Language repetition and short-term memory: an integrative framework.

PubMed

Majerus, Steve

2013-01-01

Short-term maintenance of verbal information is a core factor of language repetition, especially when reproducing multiple or unfamiliar stimuli. Many models of language processing locate the verbal short-term maintenance function in the left posterior superior temporo-parietal area and its connections with the inferior frontal gyrus. However, research in the field of short-term memory has implicated bilateral fronto-parietal networks, involved in attention and serial order processing, as being critical for the maintenance and reproduction of verbal sequences. We present here an integrative framework aimed at bridging research in the language processing and short-term memory fields. This framework considers verbal short-term maintenance as an emergent function resulting from synchronized and integrated activation in dorsal and ventral language processing networks as well as fronto-parietal attention and serial order processing networks. To-be-maintained item representations are temporarily activated in the dorsal and ventral language processing networks, novel phoneme and word serial order information is proposed to be maintained via a right fronto-parietal serial order processing network, and activation in these different networks is proposed to be coordinated and maintained via a left fronto-parietal attention processing network. This framework provides new perspectives for our understanding of information maintenance at the non-word-, word- and sentence-level as well as of verbal maintenance deficits in case of brain injury.

Language repetition and short-term memory: an integrative framework

PubMed Central

Majerus, Steve

2013-01-01

Short-term maintenance of verbal information is a core factor of language repetition, especially when reproducing multiple or unfamiliar stimuli. Many models of language processing locate the verbal short-term maintenance function in the left posterior superior temporo-parietal area and its connections with the inferior frontal gyrus. However, research in the field of short-term memory has implicated bilateral fronto-parietal networks, involved in attention and serial order processing, as being critical for the maintenance and reproduction of verbal sequences. We present here an integrative framework aimed at bridging research in the language processing and short-term memory fields. This framework considers verbal short-term maintenance as an emergent function resulting from synchronized and integrated activation in dorsal and ventral language processing networks as well as fronto-parietal attention and serial order processing networks. To-be-maintained item representations are temporarily activated in the dorsal and ventral language processing networks, novel phoneme and word serial order information is proposed to be maintained via a right fronto-parietal serial order processing network, and activation in these different networks is proposed to be coordinated and maintained via a left fronto-parietal attention processing network. This framework provides new perspectives for our understanding of information maintenance at the non-word-, word- and sentence-level as well as of verbal maintenance deficits in case of brain injury. PMID:23874280

Individual nodeʼs contribution to the mesoscale of complex networks

NASA Astrophysics Data System (ADS)

Klimm, Florian; Borge-Holthoefer, Javier; Wessel, Niels; Kurths, Jürgen; Zamora-López, Gorka

2014-12-01

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

PubMed Central

Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

2017-01-01

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

PubMed

Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

2017-03-31

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

Incorporating Resilience into Dynamic Social Models

DTIC Science & Technology

2016-07-20

solved by simply using the information provided by the scenario. Instead, additional knowledge is required from relevant fields that study these...resilience function by leveraging Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network framework[5],[6]. BKBs allow for inferencing...reasoning network framework based on Bayesian Knowledge Bases (BKBs). BKBs are central to our social resilience framework as they are used to

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

Practical use of a framework for network science experimentation

NASA Astrophysics Data System (ADS)

Toth, Andrew; Bergamaschi, Flavio

2014-06-01

In 2006, the US Army Research Laboratory (ARL) and the UK Ministry of Defence (MoD) established a collaborative research alliance with academia and industry, called the International Technology Alliance (ITA)1 In Network and Information Sciences, to address fundamental issues concerning Network and Information Sciences that will enhance decision making for coalition operations and enable rapid, secure formation of ad hoc teams in coalition environments and enhance US and UK capabilities to conduct coalition warfare. Research conducted under the ITA was extended through collaboration between ARL and IBM UK to characterize and dene a software stack and tooling that has become the reference framework for network science experimentation in support for validation of theoretical research. This paper discusses the composition of the reference framework for experimentation resulting from the ARL/IBM UK collaboration and its use, by the Network Science Collaborative Technology Alliance (NS CTA)2 , in a recent network science experiment conducted at ARL. It also discusses how the experiment was modeled using the reference framework, the integration of two new components, the Apollo Fact-Finder3 tool and the Medusa Crowd Sensing4 application, the limitations identified and how they shall be addressed in future work.

Characterizing mutation-expression network relationships in multiple cancers.

PubMed

Ghazanfar, Shila; Yang, Jean Yee Hwa

2016-08-01

Data made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics. Using somatic mutation and gene expression information across 19 cancers, we generated mutation-expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p<0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load. This study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation-expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Atlanta congestion reduction demonstration. National evaluation : content analysis test plan.

DOT National Transportation Integrated Search

2000-05-30

Commercial Vehicle Information Systems and Networks (CVISN) is the collection of information systems and communication networks that support commercial vehicle operations (CVO.) The National ITS Architecture provides a technical framework that descri...

Social Networking on the Semantic Web

ERIC Educational Resources Information Center

Finin, Tim; Ding, Li; Zhou, Lina; Joshi, Anupam

2005-01-01

Purpose: Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach: The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the…

Advanced Performance Modeling with Combined Passive and Active Monitoring

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

Dovrolis, Constantine; Sim, Alex

2015-04-15

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

Design and Implementation of an Architectural Framework for Web Portals in a Ubiquitous Pervasive Environment

PubMed Central

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework. PMID:22346693

Design and implementation of an architectural framework for web portals in a ubiquitous pervasive environment.

PubMed

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal's gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world's largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework.

Application of wireless networks-peer-to-peer information sharing

NASA Astrophysics Data System (ADS)

ellappan, Vijayan; chaki, suchismita; kumar, avn

2017-11-01

Peer to Peer communications and its applications have gotten to be ordinary construction modelling in the wired network environment. But then, they have not been successfully adjusted with the wireless environment. Unlike the traditional client-server framework, in a P2P framework, each node can play the role of client as well as server simultaneously and exchange data or information with others. We aim to design an application which can adapt to the wireless ad-hoc networks. Peer to Peer communication can help people to share their files (information, image, audio, video and so on) and communicate with each other without relying on a particular network infrastructure or limited data usage. Here there is a central server with the help of which, the peers will have the capability to get the information about the other peers in the network. Indeed, even without the Internet, devices have the potential to allow users to connect and communicate in a special way through short range remote protocols such Wi-Fi.

The study and implementation of the wireless network data security model

NASA Astrophysics Data System (ADS)

Lin, Haifeng

2013-03-01

In recent years, the rapid development of Internet technology and the advent of information age, people are increasing the strong demand for the information products and the market for information technology. Particularly, the network security requirements have become more sophisticated. This paper analyzes the wireless network in the data security vulnerabilities. And a list of wireless networks in the framework is the serious defects with the related problems. It has proposed the virtual private network technology and wireless network security defense structure; and it also given the wireless networks and related network intrusion detection model for the detection strategies.

Impact of the social networking applications for health information management for patients and physicians.

PubMed

Sahama, Tony; Liang, Jian; Iannella, Renato

2012-01-01

Most social network users hold more than one social network account and utilize them in different ways depending on the digital context. For example, friendly chat on Facebook, professional discussion on LinkedIn, and health information exchange on PatientsLikeMe. Thus many web users need to manage many disparate profiles across many distributed online sources. Maintaining these profiles is cumbersome, time consuming, inefficient, and leads to lost opportunity. In this paper we propose a framework for multiple profile management of online social networks and showcase a demonstrator utilising an open source platform. The result of the research enables a user to create and manage an integrated profile and share/synchronise their profiles with their social networks. A number of use cases were created to capture the functional requirements and describe the interactions between users and the online services. An innovative application of this project is in public health informatics. We utilize the prototype to examine how the framework can benefit patients and physicians. The framework can greatly enhance health information management for patients and more importantly offer a more comprehensive personal health overview of patients to physicians.

BioNet Digital Communications Framework

NASA Technical Reports Server (NTRS)

Gifford, Kevin; Kuzminsky, Sebastian; Williams, Shea

2010-01-01

BioNet v2 is a peer-to-peer middleware that enables digital communication devices to talk to each other. It provides a software development framework, standardized application, network-transparent device integration services, a flexible messaging model, and network communications for distributed applications. BioNet is an implementation of the Constellation Program Command, Control, Communications and Information (C3I) Interoperability specification, given in CxP 70022-01. The system architecture provides the necessary infrastructure for the integration of heterogeneous wired and wireless sensing and control devices into a unified data system with a standardized application interface, providing plug-and-play operation for hardware and software systems. BioNet v2 features a naming schema for mobility and coarse-grained localization information, data normalization within a network-transparent device driver framework, enabling of network communications to non-IP devices, and fine-grained application control of data subscription band width usage. BioNet directly integrates Disruption Tolerant Networking (DTN) as a communications technology, enabling networked communications with assets that are only intermittently connected including orbiting relay satellites and planetary rover vehicles.

Mapping and discrimination of networks in the complexity-entropy plane

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

A Framework for Network Visualisation: Progress Report

DTIC Science & Technology

2006-12-01

time; secondly a simple oscillation, in which traffic changes, but those changes repeat periodically; or thirdly, a “ strange attractor ”, a pattern of...changes that never repeats exactly, though it may appear to repeat approximately. The strange attractor is the signature of a chaotic system, which...IST-063 3 - 1 Taylor, M.M. (2006) A Framework for Network Visualisation: Progress Report. In Visualising Network Information (pp. 3-1 – 3-22

Modeling socio-cultural processes in network-centric environments

NASA Astrophysics Data System (ADS)

Santos, Eunice E.; Santos, Eugene, Jr.; Korah, John; George, Riya; Gu, Qi; Kim, Keumjoo; Li, Deqing; Russell, Jacob; Subramanian, Suresh

2012-05-01

The major focus in the field of modeling & simulation for network centric environments has been on the physical layer while making simplifications for the human-in-the-loop. However, the human element has a big impact on the capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model and analyze various socio-cultural processes within network centric environments. The key innovation in our methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional mappings between them. We represent socio-cultural information such as friendships, professional relationships and temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We will leverage previous work on a network performance modeling framework called Network-Centric Operations Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating relevant factors and providing analyses of the results.

Evaluating multiple determinants of the structure of plant-animal mutualistic networks.

PubMed

Vázquez, Diego P; Chacoff, Natacha P; Cagnolo, Luciano

2009-08-01

The structure of mutualistic networks is likely to result from the simultaneous influence of neutrality and the constraints imposed by complementarity in species phenotypes, phenologies, spatial distributions, phylogenetic relationships, and sampling artifacts. We develop a conceptual and methodological framework to evaluate the relative contributions of these potential determinants. Applying this approach to the analysis of a plant-pollinator network, we show that information on relative abundance and phenology suffices to predict several aggregate network properties (connectance, nestedness, interaction evenness, and interaction asymmetry). However, such information falls short of predicting the detailed network structure (the frequency of pairwise interactions), leaving a large amount of variation unexplained. Taken together, our results suggest that both relative species abundance and complementarity in spatiotemporal distribution contribute substantially to generate observed network patters, but that this information is by no means sufficient to predict the occurrence and frequency of pairwise interactions. Future studies could use our methodological framework to evaluate the generality of our findings in a representative sample of study systems with contrasting ecological conditions.

Collective decision dynamics in the presence of external drivers

NASA Astrophysics Data System (ADS)

Bassett, Danielle S.; Alderson, David L.; Carlson, Jean M.

2012-09-01

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

NASA Astrophysics Data System (ADS)

Aydin, Orhun; Caers, Jef Karel

2017-08-01

Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics.

Interspecific social networks promote information transmission in wild songbirds.

PubMed

Farine, Damien R; Aplin, Lucy M; Sheldon, Ben C; Hoppitt, William

2015-03-22

Understanding the functional links between social structure and population processes is a central aim of evolutionary ecology. Multiple types of interactions can be represented by networks drawn for the same population, such as kinship, dominance or affiliative networks, but the relative importance of alternative networks in modulating population processes may not be clear. We illustrate this problem, and a solution, by developing a framework for testing the importance of different types of association in facilitating the transmission of information. We apply this framework to experimental data from wild songbirds that form mixed-species flocks, recording the arrival (patch discovery) of individuals to novel foraging sites. We tested whether intraspecific and interspecific social networks predicted the spread of information about novel food sites, and found that both contributed to transmission. The likelihood of acquiring information per unit of connection to knowledgeable individuals increased 22-fold for conspecifics, and 12-fold for heterospecifics. We also found that species varied in how much information they produced, suggesting that some species play a keystone role in winter foraging flocks. More generally, these analyses demonstrate that this method provides a powerful approach, using social networks to quantify the relative transmission rates across different social relationships.

Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity.

PubMed

Koyluoglu, Onur Ozan; Pertzov, Yoni; Manohar, Sanjay; Husain, Masud; Fiete, Ila R

2017-09-07

It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain.

Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity

PubMed Central

Pertzov, Yoni; Manohar, Sanjay; Husain, Masud; Fiete, Ila R

2017-01-01

It is widely believed that persistent neural activity underlies short-term memory. Yet, as we show, the degradation of information stored directly in such networks behaves differently from human short-term memory performance. We build a more general framework where memory is viewed as a problem of passing information through noisy channels whose degradation characteristics resemble those of persistent activity networks. If the brain first encoded the information appropriately before passing the information into such networks, the information can be stored substantially more faithfully. Within this framework, we derive a fundamental lower-bound on recall precision, which declines with storage duration and number of stored items. We show that human performance, though inconsistent with models involving direct (uncoded) storage in persistent activity networks, can be well-fit by the theoretical bound. This finding is consistent with the view that if the brain stores information in patterns of persistent activity, it might use codes that minimize the effects of noise, motivating the search for such codes in the brain. PMID:28879851

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model.

PubMed

Liu, Dan; Liu, Xuejun; Wu, Yiguang

2018-04-24

This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.

Sensor Based Framework for Secure Multimedia Communication in VANET

PubMed Central

Rahim, Aneel; Khan, Zeeshan Shafi; Bin Muhaya, Fahad T.; Sher, Muhammad; Kim, Tai-Hoon

2010-01-01

Secure multimedia communication enhances the safety of passengers by providing visual pictures of accidents and danger situations. In this paper we proposed a framework for secure multimedia communication in Vehicular Ad-Hoc Networks (VANETs). Our proposed framework is mainly divided into four components: redundant information, priority assignment, malicious data verification and malicious node verification. The proposed scheme jhas been validated with the help of the NS-2 network simulator and the Evalvid tool. PMID:22163462

A modeling framework for characterizing near-road air pollutant concentration at community scales

EPA Science Inventory

In this study, we combine information from transportation network, traffic emissions, and dispersion model to develop a framework to inform exposure estimates for traffic-related air pollutants (TRAPs) with a high spatial resolution. A Research LINE source dispersion model (R-LIN...

The Perceptions of U.S.-Based IT Security Professionals about the Effectiveness of IT Security Frameworks: A Quantitative Study

ERIC Educational Resources Information Center

Warfield, Douglas L.

2011-01-01

The evolution of information technology has included new methodologies that use information technology to control and manage various industries and government activities. Information Technology has also evolved as its own industry with global networks of interconnectivity, such as the Internet, and frameworks, models, and methodologies to control…

Determination of network origin-destination matrices using partial link traffic counts and virtual sensor information in an integrated corridor management framework.

DOT National Transportation Integrated Search

2014-04-01

Trip origin-destination (O-D) demand matrices are critical components in transportation network : modeling, and provide essential information on trip distributions and corresponding spatiotemporal : traffic patterns in traffic zones in vehicular netw...

Nature-Inspired Dissemination of Information in P2P Networks

NASA Astrophysics Data System (ADS)

Guéret, Christophe

After having first been used as a means to publish content, the Web is now widely used as a social tool for sharing information. It is an easy task to subscribe to a social network, join one of the Web-based communities according to some personal interests and start to share content with all the people who do the same. It is easy once you solve two basic problems: select the network to join (go to hi5, facebook, myspace,…? join all of them?) and find/pick up the right communities (i.e., find a strict label to match non-strict centers of interest). An error of appreciation would result in getting too much of useless/non-relevant information. This chapter provides a study on the dissemination of information within groups of people and aim at answering one question: can we find an effortless way of sharing information on the Web? Ideally, such a solution would require neither the definition of a profile nor the selection of communities to join. Publishing information should also not be the result of an active decision but be performed in an automatic way. A nature-inspired framework is introduced as an answer to this question. This framework features artificial ants taking care of the dissemination of information items within the network. Centers of interest of the users are reflected by artificial pheromones laid down on connections between peers. Another part of the framework uses those pheromone trails to detect shared interests and creates communities.

A Framework for Bridging Scientists, Knowledge Brokers and Local Decision Makers in State-level Climate Assessments

NASA Astrophysics Data System (ADS)

Galford, G. L.; Nash, J. L.

2016-12-01

Large-scale analyses like the National Climate Assessment (NCA) contain a wealth of information critical to national and regional responses to climate change but tend to be insufficiently detailed for action at state or local levels. Many states now develop assessments (SCAs) to provide relevant, actionable information to state and local authorities. These assessments generate new or additional primary information, build networks and inform stakeholders. Based on our experience in the Vermont Climate Assessment (VCA), we present a SCA framework to engage local decision makers, using a fluid network of scientific experts and knowledge brokers to conduct subject area prioritization, data analysis, and writing. Knowledge brokers bridged the scientific and stakeholder communities, providing a two-way flow of information by capitalizing on their existing networks. Rich citizen records of climate and climate change impacts associated a human voice, a memorable story, or personal observation with a climate record, improving climate information salience. This engagement process that created salient climate information perceived as credible and legitimate by local and state decision makers. We present this framework as an effective structure for SCAs to foster interaction among scientists, knowledge brokers and stakeholders. We include a qualitative impact evaluation and lessons learned for future SCAs.

An information theory framework for dynamic functional domain connectivity.

PubMed

Vergara, Victor M; Miller, Robyn; Calhoun, Vince

2017-06-01

Dynamic functional network connectivity (dFNC) analyzes time evolution of coherent activity in the brain. In this technique dynamic changes are considered for the whole brain. This paper proposes an information theory framework to measure information flowing among subsets of functional networks call functional domains. Our method aims at estimating bits of information contained and shared among domains. The succession of dynamic functional states is estimated at the domain level. Information quantity is based on the probabilities of observing each dynamic state. Mutual information measurement is then obtained from probabilities across domains. Thus, we named this value the cross domain mutual information (CDMI). Strong CDMIs were observed in relation to the subcortical domain. Domains related to sensorial input, motor control and cerebellum form another CDMI cluster. Information flow among other domains was seldom found. Other methods of dynamic connectivity focus on whole brain dFNC matrices. In the current framework, information theory is applied to states estimated from pairs of multi-network functional domains. In this context, we apply information theory to measure information flow across functional domains. Identified CDMI clusters point to known information pathways in the basal ganglia and also among areas of sensorial input, patterns found in static functional connectivity. In contrast, CDMI across brain areas of higher level cognitive processing follow a different pattern that indicates scarce information sharing. These findings show that employing information theory to formally measured information flow through brain domains reveals additional features of functional connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.

An Optimization-Based State Estimatioin Framework for Large-Scale Natural Gas Networks

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

Jalving, Jordan; Zavala, Victor M.

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable ofmore » tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.« less

A Model Designed to Enhance Informed Consent: Experiences From the HIV Prevention Trials Network

PubMed Central

Woodsong, Cynthia; Karim, Quarraisha Abdool

2005-01-01

HIV prevention research in developing countries has resulted in increased attention to and discussion of ethical issues, particularly the issue of the quality of informed consent. We present a conceptual framework for an enhanced informed consent process, drawing on experiences garnered from domestic and international studies conducted by the HIV Prevention Trials Network, funded by the National Institutes of Health. This framework guides the development of an informed consent process designed to help ensure initial and continued comprehension of research participation, with an emphasis on HIV prevention research. Attention is focused at the individual and community levels and on 3 study phases: preenrollment, enrollment, and postenrollment. PMID:15727968

A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

PubMed

Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

2014-03-01

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

The Vehicular Information Space Framework

NASA Astrophysics Data System (ADS)

Prinz, Vivian; Schlichter, Johann; Schweiger, Benno

Vehicular networks are distributed, self-organizing and highly mobile ad hoc networks. They allow for providing drivers with up-to-the-minute information about their environment. Therefore, they are expected to be a decisive future enabler for enhancing driving comfort and safety. This article introduces the Vehicular Information Space framework (VIS). Vehicles running the VIS form a kind of distributed database. It enables them to provide information like existing hazards, parking spaces or traffic densities in a location aware and fully distributed manner. In addition, vehicles can retrieve, modify and delete these information items. The underlying algorithm is based on features derived from existing structured Peer-to-Peer algorithms and extended to suit the specific characteristics of highly mobile ad hoc networks. We present, implement and simulate the VIS using a motorway and an urban traffic environment. Simulation studies on VIS message occurrence show that the VIS implies reasonable traffic overhead. Also, overall VIS message traffic is independent from the number of information items provided.

A Framework for Managing Inter-Site Storage Area Networks using Grid Technologies

NASA Technical Reports Server (NTRS)

Kobler, Ben; McCall, Fritz; Smorul, Mike

2006-01-01

The NASA Goddard Space Flight Center and the University of Maryland Institute for Advanced Computer Studies are studying mechanisms for installing and managing Storage Area Networks (SANs) that span multiple independent collaborating institutions using Storage Area Network Routers (SAN Routers). We present a framework for managing inter-site distributed SANs that uses Grid Technologies to balance the competing needs to control local resources, share information, delegate administrative access, and manage the complex trust relationships between the participating sites.

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

How multiple social networks affect user awareness: The information diffusion process in multiplex networks

NASA Astrophysics Data System (ADS)

Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

2015-10-01

The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

A Framework for Integrating Multiple Biological Networks to Predict MicroRNA-Disease Associations.

PubMed

Peng, Wei; Lan, Wei; Yu, Zeng; Wang, Jianxin; Pan, Yi

2017-03-01

MicroRNAs have close relationship with human diseases. Therefore, identifying disease related MicroRNAs plays an important role in disease diagnosis, prognosis and therapy. However, designing an effective computational method which can make good use of various biological resources and correctly predict the associations between MicroRNA and disease is still a big challenge. Previous researchers have pointed out that there are complex relationships among microRNAs, diseases and environment factors. There are inter-relationships between microRNAs, diseases or environment factors based on their functional similarity or phenotype similarity or chemical structure similarity and so on. There are also intra-relationships between microRNAs and diseases, microRNAs and environment factors, diseases and environment factors. Moreover, functionally similar microRNAs tend to associate with common diseases and common environment factors. The diseases with similar phenotypes are likely caused by common microRNAs and common environment factors. In this work, we propose a framework namely ThrRWMDE which can integrate these complex relationships to predict microRNA-disease associations. In this framework, microRNA similarity network (MFN), disease similarity network (DSN) and environmental factor similarity network (ESN) are constructed according to certain biological properties. Then, an unbalanced three random walking algorithm is implemented on the three networks so as to obtain information from neighbors in corresponding networks. This algorithm not only can flexibly infer information from different levels of neighbors with respect to the topological and structural differences of the three networks, but also in the course of working the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment show that our method achieves better prediction performance than other state-of-the-art methods.

A general framework for a collaborative water quality knowledge and information network.

PubMed

Dalcanale, Fernanda; Fontane, Darrell; Csapo, Jorge

2011-03-01

Increasing knowledge about the environment has brought about a better understanding of the complexity of the issues, and more information publicly available has resulted into a steady shift from centralized decision making to increasing levels of participatory processes. The management of that information, in turn, is becoming more complex. One of the ways to deal with the complexity is the development of tools that would allow all players, including managers, researchers, educators, stakeholders and the civil society, to be able to contribute to the information system, in any level they are inclined to do so. In this project, a search for the available technology for collaboration, methods of community filtering, and community-based review was performed and the possible implementation of these tools to create a general framework for a collaborative "Water Quality Knowledge and Information Network" was evaluated. The main goals of the network are to advance water quality education and knowledge; encourage distribution and access to data; provide networking opportunities; allow public perceptions and concerns to be collected; promote exchange of ideas; and, give general, open, and free access to information. A reference implementation was made available online and received positive feedback from the community, which also suggested some possible improvements.

A General Framework for a Collaborative Water Quality Knowledge and Information Network

NASA Astrophysics Data System (ADS)

Dalcanale, Fernanda; Fontane, Darrell; Csapo, Jorge

2011-03-01

Increasing knowledge about the environment has brought about a better understanding of the complexity of the issues, and more information publicly available has resulted into a steady shift from centralized decision making to increasing levels of participatory processes. The management of that information, in turn, is becoming more complex. One of the ways to deal with the complexity is the development of tools that would allow all players, including managers, researchers, educators, stakeholders and the civil society, to be able to contribute to the information system, in any level they are inclined to do so. In this project, a search for the available technology for collaboration, methods of community filtering, and community-based review was performed and the possible implementation of these tools to create a general framework for a collaborative "Water Quality Knowledge and Information Network" was evaluated. The main goals of the network are to advance water quality education and knowledge; encourage distribution and access to data; provide networking opportunities; allow public perceptions and concerns to be collected; promote exchange of ideas; and, give general, open, and free access to information. A reference implementation was made available online and received positive feedback from the community, which also suggested some possible improvements.

Taxonomies of networks from community structure

PubMed Central

Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2014-01-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: they can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi. PMID:23030977

Taxonomies of networks from community structure

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2012-09-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

A security architecture for health information networks.

PubMed

Kailar, Rajashekar; Muralidhar, Vinod

2007-10-11

Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

Ambiguity in Social Network Data for Presence, Sensitive-Attribute, Degree and Relationship Privacy Protection.

PubMed

Rajaei, Mehri; Haghjoo, Mostafa S; Miyaneh, Eynollah Khanjari

2015-01-01

Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected entities), and relationships, as well as the presence of individuals in directed social network data whose nodes contain attributes. First, we define a privacy model that specifies privacy requirements for the above private information. Then, we introduce the technique of Ambiguity in Social Network data (ASN) based on anatomy, which specifies how to publish social network data. To employ ASN, individuals are partitioned into groups. Then, ASN publishes exact values of properties of individuals of each group with common group ID in several tables. The lossy join of those tables based on group ID injects uncertainty to reconstruct the original network. We also show how to measure different privacy requirements in ASN. Simulation results on real and synthetic datasets demonstrate that our framework, which protects from four types of private information disclosure, preserves data utility in tabular, topological and spectrum aspects of networks at a satisfactory level.

Ambiguity in Social Network Data for Presence, Sensitive-Attribute, Degree and Relationship Privacy Protection

PubMed Central

Rajaei, Mehri; Haghjoo, Mostafa S.; Miyaneh, Eynollah Khanjari

2015-01-01

Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected entities), and relationships, as well as the presence of individuals in directed social network data whose nodes contain attributes. First, we define a privacy model that specifies privacy requirements for the above private information. Then, we introduce the technique of Ambiguity in Social Network data (ASN) based on anatomy, which specifies how to publish social network data. To employ ASN, individuals are partitioned into groups. Then, ASN publishes exact values of properties of individuals of each group with common group ID in several tables. The lossy join of those tables based on group ID injects uncertainty to reconstruct the original network. We also show how to measure different privacy requirements in ASN. Simulation results on real and synthetic datasets demonstrate that our framework, which protects from four types of private information disclosure, preserves data utility in tabular, topological and spectrum aspects of networks at a satisfactory level. PMID:26110762

Organizational Application of Social Networking Information Technologies

ERIC Educational Resources Information Center

Reppert, Jeffrey R.

2012-01-01

The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Information spreading in Delay Tolerant Networks based on nodes' behaviors

NASA Astrophysics Data System (ADS)

Wu, Yahui; Deng, Su; Huang, Hongbin

2014-07-01

Information spreading in DTNs (Delay Tolerant Networks) adopts a store-carry-forward method, and nodes receive the message from others directly. However, it is hard to judge whether the information is safe in this communication mode. In this case, a node may observe other nodes' behaviors. At present, there is no theoretical model to describe the varying rule of the nodes' trusting level. In addition, due to the uncertainty of the connectivity in DTN, a node is hard to get the global state of the network. Therefore, a rational model about the node's trusting level should be a function of the node's own observing result. For example, if a node finds k nodes carrying a message, it may trust the information with probability p(k). This paper does not explore the real distribution of p(k), but instead presents a unifying theoretical framework to evaluate the performance of the information spreading in above case. This framework is an extension of the traditional SI (susceptible-infected) model, and is useful when p(k) conforms to any distribution. Simulations based on both synthetic and real motion traces show the accuracy of the framework. Finally, we explore the impact of the nodes' behaviors based on certain special distributions through numerical results.

Link predication based on matrix factorization by fusion of multi class organizations of the network.

PubMed

Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

2017-08-21

Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

A Security Architecture for Health Information Networks

PubMed Central

Kailar, Rajashekar

2007-01-01

Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a generic framework for solving the network planning problem under uncertainties. In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified framework based on robust optimization can be used to solve a rather large segment of network planning problem under uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and suitable framework for tackling generic network planning problems under uncertainties. In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be applied to the current common network planning problems under uncertain environments. Next, we list some practical considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we conclude this article with some thoughts on the future directions for applying this framework to solve other network planning problems.

A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

NASA Astrophysics Data System (ADS)

Kolosionis, Konstantinos; Papadopoulou, Maria P.

2017-04-01

Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

NHDPlusHR: A national geospatial framework for surface-water information

USGS Publications Warehouse

Viger, Roland; Rea, Alan H.; Simley, Jeffrey D.; Hanson, Karen M.

2016-01-01

The U.S. Geological Survey is developing a new geospatial hydrographic framework for the United States, called the National Hydrography Dataset Plus High Resolution (NHDPlusHR), that integrates a diversity of the best-available information, robustly supports ongoing dataset improvements, enables hydrographic generalization to derive alternate representations of the network while maintaining feature identity, and supports modern scientific computing and Internet accessibility needs. This framework is based on the High Resolution National Hydrography Dataset, the Watershed Boundaries Dataset, and elevation from the 3-D Elevation Program, and will provide an authoritative, high precision, and attribute-rich geospatial framework for surface-water information for the United States. Using this common geospatial framework will provide a consistent basis for indexing water information in the United States, eliminate redundancy, and harmonize access to, and exchange of water information.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Temporal Information Partitioning Networks (TIPNets): A process network approach to infer ecohydrologic shifts

NASA Astrophysics Data System (ADS)

Goodwell, Allison E.; Kumar, Praveen

2017-07-01

In an ecohydrologic system, components of atmospheric, vegetation, and root-soil subsystems participate in forcing and feedback interactions at varying time scales and intensities. The structure of this network of complex interactions varies in terms of connectivity, strength, and time scale due to perturbations or changing conditions such as rainfall, drought, or land use. However, characterization of these interactions is difficult due to multivariate and weak dependencies in the presence of noise, nonlinearities, and limited data. We introduce a framework for Temporal Information Partitioning Networks (TIPNets), in which time-series variables are viewed as nodes, and lagged multivariate mutual information measures are links. These links are partitioned into synergistic, unique, and redundant information components, where synergy is information provided only jointly, unique information is only provided by a single source, and redundancy is overlapping information. We construct TIPNets from 1 min weather station data over several hour time windows. From a comparison of dry, wet, and rainy conditions, we find that information strengths increase when solar radiation and surface moisture are present, and surface moisture and wind variability are redundant and synergistic influences, respectively. Over a growing season, network trends reveal patterns that vary with vegetation and rainfall patterns. The framework presented here enables us to interpret process connectivity in a multivariate context, which can lead to better inference of behavioral shifts due to perturbations in ecohydrologic systems. This work contributes to more holistic characterizations of system behavior, and can benefit a wide variety of studies of complex systems.

Metadata and network API aspects of a framework for storing and retrieving civil infrastructure monitoring data

NASA Astrophysics Data System (ADS)

Wong, John-Michael; Stojadinovic, Bozidar

2005-05-01

A framework has been defined for storing and retrieving civil infrastructure monitoring data over a network. The framework consists of two primary components: metadata and network communications. The metadata component provides the descriptions and data definitions necessary for cataloging and searching monitoring data. The communications component provides Java classes for remotely accessing the data. Packages of Enterprise JavaBeans and data handling utility classes are written to use the underlying metadata information to build real-time monitoring applications. The utility of the framework was evaluated using wireless accelerometers on a shaking table earthquake simulation test of a reinforced concrete bridge column. The NEESgrid data and metadata repository services were used as a backend storage implementation. A web interface was created to demonstrate the utility of the data model and provides an example health monitoring application.

Optimization of rainfall networks using information entropy and temporal variability analysis

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-04-01

Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.

An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

PubMed Central

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-01-01

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

PubMed

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-02-11

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736

Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.

Sensor-based architecture for medical imaging workflow analysis.

PubMed

Silva, Luís A Bastião; Campos, Samuel; Costa, Carlos; Oliveira, José Luis

2014-08-01

The growing use of computer systems in medical institutions has been generating a tremendous quantity of data. While these data have a critical role in assisting physicians in the clinical practice, the information that can be extracted goes far beyond this utilization. This article proposes a platform capable of assembling multiple data sources within a medical imaging laboratory, through a network of intelligent sensors. The proposed integration framework follows a SOA hybrid architecture based on an information sensor network, capable of collecting information from several sources in medical imaging laboratories. Currently, the system supports three types of sensors: DICOM repository meta-data, network workflows and examination reports. Each sensor is responsible for converting unstructured information from data sources into a common format that will then be semantically indexed in the framework engine. The platform was deployed in the Cardiology department of a central hospital, allowing identification of processes' characteristics and users' behaviours that were unknown before the utilization of this solution.

Measuring healthcare integration: Operationalization of a framework for a systems evaluation of palliative care structures, processes, and outcomes.

PubMed

Bainbridge, Daryl; Brazil, Kevin; Ploeg, Jenny; Krueger, Paul; Taniguchi, Alan

2016-06-01

Healthcare integration is a priority in many countries, yet there remains little direction on how to systematically evaluate this construct to inform further development. The examination of community-based palliative care networks provides an ideal opportunity for the advancement of integration measures, in consideration of how fundamental provider cohesion is to effective care at end of life. This article presents a variable-oriented analysis from a theory-based case study of a palliative care network to help bridge the knowledge gap in integration measurement. Data from a mixed-methods case study were mapped to a conceptual framework for evaluating integrated palliative care and a visual array depicting the extent of key factors in the represented palliative care network was formulated. The study included data from 21 palliative care network administrators, 86 healthcare professionals, and 111 family caregivers, all from an established palliative care network in Ontario, Canada. The framework used to guide this research proved useful in assessing qualities of integration and functioning in the palliative care network. The resulting visual array of elements illustrates that while this network performed relatively well at the multiple levels considered, room for improvement exists, particularly in terms of interventions that could facilitate the sharing of information. This study, along with the other evaluative examples mentioned, represents important initial attempts at empirically and comprehensively examining network-integrated palliative care and healthcare integration in general. © The Author(s) 2016.

Cloud Computing Services for Seismic Networks

NASA Astrophysics Data System (ADS)

Olson, Michael

This thesis describes a compositional framework for developing situation awareness applications: applications that provide ongoing information about a user's changing environment. The thesis describes how the framework is used to develop a situation awareness application for earthquakes. The applications are implemented as Cloud computing services connected to sensors and actuators. The architecture and design of the Cloud services are described and measurements of performance metrics are provided. The thesis includes results of experiments on earthquake monitoring conducted over a year. The applications developed by the framework are (1) the CSN---the Community Seismic Network---which uses relatively low-cost sensors deployed by members of the community, and (2) SAF---the Situation Awareness Framework---which integrates data from multiple sources, including the CSN, CISN---the California Integrated Seismic Network, a network consisting of high-quality seismometers deployed carefully by professionals in the CISN organization and spread across Southern California---and prototypes of multi-sensor platforms that include carbon monoxide, methane, dust and radiation sensors.

The Delta Connectome: A network-based framework for studying connectivity in river deltas

NASA Astrophysics Data System (ADS)

Passalacqua, Paola

2017-01-01

Many deltas, including the Mississippi River Delta, have been losing land at fast rates compromising the safety and sustainability of their ecosystems. Knowledge of delta vulnerability has raised global concern and stimulated active interdisciplinary research as deltas are densely populated landscapes, rich in agriculture, fisheries, oil and gas, and important means for navigation. There are many ways of looking at this problem which all contribute to a deeper understanding of the functioning of coastal systems. One aspect that has been overlooked thus far, yet fundamental for advancing delta science is connectivity, both physical (how different portions of the system interact with each other) as well as conceptual (pathways of process coupling). In this paper, I propose a framework called Delta Connectome for studying connectivity in river deltas based on different representations of a delta as a network. After analyzing the classic network representation as a set of nodes (e.g., bifurcations and junctions or regions with distinct physical or statistical behavior) and links (e.g., channels), I show that from connectivity considerations the delta emerges as a leaky network that continuously exchanges fluxes of matter, energy, and information with its surroundings and evolves over time. I explore each network representation and show through several examples how quantifying connectivity can bring to light aspects of deltaic systems so far unexplored and yet fundamental to understanding system functioning and informing coastal management and restoration. This paper serves both as an introduction to the Delta Connectome framework as well as a review of recent applications of the concepts of network and connectivity to deltaic systems within the Connectome framework.

The Command and Control of the Grand Armee: Napoleon as Organizational Designer

DTIC Science & Technology

2009-06-01

AUTHOR(S) Norman L. Durham 5. FUNDING NUMBERS 7 . PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000...served as the framework for a highly effective command and control system. This command and control network allowed Napoleon to dominate a war with...within his organizational design was a vast information network that served as the framework for a highly effective command and control system. This

Comprehensive security framework for the communication and storage of medical images

NASA Astrophysics Data System (ADS)

Slik, David; Montour, Mike; Altman, Tym

2003-05-01

Confidentiality, integrity verification and access control of medical imagery and associated metadata is critical for the successful deployment of integrated healthcare networks that extend beyond the department level. As medical imagery continues to become widely accessed across multiple administrative domains and geographically distributed locations, image data should be able to travel and be stored on untrusted infrastructure, including public networks and server equipment operated by external entities. Given these challenges associated with protecting large-scale distributed networks, measures must be taken to protect patient identifiable information while guarding against tampering, denial of service attacks, and providing robust audit mechanisms. The proposed framework outlines a series of security practices for the protection of medical images, incorporating Transport Layer Security (TLS), public and secret key cryptography, certificate management and a token based trusted computing base. It outlines measures that can be utilized to protect information stored within databases, online and nearline storage, and during transport over trusted and untrusted networks. In addition, it provides a framework for ensuring end-to-end integrity of image data from acquisition to viewing, and presents a potential solution to the challenges associated with access control across multiple administrative domains and institution user bases.

Analysis of Traffic Signals on a Software-Defined Network for Detection and Classification of a Man-in-the-Middle Attack

DTIC Science & Technology

2017-09-01

unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber events, such as a...Furthermore, we identify unique characteristics of reported anomalies in the collected traffic signals to build a classification framework. Other cyber...2]. The applications build flow rules using network topology information provided by the control plane [1]. Since the control plane is able to

IsoWAN: A NASA Science and Engineering Information and Services Framework

NASA Technical Reports Server (NTRS)

Korsmeyer, David J.; Chow, Edward T.; Conroy, Michael P.; Swanson, Keith (Technical Monitor)

2000-01-01

We believe that the next evolutionary step in supporting wide-area application and services delivery to customers is a network framework that provides for collocation of applications and services at distinct sites in the network, an interconnection between these sites that is performance optimized for these applications, and value-added services for applications. We use the term IsoWAN to describe an advanced, isolated network interconnect services framework that will enable applications to be more secure, and able to access and be in use in both local and remote environments. The main functions of an IsoWAN are virtual localization of application services, an application service interface, coordinated delivery of applications and associated data to the customer, and supporting collaborative application development for customers. An initial pilot network between three NASA Centers: Ames Research Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, has been built and its properties will be discussed.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Loops in hierarchical channel networks

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Magnasco, Marcelo

2012-02-01

Nature provides us with many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated and natural graphs extracted from digitized images of dicotyledonous leaves and animal vasculature. We calculate various metrics on the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

A Web Service-Based Framework Model for People-Centric Sensing Applications Applied to Social Networking

PubMed Central

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype. PMID:22438732

A Web Service-based framework model for people-centric sensing applications applied to social networking.

PubMed

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.

Developing Simulated Cyber Attack Scenarios Against Virtualized Adversary Networks

DTIC Science & Technology

2017-03-01

MAST is a custom software framework originally designed to facilitate the training of network administrators on live networks using SimWare. The MAST...or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services ...scenario development and testing in a virtual test environment. Commercial and custom software tools that provide the ability to conduct network

A computational framework for modeling targets as complex adaptive systems

NASA Astrophysics Data System (ADS)

Santos, Eugene; Santos, Eunice E.; Korah, John; Murugappan, Vairavan; Subramanian, Suresh

2017-05-01

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change, either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets. However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance (ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information. To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key processes, namely information aggregation and information composition. While information aggregation deals with the union, merger and concatenation of information and takes into account issues such as source reliability and information inconsistencies, information composition focuses on combining information components where such components may have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis. Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used for modeling targets with a focus on methodologies for quantifying NCO performance metrics.

From trees to forest: relational complexity network and workload of air traffic controllers.

PubMed

Zhang, Jingyu; Yang, Jiazhong; Wu, Changxu

2015-01-01

In this paper, we propose a relational complexity (RC) network framework based on RC metric and network theory to model controllers' workload in conflict detection and resolution. We suggest that, at the sector level, air traffic showing a centralised network pattern can provide cognitive benefits in visual search and resolution decision which will in turn result in lower workload. We found that the network centralisation index can account for more variance in predicting perceived workload and task completion time in both a static conflict detection task (Study 1) and a dynamic one (Study 2) in addition to other aircraft-level and pair-level factors. This finding suggests that linear combination of aircraft-level or dyad-level information may not be adequate and the global-pattern-based index is necessary. Theoretical and practical implications of using this framework to improve future workload modelling and management are discussed. We propose a RC network framework to model the workload of air traffic controllers. The effect of network centralisation was examined in both a static conflict detection task and a dynamic one. Network centralisation was predictive of perceived workload and task completion time over and above other control variables.

An efficient semi-supervised community detection framework in social networks.

PubMed

Li, Zhen; Gong, Yong; Pan, Zhisong; Hu, Guyu

2017-01-01

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

Connecting Hazard Analysts and Risk Managers to Sensor Information.

PubMed

Le Cozannet, Gonéri; Hosford, Steven; Douglas, John; Serrano, Jean-Jacques; Coraboeuf, Damien; Comte, Jérémie

2008-06-11

Hazard analysts and risk managers of natural perils, such as earthquakes, landslides and floods, need to access information from sensor networks surveying their regions of interest. However, currently information about these networks is difficult to obtain and is available in varying formats, thereby restricting accesses and consequently possibly leading to decision-making based on limited information. As a response to this issue, state-of-the-art interoperable catalogues are being currently developed within the framework of the Group on Earth Observations (GEO) workplan. This article provides an overview of the prototype catalogue that was developed to improve access to information about the sensor networks surveying geological hazards (geohazards), such as earthquakes, landslides and volcanoes.

Connecting Hazard Analysts and Risk Managers to Sensor Information

PubMed Central

Le Cozannet, Gonéri; Hosford, Steven; Douglas, John; Serrano, Jean-Jacques; Coraboeuf, Damien; Comte, Jérémie

2008-01-01

Hazard analysts and risk managers of natural perils, such as earthquakes, landslides and floods, need to access information from sensor networks surveying their regions of interest. However, currently information about these networks is difficult to obtain and is available in varying formats, thereby restricting accesses and consequently possibly leading to decision-making based on limited information. As a response to this issue, state-of-the-art interoperable catalogues are being currently developed within the framework of the Group on Earth Observations (GEO) workplan. This article provides an overview of the prototype catalogue that was developed to improve access to information about the sensor networks surveying geological hazards (geohazards), such as earthquakes, landslides and volcanoes. PMID:27879915

Examining Users' E-Satisfaction in the Usage of Social Networking Sites; Contribution from Utilitarian and Hedonic Information Systems

NASA Astrophysics Data System (ADS)

Ariff, Mohd Shoki Md; Shan, Tay Kai; Zakuan, Norhayati; Ishak, Nawawi; Ridzuan Wahi, Mohd

2014-06-01

E-satisfaction (eSAT) is an important success factor of online service providers such as social networking sites (SNSs). The utilitarian and hedonic information systems are crucial in determining users' eSAT of SNSs, especially among young users. The utilitarian aspect of an information system is productivity-oriented which aims to enhance the users' task performance, and it is important in measuring eSAT of SNSs. In this study, the original constructs of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of TAM of utilitarian information system was first developed in this research framework. The use of SNSs, such as Facebook, is pleasure-oriented, in which self-fulfilling values to the users are important in determining users' satisfaction towards the SNSs. Therefore, Perceived Enjoyment (PE) of hedonic information system is added to the framework. Thus, the research framework of this study includes both utilitarian (PEOU and PU) and hedonic (PE) aspects of information systems to determine Malaysian young users' eSAT in the usage of Facebook, a social networking site. In this framework, the effects of PEOU, PU and PE on eSAT in the usage of Facebook are examined among Facebook's users in the age of 18 - 24 years old. The effects of PEOU on PU and PE are also examined. Online questionnaire survey was employed and a total of 384 sets of questionnaires were gathered from users of Facebook. The results indicated that PEOU has positive effects on PU and PE in the context of Facebook. In addition, PEOU, PU and PE are also found to have positive effects on eSAT. PE of hedonic information system exerted higher effect on eSAT, compared to PEOU and PU of utilitarian information system, highlighting the importance of pleasure orientation in the usage of Facebook of SNSs. Managerial and theoretical implications of the study are discussed in term of measuring and enhancing users' eSAT in the usage of SNSs, particularly Facebook.

Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

PubMed

Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

2017-12-05

Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.

A Community-Building Framework for Collaborative Research Coordination across the Education and Biology Research Disciplines.

PubMed

Pelaez, Nancy; Anderson, Trevor R; Gardner, Stephanie M; Yin, Yue; Abraham, Joel K; Bartlett, Edward L; Gormally, Cara; Hurney, Carol A; Long, Tammy M; Newman, Dina L; Sirum, Karen; Stevens, Michael T

2018-06-01

Since 2009, the U.S. National Science Foundation Directorate for Biological Sciences has funded Research Coordination Networks (RCN) aimed at collaborative efforts to improve participation, learning, and assessment in undergraduate biology education (UBE). RCN-UBE projects focus on coordination and communication among scientists and educators who are fostering improved and innovative approaches to biology education. When faculty members collaborate with the overarching goal of advancing undergraduate biology education, there is a need to optimize collaboration between participants in order to deeply integrate the knowledge across disciplinary boundaries. In this essay we propose a novel guiding framework for bringing colleagues together to advance knowledge and its integration across disciplines, the "Five 'C's' of Collaboration: Commitment, Collegiality, Communication, Consensus, and Continuity." This guiding framework for professional network practice is informed by both relevant literature and empirical evidence from community-building experience within the RCN-UBE Advancing Competencies in Experimentation-Biology (ACE-Bio) Network. The framework is presented with practical examples to illustrate how it might be used to enhance collaboration between new and existing participants in the ACE-Bio Network as well as within other interdisciplinary networks.

Information theory-based decision support system for integrated design of multivariable hydrometric networks

NASA Astrophysics Data System (ADS)

Keum, Jongho; Coulibaly, Paulin

2017-07-01

Adequate and accurate hydrologic information from optimal hydrometric networks is an essential part of effective water resources management. Although the key hydrologic processes in the water cycle are interconnected, hydrometric networks (e.g., streamflow, precipitation, groundwater level) have been routinely designed individually. A decision support framework is proposed for integrated design of multivariable hydrometric networks. The proposed method is applied to design optimal precipitation and streamflow networks simultaneously. The epsilon-dominance hierarchical Bayesian optimization algorithm was combined with Shannon entropy of information theory to design and evaluate hydrometric networks. Specifically, the joint entropy from the combined networks was maximized to provide the most information, and the total correlation was minimized to reduce redundant information. To further optimize the efficiency between the networks, they were designed by maximizing the conditional entropy of the streamflow network given the information of the precipitation network. Compared to the traditional individual variable design approach, the integrated multivariable design method was able to determine more efficient optimal networks by avoiding the redundant stations. Additionally, four quantization cases were compared to evaluate their effects on the entropy calculations and the determination of the optimal networks. The evaluation results indicate that the quantization methods should be selected after careful consideration for each design problem since the station rankings and the optimal networks can change accordingly.

Lowering the barrier to a decentralized NHIN using the open healthcare framework.

PubMed

Smith, Eishay; Kaufman, James H

2006-01-01

In this paper, we discuss two important elements to lowering the barrier to creation of a National Health Information Network. The first element is the adoption of standards that will enable interoperability while guarantee open interfaces (and preventing vendor lock-in). The second element is the role of open source. While adoption of open standards by large EMR vendors is critically important to enterprise healthcare providers and payors, the availability of inexpensive (or free) standardized Healthcare Information Technology for small physician practices is critical. By analogy to the emergence of the World Wide Web, a framework for creating inexpensive and open source applications for physicians will be as important to realizing a National Health Information Network as availability of free browser technology was to the growth of the internet.

Concurrent enterprise: a conceptual framework for enterprise supply-chain network activities

NASA Astrophysics Data System (ADS)

Addo-Tenkorang, Richard; Helo, Petri T.; Kantola, Jussi

2017-04-01

Supply-chain management (SCM) in manufacturing industries has evolved significantly over the years. Recently, a lot more relevant research has picked up on the development of integrated solutions. Thus, seeking a collaborative optimisation of geographical, just-in-time (JIT), quality (customer demand/satisfaction) and return-on-investment (profits), aspects of organisational management and planning through 'best practice' business-process management - concepts and application; employing system tools such as certain applications/aspects of enterprise resource planning (ERP) - SCM systems information technology (IT) enablers to enhance enterprise integrated product development/concurrent engineering principles. This article assumed three main organisation theory applications in positioning its assumptions. Thus, proposing a feasible industry-specific framework not currently included within the SCOR model's level four (4) implementation level, as well as other existing SCM integration reference models such as in the MIT process handbook's - Process Interchange Format (PIF), the TOVE project, etc. which could also be replicated in other SCs. However, the wider focus of this paper's contribution will be concentrated on a complimentary proposed framework to the SCC's SCOR reference model. Quantitative empirical closed-ended questionnaires in addition to the main data collected from a qualitative empirical real-life industrial-based pilot case study were used: To propose a conceptual concurrent enterprise framework for SCM network activities. This research adopts a design structure matrix simulation approach analysis to propose an optimal enterprise SCM-networked value-adding, customised master data-management platform/portal for efficient SCM network information exchange and an effective supply-chain (SC) network systems-design teams' structure. Furthermore, social network theory analysis will be employed in a triangulation approach with statistical correlation analysis to assess the scale/level of frequency, importance, level of collaborative-ness, mutual trust as well as roles and responsibility among the enterprise SCM network for systems product development (PD) design teams' technical communication network as well as extensive literature reviews.

Cell Fate Reprogramming by Control of Intracellular Network Dynamics

PubMed Central

Zañudo, Jorge G. T.; Albert, Réka

2015-01-01

Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. PMID:25849586

Growing a National Learning Environments and Resources Network for Science, Mathematics, Engineering, and Technology Education: Current Issues and Opportunities for the NSDL Program; Open Linking in the Scholarly Information Environment Using the OpenURL Framework; The HeadLine Personal Information Environment: Evaluation Phase One.

ERIC Educational Resources Information Center

Zia, Lee L.; Van de Sompel, Herbert; Beit-Arie, Oren; Gambles, Anne

2001-01-01

Includes three articles that discuss the National Science Foundation's National Science, Mathematics, Engineering, and Technology Education Digital Library (NSDL) program; the OpenURL framework for open reference linking in the Web-based scholarly information environment; and HeadLine (Hybrid Electronic Access and Delivery in the Library Networked…

Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.

PubMed

Buchler, Norbou; Fitzhugh, Sean M; Marusich, Laura R; Ungvarsky, Diane M; Lebiere, Christian; Gonzalez, Cleotilde

2016-01-01

A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In addition, the information sharing network was largely imbalanced and dominated by a few key individuals so that most individuals in the network have very few email connections, but a small number of individuals have very many connections. These results highlight several major growing pains for networked organizations and military organizations in particular.

Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness

PubMed Central

Buchler, Norbou; Fitzhugh, Sean M.; Marusich, Laura R.; Ungvarsky, Diane M.; Lebiere, Christian; Gonzalez, Cleotilde

2016-01-01

A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004). We test this assumption in a large-scale, 2-week military training exercise. We conducted a social network analysis of email communications among the multi-echelon Mission Command staff (one Division and two sub-ordinate Brigades) and assessed the situational awareness of every individual. Results from our exponential random graph models challenge the aforementioned assumption, as increased email output was associated with lower individual situation awareness. It emerged that higher situation awareness was associated with a lower probability of out-ties, so that broadly sending many messages decreased the likelihood of attaining situation awareness. This challenges the hypothesis that increased information sharing improves situation awareness, at least for those doing the bulk of the sharing. In addition, we observed two trends that reflect a compartmentalizing of networked information sharing as email links were more commonly formed among members of the command staff with both similar functions and levels of situation awareness, than between two individuals with dissimilar functions and levels of situation awareness; both those findings can be interpreted to reflect effects of homophily. Our results have major implications that challenge the current conceptual framework of NEO. In addition, the information sharing network was largely imbalanced and dominated by a few key individuals so that most individuals in the network have very few email connections, but a small number of individuals have very many connections. These results highlight several major growing pains for networked organizations and military organizations in particular. PMID:27445905

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

Alienation Theory: Application of a Conceptual Framework to a Study of Information among Janitors.

ERIC Educational Resources Information Center

Chatman, Elfreda A.

1990-01-01

Alienation theory was applied to a study of information behavior among 51 janitors. Results showed that they lack an informal information network because of their work schedule, and a perception that their neighbors are undesirable associates and no more informed than they are. (34 references) (EAM)

Federated queries of clinical data repositories: Scaling to a national network.

PubMed

Weber, Griffin M

2015-06-01

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

Information theory in systems biology. Part I: Gene regulatory and metabolic networks.

PubMed

Mousavian, Zaynab; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-03-01

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Systematic Framework of Virtual Laboratories Using Mobile Agent and Design Pattern Technologies

ERIC Educational Resources Information Center

Li, Yi-Hsung; Dow, Chyi-Ren; Lin, Cheng-Min; Chen, Sheng-Chang; Hsu, Fu-Wei

2009-01-01

Innovations in network and information technology have transformed traditional classroom lectures into new approaches that have given universities the opportunity to create a virtual laboratory. However, there is no systematic framework in existing approaches for the development of virtual laboratories. Further, developing a virtual laboratory…

Comparing Networks from a Data Analysis Perspective

NASA Astrophysics Data System (ADS)

Li, Wei; Yang, Jing-Yu

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

A FORCEnet Framework for Analysis of Existing Naval C4I Architectures

DTIC Science & Technology

2003-06-01

best qualities of humans and computers. f. Information Weapons Information weapons integrate the use of military deception, psychological ...operations, to include electronic warfare, psychological operations, computer network attack, computer network defense, operations security, and military...F/A-18 ( ATARS /SHARP), S-3B (SSU), SH-60 LAMPS (HAWKLINK) and P-3C (AIP, Special Projects). CDL-N consists of two antennas (one meter diameter

The Defense Science Board Task Force on Tactical Battlefield Communications

DTIC Science & Technology

1999-12-01

impact of the system is clearly under appreciated. It could be the foundation for a common- user , QoS, Internet and could integrate legacy systems...into a common- user framework as is occurring in the private sector. Unfortunately, the networking aspects of the system are being lost; the focus...system-centric framework to a common- user , internetwork framework . Recommendation V—Information Security

A bayesian translational framework for knowledge propagation, discovery, and integration under specific contexts.

PubMed

Deng, Michelle; Zollanvari, Amin; Alterovitz, Gil

2012-01-01

The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts-rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.

A Bayesian Translational Framework for Knowledge Propagation, Discovery, and Integration Under Specific Contexts

PubMed Central

Deng, Michelle; Zollanvari, Amin; Alterovitz, Gil

2012-01-01

The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts—rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well. PMID:22779044

Data collection framework for energy efficient privacy preservation in wireless sensor networks having many-to-many structures.

PubMed

Bahşi, Hayretdin; Levi, Albert

2010-01-01

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-to-many structures evolved due to the need for conveying collected event information to multiple sinks. Privacy preserved data collection models in the literature do not solve the problems of WSN applications in which network has multiple un-trusted sinks with different level of privacy requirements. This study proposes a data collection framework bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks at the same anonymized output. If the same output is formed, it can be multicasted to all sinks. The other trivial solution is to produce different anonymized outputs for each sink and send them to related sinks. Multicasting is an energy efficient data sending alternative for some sensor nodes. Since minimization of energy consumption is an important design criteria for WSNs, multicasting the same event information to multiple sinks reduces the energy consumption of overall network.

Development of a privacy and security policy framework for a multistate comparative effectiveness research network.

PubMed

Kim, Katherine K; McGraw, Deven; Mamo, Laura; Ohno-Machado, Lucila

2013-08-01

Comparative effectiveness research (CER) conducted in distributed research networks (DRNs) is subject to different state laws and regulations as well as institution-specific policies intended to protect privacy and security of health information. The goal of the Scalable National Network for Effectiveness Research (SCANNER) project is to develop and demonstrate a scalable, flexible technical infrastructure for DRNs that enables near real-time CER consistent with privacy and security laws and best practices. This investigation began with an analysis of privacy and security laws and state health information exchange (HIE) guidelines applicable to SCANNER participants from California, Illinois, Massachusetts, and the Federal Veteran's Administration. A 7-member expert panel of policy and technical experts reviewed the analysis and gave input into the framework during 5 meetings held in 2011-2012. The state/federal guidelines were applied to 3 CER use cases: safety of new oral hematologic medications; medication therapy management for patients with diabetes and hypertension; and informational interventions for providers in the treatment of acute respiratory infections. The policy framework provides flexibility, beginning with a use-case approach rather than a one-size-fits-all approach. The policies may vary depending on the type of patient data shared (aggregate counts, deidentified, limited, and fully identified datasets) and the flow of data. The types of agreements necessary for a DRN may include a network-level and data use agreements. The need for flexibility in the development and implementation of policies must be balanced with responsibilities of data stewardship.

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

PubMed Central

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

PubMed

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

Sociospace: A smart social framework based on the IP Multimedia Subsystem

NASA Astrophysics Data System (ADS)

Hasswa, Ahmed

Advances in smart technologies, wireless networking, and increased interest in contextual services have led to the emergence of ubiquitous and pervasive computing as one of the most promising areas of computing in recent years. Smart Spaces, in particular, have gained significant interest within the research community. Currently, most Smart Spaces rely on physical components, such as sensors, to acquire information about the real-world environment. Although current sensor networks can acquire some useful contextual information from the physical environment, their information resources are often limited, and the data acquired is often unreliable. We argue that by introducing social network information into such systems, smarter and more adaptive spaces can be created. Social networks have recently become extremely popular, and are now an integral part of millions of people's daily lives. Through social networks, users create profiles, build relationships, and join groups, forming intermingled sets and communities. Social Networks contain a wealth of information, which, if exploited properly, can lead to a whole new level of smart contextual services. A mechanism is therefore needed to extract data from heterogeneous social networks, to link profiles across different networks, and to aggregate the data obtained. We therefore propose the design and implementation of a Smart Spaces framework that utilizes the social context. In order to manage services and sessions, we integrate our system with the IP Multimedia Subsystem. Our system, which we call SocioSpace, includes full design and implementation of all components, including the central server, the location management system, the social network interfacing system, the service delivery platform, and user agents. We have built a prototype for proof of concept and carried out exhaustive performance analysis; the results show that SocioSpace is scalable, extensible, and fault-tolerant. It is capable of creating Smart Spaces that can truly deliver adaptive services that enhance the users' overall experience, increase their satisfaction, and make the surroundings more beneficial and interesting to them.

A Data Scheduling and Management Infrastructure for the TEAM Network

NASA Astrophysics Data System (ADS)

Andelman, S.; Baru, C.; Chandra, S.; Fegraus, E.; Lin, K.; Unwin, R.

2009-04-01

The objective of the Tropical Ecology Assessment and Monitoring Network (www.teamnetwork.org) is "To generate real time data for monitoring long-term trends in tropical biodiversity through a global network of TEAM sites (i.e. field stations in tropical forests), providing an early warning system on the status of biodiversity to effectively guide conservation action". To achieve this, the TEAM Network operates by collecting data via standardized protocols at TEAM Sites. The standardized TEAM protocols include the Climate, Vegetation and Terrestrial Vertebrate Protocols. Some sites also implement additional protocols. There are currently 7 TEAM Sites with plans to grow the network to 15 by June 30, 2009 and 50 TEAM Sites by the end of 2010. Climate Protocol The Climate Protocol entails the collection of climate data via meteorological stations located at the TEAM Sites. This includes information such as precipitation, temperature, wind direction and strength and various solar radiation measurements. Vegetation Protocol The Vegetation Protocol collects standardized information on tropical forest trees and lianas. A TEAM Site will have between 6-9 1ha plots where trees and lianas larger than a pre-specified size are mapped, identified and measured. This results in each TEAM Site repeatedly measuring between 3000-5000 trees annually. Terrestrial Vertebrate Protocol The Terrestrial Vertebrate Protocol collects standardized information on mid-sized tropical forest fauna (i.e. birds and mammals). This information is collected via camera traps (i.e. digital cameras with motion sensors housed in weather proof casings). The images taken by the camera trap are reviewed to identify what species are captured in the image by the camera trap. The image and the interpretation of what is in the image are the data for the Terrestrial Vertebrate Protocol. The amount of data collected through the TEAM protocols provides a significant yet exciting IT challenge. The TEAM Network is currently partnering with the San Diego Super Computer Center to build the data management infrastructure. Data collected from the three core protocols as well as others are currently made available through the TEAM Network portal, which provides the content management framework, the data scheduling and management framework, an administrative framework to implement and manage TEAM sites, collaborative tools and a number of tools and applications utilizing Google Map and Google Earth products. A critical element of the TEAM Network data management infrastructure is to make the data publicly available in as close to real-time as possible (the TEAM Network Data Use Policy: http://www.teamnetwork.org/en/data/policy). This requires two essential tasks to be accomplished, 1) A data collection schedule has to be planned, proposed and approved for a given TEAM site. This is a challenging process since TEAM sites are geographically distributed across the tropics and hence have different seasons where they schedule field sampling for the different TEAM protocols. Capturing this information and ensuring that TEAM sites follow the outlined legal contract is key to the data collection process and 2) A stream-lined and efficient information management system to ensure data collected from the field meet the minimum data standards (i.e. are of the highest scientific quality) and are securely transferred, archived, processed and be rapidly made publicaly available, as a finished consumable product via the TEAM Network portal. The TEAM Network is achieving these goals by implementing an end-to-end framework consisting of the Sampling Scheduler application and the Data Management Framework. Sampling Scheduler The Sampling Scheduler is a project management, calendar based portal application that will allow scientists at a TEAM site to schedule field sampling for each of the TEAM protocols implemented at that site. The sampling scheduler addresses the specific requirements established in the TEAM protocols with the logistical scheduling needs of each TEAM Site. For example, each TEAM protocol defines when data must be collected (e.g. time of day, number of times per year, during which seasons, etc) as well as where data must be collected (from which sampling units, which trees, etc). Each TEAM Site has a limited number of resources and must create plans that will both satisfy the requirements of the protocols as well as be logistically feasible for their TEAM Site. With 15 TEAM Sites (and many more coming soon) the schedules of each TEAM Site must be communicated to the Network Office to ensure data are being collected as scheduled and to address the many problems when working in difficult environments like Tropical Forests. The Sampling Schedule provides built-in proposal and approval functionality to ensure that the TEAM Sites are and the Network office are in sync as well as provides the capability to modify schedules when needed. The Data Management Framework The Data Management framework is a three-tier data ingestion, edit and review application for protocols defined in the TEAM network. The data ingestion framework provides online web forms for field personnel to submit and edit data collected at TEAM Sites. These web forms will be accessible from the TEAM content management site. Once the data is securely uploaded, cured, processed and approved, it will be made publicly available for consumption by the scientific community. The Data Management framework, when combined with the Sampling Scheduler provides a closed loop Data Scheduling and Management infrastructure. All information starting from data collection plan, tools to input, modify and curate data, review and run QA/QC tests, as well as verify data are collected as planed are included. Finally, TEAM Network data are available for download via the Data Query and Download Application. This application utilizes a Google Maps custom interface to search, visualize, and download TEAM Network data. References • TEAM Network, http://www.teamnetwork.org • Center for Applied Biodiversity Science, Conservation International. http://science.conservation.org/portal/server.pt • TEAM Data Query and Download Application, http://www.teamnetwork.org/en/data/query

Modeling of information diffusion in Twitter-like social networks under information overload.

PubMed

Li, Pei; Li, Wei; Wang, Hui; Zhang, Xin

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

PubMed Central

Li, Wei

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations. PMID:24795541

Networking among Chevron Libraries.

ERIC Educational Resources Information Center

Linden, Margaret J.

1989-01-01

Describes the process by which librarians at the Chevron and Gulf Oil Corporations managed the merger of corporation libraries and developed a framework for a company-wide library network. The discussion covers corporate policies for information exchange, shared resources, and cost control, and examines factors that led to the success of the…

Exploring Self-Disclosure in Online Social Networks

ERIC Educational Resources Information Center

Velasco-Martin, Javier

2013-01-01

This project explores how experienced adult users of social media disclose personal information over online social networks (OSN). This work introduces a four-dimensional model to serve as a foundational framework for the study of online self-disclosure (OSD); these four dimensions are personal, social, technological and contextual, and support…

Web Information Systems for Monitoring and Control of Indoor Air Quality at Subway Stations

NASA Astrophysics Data System (ADS)

Choi, Gi Heung; Choi, Gi Sang; Jang, Joo Hyoung

In crowded subway stations indoor air quality (IAQ) is a key factor for ensuring the safety, health and comfort of passengers. In this study, a framework for web-based information system in VDN environment for monitoring and control of IAQ in subway stations is suggested. Since physical variables that describing IAQ need to be closely monitored and controlled in multiple locations in subway stations, concept of distributed monitoring and control network using wireless media needs to be implemented. Connecting remote wireless sensor network and device (LonWorks) networks to the IP network based on the concept of VDN can provide a powerful, integrated, distributed monitoring and control performance, making a web-based information system possible.

Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison

NASA Astrophysics Data System (ADS)

De Domenico, Manlio; Biamonte, Jacob

2016-10-01

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

Evaluating the Quality of Evidence from a Network Meta-Analysis

PubMed Central

Salanti, Georgia; Del Giovane, Cinzia; Chaimani, Anna; Caldwell, Deborah M.; Higgins, Julian P. T.

2014-01-01

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses. PMID:24992266

Technologies for unattended network operations

NASA Technical Reports Server (NTRS)

Jaworski, Allan; Odubiyi, Jide; Holdridge, Mark; Zuzek, John

1991-01-01

The necessary network management functions for a telecommunications, navigation and information management (TNIM) system in the framework of an extension of the ISO model for communications network management are described. Various technologies that could substantially reduce the need for TNIM network management, automate manpower intensive functions, and deal with synchronization and control at interplanetary distances are presented. Specific technologies addressed include the use of the ISO Common Management Interface Protocol, distributed artificial intelligence for network synchronization and fault management, and fault-tolerant systems engineering.

A framework using cluster-based hybrid network architecture for collaborative virtual surgery.

PubMed

Qin, Jing; Choi, Kup-Sze; Poon, Wai-Sang; Heng, Pheng-Ann

2009-12-01

Research on collaborative virtual environments (CVEs) opens the opportunity for simulating the cooperative work in surgical operations. It is however a challenging task to implement a high performance collaborative surgical simulation system because of the difficulty in maintaining state consistency with minimum network latencies, especially when sophisticated deformable models and haptics are involved. In this paper, an integrated framework using cluster-based hybrid network architecture is proposed to support collaborative virtual surgery. Multicast transmission is employed to transmit updated information among participants in order to reduce network latencies, while system consistency is maintained by an administrative server. Reliable multicast is implemented using distributed message acknowledgment based on cluster cooperation and sliding window technique. The robustness of the framework is guaranteed by the failure detection chain which enables smooth transition when participants join and leave the collaboration, including normal and involuntary leaving. Communication overhead is further reduced by implementing a number of management approaches such as computational policies and collaborative mechanisms. The feasibility of the proposed framework is demonstrated by successfully extending an existing standalone orthopedic surgery trainer into a collaborative simulation system. A series of experiments have been conducted to evaluate the system performance. The results demonstrate that the proposed framework is capable of supporting collaborative surgical simulation.

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

PubMed

van Dam, Jesse C J; Schaap, Peter J; Martins dos Santos, Vitor A P; Suárez-Diez, María

2014-09-26

Different methods have been developed to infer regulatory networks from heterogeneous omics datasets and to construct co-expression networks. Each algorithm produces different networks and efforts have been devoted to automatically integrate them into consensus sets. However each separate set has an intrinsic value that is diluted and partly lost when building a consensus network. Here we present a methodology to generate co-expression networks and, instead of a consensus network, we propose an integration framework where the different networks are kept and analysed with additional tools to efficiently combine the information extracted from each network. We developed a workflow to efficiently analyse information generated by different inference and prediction methods. Our methodology relies on providing the user the means to simultaneously visualise and analyse the coexisting networks generated by different algorithms, heterogeneous datasets, and a suite of analysis tools. As a show case, we have analysed the gene co-expression networks of Mycobacterium tuberculosis generated using over 600 expression experiments. Regarding DNA damage repair, we identified SigC as a key control element, 12 new targets for LexA, an updated LexA binding motif, and a potential mismatch repair system. We expanded the DevR regulon with 27 genes while identifying 9 targets wrongly assigned to this regulon. We discovered 10 new genes linked to zinc uptake and a new regulatory mechanism for ZuR. The use of co-expression networks to perform system level analysis allows the development of custom made methodologies. As show cases we implemented a pipeline to integrate ChIP-seq data and another method to uncover multiple regulatory layers. Our workflow is based on representing the multiple types of information as network representations and presenting these networks in a synchronous framework that allows their simultaneous visualization while keeping specific associations from the different networks. By simultaneously exploring these networks and metadata, we gained insights into regulatory mechanisms in M. tuberculosis that could not be obtained through the separate analysis of each data type.

Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory.

PubMed

Shi, Bin; Jiang, Jiping; Sivakumar, Bellie; Zheng, Yi; Wang, Peng

2018-05-01

Field monitoring strategy is critical for disaster preparedness and watershed emergency environmental management. However, development of such is also highly challenging. Despite the efforts and progress thus far, no definitive guidelines or solutions are available worldwide for quantitatively designing a monitoring network in response to river chemical spill incidents, except general rules based on administrative divisions or arbitrary interpolation on routine monitoring sections. To address this gap, a novel framework for spatial-temporal network design was proposed in this study. The framework combines contaminant transport modelling with discrete entropy theory and spectral analysis. The water quality model was applied to forecast the spatio-temporal distribution of contaminant after spills and then corresponding information transfer indexes (ITIs) and Fourier approximation periodic functions were estimated as critical measures for setting sampling locations and times. The results indicate that the framework can produce scientific preparedness plans of emergency monitoring based on scenario analysis of spill risks as well as rapid design as soon as the incident happened but not prepared. The framework was applied to a hypothetical spill case based on tracer experiment and a real nitrobenzene spill incident case to demonstrate its suitability and effectiveness. The newly-designed temporal-spatial monitoring network captured major pollution information at relatively low costs. It showed obvious benefits for follow-up early-warning and treatment as well as for aftermath recovery and assessment. The underlying drivers of ITIs as well as the limitations and uncertainty of the approach were analyzed based on the case studies. Comparison with existing monitoring network design approaches, management implications, and generalized applicability were also discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

The social determinants of oral health: new approaches to conceptualizing and researching complex causal networks.

PubMed

Newton, J Timothy; Bower, Elizabeth J

2005-02-01

Oral epidemiological research into the social determinants of oral health has been limited by the absence of a theoretical framework which reflects the complexity of real life social processes and the network of causal pathways between social structure and oral health and disease. In the absence of such a framework, social determinants are treated as isolated risk factors, attributable to the individual, having a direct impact on oral health. There is little sense of how such factors interrelate over time and place and the pathways between the factors and oral health. Features of social life which impact on individuals' oral health but are not reducible to the individual remain under-researched. A conceptual framework informing mainstream epidemiological research into the social determinants of health is applied to oral epidemiology. The framework suggests complex causal pathways between social structure and health via interlinking material, psychosocial and behavioural pathways. Methodological implications for oral epidemiological research informed by the framework, such as the use of multilevel modelling, path analysis and structural equation modelling, combining qualitative and quantitative research methods, and collaborative research, are discussed. Copyright Blackwell Munksgaard, 2005.

Suppressing disease spreading by using information diffusion on multiplex networks.

PubMed

Wang, Wei; Liu, Quan-Hui; Cai, Shi-Min; Tang, Ming; Braunstein, Lidia A; Stanley, H Eugene

2016-07-06

Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

Network Application Server Using Extensible Mark-Up Language (XML) to Support Distributed Databases and 3D Environments

DTIC Science & Technology

2001-12-01

diides.ncr.disa.mil/xmlreg/user/index.cfm] [ Deitel ] Deitel , H., Deitel , P., Java How to Program 3rd Edition, Prentice Hall, 1999. [DL99...presentation, and data) of information and the programming functionality. The Web framework addressed ability to provide a framework for the distribution...BLANK v ABSTRACT Advances in computer communication technology and an increased awareness of how enhanced information access can lead to improved

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

PubMed Central

Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J.; Auffray, Charles

2017-01-01

Abstract Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework. Contact: ibalaur@eisbm.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993779

Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

NASA Astrophysics Data System (ADS)

Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

2016-11-01

Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

A question of trust: user-centered design requirements for an informatics intervention to promote the sexual health of African-American youth.

PubMed

Veinot, Tiffany C; Campbell, Terrance R; Kruger, Daniel J; Grodzinski, Alison

2013-01-01

We investigated the user requirements of African-American youth (aged 14-24 years) to inform the design of a culturally appropriate, network-based informatics intervention for the prevention of HIV and other sexually transmitted infections (STI). We conducted 10 focus groups with 75 African-American youth from a city with high HIV/STI prevalence. Data analyses involved coding using qualitative content analysis procedures and memo writing. Unexpectedly, the majority of participants' design recommendations concerned trust. Youth expressed distrust towards people and groups, which was amplified within the context of information technology-mediated interactions about HIV/STI. Participants expressed distrust in the reliability of condoms and the accuracy of HIV tests. They questioned the benevolence of many institutions, and some rejected authoritative HIV/STI information. Therefore, reputational information, including rumor, influenced HIV/STI-related decision making. Participants' design requirements also focused on trust-related concerns. Accordingly, we developed a novel trust-centered design framework to guide intervention design. Current approaches to online trust for health informatics do not consider group-level trusting patterns. Yet, trust was the central intervention-relevant issue among African-American youth, suggesting an important focus for culturally informed design. Our design framework incorporates: intervention objectives (eg, network embeddedness, participation); functional specifications (eg, decision support, collective action, credible question and answer services); and interaction design (eg, member control, offline network linkages, optional anonymity). Trust is a critical focus for HIV/STI informatics interventions for young African Americans. Our design framework offers practical, culturally relevant, and systematic guidance to designers to reach this underserved group better.

Multi-agent coordination in directed moving neighbourhood random networks

NASA Astrophysics Data System (ADS)

Shang, Yi-Lun

2010-07-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.

Deep graphs—A general framework to represent and analyze heterogeneous complex systems across scales

NASA Astrophysics Data System (ADS)

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Deep graphs-A general framework to represent and analyze heterogeneous complex systems across scales.

PubMed

Traxl, Dominik; Boers, Niklas; Kurths, Jürgen

2016-06-01

Network theory has proven to be a powerful tool in describing and analyzing systems by modelling the relations between their constituent objects. Particularly in recent years, a great progress has been made by augmenting "traditional" network theory in order to account for the multiplex nature of many networks, multiple types of connections between objects, the time-evolution of networks, networks of networks and other intricacies. However, existing network representations still lack crucial features in order to serve as a general data analysis tool. These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales. In this paper, we introduce a collection of definitions resulting in a framework that, on the one hand, entails and unifies existing network representations (e.g., network of networks and multilayer networks), and on the other hand, generalizes and extends them by incorporating the above features. To implement these features, we first specify the nodes and edges of a finite graph as sets of properties (which are permitted to be arbitrary mathematical objects). Second, the mathematical concept of partition lattices is transferred to the network theory in order to demonstrate how partitioning the node and edge set of a graph into supernodes and superedges allows us to aggregate, compute, and allocate information on and between arbitrary groups of nodes. The derived partition lattice of a graph, which we denote by deep graph, constitutes a concise, yet comprehensive representation that enables the expression and analysis of heterogeneous properties, relations, and interactions on all scales of a complex system in a self-contained manner. Furthermore, to be able to utilize existing network-based methods and models, we derive different representations of multilayer networks from our framework and demonstrate the advantages of our representation. On the basis of the formal framework described here, we provide a rich, fully scalable (and self-explanatory) software package that integrates into the PyData ecosystem and offers interfaces to popular network packages, making it a powerful, general-purpose data analysis toolkit. We exemplify an application of deep graphs using a real world dataset, comprising 16 years of satellite-derived global precipitation measurements. We deduce a deep graph representation of these measurements in order to track and investigate local formations of spatio-temporal clusters of extreme precipitation events.

Framework for integration of informal waste management sector with the formal sector in Pakistan.

PubMed

Masood, Maryam; Barlow, Claire Y

2013-10-01

Historically, waste pickers around the globe have utilised urban solid waste as a principal source of livelihood. Formal waste management sectors usually perceive the informal waste collection/recycling networks as backward, unhygienic and generally incompatible with modern waste management systems. It is proposed here that through careful planning and administration, these seemingly troublesome informal networks can be integrated into formal waste management systems in developing countries, providing mutual benefits. A theoretical framework for integration based on a case study in Lahore, Pakistan, is presented. The proposed solution suggests that the municipal authority should draw up and agree on a formal work contract with the group of waste pickers already operating in the area. The proposed system is assessed using the integration radar framework to classify and analyse possible intervention points between the sectors. The integration of the informal waste workers with the formal waste management sector is not a one dimensional or single step process. An ideal solution might aim for a balanced focus on all four categories of intervention, although this may be influenced by local conditions. Not all the positive benefits will be immediately apparent, but it is expected that as the acceptance of such projects increases over time, the informal recycling economy will financially supplement the formal system in many ways.

The road to NHDPlus — Advancements in digital stream networks and associated catchments

USGS Publications Warehouse

Moore, Richard B.; Dewald, Thomas A.

2016-01-01

A progression of advancements in Geographic Information Systems techniques for hydrologic network and associated catchment delineation has led to the production of the National Hydrography Dataset Plus (NHDPlus). NHDPlus is a digital stream network for hydrologic modeling with catchments and a suite of related geospatial data. Digital stream networks with associated catchments provide a geospatial framework for linking and integrating water-related data. Advancements in the development of NHDPlus are expected to continue to improve the capabilities of this national geospatial hydrologic framework. NHDPlus is built upon the medium-resolution NHD and, like NHD, was developed by the U.S. Environmental Protection Agency and U.S. Geological Survey to support the estimation of streamflow and stream velocity used in fate-and-transport modeling. Catchments included with NHDPlus were created by integrating vector information from the NHD and from the Watershed Boundary Dataset with the gridded land surface elevation as represented by the National Elevation Dataset. NHDPlus is an actively used and continually improved dataset. Users recognize the importance of a reliable stream network and associated catchments. The NHDPlus spatial features and associated data tables will continue to be improved to support regional water quality and streamflow models and other user-defined applications.

Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier

PubMed Central

Akram, M. Usman; Khan, Shoab A.; Javed, Muhammad Younus

2014-01-01

National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. PMID:25136674

Local immunization program for susceptible-infected-recovered network epidemic model

NASA Astrophysics Data System (ADS)

Wu, Qingchu; Lou, Yijun

2016-02-01

The immunization strategies through contact tracing on the susceptible-infected-recovered framework in social networks are modelled to evaluate the cost-effectiveness of information-based vaccination programs with particular focus on the scenario where individuals belonging to a specific set can get vaccinated due to the vaccine shortages and other economic or humanity constraints. By using the block heterogeneous mean-field approach, a series of discrete-time dynamical models is formulated and the condition for epidemic outbreaks can be established which is shown to be not only dependent on the network structure but also closely related to the immunization control parameters. Results show that increasing the immunization strength can effectively raise the epidemic threshold, which is different from the predictions obtained through the susceptible-infected-susceptible network framework, where epidemic threshold is independent of the vaccination strength. Furthermore, a significant decrease of vaccine use to control the infectious disease is observed for the local vaccination strategy, which shows the promising applications of the local immunization programs to disease control while calls for accurate local information during the process of disease outbreak.

Visualization techniques for computer network defense

NASA Astrophysics Data System (ADS)

Beaver, Justin M.; Steed, Chad A.; Patton, Robert M.; Cui, Xiaohui; Schultz, Matthew

2011-06-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operator to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.

Generalized information fusion and visualization using spatial voting and data modeling

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.

2013-05-01

We present a novel and innovative information fusion and visualization framework for multi-source intelligence (multiINT) data using Spatial Voting (SV) and Data Modeling. We describe how different sources of information can be converted into numerical form for further processing downstream, followed by a short description of how this information can be fused using the SV grid. As an illustrative example, we show the modeling of cyberspace as cyber layers for the purpose of tracking cyber personas. Finally we describe a path ahead for creating interactive agile networks through defender customized Cyber-cubes for network configuration and attack visualization.

Designing an Integrated System of Databases: A Workstation for Information Seekers.

ERIC Educational Resources Information Center

Micco, Mary; Smith, Irma

1987-01-01

Proposes a framework for the design of a full function workstation for information retrieval based on study of information seeking behavior. A large amount of local storage of the CD-ROM jukebox variety and full networking capability to both local and external databases are identified as requirements of the prototype. (MES)

Information Flow in Interaction Networks II: Channels, Path Lengths, and Potentials

PubMed Central

Stojmirović, Aleksandar

2012-01-01

Abstract In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. PMID:22409812

A cognitive information processing framework for distributed sensor networks

NASA Astrophysics Data System (ADS)

Wang, Feiyi; Qi, Hairong

2004-09-01

In this paper, we present a cognitive agent framework (CAF) based on swarm intelligence and self-organization principles, and demonstrate it through collaborative processing for target classification in sensor networks. The framework involves integrated designs to provide both cognitive behavior at the organization level to conquer complexity and reactive behavior at the individual agent level to retain simplicity. The design tackles various problems in the current information processing systems, including overly complex systems, maintenance difficulties, increasing vulnerability to attack, lack of capability to tolerate faults, and inability to identify and cope with low-frequency patterns. An important and distinguishing point of the presented work from classical AI research is that the acquired intelligence does not pertain to distinct individuals but to groups. It also deviates from multi-agent systems (MAS) due to sheer quantity of extremely simple agents we are able to accommodate, to the degree that some loss of coordination messages and behavior of faulty/compromised agents will not affect the collective decision made by the group.

Analyzing the U.S. Marine Corps Enterprise Information Technology Framework for IT Acquisition and Portfolio Governance

DTIC Science & Technology

2012-09-01

Five Forces Model (Porter, 2008, p. 80) An example of the usefulness of these two frameworks is the case of IKEA , and its issues during the mid...from India and Pakistan, a proportion of which had been manufactured using child labor. IKEA spent years determining methods to ensure that none of...from the company’s value: a global news media, which could leverage networks and information, and present damaging issues to the public before IKEA

Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

2017-03-01

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

Organizational Analysis of the TIDES Project and the STAR-TIDES Network Using the 7-S Framework

DTIC Science & Technology

2013-04-01

data, provided some useful rec- ommendations.8 Since that time, TIDES has continued to grow and change. The present study was undertaken to update the...information across platforms and within the secure NDU network. For ex- ample, many contacts made by the Director are preserved within his Blackberry ...the active participation of STAR-TIDES network members, and to grow the network. 5. Skills Skills refers to the talents and abilities of the

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

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

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

An integrated network visualization framework towards metabolic engineering applications.

PubMed

Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

2014-12-30

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

PubMed Central

2012-01-01

Background Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach. PMID:22691450

Quantifying loopy network architectures.

PubMed

Katifori, Eleni; Magnasco, Marcelo O

2012-01-01

Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

Dynamic Policy-Driven Quality of Service in Service-Oriented Information Management Systems

DTIC Science & Technology

2011-01-01

both DiffServ and IntServ net- work QoS mechanisms. Wang et al [48] provide middleware APIs to shield applications from directly interacting with...complex network QoS mechanism APIs . Middleware frameworks transparently converted the specified application QoS requirements into low- er-level network...QoS mechanism APIs and provided network QoS assurances. Deployment-time resource allocation. Other prior work has focused on deploying ap- plications

Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies

PubMed Central

Blonder, Benjamin; Dornhaus, Anna

2011-01-01

Background An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources. Methodology/Findings Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size. Conclusions/Significance Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales. PMID:21625450

A question of trust: user-centered design requirements for an informatics intervention to promote the sexual health of African-American youth

PubMed Central

Veinot, Tiffany C; Campbell, Terrance R; Kruger, Daniel J; Grodzinski, Alison

2013-01-01

Objective We investigated the user requirements of African-American youth (aged 14–24 years) to inform the design of a culturally appropriate, network-based informatics intervention for the prevention of HIV and other sexually transmitted infections (STI). Materials and Methods We conducted 10 focus groups with 75 African-American youth from a city with high HIV/STI prevalence. Data analyses involved coding using qualitative content analysis procedures and memo writing. Results Unexpectedly, the majority of participants’ design recommendations concerned trust. Youth expressed distrust towards people and groups, which was amplified within the context of information technology-mediated interactions about HIV/STI. Participants expressed distrust in the reliability of condoms and the accuracy of HIV tests. They questioned the benevolence of many institutions, and some rejected authoritative HIV/STI information. Therefore, reputational information, including rumor, influenced HIV/STI-related decision making. Participants’ design requirements also focused on trust-related concerns. Accordingly, we developed a novel trust-centered design framework to guide intervention design. Discussion Current approaches to online trust for health informatics do not consider group-level trusting patterns. Yet, trust was the central intervention-relevant issue among African-American youth, suggesting an important focus for culturally informed design. Our design framework incorporates: intervention objectives (eg, network embeddedness, participation); functional specifications (eg, decision support, collective action, credible question and answer services); and interaction design (eg, member control, offline network linkages, optional anonymity). Conclusions Trust is a critical focus for HIV/STI informatics interventions for young African Americans. Our design framework offers practical, culturally relevant, and systematic guidance to designers to reach this underserved group better. PMID:23512830

MR connectomics: a conceptual framework for studying the developing brain

PubMed Central

Hagmann, Patric; Grant, Patricia E.; Fair, Damien A.

2012-01-01

The combination of advanced neuroimaging techniques and major developments in complex network science, have given birth to a new framework for studying the brain: “connectomics.” This framework provides the ability to describe and study the brain as a dynamic network and to explore how the coordination and integration of information processing may occur. In recent years this framework has been used to investigate the developing brain and has shed light on many dynamic changes occurring from infancy through adulthood. The aim of this article is to review this work and to discuss what we have learned from it. We will also use this body of work to highlight key technical aspects that are necessary in general for successful connectome analysis using today's advanced neuroimaging techniques. We look to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research. PMID:22707934

Effects in the network topology due to node aggregation: Empirical evidence from the domestic maritime transportation in Greece

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Polyzos, Serafeim

2018-02-01

This article studies the topological consistency of spatial networks due to node aggregation, examining the changes captured between different network representations that result from nodes' grouping and they refer to the same socioeconomic system. The main purpose of this study is to evaluate what kind of topological information remains unalterable due to node aggregation and, further, to develop a framework for linking the data of an empirical network with data of its socioeconomic environment, when the latter are available for hierarchically higher levels of aggregation, in an effort to promote the interdisciplinary research in the field of complex network analysis. The research question is empirically tested on topological and socioeconomic data extracted from the Greek Maritime Network (GMN) that is modeled as a non-directed multilayer (bilayer) graph consisting of a port-layer, where nodes represent ports, and a prefecture-layer, where nodes represent coastal and insular prefectural groups of ports. The analysis highlights that the connectivity (degree) of the GMN is the most consistent aspect of this multilayer network, which preserves both the topological and the socioeconomic information through node aggregation. In terms of spatial analysis and regional science, such effects illustrate the effectiveness of the prefectural administrative division for the functionality of the Greek maritime transportation system. Overall, this approach proposes a methodological framework that can enjoy further applications about the grouping effects induced on the network topology, providing physical, technical, socioeconomic, strategic or political insights.

Exploring information transmission in gene networks using stochastic simulation and machine learning

NASA Astrophysics Data System (ADS)

Park, Kyemyung; Prüstel, Thorsten; Lu, Yong; Narayanan, Manikandan; Martins, Andrew; Tsang, John

How gene regulatory networks operate robustly despite environmental fluctuations and biochemical noise is a fundamental question in biology. Mathematically the stochastic dynamics of a gene regulatory network can be modeled using chemical master equation (CME), but nonlinearity and other challenges render analytical solutions of CMEs difficult to attain. While approaches of approximation and stochastic simulation have been devised for simple models, obtaining a more global picture of a system's behaviors in high-dimensional parameter space without simplifying the system substantially remains a major challenge. Here we present a new framework for understanding and predicting the behaviors of gene regulatory networks in the context of information transmission among genes. Our approach uses stochastic simulation of the network followed by machine learning of the mapping between model parameters and network phenotypes such as information transmission behavior. We also devised ways to visualize high-dimensional phase spaces in intuitive and informative manners. We applied our approach to several gene regulatory circuit motifs, including both feedback and feedforward loops, to reveal underexplored aspects of their operational behaviors. This work is supported by the Intramural Program of NIAID/NIH.

IOOC Organizational Network (ION) Project

NASA Astrophysics Data System (ADS)

Dean, H.

2013-12-01

In order to meet the growing need for ocean information, research communities at the national and international levels have responded most recently by developing organizational frameworks that can help to integrate information across systems of existing networks and standardize methods of data gathering, management, and processing that facilitate integration. To address recommendations and identified challenges related to the need for a better understanding of ocean observing networks, members of the U.S. Interagency Ocean Observation Committee (IOOC) supported pursuing a project that came to be titled the IOOC Organizational Network (ION). The ION tool employs network mapping approaches which mirror approaches developed in academic literature aimed at understanding political networks. Researchers gathered data on the list of global ocean observing organizations included in the Framework for Ocean Observing (FOO), developed in 2012 by the international Task Team for an Integrated Framework for Sustained Ocean Observing. At the international scale, researchers reviewed organizational research plans and documents, websites, and formal international agreement documents. At the U.S. national scale, researchers analyzed legislation, formal inter-agency agreements, work plans, charters, and policy documents. Researchers based analysis of relationships among global organizations and national federal organizations on four broad relationship categories: Communications, Data, Infrastructure, and Human Resources. In addition to the four broad relationship categories, researchers also gathered data on relationship instrument types, strength of relationships, and (at the global level) ocean observing variables. Using network visualization software, researchers then developed a series of dynamic webpages. Researchers used the tool to address questions identified by the ocean observing community, including identifying gaps in global relationships and the types of tools used to develop networks at the U.S. national level. As the ION project goes through beta testing and is utilized to address specific questions posed by the ocean observing community, it will become more refined and more closely linked to user needs and interests.

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012

A Comparison of Product Realization Frameworks

DTIC Science & Technology

1993-10-01

software (integrated FrameMaker ). Also included are BOLD for on-line documentation delivery, printer/plotter support, and 18 network licensing support. AMPLE...are built with DSS. Documentation tools include an on-line information system (BOLD), text editing (Notepad), word processing (integrated FrameMaker ...within an application. FrameMaker is fully integrated with the Falcon Framework to provide consistent documentation capabilities within engineering

Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.

DOT National Transportation Integrated Search

2017-05-31

The overarching goal of this project was to integrate data from commercial remote sensing and spatial information (CRS&SI) technologies to create a novel data-driven decision making framework that empowers the railroad industry to monitor, assess, an...

Anterior Cingulate Cortex in Schema Assimilation and Expression

ERIC Educational Resources Information Center

Wang, Szu-Han; Tse, Dorothy; Morris, Richard G. M.

2012-01-01

In humans and in animals, mental schemas can store information within an associative framework that enables rapid and efficient assimilation of new information. Using a hippocampal-dependent paired-associate task, we now report that the anterior cingulate cortex is part of a neocortical network of schema storage with NMDA receptor-mediated…

Case analysis online: a strategic management case model for the health industry.

PubMed

Walsh, Anne; Bearden, Eithne

2004-01-01

Despite the plethora of methods and tools available to support strategic management, the challenge for health executives in the next century will relate to their ability to access and interpret data from multiple and intricate communication networks. Integrated digital networks and satellite systems will expand the scope and ease of sharing information between business divisions, and networked systems will facilitate the use of virtual case discussions across universities. While the internet is frequently used to support clinical decisions in the healthcare industry, few executives rely upon the internetfor strategic analysis. Although electronic technologies can easily synthesize data from multiple information channels, research as well as technical issues may deter their application in strategic analysis. As digital models transform access to information, online models may become increasingly relevant in designing strategic solutions. While there are various pedagogical models available to support the strategic management process, this framework was designed to enhance strategic analysis through the application of technology and electronic research. A strategic analysis framework, which incorporated internet research and case analysis in a strategic managementcourse, is described alongwith design and application issues that emerged during the case analysis process.

Snoopy--a unifying Petri net framework to investigate biomolecular networks.

PubMed

Rohr, Christian; Marwan, Wolfgang; Heiner, Monika

2010-04-01

To investigate biomolecular networks, Snoopy provides a unifying Petri net framework comprising a family of related Petri net classes. Models can be hierarchically structured, allowing for the mastering of larger networks. To move easily between the qualitative, stochastic and continuous modelling paradigms, models can be converted into each other. We get models sharing structure, but specialized by their kinetic information. The analysis and iterative reverse engineering of biomolecular networks is supported by the simultaneous use of several Petri net classes, while the graphical user interface adapts dynamically to the active one. Built-in animation and simulation are complemented by exports to various analysis tools. Snoopy facilitates the addition of new Petri net classes thanks to its generic design. Our tool with Petri net samples is available free of charge for non-commercial use at http://www-dssz.informatik.tu-cottbus.de/snoopy.html; supported operating systems: Mac OS X, Windows and Linux (selected distributions).

A Systems Engineering Framework for Implementing a Security and Critical Patch Management Process in Diverse Environments (Academic Departments' Workstations)

NASA Astrophysics Data System (ADS)

Mohammadi, Hadi

Use of the Patch Vulnerability Management (PVM) process should be seriously considered for any networked computing system. The PVM process prevents the operating system (OS) and software applications from being attacked due to security vulnerabilities, which lead to system failures and critical data leakage. The purpose of this research is to create and design a Security and Critical Patch Management Process (SCPMP) framework based on Systems Engineering (SE) principles. This framework will assist Information Technology Department Staff (ITDS) to reduce IT operating time and costs and mitigate the risk of security and vulnerability attacks. Further, this study evaluates implementation of the SCPMP in the networked computing systems of an academic environment in order to: 1. Meet patch management requirements by applying SE principles. 2. Reduce the cost of IT operations and PVM cycles. 3. Improve the current PVM methodologies to prevent networked computing systems from becoming the targets of security vulnerability attacks. 4. Embed a Maintenance Optimization Tool (MOT) in the proposed framework. The MOT allows IT managers to make the most practicable choice of methods for deploying and installing released patches and vulnerability remediation. In recent years, there has been a variety of frameworks for security practices in every networked computing system to protect computer workstations from becoming compromised or vulnerable to security attacks, which can expose important information and critical data. I have developed a new mechanism for implementing PVM for maximizing security-vulnerability maintenance, protecting OS and software packages, and minimizing SCPMP cost. To increase computing system security in any diverse environment, particularly in academia, one must apply SCPMP. I propose an optimal maintenance policy that will allow ITDS to measure and estimate the variation of PVM cycles based on their department's requirements. My results demonstrate that MOT optimizes the process of implementing SCPMP in academic workstations.

Susceptible-infected-recovered epidemics in random networks with population awareness

NASA Astrophysics Data System (ADS)

Wu, Qingchu; Chen, Shufang

2017-10-01

The influence of epidemic information-based awareness on the spread of infectious diseases on networks cannot be ignored. Within the effective degree modeling framework, we discuss the susceptible-infected-recovered model in complex networks with general awareness and general degree distribution. By performing the linear stability analysis, the conditions of epidemic outbreak can be deduced and the results of the previous research can be further expanded. Results show that the local awareness can suppress significantly the epidemic spreading on complex networks via raising the epidemic threshold and such effects are closely related to the formulation of awareness functions. In addition, our results suggest that the recovered information-based awareness has no effect on the critical condition of epidemic outbreak.

A Secure Framework for Location Verification in Pervasive Computing

NASA Astrophysics Data System (ADS)

Liu, Dawei; Lee, Moon-Chuen; Wu, Dan

The way people use computing devices has been changed in some way by the relatively new pervasive computing paradigm. For example, a person can use a mobile device to obtain its location information at anytime and anywhere. There are several security issues concerning whether this information is reliable in a pervasive environment. For example, a malicious user may disable the localization system by broadcasting a forged location, and it may impersonate other users by eavesdropping their locations. In this paper, we address the verification of location information in a secure manner. We first present the design challenges for location verification, and then propose a two-layer framework VerPer for secure location verification in a pervasive computing environment. Real world GPS-based wireless sensor network experiments confirm the effectiveness of the proposed framework.

A framework for modelling the complexities of food and water security under globalisation

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; Sivapalan, Murugesu; Stehfest, Elke; van Vuuren, Detlef P.; Wassen, Martin J.; Bierkens, Marc F. P.; Dekker, Stefan C.

2018-01-01

We present a new framework for modelling the complexities of food and water security under globalisation. The framework sets out a method to capture regional and sectoral interdependencies and cross-scale feedbacks within the global food system that contribute to emergent water use patterns. The framework integrates aspects of existing models and approaches in the fields of hydrology and integrated assessment modelling. The core of the framework is a multi-agent network of city agents connected by infrastructural trade networks. Agents receive socio-economic and environmental constraint information from integrated assessment models and hydrological models respectively and simulate complex, socio-environmental dynamics that operate within those constraints. The emergent changes in food and water resources are aggregated and fed back to the original models with minimal modification of the structure of those models. It is our conviction that the framework presented can form the basis for a new wave of decision tools that capture complex socio-environmental change within our globalised world. In doing so they will contribute to illuminating pathways towards a sustainable future for humans, ecosystems and the water they share.

A vision of network-centric military communications

NASA Astrophysics Data System (ADS)

Conklin, Ross, Jr.; Burbank, Jack; Nichols, Robert, Jr.

2005-05-01

This paper presents a vision for a future capability-based military communications system that considers user requirements. Historically, the military has developed and fielded many specialized communications systems. While these systems solved immediate communications problems, they were not designed to operate with other systems. As information has become more important to the execution of war, the "stove-pipe" nature of the communications systems deployed by the military is no longer acceptable. Realizing this, the military has begun the transformation of communications to a network-centric communications paradigm. However, the specialized communications systems were developed in response to the widely varying environments related to military communications. These environments, and the necessity for effective communications within these environments, do not disappear under the network-centric paradigm. In fact, network-centric communications allows for one message to cross many of these environments by transiting multiple networks. The military would also like one communications approach that is capable of working well in multiple environments. This paper presents preliminary work on the creation of a framework that allows for a reconfigurable device that is capable of adapting to the physical and network environments. The framework returns to the Open Systems Interconnect (OSI) architecture with the addition of a standardized intra-layer control interface for control information exchange, a standardized data interface and a proposed device architecture based on the software radio.

Visualization Techniques for Computer Network Defense

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

Beaver, Justin M; Steed, Chad A; Patton, Robert M

2011-01-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operatormore » to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.« less

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks.

PubMed

Yu, Lequan; Chen, Hao; Dou, Qi; Qin, Jing; Heng, Pheng-Ann

2017-04-01

Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition. To take full advantage of very deep networks, we propose a set of schemes to ensure effective training and learning under limited training data. First, we apply the residual learning to cope with the degradation and overfitting problems when a network goes deeper. This technique can ensure that our networks benefit from the performance gains achieved by increasing network depth. Then, we construct a fully convolutional residual network (FCRN) for accurate skin lesion segmentation, and further enhance its capability by incorporating a multi-scale contextual information integration scheme. Finally, we seamlessly integrate the proposed FCRN (for segmentation) and other very deep residual networks (for classification) to form a two-stage framework. This framework enables the classification network to extract more representative and specific features based on segmented results instead of the whole dermoscopy images, further alleviating the insufficiency of training data. The proposed framework is extensively evaluated on ISBI 2016 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset. Experimental results demonstrate the significant performance gains of the proposed framework, ranking the first in classification and the second in segmentation among 25 teams and 28 teams, respectively. This study corroborates that very deep CNNs with effective training mechanisms can be employed to solve complicated medical image analysis tasks, even with limited training data.

Ribbon networks for modeling navigable paths of autonomous agents in virtual environments.

PubMed

Willemsen, Peter; Kearney, Joseph K; Wang, Hongling

2006-01-01

This paper presents the Environment Description Framework (EDF) for modeling complex networks of intersecting roads and pathways in virtual environments. EDF represents information about the layout of streets and sidewalks, the rules that govern behavior on roads and walkways, and the locations of agents with respect to navigable structures. The framework serves as the substrate on which behavior programs for autonomous vehicles and pedestrians are built. Pathways are modeled as ribbons in space. The ribbon structure provides a natural coordinate frame for defining the local geometry of navigable surfaces. EDF includes a powerful runtime interface supported by robust and efficient code for locating objects on the ribbon network, for mapping between Cartesian and ribbon coordinates, and for determining behavioral constraints imposed by the environment.

Unified Simulation and Analysis Framework for Deep Space Navigation Design

NASA Technical Reports Server (NTRS)

Anzalone, Evan; Chuang, Jason; Olsen, Carrie

2013-01-01

As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.

Addressing practical challenges in utility optimization of mobile wireless sensor networks

NASA Astrophysics Data System (ADS)

Eswaran, Sharanya; Misra, Archan; La Porta, Thomas; Leung, Kin

2008-04-01

This paper examines the practical challenges in the application of the distributed network utility maximization (NUM) framework to the problem of resource allocation and sensor device adaptation in a mission-centric wireless sensor network (WSN) environment. By providing rich (multi-modal), real-time information about a variety of (often inaccessible or hostile) operating environments, sensors such as video, acoustic and short-aperture radar enhance the situational awareness of many battlefield missions. Prior work on the applicability of the NUM framework to mission-centric WSNs has focused on tackling the challenges introduced by i) the definition of an individual mission's utility as a collective function of multiple sensor flows and ii) the dissemination of an individual sensor's data via a multicast tree to multiple consuming missions. However, the practical application and performance of this framework is influenced by several parameters internal to the framework and also by implementation-specific decisions. This is made further complex due to mobile nodes. In this paper, we use discrete-event simulations to study the effects of these parameters on the performance of the protocol in terms of speed of convergence, packet loss, and signaling overhead thereby addressing the challenges posed by wireless interference and node mobility in ad-hoc battlefield scenarios. This study provides better understanding of the issues involved in the practical adaptation of the NUM framework. It also helps identify potential avenues of improvement within the framework and protocol.

Networked Mediated Influence 2.0

DTIC Science & Technology

2014-12-12

but they communicate the information through different frames of reference. . . . Frames work by accessing a particular perspective on an issue...nature yet attention grabbers.214 Framing. A form of communications where information is presented in a unique slant, focal point, or frame of reference...mental frameworks differ in their implications for decision making, the results can be dramatic.215 Information Communication Technologies (ICTs). A term

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Visual social network analysis: effective approach to model complex human social, behaviour & culture.

PubMed

Ahram, Tareq Z; Karwowski, Waldemar

2012-01-01

The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

NASA Astrophysics Data System (ADS)

Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad

2017-03-01

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

An information theory account of cognitive control.

PubMed

Fan, Jin

2014-01-01

Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory.

NINJA: a noninvasive framework for internal computer security hardening

NASA Astrophysics Data System (ADS)

Allen, Thomas G.; Thomson, Steve

2004-07-01

Vulnerabilities are a growing problem in both the commercial and government sector. The latest vulnerability information compiled by CERT/CC, for the year ending Dec. 31, 2002 reported 4129 vulnerabilities representing a 100% increase over the 2001 [1] (the 2003 report has not been published at the time of this writing). It doesn"t take long to realize that the growth rate of vulnerabilities greatly exceeds the rate at which the vulnerabilities can be fixed. It also doesn"t take long to realize that our nation"s networks are growing less secure at an accelerating rate. As organizations become aware of vulnerabilities they may initiate efforts to resolve them, but quickly realize that the size of the remediation project is greater than their current resources can handle. In addition, many IT tools that suggest solutions to the problems in reality only address "some" of the vulnerabilities leaving the organization unsecured and back to square one in searching for solutions. This paper proposes an auditing framework called NINJA (acronym for Network Investigation Notification Joint Architecture) for noninvasive daily scanning/auditing based on common security vulnerabilities that repeatedly occur in a network environment. This framework is used for performing regular audits in order to harden an organizations security infrastructure. The framework is based on the results obtained by the Network Security Assessment Team (NSAT) which emulates adversarial computer network operations for US Air Force organizations. Auditing is the most time consuming factor involved in securing an organization's network infrastructure. The framework discussed in this paper uses existing scripting technologies to maintain a security hardened system at a defined level of performance as specified by the computer security audit team. Mobile agents which were under development at the time of this writing are used at a minimum to improve the noninvasiveness of our scans. In general, noninvasive scans with an adequate framework performed on a daily basis reduce the amount of security work load as well as the timeliness in performing remediation, as verified by the NINJA framework. A vulnerability assessment/auditing architecture based on mobile agent technology is proposed and examined at the end of the article as an enhancement to the current NINJA architecture.

Privacy as an enabler, not an impediment: building trust into health information exchange.

PubMed

McGraw, Deven; Dempsey, James X; Harris, Leslie; Goldman, Janlori

2009-01-01

Building privacy and security protections into health information technology systems will bolster trust in such systems and promote their adoption. The privacy issue, too long seen as a barrier to electronic health information exchange, can be resolved through a comprehensive framework that implements core privacy principles, adopts trusted network design characteristics, and establishes oversight and accountability mechanisms. The public policy challenges of implementing this framework in a complex and evolving environment will require improvements to existing law, new rules for entities outside the traditional health care sector, a more nuanced approach to the role of consent, and stronger enforcement mechanisms.

SparkMed: a framework for dynamic integration of multimedia medical data into distributed m-Health systems.

PubMed

Constantinescu, Liviu; Kim, Jinman; Feng, David Dagan

2012-01-01

With the advent of 4G and other long-term evolution (LTE) wireless networks, the traditional boundaries of patient record propagation are diminishing as networking technologies extend the reach of hospital infrastructure and provide on-demand mobile access to medical multimedia data. However, due to legacy and proprietary software, storage and decommissioning costs, and the price of centralization and redevelopment, it remains complex, expensive, and often unfeasible for hospitals to deploy their infrastructure for online and mobile use. This paper proposes the SparkMed data integration framework for mobile healthcare (m-Health), which significantly benefits from the enhanced network capabilities of LTE wireless technologies, by enabling a wide range of heterogeneous medical software and database systems (such as the picture archiving and communication systems, hospital information system, and reporting systems) to be dynamically integrated into a cloud-like peer-to-peer multimedia data store. Our framework allows medical data applications to share data with mobile hosts over a wireless network (such as WiFi and 3G), by binding to existing software systems and deploying them as m-Health applications. SparkMed integrates techniques from multimedia streaming, rich Internet applications (RIA), and remote procedure call (RPC) frameworks to construct a Self-managing, Pervasive Automated netwoRK for Medical Enterprise Data (SparkMed). Further, it is resilient to failure, and able to use mobile and handheld devices to maintain its network, even in the absence of dedicated server devices. We have developed a prototype of the SparkMed framework for evaluation on a radiological workflow simulation, which uses SparkMed to deploy a radiological image viewer as an m-Health application for telemedical use by radiologists and stakeholders. We have evaluated our prototype using ten devices over WiFi and 3G, verifying that our framework meets its two main objectives: 1) interactive delivery of medical multimedia data to mobile devices; and 2) attaching to non-networked medical software processes without significantly impacting their performance. Consistent response times of under 500 ms and graphical frame rates of over 5 frames per second were observed under intended usage conditions. Further, overhead measurements displayed linear scalability and low resource requirements.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging

NASA Astrophysics Data System (ADS)

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-12-01

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging.

PubMed

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-12-02

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging

PubMed Central

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-01-01

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers. PMID:26626047

Safeguarding Your Technology: Practical Guidelines for Electronic Education Information Security.

ERIC Educational Resources Information Center

Szuba, Tom

This guide was developed specifically for educational administrators at the building, campus, district, system, and state levels, and is meant to serve as a framework to help them better understand why and how to effectively secure their organization's information, software, and computer and networking equipment. This document is organized into 10…

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

A network-based framework for assessing infrastructure resilience: a case study of the London metro system.

PubMed

Chopra, Shauhrat S; Dillon, Trent; Bilec, Melissa M; Khanna, Vikas

2016-05-01

Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems. © 2016 The Author(s).

Supporting Dynamic Spectrum Access in Heterogeneous LTE+ Networks

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

Luiz A. DaSilva; Ryan E. Irwin; Mike Benonis

As early as 2014, mobile network operators’ spectral capac- ity is expected to be overwhelmed by the demand brought on by new devices and applications. With Long Term Evo- lution Advanced (LTE+) networks likely as the future one world 4G standard, network operators may need to deploy a Dynamic Spectrum Access (DSA) overlay in Heterogeneous Networks (HetNets) to extend coverage, increase spectrum efficiency, and increase the capacity of these networks. In this paper, we propose three new management frameworks for DSA in an LTE+ HetNet: Spectrum Accountability Client, Cell Spectrum Management, and Domain Spectrum Man- agement. For these spectrum managementmore » frameworks, we define protocol interfaces and operational signaling scenar- ios to support cooperative sensing, spectrum lease manage- ment, and alarm scenarios for rule adjustment. We also quan- tify, through integer programs, the benefits of using DSA in an LTE+ HetNet, that can opportunistically reuse vacant TV and GSM spectrum. Using integer programs, we consider a topology using Geographic Information System data from the Blacksburg, VA metro area to assess the realistic benefits of DSA in an LTE+ HetNet.« less

Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

Modeling Trust in ELICIT-WEL to Capture the Impact of Organization Structure on the Agility of Complex Networks

DTIC Science & Technology

2012-06-01

Topic 8: Networks and Networking Name of Author(s) Kevin Chan, US Army Research Laboratory Mary Ruddy, Azigo Point of Contact Kevin Chan RDRL-CIN...framework. The enhanced integrated emulation platform is then used to conduct a series of agent-based ELICIT experiments whose design is informed by...NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) US Army Research

NET: a new framework for the vectorization and examination of network data.

PubMed

Lasser, Jana; Katifori, Eleni

2017-01-01

The analysis of complex networks both in general and in particular as pertaining to real biological systems has been the focus of intense scientific attention in the past and present. In this paper we introduce two tools that provide fast and efficient means for the processing and quantification of biological networks like Drosophila tracheoles or leaf venation patterns: the Network Extraction Tool ( NET ) to extract data and the Graph-edit-GUI ( GeGUI ) to visualize and modify networks. NET is especially designed for high-throughput semi-automated analysis of biological datasets containing digital images of networks. The framework starts with the segmentation of the image and then proceeds to vectorization using methodologies from optical character recognition. After a series of steps to clean and improve the quality of the extracted data the framework produces a graph in which the network is represented only by its nodes and neighborhood-relations. The final output contains information about the adjacency matrix of the graph, the width of the edges and the positions of the nodes in space. NET also provides tools for statistical analysis of the network properties, such as the number of nodes or total network length. Other, more complex metrics can be calculated by importing the vectorized network to specialized network analysis packages. GeGUI is designed to facilitate manual correction of non-planar networks as these may contain artifacts or spurious junctions due to branches crossing each other. It is tailored for but not limited to the processing of networks from microscopy images of Drosophila tracheoles. The networks extracted by NET closely approximate the network depicted in the original image. NET is fast, yields reproducible results and is able to capture the full geometry of the network, including curved branches. Additionally GeGUI allows easy handling and visualization of the networks.

MIPS: analysis and annotation of proteins from whole genomes in 2005

PubMed Central

Mewes, H. W.; Frishman, D.; Mayer, K. F. X.; Münsterkötter, M.; Noubibou, O.; Pagel, P.; Rattei, T.; Oesterheld, M.; Ruepp, A.; Stümpflen, V.

2006-01-01

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein–protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (). PMID:16381839

MIPS: analysis and annotation of proteins from whole genomes in 2005.

PubMed

Mewes, H W; Frishman, D; Mayer, K F X; Münsterkötter, M; Noubibou, O; Pagel, P; Rattei, T; Oesterheld, M; Ruepp, A; Stümpflen, V

2006-01-01

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein-protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.gsf.de).

Flow motifs reveal limitations of the static framework to represent human interactions

NASA Astrophysics Data System (ADS)

Rocha, Luis E. C.; Blondel, Vincent D.

2013-04-01

Networks are commonly used to define underlying interaction structures where infections, information, or other quantities may spread. Although the standard approach has been to aggregate all links into a static structure, some studies have shown that the time order in which the links are established may alter the dynamics of spreading. In this paper, we study the impact of the time ordering in the limits of flow on various empirical temporal networks. By using a random walk dynamics, we estimate the flow on links and convert the original undirected network (temporal and static) into a directed flow network. We then introduce the concept of flow motifs and quantify the divergence in the representativity of motifs when using the temporal and static frameworks. We find that the regularity of contacts and persistence of vertices (common in email communication and face-to-face interactions) result on little differences in the limits of flow for both frameworks. On the other hand, in the case of communication within a dating site and of a sexual network, the flow between vertices changes significantly in the temporal framework such that the static approximation poorly represents the structure of contacts. We have also observed that cliques with 3 and 4 vertices containing only low-flow links are more represented than the same cliques with all high-flow links. The representativity of these low-flow cliques is higher in the temporal framework. Our results suggest that the flow between vertices connected in cliques depend on the topological context in which they are placed and in the time sequence in which the links are established. The structure of the clique alone does not completely characterize the potential of flow between the vertices.

Context-Based Tourism Information Filtering with a Semantic Rule Engine

PubMed Central

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction. PMID:22778584

Context-based tourism information filtering with a semantic rule engine.

PubMed

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction.

Using Target Network Modelling to Increase Battlespace Agility

DTIC Science & Technology

2013-06-01

Moffat, James. (2003) Complexity Theory and Network Centric Warfare. Washington DC: CCRP Moore, David T.. Sensemaking : A Structure for an Intelligence...Ted Hopf’s “Promise of Constructivism in International Relations Theory ” presented in International Security in 1998; and Adler 1998. 5 Look to...of warfighting within a doctrinal framework. Based on 10 years of research12 informed by social theory , experimentation, NATO doctrinal studies and

Information Product Quality in Network Centric Operations

DTIC Science & Technology

2005-05-01

Signori et al.’ s NCOCF .......................................................................................................1 Figure 2...NCW Conceptual Framework Figure 1. Signori et al.’ s NCOCF 1 perspective, having led to what is currently known as the Network Centric Operations...following equation: T QS δ≥∆ , where is the change in entropy, is the change in heat energy and T is some constant S ∆ Qδ 7 temperature. Whenever heat

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

Ahn, Gail-Joon

The project seeks an innovative framework to enable users to access and selectively share resources in distributed environments, enhancing the scalability of information sharing. We have investigated secure sharing & assurance approaches for ad-hoc collaboration, focused on Grids, Clouds, and ad-hoc network environments.

A Framework of Hyperspectral Image Compression using Neural Networks

DOE PAGES

Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; ...

2015-01-01

Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows usmore » to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.« less

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

Advanced algorithms for distributed fusion

NASA Astrophysics Data System (ADS)

Gelfand, A.; Smith, C.; Colony, M.; Bowman, C.; Pei, R.; Huynh, T.; Brown, C.

2008-03-01

The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.

Collective Phenomena Emerging from the Interactions between Dynamical Processes in Multiplex Networks

NASA Astrophysics Data System (ADS)

Nicosia, Vincenzo; Skardal, Per Sebastian; Arenas, Alex; Latora, Vito

2017-03-01

We introduce a framework to intertwine dynamical processes of different nature, each with its own distinct network topology, using a multilayer network approach. As an example of collective phenomena emerging from the interactions of multiple dynamical processes, we study a model where neural dynamics and nutrient transport are bidirectionally coupled in such a way that the allocation of the transport process at one layer depends on the degree of synchronization at the other layer, and vice versa. We show numerically, and we prove analytically, that the multilayer coupling induces a spontaneous explosive synchronization and a heterogeneous distribution of allocations, otherwise not present in the two systems considered separately. Our framework can find application to other cases where two or more dynamical processes such as synchronization, opinion formation, information diffusion, or disease spreading, are interacting with each other.

Study of Water Pollution Early Warning Framework Based on Internet of Things

NASA Astrophysics Data System (ADS)

Chengfang, H.; Xiao, X.; Dingtao, S.; Bo, C.; Xiongfei, W.

2016-06-01

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

A novel framework for command and control of networked sensor systems

NASA Astrophysics Data System (ADS)

Chen, Genshe; Tian, Zhi; Shen, Dan; Blasch, Erik; Pham, Khanh

2007-04-01

In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for future military operations by graphical game theory and advanced knowledge information fusion infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach.

Optimization of hydrometric monitoring network in urban drainage systems using information theory.

PubMed

Yazdi, J

2017-10-01

Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.

Policy reconciliation for access control in dynamic cross-enterprise collaborations

NASA Astrophysics Data System (ADS)

Preuveneers, D.; Joosen, W.; Ilie-Zudor, E.

2018-03-01

In dynamic cross-enterprise collaborations, different enterprises form a - possibly temporary - business relationship. To integrate their business processes, enterprises may need to grant each other limited access to their information systems. Authentication and authorization are key to secure information handling. However, access control policies often rely on non-standardized attributes to describe the roles and permissions of their employees which convolutes cross-organizational authorization when business relationships evolve quickly. Our framework addresses the managerial overhead of continuous updates to access control policies for enterprise information systems to accommodate disparate attribute usage. By inferring attribute relationships, our framework facilitates attribute and policy reconciliation, and automatically aligns dynamic entitlements during the evaluation of authorization decisions. We validate our framework with a Industry 4.0 motivating scenario on networked production where such dynamic cross-enterprise collaborations are quintessential. The evaluation reveals the capabilities and performance of our framework, and illustrates the feasibility of liberating the security administrator from manually provisioning and aligning attributes, and verifying the consistency of access control policies for cross-enterprise collaborations.

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

PubMed

Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

2016-01-01

We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

On imputing function to structure from the behavioural effects of brain lesions.

PubMed

Young, M P; Hilgetag, C C; Scannell, J W

2000-01-29

What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures.

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 23: Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Barclay, Rebecca O.; Bishop, Ann P.; Kennedy, John M.

1992-01-01

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowled reproduction, transfer, and use as equally important components of the process of knowledge diffusion. It is argued that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of the members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

Architecture of a framework for providing information services for public transport.

PubMed

García, Carmelo R; Pérez, Ricardo; Lorenzo, Alvaro; Quesada-Arencibia, Alexis; Alayón, Francisco; Padrón, Gabino

2012-01-01

This paper presents OnRoute, a framework for developing and running ubiquitous software that provides information services to passengers of public transportation, including payment systems and on-route guidance services. To achieve a high level of interoperability, accessibility and context awareness, OnRoute uses the ubiquitous computing paradigm. To guarantee the quality of the software produced, the reliable software principles used in critical contexts, such as automotive systems, are also considered by the framework. The main components of its architecture (run-time, system services, software components and development discipline) and how they are deployed in the transportation network (stations and vehicles) are described in this paper. Finally, to illustrate the use of OnRoute, the development of a guidance service for travellers is explained.

NASA/DoD Aerospace Knowledge Diffusion Research Project. XXIII - Information technology and aerospace knowledge diffusion: Exploring the intermediary-end user interface in a policy framework

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Barclay, Rebecca O.; Bishop, Ann P.; Kennedy, John M.

1992-01-01

Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowledge production, transfer, and use as equally important components of the process of knowledge diffusion. This article argues that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.

Applying a Health Network approach to translate evidence-informed policy into practice: a review and case study on musculoskeletal health.

PubMed

Briggs, Andrew M; Bragge, Peter; Slater, Helen; Chan, Madelynn; Towler, Simon C B

2012-11-14

While translation of evidence into health policy and practice is recognised as critical to optimising health system performance and health-related outcomes for consumers, mechanisms to effectively achieve these goals are neither well understood, nor widely communicated. Health Networks represent a framework which offers a possible solution to this dilemma, particularly in light of emerging evidence regarding the importance of establishing relationships between stakeholders and identifying clinical leaders to drive evidence integration and translation into policy. This is particularly important for service delivery related to chronic diseases. In Western Australia (WA), disease and population-specific Health Networks are comprised of cross-discipline stakeholders who work collaboratively to develop evidence-informed policies and drive their implementation. Since establishment of the Health Networks in WA, over 50 evidence-informed Models of Care (MoCs) have been produced across 18 condition or population-focused Networks. The aim of this paper is to provide an overview of the Health Network framework in facilitating the translation of evidence into policy and practice with a particular focus on musculoskeletal health. A review of activities of the WA Musculoskeletal Health Network was undertaken, focussing on outcomes and the processes used to achieve them in the context of: development of policy, procurement of funding, stakeholder engagement, publications, and projects undertaken by the Network which aligned to implementation of MoCs.The Musculoskeletal Health Network has developed four MoCs which reflect Australian National Health Priority Areas. Establishment of community-based services for consumers with musculoskeletal health conditions is a key recommendation from these MoCs. Through mapping barriers and enablers to policy implementation, working groups, led by local clinical leaders and supported by the broader Network and government officers, have undertaken a range of integrated projects, such as the establishment of a community-based, multidisciplinary rheumatology service. The success of these projects has been contingent on developing relationships between key stakeholders across the health system. In WA, Networks have provided a sustainable mechanism to meaningfully engage consumers, carers, clinicians and other stakeholders; provided a forum to exchange ideas, information and evidence; and collaboratively plan and deliver evidence-based and contextually-appropriate health system improvements for consumers.

Applying a Health Network approach to translate evidence-informed policy into practice: A review and case study on musculoskeletal health

PubMed Central

2012-01-01

Background While translation of evidence into health policy and practice is recognised as critical to optimising health system performance and health-related outcomes for consumers, mechanisms to effectively achieve these goals are neither well understood, nor widely communicated. Health Networks represent a framework which offers a possible solution to this dilemma, particularly in light of emerging evidence regarding the importance of establishing relationships between stakeholders and identifying clinical leaders to drive evidence integration and translation into policy. This is particularly important for service delivery related to chronic diseases. In Western Australia (WA), disease and population-specific Health Networks are comprised of cross-discipline stakeholders who work collaboratively to develop evidence-informed policies and drive their implementation. Since establishment of the Health Networks in WA, over 50 evidence-informed Models of Care (MoCs) have been produced across 18 condition or population-focused Networks. The aim of this paper is to provide an overview of the Health Network framework in facilitating the translation of evidence into policy and practice with a particular focus on musculoskeletal health. Case presentation A review of activities of the WA Musculoskeletal Health Network was undertaken, focussing on outcomes and the processes used to achieve them in the context of: development of policy, procurement of funding, stakeholder engagement, publications, and projects undertaken by the Network which aligned to implementation of MoCs. The Musculoskeletal Health Network has developed four MoCs which reflect Australian National Health Priority Areas. Establishment of community-based services for consumers with musculoskeletal health conditions is a key recommendation from these MoCs. Through mapping barriers and enablers to policy implementation, working groups, led by local clinical leaders and supported by the broader Network and government officers, have undertaken a range of integrated projects, such as the establishment of a community-based, multidisciplinary rheumatology service. The success of these projects has been contingent on developing relationships between key stakeholders across the health system. Conclusions In WA, Networks have provided a sustainable mechanism to meaningfully engage consumers, carers, clinicians and other stakeholders; provided a forum to exchange ideas, information and evidence; and collaboratively plan and deliver evidence-based and contextually-appropriate health system improvements for consumers. PMID:23151082

Biana: a software framework for compiling biological interactions and analyzing networks

PubMed Central

2010-01-01

Background The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. Results We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. Conclusions BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules. PMID:20105306

Biana: a software framework for compiling biological interactions and analyzing networks.

PubMed

Garcia-Garcia, Javier; Guney, Emre; Aragues, Ramon; Planas-Iglesias, Joan; Oliva, Baldo

2010-01-27

The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

An information theory account of cognitive control

PubMed Central

Fan, Jin

2014-01-01

Our ability to efficiently process information and generate appropriate responses depends on the processes collectively called cognitive control. Despite a considerable focus in the literature on the cognitive control of information processing, neural mechanisms underlying control are still unclear, and have not been characterized by considering the quantity of information to be processed. A novel and comprehensive account of cognitive control is proposed using concepts from information theory, which is concerned with communication system analysis and the quantification of information. This account treats the brain as an information-processing entity where cognitive control and its underlying brain networks play a pivotal role in dealing with conditions of uncertainty. This hypothesis and theory article justifies the validity and properties of such an account and relates experimental findings to the frontoparietal network under the framework of information theory. PMID:25228875

The electronic transfer of information and aerospace knowledge diffusion

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Bishop, Ann P.; Barclay, Rebecca O.; Kennedy, John M.

1992-01-01

Increasing reliance on and investment in information technology and electronic networking systems presupposes that computing and information technology will play a motor role in the diffusion of aerospace knowledge. Little is known, however, about actual information technology needs, uses, and problems within the aerospace knowledge diffusion process. The authors state that the potential contributions of information technology to increased productivity and competitiveness will be diminished unless empirically derived knowledge regarding the information-seeking behavior of the members of the social system - those who are producing, transferring, and using scientific and technical information - is incorporated into a new technology policy framework. Research into the use of information technology and electronic networks by U.S. aerospace engineers and scientists, collected as part of a research project designed to study aerospace knowledge diffusion, is presented in support of this assertion.

Value Driven Information Processing and Fusion

DTIC Science & Technology

2016-03-01

consensus approach allows a decentralized approach to achieve the optimal error exponent of the centralized counterpart, a conclusion that is signifi...SECURITY CLASSIFICATION OF: The objective of the project is to develop a general framework for value driven decentralized information processing...including: optimal data reduction in a network setting for decentralized inference with quantization constraint; interactive fusion that allows queries and

Development and Demonstration of a Statistical Data Base System for Library and Network Planning and Evaluation. Fourth Quarterly Report.

ERIC Educational Resources Information Center

Jones, Dennis; And Others

The National Center for Higher Education Management Systems (NCHEMS) has completed the development and demonstration of a library statistical data base. The data base, or management information system, was developed for administrators of public and academic libraries. The system provides administrators with a framework of information and…

Laboratory Evaluation of Dynamic Traffic Assignment Systems: Requirements, Framework, and System Design

DOT National Transportation Integrated Search

1997-01-01

The success of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) depends on the availability and dissemination of timely and accurate estimates of current and emerging traffic network conditions. Real-time Dy...

Implementing an Integrated Network Defense Construct

DTIC Science & Technology

2013-06-01

hierarchical structure under the Air Defence of Great Britain initiative ( Checkland and Holwell, 1998). This implementation was the earliest concept...Framework for Detecting and Defending against Insider IT Attacks. Auerbach Publications. 2008. Checkland , Peter and Holwell, Sue. "Information, Systems

Large-scale P2P network based distributed virtual geographic environment (DVGE)

NASA Astrophysics Data System (ADS)

Tan, Xicheng; Yu, Liang; Bian, Fuling

2007-06-01

Virtual Geographic Environment has raised full concern as a kind of software information system that helps us understand and analyze the real geographic environment, and it has also expanded to application service system in distributed environment--distributed virtual geographic environment system (DVGE), and gets some achievements. However, limited by the factor of the mass data of VGE, the band width of network, as well as numerous requests and economic, etc. DVGE still faces some challenges and problems which directly cause the current DVGE could not provide the public with high-quality service under current network mode. The Rapid development of peer-to-peer network technology has offered new ideas of solutions to the current challenges and problems of DVGE. Peer-to-peer network technology is able to effectively release and search network resources so as to realize efficient share of information. Accordingly, this paper brings forth a research subject on Large-scale peer-to-peer network extension of DVGE as well as a deep study on network framework, routing mechanism, and DVGE data management on P2P network.

Bayesian network prior: network analysis of biological data using external knowledge

PubMed Central

Isci, Senol; Dogan, Haluk; Ozturk, Cengizhan; Otu, Hasan H.

2014-01-01

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. Contact: hasan.otu@bilgi.edu.tr Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24215027

Fundamental trade-offs between information flow in single cells and cellular populations.

PubMed

Suderman, Ryan; Bachman, John A; Smith, Adam; Sorger, Peter K; Deeds, Eric J

2017-05-30

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3-4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

Prestimulus Network Integration of Auditory Cortex Predisposes Near-Threshold Perception Independently of Local Excitability

PubMed Central

Leske, Sabine; Ruhnau, Philipp; Frey, Julia; Lithari, Chrysa; Müller, Nadia; Hartmann, Thomas; Weisz, Nathan

2015-01-01

An ever-increasing number of studies are pointing to the importance of network properties of the brain for understanding behavior such as conscious perception. However, with regards to the influence of prestimulus brain states on perception, this network perspective has rarely been taken. Our recent framework predicts that brain regions crucial for a conscious percept are coupled prior to stimulus arrival, forming pre-established pathways of information flow and influencing perceptual awareness. Using magnetoencephalography (MEG) and graph theoretical measures, we investigated auditory conscious perception in a near-threshold (NT) task and found strong support for this framework. Relevant auditory regions showed an increased prestimulus interhemispheric connectivity. The left auditory cortex was characterized by a hub-like behavior and an enhanced integration into the brain functional network prior to perceptual awareness. Right auditory regions were decoupled from non-auditory regions, presumably forming an integrated information processing unit with the left auditory cortex. In addition, we show for the first time for the auditory modality that local excitability, measured by decreased alpha power in the auditory cortex, increases prior to conscious percepts. Importantly, we were able to show that connectivity states seem to be largely independent from local excitability states in the context of a NT paradigm. PMID:26408799

A Decentralized Framework for Multi-Agent Robotic Systems

PubMed Central

2018-01-01

Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles. PMID:29389849

Leaf Extraction and Analysis Framework Graphical User Interface: Segmenting and Analyzing the Structure of Leaf Veins and Areoles1[W][OA

PubMed Central

Price, Charles A.; Symonova, Olga; Mileyko, Yuriy; Hilley, Troy; Weitz, Joshua S.

2011-01-01

Interest in the structure and function of physical biological networks has spurred the development of a number of theoretical models that predict optimal network structures across a broad array of taxonomic groups, from mammals to plants. In many cases, direct tests of predicted network structure are impossible given the lack of suitable empirical methods to quantify physical network geometry with sufficient scope and resolution. There is a long history of empirical methods to quantify the network structure of plants, from roots, to xylem networks in shoots and within leaves. However, with few exceptions, current methods emphasize the analysis of portions of, rather than entire networks. Here, we introduce the Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI), a user-assisted software tool that facilitates improved empirical understanding of leaf network structure. LEAF GUI takes images of leaves where veins have been enhanced relative to the background, and following a series of interactive thresholding and cleaning steps, returns a suite of statistics and information on the structure of leaf venation networks and areoles. Metrics include the dimensions, position, and connectivity of all network veins, and the dimensions, shape, and position of the areoles they surround. Available for free download, the LEAF GUI software promises to facilitate improved understanding of the adaptive and ecological significance of leaf vein network structure. PMID:21057114

Leaf extraction and analysis framework graphical user interface: segmenting and analyzing the structure of leaf veins and areoles.

PubMed

Price, Charles A; Symonova, Olga; Mileyko, Yuriy; Hilley, Troy; Weitz, Joshua S

2011-01-01

Interest in the structure and function of physical biological networks has spurred the development of a number of theoretical models that predict optimal network structures across a broad array of taxonomic groups, from mammals to plants. In many cases, direct tests of predicted network structure are impossible given the lack of suitable empirical methods to quantify physical network geometry with sufficient scope and resolution. There is a long history of empirical methods to quantify the network structure of plants, from roots, to xylem networks in shoots and within leaves. However, with few exceptions, current methods emphasize the analysis of portions of, rather than entire networks. Here, we introduce the Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI), a user-assisted software tool that facilitates improved empirical understanding of leaf network structure. LEAF GUI takes images of leaves where veins have been enhanced relative to the background, and following a series of interactive thresholding and cleaning steps, returns a suite of statistics and information on the structure of leaf venation networks and areoles. Metrics include the dimensions, position, and connectivity of all network veins, and the dimensions, shape, and position of the areoles they surround. Available for free download, the LEAF GUI software promises to facilitate improved understanding of the adaptive and ecological significance of leaf vein network structure.

A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics

PubMed Central

Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan

2015-01-01

Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859

Culture, social networks and HIV vulnerability among men who have sex with men in Indonesia.

PubMed

Fauk, Nelsensius Klau; Merry, Maria Silvia; Sigilipoe, Mitra Andhini; Putra, Sukma; Mwanri, Lillian

2017-01-01

The current study aimed to explore cultural and social network influence on HIV vulnerability among Men who have Sex with Men (MSM) population in Yogyakarta, Indonesia. A qualitative inquiry employing in-depth one-on-one interviews was carried out with 24 MSM participants in July 2015. Data were analysed using a framework analysis and guided by the Social Networks Theory (SNT) as a conceptual framework. Findings indicated that prohibitive cultural perspectives and norms against same-sex marriage made them to conceal their sexual orientation and thus secretively engaging in unprotected sex that increased their predisposition to HIV transmission. The prohibitive cultures were also instrumental in the formation of MSM sexual networks that provided supportive environment for HIV-risky sexual practices among network partners. These findings provide information that can be used to improve HIV/AIDS service practices and policies. However, further studies with large numbers of MSM would be needed to improve the understanding of other HIV vulnerability determinants, the unique needs of MSM, and what and how programs could be conducted to reduce HIV vulnerability among MSM population.

Progress of the European Assistive Technology Information Network.

PubMed

Gower, Valerio; Andrich, Renzo

2015-01-01

The European Assistive Technology Information Network (EASTIN), launched in 2005 as the result of a collaborative EU project, provides information on Assistive Technology products and related material through the website www.eastin.eu. In the past few years several advancements have been implemented on the EASTIN website thanks to the contribution of EU funded projects, including a multilingual query processing component for supporting non expert users, a user rating and comment facility, and a detailed taxonomy for the description of ICT based assistive products. Recently, within the framework of the EU funded project Cloud4All, the EASTIN information system has also been federated with the Unified Listing of assistive products, one of the building blocks of the Global Public Inclusive Infrastructure initiative.

Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

PubMed Central

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy. PMID:22163641

Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

PubMed

Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

2010-01-01

In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

Assessing Digital Information Literacy in Higher Education: A Review of Existing Frameworks and Assessments with Recommendations for Next-Generation Assessment. Research Report. ETS RR-16-32

ERIC Educational Resources Information Center

Sparks, Jesse R.; Katz, Irvin R.; Beile, Penny M.

2016-01-01

Digital information literacy (DIL)--generally defined as the ability to obtain, understand, evaluate, and use information in a variety of digital technology contexts--is a critically important skill deemed necessary for success in higher education as well as in the global networked economy. To determine whether college graduates possess the…

Accelerating Adverse Outcome Pathway (AOP) development via computationally predicted AOP networks

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

Cryptography for a High-Assurance Web-Based Enterprise

DTIC Science & Technology

2013-10-01

2. Other Cryptographic services - Java provides many cryptographic services through the Java Cryptography Architecture (JCA) framework. The...id=2125 [7]. Miller, Sandra Kay, Fiber Optic Networks Vulnerable to Attack, Information Security Magazine, November 15, 2006, [8]. José R.C

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

PubMed Central

Kerkhofs, Johan; Geris, Liesbet

2015-01-01

Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

PubMed

Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

2015-08-11

Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

Information at the edge of chaos in fluid neural networks

NASA Astrophysics Data System (ADS)

Solé, Ricard V.; Miramontes, Octavio

1995-01-01

Fluid neural networks, defined as neural nets of mobile elements with random activation, are studied by means of several approaches. They are proposed as a theoretical framework for a wide class of systems as insect societies, collectives of robots or the immune system. The critical properties of this model are also analysed, showing the existence of a critical boundary in parameter space where maximum information transfer occurs. In this sense, this boundary is in fact an example of the “edge of chaos” in systems like those described in our approach. Recent experiments with ant colonies seem to confirm our result.

Network-Based Real-time Integrated Fire Detection and Alarm (FDA) System with Building Automation

NASA Astrophysics Data System (ADS)

Anwar, F.; Boby, R. I.; Rashid, M. M.; Alam, M. M.; Shaikh, Z.

2017-11-01

Fire alarm systems have become increasingly an important lifesaving technology in many aspects, such as applications to detect, monitor and control any fire hazard. A large sum of money is being spent annually to install and maintain the fire alarm systems in buildings to protect property and lives from the unexpected spread of fire. Several methods are already developed and it is improving on a daily basis to reduce the cost as well as increase quality. An integrated Fire Detection and Alarm (FDA) systems with building automation was studied, to reduce cost and improve their reliability by preventing false alarm. This work proposes an improved framework for FDA system to ensure a robust intelligent network of FDA control panels in real-time. A shortest path algorithmic was chosen for series of buildings connected by fiber optic network. The framework shares information and communicates with each fire alarm panels connected in peer to peer configuration and declare the network state using network address declaration from any building connected in network. The fiber-optic connection was proposed to reduce signal noises, thus increasing large area coverage, real-time communication and long-term safety. Based on this proposed method an experimental setup was designed and a prototype system was developed to validate the performance in practice. Also, the distributed network system was proposed to connect with an optional remote monitoring terminal panel to validate proposed network performance and ensure fire survivability where the information is sequentially transmitted. The proposed FDA system is different from traditional fire alarm and detection system in terms of topology as it manages group of buildings in an optimal and efficient manner.Introduction

Encapsulating urban traffic rhythms into road networks.

PubMed

Wang, Junjie; Wei, Dong; He, Kun; Gong, Hang; Wang, Pu

2014-02-20

Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution.

Encapsulating Urban Traffic Rhythms into Road Networks

PubMed Central

Wang, Junjie; Wei, Dong; He, Kun; Gong, Hang; Wang, Pu

2014-01-01

Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution. PMID:24553203

The emerging potential for network analysis to inform precision cancer medicine.

PubMed

Ozturk, Kivilcim; Dow, Michelle; Carlin, Daniel E; Bejar, Rafael; Carter, Hannah

2018-06-14

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis and the path for such tools to the clinic. Copyright © 2018. Published by Elsevier Ltd.

Auditing Albaha University Network Security using in-house Developed Penetration Tool

NASA Astrophysics Data System (ADS)

Alzahrani, M. E.

2018-03-01

Network security becomes very important aspect in any enterprise/organization computer network. If important information of the organization can be accessed by anyone it may be used against the organization for further own interest. Thus, network security comes into it roles. One of important aspect of security management is security audit. Security performance of Albaha university network is relatively low (in term of the total controls outlined in the ISO 27002 security control framework). This paper proposes network security audit tool to address issues in Albaha University network. The proposed penetration tool uses Nessus and Metasploit tool to find out the vulnerability of a site. A regular self-audit using inhouse developed tool will increase the overall security and performance of Albaha university network. Important results of the penetration test are discussed.

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules

NASA Astrophysics Data System (ADS)

Menon, Govind; Krishnan, J.

2016-07-01

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules.

PubMed

Menon, Govind; Krishnan, J

2016-07-21

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Enterprise systems security management: a framework for breakthrough protection

NASA Astrophysics Data System (ADS)

Farroha, Bassam S.; Farroha, Deborah L.

2010-04-01

Securing the DoD information network is a tremendous task due to its size, access locations and the amount of network intrusion attempts on a daily basis. This analysis investigates methods/architecture options to deliver capabilities for secure information sharing environment. Crypto-binding and intelligent access controls are basic requirements for secure information sharing in a net-centric environment. We introduce many of the new technology components to secure the enterprise. The cooperative mission requirements lead to developing automatic data discovery and data stewards granting access to Cross Domain (CD) data repositories or live streaming data. Multiple architecture models are investigated to determine best-of-breed approaches including SOA and Private/Public Clouds.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Wireless physical layer security

NASA Astrophysics Data System (ADS)

Poor, H. Vincent; Schaefer, Rafael F.

2017-01-01

Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research devoted to using this framework to develop a greater understanding of the fundamental ability of the so-called physical layer to provide security in wireless networks. Moreover, this approach is also suggestive in many cases of coding techniques that can approach fundamental limits in practice and of techniques for other security tasks such as authentication. This paper provides an overview of these developments.

Toward implementation of a national ground water monitoring network

USGS Publications Warehouse

Schreiber, Robert P.; Cunningham, William L.; Copeland, Rick; Frederick, Kevin D.

2008-01-01

The Federal Advisory Committee on Water Information's (ACWI) Subcommittee on Ground Water (SOGW) has been working steadily to develop and encourage implementation of a nationwide, long-term ground-water quantity and quality monitoring framework. Significant progress includes the planned submission this fall of a draft framework document to the full committee. The document will include recommendations for implementation of the network and continued acknowledgment at the federal and state level of ACWI's potential role in national monitoring toward an improved assessment of the nation's water reserves. The SOGW mission includes addressing several issues regarding network design, as well as developing plans for concept testing, evaluation of costs and benefits, and encouraging the movement from pilot-test results to full-scale implementation within a reasonable time period. With the recent attention to water resource sustainability driven by severe droughts, concerns over global warming effects, and persistent water supply problems, the SOGW mission is now even more critical.

Reliable Adaptive Video Streaming Driven by Perceptual Semantics for Situational Awareness

PubMed Central

Pimentel-Niño, M. A.; Saxena, Paresh; Vazquez-Castro, M. A.

2015-01-01

A novel cross-layer optimized video adaptation driven by perceptual semantics is presented. The design target is streamed live video to enhance situational awareness in challenging communications conditions. Conventional solutions for recreational applications are inadequate and novel quality of experience (QoE) framework is proposed which allows fully controlled adaptation and enables perceptual semantic feedback. The framework relies on temporal/spatial abstraction for video applications serving beyond recreational purposes. An underlying cross-layer optimization technique takes into account feedback on network congestion (time) and erasures (space) to best distribute available (scarce) bandwidth. Systematic random linear network coding (SRNC) adds reliability while preserving perceptual semantics. Objective metrics of the perceptual features in QoE show homogeneous high performance when using the proposed scheme. Finally, the proposed scheme is in line with content-aware trends, by complying with information-centric-networking philosophy and architecture. PMID:26247057

Wireless physical layer security.

PubMed

Poor, H Vincent; Schaefer, Rafael F

2017-01-03

Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research devoted to using this framework to develop a greater understanding of the fundamental ability of the so-called physical layer to provide security in wireless networks. Moreover, this approach is also suggestive in many cases of coding techniques that can approach fundamental limits in practice and of techniques for other security tasks such as authentication. This paper provides an overview of these developments.

Wireless physical layer security

PubMed Central

Schaefer, Rafael F.

2017-01-01

Security in wireless networks has traditionally been considered to be an issue to be addressed separately from the physical radio transmission aspects of wireless systems. However, with the emergence of new networking architectures that are not amenable to traditional methods of secure communication such as data encryption, there has been an increase in interest in the potential of the physical properties of the radio channel itself to provide communications security. Information theory provides a natural framework for the study of this issue, and there has been considerable recent research devoted to using this framework to develop a greater understanding of the fundamental ability of the so-called physical layer to provide security in wireless networks. Moreover, this approach is also suggestive in many cases of coding techniques that can approach fundamental limits in practice and of techniques for other security tasks such as authentication. This paper provides an overview of these developments. PMID:28028211

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Stochastic Online Learning in Dynamic Networks under Unknown Models

DTIC Science & Technology

2016-08-02

Repeated Game with Incomplete Information, IEEE International Conference on Acoustics, Speech, and Signal Processing. 20-MAR-16, Shanghai, China...in a game theoretic framework for the application of multi-seller dynamic pricing with unknown demand models. We formulated the problem as an...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning

Simulation Methods for Design of Networked Power Electronics and Information Systems

DTIC Science & Technology

2014-07-01

Insertion of latency in every branch and at every node permits the system model to be efficiently distributed across many separate computing cores. An... the system . We demonstrated extensibility and generality of the Virtual Test Bed (VTB) framework to support multiple solvers and their associated...Information Systems Objectives The overarching objective of this program is to develop methods for fast

General practice and the Internet revolution. Use of an Internet social network to communicate information on prevention in France.

PubMed

Veuillotte, Isabelle; Morel, Gilles; Pitois, Stephane; Haler, Renaud; Mercier, Patricia; Aubry, Catherine; Cannet, Didier

2015-03-01

The popularity of social networks and the huge number of exchanges have made them immensely important for the communication of information. This French study explored prevention in hereditary breast cancer using a social Internet network to communicate information. The principal objective was to inform French women aged from 20 to 50 years, using the social network Facebook, about the warning signs of breast cancer in cases of a predisposition to the disease due to a genetic mutation. The secondary objectives were to inform people about screening. An information page entitled "hereditary breast cancer: and if I was concerned?" was distributed in 3 different ways: from friend to friend, via groups of persons, and by targeted advertising. Four articles and 11 messages were distributed over 27 days. The total number of visits for this period amounted to 1019. A total of 81 percent of the Internauts were women and 55 percent of the visitors were aged between 25 and 44 years. Other information campaigns concerning public health issues could be conducted using this tool. A legal framework is necessary to preserve the quality of the medical information provided. This new means of communication, used for prevention purposes, will add to other frequently used methods of communication. © The Author(s) 2013.

Sensor assignment to mission in AI-TECD

NASA Astrophysics Data System (ADS)

Ganger, Robert; de Mel, Geeth; Pham, Tien; Rudnicki, Ronald; Schreiber, Yonatan

2016-05-01

Sensor-mission assignment involves the allocation of sensors and other information-providing resources to missions in order to cover the information needs of the individual tasks within each mission. The importance of efficient and effective means to find appropriate resources for tasks is exacerbated in the coalition context where the operational environment is dynamic and a multitude of critically important tasks need to achieve their collective goals to meet the objectives of the coalition. The Sensor Assignment to Mission (SAM) framework—a research product of the International Technology Alliance in Network and Information Sciences (NIS-ITA) program—provided the first knowledge intensive resource selection approach for the sensor network domain so that contextual information could be used to effectively select resources for tasks in coalition environments. Recently, CUBRC, Inc. was tasked with operationalizing the SAM framework through the use of the I2WD Common Core Ontologies for the Communications-Electronics Research, Development and Engineering Center (CERDEC) sponsored Actionable Intelligence Technology Enabled Capabilities Demonstration (AI-TECD). The demonstration event took place at Fort Dix, New Jersey during July 2015, and this paper discusses the integration and the successful demonstration of the SAM framework within the AI-TECD, lessons learned, and its potential impact in future operations.

NASA/DoD Aerospace Knowledge Diffusion Research Project. Paper 30: The electronic transfer of information and aerospace knowledge diffusion

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Bishop, Ann P.; Barclay, Rebecca O.; Kennedy, John M.

1992-01-01

Increasing reliance on and investment in information technology and electronic networking systems presupposes that computing and information technology will play a major role in the diffusion of aerospace knowledge. Little is known, however, about actual information technology needs, uses, and problems within the aerospace knowledge diffusion process. The authors state that the potential contributions of information technology to increased productivity and competitiveness will be diminished unless empirically derived knowledge regarding the information-seeking behavior of the members of the social system - those who are producing, transferring, and using scientific and technical information - is incorporated into a new technology policy framework. Research into the use of information technology and electronic networks by U.S. aerospace engineers and scientists, collected as part of a research project designed to study aerospace knowledge diffusion, is presented in support of this assertion.

Vector Symbolic Spiking Neural Network Model of Hippocampal Subarea CA1 Novelty Detection Functionality.

PubMed

Agerskov, Claus

2016-04-01

A neural network model is presented of novelty detection in the CA1 subdomain of the hippocampal formation from the perspective of information flow. This computational model is restricted on several levels by both anatomical information about hippocampal circuitry and behavioral data from studies done in rats. Several studies report that the CA1 area broadcasts a generalized novelty signal in response to changes in the environment. Using the neural engineering framework developed by Eliasmith et al., a spiking neural network architecture is created that is able to compare high-dimensional vectors, symbolizing semantic information, according to the semantic pointer hypothesis. This model then computes the similarity between the vectors, as both direct inputs and a recalled memory from a long-term memory network by performing the dot-product operation in a novelty neural network architecture. The developed CA1 model agrees with available neuroanatomical data, as well as the presented behavioral data, and so it is a biologically realistic model of novelty detection in the hippocampus, which can provide a feasible explanation for experimentally observed dynamics.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

Architecture of a Framework for Providing Information Services for Public Transport

PubMed Central

García, Carmelo R.; Pérez, Ricardo; Lorenzo, Álvaro; Quesada-Arencibia, Alexis; Alayón, Francisco; Padrón, Gabino

2012-01-01

This paper presents OnRoute, a framework for developing and running ubiquitous software that provides information services to passengers of public transportation, including payment systems and on-route guidance services. To achieve a high level of interoperability, accessibility and context awareness, OnRoute uses the ubiquitous computing paradigm. To guarantee the quality of the software produced, the reliable software principles used in critical contexts, such as automotive systems, are also considered by the framework. The main components of its architecture (run-time, system services, software components and development discipline) and how they are deployed in the transportation network (stations and vehicles) are described in this paper. Finally, to illustrate the use of OnRoute, the development of a guidance service for travellers is explained. PMID:22778585

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Rahmani, Amirreza; Mesbahi, Mehran; Fathpour, Nanaz; Hadaegh, Fred Y.

2008-01-01

In this work, we develop an approach to formation estimation by explicitly characterizing formation's system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer/estimator design by relaxing the accessibility to the global state information by a centralized observer/estimator- and in turn- providing an analysis and synthesis framework for formation observers/estimators that rely on local measurements. The noveltyof our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations inthe topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.

Information processing in echo state networks at the edge of chaos.

PubMed

Boedecker, Joschka; Obst, Oliver; Lizier, Joseph T; Mayer, N Michael; Asada, Minoru

2012-09-01

We investigate information processing in randomly connected recurrent neural networks. It has been shown previously that the computational capabilities of these networks are maximized when the recurrent layer is close to the border between a stable and an unstable dynamics regime, the so called edge of chaos. The reasons, however, for this maximized performance are not completely understood. We adopt an information-theoretical framework and are for the first time able to quantify the computational capabilities between elements of these networks directly as they undergo the phase transition to chaos. Specifically, we present evidence that both information transfer and storage in the recurrent layer are maximized close to this phase transition, providing an explanation for why guiding the recurrent layer toward the edge of chaos is computationally useful. As a consequence, our study suggests self-organized ways of improving performance in recurrent neural networks, driven by input data. Moreover, the networks we study share important features with biological systems such as feedback connections and online computation on input streams. A key example is the cerebral cortex, which was shown to also operate close to the edge of chaos. Consequently, the behavior of model systems as studied here is likely to shed light on reasons why biological systems are tuned into this specific regime.

A Proposed Framework for Collaborative Design in a Virtual Environment

NASA Astrophysics Data System (ADS)

Breland, Jason S.; Shiratuddin, Mohd Fairuz

This paper describes a proposed framework for a collaborative design in a virtual environment. The framework consists of components that support a true collaborative design in a real-time 3D virtual environment. In support of the proposed framework, a prototype application is being developed. The authors envision the framework will have, but not limited to the following features: (1) real-time manipulation of 3D objects across the network, (2) support for multi-designer activities and information access, (3) co-existence within same virtual space, etc. This paper also discusses a proposed testing to determine the possible benefits of a collaborative design in a virtual environment over other forms of collaboration, and results from a pilot test.

Brain and Cognitive Reserve: Translation via Network Control Theory

PubMed Central

Medaglia, John Dominic; Pasqualetti, Fabio; Hamilton, Roy H.; Thompson-Schill, Sharon L.; Bassett, Danielle S.

2017-01-01

Traditional approaches to understanding the brain’s resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive “reserve,” associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention. PMID:28104411

Social Networks as a Source of Competitive Advantage for the Firm.

ERIC Educational Resources Information Center

Van Laere, Kristien; Heene, Aime

2003-01-01

Proposes a conceptual framework for managing relationships of small and medium-sized enterprises, based on the necessity of cooperation for survival. Describes characteristics of embedded relationship in stakeholder interactions, including trust, durability, information transfer, and collaboration. (Contains 72 references.) (SK)

Achieving biopolymer synergy in systems chemistry.

PubMed

Bai, Yushi; Chotera, Agata; Taran, Olga; Liang, Chen; Ashkenasy, Gonen; Lynn, David G

2018-05-31

Synthetic and materials chemistry initiatives have enabled the translation of the macromolecular functions of biology into synthetic frameworks. These explorations into alternative chemistries of life attempt to capture the versatile functionality and adaptability of biopolymers in new orthogonal scaffolds. Information storage and transfer, however, so beautifully represented in the central dogma of biology, require multiple components functioning synergistically. Over a single decade, the emerging field of systems chemistry has begun to catalyze the construction of mutualistic biopolymer networks, and this review begins with the foundational small-molecule-based dynamic chemical networks and peptide amyloid-based dynamic physical networks on which this effort builds. The approach both contextualizes the versatile approaches that have been developed to enrich chemical information in synthetic networks and highlights the properties of amyloids as potential alternative genetic elements. The successful integration of both chemical and physical networks through β-sheet assisted replication processes further informs the synergistic potential of these networks. Inspired by the cooperative synergies of nucleic acids and proteins in biology, synthetic nucleic-acid-peptide chimeras are now being explored to extend their informational content. With our growing range of synthetic capabilities, structural analyses, and simulation technologies, this foundation is radically extending the structural space that might cross the Darwinian threshold for the origins of life as well as creating an array of alternative systems capable of achieving the progressive growth of novel informational materials.

Social Support: A Mixed Blessing for Women in Substance Abuse Treatment

PubMed Central

Tracy, Elizabeth M.; Munson, Michelle R.; Peterson, Lance T.; Floersch, Jerry E.

2010-01-01

Using a personal social network framework, this qualitative study sought to understand how women in substance abuse treatment describe their network members' supportive and unsupportive behaviors related to recovery. Eighty-six women were interviewed from residential and outpatient substance abuse treatment programs. Positive and negative aspects of women's social networks were assessed via open-ended questions. Analysis was guided by grounded theory techniques using three coders. The findings extend classic social support concepts such as emotional, tangible, and informational support. Practice implications are presented in light of the potential roles network members may play in substance use and recovery. PMID:20953326

The Digital Anatomist Distributed Framework and Its Applications to Knowledge-based Medical Imaging

PubMed Central

Brinkley, James F.; Rosse, Cornelius

1997-01-01

Abstract The domain of medical imaging is anatomy. Therefore, anatomic knowledge should be a rational basis for organizing and analyzing images. The goals of the Digital Anatomist Program at the University of Washington include the development of an anatomically based software framework for organizing, analyzing, visualizing and utilizing biomedical information. The framework is based on representations for both spatial and symbolic anatomic knowledge, and is being implemented in a distributed architecture in which multiple client programs on the Internet are used to update and access an expanding set of anatomical information resources. The development of this framework is driven by several practical applications, including symbolic anatomic reasoning, knowledge based image segmentation, anatomy information retrieval, and functional brain mapping. Since each of these areas involves many difficult image processing issues, our research strategy is an evolutionary one, in which applications are developed somewhat independently, and partial solutions are integrated in a piecemeal fashion, using the network as the substrate. This approach assumes that networks of interacting components can synergistically work together to solve problems larger than either could solve on its own. Each of the individual projects is described, along with evaluations that show that the individual components are solving the problems they were designed for, and are beginning to interact with each other in a synergistic manner. We argue that this synergy will increase, not only within our own group, but also among groups as the Internet matures, and that an anatomic knowledge base will be a useful means for fostering these interactions. PMID:9147337

Public Health Network Structure and Collaboration Effectiveness during the 2015 MERS Outbreak in South Korea: An Institutional Collective Action Framework

PubMed Central

Andrew, Simon A.

2017-01-01

Following the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea, this research aims to examine the structural effect of public health network explaining collaboration effectiveness, which is defined as joint efforts to improve quality of service provision, cost savings, and coordination. We tested the bonding and bridging effects on collaboration effectiveness during the MERS outbreak response by utilizing an institutional collective action framework. The analysis results of 114 organizations responding during the crisis show a significant association between the bonding effect and the effectiveness of collaboration, as well as a positive association between risk communication in disseminating public health information and the effectiveness of collaboration. PMID:28914780

SHINE: Strategic Health Informatics Networks for Europe.

PubMed

Kruit, D; Cooper, P A

1994-10-01

The mission of SHINE is to construct an open systems framework for the development of regional community healthcare telematic services that support and add to the strategic business objectives of European healthcare providers and purchasers. This framework will contain a Methodology, that identifies healthcare business processes and develops a supporting IT strategy, and the Open Health Environment. This consists of an architecture and information standards that are 'open' and will be available to any organisation wishing to construct SHINE conform regional healthcare telematic services. Results are: generic models, e.g., regional healthcare business networks, IT strategies; demonstrable, e.g., pilot demonstrators, application and service prototypes; reports, e.g., SHINE Methodology, pilot specifications & evaluations; proposals, e.g., service/interface specifications, standards conformance.

A FRAMEWORK FOR ATTRIBUTE-BASED COMMUNITY DETECTION WITH APPLICATIONS TO INTEGRATED FUNCTIONAL GENOMICS.

PubMed

Yu, Han; Hageman Blair, Rachael

2016-01-01

Understanding community structure in networks has received considerable attention in recent years. Detecting and leveraging community structure holds promise for understanding and potentially intervening with the spread of influence. Network features of this type have important implications in a number of research areas, including, marketing, social networks, and biology. However, an overwhelming majority of traditional approaches to community detection cannot readily incorporate information of node attributes. Integrating structural and attribute information is a major challenge. We propose a exible iterative method; inverse regularized Markov Clustering (irMCL), to network clustering via the manipulation of the transition probability matrix (aka stochastic flow) corresponding to a graph. Similar to traditional Markov Clustering, irMCL iterates between "expand" and "inflate" operations, which aim to strengthen the intra-cluster flow, while weakening the inter-cluster flow. Attribute information is directly incorporated into the iterative method through a sigmoid (logistic function) that naturally dampens attribute influence that is contradictory to the stochastic flow through the network. We demonstrate advantages and the exibility of our approach using simulations and real data. We highlight an application that integrates breast cancer gene expression data set and a functional network defined via KEGG pathways reveal significant modules for survival.

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

PubMed Central

Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

2014-01-01

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs. PMID:25350506

Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

PubMed

Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

2018-06-01

Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

Information Transfer in the Brain: Insights from a Unified Approach

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Wu, Guorong; Pellicoro, Mario; Stramaglia, Sebastiano

Measuring directed interactions in the brain in terms of information flow is a promising approach, mathematically treatable and amenable to encompass several methods. In this chapter we propose some approaches rooted in this framework for the analysis of neuroimaging data. First we will explore how the transfer of information depends on the network structure, showing how for hierarchical networks the information flow pattern is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. This was reported to be true also for effective connectivity networks from human EEG data. Then we address the problem of partial conditioning to a limited subset of variables, chosen as the most informative ones for the driver node.We will then propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character (synergetic or redundant) which can be associated to the sign of the contribution. Applications are reported for EEG and fMRI data.

A Transparent and Transferable Framework for Tracking Quality Information in Large Datasets

PubMed Central

Smith, Derek E.; Metzger, Stefan; Taylor, Jeffrey R.

2014-01-01

The ability to evaluate the validity of data is essential to any investigation, and manual “eyes on” assessments of data quality have dominated in the past. Yet, as the size of collected data continues to increase, so does the effort required to assess their quality. This challenge is of particular concern for networks that automate their data collection, and has resulted in the automation of many quality assurance and quality control analyses. Unfortunately, the interpretation of the resulting data quality flags can become quite challenging with large data sets. We have developed a framework to summarize data quality information and facilitate interpretation by the user. Our framework consists of first compiling data quality information and then presenting it through 2 separate mechanisms; a quality report and a quality summary. The quality report presents the results of specific quality analyses as they relate to individual observations, while the quality summary takes a spatial or temporal aggregate of each quality analysis and provides a summary of the results. Included in the quality summary is a final quality flag, which further condenses data quality information to assess whether a data product is valid or not. This framework has the added flexibility to allow “eyes on” information on data quality to be incorporated for many data types. Furthermore, this framework can aid problem tracking and resolution, should sensor or system malfunctions arise. PMID:25379884

Exploring mobile health in a private online social network.

PubMed

Memon, Qurban A; Mustafa, Asma Fayes

2015-01-01

Health information is very vulnerable. Certain individuals or corporate organisations will continue to steal it similar to bank account data once data is on wireless channels. Once health information is part of a social network, corresponding privacy issues also surface. Insufficiently trained employees at hospitals that pay less attention to creating a privacy-aware culture will suffer loss when mobile devices containing health information are lost, stolen or sniffed. In this work, a social network system is explored as a m-health system from a privacy perspective. A model is developed within a framework of data-driven privacy and implemented on Android operating system. In order to check feasibility of the proposed model, a prototype application is developed on Facebook for different services, including: i) sharing user location; ii) showing nearby friends; iii) calculating and sharing distance moved, and calories burned; iv) calculating, tracking and sharing user heart rate; etc.

What drives political commitment for nutrition? A review and framework synthesis to inform the United Nations Decade of Action on Nutrition.

PubMed

Baker, Phillip; Hawkes, Corinna; Wingrove, Kate; Demaio, Alessandro Rhyl; Parkhurst, Justin; Thow, Anne Marie; Walls, Helen

2018-01-01

Generating country-level political commitment will be critical to driving forward action throughout the United Nations Decade of Action on Nutrition (2016-2025). In this review of the empirical nutrition policy literature, we ask: what factors generate, sustain and constrain political commitment for nutrition, how and under what circumstances? Our aim is to inform strategic 'commitment-building' actions. We adopted a framework synthesis method and realist review protocol. An initial framework was derived from relevant theory and then populated with empirical evidence to test and modify it. Five steps were undertaken: initial theoretical framework development; search for relevant empirical literature; study selection and quality appraisal; data extraction, analysis and synthesis and framework modification. 75 studies were included. We identified 18 factors that drive commitment, organised into five categories: actors; institutions; political and societal contexts; knowledge, evidence and framing; and, capacities and resources. Irrespective of country-context, effective nutrition actor networks, strong leadership, civil society mobilisation, supportive political administrations, societal change and focusing events, cohesive and resonant framing, and robust data systems and available evidence were commitment drivers. Low-income and middle-income country studies also frequently reported international actors, empowered institutions, vertical coordination and capacities and resources. In upper-middle-income and high-income country studies, private sector interference frequently undermined commitment. Political commitment is not something that simply exists or emerges accidentally; it can be created and strengthened over time through strategic action. Successfully generating commitment will likely require a core set of actions with some context-dependent adaptations. Ultimately, it will necessitate strategic actions by cohesive, resourced and strongly led nutrition actor networks that are responsive to the multifactorial, multilevel and dynamic political systems in which they operate and attempt to influence. Accelerating the formation and effectiveness of such networks over the Nutrition Decade should be a core task for all actors involved.

What drives political commitment for nutrition? A review and framework synthesis to inform the United Nations Decade of Action on Nutrition

PubMed Central

Baker, Phillip; Hawkes, Corinna; Wingrove, Kate; Parkhurst, Justin; Thow, Anne Marie; Walls, Helen

2018-01-01

Introduction Generating country-level political commitment will be critical to driving forward action throughout the United Nations Decade of Action on Nutrition (2016–2025). In this review of the empirical nutrition policy literature, we ask: what factors generate, sustain and constrain political commitment for nutrition, how and under what circumstances? Our aim is to inform strategic ‘commitment-building’ actions. Method We adopted a framework synthesis method and realist review protocol. An initial framework was derived from relevant theory and then populated with empirical evidence to test and modify it. Five steps were undertaken: initial theoretical framework development; search for relevant empirical literature; study selection and quality appraisal; data extraction, analysis and synthesis and framework modification. Results 75 studies were included. We identified 18 factors that drive commitment, organised into five categories: actors; institutions; political and societal contexts; knowledge, evidence and framing; and, capacities and resources. Irrespective of country-context, effective nutrition actor networks, strong leadership, civil society mobilisation, supportive political administrations, societal change and focusing events, cohesive and resonant framing, and robust data systems and available evidence were commitment drivers. Low-income and middle-income country studies also frequently reported international actors, empowered institutions, vertical coordination and capacities and resources. In upper-middle-income and high-income country studies, private sector interference frequently undermined commitment. Conclusion Political commitment is not something that simply exists or emerges accidentally; it can be created and strengthened over time through strategic action. Successfully generating commitment will likely require a core set of actions with some context-dependent adaptations. Ultimately, it will necessitate strategic actions by cohesive, resourced and strongly led nutrition actor networks that are responsive to the multifactorial, multilevel and dynamic political systems in which they operate and attempt to influence. Accelerating the formation and effectiveness of such networks over the Nutrition Decade should be a core task for all actors involved. PMID:29527338

Developing a Framework for Effective Network Capacity Planning

NASA Technical Reports Server (NTRS)

Yaprak, Ece

2005-01-01

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Towards a Framework for Evolvable Network Design

NASA Astrophysics Data System (ADS)

Hassan, Hoda; Eltarras, Ramy; Eltoweissy, Mohamed

The layered Internet architecture that had long guided network design and protocol engineering was an “interconnection architecture” defining a framework for interconnecting networks rather than a model for generic network structuring and engineering. We claim that the approach of abstracting the network in terms of an internetwork hinders the thorough understanding of the network salient characteristics and emergent behavior resulting in impeding design evolution required to address extreme scale, heterogeneity, and complexity. This paper reports on our work in progress that aims to: 1) Investigate the problem space in terms of the factors and decisions that influenced the design and development of computer networks; 2) Sketch the core principles for designing complex computer networks; and 3) Propose a model and related framework for building evolvable, adaptable and self organizing networks We will adopt a bottom up strategy primarily focusing on the building unit of the network model, which we call the “network cell”. The model is inspired by natural complex systems. A network cell is intrinsically capable of specialization, adaptation and evolution. Subsequently, we propose CellNet; a framework for evolvable network design. We outline scenarios for using the CellNet framework to enhance legacy Internet protocol stack.

A Framework for Information Theoretic Cooperative Sensing and Predictive Control

DTIC Science & Technology

2012-09-11

Miroslav Barić and Francesco Borelli , Decentralized Robust Control Invariance for a Network of Integrators, Proceeding of American Control...from http: //www.mpc.berkeley.edu. P4 Miroslav Barić and Francesco Borelli , Distributed Averaging with Flow Constraints, Proceeding of American Control

Building oceanographic and atmospheric observation networks by composition: unmanned vehicles, communication networks, and planning and execution control frameworks

NASA Astrophysics Data System (ADS)

Sousa, J. T.; Pinto, J.; Martins, R.; Costa, M.; Ferreira, F.; Gomes, R.

2014-12-01

The problem of developing mobile oceanographic and atmospheric observation networks (MOAO) with coordinated air and ocean vehicles is discussed in the framework of the communications and control software tool chain developed at Underwater Systems and Technologies Laboratory (LSTS) from Porto University. This is done with reference to field experiments to illustrate key capabilities and to assess future MOAO operations. First, the motivation for building MOAO by "composition" of air and ocean vehicles, communication networks, and planning and execution control frameworks is discussed - in networked vehicle systems information and commands are exchanged among multiple vehicles and operators, and the roles, relative positions, and dependencies of these vehicles and operators change during operations. Second, the planning and execution control framework developed at LSTS for multi-vehicle systems is discussed with reference to key concepts such as autonomy, mixed-initiative interactions, and layered organization. Third, the LSTS tool software tool chain is presented to show how to develop MOAO by composition. The tool chain comprises the Neptus command and control framework for mixed initiative interactions, the underlying IMC messaging protocol, and the DUNE on-board software. Fourth, selected LSTS operational deployments illustrate MOAO capability building. In 2012 we demonstrated the use of UAS to "ferry" data from UUVs located beyond line of sight (BLOS). In 2013 we demonstrated coordinated observations of coastal fronts with small UAS and UUVs, "bent" BLOS through the use of UAS as communication relays, and UAS tracking of juvenile hammer-head sharks. In 2014 we demonstrated UUV adaptive sampling with the closed loop controller of the UUV residing on a UAS; this was done with the help of a Wave Glider ASV with a communications gateway. The results from these experiments provide a background for assessing potential future UAS operations in a compositional MOAO.

Nature as a network of morphological infocomputational processes for cognitive agents

NASA Astrophysics Data System (ADS)

Dodig-Crnkovic, Gordana

2017-01-01

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

A Framework to Manage Information Models

NASA Astrophysics Data System (ADS)

Hughes, J. S.; King, T.; Crichton, D.; Walker, R.; Roberts, A.; Thieman, J.

2008-05-01

The Information Model is the foundation on which an Information System is built. It defines the entities to be processed, their attributes, and the relationships that add meaning. The development and subsequent management of the Information Model is the single most significant factor for the development of a successful information system. A framework of tools has been developed that supports the management of an information model with the rigor typically afforded to software development. This framework provides for evolutionary and collaborative development independent of system implementation choices. Once captured, the modeling information can be exported to common languages for the generation of documentation, application databases, and software code that supports both traditional and semantic web applications. This framework is being successfully used for several science information modeling projects including those for the Planetary Data System (PDS), the International Planetary Data Alliance (IPDA), the National Cancer Institute's Early Detection Research Network (EDRN), and several Consultative Committee for Space Data Systems (CCSDS) projects. The objective of the Space Physics Archive Search and Exchange (SPASE) program is to promote collaboration and coordination of archiving activity for the Space Plasma Physics community and ensure the compatibility of the architectures used for a global distributed system and the individual data centers. Over the past several years, the SPASE data model working group has made great progress in developing the SPASE Data Model and supporting artifacts including a data dictionary, XML Schema, and two ontologies. The authors have captured the SPASE Information Model in this framework. This allows the generation of documentation that presents the SPASE Information Model in object-oriented notation including UML class diagrams and class hierarchies. The modeling information can also be exported to semantic web languages such as OWL and RDF and written to XML Metadata Interchange (XMI) files for import into UML tools.

A distributed cloud-based cyberinfrastructure framework for integrated bridge monitoring

NASA Astrophysics Data System (ADS)

Jeong, Seongwoon; Hou, Rui; Lynch, Jerome P.; Sohn, Hoon; Law, Kincho H.

2017-04-01

This paper describes a cloud-based cyberinfrastructure framework for the management of the diverse data involved in bridge monitoring. Bridge monitoring involves various hardware systems, software tools and laborious activities that include, for examples, a structural health monitoring (SHM), sensor network, engineering analysis programs and visual inspection. Very often, these monitoring systems, tools and activities are not coordinated, and the collected information are not shared. A well-designed integrated data management framework can support the effective use of the data and, thereby, enhance bridge management and maintenance operations. The cloud-based cyberinfrastructure framework presented herein is designed to manage not only sensor measurement data acquired from the SHM system, but also other relevant information, such as bridge engineering model and traffic videos, in an integrated manner. For the scalability and flexibility, cloud computing services and distributed database systems are employed. The information stored can be accessed through standard web interfaces. For demonstration, the cyberinfrastructure system is implemented for the monitoring of the bridges located along the I-275 Corridor in the state of Michigan.

Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

NASA Astrophysics Data System (ADS)

Maiti, Saumen; Tiwari, Ram Krishna

2010-10-01

A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho-section of the KTB. The comparisons of maximum a posteriori geological sections constructed here, based on the maximum a posteriori probability distribution, with the available geological information and the existing geophysical findings suggest that the BNN results reveal some additional finer details in the KTB borehole data at certain depths, which appears to be of some geological significance. We also demonstrate that the proposed BNN approach is superior to the conventional artificial neural network in terms of both avoiding "over-fitting" and aiding uncertainty estimation, which are vital for meaningful interpretation of geophysical records. Our analyses demonstrate that the BNN-based approach renders a robust means for the classification of complex changes in the litho-facies successions and thus could provide a useful guide for understanding the crustal inhomogeneity and the structural discontinuity in many other tectonically complex regions.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

PubMed

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Epidemics in Complex Networks: The Diversity of Hubs

NASA Astrophysics Data System (ADS)

Kitsak, Maksim; Gallos, Lazaros K.; Havlin, Shlomo; Stanley, H. Eugene; Makse, Hernan A.

2009-03-01

Many complex systems are believed to be vulnerable to spread of viruses and information owing to their high level of interconnectivity. Even viruses of low contagiousness easily proliferate the Internet. Rumors, fads, and innovation ideas are prone to efficient spreading in various social systems. Another commonly accepted standpoint is the importance of the most connected elements (hubs) in the spreading processes. We address following questions. Do all hubs conduct epidemics in the same manner? How does the epidemics spread depend on the structure of the network? What is the most efficient way to spread information over the system? We analyze several large-scale systems in the framework of of the susceptible/infective/removed (SIR) disease spread model which can also be mapped to the problem of rumor or fad spreading. We show that hubs are often ineffective in the transmission of virus or information owing to the highly heterogeneous topology of most networks. We also propose a new tool to evaluate the efficiency of nodes in spreading virus or information.

Analyzing Evolving Social Network 2 (EVOLVE2)

DTIC Science & Technology

2015-04-01

Facebook friendship graph. We simulated two different interaction models: one-to-one and one-to-many interactions . Both types of models revealed...to an unbiased random walk on the reweighed “ interaction graph” W with entries wij = αiAijαj . The generalized Laplacian framework is flexible enough...Information Intelligence Systems & Analysis Division Information Directorate This report is published in the interest of scientific and technical

Where-Fi: a dynamic energy-efficient multimedia distribution framework for MANETs

NASA Astrophysics Data System (ADS)

Mohapatra, Shivajit; Carbunar, Bogdan; Pearce, Michael; Chaudhri, Rohit; Vasudevan, Venu

2008-01-01

Next generation mobile ad-hoc applications will revolve around users' need for sharing content/presence information with co-located devices. However, keeping such information fresh requires frequent meta-data exchanges, which could result in significant energy overheads. To address this issue, we propose distributed algorithms for energy efficient dissemination of presence and content usage information between nodes in mobile ad-hoc networks. First, we introduce a content dissemination protocol (called CPMP) for effectively distributing frequent small meta-data updates between co-located devices using multicast. We then develop two distributed algorithms that use the CPMP protocol to achieve "phase locked" wake up cycles for all the participating nodes in the network. The first algorithm is designed for fully-connected networks and then extended in the second to handle hidden terminals. The "phase locked" schedules are then exploited to adaptively transition the network interface to a deep sleep state for energy savings. We have implemented a prototype system (called "Where-Fi") on several Motorola Linux-based cell phone models. Our experimental results show that for all network topologies our algorithms were able to achieve "phase locking" between nodes even in the presence of hidden terminals. Moreover, we achieved battery lifetime extensions of as much as 28% for fully connected networks and about 20% for partially connected networks.

Translating knowledge into practice and policy: the role of knowledge networks in primary health care.

PubMed

Armstrong, Kylie; Kendall, Elizabeth

The translation of information into practice is a well-recognised challenge for the health sector. In the primary healthcare sector, the last decade has seen an explosion of information generated by health systems, universities and a range of other sources. Without a system for translating that knowledge into practice and sharing it in a comprehensible form, it will remain meaningless to most practitioners. We propose the establishment of Knowledge Networks as a promising method for supporting the rapid adoption and generation of health information within the primary health care sector to advance health care services. These networks will be particularly important to the implementation of the national reform agenda, responsive decision-making and the translation of new frameworks or competencies into practice. This paper describes how interdisciplinary Knowledge Networks could be established focusing on a number of priority health research areas. Local Knowledge Networks would be used as a platform to support a collaborative web of evidence designed to influence health policy and planning. Our experience with Knowledge Networks indicates that they must be comprised of health professionals from Divisions of General Practice, researchers, policy-makers, consumers, government and non-government sectors. This paper will describe these networks and show how they might support the translation of knowledge into practice, thus driving systematic and institutional change.

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

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

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

Self-organizing network services with evolutionary adaptation.

PubMed

Nakano, Tadashi; Suda, Tatsuya

2005-09-01

This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.

Establishing and sustaining a biorepository network in Israel: challenges and progress.

PubMed

Cohen, Yehudit; Almog, Ronit; Onn, Amir; Itzhaki-Alfia, Ayelet; Meir, Karen

2013-12-01

Over the past 5 years, using European and North American biobanks as models, the grass-roots establishment of independently operating biobanks has occurred virtually simultaneously in large Israeli teaching hospitals. The process of establishing a national biorepository network in Israel has progressed slowly, sustained mainly by a few proponents working together on a personal level. Slow progress has been due to limited funding and the lack of a legal framework specific to biobanking activities. Recently, due to increasing pressure from the scientific community, the government has earmarked funds for a national biorepository network, and the structure is now being established. In forming a network, Israel's biobanks face certain difficulties, particularly lack of support. Additional challenges include harmonization of standard operating procedures, database centralization, and use of a common informed consent form. In this article, we highlight some of the issues faced by Israel's biobank managers in establishing and sustaining a functional biobank network, information that could provide guidance for other small countries with limited resources.

Process and data fragmentation-oriented enterprise network integration with collaboration modelling and collaboration agents

NASA Astrophysics Data System (ADS)

Li, Qing; Wang, Ze-yuan; Cao, Zhi-chao; Du, Rui-yang; Luo, Hao

2015-08-01

With the process of globalisation and the development of management models and information technology, enterprise cooperation and collaboration has developed from intra-enterprise integration, outsourcing and inter-enterprise integration, and supply chain management, to virtual enterprises and enterprise networks. Some midfielder enterprises begin to serve for different supply chains. Therefore, they combine related supply chains into a complex enterprise network. The main challenges for enterprise network's integration and collaboration are business process and data fragmentation beyond organisational boundaries. This paper reviews the requirements of enterprise network's integration and collaboration, as well as the development of new information technologies. Based on service-oriented architecture (SOA), collaboration modelling and collaboration agents are introduced to solve problems of collaborative management for service convergence under the condition of process and data fragmentation. A model-driven methodology is developed to design and deploy the integrating framework. An industrial experiment is designed and implemented to illustrate the usage of developed technologies in this paper.

Understanding genetic variation - the value of systems biology.

PubMed

Hütt, Marc-Thorsten

2014-04-01

Pharmacology is currently transformed by the vast amounts of genome-associated information available for system-level interpretation. Here I review the potential of systems biology to facilitate this interpretation, thus paving the way for the emerging field of systems pharmacology. In particular, I will show how gene regulatory and metabolic networks can serve as a framework for interpreting high throughput data and as an interface to detailed dynamical models. In addition to the established connectivity analyses of effective networks, I suggest here to also analyze higher order architectural properties of effective networks. © 2013 The British Pharmacological Society.

The framework for simulation of bioinspired security mechanisms against network infrastructure attacks.

PubMed

Shorov, Andrey; Kotenko, Igor

2014-01-01

The paper outlines a bioinspired approach named "network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed procedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine necessary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described.

New Frameworks for Detecting and Minimizing Information Leakage in Anonymized Network Data

DTIC Science & Technology

2011-10-01

researcher the exact extent to which a particular utility is affected by the anonymization. For instance, Karr et al.’s use of the Kullback - Leibler ...technical, legal, policy, and privacy issues limit the ability of operators to produce data sets for information security testing . In an effort to...technical, legal, policy, and privacy issues limit the ability of operators to produce datasets for information security testing . In an effort to help

A Framework for Organizing Current and Future Electric Utility Regulatory and Business Models

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

Satchwell, Andrew; Cappers, Peter; Schwartz, Lisa

In this report, we will present a descriptive and organizational framework for incremental and fundamental changes to regulatory and utility business models in the context of clean energy public policy goals. We will also discuss the regulated utility's role in providing value-added services that relate to distributed energy resources, identify the "openness" of customer information and utility networks necessary to facilitate change, and discuss the relative risks, and the shifting of risks, for utilities and customers.

CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

PubMed

Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

2016-12-23

A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

A model to assess the Mars Telecommunications Network relay robustness

NASA Technical Reports Server (NTRS)

Girerd, Andre R.; Meshkat, Leila; Edwards, Charles D., Jr.; Lee, Charles H.

2005-01-01

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

PubMed Central

Wang, Xin; Wang, Ying; Sun, Hongbin

2016-01-01

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework. PMID:27034651

Elements of an integrated health monitoring framework

NASA Astrophysics Data System (ADS)

Fraser, Michael; Elgamal, Ahmed; Conte, Joel P.; Masri, Sami; Fountain, Tony; Gupta, Amarnath; Trivedi, Mohan; El Zarki, Magda

2003-07-01

Internet technologies are increasingly facilitating real-time monitoring of Bridges and Highways. The advances in wireless communications for instance, are allowing practical deployments for large extended systems. Sensor data, including video signals, can be used for long-term condition assessment, traffic-load regulation, emergency response, and seismic safety applications. Computer-based automated signal-analysis algorithms routinely process the incoming data and determine anomalies based on pre-defined response thresholds and more involved signal analysis techniques. Upon authentication, appropriate action may be authorized for maintenance, early warning, and/or emergency response. In such a strategy, data from thousands of sensors can be analyzed with near real-time and long-term assessment and decision-making implications. Addressing the above, a flexible and scalable (e.g., for an entire Highway system, or portfolio of Networked Civil Infrastructure) software architecture/framework is being developed and implemented. This framework will network and integrate real-time heterogeneous sensor data, database and archiving systems, computer vision, data analysis and interpretation, physics-based numerical simulation of complex structural systems, visualization, reliability & risk analysis, and rational statistical decision-making procedures. Thus, within this framework, data is converted into information, information into knowledge, and knowledge into decision at the end of the pipeline. Such a decision-support system contributes to the vitality of our economy, as rehabilitation, renewal, replacement, and/or maintenance of this infrastructure are estimated to require expenditures in the Trillion-dollar range nationwide, including issues of Homeland security and natural disaster mitigation. A pilot website (http://bridge.ucsd.edu/compositedeck.html) currently depicts some basic elements of the envisioned integrated health monitoring analysis framework.

GeNN: a code generation framework for accelerated brain simulations

NASA Astrophysics Data System (ADS)

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations.

PubMed

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-07

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

GeNN: a code generation framework for accelerated brain simulations

PubMed Central

Yavuz, Esin; Turner, James; Nowotny, Thomas

2016-01-01

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/. PMID:26740369

Strengthening and sustainability of national immunization technical advisory groups (NITAGs) globally: Lessons and recommendations from the founding meeting of the global NITAG network.

PubMed

Adjagba, Alex; MacDonald, Noni E; Ortega-Pérez, Inmaculada; Duclos, Philippe

2017-05-25

National Immunization Technical Advisory Groups (NITAGs) provide independent, evidence-informed advice to assist their governments in immunization policy formation. However, many NITAGs face challenges in fulfilling their roles. Hence the many requests for formation of a network linking NITAGs together so they can learn from each other. To address this request, the Health Policy and Institutional Development (HPID) Center (a WHO Collaborating Center at the Agence de Médecine Préventive - AMP), in collaboration with WHO, organized a meeting in Veyrier-du-Lac, France, on 11 and 12 May 2016, to establish a Global NITAG Network (GNN). The meeting focused on two areas: the requirements for (a) the establishment of a global NITAG collaborative network; and (b) the global assessment/evaluation of the performance of NITAGs. 35 participants from 26 countries reviewed the proposed GNN framework documents and NITAG performance evaluation. Participants recommended that a GNN should be established, agreed on its governance, function, scope and a proposed work plan as well as setting a framework for NITAG evaluation. Copyright © 2017.

The Development of a Contextual Information Framework Model as a Potential IAEA Strategy to Maintain Radioactive Waste Knowledge

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

Upshall, I.R.; McCarthy, G.J.

A contextual framework comprises 'entities' that exhibit one or more definable relationships with a particular 'event'. People, organisations, concepts, ideas, places, natural phenomena, events themselves, cultural artefacts including records, books, works of art can all be conceptualised as entities. If these entities are registered in an information management system where the relationships between them can be defined and systematically managed then it is possible to create a contextual information framework that represents a particular view of what occurs in real life. The careful identifying and mapping of the relationships between these entities and the selected event can lead rapidly tomore » the creation of an information network that closely reflects the human approach to knowledge acquisition and application. The 'event' referred to in this paper is the safe management of radioactive waste. It is widely accepted that society will expect that knowledge about the waste will be maintained for many decades, if not centuries. Delivering on this expectation will demand the application of management approaches that are both innovative and sustainable. Effective inter-generational transfer of information using many 'conventional' techniques will be highly dependent on societal stability - something that cannot be guaranteed over such long periods of time. Consequently, alternative approaches should be explored and, where appropriate, implemented to give reasonable assurance that future generations of waste custodians will not be unduly burdened by the need to recreate information about the waste long after its disposal. In actual fact, the contextual information framework model is not 'new technology' but simply a means for rationalising and representing the way humans naturally tend to use information in the pursuit of knowledge enhancement. By making use of multiple information entities and their relationships, it is often possible to convert otherwise impossibly complex socio-technical environments into information architectures or networks with remarkable and useful properties. The International Atomic Energy Agency, in its ongoing work to encourage the application of systems to manage radioactive waste information over the long term, has embraced the contextual information framework as a potentially viable approach to this particular challenge. To this end, it invited Member States to contribute to the production of a Safety Report that used the contextual information framework model, building on the wealth of existing IAEA guidance. The report focuses, not on the important area of records management, but on the benefits that can arise from the development of an information management approach that increases the likelihood that future generations will recognise the significance and value of the information contained in these records. Our understanding of 'inter-generational transfer' should extend beyond the simple physical transfer of records into an archival repository towards the establishment of a working culture that places sufficient contemporary information into a form that ensures it remains accessible, and ultimately enhances, the knowledge of future generations. Making information accessible is therefore the key and whilst the use of stable records media, storage environments and quality assurance are important elements, they cannot be considered solutions in themselves. This paper articulates some of the lessons that have been learned about using the contextual information framework model when applied to the long term management of radioactive waste. The draft IAEA Safety Report entitled 'Preservation and Transfer to Future Generations of Information Important to the Safety of Waste Disposal Facilities', on which this paper is based, is expected to be published in 2007. (authors)« less

Statistical Mechanics of Temporal and Interacting Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide a new framework to quantify the information encoded in these networks and to answer a fundamental problem in network science: how complex are temporal and growing networks. Finally, we consider two examples of critical phenomena in interacting networks. In particular, on one side we investigate the percolation of interacting networks by introducing antagonistic interactions. On the other side, we investigate a model of political election based on the percolation of antagonistic networks. The aim of this research is to show how antagonistic interactions change the physics of critical phenomena on interacting networks. We believe that the work presented in these thesis offers the possibility to appreciate the large variability of problems that can be addressed in the new framework of temporal and interacting networks.

The New Face of FLUXNET: Redesigning the Web Site and Data Organization to Enhance the User Experience

NASA Astrophysics Data System (ADS)

Shanafield, Harold; Shamblin, Stephanie; Devarakonda, Ranjeet; McMurry, Ben; Walker Beaty, Tammy; Wilson, Bruce; Cook, Robert B.

2011-02-01

The FLUXNET global network of regional flux tower networks serves to coordinate the regional and global analysis of eddy covariance based CO2, water vapor and energy flux measurements taken at more than 500 sites in continuous long-term operation. The FLUXNET database presently contains information about the location, characteristics, and data availability of each of these sites. To facilitate the coordination and distribution of this information, we redesigned the underlying database and associated web site. We chose the PostgreSQL database as a platform based on its performance, stability and GIS extensions. PostreSQL allows us to enhance our search and presentation capabilities, which will in turn provide increased functionality for users seeking to understand the FLUXNET data. The redesigned database will also significantly decrease the burden of managing such highly varied data. The website is being developed using the Drupal content management system, which provides many community-developed modules and a robust framework for custom feature development. In parallel, we are working with the regional networks to ensure that the information in the FLUXNET database is identical to that in the regional networks. Going forward, we also plan to develop an automated way to synchronize information with the regional networks.

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

PubMed

Wang, Mengmeng; Zuo, Wanli; Wang, Ying

2015-01-01

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

PubMed

Gangopadhyay, Ahana; Chakrabartty, Shantanu

2018-06-01

This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

A geometrical approach to control and controllability of nonlinear dynamical networks

PubMed Central

Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng

2016-01-01

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control. PMID:27076273

Small Worldness in Dense and Weighted Connectomes

NASA Astrophysics Data System (ADS)

Colon-Perez, Luis; Couret, Michelle; Triplett, William; Price, Catherine; Mareci, Thomas

2016-05-01

The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a one-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.

Dynamic social networks based on movement

USGS Publications Warehouse

Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

2016-01-01

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

Learning consensus in adversarial environments

NASA Astrophysics Data System (ADS)

Vamvoudakis, Kyriakos G.; García Carrillo, Luis R.; Hespanha, João. P.

2013-05-01

This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement must be reached by the networked mobile team based on environmental changes. The problem is addressed under a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over centralized decision making in the sense that a decision maker is not required to access information from all the other decision makers. The proposed framework derives three tuning laws for every agent; one associated with the cost, one associated with the controller, and one with the adversarial input.

Energy prediction using spatiotemporal pattern networks

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

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated bymore » the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.« less

A conceptual framework for the study of social capital in new destination immigrant communities.

PubMed

Bernosky de Flores, Catherine H

2010-07-01

Mexican immigration to the United States is an intragenerational phenomenon. Young adult Mexicans leave their families of origin in search of employment opportunities that pull them to new destination communities. A conceptual framework that defines and relates the concepts of human capital, personal networks, social capital, and resources is introduced. The influence of social capital on the capacity of immigrants to access resources is described. The framework informed the design of a study to examine the approaches used by Mexican immigrant women to access resources for healthy childbearing in the absence of traditional family support systems in a new destination community.

A planning-oriented sustainability assessment framework for peri-urban water management in developing countries.

PubMed

Starkl, Markus; Brunner, Norbert; López, Eduardo; Martínez-Ruiz, José Luis

2013-12-15

DPSIR and the three-pillar model are well-established frameworks for sustainability assessment. This paper proposes a planning-oriented sustainability assessment framework (POSAF). It is informed by those frameworks but differs insofar as it puts more emphasis on a constructivist conception which recognises that sustainability needs to be defined anew for each planning problem. In finding such a consensus definition, POSAF uses participatory scenario analysis and participatory planning, technical feasibility study, participatory assessment, analysis of trade-offs and social networks in an unusual combination and for goals that differ from the original conceptions of these methods. POSAF was applied in a peri-urban area of Mexico City for the design of improved water service provision, integrating solid waste management. It supported consensus amongst users about the importance of environmental issues, informed planners about the values of stakeholders and users, detected local differences, and identified possible conflicts at an early stage of decision-making. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Research on tumor information grid framework].

PubMed

Zhang, Haowei; Qin, Zhu; Liu, Ying; Tan, Jianghao; Cao, Haitao; Chen, Youping; Zhang, Ke; Ding, Yuqing

2013-10-01

In order to realize tumor disease information sharing and unified management, we utilized grid technology to make the data and software resources which distributed in various medical institutions for effective integration so that we could make the heterogeneous resources consistent and interoperable in both semantics and syntax aspects. This article describes the tumor grid framework, the type of the service being packaged in Web Service Description Language (WSDL) and extensible markup language schemas definition (XSD), the client use the serialized document to operate the distributed resources. The service objects could be built by Unified Modeling Language (UML) as middle ware to create application programming interface. All of the grid resources are registered in the index and released in the form of Web Services based on Web Services Resource Framework (WSRF). Using the system we can build a multi-center, large sample and networking tumor disease resource sharing framework to improve the level of development in medical scientific research institutions and the patient's quality of life.

Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data.

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is becoming a widely used tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse ecological and human health outcomes. However, the conventional process...

A Value-Added Framework for Analyzing Electronic and Print Publishing.

ERIC Educational Resources Information Center

Perritt, Henry H., Jr.

1991-01-01

Discusses changes in the publishing industry resulting from digital electronic networks and optical storage technologies, and explains attributes of information products as types of value for both print and electronic contexts. Electronic products necessary for a shift from paper to electronic formats are identified, and legal issues are…

Network origin-destination demand estimation using limited link traffic counts : strategic deployment of vehicle detectors through an integrated corridor management framework.

DOT National Transportation Integrated Search

2009-10-15

In typical road traffic corridors, freeway systems are generally well-equipped with traffic surveillance systems such as vehicle detector (VD) and/or closed circuit television (CCTV) systems in order to gather timely traffic information for traffic c...

Application of the Strategic Alignment Model and Information Technology Governance Concepts to Support Network Centric Warfare

DTIC Science & Technology

2006-03-01

Defense, Editor. 2001. 12. Defense, D.o., Department of Defense Architecture Framework Deskbook. 2004, Department of Defense. 13. Denzin , N. and...Y. Lincoln , Handbook of Qualitative Research. 2000, California: Sage. 14. Flick, U., An Introduction to Qualitative research: Theory, method and

Strategic Planning to Conduct Joint Force Network Operations: A Content Analysis of NETOPS Organizations Strategic Plans

DTIC Science & Technology

2007-03-01

information dominance , Joint Network Operations (NETOPS) organizations need to be strategically aligned. As result, to enhance the capabilities-based effects of NETOPS and reduce our NETOP infrastructures susceptibility to compromise. Once the key organizations were identified, their strategic plans were analyzed using a structured content analysis framework. The results illustrated that the strategic plans were aligned with the community of interests tasking to conduct NETOPS. Further research is required into the strategic alignment beyond the strategic

Aeronautical Situational Awareness - Airport Surface

NASA Technical Reports Server (NTRS)

Linetsky, Vladimir M.; Ivancic, William D.; Vaden, Karl R.

2017-01-01

This paper advocates for a specific design approach, based on simple principals, yet addresses challenges faced by the system engineers when designing complex data and information infrastructure. The document provides guidance for breaking out various work elements in the overall network architecture design, so that communication systems are conceived and effectively realized regardless of their location, size and local specifics. Although targeted at the Global Airspace System (GAS) and National Airspace System (NAS), this framework can be applied to any network-centric architecture.

Information-Theoretic Approach May Shed a Light to a Better Understanding and Sustaining the Integrity of Ecological-Societal Systems under Changing Climate

NASA Astrophysics Data System (ADS)

Kim, J.

2016-12-01

Considering high levels of uncertainty, epistemological conflicts over facts and values, and a sense of urgency, normal paradigm-driven science will be insufficient to mobilize people and nation toward sustainability. The conceptual framework to bridge the societal system dynamics with that of natural ecosystems in which humanity operates remains deficient. The key to understanding their coevolution is to understand `self-organization.' Information-theoretic approach may shed a light to provide a potential framework which enables not only to bridge human and nature but also to generate useful knowledge for understanding and sustaining the integrity of ecological-societal systems. How can information theory help understand the interface between ecological systems and social systems? How to delineate self-organizing processes and ensure them to fulfil sustainability? How to evaluate the flow of information from data through models to decision-makers? These are the core questions posed by sustainability science in which visioneering (i.e., the engineering of vision) is an essential framework. Yet, visioneering has neither quantitative measure nor information theoretic framework to work with and teach. This presentation is an attempt to accommodate the framework of self-organizing hierarchical open systems with visioneering into a common information-theoretic framework. A case study is presented with the UN/FAO's communal vision of climate-smart agriculture (CSA) which pursues a trilemma of efficiency, mitigation, and resilience. Challenges of delineating and facilitating self-organizing systems are discussed using transdisciplinary toold such as complex systems thinking, dynamic process network analysis and multi-agent systems modeling. Acknowledgments: This study was supported by the Korea Meteorological Administration Research and Development Program under Grant KMA-2012-0001-A (WISE project).

Lateral Entorhinal Cortex Lesions Impair Local Spatial Frameworks

PubMed Central

Kuruvilla, Maneesh V.; Ainge, James A.

2017-01-01

A prominent theory in the neurobiology of memory processing is that episodic memory is supported by contextually gated spatial representations in the hippocampus formed by combining spatial information from medial entorhinal cortex (MEC) with non-spatial information from lateral entorhinal cortex (LEC). However, there is a growing body of evidence from lesion and single-unit recording studies in rodents suggesting that LEC might have a role in encoding space, particularly the current and previous locations of objects within the local environment. Landmarks, both local and global, have been shown to control the spatial representations hypothesized to underlie cognitive maps. Consequently, it has recently been suggested that information processing within this network might be organized with reference to spatial scale with LEC and MEC providing information about local and global spatial frameworks respectively. In the present study, we trained animals to search for food using either a local or global spatial framework. Animals were re-tested on both tasks after receiving excitotoxic lesions of either the MEC or LEC. LEC lesioned animals were impaired in their ability to learn a local spatial framework task. LEC lesioned animals were also impaired on an object recognition (OR) task involving multiple local features but unimpaired at recognizing a single familiar object. Together, this suggests that LEC is involved in associating features of the local environment. However, neither LEC nor MEC lesions impaired performance on the global spatial framework task. PMID:28567006

Using Network Dynamical Influence to Drive Consensus

NASA Astrophysics Data System (ADS)

Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.

2016-05-01

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Generalised power graph compression reveals dominant relationship patterns in complex networks

PubMed Central

Ahnert, Sebastian E.

2014-01-01

We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified. PMID:24663099

Electronic Health Object

PubMed Central

Almunawar, Mohammad Nabil; Anshari, Muhammad; Younis, Mustafa Z.; Kisa, Adnan

2015-01-01

Electronic health records (EHRs) store health-related patient information in an electronic format, improving the quality of health care management and increasing efficiency of health care processes. However, in existing information systems, health-related records are generated, managed, and controlled by health care organizations. Patients are perceived as recipients of care and normally cannot directly interact with the system that stores their health-related records; their participation in enriching this information is not possible. Many businesses now allow customers to participate in generating information for their systems, strengthening customer relationships. This trend is supported by Web 2.0, which enables interactivity through various means, including social networks. Health care systems should be able to take advantage of this development. This article proposes a novel framework in addressing the emerging need for interactivity while preserving and extending existing electronic medical data. The framework has 3 dimensions of patient health record: personal, social, and medical dimensions. The framework is designed to empower patients, changing their roles from static recipient of health care services to dynamic and active partners in health care processes. PMID:26660486

Medical-device risk management and public safety: using cost-benefit as a measurement of effectiveness

NASA Astrophysics Data System (ADS)

Hughes, Allen A.

1994-12-01

Public safety can be enhanced through the development of a comprehensive medical device risk management. This can be accomplished through case studies using a framework that incorporates cost-benefit analysis in the evaluation of risk management attributes. This paper presents a framework for evaluating the risk management system for regulatory Class III medical devices. The framework consists of the following sixteen attributes of a comprehensive medical device risk management system: fault/failure analysis, premarket testing/clinical trials, post-approval studies, manufacturer sponsored hospital studies, product labeling, establishment inspections, problem reporting program, mandatory hospital reporting, medical literature surveillance, device/patient registries, device performance monitoring, returned product analysis, autopsy program, emergency treatment funds/interim compensation, product liability, and alternative compensation mechanisms. Review of performance histories for several medical devices can reveal the value of information for many attributes, and also the inter-dependencies of the attributes in generating risk information flow. Such an information flow network is presented as a starting point for enhancing medical device risk management by focusing on attributes with high net benefit values and potential to spur information dissemination.

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

PubMed Central

Lee, Ji-Hyun; Park, Kyoung Mii; Han, Dong-Jin; Bang, Nam Young; Kim, Do-Hee; Na, Hyeongjin; Lim, Semi; Kim, Tae Bum; Kim, Dae Gyu; Kim, Hyun-Jung; Chung, Yeonseok; Sung, Sang Hyun; Surh, Young-Joon; Kim, Sunghoon; Han, Byung Woo

2015-01-01

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org. PMID:26555441

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

Bridging gaps in health information systems: a case study from Somaliland, Somalia.

PubMed

Askar, Ahmed; Ardakani, Malekafzali; Majdzade, Reza

2018-01-02

Reliable and timely health information is fundamental for health information systems (HIS) to work effectively. This case study aims to assess Somaliland HIS in terms of its contextual situation, major weaknesses and proposes key evidence-based recommendations. Data were collected through national level key informants' interviews, observations, group discussion and scoring using the HIS framework and assessment tool developed by World Health Organization Health Metrics Network (WHO/HMN). The study found major weaknesses including: no policy, strategic plan and legal framework in place; fragmented sub-information systems; Poor information and communications technology (ICT) infrastructure; poorly motivated and under-skilled personnel; dependence on unsustainable external funds; no census or civil registration in place; data from private health sector not captured; insufficient technical capacity to analyse data collected by HIS; and information is not widely shared, disseminated or utilized for decision-making. We recommend developing a national HIS strategic plan that harmonizes and directs collective efforts to become a more integrated, cost-effective and sustainable HIS.

The ConnectinGEO Observation Inventory

NASA Astrophysics Data System (ADS)

Santoro, M.; Nativi, S.; Jirka, S.; McCallum, I.

2016-12-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations) is an EU-funded project under the H2020 Framework Programme. The primary goal of the project is to link existing coordinated Earth Observation networks with science and technology (S&T) communities, the industry sector and the GEOSS and Copernicus stakeholders. An expected outcome of the project is a prioritized list of critical gaps within GEOSS (Global Earth Observation System of Systems) in observations and models that translate observations into practice relevant knowledge. The project defines and utilizes a formalized methodology to create a set of observation requirements that will be related to information on available observations to identify key gaps. Gaps in the information provided by current observation systems as well as gaps in the systems themselves will be derived from five different threads. One of these threads consists in the analysis of the observations and measurements that are currently registered in GEO Discovery and Access Broker (DAB). To this aim, an Observation Inventory (OI) has been created and populated using the current metadata information harmonized by the DAB. This presentation describes the process defined to populate the ConnectinGEO OI and the resulting system architecture. In addition, it provides information on how to systematically access the OI for performing the gap analysis. Furthermore it demonstrates initial findings of the gap analysis, and shortcomings in the metadata that need attention. The research leading to these results benefited from funding by the European Union H2020 Framework Programme under grant agreement n. 641538 (ConnectinGEO).

The neural signature of emotional memories in serial crimes.

PubMed

Chassy, Philippe

2017-10-01

Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Game theoretic approach for cooperative feature extraction in camera networks

NASA Astrophysics Data System (ADS)

Redondi, Alessandro E. C.; Baroffio, Luca; Cesana, Matteo; Tagliasacchi, Marco

2016-07-01

Visual sensor networks (VSNs) consist of several camera nodes with wireless communication capabilities that can perform visual analysis tasks such as object identification, recognition, and tracking. Often, VSN deployments result in many camera nodes with overlapping fields of view. In the past, such redundancy has been exploited in two different ways: (1) to improve the accuracy/quality of the visual analysis task by exploiting multiview information or (2) to reduce the energy consumed for performing the visual task, by applying temporal scheduling techniques among the cameras. We propose a game theoretic framework based on the Nash bargaining solution to bridge the gap between the two aforementioned approaches. The key tenet of the proposed framework is for cameras to reduce the consumed energy in the analysis process by exploiting the redundancy in the reciprocal fields of view. Experimental results in both simulated and real-life scenarios confirm that the proposed scheme is able to increase the network lifetime, with a negligible loss in terms of visual analysis accuracy.

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

PubMed Central

Lee, Esther J.

2018-01-01

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals. PMID:29451874

Many-objective Groundwater Monitoring Network Design Using Bias-Aware Ensemble Kalman Filtering and Evolutionary Optimization

NASA Astrophysics Data System (ADS)

Kollat, J. B.; Reed, P. M.

2009-12-01

This study contributes the ASSIST (Adaptive Strategies for Sampling in Space and Time) framework for improving long-term groundwater monitoring decisions across space and time while accounting for the influences of systematic model errors (or predictive bias). The ASSIST framework combines contaminant flow-and-transport modeling, bias-aware ensemble Kalman filtering (EnKF) and many-objective evolutionary optimization. Our goal in this work is to provide decision makers with a fuller understanding of the information tradeoffs they must confront when performing long-term groundwater monitoring network design. Our many-objective analysis considers up to 6 design objectives simultaneously and consequently synthesizes prior monitoring network design methodologies into a single, flexible framework. This study demonstrates the ASSIST framework using a tracer study conducted within a physical aquifer transport experimental tank located at the University of Vermont. The tank tracer experiment was extensively sampled to provide high resolution estimates of tracer plume behavior. The simulation component of the ASSIST framework consists of stochastic ensemble flow-and-transport predictions using ParFlow coupled with the Lagrangian SLIM transport model. The ParFlow and SLIM ensemble predictions are conditioned with tracer observations using a bias-aware EnKF. The EnKF allows decision makers to enhance plume transport predictions in space and time in the presence of uncertain and biased model predictions by conditioning them on uncertain measurement data. In this initial demonstration, the position and frequency of sampling were optimized to: (i) minimize monitoring cost, (ii) maximize information provided to the EnKF, (iii) minimize failure to detect the tracer, (iv) maximize the detection of tracer flux, (v) minimize error in quantifying tracer mass, and (vi) minimize error in quantifying the moment of the tracer plume. The results demonstrate that the many-objective problem formulation provides a tremendous amount of information for decision makers. Specifically our many-objective analysis highlights the limitations and potentially negative design consequences of traditional single and two-objective problem formulations. These consequences become apparent through visual exploration of high-dimensional tradeoffs and the identification of regions with interesting compromise solutions. The prediction characteristics of these compromise designs are explored in detail, as well as their implications for subsequent design decisions in both space and time.

Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework.

PubMed

Sridharan, Devarajan; Steinmetz, Nicholas A; Moore, Tirin; Knudsen, Eric I

2017-01-18

Distinct networks in the forebrain and the midbrain coordinate to control spatial attention. The critical involvement of the superior colliculus (SC)-the central structure in the midbrain network-in visuospatial attention has been shown by four seminal, published studies in monkeys (Macaca mulatta) performing multialternative tasks. However, due to the lack of a mechanistic framework for interpreting behavioral data in such tasks, the nature of the SC's contribution to attention remains unclear. Here we present and validate a novel decision framework for analyzing behavioral data in multialternative attention tasks. We apply this framework to re-examine the behavioral evidence from these published studies. Our model is a multidimensional extension to signal detection theory that distinguishes between two major classes of attentional mechanisms: those that alter the quality of sensory information or "sensitivity," and those that alter the selective gating of sensory information or "choice bias." Model-based simulations and model-based analyses of data from these published studies revealed a converging pattern of results that indicated that choice-bias changes, rather than sensitivity changes, were the primary outcome of SC manipulation. Our results suggest that the SC contributes to attentional performance predominantly by generating a spatial choice bias for stimuli at a selected location, and that this bias operates downstream of forebrain mechanisms that enhance sensitivity. The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention. Attention involves the selection of the most relevant information for differential sensory processing and decision making. While the mechanisms by which attention alters sensory encoding (sensitivity control) are well studied, the mechanisms by which attention alters decisional weighting of sensory evidence (choice-bias control) are poorly understood. Here, we introduce a model of multialternative decision making that distinguishes bias from sensitivity effects in attention tasks. With our model, we simulate experimental data from four seminal studies that microstimulated or inactivated a key attention-related midbrain structure, the superior colliculus (SC). We demonstrate that the experimental effects of SC manipulation are entirely consistent with the SC controlling attention by changing choice bias, thereby shedding new light on how the brain mediates attention. Copyright © 2017 the authors 0270-6474/17/370480-32$15.00/0.

Designing and Implementing a Retrospective Earthquake Detection Framework at the U.S. Geological Survey National Earthquake Information Center

NASA Astrophysics Data System (ADS)

Patton, J.; Yeck, W.; Benz, H.

2017-12-01

The U.S. Geological Survey National Earthquake Information Center (USGS NEIC) is implementing and integrating new signal detection methods such as subspace correlation, continuous beamforming, multi-band picking and automatic phase identification into near-real-time monitoring operations. Leveraging the additional information from these techniques help the NEIC utilize a large and varied network on local to global scales. The NEIC is developing an ordered, rapid, robust, and decentralized framework for distributing seismic detection data as well as a set of formalized formatting standards. These frameworks and standards enable the NEIC to implement a seismic event detection framework that supports basic tasks, including automatic arrival time picking, social media based event detections, and automatic association of different seismic detection data into seismic earthquake events. In addition, this framework enables retrospective detection processing such as automated S-wave arrival time picking given a detected event, discrimination and classification of detected events by type, back-azimuth and slowness calculations, and ensuring aftershock and induced sequence detection completeness. These processes and infrastructure improve the NEIC's capabilities, accuracy, and speed of response. In addition, this same infrastructure provides an improved and convenient structure to support access to automatic detection data for both research and algorithmic development.

From Field Notes to Data Portal - A Scalable Data QA/QC Framework for Tower Networks: Progress and Preliminary Results

NASA Astrophysics Data System (ADS)

Sturtevant, C.; Hackley, S.; Lee, R.; Holling, G.; Bonarrigo, S.

2017-12-01

Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. Data quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from humans or the natural environment. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process heavily relying on visual inspection of data. In addition, notes of measurement interference are often recorded on paper without an explicit pathway to data flagging. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. We present a scalable QA/QC framework in development for NEON that combines the efficiency and standardization of automated checks with the power and flexibility of human review. This framework includes fast-response monitoring of sensor health, a mobile application for electronically recording maintenance activities, traditional point-based automated quality flagging, and continuous monitoring of quality outcomes and longer-term holistic evaluations. This framework maintains the traceability of quality information along the entirety of the data generation pipeline, and explicitly links field reports of measurement interference to quality flagging. Preliminary results show that data quality can be effectively monitored and managed for a multitude of sites with a small group of QA/QC staff. Several components of this framework are open-source, including a R-Shiny application for efficiently monitoring, synthesizing, and investigating data quality issues.

Effective communication of public health guidance to emergency department clinicians in the setting of emerging incidents: a qualitative study and framework.

PubMed

Khan, Yasmin; Sanford, Sarah; Sider, Doug; Moore, Kieran; Garber, Gary; de Villa, Eileen; Schwartz, Brian

2017-04-28

Evidence to inform communication between emergency department clinicians and public health agencies is limited. In the context of diverse, emerging public health incidents, communication is urgent, as emergency department clinicians must implement recommendations to protect themselves and the public. The objectives of this study were to: explore current practices, barriers and facilitators at the local level for communicating public health guidance to emergency department clinicians in emerging public health incidents; and develop a framework that promotes effective communication of public health guidance to clinicians during emerging incidents. A qualitative study was conducted using semi-structured interviews with 26 key informants from emergency departments and public health agencies in Ontario, Canada. Data were analyzed inductively and the analytic approach was guided by concepts of complexity theory. Emergent themes corresponded to challenges and strategies for effective communication of public health guidance. Important challenges related to the coordination of communication across institutions and jurisdictions, and differences in work environments across sectors. Strategies for effective communication were identified as the development of partnerships and collaboration, attention to specific methods of communication used, and the importance of roles and relationship-building prior to an emerging public health incident. Following descriptive analysis, a framework was developed that consists of the following elements: 1) Anticipate; 2) Invest in building relationships and networks; 3) Establish liaison roles and redundancy; 4) Active communication; 5) Consider and respond to the target audience; 6) Leverage networks for coordination; and 7) Acknowledge and address uncertainty. The qualities inherent in local relationships cut across framework elements. This research indicates that relationships are central to effective communication between public health agencies and emergency department clinicians at the local level. Our framework which is grounded in qualitative evidence focuses on strategies to promote effective communication in the emerging public health incident setting and may be useful in informing practice.

Big data and high-performance analytics in structural health monitoring for bridge management

NASA Astrophysics Data System (ADS)

Alampalli, Sharada; Alampalli, Sandeep; Ettouney, Mohammed

2016-04-01

Structural Health Monitoring (SHM) can be a vital tool for effective bridge management. Combining large data sets from multiple sources to create a data-driven decision-making framework is crucial for the success of SHM. This paper presents a big data analytics framework that combines multiple data sets correlated with functional relatedness to convert data into actionable information that empowers risk-based decision-making. The integrated data environment incorporates near real-time streams of semi-structured data from remote sensors, historical visual inspection data, and observations from structural analysis models to monitor, assess, and manage risks associated with the aging bridge inventories. Accelerated processing of dataset is made possible by four technologies: cloud computing, relational database processing, support from NOSQL database, and in-memory analytics. The framework is being validated on a railroad corridor that can be subjected to multiple hazards. The framework enables to compute reliability indices for critical bridge components and individual bridge spans. In addition, framework includes a risk-based decision-making process that enumerate costs and consequences of poor bridge performance at span- and network-levels when rail networks are exposed to natural hazard events such as floods and earthquakes. Big data and high-performance analytics enable insights to assist bridge owners to address problems faster.

Epidemic outbreaks in complex heterogeneous networks

NASA Astrophysics Data System (ADS)

Moreno, Y.; Pastor-Satorras, R.; Vespignani, A.

2002-04-01

We present a detailed analytical and numerical study for the spreading of infections with acquired immunity in complex population networks. We show that the large connectivity fluctuations usually found in these networks strengthen considerably the incidence of epidemic outbreaks. Scale-free networks, which are characterized by diverging connectivity fluctuations in the limit of a very large number of nodes, exhibit the lack of an epidemic threshold and always show a finite fraction of infected individuals. This particular weakness, observed also in models without immunity, defines a new epidemiological framework characterized by a highly heterogeneous response of the system to the introduction of infected individuals with different connectivity. The understanding of epidemics in complex networks might deliver new insights in the spread of information and diseases in biological and technological networks that often appear to be characterized by complex heterogeneous architectures.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

A hierarchical spatial framework and database for the national river fish habitat condition assessment

USGS Publications Warehouse

Wang, L.; Infante, D.; Esselman, P.; Cooper, A.; Wu, D.; Taylor, W.; Beard, D.; Whelan, G.; Ostroff, A.

2011-01-01

Fisheries management programs, such as the National Fish Habitat Action Plan (NFHAP), urgently need a nationwide spatial framework and database for health assessment and policy development to protect and improve riverine systems. To meet this need, we developed a spatial framework and database using National Hydrography Dataset Plus (I-.100,000-scale); http://www.horizon-systems.com/nhdplus). This framework uses interconfluence river reaches and their local and network catchments as fundamental spatial river units and a series of ecological and political spatial descriptors as hierarchy structures to allow users to extract or analyze information at spatial scales that they define. This database consists of variables describing channel characteristics, network position/connectivity, climate, elevation, gradient, and size. It contains a series of catchment-natural and human-induced factors that are known to influence river characteristics. Our framework and database assembles all river reaches and their descriptors in one place for the first time for the conterminous United States. This framework and database provides users with the capability of adding data, conducting analyses, developing management scenarios and regulation, and tracking management progresses at a variety of spatial scales. This database provides the essential data needs for achieving the objectives of NFHAP and other management programs. The downloadable beta version database is available at http://ec2-184-73-40-15.compute-1.amazonaws.com/nfhap/main/.

Knowledge Interaction Design for Creative Knowledge Work

NASA Astrophysics Data System (ADS)

Nakakoji, Kumiyo; Yamamoto, Yasuhiro

This paper describes our approach for the development of application systems for creative knowledge work, particularly for early stages of information design tasks. Being a cognitive tool serving as a means of externalization, an application system affects how the user is engaged in the creative process through its visual interaction design. Knowledge interaction design described in this paper is a framework where a set of application systems for different information design domains are developed based on an interaction model, which is designed for a particular model of a thinking process. We have developed two sets of application systems using the knowledge interaction design framework: one includes systems for linear information design, such as writing, movie-editing, and video-analysis; the other includes systems for network information design, such as file-system navigation and hypertext authoring. Our experience shows that the resulting systems encourage users to follow a certain cognitive path through graceful user experience.

Optimizing information flow in small genetic networks. IV. Spatial coupling

NASA Astrophysics Data System (ADS)

Sokolowski, Thomas R.; Tkačik, Gašper

2015-06-01

We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

PubMed Central

Griol, David

2016-01-01

Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users. PMID:26819592

NOAA/NCEI/Regional Climate Services: Working with Partners and Stakeholders across a Wide Network

NASA Astrophysics Data System (ADS)

Mecray, E. L.

2015-12-01

Federal agencies all require plans to be prepared at the state level that outline the implementation of funding to address wildlife habitat, human health, transportation infrastructure, coastal zone management, environmental management, emergency management, and others. These plans are now requiring the consideration of changing climate conditions. So where does a state turn to discuss lessons learned, obtain tools and information to assess climate conditions, and to work with other states in their region? Regional networks and collaboratives are working to deliver this sector by sector. How do these networks work? Do they fit together in any way? What similarities and differences exist? Is anyone talking across these lines to find common climate information requirements? A sketch is forming that links these efforts, not by blending the sectors, but by finding the areas where coordination is critical, where information needs are common, and where delivery mechanisms can be streamlined. NOAA/National Centers for Environmental Information's Regional Climate Services Directors have been working at the interface of stakeholder-driven information delivery since 2010. This talk will outline the regional climate services delivery framework for the Eastern Region, with examples of regional products and information.

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Locating multiple diffusion sources in time varying networks from sparse observations.

PubMed

Hu, Zhao-Long; Shen, Zhesi; Cao, Shinan; Podobnik, Boris; Yang, Huijie; Wang, Wen-Xu; Lai, Ying-Cheng

2018-02-08

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

Calibration Testing of Network Tap Devices

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

Popovsky, Barbara; Chee, Brian; Frincke, Deborah A.

2007-11-14

Abstract: Understanding the behavior of network forensic devices is important to support prosecutions of malicious conduct on computer networks as well as legal remedies for false accusations of network management negligence. Individuals who seek to establish the credibility of network forensic data must speak competently about how the data was gathered and the potential for data loss. Unfortunately, manufacturers rarely provide information about the performance of low-layer network devices at a level that will survive legal challenges. This paper proposes a first step toward an independent calibration standard by establishing a validation testing methodology for evaluating forensic taps against manufacturermore » specifications. The methodology and the theoretical analysis that led to its development are offered as a conceptual framework for developing a standard and to "operationalize" network forensic readiness. This paper also provides details of an exemplar test, testing environment, procedures and results.« less

Compression of Flow Can Reveal Overlapping-Module Organization in Networks

NASA Astrophysics Data System (ADS)

Viamontes Esquivel, Alcides; Rosvall, Martin

2011-10-01

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

When Information from Public Health Officials is Untrustworthy: The Use of Online News, Interpersonal Networks, and Social Media during the MERS Outbreak in South Korea.

PubMed

Jang, Kyungeun; Baek, Young Min

2018-03-20

Public health officials (PHOs) are responsible for providing trustworthy information during a public health crisis; however, there is little research on how the public behaves when their expectations for such information are violated. Drawing on media dependency theory and source credibility research as our primary theoretical framework, we tested how credibility of information from PHOs is associated with people's reliance on a particular communication channel in the context of the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea. Using nationally representative data (N = 1036) collected during the MERS outbreak, we found that less credible information from PHOs led to more frequent use of online news, interpersonal networks, and social media for acquiring MERS-related information. However, credibility of information from PHOs was not associated with the use of television news or print newspapers. The theoretical and practical implications of our results on communication channels usage are discussed.

Parameter inference in small world network disease models with approximate Bayesian Computational methods

NASA Astrophysics Data System (ADS)

Walker, David M.; Allingham, David; Lee, Heung Wing Joseph; Small, Michael

2010-02-01

Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.

The Framework for Simulation of Bioinspired Security Mechanisms against Network Infrastructure Attacks

PubMed Central

Kotenko, Igor

2014-01-01

The paper outlines a bioinspired approach named “network nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed prosedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine nessesary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described. PMID:25254229

ImTK: an open source multi-center information management toolkit

NASA Astrophysics Data System (ADS)

Alaoui, Adil; Ingeholm, Mary Lou; Padh, Shilpa; Dorobantu, Mihai; Desai, Mihir; Cleary, Kevin; Mun, Seong K.

2008-03-01

The Information Management Toolkit (ImTK) Consortium is an open source initiative to develop robust, freely available tools related to the information management needs of basic, clinical, and translational research. An open source framework and agile programming methodology can enable distributed software development while an open architecture will encourage interoperability across different environments. The ISIS Center has conceptualized a prototype data sharing network that simulates a multi-center environment based on a federated data access model. This model includes the development of software tools to enable efficient exchange, sharing, management, and analysis of multimedia medical information such as clinical information, images, and bioinformatics data from multiple data sources. The envisioned ImTK data environment will include an open architecture and data model implementation that complies with existing standards such as Digital Imaging and Communications (DICOM), Health Level 7 (HL7), and the technical framework and workflow defined by the Integrating the Healthcare Enterprise (IHE) Information Technology Infrastructure initiative, mainly the Cross Enterprise Document Sharing (XDS) specifications.

Source-Adaptation-Based Wireless Video Transport: A Cross-Layer Approach

NASA Astrophysics Data System (ADS)

Qu, Qi; Pei, Yong; Modestino, James W.; Tian, Xusheng

2006-12-01

Real-time packet video transmission over wireless networks is expected to experience bursty packet losses that can cause substantial degradation to the transmitted video quality. In wireless networks, channel state information is hard to obtain in a reliable and timely manner due to the rapid change of wireless environments. However, the source motion information is always available and can be obtained easily and accurately from video sequences. Therefore, in this paper, we propose a novel cross-layer framework that exploits only the motion information inherent in video sequences and efficiently combines a packetization scheme, a cross-layer forward error correction (FEC)-based unequal error protection (UEP) scheme, an intracoding rate selection scheme as well as a novel intraframe interleaving scheme. Our objective and subjective results demonstrate that the proposed approach is very effective in dealing with the bursty packet losses occurring on wireless networks without incurring any additional implementation complexity or delay. Thus, the simplicity of our proposed system has important implications for the implementation of a practical real-time video transmission system.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

PubMed Central

Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

2016-01-01

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications. PMID:27429547

The SLH framework for modeling quantum input-output networks

DOE PAGES

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

2017-09-04

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less

The SLH framework for modeling quantum input-output networks

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

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less

Developing an inter-organizational community-based health network: an Australian investigation.

PubMed

Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

2015-12-01

Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Building National Capacity for Climate Change Interpretation: The Role of Leaders, Partnerships, and Networks

NASA Astrophysics Data System (ADS)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. Our project represents a cross-disciplinary partnership among climate scientists, social and cognitive scientists, and informal education practitioners. We have built a growing national network of more than 250 alumni, including approximately 15-20 peer leaders who co-lead both in-depth training programs and introductory workshops. We have found that this alumni network has been assuming increasing importance in providing for ongoing learning, support for implementation, leadership development, and coalition building. As we look toward the future, we are exploring potential partnerships with other existing networks, both to sustain our impact and to expand our reach. This presentation will address what we have learned in terms of network impacts, best practices, factors for success, and future directions.

An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures

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

Schuman, Catherine D; Plank, James; Disney, Adam

2016-01-01

As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

Using social knowledge networking technology to enable meaningful use of electronic health record technology in hospitals and health systems.

PubMed

Rangachari, Pavani

2014-12-01

Despite the federal policy momentum towards "meaningful use" of Electronic Health Records, the healthcare organizational literature remains replete with reports of unintended adverse consequences of implementing Electronic Health Records, including: increased work for clinicians, unfavorable workflow changes, and unexpected changes in communication patterns & practices. In addition to being costly and unsafe, these unintended adverse consequences may pose a formidable barrier to "meaningful use" of Electronic Health Records. Correspondingly, it is essential for hospital administrators to understand and detect the causes of unintended adverse consequences, to ensure successful implementation of Electronic Health Records. The longstanding Technology-in-Practice framework emphasizes the role of human agency in enacting structures of technology use or "technologies-in-practice." Given a set of unintended adverse consequences from health information technology implementation, this framework could help trace them back to specific actions (types of technology-in-practice) and institutional conditions (social structures). On the other hand, the more recent Knowledge-in-Practice framework helps understand how information and communication technologies ( e.g. , social knowledge networking systems) could be implemented alongside existing technology systems, to create new social structures, generate new knowledge-in-practice, and transform technology-in-practice. Therefore, integrating the two literature streams could serve the dual purpose of understanding and overcoming unintended adverse consequences of Electronic Health Record implementation. This paper seeks to: (1) review the theoretical literatures on technology use & implementation, and identify a framework for understanding & overcoming unintended adverse consequences of implementing Electronic Health Records; (2) outline a broad project proposal to test the applicability of the framework in enabling "meaningful use" of Electronic Health Records in a healthcare context; and (3) identify strategies for successful implementation of Electronic Health Records in hospitals & health systems, based on the literature review and application.

Curriculum Framework (CF) Implementation Conference. Report of the Regional Educational Laboratory Network Program and the National Network of Eisenhower Mathematics and Science Regional Consortia (Hilton Head Island, South Carolina, January 26-27, 1995).

ERIC Educational Resources Information Center

Palmer, Jackie; Powell, Mary Jo

The Laboratory Network Program and the National Network of Eisenhower Mathematics and Science Regional Consortia, operating as the Curriculum Frameworks Task Force, jointly convened a group of educators involved in implementing state-level mathematics or science curriculum frameworks (CF). The Hilton Head (South Carolina) conference had a dual…

Development of computationally predicted Adverse Outcome Pathway (AOP) networks through data mining and integration of publicly available in vivo, in vitro, phenotype, and biological pathway data

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

Computationally predicted Adverse Outcome Pathway networks for liver-related diseases using publicly available data sources: Case studies and lessons learned

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework summarizes key information about mechanistic events leading to an adverse health or ecological outcome. In recent years computationally predicted AOPs (cpAOP) making use of publicly available data have been proposed as a means of accele...

Integrating child welfare, juvenile justice, and other agencies in a continuum of services.

PubMed

Howell, James C; Kelly, Marion R; Palmer, James; Mangum, Ronald L

2004-01-01

This article presents a comprehensive strategy framework for integrating mental health, child welfare, education, substance abuse, and juvenile justice system services. It proposes an infrastructure of information exchange, cross-agency client referrals, a networking protocol, interagency councils, and service integration models. This infrastructure facilitates integrated service delivery.

Academic Help Seeking: A Framework for Conceptualizing Facebook Use for Higher Education Support

ERIC Educational Resources Information Center

Amador, Paul V.; Amador, Julie M.

2017-01-01

This purpose of this study was to understand how higher education students, specifically preservice teachers, used Facebook to seek academic help. Results indicated that participants who regularly used Facebook to seek academic support formally and informally, considered the network to be social in nature, generated a sense of community through…

A Biologically Informed Framework for the Analysis of the PPAR Signaling Pathway using a Bayesian Network

EPA Science Inventory

The US EPA’s ToxCastTM program seeks to combine advances in high-throughput screening technology with methodologies from statistics and computer science to develop high-throughput decision support tools for assessing chemical hazard and risk. To develop new methods of analysis of...

Exploring molecular networks using MONET ontology.

PubMed

Silva, João Paulo Müller da; Lemke, Ney; Mombach, José Carlos; Souza, José Guilherme Camargo de; Sinigaglia, Marialva; Vieira, Renata

2006-03-31

The description of the complex molecular network responsible for cell behavior requires new tools to integrate large quantities of experimental data in the design of biological information systems. These tools could be used in the characterization of these networks and in the formulation of relevant biological hypotheses. The building of an ontology is a crucial step because it integrates in a coherent framework the concepts necessary to accomplish such a task. We present MONET (molecular network), an extensible ontology and an architecture designed to facilitate the integration of data originating from different public databases in a single- and well-documented relational database, that is compatible with MONET formal definition. We also present an example of an application that can easily be implemented using these tools.

The SysMan monitoring service and its management environment

NASA Astrophysics Data System (ADS)

Debski, Andrzej; Janas, Ekkehard

1996-06-01

Management of modern information systems is becoming more and more complex. There is a growing need for powerful, flexible and affordable management tools to assist system managers in maintaining such systems. It is at the same time evident that effective management should integrate network management, system management and application management in a uniform way. Object oriented OSI management architecture with its four basic modelling concepts (information, organization, communication and functional models) together with widely accepted distribution platforms such as ANSA/CORBA, constitutes a reliable and modern framework for the implementation of a management toolset. This paper focuses on the presentation of concepts and implementation results of an object oriented management toolset developed and implemented within the framework of the ESPRIT project 7026 SysMan. An overview is given of the implemented SysMan management services including the System Management Service, Monitoring Service, Network Management Service, Knowledge Service, Domain and Policy Service, and the User Interface. Special attention is paid to the Monitoring Service which incorporates the architectural key entity responsible for event management. Its architecture and building components, especially filters, are emphasized and presented in detail.

Layer-switching cost and optimality in information spreading on multiplex networks

PubMed Central

Min, Byungjoon; Gwak, Sang-Hwan; Lee, Nanoom; Goh, K. -I.

2016-01-01

We study a model of information spreading on multiplex networks, in which agents interact through multiple interaction channels (layers), say online vs. offline communication layers, subject to layer-switching cost for transmissions across different interaction layers. The model is characterized by the layer-wise path-dependent transmissibility over a contact, that is dynamically determined dependently on both incoming and outgoing transmission layers. We formulate an analytical framework to deal with such path-dependent transmissibility and demonstrate the nontrivial interplay between the multiplexity and spreading dynamics, including optimality. It is shown that the epidemic threshold and prevalence respond to the layer-switching cost non-monotonically and that the optimal conditions can change in abrupt non-analytic ways, depending also on the densities of network layers and the type of seed infections. Our results elucidate the essential role of multiplexity that its explicit consideration should be crucial for realistic modeling and prediction of spreading phenomena on multiplex social networks in an era of ever-diversifying social interaction layers. PMID:26887527

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Human fatigue expression recognition through image-based dynamic multi-information and bimodal deep learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Wang, Zengcai; Wang, Xiaojin; Qi, Yazhou; Liu, Qing; Zhang, Guoxin

2016-09-01

Human fatigue is an important cause of traffic accidents. To improve the safety of transportation, we propose, in this paper, a framework for fatigue expression recognition using image-based facial dynamic multi-information and a bimodal deep neural network. First, the landmark of face region and the texture of eye region, which complement each other in fatigue expression recognition, are extracted from facial image sequences captured by a single camera. Then, two stacked autoencoder neural networks are trained for landmark and texture, respectively. Finally, the two trained neural networks are combined by learning a joint layer on top of them to construct a bimodal deep neural network. The model can be used to extract a unified representation that fuses landmark and texture modalities together and classify fatigue expressions accurately. The proposed system is tested on a human fatigue dataset obtained from an actual driving environment. The experimental results demonstrate that the proposed method performs stably and robustly, and that the average accuracy achieves 96.2%.

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

Popularity versus similarity in growing networks

NASA Astrophysics Data System (ADS)

Krioukov, Dmitri; Papadopoulos, Fragkiskos; Kitsak, Maksim; Serrano, Mariangeles; Boguna, Marian

2012-02-01

Preferential attachment is a powerful mechanism explaining the emergence of scaling in growing networks. If new connections are established preferentially to more popular nodes in a network, then the network is scale-free. Here we show that not only popularity but also similarity is a strong force shaping the network structure and dynamics. We develop a framework where new connections, instead of preferring popular nodes, optimize certain trade-offs between popularity and similarity. The framework admits a geometric interpretation, in which preferential attachment emerges from local optimization processes. As opposed to preferential attachment, the optimization framework accurately describes large-scale evolution of technological (Internet), social (web of trust), and biological (E.coli metabolic) networks, predicting the probability of new links in them with a remarkable precision. The developed framework can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.

Communication between Brain Areas Based on Nested Oscillations

PubMed Central

Kastner, Sabine

2017-01-01

Abstract Unraveling how brain regions communicate is crucial for understanding how the brain processes external and internal information. Neuronal oscillations within and across brain regions have been proposed to play a crucial role in this process. Two main hypotheses have been suggested for routing of information based on oscillations, namely communication through coherence and gating by inhibition. Here, we propose a framework unifying these two hypotheses that is based on recent empirical findings. We discuss a theory in which communication between two regions is established by phase synchronization of oscillations at lower frequencies (<25 Hz), which serve as temporal reference frame for information carried by high-frequency activity (>40 Hz). Our framework, consistent with numerous recent empirical findings, posits that cross-frequency interactions are essential for understanding how large-scale cognitive and perceptual networks operate. PMID:28374013

The roosting spatial network of a bird-predator bat.

PubMed

Fortuna, Miguel A; Popa-Lisseanu, Ana G; Ibáñez, Carlos; Bascompte, Jordi

2009-04-01

The use of roosting sites by animal societies is important in conservation biology, animal behavior, and epidemiology. The giant noctule bat (Nyctalus lasiopterus) constitutes fission-fusion societies whose members spread every day in multiple trees for shelter. To assess how the pattern of roosting use determines the potential for information exchange or disease spreading, we applied the framework of complex networks. We found a social and spatial segregation of the population in well-defined modules or compartments, formed by groups of bats sharing the same trees. Inside each module, we revealed an asymmetric use of trees by bats representative of a nested pattern. By applying a simple epidemiological model, we show that there is a strong correlation between network structure and the rate and shape of infection dynamics. This modular structure slows down the spread of diseases and the exchange of information through the entire network. The implication for management is complex, affecting differently the cohesion inside and among colonies and the transmission of parasites and diseases. Network analysis can hence be applied to quantifying the conservation status of individual trees used by species depending on hollows for shelter.

MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey.

PubMed

Han, Ruisong; Yang, Wei; You, Kaiming

2016-12-16

Safety production of coalmines is a task of top priority which plays an important role in guaranteeing, supporting and promoting the continuous development of the coal industry. Since traditional wireless sensor networks (WSNs) cannot fully meet the requirements of comprehensive environment monitoring of underground coalmines, wireless multimedia sensor networks (WMSNs), enabling the retrieval of multimedia information, are introduced to realize fine-grained and precise environment surveillance. In this paper, a framework for designing underground coalmine WMSNs based on Multi-Band Orthogonal Frequency-Division Multiplexing Ultra-wide Band (MB-OFDM-UWB) is presented. The selection of MB-OFDM-UWB wireless transmission solution is based on the characteristics of underground coalmines. Network structure and design challenges are analyzed first, which is the foundation for further discussion. Then, key supporting technologies and open research areas in different layers are surveyed, and we provide a detailed literature review of the state of the art strategies, algorithms and general solutions in these issues. Finally, other research issues like localization, information processing, and network management are discussed.

MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey

PubMed Central

Han, Ruisong; Yang, Wei; You, Kaiming

2016-01-01

Safety production of coalmines is a task of top priority which plays an important role in guaranteeing, supporting and promoting the continuous development of the coal industry. Since traditional wireless sensor networks (WSNs) cannot fully meet the requirements of comprehensive environment monitoring of underground coalmines, wireless multimedia sensor networks (WMSNs), enabling the retrieval of multimedia information, are introduced to realize fine-grained and precise environment surveillance. In this paper, a framework for designing underground coalmine WMSNs based on Multi-Band Orthogonal Frequency-Division Multiplexing Ultra-wide Band (MB-OFDM-UWB) is presented. The selection of MB-OFDM-UWB wireless transmission solution is based on the characteristics of underground coalmines. Network structure and design challenges are analyzed first, which is the foundation for further discussion. Then, key supporting technologies and open research areas in different layers are surveyed, and we provide a detailed literature review of the state of the art strategies, algorithms and general solutions in these issues. Finally, other research issues like localization, information processing, and network management are discussed. PMID:27999258

A fusion network for semantic segmentation using RGB-D data

NASA Astrophysics Data System (ADS)

Yuan, Jiahui; Zhang, Kun; Xia, Yifan; Qi, Lin; Dong, Junyu

2018-04-01

Semantic scene parsing is considerable in many intelligent field, including perceptual robotics. For the past few years, pixel-wise prediction tasks like semantic segmentation with RGB images has been extensively studied and has reached very remarkable parsing levels, thanks to convolutional neural networks (CNNs) and large scene datasets. With the development of stereo cameras and RGBD sensors, it is expected that additional depth information will help improving accuracy. In this paper, we propose a semantic segmentation framework incorporating RGB and complementary depth information. Motivated by the success of fully convolutional networks (FCN) in semantic segmentation field, we design a fully convolutional networks consists of two branches which extract features from both RGB and depth data simultaneously and fuse them as the network goes deeper. Instead of aggregating multiple model, our goal is to utilize RGB data and depth data more effectively in a single model. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and achieve competitive results with the state-of-the-art methods.

A VGI data integration framework based on linked data model

NASA Astrophysics Data System (ADS)

Wan, Lin; Ren, Rongrong

2015-12-01

This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies - which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.

Physiology-based face recognition in the thermal infrared spectrum.

PubMed

Buddharaju, Pradeep; Pavlidis, Ioannis T; Tsiamyrtzis, Panagiotis; Bazakos, Mike

2007-04-01

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

Network information analysis reveals risk perception transmission in a behaviour-influenza dynamics system.

PubMed

Liao, C-M; You, S-H; Cheng, Y-H

2015-01-01

Influenza poses a significant public health burden worldwide. Understanding how and to what extent people would change their behaviour in response to influenza outbreaks is critical for formulating public health policies. We incorporated the information-theoretic framework into a behaviour-influenza (BI) transmission dynamics system in order to understand the effects of individual behavioural change on influenza epidemics. We showed that information transmission of risk perception played a crucial role in the spread of health-seeking behaviour throughout influenza epidemics. Here a network BI model provides a new approach for understanding the risk perception spread and human behavioural change during disease outbreaks. Our study allows simultaneous consideration of epidemiological, psychological, and social factors as predictors of individual perception rates in behaviour-disease transmission systems. We suggest that a monitoring system with precise information on risk perception should be constructed to effectively promote health behaviours in preparation for emerging disease outbreaks.

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

PubMed

Diederich, Nick; Bartsch, Thorsten; Kohlstedt, Hermann; Ziegler, Martin

2018-06-19

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Two Distinct Scene-Processing Networks Connecting Vision and Memory.

PubMed

Baldassano, Christopher; Esteva, Andre; Fei-Fei, Li; Beck, Diane M

2016-01-01

A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

What Motivates Young Adults to Talk About Physical Activity on Social Network Sites?

PubMed Central

Campo, Shelly; Yang, Jingzhen; Eckler, Petya; Snetselaar, Linda; Janz, Kathleen; Leary, Emily

2017-01-01

Background Electronic word-of-mouth on social network sites has been used successfully in marketing. In social marketing, electronic word-of-mouth about products as health behaviors has the potential to be more effective and reach more young adults than health education through traditional mass media. However, little is known about what motivates people to actively initiate electronic word-of-mouth about health behaviors on their personal pages or profiles on social network sites, thus potentially reaching all their contacts on those sites. Objective This study filled the gap by applying a marketing theoretical model to explore the factors associated with electronic word-of-mouth on social network sites about leisure-time physical activity. Methods A Web survey link was sent to undergraduate students at one of the Midwestern universities and 439 of them completed the survey. Results The average age of the 439 participants was 19 years (SD=1 year, range: 18-24). Results suggested that emotional engagement with leisure-time physical activity (ie, affective involvement in leisure-time physical activity) predicted providing relevant opinions or information on social network sites. Social network site users who perceived stronger ties with all their contacts were more likely to provide and seek leisure-time physical activity opinions and information. People who provided leisure-time physical activity opinions and information were more likely to seek opinions and information, and people who forwarded information about leisure-time physical activity were more likely to chat about it. Conclusions This study shed light on the application of the electronic word-of-mouth theoretical framework in promoting health behaviors. The findings can also guide the development of future social marketing interventions using social network sites to promote leisure-time physical activity. PMID:28642215

Integrating Data and Networks: Human Factors

NASA Astrophysics Data System (ADS)

Chen, R. S.

2012-12-01

The development of technical linkages and interoperability between scientific networks is a necessary but not sufficient step towards integrated use and application of networked data and information for scientific and societal benefit. A range of "human factors" must also be addressed to ensure the long-term integration, sustainability, and utility of both the interoperable networks themselves and the scientific data and information to which they provide access. These human factors encompass the behavior of both individual humans and human institutions, and include system governance, a common framework for intellectual property rights and data sharing, consensus on terminology, metadata, and quality control processes, agreement on key system metrics and milestones, the compatibility of "business models" in the short and long term, harmonization of incentives for cooperation, and minimization of disincentives. Experience with several national and international initiatives and research programs such as the International Polar Year, the Group on Earth Observations, the NASA Earth Observing Data and Information System, the U.S. National Spatial Data Infrastructure, the Global Earthquake Model, and the United Nations Spatial Data Infrastructure provide a range of lessons regarding these human factors. Ongoing changes in science, technology, institutions, relationships, and even culture are creating both opportunities and challenges for expanded interoperability of scientific networks and significant improvement in data integration to advance science and the use of scientific data and information to achieve benefits for society as a whole.

DGs for Service Restoration to Critical Loads in a Secondary Network

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

Xu, Yin; Liu, Chen-Ching; Wang, Zhiwen

During a major outage in a secondary network distribution system, distributed generators (DGs) connected to the primary feeders as well as the secondary network can be used to serve critical loads. This paper proposed a resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster. Technical issues associated with the restoration process are analyzed, including the operation of network protectors, inrush currents caused by the energization of network transformers, synchronization of DGs to the network, and circulating currents among DGs. A look-ahead load restoration framework is proposed, incorporating technical issues associated with secondary networks, limitsmore » on DG capacity and generation resources, dynamic constraints, and operational limits. The entire outage duration is divided into a sequence of periods. Restoration strategies can be adjusted at the beginning of each period using the latest information. Finally, numerical simulation of the modified IEEE 342-node low voltage networked test system is performed to validate the effectiveness of the proposed method.« less

DGs for Service Restoration to Critical Loads in a Secondary Network

DOE PAGES

Xu, Yin; Liu, Chen-Ching; Wang, Zhiwen; ...

2017-08-25

During a major outage in a secondary network distribution system, distributed generators (DGs) connected to the primary feeders as well as the secondary network can be used to serve critical loads. This paper proposed a resilience-oriented method to determine restoration strategies for secondary network distribution systems after a major disaster. Technical issues associated with the restoration process are analyzed, including the operation of network protectors, inrush currents caused by the energization of network transformers, synchronization of DGs to the network, and circulating currents among DGs. A look-ahead load restoration framework is proposed, incorporating technical issues associated with secondary networks, limitsmore » on DG capacity and generation resources, dynamic constraints, and operational limits. The entire outage duration is divided into a sequence of periods. Restoration strategies can be adjusted at the beginning of each period using the latest information. Finally, numerical simulation of the modified IEEE 342-node low voltage networked test system is performed to validate the effectiveness of the proposed method.« less

Hierarchical Representation Learning for Kinship Verification.

PubMed

Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul

2017-01-01

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender and age and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d' , and perceptual information entropy. Utilizing the information obtained from the human study, a hierarchical kinship verification via representation learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as an output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. A new WVU kinship database is created, which consists of multiple images per subject to facilitate kinship verification. The results show that the proposed deep learning framework (KVRL-fcDBN) yields the state-of-the-art kinship verification accuracy on the WVU kinship database and on four existing benchmark data sets. Furthermore, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification.

The lower saxony bank of health. rationale, principles, services, organization and architectural framework.

PubMed

Plischke, M; Wagner, M; Haarbrandt, B; Rochon, M; Schwartze, J; Tute, E; Bartkiewicz, T; Kleinschmidt, T; Seidel, C; Schüttig, H; Haux, R

2014-01-01

This article is part of a Focus Theme of METHODS of Information in Medicine on Health Record Banking. Poor communication of health care information between health care providers (HCP) is still a major problem. One recent approach is the concept of Health Record Banking. With this report we want to introduce the Lower Saxony Bank of Health (LSBH) to the international community. The main objective of this paper is to report and explain: 1) why this organization has been founded, 2) which basic principles have been set, 3) which services will be provided, 4) which type of organization has been chosen, and 5) which architectural framework has been selected. To report and discuss how we plan to achieve the intended objectives. The LSBH was founded as an entrepreneurial company, regarding itself as a neutral third-party information broker. The bank does not store medical documents on its central servers but offers a document registry with links to documents stored at participating health care providers. Subject to valid patient consent, the LSBH grants access to these documents to authorized health care providers. To implement our services, we chose the established technical frameworks of the Integrating the Healthcare Enterprise (IHE) initiative using cross-enterprise document sharing (XDS). Different approaches to establish health information exchange (HIE) are in early stages and some have failed in the past. Health Record Banking can address major challenges described in the literature about HIE. The future will show if our provider-sponsored business model is sustainable. After reaching a stable network, we intend to add additional HCPs, e.g., care homes or ambulance services, to the network.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

Digital contract approach for consistent and predictable multimedia information delivery in electronic commerce

NASA Astrophysics Data System (ADS)

Konana, Prabhudev; Gupta, Alok; Whinston, Andrew B.

1997-01-01

A pure 'technological' solution to network quality problems is incomplete since any benefits from new technologies are offset by the demand from exponentially growing electronic commerce ad data-intensive applications. SInce an economic paradigm is implicit in electronic commerce, we propose a 'market-system' approach to improve quality of service. Quality of service for digital products takes on a different meaning since users view quality of service differently and value information differently. We propose a framework for electronic commerce that is based on an economic paradigm and mass-customization, and works as a wide-area distributed management system. In our framework, surrogate-servers act as intermediaries between information provides and end- users, and arrange for consistent and predictable information delivery through 'digital contracts.' These contracts are negotiated and priced based on economic principles. Surrogate servers pre-fetched, through replication, information from many different servers and consolidate based on demand expectations. In order to recognize users' requirements and process requests accordingly, real-time databases are central to our framework. We also propose that multimedia information be separated into slowly changing and rapidly changing data streams to improve response time requirements. Surrogate- servers perform the tasks of integration of these data streams that is transparent to end-users.

Active vision and image/video understanding with decision structures based on the network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

Nebraska Earth Science Education Network: Enhancing the NASA, University, and Pre-College Science Teacher Connection with Electronic Communication

NASA Technical Reports Server (NTRS)

Gosselin, David C.

1997-01-01

The primary goals of this project were to: 1. Promote and enhance K-12 earth science education; and enhance the access to and exchange of information through the use of digital networks in K-12 institutions. We have achieved these two goals. Through the efforts of many individuals at the University of Nebraska-Lincoln (UNL), Nebraska Earth Science Education Network (NESEN) has become a viable and beneficial interdisciplinary outreach program for K-12 educators in Nebraska. Over the last three years, the NASA grant has provided personnel and equipment to maintain, expand and develop NESEN into a program that is recognized by its membership as a valuable source of information and expertise in earth systems science. Because NASA funding provided a framework upon which to build, other external sources of funding have become available to support NESEN programs.

MaNIDA: an operational infrastructure for shipborne data

NASA Astrophysics Data System (ADS)

Macario, Ana; Scientific MaNIDA Team

2013-04-01

The Marine Network for Integrated Data Access (MaNIDA) aims to build a sustainable e-Infrastruture to support discovery and re-use of data archived in a distributed network of data providers in Germany (see related abstracts in session ESSI1.2 and session ESSI2.2). Because one of the primary focus of MaNIDA is the underway data acquired on board of German academic research vessels, we will be addressing various issues related to cruise-level metadata, shiptrack navigation, sampling events conducted during the cruise (event logs), standardization of device-related (type, name, parameters) and place-related (gazetteer) vocabularies, QA/QC procedures (near real time and post-cruise validation, corrections, quality flags) as well as ingestion and management of contextual information (e.g. various types of cruise-related reports and project-related information). One of MaNIDA's long-term goal is to be able to offer an integrative "one-stop-shop" framework for management and access of ship-related information based on international standards and interoperability. This access framework will be freely available and is intended for scientists, funding agencies and the public. The master "catalog" we are building currently contains information from 13 German academic research vessels and respective cruises (to date ~1900 cruises with expected growing rate of ~150 cruises annually). Moreover, MaNIDA's operational infrastructure will additionally provide a direct pipeline to SeaDataNet Cruise Summary Report Inventory, among others. In this presentation, we will focus on the extensions we are currently implementing to support automated acquisition and standardized transfer of various types of data from German research vessels to hosts on land. Our concept towards nationwide common QA/QC procedures for various types of underway data (including versioning concept) and common workflows will also be presented. The "linking" of cruise-related information with quality-controlled data and data products (e.g., digital terrain models), publications, cruise-related reports, people and other contextual information will be additionally shown in the framework of a prototype for R.V. Polarstern.

Formation Control over Delayed Communication Network

NASA Astrophysics Data System (ADS)

Secchi, Cristian; Fantuzzi, Cesare

In this Chapter we address the problem of formation control of a group of robots that exchange information over a communication network characterized by a non negligible delay. We consider the Virtual Body Artificial Potential approach for stabilizing a group of robots at a desired formation. We show that it is possible to model the controlled group of robots as a port-Hamiltonian system and we exploit the scattering framework to achieve a passive behavior of the controlled system and to stabilize the robots in the desired formation independently of any communication delay.

Emergence of consensus as a modular-to-nested transition in communication dynamics

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics.

PubMed

Borge-Holthoefer, Javier; Baños, Raquel A; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-30

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics

PubMed Central

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources –visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems. PMID:28134358

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Formulating a Theoretical Framework for Assessing Network Loads for Effective Deployment in Network-Centric Operations and Warfare

DTIC Science & Technology

2008-11-01

is particularly important in order to design a network that is realistically deployable. The goal of this project is the design of a theoretical ... framework to assess and predict the effectiveness and performance of networks and their loads.

Role of Network Science in the Study of Anesthetic State Transitions.

PubMed

Lee, UnCheol; Mashour, George A

2018-04-23

The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

Large networks of interacting dynamical systems are well-known for the complex behaviours they are able to display, even when each node features a quite simple dynamics. Despite examples of such networks being widespread both in nature and in technological applications, the interplay between the local and the macroscopic behaviour, through the interconnection topology, is still not completely understood. Moreover, traditional analytical methods for dynamical response analysis fail because of the intrinsically large dimension of the phase space of the network which makes the general problem intractable. Therefore, in this paper we develop an approach aiming to condense all the information in a compact description based on partial differential equations. By focusing on propagative phenomena, rigorous conditions under which the original network dynamical properties can be successfully analysed within the proposed framework are derived as well. A network of Fitzhugh-Nagumo systems is finally used to illustrate the effectiveness of the proposed method.

HOLA: Human-like Orthogonal Network Layout.

PubMed

Kieffer, Steve; Dwyer, Tim; Marriott, Kim; Wybrow, Michael

2016-01-01

Over the last 50 years a wide variety of automatic network layout algorithms have been developed. Some are fast heuristic techniques suitable for networks with hundreds of thousands of nodes while others are multi-stage frameworks for higher-quality layout of smaller networks. However, despite decades of research currently no algorithm produces layout of comparable quality to that of a human. We give a new "human-centred" methodology for automatic network layout algorithm design that is intended to overcome this deficiency. User studies are first used to identify the aesthetic criteria algorithms should encode, then an algorithm is developed that is informed by these criteria and finally, a follow-up study evaluates the algorithm output. We have used this new methodology to develop an automatic orthogonal network layout method, HOLA, that achieves measurably better (by user study) layout than the best available orthogonal layout algorithm and which produces layouts of comparable quality to those produced by hand.

MIDER: network inference with mutual information distance and entropy reduction.

PubMed

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

2014-01-01

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information-theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning.

Secure smart grid communications and information integration based on digital watermarking in wireless sensor networks

NASA Astrophysics Data System (ADS)

Yan, Xin; Zhang, Ling; Wu, Yang; Luo, Youlong; Zhang, Xiaoxing

2017-02-01

As more and more wireless sensor nodes and networks are employed to acquire and transmit the state information of power equipment in smart grid, we are in urgent need of some viable security solutions to ensure secure smart grid communications. Conventional information security solutions, such as encryption/decryption, digital signature and so forth, are not applicable to wireless sensor networks in smart grid any longer, where bulk messages need to be exchanged continuously. The reason is that these cryptographic solutions will account for a large portion of the extremely limited resources on sensor nodes. In this article, a security solution based on digital watermarking is adopted to achieve the secure communications for wireless sensor networks in smart grid by data and entity authentications at a low cost of operation. Our solution consists of a secure framework of digital watermarking, and two digital watermarking algorithms based on alternating electric current and time window, respectively. Both watermarking algorithms are composed of watermark generation, embedding and detection. The simulation experiments are provided to verify the correctness and practicability of our watermarking algorithms. Additionally, a new cloud-based architecture for the information integration of smart grid is proposed on the basis of our security solutions.

TANDEM: A Trust-Based Agent Framework for Networked Decision Making

DTIC Science & Technology

2015-09-10

selective (20–80 %), while the rest are good citizens, trust acts as a method to isolate misbehaving agents. If the majority of the agents have high...competence and low selectivity, then they can use trust to isolate route information around the misbehaving agents, improving Comm and Steps. The impact is...more dramatic when only 20–40 % of the agents are misbehaving . However, using trust results in reduced SA as the information available at the

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

2018-05-01

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

PubMed Central

Song, Min

2016-01-01

In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications. PMID:27195695

Deformable image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

2018-03-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

Case retrieval in medical databases by fusing heterogeneous information.

PubMed

Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Roux, Christian; Cochener, Béatrice

2011-01-01

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

Research and Design of the Three-tier Distributed Network Management System Based on COM / COM + and DNA

NASA Astrophysics Data System (ADS)

Liang, Likai; Bi, Yushen

Considered on the distributed network management system's demand of high distributives, extensibility and reusability, a framework model of Three-tier distributed network management system based on COM/COM+ and DNA is proposed, which adopts software component technology and N-tier application software framework design idea. We also give the concrete design plan of each layer of this model. Finally, we discuss the internal running process of each layer in the distributed network management system's framework model.

Partnerships - Working Together to Build The National Map

USGS Publications Warehouse

,

2004-01-01

Through The National Map, the U.S. Geological Survey (USGS) is working with partners to ensure that current, accurate, and complete base geographic information is available for the Nation. Designed as a network of online digital databases, it provides a consistent geographic data framework for the country and serves as a foundation for integrating, sharing, and using data easily and reliably. It provides public access to high quality geospatial data and information from multiple partners to help inform decisionmaking by resource managers and the public, and to support intergovernmental homeland security and emergency management requirements.

MediaNet: a multimedia information network for knowledge representation

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.; Chang, Shih-Fu

2000-10-01

In this paper, we present MediaNet, which is a knowledge representation framework that uses multimedia content for representing semantic and perceptual information. The main components of MediaNet include conceptual entities, which correspond to real world objects, and relationships among concepts. MediaNet allows the concepts and relationships to be defined or exemplified by multimedia content such as images, video, audio, graphics, and text. MediaNet models the traditional relationship types such as generalization and aggregation but adds additional functionality by modeling perceptual relationships based on feature similarity. For example, MediaNet allows a concept such as car to be defined as a type of a transportation vehicle, but which is further defined and illustrated through example images, videos and sounds of cars. In constructing the MediaNet framework, we have built on the basic principles of semiotics and semantic networks in addition to utilizing the audio-visual content description framework being developed as part of the MPEG-7 multimedia content description standard. By integrating both conceptual and perceptual representations of knowledge, MediaNet has potential to impact a broad range of applications that deal with multimedia content at the semantic and perceptual levels. In particular, we have found that MediaNet can improve the performance of multimedia retrieval applications by using query expansion, refinement and translation across multiple content modalities. In this paper, we report on experiments that use MediaNet in searching for images. We construct the MediaNet knowledge base using both WordNet and an image network built from multiple example images and extracted color and texture descriptors. Initial experimental results demonstrate improved retrieval effectiveness using MediaNet in a content-based retrieval system.

Model-based analysis of pattern motion processing in mouse primary visual cortex

PubMed Central

Muir, Dylan R.; Roth, Morgane M.; Helmchen, Fritjof; Kampa, Björn M.

2015-01-01

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features. PMID:26300738

Diabetes Information Technology: Designing Informatics Systems to Catalyze Change in Clinical Care

PubMed Central

Lester, William T.; Zai, Adrian H.; Chueh, Henry C.; Grant, Richard W.

2008-01-01

Current computerized reminder and decision support systems intended to improve diabetes care have had a limited effect on clinical outcomes. Increasing pressures on health care networks to meet standards of diabetes care have created an environment where information technology systems for diabetes management are often created under duress, appended to existing clinical systems, and poorly integrated into the existing workflow. After defining the components of diabetes disease management, the authors present an eight-step conceptual framework to guide the development of more effective diabetes information technology systems for translating clinical information into clinical action. PMID:19885355

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

NASA Astrophysics Data System (ADS)

Faes, Luca; Marinazzo, Daniele; Stramaglia, Sebastiano; Jurysta, Fabrice; Porta, Alberto; Giandomenico, Nollo

2016-05-01

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac process η) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to the η, δ, θ, α, σ, β time series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP for δ and θ but not for η. The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Babacan, K.; Chen, L.; Sohn, G.

2017-11-01

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Data philanthropy and the design of the infraethics for information societies.

PubMed

Taddeo, Mariarosaria

2016-12-28

In mature information societies, sharing data is increasingly recognized as a crucial means to foster their development. However, competing tensions on data control and ownership, limited technical understanding, and the lack of an adequate governance framework pose serious challenges to attempts to share data among different actors. Data philanthropy, understood as the donation of data from both individuals and private companies, has been proposed as means to meet these challenges. While at first sight data philanthropy may seem an uncontroversial phenomenon, a closer analysis reveals a bewildering network of problems. In this article, I analyse the role of data philanthropy in contemporary societies and the moral problems that it yields. I argue that the solution to these problems rests on the understanding of the infraethical nature of data philanthropy and on the design of an ethical framework encompassing the right infraethics and the right ethics. This is a framework able to address the changes brought about by the information revolution and to harness the opportunities that these pose for the prosperity of current and future information societies.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

Investigation of Service Quality of Measurement Reference Points for the Internet Services on Mobile Networks

NASA Astrophysics Data System (ADS)

Lipenbergs, E.; Bobrovs, Vj.; Ivanovs, G.

2016-10-01

To ensure that end-users and consumers have access to comprehensive, comparable and user-friendly information regarding the Internet access service quality, it is necessary to implement and regularly renew a set of legislative regulatory acts and to provide monitoring of the quality of Internet access services regarding the current European Regulatory Framework. The actual situation regarding the quality of service monitoring solutions in different European countries depends on national regulatory initiatives and public awareness. The service monitoring solutions are implemented using different measurement methodologies and tools. The paper investigates the practical implementations for developing a harmonising approach to quality monitoring in order to obtain objective information on the quality of Internet access services on mobile networks.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

Regional and international integrated telemedicine network for organ transplant (HC 4028 & IN 4028 European Commission DGXIII).

PubMed

Vari, S G; Brugal, G; Godo, F; Bercic, B; Nagy, G; Avar, G; Adelh, D; Lagouarde, P

2000-01-01

A substantial portion of future medical practice will depend greatly on improved collaboration between the providers throughout the healthcare sector, and effective sharing of data and expertise by different healthcare professionals. In organ transplant it is a rule, donor organs are matched to recipients via national or multinational organ-sharing organizations. Only through close co-operation between transplant surgeons, immunologists, nephrologists, pathologists, radiologists and other physicians could one increase the efficiency of organ transplantation. Information technology (IT) has become an inevitable and inherent part of transplantation medicine. The RETRANSPLANT project interfaces and integrates IT from the European Union Fourth Framework projects to support the development of regional organ transplant information networks in Central Europe.

Inferring the effective TOR-dependent network: a computational study in yeast

PubMed Central

2013-01-01

Background Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR. Results In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies. Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition. Conclusions Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies. PMID:24005029

Public/Private Partnerships: Building Support for Employer-Sponsored Eldercare: A Guide for the Aging Network.

ERIC Educational Resources Information Center

Sheehy, Edward

This guidebook provides practical information to assist state and local aging agencies in developing strategic relationships with businesses in their communities. It focuses on the experiences of those state agencies and Area Agencies on Aging that are actively working with local employers and it presents a framework for other agencies. The book…

Scientific Knowledge and Attitude Change: The Impact of a Citizen Science Project. Research Report

ERIC Educational Resources Information Center

Brossard, Dominique; Lewenstein, Bruce; Bonney, Rick

2005-01-01

This paper discusses the evaluation of an informal science education project, The Birdhouse Network (TBN) of the Cornell Laboratory of Ornithology. The Elaboration Likelihood Model and the theory of Experiential Education were used as frameworks to analyse the impact of TBN on participants' attitudes toward science and the environment, on their…

An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring

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

Loparo, Kenneth; Kolacinski, Richard; Threeanaew, Wanchat

A central goal of the work was to enable both the extraction of all relevant information from sensor data, and the application of information gained from appropriate processing and fusion at the system level to operational control and decision-making at various levels of the control hierarchy through: 1. Exploiting the deep connection between information theory and the thermodynamic formalism, 2. Deployment using distributed intelligent agents with testing and validation in a hardware-in-the loop simulation environment. Enterprise architectures are the organizing logic for key business processes and IT infrastructure and, while the generality of current definitions provides sufficient flexibility, the currentmore » architecture frameworks do not inherently provide the appropriate structure. Of particular concern is that existing architecture frameworks often do not make a distinction between ``data'' and ``information.'' This work defines an enterprise architecture for health and condition monitoring of power plant equipment and further provides the appropriate foundation for addressing shortcomings in current architecture definition frameworks through the discovery of the information connectivity between the elements of a power generation plant. That is, to identify the correlative structure between available observations streams using informational measures. The principle focus here is on the implementation and testing of an emergent, agent-based, algorithm based on the foraging behavior of ants for eliciting this structure and on measures for characterizing differences between communication topologies. The elicitation algorithms are applied to data streams produced by a detailed numerical simulation of Alstom’s 1000 MW ultra-super-critical boiler and steam plant. The elicitation algorithm and topology characterization can be based on different informational metrics for detecting connectivity, e.g. mutual information and linear correlation.« less

End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.

PubMed

Cai, Chuangjian; Deng, Kexin; Ma, Cheng; Luo, Jianwen

2018-06-15

An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging. A residual learning framework is used to facilitate optimization and to gain better accuracy from considerably increased network depth. The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO 2 ). According to our numerical experiments, the estimations of sO 2 and indocyanine green concentration are accurate and robust against variations in both optical property and object geometry. An extremely short reconstruction time of 22 ms is achieved.

Multilayer Brain Networks

NASA Astrophysics Data System (ADS)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

SNMP-SI: A Network Management Tool Based on Slow Intelligence System Approach

NASA Astrophysics Data System (ADS)

Colace, Francesco; de Santo, Massimo; Ferrandino, Salvatore

The last decade has witnessed an intense spread of computer networks that has been further accelerated with the introduction of wireless networks. Simultaneously with, this growth has increased significantly the problems of network management. Especially in small companies, where there is no provision of personnel assigned to these tasks, the management of such networks is often complex and malfunctions can have significant impacts on their businesses. A possible solution is the adoption of Simple Network Management Protocol. Simple Network Management Protocol (SNMP) is a standard protocol used to exchange network management information. It is part of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol suite. SNMP provides a tool for network administrators to manage network performance, find and solve network problems, and plan for network growth. SNMP has a big disadvantage: its simple design means that the information it deals with is neither detailed nor well organized enough to deal with the expanding modern networking requirements. Over the past years much efforts has been given to improve the lack of Simple Network Management Protocol and new frameworks has been developed: A promising approach involves the use of Ontology. This is the starting point of this paper where a novel approach to the network management based on the use of the Slow Intelligence System methodologies and Ontology based techniques is proposed. Slow Intelligence Systems is a general-purpose systems characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptation, elimination and concentration. Therefore, the proposed approach aims to develop a system able to acquire, according to an SNMP standard, information from the various hosts that are in the managed networks and apply solutions in order to solve problems. To check the feasibility of this model first experimental results in a real scenario are showed.

Model validation of simple-graph representations of metabolism

PubMed Central

Holme, Petter

2009-01-01

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules. PMID:19158012

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network.

PubMed

Fernandez, Susel; Hadfi, Rafik; Ito, Takayuki; Marsa-Maestre, Ivan; Velasco, Juan R

2016-08-15

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors.

Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network

PubMed Central

Fernandez, Susel; Hadfi, Rafik; Ito, Takayuki; Marsa-Maestre, Ivan; Velasco, Juan R.

2016-01-01

Intelligent transportation systems are a set of technological solutions used to improve the performance and safety of road transportation. A crucial element for the success of these systems is the exchange of information, not only between vehicles, but also among other components in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be within vehicles or as part of the infrastructure, such as bridges, roads or traffic signs. Sensors can provide information related to weather conditions and traffic situation, which is useful to improve the driving process. To facilitate the exchange of information between the different applications that use sensor data, a common framework of knowledge is needed to allow interoperability. In this paper an ontology-driven architecture to improve the driving environment through a traffic sensor network is proposed. The system performs different tasks automatically to increase driver safety and comfort using the information provided by the sensors. PMID:27537878

Multiscale Embedded Gene Co-expression Network Analysis

PubMed Central

Song, Won-Min; Zhang, Bin

2015-01-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

Multiscale Embedded Gene Co-expression Network Analysis.

PubMed

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Towards the Integration of Niche and Network Theories.

PubMed

Godoy, Oscar; Bartomeus, Ignasi; Rohr, Rudolf P; Saavedra, Serguei

2018-04-01

The quest for understanding how species interactions modulate diversity has progressed by theoretical and empirical advances following niche and network theories. Yet, niche studies have been limited to describe coexistence within tropic levels despite incorporating information about multi-trophic interactions. Network approaches could address this limitation, but they have ignored the structure of species interactions within trophic levels. Here we call for the integration of niche and network theories to reach new frontiers of knowledge exploring how interactions within and across trophic levels promote species coexistence. This integration is possible due to the strong parallelisms in the historical development, ecological concepts, and associated mathematical tools of both theories. We provide a guideline to integrate this framework with observational and experimental studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

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

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

Twitmographics: Learning the Emergent Properties of the Twitter Community

NASA Astrophysics Data System (ADS)

Cheong, Marc; Lee, Vincent

This paper presents a framework for discovery of the emergent properties of users of the Twitter microblogging platform. The novelty of our methodology is the use of machine-learning methods to deduce user demographic information and online usage patterns and habits not readily apparent from the raw messages posted on Twitter. This is different from existing social network analysis performed on de facto social networks such as Face-book, in the sense that we use publicly available metadata from Twitter messages to explore the inherent characteristics about different segments of the Twitter community, in a simple yet effective manner. Our framework is coupled with the self-organizing map visualization method, and tested on a corpus of messages which deal with issues of socio politi-cal and economic impact, to gain insight into the properties of human interaction via Twitter as a medium for computer-mediated self-expression.

Quantum photonic network and physical layer security

NASA Astrophysics Data System (ADS)

Sasaki, Masahide; Endo, Hiroyuki; Fujiwara, Mikio; Kitamura, Mitsuo; Ito, Toshiyuki; Shimizu, Ryosuke; Toyoshima, Morio

2017-06-01

Quantum communication and quantum cryptography are expected to enhance the transmission rate and the security (confidentiality of data transmission), respectively. We study a new scheme which can potentially bridge an intermediate region covered by these two schemes, which is referred to as quantum photonic network. The basic framework is information theoretically secure communications in a free space optical (FSO) wiretap channel, in which an eavesdropper has physically limited access to the main channel between the legitimate sender and receiver. We first review a theoretical framework to quantify the optimal balance of the transmission efficiency and the security level under power constraint and at finite code length. We then present experimental results on channel characterization based on 10 MHz on-off keying transmission in a 7.8 km terrestrial FSO wiretap channel. This article is part of the themed issue 'Quantum technology for the 21st century'.

Information security threats and an easy-to-implement attack detection framework for wireless sensor network-based smart grid applications

NASA Astrophysics Data System (ADS)

Tuna, G.; Örenbaş, H.; Daş, R.; Kogias, D.; Baykara, M.; K, K.

2016-03-01

Wireless Sensor Networks (WSNs) when combined with various energy harvesting solutions managing to prolong the overall lifetime of the system and enhanced capabilities of the communication protocols used by modern sensor nodes are efficiently used in are efficiently used in Smart Grid (SG), an evolutionary system for the modernization of existing power grids. However, wireless communication technology brings various types of security threats. In this study, firstly the use of WSNs for SG applications is presented. Second, the security related issues and challenges as well as the security threats are presented. In addition, proposed security mechanisms for WSN-based SG applications are discussed. Finally, an easy- to-implement and simple attack detection framework to prevent attacks directed to sink and gateway nodes with web interfaces is proposed and its efficiency is proved using a case study.

Quantum photonic network and physical layer security.

PubMed

Sasaki, Masahide; Endo, Hiroyuki; Fujiwara, Mikio; Kitamura, Mitsuo; Ito, Toshiyuki; Shimizu, Ryosuke; Toyoshima, Morio

2017-08-06

Quantum communication and quantum cryptography are expected to enhance the transmission rate and the security (confidentiality of data transmission), respectively. We study a new scheme which can potentially bridge an intermediate region covered by these two schemes, which is referred to as quantum photonic network. The basic framework is information theoretically secure communications in a free space optical (FSO) wiretap channel, in which an eavesdropper has physically limited access to the main channel between the legitimate sender and receiver. We first review a theoretical framework to quantify the optimal balance of the transmission efficiency and the security level under power constraint and at finite code length. We then present experimental results on channel characterization based on 10 MHz on-off keying transmission in a 7.8 km terrestrial FSO wiretap channel.This article is part of the themed issue 'Quantum technology for the 21st century'. © 2017 The Author(s).

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

NASA Astrophysics Data System (ADS)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

On effectiveness of network sensor-based defense framework

NASA Astrophysics Data System (ADS)

Zhang, Difan; Zhang, Hanlin; Ge, Linqiang; Yu, Wei; Lu, Chao; Chen, Genshe; Pham, Khanh

2012-06-01

Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring, detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework, we also implement the statistical-based detection and sequential testing-based detection techniques and compare their respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed framework for combating cyber attacks.

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

PubMed Central

Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N.; Barahona, Mauricio

2014-01-01

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks. PMID:25297320

Empirical and Theoretical Aspects of Generation and Transfer of Information in a Neuromagnetic Source Network

PubMed Central

Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; McIntosh, Anthony Randal

2011-01-01

Variability in source dynamics across the sources in an activated network may be indicative of how the information is processed within a network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal variability and asymmetry in mutual interdependencies in a data-driven pipeline of non-linear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected as a reaction to a face recognition task. Asymmetry in non-linear interdependencies in the network was analyzed using transfer entropy, which quantifies predictive information transfer between the sources. Variability of the source activity was estimated using multi-scale entropy, quantifying the rate of which information is generated. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in variability between the dynamics of these two sources, with the directionality of net information transfer depending on the time scale at which the sample entropy was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between variability of brain signals and information transfer between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics and temporal features of brain connectivity, such as time delay. PMID:22131968

MIDER: Network Inference with Mutual Information Distance and Entropy Reduction

PubMed Central

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

2014-01-01

The prediction of links among variables from a given dataset is a task referred to as network inference or reverse engineering. It is an open problem in bioinformatics and systems biology, as well as in other areas of science. Information theory, which uses concepts such as mutual information, provides a rigorous framework for addressing it. While a number of information-theoretic methods are already available, most of them focus on a particular type of problem, introducing assumptions that limit their generality. Furthermore, many of these methods lack a publicly available implementation. Here we present MIDER, a method for inferring network structures with information theoretic concepts. It consists of two steps: first, it provides a representation of the network in which the distance among nodes indicates their statistical closeness. Second, it refines the prediction of the existing links to distinguish between direct and indirect interactions and to assign directionality. The method accepts as input time-series data related to some quantitative features of the network nodes (such as e.g. concentrations, if the nodes are chemical species). It takes into account time delays between variables, and allows choosing among several definitions and normalizations of mutual information. It is general purpose: it may be applied to any type of network, cellular or otherwise. A Matlab implementation including source code and data is freely available (http://www.iim.csic.es/~gingproc/mider.html). The performance of MIDER has been evaluated on seven different benchmark problems that cover the main types of cellular networks, including metabolic, gene regulatory, and signaling. Comparisons with state of the art information–theoretic methods have demonstrated the competitive performance of MIDER, as well as its versatility. Its use does not demand any a priori knowledge from the user; the default settings and the adaptive nature of the method provide good results for a wide range of problems without requiring tuning. PMID:24806471

"It Takes a Network": Building National Capacity for Climate Change Interpretation

NASA Astrophysics Data System (ADS)

Spitzer, W.

2014-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. More than 1,500 informal science venues (science centers, museums, aquariums, zoos, nature centers, national parks) are visited annually by 61% of the U.S. population. These visitors expect reliable information about environmental issues and solutions. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. Beyond providing in-depth training, we have found that our "alumni network" is assuming an increasingly important role in achieving our goals: 1. Ongoing learning - Training must be ongoing given continuous advances in climate and social science research. 2. Implementation support - Social support is critical as interpreters move from learning to practice, given complex and potentially contentious subject matter. 3. Leadership development - We rely on a national cadre of interpretive leaders to conduct workshops, facilitate study circle trainings, and support alumni. 4. Coalition building - A peer network helps to build and maintain connections with colleagues, and supports further dissemination through the informal science community. We are experimenting with a variety of online and face to face strategies to support the growing alumni network. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations.

PubMed

Lewis, Joanne M; DiGiacomo, Michelle; Currow, David C; Davidson, Patricia M

2014-01-01

Lower socioeconomic populations live and die in contexts that render them vulnerable to poorer health and wellbeing. Contexts of care at the end of life are overwhelmingly determined by the capacity and nature of formal and informal networks and relations to support care. To date, studies exploring the nature of networks and relations of support in lower socioeconomic populations at the end of life are absent. This qualitative study sought to identify the nature of individual, community and civic networks and relations that defined the contexts of care for this group. Semi-structured qualitative interviews were conducted with 16 patients and 6 informal carers who identified that they had social and economic needs and were from a lower socioeconomic area. A social capital questionnaire identifying individual, community and civic networks and relations formed the interview guide. Interviews were audio-taped, transcribed and analysed using framework analysis. Participants identified that individual and community networks and relations of support were mainly inadequate to meet care needs. Specifically, data revealed: (1) individual (informal caregivers) networks and relations were small and fragile due to the nature of conflict and crisis; (2) community trust and engagement was limited and shifted by illness and caregiving; (3) and formal care services were inconsistent and provided limited practical support. Some transitions in community relations for support were noted. Levels of civic and government engagement and support were overall positive and enabled access to welfare resources. Networks and relations of support are essential for ensuring quality end of life care is achieved. Lower socioeconomic groups are at a distinct disadvantage where these networks and relations are limited, as they lack the resources necessary to augment these gaps. Understanding of the nature of assets and limitations, in networks and relations of support, is necessary to inform interventions to improve end of life care for lower socioeconomic populations.

Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations

PubMed Central

2014-01-01

Background Lower socioeconomic populations live and die in contexts that render them vulnerable to poorer health and wellbeing. Contexts of care at the end of life are overwhelmingly determined by the capacity and nature of formal and informal networks and relations to support care. To date, studies exploring the nature of networks and relations of support in lower socioeconomic populations at the end of life are absent. This qualitative study sought to identify the nature of individual, community and civic networks and relations that defined the contexts of care for this group. Methods Semi-structured qualitative interviews were conducted with 16 patients and 6 informal carers who identified that they had social and economic needs and were from a lower socioeconomic area. A social capital questionnaire identifying individual, community and civic networks and relations formed the interview guide. Interviews were audio-taped, transcribed and analysed using framework analysis. Results Participants identified that individual and community networks and relations of support were mainly inadequate to meet care needs. Specifically, data revealed: (1) individual (informal caregivers) networks and relations were small and fragile due to the nature of conflict and crisis; (2) community trust and engagement was limited and shifted by illness and caregiving; (3) and formal care services were inconsistent and provided limited practical support. Some transitions in community relations for support were noted. Levels of civic and government engagement and support were overall positive and enabled access to welfare resources. Conclusion Networks and relations of support are essential for ensuring quality end of life care is achieved. Lower socioeconomic groups are at a distinct disadvantage where these networks and relations are limited, as they lack the resources necessary to augment these gaps. Understanding of the nature of assets and limitations, in networks and relations of support, is necessary to inform interventions to improve end of life care for lower socioeconomic populations. PMID:24959101

Structure-based control of complex networks with nonlinear dynamics.

PubMed

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Dimensions and dynamics of citizen observatories: The case of online amateur weather networks

NASA Astrophysics Data System (ADS)

Gharesifard, Mohammad; Wehn, Uta; van der Zaag, Pieter

2016-04-01

Crowd-sourced environmental observations are being increasingly considered as having the potential to enhance the spatial and temporal resolution of current data streams from terrestrial and areal sensors. The rapid diffusion of ICTs during the past decades has facilitated the process of data collection and sharing by the general public (so-called citizen science) and has resulted in the formation of various online environmental citizen observatory networks. Online amateur weather networks are a particular example of such ICT-mediated citizen observatories as one of the oldest and most widely practiced citizen science activities. The objective of this paper is to introduce a conceptual framework that enables a systematic review of different dimensions of these mushrooming/expanding networks. These dimensions include the geographic scope and types of network participants; the network's establishment mechanism, revenue stream(s) and existing communication paradigm; efforts required by citizens and support offered by platform providers; and issues such as data accessibility, availability and quality. An in-depth understanding of these dimensions helps to analyze various dynamics such as interactions between different stakeholders, motivations to run these networks, sustainability of the platforms, data ownership and level of transparency of each network. This framework is then utilized to perform a critical and normative review of six existing online amateur weather networks based on publicly available data. The main findings of this analysis suggest that: (1) There are several key stakeholders such as emergency services and local authorities that are not (yet) engaged in these networks. (2) The revenue stream(s) of online amateur weather networks is one of the least discussed but most important dimensions that is crucial for the sustainability of these networks. (3) Although all of the networks included in this study have one or more explicit pattern of two-way communications, there is no sign (yet) of interactive information exchange among the triangle of weather observers, data aggregators and policy makers. KEYWORDS Citizen Science, Citizen Observatories, ICT-enabled citizen participation, online amateur weather networks

Challenges to inferring causality from viral information dispersion in dynamic social networks

NASA Astrophysics Data System (ADS)

Ternovski, John

2014-06-01

Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.

Image/video understanding systems based on network-symbolic models

NASA Astrophysics Data System (ADS)

Kuvich, Gary

2004-03-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. Computer simulation models are built on the basis of graphs/networks. The ability of human brain to emulate similar graph/network models is found. Symbols, predicates and grammars naturally emerge in such networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type relational structure created via multilevel hierarchical compression of visual information. Primary areas provide active fusion of image features on a spatial grid-like structure, where nodes are cortical columns. Spatial logic and topology naturally present in such structures. Mid-level vision processes like perceptual grouping, separation of figure from ground, are special kinds of network transformations. They convert primary image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models combines learning, classification, and analogy together with higher-level model-based reasoning into a single framework, and it works similar to frames and agents. Computational intelligence methods transform images into model-based knowledge representation. Based on such principles, an Image/Video Understanding system can convert images into the knowledge models, and resolve uncertainty and ambiguity. This allows creating intelligent computer vision systems for design and manufacturing.

A unified data representation theory for network visualization, ordering and coarse-graining

PubMed Central

Kovács, István A.; Mizsei, Réka; Csermely, Péter

2015-01-01

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form. PMID:26348923

Robopedia: Leveraging Sensorpedia for Web-Enabled Robot Control

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

Resseguie, David R

There is a growing interest in building Internetscale sensor networks that integrate sensors from around the world into a single unified system. In contrast, robotics application development has primarily focused on building specialized systems. These specialized systems take scalability and reliability into consideration, but generally neglect exploring the key components required to build a large scale system. Integrating robotic applications with Internet-scale sensor networks will unify specialized robotics applications and provide answers to large scale implementation concerns. We focus on utilizing Internet-scale sensor network technology to construct a framework for unifying robotic systems. Our framework web-enables a surveillance robot smore » sensor observations and provides a webinterface to the robot s actuators. This lets robots seamlessly integrate into web applications. In addition, the framework eliminates most prerequisite robotics knowledge, allowing for the creation of general web-based robotics applications. The framework also provides mechanisms to create applications that can interface with any robot. Frameworks such as this one are key to solving large scale mobile robotics implementation problems. We provide an overview of previous Internetscale sensor networks, Sensorpedia (an ad-hoc Internet-scale sensor network), our framework for integrating robots with Sensorpedia, two applications which illustrate our frameworks ability to support general web-based robotic control, and offer experimental results that illustrate our framework s scalability, feasibility, and resource requirements.« less

A Novel Strategy Using Factor Graphs and the Sum-Product Algorithm for Satellite Broadcast Scheduling Problems

NASA Astrophysics Data System (ADS)

Chen, Jung-Chieh

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i. e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

GP Networks as enablers of quality of care: implementing a practice engagement framework in a General Practice Network.

PubMed

Pearce, Christopher; Shearer, Marianne; Gardner, Karina; Kelly, Jill; Xu, Tony Baixian

2012-01-01

This paper describes how the Melbourne East General Practice Network supports general practice to enable quality of care, it describes the challenges and enablers of change, and the evidence of practice capacity building and improved quality of care. Primary care is well known as a place where quality, relatively inexpensive medical care occurs. General practice is made up of multiple small sites with fragmented systems and a funding system that challenges a whole-of-practice approach to clinical care. General Practice Networks support GPs to synthesise complexity and crystallise solutions that enhance general practice beyond current capacity. Through a culture of change management, GP Networks create the link between the practice and the big picture of the whole health system and reduce the isolation of general practice. They distribute information (evidence-based learning and resources) and provide individualised support, responding to practice need and capacity.

Resilient distributed control in the presence of misbehaving agents in networked control systems.

PubMed

Zeng, Wente; Chow, Mo-Yuen

2014-11-01

In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks.

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Oceans of Data : the Australian Ocean Data Network

NASA Astrophysics Data System (ADS)

Proctor, R.; Blain, P.; Mancini, S.

2012-04-01

The Australian Integrated Marine Observing System (IMOS, www.imos.org.au) is a research infrastructure project to establish an enduring marine observing system for Australian oceanic waters and shelf seas (in total, 4% of the world's oceans). Marine data and information are the main products and data management is therefore a central element to the project's success. A single integrative framework for data and information management has been developed which allows discovery and access of the data by scientists, managers and the public, based on standards and interoperability. All data is freely available. This information infrastructure has been further developed to form the Australian Ocean Data Network (AODN, www.aodn.org.au) which is rapidly becoming the 'one-stop-shop' for marine data in Australia. In response to requests from users, new features have recently been added to data discovery, visualization, and data access which move the AODN closer towards providing full integration of multi-disciplinary data.

Framework and implementation of a continuous network-wide health monitoring system for roadways

NASA Astrophysics Data System (ADS)

Wang, Ming; Birken, Ralf; Shahini Shamsabadi, Salar

2014-03-01

According to the 2013 ASCE report card America's infrastructure scores only a D+. There are more than four million miles of roads (grade D) in the U.S. requiring a broad range of maintenance activities. The nation faces a monumental problem of infrastructure management in the scheduling and implementation of maintenance and repair operations, and in the prioritization of expenditures within budgetary constraints. The efficient and effective performance of these operations however is crucial to ensuring roadway safety, preventing catastrophic failures, and promoting economic growth. There is a critical need for technology that can cost-effectively monitor the condition of a network-wide road system and provide accurate, up-to-date information for maintenance activity prioritization. The Versatile Onboard Traffic Embedded Roaming Sensors (VOTERS) project provides a framework and the sensing capability to complement periodical localized inspections to continuous network-wide health monitoring. Research focused on the development of a cost-effective, lightweight package of multi-modal sensor systems compatible with this framework. An innovative software infrastructure is created that collects, processes, and evaluates these large time-lapse multi-modal data streams. A GIS-based control center manages multiple inspection vehicles and the data for further analysis, visualization, and decision making. VOTERS' technology can monitor road conditions at both the surface and sub-surface levels while the vehicle is navigating through daily traffic going about its normal business, thereby allowing for network-wide frequent assessment of roadways. This deterioration process monitoring at unprecedented time and spatial scales provides unique experimental data that can be used to improve life-cycle cost analysis models.