The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Terry L Friesz - One of the best experts on this subject based on the ideXlab platform.
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continuity of the path delay operator for Dynamic Network loading with spillback
Transportation Research Part B-methodological, 2016Co-Authors: Ke Han, Benedetto Piccoli, Terry L FrieszAbstract:This paper establishes the continuity of the path delay operators for Dynamic Network loading (DNL) problems based on the Lighthill–Whitham–Richards model, which explicitly capture vehicle spillback. The DNL describes and predicts the spatial-temporal evolution of traffic flow and congestion on a Network that is consistent with established route and departure time choices of travelers. The LWR-based DNL model is first formulated as a system of partial differential algebraic equations. We then investigate the continuous dependence of merge and diverge junction models with respect to their initial/boundary conditions, which leads to the continuity of the path delay operator through the wave-front tracking methodology and the generalized tangent vector technique. As part of our analysis leading up to the main continuity result, we also provide an estimation of the minimum Network supply without resort to any numerical computation. In particular, it is shown that gridlock can never occur in a finite time horizon in the DNL model.
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a differential variational inequality formulation of Dynamic Network user equilibrium with elastic demand
Transportmetrica, 2014Co-Authors: Terry L Friesz, Amir H MeimandAbstract:In this article, we present an original differential variational inequality formulation of Dynamic Network user equilibrium with elastic travel demand.
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solving the Dynamic Network user equilibrium problem with state dependent time shifts
Transportation Research Part B-methodological, 2006Co-Authors: Terry L Friesz, Reetabrata MookherjeeAbstract:In this paper we consider the infinite dimensional variational inequality (VI) formulation of Dynamic user equilibrium (DUE) put forward by Friesz et al. (1993) [A variational inequality formulation of the Dynamic Network user equilibrium problem. Operations Research 41, 179–191] as well as the differential variational inequality (DVI) version reported in Friesz et al. (2001) [Dynamic Network user equilibrium with state-dependent time lags. Networks and Spatial Economics 1, 319–347]. We show how the theory of optimal control and the theory of infinite dimensional variational inequalities may be combined to create a simple and effective fixed point algorithm for calculating DUE Network flows that are solutions of both formulations. A numerical example is provided.
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Dynamic Network user equilibrium with state dependent time lags
Networks and Spatial Economics, 2001Co-Authors: Terry L Friesz, David Bernstein, Zhonggui Suo, Roger L TobinAbstract:This paper recasts the Friesz et al. (1993) measure theoretic model of Dynamic Network user equibrium as a controlled variational inequality problem involving Riemann integrals. This restatement is done to make the model and its foundations accessible to a wider audience by removing the need to have a background in functional analysis. Our exposition is dependent on previously unavailable necessary conditions for optimal control problems with state-dependent time lags. These necessary conditions, derived in an Appendix, are employed to show that a particular variational inequality control problem has solutions that are Dynamic Network user equilibria. Our analysis also shows that use of proper flow propagation constraints obviates the need to explicitly employ the arc exit time functions that have complicated numerical implementations of the Friesz et al. (1993) model heretofore. We close by describing the computational implications of numerically determining Dynamic user equilibria from formulations based on state-dependent time lags.
Marwan Krunz - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Network slicing for scalable fog computing systems with energy harvesting
IEEE Journal on Selected Areas in Communications, 2018Co-Authors: Yong Xiao, Marwan KrunzAbstract:This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of Dynamic Network slicing , in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees. The resources allocated to each slice can be Dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate the distributed cooperation and joint Network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing Network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed Dynamic Network slicing within the 3GPP Network sharing architecture and evaluate the performance of our proposed framework using the real base station (BS) location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing Networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.
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Dynamic Network Slicing for Scalable Fog Computing Systems with Energy Harvesting
IEEE Journal on Selected Areas in Communications, 2018Co-Authors: Yong Xiao, Marwan KrunzAbstract:This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of Dynamic Network slicing in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain qualityof- service (QoS) guarantees. The resources allocated to each slice can be Dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate distributed cooperation and joint Network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing Network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process (B-POMDP) is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed Dynamic Network slicing within the 3GPP Network sharing architecture, and evaluate the performance of our proposed framework using the real BS location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing Networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.
James D Westaby - One of the best experts on this subject based on the ideXlab platform.
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Network goal analysis of social and organizational systems testing Dynamic Network theory in complex social Networks
The Journal of Applied Behavioral Science, 2020Co-Authors: James D Westaby, Adam K ParrAbstract:Grounded in Dynamic Network theory, this study examined Network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social Networks. Goal nodes...
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extending Dynamic Network theory to group and social interaction analysis uncovering key behavioral elements cycles and emergent states
Organizational psychology review, 2016Co-Authors: James D Westaby, Naomi Woods, Danielle L PfaffAbstract:This article proposes a markedly new conceptual approach to group and social interaction analysis, grounded in transformative advances in Dynamic Network theory. The framework first theoretically i...
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psychology and social Networks a Dynamic Network theory perspective
American Psychologist, 2014Co-Authors: James D Westaby, Danielle L Pfaff, Nicholas ReddingAbstract:Research on social Networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social Networks in the first place. In this vein, this article aims to demonstrate how a Dynamic Network theory perspective explains the way in which social Networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social Network concepts from sociology and organizational science to provide a taxonomy of social Network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social Networks (e.g., Dynamic Network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to Dynamic Network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social Networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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Dynamic Network theory how social Networks influence goal pursuit
2011Co-Authors: James D WestabyAbstract:Social Networks surround us. They are as diverse as a local community trying to help solve a neighborhood crime, a firm wondering how to streamline decision making, or a terrorist cell figuring out how to plan an attack without central coordination. This groundbreaking book explores social Networks in formal and informal organizations, using a combination of approaches from social psychology, I/O psychology, organization/management science, social learning, and helping skills. A quantum advance over conventional social Network analysis, Dynamic Network Theory examines how social Networks articulate goals and generate social capital at various levels. Geared for researchers and practitioners, Dynamic Network Theory is also written for graduate students and advanced undergraduate students. Appendixes include primers on designing and analyzing Dynamic Network charts.
Yong Xiao - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Network slicing for scalable fog computing systems with energy harvesting
IEEE Journal on Selected Areas in Communications, 2018Co-Authors: Yong Xiao, Marwan KrunzAbstract:This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of Dynamic Network slicing , in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees. The resources allocated to each slice can be Dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate the distributed cooperation and joint Network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing Network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed Dynamic Network slicing within the 3GPP Network sharing architecture and evaluate the performance of our proposed framework using the real base station (BS) location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing Networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.
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Dynamic Network Slicing for Scalable Fog Computing Systems with Energy Harvesting
IEEE Journal on Selected Areas in Communications, 2018Co-Authors: Yong Xiao, Marwan KrunzAbstract:This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of Dynamic Network slicing in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain qualityof- service (QoS) guarantees. The resources allocated to each slice can be Dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate distributed cooperation and joint Network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing Network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process (B-POMDP) is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed Dynamic Network slicing within the 3GPP Network sharing architecture, and evaluate the performance of our proposed framework using the real BS location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing Networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.
Nicol S Harper - One of the best experts on this subject based on the ideXlab platform.
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a Dynamic Network model of temporal receptive fields in primary auditory cortex
PLOS Computational Biology, 2019Co-Authors: Monzilur Rahman, Andrew J. King, Ben D B Willmore, Nicol S HarperAbstract:Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by Dynamical aspects of neurons. We constructed a neural-Network model whose output is the weighted sum of units whose responses are determined by a Dynamic firing-rate equation. The Dynamic aspect performs low-pass filtering on each unit's response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this Dynamic Network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.
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a Dynamic Network model can explain temporal receptive fields in primary auditory cortex
PLOS Computational Biology, 2019Co-Authors: Monzilur Rahman, Andrew J. King, Ben D B Willmore, Nicol S HarperAbstract:Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by Dynamical aspects of neurons. We constructed a neural-Network model whose output is the weighted sum of units whose responses are determined by a Dynamic firing-rate equation. The Dynamic aspect performs low-pass filtering on each unit9s response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this Dynamic Network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.
