The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
Andrew Zalesky - One of the best experts on this subject based on the ideXlab platform.
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Inferring neural signalling directionality from undirected structural connectomes
Nature Communications, 2019Co-Authors: Caio Seguin, Adeel Razi, Andrew ZaleskyAbstract:Neural information flow is inherently directional. To date, investigation of directional Communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network Communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive Communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and Outgoing Communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems. Neural signalling is directional, but non-invasive neuroimaging methods are unable to map directed connections between brain regions. Here, the authors show how network Communication measures can be used to infer signalling directionality from the undirected topology of brain structural connectomes.
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send receive Communication asymmetry in brain networks inferring directionality of neural signalling from undirected structural connectomes
bioRxiv, 2019Co-Authors: Caio Seguin, Adeel Razi, Andrew ZaleskyAbstract:Neural information flow is inherently directional. To date, investigation of directional Communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network Communication, applied to the undirected topology and geometry of brain networks, can predict putative directions of large-scale neural signalling. We propose the concept of send-receive Communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and Outgoing Communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.
Alexander Setzer - One of the best experts on this subject based on the ideXlab platform.
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SPAA - Breaking the $ilde$Omega($sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018Co-Authors: Peter Robinson, Christian Scheideler, Alexander SetzerAbstract:We study the consensus problem in a synchronous distributed system of n nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and Outgoing Communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde Omega(\sqrtn )$ rounds in an n -node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. % Our main contributions are randomized distributed algorithms that achieve consensus with high probability among all except a small constant fraction of the nodes (i.e.,\ "almost-everywhere'') against a late adaptive adversary who can block up to e n$ nodes in each round, for a small constant e >0$. Our first protocol achieves binary almost-everywhere consensus and also guarantees a decision on the majority input value, thus ensuring plurality consensus. We also present an algorithm that achieves the same time complexity for multi-value consensus. Both of our algorithms succeed in $O(log n)$ rounds with high probability, thus showing an exponential gap to the $\tildeOmega(\sqrtn )$ lower bound of Ben-Or and Bar-Joseph for strongly adaptive crash-failure adversaries, which can be strengthened to $Omega(n)$ when allowing the adversary to block nodes instead of permanently crashing them. Our algorithms are scalable to large systems as each node contacts only an (amortized) constant number of peers in each Communication round. We show that our algorithms are optimal up to constant (resp.\ sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $Omega(log_d n)$ rounds in the worst case, where d is an upper bound on the number of Communication requests initiated per node in each round. We complement our theoretical results with an experimental evaluation of the binary almost-everywhere consensus protocol revealing a short convergence time even against an adversary blocking a large fraction of nodes.
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Breaking the $\tilde\Omega(\sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Peter Robinson, Christian Scheideler, Alexander SetzerAbstract:We study the consensus problem in a synchronous distributed system of $n$ nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and Outgoing Communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde \Omega(\sqrt{n})$ rounds in an $n$-node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. Our main contributions are randomized distributed algorithms that achieve almost-everywhere consensus w.h.p. against a late adaptive adversary who can block up to $\epsilon n$ nodes in each round, for a small constant $\epsilon >0$. Our first protocol achieves binary and plurality consensus, and the second one achieves multi-value consensus. Both of our algorithms succeed in $O(\log n)$ rounds with high probability, thus showing an exponential gap to the aforementioned lower bound for strongly adaptive crash-failure adversaries, which can be strengthened to $\Omega(n)$ when allowing the adversary to block nodes instead of permanently crashing them. In our algorithms each node contacts only an (amortized) constant number of peers in each Communication round. We show that our algorithms are optimal up to constant (resp. sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $\Omega(\log_d n)$ rounds in the worst case, where $d$ is an upper bound on the number of Communication requests initiated per node in each round. We complement our theoretical results with an experimental evaluation of the first protocol revealing a short convergence time.
Caio Seguin - One of the best experts on this subject based on the ideXlab platform.
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Inferring neural signalling directionality from undirected structural connectomes
Nature Communications, 2019Co-Authors: Caio Seguin, Adeel Razi, Andrew ZaleskyAbstract:Neural information flow is inherently directional. To date, investigation of directional Communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network Communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive Communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and Outgoing Communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems. Neural signalling is directional, but non-invasive neuroimaging methods are unable to map directed connections between brain regions. Here, the authors show how network Communication measures can be used to infer signalling directionality from the undirected topology of brain structural connectomes.
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send receive Communication asymmetry in brain networks inferring directionality of neural signalling from undirected structural connectomes
bioRxiv, 2019Co-Authors: Caio Seguin, Adeel Razi, Andrew ZaleskyAbstract:Neural information flow is inherently directional. To date, investigation of directional Communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network Communication, applied to the undirected topology and geometry of brain networks, can predict putative directions of large-scale neural signalling. We propose the concept of send-receive Communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and Outgoing Communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.
Lawrence Rauchwerger - One of the best experts on this subject based on the ideXlab platform.
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ARMI: A high level Communication library for STAPL
Parallel Processing Letters, 2006Co-Authors: Nathan Thomas, Steven Saunders, Timmie Smith, Gabriel Tanase, Lawrence RauchwergerAbstract:ARMI is a Communication library that provides a framework for expressing fine-grain parallelism and mapping it to a particular machine using shared-memory and message passing library calls. The library is an advanced implementation of the RMI protocol and handles low-level details such as scheduling incoming Communication and aggregating Outgoing Communication to coarsen parallelism. These details can be tuned for different platforms to allow user codes to achieve the highest performance possible without manual modification. ARMI is used by STAPL, our generic parallel library, to provide a portable, user transparent Communication layer. We present the basic design as well as the mechanisms used in the current Pthreads/OpenMP, MPI implementations and/or a combination thereof. Performance comparisons between ARMI and explicit use of Pthreads or MPI are given on a variety of machines, including an HP-V2200, Origin 3800, IBM Regatta and IBM RS/6000 SP cluster.
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PPOPP - ARMI: an adaptive, platform independent Communication library
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP '03, 2003Co-Authors: Steven Saunders, Lawrence RauchwergerAbstract:ARMI is a Communication library that provides a framework for expressing fine-grain parallelism and mapping it to a particular machine using shared-memory and message passing library calls. The library is an advanced implementation of the RMI protocol and handles low-level details such as scheduling incoming Communication and aggregating Outgoing Communication to coarsen parallelism when necessary. These details can be tuned for different platforms to allow user codes to achieve the highest performance possible without manual modification. ARMI is used by STAPL, our generic parallel library, to provide a portable, user transparent Communication layer. We present the basic design as well as the mechanisms used in the current Pthreads/OpenMP, MPI implementations and/or a combination thereof. Performance comparisons between ARMI and explicit use of Pthreads or MPI are given on a variety of machines, including an HP V2200, SGI Origin 3800, IBM Regatta-HPC and IBM RS6000 SP cluster.
Peter Robinson - One of the best experts on this subject based on the ideXlab platform.
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SPAA - Breaking the $ilde$Omega($sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018Co-Authors: Peter Robinson, Christian Scheideler, Alexander SetzerAbstract:We study the consensus problem in a synchronous distributed system of n nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and Outgoing Communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde Omega(\sqrtn )$ rounds in an n -node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. % Our main contributions are randomized distributed algorithms that achieve consensus with high probability among all except a small constant fraction of the nodes (i.e.,\ "almost-everywhere'') against a late adaptive adversary who can block up to e n$ nodes in each round, for a small constant e >0$. Our first protocol achieves binary almost-everywhere consensus and also guarantees a decision on the majority input value, thus ensuring plurality consensus. We also present an algorithm that achieves the same time complexity for multi-value consensus. Both of our algorithms succeed in $O(log n)$ rounds with high probability, thus showing an exponential gap to the $\tildeOmega(\sqrtn )$ lower bound of Ben-Or and Bar-Joseph for strongly adaptive crash-failure adversaries, which can be strengthened to $Omega(n)$ when allowing the adversary to block nodes instead of permanently crashing them. Our algorithms are scalable to large systems as each node contacts only an (amortized) constant number of peers in each Communication round. We show that our algorithms are optimal up to constant (resp.\ sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $Omega(log_d n)$ rounds in the worst case, where d is an upper bound on the number of Communication requests initiated per node in each round. We complement our theoretical results with an experimental evaluation of the binary almost-everywhere consensus protocol revealing a short convergence time even against an adversary blocking a large fraction of nodes.
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Breaking the $\tilde\Omega(\sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Peter Robinson, Christian Scheideler, Alexander SetzerAbstract:We study the consensus problem in a synchronous distributed system of $n$ nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and Outgoing Communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde \Omega(\sqrt{n})$ rounds in an $n$-node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. Our main contributions are randomized distributed algorithms that achieve almost-everywhere consensus w.h.p. against a late adaptive adversary who can block up to $\epsilon n$ nodes in each round, for a small constant $\epsilon >0$. Our first protocol achieves binary and plurality consensus, and the second one achieves multi-value consensus. Both of our algorithms succeed in $O(\log n)$ rounds with high probability, thus showing an exponential gap to the aforementioned lower bound for strongly adaptive crash-failure adversaries, which can be strengthened to $\Omega(n)$ when allowing the adversary to block nodes instead of permanently crashing them. In our algorithms each node contacts only an (amortized) constant number of peers in each Communication round. We show that our algorithms are optimal up to constant (resp. sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $\Omega(\log_d n)$ rounds in the worst case, where $d$ is an upper bound on the number of Communication requests initiated per node in each round. We complement our theoretical results with an experimental evaluation of the first protocol revealing a short convergence time.
