The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Yi Zheng - One of the best experts on this subject based on the ideXlab platform.
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Long-Range Temporal CorRelations, Multifractality, and the Causal Relation between Neural Inputs and Movements.
Frontiers in Neurology, 2013Co-Authors: Jing Hu, Yi ZhengAbstract:Understanding the Causal Relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly nonstationary, and Fano factor analysis always indicates long-range corRelations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual corRelations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal corRelations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal corRelations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.
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Long-Range Temporal CorRelations, Multifractality, and the Causal Relation Between Neural Inputs and Movements
ASME 2011 Dynamic Systems and Control Conference and Bath ASME Symposium on Fluid Power and Motion Control Volume 2, 2011Co-Authors: Yi Zheng, Jing HuAbstract:Understanding the Causal Relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal corRelations. We find that MF-DFA and wavelet multifractal analysis (but not Fano factor analysis) clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal corRelations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal corRelations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.Copyright © 2011 by ASME
Michael Katz - One of the best experts on this subject based on the ideXlab platform.
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Causal Knowledge Extraction through Large-Scale Text Mining
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael KatzAbstract:In this demonstration, we present a system for mining Causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for Causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of Causal Relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a Causal Relation. It also provides evidence from an input corpus supporting the existence of a Causal Relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of Causal Relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.
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AAAI - Causal Knowledge Extraction through Large-Scale Text Mining
2020Co-Authors: Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Shirin Sohrabi, Michael P. Perrone, Michael KatzAbstract:In this demonstration, we present a system for mining Causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for Causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of Causal Relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a Causal Relation. It also provides evidence from an input corpus supporting the existence of a Causal Relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of Causal Relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.
Zheng Chen - One of the best experts on this subject based on the ideXlab platform.
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Causal Relation of queries from temporal logs
The Web Conference, 2007Co-Authors: Yizhou Sun, Kunqing Xie, Ning Liu, Shuicheng Yan, Benyu Zhang, Zheng ChenAbstract:In this paper, we study a new problem of mining Causal Relation of queries in search engine query logs. Causal Relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the Causal Relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the Causal Relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected Relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the Causal Relation of queries is detected effectively.
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WWW - Causal Relation of queries from temporal logs
Proceedings of the 16th international conference on World Wide Web - WWW '07, 2007Co-Authors: Yizhou Sun, Kunqing Xie, Ning Liu, Shuicheng Yan, Benyu Zhang, Zheng ChenAbstract:In this paper, we study a new problem of mining Causal Relation of queries in search engine query logs. Causal Relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the Causal Relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the Causal Relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected Relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the Causal Relation of queries is detected effectively.
Jing Hu - One of the best experts on this subject based on the ideXlab platform.
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Long-Range Temporal CorRelations, Multifractality, and the Causal Relation between Neural Inputs and Movements.
Frontiers in Neurology, 2013Co-Authors: Jing Hu, Yi ZhengAbstract:Understanding the Causal Relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly nonstationary, and Fano factor analysis always indicates long-range corRelations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual corRelations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal corRelations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal corRelations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.
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Long-Range Temporal CorRelations, Multifractality, and the Causal Relation Between Neural Inputs and Movements
ASME 2011 Dynamic Systems and Control Conference and Bath ASME Symposium on Fluid Power and Motion Control Volume 2, 2011Co-Authors: Yi Zheng, Jing HuAbstract:Understanding the Causal Relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal corRelations. We find that MF-DFA and wavelet multifractal analysis (but not Fano factor analysis) clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal corRelations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal corRelations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.Copyright © 2011 by ASME
Oktie Hassanzadeh - One of the best experts on this subject based on the ideXlab platform.
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Causal Knowledge Extraction through Large-Scale Text Mining
Proceedings of the AAAI Conference on Artificial Intelligence, 2020Co-Authors: Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael KatzAbstract:In this demonstration, we present a system for mining Causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for Causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of Causal Relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a Causal Relation. It also provides evidence from an input corpus supporting the existence of a Causal Relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of Causal Relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.
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AAAI - Causal Knowledge Extraction through Large-Scale Text Mining
2020Co-Authors: Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Shirin Sohrabi, Michael P. Perrone, Michael KatzAbstract:In this demonstration, we present a system for mining Causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for Causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of Causal Relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a Causal Relation. It also provides evidence from an input corpus supporting the existence of a Causal Relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of Causal Relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.