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2017 

1.  Bhargava, Srivatsa; Gorur, Pushkar; Amrutur, Bharadwaj A distributed object detectortracker aided video encoder for smart camera networks Conference Proceedings of the 11th International Conference on Distributed Smart Cameras (ICDSC), 05.07.09.17, Stanford (USA), 2017. Abstract  BibTeX  Tags: Efficient architectures and algorithms for distributed video analytics for Smart Cities, Modeling and Simulation and Analytics  Links: @conference{Srivatsa2017, title = {A distributed object detectortracker aided video encoder for smart camera networks}, author = {Srivatsa Bhargava and Pushkar Gorur and Bharadwaj Amrutur}, doi = {10.1145/3131885.3131920}, year = {2017}, date = {20170907}, booktitle = {Proceedings of the 11th International Conference on Distributed Smart Cameras (ICDSC), 05.07.09.17, Stanford (USA)}, pages = {6975}, abstract = {In this paper, we propose a Region of Interest (ROI) modulated H.264 video encoder system, based on a distributed object detectortracker framework, for smart camera networks. Locations of objects of interest, as determined by detectortracker are used to semantically partition each frame into regions assigned with multiple levels of importance. A distributed architecture is proposed to implement the object detectortracker framework to mitigate the computational cost. Further, a rate control algorithm with modified RateDistortion(RD) cost is proposed to determine Quantization Parameter(QP) and skip decision of Macro Blocks based on their relative levels of importance. Our experiments show that, the proposed system achieves upto 3x reduction in bitrate without significant reduction in PSNR of ROI(headshoulder region of pedestrians). We also demonstrate the tradeoff between total computational cost and compression possible with the proposed distributed detectortracker framework. }, keywords = {Efficient architectures and algorithms for distributed video analytics for Smart Cities, Modeling and Simulation and Analytics}, pubstate = {published}, tppubtype = {conference} } In this paper, we propose a Region of Interest (ROI) modulated H.264 video encoder system, based on a distributed object detectortracker framework, for smart camera networks. Locations of objects of interest, as determined by detectortracker are used to semantically partition each frame into regions assigned with multiple levels of importance. A distributed architecture is proposed to implement the object detectortracker framework to mitigate the computational cost. Further, a rate control algorithm with modified RateDistortion(RD) cost is proposed to determine Quantization Parameter(QP) and skip decision of Macro Blocks based on their relative levels of importance. Our experiments show that, the proposed system achieves upto 3x reduction in bitrate without significant reduction in PSNR of ROI(headshoulder region of pedestrians). We also demonstrate the tradeoff between total computational cost and compression possible with the proposed distributed detectortracker framework. 
2.  Kadavankandy, Arun; Avrachenkov, Konstantin; Cottatellucci, Laura; Sundaresan, Rajesh Belief propagation for subgraph detection with imperfect sideinformation Conference Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), 25.30.06.17, Aachen (Germany), 2017. Abstract  BibTeX  Tags: Modeling and Simulation and Analytics  Links: @conference{Kadavankandy2017, title = {Belief propagation for subgraph detection with imperfect sideinformation}, author = {Arun Kadavankandy and Konstantin Avrachenkov and Laura Cottatellucci and Rajesh Sundaresan}, url = {http://www.rbccps.org/wpcontent/uploads/2018/01/08006800.pdf}, doi = {10.1109/ISIT.2017.8006800}, year = {2017}, date = {20170815}, booktitle = {Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), 25.30.06.17, Aachen (Germany)}, pages = {16031607}, abstract = {We propose a local message passing algorithm based on Belief Propagation (BP) to detect a small hidden ErdosRényi (ER) subgraph embedded in a larger sparse ER random graph in the presence of sideinformation. We consider sideinformation in the form of revealed subgraph nodes called cues, some of which may be erroneous. Namely, the revealed nodes may not all belong to the subgraph, and it is not known to the algorithm a priori which cues are correct and which are incorrect. We show that asymptotically as the graph size tends to infinity, the expected fraction of misclassified nodes approaches zero for any positive value of a parameter λ, which represents the effective SignaltoNoise Ratio of the detection problem. Previous works on subgraph detection using BP without sideinformation showed that BP fails to recover the subgraph when λ <; 1/e. Our results thus demonstrate the substantial gains in having even a small amount of sideinformation.}, keywords = {Modeling and Simulation and Analytics}, pubstate = {published}, tppubtype = {conference} } We propose a local message passing algorithm based on Belief Propagation (BP) to detect a small hidden ErdosRényi (ER) subgraph embedded in a larger sparse ER random graph in the presence of sideinformation. We consider sideinformation in the form of revealed subgraph nodes called cues, some of which may be erroneous. Namely, the revealed nodes may not all belong to the subgraph, and it is not known to the algorithm a priori which cues are correct and which are incorrect. We show that asymptotically as the graph size tends to infinity, the expected fraction of misclassified nodes approaches zero for any positive value of a parameter λ, which represents the effective SignaltoNoise Ratio of the detection problem. Previous works on subgraph detection using BP without sideinformation showed that BP fails to recover the subgraph when λ <; 1/e. Our results thus demonstrate the substantial gains in having even a small amount of sideinformation. 
3.  Vaidhiyan, Nidhin K; Arun, S P; Sundaresan, Rajesh Neural dissimilarity indices that predict oddball detection in behaviour Journal Article IEEE Transactions on Information Theory, 63 (8), pp. 47784796, 2017. Abstract  BibTeX  Tags: Modeling and Simulation and Analytics  Links: @article{Vaidhiyan2017, title = {Neural dissimilarity indices that predict oddball detection in behaviour}, author = {Nidhin K. Vaidhiyan and S. P. Arun and Rajesh Sundaresan}, url = {http://www.rbccps.org/wpcontent/uploads/2018/01/07937887.pdf}, doi = {10.1109/TIT.2017.2707485}, year = {2017}, date = {20170601}, journal = {IEEE Transactions on Information Theory}, volume = {63}, number = {8}, pages = {47784796}, abstract = {Neuroscientists have recently shown that images that are difficult to find in visual search elicit similar patterns of firing across a population of recorded neurons. The L1 distance between firing rate vectors associated with two images was strongly correlated with the inverse of decision time in behavior. But why should decision times be correlated with L1 distance? What is the decisiontheoretic basis? In our decision theoretic formulation, we model visual search as an active sequential hypothesis testing problem with switching costs. Our analysis suggests an appropriate neuronal dissimilarity index, which correlates equally strongly with the inverse of decision time as the L1 distance. We also consider a number of other possibilities, such as the relative entropy (KullbackLeibler divergence) and the Chernoff entropy of the firing rate distributions. A more stringent test of equality of means, which would have provided a strong backing for our modeling, fails for our proposed as well as the other already discussed dissimilarity indices. However, test statistics from the equality of means test, when used to rank the indices in terms of their ability to explain the observed results, places our proposed dissimilarity index at the top followed by relative entropy, Chernoff entropy, and the L1 indices. Computations of the different indices require an estimate of the relative entropy between two Poisson point processes. An estimator is developed and is shown to have near unbiased performance for almost all operating regions.}, keywords = {Modeling and Simulation and Analytics}, pubstate = {published}, tppubtype = {article} } Neuroscientists have recently shown that images that are difficult to find in visual search elicit similar patterns of firing across a population of recorded neurons. The L1 distance between firing rate vectors associated with two images was strongly correlated with the inverse of decision time in behavior. But why should decision times be correlated with L1 distance? What is the decisiontheoretic basis? In our decision theoretic formulation, we model visual search as an active sequential hypothesis testing problem with switching costs. Our analysis suggests an appropriate neuronal dissimilarity index, which correlates equally strongly with the inverse of decision time as the L1 distance. We also consider a number of other possibilities, such as the relative entropy (KullbackLeibler divergence) and the Chernoff entropy of the firing rate distributions. A more stringent test of equality of means, which would have provided a strong backing for our modeling, fails for our proposed as well as the other already discussed dissimilarity indices. However, test statistics from the equality of means test, when used to rank the indices in terms of their ability to explain the observed results, places our proposed dissimilarity index at the top followed by relative entropy, Chernoff entropy, and the L1 indices. Computations of the different indices require an estimate of the relative entropy between two Poisson point processes. An estimator is developed and is shown to have near unbiased performance for almost all operating regions. 
2016 

4.  Ramaswamy, Arunselvan; Bhatnagar, Shalabh A generalization of the BorkarMeyn theorem for stochastic recursive inclusions Journal Article Mathematics of Operations Research, 2016. Abstract  BibTeX  Tags: Modeling and Simulation and Analytics  Links: @article{Ramaswamy2016, title = {A generalization of the BorkarMeyn theorem for stochastic recursive inclusions}, author = {Arunselvan Ramaswamy and Shalabh Bhatnagar}, url = {http://www.rbccps.org/wpcontent/uploads/2017/10/moor.2016.0821.pdf}, doi = {10.1287/moor.2016.0821}, year = {2016}, date = {20161216}, journal = {Mathematics of Operations Research}, abstract = {In this paper, the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a setvalued map. Two different sets of sufficient conditions are presented that guarantee the “stability and convergence” of stochastic recursive inclusions. Our work builds on the works of Benaïm, Hofbauer and Sorin as well as Borkar and Meyn. As a corollary to one of the main theorems, a natural generalization of the Borkar and Meyn theorem follows. In addition, the original theorem of Borkar and Meyn is shown to hold under slightly relaxed assumptions. As an application to one of the main theorems, we discuss a solution to the “approximate drift problem.” Finally, we analyze the stochastic gradient algorithm with “constanterror gradient estimators” as yet another application of our main result.}, keywords = {Modeling and Simulation and Analytics}, pubstate = {published}, tppubtype = {article} } In this paper, the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a setvalued map. Two different sets of sufficient conditions are presented that guarantee the “stability and convergence” of stochastic recursive inclusions. Our work builds on the works of Benaïm, Hofbauer and Sorin as well as Borkar and Meyn. As a corollary to one of the main theorems, a natural generalization of the Borkar and Meyn theorem follows. In addition, the original theorem of Borkar and Meyn is shown to hold under slightly relaxed assumptions. As an application to one of the main theorems, we discuss a solution to the “approximate drift problem.” Finally, we analyze the stochastic gradient algorithm with “constanterror gradient estimators” as yet another application of our main result. 