IEEE Transactions on Automatic Control Vector Quantization for Adaptive State Aggregation in Reinforcement Learning They appeal to a game-theoretic analysis, and seek equilibria at which neither decision maker has a unilateral incentive to change its policy. They show that the valuation of information is conceivable and quantifiable grounded on this trade-off. The authors in this paper study an essential property of controlled stochastic processes by making a rate-regulation trade-off defined between the packet rate and regulation cost.
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Yet, the fact is that little is known so far about this valuation of information.
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It is, therefore, rational that such information be transmitted only if it is valuable in the sense of a cost-benefit analysis, i.e., only if its benefit surpasses its cost. Touraj Soleymani, John Baras, Sandra HircheĮven though transmission of a piece of sensory information in a networked control system decreases the uncertainty of the controller, it indeed has a price from the economic perspective. We present simulation studies and application to real data to compare the performance of the ordinary least squares and LASSO in detecting Granger causal influences, which corroborate our theoretical results.Ī John Baras 2021 Value of Information in Feedback Control: Quantification We also characterize the false positive error probability of a simple thresholding rule for identifying Granger causal effects. We establish that the sufficient conditions of LASSO also suffice for robust identification of Granger causal influences. In this work, we close this gap by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. 5, 2020 Behtash Babadi 2021 Non-Asymptotic Guarantees for Robust Identification of Granger Causality via the LASSOĬlassical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data durations to be useful and are not immune to confounding effects. Invited Paper, 73rd Annual Gaseous Electronics Virtual Conference of the American Physical Society, Oct. The overall technique extracts physically significant reaction invariants and points to potential model structural problems if they exist. The second phase further reduces the dynamic dimension when species relatively minor in concentration can be identified.
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Our model reduction approach is based on a two-step procedure where in the first step, the chemical species surface balance dynamic equations are factored to decouple the (nonlinear) reaction rates, eliminating redundant dynamic modes. In this talk, I will describe our efforts to develop mathematical methods that address the numerical challenge of simulating dynamic ALD processes while providing a rational path to creating well-posed models of these deposition processes. Those data that do exist are typically derived from quantum chemical computations or experimental examinations of reaction sequences that define only a portion of the complete ALD reaction cycle. Modeling the dynamics of atomic layer deposition processes is challenging because of the nonlinear behavior of these systems, their multiple and widely-ranging timescales, and by the relative lack of validated reaction kinetics information. 26, 2019 Raymond Adomaitis 2020 Reaction Network Analysis for Atomic Layer Deposition Processes The paper studies an extension to nonlinear systems of a recently proposed approach to the definition of modal participation factors. Eyad Abed 2019 Local modal participation analysis of nonlinear systems using Poincaré linearizationīoumediene Hamz (Imperial College London) and Eyad Abed