This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specificat...
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Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tool...
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We introduce a constructive function approximation approach as a general tool, particularly useful in adaptive and data-driven methods for perception and control. The key idea is to estimate of a collection of simple ...
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Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the int...
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1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi...
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1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the individual sample level[1].
Visual object tracking aims to continuously localize the target object of interest in a video *** one of the most fundamental problems in computer vision,visual object tracking has a long list of critical applications...
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Visual object tracking aims to continuously localize the target object of interest in a video *** one of the most fundamental problems in computer vision,visual object tracking has a long list of critical applications including video surveillance,autonomous driving,human-machine interaction,augmented reality,robotics,etc.,in which the tracking system provides the capacity to report target positions in real time for subsequent visual *** the past decades,visual object tracking has been extensively explored and has witnessed considerable progress,especially in deep-learning-based tracking.
With the increasing demand for edge computing in cyber-physical system (CPS) applications, ensuring the safety and reliability of machine learning models running on edge devices during online model training and infere...
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Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents’ contributions to such relationships pose signif...
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Establishing causal relationships between actions and outcomes is fundamental for accountable multi-agent decision-making. However, interpreting and quantifying agents’ contributions to such relationships pose significant challenges. These challenges are particularly prominent in the context of multi-agent sequential decision-making, where the causal effect of an agent’s action on the outcome depends on how other agents respond to that action. In this paper, our objective is to present a systematic approach for attributing the causal effects of agents’ actions to the influence they exert on other agents. Focusing on multi-agent Markov decision processes, we introduce agent-specific effects (ASE), a novel causal quantity that measures the effect of an agent’s action on the outcome that propagates through other agents. We then turn to the counterfactual counterpart of ASE (cf-ASE), provide a sufficient set of conditions for identifying cf-ASE, and propose a practical sampling-based algorithm for estimating it. Finally, we experimentally evaluate the utility of cf-ASE through a simulation-based testbed, which includes a sepsis management environment. Copyright 2024 by the author(s)
Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast ...
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