10,563 research outputs found

    Reinforcement learning in large state action spaces

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    Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios. This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory). In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications

    Swarm Reinforcement Learning For Adaptive Mesh Refinement

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    The Finite Element Method, an important technique in engineering, is aided by Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on task-specific heuristics or expensive error estimators, hindering their use for complex simulations. Recent learned AMR methods tackle these problems, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate the effectiveness of our approach, Adaptive Swarm Mesh Refinement (ASMR), showing that it learns reliable, scalable, and efficient refinement strategies on a set of challenging problems. Our approach significantly speeds up computation, achieving up to 30-fold improvement compared to uniform refinements in complex simulations. Additionally, we outperform learned baselines and achieve a refinement quality that is on par with a traditional error-based AMR strategy without expensive oracle information about the error signal.Comment: Version 1 of this paper is a preliminary workshop version that was accepted as a workshop paper in the ICLR 2023 Workshop on Physics for Machine Learnin

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

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    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    Traversing the FFT Computation Tree for Dimension-Independent Sparse Fourier Transforms

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    We consider the well-studied Sparse Fourier transform problem, where one aims to quickly recover an approximately Fourier kk-sparse vector x^Cnd\widehat{x} \in \mathbb{C}^{n^d} from observing its time domain representation xx. In the exact kk-sparse case the best known dimension-independent algorithm runs in near cubic time in kk and it is unclear whether a faster algorithm like in low dimensions is possible. Beyond that, all known approaches either suffer from an exponential dependence on the dimension dd or can only tolerate a trivial amount of noise. This is in sharp contrast with the classical FFT of Cooley and Tukey, which is stable and completely insensitive to the dimension of the input vector: its runtime is O(NlogN)O(N\log N) in any dimension dd for N=ndN=n^d. Our work aims to address the above issues. First, we provide a translation/reduction of the exactly kk-sparse FT problem to a concrete tree exploration task which asks to recover kk leaves in a full binary tree under certain exploration rules. Subsequently, we provide (a) an almost quadratic in kk time algorithm for this task, and (b) evidence that a strongly subquadratic time for Sparse FT via this approach is likely impossible. We achieve the latter by proving a conditional quadratic time lower bound on sparse polynomial multipoint evaluation (the classical non-equispaced sparse FT) which is a core routine in the aforementioned translation. Thus, our results combined can be viewed as an almost complete understanding of this approach, which is the only known approach that yields sublinear time dimension-independent Sparse FT algorithms. Subsequently, we provide a robustification of our algorithm, yielding a robust cubic time algorithm under bounded 2\ell_2 noise. This requires proving new structural properties of the recently introduced adaptive aliasing filters combined with a variety of new techniques and ideas

    Succinct representation for (non)deterministic finite automata

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    International audienceNon)-Deterministic finite automata are one of the simplest models of computation studied in automata theory. Here we study them through the lens of succinct data structures. Towards this goal, we design a data structure for any deterministic automaton D having n states over a σ-letter alphabet using (σ − 1)n log n(1 + o(1)) bits, that determines, given a string x, whether D accepts x in optimal O (|x|) time. We also consider the case when there are N < σ n non-failure transitions, and obtain various time-space trade-offs. Here some of our results are better than the recent work of Cotumaccio and Prezza (SODA 2021). We also exhibit a data structure for non-deterministic automaton N using σ n 2 + n bits that takes O (n 2 |x|) time for string membership checking. Finally, we also provide time and space efficient algorithms for performing several standard operations on the languages accepted by finite automata

    Molecular Research in Rice: Agronomically Important Traits 2.0

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    This volume presents recent research achievements concerning the molecular genetic basis of agronomic traits in rice. Rice (Oryza sativa L.) is the most important food crop in the world, being a staple food for more than half of the world’s population. Recent improvements in living standards have increased the worldwide demand for high-yielding and high-quality rice cultivars. To develop novel cultivars with superior agronomic performance, we need to understand the molecular basis of agronomically important traits related to grain yield, grain quality, disease resistance, and abiotic stress tolerance. Decoding the whole rice genome sequence revealed that ,while there are more than 37,000 genes in the ~400 Mbp rice genome, there are only about 3000 genes whose molecular functions are characterized in detail. We collected in this volume the continued research efforts of scholars that elucidate genetic networks and the molecular mechanisms controlling agronomically important traits in rice

    Feature Papers in Compounds

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    This book represents a collection of contributions in the field of the synthesis and characterization of chemical compounds, natural products, chemical reactivity, and computational chemistry. Among its contents, the reader will find high-quality, peer-reviewed research and review articles that were published in the open access journal Compounds by members of the Editorial Board and the authors invited by the Editorial Office and Editor-in-Chief
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