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arXiv 2021年

作者： Chuang, Chun-Hsiang Chang, Kong-Yi Huang, Chih-Sheng Jung, Tzyy-Ping Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan Department of Artificial Intelligence Research and Development Elan Microelectronics Corporation Hsinchu Taiwan Institute of Engineering in Medicine Institute for Neural Computation University of California San Diego La Jolla United States

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent misinterpretations of neural signals and underperformance of brain-computer interfaces. This study developed a new artifact removal method, IC-U-Net, which is based on the U-Net architecture for removing pervasive EEG artifacts and reconstructing brain sources. The IC-U-Net was trained using mixtures of brain and non-brain sources decomposed by independent component analysis and employed an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain sources and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noises) was demonstrated in a simulation study and three real-world EEG datasets collected at rest and while driving and walking. IC-U-Net is user-friendly and publicly available, does not require parameter tuning or artifact type designations, and has no limitations on channel numbers. Given the increasing need to image natural brain dynamics in a mobile setting, ICU-Net offers a promising end-to-end solution for automatically removing artifacts from EEG recordings. Copyright © 2021, The Authors. All rights reserved.

关键词： Electroencephalography

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Efficient Learning of Continuous-Variable Quantum States

arXiv

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arXiv 2023年

作者： Wu, Ya-Dong Zhu, Yan Chiribella, Giulio Liu, Nana John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Department of Computer Science Parks Road OxfordOX1 3QD United Kingdom Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(log M) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements. © 2023, CC BY-NC-SA.

关键词： Quantum communication

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Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation

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Frontiers of computer science 2019年第1期13卷 106-126页

作者： Shan ZHONG Quan LIU Zongzhang ZHANG Qiming FU School of Computer Science and Technology Soochow UniversitySuzhou 215000China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou 215006China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China College of Electronic &Information Engineering Suzhou University of Science and TechnologySuzhou 215000China

Dyna is an effective reinforcement learning (RL) approach that combines value function evaluation with model ***,existing works on Dyna mostly discuss only its efficiency in RL problems with discrete action *** paper proposes a novel Dyna variant,called Dyna-LSTD-PA,aiming to handle problems with continuous action ***-LSTD-PA stands for Dyna based on least-squares temporal difference (LSTD)and policy ***-LSTD-PA consists of two simultaneous,interacting *** learning process determines the probability distribution over action spaces using the Gaussian distribution;estimates the underlying value function,policy,and model by linear representation;and updates their parameter vectors online by LSTD(,t).The planning process updates the parameter vector of the value function again by using ofttine LSTD(2).Dyna-LSTD-PA also uses the Sherman-Morrison formula to improve the efficiency of LSTD(,t),and weights the parameter vector of the value function to bring the two processes ***,the global error bound is derived by considering approximation,estimation,and model ***,Dyna-LSTD-PA outperforms two representative methods in terms of convergence rate,success rate,and stability performance on four benchmark RL problems.

关键词： problem solving control methods heuristic search methods dynamic programming

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Hierarchical Event Descriptor library schema for EEG data annotation

arXiv

引用

arXiv 2023年

作者： Hermes, Dora Attia, Tal Pal Beniczky, Sándor Bosch-Bayard, Jorge Delorme, Arnaud Lundstrom, Brian Nils Rogers, Christine Rampp, Stefan Shirazi, Seyed Yahya Truong, Dung Valdes-Sosa, Pedro Worrell, Greg Makeig, Scott Robbins, Kay Multimodal Neuroimaging Laboratory Department of Physiology and Biomedical Engineering Mayo Clinic RochesterMN United States Department of Computer Science University of Texas San Antonio San AntonioTX United States The Danish Epilepsy Centre Filadelfia Denmark Aarhus University Aarhus University Hospital Aarhus Denmark Department of Biological and Health Psychology Faculty of Psychology Universidad Autónoma de Madrid Spain Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego La Jolla CA United States Bioelectronics Neurophysiology and Engineering Laboratory Department of Neurology Mayo Clinic RochesterMN United States McGill Centre for Integrative Neuroscience Montreal Neurological Institute Montreal Canada Department of Neurosurgery University Hospital Erlangen Erlangen Germany Germany

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitate data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study developed a HED library schema for SCORE, making the terms machine-readable. We demonstrate that the HED-SCORE library schema can be used to annotate events in EEG data stored in the Brain Imaging Data Structure (BIDS). Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and compute on electrophysiological data obtained from the human brain. © 2023, CC BY-NC-ND.

关键词： Brain mapping

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Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor

引用

Physical Review Letters 2023年第24期130卷 240601-240601页

作者： Jonathan Wei Zhong Lau Kian Hwee Lim Kishor Bharti Leong-Chuan Kwek Sai Vinjanampathy Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Joint Center for Quantum Information and Computer Science and Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way No. 16-16 Connexis Singapore 138632 Republic of Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit Singapore UMI 3654 Singapore National Institute of Education Nanyang Technological University 1 Nanyang Walk Singapore 637616 Singapore School of Electrical and Electronic Engineering Block S2.1 50 Nanyang Avenue Singapore 639798 Singapore Department of Physics Indian Institute of Technology-Bombay Powai Mumbai 400076 India Centre of Excellence in Quantum Information Computation Science and Technology Indian Institute of Technology Bombay Powai Mumbai 400076 India

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well-known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries.

关键词： Learning theory Machine learning Open quantum systems & decoherence Quantum algorithms Nonequilibrium systems

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Learning to Predict IR Drop with Effective Training for ReRAM-based neural Network Hardware

Learning to Predict IR Drop with Effective Training for ReRA...

引用

Design Automation Conference

作者： Sugil Lee Giju Jung Mohammed E. Fouda Jongeun Lee Ahmed Eltawil Fadi Kurdahi School of Electrical and Computer Engineering UNIST Ulsan Korea Neural Processing Research Center Seoul National University Seoul Korea Department of Electrical Engineering and Computer Science University of California-Irvine CA USA

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN.

关键词：

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Abstracting fairness: Oracles, metrics, and interpretability 1

Abstracting fairness: Oracles, metrics, and interpretability

引用

1st Symposium on Foundations of Responsible Computing, FORC 2020

作者： Dwork, Cynthia Ilvento, Christina Rothblum, Guy N. Sur, Pragya Harvard John A Paulson School of Engineering and Applied Sciences CambridgeMA United States Radcliffe Institute for Advanced Study CambridgeMA United States Microsoft Research Mountain ViewCA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Harvard University Center for Research on Computation and Society CambridgeMA United States

ISBN: (纸本)9783959771429

It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We examine what can be learned from a fairness oracle equipped with an underlying understanding of "true" fairness. The oracle takes as input a (context, classifier) pair satisfying an arbitrary fairness definition, and accepts or rejects the pair according to whether the classifier satisfies the underlying fairness truth. Our principal conceptual result is an extraction procedure that learns the underlying truth;moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle. Since every "truly fair" classifier induces a coarse metric, in which those receiving the same decision are at distance zero from one another and those receiving different decisions are at distance one, this extraction process provides the basis for ensuring a rough form of metric fairness, also known as individual fairness. Our principal technical result is a higher fidelity extractor under a mild technical constraint on the weak oracles conception of fairness. Our framework permits the scenario in which many classifiers, with differing outcomes, may all be considered fair. Our results have implications for interpretablity – a highly desired but poorly defined property of classification systems that endeavors to permit a human arbiter to reject classifiers deemed to be "unfair" or illegitimately derived. © Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, and Pragya Sur;licensed under Creative Commons License CC-BY

关键词： Extraction

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Polarization dynamics of solid-state quantum emitters

arXiv

引用

arXiv 2023年

作者： Kumar, Anand Samaner, Çağlar Cholsuk, Chanaprom Matthes, Tjorben Paçal, Serkan Oyun, Yağız Zand, Ashkan Chapman, Robert J. Saerens, Grégoire Grange, Rachel Suwanna, Sujin Ateş, Serkan Vogl, Tobias Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich80333 Germany Abbe Center of Photonics Institute of Applied Physics Friedrich Schiller University Jena Jena07745 Germany Department of Physics İzmir Institute of Technology İzmir35430 Turkey Department of Photonics İzmir Institute of Technology İzmir35430 Turkey Optical Nanomaterial Group Institute for Quantum Electronics Department of Physics ETH Zurich Zurich8093 Switzerland Optical and Quantum Physics Laboratory Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand

Quantum emitters in solid-state crystals have recently attracted a lot of attention due to their simple applicability in optical quantum technologies. The polarization of single photons generated by quantum emitters is one of the key parameters that play a crucial role in the applications, such as quantum computation that uses the indistinguishability of photons. However, the degree of single photon polarization is typically quantified using time-averaged photoluminescence intensity of single emitters, which provides limited information about the dipole properties in solids. In this work, we use single defects in hexagonal boron nitride and nanodiamond as efficient room-temperature single photon sources to reveal the origin and the temporal evolution of dipole orientation in solid-state quantum emitters. The angle of excitation and emission dipoles relative to the crystal axes are determined experimentally and then calculated using density functional theory, which results in characteristic angles for every specific defect that can be used as an efficient tool for defect identification and understanding their atomic structure. Moreover, the temporal polarization dynamics reveal a strongly modified linear polarization visibility that depends on the excited state decay time of individual excitation. This effect can be traced back potentially to the excitation of excess charges in the local crystal environment. Understanding such hidden time-dependent mechanisms can further be used to improve the performance of polarization-sensitive experiments, in particular that of quantum communication with single photon emitters. © 2023, CC BY.

关键词： Density functional theory

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Overcoming entropic limitations on asymptotic state transformations through probabilistic protocols

arXiv

引用

arXiv 2022年

作者： Regula, Bartosz Lami, Ludovico Wilde, Mark M. Department of Physics Graduate School of Science The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Institut für Theoretische Physik und IQST Universität Ulm Albert-Einstein-Allee 11 UlmD-89069 Germany School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Hearne Institute for Theoretical Physics Department of Physics and Astronomy Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

The quantum relative entropy is known to play a key role in determining the asymptotic convertibility of quantum states in general resource-theoretic settings, often constituting the unique monotone that is relevant in the asymptotic regime. We show that this is no longer the case when one allows stochastic protocols that may only succeed with some probability, in which case the quantum relative entropy is insufficient to characterize the rates of asymptotic state transformations, and a new entropic quantity based on a regularization of the Hilbert projective metric comes into play. Such a scenario is motivated by a setting where the cost associated with transformations of quantum states, typically taken to be the number of copies of a given state, is instead identified with the size of the quantum memory needed to realize the protocol. Our approach allows for constructing transformation protocols that achieve strictly higher rates than those imposed by the relative entropy. Focusing on the task of resource distillation, we give broadly applicable strong converse bounds on the asymptotic rates of probabilistic distillation protocols, and show them to be tight in relevant settings such as entanglement distillation with non-entangling operations. This generalizes and extends previously known limitations that only applied to deterministic protocols. Our methods are based on recent results for probabilistic one-shot transformations as well as a new asymptotic equipartition property for the projective relative entropy. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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