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arXiv 2021年

作者： Zhou, Gui Pan, Cunhua Ren, Hong Popovski, Petar Swindlehurst, A. Lee School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electronic Systems Aalborg University Aalborg9220 Denmark Center for Pervasive Communications and Computing University of California IrvineCA92697 United States

A reconfigurable intelligent surface (RIS) is a promising device that can reconfigure the electromagnetic propagation environment through adjustment of the phase shifts of its multiple reflecting elements. However, channel estimation in RIS-aided multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive nature of the RIS and the large number of reflecting elements that can lead to high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users do not appreciably change over multiple consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much lower pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the reparameterized CSI of the common base station (BS)-RIS channel obtained in Phase I. The minimum pilot overhead is much less than the existing works. Simulation results show that the performance of the proposed method outperforms existing methods in terms of the estimation accuracy when using the same amount of pilot overhead. © 2021, CC BY.

关键词： Channel estimation

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Vector Symbolic Architectures as a computing Framework for Emerging Hardware

arXiv

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arXiv 2021年

作者： Kleyko, Denis Davies, Mike Frady, E. Paxon Kanerva, Pentti Kent, Spencer J. Olshausen, Bruno A. Osipov, Evgeny Rabaey, Jan M. Rachkovskij, Dmitri A. Rahimi, Abbas Sommer, Friedrich T. The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden The Neuromorphic Computing Laboratory Intel Labs Santa ClaraCA95054 United States The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States International Research and Training Center for Information Technologies and Systems Kyiv03680 Ukraine IBM Research – Zurich Rüschlikon8803 Switzerland

This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing. Copyright © 2021, The Authors. All rights reserved.

关键词： Vectors

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Quantifying Quantumness of Channels Without Entanglement

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PRX Quantum 2022年第2期3卷 020338-020338页

作者： Huan-Yu Ku Josef Kadlec Antonín Černoch Marco Túlio Quintino Wenbin Zhou Karel Lemr Neill Lambert Adam Miranowicz Shin-Liang Chen Franco Nori Yueh-Nan Chen Department of Physics and Center for Quantum Frontiers of Research and Technology (QFort) National Cheng Kung University Tainan 701 Taiwan Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan Palacký University in Olomouc Faculty of Science Joint Laboratory of Optics of Palacký University and Institute of Physics AS CR 17. listopadu 12 Olomouc 771 46 Czech Republic Institute of Physics of the Czech Academy of Sciences Joint Laboratory of Optics of PU and IP AS CR 17. listopadu 50A Olomouc 772 07 Czech Republic Vienna Center for Quantum Science and Technology (VCQ) Faculty of Physics University of Vienna Boltzmanngasse 5 Vienna 1090 Austria Institute for Quantum Optics and Quantum Information (IQOQI) Austrian Academy of Sciences Boltzmanngasse 3 Vienna A-1090 Austria Graduate School of Informatics Nagoya University Chikusa-ku Nagoya 464-8601 Japan Institute of Spintronics and Quantum Information Faculty of Physics Adam Mickiewicz University Poznań 61-614 Poland Department of Physics National Chung Hsing University Taichung 40227 Taiwan Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin 14195 Germany RIKEN Center for Quantum Computing Wako Saitama 351-0198 Japan Department of Physics The University of Michigan Ann Arbor Michigan 48109-1040 USA

Quantum channels breaking entanglement, incompatibility, or nonlocality are defined as such because they are not useful for entanglement-based, one-sided device-independent, or device-independent quantum-information processing, respectively. Here, we show that such breaking channels are related to complementary tests of macrorealism, i.e., temporal separability, channel unsteerability, temporal unsteerability, and the temporal Bell inequality. To demonstrate this we first define a steerability-breaking channel, which is conceptually similar to entanglement and nonlocality-breaking channels and prove that it is identical to an incompatibility-breaking channel. A hierarchy of quantum nonbreaking channels is derived, akin to the existing hierarchy relations for temporal and spatial quantum correlations. We then introduce the concept of channels that break temporal correlations, explain how they are related to the standard breaking channels, and prove the following results. (1) A robustness-based measure for non-entanglement-breaking channels can be probed by temporal nonseparability. (2) A non-steerability-breaking channel can be quantified by channel steering. (3) Temporal steerability and nonmacrorealism can be used for, respectively, distinguishing unital steerability-breaking channels and nonlocality-breaking channels for a maximally entangled state. Finally, a two-dimensional depolarizing channel is experimentally implemented as a proof-of-principle example to demonstrate the hierarchy relation of nonbreaking channels using temporal quantum correlations.

关键词： Nonlocality Quantum channels Quantum entanglement Quantum memories

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Assessing transfer entropy from biochemical data

arXiv

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arXiv 2021年

作者： Imaizumi, Takuya Umeki, Nobuhisa Yoshizawa, Ryo Obuchi, Tomoyuki Sako, Yasushi Kabashima, Yoshiyuki Department of Mathematical and Computing Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan Cellular Informatics Laboratory RIKEN Cluster for Pioneering Research 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Systems Science Kyoto University 36-1 Yoshida-Honmachi Sakyo-ku Kyoto606-8501 Japan The Institute for Physics of Intelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan

We address the problem of evaluating the transfer entropy (TE) produced by biochemical reactions from experimentally measured data. Although these reactions are generally non-linear and non-stationary processes making it challenging to achieve accurate modeling, Gaussian approximation can facilitate the TE assessment only by estimating covariance matrices using multiple data obtained from simultaneously measured time series representing the activation levels of biomolecules such as proteins. Nevertheless, the non-stationary nature of biochemical signals makes it difficult to theoretically assess the sampling distributions of TE, which are necessary for evaluating the statistical confidence and significance of the data-driven estimates. We resolve this difficulty by computationally assessing the sampling distributions using techniques from computational statistics. The computational methods are tested by using them in analyzing data generated from a theoretically tractable time-varying signal model, which leads to the development of a method to screen only statistically significant estimates. The usefulness of the developed method is examined by applying it to real biological data experimentally measured from the ERBB-RAS-MAPK system that superintends diverse cell fate decisions. A comparison between cells containing wild-type and mutant proteins exhibits a distinct difference in the time evolution of TE while apparent difference is hardly found in average profiles of the raw signals. Such comparison may help in unveiling important pathways of biochemical reactions. © 2021, CC BY.

关键词： Covariance matrix

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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Multi-granularity ensemble sample selection and label correction for classification with noisy labels

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Applied Soft computing 2025年 180卷

作者： Kecan Cai Hongyun Zhang Witold Pedrycz Duoqian Miao Chaofan Chen Department of Computer Science and Technology Tongji University Shanghai 201804 PR China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 PR China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer/Istanbul Turkiye

Sample selection is crucial in classification tasks with noisy labels, yet most existing sample selection methods rely on a single criterion. These approaches often face challenges, including low purity of selected clean samples, and underfitting due to an insufficient number of selected clean training samples. To address these challenges, this paper proposes GNet-SSLC, a novel multi-granularity network framework that integrates multiple criteria ensemble sample selection (SS) and multiple views label correction (LC). In the SS phase, this paper proposes a metric learning-based dual k-Nearest Neighbor (k-NN) sample selection method. This method first uses corrected soft labels from the initial k-NN round to guide the selection of clean samples in the subsequent k-NN round. To further enhance selection accuracy, we combine this dual k-NN approach with a small loss sample selection technique through a voting mechanism. This multiple criteria ensemble method addresses the issues of low purity and instability inherent in single criterion approaches. In the LC phase, this paper designs a multiple views label correction framework that generates high-quality pseudo-labels for selected noisy samples. A key innovation of the framework is the design of a regularized contrastive learning loss, which optimizes the semi-supervised learning process by leveraging multiple views of training samples. The additional inclusion of training samples with high-quality pseudo-labels can effectively mitigate underfitting caused by a limited number of clean training samples. Experimental results on both synthetic and real-world noisy datasets indicate that GNet-SSLC enhances the purity and stability of the selected clean samples, and significantly improves classification performance. The enhancement is particularly notable with high noise rate dataset, such as CIFAR-100 dataset with 80% noise rate, achieving a 19.3% increase in classification accuracy compared to the baseline method.

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Author Correction: Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

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Nature communications 2025年第1期16卷 535页

作者： Wenhui Li Xianyue Jiang Wuke Wang Liya Hou Runze Cai Yongqian Li Qiuxi Gu Qinchang Chen Peixiang Ma Jin Tang Menghao Guo Guohui Chuai Xingxu Huang Jun Zhang Qi Liu State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of Education Orthopaedic Department of Tongji Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. Research Center for Life Sciences Computing Zhejiang Lab Hangzhou Zhejiang China. State Key Laboratory of Reproductive Medicine and Offspring Health Women's Hospital of Nanjing Medical University Nanjing Maternity and Child Health Care Hospital Nanjing Medical University Nanjing China. Shanghai Key Laboratory of Orthopedic Implants Department of Orthopedic Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China. State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. 18alexanderm117@***. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of Education Orthopaedic Department of Tongji Hospital Frontier Science Center for Stem Cell Research Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai China. 18alexanderm117@***. National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China. 18alexanderm117@***. Research Center for Life Sciences Computing Zhejiang Lab Hangzhou Zhejiang China. xingxuhuang@zju.edu.cn. The Key Laboratory of Pancreatic Diseases of Zhejiang Province

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