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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

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arXiv 2021年

作者： Wang, Qizhou Han, Bo Liu, Tongliang Niu, Gang Yang, Jian Gong, Chen Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering The University of Sydney Australia Japan Department of Computing Hong Kong Polytechnic University Hong Kong

The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs). Existing learning methods with label noise either employ ad-hoc heuristics or restrict to specific noise assumptions. However, more general situations, such as instance-dependent label noise, have not been fully explored, as scarce studies focus on their label corruption process. By categorizing instances into confusing and unconfusing instances, this paper proposes a simple yet universal probabilistic model, which explicitly relates noisy labels to their instances. The resultant model can be realized by DNNs, where the training procedure is accomplished by employing an alternating optimization algorithm. Experiments on datasets with both synthetic and real-world label noise verify that the proposed method yields significant improvements on robustness over state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Universal conservation laws of the wave-particleentanglement triad:theory and experiment

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Light(Science & Applications) 2025年第3期14卷 861-868页

作者： Ziheng Ding Yaohao Deng Shao-Ming Fei Si-Qi Zhou Xiaojiong Chen Ziwen Rui Zhihao Ma Yunlong Xiao Jianwei Wang School of Mathematical Sciences MOE-LSCShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory for Mesoscopic Physics School of PhysicsPeking UniversityBejing 100871China Bejing Academy of Quantum Information Sciences Beijing 100871China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China Max Planck Institute for Mathematics in the Sciences Leipzig 04103Germany Department of Physics and Astronomy Stony Brook UniversityNew YorkNY11794-3800USA Shanghai Seres Information Technology Co. LtdShanghai 200040China shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China A*STAR Quantum Innovation Centre(Q.InC) Agency for ScienceTechnology and Research(A*STAR)2Fusionopolis WayInnovis\#08-03Singapore138634Republic of Singapore institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WayConnexis\#16-16Singapore 138632Republic of Singapore

When observed,a quantum system exhibits either wave-like or particle-like properties,depending on how it is ***,this duality is affected by the entanglement of the system with its quantum memory,raising a fundamental question:how are wave-particle duality and entanglement related?Here,we broaden the scope of wave-particle duality to include entanglement,introduce universal conservation laws for the wave-particle-entanglement triad,and perform demonstrations on silicon-integrated nanophotonic quantum *** experiments not only mark the first confirmation of universal conservation laws but also highlight the potential of integrated photonics for exploring complex quantum phenomena in high-dimensional systems.

关键词： high dimensional systems universal conservation laws entanglement nanophotonic quantum chips quantum systems wave particle duality quantum system

Phase transition analysis for covariance based massive random access with massive MIMO

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arXiv 2020年

作者： Chen, Zhilin Sohrabi, Foad Liu, Ya-Feng Yu, Wei The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper considers a massive random access problem in which a large number of sporadically active devices wish to communicate with a base station (BS) equipped with massive multiple-input multiple-output (MIMO) antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem for which the sample covariance matrix of the received signal is a sufficient statistic. The goal of this paper is to characterize the feasible set of problem parameters under which this covariance based approach is able to successfully recover the device activities in the massive MIMO regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing probability of detection error as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching, which relates the phase transition analysis to a recently derived scaling law. Further, we provide a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Heterodimensional superlattice with in-plane anomalous Hall effect (vol 609, pg 46, 2021)

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NATURE 2022年第7934期611卷 E1-E1页

作者： Zhou, Jiadong Zhang, Wenjie Lin, Yung-Chang Cao, Jin Zhou, Yao Jiang, Wei Du, Huifang Tang, Bijun Shi, Jia Jiang, Bingyan Cao, Xun Lin, Bo Fu, Qundong Zhu, Chao Guo, Wei Huang, Yizhong Yao, Yuan Parkin, Stuart S. P. Zhou, Jianhui Gao, Yanfeng Wang, Yeliang Hou, Yanglong Yao, Yugui Suenaga, Kazu Wu, Xiaosong Liu, Zheng Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing China State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Peking University Beijing China Max Planck Institute of Microstructure Physics Halle Germany Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen China The Institute of Scientific and Industrial Research Osaka University Osaka Japan School of Materials Science and Engineering Nanyang Technological University Singapore Singapore Advanced Research Institute of Multidisciplinary Science and School of Chemistry and Chemical Engineering Beijing Institute of Technology Beijing China CINTRA CNRS/NTU/THALES UMI 3288 Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Chemistry National University of Singapore Singapore Singapore Institute of Physics Chinese Academy of Sciences Beijing China Anhui Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory HFIPS Anhui Chinese Academy of Sciences Hefei China School of Materials Science and Engineering Shanghai University Shanghai China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory for Magnetoelectric Materials and Devices Beijing Innovation Center for Engineering Science and Advanced Technology School of Materials Science and Engineering Peking University Beijing China

Optimal rates of convergence and error localization of Gegenbauer projections

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arXiv 2020年

作者： Wang, Haiyong School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

Motivated by comparing the convergence behavior of Gegenbauer projections and best approximations, we study the optimal rate of convergence for Gegenbauer projections in the maximum norm. We show that the rate of convergence of Gegenbauer projections is the same as that of best approximations under conditions of the underlying function is either analytic on and within an ellipse and λ ≤ 0 or differentiable and λ ≤ 1, where λ is the parameter in Gegenbauer projections. If the underlying function is analytic and λ > 0 or differentiable and λ > 1, then the rate of convergence of Gegenbauer projections is slower than that of best approximations by factors of nλ and nλ−1, respectively. An exceptional case is functions with endpoint singularities, for which Gegenbauer projections and best approximations converge at the same rate for all λ > −1/2. For functions with interior or endpoint singularities, we provide a theoretical explanation for the error localization phenomenon of Gegenbauer projections and for why the accuracy of Gegenbauer projections is better than that of best approximations except in small neighborhoods of the critical points. Our analysis provides fundamentally new insight into the power of Gegenbauer approximations and related spectral *** Codes 41A10, 41A25 Copyright © 2020, The Authors. All rights reserved.

关键词： Functions

How much faster does the best polynomial approximation converge than Legendre projection?

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arXiv 2020年

We compare the convergence behavior of best polynomial approximations and Legendre and Chebyshev projections and derive optimal rates of convergence of Legendre projections for analytic and differentiable functions in the maximum norm. For analytic functions, we show that the best polynomial approximation of degree n is better than the Legendre projection of the same degree by a factor of n1/2. For differentiable functions such as piecewise analytic functions and functions of fractional smoothness, however, we show that the best approximation is better than the Legendre projection by only some constant factors. Our results provide some new insights into the approximation power of Legendre projections. Copyright © 2020, The Authors. All rights reserved.

关键词： Functions

A multi-cubic-kilometre neutrino telescope in the western Pacific Ocean

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arXiv 2022年

作者： Ye, Z.P. Hu, F. Tian, W. Chang, Q.C. Chang, Y.L. Cheng, Z.S. Gao, J. Ge, T. Gong, G.H. Guo, J. Guo, X.X. He, X.G. Huang, J.T. Jiang, K. Jiang, P.K. Jing, Y.P. Li, H.L. Li, J.L. Li, L. Li, W.L. Li, Z. Liao, N.Y. Lin, Q. Lin, J. Liu, F. Liu, J.L. Liu, X.H. Miao, P. Mo, C. Morton-Blake, I. Peng, T. Sun, Z.Y. Tang, J.N. Tang, Z.B. Tao, C.H. Tian, X.L. Wang, M.X. Wang, Y. Wang, Y. Wei, H.D. Wei, Z.Y. Wu, W.H. Xian, S.S. Xiang, D. Xu, D.L. Xue, Q. Yang, J.H. Yang, J.M. Yu, W.B. Zeng, C. Zhang, F.Y.D. Zhang, T. Zhang, X.T. Zhang, Y.Y. Zhi, W. Zhong, Y.S. Zhou, M. Zhu, X.H. Zhuang, G.J. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Laboratory for Particle Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Department of Astronomy School of Physics Peking University Being 100871 China State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Sanya Yazhou Bay Institute of Deep Sea Technology Sanya572024 China Center for High Energy Physics Peking University Beijing100871 China Department of Engineering Physics Tsinghua University Beijing100084 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Shanghai Key Lab of Electrical Insulation and Thermal Aging School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography MNR Hangzhou310012 China Kavli Institute for Astronomy and Astrophysics Peking University Bejing100871 China Center for High Performance Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China School of Oceanography Shanghai Jiao Tong University Shanghai200030 China Key Laboratory of Submarine Geosciences Second Institute of Oceanography MNR Hangzhou310012 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China MOE Key Laboratory for Laser Plasmas School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Zhangjiang Institute for Advanced Study

Next-generation neutrino telescopes with significantly improved sensitivity are required to pinpoint the sources of the diffuse astrophysical neutrino flux detected by IceCube and uncover the century-old puzzle of cosmic ray origins. A detector near the equator will provide a unique viewpoint of the neutrino sky, complementing IceCube and other neutrino telescopes in the Northern Hemisphere. Here we present results from an expedition to the north-eastern region of the South China Sea, in the western Pacific Ocean. A favorable neutrino telescope site was found on an abyssal plain at a depth of ∼ 3.5 km. At depths below 3 km, the sea current speed, water absorption and scattering lengths for Cherenkov light, were measured to be vc © 2022, CC BY-NC-ND.

关键词： Cosmic rays

一种含Tr?ger碱单元的AIE分子的独特力致荧光增强行为（英文）

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Science China Materials 2023年第5期 1959-1967页

作者：倪珏宸臧启光张浩可孙景志唐本忠 MOE Key Laboratory of Macromolecules Synthesis and Functionalization Department of Polymer Science and EngineeringZhejiang University Center of Healthcare Materials Shaoxing InstituteZhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Shenzhen Institute of Aggregate Science and Technology School of Science and EngineeringThe Chinese University of Hong Kong

本文报道了一种性能独特的聚集诱导发光分子DTPE-TB,它由位于中间的一个Tr?ger碱和两端的两个四苯基乙烯(TPE)基团构成.当DTPE-TB分子固体受到机械力作用时,Tr?ger碱的A-型结构通过改变二面角发生形变承载外力,使分子内运动受限加重,固...

本文报道了一种性能独特的聚集诱导发光分子DTPE-TB,它由位于中间的一个Tr?ger碱和两端的两个四苯基乙烯(TPE)基团构成.当DTPE-TB分子固体受到机械力作用时,Tr?ger碱的A-型结构通过改变二面角发生形变承载外力,使分子内运动受限加重,固体荧光增强.而TPE基团受力很小,其排列方式基本不变,所以发光波长仅有微小移动.我们将这种特殊的力致发光变色效应命名为力致荧光增强.

关键词：

Evaluating and Advancing Large Language Models for Nanofiltration Membrane Knowledge Tasks

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Advanced Membranes

Advanced Membranes 2025年

作者： Xinchen Xiang Zheng Cao Yukun Qian Dan Lu Jiancong Lu Jinyan Wang Shiyu Zhou Lijun Liang Zhikan Yao Lin Zhang MOE Engineering Research Center of Membrane and Water Treatment Technology College of Chemical and Biological Engineering Zhejiang University Hangzhou 310027 China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing 210044 China Future Environment Laboratory Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China Zhejiang Baima Lake Laboratory Co. Ltd Hangzhou 310053 China College of Automation Hangzhou Dianzi University Hangzhou 310018 China

Nanofiltration (NF) is a rapidly growing field, resulting in a surge of publications with diverse focuses. It’s challenging for researchers to quickly find key information from the vast amount of publications. Large language models (LLMs) have shown promise in analyzing article and reasoning about knowledge in some scientific fields, but their effectiveness in membrane research is unclear. Here, we introduced the first benchmark specifically designed for membrane studies and used it to systematically evaluate six general-purpose LLMs (i.e., Claude-3.5, Deepseek-R1, Gemini-2.0, GPT-4o-mini, Llama-3.2, and Mistral-small-3.1). Our findings revealed that the complexity and depth of NF knowledge pose a significant challenge for these LLMs, leading to poor performance, particularly in tasks involving membrane mechanisms. To enhance LLMs’ using in this field, we developed a specialized NF database and integrated it with the LLMs using Retrieval-Augmented Generation (RAG). RAG significantly improved performance across all models, with average gains of 18.5% on Question type tasks and 10.8% on Reasoning type tasks. Moreover, in areas such as membrane fabrication and characterization, several models with RAG demonstrated performance exceeding that of human experts. These results suggested that RAG is a promising strategy for leveraging LLMs in NF research. This study introduced a new path for applying LLMs to membrane research and proposes a professional benchmark to ensure the reliable and effective use of LLMs.

关键词： Nanofiltration Large language model Benchmark Retrieval-Augmented Generation