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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Tao Zhang Yaowu Zhang Tingyou Zhou School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China School of Information Management and Engineering MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China School of Data Sciences Zhejiang University of Finance and Economics Hangzhou China

Measuring the nonlinear dependence between random vectors and testing for their statistical independence is a fundamental problem in statistics. One of the most popular dependence measures is the Hilbert-Schmidt independence criterion (HSIC), which has attracted increasing attention in recent years. However, most existing works have focused on either fixed or very high-dimensional covariates. In this work, we bridge the gap between these two scenarios and provide statistical insights into the performance of HSIC when the dimensions grow at different rates. We first show that, under the null hypothesis, the rescaled HSIC converges in distribution to a standard normal distribution. Then we provide a general condition for the HSIC based tests to have nontrivial power in high dimensions. By decomposing this condition, we illustrate how the ability of HSIC to measure nonlinear dependence changes with increasing dimensions. Moreover, we demonstrate that, depending on the sample size, the covariate dimensions and the dependence structures within covariates, the HSIC can capture different types of associations between random vectors. We also conduct extensive numerical studies to validate our theoretical results.

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Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images

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National Science Review 2023年第7期10卷 114-123页

作者： Binze Tang Yizhi Song Mian Qin Ye Tian Zhen Wei Wu Ying Jiang Duanyun Cao Limei Xu International Center for Quantum Materials Peking University School of Physics Peking University Institute of Nonequilibrium Systems School of Systems Science Beijing Normal University Collaborative Innovation Center of Quantum Matter CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials Peking University Beijing Key Laboratory of Environmental Science and Engineering School of Materials Science and Engineering Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center

Relevant to broad applied fields and natural processes,interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy(AFM).However,the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images *** machine learning,we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images,including the position of each atom and the orientations of water mole ***,it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water ***,this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images,but may also help to interpret other scientific studies involving sophisticated experimental results.

关键词： machine learning transfer learning atomic force microscopy atomic scale structure identification interfacial ion hydrates

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Competitive Auctions with Imperfect Predictions

arXiv

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arXiv 2023年

作者： Lu, Pinyan Wan, Zongqi Zhang, Jialin ITCS Shanghai University of Finance and Economics China Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Ministry of Education China SKLP Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

The competitive auction was first proposed by Goldberg, Hartline, and Wright. In their paper [GHW01], they introduce the competitive analysis framework of online algorithm designing into the traditional revenue-maximizing auction design problem. While the competitive analysis framework only cares about the worst-case bound, a growing body of work in the online algorithm community studies the learning-augmented framework. In this framework, designers are allowed to leverage imperfect machine-learned predictions of unknown information and pursue better theoretical guarantees when the prediction is accurate(consistency). Meanwhile, designers also need to maintain a nearly-optimal worst-case ratio(robustness). In this work, we revisit the competitive auctions in the learning-augmented setting. We leverage the imperfect predictions of the private value of the bidders and design the learning-augmented mechanisms for several competitive auctions with different constraints, including: digital good auctions, limited-supply auctions, and general downward-closed permutation environments. We also design the learning-augmented mechanism for online auctions. For all these auction environments, our mechanisms enjoy 1-consistency against the strongest benchmark OPT, which is impossible to achieve O(1)-competitive without predictions. At the same time, our mechanisms also maintain the O(1)-robustness against all benchmarks considered in the traditional competitive analysis, including F(2),maxV, and EFO(2). Considering the possible inaccuracy of the predictions, we provide a reduction that transforms our learning-augmented mechanisms into an error-tolerant version, which enables the learning-augmented mechanism to ensure satisfactory revenue in scenarios where the prediction error is moderate. We also prove a 3 robustness ratio lower bound for mechanisms with perfect consistency. The lower bound is strictly greater than the optimal 2.42 competitive ratio of digital good auctions, sho

关键词： Forecasting

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Green Goodwill and Effectiveness of Carbon Abatement Policies: Static and Dynamic Perspectives

SSRN

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SSRN 2024年

作者： Li, Xiaoliang Zhang, Ally Quan School of Business Guangzhou College of Technology and Business Guangzhou510850 China Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai200433 China Lancaster University Management School Lancaster University LancasterLA1 4YX United Kingdom

This study investigates the effectiveness of carbon abatement policies in a Cournot duopoly framework, incorporating static and dynamic analysis. We evaluate four policy interventions— enhancing consumer green awareness, promoting corporate environmental responsibility, reducing carbon abatement costs, and increasing carbon tax rates—and their effects on firms' environmental, economic, and social performance. A static model reveals equilibrium outcomes driven by consumer preferences for firms' green goodwill, while a dynamic extension, incorporating bounded rationality, explores market stability. Our findings underscore the importance of balancing green innovation incentives with mechanisms to maintain stability, offering insights for effective and sustainable policy design in competitive markets. © 2024, The Authors. All rights reserved.

关键词： Carbon capture and utilization

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A First Order Method for Linear Programming Parameterized by Circuit Imbalance

arXiv

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arXiv 2023年

作者： Cole, Richard Hertrich, Christoph Tao, Yixin Végh, László A. Courant Institute New York University United States Université Libre de Bruxelles Belgium ITCS Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China London School of Economics United Kingdom Corvinus Institute for Advanced Studies Corvinus University Budapest Hungary Goethe-Universität Frankfurt Germany

Various first order approaches have been proposed in the literature to solve Linear Programming (LP) problems, recently leading to practically efficient solvers for large-scale LPs. From a theoretical perspective, linear convergence rates have been established for first order LP algorithms, despite the fact that the underlying formulations are not strongly convex. However, the convergence rate typically depends on the Hoffman constant of a large matrix that contains the constraint matrix, as well as the right hand side, cost, and capacity vectors. We introduce a first order approach for LP optimization with a convergence rate depending polynomially on the circuit imbalance measure, which is a geometric parameter of the constraint matrix, and depending logarithmically on the right hand side, capacity, and cost vectors. This provides much stronger convergence guarantees. For example, if the constraint matrix is totally unimodular, we obtain polynomial-time algorithms, whereas the convergence guarantees for approaches based on primal-dual formulations may have arbitrarily slow convergence rates for this class. Our approach is based on a fast gradient method due to Necoara, Nesterov, and Glineur (Math. Prog. 2019);this algorithm is called repeatedly in a framework that gradually fixes variables to the boundary. This technique is based on a new approximate version of Tardos’s method, that was used to obtain a strongly polynomial algorithm for combinatorial LPs (Oper. Res. 1986). © 2023, CC BY.

关键词： Linear programming

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DL2G: Anatomical Landmark Detection with Deep Local Features and Geometric Global Constraint

DL2G: Anatomical Landmark Detection with Deep Local Features...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Rui Yang, Wanli Xiao, Kuntao Sun, Yi Sheng, Shurong Lv, Zhao Gao, Jiahong Auhui University Auhui Hefei China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation Auhui Hefei China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Peking University McGovern Institute for BrainResearch Beijing China

ISBN: (纸本)9798350386226

Anatomical landmark detection, a pivotal research area in medical image processing, holds immense value in surgical navigation, image registration, and related fields. Traditional machine learning methods struggle with generalization and robustness. Current supervised end-to-end approaches lacks in-terpretability in capturing global information, and GPU memory constraints restrict their application to extensive 3D medical image datasets. Moreover, existing methods overlook intrinsic geometric cues within images and point sets. Herein, we introduce a novel anatomical landmark detection framework that integrates deep learning's representation capabilities with global geometric information derived from images and point sets (DL2G). This approach facilitates anatomical landmark localization in a local-to-global fashion. Initially, we train a deep feature descriptor based on self-supervised contrastive learning, which is further used to screen candidate points for the target one by comparing local patch embeddings. Subsequently, geometric constraints constructed from labeled template point sets enable the selection of geometrically consistent matching point sets. Ultimately, the framework utilizes the global mutual information of images to perform landmark localization through iterative optimization. Adhering to the AFID data annotation protocol, we evaluated our method on two public MRI head datasets, OASIS and HCP, for detecting 32 anatomical landmarks. The results convincingly demonstrate the superiority of our method over current state-of-the-art approaches in terms of both accuracy and computational efficiency. © 2024 IEEE.

关键词： Contrastive Learning

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei230088 China Sanbo Brain Hospital Capital Medical University Beijing Key Laboratory of Epilepsy Department of Neurosurgery Beijing China Peking University McGovern Institute for Brain Research Beijing China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China

ISBN: (纸本)9798350386226

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor's spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. © 2024 IEEE.

关键词： Magnetoencephalography

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Phonon-assisted transport through suspended carbon nanotube quantum dots

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Physical Review B 2011年第15期84卷 155417-155417页

作者： Tie-Feng Fang Qing-feng Sun Hong-Gang Luo Center for Interdisciplinary Studies and Key Laboratory for Magnetism & Magnetic Materials of the Ministry of Education Lanzhou University Lanzhou 730000 China Institute of Physics Chinese Academy of Sciences Beijing 100080 China Beijing Computation Science Research Center Beijing 100080 China

Motivated by several recent experiments, we investigate phonon-assisted electronic transport through suspended carbon nanotube quantum dots including electron-electron, electron-phonon, and spin-orbit interactions. By effectively decoupling the electron and phonon thermodynamics, we present an explanation for the puzzling thermoinduced vibrational absorption sidebands, as well further predict that these absorption sidebands are obviously diminished in the partially filled Coulomb diamonds due to the destructive superposition of electron and hole states. The spin-orbit coupling is shown to split all phonon-assisted sidebands while the Kramers degeneracy remains. Interestingly, in the strong spin-orbit coupling regime, some split absorption sidebands are suppressed by the Coulomb blockade effect, while all split emission sidebands always survive.

关键词： QUANTUM dots PHONONS ELECTRON-phonon interactions THERMODYNAMICS COULOMB functions

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Power transformer fault diagnosis considering data imbalance and data set fusion

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High Voltage 2021年第3期6卷 543-554页

作者： Yang Zhang Hong Cai Chen Yaping Du Min Chen Jie Liang Jianhong Li Xiqing Fan Xin Yao Department of Building Service Engineering Hong Kong Polytechnic UniversityHong KongChina Academy for Advanced Interdisciplinary Studies and Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Research Institute State Grid Zhejiang Electric Power Co.LtdHangzhouChina Zhejiang Huayun Information Technology Co. LtdHangzhouChina State Grid Zhejiang Electric Power Co. LtdLishui Power Supply BureauLishuiChina Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina

Improving the accuracy of transformer dissolved gas analysis is always an important demand for power ***,the requirement for large numbers of fault samples becomes an obstacle to this *** article creatively uses a large number of health data,which is much easier to obtain by power companies,to improve diagnosis *** investigations from the view of both data set and methodology to deal with this problem are presented.A data set consists of 9595 health samples and 993 fault samples is used for *** characteristics of the data set and the influence of the health data on diagnostic accuracy are *** performance of many state‐of‐art algorithms that handle the imbalanced prob-lem is ***,an efficient fault diagnosis algorithm named self‐paced ensemble(SPE)is *** SPE,classification hardness is proposed to include the data characteristic in the *** method can guarantee the diversity of the data set and keep high *** to the experiment results,the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.

关键词： handle fault diagnosis

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AudioVisual video summarization

arXiv

引用

arXiv 2021年

作者： Zhao, Bin Gong, Maoguo Li, Xuelong The Academy of Advanced Interdisciplinary Research Xidian University Xi’an710071 China Northwestern Polytechnical University Xi’an710072 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi’an710071 China

Audio and vision are two main modalities in video data. Multimodal learning, especially for audiovisual learning, has drawn considerable attention recently, which can boost the performance of various computer vision tasks. However, in video summarization, existing approaches just exploit the visual information while neglect the audio information. In this paper, we argue that the audio modality can assist vision modality to better understand the video content and structure, and further benefit the summarization process. Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this. Specifically, the proposed AVRN can be separated into three parts: 1) the two-stream LSTM is utilized to encode the audio and visual feature sequentially by capturing their temporal dependency. 2) the audiovisual fusion LSTM is employed to fuse the two modalities by exploring the latent consistency between them. 3) the self-attention video encoder is adopted to capture the global dependency in the video. Finally, the fused audiovisual information, and the integrated temporal and global dependencies are jointly used to predict the video summary. Practically, the experimental results on the two benchmarks, i.e., SumMe and TVsum, have demonstrated the effectiveness of each part, and the superiority of AVRN compared to those approaches just exploiting visual information for video summarization. Copyright © 2021, The Authors. All rights reserved.

关键词： Video recording

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