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arXiv 2021年

作者： Yang, Zhiyong Xu, Qianqian Bao, Shilong Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen518100 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically;(ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(p1/N) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework.. © 2021, CC BY.

关键词： Machine learning

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Determination of relative significance factor of impact categories based on fuzzy multiattribute decision-making method

Determination of relative significance factor of impact cate...

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Chinese Control and Decision Conference, CCDC

作者： Xinyang Deng Yajuan Zhang Bingyi Kang Jiyi Wu Xiaohong Sun Yong Deng School of Computer and Information Sciences Southwest University Chongqing China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Hangzhou Zhejiang China College of Food Science and Technology Shanghai Ocean University Shanghai China Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Electronics & Information Technology Shanghai Jiaotong University Shanghai China Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang China

A relative significance factor of an impact category is the external weight of the impact category and plays an important role in life cycle assessment(LCA). Many multiattribute decision-making (MADM) methods such as analytical hierarchy process(AHP), the rank-order centroid method and the fuzzy method are proposed to determine the relative significance factor of an impact category. However, in previous research, it is shown that, the AHP approach is difficult for experts rendering consistent judgment when the number of pairwise comparisons increases, the rank-order method has some insurmountable shortcomings, the key issue to use fuzzy set theory is its complexity. In order to address this problem, a new simple and efficient method is proposed to determine relative significance factor in a more efficient manner. An example to determine relative significance factor of impact categories is shown to illustrate the efficiency of the proposed method.

关键词： Decision making Educational institutions Complexity theory Pragmatics Uncertainty Fuzzy set theory Fuzzy sets

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Trapping Centers for Carriers Leads to Persistent Photoconductive Effect: Polycrystalline Β-Ga2o3 Used on Neuromorphic Information Storage

SSRN

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SSRN 2024年

作者： Xi, Zhaoying Liu, Maosheng Li, Shan Zhang, Jia-Han Jiang, Mingming Zhang, Shaohui Tang, Weihua Liu, Zeng College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China School of Electronic Information Engineering Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Inner Mongolia University Hohhot010021 China College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China Institute of Biological and Medical Engineering Guangdong Academy of Sciences Guangzhou510316 China Nanjing University China Shenzhen University China Nanjing University of Posts and Telecommunications China

Neuromorphic visual sensors (NVS) address the bottleneck of traditional devices with synaptic behaviors that help solve the challenges of large power consumption and slow processing speeds. The carrier trapping layer plays an important role in the synaptic behavior of NVSs. In this work, we propose a polycrystalline β-Ga2O3 as both photosensitive and carrier trapping material to simplify the multilayer structure of NVSs. The persistent photoconductivity effect of polycrystalline β-Ga2O3 photosensor has been enhanced by utilizing grain boundaries. The trapping effect is thoroughly investigated by characterization and electrical tests. Further, it is demonstrated that the β-Ga2O3 device can mimic biological neuromorphic behavior with a variety of basic synaptic behaviors. Finally, the device is proved to be able to store optical images for more than 520 s and ASCII messages for more than 300 s. It broadens the road for the development of Ga2O3 in neuromorphic memory devices. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on Data Engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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An Improved Heuristic Method for Subgraph Isomorphism Problem

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IOP Conference Series: Materials Science and Engineering 2017年第1期231卷

作者： Yingzhuo Xiang Jiesi Han Haijiang Xu Xin Guo National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Jiangnan Institute of Computing Technology Wuxi China School of Mathematical Sciences of UESTC Chengdu China

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game

arXiv

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arXiv 2022年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Li, Guorong Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518055 China

Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has been paid to the security issue of such algorithms, in contrast to numerous research work on the computational and statistical characteristics. Driven by huge profit, the potential adversary has strong motivation and incentives to manipulate the ranking list. Meanwhile, the intrinsic vulnerability of the rank aggregation methods is not well studied in the literature. To fully understand the possible risks, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data in this paper. From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim. To perform the targeted attack, we formulate the interaction between the adversary and the victim as a game-theoretic framework consisting of two continuous operators while Nash equilibrium is established. Then two procedures against HodgeRank and RankCentrality are constructed to produce the modification of the original data. Furthermore, we prove that the victims will produce the target ranking list once the adversary masters the complete information. It is noteworthy that the proposed methods allow the adversary only to hold incomplete information or imperfect feedback and perform the purposeful attack. The effectiveness of the suggested target attack strategies is demonstrated by a series of toy simulations and several real-world data experiments. These experimental results show that the proposed methods could achieve the attacker’s goal in the sense that the leading candidate of the perturbed ranking list is the designated one by the adversary. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Compressed sensing based on best wavelet packet basis for image processing

引用

Journal of Computers (Finland) 2013年第8期8卷 1947-1950页

作者： Li, Zhe Tao Xie, Jing Xiong Yu, Zu Guo Tu, Deng Biao Choi, Young June The College of Information Engineering Xiangtan University Hunan 411105 China School of Computer National University of Defense Technology Hunan 410073 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China School of Mathematics and Computational Science Xiangtan University Hunan 411105 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Department of Information and Computer Engineering Ajou University Suwon 443749 Korea Republic of

In this paper, an algorithm named best wavelet packet tree decomposition (BWPTD) is proposed for image compression. In order to obtain better sparse representation of image, best wavelet packet basis is introduced to decompose image signal in the algorithm. Experimental results show that BWPTD is better than single layer wavelet decompression (SLWD) and original compressed sensing (OCS) in peak signal to noise ratio (PSNR) by 2db and 8db, respectively. In addition, the reconstruction time of BWPTD is only half as that of SLWD and OCS. © 2013 ACADEMY PUBLISHER.

关键词： Compressed sensing

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

arXiv

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arXiv 2025年

作者： Bonaldi, A. Hartley, P. Braun, R. Purser, S. Acharya, A. Ahn, K. Aparicio Resco, M. Bait, O. Bianco, M. Chakraborty, A. Chapman, E. Chatterjee, S. Chege, K. Chen, X. Chen, Z. Conaboy, L. Cruz, M. Darriba, L. De Santis, M. Denzel, P. Diao, K. Feron, J. Finlay, C. Gehlot, B. Ghosh, S. Giri, S.K. Grumitt, R. Hong, S.E. Ito, T. Jiang, M. Jordan, C. Kim, S. Kim, M. Kim, J. Krishna, S.P. Kulkarni, A. López-Caniego, M. Labadie-García, I. Lee, H. Lee, D. Lee, N. Line, J. Liu, Y. Mao, Y. Mazumder, A. Mertens, F.G. Munshi, S. Nasirudin, A. Ni, S. Nistane, V. Norregaard, C. Null, D. Offringa, A. Oh, M. Oh, S.-H. Parkinson, D. Pritchard, J. Ruiz-Granda, M. Salvador López, V. Shan, H. Sharma, R. Trott, C. Yoshiura, S. Zhang, L. Zhang, X. Zheng, Q. Zhu, Z. Zuo, S. Akahori, T. Alberto, P. Allys, E. An, T. Anstey, D. Baek, J. Basavraj Brackenhoff, S. Browne, P. Ceccotti, E. Chen, H. Chen, T. Choudhuri, S. Choudhury, M. Coles, J. Cook, J. Cornu, D. Cunnington, S. Das, S. De Lera Acedo, E. Delouis, J.-M. Deng, F. Ding, J. Elahi, K.M.A. Fernandez, P. Fernández, C. Alcázar, A. Fernández Galluzzi, V. Gao, L.-Y. Garain, U. Garrido, J. Gendron-Marsolais, M.-L. Gessey-Jones, T. Ghorbel, H. Gong, Y. Guo, S. Hasegawa, K. Hayashi, T. Herranz, D. Holanda, V. Holloway, A.J. Hothi, I. Höfer, C. Jelić, V. Jiang, Y. Jiang, X. Kang, H. Kim, J.-Y. Koopmans, L.V. Lacroix, R. Lee, E. Leeney, S. Levrier, F. Li, Y. Liu, Y. Ma, Q. Meriot, R. Mesinger, A. Mevius, M. Minoda, T. Miville-Deschenes, M.-A. Moldon, J. Mondal, R. Murmu, C. Murray, S. Nirmala, S.R. Niu, Q. Nunhokee, C. O’Hara, O. Pal, S.K. Pal, S. Park, J. Parra, M. Patra, N.N. Pindor, B. Remazeilles, M. Rey, P. Rubino-Martin, J.A. Saha, S. Selvaraj, A. Semelin, B. Shah, R. Shao, Y. Shaw, A.K. Shi, F. Shimabukuro, H. Singh, G. Sohn, B.W. Stagni, M. Starck, J.-L. Sui, C. Swinbank, J.D. Sánchez, J. SKA Observatory Jodrell Bank Lower Whitington MacclesfieldSK11 9FT United Kingdom Max-Planck Institute for Astrophysics Karl-Schwarzschild-Straße 1 Garching85748 Germany Department of Earth Sciences Chosun University Gwangju61452 Korea Republic of Universidad Europea de Madrid Madrid28670 Spain Observatoire de Sauverny Versoix1290 Switzerland Department of Physics Trottier Space Institute McGill University 3600 rue University MontréalQCH3A 2T8 Canada School of Physics and Astronomy The University of Nottingham University Park NottinghamNG7 2RD United Kingdom Department of Physics and Astronomy University of the Western Cape Cape Town Bellville7535 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Astronomy The University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Avda. de los Castros s/n SantanderE-39005 Spain Glorieta de la Astronomía s/n Granada18008 Spain Saint-Imier Switzerland Centre for Artificial Intelligence ZHAW Zurich University of Applied Sciences Technikumstrasse 71 Winterthur8400 Switzerland Department of Astronomy Tsinghua University Beijing100084 China Département de Physique Théorique Center for Astroparticle Physics Université de Geneve 24 quai Ernest Ansermet Geneve 41211 Switzerland Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfv´ens väg 12 StockholmSE-106 91 Sweden Korea Astronomy and Space Science Institute Daejeon34055 Korea Republic of Astronomy Campus University of Science and Technology Daejeon34055 Korea Republic of Kumamoto University International Research Organization for Advanced Science and Technology 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan National Key Laboratory of Radar Signal Processing

We present and analyse the results of the Science data challenge 3a (SDC3a, https://***/challenges/ foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is 4.2+−204.2 × 10−4 K2h−3cMpc3 across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation. © 2025, CC BY.

关键词： Cosmology

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