检索结果-内蒙古大学图书馆

Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Power of Large Language Model for Denoising Recommendation 25

Unleashing the Power of Large Language Model for Denoising R...

引用

34th ACM Web Conference, WWW 2025

作者： Wang, Shuyao Zheng, Zhi Sui, Yongduo Xiong, Hui School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Tencent Shenzhen China Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400712746

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD’s effectiveness in enhancing denoising and recommendation accuracy. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

On the complexity of finite-sum smooth optimization under the Polyak—Łojasiewicz condition 24

On the complexity of finite-sum smooth optimization under th...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Yunyan Bai Yuxing Liu Luo Luo School of Data Science Fudan University Shanghai China School of Data Science Fudan University Shanghai China and Shanghai Key Laboratory for Contemporary Applied Mathematics Shanghai China

This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where f(·) satisfies the Polyak-Łojasiewicz (PL) condition with parameter µ and $\{f_i(\cdot)\}_{i=1}^n$ is L-mean-squared smooth. We show that any gradient method requires at least Ω(n + κ√n log(1/ε)) incremental first-order oracle (IFO) calls to find an ε-suboptimal solution, where κ ≙ L/µ is the condition number of the problem. This result nearly matches the upper bounds of IFO complexity for best-known first-order methods. We also study the problem of minimizing the PL function in the distributed setting such that the individuals f1(·), ..., fn(·) are located on a connected network of n agents. We provide lower bounds of Ω(κ/√γ log(1/ε)), Ω((κ + τκ/√γ ) log(1/ε)) and Ω(n + κ√n log(1/ε)) for communication rounds, time cost and local first-order oracle calls respectively, where γ ∈ (0, 1] is the spectral gap of the mixing matrix associated with the network and τ > 0 is the time cost of per communication round. Furthermore, we propose a decentralized first-order method that nearly matches above lower bounds in expectation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Measurement of the neutron total cross section of^(9)Be at the Back-n white neutron source of CSNS

引用

Chinese Physics C 2024年第8期48卷 140-152页

作者： Jiangbo Bai Jingyu Tang Liqun Shi Yonghao Chen Han Yi Jie Bao Yu Bao Ping Cao Haolei Chen Zhen Chen Zengqi Cui Ruirui Fan Changqing Feng Keqing Gao Xiaolong Gao MinHao Gu Changcai Han Guozhu He Yongcheng He Yang Hong Yiwei Hu Hanxiong Huang Xiru Huang Haoyu Jiang Wei Jiang Zhijie Jiang Hantao Jing Ling Kang Bo Li Chao Li Jiawen Li Qiang Li Xiao Li Yang Li Jie Liu Shubin Liu Ze Long Guangyuan Luan Changjun Ning Mengchen Niu Binbin Qi Jie Ren Xichao Ruan Zhaohui Song Kang Sun Zhijia Sun Zhixin Tan Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Zhaohui Wang Xiaoguang Wu Xuan Wu Likun Xie Xiaoyun Yang Li Yu Tao Yu Yongji Yu Guohui Zhang Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Luping Zhou Zhihao Zhou Kejun Zhu Institute of Modern Physics Fudan UniversityShanghai 200433China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China Sate Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China

The neutron total cross section data of^(9)Be are essential in the nuclear structure model research of light nuclei and nuclear power *** neutron total cross section of^(9)Be in the 0.3 eV−120 MeV energy region has been measured using time-of-flight and transmission methods with the Neutron Total Cross Sectional Spectrometer(NTOX)based on the multi-cell fast fission chamber at the China Spallation Neutron Source(CSNS)-Back-n white neutron source(Back-n).The fission count-neutron energy distributions of ^(235)U and ^(238)U without samples and with Be samples with three thicknesses were measured in the double-bunch operation mode for a beam power of 100 *** Bayesian method was used to eliminate the influence of the double-bunch problem on neutron measurement in the energy region above 10 *** neutron total cross section of^(9)Be results was consistent with ENDF/B-VIII.0 evaluation library data in the 0.3 eV−20 MeV energy *** the energy ranges of 0.3 eV to 10 keV and 0.01 to 20 MeV,the deviations between our results and the evaluation results of ENDF/B-VIII.0 were within 2.5%and 15%,*** the resonance energy region,the measured resonance energies in our experiment were 0.63,0.82,and 2.8 MeV,*** results showed that the total cross section uncertainties of three Be samples were within 2.2%in the energy region below 1 *** total cross section uncertainty of 30 mm Be from ^(235)U was the smallest and less than 5%in the energy region of 0.3 eV−120 *** results of this experiment can provide technical support for further data analysis and related nuclear data evaluation.

关键词： ^(9)Be neutron total cross section CSNS Back-n time-of-flight method transmission method multi-cell fast fission chamber

来源：评论

学校读者我要写书评

暂无评论

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

引用

computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

来源：评论

学校读者我要写书评

暂无评论

Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs 26

Path Planning based on Reinforcement Learning with Improved ...

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ding, Qun Xu, Xiaolong Gui, Wenming Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China

ISBN: (纸本)9798350331684

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

ContiFormer: continuous-time transformer for irregular time series modeling 23

ContiFormer: continuous-time transformer for irregular time ...

引用

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

作者： Yuqi Chen Kan Ren Yansen Wang Yuchen Fang Weiwei Sun Dongsheng Li School of Computer Science & Shanghai Key Laboratory of Data Science Fudan University and Microsoft Research Asia Microsoft Research Asia Microsoft Research Asia and Shanghai Jiao Tong University School of Computer Science & Shanghai Key Laboratory of Data Science Fudan University

Modeling continuous-time dynamics on irregular time series is critical to account for data evolution and correlations that occur continuously. Traditional methods including recurrent neural networks or Transformer models leverage inductive bias via powerful neural architectures to capture complex patterns. However, due to their discrete characteristic, they have limitations in generalizing to continuous-time data paradigms. Though neural ordinary differential equations (Neural ODEs) and their variants have shown promising results in dealing with irregular time series, they often fail to capture the intricate correlations within these sequences. It is challenging yet demanding to concurrently model the relationship between input data points and capture the dynamic changes of the continuous-time system. To tackle this problem, we propose ContiFormer that extends the relation modeling of vanilla Transformer to the continuous-time domain, which explicitly incorporates the modeling abilities of continuous dynamics of Neural ODEs with the attention mechanism of Transformers. We mathematically characterize the expressive power of ContiFormer and illustrate that, by curated designs of function hypothesis, many Transformer variants specialized in irregular time series modeling can be covered as a special case of ContiFormer. A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data. The project link is https://***/contiformer/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biomolecular condensates and disease pathogenesis

引用

science China(Life sciences) 2024年第9期67卷 1792-1832页

作者： Ke Ruan Ge Bai Yanshan Fang Dan Li Tingting Li Xingguo Liu Boxun Lu Qing Lu Zhou Songyang Shuguo Sun Zheng Wang Xin Zhang Wen Zhou Hong Zhang The First Affiliated Hospital&School of Life Sciences Ministry of Education Key Laboratory for Membrane-less Organelles&Cellular DynamicsHefei National Research Center for Interdisciplinary Sciences at the MicroscaleCenter for Advanced Interdisciplinary Science and Biomedicine of IHMDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230027China Nanhu Brain-computer Interface Institute Hangzhou 311100China Department of Neurology of Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou 310058China Interdisciplinary Research Center on Biology and Chemistry Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghai 201210China University of Chinese Academy of Sciences Beijing 100049China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)Shanghai Jiao Tong UniversityShanghai 200030China Department of Biomedical Informatics School of Basic Medical SciencesPeking University Health Science CenterBeijing 100191China CAS Key Laboratory of Regenerative Biology Joint School of Life SciencesGuangzhou Institutes of Biomedicine and HealthChinese Academy of SciencesGuangzhou 510530China Joint School of Life Sciences Guangzhou Medical UniversityGuangzhou 510000China Neurology Department at Huashan Hospital State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceSchool of Life SciencesFudan UniversityShanghai 200433 China Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders Ministry of EducationBio-X InstitutesShanghai Jiao Tong UniversityShanghai 200030China State Key Laboratory of Biocontrol MOE Key Laboratory of Gene Function and Regulation and Guangzhou Key Laboratory of Healthy Aging ResearchSchool of Life SciencesSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510275China Department of Human Anatomy Hist

Biomolecular condensates or membraneless organelles(MLOs)formed by liquid-liquid phase separation(LLPS)divide intracellular spaces into discrete compartments for specific *** of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration,tumorigenesis,and many other pathological ***,we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase *** then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders,hearing loss,cancers,and immunological ***,we describe the emerging discovery of chemical modulators of phase separation.

关键词： liquid-liquid phase separation biomolecular condensate membraneless organelle

来源：评论

学校读者我要写书评

暂无评论

Kernel-Whitening: Overcome dataset Bias with Isotropic Sentence Embedding

Kernel-Whitening: Overcome Dataset Bias with Isotropic Sente...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Gao, Songyang Dou, Shihan Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models. The current mainstream solution is designing an additional shallow model to pre-identify biased instances. However, such two-stage methods scale up the computational complexity of training process and obstruct valid feature information while mitigating bias. To address this issue, we utilize the representation normalization method which aims at disentangling the correlations between features of encoded sentences. We find it also promising in eliminating the bias problem by providing isotropic data distribution. We further propose Kernel-Whitening, a Nyström kernel approximation method to achieve more thorough debiasing on nonlinear spurious correlations. Our framework is end-to-end with similar time consumption to fine-tuning. Experiments show that KernelWhitening significantly improves the performance of BERT on out-of-distribution datasets while maintaining in-distribution accuracy. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Online Multi-label Streaming Feature Selection Based on Neighborhood Approximation Error Rate and Label Correlation 11th

Online Multi-label Streaming Feature Selection Based on Nei...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Pan, Siping Zhong, Juda Mao, Yu Lin, Yaojin School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou363000 China

ISBN: (纸本)9783031927430

Multi-label learning has garnered significant attention and application in the domains of machine learning, data mining, and pattern recognition. However, existing multi-label learning algorithms often fall short in adequately capturing the complex dynamic correlations among labels. To address this limitation, this paper proposes an online multi-label stream feature selection algorithm based on neigh-borhood approximation error rate and label correlation. Initially, the similarity between labels is computed, and correlated labels are partitioned into multiple subsets. The strongly correlated label sets thus obtained serve as the label space for the subsequent algorithm. Subsequently, instance boundaries are introduced to granulate all instances. Within the multi-label neighborhood approximation system, neighborhoods are determined based on the average intervals of samples, and a method for calculating feature importance is provided, offering a quantitative basis for feature selection. Finally, a dynamic stream feature selection framework is designed to iteratively select a subset of features that minimize the approximation error, thereby enhancing the efficiency and accuracy of feature selection. Experimental results demonstrate that the proposed algorithm consistently outperforms five other multi-label feature selection algorithms across all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：