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arXiv 2022年

作者： Zhang, Nick Gupta, Abhishek Chen, Zefeng Ong, Yew-Soon The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore 138634 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Artificial Intelligence Sun Yatsen University China The School of Computer Science and Engineering NTU Singapore The Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering NTU Singapore The A*Star Center for Frontier AI Research Singapore

Studies have shown evolution strategies (ES) to be a promising approach for reinforcement learning (RL) with deep neural networks. However, the issue of high sample complexity persists in applications of ES to deep RL over long horizons. This paper is the first to address the shortcoming of today's methods via a novel neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer information from a set of auxiliary tasks (of short episode length) to the target (full length) RL task at hand. The auxiliary tasks, extracted from the target, allow an agent to update and quickly evaluate policies on shorter time horizons. The evolved skills are then transferred to guide the longer and harder task towards an optimal policy. We demonstrate that the NuEMT algorithm achieves data-efficient evolutionary RL, reducing expensive agent-environment interaction data requirements. Our key algorithmic contribution in this setting is to introduce, for the first time, a multitask skills transfer mechanism based on the statistical importance sampling technique. In addition, an adaptive resource allocation strategy is utilized to assign computational resources to auxiliary tasks based on their gleaned usefulness. Experiments on a range of continuous control tasks from the Openai Gym confirm that our proposed algorithm is efficient compared to recent ES baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-source perception data fusion of vessels in visual occlusion scenarios: Leveraging prior knowledge of vessel motion

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： He, Wei He, Wenbo Lei, Jinyu Wan, Sitong Wang, Zhiyuan Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350118 China School of Computer and Data Science Minjiang University Fuzhou350108 China

Automatic Identification System (aiS) and cameras are widely used in harbor and coastal supervision to detect ship movement. Integrating both can enhance the perception and monitoring of the navigation status of surrounding vessels. However, their integration faces several challenges. First, the two data sources have different coordinate systems and sampling frequencies. Additionally, in video data, visual occlusion during vessel encounters may lead to the loss or displacement of ship detection targets, significantly affecting the fusion of ship data. To address these issues, we incorporate prior knowledge of vessel motion to strengthen the model's ability to identify and track occluded targets. Firstly, when detecting and tracking ships in video, we use occlusion prior knowledge and tracking results to evaluate and manage the occluded detection box areas. Secondly, when the occluded detection box disappears or undergoes severe deformation, we use the prior knowledge of ship motion characteristics from aiS and images to predict the detection box. Finally, we validated our improved method on the FVessel_v1.0 dataset, confirming its accuracy in data fusion under occlusion conditions. Compared with the state-of-the-art ship data fusion algorithm, our method improved the Multiple Object Fusion Accuracy (MOFA), Identification Precision (IDP), Identification Recall (IDR), and Identification F1 score (IDPF1) metrics by 3.01%, 1.33%, 1.57%, and 1.33%, and reduced MOFA by 1.85%. Additionally, by only changing the method of utilizing prior knowledge without altering the detection and tracking algorithms of the state-of-the-art ship data fusion algorithm, we improved the MOFA, IDP, IDR, and IDPF1 metrics by 2.65%, 1.94%, 0.62%, and 0.87%, respectively, and reduced MOFA by 1.16%. © 2025 Elsevier Ltd

关键词： Image fusion

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection

Machine Learning is All You Need: A Simple Token-Based Appro...

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International Conference on Software engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Causal effect of serum 25 hydroxyvitamin D concentration on cardioembolic stroke: Evidence from two-sample Mendelian randomization

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Nutrition, Metabolism and Cardiovascular Diseases 2024年第5期34卷 1305-1313页

作者： Habibi, Danial Teymoori, Farshad Ebrahimi, Navid Fateh, Sahand Tehrani Najd-Hassan-Bonab, Leila Saeidian, Amir Hossein Soleymani Taloubaghi, Alireza Asgarian, Sara Hosseinpanah, Farhad Hakonarson, Hakon Azizi, Fereidoun Hedayati, Mehdi Daneshpour, Maryam Sadat Akbarzadeh, Mahdi Mansourian, Marjan Department of Biostatistics and Epidemiology School of Health Student Research Committee Isfahan University of Medical Sciences Isfahan Iran Nutrition and Endocrine Research Center Research Institute for Endocrine Sciences Shahid Beheshti Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran School of Medicine Tehran University of Medical Sciences Tehran Iran Cellular and Molecular Endocrine Research Center Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Center for Applied Genomics The Children's Hospital of Philadelphia Abramson Research Building Suite 1016I 3615 Civic Center Boulevard Philadelphia 19104-4318 PA United States Data Science and AI Applications Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu Taiwan Obesity Research Center Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Department of Pediatrics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Division of Human Genetics The Children's Hospital of Philadelphia Philadelphia PA United States Endocrine Research Center Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Epidemiology and Biostatistics Department School of Health Child Growth and Development Research Center Research Institute for Primordial Prevention of Non-Communicable Disease Isfahan University of Medical Sciences Isfahan Iran Department of Nutrition School of Public Health Iran University of Medical Sciences Tehran Iran Department of Biostatistics and Epidemiology School of Health Isfahan University of Medical Sciences Isfahan Iran

Background and aims: The putative association between serum 25-hydroxyvitamin D concentration [25(OH)D] and the risk of cardioembolic stroke (CES) has been examined in observational studies, which indicate controversial findings. We performed Mendelian randomization (MR) analysis to determine the causal relationship of serum 25(OH)D with the risk of CES. Methods and results: The summary statistics dataset on the genetic variants related to 25(OH)D was used from the published GWAS of European descent participants in the UK Biobank, including 417,580 subjects, yielding 143 independent loci in 112 1-Mb regions. GWAS summary data of CES was obtained from GIGASTROKE Consortium, which included European individuals (10,804 cases, 1,234,808 controls). Our results unveiled a causal relationship between 25(OH)D and CES using IVW [OR = 0.82, 95% CI: 0.67–0.98, p = 0.037]. Horizontal pleiotropy was not seen [MR-Egger intercept = 0.001;p = 0.792], suggesting an absence of horizontal pleiotropy. Cochrane's Q [Q = 78.71, p-value = 0.924], Rucker's Q [Q = 78.64, p-value = 0.913], and I² = 0.0% (95% CI: 0.0%, 24.6%) statistic suggested no heterogeneity. This result remained consistent using different MR methods and sensitivity analyses, including Maximum likelihood [OR = 0.82, 95%CI: 0.67–0.98, p-value = 0.036], Constrained maximum likelihood [OR = 0.76, 95%CI: 0.64–0.90, p-value = 0.002], Debiased inverse-variance weighted [OR = 0.82, 95%CI: 0.68–0.99, p-value = 0.002], MR-PRESSO [OR = 0.82, 95%CI 0.77–0.87, p-value = 0.022], RAPS [OR = 0.82, 95%CI 0.67–0.98, p-value = 0.038], MR-Lasso [OR = 0.82, 95%CI 0.68–0.99, p-value = 0.037]. Conclusion: Our MR analysis provides suggestive evidence that increased 25(OH)D levels may play a protective role in the development of cardioembolic stroke. Determining the role of 25(OH)D in stroke subtypes has important clinical and public health implications. © 2024 The Italian Diabetes Society, the Italian Society for the Study of Atheros

关键词： Cardioembolic stroke Genome-wide association study Mendelian randomization Serum 25(OH)D concentration

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OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain

arXiv

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arXiv 2025年

作者： Yue, Wenzhen Liu, Yong Li, Haoxuan Wang, Hao Ying, Xianghua Guo, Ruohao Xing, Bowei Shi, Ji State Key Laboratory of General AI Peking University Beijing100871 China Center for Data Science Peking University Beijing100871 China School of Software BNRist Tsinghua University Beijing100084 China Department of Control Science and Engineering Zhejiang University Hangzhou310058 China

This paper presents OLinear, a linear-based multivariate time series forecasting model that operates in an orthogonally transformed domain. Recent forecasting models typically adopt the temporal forecast (TF) paradigm, which directly encode and decode time series in the time domain. However, the entangled stepwise dependencies in series data can hinder the performance of TF. To address this, some forecasters conduct encoding and decoding in the transformed domain using fixed, dataset-independent bases (e.g., sine and cosine signals in the Fourier transform). In contrast, we utilize OrthoTrans, a data-adaptive transformation based on an orthogonal matrix that diagonalizes the series’ temporal Pearson correlation matrix. This approach enables more effective encoding and decoding in the decorrelated feature domain and can serve as a plug-in module to enhance existing forecasters. To enhance the representation learning for multivariate time series, we introduce a customized linear layer, NormLin, which employs a normalized weight matrix to capture multivariate dependencies. Empirically, the NormLin module shows a surprising performance advantage over multi-head self-attention, while requiring nearly half the FLOPs. Extensive experiments on 24 benchmarks and 140 forecasting tasks demonstrate that OLinear consistently achieves state-of-the-art performance with high efficiency. Notably, as a plug-in replacement for self-attention, the NormLin module consistently enhances Transformer-based forecasters. The code and datasets are available at https://***/r/OLinear. © 2025, CC BY.

关键词： Fourier series

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Canonical Shape Reconstruction with SE(3) Equivariance Learning for Weakly-Supervised Object Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhou, Jun Chen, Kai Wei, Mingqiang Zhang, Xiao-Ping Dou, Qi Qin, Jing Hong Kong Polytechnic University Center of Smart Health School of Nursing Hong Kong Hong Kong Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China Tsinghua University Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Shenzhen China

6D object pose estimation from a single RGB-D image is a fundamental problem in computer vision and robot manipulation. Despite recent advancements, existing methods still suffer several limitations. First of all, the object shape representation extracted from the depth map is often less expressive because the object point cloud parsed from the depth map is highly incomplete due to the object self-occlusion and noisy due to the sensor artifacts. This shape representation issue further intensifies when lacking sufficient labeled data for model training, which unfortunately is another typical problem for object pose estimation considering the heavy annotation cost for real-world pose labeling. In this study, we propose to tackle the above issues in a unified way. First, we enhance the object shape representation from the partial point cloud with a novel canonical shape reconstruction module, in which an implicit canonical frame is established by incorporating the SE(3) equivariance, achieving implicit feature alignment of the partial point cloud inputs, leading to robust shape recovery. Second, based on the enhanced object representation, we further utilize the decanonicalized and pose-dependent completed object shape as the training signal, and develop a novel weakly-supervised learning framework to leverage both labeled synthetic data and unlabeled real data to train the pose estimation model in a label-efficient way. Extensive experiments on three widely used benchmarks demonstrate the effectiveness, and superiority of our framework over state-of-the-art methods. © 1991-2012 IEEE.

关键词： Self-supervised learning

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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Research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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MDKAT: Multimodal Decoupling with Knowledge Aggregation and Transfer for Video Emotion Recognition

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Jian Wang, Chenglong Guo, Lin Zhao, Shuchang Wang, Dandan Zhang, Shiqing Zhao, Xiaoming Yu, Jun Wang, Yaowei Yang, Yi Ma, Siwei Tian, Qi Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Hangzhou Dianzi University School of Computer Science Hangzhou310018 China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310027 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Huawei Cloud & AI Shenzhen518129 China

Multimodal Emotion Recognition (MER) leverages multiple input signals to identify the expressed emotions in user-generated data. Currently, effectively addressing both modality heterogeneity and homogeneity on MER tasks is a challenging issue due to the diversity of multimodal inputs in videos. To address this issue, this work proposes an efficient Multimodal Decoupling Method with Knowledge Aggregation and Transfer (MDKAT) for robust multimodal feature learning in emotional videos. MDKAT is consisted of three key steps: modality-independent feature extraction, modality-specific feature extraction, and multi-loss integration for decoupling. In these three steps, four crucial modules are individually designed to improve different aspects of multimodal learning on MER tasks, including a Cross-modal Feature Fusion (CFF) module for enhancing modality-independent features, an Adaptive Masked Self-Attention (AMSA) module for feature refinement, a Knowledge Aggregation (KA) module for ensuring the semantic similarity of modality-independent features, and a Knowledge Transfer (KT) module for balancing the strengths of different modalities. Experimental results on the typical CMU-MOSI and CMU-MOSEI datasets show that MDKAT obtains superior performance over state-of-the-art methods, demonstrating the effectiveness of MDKAT on MER tasks. © 1991-2012 IEEE.

关键词： Feature extraction

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Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

TechRxiv

引用

TechRxiv 2023年

作者： Hadikhani, Parham Lai, Daphne Teck Ching Ong, Wee-Hong Nadimi-Shahraki, Mohammad H. The School of Digital Science Universiti Brunei Darussalam Brunei Faculty of Computer Engineering Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran

Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vector-based Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94 % on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets. © 2023, CC BY.

关键词： Cluster analysis

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