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arXiv 2022年

作者： Dong, Qiujie Wang, Zixiong Li, Manyi Gao, Junjie Chen, Shuangmin Shu, Zhenyu Xin, Shiqing Tu, Changhe Wang, Wenping The School of Computer Science and Technology Shandong University Shandong Qingdao China The School of Software Shandong University Shandong Jinan China The School of Information and Technology Qingdao University of Science and Technology Shandong Qingdao China The School of Computer and Data Engineering Ningbo Institute of Technology Zhejiang University Zhejiang Ningbo China The Department of Computer Science & Engineering Texas A&M University United States

Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh structure. Motivated by the geometric spectral theory, we introduce Laplacian2Mesh, a novel and flexible convolutional neural network (CNN) framework for coping with irregular triangle meshes (vertices may have any valence). By mapping the input mesh surface to the multi-dimensional Laplacian-Beltrami space, Laplacian2Mesh enables one to perform shape analysis tasks directly using the mature CNNs, without the need to deal with the irregular connectivity of the mesh structure. We further define a mesh pooling operation such that the receptive field of the network can be expanded while retaining the original vertex set as well as the connections between them. Besides, we introduce a channel-wise self-attention block to learn the individual importance of feature ingredients. Laplacian2Mesh not only decouples the geometry from the irregular connectivity of the mesh structure but also better captures the global features that are central to shape classification and segmentation. Extensive tests on various datasets demonstrate the effectiveness and efficiency of Laplacian2Mesh, particularly in terms of the capability of being vulnerable to noise to fulfill various learning tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Mesh generation

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Integration of Gene Regulatory Pathways Found in the Literature in a Graph database

Integration of Gene Regulatory Pathways Found in the Literat...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yibo Chen Dong Xu Mihail Popescu Institute for Data Science and Informatics University of Missouri-Columbia Columbia MO USA Department of Electrical Engineering and Computer Science University of Missouri-Columbia Columbia MO USA School of Medicines University of Missouri-Columbia Columbia MO USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Gene regulatory pathways plays a significant role in personalized medicine. Because pathways often branch or converge, graphs are better at describing them. In this work, we introduced Neo4j, a graph database, to represent gene pathways collected from the KEGG Pathway database and from 217 non-small cell lung cancer (NSCLC) articles retrieved from PubMed. We found that the graph representation of disease pathways was able to display regulatory relations between two genes even though they were not mentioned in the same article. Besides, contradictory pathway relations, for example “EGFR activates RAS” and “EGFR inhibits RAS”, self-activation or self-inhibition like “AKT activates AKT”, and pathways with different direction like “EGFR activates RAS” and “RAS activates EGFR” may also be investigated. This contradictory information may come from different conditions of the relations, different scientific opinions or they be mistakes made by our deep learning pipeline that extracts information from the literature. Overall, we concluded that graph representations of disease pathways can give us a panoramic view of the pathway knowledge obtained from various kinds of sources. Although the representation may not manifest real underlying mechanisms, it helps provide research focus and propel the development of personalized medicine.

关键词： Deep learning databases Precision medicine Pipelines Lung cancer Propulsion data mining

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Miao, Jiajun Shen, Yujun Zhang, Yan Liu, Lingjie Tian, Ye Zhao, Yu Zhou, Chunyan Harbin Engineering University College of Computer Science and Technology Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350357677

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs. © 2023 IEEE.

关键词： Risk assessment

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A Survey on Fairness in Large Language Models

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable data

arXiv

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arXiv 2022年

作者： Yuan, Hang Chan, Shing Creagh, Andrew P. Tong, Catherine Acquah, Aidan Clifton, David A. Doherty, Aiden Nuffield Department of Population Health University of Oxford United Kingdom Big Data Institute University of Oxford United Kingdom Department of Engineering Science University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom

Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset–the largest of its kind to date–containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, eand environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance. © 2022, CC BY.

关键词： Supervised learning

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

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Efficient Resource Management for Real-Time AI Systems in the Cloud using Reinforcement Learning 3

Efficient Resource Management for Real-Time AI Systems in th...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vedula, Jagadeesh Baskaran, Saravanakumar Sayiram, Sathyanarayanan Muthusamy, Rajkumar Gupta, Ketan Yuvaraj, N. Digital Transformation HoustonTX United States Microsoft Corporation SeattleWA United States Ibm San JoseCA United States Ntt Data IrvingTX United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331508456

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time. © 2025 IEEE.

关键词： Reinforcement learning

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SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search

arXiv

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arXiv 2024年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT-e for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. © 2024, CC BY.

关键词： Chatbots

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Revisiting Explicit Recommendation with Dc-Gcn: Divide-and-Conquer Graph Convolution Network

SSRN

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SSRN 2024年

作者： Peng, Furong Liao, Fujin Lu, Xuan Zheng, Jianxing Li, Ru Institute of Big Data Science and Industry Shanxi University 92 Wucheng Road Taiyuan030006 China College of Physics and Electronic Engineering Shanxi University 92 Wucheng Road Taiyuan030006 China School of Computer and Information Technology Shanxi University 92 Wucheng Road Taiyuan030006 China

In recent years, Graph Convolutional Networks (GCNs) have primarily been applied to implicit feedback recommendation, with limited exploration in explicit scenarios. Large Language Models(LLMs) for implicit recommendation also have been extensively explored, but it's ill-suited for explicit recommendations due to small scale dataset cannot fully reflect LLM's ability. Although explicit recommendations can yield promising results, the conflict between the sparsity of data and the data starvation of deep learning hinders its development. Unlike implicit scenarios, explicit recommendation provides less evidence for predictions and requires distinguishing weights of edges (ratings) in the user-item *** exploit high-order relations by GCN in explicit scenarios, we propose dividing the explicit rating graph into sub-graphs, each containing only one type of rating. We then employ GCN to capture user and item representations within each sub-graph, allowing the model to focus on rating-related user-item relations, and aggregate the representations of all subgraphs by MLP for the final recommendation. This approach, named Divide-and-Conquer Graph Convolution Network (DC-GCN), simplifies each model's mission and highlights the strengths of individual modules. Considering that creating GCNs for each sub-graph may result in over-fitting and faces more serious data sparsity, we propose to share node embeddings for all GCNs to reduce the number of parameters, and create rating-aware embedding for each sub-graph to model rating-related relations. Moreover, to alleviate over-smoothing, we utilize random column mask to randomly select columns of node features to update in GCN layers. This technique can prevent node representations from becoming homogeneous in deep GCN networks. DC-GCN is evaluated on four public datasets and achieves the SOTA experimentally. Furthermore, DC-GCN is analyzed in cold-start and popularity bias scenarios, exhibiting competitive performance in various

关键词： Graph neural networks

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