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Towards robustness and generalization of point cloud representation:A geometry coding method and a large-scale object-level dataset

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Computational Visual Media 2024年第1期10卷 27-43页

作者： Mingye Xu Zhipeng Zhou Yali Wang Yu Qiao Guangdong-Hong Kong-Macao Joint laboratory of Human-Machine intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China University of Chinese Academy of Sciences Beijing065001China Shanghai AI laboratory Shanghai200001China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen518000China alibaba DAMO Academy Hangzhou242332China

Robustness and generalization are two challenging problems for learning point cloud *** tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each *** also introduce a large-scale point cloud dataset,*** consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain ***,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and *** method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised *** and our dataset are available at https://***/MingyeXu/PCNet184.

关键词： geometry coding self-supervised learning point cloud classification segmentation 3D analysis

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

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Research 2024年第2期2023卷 703-718页

作者： Minjie Mou Ziqi Pan Zhimeng Zhou Lingyan Zheng Hanyu Zhang Shuiyang Shi Fengcheng Li Xiuna Sun Feng Zhu College of Pharmaceutical Sciences The Second Affliated HospitalZhejiang University School of MedicineNational Key Laboratory of Advanced Drug Delivery and Release SystemsZhejiang UniversityHangzhou 310058China Innovation Institute for artificial intelligence in Medicine of Zhejiang University Alibaba-Zhejiang University Joint Research Center of Future Digital HealthcareHangzhou 330110China

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://***/ensemppis.

关键词： residue extracting ensemble

Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

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CAAI Transactions on intelligence Technology 2024年第4期9卷 982-995页

作者： Xiali Li Bo Liu Zhi Wei Zhaoqi Wang Licheng Wu School of Information and Engineering Minzu University of ChinaBeijingChina Key laboratory of Ethnic Language Intelligent Analysis and Security governance of MOE Minzu University of ChinaBeijingChina Department of Computer Science New Jersey Institute of TechnologyNewarkNew JerseyUSA

Mahjong,a complex game with hidden information and sparse rewards,poses significant *** Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI *** authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐*** utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile *** design reduces de-cision complexity ***,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning *** consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 *** action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its ***-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

关键词： decision making deep learning deep neural networks

Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

A Vague Set Based OWA Method for Talent Evaluation

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Journal of Systems Science and Information 2024年第4期12卷 543-553页

作者： Peilong WANG Dandan LI Wei XU School of Management University of Chinese Academy of Sciences School of Information Renmin University of China School of Smart governance Renmin University of China

In recent years, decision-making under uncertainty has attracted substantial attention in both academia and industry, with a growing number of organizations prioritizing decision support for talent evaluation. Vague set theory has been recognized as a powerful tool to address the ambiguity of problem parameters and manage uncertainty. This paper introduces a novel talent evaluation method that harnesses the potential of vague sets. We construct a vague set Ordered Weighted Averaging(OWA) operator for offering a robust solution to intricate decision-making problems, especially in talent evaluation. The application of the OWA operator augments the decision-making process by providing a mechanism to handle the aggregation of information in a more flexible and comprehensive manner. Experimental results show the effectiveness of the proposed method, presenting an alternative for decision-makers, aiding them in selecting their preferred choices amidst uncertainty.

关键词： uncertain vague set talent evaluation OWA operator

Smart Breeding Platform:A web-based tool for high-throughput population genetics,phenomics,and genomic selection

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Molecular Plant 2024年第5期17卷 677-681页

作者： Huihui Li Xin Li Peng Zhang Yingwei Feng Junri Mi Shang Gao Lele Sheng Mohsin Ali Zikun Yang Liang Li Wei Fang Wensheng Wang Qian Qian Fei Gu Wenbin Zhou State Key laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Nanfan Research Institute CAASSanyaHainan 572024China DAMO Academy Alibaba GroupHangzhou 310023China Hupan Lab Hangzhou 310023China Yazhouwan National laboratory No.8 Huanjin RoadYazhou DistrictSanya CityHainan Province 572024China State Key laboratory of Rice Biology China National Rice Research InstituteHangzhouZhejiang 310006China

Dear Editor,In the era of big data and artificial intelligence,"smart breeding"has become a broad conceptual framework encompassing the paradigm shift of crop breeding to relying on analysis of high-throughput population genetics and phenomics data to conduct genomic selection,allowing identification and optimal use of the genetic potential in crop species(Xiao et al.,2022;Xu et al.,2022;Wang et al.,2023).Most existing tools for analyzing high-throughput breeding data require extensive computational power,complex installation processes,and command-line expertise and are therefore challenging and inconvenient for the majority of researchers and breeders(Brandies and Hogg,2021).

关键词： breeding artificial breed

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古基因组揭示河西走廊两千年以来的人群历史

Science Bulletin 2024年第5期69卷 606-611页

作者：熊建雪王睿陈国科杨谊时杜盼新蒙海亮马敏敏安达陶乐王辉金力王传超文少卿 Institute of Archaeological Science Fudan UniversityShanghai 200433China State Key laboratory of Cellular Stress Biology School of Life SciencesXiamen UniversityXiamen 361102China Institute of Cultural Relics and Archaeology in Gansu Province Lanzhou 730000China State Key laboratory of Genetic Engineering Collaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciencesand Human Phenome InstituteFudan UniversityShanghai 201203China Ministry of Education Key laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesFudan UniversityShanghai 200438China Key laboratory of Western China’s Environmental Systems(Ministry of Education) College of Earth and Environmental ScienceLanzhou UniversityLanzhou 730000China Center for the Belt and Road Archaeology and Ancient Civilizations Fudan UniversityShanghai 200433China Department of Anthropology and Ethnology Institute of AnthropologySchool of Sociology and AnthropologyFujian Provincial Key Laboratory of Philosophy and Social Sciences in BioanthropologyXiamen UniversityXiamen 361005China State Key laboratory of Marine Environmental Science Xiamen UniversityXiamen 361102China Institute of artificial intelligence Xiamen UniversityXiamen 361005China MOE laboratory for National Development and Intelligent governance Fudan UniversityShanghai 200433China

The Hexi Corridor in northwest China has for millennia served as a crossroads for interactions between East Asia,Central Asia and lands further west,while also connecting the Chinese heartland and the northern Mongolian Steppe and Tibetan Plateau(TP)to the south(Fig.1a).Previous studies have revealed the details of this cross-continental material and cultural transmission as early as the Bronze Age *** and technologies exchanged along the ancient Silk Road included East Asian millets and painted pottery,West Asian wheat and barley,bronze metal-lurgy,domesticated sheep,horses,and cattle[1].To date.

关键词： connecting details northwest

Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

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Journal of Computer Science & Technology 2020年第3期35卷 592-602页

作者： Rui-Song Zhang Wei-Ze Quan Lu-Bin Fan Li-Ming Hu Dong-Ming Yan National laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China alibaba Group Hangzhou 310023China State Key laboratory of Hydro-Science and Engineering Tsinghua UniversityBeijing 100084China

With the recent tremendous advances of computer graphics rendering and image editing technologies,computergenerated fake images,which in general do not reflect what happens in the reality,can now easily deceive the inspection of human visual *** this work,we propose a convolutional neural network(CNN)-based model to distinguish computergenerated(CG)images from natural images(NIs)with channel and pixel *** key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels *** previous approaches that directly apply CNN to solve this problem,we consider the generality of the network(or subnetwork),i.e.,the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original *** results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification *** also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

关键词： natural image computer-generated image channel and pixel correlation convolutional neural network

TSNAdb v2.0:The Updated Version of Tumor-specific Neoantigen Database

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 259-266页

作者： Jingcheng Wu Wenfan Chen Yuxuan Zhou Ying Chi Xiansheng Hua Jian Wu Xun Gu Shuqing Chen Zhan Zhou Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key laboratory of Anti-Cancer Drug Research College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare Alibaba DAMO AcademyHangzhou 311121China The Second Affiliated Hospital of School of Medicine and School of Public HealthZhejiang UniversityHangzhou 310058China Innovation Institute for artificial intelligence in Medicine Zhejiang UniversityHangzhou 310018China Department of Genetics Development and Cell BiologyIowa State UniversityAmesIA 50011USA

In recent years,neoantigens have been recognized as ideal targets for tumor *** the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical *** have built the tumor-specific neoantigen database(TSNAdb)previously,which has attracted much *** this study,we provide TSNAdb v2.0,an updated version of the *** v2.0 offers several new features,including(1)adopting more stringent criteria for neoantigen identification,(2)providing predicted neoantigens derived from three types of somatic mutations,and(3)collecting experimentally validated neoantigens and dividing them according to the experimental level.

关键词： Neoantigen Tumor immunotherapy Human leukocyte antigen Somatic mutation Database