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arXiv 2024年

作者： Cheng, Qing Guo, Jing Wang, Dong Department of Mathematics Tongji University Shanghai200092 China Key Laboratory of Intelligent Computing and Applications Tongji University Ministry of Education China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we consider numerical approximations for the optimal partition problem using Lagrange multipliers. By rewriting it into constrained gradient flows, three and four steps numerical schemes based on the Lagrange multiplier approach [16, 17] are proposed to solve the constrained gradient system. Numerical schemes proposed for the constrained gradient flows satisfy the nice properties of orthogonality-preserving, norm-preserving, positivity-preserving and energy dissipating. The proposed schemes are very efficient in which only linear Poisson equations are solved at each time step. Extensive numerical results in 2D and 3D for optimal partition problem are presented to validate the effectiveness and accuracy of the proposed numerical schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Document-level Relation Extraction with Relation Correlations

arXiv

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arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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Intra-Person Camera Adversarial for Intra-Camera Supervised Person Re-Identification

Intra-Person Camera Adversarial for Intra-Camera Supervised ...

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International Conference on Automatic Face and Gesture Recognition

作者： Ruochen Tang Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Re-identification (ReID) of individuals across different cameras is a challenging task due to the high-quality large-scale datasets required for the model. Intra-camera supervised (ICS) person re-identification has been proposed to address the high cost of annotating large-scale datasets, but reducing the gap between camera domains remains a major challenge. Current approaches use intra- and inter-camera learning with contrastive learning performed separately in both phases. However, the effect of features from the same person under different cameras on the model during inter-camera learning, and the fine-grained characteristic where the same person can be classified into multiple classes based on the camera labels, still require further research. To address this issue, we propose a Camera-Based Contrastive Learning (CBCL) method that moves features away from their respective cameras and closer to other cameras to reduce domain gaps. We also introduce an Intra-Person Camera Adversarial (IPCA) loss that effectively utilizes fine-grained characteristics of person re-identification and improve IPCA by introducing camera labels to obtain IPCA_2 which achieves better model recognition performance than IPCA alone. Extensive experiments on multiple datasets demonstrate that our method outperforms existing methods and is comparable to fully-supervised methods.

关键词： Costs Scalability Face recognition Contrastive learning Gesture recognition Cameras Robustness

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Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel knowledge Sharing Mechanism

arXiv

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arXiv 2022年

作者： Wu, Yue Liu, Yibo Gong, Maoguo Gong, Peiran Li, Hao Tang, Zedong Miao, Qiguang Ma, Wenping The School of Computer Science and Technology Key Laboratory of Big Data and Intelligent Vision Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China

Multi-view point cloud registration is fundamental in 3D reconstruction. Since there are close connections between point clouds captured from different viewpoints, registration performance can be enhanced if these connections be harnessed properly. Therefore, this paper models the registration problem as multi-task optimization, and proposes a novel bi-channel knowledge sharing mechanism for effective and efficient problem solving. The modeling of multi-view point cloud registration as multi-task optimization are twofold. By simultaneously considering the local accuracy of two point clouds as well as the global consistency posed by all the point clouds involved, a fitness function with an adaptive threshold is derived. Also a framework of the co-evolutionary search process is defined for the concurrent optimization of multiple fitness functions belonging to related tasks. To enhance solution quality and convergence speed, the proposed bi-channel knowledge sharing mechanism plays its role. The intra-task knowledge sharing introduces aiding tasks that are much simpler to solve, and useful information is shared across aiding tasks and the original tasks, accelerating the search process. The inter-task knowledge sharing explores commonalities buried among the original tasks, aiming to prevent tasks from getting stuck to local optima. Comprehensive experiments conducted on model object as well as scene point clouds show the efficacy of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Multitasking

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Study on Interaction with High-Abundant Blood Proteins and Identification of Low-Abundant Proteins to 5-Phenyl-1-(P-Tolyl)-1h-1,2,3-Triazole by Serum Proteomics

SSRN

引用

SSRN 2024年

作者： He, Wenying Xie, Cong Wang, Shuai Duan, Hongye Liu, Rongqiang Si, Hongzong Yao, Xiaojun Key Laboratory of Tropical Medicinal Resource Chemistry of Ministry of Education Hainan Normal University Haikou571158 China College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University Jinan250117 China Institute for Computational Science and Engineering Qingdao University Qingdao266071 China College of Chemical and Chemical Engineering Lanzhou University Lanzhou730000 China

In this study, a comprehensive investigation was undertaken to elucidate a simple triazole compound, 5-phenyl-1-(p-tolyl)-1H-1,2,3-triazole (PPTT), its interactions with high-abundant proteins and identification of low-abundant proteins by serum proteomics. Employing a combination of spectroscopic techniques and computational chemistry, the interactions between PPTT and three high-abundance blood globular proteins, namely human serum albumin (HSA), human immunoglobulin G (HIgG), and hemoglobin (BHb), were explored, thereby ascertaining their binding constants and thermodynamic parameters at the molecular level. Subsequently, based on the differential proteomics, utilizing two-dimensional gel electrophoresis (2-DE) in conjunction with matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-MS), the research team isolated and identified differentially expressed low-abundance proteins in human blood serum samples following exposure to PPTT. The results showed that there were twenty highly expressed proteins identified from blood serum samples intervened by PPTT. Combining bioinformatics techniques, these proteins were classified, providing preliminary insights like preproprotein or precursors inhibiting the activity of elastase, defending and regulating the immune system, carrying lipid, and other functions into their biological functionalities. One of the differential proteins, apolipoprotein A-1 (ApoA-1) protein, was selected as a possible target to explore the mechanism of action of PPTT intervention on the related signaling pathways involved in Hep G2 cells. These research findings offer scientifically sound guidance for further in-depth exploration, development, and application of the 1,2,3-triazole compound. © 2024, The Authors. All rights reserved.

关键词： Molecular biology

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Foundation models in bioinformatics

引用

National Science Review 2025年第4期 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

关键词：

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A Scheme for Kinship Reasoning based on Ontology

A Scheme for Kinship Reasoning based on Ontology

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IEEE International Conference on big knowledge (ICBK)

作者： Ru Chen Guliu Liu Yi Zhu Xindong Wu Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology School of Information Engineering Yangzhou University Mininglamp Academy of Sciences Mininglamp Technology

ISBN: (纸本)9781665438599

With the rapid development of artificial intelligence and semantic networks, knowledge graphs have received extensive attention in various application domains. As a domain knowledge graph, a genealogical knowledge graph has significant research value in the family blood, family culture, and medical genetic analysis. However, as the identical relationship often has different names and complex relationships such as divorce, remarriage, and polygamy, the reasoning based on the genealogical knowledge graph is a challenging task. In response to this problem, we propose a scheme for kinship reasoning in the genealogical knowledge graph. First, based on real genealogical revision experiences, a character ontology framework in the genealogical knowledge graph is defined, and basic kinship reasoning rules are designed. Then, given different definitions of kinship in different surnames, the solution of custom reasoning rules is integrated into the reasoning framework. In addition, aiming at complex relationships in family trees, such as multiple generations of ancestors and multiple wives, a series of inference optimization methods are proposed. Finally, we implement this scheme in the Huapu system, and the experimental results conducted on a real genealogical dataset demonstrate the effectiveness and practicality of our proposed scheme.

关键词： knowledge engineering Conferences Semantics Optimization methods Ontologies Genetics Cognition

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Anchor-Based Discriminative Dual Distribution Calibration for Transductive Zero-Shot Learning

SSRN

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SSRN 2023年

作者： Zhang, Yi Huang, Sheng Yang, Wanli Tang, Wenhao Zhang, Xiaohong Yang, Dan School of Big Data and Software Engineering Chongqing University No.55 Daxuecheng South Rd. Chongqing Shapingba401331 China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University No.174 Shazhengjie Chongqing Shapingba400044 China

Zero-shot learning (ZSL) is machine learning task to recognize samples from classes that are not observed during training. Transductive ZSL (TZSL) is a more realistic and effective paradigm that leverages unlabeled unseen data during training to reduce the bias towards seen classes. However, most existing TZSL methods neglect the information gap between visual and semantic spaces, and thus fail to generate distribution-consistent unseen features. To address this issue, we propose a novel TZSL approach named Anchorbased Discriminative Dual Distribution Calibrated Feature Generative Network (AD3C-FGN), which performs anchor-based distribution calibration in both visual and semantic spaces for improving generalization ability of the model. In AD3C-FGN, we adopt conditional generative adversarial network with an unseen discriminator to construct a Y-shape generation model that mitigates the domain shift problem. Moreover, an AD3C module is elaborated for calibrating the distribution of generated and real samples in both visual and semantic spaces with real sample anchors, and also enhance the discriminability of the generated samples. AD3C enforces the generated sample to be closer to its homogenous anchor but farther away from inhomogeneous anchors in both spaces. Extensive experimental results on six popular ZSL benchmarks demonstrate that our method achieves promising performances in different settings. © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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On the Fine-Grained Crowd Analysis via Passive WiFi Sensing

TechRxiv

引用

TechRxiv 2023年

作者： Hao, Lifei Huang, Baoqi Jia, Bing Mao, Guoqiang The Engineering Research Center of Ecological Big Data Ministry of Education The Inner Mongolia A.R. Key Laboratory of Wireless Networking and Mobile Computing The College of Computer Science Inner Mongolia University Hohhot010021 China The Research Institute of Smart Transportation Xidian University Xi’an710071 China

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction;to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds;to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas. © 2023, CC BY-NC-SA.

关键词： Convolution

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