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arXiv 2023年

作者： Zhang, Hao Liu, Yang Liu, Xiaoyan Liang, Tianming Sharma, Gaurav Xue, Liang Guo, Maozu The School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States The *** HangZhou 310000 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China The Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data. Specifically, we propose a graph view of sentence bags referring to an entity pair, which enables message-passing based aggregation of information related to the entity pair over the sentence bag. The proposed framework alleviates the common problem of noisy labeling in distantly supervised relation extraction and also effectively incorporates inter-dependencies between sentences within a bag. Extensive experiments on two large-scale biomedical relation datasets and the widely utilized NYT dataset demonstrate that our proposed framework significantly outperforms the state-of-the-art methods for biomedical distant supervision relation extraction while also providing excellent performance for relation extraction in the general text mining domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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RF Sign: Signature Anticounterfeiting Real-Time Monitoring System Based on Single Tag

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Zhu, Biaokai Wei, Qing Li, Lu Yang, Zejiao Liu, Wei You, Zirong Zhou, Jiangfan Li, Ping Song, Jie Liu, Sanman Li, Deng-Ao Network Security Department Shanxi Police College No. 799 Qingdong Rd Qingxu County Taiyuan China Intelligent Policing Key Laboratory of Sichuan Province No. 34 Jiangyang West Road Jiangyang District Luzhou China School of Computer Science and Technology Anhui University No. 111 Jiulong Road Hefei China Big Data College Taiyuan University of Technology No. 209 University Street Jinzhong China

Signatures are one of the most important means to ensure the authenticity of documents and are commonly used in life and work. In identifying imitation handwriting, it is easy to make mistakes that cannot correctly identify and evaluate different writing characteristics. In this paper, from the perspective of dynamic handwriting detection, we propose RF sign, a signature anticounterfeiting real-time monitoring model, which achieves passive recognition of signature behavior using only a single antenna with a single tag. The RF sign identifies different users by extracting fine-grained reflection features from the original RF signal. We introduced a dynamic time regularization and neural network technique for similarity calculation and signature recognition matching to achieve template matching and classification. We compiled a real-time signature handwriting detection system. The system effectively identifies the person's signature by checking real-time spatial and temporal information. Comprehensive experiments show that the recognition accuracy of my signature can reach over 93% and is robust to input location, environmental changes, and user diversity. © 2023 Biaokai Zhu et al.

关键词： Template matching

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Masked Motion Encoding for Self-Supervised Video Representation Learning

arXiv

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arXiv 2022年

作者： Sun, Xinyu Chen, Peihao Chen, Liangwei Li, Changhao Li, Thomas H. Tan, Mingkui Gan, Chuang South China University of Technology China Information Technology R&d Innovation Center Peking University China UMass Amherst United States MIT-IBM Watson Ai Lab United States Key Laboratory of Big Data and Intelligent Robot Ministry of Education Peking University Shenzhen Graduate School China

How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions. However, simply masking and recovering appearance contents may not be sufficient to model temporal clues as the appearance contents can be easily reconstructed from a single frame. To overcome this limitation, we present Masked Motion Encoding (MME), a new pretraining paradigm that reconstructs both appearance and motion information to explore temporal clues. In MME, we focus on addressing two critical challenges to improve the representation performance: 1) how to well represent the possible long-term motion across multiple frames;and 2) how to obtain fine-grained temporal clues from sparsely sampled videos. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Besides, given the sparse video input, we enforce the model to reconstruct dense motion trajectories in both spatial and temporal dimensions. Pre-trained with our MME paradigm, the model is able to anticipate longterm and fine-grained motion details. Code is available at https://***/XinyuSun/MME. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification 28

A Two-phase Prototypical Network Model for Incremental Few-s...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Ren, Haopeng Cai, Yi Chen, Xiaofeng Wang, Guohua Li, Qing School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781952148279

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep learning

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Janus Msisnn4 (M= Mo;W)：High Efficiently Overall Water Splitting Photocatalyst Triggered By The Intrinsic Electric Field

SSRN

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SSRN 2023年

作者： Wang, Yi Wang, Yingchao Chen, Tengteng Li, Lei Wang, Guang Zhang, Zhengli Ding, Zhao Guo, Xiang Luo, Zijiang Liu, Xuefei College of Big Data and Information Engineering Guizhou University Guiyang550025 China Power Semiconductor Device Reliability Research Center Ministry of Education Guizhou University Guiyang550025 China Institute of Intelligent Manufacturing Shunde Polytechnic Guangdong Shunde528300 China Key Laboratory of Micro-Nano-Electronics of Guizhou Province Guiyang550025 China School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China

Photocatalytic hydrolysis is one of the most effective ways for hydrogen fuel as a strategy for turning solar energy into hydrogen fuel. Two-dimensional(2D) Janus materials with an out-of-plane polarised electric field not only help to promote photogenerated carrier separation and migration but also lead to energy band bending, which enhances photo-oxidation and retains efficient absorption of visible light. In this manuscript, a systematic first-principles analysis was conducted on the WSn2N4 and MSiSnN4 (M= Mo;W) system. The results show that Janus MSiSnN4 (M= Mo;W) systems have a suitable bandgap and band edge location that include the redox position for water, together with abundant electron mobility(>200 cm2V-1s-1) and outstanding visible light optical absorption capacity(ηSTH = 23.12% for WSiSnN4;ηSTH = 32.91% for MoSiSnN4). The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) Gibbs free energy changes of WSiSnN4 show that it can make HER and OER processes occur spontaneously under light in acidic and neutral solutions, demonstrating excellent photocatalytic performance. © 2023, The Authors. All rights reserved.

关键词： Light absorption

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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FABind: Fast and Accurate Protein-Ligand Binding

arXiv

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arXiv 2023年

作者： Pei, Qizhi Gao, Kaiyuan Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao He, Kun Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China China School of Computer Science and Technology Huazhong University of Science and Technology China Microsoft Research AI4Science China School of Information Science and Technology University of Science and Technology of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at Github3 Copyright © 2023, The Authors. All rights reserved.

关键词： Ligands

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Preface

引用

Science China Technological Sciences 2024年

作者： Gong YiHong Wang GuoYin College of Artificial Intelligence Xi'an Jiaotong University Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications College of Computer and Information Science Chongqing Normal University

Large AGI(artificial general intelligence) models, represented by OpenAI's GPT-4, DALL-E, Sora, etc., have amazed the world by exhibiting superior capabilities on a variety of NLP and text-to-image/video generation tasks. The success of these models was achieved by exploiting ultra-scale training data, ultra-scale computational models, and unlimited computing power.

关键词：

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Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Multiscale fast correlation filtering tracking algorithm bas...

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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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