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arXiv 2023年

作者： Zhang, Zaixi Lu, Zepu Hao, Zhongkai Zitnik, Marinka Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua-Bosch Joint ML Center Tsinghua China Harvard University United States

The design of de novo functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context modeling of the ligand molecule, and the inability to generate side-chain atoms. Here, we present the Full-Atom Iterative Refinement (FAIR) method, designed to address these challenges by facilitating the co-design of protein pocket sequences, specifically residue types, and their corresponding 3D structures. FAIR operates in two steps, proceeding in a coarse-to-fine manner (transitioning from protein backbone to atoms, including side chains) for a full-atom generation. In each iteration, all residue types and structures are simultaneously updated, a process termed full-shot refinement. In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions. The subsequent stage delves deeper, modeling the side-chain atoms of the pockets and updating residue types to ensure sequence-structure congruence. Concurrently, the structure of the binding ligand is refined across iterations to accommodate its inherent flexibility. Comprehensive experiments show that FAIR surpasses existing methods in designing superior pocket sequences and structures, producing average improvement exceeding 10% in AAR and RMSD metrics. FAIR is available at https://***/zaixizhang/FAIR. © 2023, CC BY.

关键词： Atoms

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Deep Neural Network based Relation Extraction: An overview

arXiv

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arXiv 2021年

作者： Wang, Hailin Qin, Ke Zakari, Rufai Yusuf Lu, Guoming Yin, Jin Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Knowledge is a formal way of understanding the world, providing a human-level cognition and intelligence for the next-generation artificial intelligence (AI). One of the representations of knowledge is semantic relations between entities. An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), a sub-task of information extraction, plays a vital role in Natural Language Processing (NLP). Its purpose is to identify semantic relations between entities from natural language text. To date, there are several studies for RE in previous works, which have documented these techniques based on Deep Neural Networks (DNNs) become a prevailing technique in this research. Especially, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE. This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and de-noise method. We further 3) cover some novel methods and recent trends as well as discuss possible future research directions for this task. © 2021, CC0.

关键词： Deep neural networks

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An Intelligent Recognition Algorithm for Urban Utility Tunnel Surface Intrusion via Spatio-temporal-spectral Joint Information Extraction with FPN Network

An Intelligent Recognition Algorithm for Urban Utility Tunne...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Tao He Zhijun Yan Shixiong Zhang Deming Liu Hao Li Qizhen Sun Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan P. R. China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan P. R. China

An intelligent recognition algorithm for utility tunnel surface intrusion based on spatio-temporal-spectral joint information extraction with FPN network is proposed. Finally, the average recognition accuracy of 93.8%... 详细信息

关键词： Target recognition Neural networks Asia Information retrieval Feature extraction Safety Monitoring

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Enhanced Adsorption of Copper Citrate Complex by Ferromanganese Oxides-Biochar from Water: Performance and Mechanism

SSRN

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SSRN 2024年

作者： Zhu, Ying Lei, Xiang Liu, Jianguo Li, Yiting Fan, Wenhong Key Laboratory of Environmental Pollution Control and Remediation Universities of Inner Mongolia Autonomous Region College of Resources and Environmental Engineering Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China School of Materials Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

Due to the stable structure of complex metals, their removal is more challenging compared to metal ions. Additionally, the removal mechanism of metal complexes is closely related to their structure. This study developed a low-cost and economically efficient ferromanganese oxide-modified biochar and explored the preparation conditions in detail. The surface of ferromanganese oxide-modified biochar was uniformly loaded with 100 nm nanoparticles, mainly comprising Mn3O4 and (FeO)0.099(MnO)0.901, and exhibited excellent copper citrate (CuCA) adsorption capacity. The removal rate of CuCA at an initial concentration of 10 mg/L by 1 g/L modified biochar reached 99.5% for Cu and 87.7% for TOC, with a copper adsorption capacity of 3.79 mg/L. The mechanism of using modified biochar to remove CuCA includes chemical adsorption at the bimetallic oxide active sites and oxygen-containing functional groups on the surface of the biochar, as well as physical adsorption due to biochar’s porous structure and other effects. The modified biochar shows good performance and stability in wide pH range and certain coexisting ions. Ferromanganese oxide-modified biochar own the advantage of efficient, economical and easy to preparation, which has significant potential for development in the removal of metal complexes in water. © 2024, The Authors. All rights reserved.

关键词： Adsorption

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Vflh: A Following-the-Leader-History Based Algorithm for Adaptive Online Convex Optimization with Stochastic Constraints

SSRN

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SSRN 2022年

作者： Yang, Yifan Chen, Lin Zhou, Pan Ding, Xiaofeng Department of Computer Science University of California Santa BarbaraCA93106 United States National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430072 China

This paper considers online convex optimization (OCO) with generated i.i.d. stochastic constraints, where the distribution of environment is changing and the performance is measured by \textit{adaptive regret}. The stochastic constraints are disclosed at each round to the learner after the decision is made. Different from the previous non-adaptive constrained OCO algorithm which is generalized based on the traditional online gradient descent algorithm, we propose a novel \textit{Virtual queue based Following-the-Leader-History} (VFLH) strategy to make the constrained OCO algorithm adaptive and design the VFLH algorithm. In this setting, the algorithm firstly generalizes all the experts in the specified time interval, then decides the optimal decision with each expert and gives out the final choice by combining the predictions of active experts. In terms of the algorithm performance, there are two performance metrics to be evaluated, i.e., the bounds of constraint violation and regret. For the constraint violation, we bring in the multi-objective drift analysis approach to deal with the constraints via a virtual queue which provides a new method to bound the constraints violations. For the regret, we give out its upper bound and then achieve a corresponding lower bound that proves the upper bound is sufficiently tight. Finally, we provide some numerical experiments to validate the feasibility of our algorithm. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Label Incorporated Graph Neural Networks for Text Classification

Label Incorporated Graph Neural Networks for Text Classifica...

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International Conference on Pattern Recognition

作者： Yuan Xint Linli Xu Junliang Guo Jiquan Li Xin Sheng Yuanyuan Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China IFLYTEK Co. Ltd. Information Science Center University of Science and Technology of China

Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider incorporating the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.

关键词： Text categorization Buildings Semantics Benchmark testing data structures Graph neural networks Natural language processing

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The Research on Disruptive technology Identification Based on Scientific and Technological Information Mining and Expert Consultation: A Case Study on the Energy Field 2nd

The Research on Disruptive Technology Identification Based o...

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2nd International Conference on Artificial Intelligence in China, ChinaAI 2020

作者： Lyu, Lucheng Wang, Xuezhao Chen, Wei Zhang, Xin Chen, Xiaoli Liu, Xiwen National Science Library Chinese Academy of Sciences Beijing100190 China Department of Library Information and Archives Management School of Economics and Management University of Chinese Academic of Sciences Beijing100190 China Wuhan Library Chinese Academy of Sciences Wuhan430071 China Hubei Key Laboratory of Big Data in Science and Technology Wuhan430071 China

ISBN: (纸本)9789811585982

Disruptive technology identification is of great significance to the development of the countries and enterprises. In this paper, both quantitative analysis and qualitative analysis are combined to propose a disruptive technology identification method based on scientific and technological information mining and expert consultation. In the part of the quantitative analysis, three kinds of scientific and technological information data are involved including papers, patents, and projects, and the word embedding model and clustering technology are applied to dimension reduction and aggregation the scientific and technological information which results in finding "seeds" or "sprouts" of disruptive technologies from the mass scientific and technological information. In the part of the expert consultation, the ranking of potential disruptive technologies is evaluated by an expert questionnaire based on a "three-dimensional index system of technology disruptive potential evaluation." The result of the energy field research is that 45 potential disruptive technologies are identified and their disruptive ranking is determined. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

Sequence-to-Sequence Knowledge Graph Completion Based On Gat...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fengge Yi Xiumei Wei Xiaojing Liu Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

关键词：

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Meta-Path Based Graph Contrastive Learning for Micro-Video Recommendation

SSRN

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SSRN 2022年

作者： He, Ying Wu, Gongqing Cai, Desheng Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

Nowadays, micro-video sharing platforms have become popular tools for people creating and viewing micro-videos in daily life. Micro-video recommendation task has attracted significant attention from researchers, recently. The key to a high-quality micro-video recommendation system is learning meaningful user and micro-video representations. Recently, many graph neural networks (GNNs) are proposed to learn node representations in graphs, which could be useful for recommendation tasks. However, we argue that directly utilizing existing GNNs in micro-video recommendations is ill-posed. The reasons are: (1) most previous GNNs fail to capture the heterogeneity of heterogeneous graphs since they are designed for homogeneous graphs;(2) they overemphasize node proximity and could hurt the robustness of node embeddings. In contrast to previous work, we propose a meta-path-based graph contrastive learning network (MPGCL) that learns more meaningful user and video embeddings for recommendation. Specifically, we yield homogeneous graphs for user type and video type to better capture heterogeneity based on a well-designed meta-path-based random walk strategy. Furthermore, to learn more robust node embeddings that are less sensitive to noise, we propose a graph contrastive learning network on different types of homogeneous graphs, which maximizes the consistency between graph representations with different views. We do extensive experiments on three real-world datasets and the results show that our model can learn more meaningful embeddings for user and micro-videos and outperforms the strong baselines. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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MFF-AMD: Multivariate Feature Fusion for Android Malware Detection 17th

MFF-AMD: Multivariate Feature Fusion for Android Malware Det...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Xu, Guangquan Feng, Meiqi Jiao, Litao Liu, Jian Dai, Hong-Ning Wang, Ding Panaousis, Emmanouil Zheng, Xi College of Intelligence and Computing Tianjin University Tianjin China Big Data School Qingdao Huanghai University Qingdao China Faculty of Information Technology Macau University of Science and Technology Macau SAR Taipa China School of EECS Peking University Beijing China University of Greenwich London United Kingdom Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9783030926342

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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