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APL Machine Learning 2023年第2期1卷

作者： Anwen Hu Yongdi Huang Qionghai Chen Wanhui Huang Xiaohui Wu Lihong Cui Yining Dong Jun Liu College of Mathematics and Physics Beijing University of Chemical Technology Beijing 100029 People’s Republic of China State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing 100029 People’s Republic of China Interdisciplinary Research Center for Artificial Intelligence Beijing University of Chemical Technology Beijing 100029 People’s Republic of China School of Data Science and Hong Kong Institute for Data Science Centre for Systems Informatics Engineering City University of Hong Kong Tat Chee Avenue Kowloon 999077 Hong Kong

The glass transition temperature (Tg) is used to determine thermophysical properties of polymer materials and is often considered one of the most important descriptors. Methods for predicting various physical properties of materials based on machine learning algorithms and key molecular descriptors are efficient and accurate. However, it still needs improvements because an overly complex model is less practical and difficult to generalize. In addition, obtaining a large number of samples to achieve accurate predictions remains a challenge due to the complex and lengthy experimental process. In this work, based on Tg of 100 polymers, we use a feature selection algorithm combining FeatureWiz and the least absolute shrinkage and selection operator to quickly select molecular descriptors that are minimally redundant and maximally relevant to Tg. The processed dataset is interpolated from the original dataset using the nearest neighbor interpolation algorithm to solve the data deficiency problem. Finally, the synthetic minority oversampling technique algorithm is used to solve the data imbalance problem. The augmented dataset is used to construct the extreme gradient boosting prediction model to achieve good prediction accuracy. The experimental results demonstrate the robustness of the proposed model and the accuracy of its prediction results.

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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Self-attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction

arXiv

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arXiv 2019年

作者： Li, Yang Long, Guodong Shen, Tao Zhou, Tianyi Yao, Lina Huo, Huan Jiang, Jing Centre of Artificial Intelligence FEIT University of Technology Sydney Paul G. Allen School of Computer Science & Engineering University of Washington School of Computer Science and Engineering University of New South Wales

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. Specifically, in the proposed framework, 1) we use an entity-aware word embedding method to integrate both relative position information and head/tail entity embeddings, aiming to highlight the essence of entities for this task;2) we develop a self-attention mechanism to capture the rich contextual dependencies as a complement for local dependencies captured by piecewise CNN;and 3) instead of using selective attention, we design a pooling-equipped gate, which is based on rich contextual representations, as an aggregator to generate bag-level representation for final relation classification. Compared to selective attention, one major advantage of the proposed gating mechanism is that, it performs stably and promisingly even if only one sentence appears in a bag and thus keeps the consistency across all training examples. The experiments on NYT dataset demonstrate that our approach achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

lMFF: Efficient and Scalable layered Materials Force Field o...

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Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

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An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection

arXiv

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arXiv 2025年

作者： Liu, Xian-Xian Wei, Yuanyuan Xu, Mingkun Guo, Yongze Zhang, Hongwei Dong, Huicong Song, Qun Zhao, Qi Luo, Wei Tien, Feng Gao, Juntao Fong, Simon Department of Computer and Information Science University of Macau 999078 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 Hong Kong Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai519031 China Department of Precision Instrument Tsinghua University Beijing100084 China Department of Gastroenterology Affiliated Hospital of Hebei University of Engineering Handan China Institute of Artificial Intelligence Chongqing Technology and Business University CO Chongqing400067 China Cancer Centre Institute of Translational Medicine Faculty of Health Sciences University of Macau Taipa519000 China MoE Frontiers Science Center for Precision Oncology University of Macau Taipa519000 China Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou1528000 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China Tsinghua University Beijing China

Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic technologies, leading to high rates of misdiagnosis and missed diagnoses. To address these challenges, we propose an integrated system that synergizes advanced hardware and software technologies to balance speed-accuracy. Our study introduces the One Class Twin Cross Learning (OCT-X) algorithm. Leveraging a novel fast double-threshold grid search strategy (FDT-GS) and a patch-based deep fully convolutional network, OCT-X maximizes diagnostic accuracy through real-time data processing and seamless lesion surveillance. The hardware component includes an all-in-one point-of-care testing (POCT) device with high-resolution imaging sensors, real-time data processing, and wireless connectivity, facilitated by the NI CompactDAQ and LabVIEW software. Our integrated system achieved an unprecedented diagnostic accuracy of 99.70%, significantly outperforming existing models by up to 4.47%, and demonstrated a 10% improvement in multirate adaptability. These findings underscore the potential of OCT-X as well as the integrated system in clinical diagnostics, offering a path toward more accurate, efficient, and less invasive early gastric cancer detection. Future research will explore broader applications, further advancing oncological diagnostics. Code is available at https://***/ liu37972/*** © 2025, CC BY-NC-ND.

关键词： computer hardware description languages

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Affine Non-negative Collaborative Representation Based Pattern Classification

arXiv

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arXiv 2020年

作者： Yin, He-Feng Wu, Xiao-Jun Feng, Zhen-Hua Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://***/yinhefeng/ANCR. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Incomplete multi-view clustering via efficient anchor tensor recovery framework

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Neural Networks 2025年 190卷 107652页

作者： Jintian Ji Songhe Feng Jie Huang Taotao Wei Xiang Feng Peiwu Lv Bing Li School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing 100044 China Jiangsu Railway Group Co. Ltd. Nanjing 210012 China Jiangsu Province Engineering Research Center of Intelligent and Green Railway Nanjing 210012 China The National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) Beijing 100190 China

Tensor-based Incomplete Multi-view Clustering (TIMC) methods have received widespread attention due to the powerful data recovery capability of capturing cross-view high-order correlation. Although such methods have achieved remarkable progress, they still suffer from the following problems: (1) The extremely high computational complexity makes it hard for tensor-based methods to handle large-scale multi-view data. (2) Geometric structure constraints in the sample space often lead to high computational complexity and redundancy of structural information. (3) The commonly used Tensor Nuclear Norm (TNN) over-penalizes the primary rank components, leading to a sub-optimal representation tensor. Being aware of these, we propose Incomplete Multi-View Clustering with E fficient A nchor Te nsor R ecovery Framework (EATER). Specifically, it learns a group of anchors to construct a low-rank anchor tensor to recover the missing data with the high-order correlation among views and the geometric structure in the learned representation tensor is enhanced by an Anchor Laplacian Regularization (ALR). Moreover, instead of employing TNN, we adopt a tighter Non-convex Tensor Rank (NTR) to capture the multi-view high-order correlation effectively. An efficient iterative optimization algorithm is designed to solve the EATER, which is time-economical and enjoys favorable convergence. Extensive experimental results on various datasets demonstrate the superiority of the proposed algorithm as compared to state-of-the-art methods.

关键词： Geometric structure constraint High-order correlation Incomplete multi-view clustering Non-convex tensor rank

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

arXiv

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arXiv 2020年

作者： Chen, Tiantian Guo, Jianxiong Wu, Weili The Department of Computer Science Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX75080 United States The BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University at Zhuhai Guangdong Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Guangdong Zhuhai519087 China

Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We mainly consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1 − 1/e) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could achieve a (1 − e−(1−∊)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies. Copyright © 2020, The Authors. All rights reserved.

关键词： Diffusion

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Integration of topological data analysis and entropy features for the prediction of lithium-ion battery state-of-health

Future Batteries

引用

Future Batteries 2025年 6卷

作者： Manoj K. Singh Anuj Kumar Sangeeta Pant Shshank Chaube Kriti Misra Jitendra Pal Singh Ketan Kotecha School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh 201310 India School of Computer Science Engineering & Applications D. Y. Patil International University (DYPIU) Pune Maharashtra 411044 India Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Maharashtra 412115 India Symbiosis Institute of Technology Hyderabad Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science & Technology Manav Rachna University Faridabad Haryana 121010 India Department of Sciences Manav Rachna University Faridabad Haryana 121010 India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed) University Pune Symbiosis (Deemed University) Pune Maharashtra 412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune Maharashtra 412115 India

Lithium-ion batteries are now widely used in many devices due to their high performance. However, their use in electric vehicles poses challenges such as limited driving range and variable cycle life. A data-driven approach can be useful to better understand the aging mechanisms of batteries. Predicting a battery’s State-of-Health (SoH) accurately is crucial to improve battery technology/life. A machine learning algorithm, combined with features extracted from voltage/temperature during a charging cycle, can be used to predict the SoH of a battery. Creating new feature vector by integrating known features which can help machine learning (ML) algorithms in solving a particular problem will be a novel work. A Topological data Analysis (TDA) technique and entropy features are utilized to create a feature vector that can predict the SoH of a battery through a machine-learning model called long short term memory neural networks. The TDA features represent the functions of one- and two-dimensional holes in the data which appear/disappear as a tolerance value is increased. The entropy features represent the amount of information present in the dataset. There are multiple ways to define entropy of a time series data. In this article, we carefully selected entropies suitable for the battery datasets. Oxford battery degradation dataset, which is publicly available, was used to apply a Long Short-Term Memory (LSTM) model. The average Mean Absolute Error (MAE) of the model with topological data analysis features is 0.02045 (2.56 %), while the average MAE of the model with entropy features is 0.02241 (2.77 %). However, the average MAE of the model with integrated entropy-TDA features is only 0.02025 (2.54 %). The low MAEs in the models suggest that the feature set created by integrating topological-entropy features will be helpful in predicting the SoH of a Lithium-ion battery.

关键词： Li ion batteries State of health Topological features Entropy features

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