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arXiv 2025年

作者： Kong, Decheng Ren, Jinlong Li, Zhuang Shan, Guangcun Wang, Zhongjian Zhang, Ruiqin Huang, Wei Dou, Kunpeng College of Physics and Optoelectronic Engineering Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Co. Ltd Chengdu610095 China Department of Physics City University of Hong Kong 999077 Hong Kong Engineering Research Center of Advanced Marine Physical Instruments and Equipment of Education Ministry of China Key Laboratory of Optics and Optoelectronics of Qingdao Ocean University of China Qingdao266100 China School of Flexible Electronics Sun Yat-sen University Shenzhen518107 China Institute of Precision Instrument and Quantum Sensing Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China

To overcome antimalarial drug resistance, carbohydrate derivatives as selective PfHT1 inhibitor have been suggested in recent experimental work with orthosteric and allosteric dual binding pockets. Inspired by this promising therapeutic strategy, herein, molecular dynamics simulations are performed to investigate the molecular determinants of co-administration on orthosteric and allosteric inhibitors targeting PfHT1. Our binding free energy analysis capture the essential trend of inhibitor binding affinity to protein from published experimental IC50 data in three sets of distinct characteristics. In particular, we rank the contribution of key residues as binding sites which categorized into three groups based on linker length, size of tail group, and sugar moiety of inhibitors. The pivotal roles of these key residues are further validated by mutant analysis where mutated to nonpolar alanine leading to reduced affinities to different degrees. The exception was fructose derivative, which exhibited a significant enhanced affinity to mutation on orthosteric sites due to strong changed binding poses. This study may provide useful information for optimized design of precision medicine to circumvent drug-resistant Plasmodium parasites with high efficacy. © 2025, CC BY-SA.

关键词： Binding sites

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Self-to-self transitions in open quantum systems:the origin and solutions

Self-to-self transitions in open quantum systems:the origin ...

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第三十九届中国控制会议

作者： Fang Gao Yaoxiong Wang Qing Gao Dewen Cao Feng Shuang College of Electrical Engineering Guangxi University Institute of Intelligent Machines Chinese Academy of Sciences School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University

The information of quantum pathways can be extracted in the framework of the Hamiltonianencoding and Observable-decoding *** closed quantum systems,only off-diagonal elements of the Hamiltonian in the Hilbert space is required to be encoded to obtain the desired *** open quantum systems,environment-related terms will appear in the diagonal elements of the Hamiltonian in the Liouville *** diagonal encodings have to be performed to differentiate different pathways,which will lead to self-to-self transitions and inconsistency of pathway amplitudes with Dyson *** this work,a well-designed transformation is proposed to avoid the counterintuitive transitions and the inconsistency,with or without control *** simulations show that the method are consistent with Dyson expansion.

关键词： Self-to-self transitions in open quantum systems:the origin and solutions origin

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Characterizing the contribution of dependent features in XAI methods

arXiv

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arXiv 2023年

作者： Salih, Ahmed Galazzo, Ilaria Boscolo Raisi-Estabragh, Zahra Petersen, Steffen E. Menegaz, Gloria Radeva, Petia William Harvey Research Institute Nihr Barts Biomedical Research Centre Queen Mary University of London Charterhouse Square LondonEC1M 6BQ United Kingdom Department of Computer Science Faculty of Science University of Zakho Zakho Way Road Kurdistan region Duhok42002 Iraq Department of Engineering for Innovation Medicine University of Verona Via S. Francesco 22 Verona37129 Italy Barts Heart Centre St Bartholomew's Hospital Barts Health Nhs Trust West Smithfield LondonEC1A 7BE United Kingdom Health Data Research London United Kingdom Alan Turing Institute London United Kingdom De Matemàtiques i Informàtica Universitat de Barcelona Gran Via de les Corts Catalanes Barcelona08007 Spain Computer Vision Center Barcelona Cerdanyola del Vallés Spain

Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparent. In this context, many XAI methods were proposed being SHAP and LIME the most popular. However, the proposed methods assume that used predictors in the machine learning models are independent which in general is not necessarily true. Such assumption casts shadows on the robustness of the XAI outcomes such as the list of informative predictors. Here, we propose a simple, yet useful proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity. © 2023, CC BY.

关键词： Lime

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Prediction of Oral Food Challenge Outcomes via Ensemble Learning

arXiv

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arXiv 2022年

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Department of Internal Medicine University of Michigan Ann ArborMI United States Department of Pediatrics University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computer Science and Engineering University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States Mary H. Weiser Food Allergy Center University of Michigan Ann ArborMI United States Department of Computer Science Queens College City University of New York New YorkNY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥ 89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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SR-HetGNN: Session-based Recommendation with Heterogeneous Graph Neural Network

arXiv

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arXiv 2021年

作者： Chen, Jinpeng Li, Haiyang Zhang, Xudong Zhang, Fan Wang, Senzhang Wei, Kaimin Ji, Jiaqi Beijing University of Posts and Telecommunications Xitucheng Road Beijing100876 China Ministry of Education Xitucheng Road Beijing100876 China School of Computer Science and Engineering Central South University yuelu district Hunan Province Changsha410083 China College of Information Science and Technology Jinan University Huangpu Avenuet Guangdong Province Guangzhou510632 China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Hebei Province Chengde067000 China Hebei Normal University for Nationalities Hebei Province Chengde067000 China

The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, local and global session embeddings are combined with the attentional network to obtain the final session embedding, considering the influence of users' long and short-term preferences. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

arXiv

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arXiv 2025年

作者： Hou, Qingshan Zhou, Yukun Goh, Jocelyn Hui Lin Zou, Ke Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Lo, Thaddaeus Lei, Xiaofeng Wagner, Siegfried K. Chia, Mark A. Yang, Dawei Jiang, Hongyang Ran, AnRan Santos, Rui Somfai, Gabor Mark Zhou, Juan Helen Chen, Haoyu Chen, Qingyu Cheung, Carol Yim-Lui Keane, Pearse A. Tham, Yih Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang China Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Singapore Institute of Ophthalmology University College London London United Kingdom Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Hong Kong Department of Ophthalmology Stadtspital Zürich Zurich Switzerland Spross Research Institute Zurich Switzerland Department of Ophthalmology Semmelweis University Budapest Hungary Centre for Sleep and Cognition Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Joint Shantou International Eye Center Shantou University the Chinese University of Hong Kong Guangdong Shantou China Shantou University Medical College Guangdong Shantou China Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States Duke-NUS Medical School Singapore

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by finetuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850–0.952 vs. 0.823-0.944, across three datasets, all P≤0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Multi-task graph convolutional neural network for calcification morphology and distribution analysis in mammograms

arXiv

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arXiv 2021年

作者： Du, Hao Yao, Melissa Min-Szu Chen, Liangyu Chan, Wing P. Feng, Mengling Saw Swee Hock School of Public Health National University of Singapore Singapore117549 Singapore Department of Radiology School of Medicine College of Medicine Taipei Medical University Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei116 Taiwan Medical Innovation Development Center Wan Fang Hospital Taipei Medical University Taiwan School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Institute of Data Science National University of Singapore

The morphology and distribution of microcalcifications in a cluster are the most important characteristics for radiologists to diagnose breast cancer. However, it is time-consuming and difficult for radiologists to identify these characteristics, and there also lacks of effective solutions for automatic characterization. In this study, we proposed a multitask deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms. Our proposed method transforms morphology and distribution characterization into node and graph classification problem and learns the representations concurrently. Through extensive experiments, we demonstrate significant improvements with the proposed multi-task GCN comparing to the baselines. Moreover, the achieved improvements can be related to and enhance clinical understandings. We explore, for the first time, the application of GCNs in microcalcification characterization that suggests the potential of graph learning for more robust understanding of medical images. Copyright © 2021, The Authors. All rights reserved.

关键词： Biomineralization

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A layered parallel algorithm for data clustering via fuzzy c-means technique

Research Square

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Research Square 2021年

作者： Mu, Yashuang Wei, Wei Guo, Hongyue Sun, Lijun Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Maritime Economics and Management Dalian Maritime University Dalian116026 China Collaborative Innovation Center for Transport Studies Dalian Maritime University Dalian116026 China

In this study, a layered parallel algorithm via fuzzy c-means (FCM) technique, called LP-FCM, is proposed in the framework of Map-Reduce for data clustering problems. The LP-FCM mainly contains three layers. The first layer follows a parallel data partitioning method which is developed to randomly divide the original dataset into several subdatasets. The second layer uses a parallel cluster centers searching method based on Map-Reduce, where the classic FCM algorithm is applied to search the cluster centers for each subdataset in Map phases, and all the centers are gathered to the Reduce phase for the confirmation of the final cluster centers through FCM technique again. The third layer implements a parallel data clustering method based on the final cluster centers. After comparing with some famous classic random initialization sequential clustering algorithms which include K-means, K-medoids, FCM and MinMaxK-means on 20 benchmark datasets, the feasibility in terms of clustering accuracy is evaluated. Furthermore, the clustering time and the parallel performance are also tested on some generated large-scale datasets for the parallelism. © 2021, CC BY.

关键词： K-means clustering

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Comparative Analysis of Pre-process Pipelines For Automatic Retinal Vessel Segmentation

Comparative Analysis of Pre-process Pipelines For Automatic ...

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Chinese Control Conference (CCC)

作者： Gaoyi Lei Yuanqing Xia Wei Zhang Duanduan Chen Defeng Wang School of automation Beijing Institute of Technology Beijing China School of Information Science and Engineering Zaozhuang University China School of Life Sciences Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Retinal vessel structure is an unique individual characteristic and important biology marker of many diseases, like Diabetic Retinopathy (DR), cardiovascular ailment, and so on. Automatic retinal vessel segmentation can be used to assist the early diagnosis of above diseases, but suffers from the poor quality and low contrast of fundus images. To eliminate the noise in the fundus images, many pre-process pipelines are designed to normalize and enhance the fundus images. However, the specific operations in pre-process pipelines of the fundus images haven't been distinguished from operations in normalization of natural images. This paper collects a dozen of pre-process pipelines from published retinal vessel segmentation researches, and proposes five general patterns of these pre-process pipelines. Furthermore, we test the flexibility of five classical pre-process pipelines on public retinal vessel datasets with a Dense-UNet model. Experiments demonstrate that the "Hiera" pre-process pipeline and the "DUNet" pre-process pipeline outperform the rest pipelines in assisting the Dense-UNet to segment the retinal vessels.

关键词： Pipelines Retinal vessels Image segmentation databases Image color analysis Histograms Feature extraction

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