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SS-Pro:a simplified siamese contrastive learning approach for protein surface representation

Frontiers of Computer science 2024年第5期18卷 243-245页

作者： Ao SHEN Mingzhi YUAN Yingfan MA Manning WANG Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China

Protein surface serves as an important representation of protein structure,revealing how protein interacts with other biomolecules to perform its *** forms the basis for pharmaceutical and fundamental biological research[1].Datadriven deep learning methods in protein surface representation face challenges of label scarcity,since labeled data are typically obtained through wet lab experiments.

关键词： revealing representation simplified

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Artificial Intelligence in Education: Perspectives and Challenges

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第4期19卷 26-47页

作者： Al-Huwail, Nabeil Al-Hunaiyyan, Ahmed Alainati, Shaikhah Alhabshi, Ayman Computing Department College of Business Studies PAAET Al-Ardiya Kuwait Management Department College of Business Studies PAAET Al-Ardiya Kuwait Library and Information Science College of Basic Education PAAET Al-Ardiya Kuwait

Understanding the integration of artificial intelligence (AI) in academic institutions is crucial given Kuwait’s commitment to innovation and educational excellence. This study explores the integration of AI into higher education in Kuwait, revealing both positive perceptions and critical concerns. Qualitative insights from faculty and students highlight AI’s potential to improve learning across multiple academic disciplines. Quantitative data, collected from 310 students, shows that a majority of students hold optimistic views on AI’s effectiveness in enhancing educational processes and project-based activities. However, concerns were raised about the ethical implications, high costs, data privacy, and the complexity of AI tools. Importantly, no statistically significant difference was found between male and female students’ views on AI’s role in education. These findings are significant for policymakers and educators, guiding how to address practical and ethical challenges while facilitating the effective incorporation of AI into the educational system. © 2025 by the authors of this article. Published under CC-BY.

关键词： Federated learning

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A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

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Frontiers of Computer science 2023年第6期17卷 147-149页

作者： Shaolei LIU Xiaoyuan LUO Kexue FU Manning WANG Zhijian SONG Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China

1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model ***,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another *** reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.

关键词： point cloud utilizing

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Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space 41

Robust Optimization in Protein Fitness Landscapes Using Rein...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Minji Vecchietti, Luiz Felipe Jung, Hyunkyu Ro, Hyunjoo Cha, Meeyoung Kim, Ho Min School of Computing KAIST Daejeon Korea Republic of Center for Mathematical and Computational Sciences Institute for Basic Science Daejeon Korea Republic of Center for Biomolecular and Cellular Structure Institute for Basic Science Daejeon Korea Republic of Department of Biological Sciences KAIST Daejeon Korea Republic of

Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method to efficiently traverse a latent space learned by an encoder-decoder leveraging a large protein language model. To escape local optima, our optimization is modeled as a Markov decision process using reinforcement learning acting directly in latent space. We evaluate our approach on two important fitness optimization tasks, demonstrating its ability to achieve comparable or superior fitness over baseline methods. Our findings and in vitro evaluation show that the generated sequences can reach high-fitness regions, suggesting a substantial potential of LatProtRL in lab-in-the-loop scenarios. Copyright 2024 by the author(s)

关键词： Markov processes

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Detecting Offensive Language in an Open Chatbot Platform 30

Detecting Offensive Language in an Open Chatbot Platform

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Song, Hyeonho Hong, Jisu Jung, Chani Chin, Hyojin Shin, Mingi Choi, Junghoi Choi, Yubin Cha, Meeyoung KAIST School of Computing Daejeon Korea Republic of Seoul National University Seoul Korea Republic of Institute for Basic Science Daejeon Korea Republic of SimSimi Inc. Seoul Korea Republic of

ISBN: (纸本)9782493814104

While detecting offensive language in online spaces remains an important societal issue, there is still a significant gap in existing research and practial datasets specific to chatbots. Furthermore, many of the current efforts by service providers to automatically filter offensive language are vulnerable to users' deliberate text manipulation tactics, such as misspelling words. In this study, we analyze offensive language patterns in real logs of 6,254,261 chat utterance pairs from the commercial chat service Simsimi, which cover a variety of conversation topics. Based on the observed patterns, we introduce a novel offensive language detection method-a contrastive learning model that embeds chat content with a random masking strategy. We show that this model outperforms existing models in detecting offensive language in open-domain chat conversations while also demonstrating robustness against users' deliberate text manipulation tactics when using offensive language. We release our curated chatbot dataset to foster research on offensive language detection in open-domain conversations and share lessons learned from mitigating offensive language on a live platform. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Chatbot Contrastive learning Offensive language detection Open-domain conversations

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Medium-assisted enhancement of χc1(3872) production from small to large colliding systems

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Physical Review C 2024年第2期110卷 L021901-L021901页

作者： Yu Guo Xingyu Guo Jinfeng Liao Enke Wang Hongxi Xing Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Physics Department and Center for Exploration of Energy and Matter Southern Center for Nuclear-Science Theory (SCNT)

Studies of exotic hadrons such as the χc1(3872) state provide crucial insights into the fundamental force governing the strong interaction dynamics, with an emerging frontier to investigate their production in high energy collisions where a partonic medium is present. The latest experimental measurements from the Large Hadron Collider show an intriguing evolution pattern of the χc1(3872)-to-ψ(2S) yield ratio from proton-proton collisions with increasing multiplicities toward proton-lead and lead-lead collisions. Here we propose a mechanism of medium-assisted enhancement for the χc1(3872) production, which competes with the more conventional absorption-induced suppression and results in a nonmonotonic trend from small to large colliding systems. Realistic simulations from this model offer a quantitative description of all available data. Predictions are made for the centrality dependence of this observable in PbPb collisions as well as for its system-size dependence from OO and ArAr to XeXe and PbPb collisions. In both cases, a nonmonotonic behavior emerges as the imprint of the competition between enhancement and suppression and can be readily tested by future data.

关键词： Jets & heavy flavor physics Particle & resonance production Charm quark Exotic mesons

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Comprehensive constraints on fermionic dark matter-quark tensor interactions in direct detection experiments

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Chinese Physics C 2024年第12期48卷 37-49页

作者： Jin-Han Liang Yi Liao Xiao-Dong Ma Hao-Lin Wang Key Laboratory of Atomic and Subatomic Structure and Quantum Control(MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of MatterInstitute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing CenterSouth China Normal UniversityGuangzhou 510006China

Effective field theory(EFT)provides a model-independent framework for interpreting the results of dark matter(DM)direct detection *** this study,we demonstrate that the two fermionic DM-quark tensor operators(χiσ^(μν)γ^(5)χ)(qσ_(μν)q)and(χσ_(μν)χ)(qσ_(μν)q)can contribute to the DM electric and magnetic dipole moments via nonperturbative QCD effects,in addition to the well-studied contact DM-nucleon *** then investigate the constraints on these two operators by considering both the contact and dipole contributions using the XENON1T nuclear recoil and Migdal effect *** also recast other existing bounds on the DM dipole operators,derived from electron and nuclear recoil measurements in various direct detection experiments,as constraints on the two tensor *** m_(χ)■1GeV,our results significantly extend the reach of constraints on the DM-quark tensor operators to masses as low as 5MeV,with the bound exceeding that obtained by the Migdal effect with only contact interactions by approximately an order of *** particular,for the operator(χσ^(μν)iγ5χ)(qσ_(μν)q)with DM mass m_(χ)■10GeV,the latest PandaX constraint on the DM electric dipole moment puts more stringent bounds than the previous direct detection *** also briefly discuss the constraints obtained from experiments other than direct detection.

关键词： fermionic dark matter effective field theories tensor interactions

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AMSFuse:Adaptive Multi-Scale Feature Fusion Network for Diabetic Retinopathy Classification

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Computers, Materials & Continua 2025年第3期82卷 5153-5167页

作者： Chengzhang Zhu Ahmed Alasri Tao Xu Yalong Xiao Abdulrahman Noman Raeed Alsabri Xuanchu Duan Monir Abdullah School of Computer Science and Engineering Central South UniversityChangsha410083China School of Humanities Central South UniversityChangsha410083China Department of Human Anatomy and Histology & Embryology Basic Medical SciencesChangsha Health Vocational CollegeChangsha410100China Glaucoma Institute Changsha Aier Eye HospitalChangsha410000China Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia

Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people *** widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective *** learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and ***,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic *** the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological *** address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual ***,our model incorporates global features for extracting high-level contextual information from retinal ***,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR *** global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR *** model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.

关键词： Diabetic retinopathy multi-scale feature fusion global features local features integrated attention mechanism retinal images

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Advancing Breast Cancer Diagnosis:The Development and Validation of the HERA-Net Model for Thermographic Analysis

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Computers, Materials & Continua 2024年第12期81卷 3731-3760页

作者： S.Ramacharan Martin Margala Amjan Shaik Prasun Chakrabarti Tulika Chakrabarti School of Computing and Informatics University of LouisianaLafayetteLA 70504USA Computer Science and Engineering St.Peter’s Engineering CollegeHyderabad500100India Director Research and Dean International Affairs Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia Department of Basic Sciences Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia

Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal *** thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 *** dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and *** prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for *** performance assessme

关键词： Cancer breast cancer detection sensitivity deep learning accuracy

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Federated Semi-supervised Learning for Medical Image Segmentation with Intra-client and Inter-client Consistency

Federated Semi-supervised Learning for Medical Image Segment...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zheng, Yubin Tang, Peng Ju, Tianjie Wang, Hao Qiu, Weidong Rajapakse, Jagath C. Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Jiao Tong University College of Basic Medical Sciences Shanghai China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350386226

Medical image segmentation plays a vital role in medical image analysis. However, it is impractical to build a large-scale centralized segmentation dataset due to the privacy of medical images. Federated learning (FL) aims to train a shared model of isolated clients without local data exchange which aligns well with the scarcity and privacy characteristics of medical images. Moreover, there is a large amount of unlabeled data in clients due to the difficulty in annotating medical images. Federated semi-supervised learning (FSSL) can leverage the unlabeled data of clients to improve the performance of the global model. Many existing FSSL methods apply the complicated semi-supervised learning protocols and some of them neglect the problem of data heterogeneity in FL. In this paper, we propose a novel federated semi-supervised learning framework for medical image segmentation incorporating intra-client and inter-client consistency learning. The intra-client consistency learning can introduce global data noise in data augmentation which can improve the generalization ability of the model and reduce the impact of data heterogeneity. The inter-client consistency learning is proposed to expand the feature search space and learn the ensemble knowledge of different clients. The two consistency learning mechanisms are achieved with the assistance of a Variational Autoencoder (VAE) trained collaboratively by clients. The experimental results illustrate that our method outperforms the state-of-the-art methods under different FSSL settings. The code is available at https://***/zyb98/FV2IC. © 2024 IEEE.

关键词： Federated learning

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