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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Chiu, Yen-Jung Chuang, Chao-Chun Hwa, Kuo-Yuan Department of Biomedical Engineering Ming Chuan University Taipei Taiwan National Center For High-Performance Computing Hsinchu Taiwan Department of Molecular Science and Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798400718274

Large Language Models (LLMs) have revolutionized natural language processing (NLP) with significant advancements in text generation. LLMs often struggle with complex domain-specific tasks, such as medical report analysis, despite their capabilities. This study focuses on enhancing LLM performance for medical applications, particularly in diagnosing and managing cardiogenic pulmonary edema (CPE). This research explores fine-tuning LLMs to develop a real-time CPE chatbot for Intensive Care Units (ICUs). The chatbot aims to provide diagnostic suggestions and explanations based on patient data. In the results, the LLaMa3-8B model performed better in predicting patients' CPE stage and keyword extraction. The accuracies achieved 72% and 86%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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Optimizing Medical Imaging: high-performance Hardware for Image Processing, Machine Learning 24

Optimizing Medical Imaging: High-Performance Hardware for Im...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Kalyan Chakravarthy, N.S. Balapanur, Mouli Chandra Pandian, Arun Nambi Pulikanti, Jyothi Prasad, D. Syed Masood, Jafar Ali Ibrahim High-Performance Computing Research Laboratory QIS College of Engineering and Technology Andhra Pradesh Ongole India Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

The demand for high-performance hardware solutions for machine learning tasks is growing as medical imaging evolves. In this paper, we will focus on the latest hardware advanced technologies: GPUs, TPUs and FPGAs that can be used for image processing optimization in medical imaging such as brain tumor segmentation or chest X-ray classification. We test these devices on the BraTS and ChestX-ray14 datasets to show how running models quicker increases diagnostic performance. The study offers a comprehensive performance and energy efficiency evaluation of all the considered hardware solutions when training deep learning models for medical imaging, providing clinical guidelines to choose appropriate new-generation accelerators before engaging in expensive leasing contracts. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： GPUS

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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Cooperative Multiagent Learning and Exploration With Min-Max Intrinsic Motivation

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IEEE Transactions on Cybernetics 2025年第6期55卷 2852-2864页

作者： Hou, Yaqing Kang, Jie Piao, Haiyin Zeng, Yifeng Ong, Yew-Soon Jin, Yaochu Zhang, Qiang Dalian University of Technology College of Computer Science and Technology Dalian116024 China Jilin University School of Artificial Intelligence Changchun130015 China Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Nanyang Technological University School of Computing and Data Science Singapore Singapore Agency for Science Technology and Research Centre for Frontier Ai Research Institute of High Performance Computing Singapore Singapore Westlake University School of Engineering Hangzhou310030 China

In the field of multiagent reinforcement learning (MARL), the ability to effectively explore unknown environments and collect information and experiences that are most beneficial for policy learning represents a critical research area. However, existing work often encounters difficulties in addressing the uncertainties caused by state changes and the inconsistencies between agents' local observations and global information, which presents significant challenges to coordinated exploration among multiple agents. To address this issue, this article proposes a novel MARL exploration method with Min-Max intrinsic motivation (E2M) that promotes the learning of joint policies of agents by introducing surprise minimization and social influence maximization. Since the agent is subject to unstable state changes in the environment, we introduce surprise minimization by computing state entropy to encourage the agents to cope with more stable and familiar situations. This method enables surprise estimation based on the low-dimensional representation of states obtained from random encoders. Furthermore, to prevent surprise minimization from leading to conservative policies, we introduce mutual information between agents' behaviors as social influence. By maximizing social influence, the agents are encouraged to interact to facilitate the emergence of cooperative behavior. The performance of our proposed E2M is testified across a range of popular StarCraft II and Multiagent MuJoCo tasks. Comprehensive results demonstrate its effectiveness in enhancing the cooperative capability of the multiple agents. © 2025 IEEE.

关键词： Adversarial machine learning

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Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning

Benchmarking Data Heterogeneity Evaluation Approaches for P...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Li, Zhilong Wu, Xiaohu Tang, Xiaoli He, Tiantian Ong, Yew-Soon Chen, Mengmeng Liu, Qiqi Lao, Qicheng Yu, Han Beijing University of Posts and Telecommunications Beijing China College of Computing and Data Science Nanyang Technological University Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore Singapore School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9783031822391

There is growing research interest in measuring the statistical heterogeneity of clients’ local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors are taking place in silos and there is a lack of a unified benchmark to provide a fair and convenient comparison among various approaches in common settings. We aim to bridge this important gap in this paper. The proposed benchmarking framework currently includes six representative approaches. Extensive experiments have been conducted to compare these approaches under five standard non-IID FL settings, providing much needed insights into which approaches are advantageous under which settings. The proposed framework offers useful guidance on the suitability of various data divergence measures in FL systems. It is beneficial for keeping related research activities on the right track in terms of: (1) designing PFL schemes, (2) selecting appropriate data heterogeneity evaluation approaches for specific FL application scenarios, and (3) addressing fairness issues in collaborative model training. The code is available at https://***/Xiaoni-61/DH-Benchmark. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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TaiwanVQA: A Benchmark for Visual Question Answering for Taiwanese Daily Life 1

TaiwanVQA: A Benchmark for Visual Question Answering for Tai...

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1st Workshop of Evaluation of Multi-Modal Generation, EvalMG 2025

作者： Hsieh, Hsin-Yi Liu, Shang-Wei Meng, Chih-Chang Chen, Chien-Hua Lin, Shuo-Yueh Lin, Hung-Ju Huang, Hen-Hsen Chen Wu, I. National Center for High-performance Computing Taiwan National Central University Taiwan National Yang Ming Chiao Tung University Taiwan National Taiwan University Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798891762138

We introduce TaiwanVQA, a novel visual question answering benchmark designed to evaluate vision language models’ (VLMs) ability to recognize and reason about Taiwan-specific multimodal content. TaiwanVQA comprises 2,000 image-question pairs covering diverse topics relevant to Taiwanese culture and daily life. We categorize the questions into recognition and reasoning tasks, further sub-classifying reasoning questions based on the level of external knowledge required. We conduct extensive experiments on state-of-the-art VLMs, including GPT-4o, Llama-3.2, LLaVA, Qwen2-VL, and InternVL2 models. Our findings reveal significant limitations in current VLMs when handling culturally specific content. The performance gap widens between recognition tasks (top score 73.60%) and reasoning tasks (top score 49.80%), indicating challenges in cultural inference and contextual understanding. These results highlight the need for more culturally diverse training data and improved model architectures that can better integrate visual and textual information within specific cultural contexts. By providing TaiwanVQA, we aim to contribute to the development of more inclusive and culturally aware AI models, facilitating their deployment in diverse real-world settings. TaiwanVQA can be accessed on our GitHub page. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Built year prediction of buddha face with heterogeneous label modeled as probabilistic distribution

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Multimedia Tools and Applications 2025年 1-24页

作者： Qian, Yiming Vaigh, Cheikh Brahim El Nakashima, Yuta Renoust, Benjamin Nagahara, Hajime Fujioka, Yutaka Institute of High Performance Computing and Agency for Science Technology and Research 1 Fusionopolis Way16-16 Connexis Singapore138632 Singapore University of burgundy / CIAD Maison de l’Université Esp. Erasme Dijon France Median Technologies and Osaka University Valbonne France Osaka University Osaka Japan

Analysis of cultural heritages, particularly their construction years, provides new insights into human history. However, due to natural disasters, wars, material deterioration, and human errors, records documenting the construction years of many artifacts have often been lost. Historians and experts can estimate construction years within specific ranges using chemical-based analysis technologies or extensive historical research. Given the vast number of collected artifacts, applying these conventional methods to every artifact is impractical. To address this challenge, we developed a deep neural network model designed for Buddha statues to estimate an artifact’s construction year from its image. One major challenge in this task is the heterogeneity of the labels: the training samples include both precise construction years and possible ranges (e.g., a dynasty or a century) estimated by historians. To unify these heterogeneous labels during training, we represent them as probabilistic distributions. In our previous work Qian et al. (2021), we assumed that the ambiguity in heterogeneous construction year labels followed a Gaussian distribution, assigning the highest likelihood to the midpoint of the designated time range. However, this assumption does not always hold. In this paper, we propose representing heterogeneous construction year labels as a uniform distribution, assigning equal probability to all points within the designated time range. Based on this label representation, we designed a semi-supervised learning loss function to leverage both labeled and unlabeled samples during training. Our experimental results demonstrate that our method achieves a mean absolute error of 34.3 years on a test set consisting of Buddha statues constructed between 400 and 1403. These results are further analyzed in two ways. First, we compared our model’s performance to the image quality BRISQUE score, revealing a correlation between higher image quality and lower prediction er

关键词： Semi-supervised learning

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An Attention Model Based Approach for Leakage Detection in Water Distribution Networks Using Normal Pressure Data 21st

An Attention Model Based Approach for Leakage Detection in...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Luo, Juan Zhou, Du Wang, Chongxiao Yang, Jielong Zhong, Xionghu Hunan University Changsha China Hunan Construction Investment Group Co. Ltd. Changsha China Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Jiangnan University Wuxi China

ISBN: (纸本)9789819601271

In the past, machine learning methods such as Convolutional Neural Networks (CNN) and Autoencoder (AE) have been employed for leakage detection in water distribution networks (WDNs). However, these methods primarily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer based model to detect leakages in WDNs. This model incorporates an attention mechanism to learn data distributions and account for correlations between historical and daily interval pressures, thereby emphasizing long-term dependencies in the pressure series. The performance of the proposed method is evaluated using detection accuracy and F1-score. Experimental studies conducted on simulated pressure datasets from two different WDNs demonstrate that the transformer based model significantly outperforms traditional CNN methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Multi-View Test-Time Adaptation for Semantic Segmentation in Clinical Cataract Surgery

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2307-2318页

作者： Li, Heng Ou, Mingyang Li, Haojin Qiu, Zhongxi Niu, Ke Fu, Huazhu Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Beijing Information Science and Technology University Computer School Beijing100192 China Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China

Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often differ among clinical centers, intensifying the issue of domain shifts. While domain adaptation offers remedies to the shifts, the necessity for data centralization raises additional privacy concerns. To overcome these challenges, we propose a Multi-view Test-time Adaptation algorithm (MUTA) to segment cataract surgical scenes, which leverages multi-view learning to enhance model training within the source domain and model adaptation within the target domain. In the training phase, the segmentation model is equipped with multi-view decoders to boost its robustness against variations in cataract surgery. During the inference phase, test-time adaptation is implemented using multi-view knowledge distillation, enabling model updates in clinics without data centralization or privacy concerns. We conducted experiments in a simulated cross-center scenario using several cataract surgery datasets to evaluate the effectiveness of MUTA. Through comparisons and investigations, we have validated that MUTA effectively learns a robust source model and adapts the model to target data during the practical inference phase. © 1982-2012 IEEE.

关键词： Semantic Segmentation

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A Multi-Hyperparameter Prediction Framework for Distributed Energy Trading on Photovoltaic Network

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Tsinghua Science and technology 2025年第2期30卷 864-874页

作者： Chun Chen Yong Zhang Boon Han Lim Li Ning Shengzhong Feng Peng Xie School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information TechnologyShenzhen 518172China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul RahmanKajang 43000Malaysia Shenzhen Institute for Advanced Study Shenzhen 518028China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesBeijing 100864China High-performance Intelligent Computing Research Group Guangdong Institute of Intelligent Science and TechnologyZhuhai 519031China

The rapid evolution of distributed energy resources,particularly photovoltaic systems,poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing *** integrated nature of distributed markets,blending centralized and decentralized elements,holds the promise of maximizing social welfare and significantly reducing overall costs,including computational and communication ***,achieving this balance requires careful consideration of various hyperparameter sets,encompassing factors such as the number of communities,community detection methods,and trading mechanisms employed among *** address this challenge,we introduce a groundbreaking neural network-based framework,the Energy Trading-based Artificial Neural Network(ET-ANN),which excels in performance compared to existing *** experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.

关键词： photovoltaic energy trading hyperparameter prediction

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