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7th International Conference on Medical and Health informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

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Quadratic stabilisation of switched affine systems

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Journal of Control and Decision 2020年第1期7卷 1-23页

作者： Minqing Xiao Guisheng Zhai Chi Huang College of Mathematics and Informatics Fujian Key Laboratory of Mathematical Analysis and ApplicationFujian Normal UniversityFuzhouPeople’s Republic of China Department of Mathematical Sciences College of Systems Engineering and ScienceShibaura Institute of TechnologyTokyoJapan Department of Computer Science and Technology School of Economic Information EngineeringSouthwestern University of Finance and EconomicsChengduPeople’s Republic of China

Minqing Xiao received the *** from Chongqing University,Chongqing,China,in *** is currently a Professor with the College of Mathematics and informatics,Fujian Normal University,Fuzhou,*** current research interests include robust control/filter theory,delta operator systems,networked control systems,and switched systems;Guisheng Zhai received his *** from Fudan University,China,in 1988,and he received his *** ***,both in system science,from Kobe University,Japan,in 1993 and 1996,*** two years of industrial experience,*** moved to Wakayama University,Japan,in 1998,and then to Osaka Prefecture University,Japan,in *** held visiting professor positions at University of Notre Dame from August 2001 to July 2002,and at Purdue University from March 2016 through February *** April 2010,he joined the faculty board of Shibaura institute of technology,Japan,where he currently is a full Professor of Mathematical *** research interests include large scale and decentralised control systems,robust control,switched systems and switching control,networked control and multi-agent systems,neural networks and signal processing,*** is on the editorial board of several academic journals including IET Control Theory&Applications,International Journal of Applied Mathematics and computer Science,Journal of Control and Decision,and Frontiers of Mechanical *** is a Senior Member of IEEE,a member of ISCIE,SICE,JSST and JSME.

关键词： Switched affine systems(SAS) switched uncertain affine systems(SUAS) quadratic stabilisation convex combination state-dependent switching output-dependent switching convergence set LMIs norm bounded uncertainties

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

arXiv

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arXiv 2023年

作者： Gao, Zhen Ke, Malong Mei, Yikun Qiao, Li Chen, Sheng Ng, Derrick Wing Kwan Poor, H. Vincent The MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China The Wireless Product Division Ruijie Network Co. Ltd. Fuzhou350108 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The School of Electrical Engineering and Telecommunications University of New South Wales Sydney2052 Australia The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

arXiv

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arXiv 2023年

作者： Zhou, Bo Xie, Huidong Liu, Qiong Chen, Xiongchao Guo, Xueqi Feng, Zhicheng Hou, Jun Zhou, S. Kevin Li, Biao Rominger, Axel Shi, Kuangyu Duncan, James S. Liu, Chi Department of Biomedical Engineering Yale University New HavenCT United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Department of Electrical Engineering Yale University New HavenCT United States Department of Biomedical Engineering University of Southern California Los AngelesCA United States Department of Computer Science University of California Irvine IrvineCA United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Nuclear Medicine Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Nuclear Medicine Inselspital Bern University Hospital University of Bern Bern Switzerland Computer Aided Medical Procedures and Augmented Reality Institute of Informatics I16 Technical University of Munich Munich Germany

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multiinstitution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network’s weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions. © 2023, CC BY.

关键词： Deep learning

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AIPNet: Action-Instance Progressive Learning Network for Instrument-tissue Interaction Detection

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IEEE Journal of Biomedical and Health informatics 2025年

作者： Lin, Wenjun Hu, Yan Hao, Luoying Fu, Huazhu Chui, Cheekong Liu, Jiang National University of Singapore Department of Mechanical Engineering Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering China University of Birmingham School of Computer Science United Kingdom Singapore

Instrument-tissue interaction detection, a task aimed at understanding surgical scenes from videos, holds immense importance in constructing computer-assisted surgery systems. Existing methods consist of two stages: instance detection and interaction prediction. This sequential and separate model structure limits both effectiveness and efficiency, making it difficult to deploy on surgical robotic platforms. In this paper, we propose an end-to-end Action-Instance Progressive Learning Network (AIPNet) for the task. The model operates in three steps: action detection, instance detection, and action class refinement. Starting with coarse-scale proposals, the model progressively refines them into coarse-grained actions, which then serve as proposals for instance detection. The action prediction results are further refined using instance features through late fusion. These progressive learning processes improve the performance of the end-to-end model. Additionally, we introduce Dynamic Proposal Generators (DPG) to create dynamic adaptive learnable proposals for each video frame. To address the training challenges of this multi-task model, semantic supervised training is introduced to transfer prior language knowledge, and a training label strategy is proposed to generate unrelated instrument-tissue pair labels for enhanced supervision. Experimental results on PhacoQ and CholecQ datasets show that the proposed method achieves superior accuracy and faster processing speed than state-of-the-art models. © 2013 IEEE.

关键词： Grafts

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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SoAy: A Solution-based LLM API-using Methodology for academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Soliton generation in AlGaAs microresonators at room temperature

arXiv

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arXiv 2022年

作者： Wu, Lue Xie, Weiqiang Xiang, Chao Chang, Lin Yu, Yan Chen, Hao-Jing Yamamoto, Yoshihisa Bowers, John E. Vahala, Kerry J. Suh, Myoung-Gyun T. J. Watson Laboratory of Applied Physics California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Physics & Informatics Laboratories NTT Research Inc SunnyvaleCA94085 United States

Chip-integrated optical frequency combs are attractive optical sources in comb applications requiring high-repetition-rate, low power consumption, or compact size. Spontaneous soliton formation via Kerr parametric oscillation is a promising generation principle in these frequency combs, and has been demonstrated in several material platforms over the past decade. Of these materials, AlGaAs has one of the largest Kerr nonlinearity coefficients allowing low pump threshold comb generation. However, bright soliton generation using this material has only been possible at cryogenic temperature because of the large thermo-optic effect at room temperature, which hinders stable access to the soliton regime. Here, we report self-stabilized single soliton generation in AlGaAs microresonators at room temperature by utilizing a rising soliton step in large free-spectral-range resonators. With sub-milliWatt optical pump power, 1 THz repetition-rate soliton generation is demonstrated. Perfect soliton crystal formation and soliton breather states are also observed. Besides the advantages of large optical nonlinearity, the devices are natural candidates for integration with III-V pump lasers. Copyright © 2022, The Authors. All rights reserved.

关键词： Aluminum gallium arsenide

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Comparative risk of severe constipation in patients treated with opioids for non-cancer pain: a retrospective cohort study in Northwest England

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BMC medicine 2025年第1期23卷 288页

作者： Belay Birlie Yimer Mehreen Soomro John McBeth Carlos Raul Ramirez Medina Mark Lunt William G Dixon Meghna Jani Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research The University of Manchester Manchester UK. NIHR Manchester Biomedical Research Centre Manchester University NHS Foundation Trust Manchester Academic Health Science Centre Manchester UK. Faculty of Medicine and Faculty of Engineering and Computer Science The University of Southampton Southampton UK. Department of Rheumatology Salford Royal Hospital Northern Care Alliance Salford UK. Division of Informatics Imaging and Data Science The University of Manchester Manchester UK. Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research The University of Manchester Manchester UK. Meghna.jani@manchester.ac.uk. Department of Rheumatology Salford Royal Hospital Northern Care Alliance Salford UK. Meghna.jani@manchester.ac.uk. NIHR Manchester Biomedical Research Centre Manchester University NHS Foundation Trust Manchester Academic Health Science Centre Manchester UK. Meghna.jani@manchester.ac.uk.

BACKGROUND:Constipation is a frequent adverse event associated with opioid medications that can have a considerable impact on patients' quality of life. In patients who require opioids for pain relief, less is known about the risk conferred by specific opioids given their diverse pharmacology and the effect of daily dose and potency. The aim of the study was to evaluate the comparative risk of severe constipation by opioid type and dose in patients with non-cancer pain admitted to hospital. METHODS:We conducted a retrospective cohort study using hospital electronic health records in Northwest England between December 1, 2009, and December 31, 2020. Patients who were ≥ 18 years and without a history of cancer were included. Opioid exposure was measured using administered drug information in hospital. The outcome was a severe constipation event defined as administration of an enema or suppository. Incidence rates by opioid use status, type of opioid class and morphine milligram equivalent (MME) per day were calculated, and a Cox regression model was used to determine associations with incident constipation after adjusting for confounders. RESULTS:The study included 80,475 eligible patients who were administered an opioid in hospital. Compared to codeine, morphine (HR 1.59, 95% CI 1.45-1.74), oxycodone (HR 1.46, 95% CI 1.32-1.63), fentanyl (HR 1.37, 95% CI 1.14-1.64) and combination opioids (HR 1.85, 95% CI 1.66-2.06) were associated with a higher risk of constipation in the fully adjusted models. Tramadol demonstrated a significantly lower risk compared to codeine (HR 0.80, 95% CI 0.64-1.00). Higher opioid doses of more than ≥ 50 MME/day in comparison to < 50 MME/day were associated with an increased risk of constipation (compared to < 50 MME/day, 50 to < 120 MME/day: HR 1.95, 95% CI 1.78-2.15; ≥ 120 MME/day: HR 1.45, 95% CI 1.32-1.60). CONCLUSIONS:Morphine, oxycodone, fentanyl and combination opioids administration were associated with a significantly higher risk of

关键词： Adverse events Chronic pain Constipation Drug safety Electronic health records Morphine milligram equivalents Opiates Opioid-related harms Opioids

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