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Artificial Intelligence (CAI), IEEE Conference on

作者： Wei Xian Lim Naheed Anjum Arafat Wai Lee Chan Wai-Kin Adams Kong Rolls-Royce@NTU Corporate Lab Nanyang Technological University (NTUsg) Singapore School of Mechanical and Aerospace Engineering (MAE) Nanyang Technological University (NTUsg) Singapore College of Computing and Data Science (CCDS) Nanyang Technological University (NTUsg) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Studies show that artificial intelligence (AI) with embedded physics solvers has improved the accuracy of predictions on various physics problems, especially those associated with fluid dynamics. The crucial element in optimizing weight training for estimating flow fields within the AI network lies in the choice of the loss function. In addressing regression-type problems, particularly those involving the temporal evolution of flow fields, the mean square error (MSE) loss function is commonly employed at the current and single time step. However, an issue arises in existing methodologies that utilize MSE-based loss functions with single-time step information for predicting unsteady flow. Most of these approaches overlook the significance of incorporating the temporal history of the flow, a factor that cannot be disregarded in the context of numerical solvers. Hence, in this work, a physics-based AI (PbAI) method with higher-order loss functions is applied to unsteady scenarios, in particular to two distinct turbulent flows where a multitude of fine structures is present, namely, forced and decaying turbulence. Direct numerical simulations on uniform Cartesian grids are conducted to simulate these scenarios, generating two distinct datasets for training and inference. Each dataset comprises 32 randomly initialized conditions spanning 4, 848 time steps for each turbulent flow type. Five distinct models are devised, incorporating features such as rollouts from coarse numerical solvers and temporal considerations in the loss function calculation. The constructed PbAI models demonstrate consistent improvements in predictive performance over the entire temporal domain. These findings are further corroborated through vorticity correlation analyses. The empirical result demonstrates that the accuracy of the baseline case improves by up to 48% and 30% for forced and decaying turbulence, respectively. These results significantly underscore the importance of the temporal histo

关键词： Training Accuracy Computational modeling Neural networks Predictive models Network architecture Numerical models

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Real-world evidence for improved outcomes with histamine antagonists and aspirin in 22,560 COVID-19 patients

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Signal Transduction and Targeted Therapy 2021年第8期6卷 2297-2299页

作者： Cameron Mura Saskia Preissner Susanne Nahles Max Heiland Philip E.Bourne Robert Preissner School of Data Science and Deportment of Biomedical Engineering University of VirginiaCharlottesvilleVAUSA Department Oral and Maxillofacial Surgery Charite-Universitatsmedizin Berlincorporate member of Freie Universitat BerlinHumboldt-Universitat zu Berlinand Berlin Institute of HealthBerlinGermany Institute of Physiology and Science-IT Charite-Universitatsmedizin Berlincorporate member of Freie Universitat BerlinHumboldt-Universitat zu Berlinand Berlin Institute of HealthBerlinGermany

Dear Editor,The COVID-19 pandemic has driven great interest in the therapeutic potential of repurposed drugs with well-established benefits and safety profiles(toxicity,bioavailability,etc.),many of which act via sign... 详细信息

关键词： drugs category COV

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NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection through Optimized Feature Reduction and Machine Learning

NeuroPCA: Enhancing Alzheimer’s disorder Disease Detection ...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Sreenivas Reddy Sagili Senthilnathan Chidambaranathan Nagarjuna Nallametti Harana M. Bodele Laxmi Raja P G. Gayathri Department of Computer Science Rochester Institute of Technology United States 20 Corporate Pl South Virtusa Corporation Piscataway United States Department of CSIT CVR College of Engineering Hyderabad Telangana India Department of Electronics and Telecommunications J D College of Engineering & Management Nagpur India Department of Computer Science and Engineering (Cyber Security) Karpagam Academy of Higher Education Coimbatore India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Alzheimer’s (memory loss disorder) disorder Disease (AD) poses a significant diagnostic challenge due to its complex etiology and subtle onset of clinical symptoms. The Early and accurate detection will be a crucial for effective management and intervention. The integration of machine Learning-Driven (ML) techniques based on neuro imaging and clinical data may offers a promising avenue for enhancing of AD detection. However, the highest dimensionality of such data could be impede the efficacy and accuracy of ML models due to curse of dimensionality and over fitting. This research will introduces the "NeuroPCA System," a novelist diagnostic framework by employing Principal Components based Analysis (PCA) for features reduction, tailored specifically for Alzheimer’s (memory loss disorder) disorder Disease detection. The NeuroPCA System will systematically reduces the dimensionality of input health data (e.g., MRI, PET scans, and genetic information) while by preserving the most informative variance components crucial for distinguish between AD patients and healthy controls. The proposed methodology may encompasses preprocessing of the multi-modal dataset to normalized feature scales, followed by the application of PCA to extracts the principal components that capturing of the maximum variance in the dataset. The reduced feature set will then utilized to train and validate a suite of machine Learning-Driven models like Supportive Vector Machines (SVM) for classify subjects into AD and non-AD groups. The effectiveness of the NeuroPCA System may evaluated depends on classification accuracy, sensitivity, specificity, and computational efficiency, by using a cross- validated dataset of diagnosed AD patients and a control group. The NeuroPCA System also demonstrates a significant improvement in model performance and computational efficiency, by highlighting the potential of PCA for feature reduction in the context of Alzheimer’s (memory loss disorder) disorder Disease detec

关键词： Training Support vector machines Accuracy Computational modeling Feature extraction Genetics Vectors Computational efficiency Alzheimer's disease Principal component analysis

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A method for clustering scientific publications based on natural language for the purpose of strategic planning of partnerships in interdisciplinary research

A method for clustering scientific publications based on nat...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Malika Shamgunova Andrii Biloshchytskyi Oleksandr Kuchanskyi Faizullin Adil Computer Science and Engineering Astana IT University Kazakhstan Technical Sciences Professor Vice-Rector for Science and Innovation Astana IT University Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Ukraine Department of Computational and Data Science Astana IT University Kazakhstan Department of Information Control Systems and Technologies Uzhhorod National University Ukraine Strategy and Corporate Governance Department Astana IT University Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The current research aims at examining the Kazakhstani scholars’ publications in order to reveal the topdiscussed topics and possible cooperation topics. By employing natural language processing, the research abstracts are grouped thematically to demonstrate the various clusters of themes present. The results reveal specific themes of the Kazakhstani research to provide the recommendations for the future academic development of the country and the cooperation between various fields. Thus, the approach illustrated the benefit of using thematic clustering for analysis and categorization of a vast amount of scholarly production.

关键词： Productivity Workability data analysis Collaboration Strategic planning Natural language processing Information systems

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding

arXiv

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arXiv 2024年

作者： Hu, Ming Xia, Peng Wang, Lin Yan, Siyuan Tang, Feilong Xu, Zhongxing Luo, Yimin Song, Kaimin Leitner, Jurgen Cheng, Xuelian Cheng, Jun Liu, Chi Zhou, Kaijing Ge, Zongyuan AIM Lab Faculty of IT Netherlands Faculty of Engineering Monash University Australia Airdoc-Monash Research Airdoc Australia Eye Hospital Wenzhou Medical University China Bosch Corporate Research King's College London United Kingdom Cornell University United States Institute for Infocomm Research A*STAR Singapore Faculty of Data Science City University of Macau China

Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of diverse and richly annotated video datasets has hindered the development of intelligent systems for surgical workflow analysis. Existing datasets face challenges such as small scale, lack of diversity in surgery and phase categories, and absence of time-localized annotations. These limitations impede action understanding and model generalization validation in complex and diverse real-world surgical scenarios. To address this gap, we introduce OphNet, a large-scale, expert-annotated video benchmark for ophthalmic surgical workflow understanding. OphNet features: 1) A diverse collection of 2,278 surgical videos spanning 66 types of cataract, glaucoma, and corneal surgeries, with detailed annotations for 102 unique surgical phases and 150 fine-grained operations. 2) Sequential and hierarchical annotations for each surgery, phase, and operation, enabling comprehensive understanding and improved interpretability. 3) Time-localized annotations, facilitating temporal localization and prediction tasks within surgical workflows. With approximately 285 hours of surgical videos, OphNet is about 20 times larger than the largest existing surgical workflow analysis benchmark. Code and dataset are available at: https://***/OphNet-benchmark/. © 2024, CC BY-NC-SA.

关键词： Robotic surgery

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LEVERAGING LLMS FOR TOP-DOWN SECTOR ALLOCATION IN AUTOMATED TRADING

arXiv

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arXiv 2025年

作者： Heng, Ryan Quek Wei Vittori, Edoardo Ong, Keane Mao, Rui Cambria, Erik Mengaldo, Gianmarco College of Design and Engineering National University of Singapore Singapore CVA Management and A.I. Investments IMI Corporate and Investment Banking Intesa Sanpaolo Italy Asian Institute of Digital Finance National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University Singapore Sustainable and Green Finance Institute National University of Singapore Singapore Honorary Research Fellow Imperial College London United Kingdom

This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross-momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level. © 2025, CC BY.

关键词： Financial markets

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Enhanced Transformers with Dilated Convolutional Attention for Predicting Lung Cancer Outcomes

Enhanced Transformers with Dilated Convolutional Attention f...

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2025 International Conference on Visual Analytics and data Visualization, ICVADV 2025

作者： Chidambaranathan, Senthilnathan Sundaramoorthy, Pandian Puli, Balaram Daruvuri, Rajesh Vamsilala, Purushothapatnapu Naga Venkata Sathya, R. Corporate Pl S PiscatawayNJ08854 United States El CIC-1W-AMI Ibm 6303 Barfield Rd NE Sandy Springs GA30328 United States Everest Computers Inc Department of Engineering and Data Science 875 Old Roswell Road Suite RoswellGA30076 United States University of Cumberlands 1105 peachtree st atlanta United States Vignan's Lara Institute of Technology and Science Vignan's Lara Institute of Technology and Science Andhra Pradesh Tenali road near Vignan's University Guntur district 522213 India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India

ISBN: (纸本)9798331521394

The study in investigation of the usage of dilated convolutional attention functions with transformers to improve the accuracy of the prognosis of lung cancer. The suggested model exploits the benefits of the dilated convolutions to seize multi-scale spatial features in medical related data while it is integrating the human attention of the transformers that are focused on the most important information. The predicted model needs to cover collected clinical data patterns from which the best results for lung cancer will be achieved. These patterns are including early detection and tracking of tumor progression. this alternative the most effective way to improve feature extraction, but it also ensures presenting the most important trends in medical data to achieve an exact prognosis. The proposed methodology is an structured methodology that health experts use to diagnose tumor at variable stages of patient's testing while also giving hints of the possibility of Lyme Infections. © 2025 IEEE.

关键词： Personalized medicine

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Wasserstein Archetypal Analysis

arXiv

引用

arXiv 2022年

作者： Craig, Katy Osting, Braxton Wang, Dong Xu, Yiming Department of Mathematics University of California Santa Barbara Department of Mathematics University of Utah Salt Lake City School of Science and Engineering & Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen Guangdong China Corporate Model Risk Wells Fargo

Archetypal analysis is an unsupervised machine learning method that summarizes data using a convex polytope. In its original formulation, for fixed k, the method finds a convex polytope with k vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared Euclidean distance between the data and the polytope is minimal. In the present work, we consider an alternative formulation of archetypal analysis based on the Wasserstein metric, which we call Wasserstein archetypal analysis (WAA). In one dimension, there exists a unique solution of WAA [7] and, in two dimensions, we prove existence of a solution, as long as the data distribution is absolutely continuous with respect to Lebesgue measure. We discuss obstacles to extending our result to higher dimensions and general data distributions. We then introduce an appropriate regularization of the problem, via a Rényi entropy, which allows us to obtain existence of solutions of the regularized problem for general data distributions, in arbitrary dimensions. We prove a consistency result for the regularized problem, ensuring that if the data are iid samples from a probability measure, then as the number of samples is increased, a subsequence of the archetype points converges to the archetype points for the limiting data distribution, almost surely. Finally, we develop and implement a gradient-based computational approach for the two-dimensional problem, based on the semi-discrete formulation of the Wasserstein metric. Our analysis is supported by detailed computational *** Codes 62H12, 62H30, 65K10, 49Q22 Copyright © 2022, The Authors. All rights reserved.

关键词： Multivariant analysis

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Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook

arXiv

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arXiv 2024年

作者： Clerckx, Bruno Mao, Yijie Yang, Zhaohui Chen, Mingzhe Alkhateeb, Ahmed Liu, Liang Qiu, Min Yuan, Jinhong Wong, Vincent W.S. Montojo, Juan The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Science and Technology ShanghaiTech University Shanghai201210 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Corporate R&D Qualcomm Inc. San DiegoCA United States

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, message, etc) to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the increasing needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutorial, survey and outlook of past, emerging and future MA techniques and pay a particular attention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, rate-splitting, code domain MAs, and MAs in other domains, and highlight the importance of researching universal multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions for different MA schemes, and MA for AI such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications, metaverse, virtual realit

关键词： Wireless networks

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