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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Yuan, Jing Tang, Shaojie Department of Computer Science and Engineering University of North Texas Denton United States Center for AI Business Innovation Department of Management Science and Systems University at Buffalo Buffalo United States

ISBN: (纸本)9789819644476

Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a single submodular function. However, in many real-world scenarios, we might have multiple user-specific functions, each of which models the utility of a particular type of user. In these settings, our goal would be to choose a set of items that performs well across all the user-specific functions. One way to tackle this problem is to select a single subset that maximizes the sum of all of the user-specific functions. Although this aggregate approach is efficient in the sense that it avoids computation of sets for individual functions, it really misses the power of personalization - for it does not allow to choose different sets for different functions. In this paper, we introduce the problem of personalized submodular maximization with two candidate solutions. For any two candidate solutions, the utility of each user-specific function is defined as the better of these two candidates. Our objective is, therefore, to select the best set of two candidates that maximize the sum of utilities of all the user-specific functions. We have designed effective algorithms for this problem. We also discuss how our approach generalizes to multiple candidate solutions, increasing flexibility and personalization in our solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films

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光：科学与应用（英文版） 2025年第2期14卷 411-421页

作者： Waqas W.Ahmed Haicheng Cao Changqing Xu Mohamed Farhat Muhammad Amin Xiaohang Li Xiangliang Zhang Ying Wu Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia College of Engineering Taibah UniversityMadinah 42353Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Physical Science and Engineering King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Division of Computer Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Computer Science and Engineering University of Notre DameNotre DameIN 46556USA

We propose and demonstrate a data-driven plasmonic metascreen that efficiently absorbs incident light over a wide spectral range in an ultra-thin silicon *** embedding a double-nanoring silver array within a 20 nm ultrathin amorphous silicon(a-Si)layer,we achieve a significant enhancement of light *** enhancement arises from the interaction between the resonant cavity modes and localized plasmonic modes,requiring precise tuning of plasmon resonances to match the absorption region of the silicon active *** facilitate the device design and improve light absorption without increasing the thickness of the active layer,we develop a deep learning framework,which learns to map from the absorption spectra to the design *** inverse design strategy helps to tune the absorption for selective spectral *** optimized design surpasses the bare silicon planar device,exhibiting a remarkable enhancement of over 100％.Experimental validation confirms the broadband enhancement of light absorption in the proposed *** proposed metascreen absorber holds great potential for light harvesting applications and may be leveraged to improve the light conversion efficiency of ultra-thin silicon solar cells,photodetectors,and optical filters.

关键词： silicon learning absorption assisted broadband enhanced films machine metascreen plasmonic

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An Array of Silicon Dual Microneedle Electrodes Integrated With mini-LEDs for Electrophysiological Recording And Simultaneous Application of Electrical and Optical Stimuli to the Retina for Artificial Vision 38

An Array of Silicon Dual Microneedle Electrodes Integrated W...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chung, Seung-Han Kim, Chaesung Kim, Yong-Kweon Lee, Seung-Ki Park, Jae-Hyoung Im, Maesoon Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of College of Engineering Korea University School of Electrical Engineering Seoul Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

We report a novel hybrid neural interface device that not only enables simultaneous application of electrical and optical stimuli but also offers electrophysiological recording capability. A 6×6 silicon dual microneedle electrode array was fabricated and each pair consisted of recording and stimulating electrodes. A mini-LED matrix was also integrated underneath the electrode array to deliver optical stimulation. Spiking activities of retinal ganglion cells of an ex-vivo retina were successfully recorded in response to electrical and optical stimuli applied from the fabricated device. © 2025 IEEE.

关键词： Light emitting diodes

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ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis

arXiv

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arXiv 2025年

作者： Wang, Xinpeng Zhou, Rong Xie, Han Tang, Xiaoying He, Lifang Yang, Carl School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Lehigh University United States Department of Computer Science Emory University United States

Multimodal Federated Learning (MFL) has emerged as a promising approach for collaboratively training multimodal models across distributed clients, particularly in healthcare domains. In the context of brain imaging analysis, modality incompleteness presents a significant challenge, where some institutions may lack specific imaging modalities (e.g., PET, MRI, or CT) due to privacy concerns, device limitations, or data availability issues. While existing work typically assumes modality completeness or oversimplifies missing-modality scenarios, we simulate a more realistic setting by considering both client-level and instance-level modality incompleteness in this study. Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness. Specifically, ClusMFL utilizes the FINCH algorithm to construct a pool of cluster centers for the feature embeddings of each modality-label pair, effectively capturing fine-grained data distributions. These cluster centers are then used for feature alignment within each modality through supervised contrastive learning, while also acting as proxies for missing modalities, allowing cross-modal knowledge transfer. Furthermore, ClusMFL employs a modality-aware aggregation strategy, further enhancing the model’s performance in scenarios with severe modality incompleteness. We evaluate the proposed framework on the ADNI dataset, utilizing structural MRI and PET scans. Extensive experimental results demonstrate that ClusMFL achieves state-of-the-art performance compared to various baseline methods across varying levels of modality incompleteness, providing a scalable solution for cross-institutional brain imaging analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Delayed Transitions in GPenSIM 1st

Delayed Transitions in GPenSIM

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1st International Symposium on Systems Modelling and Simulation, SMS 2024

作者： Feng, Yuming Samolej, Slawomir Davidrajuh, Reggie School of Computer Science and Engineering Chongqing Three Gorges University Wenzhou China Department Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland Department Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (纸本)9789819646128

The General-purpose Petri Net Simulator (GPenSIM) is a tool for modeling, simulation, and performance analysis of discrete event systems. GPenSIM is specially designed to model real-life industrial systems. Hence, the transitions in GPenSIM represent real-life events, such as machines. However, this induces a problem, namely that an enabled transition consumes tokens from its input places immediately. This act of an enabled transition immediately consuming tokens gives only monetarily visibility to the tokens, which can be a problem for certain cases where we need to see the tokens in their input places for a while. This paper proposes a method to delay an enabled transition ("delayed transitions") so that the tokens it is supposed to consume from its input places will be visible for a while. This paper, after presenting the theory and the method, also presents an application (the cat and mouse problem) of the delayed transitions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete event simulation

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Investigation of AI with OpenCV-Python for Detecting Diabetes

Investigation of AI with OpenCV-Python for Detecting Diabete...

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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Mutar, Hussein A. Yalwi, Ilyas Khudhair Mezaal, Yaqeen S. Th. Salim ALRikabi, Haider Alaidi, Abdul Hadi M. Niama ALRubeei, Ibtihal R. Magdy, Ahmed Electrical Engineering Department College of Engineering Wasit University Wasit Iraq College of Computer Science and Information Technology Wasit University Wasit Iraq College of Engineering University of Information Technology and Communications Baghdad Iraq Electrical Engineering Department Faculty of Engineering Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031828805

Plenty of different diagnosing methods have been extensively utilized to identify diabetes accurately;however, an absolutely precise and definitive diagnosis has not yet been attained. Within the context of this research, our primary objective is to leverage the cutting-edge capabilities of Artificial Intelligence (AI) coupled with OpenCV to assist medical professionals, thereby minimizing the rate of misdiagnosis. Specifically, we harness the power of AI to effectively classify diverse images portraying patients afflicted with Non-Proliferative Diabetic Retinopathy (NPDR), with the ultimate goal of determining the severity level at which they are situated. In conjunction with this, Python, with OpenCV, has a crucial role in extracting pertinent features that may be discernible within the given images. Our methodology involves the collection and preprocessing of the Eye PACS Dataset on Kaggle, followed by feature extraction and model training using some machine learning algorithms, including convolutional Neural Network CNN, decision trees, support vector machines SVM, and neural networks. OpenCV is utilized for image processing tasks, enhancing the feature extraction process, certain individual features present within the images are precluded from being considered as contributing factors in the classification process. Some of these features include but not limited to, the measurement of the luminous blobs which are present in the image, the specific area of existence of red lesion. The evaluation of the models includes the analysis of their performance based on the goal of the prediction task, specifically decimal-based accuracy, precision, recall, and F1-score. This research employs a wide-ranging dataset embracing low, medium and high level of image severity. At last, after lots of simulation, it came to a conclusion that the CNN increases its level of classification accuracy up to 98%. These findings show that the proposed application of AI improves the accuracy

关键词： Convolutional neural networks

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Efficient Smart Public Transit System Using RFID and IoT integration with MERN web applications

Efficient Smart Public Transit System Using RFID and IoT int...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Kartik Madasanal Anand Gudnavar Sreedevi Nagarmunoli Basavaraj Madagouda Keerti Naregal Department of Computer Science and Engineering Jain College of Engineering and Research Visvesaraya Technological University Belagavi India Department of Computer Science and Engineering Graphic Era University Dehradun India Department of Computer Science and Engineering S.G.Balekundri Institute of Technology Visvesaraya Technological University Belagavi India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper develops a Smart Public Transportation System using RFID technology, IoT integration, and a MERN-based web application for increasing the efficiency, accessibility, and user experience of urban transit systems. It addresses problems such as unreliable bus tracking, lack of support for multilingualism, and ineffective communication between operators and passengers. The key hardware components include RFID tags for bus identification, and IoT-enabled bus stations with RFID scanners, speakers, GSM modules for data transmission, and LCD displays. These ensure accurate real-time updates about the bus locations. On the software side, it is supported by a MERN stack, including MongoDB, ***, ***, and ***, providing a responsive web interface that’s complemented by *** for dynamic route visualization and WebSockets for live updates. Multilingual support through i18next makes it inclusive for various user groups. The system is efficient in that it only updates when RFID scanners capture bus tags at certain checkpoints, thereby reducing server load much more than GPS-based systems. Additional features include route tracking, interactive maps, ticket pricing, and anonymous passenger chats. Crowd-sourced feedback mechanisms and an admin panel for bus management ensure the reliability and scalability of the system. This work shows the feasibility of cost-effective, scalable, and user-centric modernization of public transportation systems, paving the way for further enhancements such as integrated payment systems and AI-based route optimization.

关键词： GSM Real-time systems User experience Software Multilingual Internet of Things Servers Reliability Public transportation Radiofrequency identification

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Mitigating Security Challenges in Edge Computing: Attacks, Defence Strategies, and Algorithms

Mitigating Security Challenges in Edge Computing: Attacks, D...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Venkata Durga Rao Matta V.V.R. Maheswara Rao Venkata Sai Jayasurya Battula Mahesh Babu Addagatla Computer Science and Engineering Department Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies.

关键词： Technological innovation Scalability Data processing Real-time systems Security Resource management Internet of Things Interoperability Next generation networking Edge computing

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engineering antiferromagnetic magnon bands through interlayer spin pumping

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Physical Review Applied 2025年第3期23卷 L031003-L031003页

作者： M.M. Subedi K. Deng Y. Xiong J. Mongeon M.T. Hossain P.B. Meisenheimer E.T. Zhou J.T. Heron M.B. Jungfleisch Department of Physics and Astronomy Wayne State University Detroit Michigan 48202 USA Department of Physics Boston College 140 Commonwealth Avenue Chestnut Hill Massachusetts 02467 USA Department of Physics and Astronomy University of North Carolina Chapel Hill North Carolina 27599 USA Department of Physics Oakland University Rochester Michigan 48309 USA Department of Electronic and Computer Engineering Oakland University Rochester Michigan 48309 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Department of Materials Science and Engineering University of Michigan Ann Arbor Michigan 48109 USA

Spin pumping, a central phenomenon in spintronics used to source pure spin currents, is best understood in collinear magnetic multilayers. There is not yet a unified Landau-Lifshitz-Gilbert (LLG) theory that captures the fieldlike and dampinglike torques in a generic noncollinear magnetic multilayer. Here, we theoretically expand the LLG phenomenology to incorporate both dynamic fieldlike and dampinglike torques arising from spin pumping within noncollinear magnetic materials. We find that often overlooked dynamic fieldlike torques are capable of unveiling inversion asymmetries present in magnetic multilayers. Consequently, spin pumping can be used to lift the spectral degeneracy between various magnon modes in noncollinear antiferromagnets. We experimentally confirm this magnon-magnon interaction in a synthetic antiferromagnetic tetralayer, which has highly noncollinear magnetization configurations when under the influence of an external field. Thus, we demonstrate how spin pumping can facilitate a magnon-magnon interaction, significantly expanding how magnonic interactions can be engineered into antiferromagnets and magnetic metamaterials.

关键词： Damping

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Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs

arXiv

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arXiv 2025年

作者： Jiang, Youhe Fu, Fangcheng Yao, Xiaozhe He, Guoliang Miao, Xupeng Klimovic, Ana Cui, Bin Yuan, Binhang Yoneki, Eiko Department of Computer Science University of Cambridge Cambridgeshire United Kingdom Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science Peking University Beijing China Department of Computer Science ETH Zurich Zürich Switzerland Department of Computer Science Purdue University West LafayetteIN United States

Recent advancements in Large Language Models (LLMs) have led to increasingly diverse requests, accompanied with varying resource (compute and memory) demands to serve them. However, this in turn degrades the cost-efficiency of LLM serving as common practices primarily rely on homogeneous GPU resources. In response to this problem, this work conducts a thorough study about serving LLMs over heterogeneous GPU resources on cloud platforms. The rationale is that different GPU types exhibit distinct compute and memory characteristics, aligning well with the divergent resource demands of diverse requests. Particularly, through comprehensive benchmarking, we discover that the cost-efficiency of LLM serving can be substantially optimized by meticulously determining GPU composition, deployment configurations, and workload assignments. Subsequently, we design a scheduling algorithm via mixed-integer linear programming, aiming at deducing the most cost-efficient serving plan under the constraints of price budget and real-time GPU availability. Remarkably, our approach effectively outperforms homogeneous and heterogeneous baselines under a wide array of scenarios, covering diverse workload traces, varying GPU availablilities, and multi-model serving. This casts new light on more accessible and efficient LLM serving over heterogeneous cloud resources. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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