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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Stakeholder Engagement Strategies to Build Resilience to Compound Hazards: Engaging Community-Based Organizations in Research

SSRN

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SSRN 2024年

作者： Clark, Sarah Guido, Zack Cabrera-Rivera, Laura Teresa Méndez-Lázaro, Pablo McMahan, Ben Moscoso, Federico Cintrón Crespo-Acevedo, Wanda I. Castro-Sitiriche, Marcel Arizona Institute for Resilience University of Arizona TucsonAZ United States Department of Environmental Health University of Puerto Rico Medical Sciences Campus San Juan Puerto Rico El Puente - Latino Climate Action Network Puerto Rico NOAA Climate Adaptation Partnerships Program Puerto Rico Department of Electrical and Computer Engineering University of Puerto Rico Mayagüez Puerto Rico

Climate extremes can generate impacts in one sector that cascade or amplify the impacts in others. Developing strategies that build resilience to these compound hazards requires collaboration among diverse stakeholders to understand hazard dynamics and the synergies and tradeoffs in adaptation activities. In many regions, community-based organizations (CBOs) lead in local climate adaptation, and their engagement in research can help inform research agendas and capacity-strengthening activities that support locally led adaptation. In this paper, we describe a collaborative project that convened stakeholders working in climate adaptation, public health, and energy resilience in Puerto Rico. The goals were to identify knowledge gaps and opportunities for immediate action. Based on interviews, a participatory workshop, and a survey, we report on the CBO activities, their networks and their views on the relationships between climate, public health, and energy. We also describe their perspectives on priorities to address compound hazards. Drawing on these results, we discuss five strategies that can help research projects collaborate with CBOs. They include understanding the network to inform engagement, paying attention to differential impacts and justice, employing flexible planning to accommodate multiple goals and perspectives, focusing on information sharing to advance collaboration, and exploring narratives of change to understand adaptation and maladaptation. This study informs how research can more effectively engage CBOs in climate adaptation, which, in turn, can contribute to building plans and systems that are better equipped to build resilience to compound extreme events. © 2024, The Authors. All rights reserved.

关键词： Public health

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

引用

arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Industrial Semiconductor GPT: A Question-and-Answer System that Provides Professional Advice and Problem-Solving Methods for Semiconductor and Factory Equipment and Process

Industrial Semiconductor GPT: A Question-and-Answer System t...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yi-Cheng Yu Cheng-Fu Liou Shang-Wen Chuang Chen-Yi Lee Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Labs (MZAIR Labs) Meta Zen Artificial Intelligence Tainan Taiwan Artificial Intelligence and Industrial Internet of Things Research Center Advantech Co. Ltd. Taipei Taiwan Department of Electronics Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Research on PHM(Predictive Health Management) for detecting or predicting abnormalities in semiconductors and factory equipment and processes typically focuses on improving accuracy and reducing model size. However, in actual factory settings, on-site personnel not only need to accurately detect and predict abnormalities in processes and equipment but also require an understanding of how to handle the encountered abnormal problems when they occur. This study proposes Industrial Semiconductor GPT technology, which embeds relevant professional technical documents of process equipment into a vector database and generates possible professional questions and answers. When on-site personnel inquire, a natural language processing algorithm is used to compare the generated answers with actual answers. Furthermore, this study verifies the collected expert data through prompting engineering, resulting in an approximately $9.39 \%$ increase in similarity, ensuring that factory personnel can obtain accurate and helpful answers when facing abnormal situations.

关键词： Semiconductor device modeling Accuracy Databases Search methods Production facilities Vectors Semiconductor process modeling

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Fast Data-independent KLT Approximations Based on Integer Functions

arXiv

引用

arXiv 2024年

作者： Radünz, A.P. Coelho, D.F.G. Bayer, F.M. Cintra, R.J. Madanayake, A. Graduate Program in Statistics Universidade Federal de Pernambuco Recife Brazil Calgary Canada Departamento de Estatística and LACESM Universidade Federal de Santa Maria Santa Maria97105-900 Brazil Signal Processing Group Department of Technology Universidade Federal de Pernambuco Caruaru Brazil Department of Electrical and Computer Engineering Florida International University FL33174 United States

The Karhunen-Loève transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compression into a select few main components has rendered it instrumental in various applications within image compression frameworks. However, computing the KLT depends on the covariance matrix of the input data, which makes it difficult to develop fast algorithms for its implementation. Approximations for the KLT, utilizing specific rounding functions, have been introduced to reduce its computational complexity. Therefore, our paper introduces a category of low-complexity, data-independent KLT approximations, employing a range of round-off functions. The design methodology of the approximate transform is defined for any block-length N, but emphasis is given to transforms of N = 8 due to its wide use in image and video compression. The proposed transforms perform well when compared to the exact KLT and approximations considering classical performance measures. For particular scenarios, our proposed transforms demonstrated superior performance when compared to KLT approximations documented in the literature. We also developed fast algorithms for the proposed transforms, further reducing the arithmetic cost associated with their implementation. Evaluation of field programmable gate array (FPGA) hardware implementation metrics was conducted. Practical applications in image encoding showed the relevance of the proposed transforms. In fact, we showed that one of the proposed transforms outperformed the exact KLT given certain compression ratios. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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Deep Automatic Alignment of Mpox Dermatological Hand Photography 22

Deep Automatic Alignment of Mpox Dermatological Hand Photogr...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Jiang, Bohan McNeil, Andrew Liu, Yihao Rudravaram, Gaurav Saknite, Inga Mbala-Kingebeni, Placide Mbaya, Olivier Tshiani Silaphet, Tyra Weiss, Rachel Dodd, Lori E. Nussenblatt, Veronique Moyer, Daniel Landman, Bennett A. Dawant, Benoit M. Tkaczyk, Eric R. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Dermatology Vanderbilt University Medical Center NashvilleTN United States Dermatology Service and Research Service Department of Veterans Affairs NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Institut National de Recherche Biomédicale Kinshasa Democratic Republic Congo Clinical Monitoring Research Program Frederick National Laboratory for Cancer Research FrederickMD United States Clinical Trials Research Section Division of Clinical Research National Institute of Allergy and Infectious Disease BethesdaMD United States Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases BethesdaMD United States Biophotonics Laboratory University of Latvia Riga Latvia

ISBN: (纸本)9798331520526

Mpox is a viral illness with heavy cutaneous involvement. Automatic tracking of mpox lesion progression is critical in determining the resolution of evolving lesions. This work introduces a novel application of deep learning for lesion monitoring through alignment of dermatological hand photographs. By adapting the VoxelMorph framework for 2D photographic data, we explore key point alignment across serial images. We trained our neural network model on a unique dataset of 1,658 hand images and evaluated its performance on a test set of 254 images. Additionally, we validated the method's generalizability with a supplementary set of 500 images, which included extensive Mpox infection. Our findings indicate modest yet significant improvements in key points and lesion center registration across different regularization strengths. Although promising, the complexity of hand structure presents challenges, requiring cautious application and further refinement, especially in regions with intense spatial discontinuities, such as interdigital areas. © 2025 IEEE.

关键词： Image registration

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Scalable privacy-preserving cancer type prediction with homomorphic encryption

arXiv

引用

arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

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Structural tuning magnetism and topology in a magnetic topological insulator

arXiv

引用

arXiv 2023年

作者： Eckberg, Christopher Qiu, Gang Qu, Tao Kwon, Sohee Liu, Yuhang Tai, Lixuan Graf, David Chong, Su Kong Zhang, Peng Wong, Kin L. Lake, Roger K. Neupane, Mahesh R. Wang, Kang L. Fibertek Inc. HerndonVA20171 United States DEVCOM Army Research Laboratory AdelphiMD20783 United States DEVCOM Army Research Laboratory Playa Vista CA90094 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California RiversideCA92521 United States National High Magnetic Field Laboratory Florida State University TallahasseeFL32310 United States Materials Science and Engineering Program University of California RiversideCA92521 United States

To date, the most widely-studied quantum anomalous Hall insulator (QAHI) platform is achieved by dilute doping of magnetic ions into thin films of the alloyed tetradymite topological insulator (TI) (Bi1−xSbx)2Te3 (BST) [1–4]. In these films, long-range magnetic ordering of the transition metal substituants opens an exchange gap ∆ in the topological surface states, stabilizing spin-polarized, dissipationless edge channels with a nonzero Chern number C. The long-range ordering of the spatially separated magnetic ions is itself mediated by electronic states in the host TI, leading to a sophisticated feedback between magnetic and electronic properties. Here we present a study of the electronic and magnetic response of a BST-based QAHI system to structural tuning via hydrostatic pressure. We identify a systematic closure of the topological gap under compressive strain accompanied by a simultaneous enhancement in the magnetic ordering strength. Combining these experimental results with first-principle calculations we identify structural deformation as a strong tuning parameter to traverse a rich topological phase space and modify magnetism in the magnetically doped BST system. © 2023, CC BY-NC-ND.

关键词： Topology

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