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arXiv 2025年

作者： Gu, Haogao Li, Jifan Sun, Wanying Li, Mengting Leung, Kathy Wu, Joseph T. Yuan, Hsiang-Yu Wang, Maggie H. Yang, Bingyi McKay, Matthew R. Ning, Ning Poon, Leo L.M. School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Statistics Texas A&M University United States Hong Kong Science and Technology Park Hong Kong HKJC Global Health Institute LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Biomedical Sciences Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong Centre for Applied One Health Research and Policy Advice Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong JC School of Public Health and Primary Care Faculty of Medicine the Chinese University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Microbiology and Immunology The Peter Doherty Institute for Infection and Immunity University of Melbourne MelbourneVIC3000 Australia Victorian Infectious Diseases Reference Laboratory Royal Melbourne Hospital the Peter Doherty Institute for Infection and Immunity Victoria Australia Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Centre for Immunology & Infection Hong Kong Science and Technology Park Hong Kong HKU-Pasteur Research Pole School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong

The global spread of viruses underscores the critical need for effective genomic surveillance to detect emerging variants and guide interventions. However, high costs and uneven resource distribution hinder its global implementation. This study demonstrates that optimizing genomic surveillance by targeting resources on international travelers in travel hubs, significantly improves the early detection of SARS-CoV-2 variants. Using a metapopulation multiple-strain model calibrated with extensive data, we show that targeting travelers effectively reduces detection delays without additional costs in retrospective analyses of the Omicron variant outbreak. Further, simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers (Reff) and vaccine effectiveness. These findings provide a sustainable framework for strengthening health security, offering actionable insights for optimizing genomic surveillance and improving preparedness for emerging infectious threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Coronavirus

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Design and Characterization of a Simple and Wideband Antenna for 5G mm-wave Applications

Design and Characterization of a Simple and Wideband Antenna...

引用

IEEE MTT-S Latin America Microwave Conference (LAMC)

作者： Syed Aftab Naqvi Wahaj Abbas Awan Mohammad Alibakhshikenari Francisco Falcone Ernesto Limiti Department of Electrical and Computer Eningeering COMSATS University Islamabad Sahiwal Pakistan Department of Integrated IT Eningeering Seoul National University of Science and Technology Seoul South Korea Electronic Engineering Department University of Rome &#x201C Tor Vergata&#x201D Rome Italy Electric Electronic and Communication Engineering Department Public University of Navarre Pamplona Spain

ISBN: (数字)9781728193588

ISBN: (纸本)9781728193595

An 8.5 GHz wideband and high Gain cross elliptical shape antenna is presented in this paper for the 5G mm-wave applications. A short-pin opposite to the feeding arm is deployed for the maximum current flow and consequently high bandwidth ranging from 26.7 to 35.2 GHz is achieved. The gain of the antenna varies from 6.1 dBi to 8.47 dBi in the entire frequency range with more than 76% efficiency. The compact electrical size of the antenna is 0.89λ0 × 0.89λ × 0.14λ0, where λ0 corresponds to the free space wavelength at the lowest resonance frequency. The results are compared with the state-of-the-artwork for the 5G mm-wave applications.

关键词： Microwave antennas 5G mobile communication Shape Resonant frequency Microwave theory and techniques Pins Broadband antennas

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Two-stage robust edge service placement and sizing under demand uncertainty

arXiv

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arXiv 2020年

作者： Nguyen, Duong Tung Nguyen, Hieu Trung Trieu, Ni Bhargava, Vijay K. Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada Edward J. Bloustein School of Planning and Public Policy Rutgers University New BrunswickNJ United States Department of Electrical Engineering and Computer Sciences at Uc Berkeley CA United States

Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable various Internet of Things applications. From the perspective of a service provider (SP), how to jointly optimize the service place-ment, sizing, and workload allocation decisions is an important and challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing their service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The service placement and sizing solution of the proposed model can hedge against any possible realization within the uncertainty set of traffic demand. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second-stage after the uncertainty is revealed. To solve the two-stage model, in this paper, we present an iterative solution by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max-min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the effectiveness of the proposed two-stage robust optimization model. Copyright © 2020, The Authors. All rights reserved.

关键词： Edge computing

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Chiral-Dependent Redox Capacitive Biosensor Using Cu-Cys-GSH Nanoparticles for Ultrasensitive H2O2 Detection

Biosensors

引用

Biosensors 2025年第5期15卷 315-315页

作者： Yilmaz Aydin, Duygu Wu, Jie Jayne Chen, Jiangang Department of Electrical Engineering and Computer Science The University of Tennessee KnoxvilleTN37996 United States Department of Bioengineering Malatya Turgut Ozal University Malatya44210 Turkey Department of Public Health The University of Tennessee KnoxvilleTN37996 United States

Copper-thiolate nanostructures, formed through the self-assembly of cysteine (Cys) and glutathione (GSH) with copper ions, offer a versatile platform for redox-active applications due to their structural stability and chemical functionality. In this study, Cu-Cys-GSH nanoparticles were synthesized and employed to develop a capacitive biosensor for the ultralow concentration detection of hydrogen peroxide (H2O2). The detection mechanism leverages a Fenton-like reaction, where H2O2 interacts with Cu-Cys-GSH nanoparticles to generate hydroxyl radicals (·OH) through redox cycling between Cu2+ and Cu+ ions. These redox processes induce changes in the sensor’s surface charge and dielectric properties, enabling highly sensitive capacitive sensing at gold interdigitated electrodes (IDEs). The influence of chirality on sensing performance was investigated by synthesizing nanoparticles with both L- and D-cysteine enantiomers. Comparative analysis revealed that the stereochemistry of cysteine impacts the catalytic activity and sensor response, with Cu-L-Cys-GSH nanoparticles exhibiting superior performance. Specifically, the biosensor achieved a linear detection range from 1.0 fM to 1.0 pM and demonstrated an ultra-sensitive detection limit of 21.8 aM, outperforming many existing methods for H2O2 detection. The sensor’s practical performance was further validated using milk and saliva samples, yielding high recovery rates and confirming its robustness and accuracy for real-world applications. This study offers a disposable, low-cost sensing platform compatible with sustainable healthcare practices and facilitates easy integration into point-of-care diagnostic systems. © 2025 by the authors.

关键词： Redox reactions

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RCMNet: A deep learning model assists CAR-T therapy for leukemia

arXiv

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arXiv 2022年

作者： Zhang, Ruitao Han, Xueying Gul, Ijaz Zhai, Shiyao Liu, Ying Zhang, Yongbing Dong, Yuhan Ma, Lan Yu, Dongmei Zhou, Jin Qin, Peiwu Institute of Biomedical and Health Engineering Tsinghua Shenzhen International Graduate School China Precision Medicine and Public Health Tsinghua-Berkeley Shenzhen Institute United States The First Affiliated Hospital of Harbin Medical University China Food Inspection & Quarantine Center Shenzhen Custom China Department of Computer Science Harbin Insitute of Technology Shenzhen China School of Mc chanical Electrical & Information Engineering Shandong University China

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. Copyright © 2022, The Authors. All rights reserved.

关键词： T-cells

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KNEE ARTHRITIS SEVERITY MEASUREMENT USING DEEP LEARNING: A publicLY AVAILABLE ALGORITHM WITH A MULTI-INSTITUTIONAL VALIDATION SHOWING RADIOLOGIST-LEVEL PERFORMANCE

arXiv

引用

arXiv 2022年

作者： Gu, Hanxue Li, Keyu Colglazier, Roy J. Yang, Jichen Lebhar, Michael O'Donnell, Jonathan Jiranek, William A. Mather, Richard C. French, Rob J. Said, Nicholas Zhang, Jikai Park, Christine Mazurowski, Maciej A. The Duke University of Medicine Durham27708 United States The Department of Radiology Radiology Musculoskeletal Imaging Radiology Durham27708 United States The Practice Transformation Unit The Department of Orthopaedic Surgery Duke University Durham27708 United States The Orthopaedic Surgery Orthopaedics Orthopaedics Duke University Durham27708 United States Population Health Sciences Population Health Sciences Basic Science Department Durham27708 United States Duke Clinical Research Institute Institutes and Centers Durham27708 United States Duke-Margolis Center for Health Policy Duke - Margolis Center For Health Policy Initiatives Durham27708 United States Radiology Radiology Clinical Science Department Durham27708 United States Biostatistics and Bioinformatics Biostatistics & Bioinformatics Basic Science Department Durham27708 United States The Department of Electrical and Computer Engineering Electrical and Computer Engineering Pratt School of Engineering Durham27708 United States Computer Science Computer Science Trinity College of Arts & Sciences Durham27708 United States Duke Cancer Institute Institutes and Centers in Duke University Durham27708 United States

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. © 2022, CC BY-NC-SA.

关键词： Image classification

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An operational architecture for privacy-by-design in public service applications

arXiv

引用

arXiv 2020年

作者： Agrawal, Prashant Singh, Anubhutie Raghavan, Malavika Sharma, Subodh Banerjee, Subhashis Department of Computer Science and Engineering IIT Delhi India Dvara Research School of Public Policy IIT Delhi India

Governments around the world are trying to build large data registries for effective delivery of a variety of public services. However, these efforts are often undermined due to serious concerns over privacy risks associated with collection and processing of personally identifiable information. While a rich set of special-purpose privacy-preserving techniques exist in computer science, they are unable to provide end-to-end protection in alignment with legal principles in the absence of an overarching operational architecture to ensure purpose limitation and protection against insider attacks. This either leads to weak privacy protection in large designs, or adoption of overly defensive strategies to protect privacy by compromising on utility. In this paper, we present an operational architecture for privacy-by-design based on independent regulatory oversight stipulated by most data protection regimes, regulated access control, purpose limitation and data minimisation. We briefly discuss the feasibility of implementing our architecture based on existing techniques. We also present some sample case studies of privacy-preserving design sketches of challenging public service applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Access control

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A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

arXiv

引用

arXiv 2023年

作者： Margot, Jean-Luc Li, Megan G. Pinchuk, Pavlo Myhrvold, Nathan Lesyna, Larry Alcantara, Lea E. Andrakin, Megan T. Arunseangroj, Jeth Baclet, Damien S. Belk, Madison H. Bhadha, Zerxes R. Brandis, Nicholas W. Carey, Robert E. Cassar, Harrison P. Chava, Sai S. Chen, Calvin Chen, James Cheng, Kellen T. Cimbri, Alessia Cloutier, Benjamin Combitsis, Jordan A. Couvrette, Kelly L. Coy, Brandon P. Davis, Kyle W. Delcayre, Antoine F. Du, Michelle R. Feil, Sarah E. Fu, Danning Gilmore, Travis J. Grahill-Bland, Emery Iglesias, Laura M. Juneau, Zoe Karapetian, Anthony G. Karfakis, George Lambert, Christopher T. Lazbin, Eric A. Li, Jian H. Li, Zhuofu Liskij, Nicholas M. Lopilato, Anthony V. Lu, Darren J. Ma, Detao Mathur, Vedant Minasyan, Mary H. Muller, Maxwell K. Nasielski, Mark T. Nguyen, Janice T. Nicholson, Lorraine M. Niemoeller, Samantha Ohri, Divij Padhye, Atharva U. Penmetcha, Supreethi V. Prakash, Yugantar Qi, Xinyi Rindt, Liam Sahu, Vedant Scally, Joshua A. Scott, Zefyr Seddon, Trevor J. Shohet, Lara-Lynn V. Sinha, Anchal Sinigiani, Anthony E. Song, Jiuxu Stice, Spencer M. Tabucol, Nadine M. Uplisashvili, Andria Vanga, Krishna Vazquez, Amaury G. Vetushko, George Villa, Valeria Vincent, Maria Waasdorp, Ian J. Wagaman, Ian B. Wang, Amanda Wight, Jade C. Wong, Ella Yamaguchi, Natsuko Zhang, Zijin Zhao, Junyang Lynch, Ryan S. Department of Earth Planetary and Space Sciences University of California Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States National Renewable Energy Laboratory GoldenCO80401 United States Intellectual Ventures 3150 139th Ave SE BellevueWA98005 United States LXL Technology Las VegasNV89148 United States UCLA Samueli School of Engineering University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Electrical Engineering University of California Los AngelesCA90095 United States Department of Public Affairs University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Materials Engineering University of California Los AngelesCA90095 United States UCLA College of Letters and Science University of California Los AngelesCA90095 United States Department of Mechanical Engineering University of California Los AngelesCA90095 United States Department of Aerospace Engineering University of California Los AngelesCA90095 United States Department of Neuroscience University of California Los AngelesCA90095 United States Green Bank Observatory P.O. Box 2 Green BankWV24494 United States

We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ∼11,680 stars and planetary systems in the ∼9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ∼15 times smaller at ∼6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1013 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 1016 W) is at most 3 × 10−4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023). © 2023, CC BY-NC-SA.

关键词： Transmitters

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Prediction of Occupational Physical Activities using Inertial Measurement Units and Deep Learning Models 64th

Prediction of Occupational Physical Activities using Inertia...

引用

64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Yan, Yishu Fan, Hao Li, Yibin Hoeglinger, Elias Wiesinger, Alexander Barr, Alan Adamson, Carisa Harris Department of Mechanical Engineering University of California Berkeley United States Department of Industrial Design Northwestern Polytechnic China Department of Electrical Engineering and Computer Science University of California Berkeley United States University of Applied Sciences Upper Austria Linz Austria Department of Medicine University of California San Francisco United States School of Public Health University of California Berkeley United States

Many workers engaged in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSD). Quantifying the physical demands of a job is an important legal requirement in the US that is used by human resources in the job hiring process. Most physical demands analysis (PDA) are performed using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can create problems when assigning acceptable tasks to an injured worker. In this study, various deep learning models were applied to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities (OPA). Overall, a 95% accuracy was reached by convolutional neural network (CNN) for predicting occupational physical activities when performed in isolation. More work is needed to estimate the accuracy of the model when OPA elements are combined into a more complex task. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Forecasting

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