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International Conference on ICT For Smart Society (ICISS)

作者： Tonto Claudinus Made Prayoga Wicaksana Nicolas Kornelius Sitorus Marsa Ariqi Gustiandza Tanty Oktavia Ford Lumban Gaol Takaaki Hosoda School of Computer Science Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

This paper presents the sports apps based on a mobile application that has a function to help the provider or the owner of the sports field and sports vendor to promote their business in the apps. Due to the popularity and convenience that mobile applications had and high interest from many people in having a healthy life, it is a great opportunity to combine both of them. Therefore, this study focusses to build mobile sports application using object-oriented analysis and design method, that could meet the demand of lots of peoples that wants to do a sport especially a ball sport. Otherwise, the application can help and support the community and public, especially for ball sports fans, to make reservations for the sports field easier without having to come to the location and also meet their sporting needs.

关键词： Sports Mobile applications Business Schedules computer science Real-time systems IEEE merchandise

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A Celebration of the EDGE program’s Impact on the Mathematics Community and Beyond 1st ed. 2019

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丛书名： Association for Women in Mathematics Series

2019年

作者： Susan D'Agostino Sarah Bryant Amy Buchmann Michelle Craddock Guinn Leona Harris

ISBN: (数字)9783030194864

ISBN: (纸本)9783030194857

The Enhancing Diversity in Graduate Education (EDGE) program began twenty years ago to provide support for women entering doctoral programs in the mathematical sciences. With a steadfast commitment to diversity among participants, faculty, and staff, EDGE initially alternated between Bryn Mawr and Spelman Colleges. In later years, EDGE has been hosted on campuses around the nation and expanded to offer support for women throughout their graduate school and professional careers. The refereed papers in;range from short memoirs, to pedagogical studies, to current mathematics research. All papers are written by former EDGE participants, mentors, instructors, directors, and others connected to EDGE. Together, these papers offer compelling testimony that EDGE has produced a diverse new generation of leaders in the mathematics community. This volume contains technical and non-technical works, and it is intended for a far-reaching audience, including mathematicians, mathematics teachers, diversity officers, university administrators, government employees writing educational or science policy, and mathematics students at the high school, college, and graduate levels. By highlighting the scope of the work done by those supported by EDGE, the volume offers strong evidence of the American Mathematical Society’s recognition that EDGE is 'a program that makes a difference.”;This volume offers unique testimony that a 20-year old summer program has expanded its reach beyond the summer experience to produce a diverse new generation of women leaders, nearly half of whom are underrepresented women. While some books with a women-in-math theme focus only on one topic such as research or work-life balance, this book's broad scope includes papers on mathematics research, teaching, outreach, and career paths.

关键词： Mathematics

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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

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Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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programmable threshold sensing in wireless devices using Ising dynamics

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Nature Electronics 2025年 1-8页

作者： Nicolas Casilli Hussein M. E. Hussein Ryan Tetro Luca Colombo Matteo Rinaldi Cristian Cassella Seunghwi Kim Andrea Alù Philip X.-L. Feng Institute for Nano Systems Innovation (NanoSI) Department of Electrical and Computer Engineering Northeastern University Boston MA USA Photonics Initiative Advanced Science Research Center City University of New York New York City NY USA Physics Program Graduate Center City University of New York New York City NY USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

Ising machines—comprising dissipatively coupled nodes capable of emulating the behaviour of ferromagnetic spins—can form analogue computing engines that surpass the sequential processing constraints of von Neumann architectures. However, the incorporation of Ising dynamics into radio-frequency wireless technologies remains limited, especially in terms of their potential to enhance wireless sensing capabilities. Here we report a passive wireless sensor that uses Ising dynamics to accurately implement threshold sensing. The device correlates the occurrence of violations in a sensed parameter with transitions in the coupling state of two parametric oscillators acting as Ising spins. As a result, the accuracy of the device is unaffected by distortions in its input and output signals due to multipath and is less prone to clutter caused by co-site interference. We illustrate the potential of the approach in temperature threshold sensing using a microfabricated lithium niobate microelectromechanical temperature sensor to couple two radio-frequency parametric oscillators, and show that such a system allows the sensor threshold to be wirelessly reprogrammed.

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Unitary Excitation Transfer between Coupled Cavities Using Temporal Switching

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Physical Review Letters 2021年第1期127卷 013902-013902页

作者： Yarden Mazor Michele Cotrufo Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA School of Electrical Engineering Tel-Aviv University Ramat-Aviv Tel-Aviv 69978 Israel Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center of the City University of New York New York New York 10016 USA

Tailored time variations can enable efficient control over signal flows, giving rise to exotic wave phenomena. In this work, we demonstrate how abrupt temporal switching of the coupling between two cavities can tailor the energy flow between them beyond the limitations of static scenarios, enabling unitary excitation transfer. The proposed scheme is robust with respect to a wide range of nonidealities, with implications for classical and quantum phenomena, from computing to nanophotonic systems.

关键词： Metamaterials Coupled oscillators

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Reply to “Comment on ‘Scattering Cancellation-Based Cloaking for the Maxwell-Cattaneo Heat Waves’”

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Physical Review Applied 2021年第5期15卷 058002-058002页

作者： M. Farhat S. Guenneau P.-Y. Chen A. Alù K.N. Salama Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia UMI 2004 Abraham de Moivre-CNRS Imperial College London London SW7 2AZ United Kingdom Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago Illinois 60607 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

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Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron Microscopy

arXiv

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arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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