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arXiv 2021年

作者： Naderi, Amir Mohammad Bu, Haisong Su, Jingcheng Huang, Mao-Hsiang Vo, Khuong Torres, Ramses Seferino Trigo Chiao, J.-C. Lee, Juhyun Lau, Michael P.H. Xu, Xiaolei Cao, Hung Department of Electrical Engineering and Computer Science University of California IrvineCA United States Department of Biochemistry and Molecular Biology Department of Cardiovascular Medicine Mayo Clinic RochesterMN United States Department of Biomechatronics Engineering National Taiwan University Taipei Taiwan Department of Computer Science University of California IrvineCA United States Department of Biomedical Engineering University of California IrvineCA United States Department of Electrical and Computer Engineering Southern Methodist University DallasTX United States Department of Bioengineering University of Texas ArlingtonTX United States Sensoriis Inc. EdmondsWA United States Electrical and Biomedical Engineering University of California Irvine IrvineCA92697 United States

Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages;however, the current methods for quantification and monitoring of cardiac functions mostly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5-20 frames per second (fps);thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams. © 2021, CC BY.

关键词： Heart

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Artificial Intelligence for Digital and Computational Pathology

arXiv

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arXiv 2023年

作者： Song, Andrew H. Jaume, Guillaume Williamson, Drew F.K. Lu, Ming Y. Vaidya, Anurag Miller, Tiffany R. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Harvard Medical School BostonMA United States

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis;however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data. © 2023, CC BY.

关键词： Pathology

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Nanoparticle localization within chiral liquid crystal defect lines and nanoparticle interactions

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Physical Review E 2023年第3期107卷 034701-034701页

作者： Mykola Tasinkevych Sungoh Park Haridas Mundoor Ivan I. Smalyukh SOFT Group School of Science and Technology Nottingham Trent University Clifton Lane Nottingham NG11 8NS United Kingdom Departamento de Física and Centro de Física Teórica e Computacional Faculdade de Ciências Universidade de Lisboa 1749-016 Lisboa Portugal International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University Higashihiroshima 739-8511 Japan Department of Physics University of Colorado Boulder Colorado 80309 USA Soft Materials Research Center Department of Electrical Computer and Energy Engineering and Materials Science and Engineering Program and Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder Colorado 80309 USA

Self-assembly of colloidal particles into predefined structures is a promising way to design inexpensive manmade materials with advanced macroscopic properties. Doping of nematic liquid crystals (LCs) with nanoparticles has a series of advantages in addressing these grand scientific and engineering challenges. It also provides a very rich soft matter platform for the discovery of unique condensed matter phases. The LC host naturally allows the realization of diverse anisotropic interparticle interactions, enriched by the spontaneous alignment of anisotropic particles due to the boundary conditions of the LC director. Here we demonstrate theoretically and experimentally that the ability of LC media to host topological defect lines can be used as a tool to probe the behavior of individual nanoparticles as well as effective interactions between them. LC defect lines irreversibly trap nanoparticles enabling controlled particle movement along the defect line with the use of a laser tweezer. Minimization of Landau–de Gennes free energy reveals a sensitivity of the ensuing effective nanoparticle interaction to the shape of the particle, surface anchoring strength, and temperature, which determine not only the strength of the interaction but also its repulsive or attractive character. Theoretical results are supported qualitatively by experimental observations. This work may pave the way toward designing controlled linear assemblies as well as one-dimensional crystals of nanoparticles such as gold nanorods or quantum dots with tunable interparticle spacing.

关键词： Charged colloids Complex fluids Liquid crystals

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Photonic Terahertz Phased Array

arXiv

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arXiv 2024年

作者： Niu, Li Feng, Xi Zhang, Xueqian Lu, Yongchang Wang, Qingwei Xu, Quan Chen, Xieyu Ma, Jiajun Qiu, Haidi Sha, Wei E.I. Zhang, Shuang Alù, Andrea Zhang, Weili Han, Jiaguang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering The Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin300072 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science Graduate Program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages fractional exponents to capture long-memory effects in optimization. However, its utility is often limited by the difficulty of tuning and stabilizing these exponents. We propose 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), which integrates the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to adapt the fractional exponent in a data-driven manner. By tracking model sensitivity and effective dimensionality, 2SEDFOSGD dynamically modulates the exponent to mitigate oscillations and hasten convergence. Theoretically, for onoconvex optimization problems, this approach preserves the advantages of fractional memory without the sluggish or unstable behavior observed in naïve fractional SGD. Empirical evaluations in Gaussian and α-stable noise scenarios using an autoregressive (AR) model highlight faster convergence and more robust parameter estimates compared to baseline methods, underscoring the potential of dimension-aware fractional techniques for advanced modeling and estimation tasks. © 2025, CC BY.

关键词： Convex optimization

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Learn How to Design High-Quality Qualitative Educational Research! – A Workshop for Disciplinary STEM Faculty by Disciplinary STEM Faculty

Learn How to Design High-Quality Qualitative Educational Res...

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Frontiers in Education (FIE) Conference

作者： John R. Morelock Michelle Jarvie-Eggart Heather Chenette Sara Hooshangi Betsy Chestnutt Sarah Wilson Azadeh Bolhari Kirsten Dodson Iglika Pavlova Rebecca Reck Engineering Education Transformations Institute University of Georgia Athens GA USA Engineering Fundamentals Michigan Technological University Houghton MI USA Chemical Engineering Rose-Hulman Institute of Technology Terre Haute IN USA Computer Science - Northern Virginia Center Virginia Tech Washington D.C. USA Engineering Fundamentals University of Tennessee Knoxville Knoxville TN USA Chemical Engineering University of Kentucky Lexington KY USA Environmental Engineering Program University of Colorado Boulder Boulder CO USA Mechanical Engineering Lipscomb University Nashville TN USA Department of Biology University of North Carolina Greensboro Greensboro NC USA Bioengineering University of Illinois Urbana-Champaign Champaign IL USA

The purpose of this workshop-designed for instructional and disciplinary STEM faculty interested in learning about qualitative research-is to (1) introduce participants to high-quality qualitative research design and (2) practice this design process alongside disciplinary STEM faculty to expand their STEM education research abilities and network. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods [1]. This workshop will be conducted by ProQual Institute alumni, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change [2], the workshop leaders will serve as a community of practice to help participants move their educational research ideas forward during and after the workshop. In doing so, we strive to further FIE's mission to create a collaborative, supportive, and inclusive community of educational researchers.

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

arXiv

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arXiv 2022年

作者： Li, Yueming Jiang, Ying Lan, Lu Ge, Xiaowei Cheng, Ran Zhan, Yuewei Chen, Guo Shi, Linli Wang, Runyu Zheng, Nan Yang, Chen Cheng, Ji-Xin Department of Mechanical Engineering Boston University BostonMA02215 United States Graduate Program for Neuroscience Boston University BostonMA02215 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Chemistry Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Division of Materials Science and Engineering Boston University BostonMA02215 United States

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited precision at the millimeter-level. Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane film. SOAP generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83 µm, which is two orders of magnitude smaller than that of conventional transcranial focused ultrasound (tFUS). Here, we show effective OFUS neurostimulation in vitro with a single ultrasound cycle. We demonstrate submillimeter transcranial stimulation of the mouse motor cortex in vivo. An acoustic energy of 0.6 mJ/cm2, four orders of magnitude less than that of tFUS, is sufficient for successful OFUS neurostimulation. OFUS offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision non-invasively. Copyright © 2022, The Authors. All rights reserved.

关键词： Silicones

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Effects of Surface Passivation on CdZnTeSe Nuclear Detectors

Effects of Surface Passivation on CdZnTeSe Nuclear Detectors

引用

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Stephen U. Egarievwe Utpal N. Roy Ezekiel O. Agbalagba Amir H. Davis Mordecai B. Israel Parion L. Alexander Ralph B. James Department of Electrical Engineering and Computer Science Nuclear Engineering and Radiological Science Center Alabama A&M University AL USA Department of Nonproliferation and National Security Brookhaven National Laboratory NY USA Science and Technology Savannah River National Laboratory Aiken SC USA Department of Physics Federal University of Petroleum Resources Delta State Nigeria Department of Bioengineering Stanford University CA USA Department of Electrical and Computer Engineering University of South Alabama Mobile AL USA Department of Biological and Environmental Sciences Alabama A&M University AL USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Cadmium zinc telluride selenide (CdZnTeSe) has shown great promise in reducing the cost of semiconductor nuclear detectors that can operate at room temperature without cryogenic cooling. This is due to the high yield of detector-grade materials in the CdZnTeSe crystal growth process, which can be attributed to the much smaller numbers of Te inclusions and grain boundary network in CdZnTeSe compared to other CdTe-based semiconductors such as CdZnTe. In the present work, we study the effects of surface passivation on CdZnTe detectors using a mixture of ammonium fluoride and hydrogen peroxide solution (NH 4 F + H 2 O 2 + H 2 O). Detectors fabricated from CdZnTeSe crystals showed very good energy resolutions: 1.1% for the 662-keV gamma peak of 137 Cs by Frisch-grid detectors, and 5.9% for the 59.6-keV gamma peak of 241 Am by planar detectors. Experimental results show that the leakage current is increased immediately after passivation and then decreases as the surfaces stabilizes. The resistivity of the CdZnTeSe is of the order of 10 10 Ω-cm. The surface passivation improved the energy resolution of planar detector by 18% for the 59.6-keV gamma peak of 241 Am.

关键词： Water Grain boundaries Crystal growth Energy resolution Hydrogen Detectors Leakage currents

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