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arXiv 2025年

作者： Chada, Neil K. Jasra, Ajay Maama, Mohamed Tempone, Raul Department of Mathematics City University of Hong Kong China School of Data Science The Chinese University of Hong Kong Shenzhen CN Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a solution of a differential equation and, even if exact solutions are available, an analytical intractability of the marginal likelihood and its associated gradient, which is used for parameter estimation. The focus of this article is to deliver unbiased estimates of the unknown parameters, that is, stochastic estimators that, in expectation, are equal to the maximizer of the marginal likelihood, and possess no numerical approximation error. Based upon the ideas of [4] we develop a new approach for unbiased parameter estimation for Bayesian inverse problems. We prove unbiasedness and establish numerically that the associated estimation procedure is faster than the current state-of-the-art methodology for this problem. We demonstrate the performance of our methodology on a range of problems which include a PDE and ODE. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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650 nm emitting InGaN red micro-LEDs with ITO n-electrodes

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Applied Physics Express 2025年第6期18卷 061001-061001页

作者： Cesur Altinkaya Rawan S. Jalmood Mohammed A. Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

InGaN red micro-LEDs were fabricated with indium tin oxide (ITO) and metal n-electrode designs. Micro-LEDs with ITO electrodes achieved a peak on-wafer external quantum efficiency of 2.1% (at 1.25 A cm⁻²) and wall-plug efficiency of 1.7% (at 0.64 A cm⁻²), representing 1.6 times and 1.5 times improvements compared to metal-based electrodes. Improved performance was attributed to the transparency of ITO, enabling light extraction, while metal electrodes block emission. Both configurations achieved a low leakage current density (≤ 10^–7 A cm⁻²) and a high peak emission wavelength around 650 nm. These results represent a strong potential for low-power consumption required/area-limited AR/VR applications.

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FAULT TOLERANCE ANALYSIS OF THE CLASS OF REARRANGEABLE INTERCONNECTION NETWORKS

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 1989年第1期7卷 148-164页

作者： PAKZAD, S Computer Engineering Program Department of Electrical Engineering The Pennsylvania State University University Park Pennsylvania 16802 USA

The issue of the fault tolerance of interconnection networks has received considerable attention since the last decade. The theoretical framework developed by J. P. Shen and J. P. Hays ( IEEE Trans. Comput. C-33 , 3 (Mar. 1984), 241–248) and extended by S. Pakzad and S. Lakshmivarahan ( Internat. J. Comput. Syst. Sci. Engrg. 3 , 2 (April 1988), 91–99) can be used as a guideline to analyze the fault tolerance behavior of the interconnection networks in a multicomputer environment. This paper analyzes the fault tolerance characteristics of a range of rearrangeable β-networks based on the concepts and the framework developed by S. Pakzad and S. Lakshmivarahan (cited earlier). These rearrangeable β-networks include the Benes network, the Waksman network, the Joel network, and the serial network. In addition, this paper presents a comparative analysis of the aforementioned networks according to their hardware cost, performance, and degree of fault tolerance.

关键词： computer Systems, Digital

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Sources of contrast in confocal reflectance imaging

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APPLIED OPTICS 1996年第19期35卷 3441-3446页

作者： Dunn, AK Smithpeter, C Welch, AJ RichardsKortum, R []The authors are with the Department of Electrical and Computer Engineering Biomedical Engineering Program University of Texas at Austin Austin Texas 78712

The relationship between optical properties and image contrast in confocal imaging is investigated. A Monte Carlo simulation has been developed to analyze the effects of changes in scattering, index of refraction, and absorption in a three-layer medium. Contrast was calculated from the computed signal-to-background ratios for changes in tissue optical properties. Results show that the largest source of contrast is changes in refractive index. (C) 1996 Optical Society of America

关键词： .Monte Carlo optical properties pinhole detector relative contributions signal-to-background source of contrast signal contrast

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MICROcomputer REALIZATIONS OF LYNN FAST DIGITAL-FILTERING DESIGNS

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MEDICAL & BIOLOGICAL engineering & COMPUTING 1980年第5期18卷 638-642页

作者： MALIK, NR Electrical & Computer Engineering Program University of Iowa Iowa City Iowa USA

The feasibility of real-time digital filtering using dedicated state-of-the-art microcomputers of 8-bit wordlength is demonstrated. Implementations of Lynn's integer filter structures by means of wrap-around queue data structures are shown to operate at sampling rates of over 8 kHz. An initial condition constraint unique to this filter class which must be satisfied to prevent inadvertent overflows or oscillations in microcomputers is discussed. The fast-filtering techniques are applied to the problem of calculating the average absolute value of a fast signal in real time.

关键词： Digital filters Microcomputers Online computing Signal processing

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Impact of Quantum Well Thickness on Efficiency Loss in InGaN/GaN LEDs: Challenges for Thin-Well Designs

arXiv

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arXiv 2025年

作者： Li, Xuefeng Pant, Nick Rahman, Sheikh Ifatur Armitage, Rob Rajan, Siddharth Kioupakis, Emmanouil Feezell, Daniel University of New Mexico AlbuquerqueNM87106 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Lumileds LLC San JoseCA95131 United States Department of Materials Science and Engineering The Ohio State University ColumbusOH43210 United States

We investigate the impact of quantum well (QW) thickness on efficiency loss in c-plane InGaN/GaN LEDs using a small-signal electroluminescence (SSEL) technique. Multiple mechanisms related to efficiency loss are independently examined, including injection efficiency, carrier density vs. current density relationship, phase space filling (PSF), quantum confined stark effect (QCSE), and Coulomb enhancement. An optimal QW thickness of around 2.7 nm in these InGaN/GaN LEDs was determined for quantum wells having constant In composition. Despite better control of deep-level defects and lower carrier density at a given current density, LEDs with thin QWs still suffer from an imbalance of enhancement effects on the radiative and intrinsic Auger-Meitner recombination coefficients. The imbalance of enhancement effects results in a decline in internal quantum efficiency (IQE) and radiative efficiency with decreasing QW thickness at low current density in LEDs with QW thicknesses below 2.7 nm. We also investigate how LED modulation bandwidth varies with quantum well thickness, identifying the key trends and their implications for device performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Semiconductor quantum wells

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Corrections to “Comprehensive Analysis of Design and Performance Evaluation Methodologies for a High-Resolution Read-Out IC With Ultralow 1/ f Corner for DC Measurement Applications”

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Junho Kang Woosol Han Suhwan Kim Jaehoon Jun Department of Electrical and Computer Engineering and the Inter-University Semiconductor Research Center (ISRC) Seoul National University Seoul South Korea Department of Electrical and Computer Engineering and Program in Semiconductor Convergence Inha University Incheon South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea Gwanak Analog Company Ltd. Seoul South Korea Department of Electrical and Computer Engineering and the Program in Semiconductor Convergence Inha University Incheon South Korea

Presents corrections to the paper, (Corrections to “Comprehensive Analysis of Design and Performance Evaluation Methodologies for a High-Resolution Read-Out IC With Ultralow 1/ f Corner for DC Measurement Application... 详细信息

关键词： Performance evaluation

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Next-generation pediatric care:nanotechnology-based and AI-driven solutions for cardiovascular,respiratory,and gastrointestinal disorders

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World Journal of Pediatrics 2025年第1期21卷 8-28页

作者： Waldenice de Alencar Morais Lima Jackson G.de Souza Fátima García-Villén Julia Lira Loureiro Fernanda Nervo Raffin Marcelo A.C.Fernandes Eliana B.Souto Patricia Severino Raquel de M.Barbosa Laboratory of Galenic Pharmacy Department of PharmacyFederal University of Rio Grande Do NorteNatal59012-570Brazil InovAI Lab nPITI/IMDFederal University of Rio Grande Do NorteNatalRN59078-970Brazil Department of Pharmacy and Pharmaceutical Technology School of PharmacyUniversity of GranadaCampus of Cartuja18071GranadaSpain Department of Computer Engineering and Automation Federal University of Rio Grande Do NorteNatalRN59078-970Brazil Laboratory of Pharmaceutical Technology Faculty of PharmacyUniversity of PortoRua Jorge de Viterbo Ferreira2284050-313PortoPortugal Industrial Biotechnology Program University of Tiradentes(UNIT)AracajuSergipe49032-490Brazil Department of Pharmacy and Pharmaceutical Technology School of PharmacyUniversity of SevilleC/Professor García González241012SevilleSpain

Background Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory,cardiac,and gastrointestinal disorders in children and newborns,mostly due to the complexity of therapeutic management in pediatrics and neonatology,owing to the lack of suitable dosage forms for these patients,often rendering them“therapeutic orphans”.The development and application of pediatric drug formulations encounter numerous challenges,including physiological heterogeneity within age groups,limited profitability for the pharmaceutical industry,and ethical and clinical *** drugs are used unlicensed or off-label,posing a high risk of toxicity and reduced *** these circumstances,some regulatory changes are being performed,thus thrusting research innovation in this *** sources Up-to-date peer-reviewed journal articles,books,government and institutional reports,data repositories and databases were used as main data *** Among the main strategies proposed to address the current pediatric care situation,nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive,versatile,tunable,site-specific drug *** engineering is in the spotlight as strategy to address pediatric cardiac diseases,together with theragnostic *** integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches,ushering in an era of innovative and personalized drug delivery for pediatric ***,the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great *** promises not only to enhance efficiency in drug development in general,but also in the pediatric field,hopefully boosting clinical trials for this *** Despite the long road ahead,the deepening of nanotechnology,the evolution of tissue engineering,and the combination of traditional techniques with artificia

关键词： Artificial intelligence Conventional therapy Nanotechnology Pediatrics Theragnostics

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