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arXiv 2025年

作者： Adeli, Vida Mehraban, Soroush Mirmehdi, Majid Whone, Alan Filtjens, Benjamin Dadashzadeh, Amirhossein Fasano, Alfonso Iaboni, Andrea Taati, Babak University of Toronto Computer Science Department Canada Vector Institute Canada KITE Research Institute UHN Canada University of Toronto Institute of Biomedical Engineering Canada University of Bristol School of Computer Science United Kingdom University of Bristol Translational Health Science United Kingdom University of Toronto Data Sciences Institute Canada University of Toronto Department of Medicine Division of Neurology Canada Krembil Research Institute UHN Canada Edmond J. Safra Program in Parkinson’s Disease UHN Canada University of Toronto Department of Psychiatry Canada Centre for Mental Health UHN Canada

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson’s Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis. © 2025, CC BY-NC-SA.

关键词： Pathology

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Optimal Routing in a Small-World Network

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Journal of computer science & Technology 2006年第4期21卷 476-481页

作者：曾坚阳许文经 School of Computer Engineering Nanyang Technological University Singapore 639798 Computer Science Program Singapore-MIT Alliance (SMA) Program Singapore 117576

Substantial research has been devoted to the modelling of the small-world phenomenon that arises in nature as well as human society. Earlier work has focused on the static properties of various small-world models. To examine the routing aspects, Kleinberg proposes a model based on a d-dimensional toroidal lattice with long-range links chosen at random according to the d-harmonic distribution. Kleinberg shows that, by using only local information, the greedy routing algorithm performs in O（lg^2 n） expected number of hops. We extend Kleinberg＇s small-world model by allowing each node x to have two more random links to nodes chosen uniformly and randomly within （lg n）2/d Manhattan distance from x. Based on this extended model, we then propose an oblivious algorithm that can route messages between any two nodes in O（lg n） expected number of hops. Our routing algorithm keeps only O（（lgn）β＋1） bits of information on each node, where 1 〈 β 〈 2, thus being scalable w.r.t, the network size. To our knowledge, our result is the first to achieve the optimal routing complexity while still keeping a poly-logarithmic number of bits of information stored on each node in the small-world networks.

关键词： small-world model augmented local awareness decentralized routing analysis of algorithms distributedsystems

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Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Shi, Yukai Weng, Zhipeng Lin, Yupei Shi, Cidan Guo, Xingyuan Yang, Xiaojun Lin, Liang Guangdong University of Technology School of Information Engineering Guangzhou510006 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Southern Power Grid Ltd. Guangzhou510000 China Ministry of Education of China Key Laboratory of Photonic Technology for Integrated Sensing and Communication Guangdong Guangzhou510006 China Sun Yat-sen University School of Computer Science Guangzhou510006 China

In recent years, deep neural network tasks have increasingly relied on high-quality image inputs. With the development of high-resolution representation learning, the task of image dehazing has received significant attention. Previously, many methods collected diverse image data for large-scale training to boost the performance on a target scene. Ignoring the domain gap between different data, former dehazing methods simply adopt multiple datasets for explicit large-scale training, which often makes the methods themselves violated. To address this problem, we propose a novel method of cross-data vision alignment for richer representation learning to improve the existing dehazing methodology. Specifically, we call for the internal and external knowledge should be further adapted in a self-supervised manner to fill up the domain gap. By using cross-data external alignment, the datasets inherit samples from different domains that are firmly aligned, making the model learn more robust and generalizable features. By using the internal augmentation method, the model can fully exploit local information within the images and then obtain more image details. To demonstrate the effectiveness of our proposed method, we conduct training on the natural image dataset (NID). Experimental results show that our method clearly resolves the domain gap in different dehazing datasets and presents a new pipeline for large-scale training in the dehazing task. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images. © 1963-2012 IEEE.

关键词： Self-supervised learning

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Bootstrapping in miniature language acquisition

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cognitive Systems Research 2002年第1期3卷 15-23页

作者： Desai, Rutvik Computer Science Department The Cognitive Science Program Indiana University Bloomington IN 47405 United States

Given the difficulties in learning meanings of words by observing the referent, it has been suggested that children use the syntactic context of the word to predict part of its meaning, a hypothesis known as syntactic bootstrapping. Semantic bootstrapping is the opposite theory that the knowledge of semantics helps in acquiring syntax. While there is evidence that children can apply their knowledge of correlations between syntax and semantics to perform bootstrapping, it is not clear how they come to know about these correlations in the first place. Here, a connectionist network is presented that learns to comprehend a miniature language by associating sentences with the corresponding scenes. In doing so, it learns the syntactic/semantic correlations and exhibits bootstrapping behavior. It is argued that such specialized phenomena can emerge when general mechanisms are applied to a specific task, and it is not always necessary to endow the learner with pre-existing specialized mechanisms. © 2002 Elsevier science B.V. All rights reserved.

关键词： Connectionist modeling Emergence Language acquisition Semantic bootstrapping Syntactic bootstrapping

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Polarization-Tunable Colorimetric Metasurfaces for All-Optical and Non-Destructive Structural Characterization of Polymeric Nanofibers

arXiv

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arXiv 2025年

作者： Kirya, Paula Hochberg, Justin D. Kim, Han Sol Haddadin, Zaid Bordy, Samantha Byun, Jiuk Pokorski, Jonathan K. Poulikakos, Lisa V. Department of Mechanical and Aerospace Engineering UC San Diego La Jolla CA United States Aiiso Yufeng Li Family Department of Chemical and Nano Engineering UC San Diego La Jolla CA United States Department of Electrical and Computer Engineering UC San Diego La Jolla CA United States Program in Materials Science and Engineering UC San Diego La Jolla CA United States

Metasurfaces have pioneered significant improvements in sensing technology by tailoring strong optical responses to weak signals. When designed with anisotropic subwavelength geometries, metasurfaces can tune responses to varying polarization states of light. Leveraging this to quantify structural alignment in fibrous materials unveils an alternative to destructive characterization methods. This work introduces metasurface-enhanced polarized light microscopy (Meta-PoL), which employs polarization-tunable, guided-mode-resonant colorimetric metasurfaces to characterize molecular and bulk alignment of poly(Ε-caprolactone) (PCL) nanofibers in a far-field configuration. PCL nanofibers drawn at 0%, 400%, and 900% ratios were interfaced with the studied metasurfaces. Metasurface resonances coinciding with the intrinsic drawn nanofiber resonances – confirmed by Stokes Polarimetry – produced the strongest colorimetric enhancement, resultant from alignment-specific nanofiber reflectivity. The enhancement degree corresponded with molecular and bulk alignments for each draw ratio, as measured through differential scanning calorimetry and scanning electron microscopy, respectively. Thus, Meta-PoL presents an all-optical, non-destructive, and quantitative measurement of nanofiber alignment. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential scanning calorimetry

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OPRLM: A Web Tool and a Database for Positioning and Simulations of Proteins in Realistic Lipid Membranes

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Journal of Molecular Biology 2025年 168966页

作者： Park, Sang-Jun Schnitzer, Kyle A. Kovalenko, Alexey Cherepanov, Stanislav Patro, L. Ponoop Prasad Song, Zigang Pogozheva, Irina D. Lomize, Andrei L. Im, Wonpil Department of Computer Science and Engineering Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan 1221 Beal Ave Ann Arbor 48109-2102 MI United States Department of Computer Science College of Literature Science and the Arts University of Michigan 2260 Hayward Street Ann Arbor 48109-2121 MI United States Biophysics Program University of Michigan 930 N. University Avenue Ann Arbor 48109-1065 MI United States Department of Biological Sciences Lehigh University 111 Research Drive Bethlehem 18015 PA United States Department of Medicinal Chemistry College of Pharmacy University of Michigan 428 Church St. Ann Arbor 48109-1065 MI United States

Molecular dynamics (MD) simulations in explicit lipid bilayers enable modeling of protein-lipid interactions essential for membrane protein functions and regulation. The newly developed computational web tool, OPRLM (Orientations of Proteins in Realistic Lipid Membranes), automates the assembly of membrane protein structures with explicit lipids corresponding to 18 biological membrane types with symmetric or asymmetric lipid distributions, as well as 5 types of two-component lipid bilayers with varying cholesterol content. Built upon the CHARMM-GUI toolset and the PPM method, OPRLM simplifies the setup of complex simulation system involving integral and/or peripheral membrane proteins with explicit lipid mixtures and generates all necessary files for subsequent all-atom MD simulations. OPRLM has successfully generated protein-membrane systems for 286 tested protein structures in various biomembranes, including 138 structures containing ligands. The OPRLM database, an advanced successor of the OPM database, includes explicit protein-lipid systems for tested proteins in their native biomembranes. It provides coordinates of integral and peripheral membrane proteins from the Protein Data Bank embedded in planar or curved implicit lipid bilayers. Additionally, it includes the classification of proteins into types, superfamilies, and families, along with the information on intracellular localizations and membrane topology and visualization options. The OPRLM web tool and the database are publicly accessible at https://***. © 2025 The Authors

关键词： biomembrane explicit lipid lipid bilayer peripheral membrane protein transmembrane

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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On the particle approximation of lagged Feynman–Kac formulae

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Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse 31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L₁-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L₁−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Diffusion Monte Carlo Eigenvalue approximation Feynman–Kac formula

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Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different wastewater matrices

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Water Research 2025年

作者： Yevhen Myshkevych Ibrahima N'Doye Julie Sanchez Medina Fahad K. Aljehani Yanghui Xiong Taous-Meriem Laleg-Kirati Pei-Ying Hong Environmental Science and Engineering Program Division of Biological and Environmental Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 KAUST Center of Excellence on Smart Health King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

Enumerating virus particles (VPs) at different stages of the wastewater treatment process or along the distribution network is essential for ensuring high performance and reducing public health risks. Herein, we aimed to (i) optimize the flow virometry (FVM) protocol for use in wastewater matrices, (ii) correlate FVM data with specific virus genera of interest, and (iii) develop machine learning (ML) models for determining total VP concentration. We identified and tested a comprehensive set of parameters to determine the optimal conditions for wastewater FVM. Specifically, we tested various sample preprocessing steps to enhance FVM detection sensitivity, including the use of different nucleic acid staining dyes, surfactant addition and concentration optimization, glutaraldehyde fixation, and the effect of sample freezing before FVM analysis. Spearman's rank correlation of FVM data with virus genera concentration using a conventional qPCR-based method in 206 samples showed a positive correlation for all five virus genera, ranging from 0.21 to 0.44 (p < 0.01). The extreme gradient-boosting (XGB) model using easily accessible physiochemical water parameters (such as turbidity, electroconductivity, total dissolved solids, total suspended solids, pH, chemical oxygen demand, and concentrations of nitrate nitrogen, nitrite nitrogen, and ammonium nitrogen) as input data outperformed the random forest (RF) model and can be used to estimate total virus count across all types of wastewater matrices as output data. Furthermore, XGB achieved a better root mean square error in the four treatment processes (influent, aerobic, sand, and MBR) by a mean of 23% than RF in model development. This study demonstrates that FVM, combined with ML, can significantly enhance monitoring capabilities by accurately estimating VP concentrations across diverse wastewater matrices.

关键词： flow virometry virus enumeration water reuse machine learning

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