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arXiv 2025年

作者： Gupta, Nitant Akepati, Sri V.V.R. Bianco, Simona Shah, Jay Adams, Dave J. Jayaraman, Arthi Department of Chemical and Biomolecular Engineering University of Delaware NewarkDE19716 United States Data Science Program University of Delaware NewarkDE19716 United States School of Chemistry University of Glasgow GlasgowG12 8QQ United Kingdom Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States

In this paper, we extend a recently developed machine-learning (ML) based CREASE-2D method to analyze the entire two-dimensional (2D) scattering pattern obtained from small angle X-ray scattering measurements of supramolecular dipeptide micellar systems. Traditional analysis of such scattering data would involve use of approximate or incorrect analytical models to fit to azimuthally-averaged 1D scattering patterns that can miss the anisotropic arrangements. Analysis of the 2D scattering profiles of such micellar solutions using CREASE-2D allows us to understand both isotropic and anisotropic structural arrangements that are present in these systems of assembled dipeptides in water and in the presence of added solvents/salts. CREASE-2D outputs distributions of relevant structural features including ones that cannot be identified with existing analytical models (e.g., assembled tubes’ cross-sectional eccentricity, tortuosity, orientational order). The representative three-dimensional (3D) real-space structures for the optimized values of these structural features further facilitate visualization of the structures. Through this detailed interpretation of these 2D SAXS profiles we are able to characterize the shapes of the assembled tube structures as a function of dipeptide chemistry, solution conditions with varying salts and solvents, and relative concentrations of all components. This paper demonstrates how CREASE-2D analysis of entire SAXS profiles can provide an unprecedented level of understanding of structural arrangements which has not been possible through traditional analytical model fits to the 1D SAXS data. © 2025, CC BY-NC-SA.

关键词： X ray scattering

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Spatiotemporal dilated convolution with uncertain matching for video-based crowd estimation

arXiv

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arXiv 2021年

作者： Ma, Yu-Jen Shuai, Hong-Han Cheng, Wen-Huang Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Institute of Electronics National Chiao Tung Univresity Hsinchu300 Taiwan Artificial Intelligence and Data Science Program National Chung Hsing University Taichung400 Taiwan

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo’10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods. The source codes are released at https://***/STDNet/STDNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Local Neighborhood-Based Adaptation of Weights in Multi-Objective Evolutionary Algorithms Based on Decomposition

Local Neighborhood-Based Adaptation of Weights in Multi-Obje...

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Congress on Evolutionary Computation

作者： Paulo Pinheiro Junqueira Ivan Reinaldo Meneghini Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais UFMG Belo Horizonte MG Brazil Federal Institute of Education Science and Technology of Minas Gerais Ibirité Brazil Machine Intelligence and Data Science Laboratory (MINDS) Universidade Federal de Minas Gerais Belo Horizonte Brazil

Multi-objective algorithms based on decomposition have become popular for the reason that a uniform distribution of weight vectors may result in a better distribution of solutions along the Pareto front. However, for more complex Pareto fronts with irregular shapes, the initial weight vectors may not be adequate. One alternative to overcome this problem, is to adapt the weight vectors during the evolutionary process. In this paper an adaptive version of Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is proposed to change the weight vectors based on the concept of local neighborhoods. The proposed method is called MOEA/D with local-neighborhood-based adaptation (MOEA/D-LNA). The proposed method is compared against a number of famous variants of MOEA/D in the literature. Initial experimental results have shown promising effectiveness on problems with irregular Pareto shapes.

关键词： Shape Heuristic algorithms Evolutionary computation Standards

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Variational methods and deep Ritz method for active elastic solids

arXiv

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arXiv 2022年

作者： Wang, Haiqin Zou, Boyi Su, Jian Wang, Dong Xu, Xinpeng Physics Program Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China Technion - Israel Institute of Technology Haifa32000 Israel School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [Soft Matter, 2021, 17, 3634], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as Ritz method, for active solid statics. Moreover, the power of Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development. © 2022, CC BY-NC-ND.

关键词： Deep neural networks

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Two-Month Cognitive Changes Enhance Prediction of Nonremission in Clinical High-Risk Individuals

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Biological Psychiatry 2025年第2期98卷 175-185.0页

作者： Zhang, TianHong Wei, YanYan Tang, XiaoChen Xu, LiHua Cui, HuiRu Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Tang, YingYing Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Longitudinal changes in cognitive function may be crucial in predicting clinical outcomes in clinical high-risk (CHR) individuals. In this study, we aimed to investigate the predictive value of baseline cognitive impairment and short-term cognitive changes for nonremission and conversion to psychosis in individuals at CHR for psychosis compared with healthy control individuals (HCs). Methods: This study used a multiple-group prospective design with a 3-year follow-up. CHR individuals and HCs were assessed at baseline and at a 2-month follow-up. Neuropsychological performance was evaluated using the Chinese version of the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery. Results: The study included 310 CHR individuals and 93 HCs. Significant improvements in predicting nonremission in CHR individuals were observed when incorporating cognitive changes over 2 months (area under the receiver operating characteristic curve [AUC] for baseline cognition, 0.690;AUC for changes, 0.819;z = 3.365, p < .001). Key predictors included the Hopkins Verbal Learning Test–Revised (β = 0.083, p = .003), Wechsler Memory Scale-III spatial span (β = 0.330, p < .001), and Brief Visuospatial Memory Test–Revised (β = 0.127, p < .001). Conversely, predicting conversion to psychosis showed no significant difference between baseline and 2-month cognitive changes (AUC for baseline cognition, 0.667;AUC for changes, 0.666;z = 0.021, p = .242). Conclusions: The findings underscore the importance of dynamic cognitive monitoring in CHR individuals. Short-term cognitive changes significantly enhanced the prediction of nonremission but did not add predictive value for conversion to psychosis beyond baseline assessments. Specific cognitive domains, such as verbal learning and working memory, were particularly valuable for predicting clinical outcomes. © 2025 Society of Biological Psychiatry

关键词： Cognition Functional outcome Prodromal psychosis Remission Transition Ultra-high risk

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Insights on the influence of natural co-contaminants on the photocatalytic performance of biochar supported p-n heterojunction photocatalyst in an aqueous media

Cleaner Water

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Cleaner Water 2025年 4卷

作者： Saptarshi Ghosh Manoranjan Sahu Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai 400076 India Inter-Disciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai 400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai 400076 India

In last few decades, photocatalysis have been extensively studied in a simulated wastewater under laboratory conditions to degrade wide range of organic pollutants. To transfer this technology into a large scale, several approaches have been made focusing on developing novel photocatalyst for maximizing the generation of reactive species and efficient photocatalytic reactor for maximizing photon usage. However, there are some fundamental issues exist with the presence of natural co-contaminants such as competitive ions and natural organic matter (NOM) which can significantly reduce efficiency of the photocatalytic process. Hence, this study investigated role of competitive ions and NOM on photocatalytic oxidation of DMP using p-n heterojunction photocatalyst (O 2 -TiO 2 /CuO) decorated on biochar. The study revealed that competitive ions showed quenching effect towards photogenerated charged pairs and significantly reduces degradation efficiency. Similarly, presence of NOM in small amount could enhance or does not have substantial impact on the removal efficiency of DMP. However, at higher NOM concentration, DMP removal dropped significantly by screening of photons, blocking of adsorption sites of the catalyst and scavenging of positive holes (h + ). In addition, Electron Spin Resonance (ESR) analysis was performed as an evidence for the generation of radicals and quenching experiment was conducted to identify the major contributing radical in the degradation process among multiple radicals generated from the photocatalysis. The findings revealed that among different reactive species OH • is the dominant species in the degradation process and O 2 - • has least contribution to the degradation of DMP.

关键词： Co-contaminants P-n heterojunction Photocatalysis Reactive Oxygen Species Scavengers

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Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department

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Computer Methods and programs in Biomedicine 2024年 255卷 108357页

作者： Lee, Chih-Kuo Chen, Ting-Li Wu, Jeng-En Liao, Min-Tsun Wang, Chiehhung Wang, Weichung Chou, Cheng-Ying Department of Internal Medicine National Taiwan University Hospital Hsin-Chu Branch No. 25 Ln. 442 Sec. 1 Jingguo Rd. Taiwan Hsinchu300 Taiwan Institute of Statistical Science Academia Sinica 128 Academia Rd. Nankang Taiwan Taipei11529 Taiwan Master Program in Statistics National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Data Science Degree Program National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Institute of Applied Mathematical Sciences National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Department of Biomechatronics Engineering National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan

Background and Objectives: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. Methods: Using the open-source dataset MIMIC-IV and MIMIC[sbnd]CXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. Results: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. Conclusion: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments. © 2024 Elsevier B.V.

关键词： Image segmentation

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A parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions

arXiv

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arXiv 2022年

作者： Nichols, Nathan S. Sokol, Paul Del Maestro, Adrian Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Vermont BurlingtonVT05405 United States Materials Science Program University of Vermont BurlingtonVT05404 United States Department of Physics Indiana University BloomingtonIN47408 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

We report on Differential Evolution for Analytic Continuation (DEAC): a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly, and experimentally relevant results are included for quantum Monte Carlo simulations of bulk 4He below the superuid transition temperature. Copyright © 2022, The Authors. All rights reserved.

关键词： Functional analysis

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A growth model for water distribution networks with loops

arXiv

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arXiv 2021年

作者： Sugishita, Kashin Abdel-Mottaleb, Noha Zhang, Qiong Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs which generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modeled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency, and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modeled pipe diameters. The difference between the empirical and modeled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

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Satellite-based assessment and forecasting of greenhouse gas (GHG) concentrations in Indian megacities using advanced statistical methods

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Environmental science and Pollution Research 2025年第24期32卷 2025 Jun 4页

作者： Alagade, Adarsh Sahu, Manoranjan Environmental Science and Engineering Department Indian Institute of Technology Bombay 507 Aerosol and Nanoparticle Technology Laboratory Mumbai400076 India Inter-Disciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

The urgent challenge of climate change, driven by greenhouse gas (GHG) emissions, highlights the need for reliable monitoring to inform effective mitigation strategies. However, the lack of monitoring stations in India for direct GHG measurements poses a challenge. This study explores the potential of satellite data for monitoring CO2 and CH4 concentrations in Indian megacities, specifically for Mumbai and Delhi. We assessed the reliability of Orbiting Carbon Observatory-2 (OCO-2) satellite data for CO2 and Sentinel-5 Precursor data for CH4 by comparing them with ground-based Total Carbon Column Observing Network (TCCON) measurements. Additionally, we applied the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for CO₂ forecasting in Mumbai, achieving high accuracy (mean absolute error = 2.1 ppm, root mean square error = 2.72 ppm). While data limitations restrict CO2 analysis for Delhi, the findings for Mumbai significantly contribute to understanding urban CO2 dynamics. The SARIMA model also shows promise for CH4 forecasting with MAE of 8.3 ppb (Mumbai) and 7.78 ppb (Delhi) and RMSE of 10.37 ppb (Mumbai) and 11.92 ppb (Delhi). These findings underscore the utility of satellite data and forecasting models in monitoring urban GHG emissions. The observed rise in CO2 and CH4 concentrations highlights the urgency of implementing serious actions to address climate change. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Global warming

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