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Adaptive Input Sampling: A Novel Approach for Efficient Object Detection in High Resolution Traffic Monitoring Images

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IEEE Access 2025年 13卷 73003-73021页

作者： Gebre, Tewodros Syum Wasehun, Eden Tsehaye Dorbu, Freda Elikem Agboola, Gazali Oluwasegun Hashemi-Beni, Leila Karimoddini, Ali NC AandT State University Applied Science and Technology Program GreensboroNC United States NC AandT State University Department of Computational Data Science and Engineering GreensboroNC United States NC AandT State University Geomatics Program Department of Built Environment GreensboroNC United States NC AandT State University Department of Electrical and Computer Engineering GreensboroNC United States

Real-time traffic monitoring is crucial for efficient transportation management, but it poses significant computational challenges, particularly when dealing with high-resolution videos. Most modern traffic monitoring systems employ object detection models followed by object tracking models, which require processing every frame of the video footage to detect and track vehicles. However, even efficient object detection models are computationally expensive, making it difficult to process high-resolution videos in real-time. To address this challenge, we introduce an Adaptive Input Sampler (AIS) as a preprocessing module that selectively activates the object detector only when vehicles are present in the scene. The AIS targets the area of the frame where vehicles are detected and samples that area without compressing the sample, thereby reducing the computational load on the object detector. By doing so, the AIS enables significant computational savings, especially when fewor no vehicles are present in the monitored scene. Our experiments demonstrate that the AIS achieves 96.40% traffic counting accuracy and 95.60% classification accuracy, while reducing the data size for analysis by up to 79.00%. AIS presents a promising solution to optimize the volume of input data in traffic monitoring applications, improve computational efficiency, and speed up inference results. © 2013 IEEE.

关键词： Footage counters

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Innovative biomarker exploration in ASD: Combining Graph Neural Networks and permutation testing on fMRI data

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NeuroImage: Reports 2025年第2期5卷 100249-100249页

作者： Wang, Donglin Ding, Wandi Department of Mathematical Sciences Computational and Data Science Program Middle Tennessee State University 1301 E Main St Murfreesboro 37132 TN United States

This study employed Graph Neural Networks (GNNs), specifically an unsupervised GNN, to extract node embeddings from brain regions in both Autism Spectrum Disorder (ASD) and control groups. The objective was to identify potential biomarkers by analyzing node embeddings extracted from a graph model based on functional Magnetic Resonance Imaging (fMRI) data. Permutation tests were conducted to identify regions with significant differences in their embeddings between the two groups. Our results revealed several regions exhibiting significant differences, including the cerebellum, temporal lobe, and occipital lobe. These findings align with previous studies on ASD. Moreover, novel regions such as Vermis_3, Vermis_4_5, Fusiform areas, Parietal, and Cuneus were identified, emphasizing the need for further investigation. This study underscores the potential of GNNs in analyzing brain networks for ASD biomarker discovery. The identified regions warrant additional validation and exploration to understand their association with specific domains of ASD symptoms. Our approach presents a promising avenue to advance the diagnosis of ASD and to improve our understanding of its underlying neural basis. © 2025 The Authors

关键词： Autism Spectrum Disorder (ASD) Biomarker Graph Neural Networks (GNNs) Permutation test

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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Unbiased Parameter Estimation for Bayesian Inverse Problems

arXiv

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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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Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations

arXiv

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arXiv 2025年

作者： Chen, Yuan Khaliq, Abdul Furati, Khaled M. Computational and Data Science Program Middle Tennessee State University MurfreesboroTN37132 United States Department of Mathematical Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nonlinear time-dependent partial differential equations are essential in modeling complex phenomena across diverse fields, yet they pose significant challenges due to their computational complexity, especially in higher dimensions. This study explores Quantum Recurrent Neural Networks within an encoder-decoder framework, integrating Variational Quantum Circuits into Gated Recurrent Units and Long Short-Term Memory networks. Using this architecture, the model efficiently compresses high-dimensional spatiotemporal data into a compact latent space, facilitating more efficient temporal evolution. We evaluate the algorithms on the Hamilton-Jacobi-Bellman equation, Burgers’ equation, the Gray-Scott reaction-diffusion system, and the three dimensional Michaelis-Menten reaction-diffusion equation. The results demonstrate the superior performance of the quantum-based algorithms in capturing nonlinear dynamics, handling high-dimensional spaces, and providing stable solutions, highlighting their potential as an innovative tool in solving challenging and complex systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Operative estimation of global horizontal irradiance using transfer functions through network types of artificial neural network in some selected sites in North-East Ethiopia: assessment and comparison

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Heliyon 2025年第10期11卷

作者： Woldegiyorgis, Tegenu Argaw Assamnew, Abera Debebe Benti, Natei Ermias Desalegn, Gezahegn Assefa Anose, Fikru Abiko Mossie, Sentayehu Yigzaw Department of Physics College of Natural Sciences Wollo University P.O. Box 1145 Dessie Ethiopia Computational Data Science Program College of Natural and Computational Sciences Addis Ababa University P. O. Box 1176 Addis Ababa Ethiopia Department of Physics College of Natural and Computational Sciences Wolkite University Wolkite Ethiopia

The estimation of global horizontal irradiance (GHI) for a specific location is crucial to the design, modeling, and effective operation of photovoltaic (PV) and solar thermal energy conversion systems. This study aims to predict the monthly average daily and monthly average GHI for three locations in Ethiopia (Kemissie, Haik, and Woldiya) using artificial neural networks (ANNs) with various activation functions. Days of the year, elevation, and average values (maximum and minimum temperatures, surface pressure, wind speed, relative humidity, wind direction, and precipitation) were fed into the ANN during training. For the prediction of monthly average daily GHI, the CFBP-tansig, FFBP-tansig, and FFBP-logsig networks proved to be the most accurate. Likewise, for monthly average GHI prediction, the FFBP-tansig, CFBP-tansig, and LR-tansig networks exhibited the best performance. The daily and monthly GHI predictions achieved RMSE values ranging from 0.00058 to 0.04042, with correlation coefficients (r) between 0.95417 and nearly 1.0. In comparison, traditional methods showed RMSE values between 0.1 and 0.2, indicating that the ANN models provided up to 80 % better accuracy. The EBP-purelin, CFBP-purelin, and LR-purelin models were less effective, with RMSE values between 0.13815 and 0.17307, and r from 0.91153 to 0.96719. Among the studied locations, Kemissie exhibited the highest GHI potential, emphasizing the region's significant capability for solar energy generation in Ethiopia. © 2025 The Authors

关键词： Activation function Artificial neural network Global horizontal irradiance Network types Prediction

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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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Multifaceted Disparities Associated with Translator Earnings: A Quantitative Study of Upwork Profiles

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Journal of Social Computing 2024年第1期5卷 88-100页

作者： Tyler Horan Data Analytics and Computational Social Science Program University of Massachusetts AmherstAmherstMA 01003USA

This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation *** a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language *** study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to *** findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this *** rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.

关键词： translation online employment gig economy intercultural communication

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Rural Sustainability 1

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丛书名： SpringerBriefs in Geography

1000年

作者： Qing Tian

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