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A novel biomechanical model of the proximal mouse forelimb predicts muscle activity in optimal control simulations of reaching movements

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Journal of Neurophysiology 2025年第4期133卷 1266-1278页

作者： Gilmer, Jesse I. Coltman, Susan K. Cuenu, Geraldine Hutchinson, John R. Huber, Daniel Person, Abigail L. Borno, Mazen Al Department of Computer Science and Engineering Computational Bioscience Program University of Colorado Denver j Anschutz Medical Campus Denver CO United States Department of Basic Neuroscience University of Geneva Geneva Switzerland Department of Physiology and Biophysics University of Colorado Denver j Anschutz Medical Campus Denver CO United States Department of Comparative Biomedical Sciences Royal Veterinary College London United Kingdom Department of Kinesiology The Pennsylvania State University University Park PA United States

Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors that require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics, muscle activity, or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible. NEW & NOTEWORTHY Investigations into motor planning and execution lack an accurate and complete model of the forelimb, which could bolster or expand on findings. We sought to construct such a model using high-detail scans of murine anatomy and prior research into

关键词： biophysical models motor control muscle patterning optimal control reaching behaviors

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MathReader: Text-to-Speech for Mathematical Documents

arXiv

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arXiv 2025年

作者： Hyeon, Sieun Jung, Kyudan Kim, Nam-Joon Ryu, Hyun Gon Do, Jaeyoung Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Mathematics Chung-Ang University Korea Republic of NVIDIA United States Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://***/hyeonsieun/MathReader. Copyright © 2025, The Authors. All rights reserved.

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A Comparative Evaluation of the SIR and SEIZ Epidemiological Models to Describe the Diffusion Characteristics of COVID-19 Polarizing Viewpoints on Online Social Networks 58

A Comparative Evaluation of the SIR and SEIZ Epidemiological...

引用

58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Maleki, Maryam Agarwal, Nitin Systems Engineering Program California State University Dominguez Hills United States COSMOS Research Center UA - Little Rock AR United States International Computer Science Institute University of California Berkeley United States

ISBN: (纸本)9780998133188

To understand and characterize the diffusion trends of opposing viewpoints on Twitter (X), we applied two epidemiological models to six datasets related to COVID-19. We compared the results of the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models. We collected six datasets indicative of polarizing viewpoints related to contentious subjects surrounding the COVID-19 pandemic. Three of the datasets fall into an anti-subject hashtag group, and three of the datasets fall into a pro-subject hashtag group. The timeframe of each dataset is from January 1, 2020, to the end of 2021. Our findings demonstrate that while both the SIR and the SEIZ models are able to evaluate the propagation trends of the polarizing viewpoints, the SEIZ model is more accurate with relatively less error compared to the SIR model. This work sets the stage for ultimately leading to the ability to develop methods to prevent the propagation of ideas that lack scientific evidence while promoting the spread of scientifically backed ideas. © 2025 IEEE computer Society. All rights reserved.

关键词： COVID-19 Epidemiological model Mathematical modeling SEIZ model SIR model Social contagion Social network analysis Twitter (X)

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Inferring synaptic transmission from the stochastic dynamics of the quantal content: An analytical approach

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PLoS Computational Biology 2025年第5期21卷 e1013067页

作者： Vahdat, Zahra Gambrell, Oliver Fisch, Jonas Friauf, Eckhard Singh, Abhyudai Department of Electrical and Computer Engineering University of Delaware NewarkDE United States Animal Physiology Group Department of Biology University of Kaiserslautern Kaiserslautern Germany Department of Biomedical Engineering University of Delaware NewarkDE United States University of Delaware NewarkDE United States Interdisciplinary Neuroscience Program University of Delaware NewarkDE United States

Quantal parameters of synapses are fundamental for the temporal dynamics of neurotransmitter release, which is the basis of interneuronal communication. We formulate a general class of models that capture the stochastic dynamics of quantal content (QC), defined as the number of SV fusion events triggered by a single action potential (AP). Considering the probabilistic and time-varying nature of SV docking, undocking, and AP-triggered fusion, we derive an exact statistical distribution for the QC over time. Analyzing this distribution at steady-state and its associated autocorrelation function, we show that QC fluctuation statistics can be leveraged for inferring key presynaptic parameters, such as the probability of SV fusion (release probability) and SV replenishment at empty docking sites (refilling probability). Our model predictions are tested with electrophysiological data obtained from 50-Hz stimulation of auditory MNTB-LSO synapses in brainstem slices from juvenile mice. Our results show that while synaptic depression can be explained by low and constant refilling/release probabilities, this scenario is inconsistent with the statistics of the electrophysiological data, which show a low QC Fano factor and almost uncorrelated successive QCs. Our systematic analysis yields a model that couples a high release probability to a time-varying refilling probability to explain both the synaptic depression and its associated statistical fluctuations. In summary, we provide a general approach that exploits stochastic signatures in QCs to infer neurotransmission regulating processes that cannot be distinguished from simple analysis of averaged synaptic responses. © 2025 Vahdat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Distribution functions

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

引用

IEEE Texas Power and Energy Conference (TPEC)

作者： Arash Moradzadeh Mostafa Mouhammadpourfard Yang Weng Suhas Pol S. M. Muyeen Department of Electrical Engineering Qatar University Doha Qatar National Wind Institute Texas Tech University Lubbock TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Renewable Energy Program Texas Tech University Lubbock TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets.

关键词： Deep learning Accuracy Electricity Noise reduction Bidirectional long short term memory Predictive models Data models Stakeholders Forecasting Testing

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Development of Flexible Sendust Composite Sheets with Enhanced Magnetic Permeability for Microwave Absorbers and Antennas

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IEEE Transactions on Magnetics 2025年

作者： Whalen, Matthew P. Plouff, David T. Shevchenko, Nicholas B. Mirotznik, Mark Xiao, John Q. University of Delaware Quantum Science and Engineering Program NewarkDE19716 United States University of Delaware Department of Physics and Astronomy NewarkDE19716 United States University of Delaware Center for Composite Materials NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

The demand for high-frequency soft magnetic composites with elevated permeability and flexibility is growing for applications in microwave absorbers and magnetodielectric antennas. However, balancing high permeability with mechanical flexibility remains challenging. This study presents flexible Sendust (FeSiAl) composite sheets, fabricated with a hot-pressing technique that aligns flakes within an acrylic rubber and epoxy resin matrix. The alignment significantly enhances relative permeability at high frequencies, achieving single-layer film properties of μr' = 14.8 and μr"= 20.4 at 1 GHz, alongside high saturation magnetization (118 eμg) and high hesitivity (44.7 kΩ m-1), outperforming commonly used materials such as NiZn ferrite. Our method supports customizable thickness without sacrificing permeability, establishing a versatile platform for advanced conformal microwave absorber and antenna applications. © 2025 IEEE.

关键词： Saturation magnetization

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Effects of Incident Angle on Single-Event Leakage Current Degradation in Heavy-Ion Irradiated 1800 v and 4500 v SiC Power MOSFETs

引用

IEEE Transactions on Nuclear Science 2025年

作者： Sengupta, A. Martinella, C. Ball, D.R. Niskanen, K. Senarath, A.S. Islam, S. Harris, P.M. Hutson, J.M. Osheroff, J.M. Sternberg, A.L. Kettunen, H. Kosier, S.L. Grossner, U. Javanainen, A. Galloway, K.F. Schrimpf, R.D. Witulski, A.F. Zürich8092 Switzerland Vanderbilt University Interdisciplinary Materials Science Program NashvilleTN37235 United States Lipscomb University Electrical and Computer Engineering Department NashvilleTN37204 United States NASA/GSFC Code 561.4 GreenbeltMD20771 United States Vanderbilt University Electrical Engineering and Computer Science Department B 351825 NashvilleTN37235 United States University of Jyväskylä Department of Physics Jyväskylä40014 Finland

Experimental heavy-ion responses of two variants of SiC power MOSFETs are evaluated. The devices have similar epitaxial thickness but different doping. The higher doping in the epitaxial region results in lower breakdown voltage, and typically leads to a lower threshold drain voltage at which single-event leakage current (SELC) occurs. Furthermore, the heavy-ion-induced SELC degradation in the drain and gate currents of the SiC MOSFETs exhibit a strong dependence on the heavy-ion's angle of incidence. The devices' behavior significantly alters when irradiated with heavy-ions at different incident angles under the same fixed bias and total fluence, as also confirmed by 3-D Technology computer Aided Design (TCAD) simulations. © 1963-2012 IEEE.

关键词： Power MOSFET

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A Finite Difference Method for a Normalized Time-Fractional Black–Scholes Equation

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International Journal of Applied and Computational Mathematics 2025年第3期11卷 1-17页

作者： Nam, Yunjae Wang, Jian Lee, Chaeyoung Choi, Yongho Bang, Minjoon Li, Zhengang Kim, Junseok Program in Actuarial Science and Financial Engineering Korea University Seoul 02841 South Korea School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing 210044 China Department of Mathematics Kyonggi University Suwon 16227 South Korea Computer and Information Engineering Daegu University Gyeongsan 38453 South Korea Department of Mathematics Korea University Seoul 02841 South Korea

In this article, we propose a normalized time-fractional Black–Scholes (TFBS) equation. The proposed model uses a normalized time-fractional derivative which has a distinctive feature wherein a weight function possesses the property that its integral with respect to time is always equal to one. This feature ensures a well-balanced integration over time and provides a fair comparison between different fractional orders. An implicit finite difference method is used for the numerical solution of the TFBS equation. We perform several standard numerical tests to examine the effect of the fractional parameter on the option pricing. These experiments are designed to investigate how variations in the fractional parameter influence pricing outcomes and provide valuable insights into the model’s behavior under different conditions. The computational results demonstrate the model’s potential for capturing more intricate market dynamics, which makes the proposed model a promising tool for financial analysis. In the appendix, we provide a computer program that implements the numerical methods discussed in the paper for interested readers. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Implicit finite difference method Memory effect Normalized time-fractional Black–Scholes equation

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