检索结果-内蒙古大学图书馆

A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

学校读者我要写书评

暂无评论

Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

Towards Model Reduction for Power System Transients with Physics-Informed PDE

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Pagnier, Laurent Fritzsch, Julian Jacquod, Philippe Chertkov, Michael Program in Applied Mathematics University of Arizona Tucson United States School of Applied Sciences of Western Switzerland HES-SO Sion Switzerland Department of Quantum Matter Physics University of Geneva Geneva Switzerland

This manuscript reports the first step towards building a robust and efficient model reduction methodology to capture transient dynamics in a transmission level electric power system. Such dynamics is normally modeled on seconds-to-tens-of-seconds time scales by the so-called swing equations, which are ordinary differential equations defined on a spatially discrete model of the power grid. Following Seymlyen (1974) and Thorpe, Seyler, and Phadke (1999), we suggest to map the swing equations onto a linear, inhomogeneous Partial Differential Equation (PDE) of parabolic type in two space and one time dimensions with time-independent coefficients and properly defined boundary conditions. We illustrate our method on the synchronous transmission grid of continental Europe. We show that, when properly coarse-grained, i.e., with the PDE coefficients and source terms extracted from a spatial convolution procedure of the respective discrete coefficients in the swing equations, the resulting PDE reproduces faithfully and efficiently the original swing dynamics. We finally discuss future extensions of this work, where the presented PDE-based modeling will initialize a physics-informed machine learning approach for real-time modeling, n − 1 feasibility assessment and transient stability analysis of power systems. © 2021, CC BY.

关键词： Machine learning

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory

A new method for multiancestry polygenic prediction improves performance across diverse populations

学校读者我要写书评

暂无评论

Nature Genetics 2023年第10期55卷 1757-1768页

作者： Zhang, Haoyu Zhan, Jianan Jin, Jin Zhang, Jingning Lu, Wenxuan Zhao, Ruzhang Ahearn, Thomas U. Yu, Zhi O’Connell, Jared Jiang, Yunxuan Chen, Tony Okuhara, Dayne Aslibekyan, Stella Auton, Adam Babalola, Elizabeth Bell, Robert K. Bielenberg, Jessica Bryc, Katarzyna Bullis, Emily Coker, Daniella Partida, Gabriel Cuellar Dhamija, Devika Das, Sayantan Elson, Sarah L. Eriksson, Nicholas Filshtein, Teresa Fitch, Alison Fletez-Brant, Kipper Fontanillas, Pierre Freyman, Will Granka, Julie M. Heilbron, Karl Hernandez, Alejandro Hicks, Barry Hinds, David A. Jewett, Ethan M. Kukar, Katelyn Kwong, Alan Lin, Keng-Han Llamas, Bianca A. Lowe, Maya McCreight, Jey C. McIntyre, Matthew H. Micheletti, Steven J. Moreno, Meghan E. Nandakumar, Priyanka Nguyen, Dominique T. Noblin, Elizabeth S. Petrakovitz, Aaron A. Poznik, G. David Reynoso, Alexandra Schumacher, Morgan Shastri, Anjali J. Shelton, Janie F. Shi, Jingchunzi Shringarpure, Suyash Su, Qiaojuan Jane Tat, Susana A. Tchakouté, Christophe Toukam Tran, Vinh Tung, Joyce Y. Wang, Xin Wang, Wei Weldon, Catherine H. Wilton, Peter Wong, Corinna D. Garcia-Closas, Montserrat Lin, Xihong Koelsch, Bertram L. Chatterjee, Nilanjan Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States 23andMe Inc. Sunnyvale CA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA United States Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA United States Booz Allen Hamilton Inc. McLean VA United States Division of Genetics and Epidemiology Institute of Cancer Research London United Kingdom Department of Statistics Harvard University Cambridge MA United States Department of Oncology School of Medicine Johns Hopkins University Baltimore MD United States

Polygenic risk scores (PRSs) increasingly predict complex traits;however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.

关键词：

Drone-borne calibration pulser for radio observatories detecting ultra-high-energy cosmic rays and neutrinos 38

学校读者我要写书评

暂无评论

Drone-borne calibration pulser for radio observatories detec...

38th International Cosmic Ray Conference, ICRC 2023

作者： Nam, J. Kuo, C.-Y. Shin, B.K. Chen, P. Chen, Y. Hsu, S.-Y. Huang, J.-J. Huang, M.-H.A. Leung, C.H. Liu, T.-C. Wang, S.-H. Wang, M.-Z. Wang, Y.-H. Besson, D. Novikov, A. Hornhuber, C. Young, R. Department of Physics National Taiwan University Taipei Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei Taiwan Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Energy and Resources National United University Miaoli Taiwan Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National PingTung university PingTung900 Taiwan Chung Cheng Institute of Technology National Defense University TaoYuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Physics and Astronomy University of Kansas Lawrence United States Instrumentation Design Lab University of Kansas Lawrence United States Moscow Russia Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei Taiwan

We report on the development of a drone-borne calibration pulser (cal-pulser) for radio observatories detecting ultra-high energy (UHE) cosmic rays and neutrinos. This system allows us to calibrate the radio detector in its field of view regardless of the accessibility of the site. The system is compact and sufficiently lightweight to be attached to a commercial drone. It consists of a solid-state high-power pulse generator, a programmable attenuator, a transmitting antenna, and a differential GPS (D-GPS) module. The drone, with the ∼1.3 kg payload, has a typical flight time of around 25 minutes, corresponding to a maximum flight distance of around 10 km. After developing the first version of the system in 2019, it successfully demonstrated its calibration performance with the TAROGE stations in Taiwan and the TAROGE-M station in Mt. Melbourne, Antarctica. In 2022, the system was upgraded with a dual-band D-GPS for more reliable operation and more accurate determination of the coordinates of the cal-pulser. In addition to the cal-pulser, we investigated aerial photogrammetry using the built-in camera and the D-GPS, which is an efficient method to determine the coordinates of the detector. For objects within 10 m of the drone, an accuracy of a few millimeter was achieved. These drone-borne cal-pulser and the photogrammetry have become essential calibration procedures in the TAROGE-M experiment. We suggest these approaches may find application in many other radio experiments that detect UHE cosmic rays and neutrinos, such as ARIANNA, BEACON, GRAND, as well as IceCube-Gen2. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

Feature selection using stochastic gates 37

学校读者我要写书评

暂无评论

Feature selection using stochastic gates

37th International Conference on Machine Learning, ICML 2020

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Department of Statistics and Data Science Yale University CT United States Program in Applied Mathematics Yale University CT United States School of Medicine Yale University CT United States

ISBN: (纸本)9781713821120

Feature selection problems have been extensively studied in the setting of linear estimation (e.g. LASSO), but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in neural network estimation problems. The new procedure is based on probabilistic relaxation of the `0 norm of features, or the count of the number of selected features. Our `0-based regularization relies on a continuous relaxation of the Bernoulli distribution;such relaxation allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. The proposed framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. We provide an information-theoretic justification for incorporating Bernoulli distribution into feature selection. Furthermore, we evaluate our method using synthetic and real-life data to demonstrate that our approach outperforms other commonly used methods in both predictive performance and feature selection. Copyright 2020 by the author(s).

关键词： Feature Selection

Local Order Metrics for Two-Phase Media Across Length Scales

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Torquato, Salvatore Skolnick, Murray Kim, Jaeuk Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States

The capacity to devise order metrics for microstructures of multiphase heterogeneous media is a highly challenging task, given the richness of the possible geometries and topologies of the phases that can arise. This investigation initiates a program to formulate order metrics to characterize the degree of order/disorder of the microstructures of two-phase media in d-dimensional Euclidean space Rd across length scales. In particular, we propose the use of the local volume-fraction variance σV2 (R) associated with a spherical window of radius R as an order metric. We determine σV2 (R) as a function of R for 22 different models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. We find that the local volume-fraction variance as well as asymptotic coefficients and integral measures derived from it provide reasonably robust and sensitive order metrics to categorize disordered and ordered two-phase media across all length scales. © 2022, CC BY-SA.

关键词： Volume fraction

On the Parameter Combinations that Matter and on Those that do Not

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Evangelou, Nikolaos Wichrowski, Noah J. Kevrekidis, George A. Dietrich, Felix Kooshkbaghi, Mahdi McFann, Sarah Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Mathematics and Statistics University of Massachusetts Amherst United States Department of Informatics Technical University of Munich Germany The Program in Applied and Computational Mathematics Princeton University United States Department of Chemical and Biological Engineering Princeton University United States Lewis-Sigler Institute for Integrative Genomics Princeton University United States

We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established *** Codes 37E99 (Primary), 68T07 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

The Manifold Scattering Transform for High-Dimensional Point Cloud Data

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Chew, Joyce Steach, Holly R. Viswanath, Siddharth Wu, Hau-Tieng Hirn, Matthew Needell, Deanna Krishnaswamy, Smita Perlmutter, Michael UCLA Department of Mathematics Los AngelesCA United States Yale University Department of Genetics New HavenCT United States UC Irvine Department of Computer Science IrvineCA United States Duke University Department of Mathematics DurhamNC United States Duke University Department of Statistical Science DurhamNC United States Michigan State University Department of Mathematics East Lansing United States Michigan State University Department of CMSE East Lansing United States Yale University Department of Computer Science New HavenCT United States Yale University Applied Math Program New HavenCT United States

The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model focused primarily on its theoretical stability and invariance properties but did not provide methods for its numerical implementation except in the case of two-dimensional surfaces with predefined meshes. In this work, we present practical schemes, based on the theory of diffusion maps, for implementing the manifold scattering transform to datasets arising in naturalistic systems, such as single cell genetics, where the data is a high-dimensional point cloud modeled as lying on a low-dimensional manifold. We show that our methods are effective for signal classification and manifold classification *** Codes 68T07 Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods