检索结果-内蒙古大学图书馆

SEMI-IMPLICIT HYBRID FINITE VOLUME/FINITE ELEMENT METHOD FOR THE GPR MODEL OF CONTINUUM MECHANICS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Busto, Saray Río-Martín, Laura Department of Applied Mathematics Universidade de Santiago de Compostela Santiago de Compostela15782 Spain Galician Center for Mathematical Research and Technology CITMAga Santiago de Compostela15782 Spain Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Laboratory of Applied Mathematics DICAM University of Trento Via Mesiano 77 Trento38123 Italy

We present a new hybrid semi-implicit finite volume / finite element numerical scheme for the solution of incompressible and weakly compressible media. From the continuum mechanics model proposed by Godunov, Peshkov and Romenski (GPR), we derive the incompressible GPR formulation as well as a weakly compressible GPR system. As for the original GPR model, the new formulations are able to describe different media, from elastoplastic solids to viscous fluids, depending on the values set for the model’s relaxation parameters. Then, we propose a new numerical method for the solution of both models based on the splitting of the original systems into three subsystems: one containing the convective part and non-conservative products, a second subsystem for the source terms of the distortion tensor and heat flux equations and, finally, a pressure subsystem. In the first stage of the algorithm, the transport subsystem is solved by employing an explicit finite volume method, while the source terms are solved implicitly. Next, the pressure subsystem is implicitly discretised using finite elements. Within this methodology, unstructured grids are employed, with the pressure defined in the primal grid and the rest of the variables computed in the dual grid. To evaluate the performance of the proposed scheme, a numerical convergence analysis is carried out, which confirms the second order of accuracy in space. A wide range of benchmarks is reproduced for the incompressible and weakly compressible cases, considering both solid and fluid media. These results demonstrate the good behaviour and robustness of the proposed scheme in a variety of scenarios and conditions. © 2024, CC BY-NC-ND.

关键词： Finite element method

Brain MRI Synthesis Using Stylegan2-ADA

学校读者我要写书评

暂无评论

Brain MRI Synthesis Using Stylegan2-ADA

IEEE International Symposium on Biomedical Imaging

作者： Matteo Lai Chiara Marzi Mario Mascalchi Stefano Diciotti Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" - DEI University of Bologna Cesena Italy Alma Mater Research Institute for Human-Centered Artificial Intelligence University of Bologna Bologna Italy Department of Statistics Computer Science Applications "G. Parenti" University of Firenze Firenze Italy Experimental and Clinical Biomedical Sciences "Mario Serio" University of Florence Firenze Italy Clinical Epidemiology Unit Institute for Cancer Research Prevention and Clinical Network (ISPRO) Florence Italy

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Deep learning (DL) applications in the medical field often face challenges related to limited data availability, resulting in issues like overfitting and imbalanced datasets. Synthetic data offers a promising solution to these problems by enabling data augmentation and enhancing the performance of DL models. In this study, we trained the state-of-the-art generative model StyleGAN2-ADA on 1412 images from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to generate synthetic slices of T1-weighted brain MRI of healthy subjects. The quality of the synthetic images has been evaluated through quantitative and qualitative assessments, including a visual Turing test conducted by an expert observer with 2000 images. The observer achieved an accuracy of 52.95%, indicative of a performance level comparable to random guessing. These results demonstrate the capability of StyleGAN2-ADA to generate anatomically relevant synthetic brain MRI data.

关键词： Neuroimaging Visualization Magnetic resonance imaging Observers Brain modeling Data augmentation Data models

Quantum Sensing with Erasure Qubits

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Niroula, Pradeep Dolde, Jack Zheng, Xin Bringewatt, Jacob Ehrenberg, Adam Cox, Kevin C. Thompson, Jeff Gullans, Michael J. Kolkowitz, Shimon Gorshkov, Alexey V. Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States DEVCOM Army Research Laboratory AdelphiMD20783 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

The dominant noise in an "erasure qubit" is an erasure-a type of error whose occurrence and location can be detected. Erasure qubits have potential to reduce the overhead associated with fault tolerance. To date, research on erasure qubits has primarily focused on quantum computing and quantum networking applications. Here, we consider the applicability of erasure qubits to quantum sensing and metrology. We show theoretically that, for the same level of noise, an erasure qubit acts as a more precise sensor or clock compared to its non-erasure counterpart. We experimentally demonstrate this by artificially injecting either erasure errors (in the form of atom loss) or dephasing errors into a differential optical lattice clock comparison, and observe enhanced precision in the case of erasure errors for the same injected error rate. Similar benefits of erasure qubits to sensing can be realized in other quantum platforms like Rydberg atoms and superconducting qubits. © 2023, CC BY.

关键词： Fault tolerance

Integrated high-performance error correction for continuous-variable quantum key distribution

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhou, Chuang Li, Yang Ma, Li Yang, Jie Huang, Wei Sun, Ao Wang, Heng Luo, Yujie Li, Yong Chen, Ziyang Lau, Francis C.M. Zhang, Yichen Yu, Song Guo, Hong Xu, Bingjie National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu610041 China College of Computer Science Chongqing University Chongqing400044 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

An integrated error-correction scheme with high throughput, low frame errors rate (FER) and high reconciliation efficiency under low signal to noise ratio (SNR) is one of the major bottlenecks to realize high-performance and low-cost continuous variable quantum key distribution (CV-QKD). To solve this long-standing problem, a novel two-stage error correction method with limited precision that is suitable for integration given limited on-chip hardware resource while maintaining excellent decoding performance is proposed, and experimentally verified on a commercial FPGA. Compared to state-of-art results, the error-correction throughput can be improved more than one order of magnitude given FER © 2024, CC BY.

关键词： Error correction

Deep Learning for the Recognition of Skin Cancer

学校读者我要写书评

暂无评论

Deep Learning for the Recognition of Skin Cancer

Advanced computer Applications (ACA), International Conference on

作者： Israa Akram Fadhil Alzuabidi Layla safwat Jamil Amjed Abbas Ahmed Saif Aamer Fadhil Mohammad Kamrul Hasan Ahmed Salman Ibraheem Continuing of Education/Collage of Arts/University of Baghdad Baghdad Iraq College of agricultural engineering sciences/university of Baghdad Baghdad Iraq Department of Computer Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Imam Al-Kadhum College (IKC) Baghdad Iraq

The health industry has kept growing over the years with breakthroughs. However, in skin disease recognition such as cancer, there is still a battle to improve how recognition is implemented to get the desired result. The paper focuses on skin cancer recognition using deep learning. In addition, a cloud-based model-based architecture that uses deep learning algorithms in its core applications to create models that help predict skin cancer with improved accuracy is considered. System analysis and specification are also done in conjunction with a description of procedures and requirements. Implementation and testing are also done and finalized with the conclusion and future work. The main outcomes of the results and conclusion will be drawn from the implementation and testing that are carried out on the data. In addition to the fact that the implications of the findings would become valuable and further used as a benchmark for future research work in the research domain.

关键词：

A Brief Review of Explainable Artificial Intelligence in Healthcare

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Xiu, Haoyi Liu, Xin Wang, Weimin Kim, Kyoung-Sook Shinohara, Takayuki Chang, Qiong Matsuoka, Masashi Department of Architecture and Building Engineering Tokyo Institute of Technology Tokyo Japan Artificial Intelligence Research Center AIST Tokyo Japan DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science Tokyo Institute of Technology Tokyo Japan Innovation Technology Office Research Center PASCO Corporation Tokyo Japan

Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various convolution operations. We observe that the convolution can be equivalently decomposed as a weighted combination of a local and a global component. With this observation, we explicitly decouple these two components so that the local one can be enhanced and facilitate the learning of local surface geometry. Specifically, we propose Laplacian Unit (LU), a simple yet effective architectural unit that can enhance the learning of local geometry. Extensive experiments demonstrate that networks equipped with LUs achieve competitive or superior performance on typical point cloud understanding tasks. Moreover, through establishing connections between the mean curvature flow, a further investigation of LU based on curvatures is made to interpret the adaptive smoothing and sharpening effect of LU. The code will be available. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Guan, Yu Zhang, Kunlong Qi, Qi Wang, Dong Ke, Ziwen Wang, Shaoyu Liang, Dong Liu, Qiegen School of Mathematics and Computer Sciences Nanchang University Nanchang330031 China Department of Electronic Information Engineering Nanchang University Nanchang330031 China Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL United States Paul C. Lauterbur Research Center for Biomedical Imaging SIAT Chinese Academy of Sciences Shenzhen518055 China

Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their promise as generative models. However, their application in dynamic MRI remains relatively underexplored. This is primarily due to the substantial amount of fully-sampled data typically required for training, which is difficult to obtain in dynamic MRI due to its spatio-temporal complexity and high acquisition costs. To address this challenge, we propose a dynamic MRI reconstruction method based on a time-interleaved acquisition scheme, termed the Global-to-local Diffusion Model. Specifically, fully encoded full-resolution reference data are constructed by merging under-sampled k-space data from adjacent time frames, generating two distinct bulk training datasets for global and local models. The global-to-local diffusion framework alternately optimizes global information and local image details, enabling zero-shot reconstruction. Extensive experiments demonstrate that the proposed method performs well in terms of noise reduction and detail preservation, achieving reconstruction quality comparable to that of supervised approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

MEMORY CLASSIFIERS: TWO-STAGE CLASSIFICATION FOR ROBUSTNESS IN MACHINE LEARNING

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dutta, Souradeep Yang, Yahan Bernardis, Elena Dobriban, Edgar Lee, Insup PRECISE Center Computer and Information Science University of Pennsylvania United States PRECISE Center Electrical and Systems Engineering University of Pennsylvania United States Department of Dermatology Perelman School of Medicine University of Pennsylvania United States Statistics and Data Science Wharton School of Business University of Pennsylvania United States

The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness to distribution shifts, by combining expert knowledge about the "high-level" structure of the data with standard classifiers. Specifically, we introduce two-stage classifiers called memory classifiers. First, these identify prototypical data points-memories-to cluster the training data. This step is based on features designed with expert guidance;for instance, for image data they can be extracted using digital image processing algorithms. Then, within each cluster, we learn local classifiers based on finer discriminating features, via standard models like deep neural networks. We establish generalization bounds for memory classifiers. We illustrate in experiments that they can improve generalization and robustness to distribution shifts on image datasets. We show improvements which push beyond standard data augmentation techniques. © 2022, CC BY-NC-SA.

关键词： Classification (of information)