检索结果-内蒙古大学图书馆

Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Park, Hyoung Suk Hyun, Chang Min Lee, Sang-Hwy Seo, Jin Keun Jeon, Kiwan National Institute for Mathematical Sciences Daejeon34047 Korea Republic of School of Mathematics and Computing Computational Science and Engineering Yonsei University Seoul03722 Korea Republic of Department of Oral and Maxillofacial Surgery Oral Science Research Center College of Dentistry Yonsei University Seoul03722 Korea Republic of

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan *** Codes 92C55, 15A04, 62F10 © 2023, CC BY.

关键词： Iterative methods

Non-Iterative Recovery from Nonlinear Observations using Generative Models

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liu, Jiulong Liu, Zhaoqiang LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences 100190 China Department of Computer Science National University of Singapore Singapore

In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike conventional compressed sensing where the signal is assumed to be sparse, we assume that the signal lies in the range of an L-Lipschitz continuous generative model with bounded k-dimensional inputs. This is mainly motivated by the tremendous success of deep generative models in various real applications. Our reconstruction method is non-iterative (though approximating the projection step may use an iterative procedure) and highly efficient, and it is shown to attain the near-optimal statistical rate of order √(k log L)/m, where m is the number of measurements. We consider two specific instances of the SIM, namely noisy 1-bit and cubic measurement models, and perform experiments on image datasets to demonstrate the efficacy of our method. In particular, for the noisy 1-bit measurement model, we show that our non-iterative method significantly outperforms a state-of-the-art iterative method in terms of both accuracy and efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

MACHINE LEARNING FOR ELLIPTIC PDES: FAST RATE GENERALIZATION BOUND, NEURAL SCALING LAW AND MINIMAX OPTIMALITY 10

学校读者我要写书评

暂无评论

MACHINE LEARNING FOR ELLIPTIC PDES: FAST RATE GENERALIZATION...

10th International Conference on Learning Representations, ICLR 2022

作者： Lu, Yiping Chen, Haoxuan Lu, Jianfeng Ying, Lexing Blanchet, Jose Institute for Computational & Mathematical Engineering Stanford University StanfordCA94305 United States Department of Computing and Mathematical Sciences Caltech PasadenaCA91125 United States Mathematics Department Duke University DurhamNC27708-0320 United States Department of Mathematics Stanford University StanfordCA94305 United States Department of Management Science & Engineering Stanford University StanfordCA94305 United States

In this paper, we study the statistical limits of deep learning techniques for solving elliptic partial differential equations (PDEs) from random samples using the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs). To simplify the problem, we focus on a prototype elliptic PDE: the Schrödinger equation on a hypercube with zero Dirichlet boundary condition, which is applied in quantum-mechanical systems. We establish upper and lower bounds for both methods, which improve upon concurrently developed upper bounds for this problem via a fast rate generalization bound. We discover that the current Deep Ritz Method is sub-optimal and propose a modified version of it. We also prove that PINN and the modified version of DRM can achieve minimax optimal bounds over Sobolev spaces. Empirically, following recent work which has shown that the deep model accuracy will improve with growing training sets according to a power law, we supply computational experiments to show similar-behavior of dimension dependent power law for deep PDE solvers. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Boundary conditions

Detecting and Exploiting Unit Commitment Structures in MIPs

学校读者我要写书评

暂无评论

Detecting and Exploiting Unit Commitment Structures in MIPs

International Conference on Power and Energy Technology (ICPET)

作者： Zhao-Gang Su Sheng-Jie Chen Yu-Fei Li Liang Chen Caixia Kou Zhi Cai Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

The unit commitment (UC) problem has been extensively researched in the literature, which is typically formulated as a mixed integer programming (MIP) problem. However, current studies lack effective methods to identify potential UC problem structures within a general MIP problem, making it challenging to leverage well-established valid inequalities for UC problems to accelerate the solution process. In this paper, we present a novel detection method that automatically identifies UC structures within an MIP problem by analyzing the relationships between variables and constraints. Moreover, we enhance the MIP formulation by incorporating three well-studied classes of valid inequalities as cutting planes, based on the identified UC structures. Our experiments demonstrate that our proposed UC detection method greatly improves computational efficacy of a standard MIP solver compared to the default setting.

关键词： Integer programming Production Generators Time factors Standards

Secure Data Reduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage

学校读者我要写书评

暂无评论

Secure Data Reduplication System with Efficient and Reliable...

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： B Jayaram T. Suguna Shaik Fairooz Arul Selvam P S. Vimal Department of Computer Science and Business Systems RMK Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of CSE -AIML Malla Reddy Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

Malicious hackers may compromise hundreds or millions of machines with little effort, allowing them to shut down networks, steal sensitive data, and compromise critical infrastructure. Data is stolen and used for malicious purposes by hackers who exploit security flaws in the network. And the sophisticated technology they use, like the bucks they do, are continually feeding them the information they needed, not just once. Moreover, even if Internet firewalls have improved, cybercrime has been on the rise. Then, we have a long way to go before we approach the limit, what with all the different kinds of cloud server devices that can connect to the internet nowadays. It's certain that in the future, little devices, no matter how helpful they are really in our everyday lives, will become the preferred tool of villains. In this research, we offer a novel security technique for protecting sensitive data on cloud servers. This intelligent cloud server was protected from any hacking with the aid of this algorithm. The system's output was measured against those of pre-existing algorithms. More than 96% of inputs are authenticated by the proposed technique, while password cracking operations are reduced by about 3%. The source code security management is improved to 99% and the updating issues for various devices entries is reduced 0.02%, and further the privacy breach is reduced up to 0.32%. Hence the proposed method was more secured from cyber security issues.

关键词： Cloud computing Computer hacking Source coding Software algorithms Passwords Software Virtual private networks

BOUNDED KRNET AND ITS APPLICATIONS TO DENSITY ESTIMATION AND APPROXIMATION

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zeng, Li Wan, Xiaoliang Zhou, Tao Fuzhou University Fuzhou China Department of Mathematics Center for Computation and Technology Louisiana State University Baton Rouge70803 United States Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation. Similar to KRnet, the structure of B-KRnet adapts the pseudo-triangular structure into a normalizing flow model. The main difference between B-KRnet and KRnet is that B-KRnet is defined on a hypercube while KRnet is defined on the whole space, in other words, a new mechanism is introduced in B-KRnet to maintain the exact invertibility. Using B-KRnet as a transport map, we obtain an explicit probability density function (PDF) model that corresponds to the pushforward of a prior (uniform) distribution on the hypercube. It can be directly applied to density estimation when only data are available. By coupling KRnet and B-KRnet, we define a deep generative model on a high-dimensional domain where some dimensions are bounded and other dimensions are unbounded. A typical case is the solution of the stationary kinetic Fokker-Planck equation, which is a PDF of position and momentum. Based on B-KRnet, we develop an adaptive learning approach to approximate partial differential equations whose solutions are PDFs or can be treated as PDFs. A variety of numerical experiments is presented to demonstrate the effectiveness of B-KRnet. Copyright © 2023, The Authors. All rights reserved.

关键词： Fokker Planck equation

Cluster Assumption-Guided Timestamp-Supervised Temporal Action Segmentation

学校读者我要写书评

暂无评论

IEEE Transactions on Multimedia 2025年

作者： Ren, Ziyou Li, Guozhang Cheng, Nan Wu, Anqi Wang, Nannan Gao, Xinbo Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA15213 United States Beijing Normal University School of Artificial Intelligence Haidian District Beijing100875 China Georgia Institute of Technology School of Computational Science and Engineering College of Computing AtlantaGA30308 United States Xidian University School of Electronic Engineering Xi'an710071 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Current timestamp-supervised temporal action segmentation (TS-TAS) methods typically follow a two-phase pipeline: initializing the model with timestamp labels and refining it with pseudo-labels. However, limited by the sparsity of timestamp annotations, current methods' performance is sub-optimal. Specifically, initializing the model with only timestamp annotations may cause overfitting to labeled frames. Additionally, sparse timestamp annotations cannot capture the diverse action representations throughout the whole instance, especially those near the ambiguous action boundaries, leading to pseudo-label noise. Inspired by the cluster assumption of semi-supervised learning (SSL) that points within the same manifold likely share the same label, we here model TS-TAS as an SSL problem. Specifically, we propose a Temporal Embedding Consistency (TEC) strategy to mitigate the excessive focus on annotated frames. The TEC strategy encourages frames with similar representations within the video to have similar classification probability distributions, thereby propagating labeled frames' information to implicit ones. Besides, we design a TS-Mix strategy to further leverage unlabeled data to mitigate the influence of pseudo-label noise in a consistency regularization manner. The TS-Mix strategy includes intra-mix, which adds linear interpolation of two adjacent timestamps to every frame between them, and inter-mix, which mixes frames from two different untrimmed videos frame-by-frame. Then the mixed video is trained with the correspondingly mixed pseudo-labels. Comprehensive experimental results on different benchmarks show that we achieve new state-of-the-art performances. Furthermore, the proposed method can seamlessly enhance existing methods, significantly improving their performances. © 1999-2012 IEEE.

关键词： Self-supervised learning

Multiple-scattering frequency-time hybrid solver for the wave equation in interior domains

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Bruno, Oscar P. Yin, Tao Department of Computing & Mathematical Sciences California Institute of Technology 1200 East California Blvd. CA91125 United States LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper proposes a frequency-time hybrid solver for the time-dependent wave equation in two-dimensional interior spatial domains. The approach relies on four main elements, namely, 1) A multiple scattering strategy that decomposes a given interior time-domain problem into a sequence of limited-duration time-domain problems of scattering by overlapping open arcs, each one of which is reduced (by means of the Fourier transform) to a sequence of Helmholtz frequency-domain problems;2) Boundary integral equations on overlapping boundary patches for the solution of the frequency-domain problems in point 1);3) A smooth "Time-windowing and recentering" methodology that enables both treatment of incident signals of long duration and long time simulation;and, 4) A Fourier transform algorithm that delivers numerically dispersionless, spectrally-accurate time evolution for given incident fields. By recasting the interior time-domain problem in terms of a sequence of open-arc multiple scattering events, the proposed approach regularizes the full interior frequency domain problem-which, if obtained by either Fourier or Laplace transformation of the corresponding interior time-domain problem, must encapsulate infinitely many scattering events, giving rise to non-uniqueness and eigenfunctions in the Fourier case, and ill conditioning in the Laplace case. Numerical examples are included which demonstrate the accuracy and efficiency of the proposed *** Codes 35L05, 65M80, 65T99, 65R20 Copyright © 2022, The Authors. All rights reserved.

关键词： Wave equations

A PML method for signal-propagation problems in axon

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jiang, Xue Lyu, Maohui Yin, Tao Zheng, Weiying Department of Mathematics Faculty of Science Beijing University of Technology Beijing100124 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This work is focused on the modelling of signal propagations in myelinated axons to characterize the functions of the myelin sheath in the neural structure. Based on reasonable assumptions on the medium properties, we derive a two-dimensional neural-signaling model in cylindrical coordinates from the time-harmonic Maxwell’s equations. The well-posedness of model is established upon Dirichlet boundary conditions at the two ends of the neural structure and the radiative condition in the radial direction of the structure. Using the perfectly matched layer (PML) method, we truncate the unbounded background medium and propose an approximate problem on the truncated domain. The well-posedness of the PML problem and the exponential convergence of the approximate solution to the exact solution are established. Numerical experiments based on finite element discretization are presented to demonstrate the theoretical results and the efficiency of our methods to simulate the signal propagation in axons. Copyright © 2022, The Authors. All rights reserved.

关键词： Maxwell equations