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AutoCT: Automated CT registration, segmentation, and quantification

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arXiv 2023年

作者： Bai, Zhe Essiari, Abdelilah Perciano, Talita Bouchard, Kristofer E. Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory United States Scientific Data Division Lawrence Berkeley National Laboratory United States Biological Systems and Engineering Division Lawrence Berkeley National Laboratory United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience UC Berkeley United States

The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications. © 2023, CC BY-NC-ND.

关键词： Image segmentation

Fast Barycentric-Based Evaluation over Spectral/hp Elements

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arXiv 2021年

作者： Laughton, Edward Zala, Vidhi Narayan, Akil Kirby, Robert M. Moxey, David College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom Scientific Computing and Imaging Institute School of Computing University of Utah Salt Lake CityUT84112 United States Scientific Computing and Imaging Institute Department of Mathematics University of Utah Salt Lake CityUT84112 United States Department of Engineering King’s College London London United Kingdom

As the use of spectral/hp element methods, and high-order finite element methods in general, continues to spread, community efforts to create efficient, optimized algorithms associated with fundamental high-order operations have grown. Core tasks such as solution expansion evaluation at quadrature points, stiffness and mass matrix generation, and matrix assembly have received tremendous attention. With the expansion of the types of problems to which high-order methods are applied, and correspondingly the growth in types of numerical tasks accomplished through high-order methods, the number and types of these core operations broaden. This work focuses on solution expansion evaluation at arbitrary points within an element. This operation is core to many postprocessing applications such as evaluation of streamlines and pathlines, as well as to field projection techniques such as mortaring. We expand barycentric interpolation techniques developed on an interval to 2D (triangles and quadrilaterals) and 3D (tetrahedra, prisms, pyramids, and hexahedra) spectral/hp element methods. We provide efficient algorithms for their implementations, and demonstrate their effectiveness using the spectral/hp element library Nektar++ by running a series of baseline evaluations against the ‘standard’ Lagrangian method, where an interpolation matrix is generated and matrix-multiplication applied to evaluate a point at a given location. We present results from a rigorous series of benchmarking tests for a variety of element shapes, polynomial orders and dimensions. We show that when the point of interest is to be repeatedly evaluated, the barycentric method performs at worst 50% slower, when compared to a cached matrix evaluation. However, when the point of interest changes repeatedly so that the interpolation matrix must be regenerated in the ‘standard’ approach, the barycentric method yields far greater performance, with a minimum speedup factor of 7×. Furthermore, when derivatives of th

关键词： Lagrange multipliers

Construction and Validation of an Obstetric Risk Classification Engine to Support Intensive Care Unit (ICU) Admission Using Machine Learning

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SSRN

SSRN 2022年

作者： Soares, Fabiano Miguel Rose, Lívia Ohana da Rocha Oak Cecatti, José Guilherme Luz, Adriana Gomes Awe, Oluwafunmilola Deborah Esmi, Estevão Laureano de Carvalho Pacagnella, Rodolfo Department of Obstetrics and Gynecology Faculty of Medical Sciences State University of Campinas SP Campinas Brazil Department of Applied and Computational Mathematics Institute of Mathematics Statistics and Scientific Computing State University of Campinas SP Campinas Brazil Campus Universitário R. Alexander Flemming 101. Barão Geraldo Campinas SP. 13083-872 Brazil

Introduction: Maternal mortality remains a global problem, despite the efforts of many international organizations such as the World Health Organization to reduce it. In recent times, scientific evidence useful in the reduction of maternal mortality has become well established, which include implementation of bundles in emergency, and provision of Intensive Care Units (ICU) for obstetric complications. This is a major factor in the management of pregnant women, however, there is a need for optimization of the Intensive Care Unit (ICU) when the need arises. We aimed to develop a clinical support tool to guide decisions on which obstetric population required ICU ***: This study describes the development and validation of an obstetric risk classification engine among patients with maternal morbidity We used a supervised machine learning technique in a multicentric database of pregnant population including 9,550 women with Severe Maternal Morbidity. A decision tree model and two regression tree models were tested (Random Forest, Gradient boosting Machine -GBM, and Extreme Gradient Boosting -XGBoost) and after application, the accuracy, sensitivity and specificity were compared and the differences between the predictions were analyzed using Wilcoxon ***: The XGBoost algorithm showed the highest efficiency with an accuracy of 85%, sensitivity of 42%, specificity of 97%, AUC on the ROC curve of 86.7% and the estimated prevalence of ICU use (11.6%, in contrast to the actual prevalence of ICU, 21.52%), indicating the need to optimize the use of intensive ***: The Risk engine developed showed positive results, hence, can serve as a reinforcement factor in the management of women with complications so that those who need intensive care can be correctly identified, thereby optimizing the use of intensive beds. © 2022, The Authors. All rights reserved.

关键词： Intensive care units

Convergence and Complexity of an Adaptive Planewave Method for Eigenvalue Computations

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arXiv 2021年

作者： Dai, Xiaoying Pan, Yan Yang, Bin Zhou, Aihui LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In this paper, we study the adaptive planewave discretization for a cluster of eigenvalues of second-order elliptic partial differential equations. We first design an a posteriori error estimator and prove both the upper and lower bounds. Based on the a posteriori error estimator, we propose an adaptive planewave method. We then prove that the adaptive planewave approximations have the linear convergence rate and quasi-optimal complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

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arXiv 2020年

作者： Ma, Ganggang Xu, Jie Liu, Ya-Feng Vedady Moghadam, Mohammad R. Shenzhen China Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China TransferFi Pte Ltd Singapore

Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT). However, the performance of conventional energy beamforming may seriously degrade due to the nonlinear radio frequency (RF) to direct current (DC) conversion at energy receivers (ERs). To tackle this issue, this letter proposes a new time-division energy beamforming, in which different energy beamforming matrices (of high ranks in general) are time shared to exploit the "convex-concave" shape of the RF-DC power relation at ERs. By considering a particular time duration for WPT, we maximize the minimum harvested DC energy among all ERs, by jointly optimizing the energy beamforming matrices and the corresponding time allocation. In order to solve the non-convex min-DC-energy maximization problem, we propose an efficient solution by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed time-division energy beamforming design indeed outperforms the conventional multi-beam and time-division-multiple-access (TDMA)-based energy transmissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

A simple low-degree optimal finite element scheme for the elastic transmission eigenvalue problem

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arXiv 2021年

作者： Xi, Yingxia Ji, Xia Zhang, Shuo School of Science Nanjing University of Science and Technology Nanjing210094 China School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China

The paper presents a finite element scheme for the elastic transmission eigenvalue problem written as a fourth order eigenvalue problem. The scheme uses piecewise cubic polynomials and obtains optimal convergence rate. Compared with other low-degree and nonconforming finite element schemes, the scheme inherits the continuous bilinear form which does not need extra stabilizations and is thus simple to *** Codes 31A30, 65N30, 47B07 Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

The optimal displacement of immiscible two-phase fluids in a pore doublet

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arXiv 2022年

作者： Shan, Fang Chai, Zhenhua Shi, Baochang Zhao, Meng School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

The displacement of multiphase fluid flow in a pore doublet is a fundamental problem, and is also of importance in understanding of the transport mechanisms of multiphase flows in the porous media. During the displacement of immiscible two-phase fluids in the pore doublet, the transport process is not only influenced by the capillary and viscous forces, but also affected by the channel geometry. In this paper, we first present a mathematical model to describe the two-phase fluid displacement in the pore doublet where the effects of capillary force, viscous force and the geometric structure are included. Then we derive an analytical solution of the model for the first time, and find that the displacement process is dominated by the capillary number, the viscosity ratio and the radius ratio. Furthermore, we define the optimal displacement that the wetting fluids in two daughter channels break through the branches simultaneously (both of them have the same breakthrough time), and also obtain the critical capillary number corresponding to the optimal displacement, which is related to the radius ratio of two daughter channels and viscosity ratio of two immiscible fluids. Finally, it is worthy noting that the present analytical results on the displacement in the pore doublet can be used to explain and understand the phenomenon of preferential imbibition or preferential flow in porous media. © 2022, CC BY.

关键词： Porous materials

Two Methods for Regularized Non-Monotone Submodular Maximization

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SSRN

SSRN 2024年

作者： Sun, Xin Han, Congying Wu, Chenchen Xu, Dachuan Zhou, Yang Du, Ding-Zhu School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China College of Sciecne Tianjin University of Technology Tianjin300072 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China School of Mathematics and Statistics Shandong Normal University Jinan250014 China Department of Computer Science University of Texas at Dallas RichardsonTX75080 United States

In this paper, we investigate regularized non-monotone submodular maximization over a down-monotone family of sets by applying the Lyapunov method and the distorted continuous greedy algorithm. Based on the Lyapunov method, we systematically design parameterized algorithm frameworks in two phases. In the first phase, a theoretical approximation algorithm can be derived, as long as a set of parameters is chosen. Contingent upon the selection of a suitable set of parameters, the approximation guarantee can achieve $(1/e, \frac{\gamma-\gamma/e-1}{\gamma-1})$, where $\gamma\in[0,1)\cup[e,+\infty)$ reflects the relative dominance of the positive and negative parts of the optimal value for the regular term. In the second phase, an implementable algorithm is presented through discretization in $\mathcal{O}(n^3/\epsilon)$ with arbitrarily small loss in guarantee. Additionally, we establish that no polynomial-time algorithm yields a $(0.478,\lambda)$-approximation with $\lambda\in[0,1]$. Furthermore, we demonstrate that the Lyapunov method can also be applied in regularized monotone submodular maximization with the tight $(1-1/e, 1)$-approximation ratio. © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

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SSRN

SSRN 2023年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Czech Republic Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India Departament of Industrial Engineering University of Chile Santiago Chile Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India

Selecting outstanding tech stocks for investment is challenging. Specifically, studying the investment for academic purposes is not mature enough due to the disarray of the publications and the `over' informative experience from profit-making websites. Often some authors entitle stock price prediction subject as a stock selection problem;both prediction and selection are just sub-section of portfolio management. Moreover, stock websites provide tons of potential criteria showing their perspective evaluation to simulate and monitor the stock market professionally, which causes an extra problem for academic studies screening out the less informative criteria. The paper generalizes a novel framework with a user-interactive interface for stock selection problems based on multi-source data fusion and decision-level fusion to enhance reliability that is limited by the narrow criteria performance and to enhance the strength of a model overcoming the weakness of a single-performed model. This framework benefits the time-series prediction and decision-making study. Besides, we propose adopting dynamic time warping to assist a task-learning process by customizing a loss function that improves the accuracy of data prediction. The experiment shows that the proposed method reduces the prediction log error by 28\% in a best case of real-situation data and decreases the warping cost by 20\%. Finally, we illustrate the proposed framework by implementing it in a real-world stock data selection. The results are practical and effective, further justified through a detailed ablation study. © 2023, The Authors. All rights reserved.

关键词： Investments