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Correction to: Analysis of the dynamics of anthrax epidemic model with delay

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Discover applied sciences 2024年第11期6卷 1-1页

作者： Raza, Ali Abdella, Kenzu Department of Physical Sciences The University of Chenab Gujrat Pakistan Mathematics Research Center Department of Mathematics Near East University Mersin 10 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics Applied Modelling Graduate Program Trent University Peterborough Canada

Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimes

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arXiv 2024年

作者： Zhang, Li Jindal, Basu Alaa, Ahmed Weinreb, Robert Wilson, David Segal, Eran Zou, James Xie, Pengtao Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology University of California La Jolla San DiegoCA United States Division of Pulmonary Allergy and Critical Care Medicine Department of Medicine University of Pittsburgh PittsburghPA United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Department of Medicine University of California La Jolla San DiegoCA United States

Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra low-data regimes, where annotated images are extremely limited, posing significant challenges for the generalization of conventional deep learning methods on test images. To address this, we introduce a generative deep learning framework, which uniquely generates high-quality paired segmentation masks and medical images, serving as auxiliary data for training robust models in data-scarce environments. Unlike traditional generative models that treat data generation and segmentation model training as separate processes, our method employs multi-level optimization for end-to-end data generation. This approach allows segmentation performance to directly influence the data generation process, ensuring that the generated data is specifically tailored to enhance the performance of the segmentation model. Our method demonstrated strong generalization performance across 9 diverse medical image segmentation tasks and on 16 datasets, in ultra-low data regimes, spanning various diseases, organs, and imaging modalities. When applied to various segmentation models, it achieved performance improvements of 10-20% (absolute), in both same-domain and out-of-domain scenarios. Notably, it requires 8 to 20 times less training data than existing methods to achieve comparable results. This advancement significantly improves the feasibility and cost-effectiveness of applying deep learning in medical imaging, particularly in scenarios with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Structural, optical, and thermal properties of BN thin films grown on diamond via pulsed laser deposition

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Physical Review Materials 2023年第9期7卷 094602-094602页

作者： Abhijit Biswas Gustavo A. Alvarez Tao Li Joyce Christiansen-Salameh Eugene Jeong Anand B. Puthirath Sathvik Ajay Iyengar Chenxi Li Tia Gray Xiang Zhang Tymofii S. Pieshkov Harikishan Kannan Jacob Elkins Robert Vajtai A. Glen Birdwell Mahesh R. Neupane Elias J. Garratt Bradford B. Pate Tony G. Ivanov Yuji Zhao Zhiting Tian Pulickel M. Ajayan Department of Materials Science and Nanoengineering Rice University Houston Texas 77005 USA Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca New York 14853 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA DEVCOM Army Research Laboratory RF Devices and Circuits Adelphi Maryland 20783 USA Naval Research Laboratory Chemistry Division Washington District of Columbia 20375 USA

Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap>4.0eV), such as BN and diamond, hold significant importance for the development of high-power electronics in the next generation. However, achieving in situ heteroepitaxy of BN/diamond or vice versa remains exceptionally challenging due to the complex growth kinetics involved. In this work, we grew BN thin film on (100) single-crystal diamonds using pulsed laser deposition and investigated its structural, magnetic, optical, and thermal properties. The structural analyses confirmed the growth of BN films, which exhibited diamagnetic behavior at room temperature. Notably, the film demonstrated anisotropic refractive index characteristics within the visible-to-near-infrared wavelength range. The room-temperature cross-plane thermal conductivity of BN is 1.53 ± 0.77 W/mK, while the thermal conductance of the BN/diamond interface is 20±2MW/m2K. These findings have significant implications for a range of device applications based on UWBG BN/diamond heterostructures.

关键词： Growth Thermal conductivity Boron nitride Thin films Wide band gap systems Film deposition Laser ablation Thermoreflectance

Emergent dimer-model topological order and quasi-particle excitations in liquid crystals: combinatorial vortex lattices

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arXiv 2025年

作者： Meng, Cuiling Wu, Jin-Sheng Kos, Žiga Dunkel, Jörn Nisoli, Cristiano Smalyukh, Ivan I. Department of Physics and Chemical Physics Program University of Colorado BoulderCO80309 United States Hiroshima University Hiroshima Higashihiroshima Japan Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems Center For Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as ‘Combinatorial Vortex Lattices’ (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasi-particle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CLVs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery — an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media. Copyright © 2025, The Authors. All rights reserved.

关键词： Laser surgery

General Matrix Representation For Generalized B-Splines and its Application

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SSRN

SSRN 2023年

作者： Guo, Huayuan Liu, Minchao Qin, Guiming Qin, Kaihuai He, Kunlun Medical Big Data Research Center Medical Innovation Research Division Chinese PLA General Hospital. Beijing100853 China Medical Artificial Intelligence Research Center Medical Innovation Research Division Chinese PLA General Hospital Beijing100853 China Department of Information Medical Supplies Center of Chinese PLA General Hospital Beijing100853 China Institute of Applied Physics and computational Mathematics Beijing100091 China Department of Computer science and technology Tsinghua University Beijing100084 China

Classical B-splines based on deBoor-Cox algorithm is widely used, but its recursive calculation is time-consuming and its shape control capability still needs to be improved. To address the problem, by incorporated both core functions and Toeplitz matrix theory into the deBoor-Cox recursive formula, a novel set of basis matrix formula to calculate generalized B-splines of arbitrary order is proposed to realize the general matrix representation of three types of curves, including non-uniform generalized B-splines,uniform generalized B-splines and generalized piecewise Bézier. Furthermore, based on this general matrix, we developed the shape-controllable algorithms for curve generation and curve/surface interpolation. Experiments show that our basis matrix method outperforms classical method in terms of computational time and shape controllability for curve and surface modeling. Therefore, this novel method can further enrich the B-splines modeling theory and has good application potential. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

Constraining f(R) models with cosmic chronometers and the HII galaxy Hubble diagram

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arXiv 2022年

作者： Sultana, Joseph Yennapureddy, Manoj K. Melia, Fulvio Kazanas, Demosthenes Department of Mathematics Faculty of Science University of Malta MSD Msida2080 Malta Department of Physics The University of Arizona TucsonAZ85721 United States Department of Physics The Applied Math Program Department of Astronomy The University of Arizona TucsonAZ85721 United States Astrophysics Science Division NASA/Goddard Space Flight Center GreenbeltMD20771 United States

We consider several well-known f(R) cosmological models and constrain their parameters, namely the deviation parameter b and the cosmological parameters Ωm and h. We first obtain analytical approximations for the Hubble rate H(z) and the luminosity distance dL(z) in terms of these parameters, and then test these against the observational expansion rate derived from cosmic chronometers and the distance modulus in the HII galaxy Hubble diagram, obtained in a model-independent way using Gaussian Processes (GP). We first optimize the models based solely on the cosmic chronometers and then repeat this process with a joint analysis using both the cosmic chronometers and HII galaxies. Copyright © 2022, The Authors. All rights reserved.

关键词： Chronometers

Dimensionless machine learning: Imposing exact units equivariance

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arXiv 2022年

作者： Villar, Soledad Yao, Weichi Hogg, David W. Blum-Smith, Ben Dumitrascu, Bianca Department of Applied Mathematics and Statistics Johns Hopkins University Mathematical Institute for Data Science Johns Hopkins University United States Department of Technology Operations and Statistics New York University United States Center for Cosmology and Particle Physics Department of Physics New York University Max-Planck-Institut für Astronomie Flatiron Institute Division of the Simons Foundation United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Computer Science and Technology Cambridge University United Kingdom

Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we express this symmetry in terms of a (non-compact) group action, and we employ dimensional analysis and ideas from equivariant machine learning to provide a methodology for exactly units-equivariant machine learning: For any given learning task, we first construct a dimensionless version of its inputs using classic results from dimensional analysis, and then perform inference in the dimensionless space. Our approach can be used to impose units equivariance across a broad range of machine learning methods which are equivariant to rotations and other groups. We discuss the in-sample and out-of-sample prediction accuracy gains one can obtain in contexts like symbolic regression and emulation, where symmetry is important. We illustrate our approach with simple numerical examples involving dynamical systems in physics and ecology. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

Thermal Conductivity of Water at Extreme Conditions

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arXiv 2023年

作者： Zhang, Cunzhi Puligheddu, Marcello Zhang, Linfeng Car, Roberto Galli, Giulia Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Center for Molecular Engineering Argonne National Laboratory LemontIL60439 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Chemistry University of Chicago ChicagoIL60637 United States

Measuring the thermal conductivity (κ) of water at extreme conditions is a challenging task and few experimental data are available. We predict κ for temperatures and pressures relevant to the conditions of the Earth mantle, between 1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium molecular dynamics simulations and a deep neural network potential fitted to density functional theory data. We then interpret our results by computing the equation of state of water on a fine grid of points and using a simple model for κ. We find that the thermal conductivity is weakly dependent on temperature and monotonically increases with pressure with an approximate square-root behavior. In addition we show how the increase of κ at high pressure, relative to ambient conditions, is related to the corresponding increase in the sound velocity. Although the relationships between the thermal conductivity, pressure and sound velocity established here are not rigorous, they are sufficiently accurate to allow for a robust estimate of the thermal conductivity of water in a broad range of temperatures and pressures, where experiments are still difficult to perform. Copyright © 2023, The Authors. All rights reserved.

关键词： Thermal conductivity

MathTools: An open API for convenient MathML handling

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arXiv 2021年

作者： Greiner-Petter, André Schubotz, Moritz Cohl, Howard S. Gipp, Bela Dept. of Computer and Information Science University of Konstanz Box 76 Konstanz78464 Germany Applied and Computational Mathematics Division National Institute of Standards and Technology Mission ViejoCA92694 United States

Mathematical formulae carry complex and essential semantic information in a variety of formats. Accessing this information with different systems requires a standardized machine-readable format that is capable of encoding presentational and semantic information. Even though MathML is an official recommendation by W3C and an ISO standard for representing mathematical expressions, we could identify only very few systems which use the full descriptiveness of MathML. MathML's high complexity re- sults in a steep learning curve for novice users. We hypothesize that this complexity is the reason why many community-driven projects refrain from using MathML, and instead develop problem-specific data formats for their purposes. We provide a user-friendly, open-source application programming interface for controlling MathML data. Our API is written in JAVA and allows to create, manipulate, and efficiently access commonly needed informa- tion in presentation and content MathML. Our interface also provides tools for calculating differences and similarities between MathML expressions. The API also allows to determine the distance between expressions using different similarity measures. In addition, we provide adapters for numerous conversion tools and the canonicalization project. Our toolkit facilitates processing of mathematics for digital libraries, without the need to obtain XML expertise. © 2021, CC BY.

关键词： ISO Standards