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arXiv 2023年

作者： LaBella, Dominic Adewole, Maruf Alonso-Basanta, Michelle Altes, Talissa Anwar, Syed Muhammad Baid, Ujjwal Bergquist, Timothy Bhalerao, Radhika Chen, Sully Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Godfrey, Devon Hilal, Fathi Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kent, Collin Kirkpatrick, John Koer, Florian Van Leemput, Koen Li, Hongwei Bran Liu, Xinyang Mahtabfar, Aria McBurney-Lin, Shan McLean, Ryan Meier, Zeke Moawad, Ahmed W. Mongan, John Nedelec, Pierre Pajot, Maxence Piraud, Marie Rashid, Arif Reitman, Zachary Shinohara, Russell Takeshi Velichko, Yury Wang, Chunhao Warman, Pranav Wiggins, Walter Aboian, Mariam Albrecht, Jake Anazodo, Udunna Bakas, Spyridon Flanders, Adam Janas, Anastasia Khanna, Goldey Linguraru, Marius George Menze, Bjoern Nada, Ayman Rauschecker, Andreas M. Rudie, Jeff Tahon, Nourel Hoda Villanueva-Meyer, Javier Wiestler, Benedikt Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Lab Crestview Radiology Lagos Nigeria Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Missouri University ColumbiaMO United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Sage Bionetworks United States University of California San Francisco CA United States Duke University Medical Center School of Medicine United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM Central Institute for Translational Cancer Research Technical University of Munich Germany Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Helmholtz Ai Helmholtz Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Neuroradiology Technical University of Munich Munich Germany University of Zurich Switzerland Department of Neurosurgery Thomas Jefferson Univer

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring;yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

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Constraining new physics from Higgs measurements with Lilith-2

arXiv

引用

arXiv 2020年

作者： Bertrand, Marius Kraml, Sabine Loc, Tran Quang Nhung, Dao Thi Ninh, Le Duc Laboratoire de Physique Subatomique et de Cosmologie Université Grenoble-Alpes CNRS/IN2P3 53 Avenue des Martyrs GrenobleF-38026 France Faculté des Sciences d’Orsay Univ. Paris-Saclay 15 rue Georges Clemenceau OrsayF-91405 France Department of Applied Mathematics & Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Institute for Interdisciplinary Research in Science and Education ICISE Quy Nhon55121 Viet Nam

Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. Version 2.0 of Lilith comes with an extensive XML database which includes the ATLAS and CMS Run 2 Higgs results for 36/fb, in addition the the Run 1 results. Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1.1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith-2 can make use of correlation matrices of arbitrary dimension. We will report on these novelties and ongoing developments. The importance of how correlations and uncertainties are treated will be demonstrated by means of detailed validations of the implemented experimental results. Moreover, we show the effects for global fits of reduced Higgs couplings, 2HDMs of Type I and Type II, and invisible Higgs decays. The program is publicly available on GitHub and can be used to constrain a wide class of new physics scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

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The influence of numerical error on an inverse problem methodology in PDE models

arXiv

引用

arXiv 2018年

作者： Nardini, John T. Bortz, D.M. Department of Applied Mathematics 526 UCB BoulderCO80309-0526 United States Interdisciplinary Quantitative Biology Graduate Program University of Colorado BoulderCO80309-0596 United States

The inverse problem methodology is a commonly-used framework in the sciences for parameter estimation and inference. It is typically performed by fitting a mathematical model to noisy experimental data. There are two significant sources of error in the process: 1. Noise from the measurement and collection of experimental data and 2. numerical error in approximating the true solution to the mathematical model. Little attention has been paid to how this second source of error alters the results of an inverse problem. As a first step towards a better understanding of this problem, we present a modeling and simulation study using a simple advection-driven PDE model. We present both analytical and computational results concerning how the different sources of error impact the least squares cost function as well as parameter estimation and uncertainty quantification. We investigate residual patterns to derive an autocorrelative statistical model that can improve parameter estimation and confidence interval computation for first order methods. Building on the results of our investigation, we provide guidelines for practitioners to determine when numerical or experimental error is the main source of error in their inference, along with suggestions of how to efficiently improve their results. Copyright © 2018, The Authors. All rights reserved.

关键词： Parameter estimation

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MONGE-AMPÈRE FLOW FOR GENERATIVE MODELING

arXiv

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arXiv 2018年

作者： Zhang, Linfeng Weinan, E. Wang, Lei Program in Applied and Computational Mathematics Princeton University United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University United States Center for Data Science Peking University Beijing Institute of Big Data Research Beijing100871 China Institute of Physics Chinese Academy of Sciences Beijing100190 China

We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Ampère flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Ampère equation, optimal transport, and fluid dynamics into reversible flow-based generative models. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical mechanics

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Search for K: Assessing Five Topic-Modeling Approaches to 120,000 Canadian Articles

Search for K: Assessing Five Topic-Modeling Approaches to 12...

引用

IEEE International Conference on Big Data

作者： Qiang Fu Yufan Zhuang Jiaxin Gu Yushu Zhu Huihui Qin Xin Guo Department of Sociology The University of British Columbia Vancouver BC Canada Data Science Institute Columbia University New York NY USA Urban Studies Program and School of Public Policy Simon Fraser University Vancouver BC Canada Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Topic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic modeling is still, if not increasingly, challenged by two critical issues. First, despite intense efforts toward nonparametric/post-training methods, the search for the optimal number of topics K remains a fundamental question in topic modeling and warrants input from domain experts. Second, with the development of more sophisticated models, topic modeling is now ironically been treated as a black box and it becomes increasingly difficult to tell how research findings are informed by data, model specifications, or inference algorithms. Based on about 120,000 newspaper articles retrieved from three major Canadian newspapers (Globe and Mail, Toronto Star, and National Post) since 1977, we employ five methods with different model specifications and inference algorithms (Latent Semantic Analysis, Latent Dirichlet Allocation, Principal Component Analysis, Factor Analysis, Nonnegative Matrix Factorization) to identify discussion topics. The optimal topics are then assessed using three measures: coherence statistics, held-out likelihood (loss), and graph-based dimensionality selection. Mixed findings from this research complement advances in topic modeling and provide insights into the choice of optimal topics in social science research.

关键词： Principal component analysis Covariance matrices Data models Matrix decomposition Loading Analytical models Semantics

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

引用

arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Unsupervised particle sorting for high-resolution single-particle cryo-EM

arXiv

引用

arXiv 2019年

作者： Zhou, Ye Moscovich, Amit Bendory, Tamir Bartesaghi, Alberto Department of Computer Science Duke University Durham United States Program in Applied and Computational Mathematics Princeton University United States School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Biochemistry Duke University School of Medicine Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle *** Codes 62P10 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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Implementation of a selective mean filter algorithm to reduce noise in computed radiography images

引用

AIP Conference Proceedings 2021年第1期2346卷

作者： J. H. Saragih C. Anam W. S. Budi T. I. Simarmata G. Dougherty 1Postgraduate Program Department of Physics Faculty of Sciences and Mathematics Diponegoro University Jl. Prof. Soedarto SH Tembalang Semarang 50275 Central Java Indonesia 2Department of Physics Faculty of Sciences and Mathematics Diponegoro University Jl. Prof. Soedarto SH Tembalang Semarang 50275 Central Java Indonesia 3Department of Radiology Premier Jatinegara Hospital Jl. Jatinegara Timur No. 85-87 Jatinegara Kota Jakarta Timur 13310 DKI Jakarta Indonesia 4Department of Applied Physics and Medical Imaging California State University Channel Islands Camarillo CA 93012 USA

This study implements a selective mean filter (SMF) and determines its effectiveness on computed radiography (CR) images. A phantom of the Kualitas Citra Imajing (KUCING) was used. The effectiveness of the SMF was compared to the adaptive mean filter (AMF). The assessment of image quality was based on several parameters, such as the peak signal to noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), contrast consistency (CV), contrast linearity (CL), and modulation transfer function (MTF). The results show that the PSNR value generated by the SMF is higher than with the AMF. The MSE value generated by the SMF algorithm is lower than AMF. The SSIM values showed that SMF algorithm produces a better image similarity between the original image and the filtered image compared to the AMF algorithm. In consistency and linearity, the AMF leads to significant changes from the original image, while the SMF image is closer to the original image. The 10% MTF generated by the SMF is the same as the original image, while AMF produces a significant decrease in the 10% MTF. The SMF took 17.9 ± 0.7 seconds for denoising one CR image, while AMF took 519 ± 5 seconds. SMF decreases image noise without a significant effect on the spatial resolution of the image. SMF filtering is superior to AMF filtering in terms of spatial resolution and a shorter computational time.

关键词：

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The Nystrom Extension for Signals Defined on a Graph

The Nystrom Extension for Signals Defined on a Graph

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ayelet Heimowitz Yonina C. Eldar The Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Technion Haifa Israel

ISBN: (纸本)9781538646595

In this paper we introduce a computationally efficient solution to the problem of graph signal interpolation. Our solution is derived using the Nyström extension and is due to the properties of the Markov matrix which we use as our graph shift operator, inspired by diffusion maps. We focus on graph signals that are smooth over the graph. This assumption cements the relationship between the graph and the graph signal. We experimentally verify our suggested framework on the MNIST data set of handwritten digits.

关键词： Markov processes Signal processing Interpolation Laplace equations Symmetric matrices Image edge detection Eigenvalues and eigenfunctions

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

引用

arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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