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Threshold and decay properties of transient isolated turbulent band in plane Couette flow

Applied Mathematics and Mechanics(English Edition) 2019年第10期40卷 1449-1456页

作者： Jianzhou LU Jianjun TAO Weitao ZHOU Xiangming XIONG CAPT-HEDPS SKLTCS Collaborative Innovation Center of IFSA Department of Mechanics and Engineering Science College of Engineering Peking University Beijing 100871 China The Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 U. S.A.

By direct numerical simulations of the plane Couette flow (PCF) in a large computational domain, it is shown that an isolated turbulent band decays monotonically at low Reynolds numbers but experiences transient growth before the eventual relaminarization at moderate Reynolds numbers. The lower bound Reynolds number of the transient-growth regime is determined as 286. The width, length, and tilt angle of the iso- lated band structure are defined based on the disturbance kinetic energy in the mid-plane, and the geometric characteristics of the band can be described with a tilted rectangle. It is illustrated that before its eventual fragmentation, the isolated turbulent band decays in a style of longitudinal contraction, where the center, width, and tilt angle of the band keep almost constant but the band length contracts with a statistically constant velocity.

关键词： plane Couette flow (PCF) subcritical transition turbulent band

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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CO2adsorption on graphene supported Ni catalysts 8

CO2adsorption on graphene supported Ni catalysts

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8th Asian Physics Symposium, APS 2019

作者： Muttaqien, F. Namari, N.A.P. Wella, S.A. Wungu, T.D.K. Suprijadi Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Instrumentation and Computation Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Nuclear Physics and Biophysics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Research Center for Nanosciences and Nanotechnology Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia

We perform density functional theory (DFT) study of CO 2 adsorption on graphene supported Ni catalyst. We implement ordinary PBE functional as well as van der Waals density functional (vdW-DF) to accommodate weakly interaction between CO 2 and graphene. Based on vdW-DF calculations, we obtained that CO 2 is physisorbed on pristine graphene, in contradiction with PBE results. The adsorption energy further increases when we introduce single Ni adatom and small Ni cluster on graphene. The adsorbed CO 2 bond angle and bond length on graphene with single Ni adatom and cluster are deformed from its gas phase condition, indicating that CO 2 is chemically adsorbed on the decorated graphene area. Our results provide useful insight into appropriate design of graphene supported metal catalysts. © Published under licence by IOP Publishing Ltd.

关键词： Graphene

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Quantifying random variability in decision-making in pediatric cardiac surgery

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JTCVS Open 2025年

作者： Dlugos, Kayla V. Mazwi, Mjaye Signorile, Marisa Steve Fan, Chun-Po Trbovich, Patricia Honjo, Osami Institute of Medical Science University of Toronto ON Canada Division of Cardiovascular Surgery The Labatt Family Heart Centre The Hospital for Sick Children Toronto ON Canada Department of Surgery University of Toronto ON Canada Division of Cardiac Critical Care Seattle Children's Hospital Seattle Wash United States Ted Rogers Computational Program Ted Rogers Centre for Heart Research Peter Munk Cardiac Centre UHN Toronto ON Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto ON Canada North York General Hospital Toronto ON Canada Institute of Biomedical Engineering University of Toronto Toronto ON Canada

Objective: Random variability in day-to-day decision-making referred to as “noise” is associated with variation that negatively affects both the reproducibility and quality of decision-making. Although well described in other fields, the prevalence and significance of noise in medical decision-making are understudied and largely unknown. The goal of this study was to quantify noise in medical decision-making using a noise audit. Methods: A noise audit was completed by 71 (n = 71) Heart Centre staff at the Hospital for Sick Children and Seattle Children's Hospital and involved a series of cases and questions surrounding decisions commonly encountered in the care for patients with transposition of the great arteries and critical aortic stenosis. Entropy was used to quantify total variation in responses. Because absolute entropy was not immediately comparable across audit questions with a different number of prespecified options, we reported a standardized version of entropy, which was calculated by dividing the absolute entropy by its theoretical maximum for each case. To compare responses in easy to hard questions across years of experience (<10 years and >10 years) and role, we reported aggregate entropy ratios. Aggregate entropy ratios were calculated by first stratifying by group of comparison and then calculating the standardized entropy for each question and taking the average of standardized entropies for easy questions and hard questions. Finally, to determine the ratio (easy to hard), we divided the average standardized entropy of the easy questions by the average standardized entropy of the hard questions. Results: The overall audit aggregate entropy ratio was 0.85 less than 1, indicating lower-complexity questions had less variation than higher-complexity questions. The aggregate entropy ratio for those with more than 10 years of experience was 0.8 and 0.87, respectively, for those with less than 10 years of experience. The aggregate entropy ratios for thos

关键词： congenital heart surgery entropy medical decision-making noise noise audit random variability

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Widely Tunable Berry Curvature in the Magnetic Semimetal Cr1+δTe2

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Advanced Materials 2023年第12期35卷

作者： Fujisawa, Yuita Pardo-Almanza, Markel Hsu, Chia-Hsiu Mohamed, Atwa Yamagami, Kohei Krishnadas, Anjana Chang, Guoqing Chuang, Feng-Chuan Khoo, Khoong Hong Zang, Jiadong Soumyanarayanan, Anjan Okada, Yoshinori Okinawa904-0495 Japan Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Center for Theoretical and Computational Physics National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore Department of Physics and Astronomy University of New Hampshire DurhamNH 03824 United States Materials Science Program University of New Hampshire DurhamNH 03824 United States Department of Physics National University of Singapore Singapore117551 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore138634 Singapore

Magnetic semimetals have increasingly emerged as lucrative platforms hosting spin-based topological phenomena in real and momentum spaces. Cr1+δTe2 is a self-intercalated magnetic transition metal dichalcogenide (TMD), which exhibits topological magnetism and tunable electron filling. While recent studies have explored real-space Berry curvature effects, similar considerations of momentum-space Berry curvature are lacking. Here, the electronic structure and transport properties of epitaxial Cr1+δTe2 thin films are systematically investigated over a range of doping, δ (0.33 – 0.71). Spectroscopic experiments reveal the presence of a characteristic semi-metallic band region, which shows a rigid like energy shift with δ. Transport experiments show that the intrinsic component of the anomalous Hall effect (AHE) is sizable and undergoes a sign flip across δ. Finally, density functional theory calculations establish a link between the doping evolution of the band structure and AHE: the AHE sign flip is shown to emerge from the sign change of the Berry curvature, as the semi-metallic band region crosses the Fermi energy. These findings underscore the increasing relevance of momentum-space Berry curvature in magnetic TMDs and provide a unique platform for intertwining topological physics in real and momentum spaces. © 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.

关键词： Density functional theory

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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年第5期25卷 e622-e634.e6页

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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computational Design of Anisotropic Stealthy Hyperuniform Composites with Engineered Directional Scattering Properties

arXiv

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arXiv 2023年

作者： Shi, Wenlong Keeney, David Chen, Duyu Jiao, Yang Torquato, Salvatore Materials Science and Engineering Arizona State University TempeAZ85287 United States Materials Research Laboratory University of California Santa BarbaraCA93106 United States Department of Physics Arizona State University TempeAZ85287 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Disordered hyperuniform materials are an emerging class of exotic amorphous states of matter that endow them with singular physical properties, including large isotropic photonic band gaps, superior resistance to fracture, and nearly optimal electrical and thermal transport properties, to name but a few. Here, we generalize the Fourier-space based numerical construction procedure for designing and generating digital realizations of isotropic disordered hyperuniform two-phase heterogeneous materials (i.e., composites) developed by Chen and Torquato [Acta Mater. 142, 152 (2018)] to anisotropic microstructures with targeted spectral densities. Our generalized construction procedure explicitly incorporates the vector-dependent spectral density function χ˜V (k) of arbitrary form that is realizable. We demonstrate the utility of the procedure by generating a wide spectrum of anisotropic stealthy hyperuniform (SHU) microstructures with χ˜V (k) = 0 for k ∈ Ω, i.e., complete suppression of scattering in an "exclusion" region Ω around the origin in the Fourier space. We show how different exclusion-region shapes with various discrete symmetries, including circular-disk, elliptical-disk, square, rectangular, butterfly-shaped and lemniscate-shaped regions of varying size, affect the resulting statistically anisotropic microstructures as a function of the and phase volume fraction. The latter two cases of Ω lead to directionally hyperuniform composites, which are stealthy hyperuniform only along certain directions, and are non-hyperuniform along others. We find that, while the circular-disk exclusion regions give rise to isotropic hyperuniform composite microstructures, the directional hyperuniform behaviors imposed by the shape asymmetry (or anisotropy) of certain exclusion regions give rise to distinct anisotropic structures and degree of uniformity in the distribution of the phases on intermediate and large length scales along different directions. Moreover, while the anisotr

关键词： Volume fraction

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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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Modeling liquid water by climbing up Jacob’s ladder in density functional theory facilitated by using deep neural network potentials

arXiv

引用

arXiv 2021年

作者： Zhang, Chunyi Tang, Fujie Chen, Mohan Zhang, Linfeng Qiu, Diana Y. Perdew, John P. Klein, Michael L. Wu, Xifan Department of Physics Temple University PhiladelphiaPA19122 United States HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemistry Temple University PhiladelphiaPA19122 United States Institute for Computational Molecular Science Temple University PhiladelphiaPA19122 United States

Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric-isothermal ensemble. In order to generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water towards the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. © 2021, CC BY.

关键词： Deep neural networks

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Sent2Span: Span detection for PICO extraction in the biomedical text without span annotations

arXiv

引用

arXiv 2021年

作者： Liu, Shifeng Sun, Yifang Li, Bing Wang, Wei Bourgeois, Florence T. Dunn, Adam G. The University of Sydney Australia Northeastern University China IHPC A*STAR Singapore Hong Kong University of Science and Technology Guangzhou China Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States

The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which require finding all relevant trials. This leads to policy and practice decisions based on out-of-date, incomplete, and biased subsets of available clinical evidence. Extracting and then normalising Population, Intervention, Comparator, and Outcome (PICO) information from clinical trial articles may be an effective way to automatically assign trials to systematic reviews and avoid searching and screening—the two most time-consuming systematic review processes. We propose and test a novel approach to PICO span detection. The major difference between our proposed method and previous approaches comes from detecting spans without needing annotated span data and using only crowdsourced sentence-level annotations. Experiments on two datasets show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. By removing the reliance on expert annotations for span detection, this work could be used in a human-machine pipeline for turning low-quality, crowdsourced, and sentence-level PICO annotations into structured information that can be used to quickly assign trials to relevant systematic reviews. Copyright © 2021, The Authors. All rights reserved.

关键词： Crowdsourcing

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