检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Grigas, Alex T. Liu, Zhuoyi Logan, Jack A. Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins fold to a specific functional conformation with a densely packed hydrophobic core that controls their stability. We develop a geometric, yet all-atom model for proteins that explains the universal core packing fraction of φc = 0.55 found in experimental measurements. We show that as the hydrophobic interactions increase relative to the temperature, a novel jamming transition occurs when the core packing fraction exceeds φc. The model also recapitulates the global structure of proteins since it can accurately refold to native-like structures from partially unfolded states. © 2024, CC BY.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Compressed Parametric and Non-Parametric Approximations to the Gravitational Wave Likelihood

arXiv

引用

arXiv 2022年

作者： Delfavero, Vera O’Shaughnessy, Richard Wysocki, Daniel Yelikar, Anjali NASA Postdoctoral Program Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States Center for Computational Relativity and Gravitation Rochester Institute of Technology RochesterNY14623 United States Department of Physics University of Wisconsin–Milwaukee MilwaukeeWI53201 United States

Gravitational-wave observations of quasicircular compact binary mergers imply complicated posterior measurements of their parameters. Though Gaussian approximations to the pertinent likelihoods have decades of history in the field, the relative generality and practical utility of these approximations hasn’t been appreciated, given focus on careful, comprehensive generic Bayesian parameter inference. Building on our previous work in three dimensions, we demonstrate by example that bounded multivariate normal likelihood approximations are a sufficiently accurate representation of the full likelihood of observed gravitational-wave sources. Fits for each event are published in the Gravitational-Wave Transient Catalogs at https://***/xevra/nal-data, along with a code release at https://***/xevra/gwalk. We argue our approximations are more than accurate enough for population inference and introduce much smaller errors than waveform model systematics. To demonstrate the utility of these approximations as parametric models for the likelihood of individual gravitational-wave sources, we show examples of their application to modeling the population of observed gravitational-wave sources as well as low-latency parameter inference. Copyright © 2022, The Authors. All rights reserved.

关键词： Gravity waves

来源：评论

学校读者我要写书评

暂无评论

Interpretable Symbolic Regression for Data science: Analysis of the 2022 Competition

arXiv

引用

arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Estimating Classification Confidence Using Kernel Densities

arXiv

引用

arXiv 2022年

作者： Salamon, Peter Salamon, David Cantu, V. Adrian An, Michelle Perry, Tyler Edwards, Robert A. Segall, Anca M. The Department of Mathematics and Statistics The Computational Science Research Center The Viral Information Institute San Diego State University San DiegoCA92182 United States The Department of Mathematics and Statistics San Diego State University San DiegoCA92182 United States The Computational Science Research Center The Department of Biology San Diego State University San DiegoCA92182 United States The Perelman School of Medicine The University of Pennsylvania 425 Johnson Pavilion 3610 Hamilton Walk PhiladelphiaPA19104 United States The Computational Science Research Center San Diego State University San DiegoCA92182 United States Google 1600 Amphitheater Parkway Mountain ViewCA94043 United States The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States Helix 9875 Towne Center Drive San DiegoCA92121 United States The Department of Biology The Computational Science Research Center The Viral Information Institute The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) [1], [2] as well as the classic MNIST benchmark [3]. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

Heat transport in liquid water from first-principles and deep neural network simulations

引用

Physical Review B 2021年第22期104卷 224202-224202页

作者： Davide Tisi Linfeng Zhang Riccardo Bertossa Han Wang Roberto Car Stefano Baroni SISSA–Scuola Internazionale Superiore di Studi Avanzati 34136 Trieste Italy Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing 100088 People's Republic of China Department of Chemistry Department of Physics and Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA CNR Istituto Officina dei Materiali SISSA unit 34136 Trieste Italy

We compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.

关键词： Thermal conductivity Thermal properties Water Density functional theory Linear response theory Machine learning Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Machine-learning-based non-Newtonian fluid model with molecular fidelity

引用

Physical Review E 2020年第4期102卷 043309-043309页

作者： Huan Lei Lei Wu Weinan E Department of Computational Mathematics Science & Engineering and Department of Statistics & Probability Michigan State University East Lansing Michigan 48824 USA Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

We introduce a machine-learning-based framework for constructing continuum a non-Newtonian fluid dynamics model directly from a microscale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the microscale polymer configurations and their macroscale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the microscale model, and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a generalized form of the objective tensor derivative that retains the microscale interpretations. Both the formulation of the dynamic equation and the neural network representation rigorously preserve the rotational invariance, which ensures the admissibility of the constructed model. Numerical results demonstrate the accuracy of DeePN2 where models based on empirical closures show limitations.

关键词： Learning Neuronal networks Complex fluids Non-Newtonian fluids Rheology Viscoelasticity Multiscale modeling

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

引用

Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

来源：评论

学校读者我要写书评

暂无评论

An Evolutionary Analytic Center Classifier 9th

An Evolutionary Analytic Center Classifier

引用

9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Goulart, Renan Motta Villela, Saulo Moraes Borges, Carlos Cristiano Hasenclever Neto, Raul Fonseca Postgraduate Program in Computational Modeling Federal University of Juiz de Fora Juiz de ForaMinas Gerais Brazil Department of Computer Science Federal University of Juiz de Fora Juiz de ForaMinas Gerais Brazil

ISBN: (纸本)9783030613761

Classification is an essential task in the field of Machine Learning, where developing a classifier that minimizes errors on unknown data is one of its central problems. It is known that the analytic center is a good approximation of the center of mass of the version space that is consistent with the Bayes-optimal decision surface. Therefore, in this work, we propose an evolutionary algorithm, relying on the convexity properties of the version space, that evolves a population of perceptron classifiers in order to find a solution that approximates its analytic center. Hyperspherical coordinates are used to guarantee feasibility when generating new individuals and enabling exploration to be uniformly distributed through the search space. To evaluate the individuals we consider using a potential function that employs a logarithmic barrier penalty. Experiments were performed on real datasets, and the obtained results indicate concrete possibilities for applying the proposed algorithm for solving practical problems. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning

Research Square

引用

Research Square 2021年

作者： Miyaguchi, Ikuko Sato, Miwa Kashima, Akiko Nakagawa, Hiroyuki Kokabu, Yuichi Ma, Biao Matsumoto, Shigeyuki Tokuhisa, Atsushi Ohta, Masateru Ikeguchi, Mitsunori Mitsubishi Tanabe Pharma Co. LTD Japan RIKEN Medical Sciences Innovation Hub Program Japan RIKEN Center for Computational Science Japan Kyoto university Japan Yokohama City University Japan

Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution. © 2021, CC BY.

关键词： X ray crystallography

来源：评论

学校读者我要写书评

暂无评论

Deep neural network for the dielectric response of insulators

引用

Physical Review B 2020年第4期102卷 041121(R)-041121(R)页

作者： Linfeng Zhang Mohan Chen Xifan Wu Han Wang Weinan E Roberto Car Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University Princeton New Jersey 08544 USA

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

关键词： Electric polarization Electrical properties Electronic structure Ferroelectrics Liquids Piezoelectrics Density functional theory First-principles calculations Machine learning Molecular dynamics Multiscale modeling Wannier function methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：