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arXiv 2023年

作者： Moral, Pierre Del Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux FR Bordeaux33405 France School of Mathematics and Statistics Wenlan School of Business China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Zhongnan University of Economics and Law CN China

We consider the problem of Bayesian estimation of static parameters associated to a partially and discretely observed diffusion process. We assume that the exact transition dynamics of the diffusion process are unavailable, even up-to an unbiased estimator and that one must time-discretize the diffusion process. In such scenarios it has been shown how one can introduce the multilevel Monte Carlo method to reduce the cost to compute posterior expected values of the parameters for a pre-specified mean square error (MSE);see [12]. These afore-mentioned methods rely on upon the Euler-Maruyama discretization scheme which is well-known in numerical analysis to have slow convergence properties. We adapt stochastic Runge-Kutta (SRK) methods for Bayesian parameter estimation of static parameters for diffusions. This can be implemented in high-dimensions of the diffusion and seemingly under-appreciated in the uncertainty quantification and statistics fields. For a class of diffusions and SRK methods, we consider the estimation of the posterior expectation of the parameters. We prove that to achieve a MSE of O(ϵ2), for ϵ > 0 given, the associated work is O(ϵ−2). Whilst the latter is achievable for the Milstein scheme, this method is often not applicable for diffusions in dimension larger than two. We also illustrate our methodology in several numerical examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Study of Self-Interaction Errors in Density-Functional Calculations of Magnetic Exchange Coupling Constants Using Three Self-Interaction-Correction Methods

arXiv

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arXiv 2022年

作者： Mishra, Prakash Yamamoto, Yoh Chang, Po-Hao Nguyen, Duyen B. Peralta, Juan E. Baruah, Tunna Zope, Rajendra R. Computational Science Program University of Texas at El Paso El PasoTX79968 United States Department of Physics University of Texas at El Paso El PasoTX79968 United States Physics Department Science of Advanced Materials Program Central Michigan University Mt. PleasantMI48859 United States

We examine the role of self-interaction errors (SIE) removal on the evaluation of magnetic exchange coupling constants. In particular we analyze the effect of scaling down the self-interaction-correction (SIC) for three non-empirical density functional approximations (DFAs) namely, the local spin density approximation, the Perdew-Burke-Ernzerhof generalized gradient approximation, and recent SCAN family of meta-GGA functionals. To this end, we employ three one-electron SIC methods: Perdew-Zunger [Perdew, J. P.;Zunger, A. Phys. Rev. B, 1981, 23, 5048] SIC, the orbitalwise scaled SIC method [Vydrov, O. A. et al., J. Chem. Phys. 2006, 124, 094108], and the recent local scaling method [Zope, R. R. et al., J. Chem. Phys. 2019, 151, 214108]. We compute the magnetic exchange coupling constants using the spin projection and non projection approaches for sets of molecules composed of dinuclear and polynuclear H-He models, organic radical molecules, and chlorocuprate, and compare these results against accurate theories and experiment. Our results show that for the systems that mainly consist of single electron regions, PZSIC performs well but for more complex organic systems and the chlorcuprates, an overcorrecting tendency of PZSIC combined with the DFAs utilized in this work is more pronounced, and in such cases LSIC with kinetic energy density ratio performs better than PZSIC. Analysis of the results in terms of SIC corrections to the density and to the total energy shows that both density and energy correction are required to obtain an improved prediction of magnetic exchange couplings. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic couplings

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Three-dimensional construction of hyperuniform, nonhyperuniform, and antihyperuniform disordered heterogeneous materials and their transport properties via spectral density functions

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Physical Review E 2025年第3期111卷 035310-035310页

作者： Wenlong Shi Yang Jiao Salvatore Torquato Materials Science and Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute of Materials Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Rigorous theories connecting physical properties of a heterogeneous material to its microstructure offer a promising avenue to guide the computational material design and optimization. The spectral density function χ̃V(k), which can be obtained experimentally from scattering data, enables accurate determination of various transport and wave propagation characteristics, including the time-dependent diffusion spreadability S(t) and effective dynamic dielectric constant εe for electromagnetic wave propagation. Moreover, χ̃V(k) determines rigorous upper bounds on the fluid permeability K. Given the importance of χ̃V(k), we present here an efficient Fourier-space based computational framework to construct three-dimensional (3D) statistically isotropic two-phase heterogeneous materials corresponding to targeted spectral density functions. In particular, we employ a variety of analytical functional forms for χ̃V(k) that satisfy all known necessary conditions to construct disordered stealthy hyperuniform, standard hyperuniform, nonhyperuniform, and antihyperuniform two-phase heterogeneous material systems at varying phase volume fractions. We show that by tuning the correlations in the system across length scales via the targeted functions, one can generate a rich spectrum of distinct structures within each of the above classes of materials. Importantly, we present the first realization of antihyperuniform two-phase heterogeneous materials in 3D, which are characterized by autocovariance function χV(r) with a power-law tail, resulting in microstructures that contain clusters of dramatically different sizes and morphologies. We also determine the diffusion spreadability S(t) and estimate the fluid permeability K associated with all of the constructed materials directly from the corresponding spectral densities. Although it is well established that the long-time asymptotic scaling behavior of S(t) only depends on the functional form of χ̃V(k), with the stealthy hyperuniform a

关键词： Spectral density

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Feeling the Groove: Identification of Bass patterns from polyphonic audio

Feeling the Groove: Identification of Bass patterns from pol...

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International Conference on Communication Technology (ICCT)

作者： Himadri Mukherjee Matteo Marciano Ankita Dhar Kaushik Roy Dept. of Computer Science West Bengal State University India Dept. of Arts and Humanities Gazelien Records Lab Music Program New York University Abu Dhabi UAE Dept. of Computational Science Brainware University India

Technology has found its way into disparate spheres of music, from production to performance. Researchers have attempted to automate multitudinous aspects in this domain. One of the interests has been towards the automated generation of music pieces itself. Bass grooves are an integral part of most music pieces. It makes a piece sound complete and bridges the gap between the percussion and melody sections. Thus, it is essential for machines to understand bass grooves for automated music analysis and production. Automatically distinguishing bass grooves is difficult and it aggravates even more for polyphonic music. In polyphonic music, the bass grooves tend to be at a lower volume and its frequency range has profound overlap with the percussion section which contributes to the complexity of identification. In this paper, a system is presented to distinguish bass grooves in the presence of drums. Experiments were performed with 7 grooves totaling 4473 clips which were modeled using MFCC-based features. The highest accuracy of 97.38% was obtained using multi-layer perceptron (MLP)-based classification.

关键词： Production Feature extraction Communications technology Multiple signal classification Complexity theory Mel frequency cepstral coefficient

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Cell2Sentence: Teaching Large Language Models the Language of Biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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State of the Art Models for Fake News Detection Tasks

State of the Art Models for Fake News Detection Tasks

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2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020

作者： Antoun, Wissam Baly, Fady Achour, Rim Hussein, Amir Hajj, Hazem American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon Graduate Program in Computational Science American University of Beirut Beirut Lebanon

ISBN: (数字)9781728148212

ISBN: (纸本)9781728148212

This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets. The proposed solutions achieved first place in a recent international competition on fake news. For fake news detection, we present two models. The winning model in the competition combines similarity between the embedding of each article's title and the embedding of the top five corresponding google search results. The new model relies on advances in Natural Language Understanding (NLU) end to end deep learning models to identify stylistic differences between legitimate and fake news articles. This second model was developed after the competition and outperforms the winning approach. For news domain detection, the winning model is a hybrid approach composed of named entity features concatenated with semantic embeddings derived from end to end models. For twitter bot detection, we propose to use the following features: duration between account creation and tweet date, presence of a tweet's link, presence of user's location, other tweet's features, and the tweets' metadata. Experiments include insights into the importance of the different features and the results indicate the superior performances of all proposed models. © 2020 IEEE.

关键词： Competition

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Tumor-derived interleukin-1 receptor antagonist exhibits immunosuppressive functions and promotes pancreatic cancer

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Cell and Bioscience 2023年第1期13卷 1页

作者： Fan, Yu-Ching Fong, Yu-Cin Kuo, Chun-Tse Li, Chia-Wei Chen, Wei-Yu Lin, Jian-Da Bürtin, Florian Linnebacher, Michael Bui, Quoc Thang Lee, Kuan-Der Tsai, Yuan-Chin Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University and Academia Sinica Taipei Taiwan Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Department of Pathology Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Pathology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Biochemical Science and Technology College of Life Science National Taiwan University Taipei City 10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei City 10617 Taiwan Clinic of General Surgery University Medical Center Rostock Schillingallee 35 Rostock 18057 Germany Clinic of General Surgery Molecular Oncology and Immunotherapy University Medical Center Rostock Schillingallee 69 Rostock 18057 Germany International Ph.D. Program for Cell Therapy and Regeneration Medicine (IPCTRM) School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine Natioanl Chung Hsing University Taichung Taiwan Cell Therapy and Regenerative Medicine Center and Comprehensive Cancer Center Taichung Veterans General Hospital Taichung Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: Pancreatic ductal adenocarcinoma (PDA) is a pernicious disease characterized by an immunosuppressive milieu that is unresponsive to current immunotherapies. Interleukin-1 receptor antagonist (IL-1Ra) is a natural anti-inflammatory cytokine;however, its contribution to cancer pathogenesis and immunosuppression remains elusive. In this research, we investigated the role and mechanism of IL-1Ra in malignant progression of PDA. Results: Through analyzing clinical dataset and examining the pathological tumor tissues and serum samples, we have demonstrated that IL-1Ra expression is elevated in human PDA and positively associated with malignant progression of PDA. To study the biological function of IL-1Ra in tumors, we generated a set of mouse pancreatic cancer cell lines with a knockout (KO) of the Il1rn gene, encoding IL-1Ra, and compared the tumor growth rates in immune-competent and immune-deficient mice. We found that the Il1rn KO cells exhibited greater tumor inhibition in immune-competent mice, highlighting the crucial role of a functional immune system in Il1rn KO-mediated anti-tumor response. Consistently, we found an increase in CD8⁺ T cells and a decrease in CD11b⁺Ly6G⁻ immunosuppressive mononuclear population in the tumor microenvironment of Il1rn KO-derived tumors. To monitor the inhibitory effects of IL-1Ra on immune cells, we utilized a luciferase-based reporter CD4⁺ T cell line and splenocytes, which were derived from transgenic mice expressing ovalbumin-specific T cell receptors in CD8⁺ T cells, and mice immunized with ovalbumin. We showed that IL-1Ra suppressed T cell receptor signaling and inhibited antigen-specific interferon-γ (IFN-γ) secretion and cytolytic activity in splenocytes. Conclusions: Our findings illustrate the immunosuppressive properties of the natural anti-inflammatory cytokine IL-1Ra, and provide a rationale for considering IL-1Ra-targeted therapies in the treatment of

关键词： IFN-γ Immunosuppression interleukin-1 receptor antagonist Pancreatic adenocarcinoma Patient-derived xenograft Tumor microenvironment

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SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

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 Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA 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 TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

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Diffusion curvature for estimating local curvature in high dimensional data 22

Diffusion curvature for estimating local curvature in high d...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Dhananjay Bhaskar Kincaid MacDonald Oluwadamilola Fasina Dawson Thomas Bastian Rieck Ian Adelstein Smita Krishnaswamy Department of Genetics Yale University Department of Mathematics Yale University Applied Mathematics Program Yale University Department of Mathematics Department of Physics Yale University Institute of AI for Health Helmholtz Pioneer Campus Helmholtz Munich Department of Computer Science Department of Genetics Applied Mathematics Program Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781713871088

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature. Our measure uses the framework of diffusion maps, including the data diffusion operator, to structure point cloud data and define local curvature based on the laziness of a random walk starting at a point or region of the data. We show that this laziness directly relates to volume comparison results from Riemannian geometry. We then extend this scalar curvature notion to an entire quadratic form using neural network estimations based on the diffusion map of point-cloud data. We show applications of both estimations on toy data, single-cell data and on estimating local Hessian matrices of neural network loss landscapes.

关键词：

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Plant scent and plant-insect interactions—Review and outlook from a macroevolutionary perspective

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植物分类学报 2023年第3期61卷 465-486页

作者： Orlando Schwery Breanna N.Sipley Mariana P.Braga Yan Yang Roberto Rebollo Pengjuan Zu New Mexico Consortium 4200 W.Jemez RdSuite 301Los AlamosNM 87544USA Department of Biological Sciences University of Idaho875 Perimeter Dr.MS 3051MoscowID 83844USA Department of Biological Sciences Southeastern Louisiana University2400 N.Oak StHammondLA 70402USA Program for Bioinformatics and Computational Biology University of IdahoMoscowID 83844USA Department of Biology Washington University in St.LouisSt.LouisMO 63130USA Department of Ecology Swedish University of Agricultural SciencesUppsalaSweden Helsinki Life Science Institute University of HelsinkiHelsinkiFinland Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants Ministry of EducationCollege of ForestryHainan UniversityHaikou 570228P.R.China Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Swiss Federal Institute of Aquatic Science and Technology(Eawag) Seestrasse 79CH-6047 KastanienbaumSwitzerland

The astonishing diversity of plants and insects and their entangled interactions are cornerstones in terrestrial ***-occurring with species diversity is the diversity of plant secondary metabolites(PSMs).So far,their estimated number is more than 200 000 compounds,which are not directly involved in plant growth and development but play important roles in helping plants handle their environment including the mediation of plant-insect ***,we use plant volatile organic compounds(VOCs),a key olfactory communication channel that mediates plant-insect interactions,as a showcase of *** spite of the cumulative knowledge of the functional,ecological,and microevolutionary roles of VOCs,we still lack a macroevolutionary understanding of how they evolved with plant-insect interactions and contributed to species diversity throughout the long coevolutionary history of plants and *** first review the literature to summarize the current state-of-the-art research on this *** then present various relevant types of phylogenetic methods suitable to answer macroevolutionary questions on plant VOCs and suggest future directions for employing phylogenetic approaches in studying plant VOCs and plant-insect ***,we found that current studies in this field are still very limited in their macroevolutionary ***,with the fast-growing development of metabolome analysis techniques and phylogenetic methods,it is becoming increasingly feasible to integrate the advances of these two *** highlight promising approaches to generate new testable hypotheses and gain a mechanistic understanding of the macroevolutionary roles of chemical communication in plant-insect interactions.

关键词： coevolution

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