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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Sourty, Raphael Moreno, Jose G. Tamine, Lynda Servant, Francois-Paul Univ Paul Sabatier IRIT Toulouse France Renault Boulogne France

ISBN: (纸本)9781450387323

In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://***/raphaelsty/cherche

关键词： Neural Information Retrieval python library Information Retrieval Pipelines

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New Methods for the Acquisition and Processing of Solid-State NMR Spectra

New Methods for the Acquisition and Processing of Solid-Stat...

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作者： Altenhof, Adam Robert The Florida State University

学位级别：Ph.D., Doctor of Philosophy

Solid-state nuclear magnetic resonance (SSNMR) is a powerful tool for the study of chemical structure and dynamics. The interactions that manifest in NMR spectra are anisotropic (i.e., orientation dependent) in origin, often resulting in inhomogeneous broadening that can yield spectra exceeding ca. 250 kHz in breadth; such spectra are often referred to as ultra-wideline (UW) NMR spectra. In many cases, UWNMR spectra cannot be acquired using conventional methods and rectangular radiofrequency (RF) pulses – rather, specialized pulses and techniques must be implemented. Furthermore, the large suite of high-resolution methods based on magic-angle spinning (MAS) are generally unsuitable for the acquisition of UWNMR spectra. Frequency-swept (FS) pulses are amplitude- and phase-modulated pulses that can irradiate NMR frequencies over large bandwidths. One of these is the wideband uniform-rate smooth-truncation (WURST) pulse, which features a linear effective frequency sweep. WURST pulses are used in the WURST-Carr-Purcell/Meiboom-Gill (WURST-CPMG) and broadband adiabatic inversion-cross polarization (BRAIN-CP) pulse sequences, which have been shown to be effective for the acquisition of UWNMR spectra of stationary (static) samples. These sequences have limitations, and to date, their capabilities have not been fully explored, nor have they been described from a theoretical point of view in a comprehensive manner. Signal processing is an integral component of pulsed-Fourier transform NMR spectroscopy, and comprises many protocols for enhancing spectral sensitivity and resolution. There has been much interest in advanced spectral processing methods such as alternate basis transformations (i.e., besides Fourier encoding), statistical analyses (e.g., singular value decomposition, SVD, and principal component analysis, PCA), numerical regression (e.g., non-negative least squares), and the use of deep learning and neural networks. Such methodologies are now routinely implemented

关键词： Pulse sequence design Signal processing Solid-state nuclear magnetic resonance Spin dynamics Wideband uniform-rate smooth-truncation pulse Ultra-wideline NMR spectra python library Denoising

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DoWhy-GCM: an extension of DoWhy for causal inference in graphical causal models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 6998-7004页

作者： Patrick Blöbaum Peter Götz Kailash Budhathoki Atalanti A. Mastakouri Dominik Janzing Amazon Web Services

We present DoWhy-GCM, an extension of the DoWhy python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM addresses diverse causal queries, such as identifying the root causes of outliers and distributional changes, attributing causal influences to the data generating process of each node, or diagnosis of causal structures. With DoWhy-GCM, users typically specify cause-effect relations via a causal graph, fit causal mechanisms, and pose causal queries--all with just a few lines of code. The general documentation is available at https://***/dowhy and the DoWhy-GCM specific code at https://***/py-why/dowhy/tree/main/dowhy/gcm.

关键词： python library causal inference graphical causal models structural causal models

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PyBanshee version (1.0): A python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

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SOFTWAREX 2023年 21卷

作者： Koot, Paul Mendoza-Lugo, Miguel Angel Paprotny, Dominik Morales-Napoles, Oswaldo Ragno, Elisa Worm, Daniel T. H. Delft Univ Technol Dept Hydraul Engn Stevinweg 1 NL-2628 CN Delft Netherlands Potsdam Inst Climate Impact Res Res Dept Transformat Pathways D-14473 Potsdam Germany TNO Cyber Secur & Robustness Anna Van Buerenpl 1 NL-2595 DA The Hague Netherlands Nelen & Schuurmans BV Zakkendragershof 34-44 NL-3511 AE Utrecht Netherlands

In this paper we discuss PyBanshee, which is a python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Non-Parametric Bayesian Networks BANSHEE python library Copulas

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fromage: A library for the study of molecular crystal excited states at the aggregate scale

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2020年第10期41卷 1045-1058页

作者： Rivera, Miguel Dommett, Michael Sidat, Amir Rahim, Warda Crespo-Otero, Rachel Queen Mary Univ London Sch Biol & Chem Sci Dept Chem London England

The study of photoexcitations in molecular aggregates faces the twofold problem of the increased computational cost associated with excited states and the complexity of the interactions among the constituent monomers. A mechanistic investigation of these processes requires the analysis of the intermolecular interactions, the effect of the environment, and 3D arrangements or crystal packing on the excited states. A considerable number of techniques have been tailored to navigate these obstacles;however, they are usually restricted to in-house codes and thus require a disproportionate effort to adopt by researchers approaching the field. Herein, we present the FRamewOrk for Molecular AGgregate Excitations (fromage), which implements a collection of such techniques in a python library complemented with ready-to-use scripts. The program structure is presented and the principal features available to the user are described: geometrical analysis, exciton characterization, and a variety of ONIOM schemes. Each is illustrated by examples of diverse organic molecules in condensed phase settings. The program is available at .

关键词： exciton molecular aggregate ONIOM photochemistry python library

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RocketPy: Six Degree-of-Freedom Rocket Trajectory Simulator

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JOURNAL OF AEROSPACE ENGINEERING 2021年第6期34卷 04021093-04021093页

作者： Ceotto, Giovani H. Schmitt, Rodrigo N. Alves, Guilherme F. Pezente, Lucas A. Carmo, Bruno S. Univ Sao Paulo Escola Politecn Dept Mech Engn Av Prof Mello Moraes 2231 BR-05508030 Sao Paulo Brazil Natl Inst Space Res Av Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Escola Politecn Av Prof Mello Moraes 2231 BR-05508030 Sao Paulo Brazil Univ Sao Paulo Inst Math & Stat R Matao 1010 BR-05508090 Sao Paulo Brazil

RocketPy is a new open-source python library specialized in trajectory simulations of sounding rockets and high-powered rockets. Having a modular structure and being built on top of a six degree-of-freedom flight dynamics model that includes detailed mass variation effects, RocketPy allows for accurate trajectory prediction of different rocket configurations. Additionally, weather data from several meteorological agencies, such as reanalysis, forecasts, and ensembles, are an innovative feature directly integrated into the library, providing precise information about atmospheric conditions for each simulation. The software was successfully validated for three rockets developed by different universities, with apogee predictions showing deviations of the order of 1% when compared to actual flight data. Furthermore, due to its modular nature, RocketPy can be easily employed in optimization studies and Monte Carlo analyses. The latter was a key aspect of this work, enabling the calculation of landing dispersion ellipses based on input uncertainties. Since running several Monte Carlo analyses is computationally expensive, a new algorithm was proposed to significantly speed up results based on rejection sampling. The code described here and made available online enhances the state of the art represented by several widely used software and can serve as a framework for future research on rocket dynamics, control, flight optimization, and dispersion analysis.

关键词： Rocket trajectory simulation Sounding rockets High-powered rockets Open-source software python library Flight dynamics Six degrees of freedom Monte Carlo Multivariate rejection sampling Stochastic simulation

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OBI: A computational tool for the analysis and systematization of the positive selection in proteins

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METHODSX 2022年 9卷 101786页

作者： Calvento, Julian H. Bulgarelli, Franco Leonardo Rueda, Ana Julia Velez Univ Nacl Quilmes Dept Ciencia & Tecnol CONICET Bernal Argentina Mumuki org Buenos Aires Argentina

A B S T R A C T There are multiple tools for positive selection analysis, including vaccine design and detection of variants of circulating drug-resistant pathogens in population selection. However, applying these tools to analyze a large number of protein families or as part of a comprehensive phylogenomics pipeline could be challenging. Since many standard bioinformatics tools are only available as executables, integrating them into complex Bioinformatics pipelines may not be possible. We have developed OBI, an open-source tool aimed to facilitate positive selection analysis on a large scale. It can be used as a stand-alone command-line app that can be easily installed and used as a Conda package. Some advantages of using OBI are: center dot It speeds up the analysis by automating the entire process center dot It allows multiple starting points and customization for the analysis center dot It allows the retrieval and linkage of structural and evolutive data for a protein through We hope to provide with OBI a solution for reliably speeding up large-scale protein evolutionary and structural analysis. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： python library Proteins evolution Structural bioinformatics pipeline

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A Programmatic Approach for Development of the ViewHRV Service Platform with Accurate and Reliable Results 43

A Programmatic Approach for Development of the ViewHRV Servi...

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43rd International Convention on Information, Communication and Electronic Technology (MIPRO)

作者： Shaqiri, E. Gusev, M. Innovat Dooel Skopje North Macedonia Ss Cyril & Methodius Univ Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9789532330991

There are dozens of available packages and libraries that claim to calculate HRV. This paper aims at comparing the results from the calculation of a single array of intervals between normal beats, including the most popular open source python HRV measurement packages available on GitHub. Furthermore, the same array was ran through the Kubios software and compared to the previous results. In order to compare the accuracy of the results, as a benchmark we used the C programs provided by Physionet. The results showed a huge difference in the results with almost all the indices, in fact the simplest measurement that of Standard Deviation of NN Intervals showed to be incorrect in Kubios and in most of the python packages. Results like these are the reason we decided to develop our own package to calculate HRV. Finally, the goal of this paper is to present details on developing a publicly available web service platform ViewHRV with guaranteed precision obtaining accurate and reliable results.

关键词： ECG signal Heart Rate Variability python library Physionet

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An open source library to parse and analyze online collaborative knowledge-building portals

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JOURNAL OF INTERNET SERVICES AND APPLICATIONS 2021年第1期12卷 11-11页

作者： Verma, Amit Arjun Iyengar, S. R. S. Setia, Simran Dubey, Neeru Indian Inst Technol Ropar Social Comp & Collect Intelligence Lab Rupnagar India

With the success of collaborative knowledge-building portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons;first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building *** describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark *** of the repository: Verma et al. (2020)Video Tutorial: Verma et al. (2020)Supplementary Material: Verma et al. (2020)

关键词： Knowledge building Wikipedia Stack exchange Open-source python library

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***: A library for computational imaging using the EnderScope automated microscope

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SoftwareX 2025年 31卷

作者： Sirine Gharbi Emeric Poiraud Hugo Le Guenno Erwan Grandgirard Charly Rousseau Niamh Burke Jerome Mutterer David Rousseau Université d’Angers LARIS UMR IRHS INRAe 49000 Angers France Aix Marseille Université CNRS IMM Microscopy Core Facility Marseille France IGBMC CNRS INSERM Université de Strasbourg Illkirch France Sorbonne Université Paris Brain Institute - ICM INSERM CNRS AP-HP Hôpital de la Pitié Salpêtrière Paris France School of Medicine University College Dublin Dublin Ireland IBMP CNRS Université de Strasbourg Strasbourg France

We describe a new software library written in the python programming language to perform computational imaging on the so-called EnderScope, an automated imaging system based on the 3-axis motion stage of a 3D printer. This library is designed to be easy to use for beginner users and provides high-level functions to access used devices. We provide practical use cases and example codes using the library as an educational tool for testing, experimenting or developing computational imaging and smart microscopy algorithms.

关键词： Computational imaging python library Microscope

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