Tootfinder

CrashJS: A NodeJS Benchmark for Automated Crash Reproduction
Philip Oliver, Jens Dietrich, Craig Anslow, Michael Homer
https://arxiv.org/abs/2405.05541 htt…

CrashJS: A NodeJS Benchmark for Automated Crash Reproduction
Software bugs often lead to software crashes, which cost US companies upwards of $2.08 trillion annually. Automated Crash Reproduction (ACR) aims to generate unit tests that successfully reproduce a crash. The goal of ACR is to aid developers with debugging, providing them with another tool to locate where a bug is in a program. The main approach ACR currently takes is to replicate a stack trace from an error thrown within a program. Currently, ACR has been developed for C, Java, and Python, bu…

This https://arxiv.org/abs/2304.03853 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model…

Cantera-Based Python Computer Program for Solving Steam Power Cycles with Superheating
Osama A. Marzouk
https://arxiv.org/abs/2405.00007 https://

Cantera-Based Python Computer Program for Solving Steam Power Cycles with Superheating
One of the main sources of electricity generation is power plants that use water (steam) to rotate turbines, which drive large electric generators. The steam can be generated from renewable or non-renewable energy sources, such as geothermal energy and nuclear fuels. Having an analysis tool for modeling the performance of such steam power plants can greatly help in reaching optimum designs, leading to less fuel consumption, reduced pollution, and cheaper electricity. It is further advantageous …

This https://arxiv.org/abs/2308.09375 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image pr…

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo
Thomas Berlok, L\'ena Jlassi, Ewald Puchwein, Troels Haugb{\o}lle
https://arxiv.org/abs/2404.14484

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo
We present Paicos, a new object-oriented Python package for analyzing simulations performed with Arepo. Paicos strives to reduce the learning curve for students and researchers getting started with Arepo simulations. As such, Paicos includes many examples in the form of Python scripts and Jupyter notebooks as well as an online documentation describing the installation procedure and recommended first steps. Paicos' main features are automatic handling of cosmological and physical units, computat…

Cantera-Based Python Computer Program for Solving Steam Power Cycles with Superheating
Osama A. Marzouk
https://arxiv.org/abs/2405.00007 https://

Cantera-Based Python Computer Program for Solving Steam Power Cycles with Superheating
One of the main sources of electricity generation is power plants that use water (steam) to rotate turbines, which drive large electric generators. The steam can be generated from renewable or non-renewable energy sources, such as geothermal energy and nuclear fuels. Having an analysis tool for modeling the performance of such steam power plants can greatly help in reaching optimum designs, leading to less fuel consumption, reduced pollution, and cheaper electricity. It is further advantageous …

LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks
Jean Michel de Souza Sant Ana, Arliones Hoeller Jr., Hirley Alves, Richard Demo Souza
https://arxiv.org/abs/2404.09539

LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks
This work presents the LR-FHSS-Sim, a free and open-source discrete-event simulator for LR-FHSS networks. We highlight the importance of network modeling for IoT coverage, especially when it is needed to capture dynamic network behaviors. Written in Python, we present the LR-FHSS-Sim main structure, procedures, and extensions. We discuss the importance of a modular code, which facilitates the creation of algorithmic strategies and signal-processing techniques for LR-FHSS networks. Moreover, we …

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo
Thomas Berlok, L\'ena Jlassi, Ewald Puchwein, Troels Haugb{\o}lle
https://arxiv.org/abs/2404.14484

Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo
We present Paicos, a new object-oriented Python package for analyzing simulations performed with Arepo. Paicos strives to reduce the learning curve for students and researchers getting started with Arepo simulations. As such, Paicos includes many examples in the form of Python scripts and Jupyter notebooks as well as an online documentation describing the installation procedure and recommended first steps. Paicos' main features are automatic handling of cosmological and physical units, computat…

cosmocnc: A fast, flexible, and accurate galaxy cluster number, count likelihood code for cosmology
\'I\~nigo Zubeldia, Boris Bolliet
https://arxiv.org/abs/2403.09589

cosmocnc: A fast, flexible, and accurate galaxy cluster number, count likelihood code for cosmology
We introduce cosmocnc, a Python package for computing the number count likelihood of galaxy cluster catalogues in a fast, flexible and accurate way. cosmocnc offers three types of likelihoods: an unbinned, a binned, and an extreme value likelihood. It also supports the addition of stacked cluster data, which is modelled consistently with the cluster catalogue. The unbinned likelihood, which is the main focus of the code, can take an arbitrary number of mass observables as input and deal with se…

This https://arxiv.org/abs/2402.08377 has been replaced.
initial toot: https://mastoxiv.page/@ar…

\texttt{VAMPyR} -- A High-Level Python Library for Mathematical Operations in a Multiwavelets Representation
Wavelets and Multiwavelets have lately been adopted in Quantum Chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few) they provide a framework which narrows the gap between the theoretical formalism of the fundamental equations and the practical implementa…

PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
Khaish Singh Chadha, Prabhat Kumar
https://arxiv.org/abs/2404.12244

PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type structure with dense layers added at the bottleneck region to capture complex geometrical features. The network is trained using datasets obtained by the problem-specific open-source TO codes. Tensorflow and Keras are the main libraries employed to develop and to …

\texttt{VAMPyR} -- A High-Level Python Library for Mathematical Operations in a Multiwavelets Representation
Magnar Bj{\o}rgve, Christian Tantardini, Stig Rune Jensen, Gabriel A. Gerez S., Peter Wind, Roberto Di Remigio Eik{\aa}s, Evgueni Dinvay, Luca Frediani
https://arxiv.org/abs/2402.08377

\texttt{VAMPyR} -- A High-Level Python Library for Mathematical Operations in a Multiwavelets Representation
Wavelets and Multiwavelets have lately been adopted in Quantum Chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few) they provide a framework which narrows the gap between the theoretical formalism of the fundamental equations and the practical implementa…