Tootfinder

Search-based Hyperparameter Tuning for Python Unit Test Generation
Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.08716 https://arxiv.org/pdf/…

Search-based Hyperparameter Tuning for Python Unit Test Generation
Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a high demand of resources. Meta-heuristic search algorithms -- that effectively solve the test-generation problem -- have been proposed as a solution to also efficiently tune param…

PyTrim: A Practical Tool for Reducing Python Dependency Bloat
Konstantinos Karakatsanis, Georgios Alexopoulos, Ioannis Karyotakis, Foivos Timotheos Proestakis, Evangelos Talos, Panos Louridas, Dimitris Mitropoulos
https://arxiv.org/abs/2510.00674

PyTrim: A Practical Tool for Reducing Python Dependency Bloat
Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project requires manual effort and expertise. To tackle this challenge we introduce PYTRIM, an end-to-end system to automate this process. PYTRIM eliminates unused imports and package declarations across a variety of file types,…

NuLattice: Ab initio computations of atomic nuclei on lattices
M. Rothman, B. Johnson-Toth, G. Hagen, M. Heinz, T. Papenbrock
https://arxiv.org/abs/2509.08771 https://

NuLattice: Ab initio computations of atomic nuclei on lattices
We introduce NuLattice, a Python software package for ab initio computations of atomic nuclei on lattices. The computational tools consist of Hartree Fock, the coupled cluster method, the in-medium similarity renormalization group, and full configuration interaction. At present, the employed interactions are from pion-less effective field theory at leading order and consist of two-body and three-body contacts. We present results for light nuclei $^{2}$H, $^{3,4}$He, $^{8}$Be, $^{12}$C, and $^{1…

Me and my new #yubikey5 part 2:
Now we get to the nitty-gritty parts. I'm using #mbsync to sync multiple #imap accounts to local

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
Maximilian Burzer, Tobias King, Till Riedel, Michael Beigl, Tobias R\"oddiger
https://arxiv.org/abs/2508.16604

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFl…

Controls Abstraction Towards Accelerator Physics: A Middle Layer Python Package for Particle Accelerator Control
M. King, A. D. Brynes, F. Jackson, K. Baker, D. J. Scott, E. Yang, T. Kabana, C. Garnier, S. Chowdhury, N. Neveu
https://arxiv.org/abs/2509.19794

Controls Abstraction Towards Accelerator Physics: A Middle Layer Python Package for Particle Accelerator Control
Control system middle layers act as a co-ordination and communication bridge between end users, including operators, system experts, scientists, and experimental users, and the low-level control system interface. This article describes a Python package -- Controls Abstraction Towards Acclerator Physics (CATAP) -- which aims to build on previous experience and provide a modern Python-based middle layer with explicit abstraction, YAML-based configuration, and procedural code generation. CATAP pro…

Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties
Imran Muhammad, Gerald Schuller
https://arxiv.org/abs/2509.24769 https://…

Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties
Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface absorption. A dataset of 6000 shoebox rooms with realistic dimensions, source-receiver placements, and frequency-dependent wall absorptions was synthesized. For each configuration we simulate room impulse responses (RIRs) using Pyroomacoustics and compute target EDCs.…