Tootfinder

Hyper-py: HYbrid Photometry and Extraction Routine in PYthon
Alessio Traficante, Fabrizio De Angelis, Alice Nucara, Milena Benedettini
https://arxiv.org/abs/2509.22453 https://

Hyper-py: HYbrid Photometry and Extraction Routine in PYthon
We present Hyper-Py, a fully restructured and extended Python implementation of HYPER (HYbrid Photometry and Extraction Routine, Traficante et al. 2015). HYPER was originally implemented in IDL, aiming to deliver robust and reproducible photometry of compact sources in FIR/sub-mm/mm maps. HYPER combines source detection via high-pass filtering, background estimation through local polynomial fitting, and source modeling with 2D elliptical Gaussians, simultaneously fitting multiple Gaussians to d…

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
Yirong Sun, Yizhong Geng, Peidong Wei, Yanjun Chen, Jinghan Yang, Rongfei Chen, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2508.15418

LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibility of research. Unlike in the vision-language domain, the LSLM field suffers from the common practice of releasing model weights without their corresponding training data and configurations. To address these critical gaps, we introduce LLaSO, the first fully open, end-to-end framework for large-scale speech-language …

Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines
Mandip Goswami
https://arxiv.org/abs/2509.16901 https://arxiv.or…

Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines
We present an open, reproducible reference for automotive sound quality that connects standardized psychoacoustic metrics with lightweight AI/ML baselines, with a specific focus on electric vehicles (EVs). We implement loudness (ISO 532-1/2), tonality (DIN 45681), and modulation-based descriptors (roughness, fluctuation strength), and document assumptions and parameterizations for reliable reuse. For modeling, we provide simple, fully reproducible baselines (logistic regression, random forest, …

2025 Southeast Asia Eleven Nations Influence Index Report
Wei Meng
https://arxiv.org/abs/2509.19953 https://arxiv.org/pdf/2509.19953

2025 Southeast Asia Eleven Nations Influence Index Report
This study constructs a fully data-driven and reproducible Southeast Asia Influence Index (SAII v3) to reduce bias from expert scoring and subjective weighting while mapping hierarchical power structures across the eleven ASEAN nations. We aggregate authoritative open-source indicators across four dimensions (economic, military, diplomatic, socio-technological) and apply a three-tiered standardization chain quantile-Box-Cox-min-max to mitigate outliers and skewness. Weights are obtained through…

A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature
Faruk Alpay, Bugra Kilictas, Hamdi Alakkad
https://arxiv.org/abs/2508.04612 https://

A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature
The accelerating pace of research on autoregressive generative models has produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical. We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories, filters them for relevance, extracts metadata, hyper-parameters and reported results, clusters topics, produces retrieval-augmented summaries and generates containerised scripts for …

UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations
Qiuyang Lu, Fangjian Shen, Zhengkai Tang, Qiang Liu, Hexuan Cheng, Hui Liu, Wushao Wen
https://arxiv.org/abs/2509.15789

UPRPRC: Unified Pipeline for Reproducing Parallel Resources -- Corpus from the United Nations
The quality and accessibility of multilingual datasets are crucial for advancing machine translation. However, previous corpora built from United Nations documents have suffered from issues such as opaque process, difficulty of reproduction, and limited scale. To address these challenges, we introduce a complete end-to-end solution, from data acquisition via web scraping to text alignment. The entire process is fully reproducible, with a minimalist single-machine example and optional distribute…

Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Eleonora Mancini, Joan Serr\`a, Paolo Torroni, Yuki Mitsufuji
https://arxiv.org/abs/2510.08176 https://

Leveraging Whisper Embeddings for Audio-based Lyrics Matching
Audio-based lyrics matching can be an appealing alternative to other content-based retrieval approaches, but existing methods often suffer from limited reproducibility and inconsistent baselines. In this work, we introduce WEALY, a fully reproducible pipeline that leverages Whisper decoder embeddings for lyrics matching tasks. WEALY establishes robust and transparent baselines, while also exploring multimodal extensions that integrate textual and acoustic features. Through extensive experiments…

Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
Vaibhav Srivastav, Steven Zheng, Eric Bezzam, Eustache Le Bihan, Nithin Koluguri, Piotr \.Zelasko, Somshubra Majumdar, Adel Moumen, Sanchit Gandhi
https://arxiv.org/abs/2510.06961

Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
Despite rapid progress, ASR evaluation remains saturated with short-form English, and efficiency is rarely reported. We present the Open ASR Leaderboard, a fully reproducible benchmark and interactive leaderboard comparing 60+ open-source and proprietary systems across 11 datasets, including dedicated multilingual and long-form tracks. We standardize text normalization and report both word error rate (WER) and inverse real-time factor (RTFx), enabling fair accuracy-efficiency comparisons. For E…

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Alejandro Hern\'andez-Cano, Alexander H\"agele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pasztor, Bettina Messmer, Dhia Garbaya, Eduard Frank \v{D}urech, Ido Hakimi, Juan Garc\'ia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabol\v{c}ec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Ines Altemir Marinas, Mohammad Hossein Amani, Matin An…

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for non-permissive, toxic, and personally identifiabl…

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Elham Saremi, Ignacio Trujillo, Mohammad Akhlaghi, Zohreh Ghaffari, Johan H. Knapen, Manda Banerji, Helmut Dannerbauer, S\'ebastien Comer\'on
https://arxiv.org/abs/2508.02780

NASIM: Revealing the low surface brightness Universe from legacy VISTA data
Near-infrared imaging is a powerful technique in observational astronomy, but the bright background, primarily from the Earthś atmosphere, makes the detection of faint features particularly challenging. To recover low surface brightness (LSB) structures in such data, we present NASIM (Near-infrared Automated low Surface brightness reduction In Maneage), a fully automated and reproducible data reduction pipeline optimised for VISTA/VIRCAM observations. NASIM builds on GNU Astronomy Utilities (G…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Introducing 'inrep': an R package that facilitates fully reproducible research workfows for surve...
Clievins Selva