Tootfinder

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis
Kayvan Kousha, Mike Thelwall
https://arxiv.org/abs/2509.09596 h…

How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis
This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC), Dimensions, and OpenAlex) from 2015 to 2024. Using over 2.4 million PMC open-access publications (2021-July 2025), we also analysed full texts to assess changes in the frequency and co-occurrence of these terms before and after ChatGPT's initial public release. Acro…

Cloud Detection using Night Sky Background Light at the Pierre Auger Observatory
Fedor Tairli, for the Pierre Auger Collaboration
https://arxiv.org/abs/2509.06343 https://

Cloud Detection using Night Sky Background Light at the Pierre Auger Observatory
Rejection of cloud-contaminated data is a complex and important process at the Pierre Auger Observatory, one which combines information from several sources, including IR cameras, lidars, and satellite imaging. With the deteriorating quality of the IR cameras and challenges in using other sources, we propose a new method. We use continuous detector monitoring measurements to build a large database of night sky background fluxes for each pixel across 27 telescopes. Using this database, we genera…

Varonis Systems agrees to acquire email security company SlashNext for up to $150M, including retention awards, after buying database company Cyral for ~$25M (Emily Forgash/Bloomberg)
https://www.bloomberg.com/news/articles/2025…

Geolog-IA: Conversational System for Academic Theses
Micaela Fuel Pozo, Andrea Guatumillo Saltos, Yese\~na Tipan Llumiquinga, Kelly Lascano Aguirre, Marilyn Castillo Jara, Christian Mejia-Escobar
https://arxiv.org/abs/2510.02653

Geolog-IA: Conversational System for Academic Theses
This study presents the development of Geolog-IA, a novel conversational system based on artificial intelligence that responds naturally to questions about geology theses from the Central University of Ecuador. Our proposal uses the Llama 3.1 and Gemini 2.5 language models, which are complemented by a Retrieval Augmented Generation (RAG) architecture and an SQLite database. This strategy allows us to overcome problems such as hallucinations and outdated knowledge. The evaluation of Geolog-IA's …

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

Having moved back to Canberra after 30 years, this is spot on. https://fed.brid.gy/r/https://bsky.app/profile/did:plc:y6l4xvoigjcuug4at2w7ywoj/post/3m22eezjwss2i

Asher Wolf (@asherwolf.bsky.social)
The thing about visiting places from childhood is it’s like a geographical haunting. Moments long gone, slipped away, but the place is still there

Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
Dror Aiger, Bingyi Cao, Kaifeng Chen, Andre Araujo
https://arxiv.org/abs/2509.04351

Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
The dominant paradigm in image retrieval systems today is to search large databases using global image features, and re-rank those initial results with local image feature matching techniques. This design, dubbed global-to-local, stems from the computational cost of local matching approaches, which can only be afforded for a small number of retrieved images. However, emerging efficient local feature search approaches have opened up new possibilities, in particular enabling detailed retrieval at…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Cost-Effective, Low Latency Vector Search with Azure Cosmos DB
Nitish Upreti, et al.

Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database
Qing Xiao, Yingshan Peng, PeiPei Zhang
https://arxiv.org/abs/2508.18732 https://

Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database
Dysarthric speech recognition faces challenges from severity variations and disparities relative to normal speech. Conventional approaches individually fine-tune ASR models pre-trained on normal speech per patient to prevent feature conflicts. Counter-intuitively, experiments reveal that multi-speaker fine-tuning (simultaneously on multiple dysarthric speakers) improves recognition of individual speech patterns. This strategy enhances generalization via broader pathological feature learning, mi…