Tootfinder

Pourquoi les parkings de supermarchés se couvrent de laveries automatiques dans les zones rurales et périurbaines
https://www.lemonde.fr/m-perso/article/2…

Pourquoi les parkings de supermarchés se couvrent de laveries automatiques dans les zones rurales et périurbaines
Rapides et faciles d’accès, les kiosques laveries installés sur des parkings de supermarché ont trouvé leur public, répondant à des contraintes de temps, de mobilité ou de place.

« Et si les livres nous apprenaient Š nous soucier des autres ? »
– « Le chat qui voulait sauver les livres » de Sôsuke Natsukawa, un joli petit roman japonais sur le pouvoir des livres. (Et un chat exceptionel.) #2025reads #BooksofMastodon

Cam Newton doesn't feel Micah Parsons has any leverage in contract talks with Cowboys https://www.si.com/nfl/cowboys/news/micah-parsons-no-leverage-dallas-cowboys-contract-saga-former-nfl-mvp-says

Das #Rezept des Tages:
Leberkäse trifft auf bunte Gemüsepfanne! 🍛🥒 Diese schnelle und einfache Mahlzeit kombiniert den herzhaften Geschmack von #Leberkäse mit dem frischen Aroma von #Buttergemüse

Gemüsepfanne mit Leberkäse
Dieses Rezept mit Leberkäse habe ich - glaube ich - mal aus Bayern mitgebracht. Aber der Reis passt irgendwie nicht ins Bild. So richtig kann ich mich leider nicht mehr erinnern, welche meiner Urlaubsbekanntschaften...

HLTCOE at LiveRAG: GPT-Researcher using ColBERT retrieval
Kevin Duh, Eugene Yang, Orion Weller, Andrew Yates, Dawn Lawrie
https://arxiv.org/abs/2506.22356 …

HLTCOE at LiveRAG: GPT-Researcher using ColBERT retrieval
The HLTCOE LiveRAG submission utilized the GPT-researcher framework for researching the context of the question, filtering the returned results, and generating the final answer. The retrieval system was a ColBERT bi-encoder architecture, which represents a passage with many dense tokens. Retrieval used a local, compressed index of the FineWeb10-BT collection created with PLAID-X, using a model fine-tuned for multilingual retrieval. Query generation from context was done with Qwen2.5-7B-Instruct…

EIFNet: Leveraging Event-Image Fusion for Robust Semantic Segmentation
Zhijiang Li, Haoran He
https://arxiv.org/abs/2507.21971 https://arxiv.org/pdf/2507.2…

EIFNet: Leveraging Event-Image Fusion for Robust Semantic Segmentation
Event-based semantic segmentation explores the potential of event cameras, which offer high dynamic range and fine temporal resolution, to achieve robust scene understanding in challenging environments. Despite these advantages, the task remains difficult due to two main challenges: extracting reliable features from sparse and noisy event streams, and effectively fusing them with dense, semantically rich image data that differ in structure and representation. To address these issues, we propose…

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
https://arxiv.org/abs/2508.20736

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed in a variety of ways, from word perturbations to full document rewriting, and most often under local DP. Here, an input text must be made indistinguishable from any other potential text, within some bound governed by the privacy parameter $\varepsilon$. Such a guarantee is quite demanding, and recent works show that pr…

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Weronika {\L}ajewska, Ivica Kostric, Gabriel Iturra-Bocaz, Mariam Arustashvili, Krisztian Balog
https://arxiv.org/abs/2506.22210

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses
Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget d…

Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
Zeyu Xiong, Yixuan Nan, Li Gao, Hengzhu Tang, Shuaiqiang Wang, Junfeng Wang, Dawei Yin
https://arxiv.org/abs/2508.20559

Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query, which is essential for improving user engagement and facilitating rapid decision-making. Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications. However, these approaches suffer from two key limitations: 1)…

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi, Fabio Crestani
https://arxiv.org/abs/2506.22372

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing robust approaches to identifying and evaluating such biases. In this paper, we aim to address this issue by leveraging Large Language Models (LLMs) to detect and measure gender bias in passage ranking. Existing gender fairness metrics rely on lexical- and frequency-based measures, leading to various limitations, e.g., mi…