Tootfinder

qa_user: User interactions on Q&A websites (2016)
Networks of interactions among users from four online Q&A sites: Stack Overflow, Math Overflow, Super User, and Ask Ubuntu. A directed edge (i,j) indicates a user i responded to user j's post. Edges are timestamped. For each Q&A site, four differently defined networks are provided, based on the definition of an edge: (i) a user answered a question, (ii) a user commented on a question, (iii) a user commented on an answer…

NYC-based Protege, which prepares and sells real-world datasets like lab results and sports footage for AI training, raised a $25M Series A led by Footwork (Natasha Mascarenhas/The Information)
https://www.theinformation.com/articles/one-year-old…

The One-Year-Old Startup Notching Data Deals For Model Makers
Companies like Scale AI and Surge have proven there’s a market for human-labeled data, like professionals’ answers to complex math or law questions, that AI research labs can use to train their models. Now, a young startup is tapping a similar opportunity, linking large model makers and AI ...

HistoryBankQA: Multilingual Temporal Question Answering on Historical Events
Biswadip Mandal, Anant Khandelwal, Manish Gupta
https://arxiv.org/abs/2509.12720 https://

HistoryBankQA: Multilingual Temporal Question Answering on Historical Events
Temporal reasoning about historical events is a critical skill for NLP tasks like event extraction, historical entity linking, temporal question answering, timeline summarization, temporal event clustering and temporal natural language inference. Yet efforts on benchmarking temporal reasoning capabilities of large language models (LLMs) are rather limited. Existing temporal reasoning datasets are limited in scale, lack multilingual coverage and focus more on contemporary events. To address thes…

Thousands gathered in cities and towns across 34 states on Saturday
for a national day of protest against Republican redistricting plans in Texas and elsewhere.
“Fight the Trump Takeover”
was anchored in Austin, Texas,
with other sites spread nationwide.
In Austin alone, more than 5,000 protestors gathered at the city’s capitol in a show of defiance.
Videos and photos on social media showed protestors holding various signs that read:
“Abbott is a vot…

Describe Anything Model for Visual Question Answering on Text-rich Images
Yen-Linh Vu, Dinh-Thang Duong, Truong-Binh Duong, Anh-Khoi Nguyen, Thanh-Huy Nguyen, Le Thien Phuc Nguyen, Jianhua Xing, Xingjian Li, Tianyang Wang, Ulas Bagci, Min Xu
https://arxiv.org/abs/2507.12441

Describe Anything Model for Visual Question Answering on Text-rich Images
Recent progress has been made in region-aware vision-language modeling, particularly with the emergence of the Describe Anything Model (DAM). DAM is capable of generating detailed descriptions of any specific image areas or objects without the need for additional localized image-text alignment supervision. We hypothesize that such region-level descriptive capability is beneficial for the task of Visual Question Answering (VQA), especially in challenging scenarios involving images with dense tex…

"Floating platform to produce synthetic fuels using wind, seawater, and air"
#Energy #Renewables
https://

Floating platform to produce synthetic fuels using wind, seawater, and air
Off-grid, modular, and seaworthy: KIT (Karlsruhe Institute of Technology) and partners intend to produce synthetic fuels on a floating platform using wind energy, seawater, and ambient air. For this purpose, the "PtX-Wind" project was launched under the H2Mare lead project. A corresponding modular plant was installed on a barge and lies operational at anchor in Bremerhaven. Later this year, it will start producing synthetic fuel directly on the sea just off Helgoland Island.

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
Jinghua Zhao, Hang Su, Lichun Fan, Zhenbo Luo, Jian Luan, Hui Wang, Haoqin Sun, Yong Qin
https://arxiv.org/abs/2509.12275

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
We propose Omni-CLST, an error-aware Curriculum Learning framework with guided Selective Chain-of-Thought for audio question answering. The framework efficiently leverages existing high-quality dataset through two key strategies: an error-aware curriculum that organizes samples by difficulty, and a guided thought dropout mechanism that focuses reasoning on challenging cases. Integrated with GRPO training, these strategies enable the model to learn more effectively from informative samples. Expe…

“The Art of Deal”? Yeah, if you call giving Putin & Dugin everything they want a deal.
No concessions, no ceasefire: how Putin outplayed Trump in Anchorage https://on.ft.com/3JkNXgg

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering
Changjian Wang, Weihong Deng, Weili Guan, Quan Lu, Ning Jiang
https://arxiv.org/abs/2508.11247

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering
Multi-hop question answering (MHQA) requires integrating knowledge scattered across multiple passages to derive the correct answer. Traditional retrieval-augmented generation (RAG) methods primarily focus on coarse-grained textual semantic similarity and ignore structural associations among dispersed knowledge, which limits their effectiveness in MHQA tasks. GraphRAG methods address this by leveraging knowledge graphs (KGs) to capture structural associations, but they tend to overly rely on str…

Details from Ashley Parker and Michael Scherer’s study of Trump’s cellphone habits have a way of lingering in the mind.
The president’s personal number is “broadly circulated.”
He picks up calls from unknown numbers, and keeps a photograph of his own face on his lock screen.
The Cellphone in Chief is a “portal,” Parker and Scherer write,
but it’s also a mirror, a security blanket, and a vulnerability.
Their short psychological study suggests a man who is occasio…