Tootfinder

Causal Graph Recovery in Neuroimaging through Answer Set Programming
Mohammadsajad Abavisani, Kseniya Solovyeva, David Danks, Vince Calhoun, Sergey Plis
https://arxiv.org/abs/2506.09286

Causal Graph Recovery in Neuroimaging through Answer Set Programming
Learning graphical causal structures from time series data presents significant challenges, especially when the measurement frequency does not match the causal timescale of the system. This often leads to a set of equally possible underlying causal graphs due to information loss from sub-sampling (i.e., not observing all possible states of the system throughout time). Our research addresses this challenge by incorporating the effects of sub-sampling in the derivation of causal graphs, resulting…

🤩 The latest Safari 26 Beta does not disappoint in terms of #CSS features:
⚓️ Anchor Positioning!
🪄 Scroll-driven animations!
🌯 text-wrap: pretty!
🌈 contrast-color()!
⏲️ progress()!
and more!
And this sentence: “We value the principles of progressive enhancement and separation of concerns.” 😉
Great work, @…

News from WWDC25: WebKit in Safari 26 beta
Welcome to WWDC25!

United States wondered how China had made such progress in the field of microchips. Huawei and Xiaomi are the answer, with massive development.
https://farmingdale-observer.c…

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
Emmanouil Zaranis, Ant\'onio Farinhas, Saul Santos, Beatriz Canaverde, Miguel Moura Ramos, Aditya K Surikuchi, Andr\'e Viveiros, Baohao Liao, Elena Bueno-Benito, Nithin Sivakumaran, Pavlo Vasylenko, Shoubin Yu, Sonal Sannigrahi, Wafaa Mohammed, Ben Peters, Danae S\'anchez Villegas, Elias Stengel-Eskin, Giuseppe Attanasio, Jaehong Yoon, Stella Frank, Alessandro Suglia, Chrysoula Zerva, Desmond Elliott, M…

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding
Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current benchmarks. Many focus on peripheral, ``needle-in-a-haystack'' details, encouraging context-insensitive retrieval over deep comprehension. Others rely on large-scale, semi-automatically generated questions (often produced by language models themselves) that are easier for models to answer but fail to reflect genuine und…

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
Liangliang Chen, Huiru Xie, Jacqueline Rohde, Ying Zhang
https://arxiv.org/abs/2506.08962

WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis
This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor's design philosophy and core components, including open-ended question answering and homework feedback generation. The prompts are carefully crafted to optimize responses across different problems. The smart tutor was deployed on the Microsoft Azure platform and is currently in…

The State-of-the-Art in Lifelog Retrieval: A Review of Progress at the ACM Lifelog Search Challenge Workshop 2022-24
Allie Tran, Werner Bailer, Duc-Tien Dang-Nguyen, Graham Healy, Steve Hodges, Bj\"orn {\TH}\'or J\'onsson, Luca Rossetto, Klaus Schoeffmann, Minh-Triet Tran, Lucia Vadicamo, Cathal Gurrin
https://arxiv…

The State-of-the-Art in Lifelog Retrieval: A Review of Progress at the ACM Lifelog Search Challenge Workshop 2022-24
The ACM Lifelog Search Challenge (LSC) is a venue that welcomes and compares systems that support the exploration of lifelog data, and in particular the retrieval of specific information, through an interactive competition format. This paper reviews the recent advances in interactive lifelog retrieval as demonstrated at the ACM LSC from 2022 to 2024. Through a detailed comparative analysis, we highlight key improvements across three main retrieval tasks: known-item search, question answering, a…

This https://arxiv.org/abs/1503.03852 has been replaced.
link: https://scholar.google.com/scholar?q=a

On groups of diffeomorphisms of the interval with finitely many fixed points
In [13], it is proved that any subgroup of $\mathrm{Diff}_{+}^{ω}(I)$ (the group of orientation preserving analytic diffeomorphisms of the interval) is either metaabelian or does not satisfy a law. A stronger question is asked whether or not the Girth Alternative holds for subgroups of $\mathrm{Diff}_{+}^{ω}(I)$. In this paper, we answer this question affirmatively for even a larger class of groups of orientation preserving diffeomorphisms of the interval where every non-identity element has …

"Es ist höchste Zeit, dass sich deutsche Wissenschaftsinstitutionen nicht nur mit Solidaritätsbekundungen an die USA wenden, sondern sich auch gegen die wachsende Bedrohung für Wissenschaft und Demokratie in Deutschland positionieren."
"Überpolitisierung im Namen der Depolitisierung ist das Programm, mit dem die Wissenschaft nach rechtsextremer Façon umgeformt wird."
Andreas #FischerLescano

Wer die Wissenschaft schützen will, muss die AfD verbieten
Die Angriffe auf Universitäten in den USA sind ein Warnsignal. Während deutsche Forschungsorganisationen noch Solidaritätsbekundungen formulieren, bläst die AfD zu ihrem eigenen Feldzug gegen die freie Wissenschaft. Zeit zum Handeln. Ein Gastbeitrag von Andreas Fischer-Lescano.

This https://arxiv.org/abs/2505.18492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions
Mathematical reasoning lies at the heart of artificial intelligence, underpinning applications in education, program verification, and research-level mathematical discovery. Mathematical competitions, in particular, present two challenging problem types: theorem-proving, requiring rigorous proofs of stated conclusions, and answer-construction, involving hypothesizing and formally verifying mathematical objects. Large Language Models (LLMs) effectively generate creative candidate answers but str…

This https://arxiv.org/abs/2409.08704 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

QueryCAD: Grounded Question Answering for CAD Models
CAD models are widely used in industry and are essential for robotic automation processes. However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction o…