Tootfinder

From our #cooperative, #MajavanTila: "Membership benefits for 2026".
https://www.majavantila.fi/en/posts/…

Japanese brewer Asahi delays product launches, halts deliveries after cyberattack https://therecord.media/japan-asahi-delay-cyberattack

Optimal area exploration by resetting active particles
Kristian St{\o}levik Olsen, Hartmut L\"owen, Lorenzo Caprini
https://arxiv.org/abs/2510.01087 https://

Optimal area exploration by resetting active particles
Identifying optimal strategies for efficient spatial exploration is crucial, both for animals seeking food and for robotic search processes, where maximizing the covered area is a fundamental requirement. Here, we propose position resetting as an optimal protocol to enhance spatial exploration in active matter systems. Specifically, we show that the area covered by an active Brownian particle exhibits a non-monotonic dependence on the resetting rate, demonstrating that resetting can optimize sp…

The assumption is that one day large language models and other related AI technologies fostered by Google Gemini and OpenAI ChatGPT actually will be a great and infallible productivity tool for genuine work.
It already is decent for providing basic overviews of highly-covered, well-sourced topics, even as hallucinations and sycophancy continue to dog the tech, particularly in situations where accountability is more critical.
Despite today's downsides,
many companies ar…

Analysis: OpenAI is a loss-making machine, with estimates that it has no road to profitability by 2030 — and will need a further $207 billion in funding even if it gets there
Don't call it a bubble! Loss-making monster OpenAI is on the hook for $1.4 trillion (with a T) in compute commitments. How can this go on?

Game Theory to Study Cooperation in Human-Robot Mixed Groups: Exploring the Potential of the Public Good Game
Giulia Pusceddu, Sara Mongile, Francesco Rea, Alessandra Sciutti
https://arxiv.org/abs/2509.24530

Game Theory to Study Cooperation in Human-Robot Mixed Groups: Exploring the Potential of the Public Good Game
In this study, we explore the potential of Game Theory as a means to investigate cooperation and trust in human-robot mixed groups. Particularly, we introduce the Public Good Game (PGG), a model highlighting the tension between individual self-interest and collective well-being. In this work, we present a modified version of the PGG, where three human participants engage in the game with the humanoid robot iCub to assess whether various robot game strategies (e.g., always cooperate, always free…

A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation
Kleanthis Avramidis, Myzelle Hughes, Idan A Blank, Dani Byrd, Assal Habibi, Takfarinas Medani, Richard M Leahy, Shrikanth Narayanan
https://arxiv.org/abs/2510.00422

A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal Ideation
Accurate identification of mental health biomarkers can enable earlier detection and objective assessment of compromised mental well-being. In this study, we analyze electrodermal activity recorded during an Emotional Stroop task to capture sympathetic arousal dynamics associated with depression and suicidal ideation. We model the timing of skin conductance responses as a point process whose conditional intensity is modulated by task-based covariates, including stimulus valence, reaction time, …

Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2509.25295

Calibrated Counterfactual Conformal Fairness ($C^3F$): Post-hoc, Shift-Aware Coverage Parity via Conformal Prediction and Counterfactual Regularization
We present Calibrated Counterfactual Conformal Fairness ($C^3F$), a post-hoc procedure that targets group-conditional coverage parity under covariate shift. $C^3F$ combines importance-weighted conformal calibration with a counterfactual regularizer based on path-specific effects in a structural causal model. The method estimates group-specific nonconformity quantiles using likelihood-ratio weights so that coverage degrades gracefully with the second moment of the weights. We derive finite-sampl…

The UK government underwrites a £1.5B loan to Jaguar Land Rover as the carmaker reels from cyberattack that has crippled its production for around a month (Dominic Chopping/Wall Street Journal)
https://www.

fev-bench: A Realistic Benchmark for Time Series Forecasting
Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang
https://arxiv.org/abs/2509.26468

fev-bench: A Realistic Benchmark for Time Series Forecasting
Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly given the recent rise of pretrained models. Existing benchmarks often have narrow domain coverage or overlook important real-world settings, such as tasks with covariates. Additionally, their aggregation procedures often lack statistical rigor, making it unclear whether observed performance differences reflect true improvements or random variation. Many benchmarks also fail to…

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Yeo Jin Jung, Yating Liu, Zixuan Wu, So Won Jeong, Claire Donnat
https://arxiv.org/abs/2509.24100 https://

SpeedCP: Fast Kernel-based Conditional Conformal Prediction
Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs et al. (2023), which leverages families of covariate shifts to provide approximate conditional conformal prediction intervals, an approach with strong theoretical promise, but with prohibitive computational cost. To bridge this gap, we develop a stable and efficient algorithm that computes the full solution path of the regularized RKHS confor…