Tootfinder

AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns
Marta Robledo-Moreno, Ruben Vera-Rodriguez, Ruben Tolosana, Javier Ortega-Garcia, Andres Huergo, Julian Fierrez
https://arxiv.org/abs/2508.08502

AirSignatureDB: Exploring In-Air Signature Biometrics in the Wild and its Privacy Concerns
Behavioral biometrics based on smartphone motion sensors are growing in popularity for authentication purposes. In this study, AirSignatureDB is presented: a new publicly accessible dataset of in-air signatures collected from 108 participants under real-world conditions, using 83 different smartphone models across four sessions. This dataset includes genuine samples and skilled forgeries, enabling a comprehensive evaluation of system robustness against realistic attack scenarios. Traditional an…

Der Landkreis #Esslingen hat eine Kühle-Orte-Karte. Dort kann man sich, wenn man unterwegs ist, abkühlen. Spannend, was alles unternommen wird, damit wir uns an den #Klimawandel anpassen können.

Rational Inverse Reasoning
Ben Zandonati, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling
https://arxiv.org/abs/2508.08983 https://arxiv.org/pdf/2508…

Rational Inverse Reasoning
Humans can observe a single, imperfect demonstration and immediately generalize to very different problem settings. Robots, in contrast, often require hundreds of examples and still struggle to generalize beyond the training conditions. We argue that this limitation arises from the inability to recover the latent explanations that underpin intelligent behavior, and that these explanations can take the form of structured programs consisting of high-level goals, sub-task decomposition, and execut…

On the $(k,\ell)$-multiset anonymity measure for social graphs
Alejandro Estrada-Moreno, Elena Fern\'andez, Dorota Kuziak, Manuel Mu\~noz-M\'arquez, Rolando Trujillo-Rasua, Ismael G. Yero
https://arxiv.org/abs/2507.08433

On the $(k,\ell)$-multiset anonymity measure for social graphs
The publication of social graphs must be preceded by a rigorous analysis of privacy threats against social graph users. When the threat comes from inside the social network itself, the threat is called an active attack, and the de-facto privacy measure used to quantify the resistance to such an attack is the $(k,\ell)$-anonymity. The original formulation of $(k,\ell)$-anonymity represents the adversary's knowledge as a vector of distances to the set of attacker nodes. In this article, we argue …

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Sohee Yang, Sang-Woo Lee, Nora Kassner, Daniela Gottesman, Sebastian Riedel, Mor Geva
https://arxiv.org/abs/2506.10979

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?
Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question a…

Aerodynamics and Aeroacoustics of da Vinci's Aerial Screw
Suryansh Prakhar, Jung-Hee Seo, Rajat Mittal
https://arxiv.org/abs/2506.10223 https://…

Aerodynamics and Aeroacoustics of da Vinci's Aerial Screw
Leonardo da Vinci's aerial screw, conceived in the 15th century, represents one of the earliest conceptualizations of lift-generating rotary flight. Despite its historical significance, the aerodynamic and aeroacoustic performance of this rotor has received limited scientific attention. In this study, we employ direct numerical simulations to analyze the aerodynamic forces and acoustic emissions of a modernized da Vinci aerial screw design across a range of Reynolds numbers (2000, 4000, and 800…

Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
Nicholas Vincent, Matthew Prewitt, Hanlin Li
https://arxiv.org/abs/2506.10272

Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, producers of information goods (such as journalists, researchers, and creative professionals) need to be able to collectively bargain with AI product builders in order to receive reasonable terms and a sustainable return on the informational value they contribute. We argue that without increased market coordination or collective bargaining on the side of th…

👩🏿‍🤝‍👩🏻 Americans prefer a more diverse society, argues a new study
#survey

Americans prefer a more diverse society, argues a new study
At a time marked by debate about identity, migration and national cohesion, a new study brings a surprising message: Most Americans want a more ethnically and religiously diverse society than the one they live in today.

Bregman proximal gradient method for linear optimization under entropic constraints
Luis M. Brice\~no-Arias, Ma\"el Le Treust
https://arxiv.org/abs/2506.10849

Bregman proximal gradient method for linear optimization under entropic constraints
In this paper, we present an efficient algorithm for solving a linear optimization problem with entropic constraints -- a class of problems that arises in game theory and information theory. Our analysis distinguishes between the cases of active and inactive constraints, addressing each using a Bregman proximal gradient method with entropic Legendre functions, for which we establish an ergodic convergence rate of $O(1/n)$ in objective values. For a specific cost structure, our framework provide…

Revealing the Role of Audio Channels in ASR Performance Degradation
Kuan-Tang Huang, Li-Wei Chen, Hung-Shin Lee, Berlin Chen, Hsin-Min Wang
https://arxiv.org/abs/2508.08967 http…

Revealing the Role of Audio Channels in ASR Performance Degradation
Pre-trained automatic speech recognition (ASR) models have demonstrated strong performance on a variety of tasks. However, their performance can degrade substantially when the input audio comes from different recording channels. While previous studies have demonstrated this phenomenon, it is often attributed to the mismatch between training and testing corpora. This study argues that variations in speech characteristics caused by different recording channels can fundamentally harm ASR performan…