Tootfinder

»Red Hat erleidet möglicherweise massiven Breach:
Das Unternehmen untersucht einen Vorfall in seiner Gitlab-Instanz. Cyberkriminelle behaupten, 28'000 Repositories mit Kundendaten gehackt zu haben.«
Nimmt mich wunder wie "unsicher" Rad Hat von IBM nun ist. Ich wünsche keinen deren Kunden, dass dies Auswirkungen hat. Bin aber gespannt ob es indirekt auch @…

End-of-Year Threat Intelligence Sightings Forecast
This report presents an analysis of Threat Intelligence (TI) Sightings aggregated from several key data sources, including social platforms, code repositories, and specialized TI feeds. The primary objective is to visually track historical trends per source and provide a short-term adaptive forecast for a defined period (in days).
#opensource

Viking, n.:
1. Daring Scandinavian seafarers, explorers, adventurers,
entrepreneurs world-famous for their aggressive, nautical import
business, highly leveraged takeovers and blue eyes.
2. Bloodthirsty sea pirates who ravaged northern Europe beginning
in the 9th century.
Hagar's note: The first definition is much preferred; the second is used
only by malcontents, the envious, and disgruntled owners of waterfront
property.

A Robust Proactive Communication Strategy for Distributed Active Noise Control Systems
Junwei Ji, Dongyuan Shi, Zhengding Luo, Boxiang Wang, Ziyi Yang, Haowen Li, Woon-Seng Gan
https://arxiv.org/abs/2510.00934

A Robust Proactive Communication Strategy for Distributed Active Noise Control Systems
Distributed multichannel active noise control (DMCANC) systems assign the high computational load of conventional centralized algorithms across multiple processing nodes, leveraging inter-node communication to collaboratively suppress unwanted noise. However, communication overhead can undermine algorithmic stability and degrade overall performance. To address this challenge, we propose a robust communication framework that integrates adaptive-fixed-filter switching and the mixed-gradient combi…

Geometric Backstepping Control of Omnidirectional Tiltrotors Incorporating Servo-Rotor Dynamics for Robustness against Sudden Disturbances
Jaewoo Lee, Dongjae Lee, Jinwoo Lee, Hyungyu Lee, Yeonjoon Kim, H. Jin Kim
https://arxiv.org/abs/2510.01675

Geometric Backstepping Control of Omnidirectional Tiltrotors Incorporating Servo-Rotor Dynamics for Robustness against Sudden Disturbances
This work presents a geometric backstepping controller for a variable-tilt omnidirectional multirotor that explicitly accounts for both servo and rotor dynamics. Considering actuator dynamics is essential for more effective and reliable operation, particularly during aggressive flight maneuvers or recovery from sudden disturbances. While prior studies have investigated actuator-aware control for conventional and fixed-tilt multirotors, these approaches rely on linear relationships between actua…

MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics
Changmin Lee, Jihyun Lee, Tae-Kyun Kim
https://arxiv.org/abs/2510.01619 https://

MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics
While there has been significant progress in the field of 3D avatar creation from visual observations, modeling physically plausible dynamics of humans with loose garments remains a challenging problem. Although a few existing works address this problem by leveraging physical simulation, they suffer from limited accuracy or robustness to novel animation inputs. In this work, we present MPMAvatar, a framework for creating 3D human avatars from multi-view videos that supports highly realistic, ro…

Robust State-space Reconstruction of Brain Dynamics via Bootstrap Monte Carlo SSA
Sir-Lord Wiafe, Carter Hinsley, Vince D. Calhoun
https://arxiv.org/abs/2510.00011 https://

Robust State-space Reconstruction of Brain Dynamics via Bootstrap Monte Carlo SSA
Reconstructing latent state-space geometry from time series provides a powerful route to studying nonlinear dynamics across complex systems. Delay-coordinate embedding provides the theoretical basis but assumes long, noise-free recordings, which many domains violate. In neuroimaging, for example, fMRI is short and noisy; low sampling and strong red noise obscure oscillations and destabilize embeddings. We propose bootstrap Monte Carlo SSA with a red-noise null and bootstrap stability to retain …

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Chen-An Li, Tzu-Han Lin, Hung-yi Lee
https://arxiv.org/abs/2510.00626 https…

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio, such as silence, synthetic noise, and environmental sounds, affects text reasoning tasks where audio is unnecessary. Across three text-based benchmarks, we find that even non-informative audio reduces accuracy and increases prediction volatility; the severity of interference scales with longer durations, higher amplit…

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Vincent Ochs, Florentin Bieder, Sidaty el Hadramy, Paul Friedrich, Stephanie Taha-Mehlitz, Anas Taha, Philippe C. Cattin
https://arxiv.org/abs/2510.01136

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Tabular data builds the basis for a wide range of applications, yet real-world datasets are frequently incomplete due to collection errors, privacy restrictions, or sensor failures. As missing values degrade the performance or hinder the applicability of downstream models, and while simple imputing strategies tend to introduce bias or distort the underlying data distribution, we require imputers that provide high-quality imputations, are robust across dataset sizes and yield fast inference. We …

Do You Know Where Your Camera Is? View-Invariant Policy Learning with Camera Conditioning
Tianchong Jiang, Jingtian Ji, Xiangshan Tan, Jiading Fang, Anand Bhattad, Vitor Guizilini, Matthew R. Walter
https://arxiv.org/abs/2510.02268

Do You Know Where Your Camera Is? View-Invariant Policy Learning with Camera Conditioning
We study view-invariant imitation learning by explicitly conditioning policies on camera extrinsics. Using Plucker embeddings of per-pixel rays, we show that conditioning on extrinsics significantly improves generalization across viewpoints for standard behavior cloning policies, including ACT, Diffusion Policy, and SmolVLA. To evaluate policy robustness under realistic viewpoint shifts, we introduce six manipulation tasks in RoboSuite and ManiSkill that pair "fixed" and "randomized" scene vari…