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Turkistarhaus Suomessa #eläinoikeudet

Turkistarhaus Suomessa | Oikeutta eläimille
Oikeutta eläimille -yhdistys on julkaissut kuvia ja videoita yli sadalta suomalaiselta turkistarhalta – yli 10 % maamme turkistarhoista. Ahtaat häkit, virikkeettömyys, silmätulehdukset, ienongelmat ja vammautuneet eläimet ovat arkipäivää tarhoilla. Viime vuosina yhdistyksen julkaisemat kuvat ylijalostetuista "hirviö"ketuista sekä "kannibaali"minkeistä ovat levinneet tiedotusvälineisiin ympäri maailman.

"Tulips: Dutch, Turkish, or Flemish?" @ European studies blog: #tulips 🌷

Ha good luck with that https://geeknews.chat/@theregister/114813445350914205

Isn’t it great that Swiss institutions have made themselves completely dependent on Microsoft?
https://www.theregister.com/2025/08/08/exwhite_house_cyber_and_counterterrorism/

Former Dallas Cowboy Kelvin Joseph causes tragic death and is facing several charges https://insidethestar.com/former-dallas-cowboy-kelvin-joseph-causes-tragic-death-and-is-facing-several-charges

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi, Matteo Papini, Alberto Maria Metelli
https://arxiv.org/abs/2506.06178

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. These methods learn the parameters of parametric policies via stochastic gradient ascent, typically using on-policy trajectory data to estimate the policy gradient. However, such reliance on fresh data makes them sample-inefficient. Indeed, vanilla PG methods require $O(ε^{-2})$ trajectories to reach an $ε$-approximate stationary point. A common …

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Wule Mao, Zhouheng Li, Yunhao Luo, Yilun Du, Lei Xie
https://arxiv.org/abs/2507.04384

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Safe trajectory planning remains a significant challenge in complex environments, where traditional methods often trade off computational efficiency for safety. Comprehensive obstacle modeling improves safety but is computationally expensive, while approximate methods are more efficient but may compromise safety. To address this issue, this paper introduces a rapid and safe trajectory planning framework based on state-based diffusion models. Leveraging only low-dimensional vehicle states, the d…

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Abhishek Kumar Mishra, Mathieu Cunche, Heber H. Arcolezi
https://arxiv.org/abs/2506.05611 …

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Mobility traces represent a critical class of personal data, often subjected to privacy-preserving transformations before public release. In this study, we analyze the anonymized Yjmob100k dataset, which captures the trajectories of 100,000 users in Japan, and demonstrate how existing anonymization techniques fail to protect their sensitive attributes. We leverage population density patterns, structural correlations, and temporal activity profiles to re-identify the dataset's real-world locatio…

From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Fabian Konstantinidis, Ariel Dallari Guerreiro, Raphael Trumpp, Moritz Sackmann, Ulrich Hofmann, Marco Caccamo, Christoph Stiller
https://arxiv.org/abs/2507.05254

From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Accurate motion prediction of surrounding traffic participants is crucial for the safe and efficient operation of automated vehicles in dynamic environments. Marginal prediction models commonly forecast each agent's future trajectories independently, often leading to sub-optimal planning decisions for an automated vehicle. In contrast, joint prediction models explicitly account for the interactions between agents, yielding socially and physically consistent predictions on a scene level. However…

Isn’t it great that Swiss institutions have made themselves completely dependent on Microsoft?
https://www.theregister.com/2025/08/08/exwhite_house_cyber_and_counterterrorism/