The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data.

Inparticular,weconsiderthesetting where the time series data onN entities is generated from a Gaussian mixture model with autocorrelations overk clusters inRd. Our main contribution is an algorithm to construct coresets for the maximum likelihood objective for this mixture model.

Disney advert banned for showing'disturbing' severed body A menacing Disney advert featuring a severed body has been banned by the advertising regulator, which said it was likely to frighten and cause distress to children. The Advertising Standards Authority (ASA) found the entertainment giant had broken its rules with its advert for the Predator Badlands film. Parents complained that the digital poster, which featured a large alien holding aloft the severed body of a smaller, human figure, was inappropriate and disturbing for young children. Disney said the severed body was actually that of a robot, and the fact it had been cut in two further emphasised its non-human nature. The advert, which was seen on the roadside in Giffnock, Glasgow, was promoting the Disney sci-fi film ahead of its release in November.

A recent line of work aims at specially prompting LLMs to review their own generations and generate meaningful natural language feedback, which can subsequently be used to refine them [Madaan et al.,

Moreover,doctors explicitly explore severepathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases.

A 2025 survey in the forests of Laos, Vietnam, and Cambodia uncovered several rare and endangered animals. A pig-tailed macaque is caught on camera in a Cambodian forest. Breakthroughs, discoveries, and DIY tips sent six days a week. The results of a new camera-trap survey in Southeast Asia is revealing a bevy of hidden biodiversity tucked within the Annamites mountain range . This largely unexplored wildlife hotspot has a forest stretching 683 miles (1,100 kilometers) across the countries of Laos, Vietnam, and Cambodia.

The $β$-VAE is a foundational framework for unsupervised disentanglement, using $β$ to regulate the trade-off between latent factorization and reconstruction fidelity. Empirically, however, disentanglement performance exhibits a pervasive non-monotonic trend: benchmarks such as MIG and SAP typically peak at intermediate $β$ and collapse as regularization increases. We demonstrate that this collapse is a fundamental information-theoretic failure, where strong Kullback-Leibler pressure promotes marginal independence at the expense of the latent channel's semantic informativeness. By formalizing this mechanism in a linear-Gaussian setting, we prove that for $β> 1$, stationarity-induced dynamics trigger a spectral contraction of the encoder gain, driving latent-factor mutual information to zero. To resolve this, we introduce the $λβ$-VAE, which decouples regularization pressure from informational collapse via an auxiliary $L_2$ reconstruction penalty $λ$. Extensive experiments on dSprites, Shapes3D, and MPI3D-real confirm that $λ> 0$ stabilizes disentanglement and restores latent informativeness over a significantly broader range of $β$, providing a principled theoretical justification for dual-parameter regularization in variational inference backbones.