Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' The US is facing a severe weather outbreak on Wednesday, threatening millions of people with tornadoes, giant hail and destructive winds. Forecasters warned that the dangerous storm system is expected to barrel through Texas and Louisiana before pushing northeast toward parts of Pennsylvania and New York. Northern Illinois remains the area of greatest concern, where a moderate tornado risk covers about two million people.

In this episode of, our hosts dive into WIRED's scoop about a secret Trump administration campaign extending right into your backyard. This week, hosts Brian Barrett, Leah Feiger, and Zoë Schiffer discuss WIRED's big scoop on ICE's startling plans to expand to nearly every state in the US. Plus, a WIRED writer lets the viral AI assistant OpenClaw run his life for a week to give listeners a peek of what AI agents can and can't do. ICE Is Expanding Across the US at Breakneck Speed. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . I want to continue a conversation that we started yesterday in Slack after work hours for some of us. And this is about the men's short program-- But very specifically want to pick up on the conversation where Zoë had very strong feelings about the results of men's figure skating. I feel like we need to back up because you and Leah authentically care about the Olympics so much and I think just know more about sports than I do. I deeply have never engaged with sports ever, just as a whole rule, as a category. It doesn't exist in my life. Say the lines, say the lines, Zoë, or I'm going to read them verbatim from slack. Wait, I don't even know what you're talking about. I was merely surprised when I watched because the Americans went, I thought, wow, that guy basically fell over and was clumping around the ice, and then Japan went, and they were sailing around like little swans, and then when the gold medal came, it went to the Americans. I couldn't believe what had happened. No one else seemed outraged. For a little backup for our non-ice skating Olympic fans, I was always referring to Ilia Malinin, who a number of publications and sports experts say might actually be one of the greatest figure skaters of all time.

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.

We introduced an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Rare, deep-sea encounter: California scientists observe'extraordinary' seven-arm octopus On November 6, 2025, MBARI Senior Scientist Steven Haddock and researchers in MBARI's Biodiversity and Biooptics Team observed a seven-arm octopus (Haliphron atlanticus) during an expedition in Monterey Bay with MBARI's remotely operated vehicle at a depth of approximately 700 meters. This is read by an automated voice. Please report any issues or inconsistencies here . California scientists captured rare footage of a seven-arm octopus eating a jellyfish.

As large language models become increasingly capable, there is growing concern that they may develop reasoning processes that are encoded or hidden from human oversight. To investigate whether current interpretability techniques can penetrate such encoded reasoning, we construct a controlled testbed by fine-tuning a reasoning model (DeepSeek-R1-Distill-Llama-70B) to perform chain-of-thought reasoning in ROT-13 encryption while maintaining intelligible English outputs. We evaluate mechanistic interpretability methods--in particular, logit lens analysis--on their ability to decode the model's hidden reasoning process using only internal activations. We show that logit lens can effectively translate encoded reasoning, with accuracy peaking in intermediate-to-late layers. Finally, we develop a fully unsupervised decoding pipeline that combines logit lens with automated paraphrasing, achieving substantial accuracy in reconstructing complete reasoning transcripts from internal model representations. These findings suggest that current mechanistic interpretability techniques may be more robust to simple forms of encoded reasoning than previously understood. Our work provides an initial framework for evaluating interpretability methods against models that reason in non-human-readable formats, contributing to the broader challenge of maintaining oversight over increasingly capable AI systems.

Large Language Models (LLMs), such as GPT-4 and DeepSeek, have been applied to a wide range of domains in software engineering. However, their potential in the context of High-Performance Computing (HPC) much remains to be explored. This paper evaluates how well DeepSeek, a recent LLM, performs in generating a set of HPC benchmark codes: a conjugate gradient solver, the parallel heat equation, parallel matrix multiplication, DGEMM, and the STREAM triad operation. We analyze DeepSeek's code generation capabilities for traditional HPC languages like Cpp, Fortran, Julia and Python. The evaluation includes testing for code correctness, performance, and scaling across different configurations and matrix sizes. We also provide a detailed comparison between DeepSeek and another widely used tool: GPT-4. Our results demonstrate that while DeepSeek generates functional code for HPC tasks, it lags behind GPT-4, in terms of scalability and execution efficiency of the generated code.