Survival analysis on tabular data is a well-studied problem. However, existing deep learning methods are often highly task-specific, which can limit the transfer of new approaches from other domains and introduce constraints that may affect performance. We propose TabSurv, an approach that adapts modern tabular architectures to survival analysis using either the Weibull distribution or non-parametric survival prediction. TabSurv optimizes SurvHL, a novel histogram loss function supporting censored data. In addition to a baseline feed-forward network, we implement deep ensembles of MLPs for survival analysis within TabSurv. In contrast to prior work, the ensemble components are trained in parallel, optimizing survival distribution parameters before averaging, which promotes diversity across ensemble component predictions. We perform a comprehensive empirical evaluation of different proposed architectures on 10 diverse real-world survival datasets. Our results show that TabSurv consistently outperforms on average established classical and deep learning baselines, such as RSF, DeepSurv, DeepHit, SurvTRACE. Notably, deep ensembles with Weibull parametrization instead of non-parametric models achieve the highest average rank by C-index. Overall, our study clarifies how modern tabular neural networks can be adapted and trained to tackle survival analysis problems, offering a strong and reliable approach. The TabSurv implementation is publicly available.

Sampling from unnormalized multimodal distributions with limited density evaluations remains a fundamental challenge in machine learning and natural sciences. Successful approaches construct a bridge between a tractable reference and the target distribution. Parallel Tempering (PT) serves as the gold standard, while recent diffusion-based approaches offer a continuous alternative at the cost of neural training. In this work, we introduce Conditional Diffusion Sampling (CDS), a framework that combines these two paradigms. To this end, we derive Conditional Interpolants, a class of stochastic processes whose transport dynamics are governed by an exact, closed-form stochastic differential equation (SDE), requiring no neural approximation. Although these dynamics require sampling from a non-trivial initialization distribution, we show both theoretically and empirically that the cost of this initialization diminishes for sufficiently short diffusion times. CDS leverages this by a two-stage procedure: (1) PT is used to efficiently sample the initial distribution, and then (2) samples are transported via the transport SDE. This combination couples the robust global exploration of PT with efficient local transport. Experiments suggest that CDS has the potential to achieve a superior trade-off between sample quality and density evaluation cost compared to state-of-the-art samplers.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The gift will rename USC's School of Advanced Computing as the USC Mark and Mary Stevens School of Computing and Artificial Intelligence. This is read by an automated voice. Please report any issues or inconsistencies here . USC receives a $200-million gift from venture capitalist Mark Stevens to establish artificial intelligence research and expertise across campus.

A top Google scientist warned EU antitrust regulators that its proposal requiring the company to share search engine data with rivals risked exposing users' private information. BRUSSELS - A top Google scientist sent a warning to EU antitrust regulators on Tuesday that its proposal requiring the company to share search engine data with rivals such as OpenAI risked exposing users' private information, the sternest rebuke yet in a tussle over Google's lucrative business model. The European Commission, which acts as the EU competition enforcer, has in recent years cracked down on Big Tech via a slew of legislation to ensure that users have more choices and that smaller rivals have room to compete. However, that has triggered the ire of the U.S. government. Sergei Vassilvitskii, with the title of distinguished scientist at Google since 2012 and regarded a leader in his field, will meet EU antitrust officials on Wednesday to voice his concerns and propose a broader approach with better guardrails.

Courts may restrict access to the popular abortion medication mifepristone in the United States. Telehealth providers have backup plans in place. Abortion provider Carafem's phones were ringing nonstop over the weekend after a US federal appeals court reinstated a nationwide requirement that the drug mifepristone, one of two pills used for a medication abortion, must be obtained in person. The decision, handed down on Friday, left patients unsure if they could gain access to their treatment through telehealth. "People are afraid, and they're angry," says Carafem's chief operations officer, Melissa Grant. "I had people contact us saying, .

It may not be a fully-fledged protocol droid yet, but Luke Skywalker would be impressed. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Convincing, uncannily humanoid robots are no longer the stuff of . Sure, you may not have a protocol droid at your ready like the iconic (if neurotic) C-3PO,but you can certainly construct a computer model that imitates Luke Skywalker's mechanical pal.

'Pyralis papaleonei' reflects his strong stance on environmental conservation. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The moth appears to be endemic to the island of Crete. Breakthroughs, discoveries, and DIY tips sent six days a week. Pope Leo XIV receives gifts from visitors from all over the world every year, but a newly identified insect may be the first papal tribute of its kind.

New artificial intelligence (AI) tools and capabilities from Google, Microsoft and xAI will now be tested by the US Department of Commerce before they are released to the public. The tech firms have agreed to voluntarily submit their models for testing through Commerce's Center for AI Standards and Innovation (CAISI). The new pacts are an expansion on agreements by AI companies like OpenAI and Anthropic that were reached during the Biden Administration, and will see AI models from all of the companies evaluated for their capabilities and security. These expanded industry collaborations help us scale our work in the public interest at a critical moment, CAISI's director Chris Fall said. Overall, the evaluations of the AI tools will cover testing, collaborative research and best practice development related to commercial AI systems.

Tech giants Microsoft, Google and xAI say they will allow the United States federal government access to their new artificial intelligence models for national security testing. The Center for AI Standards and Innovation (CAISI) at the Department of Commerce announced the agreement on Tuesday amid increasing concerns about the capabilities that Anthropic's newly unveiled Mythos model could give hackers. The agreement fulfils a pledge the administration of US President Donald Trump made in July to partner with technology companies to vet their AI models for "national security risks". Microsoft will work with US government scientists to test AI systems "in ways that probe unexpected behaviors", the company said in a statement. Together they will develop shared data sets and workflows for testing the company's models, the company said.

Ukraine strikes multiple sites in Moscow, following Russia's strikes on a Ukrainian gas production facility that killed at least 5 people. This escalation comes after each side announced a ceasefire - but for different days. West Bengal Chief refuses to resign after'dirty' election Hegseth says US'hasn't capitulated on anything' regarding Iran Smotrich says he promised his son'more destruction' in Lebanon Hegseth says US blockade on Iran'gift to the world'