Boeing’s record $600 billion backlog across all key business lines, representing more than 5,900 aircraft orders, provides ...

Which countries' newspapers publish the most articles on so-called alternative medicine (SCAM)? There is no a central global dataset that tracks how many newspaper articles on ...

MemryX has now signed an agreement with a next-generation 3D memory partner to execute a dedicated 2026 test chip program, validating a targeted 5um-class hybrid-bonded interface and direct-to-tile ...

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...

Learn how acceptance sampling improves quality control by evaluating random samples. Discover its methods, benefits, and historical significance in manufacturing.

The collaboration expands upon the Secure AI Data Center solution introduced by Fortinet, now enhanced through multivendor integration to provide the blueprint for deploying and scaling AI ...

After carefully checking and debugging the inference process (i.e., forward_test() for TrajectoryHead), I found that it is entirely incorrect, or at least it is not a diffusion sampling process. There ...

It would be very natural to allow users bringing an external inference engine to implement the sample endpoint. We can add a configuration to the API server that configures a URL to use, and do all ...

In their study, Diana et al. introduce a novel method for spike inference from calcium imaging data using a Monte Carlo-based approach, emphasizing the quantification of uncertainties in spike time ...

Revised: This Reviewed Preprint has been revised by the authors in response to the previous round of peer review; the eLife assessment and the public reviews have been updated where necessary by the ...

Sampling from multi-modal distributions and estimating marginal likelihoods, also known as evidences and normalizing constants, are well-known challenges in statistical computation. They can be ...

Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 ...