A US lawmaker has introduced a bill that would require AI companies to report critical incidents. The proposal’s specific requirements and timeline are not provided in the record.
Roll Call reports that the House Science Committee advanced a measure to create or advance a federal AI security center. The record does not specify enactment or final passage.
A House Republican has introduced a bill that would require AI firms to report serious safety incidents. The record is a news item about the introduction, not the bill text itself.
The FCA is consulting on guidance related to the UK’s future crypto regulatory regime. Details of the consultation scope and timeline are not provided in the record.
Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair and shadows. Recently, diffusion models have shown strong performance in medical image segmentation t
While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remains constrained by significant computational inefficiencies. Current architectures typically suffer from massive anatomical redundancy due to the direct concatenation of consecutive 2D slices and lack the flexibility to h
Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling caused by cloud masking, as well as the heterogeneous climatic conditions under which crops evolve. In this work, we propose
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction. However, modern BGs predominantly rely on normalizi
Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures. However, this identical network design results in over-semantic homogenization. To address this, we propose a novel homologous but heterogeneous network. By treating support-query pairs as d
Capital is clustering in a small number of large transactions, spanning public-market access and private strategic backing. The pattern points to continued appetite for scale, with a few names absorbing most of the flow.
Capital is clustering in a small number of large transactions, spanning public-market access and private strategic backing. The pattern points to continued appetite for scale, with a few names absorbing most of the flow.
Capital is clustering in a small number of large transactions, spanning public-market access and private strategic backing. The pattern points to continued appetite for scale, with a few names absorbing most of the flow.
Named moves remain selective but signal-bearing, with senior research leadership joining a frontier lab and a founder step creating a new company. The desk matters when a person changes the shape of a team, not just the roster.
Named moves remain selective but signal-bearing, with senior research leadership joining a frontier lab and a founder step creating a new company. The desk matters when a person changes the shape of a team, not just the roster.
Named moves remain selective but signal-bearing, with senior research leadership joining a frontier lab and a founder step creating a new company. The desk matters when a person changes the shape of a team, not just the roster.
Benchmark updates are limited to a single model family across core reasoning and math tests. That narrow surface keeps attention on whether the gains are broad capability shifts or isolated score movement.
Benchmark updates are limited to a single model family across core reasoning and math tests. That narrow surface keeps attention on whether the gains are broad capability shifts or isolated score movement.
Benchmark updates are limited to a single model family across core reasoning and math tests. That narrow surface keeps attention on whether the gains are broad capability shifts or isolated score movement.