House Democrats are demanding answers from the National Institute of Standards and Technology and urging it to halt rumored changes they say could undermine its mission. One of the US government's top scientific research labs is taking steps that could drive away foreign scientists, a shift lawmakers and sources tell WIRED could cost the country valuable expertise and damage the agency's credibility. The National Institute of Standards and Technology (NIST) helps determine the frameworks underpinning everything from cybersecurity to semiconductor manufacturing. Some of NIST's recent work includes establishing guidelines for securing AI systems and identifying health concerns with air purifiers and firefighting gloves. Many of the agency's thousands of employees, postdoctoral scientists, contractors, and guest researchers are brought in from around the world for their specialized expertise.

We decreased the confidence threshold to 0.1 to increase article and headline The following specifications were used: { resolution: 256, learning rate: 2e-3 }. This limit is binding for common words, e.g., "the". The recognizer is trained using the Supervised Contrastive ("SupCon") loss function [7], a gener-45 In particular, we work with the "outside" SupCon loss formulation We use a MobileNetV3 (Small) encoder pre-trained on ImageNet1k sourced from the timm [19] We use 0.1 as the temperature for Center Cropping, to avoid destroying too much information. C (Small) model that is developed in [2] for character recognition. If multiple article bounding boxes satisfy these rules for a given headline, then we take the highest.

Q6 How many instances are there in total (of each type, if appropriate)?

Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.

We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound.

These sentence pairs will serve as a means to evaluate machines' commonsense reasoning abilities under different extra-linguistic contexts.

The learning rate is 2e-5 with 100% warm up and a batch size of 32.