We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models. This library facilitates the creation of group-structured versions of existing datasets based on user-specified partitions, and directly leads to a variety of useful heterogeneous datasets that can be plugged into existing software frameworks. Dataset Grouper offers three key advantages. First, it scales to settings where even a single group's dataset is too large to fit in memory. Second, it provides flexibility, both in choosing the base (non-partitioned) dataset and in defining partitions.

New York City Mayor Zohran Mamdani faces criticism after Brooklyn Navy Yard drops lease with Easy Aerial, a drone manufacturer that supplies Israel's military.

Chile's Torres Del Paine is famous for its stunning landscapes, but it's also home to a fierce predator: the puma. These majestic creatures feed primarily on guanacos, although the hunting success rate is not very high, especially for female pumas. The photographer followed this female and her two cubs for several days, before witnessing her hunting. Breakthroughs, discoveries, and DIY tips sent six days a week. In Chile's famous Torres Del Paine National Park, a mother puma with her two cubs in tow attacks a guanaco.

Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SL T datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SL T.

Competing objectives arise when a model's capabilities

In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates

AudioSet) has an even larger negative impact.

IMP-MoE-L focusing on video tasks that achieves new state-of-the-art in zero-shot video classification: 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 68.3% on