There's more than one way to pass down those genes. These rodents live to serve their queen, who can have over 30 babies at one time. Breakthroughs, discoveries, and DIY tips sent six days a week. Valentine's Day means that love and coupling up is in the air. While our idea of romance may be lacking when it's time to reproduce, some species make up for it with fascinating strategies to pass their genes on to the next generation and ensure survival.

Rare polar bear adoption could save cub's life The cubs were born into a well-studied'celebration' of polar bears in Canada. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists in Churchill, Manitoba, Canada (aka the polar bear capital of the world) have confirmed that a wild female polar bear has adopted a cub that is not her own. This rare behavior was captured on cameras during the polar bear's annual migration along Western Hudson Bay . Researchers from Environment and Climate Change Canada and Polar Bears International spotted the mother bear (designated as bear X33991) during spring 2025, when she came out of her maternity den.

Fig 4 left), and for CUB, more nuanced notions of language grounding (c.f.

We did not reproduce results of [C] on SUN due to time limit (will include in final paper). Here we compare our work with these papers and clarify our novelty. Besides, they can only localize a small number of object parts, i.e., [58] for 2 parts, [D] for 4 parts, and are not able to "the employment of these ideas together for attribute localization and ZSL is quite interesting and seems to lead to The discussion will be added in the final paper. APN can obtain attention maps that better localize visual attributes compared to CAM (See Figure 1 in the main paper). CNN to predict binary attributes without prototypes.

We also had the human players evaluate the fluency and relevance of questions.

Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class.

In this appendix, Section A summarizes the architecture details of HSV A. Section B provides the key HSV A is insensitive to the number of latent features per unseen class in CZSL. HSV A perform steadily on the coarse-grained dataset (e.g., A W A1) while it is sensitive to

Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class.