Contrary to earlier work, our experiments show this is not a universal phenomenon. We comprehensively investigate the impact of DNN architecture, training data, image resolution, and augmentations on transferability.

In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting.

A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale.

In this work, we address this issue through "Sparse Activation Control".

Russian President Vladimir Putin appears uninterested in peace in Ukraine, and there is no end to the war in sight. Kyiv has increasingly reached the conclusion that the country must do all it can to guarantee its own security. The Russians have yet again shifted their tactics, says Sokil. Several months ago, they would send small units into battle, but now, it is often just single soldiers operating on their own. And they are no longer looking to fight, preferring instead to just sneak forward and wait for air support, says the Ukrainian major. Their FPV drones are everywhere," he says, using the common shorthand for first-person view." On a mild, late summer night, Sokil is resting at a farm on the western edge of the Donbas, taking a drag from his cigarette.

Despite several works trying to reduce their computational cost, most of LLMs still adopt attention layers between all pairs of tokens in the sequence, thus incurring a quadratic cost. In this study, we present a novel approach that dynamically prunes contextual information while preserving the model's expressiveness, resulting in reduced memory and computational