Obtaining effective representations of DNA sequences is crucial for genome analysis.

Sliced Wasserstein (SW) and Generalized Sliced Wasserstein (GSW) have been widely used in applications due to their computational and statistical scalability.

This oversight of potential modality combinations limits their applicability in real-world situations.

Monte-Carlo tree search (MCTS) is an influential sequential decision-making algorithm notably employed in AlphaZero.

Existing work for this task has modeled the effect of multiple treatments independently, while estimating the joint effect has received little attention but comes with non-trivial challenges. In this paper, we propose a novel method for reliable off-policy learning for dosage combinations.

Analyzing the motion of multiple biological agents, be it cells or individual animals, is pivotal for the understanding of complex collective behaviors. With the advent of advanced microscopy, detailed images of complex tissue formations involving multiple cell types have become more accessible in recent years. However, deciphering the underlying rules that govern cell movements is far from trivial.

Plus: Donald Trump wants to outlaw individual states' right to regulate AI The International Energy Agency recently released the latest version of the World Energy Outlook, the annual report that takes stock of the current state of global energy and looks toward the future. It contains some interesting insights and a few surprising figures about electricity, grids, and the state of climate change. Let's dig into some numbers . This article is from The Spark, MIT Technology Review's weekly climate newsletter. Everything is a conspiracy theory now. Our latest series " The New Conspiracy Age " delves into how conspiracies have gripped the White House, turning fringe ideas into dangerous policy, and how generative AI is altering the fabric of truth.

Existing methods typically employ a teacher-student framework with pseudo-labeling to leverage unlabeled point clouds. However, producing reliable pseudo-labels in a diverse 3D space still remains challenging.

In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.