Musk v. Altman is a fight over how much power is too much in Silicon Valley. Get your news from a source that's not owned and controlled by oligarchs. In May 2016, Elon Musk did something out of character that he has now spent years of his life trying to undo: He made what he believed to be a charitable donation. The world's richest man is also among its stingiest. Musk's private foundation often doles out less than the minimum percentage required by law.

Consider a general probabilistic43 model with latent parameters θ.

Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and hence cannot capture the temporal evolution of the objects' representation. More important, we lack an effective approach to capture such evolution from streaming data, which is common in real-world applications. To address these issues, we propose Streaming Factor Trajectory Learning (SFTL) for temporal tensor decomposition. We use Gaussian processes (GPs) to model the trajectory of factors so as to flexibly estimate their temporal evolution.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The world's militaries spent $2.88 trillion in 2025, an increase of 2.9 percent from the year before, according to the Stockholm International Peace Research Institute's (SIPRI) latest report. To put that number into perspective, $2.88 trillion amounts to $350 of military spending for each person on the planet. In this visual explainer, Al Jazeera unpacks the rise of global militarisation, including how much each nation spends, which countries sell the most weapons, and how military spending compares with spending on healthcare and education. In 2025, the five biggest military spenders were the United States ($954bn), China ($336bn), Russia ($190bn), Germany ($114bn) and India ($92bn), accounting for more than half (58 percent) of world military spending. The US is by far the biggest spender, as it has been every year since World War II.

What are the functionals of the reward that can be computed and optimized exactly in Markov Decision Processes? In the finite-horizon, undiscounted setting, Dynamic Programming (DP) can only handle these operations efficiently for certain classes of statistics. We summarize the characterization of these classes for policy evaluation, and give a new answer for the planning problem. Interestingly, we prove that only generalized means can be optimized exactly, even in the more general framework of Distributional Reinforcement Learning (DistRL). DistRL permits, however, to evaluate other functionals approximately. We provide error bounds on the resulting estimators, and discuss the potential of this approach as well as its limitations. These results contribute to advancing the theory of Markov Decision Processes by examining overall characteristics of the return, and particularly risk-conscious strategies.

While implicit function methods capture detailed clothed shapes, they require aligned shape priors and or are weak at inpainting occluded regions given an image input.

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy.