To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

This is done in two stages.

In response, numerous challenging and comprehensive benchmarks have been proposed to more accurately assess the capabilities of MLLMs. However, there is a dearth of exploration of the higher-order perceptual capabilities of MLLMs.

If there's one thing that turns people off from adopting new tech, it's being forced to look silly and feel uncomfortable for extended lengths of time. It was always the Achilles' heel for 3D in the past, and it remains the primary hurdle for VR headsets and goofy-looking smart glasses. Laptops, tablets, and even computer monitors have started embracing a new form of 3D technology that solves this problem entirely, without giving up just how compelling 3D can look. I've used the latest iteration of the technology and spoke with the creators--this might finally be the version of 3D that sticks. I was skeptical when I first saw this next generation of 3D technology. Interest in 3D comes in waves.

I have played many Assassin's Creed games over the years, but I've rarely loved them. Ubisoft's historical fiction is perennially almost-great. A lot of players would say it reached its peak in the late 2000s, with the trio of renaissance Italy games beginning with Assassin's Creed 2, and their charismatic hero, Ezio Auditore. Since then, the series has become bloated, offering hundreds of hours of repetitive open-world exploration and assassination in ancient Greece, Egypt and even Viking Britain. Odyssey (the Greek one) was the last I played seriously; I found the setting exquisite, the gameplay somewhat irritating and the scale completely overwhelming.

We present a novel method for Deep Reinforcement Learning (DRL), incorporating the convex property of the value function over the belief space in Partially Observable Markov Decision Processes (POMDPs). We introduce hard- and soft-enforced convexity as two different approaches, and compare their performance against standard DRL on two well-known POMDP environments, namely the Tiger and FieldVisionRockSample problems. Our findings show that including the convexity feature can substantially increase performance of the agents, as well as increase robustness over the hyperparameter space, especially when testing on out-of-distribution domains. The source code for this work can be found at https://github.com/Dakout/Convex_DRL.

The numbers in bold denote a significant statistical difference between the two methods (p-value < 0.001, paired t-test). We also list the IID (Table T6) and OOD (Tables T7, T8 and T9) test results of all the agents trained for this work. Some negative values should not surprise the reader, as some agents, when tested way outside of the training distribution, fail to walk, collecting more penalties (e.g., due to undesired contact force or excessive energy expenditure) than positive reward. We also show the graphs of the reward as a function for different perturbation intensity for the end-to-end trained Oracle, DMAP and TCN (Figure F2). Generally, DMAP performs similarly to the Oracle, while the TCN has lower performance especially for more challenging morphologies (Ant, Walker).

Pocketpair, the company behind last year's viral game Palworld, has a new venture: publishing indie games. Its first project, scheduled for release later this year, will be an as-yet-unnamed horror game from Surgent Studios, the developer behind 2024's Tales of Kenzera: Zau. Palworld, jokingly referred to as "Pokémon with guns," was a breakout success last year, drawing in more than 25 million players in its first few months. The company's step into publishing comes at a turbulent time for video games, especially smaller studios; last year, Among Us developer Innersloth announced its own move into publishing to help push projects forward. Pocketpair's Palworld success, it seems, is allowing it to do the same.