Thisapproach outperforms current state-of-the-art methods on StrokeRehab, as well as on the standard benchmark datasets50Salads,Breakfast,andJigsaws.

In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss have been explored, but their over-relaxed regularization often leads to imprecise object placement. We introduce BOOTPLACE, a novel paradigm that formulates object placement as a placement-by-detection problem. Our approach begins by identifying suitable regions of interest for object placement. This is achieved by training a specialized detection transformer on object-subtracted backgrounds, enhanced with multi-object supervisions. It then semantically associates each target compositing object with detected regions based on their complementary characteristics. Through a boostrapped training approach applied to randomly object-subtracted images, our model enforces meaningful placements through extensive paired data augmentation. Experimental results on established benchmarks demonstrate BOOTPLACE's superior performance in object repositioning, markedly surpassing state-of-the-art baselines on Cityscapes and OPA datasets with notable improvements in IOU scores. Additional ablation studies further showcase the compositionality and generalizability of our approach, supported by user study evaluations.

Affiliations: 1. Cedars Sinai Medical Center, Los Angeles, California 2. University of California Los Angeles, California Keywords: vision transformer, 3D convolutional neural network, assessment tool, suturing skill, video analysis Key information: 1. Research question: Can we automate the end-to-end assessment of suturing expertise, and what benefits would it offer? MANUSCRIPT Introduction We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills. While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes.

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue. A promising trend of future mobility is electric and shared. For instance, Figure 1 shows the spatial distribution of station occupancy rate (ratio and ride-sharing services [10], [11], and the de facto solution of parked vehicles to the total available space) in a real-world is to rebalance the fleet during operation.

As automotive chieftains gather in Munich this week for Europe's first major car show in two years, they'll do more than just lift the veil on shiny sheet metal. These are occasions where big deals tend to get done. Consider one of the last times the auto world descended on a European city for such a forum, in March 2019. Just before the action got underway in Geneva, the CEOs of Peugeot maker PSA and Fiat Chrysler met to sow the seeds of what blossomed into a megamerger, vaulting Stellantis NV into the same league as Toyota Motor Corp. and Volkswagen AG (VW). Toyota, Stellantis and Nissan Motor Co. aren't even attending, and carmakers that will are sending smaller contingents due to the surging delta variant.

Artificial intelligence (AI) far too often pops up as a term used vaguely to refer to any process that appears to involve more computers than it did twenty years ago. But concrete examples of how this informatics technology can improve fields like life sciences are harder to come by. I recently spoke to Krishnan Nandabalan, founder, CEO and president of InveniAI, which aims to use AI techniques to more quickly identify pharmacological compounds and get drugs to patients faster, to get the full picture. Ruairi Mackenzie (RM): How would you like to set the record straight about AI in the life sciences? Krishnan Nandabalan (KN): AI is used as a buzzword now.

ProjectFantasticFonts allows you to refine any font to heart's desire as well as to easily create text animations. This is for perfectionist solo podcasters. With ProjectSoundSeek technology, you can automatically locate specific sounds within audio recordings like extraneous ums and ahs and delete them en mass, resulting in a much cleaner listening experience. With ProjectAwesomeAudio, you can remove unwanted noise from an audio file such as when a recording from your laptop picks up fan noise or keyboard clatter or when you record an interview in a coffee shop and turns it into a studio-quality recording in an instant. ProjectSweetTalk technology allows you to use any audio speaking sample to animate any still image that includes a face…or more specifically, a mouth.

To capture the perfect lighting, Rasmus Furbo directed Spider-Man to crouch on top of a queen-size bed. The web-slinger sat there, facing the end of the bed, against a black studio-like backdrop for the hero's classic red and blue suit. The photo shoot was not for some kind of magazine promo for the next Marvel Cinematic offering. Rather the scene unfolded all within a video game. Furbo, 33, has played video games practically his entire life, but over the summer he found what he calls "a hobby within a hobby" -- what's known by some as in-game photography.

Robots make fantastic remote-sensing systems, ideal for sending in to disaster areas or for search-and-rescue. Drones in particular can move rapidly over large areas or through structures, identifying damage or looking for survivors by sending a video feed from their on-board cameras to a remote operator. While the data that drones provide can be invaluable, managing them can be quite difficult, especially once they get beyond line-of-sight. Researchers from Graz University of Technology, in Styria, Austria, led by Okan Erat, want to change the way we interface with drones, using augmented reality to turn them from complicated flying robots into remote cameras that an untrained user can easily control. Through a HoloLens--Microsoft's mixed reality head-mounted display--a drone can enable a sort of X-ray vision, allowing you to see straight through walls and making controlling the drone as easy as grabbing a virtual drone and putting it exactly where you want it to be.

ToyTalk, the startup that mashed together Barbie and the Internet of Things and let your kids chat away with Thomas the Tank Engine for hours on end, is cashing out on the bot craze with a slight realignment of philosophy -- and a new name. "When working with children," the company's CEO Oren Jacob said, "you are beholden to some pretty strict laws, and ToyTalk as a company had to work diligently to ensure we toe the line." To do that, the company developed a whole toolset to enable writers to create narratives for children's toys. The toolset was called PullString, named after the string you can pull on some dolls to make them talk. The name is undoubtedly a nod to the Andy cowboy doll from Toy Story -- Jacob did head up Pixar's technical team as its CTO, after all.