We study predictive control in a setting where the dynamics are time-varying and linear, and the costs are time-varying and well-conditioned. At each time step, the controller receives the exact predictions of costs, dynamics, and disturbances for the future: time steps.

The ability to predict action content from neural signals in real time before the action occurs has been long sought in the neuroscientific study of decision-making, agency and volition. On-line real-time (ORT) prediction is important for understanding the relation between neural correlates of decision-making and conscious, voluntary action as well as for brain-machine interfaces. Here, epilepsy patients, implanted with intracranial depth microelectrodes or subdural grid electrodes for clinical purposes, participated in a "matching-pennies" game against an opponent. In each trial, subjects were given a 5 s countdown, after which they had to raise their left or right hand immediately as the "go" signal appeared on a computer screen. They won a fixed amount of money if they raised a different hand than their opponent and lost that amount otherwise.

Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future. Such projects need a toolkit for extrapolating how much classifier accuracy may improve from a 2x, 10x, or 50x increase in data size. While existing work has focused on finding a single "best-fit" curve using various functional forms like power laws, we argue that modeling and assessing the uncertainty of predictions is critical yet has seen less attention. In this paper, we propose a Gaussian process model to obtain probabilistic extrapolations of accuracy or similar performance metrics as dataset size increases. We evaluate our approach in terms of error, likelihood, and coverage across six datasets. Though we focus on medical tasks and image modalities, our open source approach generalizes to any kind of classifier.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

AWS Center for Quantum Computing, Pasadena, CA 91125, USA (Dated: March 23, 2023) We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] Conversely, we highlight the broader algorithmic development underlying our original work [1], and (within our original framework) provide additional numerical results showing sizable improvements over our original data, thereby refuting the comment's original performance statements. Furthermore, it has already been shown that physics-inspired graph neural networks (PI-GNNs) can outperform greedy algorithms, in particular on hard, dense instances. We also argue that the internal (parallel) anatomy of graph neural networks is very different from the (sequential) nature of greedy algorithms, and (based on their usage at the scale of real-world social networks) point out that graph neural networks have demonstrated their potential for superior scalability compared to existing heuristics such as extremal optimization. Finally, we conclude highlighting the conceptual novelty of our work and outline some potential extensions.

With its blue body, big anime eyes, and head shaped like a teardrop, Moxie wants to be friends with your child. The robot companion is designed to support social, emotional and cognitive development in children between the ages of five and 10, through play-based learning and lessons on turn-taking and eye contact. Launched in 2020, Moxie was developed by Embodied, a robotics company based in Pasadena, California. "Our collective vision is to create robots that are going to benefit society," says Paolo Pirjanian, the founder and CEO of Embodied. The roboticist and former NASA scientist founded the company in 2016 and oversaw the creation of Moxie.

A robot chef named Flippy, designed to cook 300 burgers a day, has been upgraded and can now also fill up baskets of fries and place them in the deep fat fryer. Built by Miso Robotics, a food services startup from Pasadena, California, it is now capable of working an entire fry station and can do twice as many food preparation jobs as the first Flippy, including basket filling, emptying, and returning. White Castle has partnered with Miso on the Flippy project, giving feedback that has allowed the startup to improve the functionality of the product. They deployed the original Flippy to a location in the Chicagoland area in September 2020. Automatic Dispenser for high volume foods: New Automatic Dispenser options make Flippy 2 autonomous.

"The rule of thumb is that both images--the one from the satellite and the one from the autonomous vehicle--have to have identical content for current techniques to work. The differences that they can handle are about what can be accomplished with an Instagram filter that changes an image's hues," says Anthony Fragoso (MS '14, PhD '18), lecturer and staff scientist, and lead author of the Science Robotics paper. "In real systems, however, things change drastically based on season because the images no longer contain the same objects and cannot be directly compared." The process--developed by Chung and Fragoso in collaboration with graduate student Connor Lee (BS '17, MS '19) and undergraduate student Austin McCoy--uses what is known as "self-supervised learning." While most computer-vision strategies rely on human annotators who carefully curate large data sets to teach an algorithm how to recognize what it is seeing, this one instead lets the algorithm teach itself.

Washington – The Mars rover Perseverance has successfully conducted its first test drive on the red planet, the U.S. space agency NASA said Friday. The six-wheeled rover traveled about 6.5 meters (21.3 feet) in 33 minutes on Thursday, NASA said. It drove 4 meters forward, turned in place 150 degrees to the left, and then backed up 2.5 meters, leaving tire tracks in the Martian dust. "This was our first chance to'kick the tires' and take Perseverance out for a spin," said Anais Zarifian, Perseverance mobility test bed engineer at NASA's Jet Propulsion Laboratory in Pasadena, California. Zarifian said the test drive went "incredibly well" and represented a "huge milestone for the mission and the mobility team."