Abstract: Embodiments herein relate to prediction, based on previous usage of a cloud-based computing resource by a user of one or more users of the cloud-based computing resource, future usage of the cloud-based computing resource. Based on the predicted future usage, embodiments relate to identifying that throttling of access to the cloud-based computing resource is to occur, and notifying the user of the throttling. Other embodiments may be described and/or claimed.

Abstract: Embodiments provide a fast multi-agent reinforcement learning (RL) pipeline that runs the full RL workflow end-to-end on a single GPU, using a single store of data for simulation roll-outs, inference, and training. Specifically, simulations and agents in each simulation are run in tandem, taking advantage of the parallel capabilities of the GPU. This way, the costly GPU-CPU communication and copying is significantly reduced, and simulation sampling and learning rates are in turn improved. In this way, a large number of simulations may be concurrently run on the GPU, thus largely improving efficiency of the RL training.

Abstract: A chemical composition for controlling odor and to a method of using the chemical composition to impart an odor control treatment to an article, more particularly to a textile material or a building or construction material. The chemical composition comprises a metal compound selected from the group consisting of a metal oxide, a metal hydroxide, and a combination thereof and a sulfo polyester. Methods of making and of using are provided.

Abstract: A chemical composition for controlling odor and to a method of using the chemical composition to impart an odor control treatment to an article, more particularly to a textile material or a building or construction material. The chemical composition comprises a metal compound selected from the group consisting of a metal oxide, a metal hydroxide, and a combination thereof and a sulfo polyester. Methods of making and of using are provided.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent behavior prediction using keypoint data. One of the methods includes obtaining data characterizing a scene in an environment, the data comprising: (i) context data comprising data characterizing historical trajectories of a plurality of agents up to the current time point; and (ii) keypoint data for a target agent; processing the context data using a context data encoder neural network to generate a context embedding for the target agent; processing the keypoint data using a keypoint encoder neural network to generate a keypoint embedding for the target agent; generating a combined embedding for the target agent from the context embedding and the keypoint embedding; and processing the combined embedding using a decoder neural network to generate a behavior prediction output for the target agent that characterizes predicted behavior of the target agent after the current time point.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing sensor data for a vehicle to perform prediction tasks regarding a driving environment of the vehicle.

Abstract: Disclosed herein are compounds of Formula I and methods for using the same for treating or preventing an infection caused by a Mycobacterium in a subject in need thereof. The method comprises administering to the subject the compound of Formula I, or a pharmaceutically acceptable salt thereof, or a pharmaceutical composition comprising the compound of Formula I.

Abstract: Embodiments described herein are directed to training a large action model (LAM) simulator framework. The LAM framework receives a content dataset associated with a task. This dataset includes the task name and at least one user command parameter. The LAM simulator framework identifies an abstract task based on the task name and determines the available tools for the task. It then generates a user command for an artificial intelligence agent, instructing it to complete the task using the abstract task and user command parameters. The AI agent is trained to execute the task over multiple iterations. An iteration involves creating a conversation data object from the user command, available tools, and prior conversation history, generating an action plan using a generative language model and the conversation data object, executing the actions in the plan using the environment and tools, and evaluating the actions.

Abstract: A chemical composition for controlling odor and to a method of using the chemical composition to impart an odor control treatment to an article, more particularly to a textile material or a building or construction material. The chemical composition comprises a metal compound selected from the group consisting of a metal oxide, a metal hydroxide, and a combination thereof and a sulfo polyester. Methods of making and of using are provided.

Abstract: A multistage polymer is provided, having: acrylate rich stage comprising: (a) structural units of monomer selected from C1-22 alkyl (meth)acrylates and structural units of first carbosiloxane monomer of formula (I); and (b) carbosiloxane rich stage, comprising: structural units of second carbosiloxane monomer of formula (I); wherein a is 0 to 3; wherein d is 0 or 1; wherein R1 is selected from hydrogen, C1-10 alkyl group and aryl group; wherein R2 is selected from hydrogen and C1-10 alkyl group; wherein R8 is —O—Si(CH3)3 group; wherein Y is selected from formula (II), (III) and (IV); wherein R4 and R6 are selected from hydrogen and methyl group; wherein R3 and R5 are a C1-10 alkylene group; wherein R7 is C1-10 alkyl group; wherein b is 0 to 4 and wherein c is 0 or 1; and wherein the first and second carbosiloxane monomer of formula (I) are same or different.

Abstract: Systems within materials handling facilities or retail establishments are programmed to receive images from cameras, process clips of the images to generate sets of features representing product spaces and actors depicted within such images, and to classify the clips as depicting or not depicting a shopping event. The images are dynamically cropped to reduce amounts of data that must be processed to in order to determine whether the images depict shopping events. The images are cropped by calculating a center point based on positions of points on product spaces and detected overlaps of hands and the product spaces. Features of clips determined to depict a shopping event are combined into a sequence and transferred, along with classifications of such clips, to a multi-camera system that generates a shopping hypothesis based on such features.

Abstract: Embodiments described herein provide a multi-stage rating and re-ranking pipeline for selecting SWE agents for an input issue description. Specifically, a meta-policy may be selected among available agent policies corresponding to a pool of available SWE agents which maximizes the cumulative reward along the trajectory of states (such as status of a file) and actions taken at a series of time steps, and a context of relevant repository information and issue descriptions. By dynamically choosing the most suitable agent policy for each context, the selection pipeline maximizes the expected cumulative reward across all possible contexts. In this way, software issue resolve rate is improved.