Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object recognition neural network for amodal center prediction. One of the methods includes receiving an image of an object captured by a camera. The image of the object is processed using an object recognition neural network that is configured to generate an object recognition output. The object recognition output includes data defining a predicted two-dimensional amodal center of the object, wherein the predicted two-dimensional amodal center of the object is a projection of a predicted three-dimensional center of the object under a camera pose of the camera that captured the image.

Abstract: This document describes scene understanding for cross reality systems using occupancy grids. In one aspect, a method includes recognizing one or more objects in a model of a physical environment generated using images of the physical environment. For each object, a bounding box is fit around the object. An occupancy grid that includes a multiple cells is generated within the bounding box around the object. A value is assigned to each cell of the occupancy grid based on whether the cell includes a portion of the object. An object representation that includes information describing the occupancy grid for the object is generated. The object representations are sent to one or more devices.

Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.

Abstract: Medical devices and methods for using medical devices are disclosed. An example fluid reservoir and tube set arranged and configured to couple to an endoscope for use in an endoscopic procedure includes a fluid supply tube wherein the fluid supply tube includes an extendable region positioned along a length of the fluid supply tube, and a lumen in fluid communication with the fluid reservoir. Further, the extendable region is configured to shift between a first configuration having a first length and a second extended configuration having a second length longer than the first length.

Abstract: Some techniques described herein relate to determining how to optimally store datasets in a multi-tiered storage device with compression. In one example, a method includes assigning, to a data partition of a dataset, a priority based on access patterns of the data partition. Compression data is accessed describing results of compressing a data sample associated with the data partition using multiple compression schemes. Based both on the priority of the data partition and the compression data, a storage tier is determined for storing the data partition in the multi-tiered storage device. Further, based both on the priority of the data partition and the compression data, a compression scheme is determined for compressing the data partition for storage in the multi-tiered storage device. The data partition is compressed using the compression scheme to produce a compressed data partition, and the compressed data partition is stored in the storage tier.

Abstract: Systems and methods for providing enterprise-level application content customization are described herein. A method includes executing an enterprise application (from a suite of enterprise applications that are utilized by the enterprise) on a remote computing system operated by an enterprise administrator. The method includes surfacing a content customization UI on a display device of the remote computing system during execution of the enterprise application. The content customization UI includes UI elements that enable the specification of parameters for providing enterprise-specific customized content during execution of enterprise application(s) from the suite of enterprise applications on remote computing system(s) operated by enterprise user(s). The method also includes receiving, via the content customization UI, user input including the specification of parameters and generating the customized content based on the specified parameters.

Abstract: Systems and methods for providing enterprise-level application content customization are described herein. A method includes executing an enterprise application (from a suite of enterprise applications that are utilized by the enterprise) on a remote computing system operated by an enterprise administrator. The method includes surfacing a content customization UI on a display device of the remote computing system during execution of the enterprise application. The content customization UI includes UI elements that enable the specification of parameters for providing enterprise-specific customized content during execution of enterprise application(s) from the suite of enterprise applications on remote computing system(s) operated by enterprise user(s). The method also includes receiving, via the content customization UI, user input including the specification of parameters and generating the customized content based on the specified parameters.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object recognition neural network using multiple data sources. One of the methods includes receiving training data that includes a plurality of training images from a first source and images from a second source. A set of training images are obtained from the training data. For each training image in the set of training images, contrast equalization is applied to the training image to generate a modified image. The modified image is processed using the neural network to generate an object recognition output for the modified image. A loss is determined based on errors between, for each training image in the set, the object recognition output for the modified image generated from the training image and ground-truth annotation for the training image. Parameters of the neural network are updated based on the determined loss.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.

Abstract: Disclosed herein are glyco-decoy acceptor compositions that sidetrack or inhibit the activity of biosynthetic enzymes participating in synthesis of ligands binding at selectin, galectin and siglecs receptors; methods of their preparation and uses in drug discovery and in treatments of diseases.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object recognition neural network for amodal center prediction. One of the methods includes receiving an image of an object captured by a camera. The image of the object is processed using an object recognition neural network that is configured to generate an object recognition output. The object recognition output includes data defining a predicted two-dimensional amodal center of the object, wherein the predicted two-dimensional amodal center of the object is a projection of a predicted three-dimensional center of the object under a camera pose of the camera that captured the image.

Abstract: Disclosed herein are glyco-decoy acceptor compositions that sidetrack or inhibit the activity of biosynthetic enzymes participating in synthesis of ligands binding at selectin, galectin and siglecs receptors; methods of their preparation and uses in drug discovery and in treatments of diseases.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable three-dimensional (3-D) object recognition in a cross reality system. One of the methods includes maintaining object data specifying objects that have been recognized in a scene. A stream of input images of the scene is received, including a stream of color images and a stream of depth images. A color image is provided as input to an object recognition system. A recognition output that identifies a respective object mask for each object in the color image is received. A synchronization system determines a corresponding depth image for the color image. A 3-D bounding box generation system determines a respective 3-D bounding box for each object that has been recognized in the color image. Data specifying one or more 3-D bounding boxes is received as output from the 3-D bounding box generation system.

Abstract: Disclosed herein are glyco-decoy acceptor compositions that sidetrack or inhibit the activity of biosynthetic enzymes participating in synthesis of ligands binding at selectin, galectin and siglecs receptors; methods of their preparation and uses in drug discovery and in treatments of diseases.

Abstract: A system and method for stemming flow of information from a negative campaign are described. A status for each node of a set of preselected nodes in a social network graph is identified. The status indicates whether a node has been infected with information from a negative campaign. A source and a flow of the negative campaign are identified based on the status of the nodes from the set of preselected nodes and a topology of the social network graph. A susceptibility score is computed for one or more nodes of the social network. The susceptibility score is computed using a measure of vulnerability of nodes that have not received the information based on the flow of the negative campaign, and a measure of reachability of nodes from the source. Nodes susceptible to adopting and spreading the information from the negative campaign are identified based on the susceptibility score.

Abstract: A system and method for stemming flow of information from a negative campaign are described. A status for each node of a set of preselected nodes in a social network graph is identified. The status indicates whether a node has been infected with information from a negative campaign. A source and a flow of the negative campaign are identified based on the status of the nodes from the set of preselected nodes and a topology of the social network graph. A susceptibility score is computed for one or more nodes of the social network. The susceptibility score is computed using a measure of vulnerability of nodes that have not received the information based on the flow of the negative campaign, and a measure of reachability of nodes from the source. Nodes susceptible to adopting and spreading the information from the negative campaign are identified based on the susceptibility score.

Abstract: A method for producing sialylated molecules based on reverse sialylation that catalytically transfers the sialic acid moiety of sialylated donors to nucleotide monophosphates or transfer sialic acid moieties from sialylated donors to acceptor glycoproteins or glycolipids.

Abstract: A method for determining substrates specific for a transferase enzyme selected from the group consisting of glycosyltransferases and sulfotransferases.