Abstract: Embodiments herein disclose methods for generating an extended reality (XR) environment by an electronic device (100). The method includes generating a base structure (404) associated with an XR floor in an XR environment for a user from a number of users based on a distance between the user from the other users from the number of users, configuring a size of the base structure and a position of the base structure based on a distance between users, identifying a landmark point from a plurality of landmark points for the users upon measuring a distance between the users with reference to the base structure, and generating the XR environment based on the size of the base structure, the position of the base structure, and the plurality of identified landmark point for the users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

Abstract: Systems and methods for determining a placement for computational graph across multiple hardware devices. One of the methods includes generating a policy output using a policy neural network and using the policy output to generate a final placement that satisfies one or more constraints.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Provided are methods and systems for suggesting an enhanced multimodal interaction. The method for suggesting at least one modality of interaction, includes: identifying, by an electronic device, initiation of an interaction by a user with a first device using a first modality; detecting, by the electronic device, an intent of the user and a state of the user based on the identified initiated interaction; determining, by the electronic device, at least one of a second modality and at least one second device, to continue the initiated interaction, based on the detected intent of the user and the detected state of the user; and providing, by the electronic device, a suggestion to the user to continue the interaction with the first device using the determined second modality, by indicating the second modality on the first device or the at least one second device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

Abstract: Embodiments herein disclose a method for an enhanced interaction with a voice assistant using user accessories data by a first electronic device. The method includes detecting an utterance from a first user, where the utterance comprises a data item indicative of an identity of at least one second user. Further, the method includes determining at least one of position information and direction information of at least one wearable electronic device connected to the first electronic device of the first user. Further, the method includes determining the identity of the at least one second user indicated in the utterance of the first user based on at least one of the position and the direction of the at least one wearable electronic device connected to the first electronic device.

Abstract: Provided are methods and systems for suggesting an enhanced multimodal interaction. The method for suggesting at least one modality of interaction, includes: identifying, by an electronic device, initiation of an interaction by a user with a first device using a first modality; detecting, by the electronic device, an intent of the user and a state of the user based on the identified initiated interaction; determining, by the electronic device, at least one of a second modality and at least one second device, to continue the initiated interaction, based on the detected intent of the user and the detected state of the user; and providing, by the electronic device, a suggestion to the user to continue the interaction with the first device using the determined second modality, by indicating the second modality on the first device or the at least one second device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compiler optimizations using a compiler optimization network. One of the methods includes receiving an input program, wherein the input program defines a graph of operation modules, wherein each node in the graph is a respective operation module, and each edge between nodes in the graph represents one operation module receiving the output generated by another operation module. The input program is processed by a compiler optimization network comprising a graph-embedding network that is configured to encode operation features and operation dependencies of the operation modules of the input program into a graph embedding representation and a policy network that is configured to generate an optimization action for each of one or more nodes encoded in the graph embedding representation.

Abstract: The present subject matter refers a virtual object positioning method in an augmented reality (AR) or virtual reality (VR) environment. The method comprises receiving a virtual object for positioning relative to one or more objects in an AR or VR scene. During the positioning, a relative position of the virtual object is detected relative to the one or more objects in the AR or VR scene and a position due to the relative movement is detected. An occurrence of change in a physical characteristic of the virtual object is predicted based on the virtual object crossing a boundary with respect to the one or more objects. It is determined that the virtual object is likely to cross the boundary during the positioning. Upon the determination, a prior alert is generated.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: The disclosure provides a process of designing and optimizing supramolecular therapeutics. The disclosure also provides a method for designing and optimizing antibody drug conjugates.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

Abstract: The present invention discloses a process for the dissociation of natural gas hydrates comprises injecting additives or hydrate dissociation promoters into the system at the hydrate dissociation temperatures ranging from 283-293 K in conjunction with or without first depressurizing the system to pressures (50%-75%) below the hydrate equilibrium pressure and such leading to the recovery of methane or natural gases.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a catalog for multiple datasets, the method comprising accessing multiple extant data sets, the extant data sets including data sets that are independently generated and structurally dissimilar; organizing the data sets into collections, each data set in each collection belonging to the collection based on collection data associated with the data set; for each collection of data sets: determining, from a subset of the data sets that belong to the collection, metadata that describe the data sets that belong to the collection, wherein the metadata does not include the collection data, and attributing, to other data sets in the collection, the metadata determined from the subset of data sets; and generating, from the collections of data sets and the determined metadata, a catalog for the multiple datasets.

Abstract: The disclosure provides a process of designing and optimizing supramolecular therapeutics. The disclosure also provides a method for designing and optimizing antibody drug conjugates.