Abstract: A data processing system implements a method for training machine learning modes, including receiving a set of one or more unlabeled documents associated one or more first categories of documents to be used to train machine learning models to analyze the one or more unlabeled documents, and fine-tuning a first machine learning model and a second machine learning model based on the one or more unlabeled document to enable the first machine learning model to determine a semantic representation of the one or more first categories of document, and to enable the second machine learning model to classify the semantic representations according to the one or more first categories of documents, the first machine learning model and the second machine learning model having been trained using first unlabeled training data including a second plurality of categories of documents that do not include the one or more first categories of documents.

Abstract: Methods and apparatuses for performing context completion to messages in a session are provided in the present disclosure. A message may be obtained. It may be detected that there exists context ellipsis in the message. It may be determined whether the message is retained in the current domain of the session. In response to determining that the message is retained in the current domain, a complementary text for recovering the context ellipsis may be selected in the current domain. A completed message may be generated based on the message and the complementary text.

Abstract: Methods, systems, and computer storage media for providing predicted task-actions for tasks from a plurality of task applications associated with a task management system. In operation, a task associated with a task application is accessed at a task-action prediction engine. An intent of the task is determined. The intent of the task is determined based on task features and an intent prediction machine learning model that is trained to predict intents associated with natural language text. Based on the intent of the task, a predicted task-action is selected based on a task-action computation model that is associated with predefined task-actions and predefined intent-task-categories. The predicted task-action can be communicated to a task management client that causes display of the predicted task-action in combination with a predicted task-action interface element associated with executing the predicted task-action.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to generate graphical user interfaces from textual descriptions.

Abstract: Described herein is a software update distribution service that leverages reinforcement learning—a specific type machine learning algorithm—to discover or learn optimal times (e.g., a schedule) to download software updates and to install software updates for software applications installed on a group of client computing devices of a specific enterprise, in order to achieve one of several predefined goals or objectives selected for the specific enterprise, or for the specific group of client computing devices. Using reinforcement learning, a software-based agent learns to perform activities relating to software updates at specific times that minimize a penalty, wherein the penalty is derived based on a weighted combination of metrics, some of which relate to the impact of software update timing on network traffic and power consumption.

Abstract: A computer processing system and method for computing large-degree isogenies having a computer processor resident on an electronic computing device operably configured to execute computer-readable instructions programmed to perform a large-degree isogeny operation by chaining together a plurality of scalar point multiplications, a plurality of isogeny computations, and a plurality of isogeny evaluations.

Abstract: Systems and methods for opening files on a client device include receiving a request to open a file and identifying a file type pertaining to the file. A list of software applications is identified that are capable of opening the identified file type, and metadata pertaining to each of the software applications on the list is retrieved. User preference data pertaining to a user requesting to open the file is also retrieved. Scores for the software applications on the list are then determined based at least on the metadata and the user preference data. At least one software application on the list is then selected to open the file on the client device based on the determined scores.

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by a visitor to a non-profit venue, wherein the platform determines personalized information about the visitor and a recommendation engine outputs a suggestion for a personalized experience to the visitor to encourage philanthropic activity related to an ongoing philanthropic campaign; and a machine learning system for learning on a training set of photographic data or video data, the learning to train the machine learning system to predict a sentiment towards the ongoing philanthropic campaign of the visitor based, at least in part, on photographic images or video images that are taken by the visitor, that are shared to the visitor, that are shared by the visitor or that include the visitor as a subject.

Abstract: During a communication session, a virtual assistant prompts a user for information pertaining to one or more products, and identifies relevant products based on data collected from the user. These one or more products are presented to the user, and a level of intent or urgency of the user to purchase the one or more products is determined based on determining a plurality of tone scores corresponding to a plurality of tone types for each of the words, phrases, or sentences of the language input received from the user via a user interface. A network of producers is scanned and a live producer offering the identified products is identified and presented to the user. The virtual assistant determines whether or not to transfer the user to the identified live producer based on application of transfer criteria to the evaluated level of intent or urgency of the user.

Abstract: In a system for determining intent of a detected virtual contact between a user and a virtual object in a virtual reality environment, contact maybe detected between a designated selection device, manipulated by the user, and the virtual object. It may then be determined whether or not the detected contact between the designated selection device and the virtual object is intention. Determination of whether or not the detected contact is intentional may be based on at least one of an approach direction of the virtual contact with the virtual object, an approach velocity of the virtual contact with the virtual object, or an approach acceleration of the virtual contact with the virtual object.

Abstract: Methods for rendering augmented reality (AR) content are presented. An a priori defined 3D albedo model of an object is leveraged to adjust AR content so that is appears as a natural part of a scene. Disclosed devices recognize a known object having a corresponding albedo model. The devices compare the observed object to the known albedo model to determine a content transformation referred to as an estimated shading (environmental shading) model. The transformation is then applied to the AR content to generate adjusted content, which is then rendered and presented for consumption by a user.

Abstract: Systems and methods are provided for providing a personalized therapeutic presentation. A system includes a treatment presentation data store configured to store one or more data structures associated with a personalized therapeutic presentation for a client, where the one or more data structures include client biographic data, client symptom data, client goal data, and media data. A treatment generation engine is configured to generate a treatment presentation for the client based on data stored in the treatment presentation data store, wherein the treatment presentation is designed to treat a symptom identified by the client symptom data to achieve a goal identified by the client goal data, where the treatment presentation comprises a sequential presentation of media identified by the media data that is selected to treat the symptom to achieve the goal.

Abstract: A method for generating DICOM images as part of a real-time virtual medical consultation includes receiving, by a handheld patient device associated with a patient, a DICOM image associated with the patient generated by a diagnostic device, encrypting, by the handheld patient device, the DICOM image and linking the encrypted DICOM image to a unique patient identifier associated with the patient, wherein the handheld patient device is one of a tablet computer and a smartphone, and transmitting, by the handheld patient device, the encrypted DICOM image to a server configured to provide a DICOM viewer option within a secure telemedicine platform accessible by a medical professional and executed by the server.

Abstract: Permanent magnets and method of making the same are provided. The magnets include a magnetic layer having an insulation layer disposed thereon. The insulation layer is formed via additive manufacturing techniques such as laser melting such that it has discrete phases including a magnetic phase and an insulating phase.

Abstract: An example virtualized computing system includes: a host cluster having hosts and a virtualization layer executing on hardware platforms of the hosts, the virtualization layer supporting execution of virtual machines (VMs); an orchestration control plane integrated with the virtualization layer, the orchestration control plane including a master server executing in a first VM of the VMs; guest cluster infrastructure software (GCIS) executing in the master server, the GCIS configured to create a set of objects defining a container orchestration cluster, and manage lifecycles of second VMs of the VMs based on state of the set of objects; and guest software executing in the second VMs to implement the container orchestration cluster as a guest cluster of the host cluster, the guest software having components that interface with the GCIS.

Abstract: One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

Abstract: Techniques are disclosed for providing method for providing an event timer for event synchronization across Kubernetes clusters. The event timer is configured to provide event synchronization on behalf of microservice instances in the cloud computing environment. In response to a request for an event timer for a timed event, it is determined whether the requested event timer has been started for a second microservice instance. If the requested event timer has been started, a state of the requested event timer is sent to the first microservice instance If the requested event timer has not been started, the requested event timer is instantiated, and a state of the instantiated event timer is stored in a database. The instantiated event timer is independent of the first and second microservice instances. In response to an expiration of the event timer, a single callback for processing of the event is generated.

Abstract: An electronic article surveillance (“EAS”) tag for an EAS system, comprising an antenna, a radio frequency identification (“RFID”) chip configured to transmit and/or receive a wireless signal via the antenna; and a magnetically-actuatable switch configured to move between a first position and a second position. The switch is configured to electrically couple the RFID chip to the antenna in the first position. The switch is further configured to electrically decouple the RFID chip from the antenna in the second position.

Abstract: A quantum system controller configured to perform (near) real-time quantum error correction is provided. The controller comprises a processing device comprising at least one first processing element; a time-indexed command (TIC) sequencer comprising at least one second processing element; and a plurality of driver controller elements configured to control the operation of respective components and associated with respective buffers and processing elements. The processing device is configured to generate commands and the TIC sequencer is configured to cause the time-indexed execution of the commands by the appropriate driver controller elements.