Embodiments described herein are directed to a programming language trigger mechanism. The trigger mechanism is a small piece of code that a software developer utilizes in a computer program. The trigger mechanism enables computing operations or tasks to be performed asynchronously and in a parallel fashion. In particular, logic (e.g., operations or tasks) associated with the trigger mechanism are provided to a plurality of resources for processing in parallel. Each resource asynchronously processes the task provided thereto and asynchronously provides the result. The results are asynchronously returned as an enumeration. The enumeration enables the software developer to enumerate through the returned elements as a simple stream of results as they are calculated.

Various examples for providing neural network models to client devices are described. A management application can cause a training environment to be created for training a neural network using enterprise data authorized by a client device. The management application can cause the client device to send the enterprise data to the training environment. The management application can cause a training application running in the training environment to create a neural network model using the enterprise data. The management application can send a neural network model to the client device.

At an interface an analytic model for processing data is received. The analytic model is inspected to determine a language, an action, an input type, and an output type. A virtualized execution environment is generated for an analytic engine that includes executable code to implement the analytic model for processing an input data stream.

Methods and systems for processing medical images. One method includes, in response to startup of an application using an algorithm, creating a server process supporting a programming language associated with the algorithm and loading a plurality of deep learning models used by the algorithm into a memory of the server process to create in-memory models. The method also includes processing a first set of one or more medical images with the server process using the algorithm and at least one model selected from the in-memory models, maintaining the in-memory models in the memory of the server process after processing the first set of one or more medical images, and, in response to a request to process a second set of one or more medical images, processing the second set of one or more medical images using the algorithm and at least one of the in-memory models.

A digital assistant supported across devices such as smartphones, tablets, personal computers, wearable computing devices, game consoles, and the like includes an end-to-end user experience client that interfaces with extensions to applications and/or remote cloud-based services so that user experiences, content, or features can be integrated with the digital assistant and rendered as a native digital assistant user experience. The digital assistant is configured to perform as an active participant in a user experience from its initiation to its conclusion (i.e., from “end-to-end”) by determining the user's intent, performing tasks and actions, providing status, and interacting with the user as needed. The digital assistant utilizes proximity sensing so that its end-to-end participation in a user experience may span different physical locations. Such capability can facilitate the performance of location-specific actions including authenticating the user to gain access to locations, information, or services that would be restricted from non-authenticated users.

Spreadsheets and methods for programming spreadsheets are provided. In accordance with one aspect. The spreadsheet includes a plurality of cells arranged in a two-dimensional array. One or more of the plurality of cells include a plurality of adjoining variable value cells for data corresponding to a plurality of values of a variable. The plurality of adjoining variable value cells are user defined by a pair of cell values indicating a start and an end of a cell range of the plurality of adjoining variable value cells. The plurality of cells also include a spill area into which data can flow from a cell having an expression defined therein in response to the expression including a variable range defined by the plurality of values of the variable. The spill area is automatically sized to a plurality of expression solutions corresponding to the plurality of values of the variable.

Methods, non-transitory computer readable media, and devices are disclosed for rendering a visualization using a package to expand a statistical programming language. For example, a processor may load the package, which may comprise a plurality of application programming interfaces including a first application programming interface for generating a first object to contain a metadata set and a second application programming interface for generating a second object for rendering a visual output to present via a display. In one example, the metadata set includes content data and information regarding a formatting of at least one visualization based on the content data. The processor may further configure the first object via the first application programming interface, configure the second object via the second application programming interface, and render the visual output via the second object, the visual output including the at least one visualization.

Examples relate to a method for managing a runtime system for a hybrid computing architecture, a device, an apparatus and to a corresponding computer program. The apparatus is configured to create a thread pool for each work thread of a computer program, generate native code for at least one of the at least two ISAs for code segments of the computer program, assign native code sequences to a corresponding thread in the thread pool for execution, with the native code sequences comprising the native code of the code segments, and execute the native code sequences.

In one embodiment, a method includes receiving a user query inputted on a head-mounted device from the head-mounted device, wherein the user query corresponds to multiple dialog-intents, executing multiple tasks corresponding to the multiple dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the multiple tasks, wherein the stitching model combines the two or more of the execution results based on natural language processing, and wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and sending instructions to the head-mounted device for presenting the multi-perspective response on the head-mounted device.

A framework that creates an abstraction layer to configure/display system resources and process/application resource utilization while accessing the system information from a CLI. The framework is implemented using a scripting language like python and uses pre-existing OS or OEM tools to configure/display system resource information and filter the output to implement the CLI. A user argument defines the choice to use the framework as a standalone utility (running in a Linux shell) or in a library mode. Since the framework makes use of OS or OEM tools and does not access any system resource directly, the framework can be run in the virtual machine and baremetal OS. The framework can access the OS or OEM tools to configure/display system resource information locally and remotely by establishing remote communication through rsh (remote shell) connections. This approach allows reuse of the code, minimizes the implementation complexity, reduces development time and minimizes defects.