Abstract: In some examples, for process-to-process communication, such as in function linking, a virtual channel can be provisioned to provide virtual machine to virtual machine communications. In response to a transmit request from a source virtual machine, the virtual channel can cause a data copy from a source buffer associated with the source virtual machine without decryption or encryption. The virtual channel provisions a key identifier for the copied data. The destination virtual machine can receive an indication data is available and can cause the data to be decrypted using a key accessed using the key identifier and source address of the copied data. In addition, the data can be encrypted using a second, different key for storage in a destination buffer associated with the destination virtual machine. In some examples, the key identifier and source address is managed by the virtual channel and is not visible to virtual machine or hypervisor.

Abstract: A computer-implemented method includes operating a controllable physical device to perform a task. The method also includes miming forward simulations of the task by a physics engine based on one or more physics parameters. The physics engine communicates with a parameter data layer where each of the one or more physics parameters is modeled with a probability distribution. For each forward simulation run, a tuple of parameter values is sampled from the probability distribution of the one or more physics parameters and fed to the physics engine. The method includes obtaining an observation pertaining to the task from the physical environment and a corresponding forward simulation outcome associated with each sampled tuple of parameter values. The method then includes updating the probability distribution of the one or more physics parameters in the parameter data layer based on the observation from the physical environment and the corresponding forward simulation outcomes.

Abstract: Distributed neural network boosting is performed by a neural network system through operating at least one processor. A method comprises providing a boosting algorithm that distributes a model among a plurality of processing units each being a weak learner of multiple weak learners that can perform computations independent from one another yet process data concurrently. The method further comprises enabling a distributed ensemble learning which enables a programmable logic controller (PLC) to use more than one processing units of the plurality of processing units to scale an application and training the multiple weak learners using the boosting algorithm. The multiple weak learners are machine learning models that do not capture an entire data distribution and are purposefully designed to predict with a lower accuracy. The method further comprises using the multiple weak learners to vote for a final hypothesis based on a feed forward computation of neural networks.

Abstract: Autonomous operations, such as robotic grasping and manipulation, in unknown or dynamic environments present various technical challenges. For example, three-dimensional (3D) reconstruction of a given object often focuses on the geometry of the object without considering how the 3D model of the object is used in solving or performing a robot operation task. As described herein, in accordance with various embodiments, models are generated of objects and/or physical environments based on tasks that autonomous machines perform with the objects or within the physical environments. Thus, in some cases, a given object or environment may be modeled differently depending on the task that is performed using the model. Further, portions of an object or environment may be modeled with varying resolutions depending on the task associated with the model.

Abstract: A system for supporting artificial intelligence inference in an edge computing device associated with a physical process or plant includes a neural network training module, a neural network testing module and a digital twin of the physical process or plant. The neural network training module is configured to train a neural network model for deployment to the edge computing device based on data including baseline training data and field data received from the edge computing device. The neural network testing module configured to validate the trained neural network model prior to deployment to the edge computing device by leveraging the digital twin of the physical process or plant.

Abstract: According to an aspect of the present disclosure, a computer-implemented includes creating a plurality of basic skill functions for a controllable physical device of an autonomous system. Each basic skill function includes a functional description for using the controllable physical device to interact with a physical environment to perform a defined objective. The method further includes selecting one or more basic skill functions to configure the controllable physical device to perform a defined task. The method also includes determining a decorator skill function specifying at least one constraint. The decorator skill function is configured to impose, at run-time, the at least one constraint, on the one or more basic skill functions. The method further includes generating executable code by applying the decorator skill function to the one or more basic skill functions, and actuating the controllable physical device using the executable code.

Abstract: In current applications of autonomous machines in industrial settings, the environment, in particular the devices and systems with which the machine interacts, is known such that the autonomous machine can operate in the particular environment successfully. Thus, current approaches to automating tasks within varying environments, for instance complex environments having uncertainties, lack capabilities and efficiencies. In an example aspect, a method for operating an autonomous machine within a physical environment includes detecting an object within the physical environment. The autonomous machine can determine and perform a principle of operation associated with a detected subcomponent of the object, so as to complete a task that requires that the autonomous machine interacts with the object. In some cases, the autonomous machine has not previously encountered the object.

Abstract: The present disclosure discloses a method and a system for implementing a call by a media gateway, comprising: setting a master control network element in a media gateway cluster, and establishing a mapping relationship between a user number and the media gateway in the master control network element (100); when the media gateway determines that an uplink protocol interface corresponding to a calling party is interrupted, the media gateway transmits a dialing tone to the calling party and acquires the called party number, and requests the master control network element to determine a media gateway to which the called party belongs; the master control network element determines the media gateway to which the called party belongs according to the mapping relationship stored therein, and informs the media gateway initiating the request; the call between the media gateway as a calling gateway that initiates the request and the determined media gateway to which the called party belongs is completed (103).

Abstract: The present disclosure discloses a method and a system for implementing a call by a media gateway, comprising: setting a master control network element in a media gateway cluster, and establishing a mapping relationship between a user number and the media gateway in the master control network element (100); when the media gateway determines that an uplink protocol interface corresponding to a calling party is interrupted, the media gateway transmits a dialing tone to the calling party and acquires the called party number, and requests the master control network element to determine a media gateway to which the called party belongs; the master control network element determines the media gateway to which the called party belongs according to the mapping relationship stored therein, and informs the media gateway initiating the request; the call between the media gateway as a calling gateway that initiates the request and the determined media gateway to which the called party belongs is completed (103).