Abstract: A network device may identify a link aggregation group (LAG) of a plurality of links between the network device and another network device. The network device may identify link aggregation control protocol (LACP) parameters that were communicated by the network device and the other network device in association with the LAG. The network device may determine, based on the LACP parameters, a priority order of the plurality of links in the LAG. The network device may communicate with the other network device, and based on the priority order of the plurality of links of the LAG, one or more precision time protocol (PTP) messages via the LAG. For example, the network device may determine that a first link and a second link in the priority order are not available, and therefore may communicate the one or more PTP messages via a third link in the priority order.

Abstract: An exemplary embodiment of the present disclosure provides a device for use in cardiovascular interventions, the device comprising a flexible bioresorbable semilunar valve comprising a flexible circumferential body having a ring structure and a plurality of points extending from the ring structure along a longitudinal axis of the valve, a plurality of leaflets extending between the plurality of points, and a flexible cage disposed concentrically around the ring structure, the flexible cage configured to secure the flexible bioresorbable semilunar valve within a cardiac lumen. The device can be 3D printed.

Abstract: A method is provided. The method comprises obtaining local observation data of a group of one or more agents. The local observation data indicates performance of each agent included in the group. The method further comprises obtaining global state data indicating collective performance of the group and based on the obtained local observation data and the obtained global state data, determining a discount factor for each agent included in the group. The discount factor is a weight value of a future expected reward for each agent included in the group.

Abstract: In one aspect, a method includes obtaining a first and second set of word embeddings from a first local machine learning (ML) model and a second local ML model. The method includes generating first and second latent space representations by processing the first and second sets of word embeddings using an artificial neural network (ANN) trained with the first and second local ML models, wherein the first and second latent space representations comprises a plurality of first and second contexts associated with the first and second set of word embeddings. The method includes correlating the first and second sets of word embeddings based on the plurality of first and second contexts. The method includes aggregating, based on the correlating, the first set of word embeddings and the second set of word embeddings into a global Machine Learning (ML) model of word embeddings.

Abstract: A method performed by a virtual reality, VR, system for play of a VR game in a self-driving vehicle is provided. The method includes receiving a schedule of predicted acceleration for a future time period for the self-driving vehicle operating in a real world environment. The method further includes, responsive to the schedule of predicted acceleration, adjusting play of the VR game based on the schedule of predicted acceleration. Methods performed by a vehicle system, and related VR systems and vehicle systems are also provided.

Abstract: A method for distributed machine learning (ML) at a central computing device is provided.

Abstract: Embodiments described herein relate to methods and apparatuses for generating one or more answers relating to a machine learning, ML, model. A method in a first node comprises obtaining one or more queries relating to a first output of the ML model, wherein the first output of the machine learning, ML, model is intended to fulfil one or more requirements in an environment; for each of the one or more queries performing a reinforcement learning process.

Abstract: Methods and systems to prevent micro-loops between two network devices when there is a change in network topology. In one embodiment, a method is performed by a network device in a communications network, the method comprising computing a shortest path from the network device to a destination network device and identifying a backup network device for the network device to the destination network device, where the backup network device is a neighboring network device of the network device and is on an alternative path to the destination network device. The method further comprises determining a packet destined to the destination network device is received from a downstream network device of the network device, where the downstream network device is closer than the network device on the shortest path to the destination network device, and forwarding the packet to the backup network device based on the determination.

Abstract: This application relates to apparatus and methods for the monitoring of nodes within datacenters. In some examples, a computing device, such as a node, receives a monitoring file from a monitoring server, where the monitoring file includes a plurality of node health checks. The computing device is configured to execute the monitoring file based on a type of the computing device. Further, and based on the execution of the monitoring file, the computing device is configured to determine that at least one of the plurality of node health checks failed. In response to determining that the at least one of the plurality of node health checks failed, the computing device is configured to generate an alert message identifying the node health checks that failed. Further, the computing device is configured to transmit the alert message to the monitoring server for display.

Abstract: Some embodiments provide a method for quantifying quality of several service classes provided by a link between first and second forwarding nodes in a wide area network (WAN). At a first forwarding node, the method computes and stores first and second path quality metric (PQM) values based on packets sent from the second forwarding node for the first and second service classes. The different service classes in some embodiments are associated with different quality of service (QoS) guarantees that the WAN offers to the packets. In some embodiments, the computed PQM value for each service class quantifies the QoS provided to packets processed through the service class. In some embodiments, the first forwarding node adjusts the first and second PQM values as it processes more packets associated with the first and second service classes. The first forwarding node also periodically forwards to the second forwarding node the first and second PQM values that it maintains for the first and second service classes.

Abstract: The embodiments herein relate to a method performed by a cloud node. The cloud node obtains measurements from at least some of the sensor nodes. The cloud node mathematically determines a minimum number of sensor nodes and their optimal locations. Based on the obtained measurements and the mathematically determined optimal locations, the cloud node graphically determines an optimal location for each of the minimum number of sensor nodes. The cloud node compares the mathematically and the graphically determined optimal locations. When the comparison indicates that the mathematically and graphically determined optimal locations are the same, the cloud node determines a minimum number of fog nodes. Based on the optimal location of sensor nodes, the cloud node determines an optimal location for each of the minimum number of fog nodes.

Abstract: A method of performing multi-agent reinforcement learning in a system including a master node and a plurality of agents that execute actions on an environment based on respective local policies of the agents is provided. The method includes generating a ranking of the plurality of agents based on levels of variability of stochastic processes underlying the behavior of respective ones of the plurality of agents, sequentially updating the local policies of the agents in order based on the ranking, wherein the local policy of a selected agent is updated conditioned on an expected next state of at least one previously selected agent, simultaneously executing actions by agents based on their updated local policies, and updating the ranking of the plurality of agents in response to executing the actions.

Abstract: This application relates to apparatus and methods for the monitoring of nodes within datacenters. In some examples, a computing device, such as a node, receives a monitoring file from a monitoring server, where the monitoring file includes a plurality of node health checks. The computing device is configured to execute the monitoring file based on a type of the computing device. Further, and based on the execution of the monitoring file, the computing device is configured to determine that at least one of the plurality of node health checks failed. In response to determining that the at least one of the plurality of node health checks failed, the computing device is configured to generate an alert message identifying the node health checks that failed. Further, the computing device is configured to transmit the alert message to the monitoring server for display.

Abstract: Embodiments herein may e.g. relate to a method performed by a network node (12) for handling one or more operations in a communications network comprising a plurality of computing devices (10,11) performing one or more tasks. The network node (12) obtains an indication of a failure of an operation in the communications network; and obtains one or more parameters to resolve the failure. The one or more parameters relate to resources of the plurality of computing devices (10,11) and the communications network (1), wherein the one or more parameters are structured in an hierarchic manner and defined by a task of a capability, a resource used for the task, and a service level for the task. The network node (12) generates a plan by taking an aimed service level into account as well as the obtained one or more parameters; and executes one or more operations using the generated plan.

Abstract: A computer-implemented method for building a machine learning (ML) model is provided. The method includes training a ML model using a set of input data, wherein the ML model includes a plurality of layers and each layer includes a plurality of filters, and wherein the set of input data includes class labels; obtaining a set of output data from training the ML model, wherein the set of output data includes class probabilities values; determining, for each layer in the ML model, by using the class labels and the class probabilities values, a working value for each filter in the layer; determining, for each layer in the ML model, a dominant filter, wherein the dominant filter is determined based on whether the working value for the filter exceeds a threshold; and building a subset ML model based on each dominant filter for each layer, wherein the subset ML model is a subset of the ML model.

Abstract: A method for distributed machine learning (ML) which includes providing a first dataset including a first set of labels to a plurality of local computing devices including a first local computing device and a second local computing device. The method further includes receiving, from the first local computing device, a first set of ML model probabilities values from training a first local ML model using the first set of labels. The method further includes receiving, from the second local computing device, a second set of ML model probabilities values from training a second local ML model using the first set of labels and one or more labels different from any label in the first set of labels. The method further includes generating a weights matrix using the received first set of ML model probabilities values and the received second set of MIL model probabilities values. The method further includes generating a third set of ML model probabilities values by sampling using the generated weights matrix.

Abstract: A computer-implemented method includes: obtaining loss functions including: a first loss function associated with a first reinforcement learning, RL, model performed by a first local agent, wherein the first loss function is a function of one or more first parameters; and a second loss function associated with a second RL model at a second local agent, wherein the second loss function is a function of one or more second parameters; determining a combined loss function based on the loss functions; minimizing the combined loss function with respect to the first parameters and the second parameters to determine updated values for the first parameters and updated values for the second parameters; initiating execution of a first updated action by the first local agent based on the updated values of the first parameters; and initiating execution of a second updated action by the second local agent based on the updated values of the second parameters.

Abstract: This application relates to apparatus and methods for booting servers, such as cloud datacenter compute servers. The servers may execute one or more hypervisors, such as stateless hypervisors, with each hypervisor supporting one or more virtual machines. In some examples, each of a plurality of servers are configured to boot from a network. The compute servers may obtain an IP address identifying a location of hypervisor bootable images. Upon a reboot, the servers may request and obtain a hypervisor bootable image from the IP address. The servers may execute the hypervisor bootable image to run a hypervisor. In some examples, the servers also obtain virtual machine images from the network. One or more hypervisors executing on each server may obtain, and execute, one or more of the virtual machine images to run one or more virtual machines.

Abstract: A method for distributed learning at a local computing device is provided. The method includes: training a local model of a first model type on local data, wherein the local data comprises a first set of labels; testing the local model on a portion of global data pertaining to the first set of labels, wherein the global data comprises a second set of labels and the first set of labels is a strict subset of the second set of labels; as a result of testing the local model on the portion of the global data pertaining to the first set of labels, producing a first set of probabilities corresponding to the first set of labels; and sending the first set of probabilities corresponding to the first set of labels to a central computing device.

Abstract: This application relates to apparatus and methods for the monitoring of nodes within datacenters. In some examples, a computing device, such as a node, receives a monitoring file from a monitoring server, where the monitoring file includes a plurality of node health checks. The computing device is configured to execute the monitoring file based on a type of the computing device. Further, and based on the execution of the monitoring file, the computing device is configured to determine that at least one of the plurality of node health checks failed. In response to determining that the at least one of the plurality of node health checks failed, the computing device is configured to generate an alert message identifying the node health checks that failed. Further, the computing device is configured to transmit the alert message to the monitoring server for display.