Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

Abstract: In one embodiment, a service receives a plurality of device type classification rules, each rule comprising a device type label and one or more device attributes used as criteria for application of the label to a device in a network. The service estimates, across a space of the device attributes, device densities of devices having device attributes at different points in that space. The service uses the estimated device densities to identify two or more of the device type classification rules as having overlapping device attributes. The service determines that the two or more device type classification rules are in conflict, based on the two or more rules having different device type labels. The service generates a rule conflict resolution that comprises one of the device type labels from the conflicting two or more device type classification rules.

Abstract: In various embodiments, a device classification service clusters devices in a network into a device type cluster based on attributes associated with the devices. The device classification service tracks changes to the device type cluster over time. The device classification service detects an attack on the device classification service by one or more of the devices based on the tracked changes to the device type cluster. The device classification service initiates a mitigation action for the detected attack on the device classification service.

Abstract: A method and apparatus for artificial neural network precoding for massive MIMO systems are disclosed. In one embodiment, a method includes processing, by an artificial neural network, ANN, precoding engine, at least one input signal by 5 performing a low peak-to-average-power ratio, PAPR, precoding on the at least one input signal; and transmitting, via at least one antenna array having at least one antenna, at least one precoded output signal processed by the ANN precoding engine.

Abstract: In one embodiment, a networking device at an edge of a network generates a first set of feature vectors using information regarding one or more characteristics of host devices in the network. The networking device forms the host devices into device clusters dynamically based on the first set of feature vectors. The networking device generates a second set of feature vectors using information regarding traffic associated with the device clusters. The networking device models interactions between the device clusters using a plurality of anomaly detection models that are based on the second set of feature vectors.

Abstract: In one embodiment, a service detects that an event of a particular event type has occurred in a software-defined wide area network (SD-WAN). The service activates, in response to detecting the occurrence of the event, a machine learning model to assess telemetry data regarding a first tunnel in the SD-WAN. The service predicts a failure of the first tunnel, based on the assessment of the telemetry data regarding the first tunnel by the machine learning model. The service proactively reroutes at least a subset of traffic on the first tunnel onto a second tunnel in the SD-WAN, in advance of the predicted failure of the first tunnel.

Abstract: In one embodiment, a device classification service receives a plurality of device classification rulesets, each ruleset associating a set of device characteristics with a device type label. The device classification service forms a unified ruleset by resolving a conflict between conflicting device characteristics from two or more of the device classification rulesets. The device classification service trains a machine learning-based device classifier using the unified ruleset. The device classification service classifies, using telemetry data for a device in a network as input to the trained device classifier, the device with the device type label.

Abstract: A method for determining a dimension between the back and the cutting edge of a vibrating blade mounted on a cutting head includes successively of acquiring a digital image of the blade mounted on the cutting tool so a line corresponding to a cutting edge of the blade and a line corresponding to a back of the blade are visible in the image, determining on the digital image along a straight line perpendicular to the line corresponding to the cutting edge of the blade the pixels comprised between the line corresponding to the cutting edge of the blade and the line corresponding to the back of the blade, and calculating a filtered dimension between the back and the cutting edge of the blade from the number of pixels comprised between the line corresponding to the cutting edge of the blade and the line corresponding to the back of the blade.

Abstract: Systems and methods are disclosed herein that related to Peak-to-Average Power Ratio (PAPR) reduction in a (e.g., massive) Multiple-Input Multiple-Output (MIMO) Orthogonal Division Multiplexing (OFDM) transmitter system. In some embodiments, a method of operation of a MIMO OFDM transmitter system comprises, for each carrier of two or more carriers, performing precoding of a plurality of frequency-domain input signals for the carrier to provide a plurality of frequency-domain precoded signals for the carrier, the plurality of frequency-domain input signals for the carrier being for a plurality of transmit layers for the carrier, respectively. The method further comprises processing the frequency-domain precoded signals for the two or more carriers in accordance with a multi-carrier Convex Reduction of Amplitudes (CRAM) processing scheme to provide a plurality of multi-carrier time-domain transmit signals for a plurality of antenna branches, respectively, of the MIMO OFDM transmitter system.

Abstract: In one embodiment, a network assurance service maintains a first set of telemetry data from the network anonymized using a first key regarding a plurality of network entities in a monitored network. The service receives a key rotation notification indicative of a key changeover from the first key to a second key for anonymization of a second set of telemetry data from the network. The service forms, during a key rotation time period associated with the key changeover, a mapped dataset by converting anonymized tokens in the second set of telemetry data into anonymized tokens in the first set of telemetry data. The service augments, during the key rotation time period, the first set of telemetry data with the mapped dataset. The service assesses, during the time period, performance of the network by applying a machine learning-based model to the first set of telemetry data augmented with the mapped dataset.

Abstract: Systems and methods are disclosed herein that relate to Peak-to-Average Power Ratio (PAPR) reduction in a MIMO OFDM transmitter system. In some embodiments, a method of operation of a transmitter system comprises, for each carrier of two or more carriers, performing precoding of frequency-domain input signals for the carrier to provide frequency-domain precoded signals for the carrier, the frequency-domain input signals for the carrier being for a plurality of transmit layers for the carrier, respectively. The method further comprises processing the two or more pluralities of frequency-domain precoded signals for the two or more carriers, respectively, in accordance with a multi-carrier processing scheme to provide a plurality of multi-carrier time-domain transmit signals for a plurality of antenna branches, respectively, of the MIMO OFDM transmitter system.

Abstract: The present disclosure generally relates to displaying visual effects such as virtual avatars. An electronic device having a camera and a display apparatus displays a virtual avatar that changes appearance in response to changes in a face in a field of view of the camera. In response to detecting changes in one or more physical features of the face in the field of view of the camera, the electronic device modifies one or more features of the virtual avatar.

Abstract: A system and method for assessing customer satisfaction from a physical gesture of a customer, the system comprising: a video camera (5) for capturing video frames of the customer (1) making the physical gesture; and a deep-learning object-detection module for detecting the physical gesture by analysing the captured video frames, and for categorising the physical gesture as a specific customer feedback result.

Abstract: In various embodiments, a device classification service obtains traffic telemetry data for a plurality of devices in a network. The service applies clustering to the traffic telemetry data, to form device clusters. The service generates a device classification rule based on a particular one of the device clusters. The service receives feedback from a user interface regarding the device classification rule. The service adjusts the device classification rule based on the received feedback.

Abstract: The present invention relates to the field of lettuce breeding, in particular to Quantitative Trait Loci for resistance against the lettuce aphid Nasonovia ribisnigri biotype Nr:1 and to cultivated lettuce comprising one or more of these Quantitative Trait Loci.

Abstract: In various embodiments, a device classification service makes a determination that an endpoint device in a network is eligible for expedited device classification based on a policy. The device classification service obtains, after making the determination that the endpoint device in the network is eligible for expedited device classification, telemetry data regarding the endpoint device generated by actively probing the endpoint device. The device classification service determines whether the telemetry data regarding the endpoint device matches any existing device classification rules. The device classification service generates, based on the telemetry data, a device classification rule that assigns a device type to the endpoint device, when the telemetry data does not match any existing device classification rules.

Abstract: In one embodiment, a network assurance service that monitors one or more networks identifies changes in a key performance indicator for each of a plurality of network entities in the one or more networks. The service forms a peer group of network entities from the plurality of network entities whose changes in the key performance indicator are correlated. The service monitors the key performance indicator for network entities in the peer group of network entities. The service, based on the monitoring, detects an anomalous change in the key performance indicator for a particular network entity in the peer group of network entities relative to other network entities in the peer group of network entities.

Abstract: The present disclosure generally describe user interfaces related to time. In accordance with embodiments, user interfaces for displaying and enabling an adjustment of a displayed time zone are described. In accordance with embodiments, user interfaces for initiating a measurement of time are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface using a character are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface that includes an indication of a current time are described. In accordance with embodiments, user interfaces for enabling configuration of a background for a user interface are described. In accordance with embodiments, user interfaces for enabling configuration of displayed applications on a user interface are described.

Abstract: In one embodiment, a device deploys a first machine learning model to an inference location in a network. The first machine learning model is used at the inference location to make inferences about the network. The device receives, from the inference location, an indication that the first machine learning model is exhibiting poor performance. The device identifies a corrective measure for the poor performance that minimizes resource consumption by a model training pipeline of the device. The device deploys, based on the corrective measure, a second machine learning model to the inference location. The second machine learning model is used in lieu of the first machine learning model to make the inferences about the network.

Abstract: In various embodiments, a device obtains a set of device classification rules. Each device classification rule specifies one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule. The device forms a graphical representation of the set of attributes. The device performs an analysis of the graphical representation of the set of attributes. The device provides a result of the analysis to a user interface.