Abstract: The present invention enables accurate identification of organisms by analyzing their DNA sequences and, based on their DNA sequences, assessing classification annotations, such as taxonomic, systematic, or functional annotations. Sequence-based identification of life forms as described herein can be used for diagnostic purposes, for example. Further, the techniques disclosed herein offer advantages over conventional culture-based techniques. Example embodiments are related to methods for assessing classification annotations assigned to DNA sequences of organisms. One example embodiment includes a method of identifying a centroid DNA sequence of one or more organisms. The method includes obtaining a plurality of DNA sequences from one or more organisms, annotating each DNA sequence with a classification annotation, and grouping the plurality of DNA sequences into a plurality of groups.

Abstract: A spacing bar of a wheelchair frame comprising an elongated structural body having a pair of opposite ends, each of the opposite ends having a C-shaped clamp configured for being clamped to respective lateral frame members of the wheelchair frame, wherein the elongated structural body and the C-shaped clamps are integrally made of a monocoque composite construction, and a tightening assembly for each of the C-shaped clamps, the tightening assemblies being arranged in the spacing bar to loosen and tighten the C-shaped clamps to the respective lateral frame members.

Abstract: A first radio node for uplink and downlink co-scheduling is provided. The first radio node includes processing circuitry containing instructions executable to configure the first radio node to: determine a scheduling restriction for joint uplink and downlink scheduling based on passive intermodulation (PIM) generated during simultaneous uplink and downlink communications where the uplink communications corresponds to communications from a second radio node to the first radio node, and perform communications according to the scheduling restriction.

Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

Abstract: In one embodiment, a service receives software version data regarding versions of software executed by devices in a network. The service detects a version change in the version of software executed by one or more of the devices, based on the received software version data. The service makes a determination that a drop in data quality of input data for a machine learning model used to monitor the network is associated with the detected version change. The service reverts the one or more devices to a prior version of software, based on the determination that the drop in quality of the input data for the machine learning model used to monitor the network is associated with the detected version change.

Abstract: A use of pyrroloquinoline quinone (PQQ) for at least one of preserving the quality of meat obtained from farmed animals, relates to preventing the apparition of the defaults in meat, limiting, stopping the development of defaults in meat. The defaults are linked to myopathy, with farmed animals, and preparing a feed additive or a premix in order to preserve the quality of meat obtained from farmed animals and/or in order to at least one of preventing the apparition of the defaults in meat, limiting and stopping the development of defaults in meat. The defaults are linked to myopathy, with farmed animals. The use also relates to feed additive, a feed and a premix having PQQ.

Abstract: A system of bracket assemblies for a wheelchair comprises a pair of bracket assemblies adapted to be connected to respective sides of a seat frame of the wheelchair. Each of the bracket assemblies has a base adapted to be secured to a seat frame member of the seat frame, an arm adapted to be secured to a backrest frame member of a backrest frame, and a pivot pivotally connecting the base to the arm for rotational movement of the arm relative to the base. One or more blocking mechanism blocks the rotational movement of the arm relative to the base in a use orientation of the backrest frame. A rod having opposed ends connected to the base of the pair of the bracket assemblies for spacing the bracket assemblies away from one another. A rear wheel unit is also provided.

Abstract: A tube assembly for a wheelchair frame comprises a tubular member having at least a first portion and a second portion angled relative to the first portion, the first portion configured to be in a seating zone of the wheelchair frame. The second portion is configured to be in a bottom front zone of the wheelchair frame, the tubular member defining a surface concavity in its peripheral surface delimited by a peripheral step, the surface concavity located at a junction between the first portion and the second portion of the tubular member. An interface member has a peripheral contour shaped and sized for the interface member to be received in the surface concavity and contoured at least partially by the peripheral step.

Abstract: In one embodiment, a device classification service extracts, for each of a plurality of time windows, one or more sets of traffic features of network traffic in a network from traffic telemetry data captured by the network. The service represents, for the time windows, the extracted one or more sets of traffic features as feature vectors. A feature vector for a time window indicates whether each of the traffic features was present in the network traffic during that window. The service trains, using a training dataset based on the feature vectors, a cascade of machine learning classifiers to label devices with device types. The service uses the classifiers to label a particular device in the network with a device type based on the traffic features of network traffic associated with that device. The service initiates enforcement of a network policy regarding the device based on its device type.

Abstract: The disclosure describes a cryogenic device with a display device for displaying one of a plurality of user-interfaces associated with a plurality of cryogenic device states. The cryogenic device is configured to: generate an initial user-interface for display on the display device; determine that the cryogenic device is in a first state; generate, in response to determining that the cryogenic device is in the first state, instructions for rendering a first user-interface, wherein the first user-interface is associated with the first state; and cause the display device to display the first user-interface. In this way, the cryogenic device may have a dynamic user interface that is configured to response to states of the cryogenic device.

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 network assurance service that monitors a network receives key performance indicators (KPIs) for a plurality of network entities in the network. The service applies clustering to the KPIs, to form KPI clusters. The service designates the network entities associated with the particular KPI cluster as belonging to a peer group, based in part on an assessment that the network entities associated with the particular KPI cluster share one or more attributes. The service uses a machine learning model to identify one of the network entities in the peer group as anomalous among the network entities in the peer group.

Abstract: In one embodiment, a service computes a data fidelity metric for network telemetry data used by a machine learning model to monitor a computer network. The service detects unacceptable performance of the machine learning model. The service determines a correlation between the data fidelity metric and the unacceptable performance of the machine learning model. The service adjusts generation of the network telemetry data for input to the machine learning model, based on the determined correlation between the data fidelity metric and the unacceptable performance of the machine learning model.

Abstract: In various embodiments, a device classification service receives, from a networking device in a network, an indication that deep packet inspection (DPI) trace data is not available for an endpoint device in the network because the endpoint device does not match any DPI policies of the networking device. The service configures a first DPI policy on the networking device that causes it to capture a DPI trace of traffic associated with the endpoint device. The service receives, via a user interface, an indication that a subset of attributes of the endpoint device in the DPI trace is relevant to labeling the endpoint device with a device type. The service replaces the first DPI policy on the networking device with a second DPI policy that causes it to report only the subset of attributes of endpoint devices to the device classification service for endpoint devices that match the second DPI policy.

Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.

Abstract: The present disclosure generally relates to generating and modifying 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: In various embodiments, a device classification service forms a device cluster by applying clustering to attributes of endpoint devices observed in one or more networks. The device classification service applies an initial device classification rule to the endpoint devices in the device cluster, based on one or more of the endpoint devices in the device cluster matching the initial device classification rule. The device classification service computes metrics for the initial device classification rule that quantify how well the attributes of the endpoint devices in the device cluster match the initial device classification rule. The device classification service decides, based on the metrics, whether to associate the initial device classification rule with the device cluster or generate a new device classification rule based on the device cluster.

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: It is proposed a method for determining a quantity of a liquid in a tank, the tank comprising: a first ultrasound subsystem capable of measuring level of liquid present above a predetermined threshold level within the tank; and at least one second ultrasound subsystem, said second ultrasound subsystem being associated with a sensing area within the tank and being configured to measure a parameter characteristic of the liquid, said sensing area being located below the predetermined threshold level. The method comprises in the steps of: —checking the validity of the measurement performed by said first ultrasound subsystem (S10); —when it is detected that the measurement of said first ultrasound system is not valid: (a) checking (S22) the validity of the measurement performed by said second ultrasound subsystem; (b) determining (S24, S25, S26) a quantity of liquid, which quantity being based on a result of the validity check at step (a).

Abstract: In one embodiment, a network element in a network maintains a probabilistic data structure indicative of devices in the network for which telemetry data is not to be sent to a device classification service. The network element detects a traffic flow sent from a source device to a destination device. The network element determines whether the probabilistic data structure includes entries for both the source and destination devices of the traffic flow. The network element sends flow telemetry data regarding the traffic flow to the device classification service, based on a determination that the probabilistic data structure does not include entries for both the source and destination of the traffic flow.