Abstract: According to an aspect, a method may generate an activity timeline of a user based on data indicative of a search history, a watch history, and activity of the user with a television application. The method may generate a sports score using a sports model based on the activity timeline of the user, may generate a ranked list of sports related media content items for recommending to the user based on the sports score, and may associate a respective selectable information item with each of the sports related media content items included in the ranked list. The method may send, to a network-connected display device, the ranked list of sports related media content items that includes each respective selectable information item for displaying each respective selectable information item in a user interface of the television application executing on the network-connected display device.

Abstract: Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.

Abstract: In various examples, estimated field of view or gaze information of a user may be projected external to a vehicle and compared to vehicle perception information corresponding to an environment outside of the vehicle. As a result, interior monitoring of a driver or occupant of the vehicle may be used to determine whether the driver or occupant has processed or seen certain object types, environmental conditions, or other information exterior to the vehicle. For a more holistic understanding of the state of the user, attentiveness and/or cognitive load of the user may be monitored to determine whether one or more actions should be taken. As a result, notifications, AEB system activations, and/or other actions may be determined based on a more complete state of the user as determined based on cognitive load, attentiveness, and/or a comparison between external perception of the vehicle and estimated perception of the user.

Abstract: Methods, apparatus, systems, and articles of manufacture to determine execution cost are disclosed. An example apparatus includes memory; instructions included in the apparatus; and processor circuitry to execute the instruction to: cause a plurality of instructions corresponding to a mnemonic to be executed; determine an average execution cost of the plurality of instructions; determine a standard deviation of execution costs of the plurality of instructions; and generate a mapping table including an entry, the entry including the mnemonic in association with the average and the standard deviation.

Abstract: In various examples, one or more validity checks that model one or more aspects of human physiology may be applied to frames of detected human features to detect and respond to the presence of faults. Example validity checks include human feature constraints derived from the kinematics of human motion, anatomical and spatial constraints, consistency across detection modalities, and/or others. The present techniques may be utilized to validate human features detected by various computer vision tasks, such as those involving pose estimation, facial detection, gesture recognition, and/or activity monitoring, to name a few examples.

Abstract: An antenna that enables dense packing of radiators includes a plurality of first radiators configured to radiate in a first frequency band and a plurality of second radiators configured to radiate in a second frequency band, the second frequency band having higher frequencies than the first frequency band. each of the plurality of first radiators includes a plurality of dipole arms. Each of the plurality of dipole arms includes a periodic pattern of inductive choke segments, and each of the dipole arms has a broken peripheral current path.

Abstract: Stitching of multiple images into a composite representation can be performed using a set of stitching parameters determined based, at least in part, upon a subjective stitching quality assessment value. A stitched image can be compared against its constituent images to obtain one or more objective quality metrics. These objective quality metrics can be fed, as input, to a trained classifier, which can infer a subjective quality assessment metric for the stitched (or otherwise composited) image. This subjective quality assessment metric can be used to adjust one or more compositing parameter values in order to provide at least a minimum subjective quality assessment value for composited images.

Abstract: In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.

Abstract: Disclosed are various approaches for dynamically integrating a client application with multiple third-party services. An integration service receives a request to perform an action relative to a particular third-party service from a client device. The request is received through a first application programming interface (API) generic to a plurality of third-party services. The integration service communicates with the particular third-party service to perform the action using a second API specific to the particular third-party service. The integration service sends a response to the client device through the first API. The response is based at least in part on a result of the action and includes an indication of one of a plurality of predefined user interfaces.

Abstract: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

Abstract: Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

Abstract: A device may include a processor configured to collect user behavior data associated with a user equipment (UE) device and determine a user behavior pattern for the UE device based on the collected user behavior data. The processor may be further configured to generate a policy in a core network for the UE device based on the determined user behavior pattern for the UE device, wherein the core network manages a communication session for the UE device via a Radio Access Network (RAN), and provide the generated policy to a Policy Control Function (PCF) in the core network to apply to the communication session for the UE device.

Abstract: One example discloses a first wireless communication device, including: a transceiver configured to be coupled to an antenna; a controller coupled to the transceiver; wherein the controller is configured to calculate a range to a second wireless communication device based on a set of exchanged messages; wherein the controller is configured to calculate the range while limiting at least one of: a set of allowed message repetitions and a set of allowed LTF (long training frame) totals, for channel bandwidths (BW) greater than 160 MHz.

Abstract: Certain embodiments involve using machine-learning methods to generate a recommendation for sequential content items. A method involves accessing a content item associated with an interaction stage in an online environment. A stage graph, which includes a ratio of interactions, of the content item is generated. An additional content item that includes additional stage-transition content is identified. A sequencing function outcome indicating a portion of the ratio of interactions is determined. A transition probability of receiving an interaction with stage-transition content and an additional interaction with the additional stage-transition content is calculated. A content provider system is caused to provide a recipient device with interactive content that includes the additional content item.

Abstract: Methods and apparatus relating to throttling a code fetch for speculative code paths are described. In an embodiment, a first storage structure stores a reference to a code line in response to a request to be received from a cache. A second storage structure to store a reference to the code line in response to an update to an Instruction Dispatch Queue (IDQ). Logic circuitry controls additional code line fetch operations based at least in part on a comparison of a number of ongoing speculative code fetches and a determination that the code line is speculative. Other embodiments are also disclosed and claimed.

Abstract: In various examples, circadian rhythm-based data augmentation for drowsiness detection systems and applications are provided. Embodiments described herein may produce an estimated circadian rhythm for a test subject and/or vehicle driver or other machine operator or occupant, and use the pattern of that circadian rhythm to correct, confirm, calibrate, or otherwise augment drowsiness assessments derived from video image data. The position of a person in the context of their process C circadian cycle may be used as indication of their level of drowsiness. An estimated process C circadian cycle may be used to generate more accurate ground truth training data for training machine learning models, and may be used by real-time, in-vehicle drowsiness detection systems that infer driver drowsiness levels based on captured images. In various embodiments, a circadian rhythm drowsiness estimate may be used to correct, calibrate, augment, and/or replace a drowsiness score predicted by a machine learning model.

Abstract: In various examples, circadian rhythm-based data augmentation for drowsiness detection systems and applications are provided. Embodiments described herein may produce an estimated circadian rhythm for a test subject and/or vehicle driver or other machine operator or occupant, and use the pattern of that circadian rhythm to correct, confirm, calibrate, or otherwise augment drowsiness assessments derived from video image data. The position of a person in the context of their process C circadian cycle may be used as indication of their level of drowsiness. An estimated process C circadian cycle may be used to generate more accurate ground truth training data for training machine learning models, and may be used by real-time, in-vehicle drowsiness detection systems that infer driver drowsiness levels based on captured images. In various embodiments, a circadian rhythm drowsiness estimate may be used to correct, calibrate, augment, and/or replace a drowsiness score predicted by a machine learning model.

Abstract: Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

Abstract: Embodiments of the present invention relate to synchronously replicating data in a distributed computing environment. To achieve synchronous replication both an eventual consistency approach and a strong consistency approach are contemplated. Received data may be written to a log of a primary data store for eventual committal. The data may then be annotated with a record, such as a unique identifier, which facilitates the replay of the data at a secondary data store. Upon receiving an acknowledgment that the secondary data store has written the data to a log, the primary data store may commit the data and communicate an acknowledgment of success back to the client. In a strong consistency approach, the primary data store may wait to send an acknowledgement of success to the client until it receives an acknowledgment that the secondary has not only written, but also committed, the data.

Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.