Abstract: A display substrate and related devices are provided. The display substrate includes a plurality of first sub-pixels, second sub-pixels and third sub-pixels. In a first direction, the first sub-pixels and the third sub-pixels are arranged alternately to form a plurality of first sub-pixel rows, the second sub-pixels form a plurality of second sub-pixel rows, the first sub-pixel rows and the second sub-pixel rows are arranged alternately in a second direction, connection lines of center points of two first sub-pixels and two third sub-pixels form a first virtual quadrilateral, the two first sub-pixels are located at two vertex angles of the first virtual quadrilateral which are opposite to each other, one second sub-pixel is located within the first virtual quadrilateral, and the first virtual quadrilateral includes two interior angles each being equal to 90° and two interior angles each being not equal to 90°.

Abstract: A grass trimming head includes a housing assembly and a spool. The housing assembly is formed with a housing cavity and an outer aperture for allowing a cutting line to be inserted from an outside of the housing assembly into the housing cavity. At least a portion of the spool is arranged inside the housing cavity, and the spool is rotatable with respect to the housing assembly about a central axis. The spool is formed with an inner aperture for allowing the cutting line to be inserted into the spool or a clamping portion configured to fix the cutting line.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving a user request via a graphical user interface, the user request corresponding to a user search query for a product; determining whether a first processing machine of the system is operating in a first processing mode or a second processing mode; when the first processing machine is determined to be operating in the first processing mode, analyzing the user request via the first processing machine and using a process, to identify a candidate recommendation system to utilize by: determining a randomized strategy for one or more candidate recommendation systems based on a ratio of a number of the one or more candidate recommender systems, the randomized strategy to be stored in a collected history data; determining model parameters based on the collected history data; and

Abstract: Systems and methods for generating top-k recommendation using latent space representations generated by deep joint variational autoencoder processes are disclosed. A user identifier is received and a set of prior interactions associated with the user identifier is obtained. A set of latent space representations of the set of prior interactions is generated using a trained inference model. The trained inference model includes a joint variational autoencoder model. A set of k-recommended items is generated based on a comparison of the set of latent space representations of the set of prior interactions and a set of latent space representations of one or more items. A user interface including the set of k-recommended items is generated.

Abstract: Methods and systems for rendering a virtual overlay in an extended reality (XR) environment are described. Image data is received, representing a current field of view (FOV) in the XR environment. A spatial boundary is defined in the current FOV, based on user input. An image label representing a region of interest (ROI) within the defined spatial boundary, and one or more object labels representing one or more objects within the defined spatial boundary are generated. At least one relevant virtual object is identified. The virtual object is relevant to a semantic meaning of the ROI based on the image label and/or the one or more object labels. Identification of the at least one relevant virtual object is outputted, to be renderable in the XR environment.

Abstract: An approach is disclosed for providing item-to-item recommendations. The approach receives data for an item. The approach generates recommended candidate item data for the data of the received item. The approach generates feature data of the recommended candidate item data and the data of the received item. The feature data may include one or more similarities between the data of the received item and data corresponding to a respective recommended candidate item. The approach generates ranked recommended candidate item data based on the generated feature data and at least one weight determined for candidate test item data. The candidate test item data may include data corresponding to the recommended candidate item data.

Abstract: Devices, methods, systems, and media are described for providing an extended screen distributed user interface in an augmented reality environment. GUI layout information for laying out a conventional 2D GUI is processed in order to generate an extended screen DUI for display partially on a 2D display device and partially on one or more virtual screens of an AR environment viewed using an AR display device, such as a head mounted display. GUI elements are laid out in the DUI based on a primary modality of the GUI element (input or output), and/or based on spatial dependencies between GUI elements encoded in the GUI layout information. Methods for switching focus between two software application instances displayed in the DUI are also disclosed.

Abstract: This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: receiving one or more vectors representing one or more types of features for a pair of items; generating, using a similarity item model of a machine learning architecture, a prediction for a similar item, wherein the similarity item model combines a pair of separately trained machine learning models; combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items; and transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the website. Other embodiments are disclosed.

Abstract: Methods and systems for processing an image are described. A saliency map is generated from the image. The saliency map indicates one or more salient portions of the image that have saliency values satisfying a saliency criterion. A scene graph is generated for at least the one or more salient portions. The scene graph represents a plurality of objects detected in the image. The scene graph further represents one or more relationships between each respective object pairs. One or more dataset entries associated with the image are generated. Each of the one or more relationships for each of the one or more object pairs is indicated by a respective dataset entry. The one or more dataset entries are stored in a first dataset.

Abstract: Generating a user timeline for an electronic device (ED) userData is collected that includes: location data; application data; and activity data. Occurrences of predetermined types of observed events are detected based on the collected data. For each detected occurrence a respective observed event record is stored that includes information about a time and type of the observed event. Planned event records, each including information about a time and type of a respective planned event, are stored for planned events that the user is scheduled to participate in, the planned event records. Events are predicted based on the observed event records and the planned event records. Information about observed, planned and predicted events are output on a timeline user interface based on observed event records, planned event records and predicted event records stored for the predicted events, respectively.

Abstract: An approach is disclosed for providing item-to-item recommendations. The approach receives data for an item. The approach generates recommended candidate item data for the data of the received item. The approach generates feature data of the recommended candidate item data and the data of the received item. The feature data may include one or more similarities between the data of the received item and data corresponding to a respective recommended candidate item. The approach generates ranked recommended candidate item data based on the generated feature data and at least one weight determined for candidate test item data. The candidate test item data may include data corresponding to the recommended candidate item data.

Abstract: This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.

Abstract: System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the

Abstract: A method and system for displaying data content associated with a video, comprising: receiving video data for a video at the user equipment; playing the video in a user interface rendered on a display device of the user equipment; pausing the video at a selected frame; accessing, based on the selected frame, content data associated with the selected frame; and displaying information about the content data associated with the selected frame in the user interface.

Abstract: Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.

Abstract: Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.

Abstract: Methods and systems for processing an image are described. A saliency map is generated from the image. The saliency map indicates one or more salient portions of the image that have saliency values satisfying a saliency criterion. A scene graph is generated for at least the one or more salient portions. The scene graph represents a plurality of objects detected in the image. The scene graph further represents one or more relationships between each respective object pairs. One or more dataset entries associated with the image are generated. Each of the one or more relationships for each of the one or more object pairs is indicated by a respective dataset entry. The one or more dataset entries are stored in a first dataset.

Abstract: System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the

Abstract: A method and system for displaying data content associated with a video, comprising: receiving video data for a video at the user equipment; playing the video in a user interface rendered on a display device of the user equipment; pausing the video at a selected frame; accessing, based on the selected frame, content data associated with the selected frame; and displaying information about the content data associated with the selected frame in the user interface.