Abstract: An array substrate including a display area having a plurality of subpixels is provided. The plurality of subpixels includes a plurality of first subpixels in a display-bonding sub-area and a plurality of second subpixels in a regular display sub-area. The array substrate includes a plurality of thin film transistors on a first side of the base substrate and respectively in the plurality of subpixels. A respective one of the plurality of first subpixels includes a bonding pad on a second side of a base substrate; a lead line electrically connecting a respective one of a plurality of thin film transistors to the bonding pad; and a via extending through the base substrate. The lead line is unexposed in the array substrate. The lead line extends from the first side to the second side of the base substrate through the via, to connect to the bonding pad.

Abstract: A computing system may receive a request for application update information for one or more applications installed on a computing device. The computing system determines, based on the request for the application update information, a set of applications that need to be updated and applies a machine learning model to determine a respective update priority score for each application from the set of applications. The computing system sends an indication of the respective update priority scores for the set of applications to the computing device. Responsive to receiving an update request from the computing device, the computing system initiates installation of the pending update for the at least one application from the set of applications.

Abstract: A wireless communication system may include an electronic device having a wireless communication module. The wireless communication module may include an antenna radiating element on a first surface, a ground ring surrounding the antenna radiating element on the first surface, and a radio component mounted to a second surface. The wireless communication module may be incorporated into a system package that also includes other components. Encapsulation material may cover the wireless communication module and other components. A shielding material may cover the encapsulation material and be coupled to the ground ring. An opening in the shielding material may be aligned with the antenna radiating element. If desired, the wireless communication system may include external equipment having a wireless communication module communicatively coupled to the wireless communication module to convey firmware testing, debugging, restore, and/or other data.

Abstract: A wireless communication system may include an electronic device having a wireless communication module. The wireless communication module may include an antenna radiating element on a first surface, a ground ring surrounding the antenna radiating element on the first surface, and a radio component mounted to a second surface. The wireless communication module may be incorporated into a system package that also includes other components. Encapsulation material may cover the wireless communication module and other components. A shielding material may cover the encapsulation material and be coupled to the ground ring. An opening in the shielding material may be aligned with the antenna radiating element. If desired, the wireless communication system may include external equipment having a wireless communication module communicatively coupled to the wireless communication module to convey firmware testing, debugging, restore, and/or other data.

Abstract: A data processing system employs a pre-processing step to create a simplified view of a received entity graph. During the pre-processing step, only the objects and the attributes of those objects within the graph that are required for data processing are selected. Pruned source and target objects are generated by omitting those attributes that are not required for processing. The pruned objects are included in the simplified view that enhances system performance.

Abstract: Method, system, and programs for providing identifiers to objects. Input data representing a plurality of objects is received and categorized into a plurality of entity categories. A first graph of entities is generated using the plurality of entity categories. The first graph of entities are matched with a second graph of entities. A comparison of object pairs is then made, in which each object pair includes a first object from the first graph of entities and a corresponding second object from the second graph of entities. Identifiers are assigned to each object based on comparing the object pairs.

Abstract: Visual concepts contained within a video clip are classified based upon a set of target concepts. The clip is segmented into shots and a multi-layer multi-instance (MLMI) structured metadata representation of each shot is constructed. A set of pre-generated trained models of the target concepts is validated using a set of training shots. An MLMI kernel is recursively generated which models the MLMI structured metadata representation of each shot by comparing prescribed pairs of shots. The MLMI kernel is subsequently utilized to generate a learned objective decision function which learns a classifier for determining if a particular shot (that is not in the set of training shots) contains instances of the target concepts. A regularization framework can also be utilized in conjunction with the MLMI kernel to generate modified learned objective decision functions. The regularization framework introduces explicit constraints which serve to maximize the precision of the classifier.

Abstract: A data processing system employs a pre-processing step to create a simplified view of a received entity graph. During the pre-processing step, only the objects and the attributes of those objects within the graph that are required for data processing are selected. Pruned source and target objects are generated by omitting those attributes that are not required for processing. The pruned objects are included in the simplified view that enhances system performance.

Abstract: Method, system, and programs for providing identifiers to objects. Input data representing a plurality of objects is received and categorized into a plurality of entity categories. A first graph of entities is generated using the plurality of entity categories. The first graph of entities are matched with a second graph of entities. A comparison of object pairs is then made, in which each object pair includes a first object from the first graph of entities and a corresponding second object from the second graph of entities. Identifiers are assigned to each object based on comparing the object pairs.

Abstract: One or more techniques and/or systems are disclosed for detecting a quick response (QR) code. An area of an image comprising a QR code is localized by combining pixel dynamic scale (DS), black-cell ratio (BR), and edge intensity sum (EIS) criteria determination to identify the QR code. A pattern for the QR code is detected, comprising determining if a position detection pattern (PDP) is located in respective grid areas of a first grid that comprises the QR code, and identifying an alignment pattern (AP), if present. To identify the AP, an AP region is estimated using the PDPs, and a center area of the AP is found by examining respective areas of a second grid comprising the estimated AP region.

Abstract: Visual concepts contained within a video clip are classified based upon a set of target concepts. The clip is segmented into shots and a multi-layer multi-instance (MLMI) structured metadata representation of each shot is constructed. A set of pre-generated trained models of the target concepts is validated using a set of training shots. An MLMI kernel is recursively generated which models the MLMI structured metadata representation of each shot by comparing prescribed pairs of shots. The MLMI kernel is subsequently utilized to generate a learned objective decision function which learns a classifier for determining if a particular shot (that is not in the set of training shots) contains instances of the target concepts. A regularization framework can also be utilized in conjunction with the MLMI kernel to generate modified learned objective decision functions. The regularization framework introduces explicit constraints which serve to maximize the precision of the classifier.