Abstract: A method of reducing gray energy consumption and achieving optimal gray energy saving for carbon neutralization is proposed. In a cellular network, each cell or BS (group of cells) has renewable (green) and non-renewable (gray, on-grid power) energy sources. The renewable (green) energy is highly variable and unpredictable, while non-renewable (gray, on-grid power) is stable but is not renewable and thus has more carbon impact. Each cell or BS (group of cells) services is associated UEs when it is on. In one novel aspect, a cell or BS (group of cells) that consumes more non-renewable energy can give some or all of its served UEs to another cell or BS (group of cells) that consumes less non-renewable energy.

Abstract: Methods of cutting gate structures, and structures formed, are described. In an embodiment, a structure includes first and second gate structures over an active area, and a gate cut-fill structure. The first and second gate structures extend parallel. The active area includes a source/drain region disposed laterally between the first and second gate structures. The gate cut-fill structure has first and second primary portions and an intermediate portion. The first and second primary portions abut the first and second gate structures, respectively. The intermediate portion extends laterally between the first and second primary portions. First and second widths of the first and second primary portions along longitudinal midlines of the first and second gate structures, respectively, are each greater than a third width of the intermediate portion midway between the first and second gate structures and parallel to the longitudinal midline of the first gate structure.

Abstract: This application is directed to predicting vehicle actions according to a hierarchy of interconnected vehicle actions. The hierarchy of interconnected vehicle actions includes a plurality of predefined vehicle actions that are organized to define a plurality of vehicle action sequences. A first vehicle obtains one or more images of a road and a second vehicle, and predicts a sequence of vehicle actions of the second vehicle through the hierarchy of interconnected vehicle actions using the one or more images. The first vehicle is controlled to drive at least partially autonomously based on the predicted sequence of vehicle actions of the second vehicle. In some embodiments, the hierarchy of interconnected vehicle actions includes a first action level that is defined according to a stage of a trip and corresponds to three predefined vehicle actions of: “start a trip,” “move in a trip,” and “complete a trip.

Abstract: The present disclosure provides a method for treating arthritis by using a stem cell preparation. The stem cell preparation of the present disclosure can effectively delay cartilage degeneration caused by arthritis, and it is confirmed by whole blood analysis and blood biochemical analysis that the stem cell preparation in the form of three-dimensional stem cell spheres provides a safe treatment for arthritis. The present disclosure also provides a method for preparing the stem cell preparation.

Abstract: Embodiments provide reinforcement learning (RL) techniques for transferring machine learning obtained in one domain to another. A neural network (NN) of a source domain may be trained using training data associated with that domain. In some cases, the training data is inaccessible to a target domain for which a similar NN is desired. A number of target NNs may be initialized with a portion of the parameters transferred from the source NN. A computing agent may utilize RL techniques to train the target NNs using training data of the target domain. The target NN with the most beneficial subset of transferred parameters may be selected. By using the RL techniques to identify the most advantageous combination of transferred parameters, a similarly accurate NN may be provided in the target domain at a fraction of the otherwise needed time and without accessing the original training data.

Abstract: A method and system for content display. The method includes: capturing, by an imaging device, a plurality of images of an environment; generating, by a computing device, first user profiles based on appearance of users recognized in the images; detecting devices in the environment and associating the detected devices to the users to obtain associated devices that are held by the users; retrieving, by the computing device, second user profiles of the users based on identifications of the associated devices; selecting at least one content based on the first user profiles and the second user profiles; and displaying the at least one selected content.

Abstract: A surrounding environment of an autonomous vehicle is perceived to identify one or more vehicles nearby. For each of the identified vehicles, based on a current location of the identified vehicle, vehicle-independent information is obtained to determine context surrounding the identified vehicle, where the vehicle-independent information includes vehicle surrounding information that defines physical constraints imposed on the identified vehicle. For each of the identified vehicles, one or more trajectories for the identified vehicle are predicted based at least in part on the vehicle-independent information associated with the identified vehicle. The autonomous vehicle is controlled based on the one or more predicted trajectories of the one or more identified vehicles.

Abstract: A method and system for content display. The method includes: capturing, by an imaging device, a plurality of images of an environment; generating, by a computing device, first user profiles based on appearance of users recognized in the images; detecting devices in the environment and associating the detected devices to the users to obtain associated devices that are hold by the users; retrieving, by the computing device, second user profiles of the users based on identifications of the associated devices; selecting at least one content based on the first user profiles and the second user profiles; and displaying the at least one selected content.

Abstract: Embodiments provide methods and apparatuses to manage hardware resource access on a mobile device comprising detecting an attempt by the first application to access the first hardware resource when the application is executing in the background and determining that there is not a policy for the first application to access the first hardware, providing a notification requesting user input thereof, and adding a new policy accordingly.

Abstract: Embodiments provide methods and apparatuses to manage hardware resource access on a mobile device comprising detecting an attempt by the first application to access the first hardware resource when the application is executing in the background and determining that there is not a policy for the first application to access the first hardware, providing a notification requesting user input thereof, and adding a new policy accordingly.

Abstract: A user can issue a query about a process that has a number of stages. A stage of the process is determined using the query and location data associated with the query, and a stage prediction model. A stage learning system can select a sample of query logs for a category from a database of millions or billions of users' queries. Queries can be parsed into keywords. A category can be determined from location information associated with each query and from query keywords. Queries are aligned based on location and, optionally, keywords. TF-IDF values are computed for queries and are used to determine a difference significance between aligned, adjacent queries. If aligned, adjacent queries have a substantially difference in keywords and TF-IDF, then a conversion stage is identified. Content can be presented to the user based on the category, keywords, location, and conversion stage.

Abstract: Route segment information is received from a first autonomous vehicle over a network. The route segment information includes one or more route segments and estimated entrance times, each of the entrance times being associated with one of the route segments and representing a time the first autonomous vehicle enters the route segment. Whether the first autonomous vehicle is within a specific route segment is determined based on an entrance time associated with the specific route segment and a current location of the first autonomous vehicle. If the first autonomous vehicle is within the specific route segment, a request is sent to invite the first autonomous vehicle to join a vehicle group of one or more vehicles associated with the specific route segment. The vehicle group is one of a plurality of vehicle groups that were determined based on route segment information received from a plurality of autonomous vehicles.

Abstract: The disclosure relates to determining thermal insulation levels of clothing a user should wear when traveling to a destination. To do this, a thermal insulation level of clothing worn by a user and levels worn by a crowd can be analyzed from images including the user or the crowd. A thermal sensitivity bias for the user can be determined by comparing those levels for similar locations. Environment data can then be collected for the destination. A thermal insulation level of clothing worn by the crowd for the destination can be predicted based on the environment data. This level can be adjusted with the bias for the user to generate a thermal insulation level of clothing to be worn by the user at the destination. An image having clothing with that level can be displayed to recommend a style of clothing for the user to wear at the destination.

Abstract: Responsive to sensor data received from one or more sensors of an autonomous vehicle, one or more predicted trajectories are generated, with each of the predicted trajectories having an associated probability. One or more driving scenarios that trigger gesture recognition are identified. For each of the identified driving scenarios, one or more gestures from one or more vehicles are detected in accordance with a gesture detection protocol. One or more gestures from the autonomous vehicle are emitted for communication with the vehicles in accordance with a gesture emission protocol based on the detected gestures. The predicted trajectories are modified based on the detected gestures, the emitted gestures and the associated probabilities of the predicted trajectories. The autonomous vehicle is controlled based on the modified predicted trajectories.

Abstract: A surrounding environment of an autonomous vehicle is perceived to identify one or more vehicles nearby. For each of the identified vehicles, based on a current location of the identified vehicle, vehicle-independent information is obtained to determine context surrounding the identified vehicle, where the vehicle-independent information includes vehicle surrounding information that defines physical constraints imposed on the identified vehicle. For each of the identified vehicles, one or more trajectories for the identified vehicle are predicted based at least in part on the vehicle-independent information associated with the identified vehicle. The autonomous vehicle is controlled based on the one or more predicted trajectories of the one or more identified vehicles.

Abstract: The disclosure relates to determining thermal insulation levels of clothing a user should wear when traveling to a destination. To do this, a thermal insulation level of clothing worn by a user and levels worn by a crowd can be analyzed from images including the user or the crowd. A thermal sensitivity bias for the user can be determined by comparing those levels for similar locations. Environment data can then be collected for the destination. A thermal insulation level of clothing worn by the crowd for the destination can be predicted based on the environment data. This level can be adjusted with the bias for the user to generate a thermal insulation level of clothing to be worn by the user at the destination. An image having clothing with that level can be displayed to recommend a style of clothing for the user to wear at the destination.

Abstract: Embodiments provide methods and apparatuses to manage hardware resource access on a mobile device comprising detecting an attempt by the first application to access the first hardware resource when the application is executing in the background and determining that there is not a policy for the first application to access the first hardware, providing a notification requesting user input thereof, and adding a new policy accordingly.

Abstract: A surrounding environment of an autonomous vehicle is perceived to identify one or more vehicles nearby. For each of the identified vehicles, based on a current location of the identified vehicle, vehicle-independent information is obtained to determine context surrounding the identified vehicle, where the vehicle-independent information includes vehicle surrounding information that defines physical constraints imposed on the identified vehicle. For each of the identified vehicles, one or more trajectories for the identified vehicle are predicted based at least in part on the vehicle-independent information associated with the identified vehicle. The autonomous vehicle is controlled based on the one or more predicted trajectories of the one or more identified vehicles.

Abstract: A surrounding environment of an autonomous vehicle is perceived to identify one or more vehicles nearby. For each of the identified vehicles, based on a current location of the identified vehicle, vehicle-independent information is obtained to determine context surrounding the identified vehicle, where the vehicle-independent information includes vehicle surrounding information that defines physical constraints imposed on the identified vehicle. For each of the identified vehicles, one or more trajectories for the identified vehicle are predicted based at least in part on the vehicle-independent information associated with the identified vehicle. The autonomous vehicle is controlled based on the one or more predicted trajectories of the one or more identified vehicles.

Abstract: Driving statistics of an autonomous vehicle are collected. The driving statistics include driving commands issued at different points in time and route selection information of one or more routes while the autonomous vehicle was driven in a manual driving mode by one or more users. For each user of the autonomous vehicle, one or more user driving behaviors and preferences of the user are determined from at least the driving statistics for predetermined driving scenarios. One or more driving profiles for the user are generated based on the determined user behaviors and preferences under the driving scenarios, where the driving profiles are utilized to control the autonomous vehicle under similar driving scenarios when the user is riding in the autonomous vehicle that operates in an autonomous driving mode.