Abstract: A motion compensation method and apparatus applicable to laser point cloud data includes acquiring laser point cloud data of a frame; using an acquisition time of a start point as a target time; interpolating coordinate transformation relations and an end laser point, to obtain coordinate transformation relations of other laser points, and transforming coordinates of the other laser points to the coordinates at the target time. During motion compensation on each frame of laser point cloud data, the coordinate transformation relations corresponding to only the start and end laser points are obtained, and the coordinate transformation relations corresponding to the two points are interpolated to obtain transformation relations corresponding to other laser points, thus performing motion compensation. Therefore, the processing speed is improved and meets high real-time requirements on systems such as autopilot control systems.

Abstract: Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.

Abstract: In response to receiving a user inquiry describing one more symptoms of a medical condition, an online medical diagnosis system can determine one or more medical conditions related to the one or more symptoms. The online medical diagnosis system can include a parser to parse the user inquiry into keywords. The keywords can be looked up in a correlation graph model to determine a correlation of the keywords to one or more expert keywords. Keywords meeting a threshold correlation to expert keywords can be used to look up one or more medical conditions using an expert knowledge graph model. A user profile can be used to modify correlation values of keywords to expert keywords. The user profile can also be used to narrow the number medical conditions returned by the expert knowledge graph model. A medical resources crawler can use keywords parsed from stored user inquiries, and expert keywords, to update the correlation graph model.

Abstract: According to one embodiment, a set of predetermined queries are collected, where each of the predetermined queries is associated with a predetermined category (e.g., particular medical category or particular type of Web sites). For each of the predetermined queries, the predetermined query is annotated using an annotation dictionary corresponding to the predetermined category. One or more features are extracted from the predetermined query based on annotation of the predetermined query. A classification model corresponding to the predetermined category is trained and generated based on the predetermined queries and features associated with the predetermined queries. The classification model is utilized to classify users for information retrieval.

Abstract: Camera(s) capture a scene, including an object that is portable. An image of the scene is processed to segment therefrom a portion corresponding to the object, which is then identified from among a set of predetermined real world objects. An identifier of the object is used, with a set of associations between object identifiers and user identifiers, to obtain a user identifier that identifies a user at least partially from among a set of users. Specifically, the user identifier may identify a group of users that includes the user (“weak identification”) or alternatively the user identifier may identify the user uniquely (“strong identification”) in the set. The user identifier is used either alone or in combination with user input to obtain and store in memory, information to be output to the user. At least a portion of the obtained information is thereafter output, e.g. displayed by projection into the scene.