Abstract: A method operates on observed relationship data between pairs of entities of a set of entities including entities of at least two (and optionally at least three) different entity types. An observed collective symmetric matrix is constructed in which element (n,m)=element (m,n) stores the observed relationship between entities indexed n and m when the observed relationship data includes this observed relationship. A prediction collective symmetric matrix is optimized in order to minimize a loss function comparing the observed collective symmetric matrix and the prediction collective symmetric matrix. A relationship between two entities of the set of entities is predicted using the optimized prediction collective symmetric matrix. Entities of the same entity type may be indexed using a contiguous set of indices such that the entity type maps to a contiguous set of rows and corresponding contiguous set of columns in the observed collective symmetric matrix.

Abstract: Techniques for obtaining user feedback related to media content are provided. Sensor data including motion data captured by a motion sensor while media content is played on a media content terminal device may be received. The sensor data may be analyzed for an indication of one or more personal states of one or more users. The indication of a first personal state may be determined based on the motion data. User preferences may be derived from the user feedback. For example, parts of the media content (e.g., specific video frames or scenes) may be analyzed and various entities or features extracted. The entities or features may be matched against user feedback to derive user preferences at a more granular level.

Abstract: Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D?1 and P?1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.

Abstract: The present invention provides a single-layer multi-point touch-control conductive film and a method for producing the same.

Abstract: A flexible circuit connecting device is disclosed, including a base layer having a first surface and a second surface, and conductive traces having a grid-like structure and formed on the first surface and/or the second surface. The conductive traces of the above flexible circuit connecting device are nearly aligned with the base layer, and thus the probability of damage under a stress is reduced. Designed to be a grid-like structure, the conductive traces become more transparent, while satisfying a function of a connector. Besides, the above flexible circuit connecting device has a high density circuit trace, so that the size of the connector can be reduced and the interior space of the electronic components can be saved. In a manufacture process of the above flexible circuit connecting device, the manufacture process can be simplified, manufacture efficiency and production yield can be improved, and manufacture cost can be efficiently reduced.

Abstract: A transparent conductive film includes a transparent substrate and a polymer layer formed on the transparent substrate, a surface of the polymer layer is patterned to define a meshed trench, the meshed trench is filled with a conductive material to form a sending area, a periphery of the sensing area is printed with a lead, the lead is electrically connected to the conductive material in the meshed trench. Besides, a method of manufacturing the transparent conductive film is provided. In the transparent conductive film and the method, the trench is filled with a conductive material to form a sending area, and the lead is formed by printing and electrically connected to the conductive material, the yield of the lead of the transparent conductive film is relatively high.

Abstract: A method operates on observed relationship data between pairs of entities of a set of entities including entities of at least two (and optionally at least three) different entity types. An observed collective symmetric matrix is constructed in which element (n,m)=element (m,n) stores the observed relationship between entities indexed n and m when the observed relationship data includes this observed relationship. A prediction collective symmetric matrix is optimized in order to minimize a loss function comparing the observed collective symmetric matrix and the prediction collective symmetric matrix. A relationship between two entities of the set of entities is predicted using the optimized prediction collective symmetric matrix. Entities of the same entity type may be indexed using a contiguous set of indices such that the entity type maps to a contiguous set of rows and corresponding contiguous set of columns in the observed collective symmetric matrix.

Abstract: A multi-relational data set is represented by a probabilistic multi-relational data model in which each entity of the multi-relational data set is represented by a D-dimensional latent feature vector. The probabilistic multi-relational data model is trained using a collection of observations of relations between entities of the multi-relational data set. The collection of observations includes observations of at least two different relation types. A prediction is generated for an observation of a relation between two or more entities of the multi-relational data set based on a dot product of the optimized D-dimensional latent feature vectors representing the two or more entities. The training may comprise optimizing the D-dimensional latent feature vectors to maximize likelihood of the collection of observations, for example by Bayesian inference performed using Gibbs sampling.

Abstract: A system and method for generating an occupancy model are disclosed. The model is learned using occupancy data for zones, each zone including cells, which are occupied or not at a given time, each with a sensor, which may be reporting or not. The data provides an observed occupancy corresponding to a number of cells in the respective zone which have reporting sensors, and the number of those sensors which are reporting that the respective cell is occupied. The occupancy model is based on a demand model and a sensor noise model which accounts for behavior of the non-reporting sensors. The noise model assumes that the probability of a sensor being in the reporting state is dependent on whether the respective cell is occupied or not. The model can fit the occupancy data better than one which assumes that non-reporting cells are occupied with the same frequency as reporting ones.

Abstract: Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D?1 and P?1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.