Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.

Abstract: A computer-implemented method includes: identifying, from first and second data, interventions common to a target unit and one or more of a plurality of donor units as filtered donor units, the first data corresponding to the target unit under one or more interventions, the second data corresponding to the plurality of donor units each under one or more interventions; identifying, from the first data, third data corresponding to the target unit under the common interventions; identifying, from the second data, fourth data corresponding to the filtered donor units under the common interventions; identifying, from the second data, fifth data corresponding to the filtered donor units under a subject intervention; generating, from the third and fourth data, a learned model representing to a relationship between the target unit and the filtered donor units; applying the learned model to the fifth data to generate the synthetic data; and outputting the synthetic data.

Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.

Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: A system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation technique, for example singular value thresholding, to be efficiently applied. Applying such a technique produces a mean matrix that estimates latent values, of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. These latent values have been denoised (if noisy) and imputed (if missing). Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: An engine and method for tracking the influence of an entity operating within a social network are presented. A query containing social network content is received. A database for entries referencing the social network content is searched, and interactions between an entity participating within the social network and the social network content are identified. A dynamic interaction network (DIN) of a plurality of the entities is identified and a dynamic influence score for an entity in the query specific DIN is calculated.

Abstract: Described herein are new error-correction (channel) codes: permute codes, iterative ensembles of permute and spinal codes, and graphical hash codes. In one aspect, a wireless system includes an encoder configured to encode data using one of the aforementioned channel codes. The wireless system also includes a decoder configured to decode the encoded data.

Abstract: Described herein are new error-correction (channel) codes: permute codes, iterative ensembles of permute and spinal codes, and graphical hash codes. In one aspect, a wireless system includes an encoder configured to encode data using one of the aforementioned channel codes. The wireless system also includes a decoder configured to decode the encoded data.