Abstract: A method for predicting a patient or patient cohort's cognition score on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog). The method comprises obtaining data relating to a plurality of patients or patient cohorts, the data including information relating to the longitudinal trajectories of the plurality of patients', or patient cohorts', ADAS-Cog score over time, each patient or patient cohort having undergone a treatment plan selected from a plurality of treatment plans; encoding the data into a tensor across patients or patient cohorts, time and treatment plan; generating a synthetic model of a target patient or target patient cohort using a machine learning process and the tensor; and predicting an ADAS-Cog score of the target patient or target patient cohort under a target treatment plan selected from the plurality of treatment plans, using the synthetic model.

Abstract: Methods and systems for event prediction. The system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data may be communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module may receive a prediction query and, in response, supply an event prediction based on the prediction model.

Abstract: A computer-implemented method for automatically determining data relationships includes generating a graphical user interface (GUI) that allows a user to intuitively form a customized model of data from different data sources. The GUI includes icons that represent data sources, data variable selection, data modeling, and data prediction. The icons can be logically arranged to form a customized model without any additional user input or knowledge of data modeling. A prediction GUI allows the user to set customized weights of data variables in the model to form predictive controls for data prediction such as in what-if scenarios.

Abstract: A system for event prediction is configured to ingest at least partially incomplete unstructured data from at least one data source, convert at least a subset of the ingested data into a prediction model in accordance with a received model definition, receive a prediction query, and in response, supply an event prediction based on the prediction model. In some embodiments, ingesting the data involves transforming the unstructured data into a universal structured format in which the data is represented as key-value pairs each comprising a key tuple and an associated value, the key tuple specifying an operation and at least one identifier; the prediction query includes a key tuple specifying an operation and at least one identifier; and the event prediction generated with the prediction model includes a value associated with the key tuple that is missing from the transformed data.

Abstract: Methods and systems for event prediction. The system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data may be communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module may receive a prediction query and, in response, supply an event prediction based on the prediction model.

Abstract: A system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data is communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module receives a prediction query and, in response, supplies an event prediction based on the prediction model.

Abstract: Methods and apparatus for predicting unknown values given a data set of known values. A prediction engine ingests a variety of data sets. Once the dataset has been ingested, requests for predictions may be presented to the prediction engine. The engine responds with a prediction as well as a confidence score based on the ingested information using a variety of techniques. Some of these techniques identify witness values in the ingested data that are similar to the requested prediction, other techniques compute witness values from ingested data, etc. These witness values are aggregated to yield an answer and a confidence level in a way that permits the user to review the underlying witness values.

Abstract: A computer-implemented method for automatically determining data relationships includes generating a graphical user interface (GUI) that allows a user to intuitively form a customized model of data from different data sources. The GUI includes icons that represent data sources, data variable selection, data modeling, and data prediction. The icons can be logically arranged to form a customized model without any additional user input or knowledge of data modeling. A prediction GUI allows the user to set customized weights of data variables in the model to form predictive controls for data prediction such as in what-if scenarios.

Abstract: Systems and methods for completing at least one entry in a dataset. The systems and methods described herein find a rich set of dense features for each of a plurality of entities, then combine the rich set of dense features with externally provided features to estimate a target value. The systems and methods combine these features using a neural network model in which portions of the input layer are each only connected to a portion of the hidden layer.

Abstract: Systems and methods for tracking at least one entity. Systems according to various embodiments may include an interface for at least receiving training data representing movements of a plurality of training entities and a query regarding a test entity's movement, a memory, and at least one processor executing instructions stored on the memory to create a plurality of tracks, generate an index of the plurality of tracks, execute the received query, and output a feature value with respect to the test entity.

Abstract: Systems and methods for tracking at least one entity. Systems according to various embodiments may include an interface for at least receiving training data representing movements of a plurality of training entities and a query regarding a test entity's movement, a memory, and at least one processor executing instructions stored on the memory to create a plurality of tracks, generate an index of the plurality of tracks, execute the received query, and output a feature value with respect to the test entity.

Abstract: Methods and systems for event prediction. The system includes at least one connector configured to gather at least partially incomplete data from at least one data source. The gathered data may be communicated to a model definition module that converts at least a subset of the gathered data into a prediction model in accordance with a received definition. A prediction module may receive a prediction query and, in response, supply an event prediction based on the prediction model.

Abstract: Methods and apparatus for predicting unknown values given a data set of known values. A prediction engine ingests a variety of data sets. Once the dataset has been ingested, requests for predictions may be presented to the prediction engine. The engine responds with a prediction as well as a confidence score based on the ingested information using a variety of techniques. Some of these techniques identify witness values in the ingested data that are similar to the requested prediction, other techniques compute witness values from ingested data, etc. These witness values are aggregated to yield an answer and a confidence level in a way that permits the user to review the underlying witness values.

Abstract: A system and method for providing a personalized recommendation from a series of partial preferences is presented. A preference distribution of a population including a plurality of weighted ranked lists is identified. A revealed preference of a user is compared to the plurality of ranked lists. An affinity weight between the user and each of the plurality of ranked lists is assigned, and a weighted average of each of the affinity weights is taken.

Abstract: A system and method for determining a rank aggregation from a series of partial preferences is presented. A distribution is learned over preferences from partial preferences with sparse support. A computer receives a plurality of partial preferences selected from two or more preference lists. Weights are assigned to each of said plurality of partial preferences, resulting in multiple ranked lists.

Abstract: In one aspect, a wireless system includes an encoder configured to encode data using a spinal code which uses a function over the message bits to directly produce a sequence of constellation symbols for transmission. The wireless system also includes a decoder configured to decode the spinal code. The function may be at least one of a non-linear function and a hash function.

Abstract: In one aspect, a wireless system includes an encoder configured to encode data using a spinal code which uses a function over the message bits to directly produce a sequence of constellation symbols for transmission. The wireless system also includes a decoder configured to decode the spinal code. The function may be at least one of a non-linear function and a hash function.

Abstract: In one aspect, a wireless system includes an encoder configured to encode data using a spinal code which uses a function over the message bits to directly produce a sequence of constellation symbols for transmission. The wireless system also includes a decoder configured to decode the spinal code. The function may be at least one of a non-linear function and a hash function.

Abstract: A method, apparatus and computer program product for providing network based flow control is presented. A linear combination of packets to transmit from a transmit queue is determined. The linear combination of packets is transmitted across a network using a sliding window protocol. An acknowledgement (ACK) is generated, wherein a packet is acknowledged when a receiving node receives the linear combination of packets and determines which packet of the linear combination of packets has been newly seen.

Abstract: A system and method for providing a personalized recommendation from a series of partial preferences is presented. A preference distribution of a population including a plurality of weighted ranked lists is identified. A revealed preference of a user is compared to the plurality of ranked lists. An affinity weight between the user and each of the plurality of ranked lists is assigned, and a weighted average of each of the affinity weights is taken.