Abstract: One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain.

Abstract: Digitized media is received that records a conversation between individuals. Cues are extracted from the digitized media that indicate properties of the conversation. The cues are entered as training data into a machine learning module to create a trained machine learning model. The trained machine learning model is used in a processor to detect other misalignments in subsequent digitized conversations.

Abstract: One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain.

Abstract: A method utilizes a target substrate has an array of chips on a carrier with a plurality of vacancies and a plurality of donor coupons are incompletely filled with functional chips. A bounding box is defined that encompasses the vacancies on the target substrate. Outcomes are simulated by overlapping a representation of the bounding box over a representation of each of a plurality of donor coupons at a plurality of translational offsets on a substrate plane to determine matches. An optimal one of the outcomes is found at a selected one or more of the donor coupons corresponding one or more offsets. A parallel transfer of the matching functional chips fills the vacancies on the target substrate using the one or more selected donor coupons and corresponding one or more offsets.

Abstract: A computer-implemented system and method for distributed activity detection is provided. Contextual data collected for a user performing an activity is processed on a mobile computing device. The mobile computing device extracts features from the contextual data and compares the features with a set of models. Each model represents an activity. A confidence score is assigned to each model based on the comparison with the features and the mobile computing device transmits the features to a server when the confidence scores for the models are low. The server trains a new model using the features and sends the new model to the mobile computing device.

Abstract: A computer-implemented system and method for distributed activity detection is provided. Contextual data collected for a user performing an activity is processed on a mobile computing device. The mobile computing device extracts features from the contextual data and compares the features with a set of models. Each model represents an activity. A confidence score is assigned to each model based on the comparison with the features and the mobile computing device transmits the features to a server when the confidence scores for the models are low. The server trains a new model using the features and sends the new model to the mobile computing device.

Abstract: Systems and methods described receiving a set of example data and a set of knowledge based data and combine the set of example data and the set of knowledge based data to generate a set of combined data. The combined set can be used to train a machine learning model based on the set of combined data. The machine learning model is applied to a new set of received data for a new subject.

Abstract: A computer-implemented system and method for distributed activity detection is provided. Contextual data collected for a user performing an activity is processed on a mobile computing device. The mobile computing device extracts features from the contextual data and compares the features with a set of models. Each model represents an activity. A confidence score is assigned to each model based on the comparison with the features and the mobile computing device transmits the features to a server when the confidence scores for the models are low. The server trains a new model using the features and sends the new model to the mobile computing device.

Abstract: Digitized media is received that records a conversation between individuals. Cues are extracted from the digitized media that indicate properties of the conversation. The cues are entered as training data into a machine learning module to create a trained machine learning model. The trained machine learning model is used in a processor to detect other misalignments in subsequent digitized conversations.

Abstract: Systems and methods described receiving a set of example data and a set of knowledge based data and combine the set of example data and the set of knowledge based data to generate a set of combined data. The combined set can be used to train a machine learning model based on the set of combined data. The machine learning model is applied to a new set of received data for a new subject.

Abstract: Systems and method describe inputting a set of characteristic data to a machine learning model that was trained at least in part on a knowledge based data set. A predicted outcome is determined based on the output of the machine learning model and a subset of the knowledge based data set that includes terms corresponding to the set of characteristic data is identified. The predicted outcome and subset of the knowledge based data set is used to generate display information for an interface.

Abstract: A computer-implemented system and method for distributed activity detection is provided. Contextual data collected for a user performing an activity is processed on a mobile computing device. The mobile computing device extracts features from the contextual data and compares the features with a set of models. Each model represents an activity. A confidence score is assigned to each model based on the comparison with the features and the mobile computing device transmits the features to a server when the confidence scores for the models are low. The server trains a new model using the features and sends the new model to the mobile computing device.

Abstract: One embodiment of the present invention provides a system for multi-domain clustering. During operation, the system collects domain data for at least two domains associated with users, wherein a domain is a source of data describing observable activities of a user. Next, the system estimates a probability distribution for a domain associated with the user. The system also estimates a probability distribution for a second domain associated with the user. Then, the system analyzes the domain data with a multi-domain probability model that includes variables for two or more domains to determine a probability distribution of each domain associated with the probability model and to assign users to clusters associated with user roles.

Abstract: A computer-implemented system and method for personality analysis based on social network images are provided. A plurality of images posted to one or more social networking sites by a member of these sites are accessed. An analysis of the images is performed. Personality of the member is evaluated based on the analysis of the images.

Abstract: One embodiment of the present invention provides a system for clustering heterogeneous events. During operation, the system finds a partition of events into clusters such that each cluster includes a set of events. In addition, the system estimates probability distributions for various properties of events associated with each cluster. The system obtains heterogeneous event data, and analyzes the heterogeneous event data to determine the distribution of event properties associated with clusters and to assign events to clusters.

Abstract: One embodiment of the present invention provides a system for identifying anomalies. During operation, the system obtains work practice data associated with a plurality of users. The work practice data includes a plurality of user events. The system further categorizes the work practice data into a plurality of domains based on types of the user events, models user behaviors within a respective domain based on work practice data associated with the respective domain, and identifies at least one anomalous user based on modeled user behaviors from the multiple domains.

Abstract: One embodiment of the present invention provides a system for generating one or more recommendations for a customer. During operation, the system obtains transaction and image data for a plurality of existing customers. The system then trains one or more parameters of conditioning variables associated with one or more clusters based on image data as part of a predictive model. Next, the system determines a list of recommendable items for each cluster, based on the transaction data. The system obtains transaction and image data for a customer. The system then determines that the customer is a member of a cluster associated with the predictive model, based on the obtained transaction and image data. The system generates a recommendation for one or more recommendable items for the customer based on the determined cluster membership.

Abstract: A method is provided for parsing a table. The method includes: receiving an input containing the table; finding candidate separators within the table; and determining which candidate separators are at least one of real and spurious by optimizing an objective function over the set of found candidate separators. Suitably, the function measures numerically whether a parse produced by the set of real separators is accurate. The function suitably includes one or more terms that account for multiple aspects of the table including at least two of: quality of candidate separators; coherence of cells within the parse; quality of cells within the parse; coherence of entire rows within the parse; quality of entire rows within the parse; coherence of entire columns within the parse; quality of entire columns within the parse; layout consistency along an axis of the table; and repeatability along the axis of the table.

Abstract: A method is provided for parsing a document having a plurality of lines on which items are listed spanning one or more lines. It includes: obtaining a plurality of candidates, representing hypothetical items within the document, each candidate spanning one or more lines and having a local cost representing a confidence in a quality of the candidate compared to a model; determining labeling costs for intervals of the document defined between pairs of lines, each interval containing candidates therein, each labeling cost reflecting a configuration of the candidates within the interval; identifying a best labeling for each interval based on the labeling costs determined for that interval, the best labeling corresponding to one of the configurations of the candidates within the interval; defining a global objective function; and selecting a subset of the candidates such that the global objective function is optimized, based on the identified best labelings.

Abstract: One embodiment of the present invention provides a system for clustering heterogeneous events using user-provided constraints. During operation, the system estimates, based on a probabilistic model, a distribution of events across clusters such that each cluster includes a set of events. Next, the system estimates a probability distribution for an event property associated with each cluster. The system receives heterogeneous event data, and analyzes the heterogeneous event data to determine the probability distribution of event properties of clusters and to assign events to clusters. The system receives user input specifying the user-provided constraints for specializing the probabilistic model, and performs at least one of: re-computing the assignment of events to clusters, and re-determining the probability distribution of event properties of clusters based on the user input.