Abstract: Techniques are provided for training a target language model based at least in part on data associated with a reference language model. For example, language data utilized to train an English language model may be translated and provided as training data to train a German language model to recognize utterances provided in German. By utilizing the techniques herein, the efficiency of training a new language model may be improved due at least in part to replacing labor-intensive operations conventionally performed by specialized personnel with machine-generated data. Additionally, techniques discussed herein provide for reducing the time required for training a new language model by leveraging information associated with utterances of one language to train the new language model associated with a different language.

Abstract: Techniques are provided for training a language recognition model. For example, a language recognition model may be maintained and associated with a reference language (e.g., English). The language recognition model may be configured to accept as input an utterance in the reference language and to identify a feature to be executed in response to receiving the utterance. New language data (e.g., other utterances) provided in a different language (e.g., German) may be obtained. This new language data may be translated to English and utilized to retrain the model to recognize reference language data as well as language data translated to the reference language. Subsequent utterances (e.g., English utterances, or German utterances translated to English) may be provided to the updated model and a feature may be identified. One or more instructions may be sent to a user device to execute a set of instructions associated with the feature.

Abstract: Techniques are provided for training a language recognition model. For example, a language recognition model may be maintained and associated with a reference language (e.g., English). The language recognition model may be configured to accept as input an utterance in the reference language and to identify a feature to be executed in response to receiving the utterance. New language data (e.g., other utterances) provided in a different language (e.g., German) may be obtained. This new language data may be translated to English and utilized to retrain the model to recognize reference language data as well as language data translated to the reference language. Subsequent utterances (e.g., English utterances, or German utterances translated to English) may be provided to the updated model and a feature may be identified. One or more instructions may be sent to a user device to execute a set of instructions associated with the feature.

Abstract: Techniques are provided for training a target language model based at least in part on data associated with a reference language model. For example, language data utilized to train an English language model may be translated and provided as training data to train a German language model to recognize utterances provided in German. By utilizing the techniques herein, the efficiency of training a new language model may be improved due at least in part to replacing labor-intensive operations conventionally performed by specialized personnel with machine-generated data. Additionally, techniques discussed herein provide for reducing the time required for training a new language model by leveraging information associated with utterances of one language to train the new language model associated with a different language.

Abstract: A method of building a model for predicting failure of a machine, including parsing (41) daily machine event logs of one or more machines to extract data for a plurality of features, parsing (42) service notifications for the one or more machine to extract failure information data, creating (43) bags from the daily machine event log data and failure information data for multiple instance learning by grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative, where a bag is a set of feature vectors and an associated label, where each feature vector is an n-tuple of features, transforming (44) the multiple instance learning bags into a standard classification task form, selecting (45) a subset of features from the plurality of features, and training (46) a failure prediction model using the selected subset of features.

Abstract: A method to build a failure predictive model includes: receiving an input of a set of event sequences, where each sequence is labeled as representing a failure or not representing a failure, extracting a single predictive closed pattern from among the input sequences that represents a failure, creating a root node with the single closed pattern, splitting the set of event sequences into a first set that includes the single closed pattern and a second set that excludes the single pattern, and processing each of the first and second sets until at least one child node is created that is labeled as either representing a failure or not representing a failure.

Abstract: Techniques for accent translation are described herein. A plurality of audio samples may be received, and each of the plurality of audio samples may be associated with at least one of a plurality of accents. Audio samples associated with at least a first accent of the plurality of accents may be compared to audio samples associated with at least one other accent of the plurality of accents. A translation model between the first accent and a second accent may be generated. An input audio portion in a first spoken language may be received. It may be determined whether the input audio portion is substantially associated with the first accent, and if so, an output audio portion substantially associated with the second accent in the first spoken language may be outputted based, at least in part, on the translation model.

Abstract: Techniques for accent translation are described herein. A plurality of audio samples may be received, and each of the plurality of audio samples may be associated with at least one of a plurality of accents. Audio samples associated with at least a first accent of the plurality of accents may be compared to audio samples associated with at least one other accent of the plurality of accents. A translation model between the first accent and a second accent may be generated. An input audio portion in a first spoken language may be received. It may be determined whether the input audio portion is substantially associated with the first accent, and if so, an output audio portion substantially associated with the second accent in the first spoken language may be outputted based, at least in part, on the translation model.

Abstract: A method to build a failure predictive model includes: receiving an input of a set of event sequences, where each sequence is labeled as representing a failure or not representing a failure, extracting a single predictive closed pattern from among the input sequences that represents a failure, creating a root node with the single closed pattern, splitting the set of event sequences into a first set that includes the single closed pattern and a second set that excludes the single pattern, and processing each of the first and second sets until at least one child node is created that is labeled as either representing a failure or not representing a failure.

Abstract: A method of building a model for predicting failure of a machine, including parsing (41) daily machine event logs of one or more machines to extract data for a plurality of features, parsing (42) service notifications for the one or more machine to extract failure information data, creating (43) bags from the daily machine event log data and failure information data for multiple instance learning by grouping daily event log data into the bags based on a predetermined predictive interval, labeling each bag with a with a known failure as positive, and bags without known failures as negative, where a bag is a set of feature vectors and an associated label, where each feature vector is an n-tuple of features, transforming (44) the multiple instance learning bags into a standard classification task form, selecting (45) a subset of features from the plurality of features, and training (46) a failure prediction model using the selected subset of features.

Abstract: A method for identifying a plurality of patterns of events from within event log file data includes receiving a query comprising a plurality of patterns, each of the patterns comprising a plurality of events. One or more key events is determined from the plurality of patterns of events. The one or more key events is located within a database of stored event log file data. An event stream comprising the key events and all other events of the event log file data occurring within a predetermined time span from the time of the located one or more events is generated. Each of the plurality of patterns of the received query are searched for from within the event stream.

Abstract: In a support vector regression approach to forecasting power load in an electrical grid, a feature learning scheme weights each feature in the input data with its correlation with the predicted load, increasing the prediction accuracy. The kernel matrix for the input training data is computed such that features that align better with the target variable are given greater weight. The resulting load forecast may be used to compute commands sent to demand response modules.

Abstract: Systems and methods for determining critical thresholds on a number of events (k) and a window length (t) for properly defining a burst of events in a data stream. A new coverage metric Ck,t is defined and used in the determination, where the coverage metric Ck,t is defined for a particular pair (k,t) as a fraction, with the numerator defined a number of events that occur within some (k,t)-bursty window and the denominator defined as the total number of events (n) that occurred along the entire time span being analyzed. Coverage metric Ck,t is monotonic non-increasing in k and monotonic non-decreasing in t, allowing for a divide-and-conquer search strategy to be used to find the critical threshold pairs (k*, t*).

Abstract: A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.

Abstract: A method for identifying a plurality of patterns of events from within event log file data includes receiving a query comprising a plurality of patterns, each of the patterns comprising a plurality of events. One or more key events is determined from the plurality of patterns of events. The one or more key events is located within a database of stored event log file data. An event stream comprising the key events and all other events of the event log file data occurring within a predetermined time span from the time of the located one or more events is generated. Each of the plurality of patterns of the received query are searched for from within the event stream.

Abstract: An approach is provided for condition monitoring from log messages and sensor trends based on time semi-intervals. The approach may be applied to machine condition monitoring. Patterns are mined from symbolic interval data that extends previous approaches by allowing semi-intervals and partially ordered patterns. The semi-interval patterns and semi-interval partial order patterns are less restrictive than patterns using Allen's relations. Combinations and adaptations of efficient algorithms from sequential pattern and itemset mining for discovery of semi-interval patterns are described.

Abstract: Systems and methods for determining critical thresholds on a number of events (k) and a window length (t) for properly defining a burst of events in a data stream. A new coverage metric Ck,t is defined and used in the determination, where the coverage metric Ck,t is defined for a particular pair (k,t) as a fraction, with the numerator defined a number of events that occur within some (k,t)-bursty window and the denominator defined as the total number of events (n) that occurred along the entire time span being analyzed. Coverage metric Ck,t is monotonic non-increasing in k and monotonic non-decreasing in t, allowing for a divide-and-conquer search strategy to be used to find the critical threshold pairs (k*, t*).

Abstract: An approach is provided for condition monitoring from log messages and sensor trends based on time semi-intervals. The approach may be applied to machine condition monitoring. Patterns are mined from symbolic interval data that extends previous approaches by allowing semi-intervals and partially ordered patterns. The semi-interval patterns and semi-interval partial order patterns are less restrictive than patterns using Allen's relations. Combinations and adaptations of efficient algorithms from sequential pattern and itemset mining for discovery of semi-interval patterns are described.

Abstract: A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.

Abstract: Documents in a high density data stream are clustered. Incoming documents are analyzed to find metadata, such as words in a documents headline or abstract and people, places, and organizations discussed in the document. The metadata is emphasized as compared to other words found in the document. A single feature vector for each document determined based on the emphasized metadata will accordingly take into account the importance of such words and clustering efficacy and efficiency are improved.