Abstract: A system and method to assist aircraft pilots with rapid decision-making in cases where the pilot needs to make a flight diversion at low altitudes due to an emergency (for example, loss of thrust). Once an emergency need for diversion is detected, the system and method generates a list of alternative airports the plane can reach given: (i) the current conditions of the plane; (ii) a real-emergency time simulation of evolving conditions of the plane; (iii) the environment at potential landing sites; and (iv) the environment on the flight path to those sites. For airports potentially within reach, the system and method provides a confidence scores for successful landings for alternative simulated landing options. The simulations and confidence scores take into account aircraft position, altitude, speed, and possible further problems with the aircraft for the both the current flight path and for each simulated alternative.

Abstract: A system and method to assist aircraft pilots with rapid decision-making in cases where the pilot needs to make a flight diversion at low altitudes due to an emergency (for example, loss of thrust). Once an emergency need for diversion is detected, the system and method generates a list of alternative airports the plane can reach given: (i) the current conditions of the plane; (ii) a real-emergency time simulation of evolving conditions of the plane; (iii) the environment at potential landing sites; and (iv) the environment on the flight path to those sites. For airports potentially within reach, the system and method provides a confidence scores for successful landings for alternative simulated landing options. The simulations and confidence scores take into account aircraft position, altitude, speed, and possible further problems with the aircraft for the both the current flight path and for each simulated alternative.

Abstract: A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a plurality of edges, each respective edge of the plurality of edges being associated with a corresponding distance representing embedded semantic information. The deep neural network is trained to capture the embedded semantic information. A path query is received at the deep neural network. A context is determined for the received path query at the deep neural network. The deep neural network performs the traversing of the knowledge graph in response to the received path query, based upon the determined context and the embedded semantic information.

Abstract: A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.

Abstract: Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.

Abstract: Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving a personalized data set including a plurality of real-time drug doses for a first drug or drug combination and a plurality of corresponding real-time adverse drug reaction tolerance data for the first drug or drug combination for a patient. Aspects also include receiving known drug data for a candidate drug or drug pair. Aspects also include calculating, based upon the known drug data and the personalized data set, a predicted adverse drug reaction tolerance for the candidate drug or drug pair at a candidate dosage, wherein the predicted adverse drug reaction tolerance is personalized to the patient.

Abstract: Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Abstract: Methods, systems, and computer program products for iterative and targeted feature selection are provided herein. A computer-implemented method includes generating a first prediction value for a variable attribute of a set of objects by executing a predictive model that comprises a set of features for the set of objects; evaluating the prediction error of the predictive model based on said first prediction value; generating additional features upon a determination that the prediction error exceeds a threshold; incorporating the additional features into the predictive model, generating an updated predictive model; generating a second prediction value for the variable attribute by executing the updated predictive model; evaluating the prediction error of the updated predictive model based on said second prediction value; and outputting the second prediction value to a user upon a determination that the prediction error of the updated predictive model is below the threshold.

Abstract: A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a plurality of edges, each respective edge of the plurality of edges being associated with a corresponding distance representing embedded semantic information. The deep neural network is trained to capture the embedded semantic information. A path query is received at the deep neural network. A context is determined for the received path query at the deep neural network. The deep neural network performs the traversing of the knowledge graph in response to the received path query, based upon the determined context and the embedded semantic information.

Abstract: A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.

Abstract: Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving a personalized data set including a plurality of real-time drug doses for a first drug or drug combination and a plurality of corresponding real-time adverse drug reaction tolerance data for the first drug or drug combination for a patient. Aspects also include receiving known drug data for a candidate drug or drug pair.

Abstract: Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Abstract: Methods, systems, and computer program products for iterative and targeted feature selection are provided herein. A computer-implemented method includes generating a first prediction value for a variable attribute of a set of objects by executing a predictive model that comprises a set of features for the set of objects; evaluating the prediction error of the predictive model based on said first prediction value; generating additional features upon a determination that the prediction error exceeds a threshold; incorporating the additional features into the predictive model, generating an updated predictive model; generating a second prediction value for the variable attribute by executing the updated predictive model; evaluating the prediction error of the updated predictive model based on said second prediction value; and outputting the second prediction value to a user upon a determination that the prediction error of the updated predictive model is below the threshold.

Abstract: Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.

Abstract: Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving known drug data from drug databases and one or more of a candidate drug, a drug pair, and a candidate drug-patient pair. Aspects also include calculating an adverse event prediction rating representing a confidence level of an adverse drug event for the candidate drug, a drug pair, and a candidate drug-patient pair, the rating being based on the known drug data. Aspects also include associating adverse event features with the candidate drug, drug pair, or a candidate drug-patient pair, including a nature, cause, mechanism, or severity of the adverse drug event. Aspects also include calculating and outputting an adverse event prediction rating.

Abstract: Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving known drug data from drug databases and one or more of a candidate drug, a drug pair, and a candidate drug-patient pair. Aspects also include calculating an adverse event prediction rating representing a confidence level of an adverse drug event for the candidate drug, a drug pair, and a candidate drug-patient pair, the rating being based on the known drug data. Aspects also include associating adverse event features with the candidate drug, drug pair, or a candidate drug-patient pair, including a nature, cause, mechanism, or severity of the adverse drug event. Aspects also include calculating and outputting an adverse event prediction rating.

Abstract: A system for automated traffic sensor placement includes a sensor placement module configured to determine where a plurality of traffic flow monitoring sensors are to be placed within a network of roadways to observe or infer traffic flow volume through each of a plurality of roadway arcs of interest. An arc prioritization module is configured to determine a relative priority of each of the arcs of interest. A sensor selection module is configured to receive an indication of how many sensors are available to deploy and select a corresponding number of sensors for deployment from among the traffic flow monitoring sensors to be placed based on the relative arc priorities determined by the arc prioritization module.