Abstract: A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.

Abstract: There is provided a method of dynamic adaptation of a graphical user interface for exploring sensitive data, comprising: dynamically creating a hidden data presentation by applying permissions to a dataset, for hiding records, obtaining a selection of a target variable via the GUI presenting the hidden data presentation, feeding the dataset and the target variable into a hypothesis engine that extracts hypothesis features from the dataset, tests correlations between the hypothesis features and the target variable, and selects a set of insight features from the hypothesis features according to the correlations, dynamically creating a hidden result presentation by propagating the permission to the portions of the dataset used to compute the insight features, and presenting within the GUI the hidden result presentation that presents the insight features and hides the portions of the dataset used to compute the insight features according to the permissions.

Abstract: A method for monitoring an intrabody region of a patient. The method comprises intercepting electromagnetic (EM) radiation from the intrabody region in a plurality of EM radiation sessions during a period of at least 6 hours, calculating a dielectric related change of the intrabody region by analyzing respective the intercepted EM radiation, detecting a physiological pattern according to said dielectric related change and outputting a notification indicating the physiological pattern.

Abstract: A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.

Abstract: A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.

Abstract: A system comprising a plurality of electrodes adapted to measure bio impedance measurements using electrical currents passing in a target thorax area of a target therebetween during a learning phase, at least one radiofrequency (RF) sensor adapted to measure RF interaction measurements of RF radiation interacting with the target thorax area during the learning phase, and at least one processor adapted to: calculate calibration function according to the bio impedance measurements and the RF interaction measurements, and determine a target thorax area value by adjusting subsequent bio impedance measurements using subsequent electrical currents passing in the target thorax area during an operational learning phase using the calibration function.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: Methods, products and apparatus are provided for hypotheses generation using searchable unstructured data corpus. In one method, a query is generated based on at least one attribute of at least one instance in a dataset. The query is provided to a search engine searching in an unstructured data corpus. An hypothesis for the database is based on a new attribute whose value is defined based on the one or more results. Another method comprises obtaining a set of keywords from a plurality of hypotheses extracted from a database. A query is generated based on an attribute of an instance in the dataset, where the attribute corresponds to an hypothesis. A search engine executes the query to provide results which are used to augment an instance with a new attribute, where a value of the new attribute is computed based on the one or more results.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: There is provided a method of selecting subpopulations of users mapped to subpopulations of entities, comprising: receiving latent factors of a mapping between users and entities and a predicted correlation value for each undefined mapping, computed by a recommender process, for each respective latent factor: identifying, by a user semantic model, user features of the users correlated to the respective latent factor, identifying, by an entity semantic model, entity features of the entities correlated to the respective latent factor, generate combinations of pairs each including one user feature and one entity feature, for each pair, compute statistical metric(s) indicative of a change relative to the predicted correlation value for the users and the entities, select pair(s) according to a requirement of the statistical metric(s), and provide the user feature and the entity feature for each selected pair.

Abstract: A wearable monitoring apparatus for monitoring at least one biological parameter of an internal tissue of an ambulatory user. Said wearable monitoring apparatus comprises at least one transducer configured for delivering electromagnetic (EM) radiation to said internal tissue and intercepting a reflection of said EM radiation said reform in a plurality of transmission sessions during at least 24 hours, a processing unit configured for analyzing respective said reflection and identifying a change in said at least one biological parameter accordingly, a reporting unit configured for generating a report according to said change, and a housing for containing said at least one transducer, said reporting unit, and said processing unit, said housing being configured for being disposed on said body of said ambulatory user.

Abstract: A classifier is computed as follows. For a first set of values of primary field(s) of primary data instances of a labeled primary training dataset, a second set(s) of secondary fields of unclassified second data instances of secondary dataset(s) is identified. First set of values are matched to corresponding values in respective secondary field(s), and linked to other secondary fields of respective secondary data instance(s) of the respective matched secondary field. A set of classification features is generated. Each including: (i) condition(s), and (ii) a value selected from the linked other secondary fields of the respective secondary data instance(s) of the respective matched secondary field(s). The respective classification feature outputs a binary value computed by the condition(s) that compares between the value selected from the other linked secondary fields and a new received data instance. A classifier is computed according to a selected subset of the classification features.

Abstract: There is provided a method for searching an unstructured dataset with a query, comprising: receiving a query comprising a value for a first token of a triplet, and a value for a relation term defining a relationship between the first token and a second token of the triplet, wherein the second token is defined as a variable element set with an undefined value, creating a plurality of enhanced queries for the query, each one of the plurality of enhanced queries including variations of the relation term, providing the plurality of enhanced queries for search by a search engine on at least one dataset of unstructured text-based data, receiving a plurality of documents in response to the search, analyzing the plurality of documents for extracting at least one value for the variable element of the triplet, and providing the at least one value for the variable element.

Abstract: A wearable monitoring apparatus for monitoring at least one biological parameter of an internal tissue of an ambulatory user. Said wearable monitoring apparatus comprises at least one transducer configured for delivering electromagnetic (EM) radiation to said internal tissue and intercepting a reflection of said EM radiation said reform in a plurality of transmission sessions during at least 24 hours, a processing unit configured for analyzing respective said reflection and identifying a change in said at least one biological parameter accordingly, a reporting unit configured for generating a report according to said change, and a housing for containing said at least one transducer, said reporting unit, and said processing unit, said housing being configured for being disposed on said body of said ambulatory user.

Abstract: A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.

Abstract: A method for monitoring an intrabody region of a patient. The method comprises intercepting electromagnetic (EM) radiation from the intrabody region in a plurality of EM radiation sessions during a period of at least 6 hours, calculating a dielectric related change of the intrabody region by analyzing respective the intercepted EM radiation, detecting a physiological pattern according to said dielectric related change and outputting a notification indicating the physiological pattern.

Abstract: A method for monitoring thoracic tissue. The method comprises intercepting reflections of electromagnetic (EM) radiation reflected from thoracic tissue of a patient in radiation sessions during a period of at least 24 hours, detecting a change of a dielectric coefficient of the thoracic tissue by analyzing respective the reflections, and outputting a notification indicating the change. The reflections are changed as an outcome of thoracic movements which occur during the period.

Abstract: In one embodiment, payoffs to an actor during the strategic game according to a solution concept is simulated a predetermined number of times. A machine learning tool is used to predict a payoff based on the predetermined number of payoffs according to the solution concept, wherein the machine learning tool is trained with plurality of pairs, each pair comprising a set of simulated payoffs and an actual payoff, wherein the simulated payoffs are predetermined number of payoffs determined in accordance with the solution concept. In another embodiment, a process tree representative of a strategic game involving actors is obtained. The process tree may comprise computational nodes, and leaf nodes which define payoff based on computations of the computational nodes. The process tree is reduced to a game tree representing the strategic game by simulating routes in the process tree.

Abstract: A method for monitoring an intrabody region of a patient. The method comprises intercepting electromagnetic (EM) radiation from the intrabody region in a plurality of EM radiation sessions during a period of at least 6 hours, calculating a dielectric related change of the intrabody region by analyzing respective the intercepted EM radiation, detecting a physiological pattern according to said dielectric related change and outputting a notification indicating the physiological pattern.

Abstract: A system comprising a plurality of electrodes adapted to measure bio impedance measurements using electrical currents passing in a target thorax area of a target therebetween during a learning phase, at least one radiofrequency (RF) sensor adapted to measure RF interaction measurements of RF radiation interacting with the target thorax area during the learning phase, and at least one processor adapted to: calculate calibration function according to the bio impedance measurements and the RF interaction measurements, and determine a target thorax area value by adjusting subsequent bio impedance measurements using subsequent electrical currents passing in the target thorax area during an operational learning phase using the calibration function.