Abstract: Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Abstract: A system and method for evaluating an effectiveness of a therapy for a psychological condition includes selecting a therapy to be analyzed relative to psychological pathology. The selected therapy is applied to a model of the psychological condition that includes hyperdopaminergia as a function. A response is determined using an output of the model of the psychological condition. The response is compared to a control to determine a wellness metric and a report is generated indicating an effectiveness of the therapy based on the wellness metric.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

Abstract: The present disclosure relates generally to a multi-layer fraud identification and risk analysis system. For example, the system may receive a plurality of first scores associated with borrower users and a dealer user based at least in part upon output of the first ML model. The system may receive a request from a lender user device for a second score, where the dealer user and the lender user device are associated according to a correlative score. The plurality of applications and the correlative score may be used as input to the second ML model that quantifies the risk of the dealer user specifically to the lender user, based on attributes associated with the application data, dealer user, and/or lender user. Output from the second ML model may be provided to the lender user device.

Abstract: An information presentation device includes a processor that executes a procedure. The procedure includes: for respective decision making entities, calculating assessment indexes of a plurality of respective assessment criteria, based on characteristic information representing a characteristic of each of the decision making entities; selecting, from the plurality of decision making entities, at least one decision making entity having a characteristic similar to that of an information presentation target decision making entity that is a target of information presentation, based on an assessment index calculated for each of the decision making entities; and acquiring and presenting information related to decision making by the selected decision making entity from a storage section storing information related to decision making for each of the plurality of decision making entities.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: A pattern based audio searching method includes labeling a plurality of source audio data based on patterns to obtain audio label sequences of the source audio data; obtaining, with a processing device, an audio label sequence of target audio data; determining matching degree between the target audio data and the source audio data according to a predetermined matching rule based on the audio label sequence of the target audio data and the audio label sequences of the source audio data; and outputting source audio data having matching degree higher than a predetermined matching threshold as a search result.

Abstract: Embodiments are disclosed for predicting a response (e.g., an answer responding to a question) using an end-to-end memory network model. A computing device according to some embodiments includes embedding matrices to convert knowledge entries and an inquiry into feature vectors including the input vector and memory vectors. The device further execute a hop operation to generate a probability vector based on an input vector and a first set of memory vectors using a continuous weighting function (e.g., softmax), and to generate an output vector as weighted combination of a second set of memory vectors using the elements of the probability vector as weights. The device can repeat the hop operation for multiple times, where the input vector for a hop operation depends on input and output vectors of previous hop operation(s). The device generates a predicted response based on at least the output of the last hop operation.

Abstract: Adaptive image processing, image analysis, pattern recognition, and time-to-event prediction in various imaging modalities associated with assisted reproductive technology. The reference image may be processed according to one or more adaptive processing frameworks for de-speckling or noise processing of ultrasound images. The subject image is processed according to various computer vision techniques for object detection, recognition, annotation, segmentation, and classification of reproductive anatomy, such as follicles, ovaries and the uterus. An image processing framework may also analyze secondary data along with subject image data to analyze time-to-event progression of the subject image.

Abstract: Core utilization optimization by dividing computational blocks across neurosynaptic cores is provided. In some embodiments, a neural network description describing a neural network is read. The neural network comprises a plurality of functional units on a plurality of cores. A functional unit is selected from the plurality of functional units. The functional unit is divided into a plurality of subunits. The plurality of subunits are connected to the neural network in place of the functional unit. The plurality of functional units and the plurality of subunits are reallocated between the plurality of cores. One or more unused cores are removed from the plurality of cores. An optimized neural network description is written based on the reallocation.

Abstract: Systems for autonomous management of hyperconverged distributed computing and storage systems. A method embodiment commences upon receiving a set of system measurements that correspond to system metrics associated with the computing system. A user interface is presented to users to capture a set of user sentiment indications. Over a period of time, a time series of system measurements and a time series of user sentiment indications are captured and used to form a learning model that comprises dynamically-changing user sentiment correlations between the system measurements and the user sentiment. At some moment in time, a system metric threshold breach event occurs. The learning model is consulted to determine a tracking value between the set of user sentiment indications and the system metric pertaining to the system metric threshold. Based on the tracking value, the respective system metric threshold is adjusted to more closely track with the historical user sentiment indications.

Abstract: In some examples, a machine learning system may use morphological knowledge to enhance a deep learning framework for learning word embedding. The system may consider, among other things, morphological similarities between and among words in a learning process so as to handle new or rare words, edit distances, longest common substring similarities, morpheme similarities, and syllable similarities as morphological knowledge to build a relation matrix between or among words. The system may apply the deep learning framework to query classification, web search, text mining, information retrieval, and natural language processing tasks, for example. The system may accomplish such tasks with relatively high efficiency and speed, while utilizing less computing resources as compared to other systems.

Abstract: A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user's input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user's selection actions thereof. The method includes analyzing the selected items to learn the user's content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user's learned content preferences determined to be relevant to the items of the first content system.

Abstract: A learning device includes a classification unit that classifies data to be determined by using a learner configured to classify data based on a predetermined feature among features included in the data. The learning device includes an estimation unit that estimates, from classification results according to the classification unit, the behavior of the probability of a mistake occurring in the classification results according to the classification unit based on a large deviation principle. The learning device includes a determination unit that determines, based on the behavior estimated by the estimation unit, whether to add a new feature to an object to be learned for the learner.

Abstract: Techniques for implementing a safety protocol are provided. In one example, a system is provided that can execute a machine-learned model to determine cognitive data representing a prediction about a state of an environment and an action to be performed in response to the prediction. The system can determine that a connection with a remote device is unavailable, and in response activate a safety protocol.

Abstract: A machine learning device for learning a threshold value of detecting an abnormal load in a machine tool, includes a state observation unit, and a learning unit. The state observation unit observes a state variable obtained based on at least one of information about a tool, main spindle revolution rate, and amount of coolant of the machine tool, material of a workpiece, and moving direction, cutting speed, and cut depth of the tool, and the learning unit learns the threshold value of detecting an abnormal load based on training data created from an output of the state observation unit and data related to detection of an abnormal load in the machine tool and on teacher data.

Abstract: Described herein are systems and methods of alarm rationalization for an industrial control system. This can comprise building a model of the industrial control system, wherein the model includes components that are monitored or controlled by the industrial control system and alarms associated with the components; training the model by applying one or more machine learning algorithms against a historical database of alarms for the industrial control system; and applying the trained model against the industrial control system for alarm management of the industrial control system.

Abstract: According to an embodiment, a lattice finalization device finalizes a portion of a lattice that is generated by pattern recognition with respect to a signal on a frame-by-frame basis in chronological order. The device includes a detector and a finalizer. The detector is configured to detect, as a splitting position, a frame in the lattice in which the number of nodes and passing arcs is equal to or smaller than a reference value set in advance. The finalizer is configured to finalize nodes and arcs in paths from a start node to the splitting position in the lattice.

Abstract: An abrasion amount estimation device stores a learning result obtained through supervised learning performed based on a feature amount, which is extracted from a physical amount which is acquired in injection performed by an injection molding machine, and information related to an abrasion amount of a check valve which has been attached to the injection molding machine in the injection. The abrasion amount estimation device estimates an abrasion amount of a check valve which has been attached to the injection molding machine in the injection based on the learning result which is stored and the feature amount which is extracted.