Abstract: A recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.

Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating data items. A method includes reading a glimpse from a data item using a decoder hidden state vector of a decoder for a preceding time step, providing, as input to a encoder, the glimpse and decoder hidden state vector for the preceding time step for processing, receiving, as output from the encoder, a generated encoder hidden state vector for the time step, generating a decoder input from the generated encoder hidden state vector, providing the decoder input to the decoder for processing, receiving, as output from the decoder, a generated a decoder hidden state vector for the time step, generating a neural network output update from the decoder hidden state vector for the time step, and combining the neural network output update with a current neural network output to generate an updated neural network output.

Abstract: In some examples, a system includes an article of personal protective equipment (PPE) comprising one or more sensors, the one or more sensors configured to generate usage data that is indicative of an operation of the article of PPE; and at least one computing device comprising a memory and one or more computer processors that: receive the usage data that is indicative of the operation of the article of PPE; apply the usage data to a safety learning model that predicts a likelihood of an occurrence of a safety event associated with the article of PPE based at least in part on previously generated usage data that corresponds to the safety event; and perform, based at least in part on predicting the likelihood of the occurrence of the safety event, at least one operation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audio processing using neural networks. One of the systems includes multiple neural network layers, wherein the neural network system is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output for the audio sample, the plurality of neural network layers comprising: a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features; and one or more other neural network layers having respective layer parameters, wherein the one or more neural network layers are configured to process frequency domain features to generate a neural network output.

Abstract: A method, system and computer program product for enhancing privacy of event data. Event sensor data (e.g., body temperature, heart rate data) is received and analyzed by a subscriber to form a probability of assigning a user identity to the received event sensor data. The user of the event sensor data is then assigned with a temporary membership to a cohort (related group of users that share common characteristic(s) or experience(s)) to hide the identity of the user in response to the probability of assigning the user identity to the received event sensor data exceeding a threshold. Actions may then be performed based on the temporary membership to the cohort in order to ensure that the probability of assigning a user identity to the received event sensor data does not exceed the threshold. In this manner, privacy of the user's sensitive data is enhanced.

Abstract: This disclosure relates to systems and methods for performing an autonomous procedure for monitoring and diagnostics of a machine using electrical signature analysis. In one embodiment of the disclosure, a method includes providing electrical data of an electrical rotating machine associated with at least one fault frequency. While in a learning mode, the method includes converting the electrical data from a time domain to a frequency domain to obtain baseline data. While in an operational mode, the method includes converting the electrical data from the time domain to the frequency domain to obtain monitoring data. The method further includes determining, based at least on the monitoring data, a ratio value at the fault frequency, determining a rate of change of the ratio value at the fault frequency, and, optionally, issuing, based on the rate of change, an alarm concerning at least one event of the electrical rotating machine.

Abstract: According to an embodiment, a training device trains a neural network that outputs a posterior probability that an input signal belongs to a particular class. An output layer of the neural network includes N units respectively corresponding to classes and one additional unit. The training device includes a propagator, a probability calculator, and an updater. The propagator supplies a sample signal to the neural network and acquires (N+1) input values for each unit at the output layer. The probability calculator supplies the input values to a function to generate a probability vector including (N+1) probability values respectively corresponding to the units at the output layer. The updater updates a parameter included in the neural network in such a manner to reduce an error between a teacher vector including (N+1) target values and the probability vector. A target value corresponding to the additional unit is a predetermined constant value.

Abstract: A method for generating one or more push notifications to a user device is described. The method comprises: obtaining history data representing a history of online activities of a user and candidate data representing a set of candidate information; generating, based on the history data and the candidate data, user profile vectors representing a user profile associated with the user and content vectors representing a set of content profiles associated with the set of candidate information; generating, based on a machine learning model trained with a history of online activities, embedding user feature vectors and embedding content feature vectors based on the history data and the candidate data; and providing for transmission information for one or more push notifications including first candidate information of to a user device associated with the user, the first candidate information being determined from the set of candidate information based on the aforementioned vectors.

Abstract: Methods, systems, and computer program products for detection of an arbitrarily-shaped source of an abnormal event via use of a hierarchical reconstruction method are provided herein. A computer-implemented method includes detecting an abnormal event based on analysis of sensor data, wherein said analysis of the sensor data comprises comparing the sensor data to a user-defined threshold; generating a query based on the detected abnormal event; processing the query against one or more given data repositories; executing an inverse model using an output generated in relation to said processing to identify a source of the detected abnormal event, wherein the source comprises an arbitrary shape; and outputting the identified source of the detected abnormal event.

Abstract: A system, method, and computer program product are provided for providing proactive customer care for issues associated with billing or ordering processes. In use, a likelihood that a customer is going to call a call center to address at least one issue associated with at least one of an ordering process or a billing process is predicted. Additionally, it is determined whether the customer is likely to call the call center based on the predicted likelihood that the customer is going to call the call center. Further, the customer is proactively notified before the customer contacts the call center, if it is determined that the customer is likely to call the call center.

Abstract: In accordance with one embodiment, a special purpose computer can be implemented for processing a linear optimization problem capable of being represented in the form [A] [X]+[I] [Y]=[B] and wherein the linear optimization problem can also be represented in the form [E][A][X]+[E][I][Y]=[E][B]. The computer may comprise a first processor; a plurality of row processors each configured to store a row of the matrix [E]; a computer memory in communication with the first processor and in communication with each row processor so that each row processor can read from the computer memory and write to the computer memory; wherein the first processor is configured to signal all of the row processors to process data related to the linear optimization problem.

Abstract: Embodiments of the invention relate to mapping neural dynamics of a neural model on to a lookup table. One embodiment comprises defining a phase plane for a neural model. The phase plane represents neural dynamics of the neural model. The phase plane is coarsely sampled to obtain state transition information for multiple neuronal states. The state transition information is mapped on to a lookup table.

Abstract: An approach is provided in which a QA system ingests traditional sources, which includes traditional terms, into a domain dictionary. Next, the QA system ingests crowd-based sources that include crowd-based terms and corresponding crowd-based metadata. In turn, the QA system calculates weightings pertaining to the traditional terms based upon the crowd-based metadata. When the QA system receives a question from a requestor that includes question terms, the QA system identifies an answer to the question based on the calculated weightings pertaining to the traditional terms that are relevant to the question terms.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.

Abstract: A data processing system processes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that may be processed to identify frequent sets therein. When association rules are generated from such frequent sets, the complexity and/or quantity of such rules may be managed by removing redundancies from the rules, such as by removing rules providing only trivial associations, removing rules having only a part group as the consequent, modifying rules to remove redundant antecedent items and/or filtering subsumed rules from the generated rule set that do not provide sufficient lift to meet an adjustable specialization lift threshold requirement.

Abstract: A system correlates intellectual property analyzes, for example, patent claim charts, with respect to analyzed intellectual property and a target product or other intellectual property. Analyzes are stored to enable searching and/or creating reports over multiple analyzes. Units of the analysis are associated with a context, inherited, e.g., from the intellectual property document's assignment to a relative role within organizational hierarchy; and associated with a context derived from the analysis itself. The analysis and respective documents and/or targets of the analysis can be searched/retrieved/analyzed from the hierarchical analysis, the context analysis, and/or the content of the analysis. This obviates the need to store each analysis as a separate document. The target or annotations may be visually represented by an item such as a thumbnail or hyperlink, and the system automatically associates the item with the appropriate application program.

Abstract: Disclosed are various embodiments for using media reported events to generate predictions for time series data. Information retrieved from a plurality of network content sources is classified into a plurality of categories. A prediction is generated for a time series. The time series is associated with a metric observed in a computing system. The generated prediction takes into account an impact of at least one of instance of the classified information.

Abstract: Systems and methods that determine a meaning of a knowledge item using related information are described. In one aspect, a knowledge item is received, related information associated with the knowledge item is received, at least one related meaning based on the related information is determined, and a knowledge item meaning for the knowledge item based at least in part on the related meaning is determined. Several algorithms and types of related information useful in carrying out such systems and methods are described.

Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.

Abstract: Methods, non-transitory computer-readable storage media, and computer systems comprising at least one non-transitory computer-readable storage medium and at least one processor are provided for evaluating predictions regarding relationships. A computer system is controlled to manage a relationships database of relationship data records. Each relationship data record includes a person identifier for each person in the relationship. The computer system is controlled to manage a prediction database of prediction data records. Each prediction data record includes a relationship prediction for a relationship identified by a relationship data record in the relationships database. For each relationship prediction included in a prediction data record in the prediction database, the computer system is controlled to determine whether the relationship prediction is correct by accessing official records from a database via a network, and generate a prediction result indicator that indicates a result of the determination.