Abstract: A system and related method for building and deploying one or more inference models for use in remote condition monitoring of a first fleet of a first asset. The system includes model configuration data for subsequent use by a model builder application to construct one or more desired inference models for the first asset. The model configuration data is customized to the first asset and the desired one or more inference models, and is provided in a format which is easily readable and editable by a user of the system. The model configuration data is separate from the underlying processing algorithms which are employed by the model builder application in the constructing of the one or more desired inference models during a learning mode of operation of the system.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

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: Method embodiments and/or system embodiments are provided that may be utilized to recommend online content to users based, at least in part on a prediction of diffusion of online content through a social network.

Abstract: Embodiments of the invention relate to a multiplexed neural core circuit. One embodiment comprises a core circuit including a memory device that maintains neuronal attributes for multiple neurons. The memory device has multiple entries. Each entry maintains neuronal attributes for a corresponding neuron. The core circuit further comprises a controller for managing the memory device. In response to neuronal firing events targeting one of said neurons, the controller retrieves neuronal attributes for the target neuron from a corresponding entry of the memory device, and integrates said firing events based on the retrieved neuronal attributes to generate a firing event for the target neuron.

Abstract: The described technology can provide semantic translations of a selected language snippet. This can be accomplished by mapping snippets for output languages into a vector space; creating predicates that can map new snippets into that vector space; and, when a new snippet is received, generating and matching a vector representing that new snippet to the closest vector for a snippet of a desired output language, which is used as the translation of the new snippet. The procedure for mapping new snippets into the vector space can include creating a dependency structure for the new snippet and computing a vector for each dependency structure node. The vector computed for the root node of the dependency structure is the vector representing the new snippet. A similar process is used to train a transformation function for each possible node type, using language snippets already associated with a dependency structure and corresponding vectors.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for automating a proactive Safety KPI analysis. Correlated data related to a safety key performance indicator (KPI) is obtained from a correlation engine. A safety KPI prediction related to safety incidents, future safety trends, or future safety KPIs is generated based on the received correlated data and at least one safety KPI prediction model. The generated safety KPI prediction is transmitted to a proactive monitoring and alerting engine and a safety KPI alert is generated based on the safety KPI prediction, at least one alert threshold, and the at least one safety KPI prediction model. Transmission of the generated safety KPI alert is then initiated.

Abstract: A system comprises a network of computers comprising a master computer and slave computers. For a machine learning problem that is partitioned into a number of correlated sub-problems, each master computer is configured to store tasks associated with the machine learning problem, and each of the slave computers is assigned one of the correlated sub-problems. Each slave computer is configured to store variables or parameters or both associated with the assigned one of the correlated sub-problems; obtain information about one or more tasks stored by the master computer without causing conflict with other slave computers with regard to the information; perform computations to update the obtained information and the variables or parameters or both of the assigned sub-problem; send the updated information to the master computer to update the information stored at the master computer; and store the updated variables or parameters or both of the assigned sub-problem.

Abstract: Various techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: A state machine engine having a program buffer. The program buffer is configured to receive configuration data via a bus interface for configuring a state machine lattice. The state machine engine also includes a repair map buffer configured to provide repair map data to an external device via the bus interface. The state machine lattice includes multiple programmable elements. Each programmable element includes multiple memory cells configured to analyze data and to output a result of the analysis.

Abstract: Example embodiments of the present invention relate to a method, an apparatus, and a computer program product for predictive behavioral analytics for information technology (IT) operations. The method includes collecting key performance indicators from a plurality of data sources in a network. The method also includes performing predictive behavioral analytics on the collected data and reporting on results of the predictive behavioral analytics.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.

Abstract: Methods and arrangements for identifying burden comprehension in multimedia content. A contemplated method includes: accepting multimedia input; detecting components of the multimedia input; determining a comprehension burden score of each of the detected components; and thereupon calculating a total comprehension burden score for the multimedia input. Other variants and embodiments are broadly contemplated herein.

Abstract: A method and system for determining whether, when, and how an unmanned agent interrupts a human is provided. The provided method and system uses a two-step multivariate analysis to (i) process a request for human interaction to determine whether to interrupt a human, and (ii) determine when and how to interrupt the human. The provided method and system interrupts the human in a manner that reflects analysis of variables such as mission criticality, whether a delay in the mission is acceptable, and the propriety of interrupting the human.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: According to embodiments of the present invention, the energy consumption assessment is done by identifying the benchmark production processes or enterprises. First, a process similarity between a target production process and a candidate production process is determined based on energy consumption units (ECUs) involved in the target production process and the candidate production process. In response to the process similarity being greater than a first threshold, an energy consumption similarity between the target production process and the candidate production process is determined based on a factor that has influence on energy consumption of at least one of the ECUs. In response to the energy consumption similarity being greater than a second threshold, the candidate production process is identified as a benchmark for assessing energy consumption of the target production process.

Abstract: Summarizing, the application relates to a machine-learning system for adaptively changing a matching threshold of a biometric system. The machine-learning system comprises a batch aggregator device operable to receive input data from the biometric system via a communication interface and to aggregate a batch of at least some of the received input data. The machine-learning system further comprises a learning expert device operable to compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch. Finally, the machine-learning system comprises an output device operable to output the computed new suggestion for the matching threshold of the biometric system via the communication interface.

Abstract: A predictive estimator, trained on a data corpus, is used to generate a probability estimate based a sequence of data related to an entity. The predictive estimator computes an instantaneous surprise score which is a quantification of a short-term deviation of a datum from the probability estimate. To compute the instantaneous surprise score, the predictive estimator is initialized with default values of the predictive estimator. Then, for each of data input of the datum to the predictive estimator, the instantaneous surprise score is calculated, corresponding to the deviation of the data input from the probability estimate. This generates an estimate of the probability of observing the datum given past data on the entity and the predictive estimator. The predictive estimator is updated with the datum and the time step advanced.