Abstract: A Support Vector Machine (SVM) classifier employs a kernel ƒ(xi,y)??d=1DF(xi,d,yd) or ƒ(xi,y)??d=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and vector y is a vector being classified, e.g. representing a message communicated on a Controller Area Network (CAN) bus of a vehicle. The SVM classifier comprises a microprocessor or microcontroller programmed to perform classification using the SVM classifier with at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table. For those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of values. In an embedded system, the microprocessor or microcontroller performs an embedded system function such as engine control, brake control, entertainment system operation, or so forth, or an embedded system function for a vehicle, ocean buoy, or satellite.

Abstract: Embodiments for intelligent service negotiation by a processor in a cloud computing environment. One or more data points may be analyzed based on one or more service provider cost components received from one or more service providers for a service. A negotiation model may be generated using a machine learning model reflecting actual costs of the service based on the analyzed data points. A user may be cognitively consulted according to the negotiation model to provide one or more consultative recommendations for selecting the service by the one or more service providers.

Abstract: Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items.

Abstract: A system and method to accurately modify the function of a user's electronic device in response to the multiple aspects making up a user's evolving state of mind are presented. Persistent intent objects are generated to represent a user's state of mind and the relative importance of a particular state of mind to a user's instant attention. The intent objects can be related to situations or environments and could exist beyond any specific situations or environments. The collective effect of the intent objects can be used to customize the functions of a user device to match the user's state of mind extending beyond inferences drawn from an individual or instant set of circumstances.

Abstract: A method of generating a pulmonary index value of a patient, which includes receiving two or more measure patient parameters, wherein at least one of the measured parameters originates from a pulmonary sensor; and computing the pulmonary index value based on the two or more measured patient parameters.

Abstract: A method of deploying machine learning-based models may include identifying a profile of a target execution environment to implement a machine learning-based model in communication with a cloud infrastructure. The method may further include identifying, using the profile, a software module implementing the model. The method may further include causing the software module to be uploaded from a code repository associated with the cloud infrastructure to the target execution environment.

Abstract: Exemplary systems and methods for allocating capital to trading strategies may include a means for generating a virtual machine for a trading strategy in a historical server, a means for obtaining historical performance data for the trading strategy from the historical server, a means for transforming the historical performance data into metrical data, a means for transforming the historical performance data and metrical data into a neural network usable data set, a means for creating a neural network base, and a means for forming a neural network.

Abstract: Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.

Abstract: A method for removing an extracted RF signal to examine a spectrum of at least one other RF signal includes receiving a mixture signal by an ADC. The mixture signal includes a plurality of separate signals from different signal sources. The mixture signal is digitized by the ADC. A first digitized signal and a second digitized signal are generated that are the same. The first digitized signal is delayed a predetermined time delay and the second digitized signal is processed in a neuromorphic signal processor to extract an extracted signal. The predetermined time delay corresponds to a delay embedding in the neuromorphic signal processor. A phase delay and amplitude of the extracted signal is adjusted based on a phase delay and amplitude of the first digitized signal. An adjusted extracted signal is cancelled from the first digitized signal to provide an input examination signal for examination.

Abstract: A relational event history is determined based on a data set, the relational event history including a set of relational events that occurred in time among a set of actors. Data is populated in a probability model based on the relational event history, where the probability model is formulated as a series of conditional probabilities that correspond to a set of sequential decisions by an actor for each relational event, where the probability model includes one or more statistical parameters and corresponding statistics. A baseline communications behavior for the relational event history is determined based on the populated probability model, and departures within the relational event history from the baseline communications behavior are determined.

Abstract: A work support system that is suitable for dealing with a wide variety of work is provided. An AI manual system 100 extracts terms from a manual describing procedures, contents, points of attention, or other matters of work, based on a term table in which one or multiple terms are registered, and generates a rule describing a determination condition of a work object or work situation by using the extracted terms as elements. Furthermore, sensor information is acquired from the device 300, and the work object and the work situation that are associated with the terms that are the elements of the determination condition are recognized based on the acquired sensor information. Based on the rule and a recognition result, work support information is transmitted to the device 300.

Abstract: A client management tool system comprises a gateway module configured to provide access to a data store storing clinical and non-clinical data, a collection of computerized question forms configured to obtain additional data about a client, a social data model defining a structure to store and organize the client data, a predictive model including a plurality of weighted variables and thresholds in consideration of the client data to identify needs of the client and a valuation of services to address the client needs, a knowledgebase of available programs and service providers able to deliver the needed services, a client management toolkit configured to provide recommended a course of action in response to the identified client need, valuation, and available programs and services providers, and a data presentation module operable to present notifications, alerts, and outcome report related to service delivery to the client.

Abstract: In one embodiment, a computer-implemented method for action-aware communication and conversation prioritization includes: identifying an importance of one or more parties to a communication or conversation; evaluating content of the communication or conversation to identify one or more features of the communication or conversation; assessing an importance of the content included in the communication or conversation based on one or more of the identified features; determining an urgency of the communication or conversation based on one or more of the identified features; and prioritizing the communication or conversation based at least in part on the importance of one or more of the parties, the importance of the content, and the urgency of the communication or conversation. The method is adaptive in a continuous manner based on user actions responsive to the communication or conversation. Related systems and computer program products are also disclosed.

Abstract: For controlling a target system, e.g. a gas or wind turbine or another technical system, a pool of control policies is provided. The pool of control policies comprising a plurality of control policies and weights for weighting each of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. With that, the target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system are received. Furthermore, the weights are adjusted on the basis of the received performance data to improve the performance of the controlled target system. With that, the plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.

Abstract: Technical solutions are described for implementing a convolutional neural network (CNN) using resistive processing unit (RPU) array. An example method includes configuring an RPU array corresponding to a convolution layer in the CNN based on convolution kernels of the layer. The method further includes performing forward pass computations via the RPU array by transmitting voltage pulses corresponding to input data to the RPU array, and storing values corresponding to output currents from the RPU arrays as output maps. The method further includes performing backward pass computations via the RPU array by transmitting voltage pulses corresponding to error of the output maps, and storing the output currents from the RPU arrays as backward error maps. The method further includes performing update pass computations via the RPU array by transmitting voltage pulses corresponding to the input data of the convolution layer and the error of the output maps to the RPU array.

Abstract: This invention generally relates to a process, system and computer code for updating of computer applications based on collecting automation information related to a current application such as processing power, load, footprint, and performance attributes, determining a system automation profile; using an artificial intelligence based modeler for analyzing data, applying the data to an artificial intelligence model for training and predicting performance, adjusting the artificial intelligence model to achieve an updated automation criteria with optimal values, wherein the optimal values provide input to an automation criteria library for storing and updating a prior automation criteria, and exporting the upgraded automation criteria values for incorporation in a computer-to-be-updated, to achieve a reliable automatic update.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting likelihoods of conditions being satisfied using recurrent neural networks. One of the systems is configured to process a temporal sequence comprising a respective input at each of a plurality of time steps and comprises: one or more recurrent neural network layers; one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps: receive the network internal state for the time step; and process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step.

Abstract: A system for detecting an unknown undesirable event comprises an input device configured to receive a dataset comprising a plurality n of multidimensional datapoints (MDDPs), a processor configured to embed the MDDPs in an lower dimension embedded space to obtain embedded MDDPs, and a detection engine configured to calculate distributions of distances Dnni, i=1, . . . , n of each embedded MDDP from a plurality of nearest-neighbors (nn) to compute a threshold Dnnt and to classify a particular MDDP of the dataset or a newly arrived MDDP (NAMDDP) as an abnormal MDDP based on comparison with threshold Dnnt, wherein the classification is automatic and unsupervised without relying on a signature, rules or domain expertise and wherein the particular MDDP classified as abnormal is indicative of the unknown undesirable event.

Abstract: System and means generates synthetic forms of social media data such as data from microblogging services (e.g., Twitter) and social networking services (e.g., Facebook). This system and means jointly generate interaction graph structures and text features similar to input social media data. First, an interaction graph is generated by mapping social network interactions in input (real) social media data to graph structures. This interaction graph is fitted to a social network model (or a composite model) by minimizing the distance between the input and the synthetic interaction graphs (of potentially different sizes). The distance is measured statistically or based on the performance of social media analytics. Various patterns (such as anomalies), interaction types and temporal dynamics are generated synthetically. Second, text features are extracted from input social media data with topic modeling and statistical analysis of word tuple distributions.

Abstract: In one aspect, the inventions include a system for control of an entertainment state system. First, an application plane layer is adapted to receive instructions regarding operation of the entertainment state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. An adaptive control unit is trained at least in part on analyzing the behavioral responses of users in response to content provided on one or more displays to the users as sensed by one or more sensors.