Abstract: Replay of partially executed instruction blocks in a processor-based system employing a block-atomic execution model is disclosed. In one aspect, a partial replay controller is provided in a processor(s) of a central processing unit (CPU). If an instruction is detected in the instruction block associated with a potential architectural state modification, or an exception occurs during execution of instructions, the instruction block is re-executed. During re-execution of the instruction block, the partial replay controller is configured to record produced results from load/store instructions. Thus, if an exception occurs during re-execution of the instruction block, previously recorded produced results for the executed load/store instructions before the exception occurred are replayed during re-execution of the instruction block after the exception is resolved.

Abstract: To generate insights in a predictive analysis framework for support management, raw data is received as input from an in-memory database. A predictive analysis library is integrated in the predictive analysis framework. The predictive analysis framework is generated as a configurable application-programming interface (API). Predictive analysis is performed based on the raw data and the configurable data points. The predictive analysis library functions are invoked from the in-memory database to perform predictive analysis. Predictive data model is generated based on computation performed using a prediction algorithm. Predictive insights are generated based on the predictive data model. The predictive insights are displayed in a user interface associated with a device.

Abstract: A system for providing dynamic constitutional guidance. The system includes a label generator module configured to receive a periodic longevity factor, retrieve a user periodic longevity factor training set, and generate a naïve Bayes classification algorithm utilizing the user periodic longevity factor training set. The system includes a clustering module configured to receive a user adherence factor, retrieve a user adherence factor training set, and generate a k-means clustering algorithm using the user adherence factor training set. The system includes a processing module the processing module configured to retrieve a user ameliorative plan, evaluate a user ameliorative plan, generate an updated user ameliorative plan, and display the updated user ameliorative plan on a graphical user interface.

Abstract: In one embodiment, a computing device scans a plurality of available data sources associated with a profiled identity for an individual, and categorizes instances of the data sources according to recognized terms within the data sources. Once determining whether the profiled identity contributed positively to each categorized instance, categorized instances that have a positive contribution by the profiled identity may be clustered into clusters. The computing device may then rank the clusters based on size of the clusters and frequency of recognized terms within the clusters, and can then infer an expertise of the profiled identity based on one or more best-ranked clusters. The inferred expertise of the profiled identity may then be stored.

Abstract: Sensory input data signal is communicated to an AI platform and translated into a corresponding scenario. The translation is directed at an associated force and application of the force to a selected or identified object and environment. Real-time analysis of the force is applied, which includes modeling an expected behavior. An assessment response is received and compared to a corpus. A solution in the corpus proximal to the assessment response is identified, and a reaction of proximity of the response to the identified solution is created. Proximity data is converted to a sensory output signal. Receipt of the sensory output signal by a corresponding sensory output device creates a physical manifestation of generated feedback of the reaction data to a sensory medium. Embodiments are directed at both learning and assessment, and leverage a corpus with the AI platform in support of an interactive learning experience.

Abstract: A neuromorphic device is provided. The neuromorphic device may include a pre-synaptic neuron, a row line extending from the pre-synaptic neuron in a row direction, a post-synaptic neuron, a column line extending from the post-synaptic neuron in a column direction, and a synapse at an intersection region between the row line and the column line. The synapse may include a switching device and a memristor electrically connected with each other in series. The post-synaptic neuron may include a summation circuit, a variable resistor, and a comparator.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A customer self-help system employs artificial intelligence and machine learning to identify self-help content that is responsive to a user query by analyzing and searching a plurality of customer self-help systems. The customer self-help system generates a self-help relationship model by applying one or more processes/algorithms on training set data. In response to a user query, the customer self-help system identifies ones of the plurality of customer self-help systems that are relevant to the user query and searches the relevant ones of the plurality of customer self-help systems for self-help content that is responsive to the user query. The customer self-help system then provides the self-help content to the user in response to receipt of the user query from the user.

Abstract: A set of components is computed from performing NLP on a question in an input. An actual answer is computed corresponding to the question by a cognitive system. the actual answer corresponds to an actual subset of the set of components, and an expected answer corresponds to an expected subset of the subset of components. The actual answer is mapped to an actual category in a hierarchy of answer categories. A distance between the expected answer and the actual answer is computed where the distance is a function of a path in the hierarchy from the actual category to the expected category, and a degree of correctness of the actual answer is another function of the distance. A self-learning operation in the cognitive system causes a revised actual answer on the question being at a shorter distance from the expected answer.

Abstract: A set of components is computed from performing NLP on a question in an input. An actual answer is computed corresponding to the question by a cognitive system. the actual answer corresponds to an actual subset of the set of components, and an expected answer corresponds to an expected subset of the subset of components. The actual answer is mapped to an actual category in a hierarchy of answer categories. A distance between the expected answer and the actual answer is computed where the distance is a function of a path in the hierarchy from the actual category to the expected category, and a degree of correctness of the actual answer is another function of the distance. A self-learning operation in the cognitive system causes a revised actual answer on the question being at a shorter distance from the expected answer.

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: Machine-learning data generators use an additional objective to avoid generating data that is too similar to any previously known data example. This prevents plagiarism or simple copying of existing data examples, enhancing the ability of a generator to usefully generate novel data. A formulation of generative adversarial network (GAN) learning as the mixed strategy minimax solution of a zero-sum game solves the convergence and stability problem of GANs learning, without suffering mode collapse.

Abstract: Predictive analytic models are hierarchically built based on a training dataset, which includes pairs of input data and output data. First, the input data and the output data are preprocessed. A hierarchical clustering process is performed on the dataset. The hierarchical clustering process comprises level-1 input and output data clustering, level-2 input and output data clustering, and so on, up to level-K input and output data clustering, where K is an integer greater than one. A hierarchical model building process is performed. The hierarchical model building process comprises level-1 model building over level-1 clustered input and output data, level-2 model building over level-2 clustered input and output data, and so on, up to level-K model building over level-K clustered input and output data. At least one level-K predictive model is generated as the resulting built model.

Abstract: Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.

Abstract: A neuromorphic computing apparatus has a network of neuromorphic cores, with each core including an input axon and a plurality of neurons having synapses. The input axon is associated with an input data store to store an input trace representing a time series of filtered pre-synaptic spike events, and accessible by the synapses of the plurality of neurons of the core. Each neuron includes at least one dendritic compartment to store and process variables representing a dynamic state of the neuron. Each compartment is associated with a compartment-specific data store to store an output trace representing a time series of filtered post-synaptic spike events. Each neuron includes a learning engine to apply a set of one or more learning rules based on the pre-synaptic and post-synaptic spike events to produce an adjustment of parameters of a corresponding synapse to those spike events.

Abstract: A cognitive state prediction method, system, and non-transitory computer readable medium, include a receiving circuit configured to receive an electronic message sent by a first user, a labeling circuit configured to query a second user to associate a label with the electronic message based on a cognitive state of the first user, and a correlating circuit configured to correlate the label with user data at a time of sending the electronic message, the user data corresponding to data output by at least one of the wearable and an external sensor in a database.

Abstract: A signal processing module comprises at least one operational unit incorporating computation units, input and output interfaces able to be linked to a bus and a memory storing data destined for the computation units, the memory being organized so that each data word is stored column-wise over several addresses according to an order dependent on the application, a column having a width of one bit, the words being transferred in series to the computation units.

Abstract: There are provided system and method of segmentation a fabrication process (FP) image obtained in a fabrication of a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained to provide segmentation-related data, processing a fabrication process (FP) sample using the obtained trained DNN and, resulting from the processing, obtaining by the computer segments-related data characterizing the FP image to be segmented, the obtained segments-related data usable for automated examination of the semiconductor specimen. The DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image; FP sample comprises the FP image to be segmented.

Abstract: A method for receiving training data for training a neural network to perform a machine learning task and for searching for, using the training data, an optimized neural network architecture for performing the machine learning task is described. Searching for the optimized neural network architecture includes: maintaining population data; maintaining threshold data; and repeatedly performing the following operations: selecting one or more candidate architectures from the population data; generating a new architecture from the one or more selected candidate architectures; for the new architecture: training a neural network having the new architecture until termination criteria for the training are satisfied; and determining a final measure of fitness of the neural network having the new architecture after the training; and adding data defining the new architecture and the final measure of fitness for the neural network having the new architecture to the population data.