Abstract: A trained neural network that is configured to generate predictions for periods of time in the future based on input data can be received, where the neural network is trained using training data that includes time series data segmented into windows. Observed time series data can be processed to generate the input data. Using the trained neural network and the generated input data, data predictions can be generated. The predictions can be provided to a reinforcement learning model configured to generate predicted outcomes, where the reinforcement learning model varies parameters to simulate conditions for a first and second entity, and an artificial intelligence agent simulates actions performed by one of the first and second entities, the data predictions being a parameter for the simulation. Parameters for the first and second entities can be selected, where the selected parameters correspond to a predicted outcome that meets a criteria.

Abstract: An apparatus for assessing goal attainment may include a preprocessor configured to define a goal of process, all probable final results, attributes, a process execution period, and time points of assessment, acquire or check attribute values from one or more historical process instances that match the defined goal of process, all the defined probable final results, the defined attributes, the defined process execution period, and the defined time point of assessment, and extract one or more event profiles from the attribute values of the process instances; and a likelihood calculator configured to calculate prior and posterior probabilities of an ongoing process instance based on the extracted event profiles, and calculate a probability that the ongoing process instance attains each probable final result according to each time point, using the calculated prior and posterior probabilities at the defined time point of assessment.

Abstract: An example method includes receiving analysis data and output indicator, mapping data points from a transposition of the analysis data to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes, for each node, identifying data points that are members to identify similar features, grouping features as being similar to each other based on node(s), for each feature, determining correlation with at least some data associated with the output indicator and generate a correlation score, displaying at least groupings of similar features and displaying the correlation scores, receiving a selection of features, generating a set of models based on selection, determining fit of each generated model to output data and generate a model score, and generating a model recommendation report.

Abstract: Structured documents are processed using convolutional neural networks. One of the methods includes receiving a rendered form of a structured document; mapping a grid of cells to the rendered form; assigning a respective numeric embedding to each cell in the grid, comprising, for each cell: identifying content in the structured document that corresponds to a portion of the rendered form that is mapped to the cell, mapping the identified content to a numeric embedding for the identified content, and assigning the numeric embedding for the identified content to the cell; generating a matrix representation of the structured document from the numeric embeddings assigned to the cells of the grids; and generating neural network features of the structured document by processing the matrix representation of the structured document through a subnetwork comprising one or more convolutional neural network layers.

Abstract: A method and associated systems for using machine-learning methods to perform linear regression on a DNA-computing platform. One or more processors generate and initialize beta coefficients of a system of linear equations. These initial values are encoded into nucleobase chains that are then padded to a standard length. The chains are allowed to bind with complementary template chains in a DNA-computing reaction, and the resulting DNA molecules are decoded to reveal the relative the relative likelihood of each chain to bind. The initial values of the beta coefficients are weighted proportionally to these likelihoods, and the process is repeated iteratively until the beta coefficients converge to optimal values.

Abstract: A system and method for managing asynchronously receiving updates and merging updates into global versions of a statistical model using version control are disclosed. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.

Abstract: A system for deriving ontologies to support inferencing with changing context, including changes of time. Embodiment of the invention use a unique system for modeling context and the interactions among multiple contexts in order to compute functions that can modify ontologies for presentation to a reasoning system. A parallel unique system allows previous inferences to be retrospectively modified based on newly derived ontological semantics. The system allows for the creation of new ontological elements and auditable models of agency and cause. It can be implemented using methods that delay evaluation until semantic interpretation is required, either at the ontological or inferential level.

Abstract: The system classifies data using formal concept analysis (FCA). In a training phase, the system generates a FCA classification lattice, having a structure, using a set of training data. The set of training data comprises training presentations and classifications corresponding to the training presentations. In a classification phase, a set of test data having classes that are hierarchical in nature is classified using the structure of the FCA classification lattice.

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period. Access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type. Determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs. Determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: A calculation device includes an adding unit configured to add at least one new node to a network, which has multiple nodes that output results of calculations on input data are connected and which learned a feature of data belonging to a first subclass contained in a predetermined class. The calculation device includes an accepting unit configured to accept, as input data, training data belonging to a second subclass contained in the predetermined class. The calculation device includes a calculation unit configured to calculate coupling coefficients between the new node added by the adding unit and other nodes to learn a feature of the training data belonging to the second subclass based on an output result obtained when the training data accepted by the accepting unit is input to the network.

Abstract: A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring.

Abstract: Methods and systems are provided for determining the location of procedure sites, for example hair implantation sites, the method and systems enabling a natural looking randomness to be maintained to achieve a desired density while avoiding previously created procedure sites and existing features.

Abstract: Methods for identifying a case with a missing decision from a set of decision rules in violation of a decision requirement are provided. The set of decision rules and decision requirement are received, and a set of decisions made by the decision rules is obtained. A decision detection constraint graph is built, which represents, for each case used by the set of decision rules, whether each decision in the set of decisions is made or not by a decision rule in the set of decision rules. A decision requirement constraint graph is built from the decision requirement, which represents, for each case used by the set of decision rules, the decisions required. For each case used by the set of decision rules, the decision requirement constraint graph and the decision detection constraint graph for the case are used to identify if the case is a case with a missing decision.

Abstract: A method for learning parameters of a CNN based on regression losses is provided. The method includes steps of: a learning device instructing a first to an n-th convolutional layers to generate a first to an n-th encoded feature maps; instructing an n-th to a first deconvolutional layers to generate an n-th to a first decoded feature maps from the n-th encoded feature map; generating an obstacle segmentation result by referring to a feature of the decoded feature maps; generating the regression losses by referring to differences of distances between each location of the specific rows, where bottom lines of nearest obstacles are estimated as being located per each of columns of a specific decoded feature map, and each location of exact rows, where the bottom lines are truly located per each of the columns on a GT; and backpropagating the regression losses, to thereby learn the parameters.

Abstract: A system and method simulates conversation with a human user. The system and method receive media, convert the media into a system-specific format, and compare the converted media to a vocabulary. The system and method generate a plurality of intents and a plurality of sub-entities and transform them into a pre-defined format. The system and method route intents and the sub-entities to a first selected knowledge engine and a second knowledge engine. The first selected knowledge engine selects the second knowledge engine and each active grammar in the vocabulary uniquely identifies each of the knowledge engines.

Abstract: A system includes a modeler that generates a model which models a quality of findings in radiologist reports as a function of deposited dose of scans from which the radiologist reports are created and a dose optimizer that determines an optimal dose value for a planned scan based on the model and one or more optimization rules. A method includes generating a model which models a quality of findings in radiologist reports as a function of deposited dose of scans from which the radiologist reports are created and determining an optimal dose value tar a planned scan based on the model and one or more optimization rules.

Abstract: An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of a plurality of single-word terms taken from training data business names, where each of the weights is indicative of a likelihood of correlating one of a plurality of business categories. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms.

Abstract: An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of single-word terms and trigram terms of training data business names, each of the weights indicative of a likelihood of correlating a business category. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms and trigram terms.

Abstract: An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of single-word terms and bigram terms of training data business names, each of the weights indicative of a likelihood of correlating a business category. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms and bigram terms.

Abstract: An energy management system includes a neural network, a predictive model, and a dictionary reducer. The network iteratively calculates weights, resulting in a final set, for each of single-word terms and part of speech terms of training data business names, each of the weights indicative of a likelihood of correlating a business category. The predictive employs sets of the weights to predict a first corresponding one of the plurality of business categories for each of the training data business names until employment of the final set accurately predicts a correct business category for the each of the training data business names, and subsequently employs the final set of the weights to predict a second corresponding one of the plurality of business categories for each of a plurality of operational business names. The dictionary reducer eliminates unessential terms taken to determine the plurality of single-word terms and part of speech terms.