Abstract: A method for determining quality signals for offer campaigns comprises an offer system that receives information submitted by users, and analyzes it to learn features of each user and detect patterns. The offer system clusters the users, and transmits an offer to a limited number of users in each user cluster. It receives notification that a user interacted with the offer and determines a conversion rate for each cluster. If the conversion rate exceeds a pre-defined threshold, the offer system transmits the offer to the remaining users in the cluster. Alternatively, the features of the users are rendered into a multi-dimensional graph that plots the distribution of the users. The offer system marks a representation of each user that interacts with the offer on the graph to determine groupings of users. The offer system transmits the offer to the remaining users in each group.

Abstract: A mechanism is provided in a question answering system for duration similarity weighting in a question answering system. The question answering system receives an input question. The question answering system generates a plurality of candidate answers to the input question using a corpus of information. The question answering system identifies a question-side duration value for the input question and identifies a policy-side duration value for a policy to be applied to an entity identified in the input question. The question answering system determines a duration similarity weight for each of the plurality of candidate answers. The question answering system applies the duration similarity weight to each of the plurality of candidate answers. The question answering system ranks the plurality of candidate answers and outputs one or more of the candidate answers.

Abstract: The present invention provides a system comprising a neurosynaptic processing device including multiple neurosynaptic core circuits for parallel processing, and a serial processing device including at least one processor core for serial processing. Each neurosynaptic core circuit comprises multiple electronic neurons interconnected with multiple electronic axons via a plurality of synapse devices. The system further comprises an interconnect circuit for coupling the neurosynaptic processing device with the serial processing device. The interconnect circuit enables the exchange of data packets between the neurosynaptic processing device and the serial processing device.

Abstract: Apparatus and methods for developing parallel networks. In some implementations, a network may be partitioned into multiple partitions, wherein individual portions are being executed by respective threads executed in parallel. Individual portions may comprise multiple neurons and synapses. In order to reduce cross-thread traffic and/or reduce number of synchronization locks, network may be partitioned such that for given network portion, the neurons and the input synapses into neurons within the portion are executed within the same thread. Synapse update rules may be configured to allow memory access for postsynaptic neurons and forbid memory access to presynaptic neurons. Individual threads may be afforded pairs of memory buffers configured to effectuate asynchronous data input/output to/from thread. During an even iteration of network operation, even buffer may be utilized to store data generated by the thread during even iteration.

Abstract: A system and method for generating and inferring patterns with a network that includes providing a network of recursive sub-networks with a parent feature input node and at least two child feature output nodes; propagating node selection through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network, the propagation within the sub-network including enforcing a selection constraint on at least a second node of a second pool according to a constraint node of the sub-network; and compiling the state of final child feature nodes of the network into a generated output.

Abstract: Certain embodiments of the invention may include systems and methods for identifying drug targets using biological networks. According to an example embodiment of the invention, a method is provided for predicting the effects of drug targets on treating a disease. The method can include constructing a structure of a Bayesian network based at least in part on knowledge of drug inhibiting effects on a disease; associating a set of parameters with the constructed Bayesian network; determining values of a joint probability distribution of the Bayesian network via an automatic procedure; deriving a mean Bayesian network with one or more averaged parameters based at least in part on the joint probability values; and calculating a quantitative prediction based at least in part on the mean Bayesian network.

Abstract: Disclosed herein is a method for parallel mining of temporal relations in a large event file using a MapReduce model. In the method for parallel mining of temporal relations in a large even file according to the present invention, an event file is sorted based on customer identification (ID) and event time at which each event has occurred. A set of large event types satisfying a preset support or more is generated from the event file. The event file is converted into a large event sequence including the large event type set. The large event sequence is summarized and then a time interval data file is created. Candidate temporal relations are generated from the time interval data file, and frequent temporal relations satisfying a preset support or more are derived from the candidate temporal relations. A temporal relation rule is generated from the derived frequent temporal relations.

Abstract: A method comprising determining that a charge session event associated with a charge session between an apparatus and a separate apparatus has occurred, determining a separate apparatus identity of the separate apparatus, determining a non-charging operation based, at least in part, on the separate apparatus identity and the charge session event, and causing performance of the non-charging operation is disclosed.

Abstract: A computer-implemented method is disclosed for efficiently processing records with unseen data. In the method, a computer system may obtain a plurality of records and a decision tree generated in a learning process. The decision tree may include a distinction node having multiple paths extending therefrom. After arriving at the distinction node with one or more records, the computer system may determine that the one or more records correspond to data of a type not seen by the distinction node in the learning process. Thereafter, the computer system may depart the distinction node via each of the multiple paths and eventually reach multiple leaf nodes of the decision tree. Each of the multiple leaf nodes may correspond to a probability distribution. Accordingly, the computer system may combine the probability distribution of each of the multiple leaf nodes to obtain a hybrid probability distribution corresponding to the one or more records.

Abstract: Systems and methods are disclosed for distributed first- or higher-order model fitting algorithms. Determination of the parameter set for the objective function is divided into a plurality of sub-processes, each performed by one of a plurality of worker computers. A master computer coordinates the operation of the plurality of worker computers, each operating on a portion of the parameter set such that no two worker computers contain exactly the same parameter subset nor the complete parameter set. Each worker computer performs its sub-processes on its parameter subset, together with training data. For maximum efficiency, the sub-processes are performed using a compact set of instruction primitives. The results are evaluated by the master computer, which may coordinate additional sub-process operations to perform higher-order optimization or terminate the optimization method and proceed to formulation of a model function.

Abstract: Systems and methods (e.g., utilities) for use in providing automated, lightweight collection of online, open source data which may be content-based to reduce website source bias. In one aspect, a utility is disclosed for use in extracting content of interest from at least one website or other online data source (e.g., where the extracted content can be used in a subsequent search query). In other aspects, utilities are disclosed that are operable to perform various types of analyzes on such extracted content and present graphical representations of such analyzes on a display of a client device.

Abstract: A multi-task learning system and method for predicting travel demand on an associated transportation network are provided. Observations corresponding to the associated transportation network are collected and a set of time series corresponding to travel demand are generated. Clusters of time series are then formed and for each cluster, multi-task learning is applied to generate a prediction model. Travel demand on a selected segment of the associated transportation network corresponding to at least one of the set of time series is then predicted in accordance with the generated prediction model.

Abstract: A method for analyzing an electrical power system using fuzzy logic includes: (a) acquiring data representing a signal of interest of the power system; (b) analyzing the signal using at least one fuzzy logic rule; and (c) based on the analysis, detecting and classifying at least one power system event within the power system.

Abstract: An apparatus and a method are provided for learning a program with a large number of parameters. In one embodiment, a method not only distorts the input values, but also distorts some of the parameters in the program model. Such an approach not only forces the learned program to acquire parameter values to predict missing or desired data, but also to correct errors in the input data and the program parameters themselves, thereby rendering the learned program more resilient to overfitting and falling into local optima.

Abstract: Embodiments of the invention build models to predict the likelihood of entities that operate in a given identifier space also operating in a disjoined identifier space based on a source panel of entities that operate in one or both of the identifier spaces. In operation, a model building engine builds a model based on features associated with the source panel and features associated with standard populations in the given identifier space. The model is used to determine whether the target entity is more similar to those entities in the source panel that operate only in the given identifier space or those entities in the source panel that operate in both identifier spaces.

Abstract: An approach for providing a standard data processing model through machine learning is described. A machine learning data processing platform may process and/or facilitate a processing of the at least one data set associated with one or more computation closures to determine at least one data pattern. The machine learning data processing platform may also determine one or more data processing models associated with the one or more computation closures, the at least one data set, or a combination thereof. The machine learning data processing platform may further cause, at least in part, a training of the one or more data processing models to reflect the at least one data pattern.

Abstract: A framework for executing iterative grouped optimization algorithms such as machine learning and other analytic algorithms directly on unsorted data within a SQL data store without first redistributing the data comprises an architecture that provides C++ abstraction layers that include the algorithms over a SQL data store, and a higher Python abstraction layer that includes grouping and iteration controllers and call functionality to the C++ layer for invocation of the algorithms.

Abstract: A DB registration-updating unit registers, in a patron management DB, the behavior history of each player in association with camera IDs that identify the cameras that have captured images of the player. On the basis of information in the patron management DB, a potential player-determining unit considers a player, whose length of stay in a specific corner is longer than a prescribed period and who is not playing, to be a potential player who wants to play but is unable to play. An attention level DB management unit registers, in an attention level DB, the points proportional to the lengths of stay of potential players in said corner as the attention level by cumulatively storing the points for each game console. An attention level display control unit displays the information on the players' attention level that is registered in the attention level DB.

Abstract: Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.

Abstract: An adaptable classification method is provided. The method performs the classification by using a classification standard having a plurality of categories. The classification standard is classified into different categories based on probability ranges. The adaptable classification method includes training a classifying device with a plurality of samples and using the trained classifying device to determine the categories of the samples to obtain classification model scores of the samples, transferring, by using logistic-like functions, the classification model scores into probability values; and adjusting parameters of logistic-like functions to iterate the training of the classifying device such that the probability values conform to value ranges corresponding to categories of the classification standard.