A method includes inputting defect data of a source domain, to which a first mask is applied/unapplied to a reconstruction algorithm. The algorithm is trained to generate defect data of the source domain, to which the first mask is reconstructed. Normal data of the source domain is input to the algorithm, and includes data to which a second mask is applied, and data to which the second mask is not applied. The algorithm is trained to generate normal data of the source domain, to which the second mask is reconstructed. Normal data of a target domain is input to the algorithm, and the normal data of the target domain includes data to which the second mask is applied, and data to which the second mask is not applied. The algorithm is trained to generate normal data of the target domain, to which the second mask is reconstructed.

The invention discloses a method and a device for constructing a SQL statement based on reinforcement learning, wherein the method includes: initializing an actor-critic network parameter; acquiring a sequence pair of natural language and real SQL statement from a data set; inputting a natural language sequence into an actor network encoder, and inputting a real SQL sequence into a critic network encoder; using an encoded hidden state as an initialized hidden state of a corresponding decoder; gradually predicting, by an actor network decoder, a SQL statement action, and inputting the SQL statement action to a critic network decoder and an environment to obtain a corresponding reward; and using a gradient descent algorithm to update the network parameters, and obtaining a constructing model of the natural language to the SQL statement after repeated iteration training.

The present disclosure discloses a DNA matrix processing method based on a combined restriction digestion mechanism, including the following steps: constructing a single auxiliary strand-mediated combined restriction digestion mechanism; introducing an auxiliary strand based on the single auxiliary strand-mediated combined restriction digestion mechanism, to obtain a dual auxiliary strands-mediated combined restriction digestion mechanism; and constructing DNA matrix processing and a weighted sum of Boolean matrix multiplication with the dual auxiliary strands-mediated combined restriction digestion mechanism; in which the two auxiliary strands are directly used as elements involved in the matrix processing, and the 2N auxiliary strands are combined into N.sup.2 four-pronged restriction digestion structures in the presence of E6 type DNAzymes to cleave N.sup.2 substrate strands. Meanwhile, due to high-efficiency catalysis and specific recognition, the E6 type DNAzymes make the matrix processing rapid and accurate.

A system includes a processor configured to build a model of human behavior that can be assigned to a simulated human, where the model is trained on a dataset of interactions between humans, and parameters of the model can be adjusted. The processor is further configured to build a model of a task to be engaged in by a plurality of simulated humans, simulate interactions between the plurality of simulated humans, and display behavior of the simulated humans and information about the interactions between the simulated humans during simulated interactions.

Estimator mechanisms for automated computer causal effect estimation are provided. An input dataset is received that includes an initial set of covariate data. An estimation of the relevance of covariates in the initial set is performed where relevance is to one or more causal effect relationships between a given at least one action and an outcome. Based on results of the execution of the estimation, a subset of the initial set of covariates is determined that are covariates relevant to one or more causal effect relationships. A modified dataset, comprising the subset of relevant covariates and at least a portion of the input dataset is generated. The modified dataset is input to a causal effect estimator that processes the modified dataset to generate causal effect relationship estimates for specifying causal effects between the given set of actions and the outcome.

Example apparatus and methods for generating context-aware artificial intelligence characters are disclosed. An example apparatus to animate an artificial intelligence character includes a data tagger to tag data in a media data stream to generate a plurality of data files of tagged data, the data files corresponding to different time periods in a storyline, the tagged data associated with a first character in the media data stream, the artificial intelligence character to portray the first character. The example apparatus includes a trainer to generate a response model of the first character based on the data file corresponding to a current data time period and one or more data files corresponding to one or more earlier time periods of the storyline and a response generator to apply the response model based on a stimulus input to animate the artificial intelligence character.

The present invention may include a computing device receives a location data from a client device, wherein the client device comprises one or more location sensors to generate the location data. The computing device determines an altitude of the client device above a floor from the location data. The computing device determines an age and a height of a user using a trained neural network from the client device and the location data and assigns a wireless network channel to the client device based on the age and the height of the user.

Methods, systems, apparatuses, and computer program products are provided for altering the behavior of an electronic personal assistant based on a situation associated with a mobile device. A situation is sensed with a plurality of sensors to generate sensor data. A situation score is calculated based on the sensor data. Behavior of an electronic personal assistant is altered based on the calculated situation score. In one aspect, the situation is a driving situation in which a driver drives a vehicle on a roadway. In such case, a driving situation score is calculated based on the sensor data, and behavior of the electronic personal assistant is altered based on the calculated driving situation score, such as suspending interactions by the electronic personal assistant with the driver to avoid the driver being distracted.

In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

A parameter training method for a convolutional neural network (CNN) for classifying image type data representative of a biometric trait. The method includes the implementation, by a data processor of a server, the steps of (a) for at least one data item from an already classified training database, generation of several alternate versions of this data each by application of at least one transformation chosen from a set of reference transformations satisfying a statistical distribution of transformations observed in the training database and (b) training the parameters of the CNN, from the already classified training database augmented with said alternate versions of the data.