Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object embedding system. In one aspect, a method comprises providing selected images as input to the object embedding system and generating corresponding embeddings, wherein the object embedding system comprises a thumbnailing neural network and an embedding neural network. The method further comprises backpropagating gradients based on a loss function to reduce the distance between embeddings for same instances of objects, and to increase the distance between embeddings for different instances of objects.

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include estimating a queue wait time.

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Methods and systems for topology-based clinical data mining are provided. An example system includes a pre-processing module to process the clinical datasets to generate a table of outcomes and a table of predictors of trial subjects. The system includes a graph construction module to generate metric graphs based on the table of outcomes. The metric graphs include nodes representing the subjects and edges selectively connecting the nodes according to pre-determined criteria. The graph construction module may select a graph of interest from the metric graphs and generate a compressed version of the graph of interest. The system may further include an interactive visualization module to display a graphical representation of the graph of interest or the compressed version, receive selection of groups of the trial subjects, automatically highlight groups of related subjects, and perform, using the table of predictors, a statistical analysis of predictors of subjects within the selected groups.

A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.

Described are methods, systems, and computer-program product embodiments for selecting a face image based on a name. In some embodiments, a method includes receiving the name. Based on the name, a name vector is selected from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, where each name vector includes representations associated with a plurality of words associated with each name. A plurality of face vectors corresponding to a plurality of face images is received. A face vector is selected from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, where for each name vector, a similarity score is calculated based on the name vector and each face vector. The face image is output based on the selected face vector.

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting features in an urban scene comprising building entrances.