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 method and system for cross-modal manifold alignment of different data domains includes determining for a shared embedding space a first embedding function for data of a first domain and a second embedding function for data of a second domain using a triplet loss, wherein triplets of the triplet loss include an anchor data point from the first, a positive and a negative data point from the second domain; creating a first mapping for the data of the first domain using the first embedding function in the shared embedding space; creating a second mapping for the data of the second domain using the second embedding function in the shared embedding space; and generating a cross-modal alignment for the data of the first domain and the data of the second domain.

The present disclosure provides a method for joint manifold learning based heterogenous sensor data fusion, comprising: obtaining learning heterogeneous sensor data from a plurality sensors to form a joint manifold, wherein the plurality sensors include different types of sensors that detect different characteristics of targeting objects; performing, using a hardware processor, a plurality of manifold learning algorithms to process the joint manifold to obtain raw manifold learning results, wherein a dimension of the manifold learning results is less than a dimension of the joint manifold; processing the raw manifold learning results to obtain intrinsic parameters of the targeting objects; evaluating the multiple manifold learning algorithms based on the raw manifold learning results and the intrinsic parameters to determine one or more optimum manifold learning algorithms; and applying the one or more optimum manifold learning algorithms to fuse heterogeneous sensor data generated by the plurality sensors.

Embodiments are disclosed for learning structural similarity of user experience (UX) designs using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise generating a representation of a layout of a graphical user interface (GUI), the layout including a plurality of control components, each control component including a control type, geometric features, and relationship features to at least one other control component, generating a search embedding for the representation of the layout using a neural network, and querying a repository of layouts in embedding space using the search embedding to obtain a plurality of layouts based on similarity to the layout of the GUI in the embedding space.

Disclosed herein include systems, devices, computer readable media, and methods for resilience determination and damage recovery in neural networks using a weight space and a metric that together form a manifold (such as a pseudo-Riemannian manifold or a Riemannian manifold)

A sequence of stimuli produced by an electric frac pump can be generated by a treatment optimization system. Well environment responses to the sequence of stimuli may be measured by sensors and respective sensor data may be received. The sensor data may be used to select a representative system model which can then be used to control the electric frac pump. The representative system model may be used to achieve well stage objectives such as particular cluster efficiencies, complexity factors, or proximity indices.

Various examples are directed to systems and methods for detecting training data for a generative model. A computer system may access generative model sample data and a first test sample. The computer system may determine whether a first generative model sample of the plurality of generative model samples is within a threshold distance of the first test sample and whether a second generative model sample of the plurality of generative model samples is within the threshold distance of the first test sample. The computer system may determine that a probability that the generative model was trained with the first test sample is greater than or equal to a threshold probability based at least in part on whether the first generative model sample is within the threshold distance of the first test sample, the determining also based at least in part on whether the second generative model sample is within the threshold distance of the first test sample.

A computer system, computer program product and computer-implemented method for generating job recommendations for one or more candidates or applicants. The system is configured to generate a candidate, e.g. prospective candidate, vector comprising embedded data or information associated with the candidate. The prospective candidate vector is compared to vectors with embedded data for other applicants to match the prospective candidate with past applicants or candidates and generate a list of past applicants and/or the jobs applied for by the past applicants, and to generate a job title vector comprising the job titles. According to an exemplary implementation, the system is configured to generate a baseline recommendation utilizing the job title vector and comprising a plurality of open jobs with a ranking or score for the prospective candidate. According to another aspect, the system is configured to re-rank the open jobs and the recommendation based on additional data and/or weighting factors.

Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

The present disclosure provide a system, a method, and a storage medium for distributed joint manifold learning (DJML) based heterogeneous sensor data fusion. The system includes a plurality of nodes; and each node includes at least one camera; one or more sensors; at least one memory configured to store program instructions; and at least one processor, when executing the program instructions, configured to obtain heterogeneous sensor data from the one or more sensors to form a joint manifold; determine one or more optimum manifold learning algorithms by evaluating a plurality of manifold learning algorithms based on the joint manifold; compute a contribution of the node based on the one or more optimum manifold learning algorithms; update a contribution table based on the contribution of the node and contributions received from one or more neighboring nodes; and broadcast the updated contribution table to the one or more neighboring nodes.