Abstract: A computer-implemented method includes receiving a coarse mesh input that includes a first set of nodes, wherein the coarse mesh is input to a computational fluid dynamics solver with physical parameters to obtain a coarse mesh solution, receiving a fine mesh input that is of a second set of nodes, wherein the second set of nodes includes more nodes than the first set of nodes, concatenating the fine mesh input with the physical parameters and run the concatenation through a graph convolution layer to obtain a fine mesh hidden layer, upsampling the coarse mesh solution to obtain a coarse mesh upsample including a same number of nodes as the second set of nodes, and outputting a prediction in response to at least the coarse mesh upsample.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

Abstract: A linguistic system includes a controller. The controller may be configured to receive a query and document, tokenize the query into a sequence of query tokens and tokenize the document into a sequence of document tokens, generate a matrix of token pairs for each of the query and the document tokens, retrieve for each entry in the matrix of token pairs, a precomputed similarity score produced by a neural conditional translation probability network, wherein the neural network has been trained in a ranking task using a corpus of paired queries and respective relevant documents, produce a ranking score for each document with respect to each query via a product-of-sum aggregation of each of the similarity scores for the respective query; and output the document and associated ranking score of the document.

Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

Abstract: A computer-implemented method for training a machine-learning network, wherein the network includes receiving an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generating an adversarial version of the input data utilizing an optimizer, wherein the adversarial version of the input data utilizes a subset of the input data, parameters associated with the optimizer, and one or more perturbation tiles, determining loss function value in response to the adversarial version of the input data and a classification of the adversarial version of the input data, determining a perturbation tile in response the loss function value associated with one or more subsets of the adversarial version of the input data, and output a perturbation that includes at least the perturbation tile.

Abstract: A system for controlling a physical system via segmentation of an image includes a controller. The controller may be configured to receive an image of n pixels from a first sensor, and an annotation of the image from a second sensor, form a coupling matrix, k class vectors each of length n, and a bias coefficient based on the image and the annotation, generate n pixel vectors each of length n based on the coupling matrix, class vectors, and bias coefficient create a single segmentation vector of length n from the pixel vectors wherein each entry in the segmentation vector identifies one of the k class vectors, output the single segmentation vector; and operate the physical system based on the single segmentation vector.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generate an adversarial version of the input data, utilizing a generator, in response to the input data, create a training data set utilizing the input data and the adversarial version of the input data, determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification utilizing a classifier, and update the meta model and the classifier in response to the cross-entropy classification loss utilizing the training data set.

Abstract: A computer-implemented method for training a machine learning network includes receiving an input data from one or more sensors, selecting one or more batch samples from the input data, wherein the batch samples include one or more perturbed samples from a source class configured to be misclassified into a target class, identifying the one or more perturbed samples from the one or more batch samples, determining a trigger event in response to identification of a trigger pattern of the one or more batch samples, wherein the trigger pattern induces a pre-determined response on a classifier, outputting a classification in response to identification of the trigger pattern via the classifier, and outputting a set of trigger patterns extracted from the machine-learning network.

Abstract: A computer-implemented method for training a machine-learning network includes receiving an input data from a sensor, wherein the input data includes a perturbation, wherein the input data is indicative of image, radar, sonar, or sound information, obtain a worst-case bound on a classification error and loss for perturbed versions of the input data, utilizing at least bounding of one or more hidden layer values, in response to the input data, train a classifier, wherein the classifier includes a plurality of classes, including an additional abstain class, wherein the abstain class is determined in response to at least bounding the input data, outputting a classification in response to the input data, and output a trained classifier configured to detect the additional abstain class in response to the input data classifier with a plurality of classes, including an additional abstain class.

Abstract: A computer-implemented method for training a machine-learning network. The machine-learning network method includes receiving an input data from a sensor, wherein the input data includes paired cleaned-perturbed data, wherein the input data is indicative of image, radar, sonar, or sound information, generate a perturbed version of the input data, utilizing a generator, in response to the input data, create a paired training data set utilizing an data from the input data and a perturbed image utilizing the perturbed version of the input data, jointly training the generator and a classifier in response to the paired training data set, determining a latent vector utilized to generate perturbation configured to maximize classification loss of a classifier and minimize generation loss of the generator, and outputting a trained generator and a trained classifier upon convergence to a first threshold.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.

Abstract: A dynamic equilibrium (DEQ) model circuit includes a first multiplier configured to receive an input, scale the input by a first weight, and output the scaled input, second multiplier configured to receive a root, scale the root by a second weight, and output the scaled root, a summation block configured to combine the scaled input, a bias input, and the scaled root and output a non-linear input, and a first non-linear function configured to receive the non-linear input and output the root, wherein the first weight and second weight are based on a trained DEQ model of a neural network.

Abstract: Community detection for a network using low-cardinality semidefinite programming is described. Data descriptive of a set of nodes of a network is received. The data includes a set of nodes representing entities of a system and edges representing connections between the entities of the system. A modularity maximization is performed to assign each node of the set of nodes to one or more communities to generate multiple-cardinality embeddings. The multiple-cardinality embeddings are rounded to unit cardinality to recover a community assignment with maximum modularity. The community assignment is refined and aggregated to create an aggregate network defining a set of connected communities of the network. The network is operated on in accordance with the community assignment.

Abstract: A computer-implemented method for training a neural network, comprising receiving an input data, defining a perturbed version of the input data in response to a dimensional latent vector and the input data, training a variational auto encoder (VAE) utilizing the perturbed version of the input data, wherein the VAE outputs, utilizing an encoder, a latent vector in response to the input data and the perturbed version of the input data, decoding the latent vector, utilizing a decoder of the VAE, back to an input latent space to output a perturbed example, and outputting a learned perturbed set utilizing one or more perturbed examples and upon convergence to a first threshold.

Abstract: A computer-implemented method for creating a combined audio signal in a speech recognition system, the method includes sampling the audio input signal to generate a time-domain sampled input signal, then converting the time-domain sampled input signal to a frequency-domain input signal, afterwards generating perceptual weights in response to frequency components of critical bands of the frequency-domain input signal, creating a time-domain adversary signal in response to the perceptual weights; and combining the time-domain adversary signal with the audio input signal to create a combined audio signal, wherein a speech processing of the combined audio signal will output a different result from speech processing of the audio input signal.

Abstract: A computer-implemented method includes receiving a coarse mesh input that includes a first set of nodes, wherein the coarse mesh is input to a computational fluid dynamics solver with physical parameters to obtain a coarse mesh solution, receiving a fine mesh input that is of a second set of nodes, wherein the second set of nodes includes more nodes than the first set of nodes, concatenating the fine mesh input with the physical parameters and run the concatenation through a graph convolution layer to obtain a fine mesh hidden layer, upsampling the coarse mesh solution to obtain a coarse mesh upsample including a same number of nodes as the second set of nodes, and outputting a prediction in response to at least the coarse mesh upsample.

Abstract: A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.

Abstract: A computational method for training a meta-learned, evolution strategy black box optimization classifier. The method includes receiving one or more training functions and one or more initial meta-learning parameters of the meta-learned, evolution strategy black box optimization classifier. The method further includes sampling a sampled objective function from the one or more training functions and an initial mean of the sampled function. The method also includes computing a set of T number of means by running the meta-learned, evolution strategy black box optimization classifier on the sampled objective function using the initial mean for T number of steps in t=1, . . . ,T. The method also includes computing a loss function from the set of T number of means. The method further includes updating the one or more initial meta-learning parameters of the meta-learned, evolution strategy black box optimization classifier in response to a characteristic of the loss function.

Abstract: A system for training a neural work that includes an input interface for accessing input data for the neural network and a processor in communication with the input interface. The processor is programmed to receive input at the neural network and output a trained neural networking utilizing a forward prorogation and a backward propagation, wherein the forward propagation includes utilizing a root-finding procedure to identify a fixed point associated with one or more parameters of the neural network, wherein the backward propagation includes identifying a derivative of a loss associated with the parameters of the network.