An adaptive characteristic spectral line screening method and system based on atomic emission spectrum are provided, the method includes: using a set characteristic screening optimization method to perform a plurality of optimization rounds of characteristic screening, obtaining an initialized spectral dataset of each round of the characteristic screening and initialized characteristic population genes; obtaining an optimal characteristic population gene of each round by a set analysis method, a fitness function, and an iteration of a genetic algorithm; obtaining an optimized characteristic spectral information set when the plurality of optimization rounds reach set optimization rounds; performing combination statistics and discriminant analyses on the optimized characteristic spectral information set to complete an adaptive characteristic spectral line screening. The disclosure can efficiently and automatically screen out the characteristic spectral lines that meet the analysis requirements in the complex atomic emission spectrum, thus ensuring the effectiveness and accuracy of screening the characteristic spectral lines.

Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

The present disclosure relates generally to machine learning. More particularly, the present disclosure relates to systems and methods that regularize neural networks by decorrelating neurons or other parameters of the neural networks during training of the neural networks promoting these parameter to innovate over one another.

A multi-task relationship learning system 80 for simultaneously estimating a plurality of prediction models includes a learner 81 for optimizing the prediction models so as to minimize a function that includes a sum total of errors indicating consistency with data and a regularization term deriving sparsity relating to differences between the prediction models, to estimate the prediction models.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

Methods of detecting an object in an image using a convolutional neural network based architecture that processes multiple feature maps of differing scales from differing convolution layers within a convolutional network to create a regional-proposal bounding box. The bounding box is projected back to the feature maps of the individual convolution layers to obtain a set of regions of interest. These regions of interest are then processed to ultimately create a confidence score representing the confidence that the object detected in the bounding box is the desired object. These processes allow the method to utilize deep features encoded in both the global and the local representation for object regions, allowing the method to robustly deal with challenges in the problem of robust object detection. Software for executing the disclosed methods within an object-detection system is also disclosed.

Disclosed examples access media impression data via one or more wireless communications, the media impression data including panel data obtained from a meter and impression information obtained after an access of media at a computing device; determine an audience deduplication based on the panel data; determine odds ratios for platform combinations based on the audience deduplication; determine posterior distributions for the media based on the odds ratios; perform a sequential odds ratio insertion technique based on the posterior distributions to determine unique audience sizes; align the unique audience sizes based on a constraint; and generate a report including the aligned unique audience sizes.

Systems, methods, apparatuses and devices for detecting facial expressions according to EMG signals for a virtual and/or augmented reality (VR/AR) environment, in combination with a system for simultaneous location and mapping (SLAM), are presented herein.

The present application discloses a method, system, and computer system for building a model associated with a dataset. The method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.

Disclosed examples access media impression data via one or more wireless communications, the media impression data including panel data obtained from a meter and impression information obtained after an access of media at a computing device; determine an audience deduplication based on the panel data; determine odds ratios for platform combinations based on the audience deduplication; determine posterior distributions for the media based on the odds ratios; perform a sequential odds ratio insertion technique based on the posterior distributions to determine unique audience sizes; align the unique audience sizes based on a constraint; and generate a report including the aligned unique audience sizes.