A method includes receiving, from sensors, current trace data including current sensor values associated with producing products. The method further includes processing the current trace data to identify features of the current trace data and providing the features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, outputs indicative of predictive data associated with the hyperplane limit and processing the predictive data and the hyperplane limit to determine: first products associated with a first product classification and second products associated with a second product classification based exclusively on the subset of the plurality of features; and third products associated with the first product classification or the second product classification based on an additional feature not within the subset.

A quantization parameter optimization method includes: determining a cost function in which a regularization term is added to an error function, the regularization term being a function of a quantization error that is an error between a weight parameter of a neural network and a quantization parameter that is a quantized weight parameter; updating the quantization parameter by use of the cost function; and determining, as an optimized quantization parameter of a quantization neural network, the quantization parameter with which a function value derived from the cost function satisfies a predetermined condition, the optimized quantization parameter being obtained as a result of repeating the updating, the quantization neural network being the neural network, the weight parameter of which has been quantized, wherein the function value derived from the regularization term and an inference accuracy of the quantization neural network are negatively correlated.

Computer-implemented systems and methods for generating and using a location sensitive ensemble classifier for classifying content includes dividing a validation data set into regions. Each region encompasses data points of the validation data set that fall within the region. A regional ensemble classifier is generated for each region based on the data points that fall within the region. A content item is then classified in at least one of a plurality of classes using the regional ensemble classifier for the region to which the content item belongs.

A network for image processing is provided, and more particularly, for coarse-to-fine recognition of image processing. The network includes a shared convolution layer, and a first subnet and a second subnet both subsequent to the shared convolution layer; the first subnet comprises a first skipping module comprising one or more skip-dense blocks iteratively stacked with one or more transition layers, a first pooling layer subsequent to the first skipping module, and a first classification layer subsequent to the first pooling layer; the second subnet comprises a second skipping module comprising one or more skip-dense blocks iteratively stacked with one or more layers, a second pooling layer subsequent to the second skipping module, and a second classification layer subsequent to the second pooling layer; and wherein a skip-dense block of the second subnet is selected to guide a transition layer of the first subnet, and the level of the guiding skip-dense block is deeper than the level of the guided transition layer. This network is also related to a system and a method thereof.

Embodiments access a radiological image of tissue having a tumoral volume and a peritumoral volume; define a vasculature associated with the tumoral volume; generate a Cartesian two-dimensional (2D) vessel network representation; compute a first set of localized Hough transforms based on the Cartesian 2D vessel network representation; generate a first aggregated set of peak orientations based on the first set of Hough transforms; generate a spherical 2D vessel network representation; compute a second set of localized Hough transforms based on the spherical 2D vessel network representation; generate a second aggregated set of peak orientations based on the second set of Hough transforms; generate a vascular network organization descriptor based on the aggregated peak orientations; compute a probability that the tissue is a member of a positive class based on the vascular network organization descriptor; classify the ROI based on the probability; and display the classification.

Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.

A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

A data processing method by learning of a neural network may be provided. The data processing method by the learning of a neural network includes: obtaining a first set of output values by processing a first set of input values of a task by the neural network; forming a projection space on the basis of the first set of output values; obtaining a second set of output values by processing a second set of input values out of input values of the task by the neural network; projecting the second set of output values onto the projection space; and performing processing the second set of output values in the projection space.

Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.

An avatar facial expression generating system and a method of avatar facial expression generation are provided. In the method, multiple user data are obtained and related to the sensing result of a user from multiple data sources. Multiple first emotion decisions are determined, respectively, based on each user data. Whether an emotion collision occurs among the first emotion decisions is determined. The emotion collision is related that the corresponding emotion groups of the first emotion decisions are not matched with each other. A second emotion decision is determined from one or more emotion groups according to the determining result of the emotion collision. The first or second emotion decision is related to one emotion group. A facial expression of an avatar is generated based on the second emotion decision. Accordingly, a proper facial expression of the avatar could be presented.