A device and method for processing data, in particular unnormalized, multidimensional data, of a neural network, in particular a deep neural network, especially for detecting objects in an input image. The data includes at least one first classification value for a multitude of positions in the input image in each case, a classification value quantifying a presence of a class. The method includes the following steps: evaluating the data as a function of a threshold value, a first classification value for a respective position in the input image that lies either below or above the threshold value being discarded, and a first classification value for a respective position in the input image that lies either above or below the threshold value not being discarded.

Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.

Disclosed are a method and apparatus for identifying versions of a form. In an example, clients of a medical company fill out many forms, and many of these forms have multiple versions. The medical company operates in 10 states, and each state has a different version of a client intake form, as well as of an insurance identification form. In order to automatically extract information from a particular filled out form, it may be helpful to identify a particular form template, as well as the version of the form template, of which the filled out form is an instance. A computer system evaluates images of filled out forms, and identifies various form templates and versions of form templates based on the images.

A microscope system is provided with a microscope that acquires images at least at a first magnification and a second magnification higher than the first magnification, and a processor. The processor is configured to specify a type of a container in which a specimen is placed, and when starting observation of the specimen placed in the container at the second magnification, the processor is configured to specify a map region corresponding to a map image constructed by stitching together a plurality of second images acquired by the microscope at a higher magnification than the first magnification by performing object detection according to the type of the container on a first image that includes the container acquired by the microscope at the first magnification, and cause a display unit to display the first image and a range of the map region on the first image.

Methods and systems are provided for facial detection techniques for image processing neural networks. In one example, a method may include collecting multi-channel outputs of a set of context modules, providing them to both a face detection head and a landmark localization head of the neural network. The face detection head may then generate bounding boxes which are also provided to the landmark localization head. Based on the output of the context modules and the bounding boxes, the landmark localization head may provide an output including a set of landmark indicators.

The present disclosure provides a method and apparatus for evaluating a matching degree based on artificial intelligence, a device and a storage medium, wherein the method comprises: respectively obtaining word expressions of words in a query and word expressions of words in a title; respectively obtaining context-based word expressions of words in the query and context-based word expressions of words in the title according to the word expressions; generating matching features according to obtained information; determining a matching degree score between the query and the title according to the matching features. The solution of the present disclosure may be applied to improve the accuracy of the evaluation result.

The present disclosure provides a method and apparatus for evaluating a matching degree of multi-domain information based on artificial intelligence, a device and a medium. The method comprises: respectively obtaining valid words in a query, and valid words in each information domain in at least two information domains in a to-be-queried document; respectively obtaining word expressions of valid words in the query and word expressions of valid words in said each information domain in at least two information domains in the to-be-queried document; based on the word expressions, respectively obtaining context-based word expressions of valid words in the query and context-based word expressions of valid words in said each information domain; generating matching features corresponding to said each information domain according to the obtained information; determining a matching degree score between the query and the to-be-queried document according to the matching features corresponding to said each information domain.

A method for training a model. The method may include forming a data set from one or more measurements of core samples, selecting one or more parameters from the data set, inputting the one or more parameters into a kernel estimation function, determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters, and selecting an input value based at least in part on the kernel density estimation. The method may further include creating a corresponding synthetic target value based at least in part on the input value, augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set, and training a petrophysical interpretation machine learning model from the data set and the synthetic data set.

The present disclosure relates to pre-processing of video images. In particular, the video images are pre-processed in an object-based manner, i.e., by applying different pre-processing to different objects detected in the image. Moreover, the pre-processing is applied to a group of images. As such, object detection is performed in a plurality of images and the pre-processing for the plurality of images may be adapted to the decoded images and is applied to the decoded images.

System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.