A particle measurement system and method of operation thereof are described. The system and method render a characteristic for a set of particles measured while passing through a measurement volume. The system includes a source that generates a particle-laden field containing the set of particles. The system further includes a sensor that generates a raw particle data corresponding to the set particles passing through the measurement volume of the particle measurement system, where the raw particle data comprises a set of raw particle records and each of one of the raw particle records includes a particle data content. A preconditioning stage carries out a preconditioning operation on the particle data content of the set of raw particle records to render a conditioned input data. A machine learning stage processes the conditioned input data to render an output characteristic parameter value for the set of particles.

A drowsy driving detection method comprises: acquiring a side face image of a currently seated driver collected by a camera module; performing face recognition on the side face image to obtain side face feature parameters, and determining, according to the side face feature parameters, whether an ID file corresponding to the currently seated driver exists in a driver ID library; and if yes, periodically acquiring a side face image of the driver in the current period collected by the camera module, obtaining eye movement feature parameters of the driver in the current period according to the side face image of the current period, and determining whether the driver is driving while drowsy according to a comparison result between the eye movement feature parameters of the current period and the normal eye movement feature parameters of the driver.

A method for identifying by mass spectrometry an unknown microorganism subgroup among a set of reference subgroups, including a step of constructing one knowledgebase and one classifying model per associated subgroup on the basis of the acquisition of at least one set of learning spectra of microorganisms identified as belonging to the subgroups of a group and including: constructing an adjusting model allowing mass-to-charge offsets of the acquired spectra to be corrected on the basis of reference masses-to-charges that are common to the various subgroups; adjusting the masses-to-charges of all of the lists of peaks of the learning spectra and constructing one classifying model per subgroup and the associated knowledgebase on the basis of the adjusted learning spectra.

A graph processing method and apparatus are used in the field of data visualization. In this method, first, at least two subgraphs of a first graph are obtained, where each subgraph includes, in the first graph, a plurality of nodes and edges between the nodes; second, the nodes and the edges that are included in each subgraph of the at least two subgraphs are calculated, to calculate respective identifiers of the at least two subgraphs; and third, subgraphs with a same identifier in the at least two subgraphs are combined to generate a second graph; and then the second graph generated through combination is output.

A data processing method and apparatus, and a device are provided. In this application, a plurality of data processing modules may collaboratively process data. Data output by each data processing module is stored in a data set, the data set includes a plurality of pieces of data, each piece of data carries one index, and the index indicates a data processing module that generates the data. A first data processing module in the plurality of data processing modules may obtain, from the data set, first data carrying a first index, where the first index indicates a data processing module that generates the first data. Then, the first data processing module processes the first data to generate second data carrying a second index, where the second index indicates the first data processing module. Then, the first data processing module stores the second data into the data set.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Methods and systems for dynamic network link prediction include generating a dynamic graph embedding model for capturing temporal patterns of dynamic graphs, each of the graphs being an evolved representation of the dynamic network over time. The dynamic graph embedding model is configured as a neural network including nonlinear layers that learn structural patterns in the dynamic network. A dynamic graph embedding learning by the embedding model is achieved by optimizing a loss function that includes a weighting matrix for weighting reconstruction of observed edges higher than unobserved links. Graph edges representing network links at a future time step are predicted based on parameters of the neural network tuned by optimizing the loss function.

The disclosure presents processes that utilize collected resistivity data, for example, from an ultra-deep resistivity tool located downhole a borehole. In some aspects, each slice of resistivity data can generate multiple distribution curves that can be overlaid offset resistivity logs. In some aspects, an analysis can be performed to identify trends in the distribution curves that can be used to identify approximate locations of subterranean formation surfaces, shoulder beds, obstacles, proximate boreholes, and other borehole and geological characteristics. As the number of distribution curves generated increase, the confidence in the analysis also increases. In some aspects, the number of distribution curves can be twenty, one hundred, one hundred and one, or other counts of distribution curves. In some aspects, the resistivity data can be used to generate two or more synchronized view perspectives of a specific location along the borehole, where each view perspective uses the same focus area.

The disclosure presents processes that utilize collected resistivity data, for example, from an ultra-deep resistivity tool located downhole a borehole. In some aspects, each slice of resistivity data can generate multiple distribution curves that can be overlaid offset resistivity logs. In some aspects, an analysis can be performed to identify trends in the distribution curves that can be used to identify approximate locations of subterranean formation surfaces, shoulder beds, obstacles, proximate boreholes, and other borehole and geological characteristics. As the number of distribution curves generated increase, the confidence in the analysis also increases. In some aspects, the number of distribution curves can be twenty, one hundred, one hundred and one, or other counts of distribution curves. In some aspects, the resistivity data can be used to generate two or more synchronized view perspectives of a specific location along the borehole, where each view perspective uses the same focus area.