The technology disclosed uses neural networks to determine analyte metadata by (i) processing input image data derived from a sequence of image sets through a neural network and generating an alternative representation of the input image data, the input image data has an array of units that depicts analytes and their surrounding background, (ii) processing the alternative representation through an output layer and generating an output value for each unit in the array, (iii) thresholding output values of the units and classifying a first subset of the units as background units depicting the surrounding background, and (iv) locating peaks in the output values of the units and classifying a second subset of the units as center units containing centers of the analytes.

In accordance with an aspect of the present disclosure, there is provided an image analysis method comprising, inputting a target image to an image analysis model composed of at least one expert generated by learning the training image; analyzing the target image based on an output value from the at least one expert; determining validity of an analysis result for the target image based on a probability of the input target image to belong to an input category of the at least one expert; and when it is determined that the analysis result for the target image is not valid, classifying the target image into a new category.

A digital cross-network platform and method for providing controlled data- and process-driven cross-network interaction and program development between heterogeneous units with network-enabled devices on a secured cloud-based network, each unit having a unit or user account in the digital cross-network platform with assigned authentication and authorization credentials for authentication and authorization controlled network access to the digital cross-network platform and the secured cloud-based network, and each unit having an assigned relationship with one or more other units stored in a persistent storage of the digital networking platform, each assigned relationship providing a defined relationship between the one or more other units or a subgroup of the one or more other units and an associated program.

Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features. The computing devices generate, based on the multi-class plural-factored elastic cluster model, a real-time demand-based forecast for the telecommunications services, and output the demand-based forecast.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

A data test method, an electronic device, and a storage medium are provided. In the data test method, based on a Density-Based Spatial Clustering of Applications with Noise (DBSCAN), at least one cluster is obtained by removing discrete points in the target data and performing clustering, an calculation result is obtained by performing a regression analysis on the target data with the objective function, and parameters to be tested are verified according to the calculation result. Utilizing the data test method, objective function can be used to perform verification and residual analysis on the target data, related descriptions are be repeated here.

Described are systems and methods for detecting an anomaly among a plurality of components operating in a system. In some embodiments, a method includes monitoring a plurality of metrics for the plurality of components across a plurality of time periods. For each time period, the plurality of components is clustered into a plurality of clusters based on measurement information corresponding to the plurality of metrics received for the time period. For each component, a plurality of correspondences is determined between the clusters in which the component is grouped for a plurality of pairs of adjacent time periods. Then, whether each component is operating anomalously can be determined based on the plurality of determined correspondences.

The present disclosure discloses a visual relationship detection method based on adaptive clustering learning, including: detecting visual objects from an input image and recognizing the visual objects to obtain context representation; embedding the context representation of pair-wise visual objects into a low-dimensional joint subspace to obtain a visual relationship sharing representation; embedding the context representation into a plurality of low-dimensional clustering subspaces, respectively, to obtain a plurality of preliminary visual relationship enhancing representation; and then performing regularization by clustering-driven attention mechanism; fusing the visual relationship sharing representations and regularized visual relationship enhancing representations with a prior distribution over the category label of visual relationship predicate, to predict visual relationship predicates by synthetic relational reasoning. The method is capable of fine-grained recognizing visual relationships of different subclasses by mining latent relationships in-between, which improves the accuracy of visual relationship detection.

The technology disclosed relates to generating ground truth training data to train a neural network-based template generator for cluster metadata determination task. In particular, it relates to accessing sequencing images, obtaining, from a base caller, a base call classifying each subpixel in the sequencing images as one of four bases (A, C, T, and G), generating a cluster map that identifies clusters as disjointed regions of contiguous subpixels which share a substantially matching base call sequence, determining cluster metadata based on the disjointed regions in the cluster map, and using the cluster metadata to generate the ground truth training data for training the neural network-based template generator for the cluster metadata determination task.

A method for training a chromosome recognition model includes: identifying objects on a karyotype image, obtaining a mask and a minimal bounding box of each of the chromosome objects, and obtaining an organized image that includes a set of organized chromosome objects; generating a simulated metaphase image in which the chromosome objects are randomly reorganized; detecting the plurality of chromosome objects on the simulated metaphase image; obtaining a recalibrated image in which the chromosome objects are separated from one another, so as to train the chromosome recognition model for identifying feature of chromosome objects included in an image.