The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of candidate compounds, said method comprising generating (310) phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining (312) image data depicting each sample comprised in the concentration gradient, generating (314) a class-label and a class for each cell of the samples based on the image data, detecting (320) the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corresponding computer device, a computer program, and a computer program product.

Systems, methods, and apparatuses are disclosed for predicting or estimating the value of a variable speed sign (VSS). Probe data is received from multiple vehicles associated with a road segment. Location values are derived from the probe data. Center distance values are calculated based on the location values and the road segment. Clusters are derived from the probe data. Center distance values are grouped according to the respective clusters and a lane is assigned to at least one cluster based on the center distance values. The speed of the cluster predicts or estimates the corresponding lane of the VSS.

An electronic device includes a housing, a communication module positioned inside the housing, a processor positioned inside the housing and operatively connected with the communication module, a sensor module operatively connected with the processor, and a memory positioned inside the housing and operatively connected with the communication module, the sensor module, and the processor. The memory stores instructions configured to, when executed, enable the processor to gather data related to a first user, send a request for a user group corresponding to a first category among a plurality of categories to an external server using the communication module, obtain the user group corresponding to the first category based on at least part of the data related to the first user from the external server using the communication module, and provide information about at least one second user in the obtained user group.

A method includes: obtaining a plurality of clinical red blood cell images, dividing red blood cells of different shapes at different positions in each of the red blood cell images into a plurality of submasks, and synthesizing the submasks corresponding to each of the red blood cell images to generate one mask to obtain a plurality of masks corresponding to the red blood cell images; collecting shape data of a plurality of red blood cells from the masks to obtain a training data set, calculating a segmentation boundary of each red blood cell in the training data set, and establishing a red blood cell shape data set based on the segmentation boundary of each red blood cell; collecting distribution data of each red blood cell in the red blood cell shape data set; and synthesizing the red blood cell shape data set into a plurality of red blood cell images.

A computer-implemented method for implementing face recognition includes obtaining a face recognition model trained on labeled face data, separating, using a mixture of probability distributions, a plurality of unlabeled faces corresponding to unlabeled face data into a set of one or more overlapping unlabeled faces that include overlapping identities to those in the labeled face data and a set of one or more disjoint unlabeled faces that include disjoint identities to those in the labeled face data, clustering the one or more disjoint unlabeled faces using a graph convolutional network to generate one or more cluster assignments, generating a clustering uncertainty associated with the one or more cluster assignments, and retraining the face recognition model on the labeled face data and the unlabeled face data to improve face recognition performance by incorporating the clustering uncertainty.

Embodiments of the present systems and methods may provide techniques to discover features such as object categories that provide improved accuracy and performance. For example, in an embodiment, a method may comprise extracting, at the computer system, features from a dataset comprising a plurality of data samples using a backbone neural network to form a features vector for each data sample, training, at the computer system, using the features vectors for at least some of the plurality of data samples, an unsupervised generative probabilistic model to perform clustering of extracted features of the at least some of the plurality of data samples by minimizing a negative Log-Likelihood function, wherein clusters of extracted features form categories, and categorizing, at the computer system, at least some different data samples of the plurality of data samples, into the formed categories.

Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.

Enhanced techniques and circuitry are presented herein for providing responses to user questions from among digital documentation sources spanning various documentation formats, versions, and types. One example includes a method comprising receiving a user question directed to subject having a documentation corpus, determining a set of passages of the documentation corpus related to the user question, ranking the set of passages according to relevance to the user question, forming semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity, and providing a response to the user question based at least on a selected semantic cluster.

A method for two-dimensional mapping of crystal information of a polycrystalline material may include acquiring a diffraction pattern acquired by scanning an electron beam to a polycrystalline material, generating a plurality of clusters by applying a clustering algorithm to the acquired diffraction pattern based on unsupervised learning, acquiring crystal information of the polycrystalline material by applying a parallel deep convolutional neural network (DCNN) algorithm to each of the plurality of generated clusters based on supervised learning, and generating a two-dimensional image in which the acquired crystal information is mapped.

State of art techniques mostly rely of computationally intensive, time consuming Neural Networks. Embodiments provide a method and system for identification and classification of different grain and adulterant types for grain grading analysis. The method analyzes input image of grain sample of elements to determine morphological features of elements, using dynamically determined calibration factor from reference object in the image. Variation in perimeter of elements is used to perform classification of elements into target grain size, low size adulterants and higher size adulterants. The aspect ratio of target grain determines grain variety and adulterants determine adulteration percentage. Elements are classified into grain colored and non-grain colored adulterants. Grain colored adulterants are further classified as Grain Like Impurities and non-GLI, using predefined ranges of standard deviation of perimeter metric. Weight of grain colored adulterants and non-grain colored adulterant is obtained using mapping of predefined weights to the aspect ratio.