Provided is a method for calculating an evaluation score of clustering quality, based on the number of clusters into which a plurality of data is clustered. The calculating the evaluation score includes: calculating a degree of internal compactness that is a sum of values, each being defined by normalizing a first index value by a first value that is based on a number of data within each cluster, the first index value indicating a degree of dispersion of data within each cluster; calculating a degree of external separation defined by normalizing a sum of a second index value for each cluster by a second value that is based on the number of clusters, the second index value indicating an index of a distance between the clusters; and calculating the evaluation score according to a predetermined formula having, as variables, the degree of internal compactness and the degree of external separation.

A method and a system for obtaining a product prototype based on an eye movement data. The method comprises the following steps: Obtaining A front-side view of the target product for establishing a underlying sample library; The target product is divided into several detection areas according to its structure or function features, and the eye movement is detected to obtain the fixation time of the target product The invention adopts computer and image collecting technology to process the observation data of human eyes, and adopts K-means clustering to obtain the prototype of the target product, to assist the designer to grasp the categories of personal interest contour, so as to improve the design effect of product appearance.

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a first seed region criteria, the first set of tissue region seed patches comprising a subset of the set of target tissue patches; generating, by the image processing engine, a second set of tissue region seed patches by identifying target tissue patches having initial class probability scores that satisfy a second seed region criteria, the second set of tissue region seed patches comprising a subset of the set of target tissue patches; calculating, by the image processing engine, a region of interest score for each patch in the second set of tissue region seed patches as a function of initial class probability scores of neighboring patches of the second set of tissue region seed patches and a distance to patches within the first set of issue region seed patches; and generating, by the image processing engine, one or more region of interest shapes by grouping neighboring patches based on their region of interest scores.

Embodiments described herein provides an intent prototypical contrastive learning framework that leverages intent similarities between users with different behavior sequences. Specifically, user behavior sequences are encoded into a plurality of user interest representations. The user interest representations are clustered into a plurality of clusters based on mutual distances among the user interest representations in a representation space. Intention prototypes are determined based on centroids of the clusters. A set of augmented views for user behavior sequences are created and encoded into a set of view representations. A contrastive loss is determined based on the set of augmented views and the plurality of intention prototypes. Model parameters are updated based at least in part on the contrastive loss.

There is provided a neural processing unit (NPU), including a primary processing node containing primary control registers and processing circuitry configured to write control data to the primary control registers, and multiple secondary processing nodes each having respective secondary control registers and being configured to process data in accordance with control data stored by the respective secondary control registers. The NPU also includes a bus interface for transmitting data between the primary processing node and the plurality of secondary processing nodes. The primary processing node is configured to transmit first control data to a given secondary control register of each of the plurality of secondary processing nodes.

An image processing apparatus includes a processor configured to receive an image having a foreground segment and a background segment, receive a design object, calculate a position for superimposing the design object, on a basis of respective features of the foreground segment, the background segment, and the design object, and perform output to superimpose the design object at the calculated position.

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.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

A method for detecting at least one object of interest in at least one raw data x-ray image includes the steps of emitting an incident x-ray radiation beam through a scanning volume having an object therein, detecting x-ray signals transmitted through at least one of the scanning volume and the object, deriving the at least one raw data x-ray image from the detected x-ray signals, inputting the raw data x-ray image, expressed according to an attenuation scale, into a neural network, for each pixel in the raw data x-ray image, outputting from the neural network a probability value assigned to that pixel, and, classifying each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.

A modular analysis engine provided classification of variables and data in an industrial automation environment. The module may be instantiated upon receipt of an input data structure, such as containing annotated data for any desired variables related to the machine or process monitored and/or controlled. The data may be provided in a batch or the engine may operate on streaming data. The output of the module may be a data structure that can be used by other modules, such as for modeling, optimization, and control. The classification may allow for insightful analysis, such as for textual classification of alarms provided in the automation setting.