A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.

A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.

An endoscopic image observation system supports the observation of a plurality of images captured by a capsule endoscope. The endoscopic image observation system includes a distinguishing unit that outputs an accuracy score indicating the likelihood that each of the plurality of images represents an image of a region sought to be distinguished; a grouping unit that groups the plurality of images into a plurality of clusters in accordance with the accuracy score; and an identification unit that identifies a candidate image for a boundary of the region from among the plurality of images in accordance with the grouping into the plurality of clusters.

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.

Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.

A deep learning model is quantized during its training to perform a target software engineering task. During training, a portion of the full-precision floating point weights is quantized into INT4 or INT 8 data types through scalar quantization or product quantization to make the model more resilient to quantization and to reduce the noise between the quantized and full-precision model outputs. In scalar quantization, each sub-block consists of a single weight that is mapped into a codeword of a codebook. In product quantization, an identity matrix and a codebook of centroids is used to map a quantized weight into its original value.

A deep learning model is quantized during its training to perform a target software engineering task. During training, a portion of the full-precision floating point weights is quantized into INT4 or INT 8 data types through scalar quantization or product quantization to make the model more resilient to quantization and to reduce the noise between the quantized and full-precision model outputs. In scalar quantization, each sub-block consists of a single weight that is mapped into a codeword of a codebook. In product quantization, an identity matrix and a codebook of centroids is used to map a quantized weight into its original value.

Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

A method, system, and computer program product for discovering from user content, at least one tagged item that includes a product, includes identifying plural tags to be associated with each of the user-content item, and the corresponding probability that each of the plural tags is associated with products. There is also the feature of associating the plural tags and their corresponding probability of being associated with products. There are also the features of generating at least one subset of the tagged user content based upon the probability of a first one of the plural tags being associated with a product, and discovering the tagged user content comprising the product, from the subset of the tagged user content based upon the probability of the first one of the plural tags being associated with a product.