A manufacturing data analyzing method and a manufacturing data analyzing device are provided. The manufacturing data analyzing method includes the following steps. Each of at least one numerical data, at least one image data and at least one text data is transformed into a vector. The vectors are gathered to obtain a combined vector. The combined vector is inputted into an inference model to obtain a defect cause and a modify suggestion.

Systems and methods for searching a search space are disclosed. Some examples may include using a first parallel module including a first plurality of stacked searching blocks and a second plurality of stacked searching blocks to output first feature maps of a first resolution and to output second feature maps of a second resolution. In some examples, a fusion module may include a plurality of searching blocks, where the fusion module is configured to generate multiscale feature maps by fusing one or more feature maps of the first resolution received from the first parallel module with one or more feature maps of the second resolution received from the first parallel module, and wherein the fusion module is configured to output the multiscale feature maps and output third feature maps of a third resolution.

A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

A method for detecting and dimensioning a target object includes: obtaining depth data representing a target object; defining a mask having a structure which decreases in density away from a central point of the mask; overlaying the mask on the depth data and selecting a subset of the depth data comprising data points which contact the mask; detecting a cluster of data points from the subset; detecting, based on the cluster, the target object; and outputting a representation of the target object.

One example provides a computing system comprising a depth sensor comprising a plurality of pixels, and a storage machine holding instructions executable by a logic machine to, for each pixel, make K phase measurements to form a set of noisy phase measurements, determine a location at which a projection line that passes through the set of noisy phase measurements in a K-dimensional phase space passes through a lower dimensional plane, the projection line being parallel to a noise free phase evolution line, compare the location to a plurality of independent terms of a predetermined matrix of points in the lower dimensional plane, locate a corresponding set of noiseless phase orders by using a selected set of independent terms to reference a look-up table, determine a distance value for the pixel based upon the corresponding set of noiseless phase orders, and output the distance value for the pixel.

A system and method for occluding contour detection using a fully convolutional neural network is disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image by semantic segmentation; learning an array of upscaling filters to upscale the feature map into a final dense feature map of a desired size; applying the array of upscaling filters to the feature map to produce contour information of objects and object instances detected in the input image; and applying the contour information onto the input image.

Source code is analyzed to identify components. The components are each assigned a complexity score. Documentation for the source code is identified, related to the components, and given a score based on the quantity of the documentation for the component and the complexity score for the component. To determine semantic meaning of the documentation, vector embeddings for the documentation languages may be generated and aligned. Alignment causes the different machine learning models to generate similar vectors for semantically similar words in the different languages. Since the vectors of the words of the other languages are similar to the vectors of the words in a primary language with similar meanings, the vector representation of the documentation in the other languages will match the vector representation of the source code when the documentation is substantially on the same topic.

Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N−M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N−M+P) feature columns; and training the model based on the training data including the (N−M+P) feature columns.

The disclosure is a medical disease feature selection method based on an improved salp swarm algorithm. The improved salp swarm algorithm is used to optimize a feature selection problem, the accuracy of the mentioned method is estimated by means of a transfer function and classification by a K-nearest neighbor algorithm, and the salp swarm algorithm is improved by using a self-adapted control parameter and an elite grey wolf ruling policy, so that it helps the algorithm avoid premature convergence in the optimization process and jump out of local optimum, thereby achieving a target of the algorithm with the smallest selected feature quantity and the highest classification precision. The method has the advantages of high rate of convergence, higher classification precision and better robustness.

An embodiment of the present invention is directed toward machine learning to produce results encompassing a new output. A machine learning model is trained to determine a candidate output from among a plurality of candidate outputs. First embeddings associated with the plurality of candidate outputs are generated from a first set of training data by an intermediate layer of the trained machine learning model. Second embeddings associated with a new candidate output are generated from a second set of training data by the intermediate layer of the trained machine learning model. A third embedding is determined for input data by the intermediate layer of the trained machine learning model. A resulting candidate output for the input data is predicted from a group of the plurality of candidate outputs and the new candidate output based on distances for the third embedding to the first and second embeddings.