A distributed data integration device includes an acquisition unit configured to acquire, for a piece of analysis target data, an anchor data intermediate representation and an analysis target intermediate representation, the anchor data intermediate representation being an intermediate representation obtained by converting anchor data by a first function, the anchor data being data commonly used in integration of a plurality of the pieces of analysis target data that are distributed, the analysis target intermediate representation being an intermediate representation obtained by converting the analysis target data by the first function, an anchor data conversion unit configured to convert, for the piece of analysis target data, a plurality of the anchor data intermediate representations by a second function, a calculation unit configured to calculate, for the piece of analysis target data, the second function that minimizes a difference between the plurality of the anchor data intermediate representations, and an analysis target data conversion unit configured to convert, for the piece of analysis target data, the analysis target intermediate representation by the second function.

The techniques described herein relate to methods, apparatus, and computer readable media configured to determining a two-dimensional (2D) profile of a portion of a three-dimensional (3D) point cloud. A 3D region of interest is determined that includes a width along a first axis, a height along a second axis, and a depth along a third axis. The 3D points within the 3D region of interest are represented as a set of 2D points based on coordinate values of the first and second axes. The 2D points are grouped into a plurality of 2D bins arranged along the first axis. For each 2D bin, a representative 2D position is determined based on the associated set of 2D points. Each of the representative 2D positions are connected to neighboring representative 2D positions to generate the 2D profile.

Principal components analysis is applied to data sets to fingerprint the dataset or to compare the dataset to a wild file that may have been constructed from data found in the dataset. Principal components analysis allows for the reduction of data used for comparison down to a parsimonious compressed signature of a dataset. Datasets with different patterns among the variables will have different patterns of principal components. The principal components of variables (or a relevant subset thereof) in a wild file may be computed and statistically compared to the principal components of identical variables in a data provider's reference file to provide a score. This constitutes a unique and compressed signature of a file that can be used for identification and comparison with similarly defined patterns from other files.

A method for generating a synthetic image of a region of an object, includes: generating, by a charged particle microscope, a charged particle microscope image of the region of the object; calculating a sparse representation of the charged particle microscope image; wherein the sparse representation of the charged particle microscope image comprises multiple first atoms; generating the synthetic image of the region, wherein the synthetic image of the region is formed from multiple second atoms; wherein the generating of the synthetic image of the region is based on a mapping between the multiple first atoms and the multiple second atoms; wherein the charged particle microscope image and the multiple first atoms are of a first resolution; and wherein the synthetic image of the region and the multiple second atoms are of a second resolution that is finer than the first resolution.

Methods and systems for segmenting images having sparsely distributed objects are disclosed. A method may include: predicting object potential areas in the image using a preliminary fully convolutional neural network; segmenting a plurality of sub-images corresponding to the object potential areas in the image using a refinement fully convolutional neural network, wherein the refinement fully convolutional neural network is trained to segment images on a higher resolution compared to a lower resolution utilized by the preliminary fully convolutional neural network; and combining the segmented sub-images to generate a final segmented image.

A power generator system with anomaly detection and methods for detecting anomalies include a power generator that includes one or more physical components configured to provide electrical power. Sensors are configured to make measurements of a state of respective physical components, outputting respective time series of said measurements. A monitoring system includes a fitting module configured to determine a predictive model for each pair of a set of time series, an anomaly detection module configured to compare new values of each pair of time series to values predicted by the respective predictive model to determine if the respective predictive model is broken and to determine a number of broken predictive model, and an alert module configured to generate an anomaly alert if the number of broken predictive models exceeds a threshold.

In image classification, each class of a set of classes is embedded in an attribute space where each dimension of the attribute space corresponds to a class attribute. The embedding generates a class attribute vector for each class of the set of classes. A set of parameters of a prediction function operating in the attribute space respective to a set of training images annotated with classes of the set of classes is optimized such that the prediction function with the optimized set of parameters optimally predicts the annotated classes for the set of training images. The prediction function with the optimized set of parameters is applied to an input image to generate at least one class label for the input image. The image classification does not include applying a class attribute classifier to the input image.

Techniques facilitating generation of a fused kernel that can approximate a full kernel of a convolutional neural network are provided. In one example, a computer-implemented method comprises determining a first pattern of samples of a first sample matrix and a second pattern of samples of a second sample matrix. The first sample matrix can be representative of a sparse kernel, and the second sample matrix can be representative of a complementary kernel. The first pattern and second pattern can be complementary to one another. The computer-implemented method also comprises generating a fused kernel based on a combination of features of the sparse kernel and features of the complementary kernel that are combined according to a fusing approach and training the fused kernel.

A method and an apparatus for length-aware local tiling in a sparse attention module in a transformer in heterogeneous devices are provided. The method includes that a heterogeneous device including one or more GPUs: divides a transformed sparsity mask into a plurality of first tiles and obtaining one or more effective first tiles from the plurality of first tiles, where each effective first tile includes at least one non-zero element; loads the one or more effective first tiles into a shared memory in the one or more GPUs and loads a plurality of elements in a first matrix corresponding to the one or more effective first tiles into the shared memory; and performs multiplication by a first sampled dense-dense matrix multiplication (SDDMM) kernel in the sparse attention module in the transformer by fetching the one or more effective first tiles and the plurality of elements from the shared memory.

A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.