According to an embodiment, a processing apparatus includes a hardware processor. The hardware processor is configured to: cut out, from an input signal, a plurality of partial signals that are predetermined parts in the input signal; execute processing on the plurality of partial signals using neural networks having the same layer structure with each other to generate a plurality of intermediate signals including a plurality of signals corresponding to a plurality of channels; execute predetermined statistical processing on signals for each of the plurality of channels for each of the plurality of intermediate signals corresponding to the plurality of partial signals, to calculate statistics for each channel and generate a concatenated signal by concatenating the statistics of the plurality of respective intermediate signals for each channel; generate a synthetic signal by performing predetermined processing on the concatenated signal; and output an output signal in accordance with the synthetic signal.

An embodiment of the present invention provides an artificial intelligence apparatus for controlling an auto stop function, including: an input unit configured to receive brake information and velocity information of a vehicle; a storage unit configured to store a control model for the auto stop function; and a processor configured to: acquire driving information comprising the brake information and the velocity information through at the input unit, acquire base data used for determining a control of the auto stop function from the driving information, determine a control mode for the auto stop function by using the base data and the control model for the auto stop function, and control the auto stop function according to the determined control mode, wherein the control mode is one of an activation mode which activates the auto stop function or a deactivation mode which deactivates the auto stop function.

Systems and methods are provided for training and using a deep neural network with adaptively trained off-ramps for an early exit at an intermediate representation layer. The training includes, for respective intermediate representation layers of a sequence of intermediate representation layers, predicting a label based on the training data and comparing against a correct label. The training further includes generating a confidence value associated with the predicted label. The confidence value is based on optimizing an objective function that includes a weighted entropy of a probability distribution of the likelihood, weighted based on whether previous intermediate representation layer has accurately predicted the label. Use of the weighted entropy provides the training with a focus on predicting labels that the previous intermediate representation layers has performed poorly and not labels that have existed before the intermediate representation layer being trained. Among alternative methods include a distilled twin, parallel neural network for predicting labels using adaptively trained off-ramps.

A method of handling outliers is provided. The method includes determining a set of residuals, wherein each residual represents a difference between a measurement included in a set of measurements and a predetermined estimate; clustering the residuals into a plurality of clusters; calculating a consistency value for each of the plurality of clusters based on a number of measurements included in the set of measurements and a standard deviation of the measurements; identifying a cluster having a maximum consistency value among the plurality of clusters as inliers by applying the consistency function to the plurality of clusters; and handling the outliers based on an approximation of one or more parameters as a function of a statistical relationship of the inliers included in the cluster having the maximum consistency value among the plurality of clusters and an initial estimation of the one or more parameters.

A device may determine an association between a second set of parameters and a third set of parameters using a pseudoinversion network and a multiple regression procedure. The device may determine semantic embeddings based on a set of semantic descriptions of the second set of parameters. The device may determine a semantic similarity between parameters of the second set of parameters based on the semantic embeddings. The device may determine a consistency error based on the semantic similarity. The device may generate, using a regression-based learning model technique, a matrix representing an association between the second set of parameters and the third set of parameters based on the association and the consistency error. The device may perform an action based on the matrix.

Methods, systems, articles of manufacture, and apparatus to extract shape features based on a structural angle template are disclosed. An example apparatus includes a template generator to generate a template based on an input image and calculate a template value based on values in the template; a bit slicer to calculate an OR bit slice and an AND bit slice based on the input image, combine the OR bit slice with the AND bit slice to generate a fused image, group a plurality of pixels of the fused image to generate a pixel window, each pixel of the pixel window including a pixel value, and calculate a window value based on the pixel values of the pixel window; and a comparator to compare the template value with the window value and store the pixel window in response to determining the window value satisfies a similarity threshold with the template value.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A method for managing applications includes obtaining, by a client in a customer environment, an upgrade issue report for the application, making a first determination that a resynchronization of a client self-healing classification model with the production host environment (PHE) self-healing classification model is required, wherein the PHE self-healing classification model is stored in the PHE, performing the resynchronization with the PHE self-healing classification model to obtain a synchronized client self-healing classification model, applying the synchronized client self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a second determination that the state indicates a self-healable state, based on the second determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process, wherein the PHE is operatively connected to the customer environment.

Methods and computer program products for training a neural network perform multiple forms of data augmentation on sample waveforms of a training dataset that includes both normal and abnormal samples to generate normal data augmentation samples and abnormal data augmentation samples. The normal data augmentation samples are labeled according to a type of data augmentation that was performed on each respective normal data augmentation sample. The abnormal data augmentation samples are labeled according to a type of data augmentation other than that which was performed on each respective abnormal data augmentation sample. A neural network model is trained to identify a form of data augmentation that has been performed on a waveform using the normal data augmentation samples and the abnormal data augmentation samples.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.