A system for noninvasive extraction, identification, and marking of the heart valve signals to evaluate and monitor elevated left ventricular end-diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) using at rest assessment of hemodynamic performance, based on quantitative measurements of heart and lung related parameters and cardiac events for diagnostic and therapeutic purposes includes one or more signals from one or more noninvasive sensors or transducers that measure one or more physiological effects that are correlated with cardiopulmonary functions, transmission of the data to a computing device and analysis software where a trained algorithm processes the data to determine the state or condition of elevated LVEDP or PCWP and provides an output indicative of the state or condition of the analysis. The described noninvasive cardiopulmonary health assessment and monitoring systems and methods can provide effective at-home self-assessment or an integrated telehealth remote patient monitoring (RPM) system.

A system and method to compress application control data, such as weights for a layer of a convolutional neural network, is disclosed. A multi-core system for executing at least one layer of the convolutional neural network includes a storage device storing a compressed weight matrix of a set of weights of the at least one layer of the convolutional network and a decompression matrix. The compressed weight matrix is formed by matrix factorization and quantization of a floating point value of each weight to a floating point format. A decompression module is operable to obtain an approximation of the weight values by decompressing the compressed weight matrix through the decompression matrix. A plurality of cores executes the at least one layer of the convolutional neural network with the approximation of weight values to produce an inference output.

A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.

Methods and apparatuses are described for automated analysis of customer interaction text to generate customer intent information and a hierarchy of customer issues. A server captures computer text segments including a first portion comprising a transcript of an interaction and a second portion comprising notes about the interaction. The server generates interaction embeddings corresponding to the first portion of the computer text segment for a trained neural network. The server executes the neural network using the interaction embeddings to generate an interaction summary for each computer text segment. The server converts each interaction summary into a multidimensional vector and aggregates the multidimensional vectors into clusters based upon a similarity measure. The server aligns the clusters of vectors with attributes of the interaction summaries to generate a hierarchical mapping of customer issues.

For segmentation in medical imaging, a shape generative adversarial network (shape GAN) is used in training. By including shape information in a lower dimensional space than the pixels or voxels of the image space, the network may be trained with a shape loss or optimization. The adversarial loss and the shape loss are used to train the network, so the resulting generator may segment complex shapes in 2D or 3D. Other optimization may be used, such as using a loss in image space.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Methods, machines and systems for processing information are disclosed in which waves containing information select locations for processing (46). Associations between information containing waves may be made and recalled. In some embodiments information containing waves or sequences may be output as visual, auditory, tactile, motion, data or other forms. Software embodiments of the described mechanism are also included.

A method is described for determining a survival prognosis of a patient suffering from a breast tumor, using processing carried out by electronic processing and/or calculation means. The method first comprises step (a) of defining a biological network representative of a particular biological process associated with the breast tumor. The biological network comprises a plurality of nodes, a set of directional relationships between these nodes and a set of genes associated with these nodes. The method also includes step (b) of accessing a data set related to the patient, comprising gene expressions in a biological sample of the tumor isolated from the patient; and step (c) of calculating a continuous expression value for the aforesaid nodes of the biological network. If the node is associated with only one gene and it is found that the gene is present in the biological sample, the continuous expression value of the node is calculated as the expression of the associated gene detected in the biological sample. If the node is associated with multiple genes, and it is found that at least one of the aforesaid genes is present in the biological sample, the continuous expression value of the node is calculated based on the expressions of the associated genes, present in the biological sample. If the node is not associated with any gene, or the associated gene is not found in the biological sample, the node is marked as a node not associated with a continuous expression value. The method then comprises the following steps, carried out by the electronic processing and/or calculation means: (d) binarizing the data set of continuous expression values calculated for each node of the biological network to which a continuous expression value is associated, based on a comparison of the continuous expression value with a respective threshold, to thus obtain a first binarized data set of the nodes, obtained based on the detections made; (e) calculating an aggressiveness score based on the aforesaid first binarized data set of the nodes; and finally (f) determining a survival prognosis result based on the aforesaid aggressiveness score calculated.