Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example apparatus includes a data processor to process one-dimensional data captured over time with respect to patient(s). The example apparatus includes a visualization processor to transform the processed data into graphical representations and to cluster the graphical representations including the first graphical representation into at least first and second blocks arranged with respect to an indicator of a criterion to provide a visual comparison of the first block and the second block with respect to the criterion. The example apparatus includes an interaction processor to facilitate interaction, via the graphical user interface, with the first and second blocks of graphical representations to extract a data set for processing from at least a subset of the first and second blocks.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data processing are disclosed. An example time series event data processing apparatus includes memory storing instructions and one-dimensional time series healthcare-related data; and at least one processor. The example at least one processor is to: execute artificial intelligence model(s) trained on aggregated time series data to at least one of a) predict a future medical machine event, b) detect a medical machine event, or c) classify the medical machine event using the one-dimensional time series healthcare-related data; when the artificial intelligence model(s) are executed to predict the future medical machine event, output an alert related to the predicted future medical machine event to trigger a next action; and when the artificial intelligence model(s) are executed to detect and/or classify the medical machine event, label the medical machine event and output the labeled event to trigger the next action.

Abstract: Systems, apparatus, instructions, and methods for medical machine time-series event data generation are disclosed. An example synthetic time series data generation apparatus is to generate a synthetic data set including multi-channel time-series data and associated annotation using a first artificial intelligence network model. The example apparatus is to analyze the synthetic data set with respect to a real data set using a second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a first classification, the example apparatus is to adjust the first artificial intelligence network model using feedback from the second artificial intelligence network model. When the second artificial intelligence network model classifies the synthetic data set as a second classification, the example apparatus is to output the synthetic data set.

Abstract: Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

Abstract: Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model.

Abstract: Systems and computer-implemented methods for facilitating automated compression of artificial neural networks using an iterative hybrid reinforcement learning approach are provided. In various embodiments, a compression architecture can receive as input an original neural network to be compressed. The architecture can perform one or more compression actions to compress the original neural network into a compressed neural network. The architecture can then generate a reward signal quantifying how well the original neural network was compressed. In (?)-proportion of compression iterations/episodes, where ??[0,1], the reward signal can be computed in model-free fashion based on a compression ratio and accuracy ratio of the compressed neural network. In (1??)-proportion of compression iterations/episodes, the reward signal can be predicted in model-based fashion using a compression model learned/trained on the reward signals computed in model-free fashion.

Abstract: Systems and methods to generate artificial intelligence models with synthetic data are disclosed. An example system includes a deep neural network (DNN) generator to generate a first DNN model using first real data. The example system includes a synthetic data generator to generate first synthetic data from the first real data, the first synthetic data to be used by the DNN generator to generate a second DNN model. The example system includes an evaluator to evaluate performance of the first and second DNN models to determine whether to generate second synthetic data. The example system includes a synthetic data aggregator to aggregate third synthetic data and fourth synthetic data from a plurality of sites to form a synthetic data set. The example system includes an artificial intelligence model deployment processor to deploy an artificial intelligence model trained and tested using the synthetic data set.

Abstract: A fluid fitting includes a nut, a sleeve, and a union. The union and the nut may include corresponding stops and corresponding markings. Corresponding stops and corresponding marking may engage with each other when the nut is sufficiently connected with the union. A method of connecting a fitting includes connecting a sleeve of the fitting with a nut of the fitting, connecting the nut with a union, rotating at least one of the nut and the union until a stop of the nut engages a stop of the union, restricting over torque via the stop of the nut and the stop of the union, and verifying a sufficient connection if first markings of the nut align with second markings of the union.

Abstract: A fluid fitting includes a nut, a sleeve, and a union. The union and the nut may include corresponding stops. Corresponding stops may engage with each other when the nut is sufficiently connected with the union. A method of designing a fluid fitting including a union may include determining a gauge diameter of the union, determining a plane perpendicular to an axis of rotation of the union that includes a center point of the gauge diameter, determining a point of intersection of threads of the union with the perpendicular plane, and/or determining a position of a stop according to an angle from the point of intersection.

Abstract: A fitting (30) for fluid communication with a fluid conduit includes a first fluid conduit connection portion (42), a second fluid conduit connection portion (42?), a header (60) disposed axially between the first fluid conduit connection portion and the second fluid conduit connection portion, and a socket (70). A fluid fitting may include a nipple (40), a radial projection (48) connected to the nipple, and an axial protrusion (120) extending from the radial projection. The axial protrusion may be configured to protrude into an axial end of a fluid conduit (80). A fluid fitting may include a fluid conduit connection portion (42) and a dynamic tip (130) connected to an end of the fluid conduit connection portion. The dynamic tip may be configured to expand in response to an increase in fluid pressure.