Abstract: Systems, methods, and computer-readable media are described for employing a machine learning-based approach such as adaptive Bayesian optimization to learn over time the most optimized assignments of incoming network requests to service function chains (SFCs) created within network slices of a 5G network. An optimized SFC assignment may be an assignment that minimizes an unknown objective function for a given set of incoming network service requests. For example, an optimized SFC assignment may be one that minimizes request response time or one that maximizes throughput for one or more network service requests corresponding to one or more network service types. The optimized SFC for a network request of a given network service type may change over time based on the dynamic nature of network performance. The machine-learning based approaches described herein train a model to dynamically determine optimized SFC assignments based on the dynamically changing network conditions.

Abstract: Systems and method for safe over-the-air (OTA) update of electronic control units in vehicles are provided. The OTA server determines whether an operator terminal is close to a vehicle and if so, sends a request to the operator terminal requesting confirmation for proceeding with completing a firmware update for an electronic control unit in the vehicle. In response to receiving the confirmation, the OTA server sends a request to a telematics device coupled to the vehicle to complete the firmware update.

Abstract: Systems and methods for determining input data is out-of-domain of an AI (artificial intelligence) based system are provided. Input data for inputting into an AI based system is received. An in-domain feature space of the AI based system and an out-of-domain feature space of the AI based system are modelled. The in-domain feature space corresponds to features of data that the AI based system is trained to classify. The out-of-domain feature space corresponds to features of data that the AI based system is not trained to classify. Probability distribution functions in the in-domain feature space and the out-of-domain feature space are generated for the input data and for the data that the AI based system is trained to classify. It is determined whether the input data is out-of-domain of the AI based system based on the probability distribution functions for the input data and for the data that the AI based system is trained to classify.

Abstract: An AI algorithm may be used in a clinical setting to perform one or more tasks to assist medical personnel. The results produced by the AI algorithm may affect not only patient care, but also the cost of the care. The AI algorithm may be trained on auxiliary data to incorporate the impacts on patient care and cost.

Abstract: System and method for safe over-the-air (OTA) update of electronic control units in vehicles are provided. The method includes checking whether a vehicle condition allows firmware update of an electronic control unit in a vehicle. If the vehicle condition allows the firmware update, the method includes causing a telematics device to complete the firmware update for the electronic control unit.

Abstract: Systems and methods for predicting a location for acquiring a target view of an anatomical object of interest in an input image are provided. An input image of an anatomical object of interest of a patient is received. An output image is generated using a machine learning based network. The output image depicts a projection of a 3D image plane for acquiring a target view of the anatomical object of interest identified on the input image. The output image is output.

Abstract: Architectures and techniques for managing heterogeneous sets of physical GPUs. Functionality information is collected for one or more physical GPUs with a GPU device manager coupled with a heterogeneous set of physical GPUs. At least one of the physical GPUs is to be managed as multiple virtual GPUs based on the collected functionality information with the GPU device manager. Each of the physical GPUs is classified as either a single physical GPU or as one or more virtual GPUs with the device manager. Traffic representing processing jobs to be processed is received by at least a subset of the physical GPUs via a gateway programmed by a traffic manager. The GPU application to process received processing jobs scheduled by and distributed into the scheduled GPU application with a GPU scheduler communicatively coupled with the traffic manager and with the GPU device manager.

Abstract: Example method includes: receiving a plurality of intent-based network policies in a network, wherein each intent-based policy comprises at least a rule and an action to be performed by a network function on a network packet in response to the rule being matched; identifying a set of header address spaces comprising a plurality of addresses that match to a same set of rules and actions; creating an atomic address object representing the identified set of header address spaces; and verifying the plurality of intent-based network policies using the atomic address object.

Abstract: A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.

Abstract: Systems and methods are provided for updating resource allocation in a distributed network. For example, the method may comprise allocating a plurality of resource containers in a distributed network in accordance with a first distributed resource configuration. Upon determining that a processing workload value exceeds a stabilization threshold of the distributed network, determining a resource efficiency value of the plurality of resource containers in the distributed network. When a resource efficiency value is greater than or equal to the threshold resource efficiency value, the method may generate a second distributed resource configuration that includes a resource upscaling process, or when the resource efficiency value is less than the threshold resource efficiency value, the method may generate the second distributed resource configuration that includes a resource outscaling process. The resource allocation may transmit the second to update the resource allocation.

Abstract: Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

Abstract: A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest.

Abstract: Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.

Abstract: Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque.

Abstract: Data privacy is a major concern when accessing and processing sensitive medical data. Homomorphic Encryption (HE) is one technique that preserves privacy while allowing computations to be performed on encrypted data. An encoding method enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size by representing the numbers as a series of polynomial terms.

Abstract: An optical network includes top networking ports coupled to a packet switch, first media converters, second media converters, and bottom networking ports. The first media converters are coupled to top networking ports, each of the first media converters including a first ASIC transceiver that has a circuit switch function. The second media converters are coupled to the first media converter via optical cables to receive the optical signals. Each of the second media converters includes a second ASIC transceiver that has a circuit switch function. The bottom networking ports are coupled to the second media converters. The first ASIC transceiver and the second ASIC transceiver are configured to transmit a signal from one of the top networking ports to any one of the bottom networking ports, and transmit a signal from one of the bottom networking ports to any one of the top networking ports.

Abstract: Example implementations relate to scheduling of jobs for a plurality of graphics processing units (GPUs) providing concurrent processing by a plurality of virtual GPUs. According to an example, a computing system including one or more GPUs receives a request to schedule a new job to be executed by the computing system. The new job is allocated to one or more vGPUs. Allocations of existing jobs are updated to one or more vGPUs. Operational cost of operating the one or more GPUs and migration cost of allocating the new job are minimized and allocations of the existing jobs on the one or more vGPUs is updated. The new job and the existing jobs are processed by the one or more GPUs in the computing system.

Abstract: Systems and methods for determining a concordance between results of medical assessments are provided. Results of a medical assessment of a first type for an anatomical object of a patient and results of a medical assessment of a second type for the anatomical object are received. The results of the medical assessment of the first type are converted to a hemodynamic measure. A concordance analysis between the results of the medical assessment of the first type and the results of the medical assessment of the second type based on the hemodynamic measure is performed. Results of the concordance analysis are output.

Abstract: Systems and methods for determining one or more measures of interest for optimizing throughput of a catheterization laboratory are provided. A priori medical procedure data relating to a medical procedure to be performed on a patient in a catheterization laboratory is received. One or more measures of interest are predicted based on the received a priori medical procedure data using a trained machine learning model. The one or more measures of interest include an overall time for performing the medical procedure on the patient in the catheterization laboratory. The one or more predicted measures of interest are output.

Abstract: Methods, devices, and systems for aircraft identification are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to simulate virtual light detection and ranging (Lidar) sensor data for a three-dimensional (3D) model of an aircraft type to generate a first point cloud corresponding to the 3D model of the aircraft type, generate a classification model utilizing the simulated virtual Lidar sensor data of the 3D model of the aircraft type, and identify a type and/or sub-type of an incoming aircraft at an airport by receiving, from a Lidar sensor at the airport, Lidar sensor data for the incoming aircraft, generating a second point cloud corresponding to the incoming aircraft utilizing the Lidar sensor data for the incoming aircraft, and classifying the second point cloud corresponding to the incoming aircraft using the classification model.