Abstract: Methods, systems and computer program products for providing automatic determination of recommended hyper-local data sources and features for use in modeling is provided. Responsive to training each model of a plurality of models, aspects include receiving client data, a use-case description and a selection of hyper-local data sources, generating a client data profile, determining feature importance and generating a use-case profile. Aspects also include generating a feature profile relation graph including client data profile nodes, hyper-local feature nodes and a use-case profile nodes, wherein each hyper-local feature node is associated with one or more client data profile nodes and user-case profile nodes by a respective edge having an associated edge weight. Responsive to receiving a new client data set and a new use-case description, aspects also include determining one or more hyper-local features as suggested hyper-local features for use in building a new model.

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 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 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 predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

Abstract: Machine-based risk prediction or assistance is provided for peri-procedural complication, such as peri-procedural myocardial infarction (PMI). A machine-learned model is used to predict risk of PMI and/or recommend courses of action to avoid PMI in PCI. Various combinations of types or modes of information are used in the prediction, such as both imaging and non-imaging data. The prediction may be made prior to, during, and/or after PCI using the machine-learned model to more quickly reduce the chance of PMI. The workflows for prior, during, and/or post PCI incorporate the risk prediction and/or risk-based recommendations to reduce PMI for patients.

Abstract: Determination of an inflection point in congestion in a network path can include determining packet congestion on a network path using an inflection point mechanism (IPDM), detecting a potential inflection point in response to a one-way delay (OWD) of a first packet being lesser than an OWD of a second packet by a quantity greater than a function of a difference of inter departure time (IDT) between the first packet and the second packet, and determining an inflection point in congestion of the network path based on the potential inflection point.

Abstract: Systems and methods are provided for a new type of quality of service (QoS) primitive at a network device that has better performance than traditional QoS primitives. The QoS primitive may comprise a token bucket with active queue management (TBAQM). Particularly, the TBAQM may receive a data packet that is processed by the token bucket; adjust tokens associated with the token bucket, where the tokens are added based on a configured rate and subtracted in association with processing the data packet; determine a number of tokens associated with the token bucket, comprising: when the token bucket has zero tokens, initiating a first action with the data packet, and when the token bucket has more than zero tokens, determining a marking probability based on the number of tokens and initiating a second action based on the marking probability.

Abstract: Systems and methods are provided for effectuating a filtering technique that can enable available bandwidth (e.g., on a network path) to be estimated in the presence of moderate losses caused by certain queue management techniques. When packet losses exist on a network path due to certain types of packet queue transmission mechanisms, methods, or models, a bump detection algorithm (BDA) can be used to perform bandwidth estimation. When a pattern of packet loss is identified (from its signature) as being one with which BDA can be performed to accurately estimate available bandwidth on a network path, the BDA-based bandwidth estimate may be used to place/route and load balance network traffic, or otherwise used to engage in network traffic engineering, take other network-related action(s), or reported out. Otherwise, the bandwidth estimation is suppressed and not used.

Abstract: Systems and methods are provided for optimally allocating resources used to perform multiple tasks/jobs, e.g., machine learning training jobs. The possible resource configurations or candidates that can be used to perform such jobs are generated. A first batch of training jobs can be randomly selected and run using one of the possible resource configuration candidates. Subsequent batches of training jobs may be performed using other resource configuration candidates that have been selected using an optimization process, e.g., Bayesian optimization. Upon reaching a stopping criterion, the resource configuration resulting in a desired optimization metric, e.g., fastest job completion time can be selected and used to execute the remaining training jobs.

Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Abstract: In some examples, a secure compliance protocol may include a virtual computing instance (VCI) deployed on a hypervisor and may be provisioned with hardware computing resources. In some examples the VCI may also include a cryptoprocessor to provide cryptoprocessing to securely communicate with a plurality of nodes, and a plurality of agents to generate a plurality of compliance proofs; the VCI may communicate with a server corresponding to a node of the plurality of nodes; and receive a time stamp corresponding to at least one compliance proof based on a metric of a connected device.

Abstract: Embodiments described herein are generally directed to an edge-CaaS (eCaaS) framework for providing life-cycle management of containerized applications on the edge. According to an example, declarative intents are received indicative of a use case for which a cluster of a container orchestration platform is to be deployed within an edge site that is to be created based on infrastructure associated with a private network. A deployment template is created by performing intent translation on the declarative intents and based on a set of constraints. The deployment template identifies the container orchestration platform selected by the intent translation. The deployment template is then executed to deploy and configure the edge site, including provisioning and configuring the infrastructure, installing the container orchestration platform on the infrastructure, configuring the cluster within the container orchestration platform, and deploying a containerized application or portion thereof on the cluster.

Abstract: A computer implemented system and method provide for a transfer learning platform system. The method provides an introduced enterprise security policy (IESP) to a first enterprise system. During a threat, the IESP is toggled on and off. A first change element is determined that represents a change in a logging system of the first enterprise between a first and second log element of the first enterprise captured when the IESP was toggled on and off, respectively. The IESP is provided to a second enterprise system. A second change element is determined that represents a change in a logging system of the second enterprise between a first log element of the second enterprise. The method further determines that the first and second change element are different, and, conditioned upon the determining that the second change element is different than the first change element, removes the IESP from the second enterprise system.

Abstract: A method and system for detecting whether a person is engaged in an online meeting are provided. The method includes: receiving a streaming input image of a face of the person; capturing, from the streaming input image, still images of the face of the person; extracting facial features from the still images; labeling each still image as being either engaged or not engaged; and determining, based on the labels, a score that indicates a level of engagement of the person with respect to the online meeting. The labeling is performed by applying an artificial intelligence (AI) algorithm to the still images and the facial features. The AI algorithm is then applied to images of other meeting participants in order to obtain a composite score that indicates a global level of engagement for the online meeting.

Abstract: Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Abstract: Systems and methods are provided for strategically harvesting untapped compute capacity of hardware accelerators to manage transient workload spikes at computing systems, are provided. Examples provide a low-cost and scalable computing system which orchestrates seamless offloading of workloads to hardware accelerators during transient workload spikes. By utilizing hardware accelerators as short-term emergency buffers, examples improve upon existing approaches which deploy more expensive, and often significantly under-utilized servers for these emergency purposes. Accordingly, examples may reduce the occurrence of SLA violations while minimizing capital expenditure in computing power.

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 determining a robustness of a machine learning based medical analysis network for performing a medical analysis task on input medical data are provided. Input medical data is received. Results of a medical analysis task performed based on the input medical data using a machine learning based medical analysis network are received. A robustness of the machine learning based medical analysis network for performing the medical analysis task is determined based on the input medical data and the results of the medical analysis task using a machine learning based audit network. The determination of the robustness of the machine learning based medical analysis network is output.

Abstract: Systems and methods for smart selection of training data sets by using clinically driven application dependent evaluation metrics to assess the performance of deep learning models after deployment in the field. A machine trained model is deployed to a clinical environment. An evaluation metric is acquired that correlates with a clinical outcome for each instance of the machine trained model performing the task for a medical procedure. Data sets are flagged that are challenging for the machine trained model based on the evaluation metrics. The flagged data sets are prioritized during retraining of the machine trained model.