Abstract: A scheduling platform for scheduling serverless application tasks in persistent memory (PMEM) is provided. A profiler receives application requests from processes of serverless applications. The profiler categorizes the processes as persistent or non-persistent based on the application requests. A read/write batcher creates batches of the persistent requests including the read requests and write requests and assigns the batches to persistent memory banks. A scheduler creates a schedule of the batches to the persistent memory banks in a manner enabling optimization of job completion time.

Abstract: A computer implemented method of training an encoder to extract features from sensor data comprises generating a plurality of training examples, each training example comprising at least two data representations of a set of sensor data, the at least two data representations related by a transformation parameterized by at least one numerical transformation value; and training the encoder based on a self-supervised regression loss function applied to the training examples. The encoder extracts respective features from the at least two data representations of each training example, and at least one numerical output value is computed from the extracted features. The self-supervised regression loss function encourages the at least one numerical output value to match the at least one numerical transformation value parameterizing the transformation.

Abstract: A floating vessel for use as a hydrogen and/or ammonia floating production storage and offloading vessel comprising an inner hull wall; at least two bulkheads, wherein the at least two bulkheads are disposed within the inner hull wall, forming at least three separate storage spaces; a series of cross-members, wherein the series of cross members are disposed between the at least two bulkheads to provide support and stability to the at least two bulkheads; and a deck, wherein the deck is supported by and disposed upon the at least two bulkheads; and wherein the at least three separate storage spaces are configured to contain gasses and/or liquids.

Abstract: Systems and methods for automatic assessment of a vessel are provided. A temporal sequence of medical images of a vessel of a patient is received. A plurality of sets of output embeddings is generated using a machine learning based model trained using multi-task learning. The plurality of sets of output embeddings is generated based on shared features extracted from the temporal sequence of medical images. A plurality of vessel assessment tasks is performed by modelling each of the plurality of sets of output embeddings in a respective probabilistic distribution. Results of the plurality of vessel assessment tasks are output.

Abstract: System and methods for determining and implementing optimized reconstruction parameters for computer-aided diagnosis applications. A simulator generates image data using different combinations of reconstruction parameters. The image data is used to evaluate or train machine learned networks that are configured for computer-aided diagnosis applications to determine which reconstruction parameters are optimal for application or training.

Abstract: It A computer implemented method of training an encoder to extract features from sensor data comprises training a machine learning (ML) system based on a self-supervised loss function applied to a training set, the ML system comprising the encoder. The training set comprises first data representations and corresponding second data representations, wherein the encoder extracts features from each first and second data representation, and wherein the self-supervised loss function encourages the ML system to associate each first data representation with its corresponding second data representation based on their respective features.

Abstract: Systems and methods are provided for available network bandwidth estimation using a one-way-delay noise filter with bump detection. The method includes receiving one-way delay measurements for each probe packet in a probe train sent over the telecommunications path; grouping the probe packets into a plurality of pairs based on the one-way delay measurements; for each pair, computing a respective noise threshold based on the one-way delay measurements of all the probe packets transmitted after a later-transmitted probe packet of the pair; selecting one of the pairs according to the noise thresholds and the one-way delay measurements for the probe packets of the pairs; and estimating the available bandwidth on the telecommunications path based on transmission times of the probe packets in the selected pair.

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: User interface customization based on user navigational signals is described herein. A computer system causes a device to present first information associated with a first item in a first network page. The computer system receives first data indicating a first interaction with the first information and determines whether the first interaction corresponds to a first request for information about a set of items associated with the first item or to a second request for a removal of the presentation of the first information. If the first interaction corresponds to the first request, the computer system causes the device to present a second network page that shows second information associated with a second item of the set of items. If the first interaction corresponds to the second request, the computer system causes the device to remove the first information from being presented in the first network page.

Abstract: Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.

Abstract: Systems and methods for determining an evaluation of one or more patients is provided. User input for evaluating one or more patients is received. A commit bundle is retrieved from a commit database. An evaluation of the one or more patients is determined based on the user input using a medical ontology configured with the retrieved commit bundle. The medical ontology is separate from the commit database. Results of the evaluation of the one or more patients are output.

Abstract: A computer implemented method of training an encoder to extract features from sensor data comprises training a machine learning (ML) system based on a self-supervised loss function applied to a training set, the ML system comprising the encoder. The training set comprises sets of real sensor data and corresponding sets of synthetic sensor data. The encoder extracts features from each set of real and synthetic sensor data, and the self-supervised loss function encourages the ML system to associate each set of real sensor data with its corresponding set of synthetic sensor data based on their respective features.

Abstract: Disclosed herein is an AI based system and method for generating warning alerts for a location to be excavated. The method comprises obtaining, from at least one external source, at least one underground asset map of the location to be excavated. For each of the at least one underground asset map, the method comprises locating a region of interest within the underground asset map corresponding to an identified underground utility service provider and extracting at least one feature within the region of interest. The at least one extracted feature is then compared with a plurality of features stored in a repository corresponding to the identified underground utility service provider, to determine a match. In response to the determination, the extracted feature is identified as a risk feature corresponding to the identified underground utility service provider and one or more warning alerts indicative of risk assets are generated.

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: For shape determination of cardiac anatomy with a medical imager, irregularities in motion, poor image quality, and misalignment of imaging planes are counteracted by a process relying on alignment of contours in combination with selection and fitting of a motion model. Contours are extracted from 2D images and aligned for each frame, which is extracted from the sequence of 2D images. The alignment may use a translation for each frame and rotation across frames for improved performance. A motion model is fit to the aligned contours and tested. If insufficient (greater than threshold difference), other motion models are aligned and tested. Motion models may be created on demand for improved performance. If sufficient, the shape of the heart structure is determined from the fit model.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. An input medical image in a first modality is received. Features are extracted from the input medical image using a first machine learning based encoding network. A medical imaging analysis task is performed on the input medical image based on the extracted features by, in one embodiment, decoding the extracted features to generate results of the medical imaging analysis task using a machine learning based decoding network. Results of the medical imaging analysis task are output. In one embodiment, the first machine learning based encoding network is jointly trained with a second machine learning based encoding network with an unsupervised loss using unannotated pairs of training images. Each of the unannotated pairs comprise a first training image in the first modality and a second training image in a second modality.

Abstract: Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Abstract: Example implementations relate to edge acceleration by offloading network dependent applications to a hardware accelerator. According to one embodiment, queries are received at a cluster of a container orchestration platform. The cluster includes a host system and a hardware accelerator, each serving as individual worker machines of the cluster. The cluster further includes multiple worker nodes and a master node executing on the host system or the hardware accelerator. A first worker node executes on the hardware accelerator and runs a first instance of an application. A distribution of the queries is determined among the worker machines based on a queuing model that takes into consideration the respective compute capacities of the worker machines. Responsive to receipt of the queries by the host system or the hardware accelerator, the queries are directed to the master node or one of the worker nodes in accordance with the distribution.

Abstract: Systems and methods are provided for measuring available bandwidth available in a black box network by determining a probing rate of packet transmissions between a sender and receiver. The optimal probing rate and bandwidth estimate may be determined. Additional actions may be performed, like automatically rerouting packets and/or load balancing network traffic after the probing rate is determined.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images of a patient are received. The one or more input medical images are encoded into embeddings using a machine learning based encoder network. A medical imaging analysis task is performed based on the embeddings. Results of the medical imaging analysis task are output.