Abstract: An access control system controls access to a shared resource for various functional circuits. The access control system can include a comparison circuit, a processing circuit, and a selection circuit. The comparison circuit receives identification data associated with a functional circuit based on a transaction initiated by the functional circuit, and compares the identification data and reference data to generate a select signal. The processing circuit receives error data and response data outputted by the shared resource based on an execution of the transaction, and generates another response data. The selection circuit selects and outputs, based on the select signal, one of the response data outputted by the shared resource and the response data generated by the processing circuit as a transaction response that is to be provided to the functional circuit.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: A system and method for sharing communication platform data includes (a) displaying on a screen of a mobile device a first and second data field, wherein the first data field includes a QR code and the second data field includes a plurality of visual indicia corresponding to contact information corresponding to a user of the mobile device; (b) visually distinguishing on the screen at least one selected visual indicia from at least one unselected visual indicia upon receiving an input corresponding to a selection of the at least one visual indicia by the user; and (c) updating in real-time the QR code to include the contact information corresponding to the selected visual indicia and, optionally, wherein the updated QR code corresponds to a URL containing the contact information.

Abstract: A system for generating a novel molecular structure using a protein structure is disclosed. One or more processors generate a protein voxel representation of a protein structure that includes a multichannel three-dimensional (3D) grid that includes a plurality of channels. A cavity region is detected in the protein voxel representation based on a combination of rule-based detection and a deep learning based model. A cavity voxel representation of the cavity region is generated based on upscaling of a regional voxel of the detected cavity region. A ligand voxel representation of a ligand structure is generated based on the cavity voxel representation. A 3D voxel descriptor is determined for a protein-ligand complex based on the protein voxel representation and the ligand voxel representation. A simplified molecular-input line-entry system (SMILES) of a novel molecular structure is generated using a rich 3D embedding vector, which is based on the 3D voxel descriptor.

Abstract: A method of identifying a tube type. The method includes capturing one or more pixelated images of a cap affixed to a tube; identifying a color of one or more pixels of the pixilated image of the cap; identifying one or more gradients of a dimension of the cap; and identifying the tube type based at least on: the color of the one or more pixels, and the one or more gradients of a dimension of the cap. Apparatus adapted to carry out the method are disclosed as are other aspects.

Abstract: A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

Abstract: An access control system controls access to a shared resource for various functional circuits. The access control system can include a comparison circuit, a processing circuit, and a selection circuit. The comparison circuit receives identification data associated with a functional circuit based on a transaction initiated by the functional circuit, and compares the identification data and reference data to generate a select signal. The processing circuit receives error data and response data outputted by the shared resource based on an execution of the transaction, and generates another response data. The selection circuit selects and outputs, based on the select signal, one of the response data outputted by the shared resource and the response data generated by the processing circuit as a transaction response that is to be provided to the functional circuit.

Abstract: CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.

Abstract: Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.

Abstract: Vehicle allocation method and system for ride-sharing are provided. The method includes receiving a first set of booking requests is received, at a first time instance, from a set of customer devices for sharing one or more rides. A second set of booking requests is determined, at a second time instance, based on the first set of booking requests and a third set of booking requests. The third set of booking requests is determined based on at least one of historical booking data and booking requests received after the first time instance. A set of vehicles available for the one or more rides is determined at the second time instance. A graph is generated based on the second set of booking requests and the set of available vehicles. An available vehicle is allocated to one or more customers based on optimal matching between nodes of the graph.

Abstract: A system and method for sharing information includes displaying on a screen of a mobile device data fields that include a machine-readable code (e.g., QR code) and visual indicia corresponding to information that is to be shared by a user of the mobile device. The user selects which information is to be shared by selecting or unselecting the corresponding visual indicia and the machine-readable code updates in real-time to include the information corresponding to the selected visual indicia. The information is stored as a URI-encoded serialized data object in a parameter portion of the URI of the machine-readable code.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.

Abstract: A stream of virtual topograms, in particular live virtual topograms, is predicted. Sets of surface data of an outer surface of a subject are continuously received. Based on each received set of surface data a (live) virtual topogram is continuously generated by a trained machine learning algorithm (MLA). Thereto, a representation of body landmarks is updated based on each received set of surface data by a trained body marker detector (BMD), of the trained MLA, and the (live) virtual topogram is predicted based on the updated spatial marker map and on the corresponding set of surface data by a trained topogram generator (TG) of the trained MLA.

Abstract: Systems and methods for a reduced interaction CT scanning workflow. A sensor is used to capture an image of a patient on the table. Scan parameters are automatically set. A full CT scan is performed without a scout scan. During the full CT scan, the scan parameters are adjusted based on the raw CT measurements from the full CT scan. A radiology report is automatically generated from the results of the full CT scan.

Abstract: Anomalies in images are detected. A generative network and/or an autoencoder (“G/A-Network”), a Siamese network, a first training-dataset of normal images and a second training-dataset of abnormal images are provided. The G/A-network is trained to produce latent data from input images and output images from the latent data, wherein the training is performed with images of the first training-dataset, wherein a loss function is used for training at least at the beginning of training, and the loss function enhances the similarity of the input images and respective output images. The Siamese network is trained to generate similarity measures between input images and respective output images, wherein the training is performed with images of the first training-dataset and the second training-dataset in that images of both training-datasets are used as input images for the G/A-network and output images of the G/A-network are compared with their respective input images by the Siamese network.

Abstract: A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Abstract: An example device includes a control unit that provides an execution environment for a network management application and a network interface. The network management application generates a request message based at least in part on a predicted amount of time required for the device to receive a response to a request message, wherein the predicted amount of time is based at least in part on object identifier processing time information, and wherein the predicted amount of time satisfies a timeout threshold. The network interface sends the request message and receives the response message. The network management application determines an amount of time that elapsed between when the device sent the request message and received the response message, and, responsive to determining that the amount of time that elapsed does not satisfy the timeout threshold, updates at least one of the object identifier processing time information and the timeout.

Abstract: The present disclosure relates to methods, systems, and apparatuses for identifying related records in a database. The method includes receiving, via a network interface, a related records query, the related records query identifying at least one record stored in the electronic database, determining, based on transaction data, at least one related record that is related to the identified at least one record, determining, by a processor, that the at least one related record is unavailable, in response to determining that the at least one related record is unavailable, determining at least one keyword associated with the at least one related record, selecting at least one of one or more substitute records based at least in part on comparing the at least one keyword with a set of keywords associated with one or more substitute records, and providing the selected one or more substitute records as a response to the related records query.

Abstract: A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.