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 neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.

Abstract: Computer-implemented methods, systems, and products for scoring the suitability of a recipe or food to an end-user having one or more medical conditions are provided. The methods and systems can include: the nutrient content of the food or a food created following the steps of the recipe; the chronic or health or medical conditions or general health that the target user may be interested in; the extent of the end-user's medical conditions and the relative rank order of concern of each such condition to the end-user; the end-user's biological and medical profile data such as their biological and physical characteristics comprising height, weight, age, sex, daily or weekly activity levels, etc.; and any of the end-user's nutrition preferences.

Abstract: A system and method for generating customizable molecular structures for drug discovery. The system includes a processor communicably coupled to a memory and executes a deep neural network based molecular encoding model. The processor receives input datasets of drug-like molecules from private and public databases and are employed as training dataset. The processor further executes a plurality of deep generative models configured to receive input data relating to small molecules which includes desirable molecules and undesirable molecules. The plurality of deep generative models generates molecular structures like the input desirable molecules. The deep neural network based molecular encoding model is configured to map similarities between the molecular structures generated. The deep neural network based molecular encoding model computes intra-model and inter-model distances.

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: Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Abstract: The disclosure provides a computer-implemented method for detecting a failure of a device, wherein the device is connected to a sensor, the method comprising: receiving, by a machine learning model, a trace signal from the sensor indicating a status of the device; encoding, by the machine learning model, the trace signal into a plurality of vector representations; and determining, by the machine learning model, whether the trace signal is valid or invalid based on the plurality of vector representations.

Abstract: A system and method for automated personalized financial planning is disclosed. The financial planning system includes a data receiving module for receiving a data input of at least one user account, a target information from the user. A manager module manages the data input. Further in, a machine-learning based prediction module derive learning from the data input for predicting a cashflow information. The financial planning system includes a planning module generating a financial plan based on the data input comprising of recommendation for achieving the target information.

Abstract: A pedestal assembly for a laundry appliance configured to operably connect to a water supply and drain of the laundry appliance arranged above the pedestal, the pedestal may include a cabinet defining a cavity configured to receive items to be laundered; and a shoe holder having at least one shaft configured to maintain at least one shoe thereon, wherein the shoe holder is configured to rotate within the cavity during a pedestal wash cycle and the shoe holder is configured to maintain a fixed position within the cavity for storage when the pedestal is not operating in a wash cycle.

Abstract: Present disclosure provides a method and system for package movement visibility in warehouse operations. The method includes identifying, by the package management system (1000), an object entering AOE and moving in a predetermined direction and recording, by the package management system (1000), image frame of the object. The method also includes determining, by the package management system (1000), that the object in the image frame is a package and determining, by the package management system (1000), a label on the package from the image frame. Further, the method also includes determining, by the package management system (1000), a match to the label in a cloud platform (400) and sending, by the package management system (1000), tracking details associated with the package based on the match to the label in the cloud platform (400), to a client device in real-time.

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.