Abstract: Energy consumption may be reduced by tracking a measure of baseline energy over time in order to determine which of the one or more appliances have a power on mode and a power standby mode based at least in part on the measure of baseline energy delivered to each of the appliances during the baseline time period, wherein the power standby mode consumes less power than the power on mode. During subsequent operation, the supervisor identifies when an appliance is in a power standby mode, and turns off the socket receptacles of the smart sockets that corresponds to those appliances identified as being in their power standby mode.

Abstract: For each discovered smart socket, the smart socket is automatically added to a supervisor database and a predetermined set of two or more objects for the smart socket are automatically added to the supervisor database. One or more predetermined alarm extensions are automatically added to predetermined ones of the two or more objects of the smart socket in the supervisor database. One or more predetermined history extensions are automatically added to predetermined ones of the two or more objects of the smart socket in the supervisor database. Predetermined ones of the two or more objects and/or extensions of each of the discovered smart sockets are automatically mapped to a predetermined dashboard.

Abstract: A method for maintaining a measure of accumulated energy delivered to an appliance includes a supervisor repeatedly receiving from a first smart socket a measure of energy delivered to the appliance by the first smart socket and maintaining a measure of accumulated energy delivered to the appliance. When the first smart socket fails and is replaced, the method includes repeatedly receiving from the second smart socket a measure of energy delivered to the appliance by the second smart socket, maintaining the measure of accumulated energy delivered to the appliance based at least in part on the measures previously received from the first smart socket and the measures repeatedly received from the second smart socket of energy delivered to the appliance by the second smart socket, and displaying the measure of accumulated energy delivered to the appliance on a dashboard.

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: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: Systems and methods for key performance benchmarking may include receiving for a plurality of client devices of a tenant, a duration for performing a plurality of actions to log into a resource. The systems and methods can include determining metrics for each action of the plurality of actions. The systems and methods can include generating, by the one or more processors, one or more recommendations corresponding to at least one action of the plurality of actions, to reduce the duration to log into the resource.

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: Techniques for improved machine learning are provided. Resident data describing a resident is accessed, and a residential service plan is generated for the resident, comprising extracting a set of features from the resident data and generating a set of predicted fitness scores for a set of services by processing the set of features using a machine learning model trained based on one or more collaborative filtering techniques. The residential service plan is implemented for the resident based at least in part on the set of predicted fitness scores.

Abstract: An integrated circuit (IC), including a functional circuit and a security system, is disclosed. The functional circuit generates a request packet for an indirect memory access of a memory. The security system validates the functional circuit based on a security attribute and a functional identifier of the functional circuit. Based on the request packet and the validation of the functional circuit, the security system identifies an instruction sequence associated with the indirect memory access. Further, the security system determines a type of the indirect memory access based on the instruction sequence, and validates the type of the indirect memory access based on the security attribute and the request packet. Based on the validation of the type of the indirect memory access, the instruction sequence is executed, thereby facilitating the indirect memory access for the functional circuit.

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.

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: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: For training a machine learning system for representing a patient body a plurality of stored medical imaging data sets each representing at least a part of a respective patient are obtained. A first one of the plurality of stored medical imaging data sets represents a different part of the patient body than a second one of the plurality of stored medical imaging data sets. A plurality of landmarks in the stored medical imaging data sets are estimated, and each of the stored medical imaging data sets are aligned to a predefined pose using the plurality of landmarks. A plurality of points in the aligned medical imaging data sets are sampled, and the machine learning system is trained based on at least the plurality of points. The learned parameters of the machine learning system are then stored and used in a method for inferring a body representation.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

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: Systems and methods for processing a C4 and C5 stream are disclosed. A pygas stream can be separated in a depentanizer to produce a C4 and C5 stream and a C6 to C9+ stream. The C4 and C5 stream is further processed to recover C5 dienes including isoprene, pentadiene, cyclopentadiene, or combinations thereof. The C6 to C9+ stream is further processed to recover aromatics including benzene, toluene, xylene, ethylbenzene, or combinations thereof.

Abstract: Systems and methods are disclosed for generating interactive user interfaces for evaluating transactions of an organization. For example, a system can be configured to receive transactions for an organization and determine a first subset of the transactions during a trip at a first location that is different from a home location of a consumer. A respective transaction associated with a second location that occurred during the trip is identified. The respective transaction is assigned to the first subset of the of transactions for the trip. A second subset of the transactions of a spending type is determined. A parameter value associated with the spending type is identified. A parameter adjustment is determined. The system is configured to cause a display of a spending type user interface on a client device and receive a selection of a parameter component.

Abstract: For viability determination with self-attention for process optimization, various process and state information in the manufacture (e.g., forming, assembling, and/or handling) of a part are embedded. A machine-learned model generates the embedding, which is used with self-attention similarity to identify similar cases based on the embedding. The model was trained using both regression for continuous information (e.g., variable names) in the embedding and classification for non-continuous information (e.g., value of a variable) in the embedding. By including both regression and classification, the same machine-learned model may be used for reliable and nuanced viability determination.