Abstract: Described are methods, platforms, systems, and media, for enhancing well-being by using methodology including receiving media comprising an unstructured user-generated narrative; applying one or more algorithms to the unstructured user-generated narrative to extract semi-structured user context data; applying a first machine learning model to classify one or more of sentiment, intent, habits, patterns, beliefs, and motivations from at least the user context data, wherein the first machine learning model comprises an unsupervised machine learning model; applying a second machine learning model to identify one or more recommended next actions from at least the user context data, wherein the second machine learning model comprises a supervised machine learning model; and generating one or more well-being-related insights for the user based at least on the user context data, and one or more of the sentiment, intent, habits, patterns, beliefs, motivations, and one or more recommended next actions to encourage a he

Abstract: Described are methods, platforms, systems, media, and applications for enhancing well-being using a wearable recording device that records a story; applying one or more algorithms to the story to extract semi-structured user context data; applying a first machine learning model to classify one or more of sentiment, intent, habits, patterns, beliefs, and motivations from the user context data, wherein the first machine learning model comprises an unsupervised machine learning model; applying a second machine learning model to identify one or more recommended next actions from at least the user context data, wherein the second machine learning model comprises a supervised learning model; and generating one or more well-being-related insights for the user based at least on the user context data, and one or more of the sentiment, intent, habits, patterns, beliefs, motivations, and one or more recommended next actions to encourage a healthy lifestyle.

Abstract: Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients.

Abstract: A surgical assistance method can include: generating a 3D representation of a set of anatomical structures, registering the 3D representation with a subject, displaying an overlay of the 3D representation, determining a trajectory, determining surgical guides, determining observation parameters, and/or facilitating the surgical procedure. The method functions to generate a virtual cut displayed on a 3D representation of a subject to facilitate provider planning and/or performance of a surgical incision.

Abstract: In variants, a method can include determining measurements of a surgical procedure, determining a set of feature states for elements depicted and not depicted within the measurements, tracking the set of features, determining a procedure step, predicting a set of future feature states based on the set of feature states and the procedure step, and performing further analyses based on the set of feature states and the set of predicted future feature states in order to facilitate measurement and improvement of a surgical procedure

Abstract: An integrated circuit for distributing processing tasks includes a pre-selector circuit and a scheduler circuit. The pre-selector circuit is configured to receive a processing task, determine a category of the processing task, and select, from a set of task distribution techniques and based at least in part on the category of the processing task, a task distribution technique for distributing the processing task to a group of processing units. The scheduler circuit is configured to implement the selected task distribution technique to select, from the group of processing units, a target processing unit for performing the processing task.

Abstract: A method for lane detection, including A computer implemented method for lane detection, the method includes (a) receiving, at one or more processing circuits of a vehicle, a plurality of initial lane boundary estimates that represent lane boundaries within a road environment, the plurality of initial lane boundary estimates include (i) first lane boundary estimates and (ii) single-image based lane boundary estimates; wherein under one or more predefined conditions the single-image based lane boundary estimates are sent once per multiple images; and (a) generating, by the one or more processing circuits, real-world lane detection estimates based on the initial lane boundary estimates, the generating includes evaluating real-world distances between initial lane boundary estimates of the lane boundaries, the initial lane boundary estimates are associated with a same point of time.

Abstract: Various catheters are provided herein for recording, mapping, and/or ablating target tissue to reduce or eliminate unwanted electrical impulses. In one embodiment, a catheter can have a handle, an elongate body, and an end effector. The end effector has expanded and contracted configurations and can rotate about the elongate body. A plurality of electrodes can also be disposed on the end effector for recording, mapping, and/or ablating target tissue surrounding the catheter. The handle can guide the end effector through transitioning between the configurations and rotating about the elongate body.

Abstract: Systems and methods are provided to reduce the latency in accessing an input/output (I/O) hardware register by software executing on a central processing unit (CPU). The hardware register is located in a controller coupled to the CPU via an I/O bus. The CPU software can send a command to the controller for execution. The controller can execute the command and update the hardware register to indicate that the command has been executed. The controller can write contents of the hardware register to a specified address in a CPU memory that is assigned by the CPU software. The CPU software can read the specified address to determine that the command has been executed instead of reading the hardware register on the I/O bus.

Abstract: Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients.

Abstract: To accelerate the data processing of a processor, a coprocessor subsystem can be used to offload data processing operations from the processor. The coprocessor subsystem can include a coprocessor and an accelerator. The accelerator can offload operations such as data formatting operations from the coprocessor to improve the performance of the coprocessor. The coprocessor subsystem can be used to accelerate database operations.

Abstract: Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients.

Abstract: A trekking pole or the like includes a grip top fixed at the top end. A user's glove supports a puck. The puck is placed on a release button then slid into a locking channel on the grip top, from the release button into the locking channel. Thereafter, the release button automatically locks the puck in the channel. When the release button is pressed down, the puck can be removed. A magnet on the release button can assist in locating the puck properly on the release button and also in withdrawing the puck from the locking channel.

Abstract: Various catheters are provided herein for recording, mapping, and/or ablating target tissue to reduce or eliminate unwanted electrical impulses. In one embodiment, a catheter can have a handle, an elongate body, and an end effector. The end effector has expanded and contracted configurations and can rotate about the elongate body. A plurality of electrodes can also be disposed on the end effector for recording, mapping, and/or ablating target tissue surrounding the catheter. The handle can guide the end effector through transitioning between the configurations and rotating about the elongate body.

Abstract: Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.

Abstract: An aerial vehicle including a central frame electro-mechanically connected to a central frame inertial measurement unit (IMU) adapted to output central frame inertial measurements to one or more controllers. A motor frame includes a central rod mounted in perpendicular to a longitudinal axis of the central frame, and a pair of lateral arms each fixated to a different end of the central rod in parallel to the longitudinal axis of the central frame. A motor frame IMU adapted to output motor frame inertial measurements of the motor frame to the flight controller. The central frame is further electro-mechanically connected to the one or more controllers adapted to calculate control signals for rotating the central frame according to the central frame inertial measurements and the central rod inertial measurements. A stabilizing motor adapted to rotate the central frame according to the control signals.

Abstract: A method for steering assistance, the method may include obtaining by vehicle camera, an image of an environment of the vehicle; feeding the image to a machine learning process; outputting, by the machine learning process, road line metadata that comprises a line offset value, a line angle value, a line curvature value and a line curvature rate; and preforming a steering related response to the road line metadata.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting an exchange-correlation energy of an atomic system. The system obtains respective electron-orbital features of the atomic system at each of a plurality of grid points; generates, for each of the plurality of grid points, a respective input feature vector for the electron-orbital features at the grid point; and processes the respective input feature vectors for the plurality of grid points using a neural network to generate a predicted exchange-correlation energy of the atomic system.

Abstract: Systems, methods, and machine-readable media for predicting interruptions to the use of spare cloud resources and rebalancing based on those predictions are disclosed. A computing platform collects data for customers over time. The computing platform runs a machine learning algorithm on the historical data to generate a prediction classifier. The prediction classifier relates to a time window for prediction into the future, on the order of minutes or hours. The prediction classifier is run on monitored data from ongoing activity with a cloud provider to generate a risk score. Each risk score may identify an amount of risk that a spare cloud resource related to new resource metrics data will be interrupted within the future time frame corresponding to that prediction classifier. If predicted to be interrupted, the customer may be assisted in rebalancing to other resources. As a result, interruptions can be predicted hours into the future.

Abstract: Techniques are described for maintaining in-order execution when a dependency exists between write transactions. In some embodiments, a write re-order buffer (WROB) is configured to assign the same group ID to an incoming write transaction upon determining that the incoming write transaction is dependent on a pending write transaction. The WROB forwards the incoming write transaction to an interconnect fabric for routing to a completer device. The interconnect fabric enforces in-order execution when write transactions share the same group ID. The WROB can maintain a transaction log of pending write transactions and also track the statuses of responses for such transactions. Transaction responses can include responses sent from a completer to confirm that a transaction has actually been completed. Additionally, the WROB can send a response indicating completion back to the requester of the transaction. In some embodiments, the WROB is configured to send an early response to the requester.