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: 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: 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: The disclosure relates to a method including receiving a first set of images, the images being scans from one or more medical instruments; creating a patient-specific image reconstructed model based on the first set of images; defining mapping points on the reconstructed model; receiving a second set of images; defining patient recognition mapping points on the second set of images; overlaying the reconstructed model and the second set of images based on the mapping points and the recognition mapping points; and displaying the overlaid model aligned with the second set of images.

Abstract: The present device provides a storage cabinet for safely housing multiple rechargeable chemical batteries, comprising a fire-proof outer housing that defines an interior chamber; within the chamber, one or more battery storage compartments are arranged such that an insulating air-gap separates each compartment from every adjacent compartment; each battery compartment includes a closable access door that forms an air-tight seal; an ignition source is disposed within the interior chamber to deliberately ignite any combustible gas generated by a battery thermal event; and a ventilation system, having an inlet port for introducing ambient air into the interior chamber and an exhaust port leading to an external exhaust conduit and vent which is manages airflow and aids in controlled combustion and heat dissipation.

Abstract: The disclosure relates to a method including receiving a first set of images, the images being scans from one or more medical instruments; creating a patient-specific image reconstructed model based on the first set of images; defining mapping points on the reconstructed model; receiving a second set of images; defining patient recognition mapping points on the second set of images; overlaying the reconstructed model and the second set of images based on the mapping points and the recognition mapping points; and displaying the overlaid model aligned with the second set of images.

Abstract: An address decode and translate (ADT) circuit includes an address decode circuit configured to receive and decode transactions issued by a plurality of source nodes, and a resolution circuit configured to make certain resolutions and decisions based on results of decoding the transactions. For example, the resolution circuit can determine, among a plurality of target nodes of an adapter device, respective target nodes for performing the transactions, and translate transaction addresses of the transactions to local addresses of the adapter device. The resolution circuit is also configured to perform, for example, enabling monitoring of the execution of the transactions, detecting a denial-of-service (DOS) attack by the transactions, reordering the transactions, determining data caching attributes for the transactions, or a combination.

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: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.

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: 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: 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: A wake-up management circuit for implementation in circuit boards with multiple processors is disclosed. The wake-up management circuit utilizes hardware resources to generate a wake-up signal for a processor in a low power mode when traffic intended for the processor is received by the wake-up management circuit. The wake-up management circuit is configured to generate a wake-up signal for a specific processor when address information in a data packet received by the wake-up management circuit indicates that the data packet is intended for the specific processor (e.g., the specific processor is the destination for the data packet).

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: Techniques for low-latency packet processing are disclosed. A network device receives a first set of write transactions including a first set of data segments corresponding to a first DMA descriptor from a host. The network device receives a second set of write transactions including a second set of data segments corresponding to a second DMA descriptor from the host. The network device detects that the first set of data segments have been written. In response to detecting that the first set of data segments have been written, and prior to completely writing the second set of data segments and to receiving a packet notifier from the host, the network device processes the first DMA descriptor.

Abstract: A method for calculating a distance between a vehicle camera and an object, the method may include: (a) obtaining an image that was acquired by the vehicle camera of a vehicle; the image captures the horizon, the object, and road lane boundaries; (b) determining an initial row-location horizon estimate and a row-location contact point estimate, the contact point is between the object and a road on which the vehicle is positioned; (c) determining a vehicle camera roll angle correction that once applied will cause the lanes boundaries to be parallel to each other in the real world; (d) calculating a new row-location horizon estimate, wherein the calculating comprises updating the row-location horizon estimate based on the vehicle camera roll angle correction; and (e) calculating the distance between the vehicle camera based on a difference between the new row-location horizon estimate and the row-location contact point estimate.

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: 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: 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: 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.