Abstract: A method includes receiving one or more images, wherein at least one of the one or more images depicts a water region and analyzing, by one or more processors, the one or more images using a first machine learning model to determine a depth of the water region. The method also includes analyzing, by the one or more processors, the one or more images using a second machine learning model to determine a surface normal of the water region and performing, by the one or more processors, using a third machine learning model, multi-class segmentation of the one or more images. Additionally, the method includes performing one or more fusion operations on outputs of at least two of the first machine learning model, the second machine learning model and the third machine learning model to generate a classification of the water region.

Abstract: A method for selecting one or more Regions of Interest (RoIs) for human annotations includes obtaining sensor data generated by one or more sensors of a vehicle; applying at least one class-agnostic heuristic function to the sensor data to determine a presence and an approximate position of one or more objects in an RoI of the sensor data; selecting one or more RoIs having proposed annotations for the one or more objects for refinement by an annotator; and outputting the one or more selected RoIs.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A set of object detections, each respective object detection in the set of object detections corresponding to a respective object detected in an environment, is accessed. Based on the set of object detections, a graph representation comprising a plurality of nodes is generated, where each respective node in the plurality of nodes corresponds to a respective object detection in the set of object detections. A set of output features is generated based on processing the graph representation using a trained message passing network. A predicted object relationship graph is generated based on processing the set of output features using a layer of a trained machine learning model.

Abstract: Systems and methods are provided for providing a zero touch operations and self-healing data platform. Initially, data corresponding to a mobile communications network is received from a plurality of sources. A machine learning model is trained to detect anomalies corresponding to the data. Upon detecting an anomaly, self-remediation is initiated. Upon determining the self-remediation exceeds a configurable number of attempts or duration, an action is automatically performed. In various aspects, the action includes automatically initiating an alert corresponding to the anomaly or automatically creating a ticket corresponding to the anomaly.

Abstract: A method of processing image content includes constructing a first graph representation having a first level of point sparsity from a first point cloud data, and performing diffusion-based upsampling on the first graph representation to generate a second graph representation having a second level of point sparsity. Performing diffusion-based upsampling includes inputting the first graph representation into a diffusion-based trained model to generate a first intermediate graph representation having a first intermediate level of point sparsity, inputting the first intermediate graph representation into the diffusion-based trained model to generate a second intermediate graph representation having a second intermediate level of point sparsity, and generating the second graph representation based on at least on the second intermediate graph representation.

Abstract: A system for processing image data and position data, the system comprising: a memory for storing the image data and the position data; and processing circuitry in communication with the memory. The processing circuitry is configured to: apply a first encoder to extract, from the image data, a first set of features; apply a first decoder to determine, based on the first set of features, a first uncertainty score. Additionally, the processing circuitry is configured to apply a second encoder to extract, from the position data, a second set of features; apply a second decoder to determine, based on the second set of features, a second uncertainty score; and fuse the first set of features and the second set of features based on the first uncertainty score and the second uncertainty score.

Abstract: An apparatus includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to apply, based on a positional encoding model, a first feature conditioning module to a set of bird's eye view (BEV) position data features corresponding to position data to generate a set of conditioned BEV position data features, and apply, based on the position encoding model, a second feature conditioning module to a set of perspective image data features corresponding to image data to generate a set of conditioned perspective image data features. The processing circuitry is also configured to generate, based on the positional encoding model, the set of conditioned BEV position data features, and the set of conditioned perspective image data features, a weighted summation. Additionally, the processing circuitry is configured to generate, based on the weighted summation, a set of BEV image data features.

Abstract: The technology described herein is directed towards intelligently controlling the power consumed by light emitting diodes (LEDs) of network devices, such as present in a datacenter. An internal device agent, application and/or centralized monitoring system can determine when to activate and deactivate LEDs, such as to activate when a group of devices is likely being observed, and deactivate otherwise. Devices can be grouped for collective control, such as a group per rack of network devices with their LEDs activated and deactivated together. Sensors, such as rack cabinet door sensors, motion sensors and so on can be used to detect whether a group of devices is likely being observed; an action such as closing the door or walking away can be sensed to deactivate the LEDs. After LED activation, expiration of a configurable timer can be used to deactivate the LEDs. Significant power savings are thus achievable in various network scenarios.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. A method is disclosed for aligning top-down features from two sensor arrangements and generating vehicle control instructions. The method includes receiving first sensor data from a first sensor arrangement and second sensor data from a second sensor arrangement. The method further includes determining a first set of top-down and a second set of top-down features based on the sensor data. A transformation is determined based on the first set of top-down features and the second set of top-down features to align the second set of top-down features with the first set of top-down features. Finally, vehicle control instructions for a vehicle are determined based on the transformation. Other aspects and features are also claimed and described.

Abstract: A sensor data processing system includes various elements, including a perception unit that collects data representing positions of sensors on a vehicle and obtains environmental information around the vehicle via the sensors. The sensor data processing system also includes a feature fusion unit that combines the first environmental information from the sensors into first fused feature data representing first positions of objects around the vehicle, provides the first fused feature data to the object tracking unit, receives feedback for the first fused feature data from the object tracking unit, and combines second environmental information from the sensors using the feedback into second fused feature data representing second positions of objects around the vehicle. The sensor data processing system may then at least partially control operation of the vehicle using the second fused feature data.

Abstract: The present disclosure relates to limiting viewing of media content. More particularly, the present invention relates to classifying segments of content for limited viewing based on user preferences and classifications. According to a first aspect, there is provided a method for parental control of media content for a media guidance application, the method comprising, determining user preferences comprising settings for restricting viewing of segments of media content, determining media content for user consumption on a user device, determining a classification of each of a plurality of segments of the media content using a content analyzer and classifier, and comparing the determined classifications against the user preferences.

Abstract: A Layer 2 (L2) domain hierarchical address communication system includes a first computing device that provides a first host, and a second computing device that provides a second host. A first leaf switch device is connected to the first computing device, and a second leaf switch device that is connected to the second computing device and coupled to the first leaf switch device. The first leaf switch device receives a first data communication from the first host that includes a second hierarchical MAC address of the second host. The first leaf switch device identifies a hierarchical MAC prefix in the second hierarchical MAC address, and uses the hierarchical MAC prefix to forward the first data communication to the second leaf switch device.

Abstract: Disclosed are techniques for localization of an object. For example, a device can generate, based on sensor data obtained from sensor(s) associated with an object, a predicted map comprising predicted nodes associated with a predicted location of the object within an environment. The device can receive a high definition (HD) map comprising HD nodes associated with a HD location of the object within the environment. The device can further match the predicted nodes with the HD nodes to determine pair(s) of matched nodes between the predicted map and the HD map. The device can determine, based on a comparison between nodes in each pair of the pair(s) of matched nodes, a respective node score for each pair of the pair(s) of matched nodes. The device can determine, based on the respective node score for each pair of the pair(s) of matched nodes, a location of the object within the environment.

Abstract: A networking device credential information reset system includes credential information reset authorization devices coupled to a networking device. At least one of the credential information reset authorization devices receives a networking device credential information reset request from the networking device and, in response, generates a networking device credential information reset alert and provides it for display on an administrator device. Following the networking device credential information reset alert being provided for display on the administrator device, a first credential information reset authorization device receives first credential information for the first credential information reset authorization device from the administrator device, validates the first credential information and, in response, provides a credential information reset authorization to the networking device that is configured to cause the networking device to reset second credential information for the networking device.

Abstract: A method includes performing an atomic layer deposition (ALD) process with respect to a plurality of target elements to coat interiors of the plurality of target elements with a protective coating. Performing the ALD process includes alternating delivery of a first precursor inside the plurality of target elements for a first duration to form an adsorption layer on the interiors of the plurality of target elements, alternating purging of the first precursor from the plurality of target elements for a second duration, and alternating delivery of a second precursor inside the plurality of target elements for a third duration to cause the second precursor to react with the adsorption layer and form a target layer on the interiors of the plurality of target elements.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving a plurality of image frames representative of a scene; receiving point cloud data representative of the scene; determining, using a NeRF model, a three-dimensional reconstruction of the scene based on the plurality of image frames; and outputting fused data that combines first BEV features of the three-dimensional reconstruction of the scene and second BEV features of the point cloud data. Other aspects and features are also claimed and described.

Abstract: Aspects presented herein may enable a UE to detect and identify a weather condition of an environment based on the sparsity of FFT/DWT coefficients derived from a set of range images associated with the environment. In one aspect, a UE converts a set of point clouds associated with an environment to a set of range images based on a spherical projection. The UE applies at least one of FFT or DWT to the set of range images to obtain a set of FFT coefficients or a set of DWT coefficients. The UE identifies a level of a condition for the environment based on a sparsity of the set of FFT coefficients or the set of DWT coefficients.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method includes receiving an image frame representing a scene; receiving point cloud data representing the scene; determining first sets of image frame features; determining second sets of point cloud data features based on a plurality of voxels representing the point cloud data; determining a third set of features of the image frame based on a first set of features of the plurality of first sets of features of the image frame and a second set of features of the plurality of second sets of features of the point cloud data; and outputting fused data that combines the third set of features of the image frame and a fourth set of features of the point cloud data. Other aspects and features are also claimed and described.

Abstract: Systems and methods for generating communications based on user account activity data are described herein. For example, the system may receive an input activity log and generate a plurality of tokens. The system may generate a time-ordered sequence of tokens based on the plurality of tokens. The system may generate an output vector encoding based on a machine learning model. The system may generate a predicted communication based on a vector encoding model. The system may transmit the predicted communication to a user device.

Abstract: Systems and methods for generating communications based on user account activity data are described herein. For example, the system may obtain an input activity log and generate an activity log transformation. The system may input the activity log transformation into a vector encoding model to obtain a first output vector encoding. The system may input the first output vector encoding into a contrastive machine learning model to obtain a first matching vector encoding. The system may access a first plurality of vector encodings from a communication database. Based on comparing each vector encoding in the first plurality of vector encodings with the first matching vector encoding, the system may generate the matching communication.