Abstract: Apparatuses, systems, and techniques to identify orientations of objects within images. In at least one embodiment, one or more neural networks are trained to identify an orientations of one or more objects based, at least in part, on one or more characteristics of the object other than the object's orientation.

Abstract: The systems, apparatuses and methods of the present invention set forth improvements to the problems of the current pairing or duplex paradigm, resulting in a dramatic increase in fiber transmission efficiency, accomplished explicitly by restructuring presently-aligned C-Band wavelengths into innovative DWDM transmit and receive formats, and through implementing photonic-wave changes, which directs Ethernet data flow onto new path adaptations. These improvements could reduce line haul expenses significantly, believed to reach a projected 50% less requirement/deployment of fiber strands. This saving would offer owner-operators substantial fiber strand cost reductions, affecting transportation rates of high-bandwidth digital payloads traversing over DWDM networks, and lower usage rates of cross-connections amid multiple equipment inter-exchanging throughout large data centers.

Abstract: A system for creating synthetic data for testing an autonomous system, comprising at least one hardware processor adapted to execute a code for: using a machine learning model to compute a plurality of depth maps based on a plurality of real signals captured simultaneously from a common physical scene, each of the plurality of real signals are captured by one of a plurality of sensors, each of the plurality of computed depth maps qualifies one of the plurality of real signals; applying a point of view transformation to the plurality of real signals and the plurality of depth maps, to produce synthetic data simulating a possible signal captured from the common physical scene by a target sensor in an identified position relative to the plurality of sensors; and providing the synthetic data to at least one testing engine to test an autonomous system comprising the target sensor.

Abstract: Disclosed is a pain assessment method using a deep learning model, the pain assessment method including operations of receiving, by an analysis device, an image indicating activity in a specific brain area of a subject animal and allowing the analysis device to input images of regions of interest in the image into a neural network model and assess the pain of the subject animal according to a result output by the neural network model.

Abstract: Techniques are disclosed for improving upon the usage of Light Detection and Ranging (LIDAR) supervision to perform image depth estimation. The techniques use a generator and adversary network to generate respective models that “compete” against one another to enable the generator model to output a desired output image that compensates for a LIDAR image having a structured or lined data pattern. The techniques described herein may be suitable for use by vehicles and/or other agents operating in a particular environment as part of machine vision algorithms that are implemented to perform autonomous and/or semi-autonomous functions.

Abstract: A system having multiple devices that can host different versions of an artificial neural network (ANN) as well as different versions of a feature dictionary. In the system, encoded inputs for the ANN can be decoded by the feature dictionary, which allows for encoded input to be sent to a master version of the ANN over a network instead of an original version of the input which usually includes more data than the encoded input. Thus, by using the feature dictionary for training of a master ANN there can be reduction of data transmission.

Abstract: The preset invention aims to provide a method of presenting a learning condition which enables an improvement in the accuracy of image analysis. An information processing apparatus for machine learning is provided which includes a true/false information generating unit which generates true/false information of an image analysis result, a reliability determining unit which determines reliability related to analysis in image analysis processing, and a learning condition output unit which presents a learning condition, based on the true/false information and the reliability.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that perform language guided digital image editing utilizing a cycle-augmentation generative-adversarial neural network (CAGAN) that is augmented using a cross-modal cyclic mechanism. For example, the disclosed systems generate an editing description network that generates language embeddings which represent image transformations applied between a digital image and a modified digital image. The disclosed systems can further train a GAN to generate modified images by providing an input image and natural language embeddings generated by the editing description network (representing various modifications to the digital image from a ground truth modified image). In some instances, the disclosed systems also utilize an image request attention approach with the GAN to generate images that include adaptive edits in different spatial locations of the image.

Abstract: A system includes a housing and a first circuit board positioned inside the housing. The housing has a top panel, a bottom panel, a left side panel, a right side panel, a front panel, and a rear panel. The front panel is at an angle relative to the bottom panel in which the angle is in a range from 30 to 150°. The first circuit board has a length, a width, and a thickness, in which the length is at least twice the thickness, the width is at least twice the thickness, and the first circuit board has a first surface defined by the length and the width. The first surface of the first circuit board is at a first angle relative to the bottom panel in which the first angle is in a range from 30 to 150°. The first surface of the first circuit board is substantially parallel to the front panel or at a second angle relative to the front panel in which the second angle is less than 60°.

Abstract: An approach is provided for lane width estimation from incomplete lane marking detections of a road lane. The approach, for example, involves generating one or more perpendicular lines respectively from location centers of one or more first lane marking detections. A respective lane marking detection represents at least a portion of a boundary of the road lane as a line delimited by two location data points in accordance with detections by at least one sensor device onboard at least one vehicle. The approach also involves identifying second lane marking detections that each respectively intersect one of the one or more perpendicular lines. The approach further involves selecting one or more candidate lane widths based on one or more respective distances from the location centers to the second lane marking detections. The approach further involves determining an estimated lane width of the road lane based on the one or more candidate lane widths.

Abstract: An object orientation indicator for indicating the orientation of an object, such as the vertical positioning of an object to be inserted or being inserted into a surface. The object orientation indicator is configured to determine or indicate when an object being inserted into a surface parallel or non-parallel to the ground is vertically level, i.e. perpendicular to the ground. The object orientation indicator may also be configured to determine or indicate if a preexisting vertical object inserted into a surface parallel or non-parallel to the ground remains in its originally inserted orientation. The object orientation indicator may include a housing assembly and a display panel.

Abstract: A trusted graph data node classification method includes: (1) inputting a topological graph and node features, and calculating a discrete Ricci curvature of the discrete topological graph; (2) preprocessing the curvature and the node features; (3) mapping the curvature, reconstructing original features, and performing a semi-supervised training on graph data containing adversarial examples; and (4) performing a classification on unlabeled nodes. The new method uses a discrete curvature to extract topological information, and uses a residual network to reconstruct node feature vectors without knowing the technical details of the adversarial examples, and without using a large number of adversarial examples for adversarial training. Hence, the system effectively defends against attacks from adversarial examples on the graph data, outperforms the existing mainstream models in terms of accuracy when used in data without adversarial examples, and is thus a trusted node classification system.

Abstract: A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting locations of utility assets. One of the methods includes receiving an input image of an area in a first geographical region; generating, from the input image and using a generative adversarial network, a corresponding reference image; and generating, by an object detection model and from the reference image, an output that identifies respective locations of one or more utility assets with reference to the input image.

Abstract: The present disclosure discloses a method, apparatus and system for customer group analysis, and a storage medium. The method includes: obtaining video images of a customer group passing by a display apparatus; recognizing and tracking head images of the customer group in the video images, and determining behavior characteristics of individuals in the customer group; and performing statistical analysis on the behavior characteristics of the customer group corresponding to the display apparatus, so as to update a quantity of individuals corresponding to each behavior characteristic.

Abstract: Embodiments disclosed herein utilize biometric verification from wearable devices to approve access for shared spaces. Requests to occupy the shared spaces are received and conditional reservations are made pending health checks of the requesters. Prior to occupation, fresh biometric measurements are captured via the user's wearable device, which include physiological signals validating both ongoing good health as well as identity match to the original requester. When recent biometric verification indicates both healthy status and unchanged identity, access to the reserved space is approved. These embodiments securely manage reservations while protecting the health of other occupants through conditional access related to the requester's current health condition.

Abstract: Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

Abstract: A system includes a camera capturing images in a first field of view of a sports ball bouncing and rolling, a storage arrangement, and a processing arrangement. The storage arrangement includes a three-dimensional 3D model of a part of a sports play area and the processing arrangement is configured to: detect a pixel location of a ball in an image; determine, based on intrinsic and extrinsic calibration parameters, a camera-ball line comprising a straight line passing through the camera in the direction of the sports ball and to determine, based on the 3D model, an intersection point of the camera-ball line with the 3D model. The processor outputs the intersection point as a 3D position of the ball in the image.

Abstract: Disclosed is a super-resolution flow field reconstruction method, including: superimposing a plurality of frames of contrast-enhanced ultrasound images that are in one time interval and each of which includes images of a plurality of microbubbles, to obtain a superimposed image including a plurality of microbubble trajectories of the plurality of microbubbles; performing straight line fitting on the plurality of microbubble trajectories, to obtain a plurality of microbubble trajectory straight lines of the plurality of microbubbles, respectively; determining directions and velocities of instantaneous movements of the plurality of microbubbles in the time interval; and reconstructing a super-resolution flow field.