Abstract: This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for mapping coding indices associated with a rateless coding scheme to respective sets of communication resources over which a transmitting device may perform transmissions of portions of a message corresponding to each of the coding indices. In some aspects, for example, different coding indices may correspond to different cumulative portions of the message and the transmitting device may transmit a first cumulative portion of the message corresponding to a first coding index over a first set of communication resources to which the first coding index is mapped. The transmitting device may signal an indication of such a resource mapping scheme to a receiving device and the receiving device may attempt to decode transmissions of one or more cumulative portions of the message using the indicated resource mapping scheme.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus may receive a wake-up signal (WUS) indicating that the user equipment (UE) is to perform a wake-up from a sleep mode. The apparatus may determine a set of parameters for a reference sequence to be used in association with performing a synchronization after the wake-up. The apparatus may receive the reference sequence based at least in part on the set of parameters. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an UE may receive an indication of a configuration for receiving reference signals (RSs) and for transmitting a digital pre-distortion (DPD) measurement. The UE may receive the RSs from a network node. The UE may transmit a first signal that indicates the DPD measurement as observed via the RSs at the first UE, the first signal configured for coherent combination over the air with a second signal that indicates a DPD measurement as observed via the RSs at a second UE, the first signal and the second signal scheduled for transmission via overlapping network resources. Numerous other aspects are described.

Abstract: Aspects described herein relate to receiving, in a component carrier (CC) configured for a first radio access technology (RAT), a wake-up signal indicating whether to activate communications for each of multiple RATs including a second RAT, and activating, based on the wake-up signal, communications in the first RAT or the second RAT. Other aspects relate to generating and/or transmitting the wake-up signal related to multiple RATs.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration of a sleep mode, the configuration indicating a coding scheme used for a wake-up signal (WUS). The UE may receive a WUS message, the WUS message being encoded with product coding, wherein a coding stage of the product coding comprises locally-decodable coding. The UE may receive a communication based at least in part on the WUS message. Numerous other aspects are described.

Abstract: A user equipment (UE) may transmit, to a network node, an indication of support for a decoder success prediction capability. The network node may obtain the indication of support by the UE for the decoder success prediction probability. The network node may output a transmission for the UE including one or more parameters based on the indication of support by the UE for the decoder success prediction capability. The UE may receive the transmission including one or more code blocks based on the indication of support for the decoder success prediction capability. The network node may optimize a multiple incremental redundancy scheme (MIRS) schedule for at least one of transmitting the transmission or retransmitting the transmission based on the indication of support by the UE for the decoder success prediction capability. The transmission may include the MIRS schedule.

Abstract: A user equipment (UE) may transmit, to a network node, an indication of support for a decoder success prediction capability. The network node may obtain the indication of support by the UE for the decoder success prediction probability. The network node may output a transmission for the UE including one or more parameters based on the indication of support by the UE for the decoder success prediction capability. The UE may receive the transmission including one or more code blocks based on the indication of support for the decoder success prediction capability. The network node may optimize a multiple incremental redundancy scheme (MIRS) schedule for at least one of transmitting the transmission or retransmitting the transmission based on the indication of support by the UE for the decoder success prediction capability. The transmission may include the MIRS schedule.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: There is provided a computer implemented method for localizing target anatomical regions of interest (ROI) of a target individual, comprising: uniformly sub-sampling a plurality of 2D images having sequential index numbers within a 3D anatomical volume, feeding the plurality of sampled 2D images into a classifier for outputting a plurality of values on a normalized anatomical scale, fitting a linear model to the plurality of values and corresponding sequential index numbers, mapping by the linear model, the plurality of 2D images to the normalized anatomical scale, receiving an indication of at least one target anatomical ROI of a target individual, wherein each target anatomical ROI is mapped to the normalized anatomical scale, and providing a sub-set of the plurality of 2D images having values of the normalized anatomical scale corresponding to the received at least one target anatomical ROI.

Abstract: There is provided a method of training a neural network for reconstructing of a 3D point cloud from 2D image(s), comprising: extracting point clouds each represented by an ordered list of coordinates, from 3D anatomical images depicting a target anatomical structure, selecting one of the plurality of point clouds as a template, non-rigidly registering the template with each of the point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the warped templates are consistent in terms of coordinate order, receiving 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images, and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping 2D anatomical image(s) into a 3D point cloud.

Abstract: There is provided a method of training a neural network for reconstructing of a 3D point cloud from 2D image(s), comprising: extracting point clouds each represented by an ordered list of coordinates, from 3D anatomical images depicting a target anatomical structure, selecting one of the plurality of point clouds as a template, non-rigidly registering the template with each of the point clouds to compute a respective warped template having a shape of the respective point cloud and retaining the coordinate order of the template, wherein the warped templates are consistent in terms of coordinate order, receiving 2D anatomical images depicting the target anatomical structure depicted in corresponding 3D anatomical images, and training a neural network, according to a training dataset of the warped templates and corresponding 2D images, for mapping 2D anatomical image(s) into a 3D point cloud.

Abstract: There is provided a computer implemented method for localizing target anatomical regions of interest (ROI) of a target individual, comprising: uniformly sub-sampling a plurality of 2D images having sequential index numbers within a 3D anatomical volume, feeding the plurality of sampled 2D images into a classifier for outputting a plurality of values on a normalized anatomical scale, fitting a linear model to the plurality of values and corresponding sequential index numbers, mapping by the linear model, the plurality of 2D images to the normalized anatomical scale, receiving an indication of at least one target anatomical ROI of a target individual, wherein each target anatomical ROI is mapped to the normalized anatomical scale, and providing a sub-set of the plurality of 2D images having values of the normalized anatomical scale corresponding to the received at least one target anatomical ROI.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.

Abstract: A method for depth mapping of objects in a scene by a system of a moving/movable platform is disclosed, comprising actively illuminating the scene with pulsed light that is generated by at least one pulsed light illuminator; receiving, responsive to illuminating the scene, reflections on at least one image sensor that comprises a plurality of pixel elements; gating at least one of the plurality of pixel elements of the at least one image sensor for converting the reflections into pixel values for generating reflection-based images that have at least two depth-of-field ranges and an overlapping DOF region; and determining, based on at least one first pixel value of a first DOF in the overlapping DOF region, and based on at least one second pixel value of a second DOF in the overlapping DOF region, depth information of one or more objects located in the overlapping DOF region of the scene.

Abstract: An input image is received from a mobile device. A portion of the input image is determined to correspond to a test card and an image transformation is applied to that portion of the input image. The image transformation rectifies the portion of the input image. Based on the rectified image, a specific test for which the test card includes a result and the result of that test are both identified.