Abstract: A device for decoding encoded mesh data can be configured to determine, based on the encoded mesh data, a base mesh; determine, based on the encoded mesh data, a set of coefficients; receive in the encoded mesh data a quantization parameter value; receive in the encoded mesh data a bit depth offset value; determine an inverse scaling factor based on the quantization parameter value and the bit depth offset value; perform an inverse scaling on the set of coefficients based on the inverse scaling factor to determine a set of de-quantized coefficients; determine a displacement vector based on the set of de-quantized coefficients; deform the base mesh based on the displacement vector to determine a decoded mesh; and output the decoded mesh.

Abstract: A semiconductor structure includes an alternating stack of insulating layers and electrically conductive layers, a memory opening vertically extending through the alternating stack and a memory opening fill structure located in the memory opening. A Group IV-containing material portion is formed by selective deposition on an end portion of the vertical semiconductor channel. Alternatively, a backside semiconductor cap structure can be formed directly on a bottom surface of the vertical semiconductor channel by selective or non-selective deposition of a semiconductor material.

Abstract: A method of processing a point cloud includes determining that angular mode is enabled for encoding or decoding a current point of points in the point cloud, and parsing or signaling information for an azimuthal angle residual value for the current point independent of a radius value of the current point or a radius value of a previous point of the point cloud that is previous to the current point in decoding order. The azimuthal angle residual value is based on a difference between an azimuthal angle value of the current point and a predictor azimuthal angle value of the current point, and the azimuthal angle value of the current point is indicative an azimuthal angle of the current point based on a laser used to capture the points of the point cloud.

Abstract: A semiconductor structure includes an alternating stack of insulating layers and electrically conductive layers, a memory opening vertically extending through the alternating stack and a memory opening fill structure located in the memory opening. A Group IV-containing material portion is formed by selective deposition on an end portion of the vertical semiconductor channel. Alternatively, a backside semiconductor cap structure can be formed directly on a bottom surface of the vertical semiconductor channel by selective or non-selective deposition of a semiconductor material.

Abstract: Techniques and devices are described for processing point cloud data. An example device includes one or more memories and one or more processors. The one or more processors are configured to process a syntax element indicative of whether a previous previous frame can be used for inter prediction for one or more points of point cloud data. The previous previous frame includes a reference frame of a previous reference frame. The one or more processors are configured to code the one or more points based on a determination of whether the previous previous frame can be used for inter prediction for the one or more points.

Abstract: An example device for decoding point cloud data includes: a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: decode encoded point cloud geometry data for a point cloud to form reconstructed point cloud geometry data for the point cloud; downscale the point cloud geometry data to form downscaled point cloud geometry data; decode attribute data for the point cloud using the downscaled point cloud geometry; apply the attribute data to the reconstructed point cloud geometry data to form intermediate point cloud data; and apply a residual learning network to the intermediate point cloud data to form a reconstructed point cloud.

Abstract: A device for encoding point cloud data, the device comprising: a memory to store the point cloud data; and one or more processors coupled to the memory and implemented in circuitry, the one or more processors configured to: determine a horizontal plane position of a node, wherein the horizontal plane position indicates a position of a single plane that is perpendicular to a first axis of a coordinate system, wherein the first axis is a horizontal axis; determine, from a plurality of contexts consisting of 8 contexts, a context for the horizontal plane position of the node; and perform arithmetic encoding on a syntax element indicating the horizontal plane position using the determined context.

Abstract: An imaging device includes a pixel including a photoelectric conversion region, a first transfer transistor coupled to the photoelectric conversion region, a first floating diffusion, a second floating diffusion, a second transfer transistor coupled between the first floating diffusion and the second floating diffusion to control access to the second floating diffusion, a third transfer transistor coupled to the photoelectric conversion region, a third floating diffusion coupled, a fourth floating diffusion, and a fourth transfer transistor coupled between the third floating diffusion and the fourth floating diffusion to control access to the fourth floating diffusion. The imaging device includes a first wiring layer including a first wiring connected to the second floating diffusion, a second wiring connected to the fourth floating diffusion, and a third wiring connected to ground and capacitively coupled with the first wiring and the second wiring.

Abstract: In embodiments, systems and apparatus comprise a bracket having a through hole configured to receive a first connector connected to a panel spaced from the bracket, the bracket movable between first and second positions; a switch mountable on the panel, the switch having first and second switch positions; and at least one compression spring extending from the bracket to bias the bracket into the first position spaced away from the switch. When the bracket is in the first position, the switch is in the first switch position, and when the bracket is in the second position, the bracket engages and actuates the switch into the second switch position. The bracket is movable into the second position to engage and actuate the switch by coupling a second connector to the first connector. The switch is configured to trigger a signal indicating that the first connector is connected to the second connector.

Abstract: A video decoder may apply a first inverse residual modification function to first decoded modified chroma residual data to generate first inverse modified chroma residual data. Additionally, the video decoder may apply a second inverse residual modification function to second decoded modified chroma residual data to generate second inverse modified chroma residual data. The first decoded modified chroma residual data is associated with a first chroma component and the second decoded modified chroma residual data is associated with a second chroma component. The video decoder may reconstruct a block of video data based on the first inverse modified chroma residual data and the second inverse modified chroma residual data.

Abstract: An example device for coding point cloud data includes: a memory configured to store point cloud data; and one or more processors implemented in circuitry and configured to: decode encoded point cloud geometry data for a point cloud to reconstruct point cloud geometry data for the point cloud; downscale the point cloud geometry data to form downscaled point cloud geometry data; and code attribute data for the point cloud using the downscaled point cloud geometry. When encoding the attribute data, the processors may encode the point cloud geometry data using a deep learning-based geometry encoder. When decoding the attribute data, the processors may upscale the downscaled point cloud attribute data. The processors may code a value representing an amount of downscaling to apply to the decoded point cloud geometry data.

Abstract: A memory device includes an alternating stack of insulating layers and electrically conductive layers, memory openings vertically extending through the alternating stack, where a smallest unit shape of three nearest neighbor memory openings is a non-equilateral triangle, and memory opening fill structures located in the memory openings, where each of the memory opening fill structures includes a vertical semiconductor channel and a vertical stack of memory elements.

Abstract: A robot comprises a body comprising a scalable joint module having a housing including an actuator within the housing powered by a voltage regulator on the housing and defining a female connector cavity of the housing; a first plurality of axial thrust roller bearings on the housing and a second plurality of axial thrust roller bearings on an opposite side of the housing, wherein each of the first and second plurality of axial thrust roller bearings are sandwiched between two washers; a male connector opposite from the female connector cavity; a head module coupled to the body, containing an onboard computing system controlling the movement of the lunar exploration robot, and an actuated latching mechanism; and a tail module coupled to the body, wherein the tail module defines a cavity configured for the actuated latching mechanism of the head module to enter, to thereby couple the head and tail modules.

Abstract: An epilating device for extracting hairs out of a user's skin, including hair-clamping elements that are arranged adjacent to each other and rotatable relative to a housing. A drive system rotates the hair-clamping elements which co-operate in pairs and are moveable relative to each other during rotation. On each side surface facing an adjacent hair-clamping element, there is a hair-clamping section and a hair-catching section. The hair-catching sections of adjacent hair-clamping elements define a funnel area, wherein the hair-catching sections converge towards each other for guiding hairs towards the hair-clamping sections. The hair-clamping sections of adjacent hair-clamping elements are movable relative to each other from an open position, where the hair-clamping sections are at a distance from each other, to a closed position, where the hair-clamping sections are in mutual clamping engagement in a hair-clamping area.

Abstract: A method of decoding point cloud data comprises obtaining a bitstream that is encoded to comply with one or more constraints and decoding the bitstream, wherein decoding the bitstream comprises: determining a residual value of a first component of an attribute of a point; generating a predicted value of a second component of the attribute of the point; and reconstructing the second component as a sum of the predicted value of the second component and a multiplication product of a scale factor for the second component and the residual value of the first component, wherein the constraints include a constraint that limits the first component of the attribute of the point, the residual value of the first component of the attribute of the point, the second component of the attribute, and the residual value of the second component of the attribute of the point to one or more predefined bitdepths.

Abstract: A method of encoding a point cloud includes determining, by one or more processors, a quantity of lasers used to capture light detection and ranging (LIDAR) data that represents the point cloud; and encoding, by the one or more processors, a laser index for a current node of the point cloud, wherein encoding the laser index comprises: obtaining a predicted laser index value of the current node; determining a residual laser index value for the current node, wherein determining the residual laser index value comprises constraining a sum of the residual laser index value and the predicted laser index value to be less than or equal to the determined quantity of lasers minus one; and encoding, in a bitstream, one or more syntax elements that represent the residual laser index value.

Abstract: A semiconductor structure includes semiconductor devices located over a substrate, bit lines electrically connected to the semiconductor devices and having a respective reentrant vertical cross-sectional profile within a vertical plane that is perpendicular to a lengthwise direction along which the bit lines laterally extend, and dielectric portions that are interlaced with the bit lines along a horizontal direction that is perpendicular to the lengthwise direction. The dielectric portions may contain air gaps. A bit-line-contact via structure can be formed on top of a bit line. In some embodiments, dielectric cap strips may be located on top surface of the dielectric portions and may cover peripheral regions of the top surfaces of the bit lines without covering middle regions of the top surfaces of the bit lines.

Abstract: A device for encoding point cloud data, the device comprising: a memory to store the point cloud data; and one or more processors coupled to the memory and implemented in circuitry, the one or more processors configured to: determine a horizontal plane position of a node, wherein the horizontal plane position indicates a position of a single plane that is perpendicular to a first axis of a coordinate system, wherein the first axis is a horizontal axis; determine, from a plurality of contexts consisting of 8 contexts, a context for the horizontal plane position of the node; and perform arithmetic encoding on a syntax element indicating the horizontal plane position using the determined context.

Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for classifying a region and an intensity of a contact. A method includes obtaining, from a plurality of acoustic sensors provided on an inner surface of a bumper of the apparatus, a combined acoustic signal, the combined acoustic signal being based on an input signal provided to the plurality of acoustic sensors, determining, using a trained machine learning model that has been trained with acoustic signals and corresponding position and intensity information of impacts on a plurality of regions of an outer surface of the bumper, the region of the contact on the bumper with respect to the plurality of regions and the intensity of the contact, based on the combined acoustic signal, and determining a motion of the apparatus based on the region of the contact, the intensity of the contact, and an operating mode of the apparatus.

Abstract: A lighting stage includes a plurality of lights that project alternating spherical color gradient illumination patterns onto an object or human performer at a predetermined frequency. The lighting stage also includes a plurality of cameras that capture images of an object or human performer corresponding to the alternating spherical color gradient illumination patterns. The lighting stage also includes a plurality of depth sensors that capture depth maps of the object or human performer at the predetermined frequency. The lighting stage also includes (or is associated with) one or more processors that implement a machine learning algorithm to produce a three-dimensional (3D) model of the object or human performer. The 3D model includes relighting parameters used to relight the 3D model under different lighting conditions.