Abstract: A system related to providing multi-layer code rates for special event protection with reduced performance penalty for memories is disclosed. Based on an impending stress event, extra error correction code data is utilized to encode user data obtained from a host. The user data and first error correction code data are written to a first block and the extra error correction code data is written to a second block. Upon stress event completion, pages having user data with the extra error correction code data are scanned. If pages of the first block are unable to satisfy reliability requirements, a touch-up process is executed on each page in the first block to reinstate the first block so that the extra error correction code data is no longer needed. The extra error correction code data is deleted from the second block and the second block is made available for user data.

Abstract: A high-voltage optical transformer may include (1) an array of light-emitting devices that are connected in parallel with one another and are electrically coupled to an electrical input, (2) a transfer medium, and (3) an array of photovoltaic cells that (A) are optically coupled to the array of light-emitting devices via the transfer medium, (B) are connected in series with one another, and (C) produce an electrical output whose voltage is higher than the electrical input. Various other apparatuses, systems, and methods are also disclosed.

Abstract: An example flow cell includes a patterned substrate having an active region and a bonding region that at least partially surrounds the active region. The active region includes first depressions defined in a layer of the patterned substrate, surface chemistry positioned in the first depressions, and first interstitial regions surrounding the first depressions. The bonding region includes second depressions defined in the layer and second interstitial regions surrounding the second depressions. An adhesive is positioned over the second depressions and over the second interstitial regions. A cover is attached to the adhesive such that a flow channel is defined between a portion of the cover and the active region.

Abstract: A liquid lens with flexible transparent active layers on both sides of a fluid is transformed along two distinct deformation axes. The flexible transparent active layers include piezoelectric materials that actuate the lens in response to applied voltage(s). The piezoelectric properties and actuation mechanism of the transparent layers are arranged to deform the lens cylindrically along different axes resulting in a net spherical deformation or a combination of spherical and cylindrical deformation with substantially less distortion than spherically deforming layers. The piezoelectric active layers may be polymer or ceramic with isotropic or anisotropic mechanical stiffness. Alternatively, a pair of transparent, internal layers are positioned between the front and rear surfaces. The active layers, front and/or rear, are dual layers affixed together with an adhesive or single layers.

Abstract: A mechanically and piezoelectrically anisotropic polymer article is formed from a crystallizable fluoropolymer and a nucleating agent. The polymer article may be a thin film or a fiber, for example. A crystalline phase may constitute at least approximately 50% of the polymer article. In certain examples, a fluoropolymer may include vinylidene fluoride, trifluoroethylene, chlorotrifluoroethylene, hexafluoropropylene, and vinyl fluoride. The polymer article may include up to approximately 10 wt. % of the nucleating agent. Such a polymer article is optically transparent, has an elastic modulus of at least approximately 3 GPa, and an electromechanical coupling factor (k31) of at least approximately 0.15.

Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.

Abstract: The present disclosure provides methods, compositions, kits and systems for nucleic acid amplification. In some embodiments, nucleic acid amplification methods include subjecting the nucleic acid to be amplified to partially denaturing conditions. In some embodiments, nucleic acid amplification methods include amplifying without fully denaturing the nucleic acid that is amplified. In some embodiments, the nucleic acid amplification method employs an enzyme that catalyzes homologous recombination and a polymerase. In some embodiments, methods for nucleic acid amplification can be conducted in a single reaction vessel and/or in a single continuous liquid phase of a reaction mixture, without need for compartmentalization of the reaction mixture or immobilization of reaction components.

Abstract: One embodiment of a method for controlling a robot includes generating a representation of spatial occupancy within an environment based on a plurality of red, green, blue (RGB) images of the environment, determining one or more actions for the robot based on the representation of spatial occupancy and a goal, and causing the robot to perform at least a portion of a movement based on the one or more actions.

Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Abstract: In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.