Abstract: Embodiments of the present disclosure relate to an electrostatic protection structure and an electrostatic protection circuit. The electrostatic protection structure includes: a SCR structure and a trigger structure; the SCR structure includes: a well region of a second conductivity type and a first well of a first conductivity type region, a first-doped region of the first conductivity type, and a first-doped region of the second conductivity type; the trigger structure includes: a first-doped region of the second conductivity type, a second well region of the first conductivity type, a second-doped region of two conductivity types, a third-doped region of the second conductivity type, a fourth-doped region of the second conductivity type, and a first gate electrode.

Abstract: A method for forming a 3D memory device is disclosed. The method includes: forming an alternating dielectric stack on a substrate; forming a plurality of channel holes penetrating the alternating dielectric stack; forming a channel structure in each channel hole; forming a channel column structure on the channel structure in each channel hole; trimming an upper portion of each channel column structure to form a channel plug; and forming a top selective gate cut between neighboring channel plugs.

Abstract: In one example in accordance with the present disclosure, an additive manufacturing system is described. The additive manufacturing system includes a build material deposition device to form a plastically deformable three-dimensional (3D) object by depositing layers of a thermoplastic build material to form a body of the plastically deformable 3D object. The additive manufacturing system also includes a heat channel forming device to form heat channels within the plastically deformable 3D object which heat channels, responsive to an applied stimulus, are to soften adjacent regions of the body. The additive manufacturing system also includes a fusing system to selectively harden layers of thermoplastic build material to form the plastically deformable 3D object.

Abstract: A modified Kalman filter may include one or more neural networks to augment or replace components of the Kalman filter in such a way that the human interpretability of the filter's inner functions is preserved. The neural networks may include a neural network to account for bias in measurement data, a neural network to account for unknown controls in predicting a state of an object, a neural network ensemble that is trained differently based on different sensor data, a neural network for determining the Kalman gain, and/or a set of Kalman filters including various neural networks that determine independent estimated states, which may be fused using Bayesian fusion to determine a final estimated state.

Abstract: The freshness of one or more terms in a documentation, indicative of a currency of the one or more terms is computed. Each term includes one or more constituent words, and the terms are visually marked as current or out-of-date based on the computed freshness. Upon marking a term as out-of-date, a latest term for the out-of-date term is retrieved or a most possible latest term for the out-of-date term is predicted.

Abstract: Provided are a refreshing method and a display apparatus. The method includes: presenting a video on a display of the display apparatus; detecting whether a content type of the video is switched from a first content type to a second content type; obtaining first picture quality parameter information matching the second content type from preset data of the display apparatus, wherein the preset data includes a predefined relation between a content type and picture quality parameter information, updating the parameter of the at least one picture quality setting option in the picture mode settings menu according to the first picture quality parameter information, and presenting the second section of the video according to the updated parameter of the at least one picture quality setting option in the picture mode settings menu.

Abstract: Apparatuses, systems, and methods for determining a field in a path of travel of a vehicle for detection of objects in the path of travel. An apparatus includes a path identifier configured to identify a path of travel for a vehicle. At least one camera is disposed on the vehicle and is configured to capture image data of an area including the path of travel. A region of interest selector is configured to select a region of interest within the image data. A horizon identifier is configured to identify a horizon in the image data. A field determiner is configured to project the horizon onto the region of interest to isolate a field specified by the path of travel and the horizon, the field being evaluatable for the presence of objects in the path of travel of the vehicle.

Abstract: Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

Abstract: Disclosed embodiments include apparatuses, systems, and methods for determining a field in a path of travel of a vehicle for detection of objects in the path of travel. In an illustrative embodiment, an apparatus includes a path identifier configured to identify a path of travel for a vehicle. At least one camera is disposed on the vehicle and is configured to capture image data of an area including the path of travel. A region of interest selector is configured to select a region of interest within the image data. A horizon identifier is configured to identify a horizon in the image data. A field determiner is configured to project the horizon onto the region of interest to isolate a field specified by the path of travel and the horizon, the field being evaluatable for the presence of objects in the path of travel of the vehicle.

Abstract: Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

Abstract: Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

Abstract: Techniques for detecting and tracking objects in an environment are discussed herein. For example, techniques can include detecting a center point of a block of pixels associated with an object. Unimodal (e.g., Gaussian) confidence values may be determined for a group of pixels associated with an object. Proposed detection box center points may be determined based on the Gaussian confidence values of the pixels and an output detection box may be determined using filtering and/or suppression techniques. Further, a machine-learned model can be trained by determining parameters of a center pixel of the detection box and a focal loss based on the unimodal confidence value which can then be backpropagated to the other pixels of the detection.

Abstract: A gene detection method, a gene detection kit, and a gene detection device, including the following steps: providing a plurality of separation cavities on a kit, using a plunger to separate adjacent separation cavities, and respectively providing a lysate solution, a washing solution and a reaction solution in the separation cavities; when detecting a sample, pushing each plunger to align a plunger hole of the plunger with the separation cavity, thereby making the separation cavities interconnected; then, controlling magnetic beads in the kit to drive the sample to be tested to pass through the separation cavities in sequence by an electromagnetic control method, carrying out a lysing, a washing and a reaction in sequence; and finally, performing a optical detection on a gene in the reaction solution from outside.

Abstract: Disclosed embodiments include apparatuses, systems, and methods for determining a field in a path of travel of a vehicle for detection of objects in the path of travel. In an illustrative embodiment, an apparatus includes a path identifier configured to identify a path of travel for a vehicle. At least one camera is disposed on the vehicle and is configured to capture image data of an area including the path of travel. A region of interest selector is configured to select a region of interest within the image data. A horizon identifier is configured to identify a horizon in the image data. A field determiner is configured to project the horizon onto the region of interest to isolate a field specified by the path of travel and the horizon, the field being evaluatable for the presence of objects in the path of travel of the vehicle.

Abstract: A gene detection method, a gene detection kit, and a gene detection device, including the following steps: providing a plurality of separation cavities on a kit, using a plunger to separate adjacent separation cavities, and respectively providing a lysate solution, a washing solution and a reaction solution in the separation cavities; when detecting a sample, pushing each plunger to align a plunger hole of the plunger with the separation cavity, thereby making the separation cavities interconnected; then, controlling magnetic beads in the kit to drive the sample to be tested to pass through the separation cavities in sequence by an electromagnetic control method, carrying out a lysing, a washing and a reaction in sequence; and finally, performing a optical detection on a gene in the reaction solution from outside.

Abstract: A multiple camera control system includes at least two cameras shooting an image outside the vehicle when an ignition of the vehicle is turned on or a shift speed stage of the vehicle is a reverse speed stage, and an image output apparatus receiving the shot image from the cameras and selectively displaying the image received from the cameras according to a traveling direction indication signal of the vehicle and a shift speed stage information of the vehicle.

Abstract: The present invention generally relates to a process for making non-halogenated flame retardant polymeric composites with superabsorbent polymer coated nanoparticles to provide excellent flame retardant property, low toxicity and high loading efficiency. The flame retardant polymeric composites can be used as flame retarding foams, insulation sheeting materials or other composite materials.

Abstract: A method for joining elements together, the method comprising the steps of: positioning the elements relative to one another; enhancing the formability of at least a portion of at least one of the elements using a laser; and joining the elements together using a mechanical fastening method.

Abstract: An electrical connector comprises a dielectric housing and a plurality of conductive contacts retained in the dielectric housing. The dielectric housing comprises a pair of guiding portions and an engaging portion between the guiding portions. The engaging portion comprises an upper engaging plane, and a parallel opposite lower engaging plane. A mounting plane defined between the upper engaging plane and lower engaging plane. The guiding portion defining a lower face aligned with the mounting plane. Each the contact comprises a tail portion extending to the mounting plane.

Abstract: A method for joining elements together, the method comprising the steps of: positioning the elements relative to one another; enhancing the formability of at least a portion of at least one of the elements using a laser; and joining the elements together using a mechanical fastening method.