Abstract: Disclosed in the present invention are a high-strength steel composite galvanized sheet resistant to LME cracking during spot welding, and a preparation method therefor. The high-strength steel composite galvanized sheet comprises a high-strength steel body (1), low-carbon steel composite layers (2) and a galvanized layer (3), wherein two low-carbon steel composite layers (2) are respectively compounded and rolled on two surfaces of the high-strength steel body (1), the galvanized layer (3) is formed on the surface of at least one low-carbon steel composite layer (2), and a high-strength steel composite galvanized sheet is thus formed.

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

Abstract: A display module includes a display panel, at least one bonding circuit board, a plurality of chip-on-films, and a plurality of buffer devices. The at least one bonding circuit board each include first differential lines, and a first differential line includes a P-polarity differential sub-line and an N-polarity differential sub-line. An end of a chip-on-film is connected to the first differential line, and the other end of the chip-on-film is connected to the display panel. The buffer devices are arranged on the bonding circuit board, a buffer device is connected to ends, proximate to the chip-on-film, of the P-polarity differential sub-line and the N-polarity differential sub-line, and the buffer device is configured to reduce signal reflection between the first differential line and the chip-on-film.

Abstract: Catalysts and methods for catalytic hydrogenolysis of a polymer. The method comprises a) activating a catalyst with a hydrogen source to provide an activated catalyst, wherein the catalyst comprises: i) a MXene support of Formula I: Mn+1XnTx (I); wherein each M is independently an early transition metal; X is carbon or nitrogen; Tx is a surface functional group wherein x is 0-10; and n is 1, 2, 3, or 4; and ii) a supported metal, wherein loading of the supported metal on the MXene support is less than 5% w/w based on the weight of the catalyst; and b) contacting a mixture of the activated catalyst, hydrogen gas, and a polymer at a temperature of at least about 200° C. for a period of time that is sufficient for catalytic hydrogenolysis of the polymer; thereby converting the polymer to a fuel.

Abstract: In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.

Abstract: The present disclosure provides a binary code similarity detection system based on hard sample-aware momentum contrastive learning, comprising: a preprocessing device for transforming the binary code into tokens for neural networks; a feature extracting device for using the tokens to generate final representation embeddings for the binary code; and a similarity detection device for using the representation embeddings to detect the binary code similarity.

Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the estimated representation may be sparse, a deep neural network (DNN) may be used to predict values for a dense representation of the 3D surface structure from the sparse representation. For example, a sparse 3D point cloud may be projected to form a sparse projection image (e.g., a sparse 2D height map), which may be fed into the DNN to predict a dense projection image (e.g., a dense 2D height map). The predicted dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.

Abstract: A system configured to perform style-aware listener animation. By representing different listening styles (e.g., facial expressions) using an embedding space, a single model can be trained to generate unique facial animations for a number of distinct listeners. Thus, individual listening styles can be associated with a listener identifier, enabling the system to (i) animate a plurality of different listeners with unique nonverbal behavior and/or (ii) select a particular listener identifier or desired type of listener style with which to animate. This enables the model to be generalized to new listeners to generate additional listener facial responses without needing training data for each new listener. The model may process a listener representation style or listener identifier, along with input data corresponding to a speaker talking, to generate unique facial animation responsive to the speech.

Abstract: The present disclosure provides a system for IoT Botnet detection, comprising: a flow preprocessing device for processing the flow data packets to obtain NetFlow data; a flow clustering device for segmenting the NetFlow data to obtain flow points, and clustering the flow points to form botnet transactions or normal behavior transactions; a fingerprint generation device for extracting feature fingerprints from the obtained transactions and converting the feature fingerprints into RGB fingerprint images; and a classification device for detecting the fingerprint images and yielding the classification results.

Abstract: The present invention relates to a device for skin parameter measurement, comprising a housing structure (112) defining an interior cavity (120) and a first opening (122) at a skin contact end (124) of the interior cavity (120), a movable part (113, 117) connected to the housing structure (112) via an elastic connecting arrangement (126), the movable part (113, 117) being movable with respect to the housing structure (112) and configured to protrude the skin contact end (124) through the first opening (122) of the housing structure when no external force is applied to move the movable part (113, 117) with respect to the housing structure, the movable part (113, 117) further comprising a second opening (116), an optical sensing unit (118) for performing measurement of a first skin parameter when the device is in contact with a skin surface, the optical sensing unit being provided within the interior cavity of the housing structure and comprising an illuminating unit (118A) for illuminating the skin surface by

Abstract: A height measurement system includes: a first fixed assembly, configured to connect to an external fixing part so that the height measurement system is fixed on the external fixing part for use; a head plate, configured to move downwards from the first fixed assembly to the head of a user; a second fixed assembly, disposed on the head plate and being movable along with the heat plate; a digital tape measure, comprising a mounting assembly and a tape disposed in the mounting assembly, one of the mounting assembly and the tape head being attached to the first fixing assembly and the other to the second fixing assembly; a memory storing a standard height value of the first fixed assembly; and a data acquisition unit, configured to acquire a first tensile length of the tape when the head plate moves from the first fixed assembly to the user's head.

Abstract: A reversibly adhesive film, comprising: at least two hydroxyl-bearing polymer chains being crosslinked by one or more boronic ester bonds, the film having accessible hydroxyl groups at a surface of the film. An reversible adhesive, comprising: a first film layer, the first film layer comprising at least two first hydroxyl-bearing polymer chains crosslinked by first crosslinks that comprise one or more boronic ester bonds; and a second film layer, the first second layer comprising at least two second hydroxyl-bearing polymer chains crosslinked by second crosslinks that comprise one or more boronic ester bonds, wherein (1) the first hydroxyl-bearing polymer chains differ from the second hydroxyl-bearing polymer chains in one or more of composition and concentration, or (2) the boronic ester bonds are present in the first film layer at a different density than the boronic ester bonds in the second film layer, or (3) both (1) and (2).

Abstract: In various examples, an ego-machine may analyze sensor data to identify and track features in the sensor data using. Geometry of the tracked features may be used to analyze motion flow to determine whether the motion flow violates one or more geometrical constraints. As such, tracked features may be identified as dynamic features when the motion flow corresponding to the tracked features violates the one or more static constraints for static features. Tracked features that are determined to be dynamic features may be clustered together according to their location and feature track. Once features have been clustered together, the system may calculate a detection bounding shape for the clustered features. The bounding shape information may then be used by the ego-machine for path planning, control decisions, obstacle avoidance, and/or other operations.

Abstract: In various examples, sensor calibration for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that calibrate image sensors, such as cameras, using images captured by the image sensors at different time instances. For instance, a first image sensor may generate first image data representing at least two images and a second image sensor may generate second image data representing at least one image. One or more feature points may then be tracked between the images represented by the first image data and the image represented by the second image data. Additionally, the feature point(s), timestamps associated with the images, poses associated with image sensors (e.g., poses of a vehicle), and/or other information may be used to determine one or more values of one or more parameters that calibrate the first image sensor with the second image sensor.

Abstract: A method of desorbing CO2 from an aqueous liquid comprising CO2, the method comprising the steps of: a) providing said aqueous liquid in the form of an aqueous colloidal solution comprising (i) a CO2 absorbent having CO2 absorbed thereto and (ii) colloidal catalyst having a core-shell structure in which the shell comprises proton-donor groups, and b) thermally desorbing CO2 from the CO2 absorbent, wherein the thermal desorption of the CO2 is catalysed by the colloidal catalyst.

Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.

Abstract: Provided herein are systems and methods for roll-to-roll deposition of membrane electrode assemblies (MEAs) and layers thereof. Embodiments of the systems and methods may be used for producing layers, including polymer electrolyte membranes (PEMs) and catalyst layers, of an MEA. In particular embodiments, the methods and systems may be used for producing anion-exchange membranes (AEMS). In other instances, the methods and systems may be used for producing cation-exchange membranes or bipolar membranes. Also provided are MEAs and layers thereof produced by the methods described herein. In some embodiments, the MEAs are configured for electrolysis and, in particular, for carbon oxide (COx) reduction. The methods and systems may also be employed for water electrolysis.

Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

Abstract: Disclosed in the present invention are a five-membered ring-fused six-membered ring compound, a preparation method therefor, and a pharmaceutical composition and the use thereof. Provided in the present invention is a compound as represented by formula (I), or a pharmaceutically acceptable salt or an isotope compound thereof. The compound of the present invention has an inhibition and/or degradation effect on IRAK4.

Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the estimated representation may be sparse, a deep neural network (DNN) may be used to predict values for a dense representation of the 3D surface structure from the sparse representation. For example, a sparse 3D point cloud may be projected to form a sparse projection image (e.g., a sparse 2D height map), which may be fed into the DNN to predict a dense projection image (e.g., a dense 2D height map). The predicted dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.