Abstract: The present invention discloses a shed tunnel structure for preventing a falling rock, including a shed tunnel body and a buffer plate for bearing impact of the falling rock, where the shed tunnel body includes a first supporting structure, and the first supporting structure is arranged on a side away from a ramp; one end of the buffer plate is connected to the ramp; a side face of the buffer plate close to the shed tunnel body is in movable contact with the first supporting structure, and the contact position is close to the other end of the buffer plate. The objective of resisting continuous impact of the falling rock can be achieved through the structural design.

Abstract: Disclosed are a new AXL-targeting monoclonal antibody and antibody-drug conjugate. Also disclosed is a method for preparing said antibody and antibody-drug conjugate. The AXL antibody of the present invention can bind with purified human AXL protein and multiple AXL on tumor cell surface with high effectiveness and high specificity. Said humanized antibody also has high affinity and low immunogenicity. Said AXL antibody-drug conjugate has markable performance against tumors having high AXL expression.

Abstract: A device and method for preparing a fully granular cigarette/filter rod, and a prepared fully granular cigarette/filter rod are disclosed. In certain embodiments, negative pressure is used to adhere granular material onto a mesh-type conveyor belt. The granular material is preliminarily formed into fully granular cigarette/filter rod material by means of a pre-forming apparatus; the fully granular cigarette/filter rod material, traveling with the mesh-type conveyor belt, enters into a forming cavity, and undergoes heating and humidifying treatment, and a continuous solid porous substance is formed; the continuous solid porous substance is moved into the cooling/drying cavity, drying and cooling treatment are performed under the action of negative pressure, and a fully granular cigarette/filter rod with strong adhesion, porosity, permeability, and appropriate moisture is obtained.

Abstract: Techniques are disclosed relating to automatically determining image quality for images of documents. In some embodiments, a computer system receives an image of a document captured at a user computing device. Using a neural network, the computer system analyzes the image to determine whether the image satisfies a quality threshold, where the analyzing includes determining whether one or more features in the image used in an authentication process are obscured. The computer system transmits, to the user computing device, a quality result, where the quality result is generated based on an image classification output by the neural network. Automatically determining whether a received image of a document satisfies a quality threshold may advantageously improve the chances of a system being able to complete an authentication process quickly, which in turn may improve user experience while reducing fraudulent activity.

Abstract: A virtual object display method, a non-transitory computer-readable storage medium and an electronic device. The virtual object display method includes operations as follows. Localization is performed on the first device to determine a pose of the first device in a target coordinate system; a pose of a second device in the target coordinate system is determined, where the second device is configured to play audio; a virtual object corresponding to the audio is determined, and a pose of the virtual object in the target coordinate system is determined based on the pose of the second device in the target coordinate system; and in response to the second device is playing the audio, the virtual object is displayed on a display interface of the first device according to the pose of the first device in the target coordinate system and the pose of the virtual object in the target coordinate system.

Abstract: Disclosed are a positioning method, an electronic device, and a storage medium. The method includes: converting, according to an intrinsic parameter matrix of an image acquisition module and depth values of multiple pixel points in a depth image captured by the image acquisition module, pixel coordinates of the multiple pixel points into camera coordinates; and matching the camera coordinates of the respective pixel points with target world coordinates of multiple voxels in a pre-established point cloud map, and obtaining a positioning result of the image acquisition module; wherein the target world coordinates of the respective voxels are obtained by updating initial world coordinates of the respective voxels according to multiple sample image pairs, and a sample image pair comprises a two-dimensional sample image and a depth sample image.

Abstract: A fuel cell includes at least two single cells stacked adjacent to each other. A cathode plate (1) of one single cell is stacked adjacent to an anode plate (2) of an adjacent single cell. The cathode plate (1) includes a cathode plate body (11), the cathode plate body (11) has a cathode channel ridge (12) protruding towards the anode plate (2), and the cathode channel ridge (12) has a cathode channel (121) formed therein. The anode plate (2) includes an anode plate body (21), the anode plate body (21) has an anode channel ridge (22) protruding towards the cathode plate (1), and the anode channel ridge (22) has an anode channel (221) formed therein. A cooling channel (3) is formed between the cathode plate (1) and the anode plate (2). The anode channel ridge (22) and the cathode channel ridge (12) are intersected with each other.

Abstract: A fault diagnosis system is disclosed, including: a control unit, a first management board, a first pull-up unit, a second pull-up unit, a first pull-up switch, a second pull-up switch, and at least one central processing unit, the control unit is configured to receive physical partitioning information sent by the first management board, the first pull-up unit and the second pull-up unit are configured to pull up a fault indication signal of a fault diagnosis path to obtain a target signal, the first management board is configured to detect whether a level of the target signal is lower than a diagnosis threshold, and when the level of the target signal is lower than the diagnosis threshold, determine that a faulty central processing unit exists in the at least one central processing unit.

Abstract: According to embodiments of the present disclosure, a map constructing method, a positioning method, and a wireless communication terminal are provided. The map constructing method includes: a series of environment images of a current; first image feature information of the environment image is obtained, where the first image feature information includes feature point information and descriptor information and based on the first image feature information, a feature point matching is performed on the environment images to select keyframe images; depth information of matched feature points in the keyframe image are acquired, based on the feature point information; and map data of the current environment are generated based on the keyframe images, where the map data includes the image feature information and the depth information of the keyframe image.

Abstract: A map update method. The method comprises: acquiring a first image feature in a first map; matching a second image feature from a second map according to the first image feature, wherein scene information corresponding to the first map is partially the same as scene information corresponding to the second map; and adding each key frame image in the first image into the second map according to the first image feature and the second image feature so as to obtain an updated second map. Further provided are a map update apparatus, and a terminal and a storage medium.

Abstract: In some examples, a method includes receiving a test image from a user or client, the test image depicting an anatomical region or structure of a human body, obtaining a reference image corresponding to the anatomical region or structure depicted in the test image, and analyzing the test image and the reference image to obtain a set of differences between the two images. In some examples, the method further includes, based at least in part on the set of differences, detecting a possible abnormality in the test image and outputting a result to the user or client. In some examples, a fast R-CNN is used to detecting the possible abnormality in the test image.

Abstract: Technology for performing object tracking of an object in a video image using both object detection algorithm and an object tracking algorithm. Because object detection algorithms are generally slower, but accurate, than object tracking algorithms, only certain sampled video frames are subject to the object detection algorithm. The non-sampled frames locate the object using the object tracking algorithm, using the most recent previous object detected frame as a starting frame. In this way, the object detection algorithm is used to periodically, effectively calibrate the object tracking algorithm by using a lagging detection result to hide latency introduced by the object detection algorithm.

Abstract: A method, computer system, and computer program product for model selection for training a new dataset is provided. The present invention may include choosing a model from a set of models to be evaluated for training the new dataset, selecting a sample input from a subset of the new dataset, calculating a model activation score for each of the sample inputs in the chosen model, calculating an accumulated model activation score for the chosen model, depending on the model activation score of each of the sample inputs in the chosen model, calculating an accumulated model activation score for each model from the set of models to be evaluated for training the new dataset, and selecting the model for training the new dataset with the highest accumulated model activation score.

Abstract: A method includes receiving temporal-based user profile data including a first set of user profile data and a second set of user profile data. The method further includes determining an aggregated profile covariance (APC) and an aggregated profile variance (APV), and then deriving a regression coefficient based on the APC and the APV. In response to receiving an observation of desirable media content for one or more particular timeslots, the method further includes determining an updated APC based on a covariance of the first set of user profile data and the observation of desirable media content, determining an updated APV based on a variance of the first set of user profile data, and generating a prediction of desirable media content based on the updated APC, the updated APV, and the regression coefficient. The prediction of desirable media content corresponds to the one or more particular timeslots.

Abstract: An Internet of Things data transmission method includes sending, by a terminal device, a first data request to a server, and continuously receiving, by the terminal device, N data packets from the server. The first data request instructs the server to continuously send a plurality of data packets. The first data request includes a quantity N of data packets, that the terminal device is capable of continuously receiving, and a time interval for sending two consecutive data packets, where N is an integer greater than 1. The N data packets include at least one non-confirmable Constrained Application Protocol (NON) data packet, a sending time interval between two consecutive data packets in the N data packets and the at least one NON data packet indicates that sending a receiving response from the terminal device to the server is unnecessary.

Abstract: In some examples, a method includes receiving a test image from a user or client, the test image depicting an anatomical region or structure of a human body, obtaining a reference image corresponding to the anatomical region or structure depicted in the test image, and analyzing the test image and the reference image to obtain a set of differences between the two images. In some examples, the method further includes, based at least in part on the set of differences, detecting a possible abnormality in the test image and outputting a result to the user or client. In some examples, a fast R-CNN is used to detecting the possible abnormality in the test image.

Abstract: A fault diagnosis system is disclosed, including: a control unit, a first management board, a first pull-up unit, a second pull-up unit, a first pull-up switch, a second pull-up switch, and at least one central processing unit, the control unit is configured to receive physical partitioning information sent by the first management board, the first pull-up unit and the second pull-up unit are configured to pull up a fault indication signal of a fault diagnosis path to obtain a target signal, the first management board is configured to detect whether a level of the target signal is lower than a diagnosis threshold, and when the level of the target signal is lower than the diagnosis threshold, determine that a faulty central processing unit exists in the at least one central processing unit.

Abstract: Technology for performing object tracking of an object in a video image using both object detection algorithm and an object tracking algorithm. Because object detection algorithms are generally slower, but accurate, than object tracking algorithms, only certain sampled video frames are subject to the object detection algorithm. The non-sampled frames locate the object using the object tracking algorithm, using the most recent previous object detected frame as a starting frame. In this way, the object detection algorithm is used to periodically, effectively calibrate the object tracking algorithm by using a lagging detection result to hide latency introduced by the object detection algorithm.

Abstract: A method includes obtaining a first set of parameters corresponding to a first video, such parameters including a first skip number and a first segment length. The method includes obtaining a first set of image feature values corresponding to the first video and storing the first set of parameters and the first set of image feature values as reference data. The method includes calculating a second set of image feature values corresponding to a second video and comparing the second set of image feature values to the reference data, and determining, based on the comparing, that the second set of image feature values exceeds a threshold. The method includes calculating, in response to the determining, a variance. The method includes generating, based on the variance, a second set of parameters corresponding to the second video, the second set of parameters including a second skip number and a second segment length.

Abstract: An Internet of Things data transmission method includes sending, by a terminal device, a first data request to a server, and continuously receiving, by the terminal device, N data packets from the server. The first data request instructs the server to continuously send a plurality of data packets. The first data request includes a quantity N of data packets, that the terminal device is capable of continuously receiving, and a time interval for sending two consecutive data packets, where N is an integer greater than 1. The N data packets include at least one non-confirmable Constrained Application Protocol (NON) data packet, a sending time interval between two consecutive data packets in the N data packets and the at least one NON data packet indicates that sending a receiving response from the terminal device to the server is unnecessary.