Abstract: Embodiments of the present invention disclose a binocular image matching method, apparatus, device, and storage medium. The method comprises: performing target detection on a first image to obtain a first bounding box of a target in the first image; determining a second bounding box corresponding to the first bounding box in a second image; and obtaining a third bounding box of the target in the second image by regressing the second bounding box. The technical solutions realize accurate matching of targets in a binocular image without requiring performing target detection on both images in the binocular image, and then use a matching algorithm to match the targets detected in the two images, thereby greatly reducing the calculation overhead of target matching in the binocular image.

Abstract: A process for the production of a mixed metal hydroxide material for use as a cathode active material precursor is described, wherein the process utilizes cavitation to produce high quality materials through elimination of some processing steps and ingredients. In particular, a method of producing a mixed metal hydroxide material combining a first metal, a second metal, an oxidant and a liquid to form a reaction slurry is disclosed. The method may include applying cavitation to the liquid prior to the formation of the reaction slurry and/or applying cavitation to the liquid when the liquid is part of the reaction slurry. A method of forming an active material and a mixed metal hydroxide material for use as an active material precursor is also disclosed.

Abstract: Provided method for training an autonomous driving model including a video prediction model, and the method including: determining, according to at least one of an initial video frame collected by a target vehicle or scenario description metadata of an initial video frame, a scenario context of the initial video frame; determining a vehicle movement instruction of the target vehicle according to at least one of the initial video frame or trajectory data of the target vehicle corresponding to the initial video frame; and training an initial model using the initial video frame and a control text corresponding to the initial video frame, to obtain the video prediction model, where the control text comprises the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.

Abstract: A method, computer system, and a computer program product for personalized disease progression modeling is provided. The present invention may include receiving patient data, wherein the patient data is comprised of at least genetic data and clinical observations. The present invention may include building a personalized disease progression framework using one or more patient groupings and one or more disease progression pathways. The present invention may include determining a disease stage and progression pattern for each of the one or more patient groupings using a personalizes disease progression model.

Abstract: Provided method for training an autonomous driving model including a video prediction model, and the method including: determining, according to at least one of an initial video frame collected by a target vehicle or scenario description metadata of an initial video frame, a scenario context of the initial video frame; determining a vehicle movement instruction of the target vehicle according to at least one of the initial video frame or trajectory data of the target vehicle corresponding to the initial video frame; and training an initial model using the initial video frame and a control text corresponding to the initial video frame, to obtain the video prediction model, where the control text comprises the scenario context and the vehicle movement instruction, and the video prediction model is configured to output a predicted video frame.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising memory configured to store convolutional artificial neural network information comprising at least one filter definition, and at least one processing core configured to generate, from a preceding layer, a convolutional result of a succeeding layer of the artificial neural network in accordance with the at least one filter definition, and generate, from the convolutional result, an activation result of the succeeding layer by using an activation function, the activation function taking three arguments, the three arguments being derived from the convolutional result.

Abstract: The present application provides a method and a unified framework system for full-stack autonomous driving planning. The method comprises: acquiring an image of a scene and converting the image into an image feature, and converting the image feature into a bird's-eye view feature map; detecting agents from the bird's-eye view feature map through track query vectors, and continuously tracking the agents; segmenting different types of map elements from the bird's-eye view feature map through map query vectors, and continuously updating the map elements; predicting a future trajectory of each agent using an interaction between the agents and the different map elements; predicting, according to the predicted future trajectory of each agent, an occupancy grid map over multi-steps into the future; and decoding an ego-vehicle query vector to generate a planned path of an ego-vehicle, and optimizing the planned path using the predicted future multi-step occupancy grid map.

Abstract: An information synchronization method, a terminal and a system, which relates to the technical field of communication. The method includes: by the first terminal, sending a controlling request of a target terminal to the controlling server; by the first terminal, receiving a state feedback information sent by the first terminal, according to the state feedback information, updating an information of the first terminal; and by the first terminal, sending the state feedback information to each of the at least one second terminal, so that the second terminal updates an information of the second terminal according to the state feedback information.

Abstract: The embodiments of the present application discloses an image processing method, an image processing apparatus, an image processing device and a computer-readable storage medium.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising memory configured to store convolutional artificial neural network information comprising at least one filter definition, and at least one processing core configured to generate, from a preceding layer, a convolutional result of a succeeding layer of the artificial neural network in accordance with the at least one filter definition, and generate, from the convolutional result, an activation result of the succeeding layer by using an activation function, the activation function taking three arguments, the three arguments being derived from the convolutional result.

Abstract: Disclosed is a slag discharging method in a process of producing ultra-low phosphorus steel, which relates to the technical field of iron and steel smelting, and in which molten steel is mixed with lime first to produce basic slag; then converting is performed with oxygen to increase the oxidizability of the basic slag; and a carbon-containing reducing agent is finally added, so that in the process that the carbon is oxidized to release a large amount of carbon monoxide gas, phosphates are captured, and the basic slag is rapidly foamed and overflows from the opening of the steel ladle, so that conditions are no longer available for rephosphorization. Also disclosed is a method for producing ultra-low phosphorus steel, which includes the above-described slag discharging method in a process of producing ultra-low phosphorus steel, and refining and ingotting after slag discharge.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising memory configured to store convolutional artificial neural network information comprising at least one filter definition, and at least one processing core configured to generate, from a preceding layer, a convolutional result of a succeeding layer of the artificial neural network in accordance with the at least one filter definition, and generate, from the convolutional result, an activation result of the succeeding layer by using an activation function, the activation function taking three arguments, the three arguments being derived from the convolutional result.

Abstract: Methods and apparatus, including computer program products, are provided for machine learning including deep convolutional neural networks. In some example embodiments, there may be provided a method that includes receiving, at a trained machine learning model, a portion of a test image; activating, at the machine learning model, a convolutional result formed based on the portion of the test image, the activation based on neighboring regions in the test image; and providing, by the machine learning model, an activated output. Related systems, methods, and articles of manufacture are also described.

Abstract: Embodiments of the present invention disclose a binocular image matching method, apparatus, device, and storage medium. The method comprises: performing target detection on a first image to obtain a first bounding box of a target in the first image; determining a second bounding box corresponding to the first bounding box in a second image; and obtaining a third bounding box of the target in the second image by regressing the second bounding box. The technical solutions realize accurate matching of targets in a binocular image without requiring performing target detection on both images in the binocular image, and then use a matching algorithm to match the targets detected in the two images, thereby greatly reducing the calculation overhead of target matching in the binocular image.

Abstract: An information synchronization method, a terminal and a system, which relates to the technical field of communication. The method includes: by the first terminal, sending a controlling request of a target terminal to the controlling server; by the first terminal, receiving a state feedback information sent by the first terminal, according to the state feedback information, updating an information of the first terminal; and by the first terminal, sending the state feedback information to each of the at least one second terminal, so that the second terminal updates an information of the second terminal according to the state feedback information.

Abstract: A flavor system for a non-animal derived protein consumable is provided. The flavor system includes an aqueous component; a protein binder including a mixture of at least one terpene and at least one carbonyl compound; one or more off-note blocking compounds; and a flavorant.

Abstract: An object detection method, a neural network training method, an apparatus, and an electronic device include: obtaining, through prediction, multiple fused feature graphs from images to be processed, through a deep convolution neural network for target region frame detection, obtaining multiple first feature graphs from a first subnet having at least one lower sampling layer, obtaining multiple second feature graphs from a second subnet having at least one upper sampling layer, and obtaining fused graph by fusing multiple first feature graphs and multiple second feature graphs respectively; and obtaining target region frame data according to the multiple fused feature graphs. Because the fused feature graphs better represent semantic features on high levels and detail features on low levels in images, target region frame data of big and small objects in images can be effectively extracted according to the fused feature graphs, thereby improving accuracy and robustness of object detection.

Abstract: In one embodiment, a method includes accessing posts in a social-networking system. Each of the posts is unlabeled with respect to whether the post is known to be spam. The method also includes determining a posting user who submitted the post to the social-networking system and a recipient user to whom the post is addressed. The method further includes determining a first vector representation of the posting user and a second vector representation of the recipient user based on one or more features associated with the post, the posting user, and the recipient user. The method still further includes comparing the vector representations and building a machine learning model for automatically detecting spam posts in the social-networking system using a subset of the plurality of posts as non-spam training data.

Abstract: In one embodiment, a method includes accessing posts in a social-networking system. Each of the posts is unlabeled with respect to whether the post is known to be spam. The method also includes determining a posting user who submitted the post to the social-networking system and a recipient user to whom the post is addressed. The method further includes determining a first vector representation of the posting user and a second vector representation of the recipient user based on one or more features associated with the post, the posting user, and the recipient user. The method still further includes comparing the vector representations and building a machine learning model for automatically detecting spam posts in the social-networking system using a subset of the plurality of posts as non-spam training data.