Abstract: A system obtains scene context data characterizing the environment. The scene context data includes data that characterizes a trajectory of an agent in a vicinity of a vehicle up to a current time point. The system identifies a plurality of initial target locations, and generates, for each of a plurality of target locations that each corresponds to one of the initial target locations, a respective predicted likelihood score that represents a likelihood that the target location will be an intended final location for a future trajectory of the agent. For each target location in a first subset of the target locations, the system generates a predicted future trajectory for the agent given that the target location is the intended final location for the future trajectory. The system further selects, as likely future trajectories of the agent, one or more of the predicted future trajectories.

Abstract: A flexible printed circuit board includes a main flexible board and a bridging flexible board, wherein the main flexible board includes a first main pad, a second main pad, a first signal line and a second signal line, and the bridging flexible board includes a first bridging pad and a second bridging pad; the first main pad includes at least two first solder joints, the second main pad includes at least two second solder joints, the first bridging pad includes at least two third solder joints, and the second bridging pad includes at least two fourth solder joints.

Abstract: The present application provides a display device and a manufacturing method thereof. The display device includes a display module and a cover plate on the display module. The display module includes a display region including a flat display region and a plurality of curved display regions adjacent to the flat display region and extending in different directions, a non-display region including a plurality of corner regions between adjacent ones of the curved display regions, and a functional device provided in at least one of the plurality of the corner regions to increase a screen-to-body ratio of the display device.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for predicting future trajectories for an agent in an environment. A system obtains scene context data characterizing the environment. The scene context data includes data that characterizes a trajectory of an agent in a vicinity of a vehicle in an. environment up to a current time point. The system identifies a plurality of initial target locations in the environment. The system further generates, for each of a plurality of target locations that each corresponds to one of the initial target locations, a respective predicted likelihood score that represents a likelihood that the target location will be an intended final location for a future trajectory of the agent starting from the current time point.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

Abstract: The present disclosure belongs to the technical field of biological products for veterinary medicine, and specifically relates to a recombinant foot-and-mouth disease virus (FMDV) with a reduced immunosuppressive activity, a preparation method and use thereof, and a recombinant vaccine strain. According to the present disclosure, it is firstly discovered that FMDV 3B protein has an immunosuppressive function, and key sites for exerting the immunosuppressive function are found. A recombinant FMDV vaccine strain with a lost immunosuppressive function in FMDV 3B protein is constructed by introducing amino acid mutations into three repeated copies of FMDV 3B protein.

Abstract: A ridge recognition substrate and a ridge recognition apparatus. The ridge recognition substrate includes: a base substrate including a photosensitive area, and a light-shielding area located on at least one side of the photosensitive area; a plurality of photosensitive devices, arranged in the photosensitive area in an array; each photosensitive device includes a first electrode, a photoelectric conversion structure and a second electrode arranged in layers, the photoelectric conversion structure is electrically connected with the first electrode, and the photoelectric conversion structure directly contacts with the second electrode; and dummy devices, arranged in the light-shielding area in an array, each dummy device including a third electrode, an equivalent dielectric layer and a fourth electrode, the third electrode and the first electrode is in the same layer, the fourth electrode is located at the side of the layer where the second electrode is located facing away from the base substrate.

Abstract: Provided is a bispecific antibody, comprising a first antigen-binding moiety and a second antigen-binding moiety, wherein a paired domain of the first antigen-binding moiety comprises an amino acid sequence of engineered HLA-I ?3 and an amino acid sequence of engineered ?2M. Also provided are an expression method for the bispecific antibody, a polynucleotide encoding the bispecific antibody, a vector and host cell containing the polynucleotide, a pharmaceutical composition containing the bispecific antibody, a fusion protein, and a conjugate.

Abstract: A method for preparing a cementing material by sintering and activating coal gangue is provided. The method includes the following steps: (1) crushing coarse-grained coal gangue to predetermined particle size; (2) fully and evenly mixing the crushed coal gangue, the composite additive and water according to a set proportion, and granulating the obtained mixture; (3) distributing, igniting and exhausting the granulated pellets in the sintering machine to obtain decarburized coal gangue sinter; (4) cooling the decarburized coal gangue sinter in a cooler; (5) crushing and finely grinding the cooled coal gangue sinter to obtain a coal gangue active mixture; and (6) mixing the coal gangue active admixture, the fly ash, the quicklime and the gypsum according to a predetermined ratio, and then carrying out injection moulding, mold removal and curing to obtain the coal gangue-based cementitious material.

Abstract: Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for searching an autonomous vehicle sensor data repository. One of the methods includes maintaining a collection of sensor samples and, for each sensor sample, an embedding of the sensor sample; receiving a request specifying a query sensor sample, wherein the query sensor sample characterizes a query environment region; and identifying, from the collection of sensor samples, a plurality of relevant sensor samples that characterize similar environment regions to the query environment region, comprising: processing the query sensor sample through the embedding neural network to generate a query embedding; and identifying, from sensor samples in a subset of the sensor samples in the collection, a plurality of sensor samples that have embeddings that are closest to the query embedding.

Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating a new measurement of an object surrounding a vehicle with a maintained track. One of the methods includes receiving an object track for a particular object, receiving a new measurement characterizing a new object at a new time step, and determining whether the new object is the same as the particular object, comprising: generating a representation of the new object at the new and preceding time steps; generating a representation of the particular object at the new and preceding time steps; processing a first network input comprising the representations using a first neural network to generate an embedding of the first network input; and processing the embedding of the first network input using a second neural network to generate a predicted likelihood that the new object and the particular object are the same.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating rare pose data. One of the methods includes obtaining a three-dimensional model of a dynamic object, wherein the dynamic object has multiple movable elements that define a plurality of poses of the dynamic object. A plurality of template poses of the dynamic object are used to generate additional poses for the dynamic object including varying angles of one or more key joints of the dynamic object according to the three-dimensional model. Point cloud data is generated for the additional poses generated for the dynamic object.

Abstract: Systems, methods, devices, and techniques for generating object-heading estimations. In one example, methods include actions of receiving sensor data representing measurements of an object that was detected within a proximity of a vehicle; processing the sensor data with one or more preliminary heading estimation subsystems to respectively generate one or more preliminary heading estimations for the object; processing two or more inputs with a second heading estimation subsystem to generate a refined heading estimation for the object, the two or more inputs including the one or more preliminary heading estimations for the object; and providing the refined heading estimation for the object to an external processing system.

Abstract: Some aspects of the subject matter disclosed herein include a system implemented on one or more data processing apparatuses. The system can include an interface configured to obtain, from one or more sensor subsystems, sensor data describing an environment of a vehicle, and to generate, using the sensor data, (i) one or more first neural network inputs representing sensor measurements for a particular object in the environment and (ii) a second neural network input representing sensor measurements for at least a portion of the environment that encompasses the particular object and additional portions of the environment that are not represented by the one or more first neural network inputs; and a convolutional neural network configured to process the second neural network input to generate an output, the output including a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a spatio-temporal embedding of a sequence of point clouds. One of the methods includes obtaining a temporal sequence comprising a respective point cloud input corresponding to each of a plurality of time points, each point cloud input comprising point cloud data generated from sensor data captured by one or more sensors of a vehicle at the respective time point; processing each point cloud input using a first neural network to generate a respective spatial embedding that characterizes the point cloud input; and processing the spatial embeddings of the point cloud inputs using a second neural network to generate a spatio-temporal embedding that characterizes the point cloud inputs in the temporal sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a classifier to detect open vehicle doors. One of the methods includes obtaining a plurality of initial training examples, each initial training example comprising (i) a sensor sample from a collection of sensor samples and (ii) data classifying the sensor sample as characterizing a vehicle that has an open door; generating a plurality of additional training examples, comprising, for each initial training example: identifying, from the collection of sensor samples, one or more additional sensor samples that were captured less than a threshold amount of time before the sensor sample in the initial training example was captured; and training the machine learning classifier on first training data that includes the initial training examples and the additional training examples to generate updated weights for the machine learning classifier.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using spatio-temporal-interactive networks.