Abstract: The disclosure provides a modified positive electrode active material, a preparation method thereof, and an electrochemical energy storage device. The modified positive electrode active material comprises positive electrode active material substrate; first oxide layer, coated on the surface of the positive electrode active material substrate and selected from one or more of oxides of element M being selected from the group of one or more of Li, Al, Zr, Mg, Ti, Y, Si, Ca, Cr, Fe, Zn, Nb, Sn, Ba, and Cd; and second oxide layer having a continuous layered structure, coated on the surface of the first oxide layer and selected from one or more of oxides of element M? being selected from one or more of Li, B, P, As, Pb, V, Mo, and Sn. High temperature storage performance and cycling performance of electrochemical energy storage device are improved by the modified positive electrode active material.

Abstract: An FPGA hardware device obtains encrypted data of each participant of a secure computing system, where the FPGA hardware device stores at least one first key, where the at least one first key is at least one first key of all participants in the secure computing system or at least one first key of a predetermined number of trusted managers in the secure computing system, where the FPGA hardware device includes an FPGA chip. The FPGA hardware device decrypts the encrypted data of each participant by using a working key of each participant, to obtain plaintext data of each participant, where the working key of each participant is obtained based on a corresponding first key of the at least one first key. The FPGA hardware device performs computing based on the plaintext data of each participant to obtain a computing result. The FPGA hardware device outputs the computing result.

Abstract: Implementations of the present specification provide a method, an apparatus, and a device for updating a convolutional neural network by using a GPU cluster. The GPU cluster includes a first GPU and several other GPUs.

Abstract: An image processing method and apparatus using a neural network are provided. The image processing method includes generating a plurality of augmented features by augmenting an input feature, and generating a prediction result based on the plurality of augmented features.

Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

Abstract: The present application provides a positive electrode sheet for a secondary battery, the positive electrode sheet includes a positive electrode current collector and a positive electrode active material layer on a surface of the positive electrode current collector, the positive electrode active material layer includes a positive electrode active material, the positive electrode active material includes a first lithium nickel transition metal oxide and a second lithium nickel transition metal oxide, the first lithium nickel transition metal oxide includes a first substrate and a first coating layer on a surface of the first substrate, the first substrate is secondary particles, and the second lithium nickel transition metal oxide is a single crystal or single-crystal-like morphological particles.

Abstract: Implementations of the present specification provide a model-based prediction method and apparatus. The method includes: a model running environment receives an input tensor of a machine learning model; the model running environment sends a table query request to an embedding running environment, the table query request including the input tensor, to request low-dimensional conversion of the input tensor; the model running environment receives a table query result returned by the embedding running environment, the table query result being obtained by the embedding running environment by performing embedding query and processing based on the input tensor; and the model running environment inputs the table query result into the machine learning model, and runs the machine learning model to complete model-based prediction.

Abstract: An FPGA hardware device obtains encrypted data of each participant of a secure computing system, where the FPGA hardware device stores at least one first key, where the at least one first key is at least one first key of all participants in the secure computing system or at least one first key of a predetermined number of trusted managers in the secure computing system, where the FPGA hardware device includes an FPGA chip. The FPGA hardware device decrypts the encrypted data of each participant by using a working key of each participant, to obtain plaintext data of each participant, where the working key of each participant is obtained based on a corresponding first key of the at least one first key. The FPGA hardware device performs computing based on the plaintext data of each participant to obtain a computing result. The FPGA hardware device outputs the computing result.

Abstract: This application provides a positive active material, a positive electrode plate, an electrochemical energy storage apparatus, and an apparatus. The positive active material is LixNiyCozMkMepOrAm or LixNiyCozMkMepOrAm whose surface is provided with a coating layer. The positive active material is secondary particles, and a particle size Dn10 of the positive active material satisfies: 0.5 ?m?Dn10?3 ?m. In this application, particle morphology of the positive active material and the amount of micro powder in the positive active material are properly controlled, to effectively reduce side reactions between the positive active material and an electrolyte, decrease gas production of the electrochemical energy storage apparatus, and improve storage performance of the electrochemical energy storage apparatus without deteriorating energy density, cycle performance and rate performance of the electrochemical energy storage apparatus.

Abstract: This application relates to the field of battery technologies, and in particular, to a high-compacted-density positive electrode material and an electrochemical energy storage apparatus. The positive electrode material includes a lithium-nickel transition metal oxide A and a lithium-nickel transition metal oxide B. The lithium-nickel transition metal oxide A is secondary particles, whose chemical formula is shown in formula I: Lia1(Nib1Coc1Mnd1)x1M1-x1O2-e1Xe1. The lithium-nickel transition metal oxide B is a monocrystalline structure or a monocrystalline-like structure, whose chemical formula is shown in formula II: Lia2(Nib2Coc2Mnd2)x2M?1-x2O2-e2X?e2 (II). The positive electrode material of this application includes the large-particle lithium-nickel transition metal oxide A and the small-particle lithium-nickel transition metal oxide B to improve an energy density of the battery.

Abstract: The present application relates to the electrochemical field, and in particular, to a positive electrode material, and an electrochemical energy storage apparatus having thereof. The present application provides a positive electrode material, including a substrate. The substrate includes secondary particles containing primary particles. A surface of the substrate is coated with an oxide coating layer. The oxide coating layer comprises a coating element, and the coating element is selected from one or more of Al, Ba, Zn, Ti, Zr, Mg, W, Y, Si, Sn, B, Co, or P. The electrochemical energy storage apparatus comprises the foregoing positive electrode material.

Abstract: An FPGA hardware device obtains encrypted data of each participant of a secure computing system, where the FPGA hardware device stores at least one first key, where the at least one first key is at least one first key of all participants in the secure computing system or at least one first key of a predetermined number of trusted managers in the secure computing system, where the FPGA hardware device includes an FPGA chip. The FPGA hardware device decrypts the encrypted data of each participant by using a working key of each participant, to obtain plaintext data of each participant, where the working key of each participant is obtained based on a corresponding first key of the at least one first key. The FPGA hardware device performs computing based on the plaintext data of each participant to obtain a computing result. The FPGA hardware device outputs the computing result.

Abstract: The present specification provides an article damage detection method, including: obtaining at least two images that are time sequentially related and show a detected article at different angles; and inputting the images to a detection model in time order, to determine a damage detection result, where the detection model includes a first sub-model and a second sub-model, the first sub-model identifies respective features of each image, a feature processing result of each image is input to the second sub-model, the second sub-model performs time series analysis on the feature processing result to determine the damage detection result, and the first sub-model and the second sub-model are obtained by performing joint training by using training samples labeled with article damage.

Abstract: Embodiments of the present specification provide chips and chip-based data processing methods. In an embodiment, a method comprises: obtaining data associated with one or more neural networks transmitted from a server; for each layer of a neural network of the one or more neural networks, configuring, based on the data, a plurality of operator units based on a type of computation each operator unit performs; and invoking the plurality of operator units to perform computations, based on neurons of a layer of the neural network immediately above, of the data for each neuron to produce a value of the neuron.

Abstract: A gaze tracking method and apparatus, and a gaze tracking neural network training method and apparatus are provided. The gaze tracking apparatus includes one or more processors and a memory, and the one or more processors obtain output position information from an input face image of a user using a neural network model, determines a position adjustment parameter for the user, and predicts gaze position information of the user by adjusting the output position information based on the position adjustment parameter.

Abstract: N vehicle accident images are obtained, where N is a natural number greater than or equal to 2. N feature vectors are obtained by inputting the vehicle accident images into a trained convolutional neural network, where the N feature vectors respectively correspond to the vehicle accident images. A distance is calculated between any two feature vectors of the N feature vectors. A determination is made that two vehicle accident images of the N vehicle accident images corresponding to the distance are abnormal when the distance is greater than a first predetermined threshold.

Abstract: N vehicle accident images are obtained, where N is a natural number greater than or equal to 2. N feature vectors are obtained by inputting the vehicle accident images into a trained convolutional neural network, where the N feature vectors respectively correspond to the vehicle accident images. A distance is calculated between any two feature vectors of the N feature vectors. A determination is made that two vehicle accident images of the N vehicle accident images corresponding to the distance are abnormal when the distance is greater than a first predetermined threshold.

Abstract: The present specification provides methods, apparatuses, and devices for detecting damages to an article. In one aspect, the method includes: obtaining at least two images that are time sequentially related and show the article at different angles; providing the at least two images as input to a detection model in time order, wherein the detection model comprises a first sub-model and a second sub-model that have been jointly trained on training samples associated with labels indicating respective article damage degrees; processing the at least two images using the first sub-model to determine a feature processing result based on respective features identified from each image; processing the feature processing result using the second sub-model to perform time series analysis on the feature processing result to determine a damage detection result; and obtaining, as output from the detection model, the damage detection result.

Abstract: An FPGA hardware device obtains encrypted data of each participant of a secure computing system, where the FPGA hardware device stores at least one first key, where the at least one first key is at least one first key of all participants in the secure computing system or at least one first key of a predetermined number of trusted managers in the secure computing system, where the FPGA hardware device includes an FPGA chip. The FPGA hardware device decrypts the encrypted data of each participant by using a working key of each participant, to obtain plaintext data of each participant, where the working key of each participant is obtained based on a corresponding first key of the at least one first key. The FPGA hardware device performs computing based on the plaintext data of each participant to obtain a computing result. The FPGA hardware device outputs the computing result.

Abstract: A gaze estimation method includes receiving, by a processor, input data including a current image and a previous image each including a face of a user, determining, by the processor, a gaze mode indicating a relative movement between the user and a camera that captured the current image and the previous image based on the input data, and estimating, by the processor, a gaze of the user based on the determined gaze mode, wherein the determined gaze mode is one of a plurality of gaze modes comprising a stationary mode and a motion mode.