Abstract: Methods, devices, and apparatus, including computer programs encoded on computer storage media, for processing an item-picking dispatching request are provided. One of methods includes receiving a dispatching request with respect to item picking; determining a type of the dispatching request; determining, in stored picking resource status information, target picking resource status information corresponding to the type of the dispatching request according to a preset correspondence between types of dispatching requests and picking resource status information, wherein the stored picking resource status information is updated in real time; generating mirror-image information of the target picking resource status information; and generating a dispatching task for item picking based on the mirror-image information of the target picking resource status information.

Abstract: Methods, devices, and apparatus, including computer programs encoded on computer storage media, for processing an item-picking dispatching request are provided. One of methods includes receiving a dispatching request with respect to item picking; determining a type of the dispatching request; determining, in stored picking resource status information, target picking resource status information corresponding to the type of the dispatching request according to a preset correspondence between types of dispatching requests and picking resource status information, wherein the stored picking resource status information is updated in real time; generating mirror-image information of the target picking resource status information; and generating a dispatching task for item picking based on the mirror-image information of the target picking resource status information.

Abstract: Methods and systems for correcting an error of a GPS signal are provided. The method includes receiving a request for positioning service from a computing device at a location; estimating a Total Electron Content (vTEC) value associated with the location of the object based at least in part on a plurality of vTEC values of a plurality of ground stations equipped with a plurality of GPS receivers, the vTEC value being estimated based on an estimator that is trained using a filter-reweight-retrain robust algorithm; and sending information of the estimated vTEC value to the computing device to allow the computing device to correct an error of a GPS signal received by the computing device, the error of the GPS signal being associated with an ionospheric delay.

Abstract: A method including receiving, by a processor, a computing instruction for a neural network, wherein the computing instruction for the neural network includes a computing rule for the neural network and a connection weight of the neural network, and the connection weight is a power of 2; and inputting, for a multiplication operation in the computing rule for the neural network, a source operand corresponding to the multiplication operation to a shift register, and performing a shift operation based on a connection weight corresponding to the multiplication operation, wherein the shift register outputs a target result operand as a result of the multiplication operation. The neural network uses a shift operation, and a neural network computing speed is increased.

Abstract: Systems and methods are provided for online traffic dynamical adjustment and optimization. A such system may comprise one or more servers configured to monitor an incoming traffic volume for visiting an online platform, determine one or more unit traffic values of the monitored incoming traffic volume to the online platform, and monitor one or more subsets of the incoming traffic volume for respectively visiting one or more sub-platforms of the online platform.

Abstract: A method including receiving, by a processor, a computing instruction for a neural network, wherein the computing instruction for the neural network includes a computing rule for the neural network and a connection weight of the neural network, and the connection weight is a power of 2; and inputting, for a multiplication operation in the computing rule for the neural network, a source operand corresponding to the multiplication operation to a shift register, and performing a shift operation based on a connection weight corresponding to the multiplication operation, wherein the shift register outputs a target result operand as a result of the multiplication operation. The neural network uses a shift operation, and a neural network computing speed is increased.

Abstract: A method, an apparatus, a device and a computer readable storage media for neural network processing are disclosed. The method includes constructing a training function having a constraint for a neural network; and solving a constrained optimization based on the training function to obtain connection weights of the neural network. The neural network processing method, apparatus, device and computer readable storage media of the embodiments of the present disclosure model a problem of solving connection weights of a neural network from the perspective of an optimization problem, and can effectively find a solution for the problem of solving the connection weights of the neural network, thus being able to improve the speed of training of the neural network.

Abstract: A method for selecting a product to display on a webpage of an e-commerce website. Online user behavior data associated with a plurality of merchants selling products on the webpage is obtained. The behavior data is user actions performed online with respect to merchants. The behavior data is analyzed to determine a most relevant group of the users for each merchant. The most relevant group is compared to a targeted group of the webpage to determine a rate of overlap for each merchant. The rate of overlap represents a percentage of the most relevant group that matches the targeted group. A top merchant is selected based on the highest rate of overlap. A top product being sold by the top merchant is selected to display on the webpage. The top product is displayed on the webpage of the e-commerce web site to an end user.

Abstract: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

Abstract: Systems and methods are disclosed to perform performance prediction for cloud-based databases by building on a computer a cloud database performance model using a set of training workloads; and using a learned model on the computer to predict database performance in the cloud for a new workload, wherein for each reference workload r and hardware configuration h, system throughput tr,h, average throughput of ?r and standard deviation ?r, comprising normalizing each throughput as: t r , h _ = t r , h - a r ? r .

Abstract: Systems and methods are disclosed for object detection by receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; and performing selective max-pooling to choose one or more feature regions without noises.

Abstract: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

Abstract: Systems and methods for object detection by receiving an image; segmenting the image and identifying candidate bounding boxes which may contain an object; for each candidate bounding box, dividing the box into overlapped small patches, and extracting dense features from the patches; during a training phase, applying a learning process to learn one or more discriminative classification models to classify negative boxes and positive boxes; and during an operational phase, for a new box generated from the image, applying the learned classification model to classify whether the box contains an object.

Abstract: Systems and methods are disclosed for object detection by receiving an image; segmenting the image; extracting features from the image; and performing a dimension-wise spatial layout selection to pick up dimensions inside a discriminative spatial region for classification.

Abstract: Systems and methods are disclosed to search for a query image, by detecting local invariant features and local descriptors; retrieving best matching images by quantizing the local descriptors with a vocabulary tree; and reordering retrieved images with results from the vocabulary tree quantization.

Abstract: A method for fine-grained image classification on an image includes automatically segmenting one or more objects of interest prior to classification; and combining segmented and original image features before performing final classification.

Abstract: A nearest-neighbor-based distance metric learning process includes applying an exponential-based loss function to provide a smooth objective; and determining an objective and a gradient of both hinge-based and exponential-based loss function in a quadratic time of the number of instances using a computer.

Abstract: There are provided a system and method for predicting query execution time in a database system. A cost model determination device determines a cost model of a database query optimizer for the database system. The cost model models costs of queries applied to the database system. A profiling device determines profiling queries for profiling input/output cost units and processor cost units relating to the database system, and profiles the cost units using the profiling queries to output profiled cost units. A calibrating device calibrates cost units in the cost model responsive to the profiled cost units to output calibrated cost units. A sampling re-estimator samples and re-estimates a cardinality estimation of a final query plan to output an updated cardinality estimation. A predictor applies the calibrated cost units and the updated cardinality estimation in the cost model to generate a prediction of an execution time of a given query.

Abstract: Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex.

Abstract: Systems and methods are disclosed for object detection by receiving an image; segmenting the image; extracting features from the image; and performing a dimension-wise spatial layout selection to pick up dimensions inside a discriminative spatial region for classification.