Abstract: Techniques provide for data synchronization. For example, such a technique may involve: obtaining respective synchronization characteristics of a group of synchronization jobs to be processed, each synchronization characteristic indicating at least one of an expected completion time instant and an amount of data to be synchronized of a corresponding synchronization job; prioritizing the group of the synchronization jobs based on the synchronization characteristics; and controlling execution of the group of the synchronization jobs based on a result of the prioritizing. Accordingly, high priority is given to the synchronization jobs which can be rapidly completed thereby improving the Recovery Point Objective (RPO) achievement rate before occurrence of a failure.

Abstract: A computer trains a classification model. (A) An estimation vector is computed for each observation vector using a weight value, a mean vector, and a covariance matrix. The estimation vector includes a probability value for each class of a plurality of classes for each observation vector that indicates a likelihood that each observation vector is associated with each class. A subset of the plurality of observation vectors has a predefined class assignment. (B) The weight value is updated using the computed estimation vector. (C) The mean vector for each class is updated using the computed estimation vector. (D) The covariance matrix for each class is updated using the computed estimation vector. (E) A convergence parameter value is computed. (F) A classification model is trained by repeating (A) to (E) until the computed convergence parameter value indicates the mean vector for each class of the plurality of classes is converged.

Abstract: A method of preparing quantum dots and the quantum dots are provided. The quantum dots are doped with manganese ions and various kinds of halogen ions such that when the quantum dots emit light, they will have a dual luminescence system, one is intrinsic luminescence of perovskite quantum dots and the other is doping luminescence of the manganese ions. The intrinsic luminescence of perovskite is adjustable from 410 nm to 520 nm based on the ratio of the amounts of different substances having various halide ions, which increases the wavelength range of a quantum dot luminescence spectrum so that the application scenario of the quantum dots is wider.

Abstract: Apparatuses and methods are disclosed for database scheduling. An exemplary method may include obtaining a performance parameter associated with a database server. The method may also include determining whether the performance parameter matches a preset condition. After it is determined that the performance parameter matches the preset condition, the method may further include selecting a database instance operating on the database server and migrating the database instance to a receiving database server.

Abstract: A computer trains a classification model. (A) An estimation vector is computed for each observation vector using a weight value, a mean vector, and a covariance matrix. The estimation vector includes a probability value for each class of a plurality of classes for each observation vector that indicates a likelihood that each observation vector is associated with each class. A subset of the plurality of observation vectors has a predefined class assignment. (B) The weight value is updated using the computed estimation vector. (C) The mean vector for each class is updated using the computed estimation vector. (D) The covariance matrix for each class is updated using the computed estimation vector. (E) A convergence parameter value is computed. (F) A classification model is trained by repeating (A) to (E) until the computed convergence parameter value indicates the mean vector for each class of the plurality of classes is converged.

Abstract: The present disclosure is directed to a rotor blade assembly for a wind turbine having a first rotor blade segment with a first spar cap segment and a second rotor blade segment with a second spar cap segment. The first and second spar cap segments are arranged together at an interface and are constructed of a composite material. Further, the rotor blade assembly includes a joint assembly at the interface of the first and second spar cap segments. The joint assembly is constructed of a first metal joint secured to the first spar cap segment and a second metal joint secured to second spar cap segment. Moreover, the first and second metal joints are welded together at a weld area.

Abstract: Techniques involve use of an adaptor for a storage device. The adaptor includes a baseboard arranged to support an Open Compute Project (OCP) mezzanine card in the storage device; a connector coupled to the baseboard and arranged to connect with the storage device; and an adapting part coupled to the baseboard and including an insertion slot for receiving a first interface of the OCP mezzanine card.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine translation using neural networks. In some implementations, a text in one language is translated into a second language using a neural network model. The model can include an encoder neural network comprising a plurality of bidirectional recurrent neural network layers. The encoding vectors are processed using a multi-headed attention module configured to generate multiple attention context vectors for each encoding vector. A decoder neural network generates a sequence of decoder output vectors using the attention context vectors. The decoder output vectors can represent distributions over various language elements of the second language, allowing a translation of the text into the second language to be determined based on the sequence of decoder output vectors.

Abstract: A hybrid electric vehicle system comprises an internal combustion (IC) engine and an electric engine providing power to a drive shaft of the vehicle. The IC engine receives a fuel from a fuel tank. Exhaust gases from the IC engine are treated at an exhaust treatment apparatus including a reagent tank containing a reagent. A controller monitors a quality of the fuel in the fuel tank and the reagent in the reagent tank and if needed, initiates fuel degradation reduction event or a reagent degradation reduction event. These events can include running the IC engine even if a battery supplying power to the motor is not discharged. The fuel degradation reduction event includes dosing the fuel tank with an antioxidant to reduce the rate of degradation of the fuel.

Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.

Abstract: A system for resource provisioning for content delivery of multiple services is disclosed. The system may be configured to offer multiple services, wherein at least one first offered service is delivery of prerecorded content and at least one second offered service is content delivered according to a deadline. In at least one embodiment, the first offered service may be video content, and the second offered service may be broadcast television content. The system may also be configured to deliver at least a portion of the first offered service in advance of use of that portion of the first offered service such that resource demand in connection with delivery of service content of the second offered service at peak delivery may be reduced. As such, fewer resources are needed to provide uninterrupted service of the first and second offered services.

Abstract: The application relates to a method for producing a hollow support from a fiber composite material, to a core designed as a hollow body, to the use of said core, and to the use of said hollow support made from fiber composite material.

Abstract: A perovskite quantum dot preparation method and a perovskite quantum dot solution are provided. The preparation method may include: providing a first solution including caesium oleate, a second solution including lead halide and a third solution including DDAB; adding the first solution, the second solution and the third solution into a non-polar alkyl solution at a preset proportion and stifling to obtain a perovskite quantum dot solution; the perovskite quantum dot solution may include caesium-lead-halogen of pure phase. In this way, the present disclosure can easily obtain the perovskite quantum dot solution including caesium-lead-halogen of pure phase.

Abstract: A character string classification method, a character string classification system, a character string classification device, and a computer readable storage medium are provided. The method includes: acquiring a to-be-classified character string, inputting the to-be-classified character string to a feature extractor to obtain a feature vector of the to-be-classified character string, and inputting the feature vector to a classifier to obtain a classification result of the to-be-classified character string. With the character string classification method, only the features of the character string itself are used in the character string classification process. That is, the to-be-classified character string is directly inputted to the feature extractor to obtain the feature vector, and the classifier classifies the to-be-classified character string based on the feature vector, thereby eliminating requirement for other information associated with the character string.

Abstract: A diffractive optical element for a structured light projection module and a method of using the diffractive optical element are described herein. The diffractive optical element is configured to: receive two-dimensional patterned beams and generate multi-order diffractive beams, wherein the two-dimensional patterned beams are emitted from a structured light projection module, the structured light projection module includes a light source comprising a plurality of sub-light sources arranged in a two-dimensional array, and the two-dimensional patterned beams correspond to the two-dimensional array; and project a plurality of two-dimensional patterned beams, wherein each of the plurality of two-dimensional patterned beams creates a corresponding duplicated pattern, and the duplicated patterns form a speckle pattern having uniform speckle density. The two-dimensional patterned beams can overlap with each other, or not overlap with each other.

Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix used to select a label for each observation. Unique combinations of observations selected from the unlabeled observations are defined. A marginal distribution value is computed from the label distribution probability matrix. A joint distribution value is computed between observations included in each combination. A mutual information value is computed for each combination as a combination of the marginal distribution value and the joint distribution value computed for the respective combination. A predefined number of observation vector combinations is selected from the combinations that have highest values for the computed mutual information value. Labeled observation vectors are updated to include each observation vector included in the selected observation vector combinations with a respective obtained label.

Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.

Abstract: A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.

Abstract: A depth calculation processor, a data processing method, and a 3D image device are disclosed herein. The depth calculation processor includes: two input ports to receive a first image data, wherein the first image data comprises structured light image acquired under projection of structured light; an input switch connected to the input ports and to convey all or some of the first image data from the input ports; a data processing engine connected to the input switch and to process the first image data that is output through the input switch and to output a second image data, wherein the second image data comprises a depth map, wherein the data processing engine comprises a depth processing engine to process the structured light image to obtain the depth map; and one output port connected to the data processing engine and to output the second image data to a main device.

Abstract: Methods and devices are provided for allowing a mobile device (e.g., a key fob or a consumer electronic device, such as a mobile phone, watch, or other wearable device) to interact with a vehicle such that a location of the mobile device can be determined by the vehicle, thereby enabling certain functionality of the vehicle. A device may include both RF antenna(s) and magnetic antenna(s) for determining a location of a mobile device relative to the vehicle. Such a hybrid approach can provide various advantages. Existing magnetic coils on a mobile device (e.g., for charging or communication) may be re-used for distance measurements that are supplemented by the RF measurements. Any device antenna may provide measurements to a machine learning model that determines a region in which the mobile device resides, based on training measurements in the regions.