Abstract: An energy Internet system, an energy routing conversion device, and an energy control method, relating to a field of energy information. An alternating-current (AC) side energy routing conversion device of the energy Internet system includes a plurality of first route ports, and a direct-current (DC) side energy routing conversion device includes a plurality of second route ports, where each second route port is connected to a corresponding first route port by means of a corresponding DC busbar. A plurality of energy devices are connected to a DC busbar by means of corresponding first AC/DC converters or first DC converters. The AC side energy routing conversion device and the DC side energy routing conversion device collect energy information of the energy devices and adjust energy of the energy devices on a basis of energy balance constraint conditions.

Abstract: This application discloses data transmission that includes: dividing to-be-transmitted content into a plurality of data units at a session layer; transmitting the data units to the target terminal device by using a first transmission channel as a responsible channel of the to-be-transmitted content; distributing, by the session layer in a case that the session layer switches the responsible channel of the to-be-transmitted content from the first transmission channel to a second transmission channel with a different transmission protocol, the data units to the second transmission channel; and transmitting the data units distributed by the session layer to the target terminal device through the second transmission channel. In data transmission in this application, the session layer controls switching of the channels and distribution of the data units, so that the transmission sequence is free from constraints of protocols and the degree of freedom in data transmission is increased.

Abstract: A hardware accelerator that is efficient at performing computations related to a neural network. In one embodiment, the hardware accelerator includes a first data buffer that receives input data of a layer in the neural network and shift the input data slice by slice downstream. The hardware accelerator includes a second data buffer that receives kernel data of the layer in the neural network and shift the kernel data slice by slice downstream. The hardware accelerator includes a first input shift register that receives an input data slice from the first data buffer. The first input shift register may correspond to a two-dimensional shift register configured to shift values in the input data slice in x and y directions. The hardware accelerator includes a second input shift register that receives a kernel data slice from the second data buffer. A multiplication block performs convolution of the input and kernel data.

Abstract: A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Abstract: A hardware accelerator that is efficient at performing computations related to a neural network. In one embodiment, the hardware accelerator includes a first data buffer that receives input data of a layer in the neural network and shift the input data slice by slice downstream. The hardware accelerator includes a second data buffer that receives kernel data of the layer in the neural network and shift the kernel data slice by slice downstream. The hardware accelerator includes a first input shift register that receives an input data slice from the first data buffer. The first input shift register may correspond to a two-dimensional shift register configured to shift values in the input data slice in x and y directions. The hardware accelerator includes a second input shift register that receives a kernel data slice from the second data buffer. A multiplication block performs convolution of the input and kernel data.

Abstract: A hardware accelerator that is efficient at performing computations related to a neural network. In one embodiment, the hardware accelerator includes a first data buffer that receives input data of a layer in the neural network and shift the input data slice by slice downstream. The hardware accelerator includes a second data buffer that receives kernel data of the layer in the neural network and shift the kernel data slice by slice downstream. The hardware accelerator includes a first input shift register that receives an input data slice from the first data buffer. The first input shift register may correspond to a two-dimensional shift register configured to shift values in the input data slice in x and y directions. The hardware accelerator includes a second input shift register that receives a kernel data slice from the second data buffer. A multiplication block performs convolution of the input and kernel data.

Abstract: A hardware accelerator that is efficient at performing computations related to a neural network. In one embodiment, the hardware accelerator includes a first data buffer that receives input data of a layer in the neural network and shift the input data slice by slice downstream. The hardware accelerator includes a second data buffer that receives kernel data of the layer in the neural network and shift the kernel data slice by slice downstream. The hardware accelerator includes a first input shift register that receives an input data slice from the first data buffer. The first input shift register may correspond to a two-dimensional shift register configured to shift values in the input data slice in x and y directions. The hardware accelerator includes a second input shift register that receives a kernel data slice from the second data buffer. A multiplication block performs convolution of the input and kernel data.

Abstract: A system for implementing seasonal time series analysis and forecasting using a distributed tournament selection process. Time series analysis is initiated by a prediction or runway event trigger. Prediction events include a determination of the availability of one or more resources at a given point in time or over a given time period. A runway event may include a determination of when a resource is below a minimum threshold level of availability. Training of the prediction models is based data taken from different time periods, accounting for any combination of time periods and/or for differing sampling frequencies or ranges. Processes for prosecuting a tournament to identify winning models are parallelized to achieve low latency tournament results. Ranking of each model and/or some combination of models is based on how precisely and/or conclusively the models match a determined set of training data.

Abstract: A method for time series analysis of time-oriented usage data pertaining to computing resources of a computing system. A method embodiment commences upon collecting time series datasets, individual ones of the time series datasets comprising time-oriented usage data of a respective individual computing resource. A plurality of prediction models are trained using portions of time-oriented data. The trained models are evaluated to determine quantitative measures pertaining to predictive accuracy. One of the trained models is selected and then applied over another time series dataset of the individual resource to generate a plurality of individual resource usage predictions. The individual resource usage predictions are used to calculate seasonally-adjusted resource usage demand amounts over a future time period. The resource usage demand amounts are compared to availability of the resource to form a runway that refers to a future time period when the resource is predicted to be demanded to its capacity.

Abstract: Systems for computing cluster management. One embodiment commences upon receiving a set of observed workload parameters corresponding to one or more observable workloads that run in a computing cluster. While the workloads are running, workload stimulus and cluster response observations are taken and used to generate a workload resource usage predictive model based on mappings or correlations between the observable workloads parameters and observed resource usage measurements. A set of planned workloads are applied to the workload resource usage predictive model to predict a set of corresponding predicted resource usage demands. The predicted resource usage demands are then mapped to a set of recommended hardware to form resource deployment recommendations that satisfy at least some of the corresponding resource usage demands while also observing a set of hardware model compatibility constraints.

Abstract: A resin composition, a metal-resin composite formed with the resin composition and a metal substrate and a preparation method and use thereof, and an electronic product shell using the resin composition are provided. The resin composition comprises a base resin, a modified resin and a fiber, wherein the base resin is one or two or more of a polyarylene sulfide resin, a polyether resin, and a polyester resin, and the modified resin has a melting point that is 3-24° C. higher than a glass transition temperature of the base resin.

Abstract: A method performed by one or more processing devices includes obtaining a definition of a data structure; obtaining a definition of one or more operations to be performed on the data structure, the definition of the one or more operations being in accordance with a first syntax; receiving a request for instructions to perform the one or more operations on the one or more fields of the data structure, the instructions being in accordance with a second syntax; generating a translation of the definition of the one or more operations from the first syntax to the second syntax; generating instructions for accessing the one or more fields of the data structure; and generating the instructions in accordance with the second syntax for performing the one or more operations on the one or more fields of the data structure.

Abstract: A computer-implemented method includes receiving a request to populate one or more first fields of a first data structure with one or more first values that are at least partly based on one or more second values in one or more second fields of a second data structure; generating, based on the request, one or more instructions to (i) select the one or more second values, (ii) generate the one or more first values, and (iii) populate the one or more first fields of the first data structure with the one or more first values; and sending the one or more instructions to a computing device for insertion into a computer program that is executable by the computing device.

Abstract: A method performed by one or more processing devices includes receiving a request to generate a computer program; generating, in response to receiving the request, a first user interface that when rendered on a display device renders: one or more first visual representations of one or more first fields of the first resource; and one or more second visual representations of one or more second fields of the second resource; receiving, through the first user interface, a selection of: at least one of the one or more first visual representations; and at least one of the one or more second visual representations; receiving, through a second user interface, data specifying the one or more operations; and updating, based on the received data, the computer program with one or more third instructions to perform the one or more operations.

Abstract: A computer-implemented method includes receiving a request to search for a data structure associated with one or more search criteria; searching, an index in a data repository, for the data structure, with the search based on the one or more search criteria; identifying, based on searching, the data structure associated with the one or more search criteria; obtaining a definition for the data structure; generating an interface that when presented on a computing device renders a representation of the definition for the data structure; and sending the interface to the computing device associated with the request to search.