Abstract: The present disclosure provides a method of federated learning. A specific implementation solution includes: determining, for a current learning period, a target device for each task of at least one learning task to be performed, from a plurality of candidate devices according to a plurality of resource information of the plurality of candidate devices; transmitting a global model for the each task to the target device for the each task, so that the target device for the each task trains the global model for the each task; and updating, in response to receiving trained models from all target devices for the each task, the global model for the each task according to the trained models, so as to complete the current learning period. The present disclosure further provides an electronic device, and a storage medium.

Abstract: The present disclosure provides a method for asynchronous federated learning, including: in response to a request for participating in asynchronous federated learning sent by a target electronic device, determining, according to performance information of a server, a first number of electronic devices that the server supports to participate in the asynchronous federated learning, and acquiring a second number of other electronic devices that have participated in the asynchronous federated learning; if the first number is greater than the second number, sending a global model to be optimized to the target electronic device, and receiving target feedback information which is obtained by the target electronic device from training on the global model to be optimized; and optimizing, according to the target feedback information, the global model to be optimized to obtain an optimized global model.

Abstract: A traffic classification method and apparatus, a training method and apparatus, a device and a medium are provided. An implementation is: performing a preprocessing operation on each characteristic of one or more characteristics of an object to be classified; and inputting the one or more characteristics of the object to be classified into a traffic classifier to determine a traffic type of the object to be classified. The preprocessing operation includes at least one of: setting, in response to determining that a characteristic value of the characteristic is invalid data, the characteristic value to a null value; converting, in response to determining that the characteristic is a non-numeric characteristic, the characteristic value of the characteristic to an integer value, and normalizing, in response to determining that the characteristic is a non-port characteristic, the characteristic value of the characteristic.

Abstract: A method for multi-task scheduling, a device and a storage medium are provided. The method may include: initializing a list of candidate scheduling schemes, the candidate scheduling scheme being used to allocate a terminal device for training to each machine learning task in a plurality of machine learning tasks; perturbing, for each candidate scheduling scheme in the list of candidate scheduling schemes, the candidate scheduling scheme to generate a new scheduling scheme; determining whether to replace the candidate scheduling scheme with the new scheduling scheme based on a fitness value of the candidate scheduling scheme and a fitness value of the new scheduling scheme, to generate a new scheduling scheme list; and determining a target scheduling scheme, based on the fitness value of each new scheduling scheme in the new scheduling scheme list.

Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.

Abstract: A computer-implemented method is provided. The method includes: executing, for each task in a federated learning system, a first training process comprising: obtaining resource information of a plurality of terminal devices of the federated learning system; determining one or more target terminal devices corresponding to the task based on the resource information; and training a global model corresponding to the task by the target terminal devices until the global model meets a preset condition.

Abstract: The invention provides a polymerizable composition and a hydrogel obtained or obtainable from the polymerizable composition. The polymerizable composition comprises cucurbituril and a first monomer having a guest for the cucurbituril, wherein the total monomer concentration, Cmon, within the polymerizable composition is at least 0.5 M. The composition may be an aqueous composition.

Abstract: The disclosure relates to a soft rock shear rheological test system with simulation of coupled rainfall seepage and blasting vibrations, which is at least provided with a loading device and a shear box. The loading device includes a frame (2), a normal static load electric cylinder (1) disposed on a top of the frame (2) and a normal dynamic load electric cylinder (16) disposed on a lower portion of the frame (2), a horizontal static load electric cylinder (5) and a horizontal dynamic load electric cylinder (12) disposed on both sides of the frame (2), and a reaction post (10). This test system can perform a dry-wet cycle operation on the test specimen without disassembling the shear box during the shear rheological test, and can truly simulate influences of rainfall seepage and blasting vibrations on the shear rheological effect of soft rock.

Abstract: Embodiments of the present disclosure disclose a video processing method, apparatus, electronic device, and storage medium. According to the present disclosure, by playing a video, in the case where the video is played to a preset progress, suspending playing the video, and displaying interactive operation guidance information, under the guidance of the interactive operation guidance information, a user watching the video possibly will perform an interactive operation corresponding to the interactive operation guidance information. If the interactive operation of the user is detected within a preset time, a pop-up window is displayed which can comprise a target object. A target page which comprises relevant information of the target object can be displayed according to an operation of the user on the pop-up window.

Abstract: A shear box of shear rheology experiment of a soft rock for simulating the coupling of the rainfall seepage and blasting vibration includes an upper shear box, a lower shear box, a normally-loading indenter, a normally-loading cushion block and a test piece joint. The upper shear box is tightly connected to the lower shear box by a vertical roll. The vertical roll passes through the through holes at both sides of the upper shear box and is engaged with the lower shear box through female thread connection holes. The normally-loading indenter passes through a circular through hole and presses against the normally-loading cushion block. The first end of the test piece joint is installed into a water or gas outlet hole, and the second end of the test piece joint is directly mortised into a rock test piece.

Abstract: The present disclosure provides a method and apparatus for determining an encryption mask, a method and apparatus for recognizing an image, a method and apparatus for training a model, a device, a storage medium and a computer program product. A specific implementation comprises: acquiring a test image set and an encryption mask set; superimposing an image in the test image set with a mask in the encryption mask set to obtain an encrypted image set; recognizing an image in the encrypted image set using a pre-trained encrypted image recognition model and recognizing the image in the encrypted image set using a pre-trained original image recognition model to obtain a first recognition result; and determining a target encryption mask from the encryption mask set based on the first recognition result.

Abstract: A method and an apparatus for training a model are provided. The method includes obtaining an initial model and a training sample set including a plurality of training samples generated based on multimedia data, performing a model training task and a model fusion task in parallel, repeating the action of performing the model training task and the model fusion task in parallel till a predetermined training termination condition is met.

Abstract: A method of data interaction, a data interaction apparatus, an electronic device and a non-transitory computer readable storage medium are provided, related to field of computer technologies, and in particular to the field of artificial intelligence technologies. When the method of data interaction is applied to the first data platform, the method includes: sending a data request to a second data platform based on a first resource server provided by a cloud or based on a local server; acquiring response data fed back by the second data platform based on a second resource server provided by the cloud; at least one of the first resource server and the second resource server is dynamically created by the cloud.

Abstract: Embodiments are provided for compilation of a quantum program. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an identification component that selects a subgraph that is common among a first commutation directed acyclic graph (DAG) and a second commutation DAG. The subgraph has an upper-bound size that is greater than a threshold size. The first commutation DAG represents a first quantum circuit of a set of quantum circuits and the second commutation DAG represents a second quantum circuit of the set of quantum circuits. The computer-executable components also include a compilation component that compiles a quantum subcircuit corresponding to the subgraph. The computer-executable components further include a configuration component that replaces the quantum subcircuit in the first quantum circuit with the compiled quantum subcircuit.

Abstract: One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Abstract: A method and an apparatus for predicting an epidemic situation, a device, a storage medium, and a program product are provided. The method may include: estimating a variable parameter of the epidemic situation in a preset area using a Markov Chain Monte Carlo (MCMC) method; acquiring a constant parameter of the epidemic situation in the preset area; constructing a transmission model based on the variable parameter and the constant parameter; and fitting, using the transmission model, to predict epidemic information of the preset area.

Abstract: A hide for a small animal habitat is provided. The hide includes a base, four side panels, a roof and one or more coupling mechanisms for removably coupling the hide to the base. A plurality of coupling mechanisms is contemplated and include tabs having a radially extending flange, an L-shaped in cross section clip, and a rotatable disc.

Abstract: Systems and methods for screening enzyme variants are described, wherein the enzyme variants may catalyze a reaction that is cofactor-dependent or cofactor-independent.

Abstract: A data management method, apparatus, a computing device, a storage medium, and a cloud platform are provided. The data management method includes: obtaining a task request, the task request indicating to retrieve stored data to execute a task; updating execution information for the data, the execution information for the data describing one or more tasks that need retrieval of the data and the execution frequency of each of the tasks; calculating, based on the updated execution information for the data and for each of a plurality of electronic storage locations, a storage-location-specific cost value of the data; and determining a target electronic storage location of the data according to the calculated cost value.

Abstract: Embodiments of the present disclosure provide a data mining system, a data mining method, and a storage medium. The data mining system includes a transfer device, a first trusted execution space and a second trusted execution space. The transfer device is configured to receive a data calling request of the second trusted execution space, obtain data to be called from the first trusted execution space according to the data calling request, and provide the data to be called to the second trusted execution space, so as to perform data mining based on the data to be called and the mining-related data to obtain a data mining result and to provide the data mining result to a device of the data user.