Abstract: The disclosure herein describes training a document recommendation model using loss data generated from a linear score difference vector. A training data entry is provided including a query and a set of candidate documents. A document recommendation model generates a set of document prediction scores indicative of a likelihood that the candidate documents are responses to the query and a pairwise score difference matrix is generated using the set of document prediction scores. The pairwise score difference matrix is transformed into a score difference vector using a correct document vector that indicates a correct document among the set of candidate documents. Loss data of the document recommendation model is generated using the score difference vector and the document recommendation model is adjusted using the calculated loss data. Training the document recommendation model based on the linear score difference vector reduces resource usage when compared to training with a difference matrix.

Abstract: This application discloses a walkie-talkie communication method based on base station relay, a base station, a walkie-talkie, and a medium. The method includes: transmitting a time frame to a network node within a first communication range via a first voice communication channel to broadcast the network node corresponding to each voice time slot in the time frame through a synchronization time slot of the time frame; acquiring voice information transmitted by the network node and other base stations through the voice time slots of the received time frame; broadcasting the received voice information to the network node within the first communication range via the first voice communication channel, and broadcasting the received voice information to other base stations within a second communication range via a second voice communication channel.

Abstract: The present invention relates to the technical field of water surface waste treatment, and in particular to a water surface floater collecting ship. The technical problems are: picking up floaters with the cooperation of labors consumes manpower and causes disturbance of water surface, which makes the floaters float further away with water waves, and the odor emitted by the collected floating waste will pollute the environment and affect the salvage efficiency. The technical solution is: a water surface floater collecting ship, comprising a hull, a collecting system, etc.; the left part of the hull is connected with the collecting system used for collecting waste.

Abstract: The present invention relates to the field of water surface treatment, and in particular to a cleaning ship for water surface treatment for harvesting water plants. In order to solve the technical problems that the existing cleaning ship cannot adapt to the waters of different depths for harvesting water plants, and a hull has low load utilization rate for the collected water plants and affects the overall working efficiency and the fuel economy of the hull, the present invention provides a cleaning ship for water surface treatment for harvesting water plants, comprising a steel belt conveying mechanism, a driven shaft and the like. The steel belt conveying mechanism drives the driven shaft to rotate.

Abstract: Generating resource deployment health report graphs for specific virtual deployments is provided. A resource deployment health report graph that defines each respective resource dependency and a status of each respective resource and each respective dependency resource in a virtual deployment of a container-based environment is generated prior to a plurality of resources corresponding to the virtual deployment being connected in the container-based environment. An analysis of information contained in the resource deployment health report graph is performed. It is determined whether each respective resource and each respective dependency resource in the virtual deployment is in a ready state based on the analysis. In response to determining that each respective resource and each respective dependency resource in the virtual deployment is in a ready state based on the analysis of the information, it is determined that an actual deployment reflected by the virtual deployment is in a healthy state.

Abstract: Systems and methods are directed to training and utilizing a generative language model that is constrained by a predetermined template that is used to train the generative language model. Once trained, customer data is accessed and transmitted to an evaluation component associated with the generative language model. The generative language model generates one or more sentences based on a feedback input of the plurality of feedback inputs, whereby the one or more sentences each include a sentiment, a target, and a reason for the sentiment in a format defined by the predetermined template. The evaluation component then identifies the sentiment, the target, and the reason from a sentence of the one or more sentences. A communication is then presented, on a device of a user, based on at least the sentiment and the reason identified from the sentence. The communication can be an alert or a report.

Abstract: A zonal elastic shoe material and a method for manufacturing same are provided. The shoe material includes: a face layer knitted with a multifilament yarn into a chain knitting structure by a loop-forming ground bar GB1 moving with lapping notation of 1-0/0-1//; a jacquard layer superimposed on the face layer and including an elastic region and a non-elastic region, wherein the elastic region is knitted with a multifilament yarn into a mesh structure by a first pair of jacquard guide bars; the non-elastic region is knitted with a multifilament yarn into a compact knit structure by the first pair of jacquard guide bars; the first pair of jacquard guide bars perform knitting by moving with lapping notation in combination of four needle spaces of 0, 1, 2, and 3, any of which serves as a basic shogging unit; and a pattern-bar zonal-elastic layer provided on the elastic region.

Abstract: A quality prediction and adaptive compensation method and apparatus for curved surface assembly are provided. The method includes: inputting a geometric error function and a thermal error function into a spatial error model, to obtain a machining error prediction model; superimposing obtained machining errors on a theoretical surface of an assembly surface, to obtain a predicted machining surface; calculating, according to an assembly median plane determined based on the assembly surface, shape errors and assembly gap errors, and predicting curved surface assembly quality of a part by using the shape errors and the assembly gap errors; calculating an adaptive compensation amount of each assembly plane of the assembly surface based on the shape errors, the assembly gap errors, and the machining errors, when the curved surface assembly quality does not meet a preset assembly quality requirement, and compensating the corresponding assembly plane by using the adaptive compensation amount.

Abstract: Mechanisms are provided for workflow data redistribution in a hybrid computing environment comprising a private portion and a public portion of the hybrid computing environment. A received user request, that is serviced by a workflow executed by one or more computing devices of the hybrid computing environment, is parsed to extract criteria of the workflow for servicing the user request. It is determined whether a catalog data structure exists in a public catalog data store of the public portion that satisfies the criteria of the workflow and if not, the user request is processed in a workflow manager computing system of the private portion to generate a catalog that satisfies the criteria of the workflow and generate a result. A portion of the catalog is redistributed by storing the portion of the catalog in the public catalog data store of the public portion.

Abstract: The present disclosure relates to at least: grouping a set of nodes in a target scenario to obtain nodes included in each of a plurality of time-bound communities; the numbers of nodes in the communities follow a power-law distribution; generating a time window for each of the plurality of time-bound communities, wherein starting times of time windows corresponding to the plurality of time-bound communities follow a uniform distribution, and lengths of the time windows corresponding to the plurality of time-bound communities follow a power-law distribution; and constructing an index structure that is transferable between different time-bound communities, and generating temporal edges within each time-bound community and/or temporal edges between different time-bound communities of the plurality of time-bound communities based on the nodes included in the respective time-bound communities, the time windows of the respective time-bound communities, and the index structure, thus generating a temporal graph for a

Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: Provided is a call response method in the field of terminal technology. The call response method includes receiving a first call indication message and a second call indication message sent by a network device, determining a target state of the terminal device, and responding to the first call indication message or the second call indication message according to the target state to perform the PS call or the CS call.

Abstract: Systems and methods are directed to training and utilizing a generative language model that is constrained by a predetermined template that is used to train the generative language model. Once trained, customer data is accessed and transmitted to an evaluation component associated with the generative language model. The generative language model generates one or more sentences based on a feedback input of the plurality of feedback inputs, whereby the one or more sentences each include a sentiment, a target, and a reason for the sentiment in a format defined by the predetermined template. The evaluation component then identifies the sentiment, the target, and the reason from a sentence of the one or more sentences. A communication is then presented, on a device of a user, based on at least the sentiment and the reason identified from the sentence. The communication can be an alert or a report.

Abstract: This application discloses a nanoscale failure analysis method, including step 1: placing a first sample to be analyzed on a sample stage of an FIB machine, and performing cutting on a selected area of the first sample by using an ion beam in the FIB machine to form a first cross section and expose a metal pattern on the first cross section; step 2: depositing a protective layer on the first cross section by using an electron beam of the FIB machine; step 3: transferring the first sample to a nano prober, the protective layer being used for protecting the metal pattern and preventing metal diffusion in a transfer process; step 4: performing surface micro treatment on the first sample by using an ion source in the nano prober to remove the protective layer; step 5: performing probing on the metal pattern and implementing electrical testing through the nano prober.

Abstract: The present disclosure provides the use of an adenosine A2B receptor as a target in drugs for prevention and/or treatment of pruritus and belongs to the technical field of biological pharmaceuticals. Expression of genes encoding major ligand-gated ion channels is detected based on previously reported single-cell RNA sequencing data of mouse dorsal root ganglions (DRGs), and Adora2b is detected to be highly expressed in Neuron_10, suggesting that Adora2b and Adora2b receptor (the adenosine A2B receptor) may be involved in pruritus regulation. Experimental results show that histamine-induced mouse scratching behavior can be effectively prevented by blocking adenosine A2B receptor activity, indicating that the adenosine A2B receptor is involved in histamine-induced pruritus.

Abstract: Managing container updates is provided. A changed file corresponding to a new version of an application is retrieved from a persistent volume based on a mapping between the new version of the application and the changed file. The changed file corresponding to the new version of the application is loaded in a corresponding file of a running container to update the corresponding file of the running container without redeploying the running container using an updated container image.

Abstract: The disclosure herein describes training a document recommendation model using loss data generated from a linear score difference vector. A training data entry is provided including a query and a set of candidate documents. A document recommendation model generates a set of document prediction scores indicative of a likelihood that the candidate documents are responses to the query and a pairwise score difference matrix is generated using the set of document prediction scores. The pairwise score difference matrix is transformed into a score difference vector using a correct document vector that indicates a correct document among the set of candidate documents. Loss data of the document recommendation model is generated using the score difference vector and the document recommendation model is adjusted using the calculated loss data. Training the document recommendation model based on the linear score difference vector reduces resource usage when compared to training with a difference matrix.