Abstract: The present application provides a resource scheduling method and apparatus, an electronic device, and a computer readable storage medium. The method includes: selecting an optimal path from a pre-constructed resource label forest according to a weight of a path in a resource label tree in the resource label forest; and scheduling a task to a third node through which the optimal path passes. The resource label forest includes the at least one resource label tree, and each path of which includes a first node, a second node and the third node in an order from a root node to leaf nodes.

Abstract: An aircraft seat backrest with a lifesaving function, including a backrest body and a back plate assembly, wherein a containing cavity is arranged in a front face of the backrest body, the back plate assembly includes a back plate covering the containing cavity and a lifesaving parachute pack that is arranged on a back face of the back plate and contained in the containing cavity, the back plate is fixedly connected with the backrest body through a quick release mechanism in a quickly separable manner, lifesaving straps of the lifesaving parachute pack pass through strap through holes in the back plate, and an occupant leans against the back plate and is connected with the back plate and the lifesaving parachute pack through the lifesaving straps when riding.

Abstract: Some embodiments provide a method for performing data traffic monitoring. For each packet processing stage of a set of packet processing stages in a packet processing pipeline, the method determines whether a packet received by the packet processing pipeline specifies a set of monitoring actions to be performed on the packet. When the packet specifies a set of monitoring action, the method determines whether the monitoring actions in the set are supported by the packet processing stage. The method executes the supported monitoring actions on the packet in addition to processing the packet according to configuration data for the stage.

Abstract: A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Abstract: Some embodiments provide a method of aggregating and providing packet metrics collected during a live packet monitoring session performed for packets matching a specified set of characteristics. The method receives, from one or more computing devices that process packets during the live packet monitoring session, multiple metrics associated with a set of packets matching the specified set of characteristics. Metrics associated with each packet in the set are accompanied by a packet identifier (ID) used to tag the packet by an initial computing device that processed the packet. The method uses the accompanying packet IDs to aggregate the received plurality of metrics. The method provides (i) an aggregated set of session metrics for the set of packets matching the specified set of characteristics during the live packet monitoring session and (ii) individual packet metrics using the packet IDs for at least one packet in the set of packets.

Abstract: A method for anomaly detection includes receiving an anomaly detection query from a user. The anomaly detection query requests data processing hardware determine one or more anomalies in a dataset including a plurality of examples. Each example in the plurality of examples is associated with one or more features. The method includes training a model using the dataset. The trained model is configured to use a local outlier factor (LOF) algorithm. For each respective example of the plurality of examples in the dataset, the method includes determining, using the trained model, a respective local deviation score based on the one or more features. The method includes determining that the respective local deviation score satisfies a deviation score threshold and, based on the location deviation score satisfying the threshold, determining that the respective example is anomalous. The method includes reporting the respective anomalous example to the user.

Abstract: The invention discloses compounds for treatment or prevention of liver diseases. The compounds are compounds represented by a formula (I) or (II), optical isomers or pharmaceutically acceptable salts of the compounds. The compounds and optical isomers or pharmaceutically acceptable salts of the compounds can be applied to preparation of drugs for treatment or prevention of liver diseases.

Abstract: A class of ketoamide-based compounds, in particular, a ketoarnide-based compound as represented by general formula A is provided. The ketoamide compound may be used as a 2019 novel coronavirus (2019-nCov) 3 CL protease inhibitor and/or human cathepsin L inhibitor, and/or may be used in the preparation of a medicament for treating and/or preventing and relieving respiratory tract infection, pneumonia and other related diseases caused by 2019 novel coronavirus infection. Pharmaceutical compositions of the class of compounds, pharmaceutical salts, enantiomeric forms, diastereoisomers and racemic compounds thereof in the preparation of a medicament for treating and/or preventing and relieving respiratory tract infections and other related diseases caused by the 2019 novel coronavirus infection are also provided.

Abstract: Some embodiments provide a method of aggregating and providing packet metrics collected during a live packet monitoring session performed for packets matching a specified set of characteristics. The method receives, from one or more computing devices that process packets during the live packet monitoring session, multiple metrics associated with a set of packets matching the specified set of characteristics. Metrics associated with each packet in the set are accompanied by a packet identifier (ID) used to tag the packet by an initial computing device that processed the packet. The method uses the accompanying packet IDs to aggregate the received plurality of metrics. The method provides (i) an aggregated set of session metrics for the set of packets matching the specified set of characteristics during the live packet monitoring session and (ii) individual packet metrics using the packet IDs for at least one packet in the set of packets.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

Abstract: A method includes receiving a model analysis request from a user. The model analysis requests requesting the data processing hardware to provide one or more statistics of a model trained on a dataset. The method also includes obtaining the trained model. The trained model includes a plurality of weights. Each weight is assigned to a feature of the trained model. The model also includes determining, using the dataset and the plurality of weights, the one or more statistics of the trained model based on a linear regression of the trained model. The method includes reporting the one or more statistics of the trained model to the user.

Abstract: Some embodiments provide a method for performing data traffic monitoring. The method processes a packet through a packet processing pipeline that includes multiple stages. At a filtering stage, the method tags the packet with a set of monitoring actions for subsequent stages to perform on the packet based on a determination that the packet matches a particular filter. For each stage of a set of packet processing stages subsequent to the filtering stage, the method (i) executes any monitoring actions specified for the stage to perform on the packet and (ii) sends the packet to a next stage in the packet processing pipeline.

Abstract: Some embodiments provide a method for performing data traffic monitoring. For each packet processing stage of a set of packet processing stages in a packet processing pipeline, the method determines whether a packet received by the packet processing pipeline specifies a set of monitoring actions to be performed on the packet. When the packet specifies a set of monitoring action, the method determines whether the monitoring actions in the set are supported by the packet processing stage. The method executes the supported monitoring actions on the packet in addition to processing the packet according to configuration data for the stage.

Abstract: The present invention belongs to the field of medicinal chemistry. Specifically disclosed is a class of URAT1 inhibitors for promoting uric acid excretion, which are compounds as represented by the structure of formula (I) or pharmaceutically acceptable salts thereof. Experiments show that the compounds provided by the present invention have a very good inhibitory effect on the transport of uric acid by hURAT1 in HEK293 transfected cells, and that such compounds have a good application prospect in the treatment of hyperuricemia or gout.

Abstract: A method for principal component analysis includes receiving a principal component analysis (PCA) request from a user requesting data processing hardware to perform PCA on a dataset, the dataset including a plurality of input features. The method further includes training a PCA model on the plurality of input features of the dataset. The method includes determining, using the trained PCA model, one or more principal components of the dataset. The method also includes generating, based on the plurality of input features and the one or more principal components, one or more embedded features of the dataset. The method includes returning the one or more embedded features to the user.

Abstract: A method and device for characterizing microbial carbonate pores, and a server. Acquiring a user's detailed observational description of a profile of a microbial carbonate to be analyzed, and determining a full-diameter core sample on the microbial carbonate; performing pore characterization and a first pore test on the full-diameter core sample to determine a centimeter-scale pore parameter; determining a sampling position on the full-diameter core sample, and sampling on the full-diameter core sample to obtain a plunger sample; performing a second pore test on the plunger sample to determine a millimeter-scale pore parameter; determining a sampling position on the plunger sample according to the millimeter-scale pore parameter, and sampling on the plunger sample to obtain a scanning electron microscope (SEM) sample and a casting thin section sample; and performing a pore test on the SEM sample and the casting thin section sample to determine a micron-to-nanoscale pore parameter.

Abstract: Example methods and systems for logical overlay tunnel monitoring are described. One example may involve a first computer system obtaining control information identifying a list of multiple logical overlay tunnels to be monitored, including a first logical overlay tunnel between a first virtual tunnel endpoint (VTEP) and a second VTEP. Based on the control information, a first monitoring agent may configure and inject a probe packet at the first VTEP to cause the first VTEP to perform encapsulation and send an encapsulated probe packet over the first logical overlay tunnel. In response, an encapsulated response packet that includes a response packet may be received from the second monitoring agent over the first logical overlay tunnel. Based on the response packet, a tunnel performance metric associated with the first logical overlay tunnel may be determined.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

Abstract: A backlight source and a manufacturing method thereof are provided. The backlight source includes a first substrate, a plurality of light sources, a driving circuit layer, and a conductive line. The light sources are distributed on an upper surface of the first substrate. The second substrate is disposed opposite to the first substrate. The driving circuit layer is disposed on a surface of a side of the second substrate away from the first substrate. An end of the conductive line is connected to one of the light sources, and another end of the conductive line is bonded to the driving circuit layer.

Abstract: The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.