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: 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 mark hole positioning method is provided. The assembly device obtains a target image including a to-be-positioned mark hole, and extracts contour points of the mark hole from the target image. Next, the assembly device generates a circle center set of auxiliary circles by using the contour points as circle centers of the auxiliary circles. Then, the assembly device traverses each circle center in the circle center set and each radius value in a radius set, generates a plurality of auxiliary circles in the target image, and accumulates, for different radius values, a quantity of times of intersection between pixels in the target image and the plurality of auxiliary circles. Finally, sub-pixel positioning information of the mark hole is determined and the mark hole is positioned based on the sub-pixel positioning information of the mark hole.

Abstract: A method for forecasting time-series data, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving a time series forecasting query from a user requesting a time series forecast forecasting future data based on a set of current time-series data. The operations include obtaining, from the set of current time-series data, a set of training data. The operations include training, using a first portion of the set of training data, a first sub-model of a forecasting model and training, using a second portion of the set of training data, a second sub-model of the forecasting model. The second portion is different than the first portion. The operations include forecasting, using the forecasting model, the future data based on the set of current time-series data and returning, to the user, the forecasted future data for the time series forecast.

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: The present application discloses a display panel. The display panel includes a plurality of light emitting devices distributed in an array, wherein each light-emitting device includes an upper light emitting surface and a side light emitting surface configured to transmit light, and wherein the side light emitting surface is provided with a reflective layer; a plurality of light emitting devices includes a plurality of light emitting devices with different colors, and the reflective layer on the light emitting device with one color is configured to reflect light emitted by the color light emitting devices with another colors.

Abstract: Methods, systems, and apparatus, including computer-readable storage media relating to a database management system (DBMS) configured to perform data searching and machine learning (ML) pre-processing using a common user specification. Two common functions associated with large databases are searching for data in the database and using data stored in databases for data-intensive processing, such as training or executing a machine learning model. While a single DBMS may implement separate sub-systems for searching and machine learning model processing that rely on similar operations, e.g., text processing, the respective interfaces for each sub-system are different and have different requirements for properly formed user input. A specification, when parsed by either a search sub-system or ML pre-processing system, can allow for correctly pre-processing data in accordance with the specification, without the user having to provide separate specifications for either sub-system.

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: The present application discloses a thin-film transistor, a display panel, and an electronic device. The thin-film transistor includes a gate, a gate insulating layer, and a semiconductor layer sequentially disposed from an inside to an outside along a radial direction. The thin-film transistor further includes a source and a drain disposed on a side of the semiconductor layer and arranged at intervals, and the source and the drain are connected to the semiconductor layer.

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: Provided are a prediction method and a prediction system for MRKH (Mayer-Rokitansky-Küster-Hauser) syndrome, a device, a medium, and a program product, relating to the field of intelligent healthcare. The method includes the following steps: acquiring gene expression data of a sample to be tested; extracting expression data of target genes from the gene expression data, where the target genes include one or more of the following: AGPAT2, and PAN2; performing classification prediction based on the expression data of the target genes to obtain a classification result about whether the sample to be tested suffers from MRKH; if expression level of any one or more of the target genes is high, obtaining a classification result that the sample to be tested suffers from MRKH; and on the contrary, obtaining a classification result that the sample to be tested does not suffer from MRKH.

Abstract: A method includes receiving a query to determine anomalies in a set of multivariate time series data values including an endogenous variable and an exogenous variable. The method includes determining an impact of the exogenous variable on the endogenous variable. The method includes determining a set of univariate time series data values and training one or more models using the univariate time series data values. The method includes determining an expected data value for a respective time series data value and determining a difference between the expected data value and the respective time series data value. The method includes determining that the difference between the expected data value for a particular time series data value and the particular time series data value satisfies a threshold. In response, the method includes determining that the particular time series data value is anomalous and reporting the anomalous value to a user.

Abstract: A method includes receiving a text embedding generation query from a user requesting generation of a text embedding for one or more data elements stored at a data warehouse. In response, the method includes selecting, using the text embedding generation query, a text embedding model from a plurality of different text embedding models. The method includes generating, using the selected text embedding model, the text embedding for the one or more data elements and storing the text embeddings at the data warehouse. The method includes receiving a machine learning model training query from the user device requesting training of a machine learning model using the text embeddings. In response to receiving the machine learning model training query, the method includes training the machine learning model using the text embeddings. The method includes providing, to the user device, a notification indicating that training of the machine learning model is complete.

Abstract: A driver substrate and a display panel are disclosed. The driver substrate includes a base substrate and a flexible circuit board. The base substrate has a first bonding portion. The flexible circuit board is disposed on a bonding surface of the first bonding portion and bends from the bonding surface to a side of the first bonding portion away from the bonding surface. The flexible circuit board includes a bent portion, an anti-scratch structure is disposed between the bent portion and the first bonding portion, and the bent portion is attached to the anti-scratch structure.

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: The application discloses a thin film transistor and a light-emitting diode (LED) backplane. The thin film transistor includes: a first gate, a first insulating layer, a first source, a semiconductor layer, a first drain, a second insulating layer, and a second gate; the first insulating layer covers the first gate; the first source, the semiconductor layer, and the first drain are all disposed at a side of the first insulating layer away from the first gate; the second insulating layer covers the first drain, the semiconductor layer, and the first source; and the second gate is disposed on a surface of a side of the second insulating layer away from the first gate, wherein the first source, the semiconductor layer, and the first drain jointly constitute a vertical channel structure.

Abstract: Aspects of the disclosure are directed to integrating one or more large language models (LLMs) into a cloud database platform, such as a data warehouse. Users of the cloud database platform can provide queries to instruct one or more LLMs to perform generative natural language processing tasks by manipulating or generating text directly in the cloud database platform with a table valued function. Users can provide input to register or generate one or more LLMs of the cloud database platform for performing the natural language processing tasks. Integrating LLMs into the cloud database platform can improve processing capabilities of the LLMs and save computing resources, as specialized LLMs or application-specific API may no longer be necessary.

Abstract: Aspects of the disclosure are directed methods, systems, and computer readable media for in-database holiday effect modeling for time series forecasting. The modeling can be accurate, explainable, customizable, and scalable. Machine learning models can receive a first dataset for time series data and a second dataset for configurable holiday data. The models can detect and model effects of each configurable holiday on one or more forecasts, effectively accumulating effects of overlapping holidays, to manage different levels of holiday modeling. Holiday data can be customizable, including an ability to modify existing holidays and/or add new holidays, through one or more interfaces that can display default holiday information, combined holiday information based on both default and customizable holidays, effects of each holiday on forecasts, and accumulated effects of multiple holidays on forecasts.

Abstract: An example method of beacon probing in a computing system includes: sending, by cross-host beacon probing (CHBP) software executing in a first host of the computing system, a first beacon probe from a first network interface controller (NIC) of the first host to NICs on a same layer 2 domain as the first NIC, the NICs including a second NIC of the first host and cross-host NICs of at least one host other than the first host; receiving, at the CHBP software through the first NIC, acknowledgements (ACKs) to the first beacon probe from the cross-host NICs; and determining, in response to the first beacon probe, connectivity statuses of the first NIC and the second NIC by the CHBP software based on the ACKs and on whether the second NIC receives the first beacon probe.

Abstract: Disclosed are a class of URAT1 inhibitor compounds and the use of such compounds. These compounds are compounds 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 hURAT1-transported uric acid in HEK293 transfected cells, and show that such compounds have a good potential for application in the treatment of hyperuricemia or gout.