Abstract: The present disclosure provides a method and a system for inspecting goods. The method includes the steps of: obtaining a transmission image and a HSCODE of inspected goods; processing the transmission image to obtain a region of interest; retrieving from a model library a model created based on the HSCODE, in accordance with the HSCODE of the inspected goods; and determining whether there are any goods not registered in a customs declaration that are contained in the region of interest based on the model. With the above solution, it is possible to inspect goods in a container efficiently, so as to find out whether there are goods not indicated in the customs declaration that are concealed in the container.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.

Abstract: A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined.

Abstract: Provided is a GLP-1 analogue having the structure as shown in the general formula A: 7HAEX10TFTSX15VSSYLEX22QAAKEFIX30WLX33KGRG37n1X1Cn2X2 ??(general formula A), wherein X10 is glycine or cysteine, X15 is aspartic acid or cysteine, X22 is glycine or cysteine, X30 is alanine or cysteine, X33 is valine or cysteine, and at least one of X10, X15, X22, X30 and X33 is cysteine, X1 and X2 respectively is glycine, alanine or valine, n1=1-30, n2=1-30. The general formula A contains two cysteines to form disulfide bonds. Also provided are the preparation methods and the use of said GLP-1 analogue. Said GLP-1 analogue has a prolonged blood half-life compared with GLP-1, and can be used for the treatment of diabetes and obesity.

Abstract: Provided are a glucagon-like peptide-1 (GLP-1) analogue monomer and dimmer, a preparation method thereof, and an application thereof. The GLP-1 analogue monomer comprises one cysteine; and the dimer is formed by two monomer molecules connected via an intermolecular disulfide bond formed by the cysteine. The GLP-1 monomer comprising cysteine has the following general formula: 7HAEX10TFTSX15VSSYLEX22X23AAKEFIX30WLX33KGRG37, wherein X10 is glycine or cysteine, X15 is aspartate or cysteine, X22 is glycine or cysteine, X23 is glutamine or cysteine, X30 is alanine or cysteine, and X33 is valine or cysteine; and only one of X10, X15, X22, X23, X30, and X33 is cysteine. The glucagon-like peptide-1 analogue dimer of the present invention has an in vivo half-life of more than 8 to 96 hours, thus facilitating clinical promotion and application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.

Abstract: A computer-implemented method includes receiving, in a system of one or more computers, training data for predictive modeling, the training data including a plurality of categories; determining, by the system, one or more attributes of the training data; identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes; obtaining a utility function for the predictive model of the identified type, the utility function specifying importance of the plurality of categories relative to each other; and generating, based on the training data and the utility function, a predictive model of the identified type.

Abstract: An approach is provided for metrics data collection for business transactions. An online activity (e.g., Application Programming Interface (API) call) associated with a business flow is identified. It is determined whether the online activity is a part of a transaction, wherein the transaction is defined according to a business rule. Status of the transaction is updated if the online activity is a part of the transaction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for utilizing predictive models from an application scripting language.

Abstract: A hollow floor-jack web-plate type chassis side panel assembly includes a side panel, a reserved installation hole on the side panel, a side panel bent edge at a lower edge of the side panel, and a web plate having an installation hole corresponding to the web plate. The web plate has an edge including first and second web plate molded edges which are perpendicular to the web plate and arranged in the same direction; the first web plate molded edge is attached and welded onto a side panel bent edge facing away from the side panel; the second web plate molded edge is attached and welded at a position on the side panel other than the side panel bent edge; and the side panel and the web plate are parallel to each other, and a hollow web is formed between the side panel and the web plate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of first training examples, training a first predictive model using the first training examples, for each example in the first training examples, providing the first features of the example to the trained first predictive model to generate a respective first prediction, generating a second training example for each of the first training examples, wherein the second training example comprises the first features of the first training example and an answer that indicates whether the first answer of the first training example matches the respective first prediction of the first training example, training a second predictive model using the second training examples, and using the trained second predictive model to determine a confidence score for a prediction generated by the trained first predictive model.

Abstract: In general, a method includes receiving a training data set that includes a plurality of examples, wherein each example includes one or more features and an answer, generating a plurality of modified training data sets by applying one or more filters to the training data set, each of the plurality of modified training data sets being based on a different combination of the one or more filters, training a plurality of predictive models, each of the plurality of predictive models being trained using a different modified training data set of the plurality of modified training data sets, determining a respective accuracy for each of the plurality of predictive models, identifying a most accurate predictive model based on the determined accuracies, and specifying an association between the training data set and the combination of filters used to generate the modified training data set that was used to train the most accurate predictive model.

Abstract: A hollow floor-jack web-plate type chassis side panel assembly includes a side panel, a reserved installation hole on the side panel, a side panel bent edge at a lower edge of the side panel, and a web plate having an installation hole corresponding to the web plate. The web plate has an edge including first and second web plate molded edges which are perpendicular to the web plate and arranged in the same direction; the first web plate molded edge is attached and welded onto a side panel bent edge facing away from the side panel; the second web plate molded edge is attached and welded at a position on the side panel other than the side panel bent edge; and the side panel and the web plate are parallel to each other, and a hollow web is formed between the side panel and the web plate.

Abstract: The present invention is an abstraction layer that “hides” the complexity of underlying workflow engine and provides a unified application programming interface (API) to access underlying workflow functions. The abstraction layer of the present invention serves as a gateway between application flow logic and one or more workflow engines and allows an application to build flow implementation logic once and then be able to run on various workflow engines without significant code changes. The present invention also enables different workflow engines to run concurrently to support one application and without the need for applications to migrate or consolidate to one workflow engine.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for multi-label models. One of the methods includes receiving training records, each training record having an input, a first output, and a second output. The method includes generating a first classifier using as input one of the inputs and using as output a corresponding one of the first outputs. The method includes generating a second classifier using as input one of the inputs and using as output a corresponding one of the second outputs. The method includes inputting the inputs into the first classifier and generating first predictive outputs. The method includes inputting the inputs into the second classifier and generating second predictive outputs. The method also includes generating a third classifier using as input the first output and the second output and using as output the first output and the second output of the corresponding training record.

Abstract: A computer-implemented method includes obtaining a database table, the database table including data arranged in a plurality of rows and a plurality of columns, each column of data being associated with a different tag that specifies a category for data in the column, using one or more processors to identify a first predictive model, from a collection of predictive models, that can be applied to the database table to generate a predictive output, in which identifying the first predictive model is based on one or more of the different tags, adding a name associated with the first predictive model to a set of names of predictive models that are compatible with the database table, and providing the set of names of predictive models to a client device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for caching predictive models are described. Records are obtained, each record including a time of a previously submitted predictive request and an identifier of a trained predictive model. A trained scheduling model is generated using the records as training data. A set of identifiers of trained predictive models are determined from a plurality of trained predictive models that are stored in a secondary memory of a computing system. The target time is inputted to the trained scheduling model. In response, a second predictive output is received that comprises the set of identifiers. A set of trained predictive models are obtained that correspond to the set of identifiers from the secondary memory. The set of trained predictive models are stored in a primary memory of the computing system.