Abstract: A purified oligosaccharide compound having antithrombotic activity or a mixture of a homologous compound thereof and a pharmaceutically acceptable salt thereof, a preparation method for the mixture, a pharmaceutical composition containing the mixture, and uses thereof serving as an intrinsic factor X-enzyme (Xase) inhibitor in the preparation of drugs for preventing and/or treating thrombotic diseases.

Abstract: Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Abstract: The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m?3, and m>n?2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

Abstract: A similarity measurement method includes: obtaining a directional relationship between nodes in a network, and determining a transition matrix; calculating a constraint matrix according to the transition matrix and an obtained attenuation factor; constructing a system of linear equations, where a coefficient matrix of the system of linear equations is the constraint matrix, and a variable of the system of linear equations is a correction vector; solving the system of linear equations by means of iteration by using a Jacobi method, and determining the correction vector; and calculating similarities between the nodes according to the transition matrix, the attenuation factor, and a diagonal correction matrix that is generated according to the correction vector. In the method, the correction vector is determined by using the Jacobi method, and further the similarities between the nodes may be calculated.

Abstract: Embodiments of the present invention provide a machine learning model training method, including: obtaining target task training data and N categories of support task training data; inputting the target task training data and the N categories of support task training data into a memory model to obtain target task training feature data and N categories of support task training feature data; training the target task model based on the target task training feature data and obtaining a first loss of the target task model, and separately training respectively corresponding support task models based on the N categories of support task training feature data and obtaining respective second losses of the N support task models; and updating the memory model, the target task model, and the N support task models based on the first loss and the respective second losses of the N support task models.

Abstract: The method of the present disclosure includes: after a graph partitioning apparatus extracts an edge, first determining whether an aggregation degree between a currently extracted edge and an allocated edge in a first device satisfies a preset condition; then, when the preset condition is satisfied, determining whether a quantity of allocated edges stored in the first device is less than a first preset threshold; and allocating the currently extracted edge to the first device when the quantity is less than the first preset threshold. In this way, an aggregation degree between allocated edges in each device is relatively high and each device has relatively balanced load. When an edge changes and an edge associated with the particular edge needs to be synchronized, a relatively small quantity of devices need to perform synchronization and update, so that costs of communication between devices are reduced, and distributed graph computing efficiency is improved.

Abstract: A data processing method, includes receiving a data flow; generating a triplet set according to the data flow, where each triplet in the set includes three items, the first item is a first element in the data flow, the second item includes a first time point at which the first element appears in the data flow and a first quantity of times that corresponds to the first time point, and the third item includes a second time point at which the first element appears in the data flow and a second quantity of times that corresponds to the second time point; and performing data processing on the data flow according to the triplet set. In the embodiments of the present application, the triplet set may be generated based on the data flow.

Abstract: A method for compressing flow data, including: generating multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

Abstract: A recommendation result generation method, where the method includes obtaining article content information of at least one article and user score information of at least one user, where user score information of a first user of the at least one user includes a historical score of the first user for the at least one article, encoding the article content information and the user score information using an article neural network and a user neural network respectively to obtain a target article latent vector of each of the at least one article and a target user latent vector of each of the at least one user, and calculating a recommendation result for each user according to the article latent vector and the user latent vector.

Abstract: A similarity measurement method includes: obtaining a directional relationship between nodes in a network, and determining a transition matrix; calculating a constraint matrix according to the transition matrix and an obtained attenuation factor; constructing a system of linear equations, where a coefficient matrix of the system of linear equations is the constraint matrix, and a variable of the system of linear equations is a correction vector; solving the system of linear equations by means of iteration by using a Jacobi method, and determining the correction vector; and calculating similarities between the nodes according to the transition matrix, the attenuation factor, and a diagonal correction matrix that is generated according to the correction vector. In the method, the correction vector is determined by using the Jacobi method, and further the similarities between the nodes may be calculated.

Abstract: A method for compressing flow data, including: generating multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

Abstract: A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

Abstract: A method for compressing flow data, including: constructing multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

Abstract: An extract for preventing or treating thrombotic diseases, particularly, an extract of at least one of leeches and earthworms having a molecular weight of no more than 5,800 daltons is provided, wherein the extract includes 15% to 38% amino acid, 40% to 60% saccharide and 0.3% to 1% hypoxanthine. Processes for preparation, pharmaceutical compositions and uses thereof are also provided. Compared to conventional arts, the extract has safety greatly improved and drug actions maintained and even improved.

Abstract: An extract for preventing or treating thrombotic diseases, particularly, an extract of at least one of leeches and earthworms having a molecular weight of no more than 5,800 daltons is provided, wherein the extract includes 15% to 38% amino acid, 40% to 60% saccharide and 0.3% to 1% hypoxanthine. Processes for preparation, pharmaceutical compositions and uses thereof are also provided. Compared to conventional arts, the extract has safety greatly improved and drug actions maintained and even improved.

Abstract: Techniques for identifying a composition of a target fluid using a set of vectors representing known residue patterns for a two or more fluids including said target fluid is provided. An exemplary method includes storing one or more digital measurements of residue for the target fluid, extracting one or more descriptive features from the measurements; and processing descriptive features to identify the composition of the target fluid. The processing includes using a machine learning algorithm trained with data linking residue morphology to fluid composition. A distance between a vector representing said one or more descriptive features and said set of vectors representing known residue patterns is determined, and a residue is assigned to one or more of the known residue patterns.

Abstract: An extract for preventing or treating thrombotic disease, particularly, an extract of leech and/or earthworm with molecular weight of not more than 5800 Dalton, and processes for preparation, pharmaceutical compositions and uses thereof. The extract of the present invention has improved significantly safety without any reducing in pharmaceutical activities or the therapeutical effects as compared to existing products.

Abstract: An extract for preventing or treating thrombotic disease, particularly, an extract of leech and/or earthworm with molecular weight of not more than 5800 Dalton, and processes for preparation, pharmaceutical compositions and uses thereof. The extract of the present invention has improved significantly safety without any reducing in pharmaceutical activities or the therapeutical effects as compared to existing products.