Abstract: The present invention relates to the technical field of medicine, in particular to a chondroitin sulfate polysaccharide, and a semi-synthetic preparation method therefor and the use thereof. A metal salt of the chondroitin sulfate polysaccharide provided by the present invention has an anti-inflammatory effect, and can be used for preparing a drug against inflammatory diseases. In particular, the metal salt of the chondroitin sulfate polysaccharide provided by the present invention has anti-inflammatory and bone-protecting effects, and can be used for preparing a drug against rheumatoid arthritis and for preparing a drug against osteoarthritis. The present invention provides a method for preparing the metal salt of the chondroitin sulfate polysaccharide, and the metal salt of the chondroitin sulfate polysaccharide with different degrees of sulfation can be obtained by semi-synthetic means in the present invention. The method is simple to operate and suitable for large-scale production.

Abstract: Electronic devices comprising a first layer, said first layer comprising a perovskite material; and a coating layer disposed on a surface of said first layer; wherein said coating layer comprises a coating oxysalt. Also provided herein are perovskite materials comprising: a coating layer on at least a portion of a surface of said perovskite material; wherein said coating layer comprises a coating oxysalt. Further provided herein are methods for forming a coating layer on a surface of a perovskite material comprising steps of: exposing said surface to a fluid having a precursor oxysalt dissolved therein such that said coating layer forms on said surface via a chemical reaction between said perovskite material and said precursor oxysalt; wherein said coating layer comprises a coating oxysalt.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation. The method may comprise: receiving a resource request from a user; iterating through a plurality of entities to obtain a corresponding score for each entity based at least on: a projected approval rate for the entity to approve the resource request, a projected risk for the entity to serve the resource request, and one or more multipliers applied to the projected approval rate and the projected risk; and recommending one of the plurality of entities to serve the resource request for the user based on the corresponding score, wherein the one or more multipliers are obtained by solving an optimization model constructed based on historical data collected from a previous period of time, the historical data comprising approval rates projected for the plurality of entities during the previous period of time.

Abstract: The present disclosure provides a lignite char supported nano-cobalt composite catalyst and a preparation method thereof. In the method, lignite is used as a raw material, and a lignite char supported high dispersion nano-cobalt composite catalyst is obtained by a modified impregnation method followed by a high temperature pyrolysis process. The composite catalyst prepared by the present disclosure has a hierarchical pore structure, a high specific surface area, and uniformly dispersing nano-sized cobalts on the lignite char with controllable particle size, so that the obtained catalyst has an excellent catalytic activity for low-temperature CO2 methanation; moreover, the preparation process is simple and feasible, the raw materials used are cheap and easily available. Therefore, the composite catalyst is very suitable for industrial production and application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating risk are provided. One of the methods includes: obtaining a plurality of transactions each comprising a plurality of data dimensions, wherein some of the plurality of transactions are labeled as risky transactions and some of the plurality of transactions are labeled as safe transactions; obtaining at least one of the plurality of data dimensions as an output space and the plurality of data dimensions other than the at least one data dimension as an input space; initializing a first mapping from the input space to a latent space and a second mapping from the latent space to the output space, wherein the first mapping comprises mapping the input space to the latent space according to an inverse of a generalized linear model; and optimizing the first mapping and the second mapping to generate a Bezier surface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing. An exemplary method comprises: inputting a plurality of to-be-optimized routing solution candidates to a Siamese neural network comprising a plurality of value prediction networks, each of the value prediction networks being trained to predict a cost associated with a to-be-optimized routing solution candidate; identifying one or more to-be-optimized routing solution candidates from the plurality of to-be-optimized routing solution candidates based on outputs of the Siamese neural network; inputting the one or more identified to-be-optimized routing solution candidates to a routing optimizer to obtain one or more optimized routing solution candidates; and determining an optimized routing solution with a lowest cost from the one or more optimized routing solution candidates.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping a plurality of resources into a plurality of cohorts, wherein each of the plurality of resources is associated with a risk level and a benefit level, and resources in each cohort share the same risk level and the same benefit level; obtaining a plurality of provider-level adjustments; constructing an optimization model for determining a resource flow solution; and according to the plurality of cohort-provider-level adjustments, identifying one or more resources in each of the plurality of cohorts that are provided by a first provider to be transferred to a second provider; and automatically sending one or more requests to the first provider and the second provider to transfer the determined one or more first resources from the first provider to the second provider.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation. The method may comprise: receiving a resource request from a user; iterating through a plurality of entities to obtain a corresponding score for each entity based at least on: a projected approval rate for the entity to approve the resource request, a projected risk for the entity to serve the resource request, and one or more multipliers applied to the projected approval rate and the projected risk; and recommending one of the plurality of entities to serve the resource request for the user based on the corresponding score, wherein the one or more multipliers are obtained by solving an optimization model constructed based on historical data collected from a previous period of time, the historical data comprising approval rates projected for the plurality of entities during the previous period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating risk are provided. One of the methods includes: obtaining a plurality of transactions each comprising a plurality of data dimensions, wherein some of the plurality of transactions are labeled as risky transactions and some of the plurality of transactions are labeled as safe transactions; obtaining at least one of the plurality of data dimensions as an output space and the plurality of data dimensions other than the at least one data dimension as an input space; initializing a first mapping from the input space to a latent space and a second mapping from the latent space to the output space, wherein the first mapping comprises mapping the input space to the latent space according to an inverse of a generalized linear model; and optimizing the first mapping and the second mapping to generate a Bezier surface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing. An exemplary method comprises: inputting a plurality of to-be-optimized routing solution candidates to a Siamese neural network comprising a plurality of value prediction networks, each of the value prediction networks being trained to predict a cost associated with a to-be-optimized routing solution candidate; identifying one or more to-be-optimized routing solution candidates from the plurality of to-be-optimized routing solution candidates based on outputs of the Siamese neural network; inputting the one or more identified to-be-optimized routing solution candidates to a routing optimizer to obtain one or more optimized routing solution candidates; and determining an optimized routing solution with a lowest cost from the one or more optimized routing solution candidates.

Abstract: Provided are a compound of formula (I) or a pharmaceutically acceptable salt, stereoisomer, ether, ester, prodrug and solvate thereof. The compound can be used for preventing and/or treating cancers.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing. An exemplary method comprises: inputting a plurality of to-be-optimized routing solution candidates to a Siamese neural network comprising a plurality of value prediction networks, each of the value prediction networks being trained to predict a cost associated with a to-be-optimized routing solution candidate; identifying one or more to-be-optimized routing solution candidates from the plurality of to-be-optimized routing solution candidates based on outputs of the Siamese neural network; inputting the one or more identified to-be-optimized routing solution candidates to a routing optimizer to obtain one or more optimized routing solution candidates; and determining an optimized routing solution with a lowest cost from the one or more optimized routing solution candidates.

Abstract: A target detection method and apparatus, in which the method includes: obtaining a target candidate region in a to-be-detected image; determining at least two part candidate regions from the target candidate region by using an image segmentation network, where each part candidate region corresponds to one part of a to-be-detected target; and extracting, from the to-be-detected image, local image features corresponding to the part candidate regions; and learning the local image features of the part candidate regions by using a bidirectional long short-term memory LSTM network, to obtain a part relationship feature used to describe a relationship between the part candidate regions; and detecting the to-be-detected target in the to-be-detected image based on the part relationship feature. As a result, image data processing precision in target detection can be improved, application scenarios of target detection can be diversified, and target detection accuracy can be improved.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing. An exemplary method comprises: inputting a plurality of to-be-optimized routing solution candidates to a Siamese neural network comprising a plurality of value prediction networks, each of the value prediction networks being trained to predict a cost associated with a to-be-optimized routing solution candidate; identifying one or more to-be-optimized routing solution candidates from the plurality of to-be-optimized routing solution candidates based on outputs of the Siamese neural network; inputting the one or more identified to-be-optimized routing solution candidates to a routing optimizer to obtain one or more optimized routing solution candidates; and determining an optimized routing solution with a lowest cost from the one or more optimized routing solution candidates.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for risk detection. One exemplary method may comprise: obtaining a first subset of a plurality of risk-detection rules, the first subset being associated with a first coverage score; constructing, based on the first subset, a lower-bound data mapping that outputs an approximate coverage score for an input subset; and constructing, based on the first subset, an upper-bound data mapping comprising a set of parameters; and generating a third subset of the plurality of risk-detection rules; and in response to the first coverage score exceeding the third coverage score, selecting rules in the first subset for risk-detection on a new transaction.

Abstract: A virtual reality interaction method comprises: continuously acquiring image information in front of a user; recognizing a preset interactive object by recognizing a preset identifier in the image information, a surface of the preset interactive object having one or more of the preset identifiers; recognizing a hand action of the user according to the image information; and simulating an interactive action of the user on the preset interactive object in a virtual reality environment according to the hand action and a position of the preset interactive object. The virtual reality interaction method provided by the present disclosure can accurately simulate an interactive action of the user on a real object in a virtual reality environment, thereby improving user experience of virtual reality.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing resource allocation are provided. One of the methods includes: processing a plurality of first objective functions in parallel to determine a plurality of allocation plans, wherein each of the allocation plans corresponds to allocating zero or more of a plurality of resources associated with a platform to a user, and the plurality of first objective functions share one or more dual multipliers; determining a plurality of profits and costs for the platform in parallel based on the plurality of the allocation plans; aggregating the calculated profits and costs using parallel reduction; updating the one or more dual multipliers based on the aggregated costs to determine whether an exit condition is satisfied; if the exit condition is not satisfied, repeating the processing the plurality of the first objective functions based on the updated dual multipliers.