Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: A wet etching method is provided in the present disclosure. The method includes providing a substrate, where a layer to-be-etched is on a surface of the substrate; and performing etching treatments on the layer to-be-etched till a thickness of the layer to-be-etched reaches a target thickness. Each etching treatment includes performing a first etching process, where the substrate is at a first rotation speed; after the first etching process, performing a second etching process, where a rotation speed of the substrate is reduced from the first rotation speed to a second rotation speed, and a liquid film of a chemical solution on the surface of the substrate is increased to a first thickness; and after the second etching process, performing a third etching process, where the substrate is at a third rotation speed, and the third rotation speed is lower than or equal to the first rotation speed.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based project evaluation, prediction, and recommendation service is provided. The service may include obtaining performance data pertaining to key performance indicators (KPIs) and groups of a project and calculating affinity propagation using the performance data. The service may further include calculating prospective target KPI values for groups that may have deficient current KPI values based on similar performance data associated with other groups. The service may also include calculating time periods allotted to attain the prospective target KPI values.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate capturing relative importance of relational entities for building database embedding models are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components can comprise a template component that utilized natural language as a prompt template to describe a perspective of clustering and assembles description information into the prompt template to generate a base model. The computer executable components can comprise a training component that can utilize data in the prompt template to automatically build training data of an adapter to generate a final model. The computer executable components can comprise a vector generator component that inputs the prompt template to the final model to generate one or more hidden layer vectors highlighting characteristics of the natural language.

Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: An embodiment for a method of extracting information from documents using knowledge graphs and prompt-based learning. The embodiment may receive a document and perform optical character recognition (OCR) to obtain OCR text lines and associated bounding boxes. The embodiment may encode each of the obtained OCR text lines into semantic vectors and each of the associated bounding boxes into position vectors to generate a knowledge graph using fusion vectors derived therefrom. The embodiment may receive a query including a key value. The embodiment may identify a series of candidate nodes including a series of most similar nearby nodes positioned near a first node associated with the key value. The embodiment may generate prompt template to determine closeness of the candidate nodes to the key value and calculate associated confidence levels. The embodiment may output extraction information associated with the candidate node having a highest calculated confidence level.

Abstract: A predictive circuit breaker model and system for network application is disclosed. In some embodiments, the techniques described herein relate to a method that includes receiving telemetry data from one or more network applications; generating a set of feature vectors based on the telemetry data, the set of feature vectors associated with at least one network application in the one or more network applications; inputting the set of feature vectors into a machine learning (ML) model to generate a prediction, the prediction including a binary classification of the set of feature vectors; and triggering at least one circuit breaker function in response to the prediction.

Abstract: The present disclosure relates to a resource allocation method, a resource allocation apparatus and a storage medium. The method comprises: constructing a comprehensive data system from medical insurance data and clinical path data, and determining effect parameters of different diseases accordingly; determining marginal effect parameters of a plurality of diseases according to the medical insurance data and the effect parameters of the plurality of diseases; determining resource allocation increments of the plurality of diseases in the whole disease spectrum according to the marginal effect parameters and increase amount of medical insurance resources.

Abstract: A predictive circuit breaker model and system for network application is disclosed. In some embodiments, the techniques described herein relate to a method that includes receiving telemetry data from one or more network applications; generating a set of feature vectors based on the telemetry data, the set of feature vectors associated with at least one network application in the one or more network applications; inputting the set of feature vectors into a machine learning (ML) model to generate a prediction, the prediction including a binary classification of the set of feature vectors; and triggering at least one circuit breaker function in response to the prediction.

Abstract: A system for urban organic solid waste pyrolysis-gasification coupled with drying includes a sludge feeding and storage device, a pre-drying device, a cyclone separator, a specific cloth bag for sludge and a flue gas waste heat recovery device sequentially connected. The cyclone separator and a sludge outlet of the specific cloth bag for sludge are connected with a cyclone fluidized bed gasification furnace. The cyclone fluidized bed gasification furnace is connected with a high-temperature separator. The high-temperature separator is connected with a secondary combustion chamber. High-temperature flue gas generated by the secondary combustion chamber serves as a heat source of the pre-drying device. Ash generated by the high-temperature separator and secondary combustion chamber is sent to an ash bin after being cooled by a cold slag conveyor.

Abstract: A wet etching method is provided in the present disclosure. The method includes providing a substrate, where a layer to-be-etched is on a surface of the substrate; and performing etching treatments on the layer to-be-etched till a thickness of the layer to-be-etched reaches a target thickness. Each etching treatment includes performing a first etching process, where the substrate is at a first rotation speed; after the first etching process, performing a second etching process, where a rotation speed of the substrate is reduced from the first rotation speed to a second rotation speed, and a liquid film of a chemical solution on the surface of the substrate is increased to a first thickness; and after the second etching process, performing a third etching process, where the substrate is at a third rotation speed, and the third rotation speed is lower than or equal to the first rotation speed.

Abstract: A method for controlling a game character is used for controlling a simulated skydiving action of the game character in a game scene and includes: determining a target position to be landed on; obtaining a real-time location of the game character; determining a real-time flight strategy according to the target position and the real-time location during the simulated skydiving action, wherein the real-time flight strategy, comprises one of a released-parachute flight strategy and a folded-parachute flight strategy and according to the real-time flight strategy, automatically controlling the simulated skydiving action of the game character.

Abstract: A method for controlling a game character is used for controlling a simulated skydiving action of the game character in a game scene and includes: determining a target position to be landed on; obtaining a real-time location of the game character; determining a real-time flight strategy according to the target position and the real-time location during the simulated skydiving action, wherein the real-time flight strategy, comprises one of a released-parachute flight strategy and a folded-parachute flight strategy and according to the real-time flight strategy, automatically controlling the simulated skydiving action of the game character.

Abstract: A system described herein may use automated techniques, such as machine learning techniques, to identify sequences of actions that satisfy checkpoint criteria. Different sequences of actions may be used for different iterations of the same checkpoints, and may be used to refine a model that evaluates the different sequences of actions (e.g., scores the different sequences of actions). The model may be used to simulate the same or similar actions, in order to validate or discover other sequences of actions for the same checkpoint.

Abstract: A system described herein may use automated techniques, such as machine learning techniques, to identify sequences of actions that satisfy checkpoint criteria. Different sequences of actions may be used for different iterations of the same checkpoints, and may be used to refine a model that evaluates the different sequences of actions (e.g., scores the different sequences of actions). The model may be used to simulate the same or similar actions, in order to validate or discover other sequences of actions for the same checkpoint.

Abstract: A system for urban organic solid waste pyrolysis-gasification coupled with drying includes a sludge feeding and storage device, a pre-drying device, a cyclone separator, a specific cloth bag for sludge and a flue gas waste heat recovery device sequentially connected. The cyclone separator and a sludge outlet of the specific cloth bag for sludge are connected with a cyclone fluidized bed gasification furnace. The cyclone fluidized bed gasification furnace is connected with a high-temperature separator. The high-temperature separator is connected with a secondary combustion chamber. High-temperature flue gas generated by the secondary combustion chamber serves as a heat source of the pre-drying device. Ash generated by the high-temperature separator and secondary combustion chamber is sent to an ash bin after being cooled by a cold slag conveyor.

Abstract: A system described herein may allow for the enhanced identification of candidate images that are similar to a reference image. For example, a primary focus and one or more secondary foci may be identified in the reference image (where a “focus” corresponds to a visual feature of the reference image). Characteristics (e.g., size, shape, color, etc.) of the secondary foci may be identified. Positional relationships of the secondary foci to the primary focus may also be identified. Candidate images may be scored based on whether they include the primary focus and one or more secondary foci, as well as whether the secondary foci match the characteristics and/or positional relationships of corresponding secondary foci in the reference image.

Abstract: A system described herein may allow for the enhanced identification of candidate images that are similar to a reference image. For example, a primary focus and one or more secondary foci may be identified in the reference image (where a “focus” corresponds to a visual feature of the reference image). Characteristics (e.g., size, shape, color, etc.) of the secondary foci may be identified. Positional relationships of the secondary foci to the primary focus may also be identified. Candidate images may be scored based on whether they include the primary focus and one or more secondary foci, as well as whether the secondary foci match the characteristics and/or positional relationships of corresponding secondary foci in the reference image.

Abstract: A method for patterning a polished silicon surface is disclosed, the method including steps leading to an organic monolayer on at least a part of the silicon surface, the monolayer being functionalized in specific desired locations. The method can be used to produce a device comprising one or more FET structures, the gate of the FET being formed by the functionalized organic monolayer. The functionalized monolayer preferably contains oligosaccharides or oligopeptides which are capable of interacting with biological substance, such that the device acts as a bio-sensor.

Abstract: A method for patterning a polished silicon surface is disclosed, the method including steps leading to an organic monolayer on at least a part of the silicon surface, the monolayer being functionalized in specific desired locations. The method can be used to produce a device comprising one or more FET structures, the gate of the FET being formed by the functionalized organic monolayer. The functionalized monolayer preferably contains oligosaccharides or oligopeptides which are capable of interacting with biological substance, such that the device acts as a bio-sensor.