11 results about "Research environment" patented technology

A method for secure analysis of distributed data includes receiving a request from a user to perform a data analysis operation. The data analysis operation utilizes first data accessible by a first secure research environment and second data accessible by a second secure research environment. The method further includes authenticating the user at the first secure research environment and the second secure research environment, communicating a first query for the first data to the first secure research environment, and communicating a second query for the second data to the second secure research environment. The method further includes receiving, from a secure compute environment in communication with the first secure research environment and the second secure research environment, results from the data analysis operation.

The present invention relates to a computing and archiving research environment that uses multi-data integration between different information or data with high requirements for data storage and high performance computing. The invention provides a platform for easy storage, analysis and sharing of different scientific data by providing different service combinations such as high performance computing (HPC), scientific clouds and data archiving.

A method for generating a trained machine learning model trained on multiple segregated data sources includes generating a first dataset by transforming a first source dataset by generating an embedded representation of the first source dataset and adding privacy parameters. The method includes generating a second dataset by transforming a second source dataset by generating an embedded representation of the second source dataset and adding privacy parameters, generating a combined dataset that includes the first dataset and a ground truth dataset from a first segregated data environment combined with the second dataset from a second segregated data environment (e.g., within a trusted research environment or a secure processing environment). The method includes training a machine learning model with training data that includes a subset of the combined dataset, in which the model parameters of the trained machine learning model are stored in a storage device.

The present invention relates to a computing and archiving research environment using multiple data integration between different information or data that have a high requirement for data storage and a high-performance computation. The present invention provides a platform for an easy storage, analysis, and sharing of different scientific data by providing different combination of services such as a high-performing computing (HPC), science cloud, and data archiving.

The application discloses a system for self-evolution and continuous learning of a theory reasoning intelligent agent; a first intelligent agent module receives scientific literature, performs theory reasoning on the scientific literature, and generates a corresponding theory summary; a cognitive dissonance monitoring module generates a dissonance signal when detecting inconsistency between the internal knowledge state and external behavior output of the first intelligent agent module; an endogenous target generation module receives the dissonance signal and generates an endogenous learning target for correcting the inconsistency through internal drive according to the dissonance signal; and a self-reflection and model updating module updates the model parameters of the first intelligent agent module according to the endogenous learning target to eliminate the inconsistency. The application enables the theory reasoning intelligent agent to get rid of the dependence on external artificial annotation or static reward model and has the capability of continuous self-evolution in a dynamic scientific research environment.

The embodiment of the invention discloses a scientific research inference task processing method and device and a medium, and relates to the technical field of large models.The method comprises the steps that a scientific research inference task request is received, the scientific research inference task request comprises an input text sequence, and the input text sequence is from a scientific literature database or scientific research query submitted by a user; processing the input text sequence through a pre-loaded pre-training reasoning large model, and performing long-sequence attention optimization on the input text sequence of which the sequence length exceeds a preset threshold value to generate final attention output; based on the final attention output, a scientific research reasoning result is generated through a reasoning layer of the pre-training reasoning large model, and the scientific research reasoning result comprises text implication judgment, common sense reasoning answers or logic structure analysis output. And the scene requirements of common large-scale literature analysis, complex problem solving and other practical application scenes in the scientific research environment can be met.

A method includes retrieving data from a data store according to a data access mode (e.g., within a trusted research environment or secure processing environment). The data access mode is determined based on a policy profile associated with a data processing job submitted by a user. The method includes determining distributional properties of the data, determining one or more risk metrics based on the distributional properties of the data, and determining a strategy for adding noise to the data based on the one or more risk metrics. The strategy includes an amount of noise to add to the data and an optimization strategy for adding the noise to the data. The method includes adding the noise to the data according to the determined strategy to generate noisy data and executing the data processing job, which includes processing the noisy data according to the data processing job to generate an output.

The invention provides a blockchain-based platform for secure management and sharing of research data. It integrates modules for smart contract execution, decentralized data marketplaces, dynamic consent management, cross-chain interoperability, and advanced security features, including post-quantum cryptography and multi-signature authentication. The platform ensures data integrity, transparency, and compliance with evolving privacy regulations by automating research agreements, incentivizing participation through tokenomics, and enabling seamless multi-party collaborations. A decentralized reputation scoring system and comprehensive version control enhance accountability and traceability of research activities. Additionally, the platform supports data anonymization and dynamic consent updates, safeguarding participant privacy while fostering collaborative research. By leveraging AI-driven analysis and decentralized governance through a DAO model, the invention optimizes research processes, facilitates efficient data exchange, and promotes ethical standards in research environments, offering a robust and innovative solution to the challenges of modern research data management.

The invention discloses a project-oriented potential scientific research team mining method based on network diffusion, and the method comprises the following steps: obtaining and preprocessing academic data, and constructing an author-paper bipartite graph network; initializing a seed paper set according to a reference coupling relationship of the target subject, and constructing an initial influence signal in a paper layer of the bipartite graph network; parallelly executing two network diffusion processes of mass diffusion and heat conduction on the bipartite graph network, and respectively calculating to obtain an author influence vector and an author influence vector under a heat conduction mechanism; normalizing the two obtained author influence vectors, and performing linear weighted fusion by adjusting parameters to obtain a final mixed influence vector; sorting all authors according to the mixed influence vector, and selecting the top authors as potential scientific research team recommendation; the method is suitable for scientific research environments in different periods.

1. Name of the product in this design: Atmospheric Turbulence Simulation Equipment. 2. Purpose of this design: To simulate the turbulence effect caused by hot and cold convection in the actual atmosphere indoors, providing a controllable experimental and research environment for relevant personnel. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention belongs to the technical field of artificial intelligence and information retrieval, and relates to a large-model-driven intelligent recommendation method and system for a large-scale scientific data sharing platform. The method comprises the following steps: analyzing a scientific research problem expressed by a natural language input by a user, and generating structured scientific research intention information; multiple rounds of dialogues in the scientific research query process are compressed, and important context information is reserved; and performing retrieval based on the structured scientific research intention information to obtain a candidate data set, screening the candidate data set by using context information, and taking the screened data set as a recommendation result. The system comprises a scientific research intention perception module, a structured memory compression module and a trusted data set retrieval and recommendation module. According to the method, deep semantic perception, multi-round context continuous tracking and traceable credible recommendation of results for complex scientific research requirements are realized, and the applicability and user experience of a scientific data set recommendation system in an actual scientific research environment are remarkably improved.