24 results about "Hypothesis" patented technology

The invention relates to the technical field of drainage basin water environment pollution traceability, and discloses a drainage basin non-point source pollution accurate traceability method based on deep learning, which comprises the following steps: constructing a generation and verification dual-network collaborative framework comprising a pollution source hypothesis generator and a hypothesis verifier, and establishing a collaborative optimization mechanism of the pollution source hypothesis generator and the hypothesis verifier; the generation and probability evaluation of the pollution source hypothesis are realized, and an evidence link tracker is constructed to analyze the relationship between the pollution source candidate hypothesis and the support evidence; further analyzing the relationship between the pollution source candidate hypothesis and the support evidence; the analysis result of the relation between the pollution source candidate hypothesis and the support evidence is integrated, and the calculation efficiency and the system performance are optimized; according to the method, probability evaluation is provided for each pollution source hypothesis through a Bayesian posterior inference framework, the uncertainty of a traceability result is quantified, and a reliable reliability index is provided for decision making.

A system receives a user query requesting a testable hypothesis about a scientific topic. The system classifies the user query into a first theoretical framework of a plurality of theoretical frameworks each comprising of terms and principles related to a particular scientific topic. The system generates the testable hypothesis by a first machine learning (ML) model that is configured to: receive as inputs: the user query, the first theoretical framework, and information from a graph document database comprising data associated with scientific documents, generate, as an output, the testable hypothesis that can be evaluated using the first theoretical framework and that does not reiterate a hypothesis or findings from the scientific documents in the graph document database. The system outputs the testable hypothesis via a user interface in response to the user query.

An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback. The engine also uses a continuous integration module that iteratively refines and updates entities and relationships and their representations to yield higher quality of hypothesis generation over time.

This application provides a method for constructing a trustworthy scientific research agent based on an evidence-reasoning-verification chain, belonging to the field of agent construction technology. The method includes: parsing user-input scientific queries using a large language model; constructing a scientific knowledge graph using a high-quality evidence set obtained through formal research question representation retrieval; synthesizing multiple scientific hypotheses based on the high-quality evidence set and the scientific knowledge graph; constructing an integrated constraint reasoning path to output a structured reasoning chain; performing tuple-based processing; aligning and verifying the trustworthiness of the chain with the scientific knowledge graph; and generating a trustworthy reasoning chain as the core content output of a structured scientific report. This application addresses the technical problems in existing technologies where scientific research agents are unverifiable, untraceable, and lack scientific basis, achieving the technical effect of ensuring logical consistency, sufficient evidence, traceability, and academic credibility in the content generated by the scientific research agent.

A method, apparatus, and non-transitory computer-readable medium for generating a hypothesis from a knowledge graph. The method comprises processing the knowledge graph comprising a plurality of fact triples. Each fact triple of the plurality of fact triples comprises two concepts of a set of concepts and one relationship of a set of relationships. Each fact triple is also associated with at least one source. The method further comprises generating the hypothesis from data representing multiple triples, the hypothesis. The hypothesis includes at least one predicted triple having a concept-concept relationship not found in the knowledge graph. The method then outputs at least one source and / or explanation data for the hypothesis.

Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video coding. In one method, a decoder obtains, for a current chroma block, a non-linear model (non-LM) mode and a linear model (LM) mode, and the LM mode includes a cross component linear model (CCLM) mode and a multi-model linear model (MMLM) mode. The decoder then combines the non-LM mode and the LM mode for a multi-hypothesis- based chroma prediction (MCP) for the current chroma block.

The system according to this embodiment aims to analyze gene expression data, predict disease mechanisms, and generate new research hypotheses. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment method proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The analysis unit analyzes the gene expression data acquired by the acquisition unit. The prediction unit predicts the disease mechanism based on the data analyzed by the analysis unit. The hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit. The treatment method proposal unit proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit. The report generation unit generates an experimental result as a report based on the treatment method proposed by the treatment method proposal unit.

A method, apparatus, and non-transitory computer-readable medium for generating a hypothesis from a knowledge graph. The method comprises processing the knowledge graph comprising a plurality of fact triples. Each fact triple of the plurality of fact triples comprises two concepts of a set of concepts and one relationship of a set of relationships. Each fact triple is also associated with at least one source. The method further comprises generating the hypothesis from data representing multiple triples, the hypothesis. The hypothesis includes at least one predicted triple having a concept-concept relationship not found in the knowledge graph. The method then outputs at least one source and / or explanation data for the hypothesis.

The present invention relates to methods, systems, and apparatus, including computer programs, for improving the accuracy of variant identification by taking into account indications of related error events. In one aspect, a method may comprise the actions of accessing a stack of sequence reads aligned to a first region of a reference genome; obtaining information describing one or more characteristics of each of the plurality of reads of the stack; providing one or more inputs describing the one or more characteristics of the plurality of reads of the stack to a probabilistic model, wherein the probabilistic model is configured to determine, for each of one or more hypotheses selected based on the one or more inputs, a score indicating whether each hypothesis is true; obtaining output information of each hypothesis in the one or more hypotheses; and determining a likelihood of presence of a true variant at the first location based on the obtained output information.

The invention relates to the technical field of teenager teaching, in particular to a teenager scientific literacy improvement teaching system and method based on cross-domain exploration, and the system comprises an enrollment evaluation module, a teaching improvement module and a stage evaluation module. According to the invention, the traditional mode of introducing scientific conclusions into a classroom is converted into the mode of introducing scientific research processes into the classroom, so that students can develop, observe and propose good habits of problems, make hypotheses and design experiments based on the proposed problems, collect evidences and verify the hypotheses in the experiment process, and further think to make explanations when supporting the hypotheses. The method comprises the steps of obtaining a conclusion, sharing the conclusion, making deep thinking and proposing a new problem, and when the hypothesis is not supported, observing and collecting new evidence again and trying to make a new hypothesis, so that an active thinking thought can be gradually formed, and the own scientific literacy is gradually improved.

The invention discloses a multi-agent collaborative scientific hypothesis automatic generation method, and belongs to the field of creative scientific research engineering, and the method comprises the steps: inputting a to-be-researched problem and research background information of the to-be-researched problem into a creative generation agent, so as to obtain a plurality of creative hypothesis texts of the to-be-researched problem; respectively inputting the plurality of creative hypothesis texts into a hypothesis testing agent to obtain evaluation data of each creative hypothesis text under a plurality of evaluation dimensions; and according to the evaluation data of each creative hypothesis text under the plurality of evaluation dimensions, determining a target creative hypothesis text corresponding to the to-be-researched problem from all creative hypothesis texts. Through cooperative work of the creative generation agent and the hypothesis test agent, the problems of insufficient innovation, one-sided evaluation result and incomplete reasoning chain in the automatic hypothesis process are improved, and the overall working efficiency and decision quality of researchers in complex scientific research tasks are improved.

The application relates to the technical field of watershed water environment pollution tracing, and discloses a watershed non-point source pollution accurate tracing method based on deep learning, which comprises the following steps: a generation and verification double-network collaborative framework comprising a pollution source hypothesis generator and a hypothesis verifier is constructed, and a collaborative optimization mechanism of the pollution source hypothesis generator and the hypothesis verifier is established; generation and probability evaluation of pollution source hypotheses are realized; an evidence chain link tracker is constructed to analyze the relationship between pollution source candidate hypotheses and supporting evidence; the relationship between the pollution source candidate hypotheses and the supporting evidence is further analyzed; analysis results of the relationship between the pollution source candidate hypotheses and the supporting evidence are integrated; and the calculation efficiency and the system performance are optimized; the application provides probability evaluation for each pollution source hypothesis through a Bayesian posterior inference framework, quantizes the uncertainty of the tracing results, and provides reliable reliability indexes for decision making.

The present application provides a method, a program product, a device and a medium for detecting the consistency of observation results. The method includes: during the operation of a movable platform, obtaining multiple redundant observation results of the same observable quantity by the movable platform; determining the test statistic and the rejection region of each observation result in the hypothesis testing theory; the null hypothesis in the hypothesis testing theory is that all observation results are normal and follow a preset distribution; the alternative hypothesis is that there are abnormal observation results deviating from the preset distribution; for each observation result, determining the observation result whose test statistic falls into the rejection region as an abnormal observation result, and determining the observation result whose test statistic does not fall into the rejection region as passing the consistency test. Thus, abnormal observation results and normal observation results are distinguished in real time. Through automated data collection and processing, the work efficiency is improved and human errors are reduced. It also meets the detection requirements of various sensors and different environments, ensuring the long-term stable operation of the movable platform.

The present application relates to spaceflight measurement and control technical field, especially relate to a kind of generalized data detection and simplification method, system and equipment for improving the precision of space target positioning, comprising: obtaining the observation data of space target, construct extended Gaussian-Markov model containing potential modeling error, generate multiple hypothesis set by different combination of modeling error parameter;Utilize likelihood ratio test criterion to construct the test statistic between different hypotheses, according to the observation environment requirement self-adaptive selection executes generalized data detection process or generalized data simplification process;Determine the optimal model hypothesis of current observation by multiple hypothesis testing, based on the optimal model hypothesis parameter adaptive estimation is carried out, and the position parameter of space target is solved.This application guarantees high-precision requirement by forward selection strategy, enhances robustness under complex environment by backward elimination strategy, realizes the effective identification and elimination of multiple modeling error, significantly improves the precision and reliability of space target positioning.

A computer-implemented method for determining static occupancy information, comprising the following steps performed by a computer hardware component: determining a plurality of predicted hypotheses based on a plurality of existing hypotheses of static occupancy information; correcting the plurality of predicted hypotheses to obtain predicted and corrected hypotheses based on the measurements; merging the predicted and corrected hypotheses to obtain a merged hypothesis; and trimming at least a portion of the merged hypothesis to obtain a final hypothesis; wherein the method further comprises at least one of the following: during pruning, a hypothesis having a covariance above a predetermined covariance threshold is ignored; and / or during merging, for each of the merged hypotheses, at most two predicted and corrected hypotheses are merged; and / or after trimming, at least one of the measurements is added to the final hypothesis at the location of at least one of the measurements that is not covered by one of the hypotheses.

The invention discloses a potential hypothesis relationship prediction method based on a causal symbol network. The method comprises the following steps: scientific hypothesis variable identification and multi-dimensional causal relationship discrimination; constructing a causal symbol network; hypothesis inspiration heuristic search is carried out; predicting a potential hypothesis relationship; ranking the credibility of the potential hypothesis relationship; according to the method, the mediation and regulation effects can be effectively captured and utilized, so that new hypotheses including the complex mechanisms can be generated, and the method is closer to the scientific problem of the real world; scientific researchers can preferentially pay attention to the assumption which is most likely to be established, and screening and verification time is saved.

The invention discloses a pre-training model fine tuning and scientific hypothesis information extraction method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: injecting a trainable adapter into a to-be-trained linear layer of a target pre-training model; inputting the sample data set into a target pre-training model, determining sample cue word information and sample text information through the target pre-training model, and outputting predicted scientific hypothesis information according to the sample cue word information and the sample text information; determining labeled scientific hypothesis information through the target pre-training model, and training the trainable adapter according to the predicted scientific hypothesis information and the labeled scientific hypothesis information to obtain an updated weight matrix; and obtaining a scientific hypothesis extraction model after fine adjustment of the target pre-training model according to the updated weight matrix. According to the method, the accuracy and integrity of scientific hypothesis information extraction are improved, researchers can be helped to quickly extract innovation points in literatures, and the project design efficiency is improved.

A computer implemented method for determining information of static occupancy comprises the following steps carried out by computer hardware components: based on a plurality of existing hypotheses for the information of static occupancy, determining a plurality of predicted hypotheses; based on measurements, correcting the plurality of predicted hypotheses to obtain predicted and corrected hypotheses; merging the predicted and corrected hypotheses to obtain merged hypotheses; and pruning at least a portion of the merged hypotheses to obtain final hypotheses; wherein the method further comprises at least one of the following: during pruning, hypotheses with a covariance above a pre-determined covariance threshold are disregarded; and / or during merging, for each of the merged hypotheses, at most two predicted and corrected hypotheses are merged; and / or after pruning, at least one hypothesis is added to the final hypotheses at a location of at least one measurement of the measurements which is not covered by a hypothesis of the hypotheses.

The application discloses a potential hypothesis relationship prediction method based on a causal signed network, and comprises the following steps: scientific hypothesis variable identification and multi-dimensional causal relationship discrimination; construction of a causal signed network; hypothesis inspiration heuristic search; potential hypothesis relationship prediction; and potential hypothesis relationship credibility sorting. The application can effectively capture and utilize intermediary and regulation effects, thereby generating new hypotheses containing the complex mechanisms and being closer to scientific problems in the real world, and enabling scientific researchers to preferentially focus on the most likely established hypotheses and save the time for screening and verification.

An object of a system according to an embodiment is to efficiently test and verify hypotheses formulated by a generative AI in the real-world.SOLUTION: A system according to an embodiment includes a generation AI, an experimental facility, and an analysis unit. The generative AI makes hypotheses. The experimental set-up experiments the hypotheses raised by the generative AI. The analysis unit analyzes an experimental result obtained by the experimental facility.SELECTED DRAWING: Figure 1

The invention provides a fuel gas measuring instrument dead meter state detection method and system based on hypothesis testing, and relates to the technical field of equipment anomaly detection, and the method comprises the steps: obtaining the in-pipe temperature and in-pipe pressure of a gas pipeline of a fuel gas measuring instrument in a preset time period, so as to form a temperature time sequence and a pressure time sequence; the zero-assumption fuel gas measuring instrument does not have a dead meter state, and the gas volume change rate in the gas pipeline is calculated according to the temperature time sequence and the pressure time sequence, so that a volume change rate time sequence is formed; hypothesis test statistics are obtained through calculation according to the volume change rate time sequence, a probability value corresponding to the hypothesis test statistics is obtained, and a detection result in a dead table state is output when the probability value is larger than the significance level; and otherwise, outputting a detection result not in the dead table state. The method has the advantages that zero hypothesis is constructed, hypothesis testing is conducted through the temperature time sequence and the pressure time sequence, the detection accuracy of the dead table state is effectively improved, and meanwhile the detection result has effectiveness and reproducibility.

The application discloses a pre-training model fine-tuning and scientific hypothesis information extraction method and device, equipment, medium and product, and relates to the technical field of artificial intelligence, which comprises the following steps: injecting a trainable adapter into a to-be-trained linear layer of a target pre-training model; inputting a sample data set into the target pre-training model, determining sample prompt word information and sample text information through the target pre-training model, and outputting predicted scientific hypothesis information according to the sample prompt word information and the sample text information; determining labeled scientific hypothesis information through the target pre-training model, and training the trainable adapter according to the predicted scientific hypothesis information and the labeled scientific hypothesis information to obtain an updated weight matrix; and obtaining a scientific hypothesis extraction model of the fine-tuned target pre-training model according to the updated weight matrix. The application improves the accuracy and integrity of scientific hypothesis information extraction, and is beneficial to helping researchers quickly refine innovation points in literature and improving subject design efficiency.

Methods and systems correlating hypotheses outcomes using relevance scoring for intuition based forewarning are disclosed. For one example, an intuition based forewarning method includes collecting and storing core data and surroundings data, wherein the core data includes parameters describing a system and ring data includes parameters describing surroundings of the system. The collected core data and ring data are analyzed to determine one or more changing situations of the system. A relevance score is provided for each determined changing situation of the system based on the analyzed core data and ring data. Each determined situation is correlated with one or more hypotheses outcomes representing a future system state based on the relevance score. The hypotheses may be modified using, for example, auto-hypothesis generation. A system forewarning is generated based on the correlated hypotheses outcomes which can be observed by one or more users.

An object of a system according to an embodiment is to extract a useful hypothesis from data and provide the customer with the useful hypothesis.SOLUTION: A system includes a collection unit, an analysis unit, a hypothesis generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The hypothesis generator derives a hypothesis based on the data analyzed by the analyzer. The provider provides the customer with the hypothesis derived by the hypothesis generator.SELECTED DRAWING: Figure 1