111 results about "Relationship learning" patented technology

The invention relates to the technical field of deep learning, and discloses an MES digital collaborative management method and system based on deep learning, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a dimensionless multi-dimensional data set; analyzing a dynamic association relationship among the data items through node feature embedding and edge relationship learning, and generating a production line operation dependency relationship graph; performing collaborative anomaly detection and root cause positioning in combination with the dynamic association weight between the data items to obtain causal association data between the abnormal event and the data parameters; constructing a digital twinborn simulation environment to simulate an influence path of heterogeneous data parameter intervention production disturbance on collaborative anomaly so as to evaluate an anomaly influence result; constructing an iterative scheduling model according to a deep learning framework, and iteratively generating a multi-objective optimized collaborative scheduling strategy to realize dynamic configuration and exception prevention among heterogeneous data; therefore, collaborative optimization management of production operation parameter configuration and abnormity prevention of multi-source heterogeneous data in large-scale production with high dimension, high complexity and dynamic change can be realized.

The invention discloses a retrieval method and device based on a knowledge graph, computer equipment and a storage medium. The method comprises the steps that the knowledge graph is acquired, a node relation vector is output through a preset relation learning module, nodes related to user query content are retrieved from the knowledge graph, and an external knowledge representation result is generated; performing feature fusion on the user query content and the external knowledge representation result by using the correlation matrix to obtain a fused feature vector; performing image-text bidirectional semantic alignment on the fused feature vector based on an attention mechanism to generate a joint feature vector; and performing matching calculation based on the joint feature vector to obtain a matching score for representing the semantic correlation degree. According to the method, the user query content and the generated external knowledge representation result are fused through the correlation matrix, image-text semantics are aligned through an attention mechanism, the joint feature vector is generated, the matching score is calculated, and cross-modal retrieval is realized by using the matching score, so that the information retrieval accuracy of the community property management system is greatly improved.

The invention provides a relation extraction model construction method and system based on semantic enhancement and knowledge distillation, relates to the technical field of relation extraction, and aims to solve the problem that in the prior art, when continuous learning is processed, a model is often difficult to keep memorizing an old relation when a new relation is learned, and forgetting is caused. Model overfitting is easily caused by excessive dependence on a small amount of data, so that a new relationship cannot be effectively generalized. The method comprises the steps of obtaining a relationship prototype of a training sample and a typical sample, performing semantic enhancement on the relationship prototype to obtain an expanded enhanced data set and an enhanced memory set, and performing feature extraction, relationship classification prediction and relationship extraction based on a student model and a multi-teacher model. According to the method, the overfitting problem caused by scarcity of small sample data is effectively relieved through a semantic enhancement technology, old relation forgetting caused by new relation learning in the continuous learning process is reduced through a multi-teacher knowledge distillation method, and the classification accuracy and generalization ability of the model facing different relations are improved.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

The invention relates to a recommendation method based on a knowledge graph and an attention mechanism, and belongs to the field of personalized recommendation. The method comprises the following steps: defining a knowledge graph perception recommendation problem, and establishing a user and project interaction matrix and a knowledge graph as input; setting seed sets of users and projects in a knowledge graph propagation layer, propagating the seed sets along adjacent entities, and capturing knowledge-based high-order interaction information of the users and the projects; learning a corresponding potential relation representation for each relation through an attention mechanism in the knowledge perception attention embedding layer to obtain a potentially associated entity set; processing the entity set through an aggregator to obtain a corresponding aggregation vector, and predicting a preference score of the user to the item according to the aggregation vector; and designing a loss function to train the process, and performing user preference recommendation after training is completed. According to the method, the potential relationship between the user and the article can be more accurately captured, and more accurate and personalized recommendation service is provided for the user.

The invention provides a power battery health state anomaly detection method based on machine learning, and relates to the field of data anomaly detection. The invention provides an MtsNet anomaly detection model which comprises a perturbation response time modeling module, a variable relation learning module and an anomaly detection module, specifically, the perturbation response time modeling module is used for processing a dynamic mode in data, the variable relation learning module is used for processing a dynamic coupling problem of the data, and the anomaly detection module is used for detecting the anomaly of the data. The anomaly detection module is used for integrating the two deviation scores to obtain a final power battery health state anomaly score, and all the modules are matched with one another to achieve anomaly detection of the power battery health state.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

The invention discloses an intelligent recommendation method and system for an industrial process monitoring model, and the method comprises the steps: firstly, solving a problem that industrial time series data lacks visual features; extracting data set features of each case from four aspects of general statistical features, distribution shape features, task related features and exogenous knowledge embedding by adopting a data set feature engineering method; secondly, designing a mapping relation learning strategy based on width learning, mapping the data set feature of each case with the corresponding model score vector, and thus endowing the width learning network with the capability of predicting the model performance; and finally, providing a triple increment strategy for driving multiple increment demands of an industrial scene, and designing an updating strategy of a wide learning network for monitoring case increment, candidate model increment and recommendation node increment respectively, thereby continuously expanding and enhancing the recommendation capability of the method. According to the method, the detection model with the optimal performance can be recommended for the industrial process.

The invention relates to a cross-domain electroencephalogram emotion recognition method and device based on multi-scale relation learning, and the method comprises the steps: inputting an electroencephalogram signal into a multi-scale relation learning network, and obtaining a fused emotion feature through a multi-scale separation module, a connection relation learning module and a multi-scale fusion module which are connected in sequence in the multi-scale relation learning network; wherein the fused emotional features comprise information of a plurality of source domains; and inputting the fused emotion features into a general feature induction network, eliminating interference of the personalized emotion features on emotion recognition by using a Dirichlet priori network in the general feature induction network, and obtaining an emotion recognition result. According to the technical scheme, the multi-scale relation learning network is used for capturing the emotional characteristics, the brain connection relation better conforming to cranial neuroscience can be learned, emotional information of multiple source domains is comprehensively considered, the fused emotional characteristics are concluded, personalized emotional interference is eliminated, and the generalization ability is improved.

The invention discloses an uncertain knowledge graph reasoning method based on semi-supervised confidence distribution learning. The method comprises the following steps: converting triple confidence in uncertain knowledge graph training data into confidence distribution; learning embedding of the uncertainty knowledge graph on the marked data and the pseudo-marked data generated by the pseudo-marked data generator at the same time by using a relation learner based on confidence distribution learning; generating a high-quality pseudo-confidence distribution label for the unmarked data by using a pseudo-marked data generator; iteratively training a relation learner based on confidence distribution learning and a pseudo-mark data generator by utilizing element self-training until the relation learner and the pseudo-mark data generator converge; and transmitting data to be complemented into the trained relationship learner based on confidence distribution learning for reasoning to realize uncertainty knowledge graph complementation. According to the method, the supervision information of a small amount of confidence or no confidence in the marked data can be captured, and the method is suitable for the scene of unbalanced confidence distribution of the triple of the uncertainty knowledge graph.

The invention provides a multi-modal semantic-action alignment method and equipment for end-to-end automatic driving, and belongs to the technical field of automatic driving, and the method comprises the steps: carrying out semantic reasoning through a large language model based on various sensor data and language instruction information of a vehicle, and generating semantic information containing a driving intention; inputting the semantic information into a semantic-action alignment module, and converting the semantic information into corresponding driving action representation through a learned consistency mapping relation from a semantic space to an action space; and generating an executable control track of the vehicle according to the driving action representation. According to the method, the problem of insufficient semantic and action space alignment is solved, information distortion and precision limitation caused by post-processing depending on rules are avoided, the high-level driving intention can be generated based on complex multi-mode information (such as navigation instructions and traffic environments), it is ensured that the final execution action is highly consistent with the intention, and the driving intention is more accurate. And the decision-making rationality of the system in a complex scene is enhanced.

The invention discloses a traffic engineering multi-source monitoring data fusion intelligent management and control system, and the system comprises a multi-source data dynamic access module which is used for the access of multi-source heterogeneous data; a data processing module; the dynamic relation learning module is used for mining dynamic space-time association and potential laws in the multi-source data and establishing a time-varying mapping model among the data; the intelligent fusion decision module is used for generating a traffic control decision based on the processed multi-source data and an output result of the time-varying mapping model; an adaptive adjustment module; a data service module; through cooperation of the multi-source data dynamic access module, the data processing module, the dynamic relation learning module, the intelligent fusion decision-making module, the adaptive adjustment module and the data service module, full-process automation from multi-source data access to intelligent management and control decision-making can be realized, and self-adaption to changes of a complex traffic environment can be realized. And the fine management level of traffic engineering is improved.

The embodiment of the invention provides a three-dimensional metallogenic prediction method based on full incidence relation learning, and belongs to the technical field of data processing, and the method specifically comprises the steps: 1, fusing feature engineering and multi-dimensional information through data driving, and obtaining an ore control factor matrix; step 2, constructing monomer energy expression according to the ore control factor matrix; step 3, constructing an interaction energy mechanism of space and feature coupling according to an association relationship between voxels of the mineralization space; 4, constructing a global prediction framework based on monomer energy expression and an interactive energy mechanism; step 5, performing end-to-end-based multi-level parameter optimization on the global prediction framework, optimizing edge distribution of each voxel label and forming target distribution; and step 6, predicting the spatial distribution rule of the mineralization space according to the target distribution. Through the scheme of the invention, the prediction efficiency, accuracy and reliability are improved.

The invention provides a multi-modal semantic guided three-dimensional target positioning method, a medium and equipment. The method is realized based on a multi-modal semantic guided three-dimensional target positioning model, and comprises a multi-view semantic prior module, a text coding module, a double-branch point cloud coding and multi-source comparison supervision module, a sparse scene graph construction and graph relationship learning module and a positioning decoding module. The multi-view semantic prior module is used for segmenting the 3D scene point cloud into a 3D object point cloud, generating a multi-view 2D visual representation and encoding the semantics of the multi-view 2D visual representation; the double-branch point cloud coding and multi-source comparison supervision module is used for injecting semantic features into a 3D object point cloud to obtain 3D fusion features and realizing multi-source feature alignment through multi-source comparison supervision; the sparse scene graph construction and graph relation learning module is used for constructing a sparse scene graph and optimizing the sparse scene graph through a graph attention network; and the positioning decoding module is used for decoding and outputting a positioning result. The method can improve the target positioning capability of the model in a complex scene.

The invention belongs to the technical field of picture classification, discloses a high-voltage cable partial discharge identification method based on incremental learning, and provides a method combining incremental learning, knowledge distillation and a graph attention network for the problem of performance reduction caused by sample distribution change in partial discharge classification. And a dynamic temperature parameter adjustment strategy based on data driving is introduced. The dynamic temperature parameter adjustment strategy adjusts the temperature according to the feature distribution difference of new and old data, and dynamically sets the temperature by using the PRPD image distribution change condition in each batch of training. Specifically, depth feature vectors of new and old data are calculated, and the temperature is adjusted according to the feature distribution difference of the depth feature vectors, so that the temperature is increased when the distribution difference is large, and inter-class relationship learning of old classes is enhanced; when the difference is small, the temperature is reduced to promote new feature learning.

The invention discloses a motor fault diagnosis method based on a graph isomorphic network and cross-graph relation learning, and the method comprises the following steps: firstly, obtaining multi-source signals of a motor in different health states, carrying out the normalization, and building an original signal graph sample set; secondly, utilizing graph samples of a motor in different health states to train the model, capturing relation characteristics among cross-graph samples through a multi-head self-attention mechanism and a feedforward network, and combining a knowledge distillation technology to obtain an optimized cascade structure of a multilayer graph isomorphic network; and finally, testing a to-be-diagnosed sample by using the trained model, and finally outputting a diagnosis result. According to the method, the noise immunity is enhanced through fusion of multi-sensor signals, cross-graph relation feature extraction and a new dynamic training feedback strategy, and the precision and robustness of motor fault diagnosis are remarkably improved. Experimental results show that the method provided by the invention is superior to a current mainstream fault diagnosis method in the aspects of fault diagnosis accuracy and robustness.

The invention provides a text video retrieval method of a fine-grained relation learning network based on energy perception. The method comprises the following steps: giving a query text and a video clip; inputting the query text into a text encoder of the CLIP, inputting the video clip into a text encoder image encoder of the CLIP, and extracting to obtain text embedding and frame embedding; inputting the text embedding and the frame embedding into a fine-grained relation learning network for text enhancement operation to obtain enhanced text embedding; taking the enhanced text embedding as a frame fusion condition to carry out frame fusion operation on the frame embedding to obtain video embedding; calculating the similarity of a text-video pair formed by the query text and the video embedding based on a cosine similarity function; and selecting the text-video pair with the highest similarity as a retrieval output result of text video retrieval. According to the method, the problem of randomness of the random text of single sampling is solved, so that semantic information of text coding is better expanded, and the final retrieval effect is improved.

The invention provides a cross-modal remote sensing image-text retrieval method based on expert-guided trusted learning, and relates to the technical field of information processing and mining of remote sensing big data, the method comprises the following steps: using a feature extractor and an expert feature extractor to respectively extract features described by a remote sensing image and a text; aiming at the extracted features, constructing corresponding Dirichlet distribution based on internal similarity of the features; aiming at the remote sensing image features and the text description features extracted by the feature extractor, respectively constructing Dirichlet distribution based on the similarity of the image to the text and the similarity of the text to the image; and on the basis of the constructed Dirichlet distribution, performing intra-modal relationship learning, inter-modal credible learning and uncertainty consistency learning guided by experts. By the adoption of the scheme, cross-modal retrieval between the remote sensing image and the text description is achieved.

The application discloses a graphite ore grade identification method and device, equipment and medium, and relates to the technical field of graphite ore grade identification. The application introduces Laplacian wavelet convolution instead of a traditional ordinary convolution kernel, realizes parallel extraction of multi-scale features, combines a multi-head attention mechanism to capture scale features of the ore under a global view, constructs a sparse sample relation graph based on an enhanced feature matrix, and uses a Chebyshev graph convolution network to perform multi-order neighborhood information propagation and aggregation on a graph structure to realize grade identification. The process reconstructs the grade identification problem from'single sample independent judgment' to'sample relation learning' under a small sample on the basis of global multi-scale feature extraction, so that the final prediction of each sample node depends not only on its own features, but also on similar feature information of all neighbor samples, thereby realizing high-precision identification of the grade of the graphite ore under a small sample.

The application discloses an uncertainty knowledge graph reasoning method based on semi-supervised confidence distribution learning, comprising the following steps: converting the triple confidence in the uncertainty knowledge graph training data into a confidence distribution; simultaneously learning the embedding of the uncertainty knowledge graph on the labeled data and the pseudo-labeled data generated by a pseudo-labeled data generator by using a relation learner based on confidence distribution learning; generating high-quality pseudo-confidence distribution labels for unlabeled data by using the pseudo-labeled data generator; iteratively training the relation learner based on confidence distribution learning and the pseudo-labeled data generator by using meta-self-training until the two converge; and inputting the data to be completed into the trained relation learner based on confidence distribution learning to perform reasoning, thereby achieving the completion of the uncertainty knowledge graph. The application can capture the supervision information of a small number of confidence or unseen confidence in the labeled data and is suitable for the scene where the triple confidence distribution of the uncertainty knowledge graph is unbalanced.

This invention provides a learning device, an evaluation device, an evaluation system, a learning method, an evaluation method, and a recording medium. The learning device includes: a correspondence receiving unit that receives correspondences between the quality evaluations of each product of an object process and the quality evaluations of each downstream product produced in a downstream process using the products of the object process; a learning processing unit that uses at least one production parameter relating to the production of each product of the object process and the quality evaluations of each downstream product produced using the products of the object process to generate a predictive model that infers the quality evaluation of the downstream products based on the at least one production parameter; a calculation unit that calculates a model evaluation based on at least one of the accuracy or complexity of the predictive model; and a model evaluation sending unit that sends the model evaluation calculated by the calculation unit to the evaluation device, which evaluates at least one upstream process using model evaluations for each of at least one upstream process that is upstream of the downstream process.

The present invention discloses a hierarchical graph-based integrated warehouse and distribution logistics emergency supply prediction system and method, which at least includes a micrograph learning module, a macro-graph learning module, and a spatio-temporal joint prediction module. The micrograph module introduces meta-path aggregation of warehouse and distribution network routing features and adopts multi-view learning to obtain the spatial features of warehouse and distribution sites from the routing view and the event view respectively. The macro-graph learning module uses a lightweight graph convolution method to depict the relationship between warehouse clusters and distribution stations in the form of cities in emergency scenarios and learn the spatial correlation features of warehouse and distribution nodes in the macro dimension. The spatio-temporal joint prediction module captures the temporal correlation of supply data and fuses it with spatial features to accurately predict the supply capacity of future logistics sites. This method analyzes the spatio-temporal correlation features of warehouses and distribution stations in the emergency logistics network to achieve the overall goal of improving the prediction accuracy and enhancing the supply capacity at both ends of the warehouse and distribution in emergency scenarios.

The application belongs to the field of computer vision, and discloses a video description method based on semantic disambiguation structured coding. The application proposes to introduce prior knowledge such as a knowledge graph to construct the relationship between objects in a video (a concept semantic graph), so as to obtain a structured coding of a deeper level of understanding of the semantic relationship of the video. On the basis of the concept semantic graph, according to the guidance of the semantic of the video scene, a relationship most conforming to the current video context is dynamically learned from various relationships of the same pair of objects to eliminate the semantic ambiguity problem existing between the objects, so as to achieve semantic disambiguation structured coding. A cross-domain guidance relationship learning strategy is proposed, which analyzes each object and its relationship in a description sentence to fit the learning of the concept semantic graph in the model, so as to better learn each object and the relationship between the objects in the video. The method of the application can realize more accurate and comprehensive video description.

The present invention proposes a method for classifying ground objects in optical remote sensing images based on multi-level pseudo-relationship learning, which is used to solve the problem that the existing self-training method ignores the potential connections between pixel points in ground object classification. The implementation steps are as follows: obtaining the optical remote sensing image to be classified from a remote device and constructing a multi-level pseudo-relationship network model, obtaining the optimization objective function of the optimized model by constructing a source domain relationship learning loss, a target domain pixel-level pseudo-relationship loss, a source domain local patch-level pseudo-relationship loss, and a target domain local patch-level pseudo-relationship loss, then training the model using a training set, and finally classifying the optical remote sensing image. The present invention realizes the classification of ground objects in optical remote sensing images through multi-level pseudo-relationship learning, improves the classification effect, makes up for the deficiencies of the self-training method, and can be used in application fields such as urban planning, land use, and environmental detection.

The present invention provides a method, application method and system for fusing a large model with a knowledge graph. The method generates knowledge graph embedding information by embedding the acquired knowledge graph into the knowledge graph, and then generates a natural language question based on the knowledge graph embedding information. The knowledge graph embedding information and the natural language question are input into an entity relationship learning model, and the entity representation and relationship representation corresponding to the natural language question are output. The subgraph corresponding to the natural language question is extracted based on the natural language question, the entity representation and the relationship representation, and the subgraph is converted into natural language. The subgraph is then combined with the entity relationship learning model and the knowledge graph embedding information and integrated into the large model to generate a target reasoning model, thereby realizing the fusion of the knowledge graph and the large model. This enables the model to extract key information and conduct in-depth understanding and reasoning, enhances the model's reasoning ability when dealing with complex problems, and further improves the clarity and interpretability of the model's subsequent output content.

The application discloses a dangerous driving behavior detection method based on a line-of-sight direction time relationship learning. A convolutional neural network is designed to estimate the head orientation and the binocular orientation of a driver. In view of the possible inconsistency between the head orientation and the binocular orientation, a head orientation and binocular orientation joint network is designed to estimate the line-of-sight direction of the driver. In view of the problem that the line-of-sight direction changes with time during driving and it is difficult to accurately determine the dangerous line-of-sight direction state, a Gaussian time weight-based line-of-sight direction time relationship learning is designed, a line-of-sight direction time positioning network is constructed, and reliable time positioning of the dangerous line-of-sight direction is realized. When the duration of the dangerous line-of-sight direction exceeds a threshold value, a safety warning is given to the driver. The application can handle the inconsistency between the head orientation and the binocular orientation, can robustly handle different line-of-sight direction time change processes, and can effectively realize dangerous driving behavior detection.

The invention provides a spatial transcriptome cell composition inference method based on graph contrast learning. According to the method, a cross-modal low-dimensional feature spatial data simulation module is constructed, single cell and spatial transcriptome data are respectively mapped to a potential embedding space, feature space difference is reduced through aligned distribution, and structural consistency of cross-modal data is enhanced; designing a double heterogeneous graph construction and potential relation learning module, constructing a feature heterogeneous graph of two cells and spatial points based on gene expression similarity, and capturing a high-level potential relation of spatial data in double graphs by adopting a meta-path potential relation reasoning strategy; and introducing a graph representation optimization mechanism based on structure contrast learning, maximizing the consistency of the cross-modal nodes in the embedding space, and obtaining a final cell composition inference result. The method realizes inference of spatial transcriptome data cell composition, can be used for cell space positioning and tissue microenvironment analysis, and provides a reliable computational analysis basis for related biomedical research.

The invention relates to the technical field of graph neural networks, in particular to a graph data imbalance classification method for synthesizing enhanced nodes, which introduces attention-based feature extraction and neighbor aggregation methods, captures a high-order relationship among nodes by integrating a multi-head attention mechanism through attention-based feature extraction, and obtains a high-order relationship among the nodes; according to the method, global features with more representativeness are learned, the problem of insufficient feature learning is solved, and expression of features and topological information between nodes is enhanced through aggregation and splicing of node features and neighbor aggregation, so that generated samples are more uniform and have subclass representativeness, and the problem of insufficient node representativeness is solved. According to the attention-based feature extraction method, the high-order relationship between the nodes is captured by integrating a multi-head attention mechanism, and the expression of the features and topological information between the nodes is enhanced by aggregating and splicing the node features and neighbor aggregation, so that the generated sample is more uniform and has subclass representativeness; and more valuable guidance is provided for node generation.

The information processing device includes a relational learning unit that learns the relationship between a first image of an object having a plurality of joints and coordinate information, the coordinate information representing the positions of the plurality of joints and being defined in a range extended compared to the perspective of the first image, and the training model is used to estimate the coordinate information of at least one joint located outside the perspective of a second image of a newly acquired object.

The present invention relates to the technical field of graph data mining, and specifically refers to a heterogeneous graph embedding method based on information completion, including: aggregating features of a target node to obtain a preliminary completed feature vector of the target node, and learning the similarity between nodes based on the relationships between nodes in the heterogeneous graph to obtain a target adjacency matrix of the target node in each relationship; cyclically updating the node features to obtain a target feature vector of the node; based on the target adjacency matrix of the target node in each relationship and the target feature vector of the node, performing neighborhood transfer on the target node to obtain a complete completed feature vector of the target node, and aggregating using an attention mechanism to obtain a final node representation of the target node. The present invention completes the missing attribute features of the target node, improves the neighborhood information of the target node, enables the target node with fewer relationship connections to learn more information, thereby obtaining a more complete node feature representation, and further improving the performance of downstream tasks.