535 results about "Binary classification" patented technology

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention provides a multi-receptive-field network abnormal flow detection method and system based on endogenous security attributes, and relates to the technical field of network security and abnormal flow intelligent detection. The method comprises the following steps: firstly, performing multi-scale flow representation, preprocessing and data enhancement on network flow data to obtain enhanced input flow data; local features are extracted through basic convolution, and local and global fusion features are obtained based on a double-branch network comprising a multi-receptive field convolution branch and a Mama-self-attention branch; deep fusion representation is formed through multi-round feature extraction and tensor fusion, and finally binary classification and fine-grained classification results are output through global pooling and a linear classification layer. According to the method, high-precision, high-robustness and high-real-time detection of the abnormal traffic of the complex network is realized under low calculation overhead.

The invention discloses an alarm analysis method, device and equipment, and the method comprises the steps: determining a target DAG matched with a target alarm analysis scene from a preset directed acyclic graph DAG; the DAG comprises a plurality of rule judgment nodes; the rule judgment nodes comprise at least one natural language rule judgment node, and each natural language rule judgment node is used for calling a large language model based on a preset prompt text to perform binary classification judgment on the input alarm data; inputting the alarm data into the target DAG, and generating a node hit track of the alarm data; the node hit track is used for recording a rule judgment node triggered by the alarm data and a corresponding judgment result; and obtaining an alarm analysis result based on the node hit trajectory. According to the invention, the automatic, interpretable and traceable research and judgment process of the alarm can be realized, and the accuracy, interpretability and maintenance convenience of alarm analysis are improved.

To provide a method for predicting a strain distribution map and determining damage based on deep learning.SOLUTION: The present invention comprises: a step S1 of establishing an image dataset of finite element analysis results for strain distribution map prediction; a step S2 of building a deep learning model for strain distribution map prediction based on a DeepLabv3+ network and performing learning and validation; a step S3 of establishing a dataset for damage determination of a structural analysis model and performing preliminary processing and data enhancement operations; a step S4 of building a binary classification deep learning model for damage determination based on a convolutional neural network and performing learning and validation for transition learning; and a step S5 of performing an interpretability analysis on the learned binary classification structural analysis model and outputting a region in an image that more contributes to classification.SELECTED DRAWING: Figure 1

The invention relates to the technical field of message recovery, and discloses an AI-based iOS device message recovery method and system, and the method comprises the steps: connecting an iOS device, positioning a database file of a message application, and obtaining a database file path list; reading an equipment storage sector, creating a physical mirror image, and analyzing a free block position mark in an APFS container super block; binary classification identification is carried out through an AI data block classification model, an SQLite page type label and a page mapping relation are obtained, deleted messages of a recombined database and a device end are determined, differential comparison is carried out on all historical backup snapshot records of an iCloud account, and cloud end deleted messages are obtained; according to the method and the system, the reorganization success rate of the database and the extraction integrity of the deleted messages are improved, and a technical solution is provided for comprehensive, accurate and efficient recovery of the iOS device messages.

A system and method for facilitating training of large language model based recommender systems are provided. The system may utilize one or more LLMs to create probability distributions for binary classification tasks associated with specific user-item pairs. The probabilities may be utilized to rank one or more tasks directly. The training of the one or more LLMs may involve the use of Knowledge Distillation methods and may be based on incorporating a dual-label system such as, for example, hard labels and soft labels. The one or more LLMs training data may consist of user-item pairs and their corresponding features. The labels used in the training process may include binary classification labels and their respective probabilities. The system may further implement the trained one or more LLMs to determine rankings or recommendations associated with user engagement of one or more content items.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention discloses an improved hemodialysis data generation method and system based on a generative adversarial network, and relates to the technical field of medical data generation and artificial intelligence, and the method comprises the steps: 1, collecting hemodialysis multivariable time series data; 2, mapping the original time sequence data to a low-dimensional potential space through an embedded network; original data are reconstructed from the low-dimensional potential space through the recovery network; step 3, a multi-scale attention mechanism is introduced into the generator, weights are dynamically distributed by calculating the similarity of query vectors and key vectors, and weighted context vectors are generated in combination with value vectors; the discriminator executes a dichotomy task and introduces a dynamic gradient clipping strategy: if the norm of the gradient L2 exceeds a preset threshold value c, scaling the gradient according to a proportion, otherwise, keeping the original value; 4, introducing a supervision loss function, and learning a time sequence dynamic rule through an autoregressive prediction task constraint generator; and 5, jointly optimizing the reconstruction loss, the confrontation loss and the supervision loss, and outputting synthetic hemodialysis data.

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

The invention relates to a Parkinson's disease walking state recognition method based on tensor singular value decomposition and automatic hyper-parameter optimization. The Parkinson's disease walking state recognition method comprises the following steps: acquiring a sensor signal of a target person; constructing a tensor according to the sensor signal; performing t-SVD decomposition processing on the tensor to obtain a frequency domain core tensor; the frequency domain core tensor and the sensor signal are subjected to feature extraction, the extracted features are input into a trained classification model, the binary classification recognition result of the Parkinson's disease patient and the healthy person is obtained, and the trained classification model is obtained through training of a training set marked with a patient and health contrast label. According to the method, efficient dimension reduction and noise suppression are realized by using t-SVD, and adaptive optimization is performed on the key hyper-parameters of the classification model in combination with an automatic machine learning technology, so that the dichotomy recognition accuracy and robustness of the Parkinson's disease patient and the health control are improved.

The invention provides an attack technology and tactical identification method oriented to honey array linkage. The method comprises the following steps: coding according to malicious sample data and associated TTP labels, and constructing a training data set; constructing an interpretable discrimination model for binary classification modeling to obtain a behavior triggering weight vector and an offset item of the malicious sample; extracting dominant trigger rules to construct a mapping inference rule base; obtaining alarm data, extracting a behavior clue based on a preset standard behavior dimension set, converting the behavior clue to obtain a trigger identifier, and mapping the alarm data into an alarm behavior vector; constructing a prediction TTP label set of the alarm behavior vector and recording a trigger path; and encapsulating the behavior semantic data, constructing an execution strategy response rule set, performing mapping in the execution strategy response rule set according to the behavior semantic data to obtain an execution strategy, and adjusting honey array deployment according to the execution strategy. By applying the method, accurate identification and transparent reasoning of the potential TTP in the attack activity can be constructed and realized, and linkage scheduling of a defense system is supported.

The present invention relates to the technical field of computer vision, and provides a pseudo-label filtering-based online domain change continual learning method and system. The method comprises: acquiring a pre-trained model, using the pre-trained model to predict changing target-domain data, and generating a pseudo-label for online adaptation; deriving a lemma for threshold-based pseudo-label filtering in online domain change continual learning on the basis of binary classification, and designing a threshold setting principle in the online domain change continual learning on the basis of the lemma; using the designed threshold setting principle to filter a pseudo-label having a low confidence level in model prediction, and introducing a class prior alignment method to encourage the model to perform fair prediction on an unknown-domain sample; and using the filtered pseudo-label to update and optimize the model to obtain a classification prediction result in the online domain change continual learning. In the present invention, an adaptive threshold capable of adapting to a CTTA process is established, thereby ensuring the quality of pseudo-labels.

The invention discloses an electric vehicle rear-end collision accident prediction method and system based on large model knowledge and trajectory data. According to the method, two vehicle types of an electric vehicle and a fuel vehicle are identified based on license plate colors, the vehicle types and vehicle tracks are bound, and the following process is divided into three states according to the combination of the front and rear vehicle types. On the basis, multi-dimensional features extracted from historical tracks are input into a large language model, expert-level rule knowledge used for distinguishing accidents and non-accidents is extracted through structured cues, and a final feature matrix is constructed according to the knowledge. And finally, based on the final feature matrix, performing differentiation processing on the three car-following states, training and calibrating a dichotomy model, and realizing real-time estimation of the rear-end collision probability at a specific moment in the future. According to the invention, by fusing the reasoning ability of the large model and the real trajectory data, the accuracy and interpretability of rear-end collision risk identification under the electric vehicle participation scene can be significantly improved, and the method has engineering deployment value.

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

The invention belongs to the field of general computers, and provides a hydraulic data processing method and system based on artificial intelligence, and the method comprises the steps: constructing a first time sequence and a second time sequence corresponding to nodes; calculating a time difference vector by using a variational encoder; constructing a graph structure in combination with the physical connection relationship between the monitoring points, and determining the structural representation of each node; performing image segmentation on the remote sensing image data, extracting image region features corresponding to node positions, splicing the image region features with meteorological data at the same position, and determining image-meteorological representation of each node; for each node, splicing the structure representation output by the graph convolutional neural network with the image-weather representation to form fusion representation; a disturbance vector of a preset amplitude is injected into the fusion representation, disturbance representation is obtained, and the disturbance direction is the gradient direction of the loss function relative to the fusion representation; and jointly inputting the fusion representation and the disturbance representation into a dichotomy neural network, and outputting a judgment result about whether abnormal mutation occurs or not.

The invention relates to the technical field of crossing of remote sensing image processing and computer vision, and discloses a remote sensing image change detection method which comprises the following steps: obtaining a first time phase bottom layer pixel feature and a second time phase bottom layer pixel feature according to a first time phase remote sensing image and a second time phase remote sensing image; according to the first time phase bottom layer pixel features and the second time phase bottom layer pixel features, enhanced visual features are obtained; according to the first pixel-text similarity score plot and the second pixel-text similarity score plot, semantic guidance features are obtained; according to the enhanced visual features and semantic guidance features, obtaining fusion features of multiple levels; pixel-level binary classification is carried out on the fusion features of the multiple levels, and a change detection binary image is output. According to the invention, the precision and accuracy of remote sensing image change detection are improved.

The invention discloses a power frequency waveform classification method and device, a medium and equipment, and belongs to the field of waveform classification, and the method comprises the steps: obtaining preprocessed power frequency waveform data of a power transmission line, calculating various waveform features, and inputting the features into a preset cascade classifier model. The model is composed of a plurality of XGBoost binary classifiers which are cascaded in sequence, and each classifier is responsible for executing a specific binary classification task. The output result of the previous classifier is used for triggering the starting of the next classifier, or the final classification result is directly output. The cascade structure not only improves the accuracy and efficiency of classification, but also reduces the complexity of the model, and effectively solves the problem that the fault positioning power frequency of the power transmission line cannot be accurately and efficiently classified in the prior art.

The invention discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects that noise is introduced and key evidences are omitted due to the fact that traditional RAG only executes'query-document 'one-way retrieval, a two-way semantic perception retrieval enhancement generation model and a two-stage training framework are constructed, wherein in the first stage, the positive / negative example distance is increased in an embedded space in a contrast learning self-supervision mode; in the second stage, fine-grained correlation discrimination is carried out on query-document bidirectional sentences through supervised dichotomy, and probabilistic correlation scores are output; in the reasoning stage, the bidirectional probabilities are fused according to Bayesian to obtain final relevancy, document reordering is carried out, and plug and play can be achieved without fine adjustment of LLM in the whole process. According to the method, the accuracy and consistency of single-hop and multi-hop questions and answers and fact checking tasks are remarkably improved, and the method has the advantages of light weight and low deployment cost.

The invention relates to the field of medical image classification, and provides a medical image classification method and device based on a coherent Isin machine, equipment and a medium, and the method comprises the steps: constructing an SVM model for medical image binary classification; constructing a QUBO model of the SVM model; solving the QUBO model by using a coherent Isin machine to obtain optimal parameters of the SVM model; and determining the type of the medical image to be classified by using the SVM model adopting the optimal parameters. According to the method and the device, the SVM of the medical image binary classification problem is modeled into the QUBO problem in the quantum solvable form, and the SVM problem in the QUBO form is processed and converted by using the coherent Isin machine, so that the optimization process is accelerated by using quantum hardware, the medical image binary classification problem can be effectively and quickly solved, and in addition, the method and the device can be applied to the field of medical image processing. And the SVM model is solved by using coherent Isin machine evolution, so that accurate quantum gate operation is avoided, the control difficulty of the quantum system is reduced, and the realizability is higher.

The invention discloses an illusion detection method and device for a large language model and a medium, and relates to the technical field of natural language processing and artificial intelligence. The method comprises the steps of extracting internal state data when an answer is generated by a large language model; wherein the internal state data comprises a hidden state vector of the last token when the answer is generated and the number of tokens for generating the answer; performing feature engineering processing on the internal state data to obtain an internal confidence score; judging through an external language model to obtain an external judgment score; fusing the internal confidence score with the external judgment score to obtain a comprehensive confidence score; and outputting an illusion or non-illusion binary classification result according to whether the comprehensive confidence score exceeds a predetermined threshold.

The invention provides a coal gangue recognition system and method based on SAF-YOLOv11, and relates to the technical field of computer vision and target detection. According to the backbone network P-HGNetV2, a dual-path HGStem module is adopted to extract multi-scale features, and Stage2-4 is embedded into an LCU unit to reduce the calculation amount; a probabilistic spatial attention module PSA generates spatial weights based on the SVM. The semantic perception fusion network SAF-Neck realizes feature weighted fusion from top to bottom and from bottom to top through bidirectional cross-scale connection, and adapts to complex illumination and texture scenes in combination with dynamic convolution. And training optimization: using Softmax to normalize the classification probability. According to hardware configuration, an image acquisition module carries six strip-shaped light sources with the wavelength of 440-650 nm, and the speed of a conveying belt is 0.5 m / s; and the processing module is used for running an SAF-YOLOv11 model in real time on the basis of NVIDIA Jetson AGX Xavier. And the output module is used for generating a coal / gangue binary classification result and bounding box coordinates. According to the scheme, the precision and efficiency are balanced through depth separable convolution and lightweight design, and the real-time requirement of an industrial sorting scene is met.

The invention relates to a method and a device for generating a report under the assistance of a label, and the method comprises the following steps: 1) extracting a structured label set from a text report of a sample based on a large language model, wherein the structured label set comprises a multi-classification group consisting of dichotomous labels and mutual exclusion options; 2) aggregating the labels in batches, after a threshold value is reached, merging and de-weighting, performing specification and mutual exclusion group merging on synonymous, near-synonymous and redundant labels, and converging into a unified label library; 3) based on the text report and the tag library, outputting a tag subset of each sample through a large language model; 4) multi-modal multi-label classification model training: extracting each visual modal feature, performing weighted aggregation and splicing, and outputting each label group logits through a classification head to perform weighted group loss optimization; (5) carrying out joint training on the multi-modal large language model by using samples of'only images-reports' and'images + labels-reports', and (6) carrying out label prediction and screening on the images by using the classification model, and inputting'images + prediction labels' into the multi-modal large language model to obtain a final report.

The invention discloses a depressive emotion intelligent identification and intervention system. The system comprises a data acquisition module used for acquiring original data of a user in real time; the data preprocessing module is used for performing layered preprocessing on original data; the feature extraction module is used for extracting each modal feature; the multi-modal fusion module realizes multi-modal dynamic fusion based on a bidirectional long short-term memory network and a cross attention mechanism, and supports multi-task output of depression dichotomy, PHQ-9 scale regression prediction and the like through a full-connection network; the user interaction module is used for providing task guidance, data acquisition control and intervention suggestion visualization for a user; the system integrates a self-developed VR (Virtual Reality) mind game and an AI (Artificial Intelligence) psychological big model, and provides intervention tools such as mind training, dialogue intervention and PC (Personal Computer) terminal severe games for different depression levels. The data management module is used for storing user information, original data, a depression emotion recognition process and evaluation result data, and a feasible technical means is provided for early recognition and auxiliary evaluation of depression risks.

The invention relates to construction and application of a pesticide interaction relationship prediction model based on multi-source information fusion, and aims to solve the problem of inaccurate antagonism prediction caused by one-sided feature expression, node explosion, complex calculation and rigid weight distribution in pesticide mixing. According to the method, the pesticide registration information triple is fused with molecular characteristics and physicochemical attributes, a knowledge graph fusing multi-source information is constructed, and the analysis limitation of a single data dimension is broken through; a neighbor node sampling mechanism is introduced into a graph random sampling layer, so that the problem of node explosion in large-scale graph data is inhibited while multi-order neighbor information is effectively utilized; a self-adaptive attention mechanism is used for weighting neighbor nodes in a graph attention coding layer, so that the feature expression capability of the model on a complex graph structure is improved; a binary classification task is completed on the interaction decoding layer based on the processed pesticide characteristics, and the interaction relation between the pesticides is deduced. And the breakthrough solution of the prediction precision (the AUC is improved by 2.4%, and the F1 value is improved by 4.5%) and the calculation efficiency is realized.

The invention discloses a policy document identification and judgment method and system based on a large model, and the method comprises the following steps: firstly, carrying out the preprocessing of an original document, completing the format conversion and text cleaning, and extracting at least one basic feature; inputting the preprocessed document into a pre-training dichotomy model, and obtaining a probability value P1 belonging to the policy document; screening P1 through a first threshold value T1 and a second threshold value T2, clearly judging a policy or non-policy document, and if a fuzzy interval is processed, executing keyword matching analysis and calculating a keyword matching score S; and screening S through a third threshold T3 and a fourth threshold T4, when S is still a fuzzy interval, extracting multi-dimensional policy characteristics to construct a structured context, inputting the structured context and the original text of the document into a large language model, reasoning according to a preset cue word template, and outputting a result containing confidence and a judgment reason. The policy document identification method has high accuracy, strong interpretability and good adaptability, and can efficiently, accurately and transparently identify the policy document.

The invention discloses a universal cost estimation optimization method based on multi-task cross-database learning, and relates to the technical field of database system performance optimization, and the method comprises the steps: constructing a vectorization tree structure; designing a multi-task learning model of hard shared parameters, wherein the model comprises a shared feature extraction module and independent branch networks respectively corresponding to cost estimation, query optimization and index selection tasks; cost estimation is defined as a regression task prediction cost value, and query optimization and index selection are defined as advantages and disadvantages of a dichotomy task judgment strategy; a meta-learning algorithm is adopted to jointly train a model on a plurality of heterogeneous database query data sets, and universal initial parameters with the capacity of quickly adapting to a new database are obtained. According to the method, implicit knowledge migration and collaborative optimization among tasks are realized, a single-task barrier is overcome, and the overall performance is improved; a cross-database meta-learning strategy enables the model to obtain a strong generalization general initial state; in the face of a new database environment, the deployment period can be remarkably shortened, and expensive retraining is avoided.

The invention discloses a radar target detection method based on graph node dual-channel feature attention fusion, and belongs to the technical field of radar signal detection, and the method comprises the following steps: 1, carrying out the graph node division of received frame radar echo data; 2, respectively extracting time domain amplitude and time frequency characteristics from echo time sequence data corresponding to each graph node; step 3, establishing a feature preprocessing sub-network; step 4, constructing a node feature fusion sub-network; 5, constructing a signal classification graph neural sub-network; 6, connecting the feature preprocessing sub-network, the node feature fusion sub-network and the signal classification graph neural sub-network in series to form a radar target detection neural network; and 7, inputting the test set into the trained radar target detection neural network, and outputting a dichotomy result of which the corresponding node is a target or clutter signal. Through the scheme, the target detection capability of the radar in the clutter environment can be improved.

The invention discloses a lithofacies paleogeography single factor analysis method, medium, equipment and product, and relates to the technical field of lithofacies paleogeography analys.The method comprises the steps that lithofacies description, well position coordinates and stratigraphic stratification data in logging interpretation data are obtained; performing binary classification on lithofacies types according to whether the lithofacies description accords with the definition of the target lithofacies; based on the stratigraphic data and the binary lithofacies type, three single factor indexes of each well are calculated, and the three single factor indexes are the total stratum thickness, the target lithofacies cumulative thickness and the rock-to-ground ratio; according to the well position coordinate data and the three single-factor indexes, well points in the whole area are paired in pairs, experimental variation function values under different lag distances are calculated, and experimental variation scatter diagrams of the three single-factor indexes are drawn respectively; kriging gridding interpolation is carried out on the scatter points, contour maps of the three single-factor indexes are drawn, and lithofacies paleogeography analysis is carried out by using the three contour maps. The lithofacies paleogeography single factor analysis is realized based on the logging interpretation data.

The invention discloses an intelligent detection method and system for AI texts, and relates to the technical field of AI text detection.The method comprises the steps that AI texts and non-AI texts corresponding to multiple themes are obtained, and a training set of theme contrast is constructed; performing word segmentation processing on each text in the training set, and quantifying vocabulary features of each text; analyzing each sentence in each text in the training set, and quantifying syntactic features of each text; on the basis of the vocabulary features and syntactic features of all the texts, the style feature distinction degree of each theme is calculated; calculating the text weight of each subject based on the style feature distinction degree, constructing a dichotomy model, and improving a loss function of the model in combination with the text weight; and training the model based on the improved loss function and the training set, and realizing intelligent detection of the AI text through the trained dichotomy model. According to the method, excessive dependence of the model on specific content vocabularies can be effectively avoided, and adaptability and discrimination accuracy of the model to different topic texts are improved.

The invention relates to the technical field of vehicle networks and deep learning, in particular to a two-stage vehicle network intrusion detection method, which comprises the steps of inputting a to-be-detected vehicle data sequence into a trained intrusion detection model, and outputting a corresponding attack detection result; the intrusion detection model is processed by the following steps: S201, inputting a to-be-detected vehicle data sequence into a two-dimensional convolution classifier, and outputting a corresponding binary classification result; s202, if the dichotomy result is attack data, executing the step S203, and if the dichotomy result is legal data, executing the step S205; s203, inputting the data sequence of the vehicle to be detected into the Robust LSTM classifier, and outputting an attack type label; s204, outputting the corresponding attack data labels and attack type labels as attack detection results; and S205, outputting the corresponding legal data label as an attack detection result. According to the invention, the real-time performance, pertinence and reliability of Internet of Vehicles intrusion detection and an Internet of Vehicles system are improved.