865 results about "Positive sample" patented technology

A system for increasing the accuracy in summarization techniques is disclosed. The system generates a set of summaries for text. The system determines a label for each summary based on a set of composite metrics. The label for the summary indicates the truthfulness and faithfulness of the summary with respect to the text. The system determines that more than a threshold number of the set of composite metrics indicate that a first summary is assigned with a first label. In response, the system adds the first summary paired with the text as a positive sample to a dataset. The system determines that more than a threshold number of composite metrics indicate that a second summary is assigned with a second label. In response, the system adds the second summary paired with the text as a negative sample to the dataset. The system trains a summarization algorithm with the dataset.

The invention provides an information recommendation method and device based on collaborative sampling of a knowledge graph and an adjacent graph, and relates to the technical field of information recommendation. According to the method, the triple is constructed by obtaining the historical interaction data of the user, and the triple is fused with the project attribute graph to construct the collaborative knowledge graph; performing expression alignment through projection transformation to obtain initial embedding expression; performing multi-hop neighbor sampling through biased random walk to obtain a multi-hop neighbor set; determining a neighbor which is most similar to the embedding representation of the positive sample item as an anchor neighbor; then linear interpolation is carried out, and the embedding representation of the difficult negative sample is synthesized; on the basis of the multi-head graph attention network, training by adopting a positive sample and a synthesized difficult negative sample, and aggregating to obtain an updated embedded representation; and calculating an interaction probability based on the updated embedded representation, and generating a recommended item for the user. According to the method, the problems of user-project interaction sparsity and false negative example interference are effectively solved, and stronger semantic comprehension and generalization ability are provided for a recommendation system.

The invention relates to the technical field of recommendation algorithms, provides a graph collaborative filtering course recommendation method based on interactive attention and comparative learning, and aims to solve the problem that a traditional recommendation system is insufficient in modeling ability in a sparse interaction scene. The method comprises the following steps: firstly, constructing a user-course bipartite graph, and utilizing a dynamic attention mechanism guided by an interactive opposite-end node: carrying out vector dot product through original embedding of the opposite-end node (for example, course embedding is used during user aggregation) and current embedding of a neighbor node, and generating an attention coefficient in combination with temperature parameter normalization; and multi-level structure information aggregation is realized. Afterwards, local context features are fused through a multilayer graph convolutional network, random noise disturbance is introduced to generate a multi-view comparison sample, the consistency of positive samples is maximized in combination with an InfoNCE loss function, and the robustness of the model to noise and sparse data is enhanced; and finally, optimizing user-course embedding in combination with Bayesian personalized sorting loss and comparison loss, and generating a personalized recommendation list. According to the method, the key interaction relationship is screened through guided attention, the representation discrimination is improved in combination with comparative learning, and the recommendation precision in cold start and data sparse scenes can be improved.

The invention discloses a consistent learning-oriented sample enhancement and optimization method, which comprises the following steps of: respectively performing knowledge extraction on professional knowledge data, professional vocabulary interpretation, historical practice data and an expert experience set in an unstructured text to obtain an original question and answer pair and a professional knowledge base; processing the original question and answer pairs through a diversified strategy to generate enhanced sample question and answer pairs with consistent semantics and various forms, and expanding or simplifying the enhanced sample question and answer pairs to obtain positive samples; introducing an interference expression or a fact error into the enhanced sample question and answer pair to obtain a negative sample; adjusting the positive and negative sample proportion according to the large model accuracy; and taking the positive sample and the negative sample as training data, and performing parameter fine tuning based on the pre-trained large model and the professional knowledge base to obtain a professional large model. According to the method, the adaptability and robustness of a large model in different scenes are improved, and the occurrence probability of irrelevant information and wrong answers is reduced.

The invention discloses a watermark imperceptible embedding and recovering method based on a deep learning network structure. Firstly, a mask guide watermark embedding scheme is designed, a mask generation module is constructed by using a residual dense feature extraction module and an attention mask generation module, and a watermark is adaptively guided to be embedded into an image texture rich area so as to improve the invisibility of the watermark. And secondly, constructing a watermark decoding network based on comparative learning, and by comparing a loss function, taking the decoding features of the same watermark image under different noise conditions as positive samples and taking the decoding features of different watermark images as negative samples so as to enhance the consistency of the decoding features, thereby improving the robustness of the watermark. The deep learning network structure can improve the robustness of the watermark in a real screen shooting scene, and has a huge application value in copyright protection and traceability tracking.

The invention belongs to the technical field of industrial operation and maintenance term processing, and provides a big language model training method based on a knowledge graph, and the method comprises the steps: carrying out the cooperation with operation and maintenance personnel in advance to set a fault dictionary, collecting fault instances, carrying out the classification and preprocessing, extracting causal trigger words of the fault instances, and setting a text dependency relationship rule. Splitting and extracting a triple of the fault instance, and importing to form a knowledge graph; mapping the instance attribute into a node feature vector, outputting an embedded representation of a node, and fusing the embedded representation with the multi-modal feature vector to form a fused feature embedded representation; replacing semantic and degree words with fault instances in the knowledge graph to generate positive samples, randomly replacing subjects, objects and causal relationships to generate negative samples, inputting the negative samples into a semantic encoder, mapping the negative samples to the same metric space, and calculating similarity to obtain a comparison loss value to adjust graph convolutional network parameters; and after the fusion feature embedding representation retrieval of the fault instance is carried out and a subgraph is generated, maintenance suggestions are given step by step along a causal chain.

The invention discloses a protein generation model optimization method based on deep learning, and relates to the technical field of protein generation. The optimization method comprises the following steps: generating a candidate protein electron density distribution diagram based on the structural characteristics of a target spot by adopting a pre-trained diffusion model; converting the candidate protein electron density distribution diagram into a corresponding amino acid sequence; determining a functional index value corresponding to the amino acid sequence, and constructing a feedback data set; setting a reward threshold value, and marking the feedback data set as a positive sample data set and a negative sample data set according to the reward threshold value; constructing a utility function taking the reward threshold as a reference point, and respectively calculating utility values of the positive sample data set and the negative sample data set; and performing iterative optimization on the diffusion model according to the utility value. By adopting the technology provided by the invention, the dependence on preference paired data of large-scale and high-quality protein sequences can be avoided, and the performance of the protein generation model can be effectively and continuously improved.

The present disclosure provides a fault prediction method, a fault prediction model training method, a computing device, a storage medium, and a computer program product. The fault prediction method comprises: obtaining abnormal log data and unit attribute information of a service processing unit; determining an abnormal event sequence on the basis of the abnormal log data; and inputting the unit attribute information and the abnormal event sequence into a fault prediction model to obtain a fault prediction result of the service processing unit, wherein the fault prediction model is obtained by performing training on the basis of a positive sample, a first sample label corresponding to the positive sample, a negative sample, and a second sample label corresponding to the negative sample, the positive sample comprises a positive sample abnormal event sequence and sample unit attribute information, and the negative sample comprises a negative sample abnormal event sequence and sample unit attribute information. The training data of the fault prediction model is richer, so that the accuracy of prediction results of the fault prediction model during applications is improved.

The invention provides a dense target detection method based on comparative learning characterization and reinforcement learning decision, which is suitable for dense animal individual detection in a livestock breeding scene. The method comprises the following steps: constructing a dense target detection model comprising a feature encoder, a reinforcement learning decision module and a detection head; the training process is divided into two stages: in the contrast learning characterization stage, an anchor sample, a positive sample and a negative sample are constructed, a first loss function is utilized to optimize a feature encoder, and an optimal feature encoder is obtained; in the reinforcement learning decision-making stage, the feature vector is used as an input state, a decision-making module dynamically selects a threshold lowering, maintaining or improving action, a detection head outputs a detection result in combination with the feature and the action, and parameters of the decision-making module are optimized through a reward function. During detection, after features of a to-be-detected image are extracted through the feature encoder, the optimized decision module selects the optimal action, and finally a detection result is output. The method has relatively high robustness and accuracy.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention provides an intention perception recommendation method based on knowledge graph and comparative learning fusion, relates to the technical field of artificial intelligence, and aims to optimize node representation by fusing knowledge graph and comparative learning technologies so as to improve the recommendation precision of a recommendation system for long-tail projects. Meanwhile, on the basis of data enhancement, positive sample selection in contrast learning is expanded in combination with user intentions, the ability of the model in the aspect of user intention modeling is further enhanced, and finally the accuracy and correlation of recommendation results are improved. The objective of the invention is to solve the problems of poor long-tail project representation quality, insufficient user intention modeling and limited recommendation precision caused by knowledge graph noise interference in the prior art.

The invention belongs to the technical field of intelligent dialogues, and provides a retrieval enhancement method and system for multi-round dialogue type questions and answers and application. A quaternion data set containing consultation questions, documents, answers and rewriting retrieval texts is constructed, and a retrieval result is obtained by combining dialogue state caching and a mixed retrieval strategy. The method comprises the following steps: generating a rewritten text taking a retrieval task as a main guide by utilizing a Rewriter model, dynamically mining a new difficult case according to the rewritten text, constructing a joint loss function by combining a generation loss function and a multi-positive sample comparison loss function, and jointly optimizing the Rewriter model. According to the method, a multi-positive sample comparison loss function is provided to make full use of supervision signals in a multi-round data set, and the ability of a rewriting model to understand the context is improved; a dynamic hard case mining method is provided, and dynamic mining is performed in real time according to a rewriting result in a training process, so that the problem that rewriting query cannot be optimized in real time according to retrieval feedback is solved.

The invention discloses a multi-modal large language model training method and system, and relates to the technical field of multi-modal large model data processing, and the method comprises the following steps: training a first large model through querying a question set, a positive sample and a hard negative sample; inputting the plurality of test samples into the trained first large model, and generating a plurality of second answers based on a first preset prompt; sorting each test sample based on the plurality of second answers, and retrieving to obtain a previous candidate multi-modal document related to the test question; and training the second large model through the test problem and the corresponding previous multi-modal document. According to the method, the positive samples and the hard negative samples are jointly used for training, the model is forced to capture the fine-grained semantic boundary of correlation judgment through a contrast learning mechanism, the distinguishing capacity of the model for difficult samples is remarkably improved, and the mistaken arrangement phenomenon is avoided.

The invention discloses a bacteriophage host prediction method based on a weighted integrated convolutional neural network. The method comprises the following steps: obtaining a joint feature vector for an input bacteriophage protein sequence and a host receptor protein sequence; taking the bacteriophage-host pair as a positive sample, randomly selecting non-host bacteria for each bacteriophage to generate a negative sample, dividing the negative sample into M subsets, and combining each subset with all the positive samples to form M balance training subsets; for each balance training subset, training an independent one-dimensional convolutional neural network model; independently training a one-dimensional convolutional neural network sub-model for each balance training subset to obtain a plurality of sub-models with different performances; and distributing weights based on the performance indexes of the sub-models on the verification set, performing weighted fusion on the prediction probabilities of the M sub-models, and outputting a final host interaction probability. The method realizes accurate prediction of phage-host interaction on the premise of only needing basic sequence information, and has the advantages of light weight, high efficiency and wide applicability.

The invention provides a model training method, mobile equipment, electronic equipment and a medium, and the model training method comprises the steps: executing a test task through a task execution model, and obtaining test data corresponding to a target time period in the process of executing the test task; inputting the test data into the initial state judgment model to obtain an initial state judgment result; under the condition that a test result corresponding to the test data meets a preset requirement and the initial state judgment result is inconsistent with the test result, storing the test data as positive sample data into a positive sample database; under the condition that the test result does not meet the preset requirement and the initial state judgment result is inconsistent with the test result, the test data serve as negative sample data to be stored in a negative sample database; and training the initial state judgment model by using the positive sample data in the positive sample database and the negative sample data in the negative sample database. According to the technical scheme, the whole training period of the model can be shortened, and the training efficiency is improved.

The invention discloses a malicious instruction detection method and system fusing self-supervised learning and a knowledge graph. The method comprises the steps of obtaining user instruction data and compliance labels, constructing an adversarial sample generation matrix, and generating an adversarial sample library through hierarchical variation and compliance correction; inputting the original instruction and the adversarial sample into a multi-modal detection model, wherein the model comprises a dual-channel encoder and a dynamic knowledge graph module; through a self-supervised comparison loss function optimization model, positive samples need to meet semantic and attack feature similarity threshold requirements, and negative samples are violation instructions; inputting a to-be-detected instruction into the trained model, extracting features, performing multi-hop reasoning on the features and the knowledge graph, and calculating a risk score; and when a malicious instruction is detected, generating a disposal strategy and updating the knowledge graph node. Through fusion of self-supervised learning and the dynamic knowledge graph, accurate detection is realized, the generalization ability and robustness of the model are enhanced, and the detection precision is improved.

The invention belongs to the technical field of artificial intelligence, and discloses a method for generating a migratable adversarial sample for a vision-language pre-training model based on comparative learning, and the method comprises the steps: carrying out the discrimination of an image-text pair set obtained through the data enhancement of the vision-language pre-training model, and obtaining a positive sample set and a negative sample set; in the positive sample guided adversarial sample generation process, negative samples are introduced to further enrich the diversity of the adversarial samples, so that cross-modal interaction is realized to the greatest extent; in the adversarial sample iteration generation process, through combination of three different types of learning modes of positive sample learning, negative sample learning and contrast learning, a transferable vision-language pre-training model adversarial sample is guided to be generated. Wherein the adversarial samples generated by fusing three learning methods show better mobility in different vision-language pre-training models and downstream tasks of the different vision-language pre-training models.

The invention discloses a model training method and device, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a weight positive sample training set and a weight negative sample training set, the weight positive sample training set and the weight negative sample training set are obtained by labeling and screening a preset unlabeled sample set; based on a preset unbiased positive sample and unmarked sample learning algorithm, according to the weight positive sample training set and the weight negative sample training set, training to obtain a dichotomy model; iterating the dichotomy model through a loss function and a regular function corresponding to the dichotomy model based on a preset expectation maximization algorithm until the dichotomy model converges, and obtaining a converged dichotomy model; and taking the converged dichotomy model as a prediction model. The problems that an existing method is poor in universality and generalization and needs to label samples manually are solved, and the universality and generalization of model training are improved.

A sample processing method includes: obtaining a positive sample image; obtaining, based on the positive sample image, an anomaly labeling mask map corresponding to the positive sample image; and generating a forged negative sample image based on the positive sample image and the anomaly labeling mask map, where an anomaly region in the forged negative sample image corresponds to an anomaly labeling region in the anomaly labeling mask map. In this way, a large quantity of forged negative sample images can be automatically generated, to provide sufficient sample datasets for task detection in a scenario with scarce samples.

The invention provides an operation decision-making method, device and system of an operation inspection robot and a medium. According to the implementation scheme, a plurality of positive sample pairs and a plurality of negative sample pairs are constructed; training a cross-modal semantic alignment model based on the plurality of positive sample pairs and the plurality of negative sample pairs, performing semantic alignment on each positive sample pair by adopting the trained cross-modal semantic alignment model to obtain a knowledge vector of semantic alignment, and storing the knowledge vector into a multi-modal operation inspection knowledge base; in response to an operation request text for the operation inspection robot, performing semantic alignment on the operation request text based on a cross-modal semantic alignment model to obtain a semantic-aligned operation request text vector; searching a target knowledge vector similar to the job request text vector in a multi-modal operation inspection knowledge base; and based on the target knowledge vector and the operation request text, generating an operation action instruction of the operation inspection robot. According to the embodiment of the invention, the operation decision accuracy of the operation inspection robot can be improved.

Embodiments described herein provide a method for training a neural network based language model (LM). The method includes receiving, via a data interface, a training dataset including pairs of user queries and ground-truth responses; generating, via the LM, a plurality of responses based on a query from the training dataset; identifying, from the plurality of responses, a first response having a first similarity metric value below a threshold, based on a similarity metric associated with a corresponding ground-truth response from the training dataset; training the LM using the first response as a negative sample and a second response as a positive sample such that the LM after training is more likely to generate the positive sample and less likely to generate the negative sample; receiving, via a user interface, a query; and generating a response to the query via the trained LM.

The invention relates to the field of network security and the deep learning field, in particular to a satellite internet-oriented Tor website fingerprint identification method, which is implemented by constructing a self-supervised contrast learning framework and is implemented by generating a multi-view sample; extracting a feature matrix of the time sequence granularity; generating a synthesized negative sample based on linear insertion, and constructing a contrast with the positive sample; and track representation is extracted, and joint optimization is carried out in combination with cross entropy loss and mask reconstruction loss. Compared with a traditional website fingerprint identification method, the robustness of the model to satellite inherent noise caused by the Doppler effect can be enhanced. According to the method, multi-scale semantic features are provided for the weak enhancement view and the strong enhancement view, the inter-class distinction degree is improved by synthesizing negative samples, and the misjudgment rate is reduced; the joint loss optimization framework enables the model to give consideration to global statistical characteristics and local burst characteristics, and under the conditions of strong dynamic topology and limited labeling of satellite internet constellations, the accuracy and adaptability of website fingerprint recognition are remarkably improved.

The invention discloses a knowledge representation learning model training, link prediction and triple evaluation method. The method comprises the steps that firstly, a plurality of triads are automatically extracted from structured data of a target domain, and each triad definitely comprises a head entity, a relation and a tail entity; subsequently, positive samples are constructed for each triple, and negative samples are created by replacing entities. An initial learning model is constructed, the core innovation of the model lies in generating vector representation containing entity embedding vectors, rotation angles and relation mapping attributes, the rotation angles allow the entities to flexibly transform on hyperplanes corresponding to relations, and the relation mapping attributes quantify the correlation degree between the entities. And finally, training and optimizing the initial model by using the positive and negative sample sets. The technical problem that a related knowledge representation learning model is difficult to meet requirements when processing complex relations is solved.

The invention discloses a combined multi-modal entity relationship extraction and generation method based on multi-view comparative learning, and particularly relates to the technical field of entity relationship extraction. The method comprises the following steps: converting triples of entity relationships in all extracted texts into a sequence consisting of position indexes of a head entity and an entity type thereof, a tail entity and an entity type thereof and a relationship between two entities, and generating a target index sequence from end to end in multi-modal input through a BART-based coding-decoding model; three positive samples are constructed for each training sample based on entity, image and context enhancement, a multi-view comparative learning algorithm is introduced, the algorithm adopts a cross entropy target of in-batch negative samples to minimize the distance between the positive samples, and intervals of a head entity and a tail entity in a sentence are specified through a target index sequence to obtain a multi-view comparative learning algorithm; and a category of the relationship so that the multi-modal representation can capture semantic similarities between samples with similar entities and relationship mentions.

Provided in the embodiments of the present application are a data processing method and apparatus, a device and a medium. The method can be applied to the field of natural language processing, and is used for enhancing the effectiveness of item embedding representations. The method comprises: acquiring a first text representation of each item and a second text representation of a historical item sequence of an interactive character; performing text prediction on hidden positions in first training samples on the basis of a pre-trained language model, so as to obtain prediction probabilities of the hidden positions; performing feature analysis on the first training samples and a first positive sample on the basis of the pre-trained language model, so as to obtain a first sample similarity; training the pre-trained language model on the basis of the first sample similarity and the prediction probabilities of the hidden positions; performing feature analysis on second training samples and a second positive sample on the basis of said pre-trained language model obtained after the training, so as to obtain a second sample similarity; and on the basis of the second sample similarity, adjusting said pre-trained language model obtained after the training, so as to obtain an adjusted pre-trained language model.

The invention belongs to the technical field of visual anomaly detection, and discloses an image small sample anomaly detection system and method based on computer vision. The system is composed of a cloud-edge environment perception federated learning module, an edge end training and pushing integrated lightweight detection module, a small sample adaptive loss optimization module and an edge-cloud collaborative iteration updating module, and a category balance comparison loss function is designed through the small sample adaptive loss optimization module. In combination with inverse frequency weighting, dynamic threshold adjustment and environmental context constraints, the model focuses on rare samples, sample differences are accurately measured, and abnormity is accurately judged according to the environmental background; self-supervised learning is utilized to generate a positive sample pseudo label, data enhancement is performed on a negative sample, the data quantity and diversity are expanded, the small sample utilization rate is improved, the influence of data imbalance on model training is effectively relieved, the labeling cost and subjective errors are reduced, the data quality is optimized, the generalization ability of the model to an abnormal mode is enhanced, and the detection effect is improved.

The invention discloses a greenhouse agricultural machinery data anomaly detection method and system based on an isolated forest algorithm, and the method is characterized in that the method comprises the following steps: S1, obtaining data, and carrying out the preprocessing of the data; s2, performing feature derivation calculation to form derived features; s3, performing feature screening to form data selection features; s4, constructing an isolated forest algorithm model; s5, greenhouse agricultural machine data detection: carrying out real-time detection work; and S6, result interpretation and fault location are carried out. The detection system comprises a data acquisition and preprocessing module, a feature derivation calculation module, a feature screening module, a model training and parameter adjustment module, a real-time detection deployment module and a result interpretation and fault positioning module. According to the method, the problem that the accuracy judgment of the abnormal node is influenced due to the defects that a detection method in the prior art is easy to leak real anomalies, detection fails in a high dimension, real-time streaming data is difficult to deal with and training is difficult due to lack of positive samples is solved.

According to the endometrial cancer cell detection method and system provided by the embodiment of the invention, firstly, an image block is paired with a pathological report text, and unified semantic features are obtained by utilizing multi-positive sample comparison learning; performing semantic entropy-driven dynamic shielding-random shuffling self-supervision training on the visual neural network; secondly, after cell nucleuses are positioned, a weighted cell graph is constructed with cells as nodes and proximity relations as edges, topological features are obtained, and then the topological features, visual features and semantic features are subjected to cross-scale attention and gating fusion to generate multi-modal features; a detection head jointly outputs a category, a frame and a mask, and comprehensive cross-modal consistency loss is subjected to supervision or weak supervision training; and a'cancer-normal 'preference pair is automatically generated by further utilizing high and low probability regions output by the initial model, a reward model is trained, and a PPO strategy is adopted for iterative fine tuning, so that cell-level cancer focus detection can be accurately and robustly completed without a large number of pixel-level labels, and the pathological screening efficiency and accuracy are remarkably improved.

The invention discloses a subject corpus labeling method and system based on classifier multi-round iteration, and belongs to the field of natural language processing and machine learning. The method comprises the steps that a multi-level subject classification system is constructed, multiple rounds of iterative reasoning are conducted on a target subject based on a Fasttext classifier and a seed set, positive and negative samples are dynamically generated in each round of iteration, and classification accuracy is gradually improved; performing multi-dimensional scoring on the positive samples by using the large model to screen high-quality positive samples, and extracting keywords to optimize classification boundaries; a subject problem is generated through a WebQA method, and a low-recall-rate subject corpus is retrieved and supplemented; training an information filter to identify, filter and reject low-quality contents such as advertisements and garbage; and finally, efficient and accurate subject labeling is realized through a classifier and filter series connection process. According to the method, the problems of low efficiency, large resource consumption and subject understanding deviation of traditional labeling are solved, and the method is suitable for efficient and accurate subject labeling scenes of large-scale text data.

The invention discloses similar object group expansion model training and similar object group expansion methods and devices, and belongs to the technical field of similar object group expansion, and the training method comprises the steps: obtaining a training set and a target time point corresponding to each training sample in the training set; a positive sample in the training set is a seed user, a target time point corresponding to the positive sample represents a time point of the seed user meeting a preset behavior condition, and a target time point corresponding to the negative sample is consistent with the target time point corresponding to the positive sample in distribution; dividing the user behavior sequence of the training sample before the corresponding target time point into a plurality of subsequences by using different preset time intervals, respectively extracting sequence features from the plurality of subsequences, and generating user features of the training sample based on a splicing result of the sequence features; and training a preset model based on the user features of the training samples to obtain a trained similar object group expansion model. Therefore, the extraction efficiency of the long-term and short-term behavior characteristics can be optimized.