Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate mode...Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate model to predict the heat transfer during the casting process instead of numerical simulation.The conditional diffusion model was established and trained with the geometry shapes,initial temperature fields and temperature fields at t_(i) as the condition and random noise sampled from standard normal distribution as the input.The output was the temperature field at t_(i+1).Therefore,the temperature field at t_(i+1)can be predicted as the temperature field at t_(i) is known,and the continuous temperature fields of all the time steps can be predicted based on the initial temperature field of an arbitrary 2D geometry.A training set with 3022D shapes and their simulated temperature fields at different time steps was established.The accuracy for the temperature field for a single time step reaches 97.7%,and that for continuous time steps reaches 69.1%with the main error actually existing in the sand mold.The effect of geometry shape and initial temperature field on the prediction accuracy was investigated,the former achieves better result than the latter because the former can identify casting,mold and chill by different colors in the input images.The diffusion model has proved the potential as a surrogate model for numerical simulation of the casting process.展开更多
The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these...The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.展开更多
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov...Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.展开更多
Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(...Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.展开更多
Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions.However,unsupervised anomaly detection methods based o...Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions.However,unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images.In this work,we have established a rail fastener anomaly detection framework called Diff-Fastener,the diffusion model is introduced into the fastener detection task,half of the normal samples are converted into anomaly samples online in the model training stage,and One-Step denoising and canonical guided denoising paradigms are used instead of iterative denoising to improve the reconstruction efficiency of the model while solving the problem of excessive smoothing.DACM(Dilated Attention Convolution Module)is proposed in the middle layer of the reconstruction network to increase the detail information of the reconstructed image;meanwhile,Sparse-Skip connections are used instead of dense connections to reduce the computational load of themodel and enhance its scalability.Through exhaustive experiments onMVTec,VisA,and railroad fastener datasets,the results show that Diff-Fastener achieves 99.1%Image AUROC(Area Under the Receiver Operating Characteristic)and 98.9%Pixel AUROC on the railroad fastener dataset,which outperforms the existing models and achieves the best average score on MVTec and VisA datasets.Our research provides new ideas and directions in the field of anomaly detection for rail fasteners.展开更多
Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deplo...Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deployment.Diffusion model-based methods face security vulnerabilities,particularly due to potential information leakage during generation.We propose a fixed neural network image steganography framework based on secure diffu-sion models to address these challenges.Unlike conventional approaches,our method minimizes cover modifications through neural network optimization,achieving superior steganographic performance in human visual perception and computer vision analyses.The cover images are generated in an anime style using state-of-the-art diffusion models,ensuring the transmitted images appear more natural.This study introduces fixed neural network technology that allows senders to transmit only minimal critical information alongside stego-images.Recipients can accurately reconstruct secret images using this compact data,significantly reducing transmission overhead compared to conventional deep steganography.Furthermore,our framework innovatively integrates ElGamal,a cryptographic algorithm,to protect critical information during transmission,enhancing overall system security and ensuring end-to-end information protection.This dual optimization of payload reduction and cryptographic reinforcement establishes a new paradigm for secure and efficient image steganography.展开更多
The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(S...The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for modeling.However,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and generation.In this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule generation.TGDD combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural language.By leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural language.TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.展开更多
Federated semi-supervised learning(FSSL)faces two major challenges:the scarcity of labeled data across clients and the non-independent and identically distributed(Non-IID)nature of data among clients.To address these ...Federated semi-supervised learning(FSSL)faces two major challenges:the scarcity of labeled data across clients and the non-independent and identically distributed(Non-IID)nature of data among clients.To address these issues,we propose diffusion model-based data synthesis aided FSSL(DDSA-FSSL),a novel approach that leverages diffusion model(DM)to generate synthetic data,thereby bridging the gap between heterogeneous local data distributions and the global data distribution.In the proposed DDSA-FSSL,each client addresses the scarcity of labeled data by utilizing a federated learningtrained classifier to perform pseudo labeling for unlabeled data.The DM is then collaboratively trained using both labeled and precision-optimized pseudolabeled data,enabling clients to generate synthetic samples for classes that are absent in their labeled datasets.As a result,the disparity between local and global distributions is reduced and clients can create enriched synthetic datasets that better align with the global data distribution.Extensive experiments on various datasets and Non-IID scenarios demonstrate the effectiveness of DDSA-FSSL,achieving significant performance improvements,such as increasing accuracy from 38.46%to 52.14%on CIFAR-10 datasets with 10%labeled data.展开更多
With the rapid development of Internet of Things technology,the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast,bringing unprecedented challenges to the field of ...With the rapid development of Internet of Things technology,the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast,bringing unprecedented challenges to the field of network security,especially in identifying malicious attacks.However,due to the uneven distribution of network traffic data,particularly the imbalance between attack traffic and normal traffic,as well as the imbalance between minority class attacks and majority class attacks,traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data.To effectively tackle this challenge,we have designed a lightweight intrusion detection model based on diffusion mechanisms,named Diff-IDS,with the core objective of enhancing the model’s efficiency in parsing complex network traffic features,thereby significantly improving its detection speed and training efficiency.The model begins by finely filtering network traffic features and converting them into grayscale images,while also employing image-flipping techniques for data augmentation.Subsequently,these preprocessed images are fed into a diffusion model based on the Unet architecture for training.Once the model is trained,we fix the weights of the Unet network and propose a feature enhancement algorithm based on feature masking to further boost the model’s expressiveness.Finally,we devise an end-to-end lightweight detection strategy to streamline the model,enabling efficient lightweight detection of imbalanced samples.Our method has been subjected to multiple experimental tests on renowned network intrusion detection benchmarks,including CICIDS 2017,KDD 99,and NSL-KDD.The experimental results indicate that Diff-IDS leads in terms of detection accuracy,training efficiency,and lightweight metrics compared to the current state-of-the-art models,demonstrating exceptional detection capabilities and robustness.展开更多
Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges...Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.展开更多
High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher reso...High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model(DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.展开更多
Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by ut...Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.展开更多
Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore ...Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore the original appearance of the cultural relics mural images,an image restoration based on the denoising diffusion probability model(Denoising Diffusion Probability Model(DDPM))and the Transformer method.The process involves two steps:in the first step,the damaged mural image is firstly utilized as the condition to generate the noise image,using the time,condition and noise image patch as the inputs to the noise prediction network,capturing the global dependencies in the input sequence through the multi-attentionmechanismof the input sequence and feedforward neural network processing,and designing a long skip connection between the shallow and deep layers in the Transformer blocks between the shallow and deep layers using long skip connections to fuse the feature information of global and local outputs to maintain the overall consistency of the restoration results;In the second step,taking the noisy image as a condition to direct the diffusion model to back sample to generate the restored image.Experiment results show that the PSNR and SSIM of the proposedmethod are improved by 2%to 9%and 1%to 3.3%,respectively,which are compared to the comparison methods.This study proposed synthesizes the advantages of the diffusionmodel and deep learningmodel to make themural restoration results more accurate.展开更多
Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion...Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend.However,existing studies on Gelugpa lack geographical perspective,making it difficult to explore the spatial characteristics.Furthermore,the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes.Therefore,taking monastery as the carrier,this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries,as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery.The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries.Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases,speeds,stages,as well as diffusion regions and centers.A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang.Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other.The spatial diffusion process of Amdo was the most complex,with the highest diffusion intensity.Amdo possessed the most influential diffusion centers,with different diffusion shapes and diffusion ranges crossing and overlapping with each other.Multiple natural and human factors may contribute to the formation of Gelugpa monasteries.This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion.展开更多
The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment.Yet,noise during capture and transmission can compromise image accuracy and reliability,compl...The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment.Yet,noise during capture and transmission can compromise image accuracy and reliability,complicating clinical decisions.The rising interest in diffusion models has led to their exploration of denoising images.We present Be-FOI(Better Fluoro Images),a weakly supervised model that uses cine images to denoise fluoroscopic images,both DR types.Trained through precise noise estimation and simulation,BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance.Our tests show that BeFOI outperforms other methods,reducing noise and enhancing clar-ity and diagnostic utility,making it an effective post-processing tool for medical images.展开更多
This article introduces an element diffusion behavior model for a titanium/steel explosive clad plate characterized by a typical curved interface during the heat-treatment process. A series of heat-treatment experimen...This article introduces an element diffusion behavior model for a titanium/steel explosive clad plate characterized by a typical curved interface during the heat-treatment process. A series of heat-treatment experiments were conducted in the temperature range from 750℃ to 950℃, and the effects of heat-treatment parameters on the microstructural evolution and diffusion behavior were investigated by optical microscopy, scanning electron microscopy, X-ray diffraction analysis, and electron-probe microanalysis. Carbon atoms within the steel matrix were observed to diffuse toward the titanium matrix and to aggregate at the bonding interface at 850℃ or lower; in contrast, when the temperature exceeded 850℃, the mutual diffusion of Ti and Fe occurred, along with the diffusion of C atoms, resulting in the for- marion of Ti-Fe intermetallics （Fe2Ti/FeTi）. The diffusion distances of C, Ti, and Fe atoms increased with increasing heating temperature and/or holding time. On the basis of this diffusion behavior, a novel diffusion model was proposed. This model considers the effects of various factors, including the curvature radius of the curved interface, the diffusion coefficient, the heating temperature, and the holding rime. The experimental results show good agreement with the calculated values. The proposed model could clearly provide a general prediction of the elements＇ diffusion at both straight and curved interfaces.展开更多
In this article, an innovation diffusion model with the nonlinear acceptance is proposed to describe the dynamics of three competing products in a market. It is proved that the model admits a unique positive equilibri...In this article, an innovation diffusion model with the nonlinear acceptance is proposed to describe the dynamics of three competing products in a market. It is proved that the model admits a unique positive equilibrium, which is globally stable by excluding the existence of periodic solutions and by using the theory of three dimensional competition systems.展开更多
In this article,a high-order scheme,which is formulated by combining the quadratic finite element method in space with a second-order time discrete scheme,is developed for looking for the numerical solution of a two-d...In this article,a high-order scheme,which is formulated by combining the quadratic finite element method in space with a second-order time discrete scheme,is developed for looking for the numerical solution of a two-dimensional nonlinear time fractional thermal diffusion model.The time Caputo fractional derivative is approximated by using the L2-1formula,the first-order derivative and nonlinear term are discretized by some second-order approximation formulas,and the quadratic finite element is used to approximate the spatial direction.The error accuracy O(h3+t2)is obtained,which is verified by the numerical results.展开更多
This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geomet...This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.展开更多
In terms of numerical method of Smoluchowski equation the behavior of fission process in diffusion model has been described and analyzed, including the reliance upon time, as well as the deformation parameters at seve...In terms of numerical method of Smoluchowski equation the behavior of fission process in diffusion model has been described and analyzed, including the reliance upon time, as well as the deformation parameters at several nuclear temperatures in this paper. The fission rates and the residual probabilities inside the saddle point are calculated for fissile nucleus n＋^238U reaction and un-fissile nucleus p＋^208Pb reaction. The results indicate that there really exists a transient fission process, which means that the pre-equillbrium fission should be taken into account for the fissile nucleus at the high temperature. Oppositely, the pre-equilibrlum fission could be neglected for the un-fissile nucleus. In the certain case the overshooting phenomenon of the fission rates will occur, which is mainly determined by the diffusive current at the saddle point. The higher the temperature is, the more obvious the overshooting phenomenon is. However, the emissions of the light particles accompanying the diffusion process may weaken or vanish the overshooting phenomenon.展开更多
基金sponsored by Tsinghua-Toyota Joint Research Fund
文摘Deep learning has achieved great progress in image recognition,segmentation,semantic recognition and game theory.In this study,a latest deep learning model,a conditional diffusion model was adopted as a surrogate model to predict the heat transfer during the casting process instead of numerical simulation.The conditional diffusion model was established and trained with the geometry shapes,initial temperature fields and temperature fields at t_(i) as the condition and random noise sampled from standard normal distribution as the input.The output was the temperature field at t_(i+1).Therefore,the temperature field at t_(i+1)can be predicted as the temperature field at t_(i) is known,and the continuous temperature fields of all the time steps can be predicted based on the initial temperature field of an arbitrary 2D geometry.A training set with 3022D shapes and their simulated temperature fields at different time steps was established.The accuracy for the temperature field for a single time step reaches 97.7%,and that for continuous time steps reaches 69.1%with the main error actually existing in the sand mold.The effect of geometry shape and initial temperature field on the prediction accuracy was investigated,the former achieves better result than the latter because the former can identify casting,mold and chill by different colors in the input images.The diffusion model has proved the potential as a surrogate model for numerical simulation of the casting process.
基金supported by the Fundamental Research Funds for the Central Universities(No.226-2024-00038),China.
文摘The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.
基金partially supported by the National Natural Science Foundation of China(62271485)the SDHS Science and Technology Project(HS2023B044)
文摘Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.
基金supported by the National Natural Science Foundation of China(Nos.81974355 and 82172524)Key Research and Development Program of Hubei Province(No.2021BEA161)+2 种基金National Innovation Platform Development Program(No.2020021105012440)Open Project Funding of the Hubei Key Laboratory of Big Data Intelligent Analysis and Application,Hubei University(No.2024BDIAA03)Free Innovation Preliminary Research Fund of Wuhan Union Hospital(No.2024XHYN047).
文摘Objective This study aimed to explore a novel method that integrates the segmentation guidance classification and the dif-fusion model augmentation to realize the automatic classification for tibial plateau fractures(TPFs).Methods YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital.Additionally,a segmentation-guided classification approach was proposed.To enhance the dataset,a diffusion model was further demonstrated for data augmentation.Results The novel method that integrated the segmentation-guided classification and diffusion model augmentation sig-nificantly improved the accuracy and robustness of fracture classification.The average accuracy of classification for TPFs rose from 0.844 to 0.896.The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training,with both the macro-area under the curve(AUC)and the micro-AUC increasing from 0.94 to 0.97.By utilizing diffusion model augmentation and segmentation map integration,the model demonstrated superior efficacy in identifying SchatzkerⅠ,achieving an accuracy of 0.880.It yielded an accuracy of 0.898 for SchatzkerⅡandⅢand 0.913 for SchatzkerⅣ;for SchatzkerⅤandⅥ,the accuracy was 0.887;and for intercondylar ridge fracture,the accuracy was 0.923.Conclusion The dual-stream attention-based classification network,which has been verified by many experiments,exhibited great potential in predicting the classification of TPFs.This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.
基金funded by the National Natural Science Foundation of China,grant number 52272385 and 52475085.
文摘Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions.However,unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images.In this work,we have established a rail fastener anomaly detection framework called Diff-Fastener,the diffusion model is introduced into the fastener detection task,half of the normal samples are converted into anomaly samples online in the model training stage,and One-Step denoising and canonical guided denoising paradigms are used instead of iterative denoising to improve the reconstruction efficiency of the model while solving the problem of excessive smoothing.DACM(Dilated Attention Convolution Module)is proposed in the middle layer of the reconstruction network to increase the detail information of the reconstructed image;meanwhile,Sparse-Skip connections are used instead of dense connections to reduce the computational load of themodel and enhance its scalability.Through exhaustive experiments onMVTec,VisA,and railroad fastener datasets,the results show that Diff-Fastener achieves 99.1%Image AUROC(Area Under the Receiver Operating Characteristic)and 98.9%Pixel AUROC on the railroad fastener dataset,which outperforms the existing models and achieves the best average score on MVTec and VisA datasets.Our research provides new ideas and directions in the field of anomaly detection for rail fasteners.
基金supported in part by the National Natural Science Foundation of China under Grants 62102450,62272478 and the Independent Research Project of a Certain Unit under Grant ZZKY20243127。
文摘Traditional steganography conceals information by modifying cover data,but steganalysis tools easily detect such alterations.While deep learning-based steganography often involves high training costs and complex deployment.Diffusion model-based methods face security vulnerabilities,particularly due to potential information leakage during generation.We propose a fixed neural network image steganography framework based on secure diffu-sion models to address these challenges.Unlike conventional approaches,our method minimizes cover modifications through neural network optimization,achieving superior steganographic performance in human visual perception and computer vision analyses.The cover images are generated in an anime style using state-of-the-art diffusion models,ensuring the transmitted images appear more natural.This study introduces fixed neural network technology that allows senders to transmit only minimal critical information alongside stego-images.Recipients can accurately reconstruct secret images using this compact data,significantly reducing transmission overhead compared to conventional deep steganography.Furthermore,our framework innovatively integrates ElGamal,a cryptographic algorithm,to protect critical information during transmission,enhancing overall system security and ensuring end-to-end information protection.This dual optimization of payload reduction and cryptographic reinforcement establishes a new paradigm for secure and efficient image steganography.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62476247 and 62072409)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant No.2024C01214)the Zhejiang Provincial Natural Science Foundation(Grant No.LR21F020003).
文摘The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs.Mainstream methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for modeling.However,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and generation.In this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule generation.TGDD combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural language.By leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural language.TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.
基金supported in part by NSF of China under Grant 62222111 and Grant 62431015in part by the Science and Technology Commission Foundation of Shanghai under Grant 24DP1500702.
文摘Federated semi-supervised learning(FSSL)faces two major challenges:the scarcity of labeled data across clients and the non-independent and identically distributed(Non-IID)nature of data among clients.To address these issues,we propose diffusion model-based data synthesis aided FSSL(DDSA-FSSL),a novel approach that leverages diffusion model(DM)to generate synthetic data,thereby bridging the gap between heterogeneous local data distributions and the global data distribution.In the proposed DDSA-FSSL,each client addresses the scarcity of labeled data by utilizing a federated learningtrained classifier to perform pseudo labeling for unlabeled data.The DM is then collaboratively trained using both labeled and precision-optimized pseudolabeled data,enabling clients to generate synthetic samples for classes that are absent in their labeled datasets.As a result,the disparity between local and global distributions is reduced and clients can create enriched synthetic datasets that better align with the global data distribution.Extensive experiments on various datasets and Non-IID scenarios demonstrate the effectiveness of DDSA-FSSL,achieving significant performance improvements,such as increasing accuracy from 38.46%to 52.14%on CIFAR-10 datasets with 10%labeled data.
基金supported by the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2024GXJS014,ZDYF2023GXJS163)the National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)Collaborative Innovation Project of Hainan University(XTCX2022XXB02).
文摘With the rapid development of Internet of Things technology,the sharp increase in network devices and their inherent security vulnerabilities present a stark contrast,bringing unprecedented challenges to the field of network security,especially in identifying malicious attacks.However,due to the uneven distribution of network traffic data,particularly the imbalance between attack traffic and normal traffic,as well as the imbalance between minority class attacks and majority class attacks,traditional machine learning detection algorithms have significant limitations when dealing with sparse network traffic data.To effectively tackle this challenge,we have designed a lightweight intrusion detection model based on diffusion mechanisms,named Diff-IDS,with the core objective of enhancing the model’s efficiency in parsing complex network traffic features,thereby significantly improving its detection speed and training efficiency.The model begins by finely filtering network traffic features and converting them into grayscale images,while also employing image-flipping techniques for data augmentation.Subsequently,these preprocessed images are fed into a diffusion model based on the Unet architecture for training.Once the model is trained,we fix the weights of the Unet network and propose a feature enhancement algorithm based on feature masking to further boost the model’s expressiveness.Finally,we devise an end-to-end lightweight detection strategy to streamline the model,enabling efficient lightweight detection of imbalanced samples.Our method has been subjected to multiple experimental tests on renowned network intrusion detection benchmarks,including CICIDS 2017,KDD 99,and NSL-KDD.The experimental results indicate that Diff-IDS leads in terms of detection accuracy,training efficiency,and lightweight metrics compared to the current state-of-the-art models,demonstrating exceptional detection capabilities and robustness.
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innvation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065)。
文摘Air target intent recognition holds significant importance in aiding commanders to assess battlefield situations and secure a competitive edge in decision-making.Progress in this domain has been hindered by challenges posed by imbalanced battlefield data and the limited robustness of traditional recognition models.Inspired by the success of diffusion models in addressing visual domain sample imbalances,this paper introduces a new approach that utilizes the Markov Transfer Field(MTF)method for time series data visualization.This visualization,when combined with the Denoising Diffusion Probabilistic Model(DDPM),effectively enhances sample data and mitigates noise within the original dataset.Additionally,a transformer-based model tailored for time series visualization and air target intent recognition is developed.Comprehensive experimental results,encompassing comparative,ablation,and denoising validations,reveal that the proposed method achieves a notable 98.86%accuracy in air target intent recognition while demonstrating exceptional robustness and generalization capabilities.This approach represents a promising avenue for advancing air target intent recognition.
基金supported by the National Natural Science Foundation of China (NSFC): Grant number 42274147。
文摘High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement,there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model(DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement,but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.
基金supported in part by the National Natural Science Foundation of China under Grants 61502162,61702175,and 61772184in part by the Fund of the State Key Laboratory of Geo-information Engineering under Grant SKLGIE2016-M-4-2+4 种基金in part by the Hunan Natural Science Foundation of China under Grant 2018JJ2059in part by the Key R&D Project of Hunan Province of China under Grant 2018GK2014in part by the Open Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN17-14Chinese Scholarship Council(CSC)through College of Computer Science and Electronic Engineering,Changsha,410082Hunan University with Grant CSC No.2018GXZ020784.
文摘Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.
基金financial support from Hunan Provincial Natural Science and Technology Fund Project(Grant No.2022JJ50077)Natural Science Foundation of Hunan Province(Grant No.2024JJ8055).
文摘Due to the limitations of a priori knowledge and convolution operation,the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration,in order to more accurately restore the original appearance of the cultural relics mural images,an image restoration based on the denoising diffusion probability model(Denoising Diffusion Probability Model(DDPM))and the Transformer method.The process involves two steps:in the first step,the damaged mural image is firstly utilized as the condition to generate the noise image,using the time,condition and noise image patch as the inputs to the noise prediction network,capturing the global dependencies in the input sequence through the multi-attentionmechanismof the input sequence and feedforward neural network processing,and designing a long skip connection between the shallow and deep layers in the Transformer blocks between the shallow and deep layers using long skip connections to fuse the feature information of global and local outputs to maintain the overall consistency of the restoration results;In the second step,taking the noisy image as a condition to direct the diffusion model to back sample to generate the restored image.Experiment results show that the PSNR and SSIM of the proposedmethod are improved by 2%to 9%and 1%to 3.3%,respectively,which are compared to the comparison methods.This study proposed synthesizes the advantages of the diffusionmodel and deep learningmodel to make themural restoration results more accurate.
基金supported by the Humanities and Social Sciences Foundation of the Ministry of Education of China(Grant No.18YJAZH140).
文摘Gelugpa is the most influential extant religious sect of Tibetan Buddhism,which is the spiritual prop for Tibetans,with thousands of monasteries and followers in Tibetan areas of China.Studies on the spatial diffusion processes of Gelugpa can not only reveal its historical geographical development but also lay the foundation for anticipating its future development trend.However,existing studies on Gelugpa lack geographical perspective,making it difficult to explore the spatial characteristics.Furthermore,the prevailing macro-perspective overlooks spatiotemporal heterogeneity in diffusion processes.Therefore,taking monastery as the carrier,this study establishes a multi-level diffusion model to reconstruct the diffusion networks of Gelugpa monasteries,as well as a framework to explore the detailed features in the spatial diffusion processes of Gelugpa in Tibetan areas of China based on a geodatabase of Gelugpa monastery.The results show that the multi-level diffusion model has a considerable applicability in the reconstruction of the diffusion networks of Gelugpa monasteries.Gelugpa monasteries in the Three Tibetan Inhabited Areas present disparate spatial diffusion processes with diverse diffusion bases,speeds,stages,as well as diffusion regions and centers.A powerful single-center diffusion-centered Gandan Monastery was rapidly formed in U-Tsang.Kham experienced a slower and more varied spatial diffusion process with multiple diffusion systems far apart from each other.The spatial diffusion process of Amdo was the most complex,with the highest diffusion intensity.Amdo possessed the most influential diffusion centers,with different diffusion shapes and diffusion ranges crossing and overlapping with each other.Multiple natural and human factors may contribute to the formation of Gelugpa monasteries.This study contributes to the understanding of the geography of Gelugpa and provides reference to studies on religion diffusion.
文摘The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment.Yet,noise during capture and transmission can compromise image accuracy and reliability,complicating clinical decisions.The rising interest in diffusion models has led to their exploration of denoising images.We present Be-FOI(Better Fluoro Images),a weakly supervised model that uses cine images to denoise fluoroscopic images,both DR types.Trained through precise noise estimation and simulation,BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance.Our tests show that BeFOI outperforms other methods,reducing noise and enhancing clar-ity and diagnostic utility,making it an effective post-processing tool for medical images.
文摘This article introduces an element diffusion behavior model for a titanium/steel explosive clad plate characterized by a typical curved interface during the heat-treatment process. A series of heat-treatment experiments were conducted in the temperature range from 750℃ to 950℃, and the effects of heat-treatment parameters on the microstructural evolution and diffusion behavior were investigated by optical microscopy, scanning electron microscopy, X-ray diffraction analysis, and electron-probe microanalysis. Carbon atoms within the steel matrix were observed to diffuse toward the titanium matrix and to aggregate at the bonding interface at 850℃ or lower; in contrast, when the temperature exceeded 850℃, the mutual diffusion of Ti and Fe occurred, along with the diffusion of C atoms, resulting in the for- marion of Ti-Fe intermetallics （Fe2Ti/FeTi）. The diffusion distances of C, Ti, and Fe atoms increased with increasing heating temperature and/or holding time. On the basis of this diffusion behavior, a novel diffusion model was proposed. This model considers the effects of various factors, including the curvature radius of the curved interface, the diffusion coefficient, the heating temperature, and the holding rime. The experimental results show good agreement with the calculated values. The proposed model could clearly provide a general prediction of the elements＇ diffusion at both straight and curved interfaces.
基金Project Supported by the National Natural Science Fund of China(10571143) the Research Fund of Southwest Normal University
文摘In this article, an innovation diffusion model with the nonlinear acceptance is proposed to describe the dynamics of three competing products in a market. It is proved that the model admits a unique positive equilibrium, which is globally stable by excluding the existence of periodic solutions and by using the theory of three dimensional competition systems.
基金the National Natural Science Fund(11661058,11761053)Natural Science Fund of Inner Mongolia Autonomous Region(2016MS0102,2017MS0107)+1 种基金Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT-17-A07)National Undergraduate Innovative Training Project of Inner Mongolia University(201710126026).
文摘In this article,a high-order scheme,which is formulated by combining the quadratic finite element method in space with a second-order time discrete scheme,is developed for looking for the numerical solution of a two-dimensional nonlinear time fractional thermal diffusion model.The time Caputo fractional derivative is approximated by using the L2-1formula,the first-order derivative and nonlinear term are discretized by some second-order approximation formulas,and the quadratic finite element is used to approximate the spatial direction.The error accuracy O(h3+t2)is obtained,which is verified by the numerical results.
基金Supported by The National Natural Science Foundation of China(71261015)Humanity and Social Science Youth Foundation of Education Ministry in China(10YJC630334)Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region
文摘This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.
基金The project supported by National Natural Science Foundation of China under Grant No. 10547005
文摘In terms of numerical method of Smoluchowski equation the behavior of fission process in diffusion model has been described and analyzed, including the reliance upon time, as well as the deformation parameters at several nuclear temperatures in this paper. The fission rates and the residual probabilities inside the saddle point are calculated for fissile nucleus n＋^238U reaction and un-fissile nucleus p＋^208Pb reaction. The results indicate that there really exists a transient fission process, which means that the pre-equillbrium fission should be taken into account for the fissile nucleus at the high temperature. Oppositely, the pre-equilibrlum fission could be neglected for the un-fissile nucleus. In the certain case the overshooting phenomenon of the fission rates will occur, which is mainly determined by the diffusive current at the saddle point. The higher the temperature is, the more obvious the overshooting phenomenon is. However, the emissions of the light particles accompanying the diffusion process may weaken or vanish the overshooting phenomenon.
