Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO...MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.展开更多
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,th...In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.展开更多
Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the...Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the broken spatial translational symmetry.However,the intensity of first-order Raman scattering at crystal edges has been rarely explored,although the mechanical stress and edge characteristics have been thoroughly studied by the Raman peak shift and the spectral features of the edge-related Raman modes.Here,by taking Ga As crystal with a well-defined edge as an example,we reveal the intensity enhancement of Raman-active modes and the emergence of Raman-forbidden modes under specific polarization configurations at the edge.This is attributed to the presence of a hot spot at the edge due to the redistributed electromagnetic fields and electromagnetic wave propagations of incident laser and Raman signal near the edge,which are confirmed by the finite-difference time-domain simulations.Spatially-resolved Raman intensities of both Raman-active and Raman-forbidden modes near the edge are calculated based on the redistributed electromagnetic fields,which quantitatively reproduce the corresponding experimental results.These findings offer new insights into the intensity enhancement of Raman scattering at crystal edges and present a new avenue to manipulate light–matter interactions of crystal by manufacturing various types of edges and to characterize the edge structures in photonic and optoelectronic devices.展开更多
Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevita...Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.展开更多
Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approac...Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.展开更多
Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face sev...Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.展开更多
Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marki...Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marking the highest SE enhancement to date[1].展开更多
Flammable ionic liquids exhibit high conductivity and a broad electrochemical window,enabling the generation of combustible gases for combustion via electrochemical decomposition and thermal decomposition.This charact...Flammable ionic liquids exhibit high conductivity and a broad electrochemical window,enabling the generation of combustible gases for combustion via electrochemical decomposition and thermal decomposition.This characteristic holds significant implications in the realm of novel satellite propulsion.Introducing a fraction of the electrical energy into energetic ionic liquid fuels,the thermal decomposition process is facilitated by reducing the apparent activation energy required,and electrical energy can trigger the electrochemical decomposition of ionic liquids,presenting a promising approach to enhance combustion efficiency and energy release.This study applied an external voltage during the thermal decomposition of 1-ethyl-3-methylimidazole nitrate([EMIm]NO_(3)),revealing the effective alteration of the activation energy of[EMIm]NO_(3).The pyrolysis,electrochemical decomposition,and electron assisted enhancement products were identified through Thermogravimetry-Differential scanning calorimetry-Fourier transform infrared-Mass spectrometry(TG-DSC-FTIR-MS)and gas chromatography(GC)analyses,elucidating the degradation mechanism of[EMIm]NO_(3).Furthermore,an external voltage was introduced during the combustion of[EMIm]NO_(3),demonstrating the impact of voltage on the combustion process.展开更多
In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symm...In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.展开更多
Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenari...Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.展开更多
Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 pat...Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 patients with breast diseases were selected as samples and grouped according to disease type.Twenty-eight patients with breast cancer were included in Group A,and 28 patients with NPM were included in Group B.All patients underwent multimodal MRI enhancement scanning and diffusion-weighted imaging.The MRI results,time-signal intensity curves,ADC values,lesion intensity,and imaging signs were compared between the two groups.Results:There were no significant differences in enhancement characteristics,lymph node enlargement,and margins between Group A and Group B(P>0.05).The proportion of outflow curves in Group A was higher than that in Group B(P<0.05).The ADC value in Group A was lower than that in Group B,and the lesion intensity was higher than that in Group B(P<0.05).There were significant differences in imaging signs,such as abscess or sinus,ascending time-signal curve,and mammary duct dilation between Group A and Group B(P<0.05).Conclusion:Multimodal MRI enhancement scanning and diffusion-weighted imaging techniques can be used to diagnose breast diseases.Comprehensive analysis of time-signal intensity curves,lesion intensity,imaging signs,and ADC values can differentiate between NPM and breast cancer.展开更多
Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as t...Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as transcranial magnetic stimulation,transcranial direct-current stimulation,and transcranial ultrasound stimulation)have shown significant potential in enhancing cognitive functions[1,2].Existing technologies are limited mainly to superficial cortical regions,with limited efficacy in targeting deep brain areas and inadequate methods for evaluating their modulatory effects.Selecting stimulation parameters(such as locus,depth,and intensity)and assessing the impact of neuromodulation remains incompletely understood.展开更多
With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control...With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.展开更多
Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should fa...Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should face up to the infinite possibilities that generative AI brings to enterprise management.Based on this,this paper will briefly analyze the value connotation of generative AI empowering the improvement of enterprise leadership and the relevant influencing factors,and discuss the strategies for enhancing enterprise leadership in the generative AI era,in order to promote the smooth progress of enterprises’digital transformation and upgrading.展开更多
Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning...Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.展开更多
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
基金financially sponsored by the National Natural Science Foundation of China(No.52204414)the National Energy-Saving and Low-Carbon Materials Production and Application Demonstration Platform Program,China(No.TC220H06N)+1 种基金the National Key R&D Program of China(No.2021YFC1910504)the Fundamental Research Funds for the Central Universities,China(No.FRFTP-20-097A1Z)。
文摘MnO_(x)-CeO_(2)catalysts for the low-temperature selective catalytic reduction(SCR)of NO remain vulnerable to water and sulfur poisoning,limting their practical applications.Herein,we report a hydrophobic-modified MnO_(x)-CeO_(2)catalyst that achieves enhanced NO conversion rate and stability under harsh conditions.The catalyst was synthesized by decorating MnOx crystals with amorphous CeO_(2),followed by loading hydrophobic silica on the external surfaces.The hydrophobic silica allowed the adsorption of NH_(3)and NO and diffusion of H,suppressed the adsorption of H_(2)O,and prevented SO_(2)interaction with the Mn active sites,achieving selective molecular discrimination at the catalyst surface.At 120℃,under H_(2)O and SO_(2)exposure,the optimal hydrophobic catalyst maintains 82%NO conversion rate compared with 69%for the unmodified catalyst.The average adsorption energies of NH_(3),H_(2)O,and SO_(2)decreased by 0.05,0.43,and 0.52 eV,respectively.The NO reduction pathway follows the Eley-Rideal mechanism,NH_(3)^(*)+*→NH_(2)^(*)+H^(*)followed by NH_(2)^(*)+NO^(*)→N_(2)^(*)+H_(2)O^(*),with NH_(3)dehydrogenation being the rate determining step.Hydrophobic modification increased the activation energy for H atom transfer,leading to a minor decrease in the NO conversion rate at 120℃.This work demonstrates a viable strategy for developing robust NH_(3)-S CR catalysts capable of efficient operation in water-and sulfur-rich environments.
基金supported by the Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
基金supported by the Natural Science Foundation of Shandong Province(nos.ZR2023MF047,ZR2024MA055 and ZR2023QF139)the Enterprise Commissioned Project(nos.2024HX104 and 2024HX140)+1 种基金the China University Industry-University-Research Innovation Foundation(nos.2021ZYA11003 and 2021ITA05032)the Science and Technology Plan for Youth Innovation of Shandong's Universities(no.2019KJN012).
文摘In low-light environments,captured images often exhibit issues such as insufficient clarity and detail loss,which significantly degrade the accuracy of subsequent target recognition tasks.To tackle these challenges,this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis.The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image,followed by image segmentation using a quadtree method.To improve the accuracy and robustness of atmospheric light estimation,the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions.Additionally,this method employs an adaptive window adjustment mechanism to derive the dark channel prior image,which is subsequently refined using morphological operations and guided filtering.The final enhanced image is reconstructed through the hazy image degradation model.Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework,achieving a peak signal-to-noise ratio(PSNR)of 17.09 and a structural similarity index(SSIM)of 0.74.These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFA1407000)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0460000)+4 种基金the National Natural Science Foundation of China(Grant Nos.12322401,12127807,and 12393832)CAS Key Research Program of Frontier Sciences(Grant No.ZDBS-LY-SLH004)Beijing Nova Program(Grant No.20230484301)Youth Innovation Promotion Association,Chinese Academy of Sciences(Grant No.2023125)CAS Project for Young Scientists in Basic Research(Grant No.YSBR-026)。
文摘Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the broken spatial translational symmetry.However,the intensity of first-order Raman scattering at crystal edges has been rarely explored,although the mechanical stress and edge characteristics have been thoroughly studied by the Raman peak shift and the spectral features of the edge-related Raman modes.Here,by taking Ga As crystal with a well-defined edge as an example,we reveal the intensity enhancement of Raman-active modes and the emergence of Raman-forbidden modes under specific polarization configurations at the edge.This is attributed to the presence of a hot spot at the edge due to the redistributed electromagnetic fields and electromagnetic wave propagations of incident laser and Raman signal near the edge,which are confirmed by the finite-difference time-domain simulations.Spatially-resolved Raman intensities of both Raman-active and Raman-forbidden modes near the edge are calculated based on the redistributed electromagnetic fields,which quantitatively reproduce the corresponding experimental results.These findings offer new insights into the intensity enhancement of Raman scattering at crystal edges and present a new avenue to manipulate light–matter interactions of crystal by manufacturing various types of edges and to characterize the edge structures in photonic and optoelectronic devices.
文摘Low-light image enhancement is one of the most active research areas in the field of computer vision in recent years.In the low-light image enhancement process,loss of image details and increase in noise occur inevitably,influencing the quality of enhanced images.To alleviate this problem,a low-light image enhancement model called RetinexNet model based on Retinex theory was proposed in this study.The model was composed of an image decomposition module and a brightness enhancement module.In the decomposition module,a convolutional block attention module(CBAM)was incorporated to enhance feature representation capacity of the network,focusing on crucial features and suppressing irrelevant ones.A multifeature fusion denoising module was designed within the brightness enhancement module,circumventing the issue of feature loss during downsampling.The proposed model outperforms the existing algorithms in terms of PSNR and SSIM metrics on the publicly available datasets LOL and MIT-Adobe FiveK,as well as gives superior results in terms of NIQE metrics on the publicly available dataset LIME.
基金Graduate Student Innovation Projects of Beijing University of Civil Engineering and Architecture(No.PG2024121)。
文摘Underwater images are inherently degraded by color distortion,contrast reduction,and uneven brightness,primarily due to light absorption and scattering in water.To mitigate these challenges,a novel enhancement approach is proposed,integrating Local Adaptive Color Correction(LACC)with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE(LACC-RCE).Conventional color correction methods predominantly employ global adjustment strategies,which are often inadequate for handling spatially varying color distortions.In contrast,the proposed LACC method incorporates local color analysis,tone-weighted control,and spatially adaptive adjustments,allowing for region-specific color correction.This approach effectively enhances color fidelity and perceptual naturalness,addressing the limitations of global correction techniques.For contrast enhancement,the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast,while CLAHE is employed to adaptively enhance local regions.A weighted fusion strategy is then applied to synthesize high-quality underwater images.Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration,contrast optimization,and detail preservation,thereby enhancing the visual quality of underwater images.This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.
文摘Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.
文摘Recently,an article was published on solid effect(SE)dynamic nuclear polarization(DNP)enhancement,where the au-thors reported achieving 1H enhancement factors up to 500 by increasing the microwave power at 9.4 T,marking the highest SE enhancement to date[1].
基金supported by the National Natural Science Foundation of China(Grant No.52206165)。
文摘Flammable ionic liquids exhibit high conductivity and a broad electrochemical window,enabling the generation of combustible gases for combustion via electrochemical decomposition and thermal decomposition.This characteristic holds significant implications in the realm of novel satellite propulsion.Introducing a fraction of the electrical energy into energetic ionic liquid fuels,the thermal decomposition process is facilitated by reducing the apparent activation energy required,and electrical energy can trigger the electrochemical decomposition of ionic liquids,presenting a promising approach to enhance combustion efficiency and energy release.This study applied an external voltage during the thermal decomposition of 1-ethyl-3-methylimidazole nitrate([EMIm]NO_(3)),revealing the effective alteration of the activation energy of[EMIm]NO_(3).The pyrolysis,electrochemical decomposition,and electron assisted enhancement products were identified through Thermogravimetry-Differential scanning calorimetry-Fourier transform infrared-Mass spectrometry(TG-DSC-FTIR-MS)and gas chromatography(GC)analyses,elucidating the degradation mechanism of[EMIm]NO_(3).Furthermore,an external voltage was introduced during the combustion of[EMIm]NO_(3),demonstrating the impact of voltage on the combustion process.
基金supported by the National Natural Science Foundation of China(Grant No.12071042)the Beijing Natural Science Foundation(Grant No.1202004)Promoting the Development of University Classification-Student Innovation and Entrepreneurship Training Programme(Grant No.5112410857)。
文摘In this paper,we propose a symmetric difference data enhancement physics-informed neural network(SDE-PINN)to study soliton solutions for discrete nonlinear lattice equations(NLEs).By considering known and unknown symmetric points,numerical simulations are conducted to one-soliton and two-soliton solutions of a discrete KdV equation,as well as a one-soliton solution of a discrete Toda lattice equation.Compared with the existing discrete deep learning approach,the numerical results reveal that within the specified spatiotemporal domain,the prediction accuracy by SDE-PINN is excellent regardless of the interior or extrapolation prediction,with a significant reduction in training time.The proposed data enhancement technique and symmetric structure development provides a new perspective for the deep learning approach to solve discrete NLEs.The newly proposed SDE-PINN can also be applied to solve continuous nonlinear equations and other discrete NLEs numerically.
文摘Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.
文摘Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 patients with breast diseases were selected as samples and grouped according to disease type.Twenty-eight patients with breast cancer were included in Group A,and 28 patients with NPM were included in Group B.All patients underwent multimodal MRI enhancement scanning and diffusion-weighted imaging.The MRI results,time-signal intensity curves,ADC values,lesion intensity,and imaging signs were compared between the two groups.Results:There were no significant differences in enhancement characteristics,lymph node enlargement,and margins between Group A and Group B(P>0.05).The proportion of outflow curves in Group A was higher than that in Group B(P<0.05).The ADC value in Group A was lower than that in Group B,and the lesion intensity was higher than that in Group B(P<0.05).There were significant differences in imaging signs,such as abscess or sinus,ascending time-signal curve,and mammary duct dilation between Group A and Group B(P<0.05).Conclusion:Multimodal MRI enhancement scanning and diffusion-weighted imaging techniques can be used to diagnose breast diseases.Comprehensive analysis of time-signal intensity curves,lesion intensity,imaging signs,and ADC values can differentiate between NPM and breast cancer.
基金supported by the National Natural Science Foundation of China(82172018 and 62333002).
文摘Cognitive enhancement is essential for maintaining the quality of life in healthy individuals and improving the ability of those with mental impairments.In recent years,noninvasive neuromodulation techniques(such as transcranial magnetic stimulation,transcranial direct-current stimulation,and transcranial ultrasound stimulation)have shown significant potential in enhancing cognitive functions[1,2].Existing technologies are limited mainly to superficial cortical regions,with limited efficacy in targeting deep brain areas and inadequate methods for evaluating their modulatory effects.Selecting stimulation parameters(such as locus,depth,and intensity)and assessing the impact of neuromodulation remains incompletely understood.
基金supported by the National Natural Science Foundation of China(62473341)Key Technologies R&D Program of Henan Province(242102211071,252102211086,252102210166).
文摘With the rapid development of the industrial Internet,the network security environment has become increasingly complex and variable.Intrusion detection,a core technology for ensuring the security of industrial control systems,faces the challenge of unbalanced data samples,particularly the low detection rates for minority class attack samples.Therefore,this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network(SA-WGAN)to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios.The proposed method integrates a selfattention mechanism with a Wasserstein Generative Adversarial Network(WGAN).The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors,providing strong guidance for subsequent data generation.The WGAN generates new data samples through adversarial training to expand the original dataset.In the SA-WGAN framework,the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism,ensuring that the generated samples exhibit both diversity and similarity to real data.Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes,addresses issues of insufficient data and category imbalance,and enhances the generalization ability and overall performance of the intrusion detection model.
文摘Currently,the transformation and upgrading of digitalization have become a new task that enterprises urgently need to address.To further enhance the leadership of enterprise leaders,relevant enterprise staff should face up to the infinite possibilities that generative AI brings to enterprise management.Based on this,this paper will briefly analyze the value connotation of generative AI empowering the improvement of enterprise leadership and the relevant influencing factors,and discuss the strategies for enhancing enterprise leadership in the generative AI era,in order to promote the smooth progress of enterprises’digital transformation and upgrading.
基金supported by the Key Laboratory of Forensic Science and Technology at College of Sichuan Province(2023YB04).
文摘Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.
