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
Local Colorism can be seen as a new literature type, which sprung up in 1920s. As a literature trend, local novels open a new road for realistically sentimental novels of view literature. Therefore, this paper is aim ...Local Colorism can be seen as a new literature type, which sprung up in 1920s. As a literature trend, local novels open a new road for realistically sentimental novels of view literature. Therefore, this paper is aim to study the Local Colorism American Novels of 19th Century, especially in west and south of American. American southern literature and western literature although established respectively unique sense of place suffer from complex history. And it proves that American literature has been increasingly building it popularity and a new type of literature.展开更多
The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographica...The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.展开更多
Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets...Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin.Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales,from nanometers to micrometers.Single molecule localization microscopy(SMLM)methods,such as STORM,are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization.Multi-label imaging of chromatin is necessary to unpack its structural complexity.However,these efforts are challenged by the high-density nuclear environment,which can affect antibody binding affinities,diffusivity and non-specific interactions.Optimizing buffer conditions,fluorophore stability,and antibody specificity is crucial for achieving effective antibody conjugates.Here,we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment.This protocol couples multiplexed localization datasets with a robust analysis algorithm,which utilizes localizations from one target as seed points for distance,density and multi-label joint affinity measurements to explore complex organization of all three targets.Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories,but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters.This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.展开更多
An r-acyclic edge chromatic number of a graph G,denoted by α＇r（G）,is the minimum number of colors used to produce an edge coloring of the graph such that adjacent edges receive different colors and every cycle C h...An r-acyclic edge chromatic number of a graph G,denoted by α＇r（G）,is the minimum number of colors used to produce an edge coloring of the graph such that adjacent edges receive different colors and every cycle C has at least min {｜C｜,r} colors.We prove that α＇r（G） ≤（4r ＋ 1）△（G）,when the girth of the graph G equals to max{50,△A（G）} and 4 ≤ r ≤ 7.If we relax the restriction of the girth to max {220,A（G）},the upper bound of a＇r（G） is not larger than（2r ＋ 5）△（G） with 4 ≤r≤ 10.展开更多
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Local Colorism can be seen as a new literature type, which sprung up in 1920s. As a literature trend, local novels open a new road for realistically sentimental novels of view literature. Therefore, this paper is aim to study the Local Colorism American Novels of 19th Century, especially in west and south of American. American southern literature and western literature although established respectively unique sense of place suffer from complex history. And it proves that American literature has been increasingly building it popularity and a new type of literature.
文摘The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.
基金supported by NIH grants U54CA268084,U54CA261694,and R01CA228272National Science Foundation grants EFMA-1830961 and CBET-2430743+1 种基金philanthropic support from Rob and Kristin Goldman,Mr.David Sachsthe Christina Carinato Charitable Foundation.
文摘Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin.Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales,from nanometers to micrometers.Single molecule localization microscopy(SMLM)methods,such as STORM,are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization.Multi-label imaging of chromatin is necessary to unpack its structural complexity.However,these efforts are challenged by the high-density nuclear environment,which can affect antibody binding affinities,diffusivity and non-specific interactions.Optimizing buffer conditions,fluorophore stability,and antibody specificity is crucial for achieving effective antibody conjugates.Here,we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment.This protocol couples multiplexed localization datasets with a robust analysis algorithm,which utilizes localizations from one target as seed points for distance,density and multi-label joint affinity measurements to explore complex organization of all three targets.Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories,but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters.This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.
基金Supported by the National Natural Science Foundation of China(No.11371355)
文摘An r-acyclic edge chromatic number of a graph G,denoted by α＇r（G）,is the minimum number of colors used to produce an edge coloring of the graph such that adjacent edges receive different colors and every cycle C has at least min {｜C｜,r} colors.We prove that α＇r（G） ≤（4r ＋ 1）△（G）,when the girth of the graph G equals to max{50,△A（G）} and 4 ≤ r ≤ 7.If we relax the restriction of the girth to max {220,A（G）},the upper bound of a＇r（G） is not larger than（2r ＋ 5）△（G） with 4 ≤r≤ 10.
