Neighborhood Of Edges Research Articles

Maritime infrared image enhancement based on morphological pseudotransmittance modulation and radiation source enhancement

To address the problems of low contrast and fuzzy details in maritime infrared images, this paper constructs a pseudotransmittance model based on the principles of infrared radiation imaging, and proposes an enhancement algorithm of morphology single-scale pseudotransmittance modulation and radiation source enhancement. First, the original image is adaptively segmented to generate the radiation source region and the radiation source suppressed background. Then, the pseudotransmittance is calculated by estimating the radiation of the background through morphological methods. Finally, the pseudotransmittance is used to modulate the original image for background enhancement and the radiation source detail is enhanced as well. On this basis, an algorithm for maritime infrared image enhancement with morphological multiscale pseudotransmittance modulation and radiation source hierarchical fusion is developed to overcome the problem of edge overenhancement and neighbourhood enhancement. Experiments show that our methods perform well in detail and contrast enhancement and outperform other methods in both qualitative and quantitative evaluations.

Saliency and edge features-guided end-to-end network for salient object detection

The rapid development of the Vision Transformer backbones has enabled the capture of feature information with global dependencies, leading to excellent performance in salient object detection tasks. However, it fails to adequately emphasize fine local features of edges, resulting in coarse and blurry edges in the final output. Therefore, this paper proposes a set prediction method for salient object detection. The approach enables the model to simultaneously consider both saliency (salient object) and edge features, achieving an end-to-end edge feature fusion. There is no need to design multiple complex branch structures and multiple training as with other methods. The model integrates random edge neighborhood sampling to enhance the recognition accuracy of local edge features in images. This approach addresses the weak perception of local features by Transformers and the issue encountered during actual training, where edge pixels typically occupy a much smaller proportion of the image compared to the background, resulting in insufficient learning of edge features by the model. The proposed end-to-end model in this paper fuses multiple features, including edge and salient objects. They are extracted within a unified framework while simultaneously outputting edge and salient object maps. Experimental results on six public datasets show that the proposed method significantly improves model performance benchmarks for detecting salient objects.

Quasi-Laplacian energy of fractal graphs

Graph energy is a measurement of determining the structural information content of graphs. The first Zagreb index can be handled with its connection to graph energy. In this paper, a novel and significant application of the first Zagreb index to composite graphs based on fractal graphs is revealed, and by the relation between quasi-Laplacian energy and the vertex degrees of a graph, we derive closed-form formulas for the quasi-Laplacian energy of fractal graphs or namely R-graphs, R-vertex and edge join, R-vertex and edge corona, R-vertex and edge neighborhood graphs in terms of the corresponding energy, the first Zagreb indices, number of vertices and edges of the underlying graphs.

Film Cooling Forming Features of the Turbine Nozzle Vanes Leading Edge Neighborhood Taking into Account Entry Flow Irregularity

Film Cooling Forming Features of the Turbine Nozzle Vanes Leading Edge Neighborhood Taking into Account Entry Flow Irregularity

“Did You Hear? Mavericks Is Closing!” Punk Refusal of Gentrified Endings

Decades of experience with closed, relocated, and renamed venues, make punks very familiar with cycles of gentrification. Often established in edge neighborhoods, punk venues participate in reproducing the “grit” of urban decline and subculture. Urban revitalization plans that promise community, livability, and culture, rarely leave spaces for established punk community and subculture. The newly branded Retail, Arts, and Theatre District in Ottawa, Canada is a case study in cultural urban development that operationalizes creative placemaking and its future-oriented visions of urban revitalization through cultural spaces and activities. Although the city’s Official Plan celebrates that existing cultural venues add diversity to the district, the pressure placed on punk venues by surrounding development reveal that not all venues are to be recognized by the city as legitimate or desirable forms of either diversity or culture. Close readings of official city planning documents, urban histories, development proposals, and marketing literature are juxtaposed with auto-ethnographic, storytelling, punk histories, and song writing. I argue that punk counter-cultural placemaking practices provide counter-information, counter-environments, and counter-temporalities to space in the city to resist gentrification and refuse displacement as endings. Gentrification kills punk. But punk always comes back, finds new places, haunts old sites, and remembers its past.

Edge Neighborhood Contrastive Learning for Building Change Detection

Building change detection aims to identify the change in buildings in the same geographic area. Recently, many methods based on deep learning (DL) have achieved encouraging performance. However, some challenges remain in effectively exploiting the temporal–spatial correlation and achieving good discrimination in the neighborhood of the edge. To relieve these issues, we develop a selective attention module (SAM) to model the relationship between the semantic and the state (i.e., unchanged or changed) of the pixel, which is integrated into an existing metric learning-based architecture. Moreover, inspired by recent advances in contrastive learning, we present a novel edge neighborhood contrastive learning method to force the network to learn discriminative and compact features, leading to improving the accuracy of building change detection. Experimental results demonstrate that our method achieves competitive performance in terms of objective metrics and visual comparisons.

Important edge identification in complex networks based on local and global features

Identifying important nodes and edges in complex networks has always been a popular research topic in network science and also has important implications for the protection of real-world complex systems. Finding the critical structures in a system allows us to protect the system from attacks or failures with minimal cost. To date, the problem of identifying critical nodes in networks has been widely studied by many scholars, and the theory is becoming increasingly mature. However, there is relatively little research related to edges. In fact, critical edges play an important role in maintaining the basic functions of the network and keeping the integrity of the structure. Sometimes protecting critical edges is less costly and more flexible in operation than just focusing on nodes. Considering the integrity of the network topology and the propagation dynamics on it, this paper proposes a centrality measure based on the number of high-order structural overlaps in the first and second-order neighborhoods of edges. The effectiveness of the metric is verified by the infection–susceptibility (SI) model, the robustness index R, and the number of connected branches θ. A comparison is made with three currently popular edge importance metrics from two synthetic and four real networks. The simulation results show that the method outperforms existing methods in identifying critical edges that have a significant impact on both network connectivity and propagation dynamics. At the same time, the near-linear time complexity can be applied to large-scale networks.

Edge Neighbor Toughness of Graphs

A new graph parameter, edge neighbor toughness is introduced to measure how difficult it is for a graph to be broken into many components through the deletion of the closed neighborhoods of a few edges. Let G=(V,E) be a graph. An edge e is said to be subverted when its neighborhood and the two endvertices are deleted from G. An edge set S⊆E(G) is called an edge cut-strategy if all the edges in S has been subverted from G and the survival subgraph, denoted by G/S, is disconnected, or is a single vertex, or is. The edge neighbor toughness of a graph G is defined to be tEN(G)=minS⊆E(G){|S|c(G/S)}, where S is any edge cut strategy of G, c(G/S) is the number of the components of G/S. In this paper, the properties of this parameter are investigated, and the proof of the computation problem of edge neighbor toughness is NP-complete; finally, a polynomial algorithm for computing the edge neighbor toughness of trees is given.

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network.

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.

Edge and neighborhood guidance network for 2D medical image segmentation

Accurate automatic image segmentation is important in medical image analysis. A perfect segmentation using fully convolutional network (FCN) means an accurate classification of each pixel. However, it is still a great challenge to accurately differentiate edge pixels from neighborhood pixels in weak edge regions. Many previous segmentation methods have focused on edge information to mitigate weak edge problems, but the more important neighborhood information is undervalued. To tackle this problem, in this paper, we propose a novel yet effective Edge and Neighborhood Guidance Network (ENGNet). Specifically, instead of just utilizing the edge information as the shape constraints, the edge and neighborhood guidance (ENG) module is designed to exploit the edge information and fine-grained neighborhood spatial information simultaneously, so as to improve the ability of network to classify edge pixels and neighborhood pixels in weak edge regions. Moreover, the ENG modules are adopted in different scales to learn sufficient feature representations of edge and neighborhood. To extract complementary features more effectively in channel dimension, we also design a multi-scale adaptive selection (MAS) module at channel-wise to extract multi-scale context information and adaptively fuse different-scale features. Two 2D public segmentation datasets including skin lesion dataset and endoscopic polyp dataset are used to evaluate the performance of the proposed ENGNet. Experimental results demonstrated that by exploiting edge information and neighborhood spatial information in different scales simultaneously, the proposed ENGNet can effectively alleviate the misclassification in weak edge regions and achieve better performance than other state-of-the-art methods.

Netpro2vec: A Graph Embedding Framework for Biomedical Applications.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

A method for estimating neighborhood characterization in studies of the association with availability of sit-down restaurants and supermarkets

BackgroundAlthough neighborhood-level access to food differs by sociodemographic factors, a majority of research on neighborhoods and food access has used a single construct of neighborhood context, such as income or race. Therefore, the many interrelated built environment and sociodemographic characteristics of neighborhoods obscure relationships between neighborhood factors and food access.MethodsThe objective of this study was to account for the many interrelated characteristics of food-related neighborhood environments and examine the association between neighborhood type and relative availability of sit-down restaurants and supermarkets. Using cluster analyses with multiple measures of neighborhood characteristics (e.g., population density, mix of land use, and sociodemographic factors) we identified six neighborhood types in 1993 in the Twin Cities Region, Minnesota. We then used mixed effects regression models to estimate differences in the relative availability of sit-down restaurants and supermarkets in 1993, 2001, and 2011 across the six neighborhood types.ResultsWe defined six types of neighborhoods that existed in 1993, namely, urban core, inner city, urban, aging suburb, high-income suburb, and suburban edge. Between 1993 and 2011, inner city neighborhoods experienced a greater increase in the percent of sit-down restaurants compared with urban core, urban, and aging suburbs. Differences in the percent of sit-down restaurants between inner city and aging suburbs, high-income suburbs and suburban edge neighborhoods increased between 1993 and 2011. Similarly, aging suburb neighborhoods had a greater percent of supermarkets compared with urban and high-income suburb neighborhoods in 2001 and 2011, but not in 1993, suggesting a more varied distribution of food stores across neighborhoods over time. Thus, the classification of neighborhood type based on sociodemographic and built environment characteristics resulted in a complex and increasingly varied distribution of restaurants and food stores.ConclusionsThe temporal increase in the relative availability of sit-down restaurants in inner cities after accounting for all restaurants might be partly related to a higher proportion of residents who eat-away-from-home, which is associated with higher calorie and fat intake.

A Strange Vertex Condition Coming from Nowhere

We prove norm-resolvent and spectral convergence in $L^2$ of solutions to the Neumann Poisson problem $-\Delta u_\varepsilon = f$ on a domain $\Omega_\varepsilon$ perforated by Dirichlet-holes and shrinking to a 1-dimensional interval. The limit $u$ satisfies an equation of the type $-u''+\mu u = f$ on the interval $(0,1)$, where $\mu$ is a positive constant. As an application we study the convergence of solutions in perforated graph-like domains. We show that if the scaling between the edge neighbourhood and the vertex neighbourhood is chosen correctly, the constant $\mu$ will appear in the vertex condition of the limit problem. In particular, this implies that the spectrum of the resulting quantum graph is altered in a controlled way by the perforation.

A Displacement Discontinuity Method of High-Order Accuracy in Fracture Mechanics

In this paper the displacement discontinuity method of high-order accuracy and its application to the problems of fracture mechanics are considered. In common practice of applications of boundary element methods, the methods with the piecewise-constant function of boundary displacement are often used. Their advantage against other algorithms is the simplicity of calculation scheme with a rather good accuracy of the solution at the points of region distant from the boundary. In the fracture mechanics (with lines of surfaces of discontinuity of the displacement field), it is required to describe the stress behavior in the proximity of the crack edges with the highest accuracy possible, which leads to necessity of increasing the degree of accuracy of the used numerical methods. It is shown that the methods with high-order continuity of displacements at the boundary proposed in this work substantially improve the accuracy of computation of displacement and stress fields in the neighborhood of crack edges within the region.

Local structure graph models with higher‐order dependence

AbstractLocal structure graph models (LSGMs) describe random graphs and networks as a Markov random field (MRF)—each graph edge has a specified conditional distribution dependent on explicit neighbourhoods of other graph edges. Centred parameterizations of LSGMs allow for direct control and interpretation of parameters for large‐ and small‐scale structures (e.g., marginal means vs. dependence). We extend this parameterization to account for triples of dependent edges and illustrate the importance of centred parameterizations for incorporating covariates and interpreting parameters. Using a MRF framework, common exponential random graph models are also shown to induce conditional distributions without centred parameterizations and thereby have undesirable features. This work attempts to advance graph models through conditional model specifications with modern parameterizations, covariates and higher‐order dependencies.

Vertex Edge Neighborhood Prime Valuation of Union of Graphs

Vertex Edge Neighborhood Prime Valuation of Union of Graphs

Back stress and strength contributions evolution of a heterogeneous austenitic stainless steel obtained after one pass by equal channel angular sheet extrusion (ECASE)

This research work studies the different strength contributions of a sheet-shaped heterogeneous austenitic stainless steel, after having been processed at room temperature by one ECASE pass. A significant hardness increase was found throughout its thickness, showing higher values near the edges while the middle area presents the smallest gains. The material heterogeneity gives rise to a plastic gradient deformation between the soft and hard areas of the microstructure. As a consequence, a more significant amount of geometrically necessary dislocations concentrates on the interfaces of these areas, which helps the material to maintain a reliable strength-ductility ratio. After the dislocations contribution to the material strength, the second contribution comes from the back stress mechanism, followed by grain size contributions. All types of contributions were higher in the edge neighborhoods than in the middle zone. Unlike the contributions from dislocations and grain size that decrease as they approach the interfaces between the hard and soft areas, the back stress contribution does the opposite, showing high increments in these zones.

Adjacency and Laplacian spectra of variants of neighborhood corona of graphs constrained by vertex subsets

In this paper, we define some variants of corona of graphs namely, subdivision (respectively, [Formula: see text]-graph, [Formula: see text]-graph, total) neighborhood corona, [Formula: see text]-graph (respectively, [Formula: see text]-graph, total) semi-edge neighborhood corona, [Formula: see text]-graph (respectively, total) semi-vertex neighborhood corona of graphs constrained by vertex subsets. These corona operations generalize some existing corona operations such as subdivision ([Formula: see text]-graph, [Formula: see text]-graph, total) double neighborhood corona, subdivision vertex (respectively, edge) neighborhood corona, [Formula: see text]-graph vertex (respectively, edge) neighborhood corona of graphs. First, we consider a matrix in specific form and determine its spectrum. Then by using this, we derive the characteristic polynomials of the adjacency and the Laplacian matrices of the new graphs when the base graph is regular. Also, we deduce the characteristic polynomials of the adjacency and Laplacian matrices of the above mentioned particular cases from our results.

IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK

Abstract. We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.

Geometric and LP-based heuristics for angular travelling salesman problems in the plane

The angular-metric travelling salesman problem (AngleTSP) asks for a Hamiltonian cycle for given points in the Euclidean plane mimimizing the sum of all turning angles. Setting as objective the linear combination of these angles with the distances of the classical TSP, gives rise to the angular-distance-metric travelling salesman problem (AngleDistanceTSP).Since both problems are NP-hard, we first introduce a wide range of heuristic approaches, motivated by the typical geometric structure of optimal solutions. In particular, we exploit lens-shaped neighbourhoods of edges and a decomposition of the graph into layers of convex hulls, which are then merged into a tour. Secondly, we consider an ILP model based on the more general quadratic travelling salesman problem (QTSP). By rounding its fractional solutions we obtain a collection of subtours, paths and isolated points, which are then combined into a tour by various strategies, all of them involving auxiliary ILP models. Finally, different improvement heuristics are proposed, most notably a matheuristic which locally reoptimizes the solution for rectangular sectors of the given point set by an ILP approach.Results of extensive computational experiments using benchmark instances from the literature illustrate the Pareto-efficient frontier of algorithms in a (running time, objective value)-space. It turns out that our new methods clearly dominate previously published heuristics.