Edge Orientation Research Articles

Resource efficient stabilization for local tasks despite unknown capacity links

Self-stabilizing protocols enable distributed systems to recover correct behavior starting from any arbitrary configuration. In particular, when processors communicate by message passing and the communication links are unbounded, fake messages may be placed in communication links by an adversary. When the number of such fake messages is unknown, self-stabilization may require huge resources:•generic solutions (a.k.a. data link protocols) require unbounded resources, which makes them unrealistic to deploy,•specific solutions (e.g., census or tree construction) require O(nlog⁡n) or O(Δlog⁡n) bits of memory per node, where n denotes the network size and Δ its maximum degree, which may prevent scalability.We investigate the possibility of resource-efficient self-stabilizing protocols in this context.Specifically, we present self-stabilizing protocols for (Δ+1)-coloring and maximal independent set construction in any n-node graph, under the asynchronous message-passing model. The problems of (Δ+1)-coloring and maximal independent set construction are widely regarded as benchmark problems for evaluating local algorithms. Our protocols offer many desirable features. They are deterministic, converge in O(kΔn2log⁡n) message exchanges, where k is the (unknown) initial number of (possibly corrupted) messages in a communication link. They use messages of O(log⁡log⁡n+log⁡Δ) bits with a memory of O(Δlog⁡Δ+log⁡log⁡n) bits at each node. The resource consumption of our protocols is thus almost oblivious to the number of nodes, enabling scalability. Moreover, a striking property of our protocols is that the nodes do not need to know the number, or any bound on the number of messages initially present in each communication link of the initial (potentially corrupted) network configuration. This permits our protocols to handle any future network with unknown message capacity communication links.A key building block of our coloring and maximal independent set schemes is an algorithm to obtain an acyclic orientation of graph edges, that is of independent interest, and can serve as a useful tool for solving other tasks in this challenging setting.

Numerical characterization of thermal transport in hexagonal tungsten disulfide (WS2) nanoribbons

In this study, we have investigated the thermal transport characteristics of single-layer tungsten disulfide, WS2 nanoribbons (SLTDSNRs) using equilibrium molecular dynamics simulations with the help of Green-Kubo formulation. Using Stillinger-Weber (SW) inter-atomic potential, the calculated room temperature thermal conductivities of 15 nm × 4 nm pristine zigzag and armchair SLTDSNRs are 126 ± 10 W m−1K−1 and 110 ± 6 W m−1K−1, respectively. We have explored the dependency of thermal conductivity on temperature, width, and length of the nanoribbon. The study shows that the thermal conductivity of the nanoribbon decreases with the increase in temperature, whereas the thermal conductivity increases with an increase in either the width or length of the ribbon. The thermal conductivity does not increase uniformly as the size of the ribbon changes. We have also observed that the thermal conductivity of SLTDSNRs depends on edge orientations; the zigzag nanoribbon has greater thermal conductivity than the armchair nanoribbon, regardless of temperature or dimension variations. Our study additionally delves into the tunable thermal properties of SLTDSNRs by incorporating defects, namely vacancies such as point vacancy, edge vacancy, and bi-vacancy. The thermal conductivities of nanoribbons with defects have been found to be considerably lower than their pristine counterparts, which aid in enhanced values for the thermoelectric figure of merit (zT). We have varied the vacancy concentration within a range of 0.1% to 0.9% and found that a point vacancy concentration of 0.1% leads to a 64% reduction in the thermal conductivity of SLTDSNRs. To elucidate these phenomena, we have calculated the phonon density of states for WS2 under different aspects. The findings of our work provide important understandings of the prospective applications of WS2 in nanoelectronic and thermoelectric devices by tailoring the thermal transport properties of WS2 nanoribbons.

Counting kernels in directed graphs with arbitrary orientations

A kernel of a directed graph is a subset of vertices that is both independent and absorbing (every vertex not in the kernel has an out-neighbour in the kernel).Not all directed graphs contain kernels, and computing a kernel or deciding that none exist is NP-complete even on low-degree planar digraphs. The existing polynomial-time algorithms for this problem are all obtained by restricting both the undirected structure and the edge orientations of the input: for example, to chordal graphs without bidirectional edges (Pass-Lanneau, Igarashi and Meunier, Discrete Appl Math 2020) or to permutation graphs where each clique has a sink (Abbas and Saoula, 4OR 2005).By contrast, we count the kernels of a fuzzy circular interval graph in polynomial time, regardless of its edge orientations, and return a kernel when one exists. (Fuzzy circular interval graphs were introduced by Chudnovsky and Seymour in their structure theorem for claw-free graphs.)We also consider kernels on cographs, where we establish NP-hardness in general but linear-time solvability on the subclass of threshold graphs.

Orientation distribution and deviation behaviors of dislocation loops in a quenched Al-Cu alloy

Dislocation loops in quenched aluminum alloys are usually seen deviating from their nominal habit planes, while the orientation distribution and deviation mechanism are far from understanding. Based on the method of tomographic crystallography of dislocations and correlative in-situ heating experiments in transmission electron microscopy (TEM), we revisit the three-dimensional (3D) characteristics and deviation behaviors of dislocation loops in a quenched Al-Cu alloy with an aim to fully unravel the relationship between dislocation crystallography and deviation behaviors. The results show that all the dislocation loops exhibit approximately circular shapes and have a/2 < 110>-type Burgers vectors, with loop orientations deviating from their pure edge orientation {110} of 0°-17.8° and distributing mainly close to the [101]-[001] and [101]-[111] edges of the standard stereographic triangle. Three types of deviation behaviors of dislocation loops are then defined, which are characterized by loop rotation around 〈100〉, 〈110〉 and irregular axes, respectively. Theoretical calculations of strain energy variation with increasing deviation angle indicate that the relatively low energy barrier may account for a high frequency of loop deviation in the low angle range. Detailed in-situ TEM heating experiments and correlative tomographic crystallography analyses strongly demonstrate the specific roles of dislocation gliding and climbing at elevated temperatures in the shrinkage, polygonization and deviation processes of dislocation loops, and the competitive and cooperative motion of dislocation segments close to slip planes {111} are the fundamental reasons for the formation of three types of deviated dislocation loops.

Canonical decompositions and algorithmic recognition of spatial graphs

AbstractWe prove that there exists an algorithm for determining whether two piecewise-linear spatial graphs are isomorphic. In its most general form, our theorem applies to spatial graphs furnished with vertex colourings, edge colourings and/or edge orientations.We first show that spatial graphs admit canonical decompositions into blocks, that is, spatial graphs that are non-split and have no cut vertices, in a suitable topological sense. Then, we apply a result of Haken and Matveev in order to algorithmically distinguish these blocks.

Experimental analyses of temperature and pressure oscillation frequencies of a flat plate pulsating heat pipe tested under various edge orientation angles and heat loads

Experimental analyses of temperature and pressure oscillation frequencies of a flat plate pulsating heat pipe tested under various edge orientation angles and heat loads

Gradient-Based Local Causal Structure Learning.

Finding the causal structure from a set of variables given observational data is a crucial task in many scientific areas. Most algorithms focus on discovering the global causal graph but few efforts have been made toward the local causal structure (LCS), which is of wide practical significance and easier to obtain. LCS learning faces the challenges of neighborhood determination and edge orientation. Available LCS algorithms build on conditional independence (CI) tests, they suffer the poor accuracy due to noises, various data generation mechanisms, and small-size samples of real-world applications, where CI tests do not work. In addition, they can only find the Markov equivalence class, leaving some edges undirected. In this article, we propose a GradieNt-based LCS learning approach (GraN-LCS) to determine neighbors and orient edges simultaneously in a gradient-descent way, and, thus, to explore LCS more accurately. GraN-LCS formulates the causal graph search as minimizing an acyclicity regularized score function, which can be optimized by efficient gradient-based solvers. GraN-LCS constructs a multilayer perceptron (MLP) to simultaneously fit all other variables with respect to a target variable and defines an acyclicity-constrained local recovery loss to promote the exploration of local graphs and to find out direct causes and effects of the target variable. To improve the efficacy, it applies preliminary neighborhood selection (PNS) to sketch the raw causal structure and further incorporates an l1 -norm-based feature selection on the first layer of MLP to reduce the scale of candidate variables and to pursue sparse weight matrix. GraN-LCS finally outputs LCS based on the sparse weighted adjacency matrix learned from MLPs. We conduct experiments on both synthetic and real-world datasets and verify its efficacy by comparing against state-of-the-art baselines. A detailed ablation study investigates the impact of key components of GraN-LCS and the results prove their contribution.

ВИЯВЛЕННЯ КАУЗАЛЬНИХ ВІДНОШЕНЬ З МАРКОВСЬКИХ ВЛАСТИВОСТЕЙ В УМОВАХ ЛАТЕНТНИХ КОНФАУНДЕРІВ

We address the problems of causal structure reconstruction given conditional independence facts when latent confounders are allowed. We examine the conditions that allow one to partially or fully identify authentic causal links and latent confounders. The updated implicative rules for orienting edges under confounding are suggested. As demonstrated, it is possible to construct the new rules, which can reveal confounded causal edges and bows. The rules rely on facts of the absence of certain authentic edges (such facts may be justified by non-independence constraints, like Verma constraint, or subject-based requirements). Keywords: causal relation, d-separation, conditional independence, latent confounder, edge orientation, bow (arc).

Human-verifiable proofs in the theory of word-representable graphs

A graph is word-representable if it can be represented in a certain way using alternation of letters in words. Word-representable graphs generalise several important and well-studied classes of graphs, and they can be characterised by semi-transitive orientations. Recognising word-representability is an NP-complete problem, and the bottleneck of the theory of word-representable graphs is convincing someone that a graph is non-word-representable, keeping in mind that references to (even publicly available and user-friendly) software are not always welcome. (Word-representability can be justified by providing a semi-transitive orientation as a certificate that can be checked in polynomial time). In the literature, a variety of (usually ad hoc) proofs of non-word-representability for particular graphs, or families of graphs, appear, but for a randomly selected graph, one should expect looking at O(2#edges) orientations and justifying that none of them is semi-transitive. Even if computer would print out all these orientations and would point out what is wrong with each of the orientations, such a proof would be essentially non-checkable by a human. In this paper, we develop methods for an automatic search of human-verifiable proofs of graph non-word-representability. As a proof-of-concept, we provide “short” proofs of non-word-representability, generated automatically by our publicly available user-friendly software, of the Shrikhande graph on 16 vertices and 48 edges (6 “lines” of proof) and the Clebsch graph on 16 vertices and 40 edges (10 “lines” of proof). Producing such short proofs for relatively large graphs would not be possible without the instrumental tool we introduce (allowing to assume orientations of several edges in a graph, not just one edge as it was previously used) that is a game changer in the area. As a bi-product of our studies, we correct two mistakes published multiple times (two graphs out of the 25 non-word-representable graphs on 7 vertices were actually word-representable, while two non-word-representable graphs on 7 vertices were missing).

Towards Privacy-Aware Causal Structure Learning in Federated Setting

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attached much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in federated learning setting.

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

Exploring factors affecting the conversion of the coefficient of restitution to equivalent free fall drop height

AbstractThe coefficient of restitution (COR) is used to estimate the equivalent free fall drop height of packaging during handling and transportation. Various experimental studies were designed to explore factors affecting the conversion of the COR to equivalent free fall drop height. These factors include the number of drops, drop height, drop orientation, drop base, temperature and humidity on the COR. The results indicate that the number of drops and drop orientation are the two most influential factors affecting COR values. As the package undergoes more drops, the COR increases. Among the various orientations, the COR is largest for flat drops, followed by edge and corner orientations. Within flat drops, a smaller impact area results in a smaller COR. Manufacturing joints and the flats, edges and corners adjacent to manufacturing joints tend to yield smaller COR values. A COR correction matrix was developed to correct the conventional COR calculation. This matrix allows engineers to refine COR values based on the number of drops and drop orientations in the field. The matrix‐corrected equivalent free fall drop heights demonstrated an average accuracy of 96%. Compared to the conventional COR calculation method, it effectively reduces errors by a factor of 2–3.

Computing Data-driven Multilinear Metro Maps

ABSTRACT Traditionally, most schematic metro maps in practice as well as metro map layout algorithms adhere to an octolinear layout style with all paths composed of horizontal, vertical, and 45 ∘ -diagonal edges. Despite growing interest in more general multilinear metro maps, generic algorithms to draw metro maps based on a system of k ≥ 2 not necessarily equidistant slopes have not been investigated thoroughly. In this paper, we present and implement an adaptation of the octolinear mixed-integer linear programming approach of Nöllenburg and Wolff (2011) that can draw metro maps schematized to any set C of arbitrary orientations. We further present a data-driven approach to determine a suitable set C by either detecting the best rotation of an equidistant orientation system or by clustering the input edge orientations using a k-medians algorithm. We demonstrate the new possibilities of our method using several real-world examples.

Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail

Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.

Stimulus edges induce orientation tuning in superior colliculus

Orientation columns exist in the primary visual cortex (V1) of cat and primates but not mouse. Intriguingly, some recent studies reported the presence of orientation and direction columns in the mouse superficial superior colliculus (sSC), while others reported a lack of columnar organization therein. Using in vivo calcium imaging of sSC in the awake mouse brain, we found that the presence of columns is highly stimulus dependent. Specifically, we observed orientation and direction columns formed by sSC neurons retinotopically mapped to the edge of grating stimuli. For both excitatory and inhibitory neurons in sSC, orientation selectivity can be induced by the edge with their preferred orientation perpendicular to the edge orientation. Furthermore, we found that this edge-induced orientation selectivity is associated with saliency encoding. These findings indicate that the tuning properties of sSC neurons are not fixed by circuit architecture but rather dependent on the spatiotemporal properties of the stimulus.

Infants are sensitive to the Edge Orientation Entropy of Natural Scenes

Infants are sensitive to the Edge Orientation Entropy of Natural Scenes

Directional lifting wavelet transform for image edge analysis

In this paper, we propose a new two-dimensional directional discrete wavelet transform that can decompose an image into 12 multiscale directional edge components. The proposed transform is designed in a fully discrete setting and thus is easy to implement in actual computations. The proposed transform is viewed as a category of redundant discrete wavelet transforms implemented by fast in-place computational algorithms by a lifting scheme that has been modified to incorporate redundancy. The redundancy is limited to (N×J+1)/4, where N=12 is the directional selectivity and J is a decomposition level of the multiscale transform. Numerical experiments in edge analysis using various images demonstrate the advantages of the proposed method over some conventional standard methods. The proposed method outperforms several conventional edge detection methods in identifying both the location and orientation of edges, and well captures the directional and geometrical features of images.

Super-resolution image visual quality assessment based on structure–texture features

Assessing the visual quality of super-resolution images (SRIs) is crucial for advancing algorithm development, but it remains an unsolved problem. In this paper, we present a novel reduced-reference image quality assessment (RR-IQA) method specifically suited for evaluating SRIs. Our approach leverages information from the input low-resolution (LR) image as a reference signal to extract features that are most relevant to modeling the visual quality of SRIs. We analyze the artifact characteristics of SRIs and demonstrate that features describing edge orientations, high frequency components, and textures are the most important for this task. To extract these features, we first perform structure–texture decompositions (STD) on both the SRI and its LR input, then obtain the edge orientation feature through a traditional hand-crafted approach, and deep neural networks to extract features related to high frequency components and textures. We employ a shallow multilayer perceptron (MLP) to predict an image quality score based on these quality-relevant features. To improve feature representation ability and prevent overfitting, we pretrain the feature extraction module using a large number of unlabeled samples, which accounts for over 99.8% of the total model parameters. We use precious samples with mean opinion score (MOS) labels to train a high-quality shallow MLP predictor. Our experimental results show that the proposed method outperforms classical and state-of-the-art models.

Quantification of structural response and edge orientation of Chopped Tape Thermoplastic Composites in net-shaped specimens

Chopped Tape Thermoplastic Composites (CTTCs) offer high formability and performance for complex-shaped components in the aerospace and automotive industries. However, the mesoscopic discontinuity leads to spatial variabilities and correspondingly high scatter in the elastic properties of CTTCs due to the random orientations of chopped tapes and chopped tape-cavity edge interactions. Here we propose a new approach to investigate the effect of mould cavity edges on chopped tape orientation and hence the mechanical properties of CTTCs. Based on this approach, a Set Voronoi tessellation was implemented to represent the variability of local Young’s Modulus and chopped tape-cavity edge interactions occurring during the manufacturing process. It was confirmed that the chopped tapes align along the edges, and progressively transition to a random orientation towards the middle of the specimen. The results were validated on moulded specimens and demonstrated the ability to deconvolute the edge interaction.

Combining forest exploitation and heathland biodiversity: Edges structure drives microclimates quality and reptile abundance in a coniferous plantation

In Western Europe, natural habitats such as heathlands have been converted into many commercially managed forests, with severe impacts on biodiversity. In dense planted forests, forest edges are often the only suitable areas for ectothermic organisms highly dependent on open habitats for thermoregulation. Surprisingly, the influence of forest edges structure on the thermal quality of microhabitats and reptile species distribution remains poorly understood. In this study, we examined two reptile species and thermal quality of interior forest edges of a coniferous forest in Western France (Brittany). We focused on two hypotheses (i) the vegetation structure of the forest edges drives the thermal quality of the habitat and (ii) structural complexity of the forest edges influences the abundance of two heliothermic reptiles: the common adder (Vipera berus) and the common lizard (Zootoca vivipara). We first deployed temperature sensitive data loggers to quantify thermal conditions along 16 cross-sections of inner forest margins. For each section, 4 temperature loggers were placed at 1, 3, 5 and 7 m from the driveway and we examined the relation to vegetation structure and canopy cover. Second, we carried visual encounter surveys in 55 edges in order to measure the response of two reptiles to the structure of the forest margin along exploitation driveway. Our results show that high local canopy cover decreases microhabitat quality within interior forest edges. We also found that common lizard abundance was significantly influenced by the edge orientation and increased with global canopy openness and ground level vegetation. Adder abundance only increased significantly with the driveway width, suggesting the unmodelled effect of other biotic/abiotic variables. Our study shows that thermal quality of interior edges and driveway characteristics are relevant to support heathland reptile populations. We posit that maintain strips of favourable microhabitats should be an efficient land sharing strategy to combine forestry activities and biodiversity conservation.