Edge Direction Research Articles

On the Dynamism of Paintings Through the Distribution of Edge Directions

The digitization of artworks has recently offered new computational perspectives on the study of art history. While much of the focus has been on classifying styles or identifying objects, the analysis of more abstract concepts, such as the perception of motion or dynamism in still images, remains largely unexplored. Semioticians and artists have long explored the representation of dynamism in still images, but they often did so through theoretical frameworks or visual techniques, without a quantitative approach to measuring it. This paper proposes a method for computing and comparing the dynamism of paintings through edge detection. Our approach is based on the idea that the dynamism of a painting can be quantified by analyzing the edges in the image, whose distribution can be used to identify patterns and trends across artists and movements. We demonstrate the applicability of our method in three key areas: studying the temporal evolution of dynamism across different artistic styles, as well as within the works of a single artist (Wassily Kandinsky), visualizing and clustering a large database of abstract paintings through PixPlot, and retrieving similarly dynamic images. We show that the dynamism of a painting can be effectively quantified and visualized using edge detection techniques, providing new insights into the study of visual culture.

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCNs) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node’s extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Specifically, TrustSGCN leverages topological information to measure trustworthiness on edge sign for high-order relationships inferred by balance theory. It then considers structural properties inherent to an input network, such as the ratio of triads, to correct for incorrect embedding propagation. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known social theories, i.e., balance and status, to jointly preserve the edge sign and direction between nodes connected by existing edges in the embedding space. The experiments on six real-world signed network datasets demonstrate that TrustSGCN consistently outperforms six state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN .

Laser directed energy deposition remanufacturing of LPBF-processed TA15 parts: Hybrid process interface induced microstructure inheritance and mechanical performance

The laser additive hybrid manufacturing technology, which combines the precision of Laser Powder Bed Fusion (LPBF) and the flexibility of Laser Directed Energy Deposition (LDED), provides an effective solution for efficient forming and remanufacturing of large, complex components. The bonding zone (BZ) between LPBF and LDED forming regions is crucial for the final component performance. This study utilized LDED to remanufacture annealed LPBF substrates and investigated the microstructure and mechanical properties of TA15 alloy remanufactured parts. Results shows that a gradient microstructure forms at the bonding zone (BZ), where the bottom region exhibits a mixed structure including transitional β phase (βt), lamellar α and secondary α phase (αs). The top region shows a "ghost structure" of fine needle-like α phases. The mechanical property matched the microstructure well, as the hardness in the upper BZ reaches up to 463 MPa, whereas the LFZ exhibits the lowest hardness at 401 MPa. As the angle between the LDED region edge and tensile direction increases, the tensile strength first increases and then stabilizes, while elongation increases initially and then decreases. At a tilt angle of 60°, the strength peaks at 1098 MPa; at a 30° angle, the elongation reaches a maximum of 10.336%. All samples fracture at the LPBF zone, indicating that the formation of fine lamellar α phases in the BZ contributes to its higher strength compared to LPBF substrate and LDED deposition zones.

Network embedding-based directed community detection with unknown community number

Community detection of network analysis plays an important role in numerous application areas, in which estimating the number of communities is a fundamental issue. However, many existing methods focus on undirected networks ignoring the directionality of edges or unrealistically assume that the number of communities is known a priori. In this article, we develop a data-dependent community detection method for the directed network to determine the number of communities and recover community structures simultaneously, which absorbs the ideas of network embedding and penalized fusion by embedding the out- and in-nodes into low-dimensional vector space and forcing the embedding vectors towards its center. The asymptotic consistency properties of the proposed method are established in terms of network embedding, directed community detection, and estimation of the number of communities. The proposed method is applied on synthetic networks and real brain functional networks, which demonstrate the superior performance of the proposed method against a number of competitors.

基于弓背蚁上颚切-锯运动机理的农业仿生刀开发研究

Aiming at issues such as low instantaneous grasping ability, unsatisfactory cutting quality, and proneness to damage of the blades for existing harvesters, by observing the physiological structure and movement characteristics of the ant's mandible and by bionic design theory, a bionic blade was designed to optimize its performance. In this paper, Camponotus was selected as the research object to observe the movement of the right mandibular teeth. It was concluded that the movement of the ant's mandibular teeth was a cutting-sawing motion. A comparative analysis was carried out using the flat blade and the mandibular teeth blade, uncovering that the mandibular teeth movement formed a sliding cut with a variable sliding cutting angle. The mandibular teeth were beneficial for clamping the target and boosting the instantaneous grasping force. The fourth tooth of the mandible was selected as the bionic prototype. from which the contour curve was extracted and analyzed, followed by the design of the bionic blade. Through the finite element method, the influence laws of parameters such as the tooth pitch, structural angle, and blade inclination angle on the stress field and deformation of the bionic blade were analyzed under two force application circumstances: along the inclination direction of the tooth edge and the blade face direction. The results showed that when the applied force was along the tooth edge inclination, the total deformation of the bionic blade initially decreased and then increased with the tooth pitch increase. The maximum equivalent stress of the bionic blade rose gradually with the tooth pitch increase, and the total deformation decreased with the increase of the inclination angle of the tooth. The equivalent stress diminished with the rise in the inclination angle. With the increase of the structural edge angle, the total deformation of the bionic blade rose gradually. When the force was applied along the blade face direction, the deformation and stress values of the blade were significantly lower than those when the force was along the tooth edge inclination. The research findings can offer theoretical references for the design of bionic blades for harvesters.

Hermitian random walk graph Fourier transform for directed graphs and its applications

Signal processing on directed graphs present additional challenges since a complete set of eigenvectors is unavailable generally. To solve this problem, in this paper, a novel graph Fourier transform is constructed for representing and processing signals on directed graphs. Firstly, we introduce a Hermitian random walk Laplacian operator and derive that it is Hermitian positive semi-definite. Hence, the obtained Laplacian operator is diagonalizable and yields orthogonal eigenvectors as graph Fourier basis. Secondly, we propose the Hermitian random walk graph Fourier transform (HRWGFT) with good properties including unitary and preserving inner products. Furthermore, HRWGFT records the directionality of edges without sacrificing the information about the graph signal. Then, using these favorable properties, we derive spectral convolution to define the graph filter which is the core tool for processing graph signals. Finally, based on the proposed Laplacian matrix and HRWGFT, we present several applications on synthetic and real-world networks, including signal denoising, data classification. The rationality and validity of our work are verified by simulations.

Cognitive customer profiling at the edge: Challenges and research directions

The proliferation of automated services and applications has generated large amounts of data that must be handled with artificial intelligence and machine learning techniques, impacting businesses in different fields. Cognitive customer profiling services allow a better user experience enabling the tailoring of the customer experience to specific consumer patterns and preferences. Cognitive customer profiling services require heavy processing power in order to get accurate results from the artificial intelligence/machine learning models, which has led to services taking advantage of the cloud-to-edge continuum. This trending approach brings a set of challenges that have to be addressed. The more recent edge intelligence paradigm introduces artificial intelligence capacities to edge nodes, enabling a new set of solutions. However, artificial intelligence techniques must be adapted to deal with the specific characteristics of these scenarios. This paper identifies the challenges of running cognitive customer profiling in centralized scenarios, such as the cloud, to later introduce other approaches more suited for scenarios like the edge, identifying challenges and future research directions in the area.

Mechanical and failure behaviour of PLA FFF printed part under sweat absorption: Splint arm application

This study focuses on the selection of printing parameters for arm splint to support forces under sweat exposure. We investigated the influence of varying infill density and raster angle on flexural properties after sweat diffusion. In particular, polylactic acid (PLA) beams were printed in the edge direction with different infill densities (60%, 80% and 100%) and raster angles (0°, 45° and 90°). We evaluated the flexural properties of the beams before and after aging in sweat, and analysed the failure mode based on the printing parameters. Additionally, we quantified the porosity of the samples and correlated it with sweat absorption and mechanical properties. Degradation of PLA in water and sweat is assessed by the Fourier transform infrared spectroscopy analysis. Our findings revealed that a higher strength and stiffness were achieved with a 45° raster angle and higher infill density. Interestingly, the parameters that resulted in optimal properties before and after sweat absorption aren’t the same. Sweat absorption depends on the architecture leads to the change in failure mode which initiated fracture. The findings provide valuable insights into the optimisation of printing parameters for arm splints, considering both mechanical performance and the impact of sweat interaction on fracture behaviour.

Liquid-immersion inclined-rotated exposure system for fabricating three-dimensional microstructures with large inclination angles

Abstract This study utilized liquid-immersion inclined-rotated ultraviolet lithography to fabricate three-dimensional (3D) microstructures. The maximum achievable inclination angles obtained through conventional inclined-rotated exposure (IRE) methods were limited by the significant refractive index differences in material. We proposed an IRE with liquid-immersion and adjustable mirrors, which enabled greater inclination angles with improved adjustability. Using liquid as a medium helped minimize the refractive index disparities between materials. We fabricated polydimethylsiloxane molds for micro suction cup (MSC) array sheets to evaluate the performance of the developed liquid-immersion IRE. The resulting MSC array sheets (10 mm2) with a suction cup diameter of 500 μm, achieved inclination angles up to 51°, approximately double those obtained with the conventional IRE method. In addition, the suction force of the fabricated MSC arrays were evaluated by pulling along the vertical, horizontal, and edge directions under wet conditions. The maximum measured suction force was 0.15 N, confirming the effectiveness of the proposed liquid-immersion IRE in fabricating 3D microstructures, as demonstrated by the fabricated MSC array sheets.

Study on separation of 6–0 mm low rank high sulfur lignite by FGX dry separation bed

ABSTRACT Coal is an essential primary energy for the national economy and people’s livelihood. In recent years, with the rapid growth of the consumption of high-quality coal resources, Lignite with a low degree of metamorphism has been extensively exploited and utilized. In this paper, desulfurization and ash reduction treatment of 6–0 mm fine-grade low-grade high-sulfur Lignite was carried out using a FGX dry separation bed (FGX FGX compound dry separation bed). The migration and distribution of differentiated density particles in the bed under the action of cascade air flow were mainly studied, and the influence of vibration intensity (amplitude and frequency) on the separation effect of fine-grade low-grade high-sulfur Lignite was explored. At the same time, the material distribution characteristics of each section in the direction of the discharge edge of the bed were systematically analyzed, the material distribution area of the bed was determined, the optimal operating parameters (A = 2.8 mm, f = 26 Hz, V = 2.29 m/s) were obtained, and the sorting test was carried out under the optimal parameter conditions. The test results show that when the vibration frequency f = 26 Hz, the sulfur segregation degree of bedding materials reaches the maximum Ss = 0.36, and the sulfur content of materials in I, II, and III regions is 0.85%, 5.47%, and 18.78%, respectively. The stratification effect of bedding materials is the best. When A = 2.8 mm, the material sulfur segregation reaches the minimum value of Ss = 0.28, and the average sulfur content of materials in I, II, and III regions is 0.78%, 5.46% and 18.27%, the material is stratified according to density. When the airflow rate is 2.55 m/s. The stratification effect of bed material is best according to density. The clean coal yield is 69.87%, and the sulfur content is 0.98%. The gangue yield is 30.13%, and the sulfur content is 18.66%. The Ep value of energy deviation is 0.11 g/cm3, which realizes high precision dry desulfurization and upgrading fine-grade, low-grade, and high sulfur lignite.

Adaptive orthogonal directional total variation with kernel regression for CT image denoising.

Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression. The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation. Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance. The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.

Ant Colony Optimization for solving Directed Chinese Postman Problem

The Chinese Postman Problem (CPP) is a well-known optimization problem involving determining the shortest route, modeling the system as an undirected graph, for delivering mail, ensuring all roads are traversed while returning to the post office. The Directed Chinese Postman Problem (DCPP) extends the Chinese Postman Problem (CPP), where the underlying graph representing the system incorporates exclusively directed edges. Similarly to CPP, this problem has plenty of applications in route optimization, interactive system analysis, and circuit design problems. However, due to the added constraint (directionality of edges), DCPP results are more challenging to solve. Although methods to solve it in literature are proposed, typically by using minimum-cost-flow algorithms, the meta-heuristics approaches proposed to deal with it are very limited. In this paper, we propose an innovative meta-heuristic approach to solve DCPP by using an ant colony optimization (ACO) algorithm, i.e., an algorithm that simulates in a simplified way the behavior of some species of ants to solve optimization problems. The efficiency of our ant colony optimization for solving the Directed Chinese Postman Problem (ACO-DCPP) is measured by comparing the ACO outcomes with the results obtained by a recursive algorithm that explores all the possible solutions. Results show that ACO-DCPP is stable and gets the global optimum frequently by using an extremely limited number of solutions explored.

Classifying of Isolated Handwritten Arabic Characters (on-line) Using Machine Learning Methods

The prediction of Arabic handwritten characters is one of the fascinating matters in the area of artificial intelligence (AI) and machine learning, especially in the case of on-line handwriting. In the handwriting recognition system, characters are classified according to their particular categories by utilizing -as input- the values of the features extracted from them. Our major objective in this paper is to classify the isolated characters for online Arabic handwriting (? to ?) using machine learning techniques with the features from EDMs (Edge Direction Matrixes) as opposed to EEDMs (Extended Edge Direction Matrixes). With respect to all classification methods used in this study, EEDMs with machine learning techniques had better performance than EDMs in recognizing isolated Arabic online characters.

Acute limbic system connectivity predicts chronic cognitive function in mild traumatic brain injury: An individualized differential structural covariance network study

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.

A Method for Roof Wireframe Reconstruction Based on Self-Supervised Pretraining

Abstract. In this paper, we present a two-stage method for roof wireframe reconstruction employing a self-supervised pretraining technique. The initial stage utilizes a multi-scale mask autoencoder to generate point-wise features. The subsequent stage involves three steps for edge parameter regression. Firstly, the initial edge directions are generated under the guidance of edge point identification. The next step employs edge parameter regression and matching modules to extract the parameters (namely, direction vector and length) of edge representation from the obtained edge features. Finally, a specifically designed edge non-maximum suppression and an edge similarity loss function are employed to optimize the representation of the final wireframe models and eliminate redundant edges. Experimental results indicate that the pre-trained self-supervised model, enriched by the roof wireframe reconstruction task, demonstrates superior performance on both the publicly available Building3D dataset and its post-processed iterations, specifically the Dense dataset, outperforming even traditional methods.

What Goes Around Comes Around: Covering Tours and Cycle Covers with Turn Costs

We investigate several geometric problems of finding tours and cycle covers with minimum turn cost, which have been studied in the past, with complexity, approximation results, and open problems dating back to work by Arkin et al. in 2001. Many new practical applications have spawned variants: For full coverage, all points have to be covered, for subset coverage, specific points have to be covered, and for penalty coverage, points may be left uncovered by incurring a penalty. We show that finding a minimum-turn (full) cycle cover is NP-hard even in 2-dimensional grid graphs, solving the long-standing open Problem 53 in The Open Problems Project edited by Demaine, Mitchell and O’Rourke. We also prove NP-hardness of finding a subset cycle cover of minimum turn cost in thin grid graphs, for which Arkin et al. gave a polynomial-time algorithm for full coverage; this shows that their boundary techniques cannot be applied to compute exact solutions for subset and penalty variants. We also provide a number of positive results. In particular, we establish the first constant-factor approximation algorithms for all considered subset and penalty problem variants for grid-based instances, based on LP/IP techniques. These geometric versions allow many possible edge directions (and thus, turn angles, such as in hexagonal grids or higher-dimensional variants); our approximation factors improve the combinatorial ones of Arkin et al.

Building functional networks for complex response analysis in systems of coupled nonlinear oscillators

Some aspects of engineering dynamics, such as nonlinearities and transient motion of many interconnected parts, remain difficult to handle today. To comply with increasing demands on resilience and safety, the dynamics of large machines need to be better understood. Complex network methods, already present in many scientific disciplines, provide a tool set complementary to conventional methods of system analysis. This work aims at providing a new, function-based view on mechanical systems by generating functional networks. To this end, a network algorithm is applied to sets of cyclically coupled Duffing oscillators as a common example of a complex nonlinear mechanical system. In the functional network, each node represents an oscillator while the direction of the network edges represents a functional coupling. Results show that the network method is capable of identifying dynamical transitions and synchronization between components, as well as determining the number of different states present within a system. Additionally, the time evolution of the component interactions, especially in response to a disturbance, is studied via a sliding-window approach. The results of this analysis might hopefully open new ways for a more efficient system analysis through optional sensor placement, and for effective countermeasures against unwanted dynamics through improved analysis of transient dynamics.

Modeling and mathematical approach for mechanical natural frequencies of structural plates with free edges

The current study aims to demonstrate an easy mathematical procedure and simple to be used by engineers to maintain the mechanical natural frequencies (Eigen values of free vibration) of thin rectangular structural plates with free edges included in boundary conditions. The reason for this easiness and simplicity is that new method of analysis depends mainly on using mechanical minimum energy concept represented in the Rayleigh–Ritz method with appropriate real polynomials as admissible functions. The new procedure guides these functions to satisfy the boundary conditions at the middle points on the free edges of the plate beside the nodal points of the vibration modes along each edge direction. Therefore, there are no complicated or imaginary terms in the developed functions while they lead to surprisingly acceptable and even accurate results when compared with other works and Ansys program models.

A Team Cognition Measurement Method for Single Pilot Operations Human-Machine System Design

Team cognition is an important indicator to reflect the trend of single pilot operation (SPO) human-machine system team performance. How to measure team cognition to explore different function allocation decisions before the implementation of prototypes or simulators has become a significant step in the early SPO human-machine system design. Therefore, this article proposed a team cognition measurement method based on the social network analysis (SNA) method, namely cognitive social network analysis (CSNA). The CSNA method firstly identified the nodes, edge directions and channel resource values of each task; secondly calculated the cognitive demands of each task based on multiple resource theory; thirdly summed the cognitive demands of the same task to construct the association matrix; and finally calculated global and nodal network metrics. The results of the study showed that (1) the CSNA method could identify design flaws of the initial SPO human-machine system; (2) the CSNA method increased the ability to identify the changes in both the cognitive demands of tasks and the overall workloads of human agents compared to the SNA method; (3) the CSNA method could help researchers make reasonable design suggestions to improve the team performance of the redesigned SPO human-machine system. The above results not only verify the feasibility of the CSNA method, but also show that the CSNA method is superior to the current SNA method.

Learning Bayesian Networks: A Copula Approach for Mixed-Type Data.

Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e.,possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data .