Which Edges Matter?

to representation the complex technical systems, we propose the use of fuzzy GH-models based on graphs, which differ from known that allow to take into account, in addition to the same type of edges, different types of edges and multiple edges in the form of a vector. The paper notes the advantages of GH-graphs due to the use of edges in the form of a vector, which reduces the time spent on calculations on graphs and at the same time takes into account the necessary relations in the system. The classification of edges in GH-graphs is performed, due to the need to use all possible relations between the objects of the system.

In this paper we provide a short tuto- rial illustrating the new functions in the package ggm that deal with ancestral, summary and rib- bonless graphs. These are mixed graphs (con- taining three types of edges) that are impor- tant because they capture the modified inde- pendence structure after marginalisation over, and conditioning on, nodes of directed acyclic graphs. We provide functions to verify whether a mixed graph implies that A is independent of B given C for any disjoint sets of nodes and to generate maximal graphs inducing the same independence structure of non-maximal graphs. Finally, we provide functions to decide on the Markov equivalence of two graphs with the same node set but different types of edges. Introduction and background Graphical Markov models have become a part of the mainstream of statistical theory and application in recent years. These models use graphs to represent conditional independencies among sets of random variables. Nodes of the graph correspond to random variables and edges to some type of conditional de- pendency.

The article analyzes a number of methods of knowledge formation using various graph models, including oriented, undirected graphs with the same type of edges and graphs with multiple and different types of edges. This article shows the possibilities of using graphs to represent a three-level structure of knowledge in the field of complex technical systems modeling. In such a model, at the first level, data is formed in the form of unrelated graph vertices, at the second level – information presented by a related undirected graph, and at the third level – knowledge in the form of a set of graph paths. The proposed interpretation of the structure of knowledge allows to create new opportunities for analytical study of knowledge and information, their properties and relationships.

The goal of color constancy is to measure image colors despite differences in the color of the light source. Traditionally, the computational method of obtaining this ability is by using pixel values only. Recently, methods using edges instead of pixel values have been proposed. However, different edge types exist, such as material, shadow and specular edges. Therefore, in this paper, the main goal is to analyze the influence of different edge types on the performance of edge-based color constancy. It is shown that, on generated data without color clipping, specular edges deliver near-perfect color constancy and that shadow edges are more valuable than material edges. However, with color clipping, the performance using the specular edges decreases significantly, while the performance using the material or shadow edges is less affected.

Estradiol or diarylpropionitrile administration to wild type, but not estrogen receptor beta knockout, mice enhances performance in the object recognition and object placement tasks

Spatial Responses of Bobolinks (Dolichonyx oryzivorus) Near Different Types of Edges in Northern Iowa

Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. To enhance the learning of task dependencies, TaG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TaG surpasses previous methods by a large margin and achieves state-of-the-art results.

Hub network design problems with profits

Performance evaluation of wind lens in turbulent environment

In image processing edges are observed as singularities. Calculating gradient is regular procedure for locating edges within the image. However, there are edges with different order of that given by gradient. Holder exponent gives mathematical background for different types of edges considering differentiation. In this paper we analyze some well known capacities, needed in pointwise Holder exponent calculation, and introduce a new capacity measure based on local image statistics.

The cross-lingual text classification task remains a long-standing challenge that aims to train a classifier on high-resource source languages and apply it to classify texts in low-resource target languages, bridging linguistic gaps while maintaining accuracy. Most existing methods achieve exceptional performance by relying on multilingual pretrained language models to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which leads to the degradation of classification performance in the target languages. This study proposes a novel framework, a heterogeneous graph neural network with multi-view contrastive learning for cross-lingual text classification, which integrates a heterogeneous graph architecture with multi-view contrastive learning for the cross-lingual text classification task. This study constructs a heterogeneous graph to capture both syntactic and semantic knowledge by connecting document and word nodes using different types of edges, including Part-of-Speech tagging, dependency, similarity, and translation edges. A Graph Attention Network is applied to aggregate information from neighboring nodes. Furthermore, this study devises a multi-view contrastive learning strategy to enhance model performance by pulling positive examples closer together and pushing negative examples further apart. Extensive experiments show that the framework outperforms the previous state-of-the-art model, achieving improvements of 2.20% in accuracy and 1.96% in F1-score on the XGLUE and Amazon Review datasets, respectively. These findings demonstrate that the proposed model makes a positive impact on the cross-lingual text classification task overall.

Quantitative assessment of basal-, edge- and defect-surfaces of carbonaceous materials and their influence on electric double-layer capacitance

Positive modulation of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors reverses sub-chronic PCP-induced deficits in the novel object recognition task in rats

Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.

T3D edge detection from a depth image is an important technique of 3D object recognition in preprocessing. There are three types of 3D edges in a depth image called jump, convex roof, and concave roof edges. Conventional 3D edge detection based on ring operators has been proposed. The conventional ring operator can detect three types of 3D edges by classifying the response of Fourier transforms. Since the conventional method needs to apply Fourier transforms to all pixels of a depth image, real-time processing cannot be done due to high computational cost. Therefore, this paper presents a fast and reliable method of detecting three types of 3D edges by using a decision tree. The decision tree is trained under supervised learning from numerous synthesized depth images and labels by capturing depth relations between candidate pixels and pixels on a ring operator to classify 3D edges. The experimental results revealed that the proposed method has 25 times faster than the conventional method. This paper also presents some examples of 3D line and 3D convex corner detection based on results obtained with the proposed method.