Self-similar Network Research Articles

Camouflaged Object Detection via Dual-branch Fusion and Dual Self-similarity constraints

Camouflaged Object Detection (COD) remains a challenging task due to the inherent difficulty in distinguishing concealed objects from their surroundings, primarily owing to their high visual similarity. Current research efforts have been insufficient in distinguishing the similarity differences between camouflaged objects and their corresponding backgrounds, leading to suboptimal performance, particularly when locating small-scale objects. To solve this issue, we introduce a novel Dual-branch Fusion and Dual Self-similarity Network (DSNet) comprising three modules. The first, a dual-branch fusion, mimics human observation of camouflaged objects and extracts multi-angle information. Dual-branch features are then decoded using a symmetric joint decoder module with channel interaction via multi-stage inter-group interaction. Inspired by natural organisms’ self-similarity, the self-similarity constraint module employs global and mutual constraints to identify subtle foreground-background differences. DSNet shows superior performance in experiments, and the constraint module can enhance other models as a plug-and-play component, further boosting overall performance.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks

Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

Scaling laws for optimized power-law fluid flow in self-similar tree-like branching networks

The power-law fluid flow in tree-like self-similar branching networks is prevalent throughout the natural world and also finds numerous applications in technology such as oil recovery and microfluidic devices. We investigate analysis of optimal power-law fluid flow conditions and the optimal structures within tree-like branching networks, focusing on maximizing flow conductance under the constraint of the network tube’s volume and the surface area. The study considered fully developed laminar power-law fluid flow regimes without considering any losses in the network system. A key observation was the sensitivity of the dimensionless effective flow conductance to the network’s geometrical parameters. We found that the maximum flow conductance occurs when a dimensionless radius ratio β∗ satisfies the equation β∗=N−1/3 and β∗=N−(n+1)/(3n+2) under constrained tube-volume and surface-area, respectively. Here, N represents the bifurcation number of branches splitting at each junction, and n is the fluid power-law index. We further find that this optimal condition occurs when pressure drops are equipartition across each branching level. We validated our results with various experimental results and theories under limiting conditions. Further, Hess–Murray’s law is justified and extended for the shear-thinning and shear-thickening fluid flows for an arbitrary number of branches N. Further, in this study, we also derive the relationships between the geometrical and flow characteristics of the parent and daughter tubes as well as the generalized scaling laws at the optimal conditions for the other essential parameters such as tube-wall stresses, length ratios, mean velocities, tube-volume, and surface-area of the tube distributing within the networks. We find that the fluid power-law index n does not influence the constrained tube-volume scaling at the optimal conditions; however, the scaling laws vary with n under the constrained tube’s surface area. These findings offer valuable design principles for developing efficient transport and flow systems.

Fractal and first-passage properties of a class of self-similar networks.

A class of self-similar networks, obtained by recursively replacing each edge of the current network with a well-designed structure (generator) and known as edge-iteration networks, has garnered considerable attention owing to its role in presenting rich network models to mimic real objects with self-similar structures. The generator dominates the structural and dynamic properties of edge-iteration networks. However, the general relationships between these networks' structural and dynamic properties and their generators remain unclear. We study the fractal and first-passage properties, such as the fractal dimension, walk dimension, resistance exponent, spectral dimension, and global mean first-passage time, which is the mean time for a walker, starting from a randomly selected node and reaching the fixed target node for the first time. We disclose the properties of the generators that dominate the fractal and first-passage properties of general edge-iteration networks. A clear relationship between the fractal and first-passage properties of the edge-iteration networks and the related properties of the generators are presented. The upper and lower bounds of these quantities are also discussed. Thus, networks can be customized to meet the requirements of fractal and dynamic properties by selecting an appropriate generator and tuning their structural parameters. The results obtained here shed light on the design and optimization of network structures.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

RESISTANCE DISTANCES OF STRETCHED SIERPINSKI NETWORKS

Stretched Sierpinski Gasket Networks are neither substitution networks nor self-similar networks. In this paper, with equivalent transformations and some classical principles in Circuit Theory, we give explicit formulae for the resistance distances of some nodes of the Stretched Sierpinski Gasket Networks.

Tutte polynomial of self-similar network models with applications

Determining the Tutte polynomial T ( G ; x , y ) of a graph network G is a challenging problem for mathematicians, physicians, and statisticians. This paper investigates a self-similar network model M ( t ) and derives its Tutte polynomial. In addition, we evaluate exact explicit formulas for the number of acyclic orientations and spanning trees of it as applications of the Tutte polynomial. Finally, we use the derived T ( M ( t ) ; x , y ) to obtain the Tutte polynomial of another self-similar model N ( t ) presented in [1] and correct the main result discussed in [1] by Ma et al. and test our result numerically by using Matlab.

Enumeration Of Subtrees Of Two Families Of Self-Similar Networks Based On Novel Two-Forest Dual Transformations

Abstract As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application.

An approach to detecting anomalies in a self-similar network traffic

Aim. The effects of cyber attacks cause failures of network elements, theft of information and other unlawful actions. Cyber attacks are often accompanied by untypical traffic activity and anomalies. The paper aims to develop an approach to detecting anomalies in network traffic by identifying the degree of self-similarity of the traffic using fractal analysis and statistical methods. Methods. The paper uses methods of mathematical statistics, mathematical analysis, fractal analysis. Results. The paper suggests an approach to identifying anomalies in network traffic by evaluating self-similarity and using statistical methods for improving the accuracy of cyber attack detection. At the first stage, the Hurst exponent is calculated for the reference traffic. At the second stage, actual traffic is divided into optimal time intervals; for each interval, the Hurst exponent is calculated. If the identified value of the Hurst exponent differs from the one obtained for the reference traffic, it is decided that there is an anomaly. At the final stage, statistical analysis is used in order to precisely localise the anomaly. The authors analysed fractal and statistical methods that resulted in the identification of more efficient methods to be used as part of the proposed approach. For fractal analysis, the DFA method was proposed, while for statistical analysis, the ARFIMA method was proposed. Conclusion. The suggested approach allows identifying cyber attacks in real time or near-real time.

A flexible and sensitive 3D carbonized biomass fiber for hybrid strain sensing and energy harvesting

Fabricating strain sensors with environmental-friendly, stretchable, and highly sensitive properties remains challenging. Nature has designed an abundance of biomaterials with distinctive biological structures, which may promote the development of ideal strain sensors. Herein, we present a carbonized Juncus effusus (CJE) fiber-based wearable electronic for strain sensing and energy harvesting. The CJE strain sensor demonstrated a combined superiority of large tolerable strain (over 80%), high sensitivity (GF 4.5–31.0), low detection limit (0.1% strain), fast response (within 100 ms), and excellent durability (3500 cycles), which benefit from the self-similar and homogenous three-dimensional (3D) networks of CJE fibers. Moreover, CJE-based woven fabrics exhibited enhanced sensing characteristics (GF up to 101.9, over 110% strain) and great application prospects as triboelectric nanogenerators (TENGs). Finally, the applications in detecting human activities and energy transformation were evaluated in detail. The structural advantages made CJE as a promising candidate for the feasible integration of flexible strain sensors and portable power source in wearable electronics.

Exploring the Relationship Between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification

To mine the spectral-spatial information of target pixel in hyperspectral image classification (HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input pattern, where a patch represents its central pixel and the neighbor pixels play auxiliary roles in the classification process. However, compared to the central pixel, its neighbor pixels often have different contributions for classification. Although many existing patch-based CNNs could adaptively emphasize the spatial neighbor information, most of them ignore the latent relationship between the center pixel and its neighbor pixels. Moreover, efficient spectral-spatial feature extraction has been a difficult yet vital topic for HSIC. To address the mentioned problems, a central vector oriented self-similarity network (CVSSN) is proposed for HSIC. Specifically, based on two similarity measures, we firstly design an adaptive weight addition based spectral vector self-similarity module (AWA-SVSS) in input space and a Euclidean distance based feature vector self-similarity module (ED-FVSS) in feature space to fully mine the central vector oriented spatial relationships. Besides, a spectral-spatial information fusion module (SSIF) is formulated as a new pattern to fuse the central 1D spectral vector and the corresponding 3D patch for efficient spectral-spatial feature learning of the subsequent modules. Moreover, we implement a channel spatial separation convolution module (CSS-Conv) and a scale information complementary convolution module (SIC-Conv) for efficient spectral-spatial feature learning. Extensive experimental results on four popular HSI data sets demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art methods. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lms-07/CVSSN</uri>

Percolation on Fractal Networks: A Survey

The purpose of this survey is twofold. First, we survey the studies of percolation on fractal networks. The objective is to assess the current state of the art on this topic, emphasizing the main findings, ideas and gaps in our understanding. Secondly, we try to offer guidelines for future research. In particular, we focus on effects of fractal attributes on the percolation in self-similar networks. Some challenging questions are outlined.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Patch-local self-similar network for ground terrain recognition

Patch-local self-similar network for ground terrain recognition

Patch-Local Self-Similar Network for Ground Terrain Recognition

Patch-Local Self-Similar Network for Ground Terrain Recognition

Neural Network Prediction of Self-Similarity Network Traffic

Neural Network Prediction of Self-Similarity Network Traffic

A one dimensional ubiquitiform damage model for quasi-brittle materials

Considering the self-similar characteristic of irregular pore network of materials during the damage evolution process, a pore ubiquitiform model (PUM) is developed to characterize the multiscale pore or microcrack network of quasi-brittle materials, and then based on the PUM, the ubiquitiform damage model (UDM) is proposed to describe the damage evolution process of quasi-brittle materials under quasi-static uniaxial tensile loading. The values of porosity and pore specific surface area estimated from the PUM and the stress-deformation curves including the softening curve calculated from the UDM are in good agreement with previous experimental data, respectively. It can be found that, the PUM can be adopted to characterize the self-similar multiscale pore network and estimate the porosity and pore specific surface area, and the UDM can describe the damage evolution process of quasi-brittle materials under quasi-static uniaxial tensile loading. Meanwhile, the UDM can characterize the self-similar characteristic of pore network during the damage evolution process of quasi-brittle materials through the evolving ubiquitiform complexity.

ECCENTRIC STEINER DISTANCE SUM OF VICSEK NETWORKS

The topological indexes, such as the Wiener sum and the eccentric distance sum, play important roles in Chemical Graph Theory, where the eccentric distance sum characterizes the geodesic distance of two nodes. In this paper, for a family of self-similar Vicsek networks, we discuss their eccentric distance sums related to the Steiner distance of four nodes.

Conformal fabrication of thick superhydrophobic coatings via reduction of sorption barrier against multiple damages

Bioinspired superhydrophobic (SH) surfaces have attracted great interest due to their wide applications. However, it is still a big challenge to construct durable SH coatings against both intrafacial abrasion and interfacial shearing. Here we report a conformal thick SH coating composed of 3D interconnected polymeric skeleton and perfluorinated nanoparticle (NPs). Due to the reduction of sorption barrier, a conformal fabrication of thick SH coating (≥100 μm) is accomplished. The durability against bidirectional linear abrasion (10 kPa) appears linear growth with the thickness, while the coating (thickness of 100 μm) remains superhydrophobicity after 1200 cycles due to the 3D interconnected self-similar network. Furthermore, the severe interfacial cut can be completely healed due to the re-formation of 3D network via hot-pressing. The healed coating shows good durability against abrasion, water injecting and freeze-thaw. This scalable yet effective strategy not only provides a novel approach for conformal fabrication of coatings via spraying but also highlights the potential applications of superhydrophobic coatings on large-scale basic facilities.

Frequency Response and Transfer Functions of Large Self-Similar Networks

Abstract Large-scale dynamical systems, no matter whether possessing interconnected appearances, are frequently modeled as networks. For instance, graphs, multi-agent systems, and materials' intricate behaviors are often treated as networked dynamical systems. However, only a few studies have approached the problem in the frequency domain, mostly due to the complexity of evaluating their frequency response. That gap is filled by this paper, which proposes algorithms computing a general class of self-similar networks' frequency response and transfer functions, no matter they are finite or infinite, damaged or undamaged. In addition, this paper shows that for infinite self-similar networks, even when they are damaged, fractional-order and irrational dynamics naturally come into sight. Most importantly, this paper illustrates that for a network under different operating conditions, its frequency response would form a set of neighboring plants, which sets the basis of applying robust control methods to dynamic networks.