Symmetric Network Research Articles

ECCENTRIC DISTANCE SUM OF SUBSTITUTION TREE NETWORKS

In this paper, we study the eccentric distance sum of substitution tree networks. Calculation of eccentric distance sum naturally involves calculation of average geodesic distance and it is much more complicated. We obtain the asymptotic formulas of average geodesic distance and eccentric distance sum of both symmetric and asymmetric substitution tree networks. Our result on average geodesic distance generalizes the result of [T. Li, K. Jiang and L. Xi, Average distance of self-similar fractal trees, Fractals 26(1) (2018) 1850016.] from symmetric case to asymmetric case. To derive formulas, we investigate the corresponding integrals on self-similar measure and use the self-similarity of distance and measure. For simplicity, we introduce some systematic symbolic assignments and make some assumptions on the graph. We verify that our formulas are correct using the numerical calculation results.

Performance Evaluation of Heuristics and Meta-Heuristics Traffic Control Strategies Using the UTNSim Traffic Simulator

In the past few decades, intelligent traffic controllers have been developed to responsively cope with the increasing traffic demands and congestions in urban traffic networks. Various studies to compare and evaluate the performance of traffic controllers have been conducted to investigate its effect on traffic performances such as its ability to reduce delay time, stops, throughputs and queues within a traffic network. In this paper, the authors aim to present another comparative study on heuristics versus meta-heuristics traffic control methods. To our knowledge, such comparison has not been conducted and could provide insights into a purely heuristic controller compared to meta-heuristics. The study aims to answer the research question “Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computational costs?” For this purpose, a heuristics model-based control strategy (MCS) which was previously developed by the authors is compared to genetic algorithms (GA) and evolution strategy (ES) respectively on a nine intersections symmetric network. These control strategies were implemented via simulations on a traffic simulator called UTNSim for three different types of traffic scenarios. Performance indices such as average delays, vehicle throughputs and the computational time of these controllers were evaluated. The results revealed that the heuristic MCS outperformed GA and ES with superior performance in average delays whereas vehicle throughputs were in close agreement. The computation time of the MCS is also feasible for real-time application compared to GA and ES that has longer convergent time.

Detecting transition between Abelian and non-Abelian topological orders through symmetric tensor networks

We propose a unified scheme to identify phase transitions out of the $\mathbb{Z}_2$ Abelian topological order, including the transition to a non-Abelian chiral spin liquid. Using loop gas and and string gas states [H.-Y. Lee, R. Kaneko, T. Okubo, N. Kawashima, Phys. Rev. Lett. 123, 087203 (2019)] on the star lattice Kitaev model as an example, we compute the overlap of minimally entangled states through transfer matrices. We demonstrate that, similar to the anyon condensation, continuous deformation of a $\mathbb{Z}_2$-injective projected entangled-pair state (PEPS) also allows us to study the transition between Abelian and non-Abelian topological orders. We show that the charge and flux anyons defined in the Abelian phase transmute into the $\sigma$ anyon in the non-Abelian topological order. Furthermore, we show that contrary to the claim in [Phys. Rev. B 101, 035140 (2020)], both the LG and SG states have infinite correlation length in the non-Abelian regime, consistent with the no-go theorem that a chiral PEPS has a gapless parent Hamiltonian.

Design of a novel chaotic neural network based encryption system for security applications

ABSTRACT Cryptography is one of the vital domains in computer systems. It is continually evolving with a tremendous amount of active research. With the widespread use of technology, there is a constant requirement for faster, secure cryptosystems. The advance of neural networks has provided us with a wide range of opportunities to optimize cryptosystems. Chaotic neural networks (CNN) are sensitive to their initial conditions. This unique property allows us to improve the security of cryptosystems. In this paper, a chaotic neural network is used to design encryption and decryption algorithms. The algorithms are experimented on using a cipher block chaining mechanism. The cryptographic algorithm was optimized by thoroughly evaluating performance parameters such as autocorrelation, cross-correlation and throughput. This paper experiments with various chaotic maps such as 1-D Logistic map, complex squaring map and cubic map to identify the most efficient map for the outlined CNN in this paper. This paper provides a symmetrical chaotic neural network for a cryptographic application that works for a wide range of inputs. The inputs are not sensitive dependent on characters. Also, the network works seamlessly with UTF-8-based characters.

U(1)-symmetric recurrent neural networks for quantum state reconstruction

Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experimental measurements, and can aid in the calculation of physical observables. In this paper, we employ a recurrent neural network (RNN) to reconstruct the ground state of the spin-1/2 XY model, a prototypical Hamiltonian explored in trapped ion simulators. We explore its performance after enforcing a U(1) symmetry, which was recently shown by Hibat-Allah et al. [Phys. Rev. Research 2, 023358 (2020)] to preserve the autoregressive nature of the RNN. By studying the reconstruction of the XY model ground state from projective measurement data, we show that imposing U(1) symmetry on the RNN significantly increases the efficiency of learning, particularly in the early epoch regime. We argue that this performance increase may result from the tendency of the enforced symmetry to alleviate vanishing and exploding gradients, which helps stabilize the training process. Thus, symmetry-enforced RNNs may be particularly useful for applications of quantum simulators where a rapid feedback between optimization and circuit preparation is necessary, such as in hybrid classical-quantum algorithms.

Integrated Inference of Asymmetric Protein Interaction Networks Using Dynamic Model and Individual Patient Proteomics Data

Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.

COMBINED ELECTROOSMOTIC AND PRESSURE DRIVEN FLOW IN TREE-LIKE MICROCHANNEL NETWORK

The present work presents a simplified mathematical model to calculate the flowrate of the combined electroosmotic flow (EOF) and pressure driven laminar flow (PDLF) in the symmetric tree-like microchannel network under the assumptions of small zeta potential and thin electrical double layer. A numerical analysis of the combined EOF and PDLF in symmetric Y-shaped microchannel is also carried out to validate the mathematical model. The analytical results and numerical results are found to be in good agreement with each other. Using the mathematical model, the present work further investigates the effect of diameter ratio of the tree-like network on the flowrate of the combined EOF and PDLF to recognize a possible conclusion being similar to the Murray’s law. Based on the present work, it is found that the symmetric tree-like network has an optimal diameter ratio to achieve the maximum flowrate for the combined EOF and PDLF when the total microchannel volume is constant; however, this optimal diameter ratio for the combined flow disobeys the generalized Murray’s law in a simple form of power function of the branching number [Formula: see text], and it is not only related on the branching number, but also depends on the branching level and channel length ratio of the tree-like network. Furthermore, the optimal diameter ratio shows a monotonous transition from [Formula: see text] for the pure PDLF to [Formula: see text] for the pure EOF with the increasing ratio of the driven voltage and driven pressure. The present work discusses the effects of these parameters on the optimal diameter ratio for the combined EOF and PDLF.

On the Upper Bounds of Fractional Metric Dimension of Symmetric Networks

Distance-based numeric parameters play a pivotal role in studying the structural aspects of networks which include connectivity, accessibility, centrality, clustering modularity, complexity, vulnerability, and robustness. Several tools like these also help to resolve the issues faced by the different branches of computer science and chemistry, namely, navigation, image processing, biometry, drug discovery, and similarities in chemical compounds. For this purpose, in this article, we are considering a family of networks that exhibits rotationally symmetric behaviour known as circular ladders consisting of triangular, quadrangular, and pentagonal faced ladders. We evaluate their upper bounds of fractional metric dimensions of the aforementioned networks.

Connecting minimal chimeras and fully asymmetric chaotic attractors through equivariant pitchfork bifurcations.

Highly symmetric networks can exhibit partly symmetry-broken states, including clusters and chimera states, i.e., states of coexisting synchronized and unsynchronized elements. We address the S_{4} permutation symmetry of four globally coupled Stuart-Landau oscillators and uncover an interconnected web of solutions with different symmetries. Among these are chaotic 2-1-1 minimal chimeras that arise from 2-1-1 periodic solutions in a period-doubling cascade, as well as fully asymmetric chaotic states arising similarly from periodic 1-1-1-1 solutions. A backbone of equivariant pitchfork bifurcations mediate between the two cascades, culminating in equivariant pitchforks of chaotic attractors.

Balanced Colorings and Bifurcations in Rivalry and Opinion Networks

Balanced colorings of networks classify robust synchrony patterns — those that are defined by subspaces that are flow-invariant for all admissible ODEs. In symmetric networks, the obvious balanced colorings are orbit colorings, where colors correspond to orbits of a subgroup of the symmetry group. All other balanced colorings are said to be exotic. We analyze balanced colorings for two closely related types of network encountered in applications: trained Wilson networks, which occur in models of binocular rivalry, and opinion networks, which occur in models of decision making. We give two examples of exotic colorings which apply to both types of network, and prove that Wilson networks with at most two learned patterns have no exotic colorings. We discuss in general terms how exotic colorings affect the existence and stability of branches for local bifurcations of the corresponding model ODEs, both to equilibria and to periodic states.

Melanocytic lesions ≤ 6mm: Prospective series of 481 melanocytic trunk and limb lesions in Brazil.

Early diagnosis when melanoma is still small and thin is essential for improving mortality and morbidity. However, the diagnosis of small size melanoma might be particularly difficult, not only clinically but also dermoscopically. This study aimed to define clinical and dermatoscopic parameters in the diagnosis of suspicious pigmented cutaneous lesions with a diameter of ≤ 6mm and determine the sensitivity, specificity, positive and negative predictive values as well as the accuracy of each clinical and dermatoscopic criterion. This is a transversal, descriptive and analytical study of dermatoscopic analysis with the gold standard being the pathologic examination obtained from the excisional biopsy of suspicious melanocytic lesions with a diameter of ≤ 6mm. Trunk and limb lesion data from a public health service and a private clinic were prospectively collected from 2011 to 2017 by a unique observer. In total, 481 melanocytic lesions were included, with 73.8% being ≤ 4mm in diameter. Overall, 123 were diagnosed as melanoma, 56.0% in situ and 22.0% as thin melanomas (Breslow index 0.1 to 1.0mm). Melanoma presented symmetry in 53.7% of cases, regular borders in 54.5% and a single color in 60.2%. Regarding evolution, 13.8% of melanomas versus 10.9% of benign lesions (p = 0.116) were new by comparing photos from baseline with photos from the follow-up. The majority of melanomas (65%) were found on the limbs compared to 37.2% of the benign lesions at this location (p<0.001). A multiple logistic regression model adjusted for age, gender and location was created. The independent variables associated with the diagnosis of melanoma ≤ 6mm, adjusted for age, gender and location, were: streaks (adjusted Odds Ratio [aOR] 2.5; 95% CI 1.3–4.7; p = 0.006), and the presence of a structureless area (aOR 2.2, 95% CI 1.2–4.0, p = 0.011). Conversely, a symmetric typical pigment network was a protection variable (aOR 0.4, 95% 0.7–0.9, p = 0.040). In conclusion, dermatoscopic criteria have been identified which help to diagnose cases of small size melanoma. These include streaks and structureless areas that can be taken, particularly in consideration for the diagnosis of this subset of small difficult melanomas.

A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald’s Protocol and Bayesian Optimization

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.

Parameterized Algorithms for Power-Efficiently Connecting Wireless Sensor Networks: Theory and Experiments

We study a problem of energy-efficiently connecting a symmetric wireless communication network: given an n-vertex graph with edge weights, find a connected spanning subgraph of minimum cost, where the cost is determined by each vertex paying the heaviest edge incident to it in the subgraph. The problem is known to be NP-hard. Strengthening this hardness result, we show that even o(log n)-approximating the difference d between the optimal solution cost and a natural lower bound is NP-hard. Moreover, we show that under the exponential time hypothesis, there are no exact algorithms running in 2o(n) time or in [Formula: see text] time for any computable function f. We also show that the special case of connecting c network components with minimum additional cost generally cannot be polynomial-time reduced to instances of size cO(1) unless the polynomial-time hierarchy collapses. On the positive side, we provide an algorithm that reconnects O(log n)-connected components with minimum additional cost in polynomial time. These algorithms are motivated by application scenarios of monitoring areas or where an existing sensor network may fall apart into several connected components because of sensor faults. In experiments, the algorithm outperforms CPLEX with known integer linear programming (ILP) formulations when n is sufficiently large compared with c. Summary of Contribution: Wireless sensor networks are used to monitor air pollution, water pollution, and machine health; in forest fire and landslide detection; and in natural disaster prevention. Sensors in wireless sensor networks are often battery-powered and disposable, so one may be interested in lowering the energy consumption of the sensors in order to achieve a long lifetime of the network. We study the min-power symmetric connectivity problem, which models the task of assigning transmission powers to sensors so as to achieve a connected communication network with minimum total power consumption. The problem is NP-hard. We provide perhaps the first parameterized complexity study of optimal and approximate solutions for the problem. Our algorithms work in polynomial time in the scenario where one has to reconnect a sensor network with n sensors and O(log n)-connected components by means of a minimum transmission power increase or if one can find transmission power lower bounds that already yield a network with O(log n)-connected components. In experiments, we show that, in this scenario, our algorithms outperform previously known exact algorithms based on ILP formulations.

Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Mesoscopic fluorescent molecular tomography (MFMT) enables to image fluorescent molecular probes beyond the typical depth limits of microscopic imaging and with enhanced resolution compared to macroscopic imaging. However, MFMT is a scattering-based inverse problem that is an ill-posed inverse problem and hence, requires relative complex iterative solvers coupled with regularization strategies. Inspired by the potential of deep learning in performing image formation tasks from raw measurements, this work proposes a hybrid approach to solve the MFMT inverse problem. This methodology combines a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure. By the proposed deep neural network, the principal components of the sensitivity matrix are extracted and the accompanying noise in measurements is suppressed, which helps to accelerate the reconstruction and improve the accuracy of results. We apply the proposed method to reconstruct in silico and vascular tree models. The results demonstrate that reconstruction accuracy and speed are highly improved due to the reduction of redundant entries of the sensitivity matrix and noise suppression.

Symmetrical lattice generative adversarial network for remote sensing images compression

Image compression usually includes two important operations: compression and decompression. The compression process includes the operation of discarding information, while the decompression process is to retrieve the lost information. In order to make the decompressed image more similar to the original image, the classic compression methods generally adopt the process of approximately reversible compression and decompression. Inspired by the symmetric structure in classic compression methods, we propose a new symmetrical lattice generating adversarial network (SLGAN) for the remote sensing images (RSIs) compression in this paper. Several pairs of symmetrical encoder-decoder lattices are constructed to build a generator to first generate deep representative codes of images and then decode them. For each pair of encoded lattice and decoded lattice, one discriminator is constructed to perform adversarial learning with the generator. When multiple discriminators are used for all the lattices, a cooperative learning algorithm is proposed to train jointly pairs of symmetric lattices in the generator. Moreover, to enhance edges, contours, and textures in the decomposed RSIs, an enhanced Laplacian of gaussian (ELoG) loss is designed as a regularizer to train the SLGAN. Experimental results on the panchromatic images from GF2 satellite show that SLGAN outperforms other existing state-of-the-art methods.

Symmetric pyramid attention convolutional neural network for moving object detection

Moving object detection (MOD) is a crucial research topic in the field of computer vision, but it faces some challenges such as shadows, illumination, and dynamic background in practical application. In the past few years, the rise of deep learning (DL) has provided fresh ideas to conquer these issues. Inspired by the existing successful deep learning framework, we design a novel pyramid attention-based architecture for MOD. On the one hand, we propose a pyramid attention module to get pivotal target information, and link different layers of knowledge through skip connections. On the other hand, the dilated convolution block (DCB) is dedicated to obtain multi-scale features, which provides sufficient semantic information and geometric details for the network. In this way, contextual resources are closely linked and get more valuable clues. It helps to obtain a precise foreground in the end. Compared with the existing conventional techniques and DL approaches on the benchmark dataset (CDnet2014), the experiments indicate that the performance of our algorithm is better than previous methods.

Two-Dimensional Covalent Organic Framework Solid Solutions.

Covalent organic frameworks (COFs) generally leverage one or two monomers with specific sizes and shapes to access highly symmetric and periodic polymer networks. Almost all reported COFs employ the minimum sets of monomers needed for the polymerization (usually two, sometimes one) and crystallize in high-symmetry topologies. COFs synthesized from more than two monomers usually employ mixtures with different pendant functionalities to distribute these groups statistically throughout the structure, or monomers with different sizes in ratios targeting lower symmetry topologies. Here, we demonstrate that mixtures of monomers with different lengths generate single-phase, hexagonal two-dimensional covalent organic framework (2D COF) solid solutions at continuously variable feed ratios. X-ray diffraction measurements, Fourier-transform infrared spectroscopy, and Pawley refinement indicate that both monomers distribute randomly within the same lattice, and the lattice parameters continuously increase as more of the larger linker is incorporated. Furthermore, COF solid solutions are accessed directly by polymerizing a mixture of monomers but not via linker exchange from a preformed COF. As strain develops from the lattice accommodating monomers with different sizes, the nonlinear relationship between the monomer incorporation and the COF's lattice parameters suggests that bond-bending of the monomers plays a role in incorporating monomers of different lengths into the solid solutions. Solid solution formation represents a new strategy to design 2D COFs and increase their complexity. Specifically, varying the monomer composition of a given network enables many properties, such as the average pore size, to be continuously tuned between those of corresponding pure COFs.

Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images.

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

TVD‐PB logic circuit based on camouflaging circuit for IoT security

Internet of Things (IoT) devices are vulnerable to many physical attacks, including reverse engineering and side-channel analysis because the sensitive information of circuits may be leaked through the physical characteristics of the device. A logic camouflaging circuit is proposed that uses a balanced power consumption and threshold voltage-defined technique to provide an antiphysical attack scheme to protect the hardware security for IoT devices. The proposed circuit uses a symmetric differential pull-down network in implementing the different logic functions through the threshold voltage reconfiguration circuit. As a result, the power consumption of the circuit attains balance and stability between two different logical operations. The proposed threshold voltage-defined power-balance (TVD-PB) design is fabricated using a 65-nm CMOS technology, and the core area occupies approximately 0.0044 mm2, composed of NAND, NOR, XOR, and INV components and multiplier gates of the proposed TVD-PB circuit. The entire chip passed the logic function tests. The measured results show that the average similarity of the TVD-PB universal gate is 99.68%. In addition, the current margin is higher than 55 μA and power consumption of 0.455 mW during each clock cycle at 1.2 V derives 0.1072% of the normalized energy deviation and 0.0453% of the normalized standard deviation. Compared with other state-of-the-art techniques, the power dependency against power attacks is improved effectively.

Design of Broadband Dual-Polarized Rectenna Array for WPT Applications

A broadband dual-polarized microstrip array antenna designed is proposed. To achieve wide 10 dB bandwidth for broadband operation, the technique of applying a ladder-shaped monopole antenna type with a rectangular slot insertion in the ground plane is implemented. The proposed design showed wide impedance bandwidth of the 1702-2755 MHz (47.2%). In addition, adding an open slot into the rectangular radiating element with an asymmetric ground plane was used and resulted in a slightly displacement of the radiation pattern. The 1 × 2 array type for two ladder-shaped patch array elements are arranged in symmetric feed network. By meticulously arrangement the two array antennas’ positions to achieved good ports isolation, with 10 dB bandwidth for the operating bands in free-space can be achieved. This antenna is used as a rectenna (rectifying antenna), which receives the RF energy of vertical and horizontal polarization wave in free space for 2.4 GHz wireless power transmission. The rectifier circuit setup using two zero biased rectifier and voltage doubler circuit. A matching network designed with small size chip components have a significant improvement in impedance matching and eliminate high order harmonics between the antenna and rectifying circuit. The proposed dual-polarized rectenna provided the RF-to-DC conversion efficiency as high as 78.8% when 14 dBm microwave power was received at 2.4 GHz with a 1 KΩ load.