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Deep graph clustering approaches employ deep graph neural networks to encode node embeddings and subsequently partition nodes based on these representations. Recent one-step methods have demonstrated progress; however, they often encounter issues during the representation learning phase. A primary challenge is that the encoding networks fail to capture the high-order structural information of the graph for effective clustering. We believe that combining the two distributions comprehensively characterizes the real node distribution in terms of neighborhood proximity and community aggregation to achieve better clustering. Additionally, the prevalent representation learning paradigms based on adjacency matrix reconstruction are computationally intensive and increase optimization challenges. This paper presents a novel deep graph clustering model, termed Deep Integration of Community structure and Neighborhood information (DICN). The model incorporates a graph self-attention mechanism to aggregate neighborhood-level node information, and utilizes a probabilistic generative model for edge community detection to captain community-level node information. These two-level embeddings are employed to compute explicit node membership distributions for the clustering loss. This ensures that the clustering objective effectively integrates both low-order and high-order node characteristics. The comprehensive learning process of DICN optimizes not only the node embeddings but also the clustering results by integrating the two-level losses from representation learning and the clustering loss with a weak guidance loss for clustering. Experiments on some benchmark datasets illustrate that DICN model has superior performance over existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A Gaussian propagation model is explored to improve Influence Maximization. • Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions. • An improved CELF algorithm to accelerate the efficiency of the greedy algorithm. The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space's circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Identifying faulty program entities has been recognized as one of the most expensive, tedious and time-consuming processes in software debugging activity. Fault localization techniques are designed to assist developers in locating faults by giving a ranking of the probability that program entities incur failures. Mutation-based fault localization (MBFL) is a recently proposed fault localization approach via mutation analysis, which uses the location of mutants to identify the faulty statements. With improved effectiveness, the MBFL also brings huge execution cost. To reduce the execution cost of MBFL technique, this paper proposes a dynamic mutation execution strategy (DMES) to prioritize the execution on both mutants and test cases. As fewer mutants and test cases are executed with DMES, the whole process will become faster and the cost will be decreased. At the same time, it is proved that the fault localization accuracy of MBFL with DMES is the same as that of the original MBFL. Furthermore, this paper discusses the lowest mutation execution cost of MBFL in theory and gives a quick solution to compute the theoretical minimal mutation execution cost of MBFL in the case of keeping fault localization accuracy non-decreasing. The empirical studies show that MBFL with the dynamic strategy proposed in this paper can reduce mutant-test execution cost by 32.4–87% and is close to the theoretical optimal cost. Furthermore, the additional run time required by utilizing our strategy is minimum and can be ignored. [ABSTRACT FROM AUTHOR]

A shape-memory polymer (SMP) is capable of memorizing its original shape, and can acquire a temporary shape upon deformation and returns to its permanent shape in response to an external stimulus such as a temperature change. SMPs have been widely used industrial and medical applications. Previously, differential equation models were developed to describe SMPs and their applications. However, these models are often of very complicated form, which require accurate numerical simulations. In this paper we argue that a variable-order fractional differential equation model of the shape-memory behavior is more suitable than constant-order fractional differential equation models in terms of modeling the memory behavior of SMPs. We develop a numerical method to simulate the variable-order model and, in particular, to identify the unknown variable order of the model. Numerical experiments are presented to show the utility of the method. [ABSTRACT FROM AUTHOR]

We study the problem of predictive control of linear time-varying systems and develop a generalized predictive controller for optimal control of a standard single-input single-output controlled autoregressive moving average model. Linear time-varying output filtering and incremental of control is included for flexibility for applications. The controller is applicable to a large class of linear time-varying systems. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to test an efficiency algorithm based on lattice Boltzmann method (LBM) and using it to analyze two-dimensional natural convection with low Prandtl number. Design/methodology/approach – Steady state or oscillatory results are obtained using double multiple-relaxation-time thermal LBM. The velocity and temperature fields are solved using D2Q9 and D2Q5 models, respectively. Findings – With different Rayleigh number, the tested natural convection can either achieve to steady state or oscillatory. With fixed Rayleigh number, lower Prandtl number leads to a weaker convection effect, longer oscillation period and higher oscillation amplitude for the cases reaching oscillatory solutions. At fixed Prandtl number, higher Rayleigh number leads to a more notable convection effect and longer oscillation period. Originality/value – Double multiple-relaxation-time thermal LBM is applied to simulate the low Prandtl number (0.001-0.01) fluid natural convection. Rayleigh number and Prandtl number effects are also investigated when the natural convection results oscillate. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to suggest a new analytical technique called the fractional series expansion method for solving linear fractional differential equations (FDEs). Design/methodology/approach – This method is based on the idea of Kantorovich method, convergent series, and the modified Riemann-Liouville derivative. Findings – This work suggests a new analytical technique. The FDEs are described in Jumarie’s sense. Originality/value – It finds a new method for solving linear FDEs. The solution procedure is elucidated by two examples. [ABSTRACT FROM AUTHOR]

To investigate accurately unique and advanced wave properties in elastic metamaterials advanced and efficient numerical methods are needed. The paper presents an extended boundary integral equation method for simulating elastic wave scattering by doubly periodic arrays of rectangular crack-like voids. The convergence of the arising double series is proved and the convergence rate is carefully analyzed. Employing the results of the investigation of the convergence rate, an algorithm for fast numerical calculation of double series is proposed to reduce computational costs through preliminary analytical evaluation and the proposed summation strategy. The efficiency of the extended boundary integral equation method is demonstrated and examples of the calculated transmission coefficients exhibiting the influence of the introduction of the doubly periodic array are given. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of two-dimensional (2-D) direction of arrival (DOA) estimation for L-shaped monostatic MIMO radar with two-level nested linear array (NLA). To obtain more degrees of freedom (DOFs) than traditional nested L-shaped (NLs) MIMO radar, we propose the extensional NLs (ENLs) MIMO radar by extending all of the sensor positions in transmitter array with an identical magnification compared with NLs MIMO radar. The ENLs MIMO radar can offer O (M 1 M 2 N 1 N 2) DOFs with only O (M 1 + M 2 + N 1 + N 2) sensors, where M 1 (N 1) and M 2 (N 2) respectively represents the numbers of the first-level and second-level sensors of NLA in transmitter (receiver) array. Furthermore, to avoid the eigenvalue decomposition, spatial spectral search and polynomial root finding technique in spatial smoothing multiple signals classification (SS-MUSIC) and SS-ROOT-MUSIC, and to improve the estimation accuracy simultaneously, we propose a search-free iterative Taylor expansion (SF-ITE) algorithm to perform DOA estimation, which employs the Discrete Fourier transform (DFT) method to get the coarse estimation, and then utilizes the iterative Taylor expansion technique to obtain fine estimation. SF-ITE can get more precise estimation by performing iteration only once than both SS-MUSIC and SS-ROOT-MUSIC. In addition, we derive an angle-pairing procedure to help to eventually resolve more targets than the total number of sensors. Finally, simulation results are provided to validate the superiority of the ENLs MIMO radar and SF-ITE algorithm. [ABSTRACT FROM AUTHOR]

This article designs and analyzes a recurrent neural network (RNN) for the visual servoing of a flexible surgical endoscope. The flexible surgical endoscope is based on a commercially available UR5 robot with a flexible endoscope attached as an end-effector. Most of the existing visual servo control frameworks of the robotic endoscopes or robot arms have not considered either the physical limits of the robot or the remote center of motion (RCM) constraints (i.e., the fulcrum effect). To tackle this issue, this article first conducts the kinematic modeling of the flexible robotic endoscope to achieve automation by visual servo control. The kinematic modeling results in a quadratic programming (QP) framework with physical limits and RCM constraints involved, making the UR5 robot applicable to surgical field. To solve the QP problem and accomplish the visual task, an RNN activated by a sign-bi-power activation function (AF) is proposed. The motivation of using the sign-bi-power AF is to enable the RNN to exhibit an accelerated finite-time convergence, which is more preferred in time-critical applications. Theoretically, the finite-time convergence of the RNN is rigorously proved using the Lyapunov theory. Compared with the previous AFs applied to the RNN, theoretical analysis shows that the RNN activated by the sign-bi-power AF delivers an accelerated convergence speed. Comparative validations are performed, showing that the proposed finite-time convergent neural network is effective to achieve visual servoing of the flexible endoscope with physical limits and RCM constraints handled simultaneously. [ABSTRACT FROM AUTHOR]

0–1 integer programming problem is very important in opsearch with widespread application. In this paper, a new DNA computing model based on self-assembled nanoparticle probes is presented to solve it, in which the nanoparticle and oligonucleotides are integrated for the first time. Its essence is that all possible combinations of variable 0 or 1 for given problem are encoded in the recognition zone of self-assembled nanoparticle probes, and feasible solutions are judged through their hybridization of target sequences. Major benefits of this method include vast parallelism, extraordinary information density and easy controllable operation. The result reveals the potential of DNA computation based on nano technology in solving complex integer programming problem. [ABSTRACT FROM AUTHOR]

In this paper, a class of even order WENO schemes, including fourth-order, sixth-order and eighth-order schemes, are presented in finite volume framework for hyperbolic conservation laws. Instead of two upwind stencils in the classical WENO reconstruction [10] for each cell interface, a common symmetrical stencil is used in the reconstruction of the variables at its both sides. For each cell interface, the 2 r th order scheme shares the same number of cell averaged values with the ( 2 r − 1 ) th order classical WENO scheme. To suppress the spurious oscillation and improve the resolution in the region with discontinuities, a convex combination and a nonlinear weighting strategy are given with the unequal degree polynomials from the candidate sub-stencils. In smooth region, the current schemes achieve one order of improvement in accuracy compared with the classical WENO schemes. A variety of numerical tests are presented to validate the performance of the current schemes. Complex flow structures and discontinuities can be well resolved, and the robustness is as good as that of the classical WENO schemes. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) is a recently proposed population-based algorithm that simulates the process of teaching and learning. Compared with other evolutionary algorithms, TLBO has fewer parameters that must be determined during the renewal process, and is very efficient for certain optimization problems. However, as a population-based algorithm, certain complex problems cause TLBO to exhibit local convergence phenomena. Therefore, to improve the global performance of TLBO, we have designed local learning and self-learning methods to enhance the search ability of TLBO. In the learner phase, every individual learns from both the teacher of the current generation and other individuals. Whether these individuals are neighbours or random individuals from the whole class is determined probabilistically. In the self-learning phase, individuals either renew their positions according to their own gradient information, or randomly exploit new positions according to a design based on the means and variances. To maintain local diversity, all individuals are rearranged after a set number of iterations. The proposed algorithm is tested on a number of functions, and its performance is compared with that of other well-known optimization algorithms. The results indicate that the improved TLBO attains good performance. [ABSTRACT FROM AUTHOR]

Purpose – Academic and industrial researches on nanoscale flows and heat transfers are an area of increasing global interest, where fascinating phenomena are always observed, e.g. admirable water or air permeation and remarkable thermal conductivity. The purpose of this paper is to reveal the phenomena by the fractional calculus. Design/methodology/approach – This paper begins with the continuum assumption in conventional theories, and then the fractional Gauss’ divergence theorems are used to derive fractional differential equations in fractal media. Fractional derivatives are introduced heuristically by the variational iteration method, and fractal derivatives are explained geometrically. Some effective analytical approaches to fractional differential equations, e.g. the variational iteration method, the homotopy perturbation method and the fractional complex transform, are outlined and the main solution processes are given. Findings – Heat conduction in silk cocoon and ground water flow are modeled by the local fractional calculus, the solutions can explain well experimental observations. Originality/value – Particular attention is paid throughout the paper to giving an intuitive grasp for fractional calculus. Most cited references are within last five years, catching the most frontier of the research. Some ideas on this review paper are first appeared. [ABSTRACT FROM AUTHOR]

Both principle and error constitution of the Collocation method are introduced in this paper. With a view of its application in filter orbit determination, the performance of the small-steplength and low-order Collocation method is researched. Based on a set of both orbit integration calculations of two-body problem and GPS constellation, some meaningful conclusions are obtained that performance of Collocation method is better than that of traditional numerical integration method; it is practical and effective to adopt small-steplength and low-order Collocation method in filter orbit determination, but when the steplength is much small ; instead of large-step and high-order Collocation method, the small-steplength and low-order method has higher efficiency and can inhibit integration divergence caused by eclipse. It is most suitable to use the fourth order collocation method of integration steplength of 900s. [ABSTRACT FROM AUTHOR]

New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By exploiting the techniques of the Lagrange dual problem like Vapnik's support vector machine (SVM), the solution of the optimization problem can be obtained iteratively and the iteration process is similar to that of the NORMA. This further strengthens the foundation of OLK and enriches the research area of SVM. We also apply the obtained OLK algorithms to problems in classification, regression, and novelty detection, including real time background substraction, to show their effectiveness. It is illustrated that, based on the experimental results of both classification and regression, the accuracy of OLK algorithms is comparable with traditional SVM-based algorithms, such as SVM and least square SVM (LS-SVM), and with the state-of-the-art algorithms, such as Kernel recursive least square (KRLS) method and projectron method, while it is slightly higher than that of NORMA. On the other hand, the computational cost of the OLK algorithm is comparable with or slightly lower than existing online methods, such as above mentioned NORMA, KRLS, and projectron methods, but much lower than that of SVM-based algorithms. In addition, different from SVM and LS-SVM, it is possible for OLK algorithms to be applied to non-stationary problems. Also, the applicability of OLK in novelty detection is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

To mitigate multipath in monitoring (reference) stations of satellite navigation systems, a weighting criterion for antenna arrays called Down-up-ratio Constrained Minimum Variance (DCMV) criterion is proposed in this paper. The proposed criterion aims at minimizing the array output power under the constraint of down-up-ratio not greater than some threshold r. Therefore, this criterion is able to mitigate both interference and multipath. Simulation results show that it outperformed other criteria in satellite navigation systems, such as Power Inversion, Beam Steering, Maximum Signal-to- Interference-plus-Noise Ratio criterion, etc. The DCMV criterion is able to quantitatively control the incoming muhipath energy, however, it losses some array gain as a cost. [ABSTRACT FROM AUTHOR]

We propose two schemes for generating entanglement and quantum-state transfer (QST) between two spatially separated semiconductor quantum dot molecules (QDMs) based on voltage-controlled tunneling effects. In the present schemes, two QDMs are trapped in two spatially separated cavities connected by a fiber. By numerically simulating the evolution of the system, we show that the generation of entanglement and QST can be controlled by an external gate voltage in our schemes. Moreover, proposed schemes are robust against the noise of system parameters. [ABSTRACT FROM AUTHOR]

This article presents a small-area Bluetooth Low-Energy (BLE) transceiver (TRX) for short-range Internet-of-Things (IoT) applications in 65-nm CMOS. An integrated Radio-Frequency Input-Output (RFIO) circuitry embedded with transmitter/receiver (TX/RX) switch function and on-chip impedance matching is proposed. A hybrid-loop TRX structure based on a wide-bandwidth fractional-N digital phase-locked loop (DPLL) is implemented to achieve the maximum power reduction. A −94dBm receiver sensitivity is achieved with 2.3mW receiver power consumption, while a RF receiving bypass route integration enhances the input power tolerance. The BLE transceiver delivers −6dBm output power while consuming 2.6mW and delivers 0dBm output power while achieving 18.5% maximum TX efficiency. Thanks to the RFIO with harmonic suppression, −56dBc of 2 $^{\mathrm{ nd}}$ -order harmonic distortion (HD2) and −48dBc of 3 $^{\mathrm{ rd}}$ -order harmonic distortion (HD3) suppression are achieved with 0.85mm2 on-chip area. This transceiver satisfied the BLE radio specification without the need for external filters and with low-power consumption, which enables minimum size and long life-time modules. [ABSTRACT FROM AUTHOR]

We employ a spatio-temporal network modelling approach to identify possible relations between strong earthquakes and spatial regions worldwide. A global strong earthquake dataset containing 7736 events (Mw ≥ 6.0) from 1964 to 2018 is used. Statistical results identify power-law relationships and heavy tail phenomena in the spatial patterns of strong earthquakes. The interactions between regions follow the same law, with a few regions that may be hit by successive strong earthquakes with high probability. Also, we find that the interconnections between regions are mainly related to the succession of events in time, whereas the distribution of events is extremely inhomogeneous in space. This study provides a research prototype for the spatio-temporal analysis of global strong earthquakes, laying a foundation for obtaining insights into the network modelling approach for global strong earthquakes. [ABSTRACT FROM AUTHOR]

Currently, companies and individuals tend to use social media to publish information for promoting products and options. Although an increasing body of research has focused on promotional skills on social media, the role of post-publication time is rarely studied. However, publication time of a post plays an important role in its popularity. To select suitable publication times, an effective approach is to predict popularity values of a post when it is published at a series of future time points. However, this task is not trivial because, (i) except for publication time, all the features are inefficient, as they are the same, and (ii) the new model needs to output multiple popularity values for each input post. To address these challenges, we introduce a latent factor model to build a time sensitivity-based predictive model that can predict posts' popularity values when they are published at various times. In this model, to alleviate data sparsity, we decompose posts into syntactic units that are derived from dependency parsing results. To take advantage of auxiliary information, we exploit the features of temporal information and neighborhood influence. Experiments with Twitter data demonstrate that the proposed model significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Community detection has been an active field of study in complex network analysis in recent years. It can be modeled as a seriously nonlinear optimization problem. Many intelligent optimization techniques have shown promising results for this problem. The teaching–learning-based optimization (TLBO) algorithm is a recently proposed swarm intelligent algorithm. In this paper, a discrete variant of TLBO (DTLBO) is proposed to address discrete optimization problems. In the proposed method, the learner representation scheme is redefined, and the updating rules for learners are also redesigned. Moreover, based on the proposed discrete variant DTLBO, a multi-objective discrete method (MODTLBO/D) is proposed to solve community detection problems for complex networks. The multi-objective decomposition mechanism is adopted and neighbor-based mutation is introduced to maintain the diversity of the population and avoid being trapped in the local optima. Finally, to verify the performance of the proposed algorithm, real-world networks are examined. The experimental results indicate that MODTLBO/D is effective compared with other algorithms used for community detection in complex networks. [ABSTRACT FROM AUTHOR]

The nonlinear quantum regime is crucial for implementing interesting quantum effects, which have wide applications in modern quantum science. Here we propose an effective method to reach the nonlinear quantum regime in a modulated optomechanical system (OMS), which is originally in the weak-coupling regime. The mechanical spring constant and optomechanical interaction are modulated periodically. This leads to the result that the resonant optomechanical interaction can be effectively enhanced into the single-photon strong-coupling regime by the modulation-induced mechanical parametric amplification. Moreover, the amplified phonon noise can be suppressed completely by introducing a squeezed vacuum reservoir, which ultimately leads to the realization of photon blockade in a weakly coupled OMS. The reached nonlinear quantum regime also allows us to engineer the nonclassical states (e.g., Schrödinger cat states) of the cavity field, which are robust against the phonon noise. This work offers an alternative approach to enhance the quantum nonlinearity of an OMS, which should expand the applications of cavity optomechanics in the quantum realm. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the filtering problem for multirate nonlinear sampled-data systems. By employing the Takagi–Sugeno (T-S) fuzzy model to represent the nonlinear system, the original multirate nonlinear system can be expressed as a set of linear multirate subsystems. Then the lifting technique is applied to reformulate the multirate system as a single-rate system. Finally, the fuzzy multirate filter with guaranteed energy-to-peak () performance is designed by solving a set of linear matrix inequalities (LMIs). Simulation examples are provided to illustrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

The mechanical Pyrochlore lattice was experimentally tested to demonstrate an intrinsically polar behavior of the material, which is soft on one side and hard on the opposite side (Bilal et al., 2017). The topological polarization in Pyrochlore lattices begs for developing a new effective medium theory because conventional Cauchy effective theories cannot predict the polarization phenomenon. In this study, we develop a 3D microtwist effective theory of Pyrochlore lattices to capture the P-asymmetric zero modes by which polarization emerges or fades on a macroscopic scale. By mapping three periodic zero modes to three macroscopic degrees of freedom, the 3D microtwist theory ends up being a kinematically enriched theory. The 3D microtwist elasticity is formulated by using two-scale asymptotic approach and its constitutive and balance equations are derived for a fairly generic isostatic lattice. Performance of the proposed theory is validated by the exact solution of the discrete model for reproducing zero modes and dispersion relations and quantitatively predicting asymmetric indentation responses. The study could shed lights on novel elastic theory of 3D polarized metamaterials outside the conventional framework of symmetry groups, which is never reported before. [ABSTRACT FROM AUTHOR]