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Purpose: The emergence of the Software-as-a-service (SaaS) licensing model dramatically changes how enterprise software is released. Especially, it is favored by small and medium enterprises (SMEs) because of the cost-friendly feature. In contrast, many large enterprises (LEs) own relatively abundant budgets and prefer the on-premise software to fulfill demands through customization. Considering the differentiated cost-acceptance level among customers, this study aims to address the versioning problem of the enterprise software faced by software firms. Design/methodology/approach: A two-point distribution model is formulated to calculate the maximal profits software firm earned from both LEs and SMEs under three strategies (On-premise, SaaS and Hybrid). Then through profit comparison, this paper obtains the optimal versioning strategy and corresponding feasible conditions. Finally, the optimal solutions are derived concerning social welfare. Findings: A significant finding is that moving to SaaS becomes necessary for the software firms in product releases since the on-premise strategy will not be optimal. Based on this, this paper discovers that when LEs own a cost-acceptance level close to that of SMEs, the hybrid strategy is the only optimal choice. When LEs become less sensitive to costs, the hybrid strategy is suggested if the customization cost falls below the threshold. Otherwise, the SaaS strategy becomes the optimal option. The conclusions explain why some software vendors transit to "cloud companies" thoroughly and provide practical insights for software firms' future decisions. Originality/value: To the best of the authors' knowledge, this paper is the first information economics study to consider consumer cost sensitivity in discussing enterprise software versioning. The differentiated cost-acceptance level is introduced to describe the customer utilities, and the results uncover the necessity of moving to SaaS under diversified customer composition. This work provides significant theoretical value and practical insights. [ABSTRACT FROM AUTHOR]

Real-time condition monitoring is the foundation of prognostics and health management (PHM) for mechanical systems. Constraint by extravagant cost and insurmountable obstacles to simulate real faults in actual working conditions, establishing fault detection models with historical normal data is the most promising way. Meanwhile, non-stationary fault detection is rarely studied in the literature but fits most in real working conditions. Thus, an innovative method named KGMem-DirAE is proposed to conduct real-time fault detection under non-stationary working conditions using normal data only. In the training stage, normal patterns with different semantics are recorded in the developed key-group memory module (KGMem) combined with auto-encoder. Considering the matching probabilities of encoded latent features and recorded memory units (normal patterns), a statistical anomaly score defined by aggregated negative log-likelihood of Dirichlet distributions is proposed to detect incipient failures. In order to deal with time-varying working conditions and capture fault degradation trends concurrently, normalized time-frequency maps containing fault-evolving information are obtained from vibration data. Both widely studied non-stationary and stationary run-to-failure data sets are deeply researched using the proposed KGMem-DirAE and the experimental results proved its superiority against other comparative anomaly detection methods. • Early fault detection is the cornerstone of mechanical fault diagnosis. • Recording different normal patterns with key-group memory auto-encoder under non-stationary working conditions. • Modeling matching matrices of normal samples and learning aggregated Dirichlet log-likelihood. • Discovering minor anomalies of data samples from the feature level with statistical learning based anomaly score. • Both non-stationary and run-to-failure data set are experimented to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to study the relationship between leakage flux coefficient and the coreless axial magnetic field permanent magnet synchronous generator (AFPMSG) size and obtain the expressions of leakage flux coefficient. Design/methodology/approach: In this paper, a magnetic circuit model of coreless AFPMSG is proposed. Four kinds of leakage permeances of permanent magnet (PM) are considered, and the expression of no-load leakage flux coefficient is obtained. Solving the integral region of leakage permeances by generator size, which improves the accuracy of the solution. Findings: Finite element method and magnetic circuit method are used to obtain the no-load leakage flux coefficient and its variation trend charts with the change of pole arc coefficient, air gap length and PM thickness. The average errors of the two methods are 2.835%, 0.84% and 1.347%, respectively. At the same time, the results of single-phase electromotive force obtained by magnetic circuit method, three dimensional finite element method and prototype experiments are 19.36 V, 18.82 V and 19.09 V, respectively. The results show that the magnetic circuit method is correct in calculating the no-load leakage flux coefficient. Originality/value: The special structure of the coreless AFPMSG is considered in the presented equivalent magnetic circuit and equations, and the equations in this paper can be applied for leakage flux evaluating purposes and initial parameter selection of the coreless AFPMSG. [ABSTRACT FROM AUTHOR]

This paper aims to study the seismic responses of a long lined tunnel embedded in a water-saturated poro-viscoelastic half-space, with focus on wave passage effects. The poro-viscoelastic half-space is modeled by the Biot's theory and the soil nonlinearity is considered via an equivalent linear approach. The seismic responses of the tunnel structure are simulated by a 2.5D hybrid FE-BE method. A total of 72 cases of different incident angles for plane SV- and P1-waves are simulated, in order to conduct a systematic study on the tunnel seismic responses, in terms of longitudinal internal forces and strains, as well as seismic pore pressure and soil stresses. It is shown that the spatial variation of earthquake ground motion due to wave passage effects results in considerable longitudinal internal forces of the tunnel lining, which cannot be overlooked for seismic design. The responses of the tunnel depend on both the horizontal and vertical incident angles of the seismic waves. It is also shown that the 2D scenario with perpendicularly incident SV-waves, which is commonly considered in engineering practice, may lead to nonconservative seismic design, especially for tunnels in saturated soil. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered dynamic output feedback tracking control for large-scale interconnected systems with disturbances. For each node, a novel event-triggered mechanism is driven by local relative output tracking error to determine whether the signal will be transmitted. A two-step optimization is applied for dynamic output feedback controller design which guarantees robust stability of the system with an optimal H ∞ disturbance attenuation level. Finally, a simulation example of master-slave multiple vehicles is given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical decision-making and control algorithm for the shepherd game, the seventh mission in the International Aerial Robotics Competition (IARC). In this game, the agent (a multirotor aerial robot) is required to contact targets (ground vehicles) sequentially and drive them to a certain boundary to earn score. During the game of 10 min, the agent should be fully autonomous without any human interference. Regarding the lower-level controller and dynamics of the agent, each action takes a duration of time to accomplish. Denoted as an action delay, in this paper, this action duration is nonconstant and is related to the final reward. Therefore, the challenging point is making the agent “aware of time” when applying a certain action. We solve this problem by two approaches: deep Q-networks and lookup table. The action delay predictor in the decision-level is fitted by a lower-level controller. Through simulations by the example of the shepherd game, the effectiveness and efficiency of this approach are validated. This paper helps our team winning the first prize in IARC 2017, and keeps the best record of this mission since it was released in 2013. [ABSTRACT FROM AUTHOR]

• A new fifth order hybrid WENO scheme is presented. • New indicator to switch using WENO or linear approximation. • No characteristic projection for linear approximation. • More efficient and smaller numerical errors than the simple WENO. In [28], a simple fifth order weighted essentially non-oscillatory (WENO) scheme was presented in the finite difference framework for the hyperbolic conservation laws, in which the reconstruction of fluxes is a convex combination of a fourth degree polynomial with two linear polynomials. In this follow-up paper, we propose a new fifth order hybrid weighted essentially non-oscillatory (WENO) scheme based on the simple WENO. The main idea of the hybrid WENO scheme is that if all extreme points of the reconstruction polynomial for numerical flux in the big stencil are located outside of the big stencil, then we reconstruct the numerical flux by upwind linear approximation directly, otherwise use the simple WENO procedure. Compared with the simple WENO, the major advantage is its higher efficiency with less numerical errors in smooth regions and less computational costs. Likewise, the hybrid WENO scheme still keeps the simplicity and robustness of the simple WENO scheme. Extensive numerical results for both one and two dimensional equations are performed to verify these good performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Deep generative models (DGMs), which are often organized in a hierarchical manner, provide a principled framework of capturing the underlying causal factors of data. Recent work on DGMs focussed on the development of efficient and scalable variational inference methods that learn a single model under some mean-field or parameterization assumptions. However, little work has been done on extending Markov chain Monte Carlo (MCMC) methods to Bayesian DGMs, which enjoy many advantages compared with variational methods. We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of DGMs in a collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a mini-batch of data samples to estimate the gradient of log-posterior and further estimates the intractable expectation over hidden variables via a neural adaptive importance sampler, where the proposal distribution is parameterized by a deep neural network and learnt jointly along with the sampling process. We demonstrate the effectiveness of learning various DGMs on a wide range of tasks, including density estimation, data generation, and missing data imputation. Our method outperforms many state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Generating collective opinion based on probability distribution function aggregation occupies crucial roles in accomplishing probabilistic risk analysis, probabilistic-forecast-based prediction, and uncertain assessment tasks. However, rare efforts have been paid to exploring collective opinion generation based on massive quantified judgments from large-scale experts. In this study, we establish a novel large-scale collective opinion generation paradigm based on probability distribution function aggregation to accomplish complicated assessment and evaluation tasks in decision analysis. To this end, we reformulate the existing bi-objective optimization model with the exclusion of the consensus dimension and inclusion of fairness concern among subject matter experts. The formulation of collective fairness utility uses the notion of aggregation functions and contributes to the establishment of large-scale collective opinion generation paradigm with capabilities of modeling distinct fairness distributions among the subject matter experts. The established fairness-aware large-scale collective opinion generation model advocates an adjusted bi-objective optimization model that maximizes the confidence level and fairness utility within the decision group for opinion aggregation on both intra-group and inter-group levels. It is endowed with the flexibility to be combined with context-specific expert classification and criterion weight assignment techniques to generate powerful large-scale group decision making prototypes that accommodate with diversifying decision-making scenarios. We apply the proposed fairness-aware large-scale collective opinion generation framework to the assessment of blockchain adoption barriers in medical supply chain to demonstrate its rationality and effectiveness. • We define the individual and collective fairness utilities. • We build the fairness-aware collective opinion generation framework. • We establish the fairness-aware large-scale collective opinion generation paradigm. • We apply the proposed model to the assessment of blockchain adoption barriers. [ABSTRACT FROM AUTHOR]

This paper considers the distributed tracking control problem for linear multi-agent systems with disturbances and a leader whose control input is nonzero and not available to any follower. Based on the relative output measurements of neighboring agents, a novel distributed observer-based tracking protocol is proposed, where the distributed intermediate estimators are constructed to estimate the leader’s unknown control input and the states of the tracking error system simultaneously, then a distributed tracking protocol is designed based on the derived estimates. It is proved that the states of the tracking error system are uniformly ultimately bounded and an explicit tracking error bound is obtained. A simulation example of aircrafts verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This technical note investigates the problem of fault estimation for a class of nonlinear systems with Lipschitzian nonlinearities. In most of the existing fault estimation approaches, the observer matching condition should be satisfied. However, this condition is hard to meet for many systems. To overcome this restriction, a new fault estimation method is proposed, where an intermediate variable is introduced and a specially designed intermediate estimator is proposed to estimate the state and the faults simultaneously by exploiting the properties of fault distribution matrix. It is proved that the states of the error system are uniformly ultimately bounded. Simulation examples verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper designs a new finite difference mapped unequal-sized trigonometric weighted essentially non-oscillatory (MUS-TWENO) scheme for solving hyperbolic conservation laws, highly oscillation problems, and some extreme problems containing low density, low pressure, or low energy. A new mapping function and associated new mapped nonlinear weights are proposed to reduce the difference between the linear weights and nonlinear weights in trigonometric polynomial space. It could get smaller numerical errors and obtain optimal fifth-order convergence with a tiny ɛ even near critical points in smooth regions when simulating some highly oscillatory problems. This new MUS-TWENO scheme uses three unequal-sized stencils to design three unequal degree trigonometric polynomials and the sophisticated optimal linear weights can be set as any positive numbers on condition that their summation is one. It is the first time that we can reconstruct a high degree trigonometric polynomial over the whole big stencil, while many classical high-order WENO spatial reconstructions only reconstruct the values at the boundary points or discrete quadrature points. Extensive benchmark examples including highly oscillatory problems and some extreme problems are used to testify the good representations of this new finite difference MUS-TWENO scheme. [ABSTRACT FROM AUTHOR]

The Runge–Kutta discontinuous Galerkin method together with the front tracking method for solving the compressible two-medium flow is presented. The fluid interface boundary condition is defined via a non-conservative method based on the real ghost fluid method. A Riemann problem is constructed in the normal direction of the interface, the corresponding Riemann solutions are used to determine the motion of the interface and obtain the ghost fluid states directly. Extensive numerical tests including the gas–gas and gas–liquid flows are provided to illustrate that the proposed methods possess the capability of enhancing the resolutions nearby the discontinuities in the single medium and material interfacial vicinities. [ABSTRACT FROM AUTHOR]

Undirected latent variable models represent an important class of graphical models that have been successfully developed to deal with various tasks. One common challenge in learning such models is to determine the number of hidden units that are unknown a priori. Although Bayesian nonparametrics have provided promising results in bypassing the model selection problem in learning directed Bayesian Networks, very little effort has been made toward applying Bayesian nonparametrics to learn undirected latent variable models. In this paper, we present the infinite exponential family Harmonium (iEFH), a bipartite undirected latent variable model that automatically determines the number of latent units from an unbounded pool. We also present two important extensions of iEFH to 1) multiview iEFH for dealing with heterogeneous data, and 2) infinite maximum-margin Harmonium (iMMH) for incorporating supervising side information to learn predictive latent features. We develop variational inference algorithms to learn model parameters. Our methods are computationally competitive because of the avoidance of selecting the number of latent units. Our extensive experiments on real image datasets and text datasets appear to demonstrate the benefits of iEFH and iMMH inherited from Bayesian nonparametrics and max-margin learning. Such results were not available until now and contribute to expanding the scope of Bayesian nonparametrics to learn the structures of undirected latent variable models. [ABSTRACT FROM AUTHOR]

In this paper, a more efficient fifth-order finite difference multi-resolution WENO scheme is designed to solve the compressible inviscid and viscous flow problems based on the original multi-resolution WENO scheme presented in Zhu and Shu (2018). This new improved method inherits all the following features of the original multi-resolution WENO scheme (Zhu and Shu , 2018): (1) a series of unequal-sized central stencils are used to obtain the spatial reconstruction polynomials; (2) any positive numbers whose sum is one can be set as the linear weights; (3) smaller L 1 and L ∞ errors could be obtained for smooth problems; (4) it is easier to extend to the unstructured finite volume framework. However, different from the original multi-resolution WENO scheme, this improved method simplifies the reconstruction process, significantly improves the computational efficiency, and has greater engineering application potential. Numerical results show that the two types of fifth-order finite difference multi-resolution WENO schemes have similar results, but the CPU time of this new multi-resolution WENO scheme is about 0.7 times that of the original one. Moreover, the new fifth-order multi-resolution WENO scheme with a small increase in the computational cost shows less dissipation error than the classical WENO scheme (Jiang and Shu, 1996), and can capture more subtle flow structures for solving inviscid and viscous flow problems on the same mesh level. Several benchmark inviscid and viscous problems are illustrated to verify the above conclusions and the improved performance of this fifth-order multi-resolution WENO scheme. • An improved multi-resolution WENO scheme is designed to solve inviscid and viscous problems. • This WENO scheme is designed based on a series of unequal-sized hierarchical central spatial stencils. • This new multi-resolution WENO scheme is more efficient than the original multi-resolution WENO scheme. • The improved multi-resolution WENO scheme is less dissipative than the classical WENOJS scheme. [ABSTRACT FROM AUTHOR]

The new generation of DORIS instruments called 'DGXX' is now flying on HY-2. This instrument provide synchronous dual frequency phase and pseudo-range measurements. These measurements are now described extension of RINEX 3.0 format. In this paper, a method of phase measurement processing is put forward, in which phase measurements are translated to velocity measurements by using differential DORIS phase measurements observation error correcting accordingly. By introducing macro-model force and empirical force, the HY-2 DORIS dynamic Precise Orbit Determination (POD) is implemented. The primary result indicates that, DORIS dynamic POD for achieves centimeter orbit determination accuracy with below 2cm radial error and 10cm position error. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to establish a situation evaluation model of the robot and ball in SimuroSot5vs5 platforms and enhance the strength of the team in a SimuroSot5vs5. Design/methodology/approach – This paper presents a mathematical model based on situation evaluations which can improve the strength of the team in SimuroSot5vs5. The situation evaluation focus on four aspects includes scores of both sides, possession of teams on ground, ball strategy, and treat. The statistical analysis of the score can verify validity and stability of current strategy in confrontation. To evaluate the situation more effectively without blindness, possession on both teams is, respectively, evaluated. Ball strategy analyzes coordinate transformation to the ball on the ground and illustrates the circumstance of both teams on the offensive position accurately in length and breadth. To know the circumstance on the field more completely and synthetically, a threat situation evaluation model is built. An effective and practical algorithm for comprehensive situation evaluation is successfully finished. The experiments prove validity and performance of the proposed situation evaluation model. Findings – A mathematical model is designed to achieve situation evaluation, and the strategy can change in accordance with different situations on the ground. Research limitations/implications – The system is specifically applied to SimuroSot5vs5 platform. The extensibility of the system is limited. Practical implications – When the robot and ball is in high speed movement, a large calculated amount will slow the speed of the system. Originality/value – The paper shows that situation evaluation in SimuroSot decision support systems will enhance the battle effectiveness of the soccer team. [ABSTRACT FROM AUTHOR]

Synchronous Nyquist folding receiver (SNYFR) is a new kind of interception architecture, which could simultaneously intercept wideband signals in multi-Nyquist zones with two analog-to-digital converters (ADCs). A parameter estimation algorithm is presented for the linear frequency modulated (LFM) signal intercepted by an SNYFR. First, taking the local oscillator signal (LOS) whose spectrum is symmetrical about the sampling rate as an example, the Nyquist zone does not exist under certain conditions. Second, the left sideband LOS SNYFR, whose zone does exist, is proposed. Then, the frequency decimation factor is designed as the modulated frequency of the sinusoidal frequency modulation (SFM) LOS to estimate the Nyquist zone. Finally, the under-sampled LFM signal is reconstructed by using the zone, and it is used to estimate the parameters of signal. Simulations show that all the parameter estimation accuracies are all close to Cramer-Rao lower bounds when the signal to noise ratio (SNR) is above 10dB. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we develop the Runge–Kutta discontinuous Galerkin (RKDG) methods with the finite volume weighted essentially non-oscillatory (WENO) reconstruction as limiters to solve for the unsteady cavitating flow under the employment of the isentropic one-fluid model. To treat the cavitating flow and suppress the possible spurious oscillations in the vicinity of the cavitation boundary, the TVB limiter is used as an indicator to detect the “troubled cells” and hence take the advantage of utilizing the WENO reconstruction for the freedoms of the RKDG methods. Numerical results are provided to illustrate the viability of these procedures. [Copyright &y& Elsevier]

Abstract: Solving a geographic problem usually requires collaborative work among a group of people in different geographic locations. Collaborative virtual geographic environment (CVGE), an integrated technology, offers an intuitive, efficient, and interactive visualization environment through which geographically separated users can explore complicated spatial information and conduct collaborative work. In this paper, two new technologies, Peer-to-Peer (P2P) and Grid computing, are tightly coupled to develop a CVGE system. This paper evaluates the potential contributions of the P2P and Grid technology to CVGE systems. Using a Grid based system architecture efficiently integrates and shares geographically distributed resources as well as modeling procedures built on different platforms. To offer a shared and interactive virtual collaborative geographic environment for resolving geographic problems, we developed several P2P services including a terrain visualization collaboration service and a video collaboration service. Finally, a CVGE prototype system is implemented for collaboration on silt dam planning on the Loess plateau. The experimental results show that the scheme developed in this paper is efficient and feasible. [Copyright &y& Elsevier]

When the conditional expectation of a complete-data likelihood in an EM algorithm is analytically intractable, Monte Carlo integration is often used to approximate the E-step. While the resulting Monte Carlo EM algorithm (MCEM) is flexible, assessing convergence of the algorithm is a more difficult task than the original EM algorithm, because of the uncertainty involved in the Monte Carlo approximation. In this note, we propose a convergence criterion using a likelihood-based distance. Because the likelihood is approximated by Monte Carlo integration, we make the distance small with a large probability by selecting the Monte Carlo sample size adaptively at each step of the MCEM algorithm. We implement the proposed convergence criterion along with the simulation size selection in a one-way random effects model. The result shows that our MCEM iterations match the exact EM iterations closely. [Copyright &y& Elsevier]

As deep learning models become increasingly integral to various 3D applications, concerns about their vulnerability to adversarial attacks grow in tandem. This paper addresses the challenge of enhancing the transferability of 3D adversarial attacks, a critical aspect for evaluating model robustness across diverse scenarios. We propose a novel approach leveraging scale and shear transformations to generate adversarial examples that exhibit improved transferability across multiple 3D models. Our methodology involves carefully integrating scale and shear transformations into the adversarial perturbation generation process with only a marginal increase in computational time. The proposed attack method operates within the Carlini-Wagner (CW) optimization framework. For each iteration, it employs two hyperparameters: p a , determining the probability of transforming the input point cloud, and p s , deciding whether to shear or scale the point cloud. Limited to scaling and shearing transformations, Scale and Shear (SS) attack seamlessly integrates with established attack methods, enhancing flexibility and compatibility in adversarial attacks on 3D models. Extensive experiments show that the SS attack proposed in this paper can be seamlessly combined with the existing state-of-the-art (SOTA) 3D point cloud attack methods to form more powerful attack methods, and the SS attack improves the transferability over 3.6 times compared to the baseline. Moreover, while substantially outperforming the baseline methods, the SS attack achieves SOTA transferability under various defenses. Our code will be available online at: https://github.com/cuge1995/SS-attack. [ABSTRACT FROM AUTHOR]

• TWENO schemes are designed for some highly oscillatory problems. • The linear weights of such TWENO schemes can be any positive numbers. • These new trigonometric WENO schemes are simple to construct and can be easily implemented. In this paper, we investigate designing a new type of high-order finite difference multi-resolution trigonometric weighted essentially non-oscillatory (TWENO) schemes for solving hyperbolic conservation laws and some benchmark highly oscillatory problems. We only use the information defined on a hierarchy of nested central spatial stencils in a trigonometric polynomial reconstruction framework without introducing any equivalent multi-resolution representations. These new finite difference trigonometric WENO schemes use the same large stencils as the classical WENO schemes (Jiang and Wu, 1996; Shu, 2009), could obtain the optimal order of accuracy in smooth regions, and simultaneously suppress spurious oscillations near strong discontinuities. The linear weights of such multi-resolution trigonometric WENO schemes can be any positive numbers on condition that their summation is one. This is the first time that a series of unequal-sized hierarchical central spatial stencils are used in designing high-order finite difference trigonometric WENO schemes. These new trigonometric WENO schemes are simple to construct and can be easily implemented to arbitrary high-order accuracy in multi-dimensions. Some benchmark examples including some highly oscillatory problems are given to demonstrate the robustness and good performance of these new trigonometric WENO schemes. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid unequal-sized weighted essentially non-oscillatory (US-WENO) scheme is developed to reduce the computational cost caused by the computation of nonlinear weights and local characteristic decompositions in its procedures. First, an improved discontinuous sensor is designed based on the mathematical properties of the fourth-degree reconstruction polynomial in the US-WENO scheme. The proposed hybridization strategy can automatically and efficiently identify the troubled cells, and does not contain artificial parameters related to the problems. Second, the efficient linear scheme and the existing US-WENO scheme are combined to simulate the inviscid and viscous compressible flow problems. Finally, some benchmark numerical examples are used to verify the performance of the presented hybrid WENO scheme in various aspects, such as computational efficiency, low dissipation characteristics, shock capture ability, etc. The numerical experiments show that the proposed hybrid method can inherit all the features of the existing US-WENO scheme while improving its computational efficiency. • A new hybrid unequal-sized WENO scheme is designed to solve compressible problems. • A simple and artificial parameter free hybridization method is proposed in this paper. • The presented method is more efficient than the existing unequal-sized WENO scheme. • The improved WENO scheme is less dissipative than the classical WENO-JS scheme. [ABSTRACT FROM AUTHOR]

Abstract Since noise is an inevitable factor in real-world applications, designing robust image recovery technologies is crucial for the application of a compressive sensing (CS) system. However, current CS approaches rarely consider noise, or they only consider the noise generated in the process of transmission. Almost all CS approaches will breakdown when the original image is corrupted by noise due to natural or artificial factors, especially non-Gaussian noise. In this paper, we propose a nonconvex m-estimator-based robust image CS approach to recover the original clean images from the compressed measurements of their corrupted versions. We simultaneously conduct reconstructing and denoising in a unified framework. We also use a weighted schatten- p norm to better exploit the nonlocal low-rank property. In addition, we developed an efficient iterative scheme based on the half-quadratic theory and the alternating direction method of multipliers to solve the resulting nonconvex optimization problem. The experimental results show that the proposed algorithm can recover the clean images accurately when the original images are corrupted by non-Gaussian noise. [ABSTRACT FROM AUTHOR]

Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task but then fails to conduct the old tasks well. By contrast, the biological brain can effectively address catastrophic forgetting through consolidating memories as more specific or more generalized forms to complement each other, which is achieved in the interplay of the hippocampus and neocortex, mediated by the prefrontal cortex. Inspired by such a brain strategy, we propose a novel approach named triple-memory networks (TMNs) for continual learning. TMNs model the interplay of the three brain regions as a triple-network architecture of generative adversarial networks (GANs). The input information is encoded as specific representations of data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in the generation process. TMNs achieve the state-of-the-art performance of generative memory replay on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10, and ImageNet-50. [ABSTRACT FROM AUTHOR]

We study the local indistinguishability of mutually orthogonal product basis quantum states in the highdimensional quantum system. In the quantum system of Cd Cd, where d is odd, Zhang et al. [Z.-C. Zhang et al., Phys. Rev. A 90. 022313 (2014)] have constructed d² orthogonal product basis quantum states that are locally indistinguishable. We find a subset that contains 6d -- 9 orthogonal product states that are still locally indistinguishable. We generalize our method to an arbitrary bipartite quantum system €"' C". We present a small set with only 3(m + n) -- 9 orthogonal product states and prove that these states are local operations and classical communication (LOCC) indistinguishable. Even though these 3(m + n) -- 9 product states are LOCC indistinguishable, they can be distinguished by separable measurements. This shows that separable operations are strictly stronger than the local operations and classical communication. [ABSTRACT FROM AUTHOR]

Abstract: Traditional multivariate tests such as Hotelling’s test or Wilk’s test are designed for classical problems, where the number of observations is much larger than the dimension of the variables. For high-dimensional data, however, this assumption cannot be met any longer. In this article, we consider testing problems in high-dimensional MANOVA where the number of variables exceeds the sample size. To overcome the challenges with high dimensionality, we propose a new approach called a shrinkage-based regularization test, which is suitable for a variety of data structures including the one-sample problem and one-way MANOVA. Our approach uses a ridge regularization to overcome the singularity of the sample covariance matrix and applies a soft-thresholding technique to reduce random noise and improve the testing power. An appealing property of this approach is its ability to select relevant variables that provide evidence against the hypothesis. We compare the performance of our approach with some competing approaches via real microarray data and simulation studies. The results illustrate that the proposed statistics maintains relatively high power in detecting a wide family of alternatives. [Copyright &y& Elsevier]

Abstract: In this paper, a host–parasitoid model with prolonged diapause for parasitoid is proposed and analyzed. The asymptotic stability analysis of the system is performed. For a biologically reasonable range of parameter values, the global dynamics of the system have been studied numerically. In particular, the effect of prolonged diapause and parasitism on the system has been investigated. Many forms of complex dynamics are observed. The complexities include: (1) chaotic bands with periodic windows; (2) pitchfork and tangent bifurcations; (3) period-doubling and period-halving cascades; (4) intermittency; (5) supertransients; (6) non-unique dynamics, meaning that several attractors coexist; and (7) attractor crises. Furthermore, the complex dynamic behaviors of the model are confirmed by the largest Lyapunov exponents. [ABSTRACT FROM AUTHOR]

Abstract: Chaotic dynamics have been observed in a wide range of population models. However, much of the research on the persistence of chaos has focused on external perturbations of ecosystems, such as climatic change or anthropogenic factors. In this paper, the effects of a non-zero population floor in a mutual interference host–parasitoid model are described. Such a perturbation generally reduces the likelihood of observing chaos. Furthermore, the computational simulation of the largest Lyapunov exponent also demonstrates the chaotic dynamic behavior of the model and describes a process which reduces the likelihood of observing chaos. The numerical results indicate that computer simulation is a useful method for studying chaos. [Copyright &y& Elsevier]

Abstract: Feed-forward neural network is employed to model the nonlinear oil-film force database of a finite-length hydrodynamic journal bearing, which is constructed by continuous transformation of Reynolds equation. Neural network models trained are utilized to investigate motion characteristics of a rigid unbalanced rotor supported on elliptical bearings in 300MW steam turbine generator set. There exist various forms of periodic, quasi-periodic and chaotic motions at different rotating speeds. Periodic doubling bifurcation and quasi-periodic routes to chaos may be found when rotating speed is used as the control parameter. Computational results show that there exist similar motion behaviors between neural networks and numerical method. It is available for neural network models of oil-film forces to research nonlinear dynamic problems of rotating machinery. [Copyright &y& Elsevier]

• This paper presents the first decentralized secret sharing scheme using the Blockchain. • Leverage the smart contract to force all participants to implement the protocol honestly. • Reconstruct the secret fairly without the trusted party in one round. • Experiments show that our scheme can run reasonably fast and is practical. Secret sharing has a number of practical applications in network-based scenarios, such as key transfer protocols, attribute-based encryption and secure multiparty computation. However, existing secret sharing schemes cannot efficiently achieve fairness. They either rely on a trusted third party or require multiple rounds of communication. In this paper, we propose the first decentralized and fair hierarchical threshold secret sharing (HTSS) scheme using blockchain. In the scheme, secret shares are distributed to different levels of parties, and any authorized subset of parties can obtain the secret. We leverage a smart contract to force all participants to commit to the secret shares; otherwise, the committer either reveals his secret share within a certain time frame or pays a fine. Thus, unlike previous HTSS schemes, the participants can reconstruct the secret fairly using Birkhoff interpolation without a trusted party and complete the computation in one round. We formally prove that our scheme is secure. We evaluate the performance of the scheme by implementing our scheme on Ethereum's official test network. Our experiments show that our scheme can run reasonably fast and is practical. [ABSTRACT FROM AUTHOR]

The analytic expressions of peaked solitary wave solutions and peaked periodic wave solutions of Camassa–Holm equation are obtained by using bifurcation method of planar dynamical systems. The convergence of the peaked periodic wave solutions is proved. Numerical simulation results show the consistence with the theoretical analysis. The results in this paper are wider than those already known. [Copyright &y& Elsevier]

We solve the unextendible maximally entangled basis (UMEB) problem in Cd ...Cd'(d ≠ d'). The results show that a UMEB will always exist. In addition, there might be two sets of UMEBs with different numbers. The main difficulty is to prove the unextendibility of the set of states. We give an explicit construction of UMEBs by considering the Schmidt number of the complementary space of the states we construct. [ABSTRACT FROM AUTHOR]

We give an explicit construction of d locally indistinguishable, orthogonal maximally entangled states in Cd ⊗ Cd for any d ≥ 4. This gives an answer to the conjecture proposed by Bandyopadhyay et al. [New J. Phys. 13, 123013 (2011)]. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, using the theories and methods of ecology and ordinary differential equations, an ecological model with an impulsive control strategy and a distributed time delay is defined. Using the theory of the impulsive equation, small-amplitude perturbations, and comparative techniques, a condition is identified which guarantees the global asymptotic stability of the prey-(x) and predator-(y) eradication periodic solution. It is proved that the system is permanent. Furthermore, the influences of impulsive perturbations on the inherent oscillation are studied numerically, an oscillation which exhibits rich dynamics including period-halving bifurcation, chaotic narrow or wide windows, and chaotic crises. Computation of the largest Lyapunov exponent confirms the chaotic dynamic behavior of the model. All these results may be useful for study of the dynamic complexity of ecosystems. [Copyright &y& Elsevier]

Abstract: The integration of high dimensional geo-visualization, geo-data management, geo-process modeling and computation, geospatial analysis, and geo-collaboration is a trend in GIScience. The technical platform that matches the trend forms a new framework unlike that of GIS and is conceptualized in this paper as a collaborative virtual geographic environment (CVGE). This paper focuses on two key issues. One is scientific research on CVGE including the concept definition and the conceptual and system framework development. The other is a prototype system development according to CVGE frameworks for air pollution simulation in the Pearl River Delta. The prototype system integrates air pollution source data, air pollution dispersion models, air pollution distribution/dispersion visualization in geographically referenced environments, geospatial analysis, and geo-collaboration. Using the prototype system, participants from geographically distributed locations can join in the shared virtual geographic environment to conduct collaborative simulation of air pollution dispersion. The collaborations supporting this simulation happen on air pollution source editing, air pollution dispersion modeling, geo-visualization of the output of the modeling, and geo-analysis. [Copyright &y& Elsevier]

Abstract: Virtually vibration signals are always composed of many frequency components. Using the least squares method, a new multi-frequency identification algorithm has been proposed to identify the amplitude and phase of each component of a multi-frequency signal, and it will work well as long as the main frequency values are known. Based on this algorithm, two similar optimization procedures have been presented to obtain accurate frequency values for a sine-wave signal and a bi-tone signal, respectively. Assisted with band-pass filters and these optimization procedures, the multi-frequency identification algorithm can successfully identify the multi-frequency components of a signal. The multi-frequency identification method can be used to identify oil-film coefficients of journal bearing. And some reliable oil-film coefficients have been reached. [Copyright &y& Elsevier]

Abstract: This paper aims to design and develop a Distributed Virtual Geographic Environment (DVGE) system. A DVGE system is an Internet-based virtual 2D and 3D environment that provides users with a shared space and a collaborative platform for publishing multidimensional geo-data, and for simulating and analyzing complex geo-phenomena. Users logging into the system from different clients can share distributed geo-information resources, including geo-data and geo-models, and can complete collaborative tasks. Web service technology provides effective solutions for constructing DVGE systems because of its ability to support multi-platform, multi-architecture, and multi-program-language interoperability on the Internet, but also because of its ability to share programs, data, and software. This paper analyzes the characteristics, relevant technologies, and specifications of web services, such as grid services, Open Geo-data Interoperability Specifications (OpenGIS), and Geography Markup Languages (GML). The architecture and working mechanisms of the DVGE system based on web services are then elaborated. To demonstrate DVGE systems based on web services, we examine a case study of water pollution in Yangzhou City, Jiangsu Province, China, using a prototype DVGE system that is developed with Jbuilder9.0 and Java3D 1.0 packages, and the Weblogic platform 8.1. [Copyright &y& Elsevier]