Search

Since the turn of the century, metamaterials have gained a large amount of attention due to their potential for possessing highly nontrivial and exotic properties such as cloaking or perfect lensing. There has been a great push to create reliable mathematical models that accurately describe the required material composition. Here, we consider a quantum graph approach to metamaterial design. An infnite square periodic quantum graph, constructed from vertices and edges, acts as a paradigm for a 2D metamaterial. Wave transport occurs along the edges with vertices acting as scatterers modelling sub-wavelength resonant elements. These resonant elements are constructed with the help of fnite quantum graphs attached to each vertex of the lattice with customisable properties controlled by a unitary scattering matrix. The metamaterial properties are understood and engineered by manipulating the band diagram of the periodic structure. The engineered properties are then demonstrated in terms of the refection and transmission behaviour of Gaussian beam solutions at an interface between two diferent metamaterials. We extend this treatment to N layered metamaterials using the Transfer Matrix Method. We demonstrate both positive and negative refraction and beam steering. Our proposed quantum graph modelling technique is very fexible and can be easily adjusted making it an ideal design tool for creating metamaterials with exotic band diagram properties or testing promising multi-layer set ups and wave steering efects

In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time series models. Containment or lockdown policies, as instigated at the national-level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black-box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time-variation in the effectiveness of specific containment policies., Comment: 44 pages, 16 figures

In this paper we develop inference for high dimensional linear models, with serially correlated errors. We examine Lasso under the assumption of strong mixing in the covariates and error process, allowing for fatter tails in their distribution. While the Lasso estimator performs poorly under such circumstances, we estimate via GLS Lasso the parameters of interest and extend the asymptotic properties of the Lasso under more general conditions. Our theoretical results indicate that the non-asymptotic bounds for stationary dependent processes are sharper, while the rate of Lasso under general conditions appears slower as $T,p\to \infty$. Further we employ the debiased Lasso to perform inference uniformly on the parameters of interest. Monte Carlo results support the proposed estimator, as it has significant efficiency gains over traditional methods.

Chiral phononic crystals provide unique properties not offered by conventional phononic material based on Bragg scattering and local resonance. However, it is insufficient to only consider the inertial amplification effect in chiral phononic crystals. Here, we theoretically and experimentally introduce the analogy with Thomson scattering to characterize the bandgap phenomena in chiral phononic crystals. Two phononic structures are proposed and discussed, one with translationrotation coupling and another with translation-translation coupling. The two lattices are different in appearance but have similar bandgaps. Thomson scattering in electromagnetic waves was drawn on to describe the coupling motion of the unit cells. We evidence that the bandgap generation is essentially based on the analogous Thomson scattering aiming to achieve an anti-phase superposition of the waves in the same polarization mode. This finding sheds new light on the physics of the elastodynamic wave manipulation in chiral phononic crystals and opens a remarkable route for their pragmatic implementation.

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of edge servers' resource allocation has been widely studied. Most of previous works focus on a single edge server (ES) serving multiple terminal entities (TEs), which restricts their access to sufficient resources. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs' selection on ESs and conflicting interests of both parties. Game theory can be used as an effective tool to realize the interests of two or more conflicting individuals in the trading market. Therefore, we propose a bilateral game framework among multiple ESs and multiple TEs by modeling the task outsourcing problem as two noncooperative games: the supplier and customer side games. In the first game, the supply function bidding mechanism is employed to model the ESs' profit maximization problem. The ESs submit their bids to the scheduler, where the computing service price is computed and sent to the TEs. While in the second game, TEs determine the optimal demand profiles according to ESs' bids to maximize their payoff. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Simulation results have demonstrated the superior performance of DTOA in increasing the ESs' profit and TEs' payoff, as well as flattening the peak and off-peak load.

Revenue generation is the main concern of any business, particularly in the cloud, where there is no direct interaction between the provider and the consumer. Cloud computing is an emerging core for today's businesses, however, Its complications (e.g, installation, and migration) with traditional markets are the main challenges. It earns more but needs exemplary performance and marketing skills. In recent years, cloud computing has become a successful paradigm for providing desktop services. It is expected that more than \$ 331 billion will be invested by 2023, likewise, 51 billion devices are expected to be connected to the cloud. Infrastructure as a Service (IaaS) provides physical resources (e.g, computing, memory, storage, and network) as VM instances. In this article, the main revenue factors are categorized as SLA and penalty management, resource scalability, customer satisfaction and management, resource utilization and provision, cost and price management, and advertising and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered., Comment: 28 Pages, 3 Figures, 5 Tables, 110 References

The Orthomin ( Omin ) and the Generalized Minimal Residual method ( GMRES ) are commonly used iterative methods for approximating the solution of non-symmetric linear systems. The s-step generalizations of these methods enhance their data locality parallel and properties by forming s simultaneous search direction vectors. Good data locality is the key in achieving near peak rates on memory hierarchical supercomputers. The theoretical derivation of the s-step Arnoldi and Omin has been published in the past. Here we derive the s-step GMRES method. We then implement s-step Omin and GMRES on a Cray-2 hierarchical memory supercomputer.

A vital element of a cyberspace infrastructure is cybersecurity. Many protocols proposed for security issues, which leads to anomalies that affect the related infrastructure of cyberspace. Machine learning (ML) methods used to mitigate anomalies behavior in mobile devices. This paper aims to apply a High Performance Extreme Learning Machine (HP-ELM) to detect possible anomalies in two malware datasets. Two widely used datasets (the CTU-13 and Malware) are used to test the effectiveness of HP-ELM. Extensive comparisons are carried out in order to validate the effectiveness of the HP-ELM learning method. The experiment results demonstrate that the HP-ELM was the highest accuracy of performance of 0.9592 for the top 3 features with one activation function., Comment: 22 pages

A telecommunication system uses carriers in order to transmit information through a cable or wirelessly. If each time only one carrier is transmitted, then the system s signal will not be immune to frequency selective fading. If frequency selective fading includes the working frequency of the system, then the wireless link will not be established. Orthogonal frequency division multiplexing OFDM is the primary solution for coping with inter signal interference and frequency selective fading. Many carriers can be produced by splitting a fast information stream to slower data series. Different orthogonal frequencies carry slower data series. System s performance can be further enhanced with the utilization of turbo codes. Turbo codes make the system more immune to noise effects with excellent BER results. This paper presents the thorough analysis of a turbo coded OFDM scheme using a PCCC technique in the presence of a channel which includes AWGN, phase noise, Rayleigh fading, Rician fading and Doppler shift., Comment: 21 pages, 11 figures

As one of open-source codes widely used in computational ocean acoustics, FOR3D can provide a very good estimate for underwater acoustic propagation. In this paper, we propose a performance optimization and parallelization to speed up the running of FOR3D. We utilized a variety of methods to enhance the entire performance, such as using a multi-threaded programming model to exploit the potential capability of the many-core node of high-performance computing (HPC) system, tuning compile options, using efficient tuned mathematical library and utilizing vectorization optimization instruction. In addition, we extended the application from single-frequency calculation to multi-frequency calculation successfully by using OpenMP+MPI hybrid programming techniques on the mainstream HPC platform. A detailed performance evaluation was performed and the results showed that the proposed parallelization obtained good accelerated effect of 25.77X when testing a typical three-dimensional medium-sized case on Tianhe-2 supercomputer. It also showed that the tuned parallel version has a weak-scalability. The speed of calculation of underwater sound field can be greatly improved by the strategy mentioned in this paper. The method used in this paper is not only applicable to other similar computing models in computational ocean acoustics but also a guideline of performance enhancement for scientific and engineering application running on modern many-core-computing platform., Comment: 9 pages, 8 figures, 3 tables. preprint for the International Conference on Computer Science and Application Engineering (CSAE2017). 2017.10.21-23, Shanghai, China

For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern supercomputer system, especially with heterogeneous computing resources such as hybrid CPU+GPU, or a CPU + Intel Xeon Phi (MIC) co-processors, is still a great challenge. An in-house parallel CFD code capable of simulating three dimensional structured grid applications is developed and tested in this study. Several methods of parallelization, performance optimization and code tuning both in the CPU-only homogeneous system and in the heterogeneous system are proposed based on identifying potential parallelism of applications, balancing the work load among all kinds of computing devices, tuning the multi-thread code toward better performance in intra-machine node with hundreds of CPU/MIC cores, and optimizing the communication among inter-nodes, inter-cores, and between CPUs and MICs. Some benchmark cases from model and/or industrial CFD applications are tested on the Tianhe-1A and Tianhe-2 supercomputer to evaluate the performance. Among these CFD cases, the maximum number of grid cells reached 780 billion. The tuned solver successfully scales up to half of the entire Tianhe-2 supercomputer system with over 1.376 million of heterogeneous cores. The test results and performance analysis are discussed in detail., Comment: 12pages, 12 figures

The residual cutting (RC) method has been proposed for efficiently solving linear equations obtained from elliptic partial differential equations. Based on the RC, we have introduced the generalized residual cutting (GRC) method, which can be applied to general sparse matrix problems. In this paper, we study the mathematics of the GRC algorithm and and prove it is a Krylov subspace method. Moreover, we show that it is deeply related to the conjugate residual (CR) method and that GRC becomes equivalent to CR for symmetric matrices. Also, in numerical experiments, GRC shows more robust convergence and needs less memory compared to GMRES, for significantly larger matrix sizes., Comment: 15 pages, 13 figures

A systematic expression quantifying the wave energy skewing phenomenon as a function of the mechanical characteristics of a non-isotropic structure is derived in this study. A structure of arbitrary anisotropy, layering and geometric complexity is modelled through Finite Elements (FEs) coupled to a periodic structure wave scheme. A generic approach for efficiently computing the angular sensitivity of the wave slowness for each wave type, direction and frequency is presented. The approach does not involve any finite differentiation scheme and is therefore computationally efficient and not prone to the associated numerical errors.

The terms "acoustic/elastic meta-materials" describe a class of periodic structures with unit cells exhibiting local resonance. This localized resonant structure has been shown to result in negative effective stiffness and/or mass at frequency ranges close to these local resonances. As a result, these structures present unusual wave propagation properties at wavelengths well below the regime corresponding to band-gap generation based on spatial periodicity, (i.e. "Bragg scattering"). Therefore, acoustic/elastic meta-materials can lead to applications, especially suitable in the low-frequency range. However, low frequency range applications of such meta-materials require very heavy internal moving masses, as well as additional constraints at the amplitudes of the internally oscillating locally resonating structures, which may prohibit their practical implementation. In order to resolve this disadvantage, the K-Damping concept will be analyzed. According to this concept, the acoustic/elastic meta-materials are designed to include negative stiffness elements instead or in addition to the internally resonating added masses. This concept removes the need for the heavy locally added heavy masses, while it simultaneously exploits the negative stiffness damping phenomenon. Application of both Bloch's theory and the classical modal analysis at the one-dimensional mass-in-mass lattice is analyzed and corresponding dispersion relations are derived. The results indicate significant advantages over the conventional mass-in-a mass lattice, such as broader band-gaps and increased damping ratio and reveal significant potential in the proposed solution. Preliminary feasibility analysis for seismic meta-structures and low frequency acoustic isolation-damping confirm the strong potential and applicability of this concept., Comment: Keywords: Acoustic meta-materials, elastic meta-materials, low-frequency vibration absorption, seismic meta-structures, noise absorption

This article deals with the problem of the statistical performance analysis of the MUSIC ( Multiple Signal Classification ) algorithm which is an eigen decomposition based method for the estimation of the angles of arrival of signals received by an array of sensors. In past work the performance of the MUSIC algorithm was studied ( via an asymptotic statistical analysis of the null spectrum of the algorithm ) for the case of two plane waves of equal power in noise. In this article, a new theoretical formula is derived for the signal to noise ratio resolution threshold of two uncorrelated, narrow band plane waves with equal powers in angular sectors received by an array of sensors. The accuracy of the formula is assessed using examples which compute the theoretical signal to noise ratio resolution threshold and compare it with the threshold obtained from simulations.

We have discovered a very extended shock structure (i.e., with a diameter of about 24 arcmin) surrounding the well-known carbon star IRC+10216 in ultraviolet images taken with the GALEX satellite. We conclude that this structure results from the interaction of IRC+10216's molecular wind with the interstellar medium (ISM), as it moves through the latter. All important structural features expected from theoretical models of such interactions are identified: the termination shock, the astrosheath, the astropause, the bowshock, and an astrotail (with vortices). The extent of the astropause provides new lower limits to the envelope age (69,000 years) and mass (1.4 solar masses, for a mass-loss rate of 2 x 10^(-5) solar masses/year). From the termination-shock standoff distance, we find that IRC+10216 is moving at a speed of about > 91 km/s [1 cm^(-3)/n_(ISM)]^1/2 through the local ISM., Comment: 4 pages, 3 color figures; accepted for Astrophysical Journal (Letters)