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The combinations of artificial intelligence and lasers provide powerful ways to form smart light sources with ground-breaking functions. Here, a Raman fiber laser (RFL) with reconfigurable and programmable spectra in an ultra-wide bandwidth is developed based on spectral-spatial manipulation of light in multimode fiber (MMF). The proposed fiber laser uses nonlinear gain from cascaded stimulated Raman scattering, random distributed feedback from Rayleigh scattering, and point feedback from an MMF-based smart spectral filter. Through wavefront shaping controlled by a genetic algorithm, light of selective wavelength(s) can be focused in the MMF, forming the filter that, together with the active part of the laser, actively shape the output spectrum with a high degree of freedom. We achieved arbitrary spectral shaping of the cascaded RFL (e.g., continuously tunable single-wavelength and multi-wavelength laser with customizable linewidth, mode separation, and power distribution) from the 1st- to the 3rd-order Stokes emission by adjusting the pump power and auto-optimization of the smart filter. Our research uses artificial-intelligence controlled light manipulation in a fiber platform with multi-eigenmodes and nonlinear gain, mapping the spatial control into the spectral domain as well as extending the linear control of light in MMF to active light emission, which is of great significance for applications in optical communication, sensing, and spectroscopy., Comment: 10 pages,6 figures

Refractive index enhancement is crucial in the field of lithography, imaging, optical communications, solar devices and many more. We present a review of advancements in the process of designing high refractive index metamaterials, starting from quantum coupling and photonic bandgap materials to metamaterials utilizing deep subwavelength coupling to achieve ever-high values of refractive index. A crisp and critical impression of each scheme are presented. The understanding of evolution of material design from intrinsic electronic states manipulation to meta-atoms design is not only fascinating but prerequisite to developing successful devices and applications.

Using metasurfaces to control the wave propagation at will has been very successful over the broad electromagnetic spectrum in recent years. By encoding specially designed abrupt changes of electromagnetic parameters into metasurfaces, such as phase and amplitude, nearly arbitrary control over the output wavefronts could be realized. Constituted by a single- or fewlayer of planar structures, metasurfaces are straightforward in design and fabrication, thus promising many realistic applications. Moreover, such control concept can be further extended to the surface wave regime. In this review, we present our recent progress on metasurfaces capable of tailoring the propagation of both free-space and surface terahertz waves. Following an introduction of the basic concept and theory, a number of unique terahertz metasurfaces are presented, showing the ability in devising ultra-thin and compact functional terahertz components.

Ultrasensitive terahertz sensing of highly absorptive aqueous solutions remains challenging due to strong absorption of water in the terahertz regime. Here, we experimentally demonstrate a cost-effective metamaterial-based sensor integrated with terahertz time-domain spectroscopy for highly absorptive water-methanol mixture sensing. This metamaterial has simple asymmetric wire structures that support multiple resonances including a fundamental Fano resonance and higher order dipolar resonance in the terahertz regime. Both the resonance modes have strong intensity in the transmission spectra which we exploit for detection of the highly absorptive water-methanol mixtures. The experimentally characterized sensitivities of the Fano and dipole resonances for the water-methanol mixtures are found to be 160 and 305 GHz/RIU, respectively. This method provides a route for readily available metamaterial-assisted terahertz spectroscopy for ultrasensitive sensing of highly absorptive chemical and biochemical materials with multiple resonances and high accuracy.

We report extensive enhancement in the refractive index of artificially designed metamaterials by exploiting the deep subwavelength coupling in a free-standing, thin-film metal-dielectric-metal checkboard structure. A record high refractive index of 77.02+43.22i is obtained at terahertz frequencies. The detailed investigations reveal that the enhancement of the effective refractive index of the structure via deep subwavelength coupling in a bilayer design is governed by a power law, which is an effective and simpler approach to design high index metamaterial. The approach relies on deep subwavelength coupling to obtain extremely high refractive index values that can lead to many practical applications in the field of imaging, lithography, design of delay lines and interferometers.

We investigate the dynamical behavior of pull-back trajectories for nonautonomous stochastic feedback systems with multiplicative noise. We proved that there exists a random periodic solution of this system and all pull-back trajectories converge to this random periodic solution as time goes to infinitely almost surely. Our results can be applied to nonautonomous stochastic Goodwin negative feedback system, nonautonomous stochastic Othmer-Tyson positive feedback system and nonautonomous stochastic competitive systems etc.

Losses are ubiquitous and unavoidable in nature inhibiting the performance of most optical processes. Manipulating losses to adjust the dissipation of photons is analogous to braking a running car that is as important as populating photons via a gain medium. Here, we introduce the transient loss boundary into a photon populated cavity that functions as a photon brake and probe photon dynamics by engineering the brake timing and brake strength. Coupled cavity photons can be distinguished by stripping one photonic mode through controlling the loss boundary, which enables the transition from a coupled to an uncoupled state. We interpret the transient boundary as a perturbation by considering both real and imaginary parts of permittivity, and the dynamic process is modelled with a temporal two-dipole oscillator, one with the natural resonant polarization and the other with a frequency-shift polarization. The model unravels the underlying mechanism of concomitant coherent spectral oscillations and generation of tone-tuning cavity photons in the braking process. By synthesizing the temporal loss boundary into a photon populated cavity, a plethora of interesting phenomena and applications are envisioned such as the observation of quantum squeezed states, low-loss nonreciprocal waveguides and ultrafast beam scanning devices., Comment: 18 pages,5 figures,accepted in Nature Communications

Superluminal tunneling of light through a barrier has attracted broad interest in the last several decades. Despite the observation of such phenomena in various systems, it has been under intensive debate whether the transmitted light truly carry the information of the original pulse. Here we report observation of anomalous time response for terahertz electromagnetic pulses passing through thin metal films, with the pulse shape of the transmitted beam faithfully resembling that of the incident beam. A causal theoretical analysis is developed to explain the experiments, though the theory of Special Relativity may confront a challenge in this exceptional circumstance. These findings may facilitate future applications in high-speed optical communication or signal transmission, and may reshape our fundamental understanding about the tunneling of light., Comment: 16 pages, 9 figures. To be submitted to PHYSICAL REVIEW LETTERS

In this paper, we study the existence of random periodic solutions for nonlinear stochastic differential equations with additive white noise. We extend the input-to-state characteristic operator of the system to the non-autonomous stochastic differential equation via the pull-back of the discretised stochastic differential equation. We then use the completeness of the measurable function space which we construct skillfully and the Banach fixed point theorem to prove the existence of a fixed point of the gain operator. And we prove that the image for the input-to-state characteristic operator at this fixed point is a random periodic solution for the forward stochastic flow generated by the non-autonomous stochastic differential equation. Finally, we present some examples.

In this paper, we first give the definition of random almost periodic solutions of random dynamical systems and give some examples. Then, we prove the existence of such random almost periodic solutions. Further, we introduce the definition of random almost periodic measure. Finally, we discuss the relationship between random almost periodic solutions and almost periodic measures.

The dynamic slope scaling procedure is an approximation method successfully which solves the fixed charge network flow (FCNF) problem by iteratively linearizing the fixed cost. The parameterized dynamic slope scaling procedure adds an additional $\psi$ parameter to the procedure which can significantly improve the solution quality. Finding the optimal value of $\psi$ for a given problem is non-trivial. This paper employs multiple metaheuristic techniques, including simulated annealing, tabu search, and particle swarm optimization, to guide the search for good parameter values. In rigorous testing, we examine the search results, compare the improvement efficiencies among the techniques, and evaluate the final solution quality of the FCNF problem. The experiments show that the solution improvement is robust with respect to these metaheuristics and the complexity of FCNF problem., Comment: journal paper

The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the search space. An empirical analysis on publicly available benchmark problems demonstrate the efficacy of our approach by outperforming standard solution strategies implemented in modern optimization software., Comment: journal

This study introduces a comprehensive stage-wise decision framework to support resilience planning for roadway networks regarding pre-disaster mitigation (Stage I), post-disaster emergency response (Stage II) and long-term recovery (Stage III). Three decision metrics are first defined, each based on a derivation of the number of independent pathways (IPW) within a roadway system, to measure the performance of a network in term of its robustness, redundancy, and recoverability, respectively. Using the three IPW-based decision metrics, a stage-wise decision process is then formulated as a stochastic multi-objective optimization problem, which includes a project ranking mechanism to identify pre-disaster network retrofit projects in Phase I, a prioritization approach for temporary repairs to facilitate immediate post-disaster emergency responses in Phase II, and a methodology for scheduling network-wide repairs during the long-term recovery of the roadway system in Phase III. Finally, this stage-wise decision framework is applied to the roadway network of Shelby County, TN, USA subjected to seismic hazards, to illustrate its implementation in supporting community network resilience planning., Comment: submit to Journal

This research presented a novel resilience-based framework to support resilience planning regarding pre-disaster mitigation and post-disaster recovery. First, the author proposes a new performance metric for transportation network, weighted number of independent pathways (WIPW), integrating the network topology, redundancy level, traffic patterns, structural reliability of network components, and functionality of the network during community's post-disaster recovery in a systematical way. To the best of our knowledge, WIPW is the only performance metric that permits risk mitigation alternatives for improving transportation network resilience to be compared on a common basis. Based on the WIPW, a decision methodology of prioritizing transportation network retrofit projects is developed. Second, our studies extend from pre-disaster mitigation to post-hazard recovery, in which this research presents two metrics to evaluate the restoration over the horizon after disasters . That is, total recovery time and the skew of the recovery trajectory. Both metrics are involved in the multi-objective stochastic optimization problem of restoration scheduling. The metrics provided a new dimension to evaluate the relative efficiency of alternative network recovery strategies. The author then develops a restoration scheduling methodology for network post-disaster recovery that minimizes the overall network recovery time and optimizes the recovery trajectory, which ultimately will reduce economic losses due to network service disruption., Comment: Dissertation

This thesis employs statistical learning technique to analyze, predict and solve the fixed charge network flow (FCNF) problem, which is common encountered in many real-world network problems. The cost structure for flows in the FCNF involves both fixed and variable costs. The FCNF problem is modeled mixed binary linear programs and can be solved with standard commercial solvers, which use branch and bound algorithm. This problem is important for its widely applications and solving challenges. There does not exist a efficient algorithm to solve this problem optimally due to lacking tight bounds. To the best of our knowledge, this is the first work that employs statistical learning technique to analyze the optimal flow of the FCNF problem. Most algorithms developed to solve the FCNF problem are based on the cost structure, relaxation, etc. We start from the network characteristics and explore the relationship between properties of nodes, arcs and networks and the optimal flow. This is a bi-direction approach and the findings can be used to locate the features that affect the optimal flow most significantly, predict the optimal arcs and provide information to solve the FCNF problem. In particular, we define 33 features based on the network characteristics, from which using step wise regression, we identify 26 statistical significant predictors for logistic regression to predict which arcs will have positive flow in the optimal solutions. The predictive model achieves 88% accuracy and the area under receiver operating characteristic curve is 0.95. Two applications are investigated. Firstly, the predictive results can be used directly as component critical index. The failure of arcs with higher critical index result in more cost increase over the entire network., Comment: Thesis