Effective Electromagnetic Wave Properties of Disordered Stealthy Hyperuniform Layered Media Beyond the Quasistatic Regime

Disordered stealthy hyperuniform dielectric composites exhibit novel electromagnetic wave transport properties in two and three dimensions. Here, we carry out the first study of the electromagnetic properties of one-dimensional (1D) disordered stealthy hyperuniform layered media. From an exact nonlocal theory, we derive an approximation formula for the effective dynamic dielectric constant tensor ${\boldsymbol \varepsilon}_e({\bf k}_q,\omega)$ of general 1D media that is valid well beyond the quasistatic regime and apply it to 1D stealthy hyperuniform systems. We consider incident waves of transverse polarization, frequency $\omega$, and wavenumber $k_q$. Our formula for ${\boldsymbol \varepsilon}_e({k}_q,\omega)$, which is given in terms of the spectral density, leads to a closed-form relation for the transmittance $T$. Our theoretical predictions are in excellent agreement with finite-difference time-domain (FDTD) simulations. Stealthy hyperuniform layered media have perfect transparency intervals up to a finite wavenumber, implying no Anderson localization, but non-stealthy hyperuniform media are not perfectly transparent. Our predictive theory provides a new path for the inverse design of the wave characteristics of disordered layered media, which are readily fabricated, by engineering their spectral densities.Comment: 8 pages, 6 figure

Novel Diagnostic Model for the Deficient and Excess Pulse Qualities

The deficient and excess pulse qualities (DEPs) are the two representatives of the deficiency and excess syndromes, respectively. Despite its importance in the objectification of pulse diagnosis, a reliable classification model for the DEPs has not been reported to date. In this work, we propose a classification method for the DEPs based on a clinical study. First, through factor analysis and Fisher's discriminant analysis, we show that all the pulse amplitudes obtained at various applied pressures at Chon, Gwan, and Cheok contribute on equal orders of magnitude in the determination of the DEPs. Then, we discuss that the pulse pressure or the average pulse amplitude is appropriate for describing the collective behaviors of the pulse amplitudes and a simple and reliable classification can be constructed from either quantity. Finally, we propose an enhanced classification model that combines the two complementary variables sequentially

Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments

The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel algorithm that combines meta-reinforcement learning (meta-RL) with model predictive control (MPC). Our method employs an off-policy meta-RL algorithm as a baseline to train a policy using transition samples generated by MPC when the robot detects certain events that can be effectively handled by MPC, with its explicit use of robot dynamics. The key idea of our method is to switch between the meta-learned policy and the MPC controller in a randomized and event-triggered fashion to make up for suboptimal MPC actions caused by the limited prediction horizon. During meta-testing, the MPC module is deactivated to significantly reduce computation time in motion control. We further propose an online adaptation scheme that enables the robot to infer and adapt to a new task within a single trajectory. The performance of our method has been demonstrated through simulations using a nonlinear car-like vehicle model with (i) synthetic movements of obstacles, and (ii) real-world pedestrian motion data. The simulation results indicate that our method outperforms other algorithms in terms of learning efficiency and navigation quality.Comment: Accepted for publication in the IEEE Robotics and Automation Letter

Generating large disordered stealthy hyperuniform systems with ultra-high accuracy to determine their physical properties

Hyperuniform many-particle systems are characterized by a structure factor $S({\mathbf{k}})$ that is precisely zero as $|\mathbf{k}|\rightarrow0$; and stealthy hyperuniform systems have $S({\mathbf{k}})=0$ for the finite range $0 < |{\mathbf{k}}| \le K$, called the "exclusion region." Through a process of collective-coordinate optimization, energy-minimizing disordered stealthy hyperuniform systems of moderate size have been made to high accuracy, and their novel physical properties have shown great promise. However, minimizing $S(\mathbf{k})$ in the exclusion region is computationally intensive as the system size becomes large. In this Letter, we present an improved methodology to generate such states using double-double precision calculations on GPUs that reduces the deviations from zero within the exclusion region by a factor of approximately $10^{30}$ for systems sizes more than an order of magnitude larger. We further show that this ultra-high accuracy is required to draw conclusions about their corresponding characteristics, such as the nature of the associated energy landscape and the presence or absence of Anderson localization, which might be masked, even when deviations are relatively small.Comment: 7 pages, 3 figure