Manipulating Electromagnetic Waves with Zero Index Materials

Zero-index material is a typical metamaterial with an effective zero refractive index, possessing a variety of exotic electromagnetic properties and particular functionalities. We have considered two kinds of zero-index materials with the first one a nearly matched zero index made of magnetic metamaterial and the second one a radially anisotropic zero index. The magnetic metamaterial-based systems are shown to be significant in wavefront engineering and flexibly tunable by an external magnetic field and a temperature field. The radially anisotropic zero-index-based systems can remarkably enhance the omnidirectional isotropic radiation by enclosing a line source and a dielectric particle within a shell configuration. The physical origin lies in that the dielectric particle effectively rescatters the trapped anisotropic higher order modes and converts them into the isotropic 0th order mode radiated outside the system. The case for the system with the loss is then examined and the energy compensation with a gain particle is also demonstrated

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation. Most of the existing methods for safe reinforcement learning can only handle transition disturbance or observation disturbance since these two kinds of disturbance affect different parts of the agent; besides, the popular worst-case return may lead to overly pessimistic policies. To address these issues, we first theoretically prove that the performance degradation under transition disturbance and observation disturbance depends on a novel metric of Value Function Range (VFR), which corresponds to the gap in the value function between the best state and the worst state. Based on the analysis, we adopt conditional value-at-risk (CVaR) as an assessment of risk and propose a novel reinforcement learning algorithm of CVaR-Proximal-Policy-Optimization (CPPO) which formalizes the risk-sensitive constrained optimization problem by keeping its CVaR under a given threshold. Experimental results show that CPPO achieves a higher cumulative reward and is more robust against both observation and transition disturbances on a series of continuous control tasks in MuJoCo

Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation

Embodied agents in vision navigation coupled with deep neural networks have attracted increasing attention. However, deep neural networks have been shown vulnerable to malicious adversarial noises, which may potentially cause catastrophic failures in Embodied Vision Navigation. Among different adversarial noises, universal adversarial perturbations (UAP), i.e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied Vision Navigation since they are computation-efficient and application-practical during the attack. However, existing UAP methods ignore the system dynamics of Embodied Vision Navigation and might be sub-optimal. In order to extend UAP to the sequential decision setting, we formulate the disturbed environment under the universal noise $\delta$, as a $\delta$-disturbed Markov Decision Process ($\delta$-MDP). Based on the formulation, we analyze the properties of $\delta$-MDP and propose two novel Consistent Attack methods, named Reward UAP and Trajectory UAP, for attacking Embodied agents, which consider the dynamic of the MDP and calculate universal noises by estimating the disturbed distribution and the disturbed Q function. For various victim models, our Consistent Attack can cause a significant drop in their performance in the PointGoal task in Habitat with different datasets and different scenes. Extensive experimental results indicate that there exist serious potential risks for applying Embodied Vision Navigation methods to the real world

Task Aware Dreamer for Task Generalization in Reinforcement Learning

A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with different reward functions. A general challenge is to quantitatively measure the similarities between these different tasks, which is vital for analyzing the task distribution and further designing algorithms with stronger generalization. To address this, we present a novel metric named Task Distribution Relevance (TDR) via optimal Q functions of different tasks to capture the relevance of the task distribution quantitatively. In the case of tasks with a high TDR, i.e., the tasks differ significantly, we show that the Markovian policies cannot differentiate them, leading to poor performance. Based on this insight, we encode all historical information into policies for distinguishing different tasks and propose Task Aware Dreamer (TAD), which extends world models into our reward-informed world models to capture invariant latent features over different tasks. In TAD, we calculate the corresponding variational lower bound of the data log-likelihood, including a novel term to distinguish different tasks via states, to optimize reward-informed world models. Extensive experiments in both image-based control tasks and state-based control tasks demonstrate that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and demonstrate a strong generalization ability to unseen tasks

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system

Measuring charge distribution of molecular cations by atomic Coulomb probe microscope

Imaging the charge distributions and structures of molecules and clusters will promote the understanding of the dynamics of the quantum system. Here, we report a method by using an Ar atom as a tip to probe the charge distributions of benzene (Bz) cations in gas phase. Remarkably, the measured charge distributions of Bz cation (QH =0.204,QC=-0.037)and dication (QH =0.248,QC=0.0853)agree well with the calculated Mulliken distributions,and the structures of Bz dimer is reconstructed by using the measured charge distributions. The structures of two Bz dimer isomers (T-shaped and PD isomers) can be resolved from the measured inter-molecular potential V(R) between two Bz ions, and the structures of Bz dimer agree well with the theoretical predictions.Comment: 7 pages, 3 Figure

Theoretical and Experimental Studies of Schottky Diodes That Use Aligned Arrays of Single Walled Carbon Nanotubes

We present theoretical and experimental studies of Schottky diodes that use aligned arrays of single walled carbon nanotubes. A simple physical model, taking into account the basic physics of current rectification, can adequately describe the single-tube and array devices. We show that for as grown array diodes, the rectification ratio, defined by the maximum-to-minimum-current-ratio, is low due to the presence of m-SWNT shunts. These tubes can be eliminated in a single voltage sweep resulting in a high rectification array device. Further analysis also shows that the channel resistance, and not the intrinsic nanotube diode properties, limits the rectification in devices with channel length up to ten micrometer.Comment: Nano Research, 2010, accepte