Emergence of a Chern-insulating state from a semi-Dirac dispersion

A Chern insulator (quantum anomalous Hall insulator) phase is demonstrated to exist in a typical semi-Dirac system, the TiO2/VO2 heterostructure. By combining first-principles calculations with Wannier-based tight-binding model, we calculate the Berry curvature distribution, finding a Chern number of -2 for the valence bands, and demonstrate the existence of gapless chiral edge states, ensuring quantization of the Hall conductivity to 2e^2/h. A new semi-Dirac model, where each semi-Dirac cone is formed by merging three conventional Dirac points, is proposed to reveal how the nontrivial topology with finite Chern number is compatible with a semi-Dirac electronic spectrum.Comment: 12 pages, 3 figure

Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems

PID Controller Optimization by GA and Its Performances on the Electro-hydraulic Servo Control System

AbstractA proportional integral derivative (PID) controller is designed and attached to electro-hydraulic servo actuator system (EHSAS) to control the angular position of the rotary actuator which control the movable surface of space vehicles. The PID gain parameters are optimized by the genetic algorithm (GA). The controller is verified on the new state-space model of servo-valves attached to the physical rotary actuator by SIMULINK program. The controller and the state-space model are verified experimentally. Simulation and experi-mental results verify the effectiveness of the PID controller adaptive by GA to control the angular position of the rotary actuator as com-pared with the classical PID controller and the compensator controller

Depletion attraction in colloidal and bacterial systems

Depletion attraction is a common entropy force observed in colloidal systems. As a common phenomenon in colloidal and bacterial systems, studying the mechanism and application of depletion attraction is of great value for controlling the state of colloidal solutions, regulating the morphology of colloidal particles, disease treatment, and water pollution treatment. Based on the current research status, we briefly introduce the calculation and measurement methods of depletion attractions. And we review the application of depletion attractions in colloidal systems, and summarize the different phenomena and aggregation mechanisms caused by depletion attraction in active colloidal particle-bacterial systems. Understanding the specific role of depletion aggregation in colloidal and bacterial systems provides more possibilities for further exploring depletion aggregation mechanisms and utilizing depletion aggregation phenomena in nature

Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling

Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established. This paper provides the first such convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning that incorporate AMSGrad updates (a standard alternative of Adam in theoretical analysis), referred to as PG-AMSGrad and TD-AMSGrad, respectively. Moreover, our analysis focuses on Markovian sampling for both algorithms. We show that under general nonlinear function approximation, PG-AMSGrad with a constant stepsize converges to a neighborhood of a stationary point at the rate of $\mathcal{O}(1/T)$ (where $T$ denotes the number of iterations), and with a diminishing stepsize converges exactly to a stationary point at the rate of $\mathcal{O}(\log^2 T/\sqrt{T})$. Furthermore, under linear function approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood of the global optimum at the rate of $\mathcal{O}(1/T)$, and with a diminishing stepsize converges exactly to the global optimum at the rate of $\mathcal{O}(\log T/\sqrt{T})$. Our study develops new techniques for analyzing the Adam-type RL algorithms under Markovian sampling

Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent

Existing convergence analyses of Q-learning mostly focus on the vanilla stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment Estimation (Adam) has been commonly used for practical Q-learning algorithms, there has not been any convergence guarantee provided for Q-learning with such type of updates. In this paper, we first characterize the convergence rate for Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly adopted alternative of Adam for theoretical analysis). To further improve the performance, we propose to incorporate the momentum restart scheme to Q-AMSGrad, resulting in the so-called Q-AMSGradR algorithm. The convergence rate of Q-AMSGradR is also established. Our experiments on a linear quadratic regulator problem show that the two proposed Q-learning algorithms outperform the vanilla Q-learning with SGD updates. The two algorithms also exhibit significantly better performance than the DQN learning method over a batch of Atari 2600 games.Comment: This paper extends the work presented at the 2020 International Joint Conferences on Artificial Intelligence with supplementary material