412 research outputs found
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
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
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
(where denotes the number of iterations), and with a
diminishing stepsize converges exactly to a stationary point at the rate of
. Furthermore, under linear function
approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood
of the global optimum at the rate of , and with a diminishing
stepsize converges exactly to the global optimum at the rate of
. Our study develops new techniques for analyzing
the Adam-type RL algorithms under Markovian sampling
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