The gapless surface states of topological insulators could enable quantitatively different types of electronic device. A study of the topological insulating Bi2Se3 thin films finds that a gap in these states opens up in films below a certain thickness. This in turn suggests that in thicker films, gapless states exist on both upper and lower surfaces.
We describe a new effect in semiconductor spintronics that leads to dissipationless spin currents in paramagnetic spin-orbit coupled systems. We argue that in a high-mobility two-dimensional electron system with substantial Rashba spin-orbit coupling, a spin current that flows perpendicular to the charge current is intrinsic. In the usual case where both spin-orbit split bands are occupied, the intrinsic spin-Hall conductivity has a universal value for zero quasiparticle spectral broadening.
Linear spin wave theory provides the leading term in the calculation of the excitation spectra of long-range ordered magnetic systems as a function of . This term is acquired using the Holstein–Primakoff approximation of the spin operator and valid for small δS fluctuations of the ordered moment. We propose an algorithm that allows magnetic ground states with general moment directions and single-Q incommensurate ordering wave vector using a local coordinate transformation for every spin and a rotating coordinate transformation for the incommensurability. Finally we show, how our model can determine the spin wave spectrum of the magnetic C-site langasites with incommensurate order.
Abstract Reinforcement learning is a new research hotspot in the artificial intelligence community. Q learning as a famous reinforcement learning algorithm can achieve satisfactory control performance without need to clarify the complex internal factors in controlled objects. However, discretization state is necessary which limits the application of Q learning in energy management for hybrid electric bus (HEB). In this paper the deep Q learning (DQL) is adopted for energy management issue and the strategy is proposed and verified. Firstly, the system modeling of bus configuration are described. Then, the energy management strategy based on deep Q learning is put forward. Deep neural network is employed and well trained to approximate the action value function (Q function). Furthermore, the Q learning strategy based on the same model is mentioned and applied to compare with deep Q learning. Finally, a part of trained decision network is analyzed separately to verify the effectiveness and rationality of the DQL-based strategy. The training results indicate that DQL-based strategy makes a better performance than that of Q learning in training time consuming and convergence rate. Results also demonstrate the fuel economy of proposed strategy under the unknown driving condition achieves 89% of dynamic programming-based method. In addition, the technique can finally learn to the target state of charge under different initial conditions. The main contribution of this study is to explore a novel reinforcement learning methodology into energy management for HEB which solve the curse of state variable dimensionality, and the techniques can be adopted to solve similar problems.
The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of the network intrusion detection method, which combines a Q-learning based reinforcement learning with a deep feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor, which is set as 0.001 under 250 episodes of training, yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.
Subwavelength confinement of light with plasmonics is promising for nanophotonics and optoelectronics. However, it is nontrivial to obtain narrow plasmonic resonances due to the intrinsically high optical losses and radiative damping in metallic structures. In this review, a thorough summary of the recent research progress on achieving high‐quality (high‐Q) factor plasmonic resonances is provided, emphasizing the fundamentals and six resonant mode types, including surface lattice resonances, multipolar resonances, plasmonic Fano resonances, plasmon‐induced transparency, guided‐mode resonances, and Tamm plasmon resonances. The applications of high‐Q plasmonic resonances in spectrally selective thermal emission, sensing, single‐photon emission, filtering, and band‐edge lasing are also discussed.
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto
et al.
Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks. This superior sample efficiency is made possible by using a large Q-function ensemble. However, REDQ is much less computationally efficient than non-ensemble counterparts such as Soft Actor-Critic (SAC) (Haarnoja et al., 2018a). To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions. Our dropout Q-functions are simple Q-functions equipped with dropout connection and layer normalization. Despite its simplicity of implementation, our experimental results indicate that DroQ is doubly (sample and computationally) efficient. It achieved comparable sample efficiency with REDQ, much better computational efficiency than REDQ, and comparable computational efficiency with that of SAC.