Friedel oscillations in graphene gapped by breaking \u3ci\u3eƤ\u3c/i\u3e and \u3ci\u3eT\u3c/i\u3e symmetries: Topological and geometrical signatures of electronic structure

The measurement of Friedel oscillations (FOs) is conventionally used to recover the energy dispersion of electronic structure. Besides the energy dispersion, the modern electronic structure also embodies other key ingredients such as the geometrical and topological properties; it is one promising direction to explore the potential of FOs for the relevant measurement. Here, we present a comprehensive study of FOs in substrate-supported graphene under off-resonant circularly polarized light, in which a valley-contrasting feature and topological phase transition occur due to the combined breaking of inversion (Ƥ) and time reversal (T) symmetries. Depending on the position of the Fermi level, FOs may be contributed by electronic backscattering in one single valley or two valleys. In the single-valley regime, the oscillation periods of FOs can be used to determine the topological phase boundary of electronic structure, while the amplitudes of FOs distinguish trivial insulators and topological insulators in a quantitative way. In the two-valley regime, the unequal Fermi surfaces lead to a beating pattern (robust two-wave-front dislocations) of FOs contributed by intravalley (intervalley) scattering. This study implies the great potential of FOs in characterizing topological and geometrical properties of the electronic structure of two-dimensional materials

Robust wavefront dislocations of Friedel oscillations in gapped graphene

Friedel oscillation is a well-known wave phenomenon, which represents the oscillatory response of electron waves to imperfection. By utilizing the pseudospin-momentum locking in gapless graphene, two recent experiments demonstrate the measurement of the topological Berry phase by corresponding to the unique number of wavefront dislocations in Friedel oscillations. Here, we study the Friedel oscillations in gapped graphene, in which the pseudospin-momentum locking is broken. Unusually, the wavefront dislocations do occur as that in gapless graphene, which expects the immediate verification in the current experimental condition. The number of wavefront dislocations is ascribed to the invariant pseudospin winding number in gaped and gapless graphene. This study deepens the understanding of correspondence between topological quantity and wavefront dislocations in Friedel oscillations, and implies the possibility to observe the wavefront dislocations of Friedel oscillations in intrinsic gapped two-dimensional materials, e.g., transition metal dichalcogenides.Comment: 5 pages, 3 figure

Make Transformer Great Again for Time Series Forecasting: Channel Aligned Robust Dual Transformer

Recent studies have demonstrated the great power of deep learning methods, particularly Transformer and MLP, for time series forecasting. Despite its success in NLP and CV, many studies found that Transformer is less effective than MLP for time series forecasting. In this work, we design a special Transformer, i.e., channel-aligned robust dual Transformer (CARD for short), that addresses key shortcomings of Transformer in time series forecasting. First, CARD introduces a dual Transformer structure that allows it to capture both temporal correlations among signals and dynamical dependence among multiple variables over time. Second, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue. This new loss function weights the importance of forecasting over a finite horizon based on prediction uncertainties. Our evaluation of multiple long-term and short-term forecasting datasets demonstrates that CARD significantly outperforms state-of-the-art time series forecasting methods, including both Transformer and MLP-based models

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate

Vortex Dynamics in Rotating Rayleigh-B\'enard Convection

We investigate the spatial distribution and dynamics of the vortices in rotating Rayleigh-B\'enard convection in a reduced Rayleigh-number range $1.3{\le}Ra/Ra_{c}{\le}166$. Under slow rotations ($Ra{\gtrsim}10Ra_{c}$), the vortices are randomly distributed. The size-distribution of the Voronoi cells of the vortex centers is well described by the standard $\Gamma$ distribution. In this flow regime the vortices exhibit Brownian-type horizontal motion. The probability density functions of the vortex displacements are, however, non-Gaussian at short time scales. At modest rotating rates ($4Ra_{c}{\le}Ra{\lesssim}10Ra_{c}$) the centrifugal force leads to radial vortex motions, i.e., warm cyclones (cold anticyclones) moving towards (outward from) the rotation axis. The mean-square-displacements of the vortices increase faster than linearly at large time. This super-diffusive behavior can be satisfactorily explained by a Langevin model incorporating the centrifugal force. In the rapidly rotating regime ($1.6Ra_{c}{\le}Ra{\le}4Ra_{c}$) the vortices are densely distributed, with the size-distribution of their Voronoi cells differing significantly from the standard $\Gamma$ distribution. The hydrodynamic interaction of neighboring vortices results in formation of vortex clusters. Inside clusters the correlation of the vortex velocity fluctuations is scale free, with the correlation length being approximately $30\%$ of the cluster length. We examine the influence of cluster forming on the dynamics of individual vortex. Within clusters, cyclones exhibit inverse-centrifugal motion as they submit to the motion of strong anticyclones, while the velocity for outward motion of the anticyclones is increased. Our analysis show that the mobility of isolated vortices, scaled by their vorticity strength, is a simple power function of the Froude number

Universal critical properties of the Eulerian bond-cubic model

We investigate the Eulerian bond-cubic model on the square lattice by means of Monte Carlo simulations, using an efficient cluster algorithm and a finite-size scaling analysis. The critical points and four critical exponents of the model are determined for several values of $n$. Two of the exponents are fractal dimensions, which are obtained numerically for the first time. Our results are consistent with the Coulomb gas predictions for the critical O($n$) branch for $n < 2$ and the results obtained by previous transfer matrix calculations. For $n=2$, we find that the thermal exponent, the magnetic exponent and the fractal dimension of the largest critical Eulerian bond component are different from those of the critical O(2) loop model. These results confirm that the cubic anisotropy is marginal at $n=2$ but irrelevant for $n<2$

EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System

IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices due to the low maintenance cost and wide availability of the energy sources. However, the power provided by the energy harvester is low and has an intrinsic drawback of instability since it varies with the ambient environment. This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Those shared models incur significantly reduced memory footprint with different levels of model sparsity, latency, and accuracy to adapt to the environmental changes. An efficient on-device implementation architecture is further developed to efficiently execute each model on device. A run-time model extraction algorithm is proposed that retrieves individual model with negligible overhead when a specific model mode is triggered. Experimental results show that the neural networks models generated by EVE is on average 2.5X times faster than the baseline models without pruning and shared weights

Geometric density of states of electronic structures for local responses: Phase information from the amplitudes of STM measurement

Electronic band structures underlie the physical properties of crystalline materials, their geometrical exploration renovates the conventional cognition and brings about novel applications. Inspired by geometry phases, we introduce a geometric amplitude named as the geometric density of states (GDOS) dictated by the differential curvature of the constant-energy contour. The GDOS determines the amplitude of the real-space Green's function making it attain the ultimate expression with transparent physics. The local responses of crystalline materials are usually formulated by the real-space Green's function, so the relevant physics should be refreshed by GDOS. As an example of local responses, we suggest using scanning tunneling microscopy (STM) to characterize the surface states of three-dimensional topological insulator under an in-plane magnetic field. The GDOS favors the straightforward simulation of STM measurement without resorting to Fourier transform of the real-space measurement, and also excavates the unexplored potential of STM measurement to extract the phase information of wavefunction through its amplitude, i.e., the spin and curvature textures. Therefore, the proposed GDOS deepens the understanding of electronic band structures and is indispensable in local responses, and it should be universal for any periodic systems.Comment: 6 pages, 2 figure

Scale-free download network for publications

The scale-free power-law behavior of the statistics of the download frequency of publications has been, for the first time, reported. The data of the download frequency of publications are taken from a well-constructed web page in the field of economic physics (http://www.unifr.ch/econophysics/). The Zipf-law analysis and the Tsallis entropy method were used to fit the download frequency. It was found that the power-law exponent of rank-ordered frequency distribution is $\gamma \sim 0.38 \pm 0.04$ which is consistent with the power-law exponent $\alpha \sim 3.37 \pm 0.45$ for the cumulated frequency distributions. Preferential attachment model of Barabasi and Albert network has been used to explain the download network.Comment: 3 pages, 2 figure