We demonstrate that for surfaces that have a nonzero Schwoebel barrier the application of an ac field parallel to the surface induces a net electromigration current, that points in the descending step direction. The magnitude of the current is calculated analytically and compared with Monte Carlo simulations. Since a downhill current smoothes the surface, our results imply that the application of ac fields can aid the smoothing process during annealing and can slow or eliminate the Schwoebel barrier induced mound formation during growth.PACS numbers: 68.35. Ct, 68.35.Md Growing epitaxial films with smooth surfaces is one of the ongoing challenges of the thin film community. However, this goal is hampered by a series of basic physical effects that lead to the development of unavoidable surface roughness during growth. In particular, there is abundant experimental and theoretical evidence that during deposition the diffusion bias generated by the Schwoebel barrier (see Fig. 1) results in a net uphill current, which in turn leads to the formation of mounds and to a fast and unwanted increase in the interface roughness [1]. As Fig. 1 demonstrates, the Schwoebel barrier introduces spatial asymmetry in the otherwise symmetric lattice potential. Interestingly, in the recent years has been recognized that in such periodic and spatially asymmetric systems (ratchets) non-equilibrium fluctuations can rectify Brownian motion and induce a nonzero net current [2]. This fluctuation driven transport is believed to play an essential role in the operation of motor proteins or molecular motors, and might result in new separation techniques [3]. In this paper we propose the first nano-scale application of this transport mechanism based on the atomic electromigration on vicinal surfaces induced by alternating electric fields. We demonstrate that the Schwoebel barrier induced asymmetry in the lattice potential can be used to generate a downhill current, aiding the smoothing of surfaces during growth or annealing.Atom diffusion on crystal surfaces is a thermally activated process: atoms can hop from their position to a neighboring one by overcoming a potential barrier ∆E. The hopping rate is given by the Arrhenius law k = ν 0 exp(−∆E/k B T ), where T is the temperature and ν 0 is the vibration frequency of the surface atoms. Fig. 1 illustrates the lattice potential of a vicinal surface that consists of long flat terraces separated by monatomic steps. The barrier height for diffusion on a flat surface is denoted by E 0 . Near a step atoms form additional lateral bonds of energy E 1 with the step atoms, leading to a deeper potential valley. Finally, jumping over a step requires passing an additional potential barrier, the Schwoebel barrier,For most metals and semiconductors the otherwise random surface diffusion of the atoms can be biased by an external electric field applied parallel to the surface, a phenomenon known as surface electromigration [5]. The effective force, F , acting on the surface atoms is proportional to the field...

Developmental canalization, which leads to a reduction in the variation of phenotype expression relative to the complexity of the genome, has long been thought to be an important property of evolving biological systems. We demonstrate that a highly canalized state develops in the process of self-organization recently discovered in N-K Boolean networks that evolve based on a competition between the nodes. The model provides a simplified description of the evolution of genetic regulatory networks in developmental systems. The mechanism responsible for the evolution is shown to be a balance of two dynamical effects which compete to bring the network to a nonrandom critical steady state. Unlike other proposed evolutionary mechanisms that select for canalization, this mechanism does so while maintaining the system's capacity for further evolution in the steady state.

We demonstrate that growth on a sample patterned with an ordered defect array can lead to islands with rather narrow size distribution. However, improvement in the size distribution is achieved only if the growth conditions ͑flux and temperature͒ have optimal values, determined by the patterning length scale. Since the scanning tunelling and the atomic force microscopes are capable of inducing surface perturbations that act as potential preferential nucleation sites, our work demonstrates that nanoscale surface patterning can improve the ordering of platelets and self-assembled quantum dots.

Sleep experts manually label sleep stages via polysomnography (PSG) to diagnose sleep disorders. However, this process is time-consuming, requires a lot of labor from sleep experts, and makes the participants uncomfortable with the attachment of multiple sensors. Thus, automatic sleep scoring methods are essential for practical sleep monitoring in our daily lives. In this study, we propose an automatic sleep scoring model based on intrinsic oscillations in a single channel electroencephalogram (EEG) signal. We applied noise assisted bivariate empirical mode decomposition (NA-BEMD) to extract the intrinsic mode components and an attention mechanism in deep neural networks to provide weights to the components depending on their significance to sleep scoring. In particular, through the attention mechanism, we found an interpretable model by examining the oscillations that correspond to specific sleep stages. Therefore, we analyzed which frequency components are more weighted to a sleep stage than the others, when the model classifies sleep stages, and, as a result, confirmed that the model assigns convincing weights to the frequency components for each sleep stage. Additionally, the model consists of a one-dimensional convolutional neural network (1D-CNN) to extract features of an epoch and bidirectional long short-term memory (Bi-LSTM) to learn the sequential information of the consecutive epochs. We evaluated proposed model using Fpz-Cz, Pz-Oz, and F3-M2 channel EEG from three different public datasets (Sleep-EDF-2013, Sleep-EDF-2018 and demonstrated that our model yielded the best overall accuracy (Fpz-Cz: 86.22%-82.67%, Pz-Oz: 83.63%-80.15%, F3-M2: 84.20%) and macro F1-score (Fpz-Cz: 80.79%-76.90%, Pz-Oz: 76.89%-72.98%, F3-M2: 74.88%) compared with the state-of-the-art sleep scoring algorithms using single channel EEG. As a benchmark test, FIR bandpass filters were compared, and it was confirmed that NA-BEMD was superior to the traditional filters in all experiments, demonstrating that the proposed model is interpretable and a state-of-the-art sleep scoring algorithm.INDEX TERMS Electroencephalogram (EEG), automatic sleep scoring, deep neural networks, attention mechanism, bivariate empirical mode decomposition.

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