Instabilities at vicinal crystal surfaces - competition between the electromigration of the adatoms and the kinetic memory effect

We studied the step dynamics during sublimation and growth in the presence of electromigration force acting on the adatoms. In the limit of fast surface diffusion and slow kinetics of atom attachment-detachment at the steps we formulate a model free of the quasi-static approximation in the calculation of the adatom concentration on the terraces. Numerical integration of the equations for the time evolution of the adatom concentrations and the equations of step motion reveals two different step bunching instabilities: 1) step density waves (small bunches which do not manifest any coarsening) induced by the kinetic memory effect and 2) step bunching with coarsening when the dynamics is dominated by the electromigration. The model developed in this paper also provides very instructive illustrations of the Popkov-Krug dynamical phase transition during sublimation and growth of a vicinal crystal surface.Comment: 15 pages, 6 figure

Photometric and spectroscopic variability of the FUor star V582 Aurigae

We carried out BVRI CCD photometric observations in the field of V582 Aur from 2009 August to 2013 February. We acquired high-, medium-, and low-resolution spectroscopy of V582 Aur during this period. To study the pre-outburst variability of the target and construct its historical light curve, we searched for archival observations in photographic plate collections. Both CCD and photographic observations were analyzed using a sequence of 14 stars in the field of V582 Aur calibrated in BVRI. The pre-outburst photographic observations of V582 Aur show low-amplitude light variations typical of T Tauri stars. Archival photographic observations indicate that the increase in brightness began in late 1984 or early 1985 and the star reached the maximum level of brightness at 1986 January. The spectral type of V582 Aur can be defined as G0I with strong P Cyg profiles of H alpha and Na I D lines, which are typical of FU Orionis objects. Our BVRI photometric observations show large amplitude variations V~2.8 mag. during the 3.5 year period of observations. Most of the time, however, the star remains in a state close to the maximum brightness. The deepest drop in brightness was observed in the spring of 2012, when the brightness of the star fell to a level close to the pre-outburst. The multicolor photometric data show a color reversal during the minimum in brightness, which is typical of UX Ori variables. The corresponding spectral observations show strong variability in the profiles and intensities of the spectral lines (especially H alpha), which indicate significant changes in the accretion rate. On the basis of photometric monitoring performed over the past three years, the spectral properties of the maximal light, and the shape of the long-term light curve, we confirm the affiliation of V582 Aur to the group of FU Orionis objects.Comment: 9 pages, 8 figures, accepted for publication in A&

Evaporation and growth of crystals - propagation of step density compression waves at vicinal surfaces

We studied the step dynamics during crystal sublimation and growth in the limit of fast surface diffusion and slow kinetics of atom attachment-detachment at the steps. For this limit we formulate a model free of the quasi-static approximation in the calculation of the adatom concentration on the terraces at the crystal surface. Such a model provides a relatively simple way to study the linear stability of a step train in a presence of step-step repulsion and an absence of destabilizing factors (as Schwoebel effect, surface electromigration etc.). The central result is that a critical velocity of the steps in the train exists which separates the stability and instability regimes. When the step velocity exceeds its critical value the plot of these trajectories manifests clear space and time periodicity (step density compression waves propagate on the vicinal surface). This ordered motion of the steps is preceded by a relatively short transition period of disordered step dynamics.Comment: 18 pages, 6 figure

Deep Learning Based Robotic Tool Detection and Articulation Estimation with Spatio-Temporal Layers

Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder-decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution

Classification of All Poisson-Lie Structures on an Infinite-Dimensional Jet Group

A local classification of all Poisson-Lie structures on an infinite-dimensional group $G_{\infty}$ of formal power series is given. All Lie bialgebra structures on the Lie algebra {\Cal G}_{\infty} of $G_{\infty}$ are also classified.Comment: 11 pages, AmSTeX fil

Arthroscopic simulation using a knee model can be used to train speed and gaze strategies in knee arthroscopy

Purpose This study aimed to determine the effect of a simulation course on gaze fixation strategies of participants performing arthroscopy. Methods Participants (n = 16) were recruited from two one-day simulation-based knee arthroscopy courses, and were asked to undergo a task before and after the course, which involved identifying a series of arthroscopic landmarks. The gaze fixation of the participants was recorded with a wearable eye-tracking system. The time taken to complete the task and proportion of time participants spent with their gaze fixated on the arthroscopic stack, the knee model, and away from the stack or knee model were recorded. Results Participants demonstrated a statistically decreased completion time in their second attempt compared to the first attempt (P = 0.001). In their second attempt, they also demonstrated improved gaze fixation strategies, with a significantly increased amount (P = 0.008) and proportion of time (P = 0.003) spent fixated on the screen vs. knee model. Conclusion Simulation improved arthroscopic skills in orthopaedic surgeons, specifically by improving their gaze control strategies and decreasing the amount of time taken to identify and mark landmarks in an arthroscopic task

Feature Aggregation Decoder for Segmenting Laparoscopic Scenes

Laparoscopic scene segmentation is one of the key building blocks required for developing advanced computer assisted interventions and robotic automation. Scene segmentation approaches often rely on encoder-decoder architectures that encode a representation of the input to be decoded to semantic pixel labels. In this paper, we propose to use the deep Xception model for the encoder and a simple yet effective decoder that relies on a feature aggregation module. Our feature aggregation module constructs a mapping function that reuses and transfers encoder features and combines information across all feature scales to build a richer representation that keeps both high-level context and low-level boundary information. We argue that this aggregation module enables us to simplify the decoder and reduce the number of parameters in the decoder. We have evaluated our approach on two datasets and our experimental results show that our model outperforms state-of-the-art models on the same experimental setup and significantly improves the previous results, 98.44% vs 89.00% , on the EndoVis’15 dataset

A quantitative theory of current-induced step bunching on Si(111)

We use a one-dimensional step model to study quantitatively the growth of step bunches on Si(111) surfaces induced by a direct heating current. Parameters in the model are fixed from experimental measurements near 900 deg C under the assumption that there is local mass transport through surface diffusion and that step motion is limited by the attachment rate of adatoms to step edges. The direct heating current is treated as an external driving force acting on each adatom. Numerical calculations show both qualitative and quantitative agreement with experiment. A force in the step down direction will destabilize the uniform step train towards step bunching. The average size of the step bunches grows with electromigration time as t^beta, with beta = 0.5, in agreement with experiment and with an analytical treatment of the steady states. The model is extended to include the effect of direct hopping of adatoms between different terraces. Monte-Carlo simulations of a solid-on-solid model, using physically motivated assumptions about the dynamics of surface diffusion and attachment at step edges, are carried out to study two dimensional features that are left out of the present step model and to test its validity. These simulations give much better agreement with experiment than previous work. We find a new step bending instability when the driving force is along the step edge direction. This instability causes the formation of step bunches and antisteps that is similar to that observed in experiment.Comment: 11 pages, 7 figure