Scaling properties of step bunches induced by sublimation and related mechanisms: A unified perspective

This work provides a ground for a quantitative interpretation of experiments on step bunching during sublimation of crystals with a pronounced Ehrlich-Schwoebel (ES) barrier in the regime of weak desorption. A strong step bunching instability takes place when the kinetic length is larger than the average distance between the steps on the vicinal surface. In the opposite limit the instability is weak and step bunching can occur only when the magnitude of step-step repulsion is small. The central result are power law relations of the between the width, the height, and the minimum interstep distance of a bunch. These relations are obtained from a continuum evolution equation for the surface profile, which is derived from the discrete step dynamical equations for. The analysis of the continuum equation reveals the existence of two types of stationary bunch profiles with different scaling properties. Through a mathematical equivalence on the level of the discrete step equations as well as on the continuum level, our results carry over to the problems of step bunching induced by growth with a strong inverse ES effect, and by electromigration in the attachment/detachment limited regime. Thus our work provides support for the existence of universality classes of step bunching instabilities [A. Pimpinelli et al., Phys. Rev. Lett. 88, 206103 (2002)], but some aspects of the universality scenario need to be revised.Comment: 21 pages, 8 figure

Learning to Calibrate - Estimating the Hand-eye Transformation without Calibration Objects

Hand-eye calibration is a method to determine the transformation linking between the robot and camera coordinate systems. Conventional calibration algorithms use a calibration grid to determine camera poses, corresponding to the robot poses, both of which are used in the main calibration procedure. Although such methods yield good calibration accuracy and are suitable for offline applications, they are not applicable in a dynamic environment such as robotic-assisted minimally invasive surgery (RMIS) because changes in the setup can be disruptive and time-consuming to the workflow as it requires yet another calibration procedure. In this paper, we propose a neural network-based hand-eye calibration method that does not require camera poses from a calibration grid but only uses the motion from surgical instruments in a camera frame and their corresponding robot poses as input to recover the hand-eye matrix. The advantages of using neural network are that the method is not limited by a single rigid transformation alignment and can learn dynamic changes correlated with kinematics and tool motion/interactions. Its loss function is derived from the original hand-eye transformation, the re-projection error and also the pose error in comparison to the remote centre of motion. The proposed method is validated with data from da Vinci Si and the results indicate that the designed network architecture can extract the relevant information and estimate the hand-eye matrix. Unlike the conventional hand-eye approaches, it does not require camera pose estimations which significantly simplifies the hand-eye problem in RMIS context as updating the hand-eye relationship can be done with a trained network and sequence of images. This introduces a potential of creating a hand-eye calibratio

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

Numerical Analysis of Aerodynamic Characteristics of the Finned Surfaces with Cross-inclined Fins

This paper presents results of numerical research and analyses air-side hydraulic performance of tube bundles with cross inclined fins. The numerical simulation of the fin-tube heat exchanger was performed using the Comsol Femlab software. The results of modeling show the influence of fin inclination angle and tube pitch on hydraulic characteristics of finned surfaces. A series of numerical tests were carried out for tube bundles with different inclination angles (γ =900, 850, 650, 60), the fin pitch u=4 mm. The results indicate that tube bundles with cross inclined fins can significantly enhance the average integral value of the air flow rate in channel between fins in comparison with conventional straight fins. Aerodynamic processes on both sides of modificated channel between inclined fins were analyzed. The verification procedures for received results of numerical modeling with experimental data were performed

Fully convolutional neural networks for polyp segmentation in colonoscopy

Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively

Current-Induced Step Bending Instability on Vicinal Surfaces

We model an apparent instability seen in recent experiments on current induced step bunching on Si(111) surfaces using a generalized 2D BCF model, where adatoms have a diffusion bias parallel to the step edges and there is an attachment barrier at the step edge. We find a new linear instability with novel step patterns. Monte Carlo simulations on a solid-on-solid model are used to study the instability beyond the linear regime.Comment: 4 pages, 4 figure

A Combined EM and Visual Tracking Probabilistic Model for Robust Mosaicking: Application to Fetoscopy

Twin-to-Twin Transfusion Syndrome (TTTS) is a progressive pregnancy complication in which inter-twin vascular connections in the shared placenta result in a blood flow imbalance between the twins. The most effective therapy is to sever these connections by laser photo-coagulation. However, the limited field of view of the fetoscope hinders their identification. A potential solution is to augment the surgeon’s view by creating a mosaic image of the placenta. State-of-the-art mosaicking methods use feature-based ap- proaches, which have three main limitations: (i) they are not robust against corrupt data e.g. blurred frames, (ii) tem- poral information is not used, (iii) the resulting mosaic suf- fers from drift. We introduce a probabilistic temporal model that incorporates electromagnetic and visual tracking data to achieve a robust mosaic with reduced drift. By assuming planarity of the imaged object, the nRT decomposition can be used to parametrize the state vector. Finally, we tackle the non-linear nature of the problem in a numerically stable manner by using the Square Root Unscented Kalman Filter. We show an improvement in performance in terms of robustness as well as a reduction of the drift in comparison to state-of-the-art methods in synthetic, phantom and ex vivo datasets

Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia

OBJECTIVES: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. METHODS: An AI algorithm was evaluated on 4 video test datasets containing 173 polyps (35,114 polyp positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by 8 endoscopists (4 independent, 4 trainees, according to Joint Advisory Group on GI endoscopy (JAG) standards in United Kingdom). RESULTS: In the first 2 video datasets, the algorithm achieved per-polyp sensitivities of 100% and 98.9%. Per-frame sensitivities were 84.1% and 85.2% . In the subtle dataset, the algorithm detected a significantly higher number of polyps (P<0.0001), compared to JAG-independent and trainee endoscopists, achieving per-polyp sensitivities of 79.5%, 37.2% and 11.5% respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. CONCLUSIONS: The AI based algorithm achieved high per-polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer

Numerical test of the damping time of layer-by-layer growth on stochastic models

We perform Monte Carlo simulations on stochastic models such as the Wolf-Villain (WV) model and the Family model in a modified version to measure mean separation $\ell$ between islands in submonolayer regime and damping time $\tilde t$ of layer-by-layer growth oscillations on one dimension. The stochastic models are modified, allowing diffusion within interval $r$ upon deposited. It is found numerically that the mean separation and the damping time depend on the diffusion interval $r$, leading to that the damping time is related to the mean separation as ${\tilde t} \sim \ell^{4/3}$ for the WV model and ${\tilde t} \sim \ell^2$ for the Family model. The numerical results are in excellent agreement with recent theoretical predictions.Comment: 4 pages, source LaTeX file and 5 PS figure