Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.Comment: 21 pages, 4 figure

Peripheral Vision Transformer

Human vision possesses a special type of visual processing systems called peripheral vision. Partitioning the entire visual field into multiple contour regions based on the distance to the center of our gaze, the peripheral vision provides us the ability to perceive various visual features at different regions. In this work, we take a biologically inspired approach and explore to model peripheral vision in deep neural networks for visual recognition. We propose to incorporate peripheral position encoding to the multi-head self-attention layers to let the network learn to partition the visual field into diverse peripheral regions given training data. We evaluate the proposed network, dubbed PerViT, on the large-scale ImageNet dataset and systematically investigate the inner workings of the model for machine perception, showing that the network learns to perceive visual data similarly to the way that human vision does. The state-of-the-art performance in image classification task across various model sizes demonstrates the efficacy of the proposed method.Comment: Technical repor

A Joint Design of Congestion Control and Burst Contention Resolution for Optical Burst Switching Networks

This paper revisits burst contention resolution problems in optical burst switching (OBS) networks from the viewpoint of network utility maximization. Burst collision occurs when two or more bursts access the same wavelength simultaneously, and the occurrence becomes more frequent as the offered load increases. In particular, when the network is overloaded, no contention resolution scheme would effectively avoid the collision without the help of congestion control. We formulate a joint optimization problem where two variables, the length and the time at which each burst is injected into the network, are jointly optimized in order to maximize aggregate utility while minimizing burst loss. A distributed algorithm is also developed, which explicitly reveals how burst contention resolution and congestion control must interact. The simulation results show that the joint control decouples throughput performance from burst loss performance so that burst loss ratio does not increase as network throughput increases. This is not the case in conventional contention resolution schemes where burst loss ratio increases as network throughput increases so that achievable network throughput is limited. Our work is the first attempt to the joint design of congestion and contention control and might lead to an interesting development in OBS research.Institute of Information Technology Advancement (South Korea) (IITA-2008-C1090-0801-0037

Dual-Resource TCP/AQM for Processing-Constrained Networks

Abstract—This paper examines congestion control issues for TCP flows that require in-network processing on the fly in network elements such as gateways, proxies, firewalls and even routers. Applications of these flows are increasingly abundant in the future as the Internet evolves. Since these flows require use of CPUs in network elements, both bandwidth and CPU resources can be a bottleneck and thus congestion control must deal with “congestion” on both of these resources. In this paper, we show that conventional TCP/AQM schemes can significantly lose throughput and suffer harmful unfairness in this environment, particularly when CPU cycles become more scarce (which is likely the trend given the recent explosive growth rate of bandwidth). As a solution to this problem, we establish a notion of dual-resource proportional fairness and propose an AQM scheme, called Dual-Resource Queue (DRQ), that can closely approximate proportional fairness for TCP Reno sources with in-network processing requirements. DRQ is scalable because it does not maintain per-flow states while minimizing communication among different resource queues, and is also incrementally deployable because of no required change in TCP stacks. The simulation study shows that DRQ approximates proportional fairness without much implementation cost and even an incremental deployment of DRQ at the edge of the Internet improves the fairness and throughput of these TCP flows. Our work is at its early stage and might lead to an interesting development in congestion control research. Index Terms—CPU capacity, efficiency, fairness, proportional fairness, TCP-AQM, transmission link capacity. I

Moving Small Cells in Public Safety Networks

In order to accommodate the growing traffic demands from moving users, the next generation heterogeneous networks (HetNets) are considering the deployment of moving small cells (mSCs). mSC is a user-centric network and unlike other fixed small cells such as femto and pico cells, these mSCs use wireless backhaul links. mSC provides network services while on the move. In this paper we propose a novel use of mSCs in public safety (PS) networks. In PS environment where there is no network coverage available and the network infrastructure is completely or partially destroyed, these mSCs can provide network services to out-of-coverage users by establishing a connection with their neighboring in-coverage mSC who will act as a relay and further connect them to the core network. Our system-level simulations show that the performance of out-of-coverage users in-terms of achievable throughput significantly improves when mSCs are used in public safety environment

Predictive Model for Differential Diagnosis of Inflammatory Papular Dermatoses of the Face

Background: The clinical features of inflammatory papular dermatoses of the face are very similar. Their clinical manifestations have been described on the basis of a small number of case reports and are not specific. Objective: This study aimed to use computer-aided image analysis (CAIA) to compare the clinical features and parameters of inflammatory papular dermatoses of the face and to develop a formalized diagnostic algorithm based on the significant findings. Methods: The study included clinicopathologically confirmed inflammatory papular dermatoses of the face: 8 cases of eosinophilic pustular folliculitis (EPF), 13 of granulomatous periorificial dermatitis-lupus miliaris disseminatus faciei (GPD-LMDF) complex, 41 of granulomatous rosacea-papulopustular rosacea complex (GR-PPR) complex, and 4 of folliculitis. Clinical features were evaluated, and area density of papular lesions was quantitatively measured with CAIA. Based on these variables, we developed a predictive model for differential diagnosis using classification and regression tree analysis. Results: The EPF group showed lesion asymmetry and annular clusters of papules in all cases. The GPD-LMDF complex group had significantly higher periocular density. The GR-PPR complex group showed a higher area density of unilateral cheek papules and the highest to-tal area density. According to the predictive model, 3 variables were used for differential diagnosis of the 4 disease groups, and each group was diagnosed with a predicted probability of 67%similar to 100%. Conclusion: We statistically confirmed the distinct clinical features of inflammatory papular dermatoses of the face and proposed a diagnostic algorithm for clinical diagnosis.Y

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques