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The peculiar nonlinear optical responses of two-dimensional (2D) materials endow them highly attractive for photonics applications. This review systematically discusses the performance of pulsed lasers employing 2D materials as their modulation device. First, it is crucial to categorize 2D materials that can act as saturable absorbers, encompassing a range of substances such as Graphene, Black Phosphorus, Topological Insulators, MXene, and even heterojunction. Furthermore, a thorough investigation is conducted to analyze the performance indexes to study the nonlinear characteristics of 2D materials in the pulse generation. Especially, the focus is on the 1.5 μ m band ultrafast lasers utilizing 2D materials. This paper also provides a comprehensive analysis of the obstacles and prospects in 2D materials preparation, and their utilization in the pulse laser systems. [ABSTRACT FROM AUTHOR]

Coronavirus disease-19 (COVID-19), an infectious disease that spreads when people live in close proximity has greatly impacted healthcare systems worldwide. The pandemic has so disrupted human life economically and socially that the scientific community has been impelled to devise a solution that assists in the diagnosis, prevention and outbreak prediction of COVID-19. This has generated an enormous quantum of unstructured data that cannot be processed by traditional methods. To alleviate COVID-19 threat and to process these unstructured data, big data analytics can be used. The main objective of this paper is to present a multidimensional survey on open source datasets, techniques and tools in big data to fight COVID-19. To this end, state-of-the-art articles have been analyzed, qualitatively and quantitatively, to put together a body of work in the prediction of COVID-19. The findings of this review show that machine learning classification algorithms in big data analytics helps design a predictive model for COVID-19 using the open source datasets. This survey may serve as a starting point to enhance the research in COVID-19. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is an important preprocessing step in data analytics. It is used to select a subset of the original feature set such that the selected subset does not affect the classification performance significantly. Its objective is to remove irrelevant and redundant features from the original dataset. FS can be done either in offline mode or in online mode. The basic assumption in the former mode is that the entire dataset has been available for the FS algorithm; and the FS algorithm takes multiple epochs to select optimal feature subset that gives good accuracy. In contrast, the FS algorithms in online mode take input data one instance at a time and accumulate knowledge by learning each one of them. In online mode each instance of the original dataset is considered as training and testing sample as well. The offline FS algorithms require long time periods, if the data to be processed is large such as Big data. Whereas online FS algorithms will take only one epoch to learn the entire data and can produce the results swiftly which is highly desirable in the case of Big data. This paper deals with the online FS problem and provides a novel Feature Selection algorithm which uses the Sparse Gradient method to build a sparse classifier. In this proposed method, an online classifier is built and maintained throughout the learning process and feature weights, which are limited to a particular boundary limit, are reduced in a step by step decrement process. This method creates sparsity in the classifier. Effectively, the built classifier is used to select optimal feature subset from the incoming data. As this method reduces the weights in the classifier in step by step manner, only those important features which have value higher than the boundary survive from this repeated decrement process. The resultant optimal feature subset is formed using these non-zero weighted features. Most significantly, this particular method can be used with any learning algorithm. To show its applicability with different learning algorithms, various online feature selection models have been built using Learning Vector Quantization, Radial Basis Function Networks and Adaptive Resonance Theory MAP. In all these models, the proposed Sparse Gradient method is used. The encouraging results shows the effectiveness of the proposed method with different learning algorithms in medium and large sized benchmark datasets. [ABSTRACT FROM AUTHOR]

In this paper, we examine the properties of the Jones polynomial using dimensionality reduction learning techniques combined with ideas from topological data analysis. Our data set consists of more than 10 million knots up to 17 crossings and two other special families up to 2001 crossings. We introduce and describe a method for using filtrations to analyze infinite data sets where representative sampling is impossible or impractical, an essential requirement for working with knots and the data from knot invariants. In particular, this method provides a new approach for analyzing knot invariants using Principal Component Analysis. Using this approach on the Jones polynomial data, we find that it can be viewed as an approximately three-dimensional subspace, that this description is surprisingly stable with respect to the filtration by the crossing number, and that the results suggest further structures to be examined and understood. [ABSTRACT FROM AUTHOR]