Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman scattering by surface phonon polaritons

In this paper, we present the study of raman scattering by surface polaritons (SPs). Based on the attenuated total reflection method (ATR method) and raman scattering method and referring to the method of raman scattering under ATR conditions (RSATR) in the Kretchmann configuration, we have suggested a method of RSATR with Otto configuration and have made a series of experiments on a single crystal sample of CaF 2 . Theoretically we have obtained a phenomenological dispersion relation of SPs on the boundary between CaF 2 and air using energy and momentum conservation parallel to the sample surface, as well as a general dispersion relation of SPs derived from surface Green's function. With the help of a specially designed sample carrier, the experiment was carried out to measure the dispersion relation of SPs. Comparison indicates that the experimental results are in good agreement with theory
Optimization strategies with resource scarcity: From immunization of networks to the traveling salesman problem

The best strategy to immunize a complex network is usually evaluated in terms of the percolation threshold, i.e. the number of vaccine doses which make the largest connected cluster (LCC) vanish. The strategy inducing the minimum percolation threshold represents the optimal way to immunize the network. Here we show that the efficacy of the immunization strategies can change during the immunization process. This means that, if the number of doses is limited, the best strategy is not necessarily the one leading to the smallest percolation threshold. This outcome should warn about the adoption of global measures in order to evaluate the best immunization strategy.
Magnetism and magnetocrystalline anisotropy in single-layer PtSe2: Interplay between strain and vacancy

The electronic and magnetic properties of the newly synthesized single-layer (1 L) transition-metal dichalcogenide (TMD) PtSe2 are studied by first-principles calculations. We find the strain or selenium vacancy (VSe) alone cannot induce the magnetism. However, an interplay between strain and VSe leads to the magnetism due to the breaking of Pt-Pt metallic bonds. Different from the case of 1 L-MoS2 with VS, the defective 1 L-PtSe2 has the spatially extended spin density, which is responsible for the obtained long range ferromagnetic coupling. Moreover, the 1 L-PtSe2 with VSe undergoes a spin reorientation transition from out-of-plane to in-plane magnetization, accompanying a maximum magnetocrystalline anisotropy energy of ∼9–10.6 meV/VSe. These results indicate the strain not only can effectively tune the magnetism but also can manipulate the magnetization direction of 1 L-TMDs.
The perceptual organization of visual flows

Locally parallel dense patterns—visual flows—define a perceptually coherent structure of particular significance to perceptual organization. Geometrically, it is argued that a proper way to investigate these structures requires the frame field approach from differential geometry, a study that leads to the notion of visual flow curvatures and to constraints on their mutual behavior. These curvatures are then used to develop a theory, and a rigorous model, of visual flow “good continuation” that extends common terminology from Gestalt psychology and from computational studies of curves. The geometrical theory is then applied in three ways. Firstly, psychophysical exploration of the role of visual flow curvatures in human perception shows that sensitivity to these curvatures greatly affects orientation-based texture segmentation. Secondly, a contextual framework for the computation of coherent visual flows from images is developed and applied to texture, shading, and color analysis. Unlike existing approaches, the proposed framework is able to handle both sparse, dense, and multivalued data sets, while preserving line and point singularities and rejecting large scale non-flow structures. Lastly, the geometrical theory is linked to the functional organization of primary visual cortex to accurately predict the distribution of long range horizontal connections and to support their identification with those obtained mathematically.
On Rocket Plume, Lunar Crater and Lunar Dust Interactions

This paper presents our recent progress on the investigations of rocket Plume-Crate interaction and Lunar/Martian Dust Interaction (PDI). PDI is a very challenging problem of complex flow/soil interactions, involved with multi-physics, multi-scales, multi-dimensions, and multi-species. We are developing hybrid computational fluid dynamics (CFD) package, with the direct simulation Monte Carlo (DSMC) and gaskinetic BGK method to simulate the lunar rarefied flowfield in an accurate and efficient manner. For dust liftoff/trajectory, we adopt discrete element method (DEM) to simulate single dust particle ejection, and the overlay method to move the dust particles in the flow field obtained by the DSMC and gaskinetic BGK method. Meanwhile two physical-based models employing DEM are under development as improved computational tools for the predictions of crater formulation/evolution.
The generalized inverse Lindley distribution: A new inverse statistical model for the study of upside-down bathtub data

ABSTRACT In this article, a two-parameter generalized inverse Lindley distribution capable of modeling a upside-down bathtub-shaped hazard rate function is introduced. Some statistical properties of proposed distribution are explicitly derived here. The method of maximum likelihood, least square, and maximum product spacings are used for estimating the unknown model parameters and also compared through the simulation study. The approximate confidence intervals, based on a normal and a log-normal approximation, are also computed. Two algorithms are proposed for generating a random sample from the proposed distribution. A real data set is modeled to illustrate its applicability, and it is shown that our distribution fits much better than some other existing inverse distributions.
Performance Comparison of Various Neural Networks for Speech Recognition

Speech recognition is a method where an audio signal is translated into text, words, or commands and also tells how the speech is recognized. Recently, many deep learning models have been adopted for automatic speech recognition and proved more effective than traditional machine learning methods like Artificial Neural Networks(ANN). This work examines the efficient learning architectures of features by different deep neural networks. In this paper, five neural network models, namely, CNN, LSTM, Bi-LSTM, GRU, and CONV-LSTM, for the comparative study. We trained the networks using Audio MNIST dataset for three different iterations and evaluated them based on performance metrics. Experimentally, CNN and Conv-LSTM network model consistently offers the best performance based on MFCC Features.
Classification of WBC cell classification using fully connected convolution neural network

White blood cells (WBCs) are cells that is key factor of the immune systems which is help to our body fight off contagions and other diseases. In order to enhance the diagnosis of various diseases in medical field by using image processing techniques from the blood cells. In that, Leukemia is associated with one type of cancer of the blood and bone marrow. It is look like spongy tissue inside the bones where blood cells are made. In this paper, a fully connected. Convolution neural network is used to segmented and classification of blood cell microscope WBC images for healthy and unhealthy conditions. The performance of the classifier was analyzed. The accuracy sensitivity specificity and pression are 96.84%, 96.26%,97.35% and 96.39% respectively.
Surface form inspection with contact coordinate measurement: a review

Parts with high-quality freeform surfaces have been widely used in industries, which require strict quality control during the manufacturing process. Among all the industrial inspection methods, contact measurement with coordinate measuring machines or computer numerical control machine tool is a fundamental technique due to its high accuracy, robustness, and universality. In this paper, the existing research in the contact measurement field is systematically reviewed. First, different configurations of the measuring machines are introduced in detail, which may have influence on the corresponding sampling and inspection path generation criteria. Then, the entire inspection pipeline is divided into two stages, namely the pre-inspection and post-inspection stages. The typical methods of each sub-stage are systematically overviewed and classified, including sampling, accessibility analysis, inspection path generation, probe tip radius compensation, surface reconstruction, and uncertainty analysis. Apart from those classical research, the applications of the emerging deep learning technique in some specific tasks of measurement are introduced. Furthermore, some potential and promising trends are provided for future investigation.
Local Lorentz force flowmeter at a continuous caster model using a new generation multicomponent force and torque sensor

Lorentz force velocimetry is a non-invasive velocity measurement technique for electrical conductive liquids like molten steel. In this technique, the metal flow interacts with a static magnetic field generating eddy currents which, in turn, produce flow-braking Lorentz forces within the fluid. These forces are proportional to the electrical conductivity and to the velocity of the melt. Due to Newton’s third law, a counter force of the same magnitude acts on the source of the applied static magnetic field which is in our case a permanent magnet. In this paper we will present a new multicomponent sensor for the local Lorentz force flowmeter (L2F2) which is able to measure simultaneously all three components of the force as well as all three components of the torque. Therefore, this new sensor is capable of accessing all three velocity components at the same time in the region near the wall. In order to demonstrate the potential of this new sensor, it is used to identify the 3-dimensional velocity field near the wide face of the mold of a continuous caster model available at the Helmholtz-Zentrum Dresden-Rossendorf. As model melt, the eutectic alloy GaInSn is used.
Band gap renormalization and Burstein-Moss effect in silicon- and germanium-doped wurtzite GaN up to 10 20 cm − 3

The interplay between band gap renormalization and band filling (Burstein-Moss effect) in n-type wurtzite GaN is investigated. For a wide range of electron concentrations up to 1.6×1020 cm−3 spectroscopic ellipsometry and photoluminescence were used to determine the dependence of the band gap energy and the Fermi edge on electron density. The band gap renormalization is the dominating effect up to an electron density of about 9×1018 cm−3; at higher values the Burstein-Moss effect is stronger. Exciton screening, the Mott transition, and formation of Mahan excitons are discussed. A quantitative understanding of the near gap transition energies on electron density is obtained. Higher energy features in the dielectric functions up to 10 eV are not influenced by band gap renormalization.
X-ray multimodal imaging using a random-phase object

We demonstrate an extension of the x-ray grating interferometer three modal imaging method to a generalized stepping scheme using a phase object with small, random features. The method allows the recovery of the absorption, scattering, and two-dimensional phase image of the sample from a raster scan of the phase object. An additional extension of the method to recover the effective wave-front curvature is also described. The technique provides fine sensitivity and high spatial resolution and has only low requirements on spatial and longitudinal coherence of the x-ray beam. Imaging modes and processing methods are explained, and an experimental demonstration of the technique is provided by imaging a feather and the quantitative characterization of a compound refractive lens.
Pole-Residue Method for Numerical Dynamic Analysis

AbstractSystems of second-order linear ordinary differential equations (ODEs) to arbitrary input functions appear in many fields of physics and engineering. Numerical methods for solving these kinds of problems have been mainly performed in the time and frequency domains. In contrast, this paper develops an efficient, seminumerical pole-residue method implemented in the Laplace domain. In this article, a key concept and development is on how to compute the poles and residues of the output from those of the input and system transfer functions. Once the poles and residues of the output are known, the corresponding time history of the output is readily obtained. Even though the correctness of the new method has been verified by using a step-by-step time-domain solution, the accuracy of the new method in theory is higher than that of any time-domain approach, partially because the output obtained from the new method is a continuous function of time.
Embedding Theorems for Abelian Groups

Given an abelian group G and a mapping θ that maps a subgroup A of G homomorphically onto another subgroup B of G, then it is known (3) that there always exists an embedding group G* ⊇ G which is abelian and possesses an endomorphism θ* which coincides with θ on A, i.e. aθ = aθ* whenever aθ is defined. θ is called a partial endomorphism of G and θη a total endomorphism or simply an endomorphism that extends or continues θ.
An Improved Semi-Supervised K-Means Clustering Algorithm

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning.For the poor ability of the clustering algorithm based on the K-means for non-spherical clusters problems,this paper presents a new idea that considers the influence of the labeled data-points on the unlabeled data-points in allocating category,puts forward a definition of gravitational influence degree between category and data-point,and designs a semi-supervised K-means clustering algorithm with a gravitational parameter.The experiments show that the new algorithm has better effect than the traditional semi-supervised K-means clustering method in dealing with the distribution of non-spherical cluster clustering.
Prediction of the return period of drought in Panjiakou hydrological station by Peason-III probability distribution

Peason-III probability distribution is chosen to simulate monthly average runoff of Panjiakou hydrological station, and calculate the minimum value of monthly average runoff at different return periods throughout March, April, May, and the whole spring. The results show that minimum value of monthly average runoff was 85.59, 62.04, 50.24, 40.74, 30.33, 23.55, and 17.46 m3/s when return periods were fixed at 2, 10, 20, 50, 100, and 200 years, respectively. In addition, the fitting results were credible by comparing the observed return periods. The return periods of monthly average runoff of Panjiakou hydrological station in March, April, May, and the whole spring of 2001 were 147.00, 254.07, 1.80, and 34.07 years, respectively.
The Computation of a Common Quadratic Lyapunov Function for a Linear Control System

In this paper, we consider the existence of common quadratic Lyapunov functions for a linear control system. For stable LTI systems whose system matrices are upper triangular form, or lower triangular form, it is proved that there exit common quadratic Lyapunov functions. Then, two algorithms of computing a common Lyapunov function are given. Finally, two examples are listed.
Certainty of Punishment versus Severity of Punishment: Enforcement of Environmental Protection Laws

ABSTRACT:According to the standard enforcement model, key deterrence components are punishment certainty and severity. Theory predicts the relative efficacy of certainty versus severity, but empirical and experimental evidence are mixed. Our study is the first to systematically compare the effects of certainty and severity in the environmental protection context. Our empirics examine wastewater discharged by chemical manufacturing facilities permitted under the Clean Water Act. We find that when enforcement certainty and severity are high, both components effectively deter pollution, with certainty being more effective. In contrast, certainty and severity increases prove counterproductive when certainty and severity are low.
Whittle-Matérn priors for Bayesian statistical inversion with applications in electrical impedance tomography

We study flexible and proper smoothness priors for Bayesian statistical inverse problems by using Whittle-Matern Gaussian random fields. We review earlier results on finite-difference approximations of certain Whittle-Matern random field in $\mathbb{R}^2$. Then we derive finite-element method approximations and show that the discrete approximations can be expressed as solutions of sparse stochastic matrix equations. Such equations are known to be computationally efficient and useful in inverse problems with a large number of unknowns. The presented construction of Whittle-Matern correlation functions allows both isotropic or anisotropic priors with adjustable parameters in correlation length and variance. These parameters can be used, for example, to model spatially varying structural information of unknowns. As numerical examples, we apply the developed priors to two-dimensional electrical impedance tomography problems.
Afterword:: Recompiling

Curiously, none of the articles in this issue of Criticism are about open source. For an issue boldly labeled “Open Source Culture and Aesthetics,” this might appear to be a problem. The claim is unfair, of course, but not inaccurate: everything depends on the scope of the variable. The local variable—very local—is a fairly mundane practice of software production and distribution (also called free software), diverse in itself to be sure, but really quite distinctive; and, in terms of the great swirl of cultural and technical production, not nearly as widespread or important as, say, standardized machine tools or spreadsheet accounting. The global variable, however, is something more like a zeitgeist, a discourse, a social form, an aesthetic, and a political cipher. It is an unordered array of ideas, practices, capitalisms, technologies, moments, and movements, from the cravenly enthusiastic to the naively critical to the dismissive. Knowledge economies, distributed collaboration, Web 2.0, critical approaches to intellectual property law, crowdsourcing, fan culture, prosumption, peer production, new configurations of property and labor, changing conceptions of creativity, novelty and aesthetic norms, doit-yourself (DIY) culture, and so on. As the editors of this issue expertly explore in the introduction, over the last decade this local variable has been progressively redefined as a global one. Open source—the local, specific software development practice—has been modulated and transformed into something anyone can “call” (as it were) from within any function. Consider how open source was introduced on Wikipedia in 2001:
The pair-wise deletion correlation coefficient testing method and its applications

To quantitatively investigate the stability and authenticity of the statistical correlation coefficient,we propose a pairwise deletion correlation coefficient(PWDCC) testing method which is based on x~2 test,through pair-wise deleting elements of a data sample and constructing a correlation coefficient series.The analysis using both ideal data sample and real climate data confirms the validity and feasibility of the PWDCC.The results show that,this method can objectively and quantitatively determine whether there exist extreme values which lead to large variation of the sample correlation coefficient.Furthermore,the method is simple and has clear physical significance,and it also overcomes some weakness of the traditional methods(the scatter diagram,the slide correlation method,etc).
On memristor based five-order chaotic circuit

By replacing Chua's diode with an active flux-controlled memristor circuit,a memristor based five-order chaotic circuit is derived from four-order Chua's oscillator and its corresponding differential equations for the circuit state variables are established.The theoretical analysis shows that this memristor based chaotic circuit has an equilibrium set with its stability dependent on the initial state of the memristor.By using conventional dynamical analysis methods,the dynamical behaviors of the circuit with the variation of initial state are investigated.The numerical simulation results verify the correctness of theoretical analysis.
Structural Determinism of Linear Baroclinic Waves and Simple Nonlinear Equilibration

Abstract The linear evolution of arbitrarily specified perturbations in a zonally homogeneous, two-layer model is analyzed in a dynamical system which describes the disturbances in terms of the phase difference and amplitude ratio between the temperature and streamfunction components. The stationary equilibria of this system are equivalent to the individual eigenmodes of normal mode analysis and in the inviscid case, simple analytical expressions for these equilibria are possible. These expressions allow us to quantitatively examine the physical processes that determine the normal mode structures in the inviscid case and how the processes of friction then affect them. The advantage to this approach is that a conceptual understanding for the processes determining the so-called “preferred structure” and their characteristics (e.g., growth rates) of normal mode analysis is obtained in terms of the basic Quid flow properties of advection, dispersion and dissipation. But the most important conceptual property .
Blind deconvolution parallel algorithm based on a simplified mixing model

The distance between sensors is usually closely in telecom meeting.MIMO can be simplified to SISO according as there is similar impact response when microphones are very nearly each other.A new kind of multi-input multi-output blind deconvolution using a measure of temporal predictability is presented.The algorithm can use an eigenvalue problem to solve directly a deconvolution filter by a simplified mixing model and parallel algrithm.Thus the complexity of the algorithm is small.The results of computer simulations are presented for sound signals.
A Least Squares Regression Realised Covariation Estimation: A Comprehensive Simulation Study

In this paper we provide a simple framework for the estimation of the whole variancecovariance matrix in the presence of MMS noise and non-synchronous trading. To accomplish that we start from the formula of the realized variance and the Hayashi-Yoshida realized covariance estimator and derive two separate pooled OLS regressions whose byproducts are the intergrated variance and covariance, respectively. An empirically motivated simulation study shows that the least square approach gives rise to very precise estimators for all elements of the covariation matrix and outperforms other widely applied estimation techniques.
Numerical Simulation of a New Probe for the Alternate Cooling and Heating of a Subcutaneous Mouse Tumor Model

A new probe for the alternate cooling and heating of a subcutaneous tumor was designed based on numerical simulations in this study. In conjunction with a thermal system built in our lab, it facilitates liquid nitrogen cooling and radiofrequency (RF) heating within one probe. Simulations were performed to study the effects of the probe length, probe inner tube diameter, and liquid nitrogen flow pressure on the probe cooling capacity. Three probes were fabricated with optimized parameters. Experimental measurements were made in gelatin to validate the simulated results and to demonstrate the probe efficiency.