Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of turbulence induced by micro vortex generators on shockwave : boundary layer interactions

Interactions between viscous and transonic effects in air flow around a laminar wing were investigated computationally by means of the solution of unsteady Reynolds-averaged Navier-Stokes Equations. The subject is important from the point of view of applications of Natural Laminar Flow technology in modern, economically efficient passenger aircraft. The research was focused on simulations and analyses of influence of turbulence induced by micro vortex generators on intensity of harmful transonic phenomena like strong shock waves and buffet. Two ways of modelling of the effects of turbulators – the micro vortex generators were taken into consideration. The first way consisted in resolving the shape and inclination angle of the generator in the grid over airfoil and setting the non-slip wall boundary condition on the surface of the generator. The second way, taking advantage of the BAY model of vortex generator, was implemented on orthogonal grid without the need of resolving the shape of the vortex generator in the grid. Calibration of the BAY model was aimed at producing similar distribution of vorticity and velocity circulation behind the model of the vortex generator, as obtained for the grid-resolved model of the vortex generator. The calibration procedure resulted, however, in overestimated turbulisation of the boundary layer in the BAY model, compared to the effects of the grid-resolved vortex generator. The flow simulations indicated, however, that turbulisation of boundary layer induced by micro vortex generators can reduce or eliminate the oscillation of strong shock wave and buffet in off-design conditions and that further adjusting of the BAY model is an efficient strategy for modelling the interactions between viscous and transonic effects in air flow around a laminar wing.
Improving covariate balance in 2K factorial designs via rerandomization with an application to a New York City Department of Education High School Study

A few years ago, the New York Department of Education (NYDE) was planning to conduct an experiment involving five new intervention programs for a selected set of New York City high schools. The goal was to estimate the causal effects of these programs and their interactions on the schools’ performance. For each of the schools, about 50 premeasured covariates were available. The schools could be randomly assigned to the 32 treatment combinations of this 25 factorial experiment, but such an allocation could have resulted in a huge covariate imbalance across treatment groups. Standard methods used to prevent confounding of treatment effects with covariate effects (e.g., blocking) were not intuitive due to the large number of covariates. In this paper, we explore how the recently proposed and studied method of rerandomization can be applied to this problem and other factorial experiments. We propose how to implement rerandomization in factorial experiments, extend the theoretical properties of rerandomization from single-factor experiments to 2K factorial designs, and demonstrate, using the NYDE data, how such a designed experiment can improve precision of estimated factorial effects.
Best-first search for property maintenance in reactive constraints systems

Real-life dynamic problems may lead to inconsistent constraints systems for which a solution must be found even if constraints have to be relaxed. In this paper, we propose a best-first search to handle such problems. Classical backtracking search algorithms are extended in two ways: identification of good backtrack points as in Intelligent Backtracking techniques and maximum use of independant work (that would have been discarded with a mere backtrack). We first describe an operational semantics for our search method. Then we specialize it to handle constraint relaxation over finite domains. The practical use of this approach is demonstrated by theoretical complexity analysis and experiments.
K-means seeding via MUS algorithm

K-means algorithm is one of the most popular procedures in data clustering. Despite its large use, one major criticism is the impact of the initial seeding on the final solution. A modified version of K-means is proposed, based on a suitable choice of the initial centers. Similarly to clustering ensemble methods, our approach takes advantage of the information contained in a co-association matrix. Such matrix is given as input for the MUS algorithm that allows to define a pivot-based initialization step. Preliminary results concerning the comparison with the classical approach are discussed.
A characterization method for the metal thin film

The evanescent wave, occurred when the incident light generates total internal reflection on the interface between glass and metallic film, can raise the surface plasmon (SP) on the metallic film. SP and evanescent wave can resonate under certain angle of incidence when they have the same frequency and wave number. In this case, the power of reflection beam decreases dramatically, and the resonance peak appears in the reflection spectroscopic. The positions of resonance peaks are different when the refraction indexes of medium on the metallic film or the thicknesses of the metallic film are different. And it is found that the phase position of p-component of reflected light changes with the metal film thickness, while the phase position of s-component almost doesn’t change in the Surface Plasmon Resonance effect. S-polarized light is taken as reference and interferometry is adopted to turn the change of the phase position into the change of interference fringes position in the paper, and the film thickness can be derived from it. The simulation results indicated that, through making use of piecewise quadratic fitting on the phase data, the inaccuracy with the range of film thickness is between 30 and 80 nanometers is not more than 0.33 nm.
When accent preservation leads to clash

In English, some complex words can display exceptional accent preservation (EAP): they can preserve an accent from their base even when this would violate a general restriction against adjacent accents (e.g. retúrn → retùrnée). This article analyses EAP both empirically and theoretically. The analysis of a set of 291 derivatives from Wells (2008) shows that this phenomenon can be partially attributed to the relative frequency of the base and its derivative and partially also to syllable structure, and that these two factors have a cumulative effect. It is also shown that the existence of a more deeply embedded base (e.g. colléct → colléctive → còllectívity ~ collèctívity) can increase the likelihood for a derivative to display EAP. A formal account of the phenomenon is proposed building on Collie's (2007, 2008) ‘fake cyclicity’ analysis, using weighted constraints (Pater 2009, 2016) and Max-Ent-OT (Goldwater & Johnson 2003). Finally, a model of lexical access building on Hay's (2001, 2003) model and integrating more deeply embedded bases is proposed.
CONSTRUCTIVE SOLID GEOMETRY

The term 'constructive solid geometry' denotes a class of schemes for describing solid objects as compositions (usually 'additions' and 'subtractions') of primitive solid 'building blocks. ' The notion of adding and subtracting solids has been used by mechanical designers and others for generations, but attempts to embody it in computer-based modelling systems have been hindered by the absence of a firm mathematical foundation. This paper provides such a foundation by drawing on established results in modern axiomatic geometry and point set topology. The paper also initiates a broader discussion, to be continued in subsequent papers, of three seminal topics: mathematical modelling of solids, representation of solids, and calculation of geometrical properties of solids
Superconvergence of the Gradient Approximation for Weak Galerkin Finite Element Methods on Rectangular Partitions

This article presents a superconvergence result for the gradient approximation of the second order elliptic equation discretized by the weak Galerkin finite element method with rectangular partitions. The result shows an error estimate of order $O(h^2)$ for the approximate gradient obtained from the weak Galerkin using the lowest order element consisting of piecewise linear and constant functions. For this numerical scheme, the optimal order of convergence for the gradient approximation has shown to be $O(h)$. The $O(h^2)$-convergence outperforms the optimal order, and therefore reveals a superior property for the corresponding weak Galerkin finite element method. Some computational results are reported to support and illustrate the superconvergence theory numerically.
Sewer overflow simulation evaluation of urban runoff model according to detailed terrain scale

Frequently torrential rain is occurred by climate change and urbanization. Urban is formed with road, residential and underground area. Without detailed topographic flooded analysis consideration can take a result which are wrong flooded depth and flooded area. Especially, flood analysis error of population and assets in dense downtown is causing a big problem for establishments and disaster response of flood measures. It can lead to casualties and property damage. Urban flood analysis is divided into sewer flow analysis and surface inundation analysis. Accuracy is very important point of these analysis. In this study, to confirm the effects of the elevation data precision in the process of flooded analysis were studied using 10m DEM, LiDAR data and 1:1,000 digital map. Study area is Dorim-stream basin in the Darim drainage basin, Sinrim 3 drainage basin, Sinrim 4 drainage basin. Flooding simulation through 2010's heavy rain by using XP-SWMM. Result, from 10m DEM, shows wrong flood depth which is more than 1m. In particular, some of the overflow manhole is not seen occurrence. Accordingly, detailed surface data is very important factor and it should be very careful when using the 10m DEM.
Leveraging the nonuniform PSO network model as a benchmark for performance evaluation in community detection and link prediction

Advances in network geometry pointed out that structural properties observed in networks derived from real complex systems can emerge in the hyperbolic space (HS). The nonuniform popularity-similarity-optimization (nPSO) is a generative model recently introduced in order to grow random geometric graphs in the HS, reproducing networks that have realistic features such as high clustering, small-worldness, scale-freeness and rich-clubness, with the additional possibility to control the community organization. Generative models allowing to tune the structural properties of ‘realistic’ synthetic networks are fundamental, because they offer a ground truth to investigate how predictive algorithms react to controlled topological variations. Here, we discuss how to leverage the nPSO model as a synthetic benchmark to compare the performance of methods for community detection and link prediction; and we prove that the nPSO offers a reliable and realistic testing framework which can complement other existing benchmarks not based on latent geometry. Furthermore, we confirm that network embedding information can improve community detection, whereas boosting link prediction in HS still needs further investigations. Indeed, we find that the presence of communities in nPSO significantly modifies the performance of link predictors and is fundamental for the reproducibility of results observed on real networks. The nPSO can trigger valuable insights to understand the intrinsic rules of link-growth and self-organization that connect topology to geometry and that are encoded in link prediction algorithms differentiating their performance.
Episodic Multi-armed Bandits

We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed of several {\em steps}, in which it chooses an action and observes a feedback signal. Moreover, in each step, it can take a special action, called the $stop$ action, that ends the current episode. After the $stop$ action is taken, the learner collects a terminal reward, and observes the costs and terminal rewards associated with each step of the episode. The goal of the learner is to maximize its cumulative gain (i.e., the terminal reward minus costs) over all episodes by learning to choose the best sequence of actions based on the feedback. First, we define an {\em oracle} benchmark, which sequentially selects the actions that maximize the expected immediate gain. Then, we propose our online learning algorithm, named {\em FeedBack Adaptive Learning} (FeedBAL), and prove that its regret with respect to the benchmark is bounded with high probability and increases logarithmically in expectation. Moreover, the regret only has polynomial dependence on the number of steps, actions and states. eMAB can be used to model applications that involve humans in the loop, ranging from personalized medical screening to personalized web-based education, where sequences of actions are taken in each episode, and optimal behavior requires adapting the chosen actions based on the feedback.
Tunable dual-wavelength operation of an all-fiber thulium-doped fiber laser based on tunable fiber Bragg gratings

Tunable dual-wavelength emission of a Tm-doped fiber laser based on two fiber Bragg gratings (FBGs) is experimentally demonstrated. By using two FBGs with central wavelengths at 2069.30 and 2069.44 nm, stable dual-wavelength laser generation in the 2 μm wavelength region is achieved by adjusting the differential loss of the two wavelengths in the laser cavity. Strain applied on the FBG allows independent tuning of the simultaneously generated wavelengths with separation between the laser lines in a range from 0.54 to 9 nm. The laser has output power fluctuations less than 0.093% for an output power of 77.3 mW.
A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Now a days Image forgery (IF) makes many problems in a society. Super Pixels Segmentation and ScaleInvariant Feature Transform based Image Forgery Detection (SPS-SIFT-IFD) has introduced to detect the forgeries in the images. The forgery region extraction algorithm replaces the features point with small SP as feature blocks and neighboring blocks that have similar local color features has been identified by the Scale-Invariant Feature Transform (SIFT) technique. Artificial Neural Network (ANN) classifier is used for better detection of forged parts in the original images. It applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the SPS-SIFT-IFD scheme achieve much better detection results even under various challenging conditions compared with the existing Forgery Detection (FD) methods. Finally, SPSSIFT-IFD method performance is measure, in terms of Recall, Precision, False Measure, Sensitivity, Sensitivity, Accuracy and Gmean.
Predictive Intelligence in Analytics Aggregation of Partial Ordered Subsets

Nowadays, the increased amount of users’ devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature.
Biological Information as Choice and Construction

A causal approach to biological information is outlined. There are two aspects to this approach: information as determining a choice between alternative objects and information as determining the construction of a single object. The first aspect has been developed in earlier work to yield a quantitative measure of biological information that can be used to analyze biological networks. This article explores the prospects for a measure based on the second aspect and suggests some applications for such a measure. These two aspects are not suggested to exhaust all the facets of biological information.
Regularization and analytic option pricing under $\alpha$-stable distribution of arbitrary asymmetry

We consider a non-Gaussian option pricing model, into which the underlying log-price is assumed to be driven by an $\alpha$-stable distribution. We remove the a priori divergence of the model by introducing a Mellin regularization for the L\'evy propagator. Using distributional and $\mathbb{C}^n$ tools, we derive an analytic closed formula for the option price, valid for any stability $\alpha\in]1,2]$ and any asymmetry. This formula is very efficient and recovers previous cases (Black-Scholes, Carr-Wu); we calibrate the formula on market datas, make numerical tests, and discuss its many interesting properties.
A cross- validation method for non- parametric estimation

This paper deals with the general problem of estimating a sig nal from a finite number of independent blurred observations. The method consists of obtaining unbiased estimators for a family of smoothed versions determined by approximations to the identity (or, delta sequences), and using a regularization of the inverse of the blurring operator. This results in the problem of determining two regularizing parameters. Both apriori and data-driven methods are presented for choosing them. Numerical examples are presented which demonstrate satisfactory estimation even for small data sets.
Breaking of waves on thin films over topographies

In the present study, we performed experiments on the evolution of gravity-driven nonlinear traveling free surface waves over strongly undulated inclines. We focused on the impact of the excitation frequency and amplitude and the substrates’ shape and periodicity on the traveling wave. Thereby, we revealed phenomena concerning the amplitude evolution of convectively unstable waves. We can classify the wave evolution in three categories: (1) A normal exponential growth of the wave until it reaches a saturation amplitude. (2) An exponential growth of the wave and an abrupt collapse of the wave leading to a wave with a saturated amplitude, which is sensibly smaller than the maximal amplitude. (3) An alternating exponential growth and breaking of the wave. By using Fourier analysis, we investigated the waves in more detail. Furthermore, we report (a) a selection of excitation frequencies for the wave breaking, (b) a major impact of the steady state free surface, especially its mean or global curvature, (c) a bubble formation occurring at the wave breaking, (d) an overlap of the wave front, and (e) a formation of a jet during wave breaking.In the present study, we performed experiments on the evolution of gravity-driven nonlinear traveling free surface waves over strongly undulated inclines. We focused on the impact of the excitation frequency and amplitude and the substrates’ shape and periodicity on the traveling wave. Thereby, we revealed phenomena concerning the amplitude evolution of convectively unstable waves. We can classify the wave evolution in three categories: (1) A normal exponential growth of the wave until it reaches a saturation amplitude. (2) An exponential growth of the wave and an abrupt collapse of the wave leading to a wave with a saturated amplitude, which is sensibly smaller than the maximal amplitude. (3) An alternating exponential growth and breaking of the wave. By using Fourier analysis, we investigated the waves in more detail. Furthermore, we report (a) a selection of excitation frequencies for the wave breaking, (b) a major impact of the steady state free surface, especially its mean or global curvature, (c) a .
Discontinuous approximation of flow in porous media with adsorption

Polymer flooding in petroleum reservoir engineering can be described by a Brinkman‐based model of flow in porous media coupled to a non‐strictly hyperbolic system of conservation laws for multiple components that form the aqueous phase. The coupled flow‐transport problem is discretized by coupling an H(div)‐conforming discontinuous Galerkin (DG) method for the Brinkman flow problem with a classical DG method for the transport equations.
Metrized laminations and quasisymmetric maps

Teichmuller space is defined as a space of hyperbolic structures on a surface rather than as a space of conformal structures. Earthquakes are defined and we see how they correspond to hyperbolic structures, via homeomorphisms of the circle. Metrised laminations are defined and we obtain a correspondence with earthquakes. We deduce a correspondence between measured laminations and earthquakes. We define uniform boundedness of earthquakes and show that such earthquakes are surjective. Quasisymmetric maps are defined and investigated. We show that an earthquake is uniformly bounded if and only if its boundary mapping is quasisymmetric. Finally we show how a uniformly bounded earthquake can be approximated, in a natural fashion, by a bi-Lipechits diffeomorphism.
Prueba de Mutación Evolutiva en Entornos Orientados a Objetos

Este trabajo fue financiado por la beca de investigacion PU-EPIF-FPI-PPI-BC 2012- 037 de la Universidad de Cadiz, por el proyecto DArDOS (TIN2015-65845-C3-3-R) del Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad del Ministerio de Economia y Competitividad, y por la Red de Excelencia SEBASENET (TIN2015-71841-REDT) del Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia del Ministerio de Economia y Competitividad.