Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification in general finite dimensional spaces with the k-nearest neighbor rule

Given an n -sample of random vectors (X i ,Y i ) 1 ≤ i ≤ n whose joint law is unknown, the long-standing problem of supervised classiﬁcation aims to optimally predict the label Y of a given new observation X . In this context, the k -nearest neighbor rule is a popular ﬂexible and intuitive method in non-parametric situations. Even if this algorithm is commonly used in the machine learning and statistics communities, less is known about its prediction ability in general ﬁnite dimensional spaces, especially when the support of the density of the observations is R d . This paper is devoted to the study of the statistical properties of the k -nearest neighbor rule in various situations. In particular, attention is paid to the marginal law of X , as well as the smoothness and margin properties of the regression function η(X) = E [ Y | X ] . We identify two necessary and sufﬁcient conditions to obtain uniform consistency rates of classiﬁcation and derive sharp estimates in the case of the k -nearest neighbor rule. Some numerical experiments are proposed at the end of the paper to help illustrate the discussion.
Generalized BSDEs, weak convergence, and homogenization of semilinear PDEs with the Wentzell-type boundary condition

ABSTRACT We study the homogenization property of semilinear second-order PDEs on the domain according to the Wentzell-type boundary condition, periodic coefficients, highly oscillating drift, and nonlinear term. Our method builds on earlier work on homogenization of Tanaka [22], which deals with of diffusion with Wentzell boundary and for semilinear PDEs, Pardoux [13], who provides a probabilistic approch in whole space, and Ouknine-Pardoux [11], who is devoted to the domain D with the Neumann boundary condition. In particular, we use the weak convergence of reflected BSDEs and then obtain pointwise convergence of PDEs but not functional convergence as with the analytical method.
Optimum Allocation of Selection Intensity in Two Stages of Truncation Selection

This paper discusses the problem of estimating gain when sequential truncation selection is followed, and develops its solution in terms of bivariate truncation. Gain is maximized in terms of the selection differential (e.g., truncation points) required to balance the costs of operations. The solution depends on the correlation between the measurements of the two stages and the cost iratio for testing in the two stages. Tabular solutions are presented for a wide range in both parameters and show how first-stage selection intensity should increase with higher initial heritabilities and with higher progeny-testing costs. In forest tree breeding, it will often occur that progeny testiing cannot be justified solely in terms of gain per unit cost, though other utilities of the testing may provide juistification.
Effect of color spaces on image compression using hybrid wavelet transform generated with varying proportions of constituent transforms

Today, enormous amount of multimedia data is generated, transmitted and stored on the internet which has opened new research dimensions for computing field. In recent work, hybrid wavelet transforms (HWT) generated with various constituent transforms is proven to be better than individual orthogonal transforms [7]. Later work as proved that HWT generated with varying proportions of constituent transforms gives better compression quality as compared to equal proportions of constituent orthogonal transforms in HWT depending upon the compression ratio [1, 2]. Here the appraise of the effect of color spaces on image compression using HWT generated with varying proportions of constituent transforms and constituent transforms is presented. The experimentation is done on the test bed having 15 images of varied sizes and eight compression ratios (60% to 95%). The results show that for higher compression ratio of 95%, the LUV color space gives better compression quality as compared to other considered color spaces with HWT generated with 4:1 proportion of Cosine- Sine constituent transforms. For 65% to 90% compression ratios, HWT generated from 1:1 proportion of Cosine-Kekre constituent transform with RGB color space gives less average mean square error (MSE). For lower compression ratio of 60%, HWT generated with 1:4 proportion of Cosine-Kekre constituent transform gives better image compression quality with RGB color space.
An Experimental Investigation of Fluid-Structure Interaction Inside Solid Propellant Rocket Motors

Fluid Structure Interaction of an inhibitor with the internal flow induced by wall injection was studied in an axisymmetric cold flow apparatus. Experiments were carried out over a wide range of injection velocities (0.6m.s−1≤Vinj≤1.7m.s−1) in order to underline how the obstacle not only modifies the mean flow field but also its entire dynamic behaviour. The resulting instability (from the interaction of the unstable shear layer with the TaylorCulick flow) exhibits a significant shift with respect to the Taylor-Culick instability and therefore emphasizes the strong impact of the inhibitor on hydrodynamics. The latter was never previously observed and may have serious impact when trying to predict pressure oscillations in Large Solid Propellant Rocket Motors.
Optical characterization of free electron concentration in heteroepitaxial InN layers using Fourier transform infrared spectroscopy and a 2 × 2 transfer-matrix algebra

Fourier Transform Infrared (FTIR) reflectance spectroscopy has been implemented as a non-destructive, non-invasive, tool for the optical characterization of a set of c-plane InN single heteroepitaxial layers spanning a wide range of thicknesses (30–2000 nm). The c-plane (0001) InN epilayers were grown by plasma-assisted molecular beam epitaxy (PAMBE) on GaN(0001) buffer layers which had been grown on Al2O3(0001) substrates. It is shown that for arbitrary multilayers with homogeneous anisotropic layers having their principal axes coincident with the laboratory coordinates, a 2 × 2 matrix algebra based on a general transfer-matrix method (GTMM) is adequate to interpret their optical response. Analysis of optical reflectance in the far and mid infrared spectral range has been found capable to discriminate between the bulk, the surface and interface contributions of free carriers in the InN epilayers revealing the existence of electron accumulation layers with carrier concentrations in mid 1019 cm−3 at both t.
Application of improved quantum genetic algorithm in PID parameter tuning

Parameter tuning is the key of PID controller design,for which,a method based on the improved quantum genetic algorithm is proposed.As an optimization algorithm,the quantum crossover,quantum mutation and population catastrophe are introduced to it,which carries out the evolution computation to transform the parameter tuning into the parameter optimization.Simulation results show its feasibility with better control performances.
Pertinence des champs bidimensionnels dans l'analyse des phénomènes instationnaires tridimensionnels

Coherent structures identi cation constitutes an important step in the study of uid dynamics and ow control. The acces to instantaneous velocity eld measurements, through a rapid development of particule image velocimetry (PIV), has deeply enriched the experimental approach to this eld. Originally two-dimensional, this technique now evolves to the measurement of complete three-dimensional elds, in order to address the intrinsically 3D nature of the space-time dynamics of the ow. However, the constraints imposed by 3D techniques, still largely justify 2D exploration. This is why in this talk we approach the validity and the possibilities o ered by modal decompositions of sets of 2D velocity eld snapshots, times resolved (or not), issue from experiments or numerical simulations in strongly unsteady ows. Three modal decompositions are compared analysing a data set coming from a reference open cavity ow : the proper orthogonal decomposition (POD), the global Fourier mode decomposition (GFD), the dynamic modes decomposition (DMD). Mots clefs : structures coherentes d'ecoulement ; analyse 2D de champs de vitesse 3D ; decompositions modales
Incorporation of Hypervolume Approximation with Scalarizing Functions into Indicator-Based Evolutionary Multiobjective Optimization Algorithms

The handling of many-objective problems is a hot issue in the evolutionary multiobjective optimization (EMO) community. Whereas Pareto-based EMO algorithms usually work very well on two-objective problems, they do not work well on many-objective problems. A promising approach to the search for the non-dominated solutions of many-objective problems is a class of indicator-based EMO algorithms. The goal of indicator-based EMO algorithms is to maximize an indicator function which evaluates the quality of a set of solutions. The hypervolume has been frequently used as an indicator function. The main difficulty of the use of hypervolume is that the computation load for its calculation increases exponentially with the number of objectives. Thus the application of indicator-based EMO algorithms to many-objective problems is time-consuming. In our former study, we proposed an idea of approximating the hypervolume using a number of achievement functions with uniformly distributed weight vectors. In this paper, we incorporate our hypervolume approximation into indicator-based EMO algorithms. Experimental results show that the computation time of indicator-based EMO algorithms for many-objective problems is drastically decreased by the use of our hypervolume approximation method with no severe deterioration in its search ability.
Smoke Particles' Recognition and Monte Carlo Simulation of Laser Scattering Characters

Based on the particle laser scattering, the paper puts forward a smoke recognition system with multi sensor array. And then a Monte Carlo method is described to simulate the scattering characters of smoke particles. By changing the sizes, refractive index of the smoke particles and incident angles of light source, the simulation results fit well with the classical results derived from Mie scattering theory.
Multi-Robot Inverse Reinforcement Learning Under O cclusion with State Transition Estimation (Extended Abstract)

Multi-robot inverse reinforcement learning (mIRL) is broadly useful for learning, from passive observations, the behaviors of multiple robots executing fixed trajectories and interacting with each other. In this paper, we relax a crucial assumption in IRL to make it better suited for wider robotic applications: we allow the transition functions of other robots to be stochastic and do not assume that the transition error probabilities are known to the learner. Challenged by occlusion where large portions of others’ state spaces are fully hidden, we present a new approach that maps stochastic transitions to distributions over features.
On the Small Effect of Boundary Layer Thicknesses on Slat Noise

In landing procedure the airframe noise is comparable to the noise generated by the engines of an aircraft, being the leading edge slat one of the most important components. Experiments on slat noise generation usually are limited to much lower Reynolds numbers than those obtained in ight conditions. The present paper describes analyses made to evaluate numerically the in uence of the boundary layer thickness on the unsteady ow and noise generation at the slat cove. Time accurate simulations were done using a LatticeBoltzmann solver. Grid independence studies of the Power Spectral Density (PSD) are presented. Slat boundary layer thicknesses were found to have little in uence on turbulence and pressure spectra at the slat cove, leading to investigations of the use of free-slip walls at the slat. Very small level di erences were obtained in the slat noise produced. This assumption allowed not only a coarser grid to be employed in certain regions but also the possibility to predict slat noise in high Reynolds number ows with no major increase in simulation cost.
Optimization on primary mirror support position of space remote sensor based on finite element method

To achieve the more accurate design of space remote sensor,the primary mirror surface deformation affected by its self-weight is studied based on the finite element method.Under the fact that the shape of primary mirror meets the requirements of actual engineering,optimization is processed by MSC Nastran,the best primary mirror support position is finally determined and the primary mirror lightweight structure is identified.An example of optimization analysis is given,in which the coordinates of three support center at the back of primary mirror are taken as design variables,and the maximum deformation value of primary mirror is taken as objective function.The comparison of the results obtained by the example and the traditional calculation method indicates that the optimization based on the finite element method has better advantages,which lays a good foundation for the development of a space remote sensor.
Novel Raman Parametric Hybrid L-Band Amplifier with Four-Wave Mixing Suppressed Pump for Terabits Dense Wavelength Division Multiplexed Systems

We demonstrate improved performance of parametric amplifier cascaded with Raman amplifier for gain of 54.79 dB. We report amplification of L-band using 100 × 10 Gbps Dense Wavelength Division Multiplexed (DWDM) system with 25 GHz channel spacing. The gain achieved is the highest reported so far with gain flatness of 3.38 dB without using any gain flattening technique. Hybrid modulated parametric pump is used for suppressing four-wave mixing (FWM) around pump region, resulting in improvement of gain flatness by 2.42 dB. The peak to peak variation of gain is achieved less than 1.6 dB. DWDM system with 16-channel, 25 GHz spaced system has been analyzed thoroughly with hybrid modulated parametric pump amplified Raman-FOPA amplifier for gain flatness and improved performance in terms of BER and -factor.
A Dose-Response Equation for the Invasion of Micro-Organisms

A series of kc doses (n', n , , nk micro-organisms) is administered to ml , m2 , . , mk test animals respectively, and the corresponding survivors are observed to number r, r2, , . rk (The terms "survivors" and "killed" whenever they occur subsequently, are meant to cover also the case when "not infected" are compared with "infected"). This paper derives a dose-response relation from a hypothesis based on the mode of action of the micro-organisms against their host first suggested by H. A. Druett (2). Assuming that
Timeliness attributes and the extent of accounting disclosure: a study of banking companies in Bangladesh

Timeliness of annual reports is an important attribute of their usefulness. There is a paucity of research about the timeliness of the published audited accounts of the banking sector companies in developing countries in general and audit delays in particular. This paper empirically examined the relationship between the disclosure score and audit delay in a developing country, Bangladesh. The objective of the study is to establish the impact of discrosure score or timeliness. The analysis in this study involves developing a regression middel to explain the level of delay of the sample banks under study. This study examine, whether timeliness of corporate financial reporting in the banking sector has improved in Bangladesh following the creation of the Securities and Exchange Commission (SEC) in 1993, the enactment of the Companies Act in 1994, the amendment of the SEC Rules in 1997, and the Banking Companies Act, 1991. Using 25% of the population (12 banks) observations over a period of 5 years (2002–2006), I find that regulatory changes have not improved timeliness in corporate reporting in the banking sector of Bangladesh as measured by the extent of audit lag, AGM lag and total lag.
Equilibrium conditions for the Malmquist-Takenaka systems

In this paper we give an overview of the discretization results connected to Malmquist-Takenaka systems for the unit disc and upper half-plane. We prove that the discretization nodes on the real line have similar properties like the discretization nodes on the unit circle: for example they satisfy some equilibrium conditions and they are stationary points of some logarithmic potential. The problems whether they are the minimum of a logarithmic potential is formulated and solved in a special case.
High performance multi-physics simulations with FEniCS/DOLFIN

This report presents new software developments implemented in the FEniCS/DOLFIN libraries to support eﬃcient block preconditioning for linear systems, and the assembly of discontinuous Galerkin methods in parallel. For block preconditioning, support for the PETSc MatNest framework has been implemented. This provides memory savings of a factor of more than three over the previous implementation, as well as reduced run times. For discontinuous Galerkin methods, the DOLFIN distributed mesh data structure has been extended to allow ghost/halo layers of cells. These layers are necessary to compute, on process boundaries, the numerical ﬂuxes that characterise discontinuous Galerkin methods. During the development of the above functionality, a number of other performance improvements (in memory and run time) have been made, and some new platform-independent proﬁling tools have been added to DOLFIN. These are detailed in this report. Performance data for the newly developed functionality on ARCHER are reported, and show very good scaling over a large range of process/core counts. The primary objectives of this project have been achieved, and the developed code is publicly available in the FEniCS Project development repositories. The functionality will be included in future releases of the FEniCS Project libraries. Developments from this project have already been used in EPSRC and NERC funded projects, as well as being made available through third-party tools that build on FEniCS.
Efficiency and robustness of multigrid methods for hypersonic flows

Some simple modifications to ensure a robust multigrid in hypersonic flow regions are proposed. Based upon a theoretical analysis, a modification of the boundary treatment for solid walls is proposed in order to avoid negative pressures around stagnation points. Secondly, a new 'damped' prolongation is designed to avoid the appearance of negative pressures due to multigrid prolongation. Thirdly, a new family of non-separable limiters for symmetric TVD schemes is presented. They replace the separable limiters used in the context of symmetric TVD, which may allow oscillatory solutions and produce negative pressures around strong shocks. The efficiency of the resulting multigrid algorithm for hypersonic flows is illustrated by several numerical examples. (Author)
Principal Coefficients Embedding: Theory and Algorithm

Low Rank Representation (LRR) and its extensions seek the lowest rank representation of a given data set by solving a rankminimization problem, which have attracted a lot of interests. Since the rank operator is nonconvex and discontinuous, LRR uses the Nuclear norm as a convex relaxation and most theoretical studies argue that the Nuclear norm may be the only one surrogate for the rank operator. In this paper, we prove the equivalence between the Frobenius-normand the Nuclear-norm-based representation. Specifically, when the data set is error-free, the Frobenius-norm-based representation is exactly the Nuclear-normbased one; When the data set contains a small amount of additive errors, the Frobenius norm is equivalent to the truncated Nuclear norm. Our theoretical result not only provides a new surrogate (i.e., the Frobenius norm) for the rank-minimization problem, but also gives some novel theoretical insights to understand the success of Frobenius-norm-based methods in subspace clustering and pattern classification. Based on our theoretical results, we propose a robust subspace learning algorithm, i.e., Principal Coefficients Embedding (PCE), which builds a similarity graph using k largest Frobenius-norm-based coefficients and embeds the graph into a low-dimensional space. Extensive experimental results show that PCE is superior to six feature extraction methods on four popular facial databases with respect to accuracy and robustness to corruptions and disguises.
Face recognition based on symmetrical kernel principal component analysis

In order to improve the practicability of face recognition technology,a new face recognition method was proposed by adopting the facial mirror symmetry and Kernel Principle Component Analysis(KPCA). Firstly,the original images were decomposed by wavelet transform,and the low frequency components could be obtained.Then,the odd symmetry samples and the even symmetry samples were obtained by mirror transforming.Odd/even eigen vector were separately extracted through KPCA and fused to composite features by an odd-even weighted factor.A nearest neighbor classifier was used to classify the images.The proposed method was tested on the ORL face image database.The experimental results show the method can increase the sample capacity,overcome the effect of illumination and posture,and raise the recognition rate.Besides,in the comprehensive performance,it is better than contrast method.
An Approach to the Log-Euclidean Mean via the Karcher Mean on Symmetric Cones

In a general symmetric cone, we show that certain sequence of the Karcher means converges to the Log-Euclidean mean by using the fact that the Karcher mean is the limit of inductive means. One can see this as a generalization of the Lie-Trotter formula of positive definite matrices into a symmetric cone setting via the least squares mean.
GRASP: a versatile algorithm for characterizing the atmosphere

GRASP (Generalized Retrieval of Aerosol and Surface Properties) is the first unified algorithm to be developed for characterizing atmospheric properties gathered from a variety of remote sensing observations (an introductory video is available elsewhere1). GRASP is based on a recent algorithm2 created to improve aerosol retrieval from the French Space Agency’s PARASOL3 imager over bright surfaces like deserts where high surface reflectance dwarfs the signal from aerosols. Moreover, GRASP relies on the heritage of retrieval advances4–7 implemented for AERONET,8 a worldwide network of over 200 radiometer sites that generate the data used to validate nearly all satellite observations of atmospheric aerosols. The AERONET retrievals derive detailed aerosol properties,6 including absorption, providing information of vital importance for reducing uncertainty in assessments of climate change. GRASP is based on several generalization principles with the idea of developing a scientifically rigorous, versatile, practically efficient, transparent, and accessible algorithm. There are two main independent modules. The first, numerical inversion, includes general mathematical operations not related to the particular physical nature of the inverted data (in this case, remote sensing observations). The second module, the forward model, was developed to simulate various atmospheric remote sensing observations. Numerical inversion is implemented as a statistically optimized fitting of observations following the multi-term least squares method (LSM) strategy, which combines9 the advantages of a variety of approaches and provides transparency and flexibility in developing algorithms that invert passive and/or active observations and derive several groups of Figure 1. Diagram illustrating the principle of combined synergetic processing of complementary observations using a multi-pixel2 retrieval approach. CALIPSO is a joint lidar mission of NASA and the French Space Agency, which also manages the PARASOL imager. AERONET is a worldwide network of radiometer sites.