Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation Of Regression Parameters of Two Dimensional Probability Distribution Mixtures

We use two methods of estimation parameters in a mixture regression: maximum likelihood (MLE) and the least squares method for an implicit interdependence. The most popular method for maximum likelihood esti-mation of the parameter vector is the EM algorithm. The least squares method for an implicit interdependence is based solving systems of nonlinear equations. Most frequently used method in the estimation of parameters mixtures regression is the method of maximum likelihood. The article presents the possibility of using a different the least squares method for an implicit interdependence and compare it with the maximum likelihood method. We compare accuracy of two methods of estimation by simulation using bias: root mean square error and bootstrapping standard errors of estimation.
Quantum dynamics of attosecond electron pulse compression

If an electron beam is periodically modulated in velocity, for example by laser field cycles, it can transform upon further propagation into a train of attosecond or shorter electron pulses. Here, I investigate the quantum mechanics of such an approach by numerically solving the Schrodinger equation in the time domain. There is a limit for the shortest electron pulses that can be achieved, and it depends on simple relations between the electron energy, the laser period, and the modulation strength. These results allow to design future experiments and to compare the measured electron pulse shapes to their quantum limit.
Sobre a necessidade de conceptualizar o fenómeno das fake news

Donald Trump comecou a usar a expressao fake news em 2017 e pouco tempo depois estava banalizada na opiniao publica. A apropriacao do conceito levou ao seu desvirtuamento. Ao mesmo tempo aumentou a confusao com false news. Mas com o aumento das tentativas de criminalizar a desinformacao online, parece fazer sentido uma caracterizacao do conceito, de modo a ajudar a fixar o que esta realmente em causa. Este trabalho propoe e justifica uma definicao de fake news.
Benjamini-Schramm convergence of locally symmetric spaces

The main theme of this work is the study of geometry and topology of locally symmetric spaces Gamma\ X as ther volume Vol(\Gamma\ X) tends to infinity. Our first main result concerns the Benjamini-Schramm convergence for arithmetic hyperbolic 2 or 3-manifolds. A sequence of locally symmetric spaces (Gamma_n\ X) converges Benjamini-Schramm to X if and only if for every radius R>0 the limit Vol((Gamma\ X)_{<R}/Vol (Gamma\ X) as n goes to infinity is 0, where (\Gamma\X)_{<R} stands for the R-thin part of Gamma\ X. We prove that there exists a positive constant C=C_R with the following property: for every torsion free, uniform, congruence arithmetic lattice Gamma in PGL(2,R) or PGL(2,C) Vol ((Gamma\ X)_{<R})<= C Vol (Gamma\X))^0.986. There is only finitely many arithmetic lattices of covolume bounded by a constant so the result above implies the Benjamini-Schramm convergence for any sequence of congruence arithmetic hyperbolic 3-manifolds. We also prove a similar but slightly weaker inequality for non-congruence subgroups. Our results are deduced form a strong form of the limit multiplicity property that holds for arithmetic lattices in PGL(2,R) of PGL(2,C). As an application of our bounds we confirm Gelander's conjecture on the triangulations of arithmetic hyperbolic 3-manifolds: we show that every arithmetic hyperbolic 3-manifold M admits a triangulation with O(Vol(M)) simplices and degrees of vertices bounded uniformly by an absolute constant. Next, we move to the setting of higher rank locally symmetric spaces. Let M_n=Gamma_n\ X be a sequence of pairwise distinct locally symmetric spaces modeled after a higher rank symmetric space X. We show that the dimension of the first homology group with coefficients in F_2 is sublinear in volume. This can be compared with the results of Calegari and Emerton on mod-p homology growth in p-adic analytic towers of 3-manifolds as well as the results of Abert, Gelander and Nikolov on the rank gradient of right-angled lattices in higher rank Lie groups.The main strength of our theorem is that we do not need to assume that the manifolds in question are commensurable. Our third result is independent of the first two. Kesten theorem asserts that if Gamma is group generated by a finite symmetric set S and N is a normal subgroup of Gamma then N is amenable if and only if the spectral radii of the Cayley graphs Cay(Gamma, S) and the Schreier graph Sch(Gamma/N,S) are equal. Building on the work of Abert, Glasner and Virag we extend Kesten's theorem to uniformly recurrent subgroups.
Microclimatic characteristic of the Chelevechnitsata cave in Western Rhodopes

Chelevchinitsa Cave is formed in the southwestern part of the Dobrostan karst region, Western Rhodopes. It has been subjected to microclimatic monitoring since April 2016. Air temperature, cave water and sediments are periodically measured at eight points, and thus the air humidity is determined. A micro-climatic zoning of the cave was being done on the basis of the analysis of the data on the temperature regimes for the period 2016–2018. A link has been sought between cave microclimate and global climate change using available data for the period 1979–2017 . The issue of the influence of the anthropogenic load on the speleomicroclimate as well as the impact of the cave enviorment on the health and safety of the tourists is also discussed. Particular attention is paid to the role of cave ventilation on the gas composition of the cave atmosphere, and especially on the regime of carbon dioxide (CO2 ) and radon (222Rn).
Machine Learning Based Image Recognition Technology

After decades of machine literacy research, image recognition, position discovery, image bracket, image product, audio recognition, natural language processing, and others remain unsolved. Deep literacy's most fundamental study direction is image categorization. concurrently, intelligent computer picture technology allows progressive enhancement in response to transnational links and the development and progress of multiple disciplines. Therefore, machine literacy-based technology for image processing is popular in a variety of industries and is utilised extensively in point image, bracket, segmentation, and recognition. The complexity of VHS filmland and the placement of items over colourful operation backgrounds make bracket delicacy noteworthy and sensitive. The paper transportation assiduity uses image recognition technology to recognise licence plates from complex backgrounds, honour licence plate characters, and create a machine learning non-license plate automatic generation algorithm to improve non-license plate recognition. Diverse and fast-created licence plate training sample sets teach powerful classifiers. A method that optimises a BP neural network to categorise licence plates improves anti-interference and identification delicacy
Quadratic Majorization for Nonconvex Loss with Applications to the Boosting Algorithm

ABSTRACT Classical robust statistical methods dealing with noisy data are often based on modifications of convex loss functions. In recent years, nonconvex loss-based robust methods have been increasingly popular. A nonconvex loss can provide robust estimation for data contaminated with outliers. The significant challenge is that a nonconvex loss can be numerically difficult to optimize. This article proposes quadratic majorization algorithm for nonconvex (QManc) loss. The QManc can decompose a nonconvex loss into a sequence of simpler optimization problems. Subsequently, the QManc is applied to a powerful machine learning algorithm: quadratic majorization boosting algorithm (QMBA). We develop QMBA for robust classification (binary and multi-category) and regression. In high-dimensional cancer genetics data and simulations, the QMBA is comparable with convex loss-based boosting algorithms for clean data, and outperforms the latter for data contaminated with outliers. The QMBA is also superior to boosting when directly implemented to optimize nonconvex loss functions. Supplementary material for this article is available online.
Direct numerical simulations of multifluid flows in a vertical channel undergoing topology changes

Multifluid flows in a vertical channel are examined by direct numerical simulations, for situations where the topology of the interface separating the different fluids changes. Several bubbles are initially placed in a turbulent channel flow at a sufficiently high void fraction so that the bubbles collide and the liquid film between them becomes very thin. This film is ruptured at a predetermined thickness and the bubbles are allowed to coalesce. For the cases with high surface tension the bubbles continue to coalesce, eventually forming one large bubble. At low surface tension, on the other hand, the large bubbles break up again, sometimes undergoing repeated coalescence and breakup. The evolution of various integral quantities, such as the average flow rate, wall-shear, and interface area are monitored and compared for different governing parameters. Averages of the flow field and the phase distribution over planes parallel to the walls are also examined, and the microstructure, at statistically steady state, is examined using low order probability functions.
Development of Numbers Material Test Using the Item Response Theory (IRT) Approach for SD Students

The purpose of the research is to produce a product in the form of a valid and reliable measuring instrument for student numeracy that can be used in schools and in the general public. The research stages will be carried out based on the test development design, namely Preparing Test Specifications, Preparing Test Items, Testing Test Items in the Field, Revision of Test Items
Consistent significance controlled variable selection in high‐dimensional regression

In regression analysis, selecting, out of a pool of available predictors, those that compose the true underlying data‐generating mechanism is a fundamental part of model building. This paper introduces a forward selection method that uses a novel entry criterium based on a combination of p‐values of the predictors already selected. Moreover, the proposed variable selection procedure controls the significance of all selected predictors at each step using False Discovery Rate corrections (or Bonferroni, or other correction criteria). Monte Carlo simulations suggest that the proposed method performs competitively against classical competitors. The proposed variable selection procedure is illustrated on a real data set.
New approaches for high-dimensional multivariate GARCH models

This document contributes to high-dimensional statistics for multivariate GARCH processes. First, the author proposes a new dynamic called vine-GARCH for correlation processes parameterized by an undirected graph called vine. The proposed approach directly specifies positive definite matrices and fosters parsimony. The author provides results for the existence and uniqueness of stationary solution of the vine-GARCH model and studies its asymptotic properties. He then proposes a general framework for penalized M-estimators with dependent processes and focuses on the asymptotic properties of the adaptive Sparse Group Lasso regularizer. The high-dimensionality setting is studied when considering a diverging number of parameters with the sample size. The asymptotic properties are illustrated through simulation experiments. Finally, the author proposes to foster sparsity for multivariate variance covariance matrix processes within the latter framework. To do so, the multivariate ARCH family is considered and the corresponding parameterizations are estimated thanks to penalized ordinary least square procedures.
Vehicle self-localization in GPS-denied zones by multi-band imaging and analysis of prominent scene features

The ability to reliably and accurately ascertain a vehicle’s position is imperative for military operations as well as civilian and commercial navigation systems. Due to the susceptibility of GPS signals to RF spoofing and jamming, alternative means of vehicle self-localization are garnering substantial interest. Vision-based methods are among the most promising in environments with sufficiently distinguishable features such as towers, high-rise structures, and prominent identifiable topographical features. Here, we present a localization approach exploiting multiple spectral bands to identify key prominent scene features and determine vehicle position relative to those features to calculate a global vehicle position and heading. We employ geometric dead-reckoning using visible and LWIR imagery to quantify positional accuracy that is achievable with these bands. We utilize image recognition algorithms to identify features and map these into useful parameters for position extraction, leveraging geospatial data when possible.
Unsupervised sparsity-based unmixing of hyperspectral imaging data using an online sparse coding dictionary

Due to the low spatial resolution of the hyperspectral cameras, the acquired spectral pixels are mixtures of present materials in the scene called endmembers. All spectral pixels are assumed to be mixtures of these endmembers with different amounts called abundances. Unmixing of the spectral pixels is a very important task for the analysis of these hyperspectral data cubes. Unsupervised unmixing aims to estimate the endmembers signatures and their abundances in each pixel without any prior knowledge about the given cube. Sparsity is one of the recent approaches used in the unmixing techniques. Solving the basis pursuit problem could be used as a sparsity-based approach to solving this unmixing problem where the endmembers are assumed to be sparse in a domain known as a dictionary. The choice of an appropriate dictionary is important for obtaining sparser representations of the given spectral pixels for better unmixing results. Two main approaches of dictionaries for sparse representation; analytic dictionaries approach and learned custom dictionaries approach. The contribution of this paper is the use of the recent online sparse coding dictionary as a learned custom dictionary for more fitting of the spectral pixels in solving the basis pursuit unmixing problem. While using the online sparse coding algorithm is more computational than any predefined transform family, but it has the advantage of no need for choosing a suitable family for the given spectral pixels. The online dictionary learning was successfully used in solving the basis pursuit unmixing problem to unmix both real AVIRIS data cube obtained from AVIRIS-NASA website and a synthetic cube made up from few materials selected from the given ASTER spectral library.
Simultaneous inference for treatment regimes

ABSTRACT A viable dynamic treatment regime refers to decisions regarding how different treatments and dose levels are tailored through time to match with the patient’s health status. In the therapy for cancer or diseases that require multiple stages of treatments, the effect of preceding treatment (such as growing back of solid tumors or regime-related toxicity) critically influences the selection of treatment in the following stage. So far, analyses of dynamic regimes mainly focus on marginal mean models under the assumption of sequential randomization in a clinical trial. Inference conclusions regarding multiple regimes are normally conservative due to different combinations in the formation of treatment regimes. In this article, we propose a simultaneous confidence interval method to identify treatment regimes that are significantly different from the bulk of the treatment combinations. The new method is applied to analyze the dynamic treatment regimes (DTRs) prostate cancer trial which includes treatment combinations of four chemotherapies at multiple stages. The new method detects a discernible effect of the regime taxane–estramustine–carboplatin (TEC) followed by cyclophosphamide, vincristine, and dexamethasone (CVD), when it is compared with other four regimes starting with CVD.
Vision-based control of an underactuated flying robot with input delay

In this paper a vision-based tracking controller is designed for the quadrotor vertical take-off and landing of an unmanned aerial vehicle. An imaged-based visual servoing approach is utilised to localise the quadrotor with respect to a moving target. Perspective image moments are used to define the visual features, which are projected on a rotated image plane to simplify the image dynamics. Attitude information and angular velocities are assumed to be available and the controller uses the flow of image features as the linear velocity cue. Presence of delay in processing and communication is modelled as a constant time delay in the force input of the translational dynamics, where a controller is designed for theses dynamics to compensate the delay effect. This controller is saturated in order to meet the quadrotor model constraint. A dynamic surface control approach is utilised for the rotational dynamics to track the desired attitude, defined through the position control loop. The stability properties of the complete control scheme are analysed using a theory of nonlinear cascaded systems. Simulation examples are provided in both nominal and perturbed conditions which show the effectiveness of the proposed theoretical results.
Development of an expert system based on fuzzy logic as support for heat pipes design

Heat pipe design and selection require specialist knowledge due to numerous possible combinations and restrictions that should be taken into account. The general objective of this work is to design a Specialist System that assists future engineers in material and working fluid selection for a suitable heat pipe application, based on the technical operating requirements. The methodology consisted of a qualitative perspective through interviews with two specialist engineers in the heat pipes area. The resulting information from the interviews was organized into a library, working as a source for the specialist system. In addition, several books from the literature completed the information in the library. Based on the operating conditions and the provided library, the program recommends suitable materials and working fluids and the necessity of porous media for the application, similar to a consult with a heat pipe specialist. The new expert system can be a tool for researchers and engineers in heat pipe design as passive control systems, providing more suitable solutions for each application
La educación para el cambio climático como herramienta de mitigación

Los datos proporcionados por los informes de evaluación del Grupo Intergubernamental de Expertos sobre el Cambio Climático (IPCC) no dejan dudas sobre la crisis climática que enfrenta el planeta y la humanidad como parte de este. Es por esto que se vuelve esencial tomar medidas de mitigación del cambio climático para frenar esta crisis. Una de las medidas de mitigación que se presenta con grandes posibilidades de cambio es la Educación para el Cambio Climático, por lo que es importante evaluar sus posibilidades de mitigación. En Uruguay no hay investigaciones relacionadas con la Educación para el Cambio Climático específicamente, sino algunas relativas a la Educación Ambiental, aunque no de manera sistemática. Esta investigación tuvo como objetivo desarrollar un programa de Educación para el Cambio Climático para alumnos de secundaria con el fin de evaluar el potencial de mitigación que este puede tener. Para esto se aplicó el programa en alumnos de 1º año de secundaria en una institución privada y se evaluaron mediante encuestas y observaciones los cambios de actitudes y hábitos de mitigación antes y después de la aplicación del mismo. Los resultados mostraron que las actitudes hacia el cambio climático mejoraron y los alumnos comenzaron a desarrollar más hábitos de mitigación y demostraron más conciencia e interés en aplicarlos a futuro. Esto evidencia que un programa de Educación para el Cambio Climático puede ser beneficioso para promover la mitigación del cambio climático, por lo que es importante profundizar la investigación en este campo utilizando distintas metodologías.
Bootstrap standard error estimations of nonlinear transport models based on linearly projected data

ABSTRACT Linear data projection is a commonly leveraged data scaling method for unbiased traffic data estimation. However, recent studies have shown that model estimations based on linearly projected data would certainly result in biased standard errors. Although methods have been developed to remove such biases for linear regression models, many transport models are nonlinear regression models. This study outlines the practical difficulties of the traditional approach to standard error estimation for generic nonlinear transport models, and proposes a bootstrapping mean value restoration method to accurately estimate the parameter standard errors of all nonlinear transport models based on linearly projected data. Comprehensive simulations with different settings using the most commonly adopted nonlinear functions in modeling traffic flow demonstrate that the proposed method outperforms the conventional method and accurately recovers the true standard errors. A case study of estimating a macroscopic fundamental diagram that illustrates situations necessitating the proposed method is presented.
Eliciting Binary Performance Metrics

Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of metric elicitation. In particular, we focus on eliciting binary performance metrics from pairwise preferences, where users provide relative feedback for pairs of classifiers. By exploiting key properties of the space of confusion matrices, we obtain provably query efficient algorithms for eliciting linear and linear-fractional metrics. We further show that our method is robust to feedback and finite sample noise.
Spectral Study of A1Π–X1Σ+ Transitions of CO Relevant to Interstellar Clouds

Highly correlated ab initio calculations were performed for an accurate determination of the A1Π–X1Σ+ system of the CO molecule. A highly accurate multi-reference configuration interaction approach was used to investigate the potential energy curves (PECs) and the transition dipole moment curve (TDMC). The resultant PECs and TDMC found by using the aug-cc-pV5Z (aV5Z) basis set and 5330 active spaces are in good agreement with the experimental data. Moreover, the Einstein A coefficients, lifetimes, ro-vibrational intensities, absorption oscillator strengths, and integrated cross sections are calculated so that the vibrational bands include v″ = 0–39 v′ = 0–23. For applications in the atmosphere and interstellar clouds, we studied the transition lineshapes to Gaussian and Lorentzian profiles at different temperatures and pressures. The intensities were calculated at high temperature that was used to satisfy some astrophysical applications, such as in planetary atmospheres. The results are potentially useful for important SAO/NASA Astrophysics Data System and databases such as HITRAN, HITEMP, and the National Institute of Standards and Technology. Because the results from many laboratory techniques and our calculations now agree, analyses of interstellar CO based on absorption from A1Π–X1Σ+ are no longer hindered by present spectral parameters.