Miracle-dz/UniFAR-FTUs huggingface dataset:
Gain-Enhanced Planar Log-Periodic Dipole Array Antenna Using Nonresonant Metamaterial

In this communication, a novel nonresonant metamaterial (MTM) structure is proposed to enhance the gain of the clamped-mode planar log-periodic dipole array (CMPLPDA) antenna fed by substrate integrated waveguide. The proposed MTM structures composed of a series of metallic semiring patches are printed on both sides of the antenna substrate, featuring low loss, broadband, and easy integration. Different arrangements of the nonresonant MTM with respect to the dipoles of the CMPLPDA antenna are investigated and compared. The proposed gain-enhanced method based on the nonresonant MTM has been studied through both numerical simulations and experiments. Compared to the original CMPLPDA antenna without any MTM inclusions, the proposed CMPLPDA antenna fully loaded with nonresonant MTM inclusions exhibits a gain enhancement of at least 1.6 dB (up to 4 dB at 27 GHz) within the operating band of 26–40 GHz in the simulations, which is also validated by the experiments.
Nyström subsampling method for coefficient-based regularized regression

Kernel methods are attractive in data analysis as they can model nonlinear similarities between observations and provide means to rich representations, both of which are useful for the regression problems in general domains. Despite their popularity, they suffer from two primary inherent drawbacks. One drawback is the positive definiteness requirement of the kernel functions, which greatly restricts their applications to some real data analysis. The other drawback is their poor scalability in massive data scenarios. In this paper, we aim to address these two problems by considering the Nyström subsampling approach for coefficient-based regularized regression (or Nyström CRR for short). Nyström subsampling is an effective approach to reduce the space and time complexity by constructing a low-rank approximation of the original kernel matrix through column subsampling. Coefficient-based regularized regression can provide a simple paradigm for designing indefinite kernel methods. We show that a combination of these two schemes is not only computationally efficient but also statistically consistent with a mini-max optimal rates of convergence. We explicitly determine the lower bound of subsampling level as a function of the sample size such that the mini-max optimal convergence rates can be preserved. From our analysis, the subsampling level plays a role as a trade-off between computational and asymptotic behaviors of Nyström CRR, and hence is pivotal for algorithmic performances. In order to choose an appropriate subsampling level, we develop an incremental Nyström CRR for nested subsampling sets. The proposed algorithm can greatly reduce the cost of cross-validation, and even allows to compute the whole solution path through all possible subsampling levels. Based on our empirical studies, the incremental Nyström CRR can perform effective model selections and achieve the state-of-the-art results on both synthetic and real data sets.
Approximations of the Heteroclinic Orbits Near a Double-Zero Bifurcation with Symmetry of Order Two. Application to a Liénard Equation

A dynamical system possessing an equilibrium point with two zero eigenvalues is considered. We assume that a degenerate Bogdanov–Takens bifurcation with symmetry of order two is present and, in the parameter space, a curve of heteroclinic bifurcation values emerges from the codimension two bifurcation point. Using a blow-up transformation and a perturbation method, we obtain second order approximations both for the heteroclinic orbits and for the curve of heteroclinic bifurcation values. Applications of our results for the truncated normal form and for a Liénard equation are presented. Some numerical simulations illustrating the accuracy of our results are performed.
Human detection and distance estimation with monocular camera using YOLOv3 neural network

Inimestega vahemalt samal tasemel keskkonnast aru saamine masinate poolt oleks kasulik paljudes domeenides. Mitmed erinevad sensored aitavad selle ulesande juures, enim on kasutatud kaameraid. Objektide tuvastamine on tahtis osa keskkonnast aru saamisel. Selle tapsus on viimasel ajal palju paranenud tanu arenenud masinoppe meetoditele nimega konvolutsioonilised narvivorgud (CNN), mida treenitakse kasutades margendatud kaamerapilte. Monokulaarkaamerapilt sisaldab 2D infot, kuid ei sisalda sugavusinfot. Teisalt, sugavusinfo on tahtis naiteks isesoitvate autode domeenis. Inimeste ohutus tuleb tagada naiteks tootades autonoomsete masinate laheduses voi kui jalakaija uletab teed autonoomse soiduki eest. Antud toos uuritakse voimalust, kuidas tuvastada inimesi ning hinnata nende kaugusi samaaegselt, kasutades RGB kaamerat, eesmargiga kasutada seda autonoomseks soitmiseks maastikul. Selleks taiustatakse hetkel parimat objektide tuvastamise konvolutsioonilist narvivorku YOLOv3 (ingl k. You Only Look Once). Selle too valiselt on simulatsioonitarkvaradega AirSim ning Unreal Engine loodud lumine metsamaastik koos inimestega erinevates kehapoosides. YOLOv3 narvivorgu treenimiseks voeti simulatsioonist valja vajalikud andmed, kasutades skripte. Lisaks muudeti narvivorku, et lisaks inimese asukohta tuvastavale piirikastile valjastataks ka inimese kauguse ennustus. Antud too tulemuseks on mudel, mille ruutkesmine viga RMSE (ingl k. Root Mean Square Error) on 2.99m objektidele kuni 50m kaugusel, sailitades samaaegselt originaalse narvivorgu inimeste tuvastamise tapsuse. Vorreldavate meetodite RMSE veaks leiti 4.26m (teist andmestikku kasutades) ja 4.79m (selles toos kasutatud andmestikul), mis vastavalt kasutavad kahte eraldiseisvat narvivorku ning LASSO meetodit. See naitab suurt parenemist vorreldes teiste meetoditega. Edasisteks eesmarkideks on meetodi treenimine ning testimine paris maailmast kogutud andmetega, et naha, kas see uldistub ka sellistele keskkondadele.
Using developmental sequences to estimate ability with English grammar: Preliminary design and investigation of a web-based test

The usefulness of open-access, web-based language learning resources can be enhanced considerably by aligning learner skill or proficiency levels with materials, activities, lessons, and other instructional media that are gauged accordingly. This paper reports on the design of an English grammar test for the purpose of making broad initial distinctions among learner ability levels, such that subsequent webbased assessment and learning tools could be articulated to their needs. In order to estimate initial learner differences within a very brief period of testing time, and in a format amenable to the online environment, developmental sequences in the acquisition of English morphosyntax were investigated as a basis for test design. Resulting test items tapped learners’ abilities to apply five word order rules in a variety of linguistic contexts; these items were piloted with 57 English learners, from pre-academic to university-entrance levels of proficiency. Findings indicated that the developmentally most advanced items (testing (a) the cancel-inversion rule in embedded questions and (b) relative clauses) discriminated well among the different proficiency levels in the population sample; in addition, relatively reliable twelve-item tests could be constructed on the basis of the items investigated. In conclusion, recommendations for follow-up web-based test implementation and validity evaluation are provided. Pilot test items and forms are also appended.
Does Foreign Aid Reduce Poverty?: Empirical Evidence from Nongovernmental and Bilateral Aid

This paper assesses the effectiveness of foreign aid in reducing poverty through its impact on human development indicators. We use a dataset of both bilateral aid and NGO aid flows. Our results show that NGO aid reduces infant mortality and does so more effectively than official bilateral aid. The impact on illiteracy is less significant. We also test whether foreign aid reduces government efforts in achieving developmental goals and find mixed evidence of a substitution effect.
Design and Development of a Video-Mediated Test of Communicative Proficiency

This paper discussed the development of the Kanda English Proficiency Test (KEPT) as a communicative test of English as Foreign Language for Japanese university students. The paper first considers the framework of communicative testing, examines each section of the test, and discusses issues related to the role of video in testing. ~£~=~-~~~~~~. ~~~ ~ ~7*~~mL~7A~~7~~~ tHft *. ~;t8*). :*~~(J )~(J):J ~ . :L. ::. 'T-y 3 ~mm~. t L,LM~~ tLt. : KEPT(WEB~mt$~~1t J)~m~~~~~~,~PT(J )~~~~ML"
Manipulation of Dirac points in graphene superlattices

This study theoretically investigates the changes in the energy spectrum of the graphene monolayer subjected to different periodic potential to allow manipulation of the energy spectrum. Floquet theory and the resonance approximation are used to analyse the energy spectrum. Thus, we reviewed the application of single laser potential; linearly polarised and circularly polarised and concluded that the gap opening in the spectrum is determined by the polarisation of the laser field. Then we apply a time periodic electric filed and found that such single potential is not enough to break the topological symmetry. We investigate the manipulation of the spectrum in 1- spatial periodic magnetic field and 2- linearly polarised laser beam with an external periodically modulated static magnetic/electric field. We investigated in particular, the creation and the destruction of the Dirac-Weyl points. We found that at certain conditions the graphene is transformed into the two-dimensional Weyl metals, where each of the two original graphene Dirac cones is split into pairs of the Weyl cones. We also show that altering the laser's beam incidence (tilting) angle may lead to appearing and disappearing of the pairs of Weyl points, the opening gap in the spectrum, and its efficient manipulation. Deformation and symmetry breaking can be achieved via different laser s frequencies and amplitudes, hence the anisotropy can be controlled.
Teaching Large Sections of a Business Communication Course

The purpose of this research is to examine specific examples of how business communication courses are delivered in large, face-to-face university classes to discover implications of these large courses. This case study reviewed four classes from two different midsized universities whose classes range from 48 to 300 students. Findings suggest that, when faced with the possibility of teaching more students, it is important to understand that pedagogical strategies may need to be adjusted to maintain student learning. These strategies include modifying the course to the lecture/lab structure, limiting the amount of writing, or allowing the instructor to teach fewer courses.
Ultrahigh Gain from Plasmonic Quantum Dot Nanolaser

This work studies the gain from quantum dot plasmonic nanolaser. A metal/semiconductor/metal structure was considered to attain plasmonic nanocavity with active region contains: quantum dot, wetting layer and barrier layers. Band alignment between layers was used to predict their parameters. Momentum matrix element for transverse magnetic mode in quantum dot structure was formulated. Waveguide Fermi energy was introduced and formulated, for the first time, in this work to cover the waveguide contribution (Ag metal layer) in addition to the active region. The gain obtained here overcomes the electron scattering losses which promises in high gain, high power and high speed applications. The waveguide Fermi energy goes deep in the valence band which explains the high gain, where it is shown that covering the structure by a metal makes valence band quantum dot states fully occupied which refers to an efficient hole contribution.
Peppers Of The World An Identification Guide

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Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery

ABSTRACT Efficiently representing and recognizing the semantic classes of the subregions of large-scale high spatial resolution (HSR) remote-sensing images are challenging and critical problems. Most of the existing scene classification methods concentrate on the feature coding approach with handcrafted low-level features or the low-level unsupervised feature learning approaches, which essentially prevent them from better recognizing the semantic categories of the scene due to their limited mid-level feature representation ability. In this article, to overcome the inadequate mid-level representation, a patch-based spatial-spectral hierarchical convolutional sparse auto-encoder (HCSAE) algorithm, based on deep learning, is proposed for HSR remote-sensing imagery scene classification. The HCSAE framework uses an unsupervised hierarchical network based on a sparse auto-encoder (SAE) model. In contrast to the single-level SAE, the HCSAE framework utilizes the significant features from the single-level algorithm in a feedforward and full connection approach to the maximum extent, which adequately represents the scene semantics in the high level of the HCSAE. To ensure robust feature learning and extraction during the SAE feature extraction procedure, a ‘dropout’ strategy is also introduced. The experimental results using the UC Merced data set with 21 classes and a Google Earth data set with 12 classes demonstrate that the proposed HCSAE framework can provide better accuracy than the traditional scene classification methods and the single-level convolutional sparse auto-encoder (CSAE) algorithm.
“You’re Fired,” Says the Robot

The rapid adoption of new technologies in the workplace, especially robotics and artificial intelligence (AI), has motivated some researchers to determine what effects such technologies may have. Few scholars, however, have examined the possibility that a large segment of the population is apprehensive about the quick pace of technological change and encroachment into modern life. Drawing from economic projections about the future of the digital economy and from literature in the sociology of technology and emotions, this article explores whether certain fears of technology exacerbate fears of unemployment and financial insecurity. Using data from Wave 2 of the Chapman Survey of American Fears (N = 1,541), I find that there exists a sizable population of “technophobes” or those who fear robots, AI, and technology they do not understand. Technophobes are also more likely than nontechnophobes to report having anxiety-related mental health issues and to fear unemployment and financial insecurity. With advances in robotics and AI, the threat of technological unemployment is discussed as a real concern among a substantial portion of the American population.
Data-driven techniques for mass appraisals. Applications to the residential market of the city of Bari Italy

The need for evaluation models capable of returning 'slender' and reliable mass appraisals of properties belonging to different market segments has been made mandatory by the events that are covering the global real estate finance, because of the emergence of non-performing loans in the banks' balance sheets. In Italy, the non-performing loans have been estimated by the Italian Banking Association equal to about 300 billion euro in 2014. In the present paper, three approaches of data-driven techniques hedonic price model, artificial neural networks and evolutionary polynomial regression have been applied to a sample of residential apartments recently sold in a district of the city of Bari Italy, in order to test the respective performance for mass appraisals. The models obtained by the implementation of the three procedures have been compared in terms of statistical accuracy, empirical compliance of the results and complexity of the functional relationships.
Student-t Processes for Degradation Analysis

Abstract Stochastic processes are widely used to analyze degradation data, and the Gaussian process is a particularly common one. In this article, we propose a robust statistical model using a Student-t process to assess the lifetime information of highly reliable products. This model is statistically plausible and demonstrates a substantially improved fit when applied to real data. A computationally accurate approach is proposed to calculate the first-passage-time density function of the Student-t degradation-based process; related properties are investigated as well. In addition, this article provides parameter estimation using the EM-type algorithm and a simple model-checking procedure to evaluate the appropriateness of the model assumptions. Several case studies are performed to demonstrate the flexibility and applicability of the proposed model with random effects and explanatory variables. Technical details, datasets, and R codes are available as supplementary materials.
Spectral properties of elliptic operators in singular settings and applications

The present thesis is focused on the investigation of the spectral properties of the linear elliptic operators in the presence of singularities. It is divided into three chapters. In the first chapter, we consider geometric singularities. We construct the heat kernel on surfaces with corners for Dirichlet, Neumann, and Robin boundary conditions as well as mixed problems. We compute the short time asymptotic expansion of the heat trace and apply this expansion to demonstrate a collection of results showing that corners are spectral invariants. The second chapter deals with linear elliptic second-order partial differential operators with bounded real-valued measurable coefficients. We emphasize that no smoothness assumptions are made on the coefficients. In the first half of this chapter, we study a time-harmonic electromagnetic and acoustic waveguide, modeled by an infinite cylinder with a non-smooth cross section. We introduce an infinitesimal generator for the wave evolution along the cylinder and prove estimates of the functional calculi of these first order non-self adjoint differential operators with nonsmooth coefficients. Applying our new functional calculus, we obtain a one-to-one correspondence between polynomially bounded time-harmonic waves and functions in appropriate spectral subspaces. In the second half, we derive Weyl’s law for the weighted Laplace equation on Riemannian manifolds with rough metric. Key ingredients in the proofs were demonstrated by Birman and Solomyak nearly fifty years ago in their seminal work on eigenvalue asymptotics. In the last chapter, we investigate spectral properties of Sturm-Liouville operators with singular potentials. We consider different types of singularities. We find asymptotic formulas for the eigenvalues of the Sturm-Liouville operator on the finite interval, with potentials having a strong negative singularity at one endpoint. We establish that, unlike the case of an infinite interval, the asymptotics for positive eigenvalues does not depend on the potential, and it is the same as in the regular case. The asymptotics of the negative eigenvalues may depend on the potential quite strongly. Next, we study the perturbation of the generalized anharmonic oscillator. We consider a piecewise Hölder continuous perturbation and investigate how the Hölder constant can affect the eigenvalues. Finally, for the the Sturm-Liouville operator with δ-interactions, two-sided estimates of the distribution function of the eigenvalues and a criterion for the discreteness of the spectrum in terms of the Otelbaev function are obtained.
Fast and accurate mean-shift vector based wavelength extraction for chromatic confocal microscopy

Fast and accurate peak wavelength extraction of chromatic confocal signals is of vital importance for efficient chromatic confocal measurement. Existing peak extraction algorithms do not effectively balance calculation efficiency and extraction accuracy. For example, centroid algorithms have high computational efficiency but low accuracy; model-based fitting algorithms have high extraction accuracy but low calculation efficiency. In this paper, a fast and accurate mean-shift vector based peak wavelength extraction algorithm is proposed. In this algorithm, the initial peak wavelength is preset with a centroid algorithm; then the corresponding mean-shift vector is calculated, after which, a new peak wavelength can be calculated by shifting the peak wavelength along the mean-shift vector. This process is then iteratively carried out until a preset ending condition is met. Our experimental results show that the mean-shift vector based peak extraction algorithm has an accuracy equal to that achieved with Gaussian fitting while the efficiency improves by over 70 times.
Group Processes In The Classroom

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The Role of Cross-Linguistic Influences in the Multilingual Communicative Competence Acquisition: A Classroom Research in the Greek Primary School Based on the Use of Free Online Linguistic Resources

The Council of Europe promotes the development of multilingual communicative competence, whereas many studies focus on the development of multilingual awareness in children. The main goal of our article is to study the effectiveness of online linguistic resources on the development of multilingual communicative competence, through a cross-linguistic study of several foreign languages. In specific, the necessary linguistic material was retrieved exclusively from free online resources. The survey was held over 117 5th and 6th graders in the Greek primary education. The activities presented the same linguistic content in four, unknown languages to the pupils: Dutch, Hungarian, Swedish and Portuguese. Internet offers very important auxiliary tools for teaching materials’ development for a multilingual awareness, provided that they are not used unreasonably, but in an appropriate way so as to equip teacher with valid teaching material. Analysis of the data received from both the pupils’ answer sheets but also from the verification process of the validity and effectiveness of the linguistic content retrieved from the online tools shows a significant difference between words and sentences’ translation accuracy, mainly regarding online translators with many languages. However, the analysis of the pupils’ worksheets shows relatively high scores, which means that online linguistic resources can be used only in accordance with the learning goals.