Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Automated Testing Framework for IoT Devices

As the Internet of Things (IoT) devices become increasingly available, there is an increasing demand for automated testing. Starting from the advantages of automated testing in the IoT, this paper first examines that automated testing has significantly improved compared to traditional manual testing in terms of test efficiency, test level, cost savings, and product stability. We then explain in detail the steps and key points of implementing automatic tests. Finally, a variety of automatic client frameworks are developed based on the Python language and connected to an automated testing environment, which has achieved significant benefits in equipment stability testing and performance testing.
AN IMPROVED GENERALIZED SPECTRAL TEST FOR CONDITIONAL MEAN MODELS IN TIME SERIES WITH CONDITIONAL HETEROSKEDASTICITY OF UNKNOWN FORM

Dynamic economic theories usually have implications on and only on the conditional mean dynamics of economic processes. Using a generalized spectral derivative approach, Hong and Lee (2005, Review of Economic Studies 72, 499–541) recently proposed a new class of omnibus nonparametric specification tests for linear and nonlinear time series conditional mean models, where the dimension of the conditioning information set may be infinite. The tests can detect a wide range of model misspecifications in mean while being robust to conditional heteroskedasticity and time-varying higher order moments of unknown form. They enjoy an asymptotic “nuisance parameter–free” property in the sense that parameter estimation uncertainty has no impact on the asymptotic N(0,1) distribution of the test statistics. As a result, only the estimated residuals from the null parametric model are needed to implement the tests, and no specific estimation is required. Although parameter estimation uncertainty has no impact on the asymptotic distribution of the tests, it may have significant impact on the finite-sample distribution, and such an impact may become more substantial as the number of estimated parameters increases. In this paper, we adopt the Wooldridge (1990, Econometric Theory 6, 17–43) device for parametric m-tests to the Hong and Lee (2005) nonparametric tests to reduce the impact of parameter estimation uncertainty. Asymptotic size and power properties of the modified tests are investigated, and simulation studies show that the modified tests generally have better sizes in finite samples and are robust to parameter estimation uncertainty. In the meantime, the size improvement does not cause loss of power against a wide range of alternatives when using the empirical critical values for the tests. These results suggest that the modified generalized spectral derivative tests can be a useful tool in time series conditional mean modeling.We thank Bruce Hansen (the co-editor) and three referees for insightful and helpful comments and suggestions. We also are grateful for financial support from the National Science Foundation Grant SES–0111769 and the Cheung Kong Visiting Scholarship by the Chinese Ministry of Education and Xiamen University, China.
An efficient scheme of intermodal distributed Raman amplification using tailored doping profiles in spatial-division multiplexed coherent fiber-optic transmission systems

For next-generation long-haul communication systems, space-division multiplexing (SDM) is suggested as a promising technique for providing orders of magnitude increase in transmission capacity. Optical amplifiers for multimode fibers are the crucial components to realize SDM systems, while few-mode fiber (FMF) has the advantage of strong intensity overlap between different modes that allows sharing of single pump across multiple signal modes. FMF-based erbiumdoped fiber amplifiers (FM-EDFA) have been intensively studied. Since SDM is exceedingly reliant on DSP technique and thus has more stringent requirement on noise performance, FMF-based distributed Raman amplifier (FM-DRA) benefits from relatively lower noise figure (NF) compared with FM-EDFA. Yet much less work has been done in this area. To implement FM-DRAs in SDM systems, their performance should be carefully optimized. In this paper, modal gain and saturation characteristics of intermodal Raman amplification in FMFs have been fully investigated by tailoring the refractive-index profiles and doping levels of different FMF designs. For 50-km-long FMF with -3 dBm signal at 1550 nm and a 20-25 dBm pump at 1455 nm, the optimized modal-equalization of Raman gain and NF are provided depending on various fiber cross-section and pumping configuration with respect to modes/wavelengths. The pumpsignal modal-overlap integrals for each of the four mode-groups with normalized frequency V=5 have been exploited, resulting in a mean gain of 10 dB within 1 dB of equalization for 16 gain coefficients. Our results show it should be possible to design FMF with larger intermodal nonlinearity and better dispersion characteristics to achieve desirable Raman gain.
Optimal adaptive control of uncertain stochastic linear systems

The problem of optimal control of stochastic linear time-invariant uncertain systems on finite time interval is formulated and solved. This optimal solution shows that previously published adaptive optimal control schemes and indirect adaptive control schemes do not need heuristics for their rationalization. It is shown that these schemes are suboptimal causal approximations of the optimal solution. The solution is achieved by the introduction of the state and parameters observability form (SPOF). This new representation of linear time-invariant systems enables direct application of the existing LQR-LQG theory of control and estimation of linear time-varying systems. The optimal solution is exact and non-causal. It is composed of a linear time varying optimal estimator of the augmented state composed of the state of the system and the parameters, and of the optimal LQR controller. The estimator is causal. The controller is non-causal. As causal approximation control schemes based on the SPOF and the certainty equivalence principle and separation theorems are presented.
Reconfiguration of distribution network based on improved particle swarm optimization

Distribution network reconfiguration for loss minimization is a complex,large-scale combinatorial optimization problem.In this problem,ensuring a radiant network is the key.In the paper,the configuration of distribution network is predigested,working with breaking-cycle-basis method to update the particles can gain 100% effective keys and greatly improve the speed of calculation.An improved binary particle swarm optimization applied to solve the distribution network reconfiguration problem is presented.Working with tabu search algorithm,this method can escape partial best key,improve the searching effect of basic PSO and accelerate the speed of searching the best keys.The optimization calculations of sixty-nine-node IEEE testing system by the presented method are conducted and the calculation results are compared with interrelated articles.Its compared results can show that the presented method has fast and high efficient ability to find the best key.
Approximation nichtlinearer prozesse mit Hinging Hyperplane Baummodellen

Due to their transparency, local model approaches represent a promising alternative to other well-known black-box model architectures. Local models approximate nonlinear functions by a number of simple submodels being valid in different regions of the input space. Major differences between local model approaches arise from the underlying partitioning scheme. In this paper the local linear Hinging Hyperplane Trees are presented which are characterized by an axis-oblique hierarchical partitioning of the input space. Based on this flexible partitioning and the possibility of a knowledge-based pre-structuring, these models are very well suited for higher-dimensional and strongly nonlinear processes. In this paper the Hinging Hyperplane Trees are utilized for the approximation of an exhaust gas component of modern combustion engines.
MUSIC:An Efficient Algorithm of Mining Frequent Subgraph Patterns in Uncertain Graph Databases

In recent years,there is a growing interest in mining frequent subgraph patterns in uncertain database.Solving this problem has two main difficulties.The first one is the large number of candidate subgraph patterns to be examined and the second one is the large number of subgraph isomorphism tests required to find the graphs that contain a given pattern.Although a typical algorithm computes expected support approximately,the cost required is still very high.This paper proposes an algorithm of mining frequent subgraph patterns in uncertain graph database.The algorithm relies on the apriori property for enumerating candidate subgraph patterns efficiently.Furthermore,an index can be used to reduce the cost of computing expected support.The paper also shows the efficiency of the algorithm with the realized experiments.
The Orbit-Stabilizer Problem for Linear Groups

Let G be a subgroup of the general linear group GL(n, Q) over the rational field Q, and consider its action by right multiplication on the vector space Q n of n-tuples over Q. The present paper investigates the question of how we may constructively determine the orbits and stabilizers of this action for suitable classes of groups. We suppose that G is specified by a finite set {x 1, …, xr ) of generators, and investigate whether there exist algorithms to solve the two problems: (Orbit Problem) Given u, v ∊ Q n , does there exist x ∊ G such that ux = v; if so, find such an element x as a word in x 1, …, xr and their inverses. (Stabilizer Problem) Given u, v ∊ Q n , describe all words in x 1, …, xr and their inverses which lie in the stabilizer
Sources of International Comparative Advantage: Theory and Evidence.

This is the first book to present a clear empirical picture of the international exchange of goods and of the resources that account for the exchanges that occur. It fully articulates the Heckscher-Ohlin theory of international comparative advantage, in which a country's factor endowments (land, labor, capital) play a crucial role in determining trade patterns. The theory is carefully link to the book's analysis. Using tables, graphs, and econometric data summaries, Learner describes the patterns of trade and the patterns of resource supplies of fifty-nine countries and explains these trade patterns in terms of the abundance of eleven resources. His study should create a standard by which other data analyses will be judged in the future.Edward E. Learner is Professor of Economics at the University of California at Los Angeles.
Design and implementation of flexible laboratory system for beam propagation study through weak atmospheric turbulence

Different applications such as astronomy, remote optical sensing and free space optical communications, among others, require both numerical and laboratory experimental simulations of beam propagation through turbulent atmosphere prior to an outdoor test. While rotating phase plates or hot chambers can be applied to such studies, they do not allow changing the atmospheric conditions and the propagation distance in situ. In contrast, the spatial light modulators (SLMs) are a flexible alternative for experimental turbulence simulation. In this work we consider an experimental setup comprising two SLMs for studying laser beam propagation in weak atmospheric turbulence. The changes of atmospheric conditions and propagation distances are properly achieved by the adjustment of the phase screens and the focal distances of digital lenses implemented in both SLMs. The proposed system can be completely automatized and all its elements are in fixed positions avoiding mechanical misalignment. Its design, propagation distance and atmospheric condition adjustment are provided. The setup performance is verified by numerical simulation of Gaussian beam propagation in the weak turbulence regime. The obtained parameters: scintillation index, beam wander and spreading are compared to their theoretical counterparts for different propagation distances and atmospheric conditions.
Étude du disque galactique par marquage chimique de ses populations stellaires

L'etude de la composition chimique et de la cinematique des etoiles de la Voie Lactee est essentielle afin de comprendre comment les grandes structures de notre Galaxie se sont formees. Les etoiles de faible masse gardent en memoire dans leur atmosphere la composition chimique du milieu interstellaire dans lequel elles sont nees, et leur cinematique est essentielle afin de caracteriser les differentes populations stellaires. Dans cette these, nous etudions le disque galactique, composante majeure de notre Galaxie. Dans le cadre de la mission spatiale Gaia, nous avons developpe une procedure automatique de mesure d'abondances chimiques, GAUGUIN, utilisee dans le cadre du Gaia-ESO Survey GES (abondances chimiques d'elements alpha et du pic du fer pour 10000 etoiles) et du projet AMBRE (abondances de lithium pour 7300 etoiles). GAUGUIN va etre integre au pipeline d'analyse des spectres RVS de Gaia. Nous avons etudie l'evolution des dispersions des vitesses dans le disque galactique en fonction du [Mg/Fe], utilise comme proxy de l'âge. A partir de 6800 etoiles de GES, nous avons detecte la presence d'etoiles du disque epais cinematiquement froides mais avec des valeurs elevees du rapport [Mg/Fe], donc possiblement âgees. Dans le contexte d'un milieu turbulent, nous discutons la presence de ces etoiles dans le cadre des differents modeles de formation du disque galactique. Nous avons egalement montre que l'abondance du lithium dans le disque montre une croissance avec la metallicite sur le domaine -1<[M/H]<+0 dex et decroit pour les metallicites super-solaires. Enfin, le disque mince et le disque epais seraient caracterises par des evolutions chimiques differentes en abondance de lithium.
Stochastic phase dynamics in neuron models and spike time reliability

The present thesis is concerned with the stochastic phase dynamics of neuron models and spike time reliability. It is well known that noise exists in all natural systems, and some beneficial effects of noise, such as coherence resonance and noise-induced synchrony, have been observed. However, it is usually difficult to separate the effect of the nonlinear system itself from the effect of noise on the system’s phase dynamics. In this thesis, we present a stochastic theory to investigate the stochastic phase dynamics of a nonlinear system. The method we use here, called “the stochastic multi-scale method”, allows a stochastic phase description of a system, in which the contributions from the deterministic system itself and from the noise are clearly seen. Firstly, we use this method to study the noise-induced coherence resonance of a single quiescent “neuron” (i.e. an oscillator) near a Hopf bifurcation. By calculating the expected values of the neuron’s stochastic amplitude and phase, we derive analytically the dependence of the frequency of coherent oscillations on the noise level for different types of models. These analytical results are in good agreement with numerical results we obtained. The analysis provides an explanation for the occurrence of a peak in coherence measured at an intermediate noise level, which is a defining feature of the coherence resonance. Secondly, this work is extended to study the interaction and competition of the coupling and noise on the synchrony in two weakly coupled neurons. Through numerical simulations, we demonstrate that noise-induced mixed-mode oscillations occur due to the existence of multistability states for the deterministic oscillators with weak coupling. We also use the standard multi-scale method to approximate the multistability states of a normal form of such a weakly coupled system. Finally we focus on the spike time reliability that refers to the phenomenon: the repetitive application of a stochastic stimulus to a neuron generates spikes with remarkably reliable timing whereas repetitive injection of a constant current fails to do so. In contrast to many numerical and experimental studies in which parameter ranges corresponding to repetitive spiking, we show that the intrinsic frequency of extrinsic noise has no direct relationship with spike time reliability for parameters corresponding to quiescent
MEAN SQUARE STABILITY IN A MODIFIED LESLIE-GOWER AND HOLLING-TYPE II PREDATOR-PREY MODEL

Of concern in the paper is a Holling-Tanner predator-prey model with modifled version of the Leslie-Gower functional response. Dynamical behaviours such as stability, permanence and Hopf bifurcation have been carried out deterministically. Using the normal form theory and center manifold theorem, the explicit formulae determining the stability and direction of Hopf bifurcation have been derived . The deterministic model is extended to a stochastic one by perturbing the growth equation of prey and predator by white and colored noises and flnally the mean square stability of the stochastic model systems is investigated analytically. An extensive quantitative analysis has been performed based on numerical computation so as to validate the applicability of the proposed mathematical model.
Litchi image segmentation algorithm based on ant colony and space constraints FCM

Accurate extracting of litchi fruits'complete contour is very important to automatic recognition and picking of robots.On the basis of ant colony and Fuzzy C-Means(FCM)clustering algorithm,an effective color images segmentation algorithm using the suitable Lab color space is proposed.The algorithm firstly uses of Lab color space that the positive and negative values of a channel represent red and green color for initial segmentation,and then determines the FCM initial clustering center according to ant colony algorithm,Finally,using the space constraints FCM completely segments litchi fruit.The results show that the proposed algorithm not only completely segments the litchis,but also meets the robot subsequent litchi recognition and picking,and the correct segmentation rate is up to 87%.
Study on the Standardization of Similarity Evaluation Method of Chromatographic Fingerprints (PartI )

Objective To study the standardization of similarity evaluation method of chromatographic fingerprints. Methods Computer simulation and HPLC method were used to investigate the characteristics of different similarity evaluation methods and the criteria of characteristic variables selection in chromatographic fingerprints. Results Cosine ratio and correlation coefficient should be the first choice for similarity calculation based on chromatographic peak area. Peak area is recommend to be used as the characteristic variable for chromatographic fingerprints. Conclusion The features of the commonly used chromatographic fingerprint evaluation methods are described and their range of application are defined.
Subtopic Division in News Topic Based on Latent Dirichlet Allocation

Nowadays it is difficult to distinguish the subtopics in a hot news topic on the internet.To solve this problem,in the paper,the method of subtopic division based on Latent Dirichlet Allocation is presented.It describes a news document by Latent Dirichlet Allocation,and uses Bayes standard method to determine the optimal number of topics in order to fit documents best.According to the high similarity of documents between subtopics,the relativity analysis of feature words is introduced.Using the improved Kullback-Leibler distance to calculate the similarity of news stories can distinguish the stories which have similar content but belong to different topics effectively.Finally,it divides a hot news topic to subtopics by clustering the news documents with the single-pass incremental clustering algorithm.Experimental results verify the availability of the improved similarity calculation method,and it shows that this method can improve the performance of subtopic division effectively comparing to the baseline method.
Minimal discrete curves and surfaces

In this thesis, we apply the ideas from combinatorial optimization to find globally optimal solutions arising in discrete and continuous minimization problems. Though we limit ourselves with the 1- and 2-dimensional surfaces, our methods can be easily generalized to higher dimensions. We start with a continuous N-dimensional variational problem and show that under very general conditions it can be approximated with a finite discrete spatial complex. It is possible to construct and refine the complex such that the globally minimal discrete solution on the complex converges to the continuous solution of the initial problem. We develop polynomial algorithm to find the minimal discrete solution on the complex and show that embeddability is a crucial property for such a solution to exist. Later on, we demonstrate that the spatial complex arising in the minimum weight triangulation problem is not embeddable. Linear programming becomes the main tool to investigate this problem and to develop a practical algorithm for constructing the minimum weight triangulation of a random point set. Finally, we consider two curve minimization problems arising in computer graphics. Both of them are described by the curves on the piece-wise linear triangulated surface. We revisit the geodesic problem, simplify and enhance the well-known exact algorithm and construct the fast approximate algorithm. We also show that the silhouette simplification can be done efficiently using the minimization techniques that we develop in the previous chapters. In many cases, the algorithms described in this thesis can be considered as a generalization of the classical Dijkstra's shortest path algorithm.
Semiconductor laser with filtered optical feedback: from optical injection to conventional feedback.

Abstract We study a model for a semiconductor laser subject to filtered optical feedback, that is a system of delay differential equations (DDEs). In this model the filter is characterised by a mean frequency Ωm and a filter width λ. In the limit of a narrow filter (λ → 0) the laser equations reduce to the equations for a laser with optical injection, whereas they become the Lang-Kobayashi equations in the limit of an unbounded filterwidth (λ→∞). We perform a bifurcation analysis of steady state solutions, with the parameter λ as main bifurcation parameter. In this way, we get insight in the relation between the filter width and the number of possible steady states. In particular we obtain the result, that there exist parameter regions in which the number of possible steady states decreases when λ is increased. From a mathematical point of view, our approach to combine information about limiting systems with precise bifurcation results may be of interest for a wider class of systems of DDEs.
Rationality Through Analogy - Towards a Positive Theory and Implementation of Human-Style Rationality

Humans are without any doubts the prototypical example of agents that can hold rational beliefs and can show rational behaviour. When modeling human decision-making, it seems reasonable to take the remarkable abilities of humans into account with respect to rational behaviour, but also the apparent deciencies of humans shining up in certain rationality tasks. Based on previous work on a computational system for analogy-making, we outline a possibility for implementing a system for simulating and, thus, predicting human-style rational behaviour.