Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved QS algorithm for pattern matching of bit stream

On the condition of bit stream, for the reason of simple character sets of target strings and Pattern strings, the probability of appearance of absolutely bad character is very low. The bad character shift is not suited for pattern matching, because of it's low efficiency. This paper gives the definition of bad string on the base of bad character shift, as well as comes up with bad sting shift. In the improved QS algorithm, the bad character shift is regarded as the trigger condition of bad string shift, and the bad string shift can expand the average skip length of bad character shift. In this way it can make the matching widows skip longer.
Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).
Numerical Investigations on a Small-Scale Air-Slide Conveyor

This study reports upon numerical investigations on a small-scale air-slide conveyor (ASC) model that is based on a full-scale industrial ASC currently in operation. For validation of the ASC model and prediction of details of cement flow distribution on time-basis, both steady-state and transient simulations are performed. Simulation results demonstrate the development of cement flow phenomenon and predict critical flow features in the system, which will be useful for predicting corresponding behavior in the full-scale industrial ASC. Mass balance is achieved and potential optimization technique is supported by simulation data. Both steady-state converged solution and results on time-basis are discussed. This study demonstrates the validity of ASC model design and paves the way for using similar numerical tools for future particle-laden flow studies. Lastly, it also offers design and optimization insights into such industrial ASC systems.
A fully coupled computational framework for fluid pressurised crack evolution in porous media

10 This paper presents a computational framework for modelling hydraulic fracture on the basis 11 of combining continuum porous media and damage theories. By considering the continuum 12 as two separate domains of damaged and intact porous domains, model components are 13 isolated and considered separately. This simplifies the whole modelling approach. The 14 mathematical model used consists of a set of coupled partial differential equations in 15 continuum space that govern compressible flow in damaged and intact porous media, 16 mechanical deformation of the domains and damage evolution. We particularly focus on the 17 flow of fluid within the intact and damaged porous zones. The porous domain typically has a 18 lower permeability than the fractured zone, therefore a more complicated flow of fluid is 19 expected within the damage zone. To model the exchange of fluid in the interface of damage 20 zone and intact porous domain, a double permeability concept has been utilized. The 21 evolution of cracks is modelled using Francfort and Marigo’s variational theory which 22 approximates the fracture by a diffusive damage zone using a phase field variable. The 23 governing model equations are discretised and solved using finite element method. The 24 framework capabilities are verified using experimental data from a one-dimensional 25 consolidation test and a plane stress pressured penny crack benchmark example. The 26 framework performance highlights its capabilities in analysing hydraulic driven fracture 27 process and the associated permeability variations.
Automatic Generation of Basis Path Set in Path Test

Path test is an important white-box test method,which can detect more program errors than others.Basis path set contains so many program paths that it will cover all the statements and branches in a program.It gains the better result by testing all the paths in a basis set under the limited test resource.This paper proposes an automatic generation of basis path set method based on depth-first searching.In order to avoid the condition that the algorithm never stops and reduces the searching procedure,and to improve its efficiency,the generation of sub-path method is adopted.
Etude des mécanismes fondamentaux d'interaction entre impulsions laser ultra-brèves et matériaux diélectriques

L'interaction entre impulsions lasers ultra-breves et materiaux dielectriques est un sujet d'etude en constant renouvellement, motive aussi bien par la naissance d'une multitude d'applications (micro-usinage laser, operations de la cornee, .) que par ses aspects fondamentaux (generation d'harmoniques d'ordre eleve, electronique au cycle optique, .). Pourtant, les mecanismes sous-jacents a cette interaction sont encore mal compris, en particulier d'un point de vue quantitatif. En effet, une bonne partie des phenomenes ont lieu durant le passage de l'impulsion laser, c'est-a-dire pendant quelques dizaines de femtosecondes. De surcroit, les eclairements impliques dans l'interaction sont eleves : quelques dizaines de TW/cm^2. La comprehension et la quantification des processus elementaires ayant lieu durant l'interaction requiert donc de concevoir des experiences sur mesure ainsi que d'effectuer un travail approfondi de modelisation. Dans ce manuscrit sont presentees deux techniques experimentales complementaires utilisees durant la these : l'interferometrie frequentielle et la spectroscopie d'absorption resolues en temps. Ces deux techniques sont employees pour etudier les processus electroniques d'excitation et de relaxation dans trois materiaux distincts : le quartz, le saphir et l'oxyde de magnesium. La quasi-integralite des resultats experimentaux sont analyses a l'aide d'un modele en equation de taux multiples (MRE) permettant de discriminer l'importance relative de chacun des processus pris en compte. Tout d'abord, une preuve experimentale directe de l'existence de l'ionisation par impact dans le quartz est presentee. Ces resultats ont ete obtenus par l'exposition de l'echantillon a une serie de deux impulsions, ce qui permet de moduler independamment la densite et la temperature du plasma. Les resultats experimentaux sont reproduits avec succes par le modele MRE integrant l'ionisation multiphotonique, le chauffage des porteurs photo-excites ainsi que l'ionisation par impact comme processus d'excitation. Nous nous concentrons ensuite sur une seconde serie de resultats concernant la relaxation du saphir apres excitation induite par laser. Un nouveau mecanisme de relaxation est propose et teste par la modelisation pour tenter d'expliquer la dynamique de ce processus. Ce mecanisme implique la formation d'excitons auto-pieges puis leur recombinaison. Enfin, le modele MRE est applique a la detection de seuils d'ablation dans les trois solides. Le choix du critere physique determinant ce seuil, sujet soumis a d'intenses debats dans la litterature, est alors discute.
Morphological segmentation for speech processing in Basque

Morphological information is traditionally used to develop high quality text to speech (TTS) and automatic speech recognition (ASR) systems. The use of this information improves the naturalness and intelligibility of the TTS synthesis and provides an appropriated way to select lexical units (LU) for ASR. Basque is an agglutinative language with a complex structure inside the words and the morphological information is essential both in TTS and ASR. In this work an automatic morphological segmentation tool oriented to TTS and ASR tasks is presented.
Uma ferramenta para monitoramento da entropia de mudança e sua relação com métricas de software

Contexto: A entropia de mudanca e uma medida para indicar o quanto um software sofre alteracoes em um determinado periodo de tempo. Estudos mostraram que o aumento da entropia pode causar desordem no processo de desenvolvimento podendo levar ao aumento no numero de defeitos do software. Dado esse contexto, nao existem ferramentas que oferecam suporte para monitoramento da relacao entre entropia e diferentes metricas de software, como por exemplo, numero de autores que modificaram um arquivo, numero de commits, authorship e ownership. Objetivo: Implementar e avaliar uma ferramenta que possibilite o monitoramento da entropia e das metricas de softwares de projetos armazenados no Github, para ajudar os desenvolvedores no gerenciamento de projeto. Ferramenta: A ferramenta e dividida em coleta de dados, calculo da entropia e das metricas, calculo da correlacao de Spearman e visualizacao de dados. Na coleta de dados, os dados sao extraidos por meio da API Github e em seguida e realizado o calculo da entropia, metricas de software e da correlacao Spearman entre a entropia e as metricas. Na etapa de visualizacao de dados, os valores da entropia de mudanca e das metricas de software sao exibidos utilizando graficos de Treemap, Heat Map e series temporais. A ferramenta foi avaliada por meio da analise das visualizacoes geradas sobre o historico do projeto Angular. Resultados: Na analise da ferramenta, foi possivel localizar arquivos mais relevantes do projeto, medindo o valor de entropia de mudanca e gerando a visualizacao de Treemaps em diferentes periodos. Calculando a correlacao de Spearman e analisando a visualizacao de Heat Map em diferentes periodos foi possivel observar quais metricas apresentam maior nivel de correlacao com o valor de entropia dos arquivos. Conclusoes: Foi concluido que a ferramenta pode ser utilizada para encontrar os arquivos que merecem maior atencao dos desenvolvedores no projeto, devido ao valor alto da entropia e das metricas correlaciondas a entropia.
Modeling and Forecasting the Volatility of Gasoline Prices Using Symmetric and Asymmetric GARCH Models in Erbil City

The paper aims to compare the performance of several univariate symmetric and asymmetric GARCH volatility models in modeling and forecasting the volatility of daily Gasoline prices in Erbil city. This paper chooses the GARCH, GARCH-M, TGARCH, E-GARCH and Power GARCH model to analyze the daily return of Gasoline under three different error distributions: normal distribution, student-t distribution and generalized error distribution and then compare the results and choose the appropriate model to forecast the volatility. The sample is divided into two subsamples. The first subsample is called in-sample data set (Training sample) used to estimate the ARMA-GARCH models for underlying data and the second subsample is called out-sample data set (Testing sample) used to investigate the performance of volatility forecasting. As a result of analyses, we conclude that the best model fits the volatility of Gasoline returns series is AR(2)-Power GARCH(2,1,1) non-linear asymmetric model with innovation student-t distribution (d.f =10), and has better forecasting performance than others models. This result is important in many fields of finance such as investment decisions, asset pricing, portfolio allocation and risk management. Keywords : Conditional Variance; Volatility Clustering; Symmetric and Asymmetric GARCH Models; Error Distribution; Volatility Forecasting; Root Mean Square Error (RMSE)
Évaluation métrologique d'algorithmes de corrrélation d'images numériques

Optical full field measurement techniques based on digital image correlation get more and more popular. We propose a methodology for the assessment of the metrological performances of the image processing algorithms that constitute their central component, the knowledge of which is required for a global assessment of the whole measurement system. It is based on the generation of synthetic images representative of real speckle patterns, deformed according to a sinusoidal displacement field with varying amplitude and period, and the statistical analysis of the discrepancies between imposed and measured values. It has been applied to various softwares used in the french community. Results show general tendencies rather independent from the implementations but strongly correlated with the assumptions of the underlying algorithms. Various error regimes are identified, for which the dependence of the accuracy with the parameters of the algorithms, such as correlation window size or shape functions can be precised.
Improving the Transverse Resolution of Optical Coherence Tomography with a Finite Impulse Response Filter and a Series of Numerically Refocused Images

Among the numerous methods for improving the informative value of the optical coherence tomography (OCT), a special place is taken by the methods for increasing the spatial resolution of the resulting images. Increasing the resolution allows one to identify more clinically significant structures in OCT images and thus improve the diagnostic value of OCT. Since the transverse resolution of OCT images is determined by the physical principles different from those for the longitudinal resolution, the ways of their improvement are also different. The aim of the study is to develop a method for increasing the transverse resolution of OCT by using a finite impulse response (FIR) filter for numerical refocusing and by combining the numerically refocused images. Results. We have developed a FIR filter able to transfer the focal plane of an OCT image using the data from 17 consecutive measurements of the field scattered by the object. In addition, we propose an original method for automatic synthesis of the final OCT image with an improved transverse resolution over the entire object depth from a series of images having different focal plane positions. This will enable the OCT instrument to produce a sharply focused beam for the scanning and thus obtain images with an improved transverse resolution in the focal plane, restore the required resolution in the out-of-focus areas using numerical transfer of the focal plane, and also synthesize the final OCT image with an improved transverse resolution. The method has been tested with model objects using an OCT device operating at a central wavelength of 1 μm with the 60 nm range and a beam focused onto a spot with a diameter of 5 μm. Conclusion. The proposed method of numerically increasing the transverse resolution of OCT images allows one to obtain images with an improved transverse resolution using only a small number of OCT measurements; in the future, it will allow for creating an OCT device that produces high-resolution images in real time.
Assessment water productivity of barley varieties under water stress by AquaCrop model

Drought is the major a biotic stress that reduces plant growth and crop productivity worldwide. AquaCrop model is one of the most widely used simulation models, which simulates the growth, yield and water productivity of the crops. Field experiment was carried out in the Experimental Research Station of Nubaria, during two seasons of 2016/2017 and 2017/2018. The study to assess water productivity of Mediterranean barley (Egyptian, Tunisian, Algerian and Morocco) varieties grown under water stress condition (40% of water holding capacity) compared to normal one (75% of water holding capacity) by AquaCrop model. Deviations (%) between observed under normal irrigation water and observed water deficit (stress), AquaCrop model normal and AquaCrop model stress, data obtained were (2.36, 1.84, 2.25 and 1.59%) for Egyptian barley varieties, respectively. For Tunisian barley varieties, deviations (%) were (2.06, 1.59, 2.78 and 3.62%), respectively. For Algerian barley varieties, were (2.12, 1.66, 2.88 and 3.71%), respectively. The percentage of absolute difference between the percentage of variation in the case of water stress and the case without water stress treatment was 5.36% under Egyptian varieties, followed by Morocco varieties (12.78%). Egyptian varieties are the least tolerant of water stress treatment where the percentage difference in the absolute difference between the cases is equal to 25%.
Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Data Imbalanced (Studi Kasus: Klasifikasi Rumah Tangga Miskin di Kabupaten Karangasem, Bali Tahun 2017)

Penelitian ini bertujuan untuk mendapatkan model terbaik untuk klasifikasi data imbalanced, yaitu rumah tangga sampel Susenas Maret 2017 di Kabupaten Karangasem, ke dalam kategori miskin atau tidak. Metode yang digunakan adalah Regresi Logistik dan Random Forest dimana masing-masing diterapkan skema cross validation (CV), yaitu stratified 5-fold CV, skema under sampling, oversampling dan combine sampling untuk mengatasi masalah data imbalanced serta proses feature selection. Hasil penelitian menunjukkan bahwa penerapan skema under sampling, oversampling dan combine sampling pada model Regresi Logistik memberikan efek meningkatnya rata-rata nilai sensitivity dan turunnya rata-rata nilai akurasi dan specificity. Sedangkan pada model Random Forest, efek tersebut hanya terlihat dari hasil skema under sampling saja. Proses feature selection dapat menurunkan varian nilai akurasi, specificity, sensitivity dan AUC pada model Regresi Logistik dan Random Forest hanya pada skema tertentu. Model terbaik secara keseluruhan adalah model model Regresi Logistik dengan skema combine sampling dan tanpa proses feature selection dengan rata-rata nilai akurasi, specificity, sensitivity dan AUC masing-masing sebesar 78,13%, 79,16%, 64,44% dan 77,77%.
Real-time analytics for complex structure data

The advancement of data acquisition and analysis technology has resulted in many realworld data being dynamic and containing rich content and structured information. More specifically, with the fast development of information technology, many current real-world data are always featured with dynamic changes, such as new instances, new nodes and edges, and modifications to the node content. Different from traditional data, which are represented as feature vectors, data with complex relationships are often represented as graphs to denote the content of the data entries and their structural relationships, where instances (nodes) are not only characterized by the content but are also subject to dependency relationships. Plus, real-time availability is one of outstanding features of today’s data. Real-time analytics is dynamic analysis and reporting based on data entered into a system before the actual time of use. Real-time analytics emphasizes on deriving immediate knowledge from dynamic data sources, such as data streams, and knowledge discovery and pattern mining are facing complex, dynamic data sources. However, how to combine structure information and node content information for accurate and real-time data mining is still a big challenge. Accordingly, this thesis focuses on real-time analytics for complex structure data. We explore instance correlation in complex structure data and utilises it to make mining tasks more accurate and applicable. To be specific, our objective is to combine node correlation with node content and utilize them for three different tasks, including (1) graph stream classification, (2) super-graph classification and clustering, and (3) streaming network node classification. Understanding the role of structured patterns for graph classification: the thesis introduces existing works on data mining from an complex structured perspective. Then we propose a graph factorization-based fine-grained representation model, where the main objective is to use linear combinations of a set of discriminative cliques to represent graphs for learning. The optimization-oriented factorization approach ensures minimum information loss for graph representation, and also avoids the expensive sub-graph isomorphism validation process. Based on this idea, we propose a novel framework for fast graph stream classification.
Proton Perpendicular Heating by Kinetic Alfvén Waves

The preferential heating of protons in the perpendicular direction with respect to the ambient magnetic field is a well-known phenomenon in the solar corona. One of the physical mechanisms proposed to explain such a feature is the nonresonant wave–particle interaction between protons and kinetic Alfvén waves. The present paper examines this mechanism by employing a novel analytical method based upon the Hamiltonian dynamical test particle approach. The analytical nature of the present method reveals an interesting fact that the pure inductive component of parallel electric field associated with the kinetic Alfvén wave in the limit Ti/Te ≪ 1 has little contribution. The parallel and perpendicular diffusion of protons by kinetic Alfvén waves is investigated through computing the autocorrelation function. The calculations unambiguously demonstrate that the proton heating is achieved mainly by nonresonant wave–particle interaction involving the wave magnetic field, and that the effects of the parallel electric field are minimal. It is also found that the perpendicular diffusion is orders of magnitude stronger than parallel diffusion, implying effective perpendicular heating.
Bioinspired Approaches for Autonomous Small-Object Detection and Avoidance

Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement-based solutions suffer either from restricted fields-of-view or significant computational complexity and are, hence, not easily portable to small platforms. In order to overcome these drawbacks, in this paper, two novel bioinspired approaches are proposed to extract information about small-field objects contained in planar optic flow. The first approach, which is analogous to the small-field extraction process hypothesized to occur in the lobula plate of the fly visual system, is based on the Fourier residual analysis of instantaneous optic flow. Alternatively, the flow-of-flow method is the engineering analogue of the small-field extraction process thought to occur in the fruit-fly's medulla, and extracts high-frequency content of optic flow by means of an elementary motion detector array. Both approaches extract instantaneous relative range and bearing of small-field obstacles from planar optic flow in a local environment characterized by small and wide-field obstacles, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bioinspired approach is computationally efficient, which renders extraction of vehicle velocity and local environment structure superfluous, and thus, serves as a robust, reflexive solution to the problem of small-object detection, and avoidance for small autonomous robots.