Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Independence Statistics to Goodness-Of-Fit, Multivariate Change Point Estimation and Clustering of Variables

Sebastian Jose Teran Hidalgo: Applications of Independence Statistics to Goodness-of-Fit, Multivariate Change Point Estimation and Clustering of Variables (Under the direction of Michael R. Kosorok and Michael C. Wu) Independence statistics try to evaluate the statistical dependence between two random vectors of general dimension and type. Independence statistics do not assume a speci c form of dependence, but they are sensitive to all forms of departures from independence. The current manuscript seeks to extend the use of independence statistics to three settings. In the rst part of the dissertation, we developed a goodness-oft test for smoothing spline ANOVA models, which are a nonparametric regression methodology with the useful property that the contribution of the covariates can be decomposed in a ANOVA fashion. The proposed method derives estimated residuals from the model. Then, statistical dependence is evaluated between the estimated residuals and the covariates using independence statistics. If no dependence exists, the model ts the data well. Application of the method is demonstrated with a neonatal mental development data analysis. In the second part, we develop a method for the change point problem where two sets of random vectors are observed sequentially over a dimension, but at some unknown point, the relationship between these two vectors changes. We propose a methodology to estimate the unknown change point without assuming a model. This is accomplished by assessing, with an independence statistic, the strength of the association before and
A Regulation and corpus-based English to Tibetan Machine translation system Design Model

Now days, in the Internet swiftly developing Information Age. We really more depend on exchanging information based on it, the main issue is that there is some obstacle between different languages. So, we need a machine translation to eliminate the obstacle between different languages urgently. The paper gives a regulation and corpus -based machine translation system design model, discusses the design method and principle. On choosing translation method, the paper elaborates the overall structure, translation algorithm and linguistic model, and then gives a further study to the design model.
Longitudinal predictions using regression-corrected grouping to reduce regression to the mean

Coarse-graining procedures make it possible to model the movement of large numbers of objects, such as particles in a fluid. Longitudinal student performance data can be modeled similarly by sorting students into score bins and following the flow of scores through time: trajectories depict the average scores over time for initial score bins and streamlines provide an approximate way to calculate the flow of student scores over many years based on only two consecutive years of data. However, due to the partially stochastic nature of observed scores, the coarse-graining procedure that sorts students into score bins amplifies a statistical phenomenon known as regression to the mean (RTM). As a result, streamlines do not provide an accurate prediction for the future performance of students. Here we discuss a new coarse-graining procedure, regression-corrected (RC) grouping, which reduces RTM in the streamlines. We apply RC streamlines to the Texas State Longitudinal Data System, which contains standardized testing data for students throughout primary and secondary school since 2003. We show that the RC streamlines accurately predict trajectories, using two or three years of data. Therefore RC streamlines can be used to identify the e↵ects of academic interventions on a time scale comparable to that of policy changes. We illustrate this assertion by examining a particular policy intervention, Texas’ Student Success Initiative.
Application of improved PSO in speaker identification

Aiming at the shortcoming of Particle Swam Optimization(PSO)which is easily relapsing into local extremum,an im-proved PSO is proposed in this paper.This approach applies the evolution speed factor as the Trigger conditions to stochastically disturb the local optimal solution.The improved PSO algorithm can not only improve extraordinarily the convergence velocity in the evolutionary optimization,but also can adjust the balance between global and local exploration suitably.Then a speaker identification approach using this improved algorithm to train SVM is presented.The SVM can receive the optimal hyper plane with less support vectors by the improved PSO,and then the training samples are reduced and the identification speed is improved.
Implementation of Role-based Access Control in Authentication Manager

Based on the authentication model for user, the multilayer- authentication model of authentication manager was advocated. Through deep analysis on the idea of multilayer- authentication model and establishing authentication policy making use of authentication manager, a lot of essential and important methods which using authentication manager in program ware advocated. Finally,the paper realized the user's authentication using authentication manager in program. It was shown that using authentication manager was easy to realize the user's authentication using authentication manager in program.
Three-process cascaded frequency conversion based on Stark-chirped rapid adiabatic passage

In the field of nonlinear frequency conversion, the cascaded frequency conversion scheme is an effective means to obtain new wavelengths. In this paper, we demonstrate efficient frequency conversion through three cascaded three-wave mixing processes based on Stark-chirped rapid adiabatic passage, which are different from previous cascaded wavelength conversion processes based on stimulated Raman adiabatic passage. Further, by analyzing the relevant parameters in the three-process cascaded frequency conversion models we have established, we find that the new scheme increases the conversion range and has lower restrictions on the conversion conditions while maintaining extremely low intermediate frequency intensity. These advantages allow our work to be applied to the acquisition of a mid-infrared or visible laser.
A Comparative Study of Data Mining Techniques for Predicting Disease Using Statlog Heart Disease Database

Data Mining (DM), frequently treated as synonymous to Knowledge Discovery in Databases (KDD) is actually a part of knowledge discovery process and is the process of extracting information including hidden patterns, trends and relationships between variables from large databases in order to make the information understandable and meaningful. The ultimate goal of data mining is prediction of unknown patterns and predictive data mining is the most common type of that which has the most direct real life applications. The process basically consists of three stages: (1) the initial data exploration, (2) model building or pattern identification with validation/verification process and (3) deployment of the data mining model. Therefore, in this research paper data mining techniques will be compared using the benchmark datasets. The different types of data classification methods and techniques are available such as Statistics, Visualization, Clustering, Decision Tree, Association Rule, Neural Networks, K-Nearest Neighbor Method and Genetic algorithms. The objective of this research paper is to do the comparative study and evaluation of decision tree, artificial neural network with the help of Statlog Heart Diseases Database collected from UCI machine learning repository. The advantages and disadvantages, of the data mining techniques depend on the capability and efficiency of the data mining techniques or algorithms to classify the large volume of database and predicting the relevant patterns for decision making process. The consequences of choosing any technique and the methods of implementation is very important factor. Data mining techniques such as Decision Tree and Artificial Neural Networks are used for the classification of Statlog heart disease datasets. These supervise machine learning algorithms are compared on the basis of classification accuracy and performance matrices.
Interactive Textbook--A New Tool in Off-Line and On-Line Education.

Implementation of ICT in the educational process has allowed us especially in the last decade to fully develop cognitive and psychomotor skills of students. A typical example is the use of multimedia, through which the learner gradually formed and developed their skills in the area of sensory perception. The paper is focused on one of the modern tools of education geography for interactive textbook for grammar (high) schools. The new generation of interactive IRS systems allows not only listen to audio files of the book, but the book also communicate with the computer. This function can be used in on-line teaching process, where the teacher receives immediate feedback regarding the work of students in class. The new generation of interactive textbooks it can perform the function of the voting equipment. Talking books as exercise books are intended primarily for testing and practicing curriculum.
HiCPlus: Resolution Enhancement of Hi-C interaction heatmap

Motivation The Hi-C technology has become an efficient tool to measure the spatial organization of the genome. With the recent advance of 1Kb resolution Hi-C experiment, some of the essential regulatory features have been uncovered. However, most available Hi-C datasets are in coarse-resolution due to the extremely high cost for generating high-resolution data. Therefore, a computational method to maximum the usage of the current available Hi-C data is urgently desired. Results Inspired by the super-resolution image technique, we develop a computational approach to impute the high-resolution Hi-C data from low-resolution Hi-C data using the deep convolutional neural network. We hypothesize that the Hi-C interaction heatmap contains the repeating features, and develop an end-to-end framework to map these features from low-resolution Hi-C heatmap to high-resolution Hi-C heatmap at the feature level. Our approach successfully reconstructs the high-resolution Hi-C interaction map from the low-resolution counterpart, which also proves that the Hi-C interaction matrix is a combination of the regional features. Besides, our approach is highly expandable, and we can also increase prediction accuracy by incorporating ChIA-PET data. Availability Source code is publicly available at https://github.com/zhangyan32/HiCPlus Contact jtang@cse.sc. edu, fyue@hmc.psu. edu
Smoothing processing method of local reference output speed in response to master reference speed error jump

The master reference speed error jump in shipboard missiles not only influence the alignment or calibration result of local reference,but also cause the corresponding output speed error jump of local reference,and this in turn influence the alignment process of slave INS that uses local reference speed as reference information for alignment.In response to this problem,a smoothing processing method of local reference output speed is presented.Based on Kalman filtering,the method uses residual chi-square test to effectively detect the master reference speed error jump.After the time of jump is found,the speed error states of Kalman filtering are used to smooth the local reference output speed with no error jump.From the simulations with three different master reference speed error jump conditions,the results show that local reference output speed error rate of change can be controlled to satisfy the alignment requirement of slave INS after using the smoothing processing method,which is valuable in engineering application.
Dynamics of a predator-prey system with prey refuge

In this paper, we investigate the dynamical complexities of a prey predator model prey refuge providing additional food to predator. We determine dynamical behaviours of the equilibria of this system and characterize codimension 1 and codimension 2 bifurcations of the system analytically. Hopf bifurcation conditions are derived analytically. We especially approximate a family of limit cycles emanating from a Hopf point. The analytical results are in well agreement with the numerical simulation results. Our bifurcation analysis indicates that the system exhibits numerous types of bifurcation phenomena, including Hopf, and Bogdanov-Takens bifurcations.
Algorithm of Implementing 3D Calibration Board-based Camera Calibration

A linear method for camera parameter calibration is studied by the mathematical model of camera on the basis of projective theory.The proposed method requires calibration board,the light spots that array in the calibration board is illuminated through the optical fiber by light source.The algorithm of camera calibration based on three dimensional calibration board is implemented by moving the calibration board precisely.
Long quasi-periodic oscillations of the faculae and pores

Aims. The main goal of this work is to analyze the structural and temporal evolution of small-scale magnetic structures (SSMSs) observed in the solar atmosphere, such as solitary faculae and pores, and reveal long quasi-periodic oscillations of these structures. Methods. The statistical method of regression analysis and the wavelet transform were used to obtain the periods of oscillations and dependences between the parameters of magnetic structures and periods of oscillations. Results. Long-period oscillations with periods in the interval of 18−260 min are found for the structurally stable phase of SSMSs at the level of the solar photosphere. These long-period oscillations were interpreted as natural oscillations of the structurally stable long-lived magnetic structures around their equilibrium position. These oscillations, which are of similar nature, are observed in the chromospheric bright formations associated with photospheric SSMSs. Dependences between the magnetic field and the continuum intensity of the facula elements were found. It is shown that the continuum intensity of a SSMS decreases when its magnetic field increases.
Signboard Recognition by Consistency Checking of Local Features

The problem of recognizing signboards in street scenes is defined as matching the input image to pre-stored 2D signboard images. This problem is not as simple as it appears to be due to arbitrary drawings and relative 3D positions. We approached this problem by matching characteristic local features of input image to those of images in the database. Local decisions are verified by the global viewpoint of the homographic consistency and color consistency. The well-known SIFT feature is used as a local feature and the homographic consistency checking is performed using RANSAC, a random sampling method. In order to handle highly perspective-distorted signboards, several perspective-transformed templates are generated offline. In our experiment, with a database of 35 images, our proposed method achieved 95% recognition rate, showing good results despite the highly distorted input images.
Hybrid particle swarm optimization algorithm for cost minimization in service-workflows with due dates

The cost minimization with due dates in service-workflow systems is an intractable problem. Traditional level-based algorithms restricted the due date within a time-window and the search scope was limited. HPSO (hybrid particle swarm optimization) algorithm was proposed by adapting a PSO algorithm effective for continuous problems. The proposed algorithm introduced operators of the particle randomly disturbing extremum, the position trace back and the crossover. By extending the research scope to the entire space, HPSO could obtain the optimal solution with a higher probability. Experimental results show that the HPSO reduces the cost 12.82% on average to that of the best currently available algorithm, as well as only 6.5s for a problem with 120 activities.
Motion-corrected magnetic resonance imaging of the fetal heart

Magnetic resonance imaging (MRI) is a versatile imaging modality that is widely used to assess the morphology, function and tissue characteristics of the human heart. However, evaluation of the fetal heart relies principally on ultrasound, as fetal cardiac MRI is limited by the challenges associated with imaging a small, rapidly beating heart that is subject to various regular and spontaneous movements. If these challenges can be overcome, MRI has the potential to provide multi-planar and volumetric images that may serve as an adjunct to ultrasound for screening of structural cardiac anomalies. This thesis explores the strengths and limitations of MRI with the aim of developing an acquisition and reconstruction strategy to visualise the fetal cardiovascular system in the presence of fetal and maternal motion. Methods are presented that use rapid dynamic MRI to resolve the beating fetal heart in utero and retrospective image-based techniques to achieve cardiac synchronisation, motion correction, outlier rejection and cine reconstruction. When combined, these techniques form a motion-tolerant framework for fetal cardiac MRI. The framework is first established for two-dimensional cine reconstruction from singleslice dynamic balanced steady state free precession MRI. The framework is then extended to three-dimensions using volumetric reconstruction techniques for scattered multi-planar images. Finally, the potential for simultaneous reconstruction of a three-dimensional cine and fully-encoded velocity mapping is explored in a preliminary study using the velocity information encoded in the phase of MRI data. The proposed frameworks were tested on human fetal subjects, including many with congenital heart disease, imaged at 1.5T, and validated using in vivo and simulation data. The results show this is a promising framework for comprehensive fetal cardiac MRI, overcoming the key challenge of motion to enable further detail in studies of the fetal heart and great vessels.
Face Recognition Using Collaborative Image Set

Digital imaging and communication technologies are developing in recent year; the image set based face recognition (ISFR) is becoming increasingly important. The difficulty in ISFR is how effectively and efficiently represents the query image set by using the gallery image sets. The set-to-set distance based methods ignore the relationship between gallery sets, while representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.
Meeting Teachers' Real Needs: New Tools in the Secondary Modern Foreign Languages Classroom

Discussion of UK government policy for education is complicated by degrees of devolution to Northern Ireland, Wales and Scotland, whereas England has no independent governing body. Educational policy in England, therefore, is decided by the UK government, whereas other areas of the UK have degrees of independence. The following refers only to policy as implemented in England. Readers from beyond the UK might also need to know that the government department responsible for English education policy has been renamed several times in recent decades; relevant for this chapter are the changes from Department for Education and Employment (DfEE) to Department for Education and Skills ABSTRACT
Control of oblique-type breakdown in a supersonic boundary layer employing streaks

The effectiveness of streak modes in controlling the oblique-type breakdown in a supersonic boundary-layer at Mach 2.0 is investigated using direct numerical simulations. Investigations in the literature have shown the effectiveness of streak modes in delaying the onset of transition dominated by two-dimensional waves, but in oblique breakdown, three-dimensional waves and a strong streak mode dominate the transition process. Paredes et al. (J. Fluid Mech., vol. 831, 2017, pp. 524–553) discussed the possible stabilization of supersonic boundary layers by optimally growing streaks using parabolized stability equations. However, no study has as yet been reported regarding direct nonlinear control of oblique breakdown. This study deals with the effects of large-amplitude decaying streak modes generated by a blowing–suction strip at the wall to control full breakdown in a reference case. Modes with four to five times the fundamental wavenumber are found to be beneficial for controlling the transition. In the first region after the control-mode forcing, the beneficial mean-flow distortion (MFD), generated by inducing the control mode, is solely responsible for hampering the growth of the fundamental-mode. On the whole, the MFD and the three-dimensional part of the control contribute equally towards controlling the oblique breakdown. The results show significant suppression of transition, and substantial improvements have been observed in the levels of the skin-friction coefficient and wall-temperature in comparison to the uncontrolled case. Moreover, refreshing the control using an additional downstream control strip increases the gain. However, the forcing amplitude must be carefully chosen in order not to introduce a generalized inflection point in the spanwise averaged mean flow invoking enhanced disturbance growth.
Detection of excessive activities in time series of graphs

ABSTRACT Considerable efforts have been made to apply scan statistics in detecting fraudulent or excessive activities in dynamic email networks. However, previous studies are mostly based on the fixed and disjoint windows, and on the assumption of short-term stationarity of the series, which might result in loss of information and error in detecting excessive activities. Here we devise scan statistics with variable and overlapping windows on stationary time series of organizational emails with a two-step process, and use likelihood function to rank the clusters. We initially estimate the log-likelihood ratio to obtain a primary cluster of communications using the Poisson model on email count series, and then extract neighborhood ego subnetworks around the observed primary cluster to obtain more refined cluster by invoking the graph invariant betweenness as the locality statistic using the binomial model. The results were then compared with the non-parametric maximum likelihood estimation method, and the residual analysis of ARMA model fitted to the time series of graph edit distance. We demonstrate that the scan statistics with two-step process is effective in detecting excessive activity in large dynamic social networks.