Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Bayesian Models for High-Dimensional Biomedical Data

EUNJEE LEE: ADVANCED BAYESIAN MODELS FOR HIGH-DIMENSIONAL BIOMEDICAL DATA (Under the direction of Joseph G. Ibrahim and Hongtu Zhu) Alzheimer’s Disease (AD) is a neurodegenerative and firmly incurable disease, and the total number of AD patients is predicted to be 13.8 million by 2050 [68]. Our motivation comes from needs to unravel a missing link between AD and biomedical information for a better understanding of AD. With the advent of data acquisition techniques, we could obtain more biomedical data with a massive and complex structure. Classical statistical models, however, often fail to address the unique structures, which hinders rigorous analysis. A fundamental question this dissertation is asking is how to use the data in a better way. Bayesian methods for high-dimensional data have been successfully employed by using novel priors, MCMC algorithms, and hierarchical modeling. This dissertation proposes novel Bayesian approaches to address statistical challenges arising in biomedical data including brain imaging and genetic data. The first and second projects aim to quantify effects of hippocampal morphology and genetic variants on the time to conversion to AD within mild cognitive impairment (MCI) patients. We propose Bayesian survival models with functional/high-dimensional covariates. The third project discusses a Bayesian matrix decomposition method applicable to brain functional connectivity. It facilitates estimation of clinical covariates, the examination of whether functional connectivity is different among normal, MCI, and AD subjects.
Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

In this paper, we propose a new pre-processing technique for efficient multidimensional wideband parameter estimation. One application is provided by an orthogonal frequency division multiplexing-(OFDM) based joint radar and communication system, which uses SIMO architecture. In this paper, the estimated parameters are given by the range (time delay), the relative velocity, and the direction of arrival (DoA) pairs of the dominant radar targets. Due to the wideband assumption, the received signals on different subcarriers are incoherent and, therefore, cannot fully exploit the frequency diversity of the OFDM waveform. To estimate the parameters jointly and coherently on different subcarriers, we propose an interpolation-based coherent multidimensional parameter estimation framework, where the wideband measurements are transformed into an equivalent narrowband system. Then, narrowband multidimensional parameter estimation algorithms can be applied. In particular, a wideband $R$ -D periodogram is introduced as a benchmark algorithm, and we develop the $R$ -D Wideband Unitary Tensor-ESPRIT algorithm. The simulations show that the proposed coherent parameter estimation method significantly outperforms the direct application of narrowband parameter estimation algorithms to the wideband measurements. If the fractional bandwidth is significant and the SNR is not too low, the estimates provided by the narrowband estimation algorithms can become inconsistent. Moreover, the interpolation order should be chosen according to the SNR regime. In the low SNR regime, interpolation with a lower-order (i.e., linear interpolation) is recommended. For higher SNRs, we propose an interpolation with higher-order polynomials, e.g., fourth-order (cubic splines) or even higher.
Preliminary Analysis of Technical Characteristics of the Basic Technology Competencies for Educators Inventory-Revised

The purpose of the present study was to update and evaluate technical characteristics of the Basic Technology Competencies for Educators Inventory . Undergraduate and post-baccalaureate teacher education majors provided data for analysis. The principal component analysis supported the theoretical structure of the 10 components hypothesized in the design. The before and after instruction study supported the predicted change that would be expected after instruction in basic technology, with large effect sizes. The internal consistency reliability coefficients indicated a high level of homogeneity within components and across all items on the BTCEI-R scores, which will result in consistent scores. The results of this study support the use of the BTCEI-R for measuring educators’ technology competencies.
Cholesky–ANN models for predicting multivariate realized volatility

Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The Cholesky-Artificial Neural Networks specification here presented provides a twofold advantage for this topic. On the one hand, the use of the Cholesky decomposition ensures positive definite forecasts. On the other hand, the implementation of artificial neural networks allows to specify nonlinear relations without any particular distributional assumption. Out-of-sample comparisons reveal that Artificial neural networks are not able to strongly outperform the competing models. However, long-memory detecting networks, like Nonlinear Autoregressive model process with eXogenous input and long shortterm memory, show improved forecast accuracy respect to existing econometric models.
Narrow Convolutional Neural Network for Arabic Dialects Polarity Classification

The complexities and tangles of Arabic dialect in orthography and morphology typically make the sentimental analysis quite challenging. Moreover, most of the classification approaches have addressed this problem based on hand-crafted features. Since the Arabic language has multi-dialects and the language has no word-based order, the extraction process and the classification tasks are more difficult and time consuming. Deep neural network approaches applied to the Arabic language colloquial are very limited. These deep learning approaches typically comprise a structure that is very complex for small quantities of data. The structures are based on wide convolutional networks that are not capable of capturing the entire semantic and sentiment features for Arabic dialects. In this paper, a narrow structure of the convolutional neural network (CNN) has been proposed in order to obtain the tweets representations and classify the Arabic tweets into five, three and two polarities. Sensitivity analysis has been conducted to evaluate the impact of various combination structural properties, such as the number of convolutional filters, pooling size, and filter size on the classification performances. The proposed Arabic narrow convolutional neural network (NCNN) has captured the entire semantic and sentiment information contained in the tweet by maximizing the features of the detector’s range. The NCNN performances were estimated to be at its optimum when structured by three convolutional layers, each one followed by the max pooling layer. The model has been developed without using lexicon resources and lexical features or augmented the dataset with extra training data. The narrow model is the first baseline model for Arabic dialects sentiment classifications for a sentence level as it is the first narrow CNN model addressing the Arabic Dialect tweets. NCNN model achieved the lowest macro average mean absolute error (MAEM) for five polarity and higher Macro average recall (P) for three and two polarities on the SemEval-2017 Arabic dialect Twitter datasets when compared to the other state-of-the-art approaches.
Stochastic equations, flows and measure-valued processes

We first prove some general results on pathwise uniqueness, comparison property and existence of nonnegative strong solutions of stochastic equations driven by white noises and Poisson random measures. The results are then used to prove the strong existence of two classes of stochastic flows associated with coalescents with multiple collisions, that is, generalized Fleming--Viot flows and flows of continuous-state branching processes with immigration. One of them unifies the different treatments of three kinds of flows in Bertoin and Le Gall [Ann. Inst. H. Poincar\'{e} Probab. Statist. 41 (2005) 307--333]. Two scaling limit theorems for the generalized Fleming--Viot flows are proved, which lead to sub-critical branching immigration superprocesses. From those theorems we derive easily a generalization of the limit theorem for finite point motions of the flows in Bertoin and Le Gall [Illinois J. Math. 50 (2006) 147--181].
Identification of induction motor parameters with measurement errors

This paper proposes an algorithm for estimating parameters of asynchronous traction motors in the presence of measurement errors. The presence of measurement errors of voltage and current leads to biased estimates of motor parameters. Algorithmic approach to eliminating the influence of noise is used most often as it increases the accuracy of estimates without precision current and voltage meters. Known modifications of algorithms based on the method of total least squares suggest filtering the derivatives of noise, which increases the complexity of the algorithm and also changes the statistical properties of noise. The proposed algorithm is a generalization of the total least-squares technique and does not require knowledge of the laws governing the distribution of measurement errors. The results of modeling show that the parameter estimation is highly accurate than the ordinary least square. The proposed algorithm can be used to create precision control systems, as well as to diagnose faults in electric motors by K parameters.
On 2‐factorizations of the complete graph: From the k‐pyramidal to the universal property

We consider 2‐factorizations of complete graphs that possess an automorphism group fixing k⩾0 vertices and acting sharply transitively on the others. We study the structures of such factorizations and consider the cases in which the group is either abelian or dihedral in some more details. Combining results of the first part of the paper with a result of D. Bryant, J Combin Des, 12 (2004), 147–155, we prove that the class of 2‐factorizations of complete graphs is universal. Namely each finite group is the full automorphism group of a 2‐factorization of the class. © 2009 Wiley Periodicals, Inc. J Combin Designs 17: 211‐228, 2009
A Digital Game-Based Learning Method to Improve Students’ Critical Thinking Skills in Elementary Science

For this paper, an educational computer game called Ecoship Endeavour was designed based on the guidelines of the inquiry, communication, mystery, decision making, challenge, and rewards (ICMDCR) framework to help facilitate critical thinking skills in students. To examine the effectiveness of the proposed digital game-based learning (DGBL) approach, a quasi-experiment was conducted in the units of “food relationship among living things”, “life processes in plants”, and “energy” of an elementary science course. A total of 127 fifth grade students (females only) from four classes consented to participate in this learning activity. Two classes ( $N = 62$ ) were assigned to the experimental group and guided by the developed game, and the other two classes ( $N = 65$ ) were allocated to the control group and taught by the conventional method of science teaching. The experimental results revealed that Ecoship Endeavour significantly improved the critical thinking skills of students; however, it was also revealed that the DGBL activity had no significant effects on students’ learning motivation and self-efficacy for science learning. Finally, more research is needed to examine the instructional benefits of the proposed method with more inclusive research samples consisting of both male and female learners. The outcome of this research may help researchers, teachers, and practitioners in the educational sector to evaluate the effectiveness of serious games in facilitating students’ critical thinking.
An integrated control method for designing non-blocking supervisors using Petri nets

This article presents an efficient integrated approach on designing a non-blocking supervisor for the most general classes of Petri nets, called G-systems that allow multiple resource acquisitions. This work mainly focuses on developing a deadlock prevention policy with a polynomial computational complexity. First, an extraction algorithm of liveness requirement constraints is presented according to the concept of resource partial orders. By considering the different resource requirements of various processes, monitors are added for the uncontrolled G-system on the basis of those precise linear inequality constraints. Afterward, we explore an iterative control policy by utilizing the traditional mathematical programming method, which can ensure the liveness of the resultant controlled system. Comparing with the existing deadlock control policies reported in the literature, the proposed method can achieve a non-blocking controlled G-system with simple structure and high computational efficiency. Finally, a benchmark G-system example is used to substantiate the efficiency of the new method.
Conceptual Design Methods for Sizing and Performance of Hybrid-Electric Transport Aircraft

The consideration of hybrid-electric propulsion systems disrupts the traditional methods for aircraft sizing and performance. Conceptual design methods for sizing, performance and identification of flight techniques for hybrid-electric transport aircraft are proposed in this thesis. The integrated performance and flight technique optimality of narrow-body hybrid-electric aircraft concepts are investigated to derive design heuristics, sizing axioms and to determine suitable market application.
The effect of tutor-specific and other motivational factors on student retention on Icelandic Online

Abstract This article presents an analysis of tracking and survey data collected over eight years on 400 users of one of seven Icelandic Online (IOL) courses: IOL 2; an open online program in Icelandic as a second language. The course is offered in three delivery modes; blended, distance, and open self-directed mode. This article introduces the second phase of a three-tiered mixed-method study and follows up previous findings on IOL that drew on tracking data from 43,000 learners, which revealed overall low completion rates and that the blended mode is more effective in retaining learners than other modes (Friðriksdóttir, 2018). The main goal of the current study is to identify factors which may affect student retention on IOL, and which may explain why the blended mode is more effective. This study attempts to assess whether (a) the initial goal to complete a course and (b) certain tutor-specific factors influence retention, and also to investigate (c) self-reported motives that drive learners towards completing. The findings reveal that the intent to complete is a significant predictor of course completion and that the majority of blended learners had the initial goal of completing. Furthermore, the study illuminates that most of the learners in the tutorial modes report that tutor-specific factors were important in motivating them. Finally, course completers report diverse reasons for completion; the majority of the blended learners are driven in their goal to complete by the credits offered.
Learner interactions in face-to-face collaborative writing with the support of online resources

Abstract This study explores interactions among language learners with the support of online resources in a collaborative writing task and how online resources assisted collaborating learners in the meaning-making process. The study was conducted in the freshman English course at a national university in Taiwan. Fifty-six students constructed an essay in pairs firstly without the support of online resources, and subsequently constructed another essay with the support of online resources. Each pair’s interactional patterns and dynamics of peer scaffolding across the two settings were examined. The findings show that the availability of online resources fosters a variety of interaction characteristics among learners with varied collaboration orientation. Results also suggest that learners’ collaboration predisposition at the onset plays a critical role in influencing the way they used online resources to support their interaction. This study thus suggests that learners’ collaborative patterns and their use of online resources have mutual impact, which may inform teachers seeking to integrate online resources to enhance their students’ collaborative learning.
The Impact of Climatic Variability on Phenological Change, Yield and Fruit Quality of Mangosteen in Phatthalung Province, Southern Thailand

Southern Thailand is a traditional area for tropical fruit plantations. Recently, the phenology in many fruit trees, including mangosteen, has been changed due to climatic variability. Therefore, the impact of climatic variability on phenological change, yield and quality of mangosteen needs to be investigated. In 2008, a study was established in an orchard in Phatthalung province, southern Thailand. Eighteen-year old mangosteen trees with 8 × 8 m spacing were used to investigate fl owering during the in-season, off-season and even, alternate bearing during three consecutive years (2008-2010). The 30- year weather data from 1981 to 2010 showed trends of change in the annual rainfall, the number of rainy days, and the maximum and minimum temperature. A marked change in rainfall distribution affected the phenological change in fl owering, productivity and fruit quality. In 2008 and 2009, the mangosteen trees experienced the required dry period that regulates fl oral induction before fl owering. Thus, fl owering was found in both the in-season and off-season. However, in 2010, a prolonged drought in summer followed by rain during July-August caused leaf fl ushing instead of fl owering and this resulted in no off-season fruit production. This indicated that climatic variability resulted in a phenological change of mangosteen in Phatthalung province, southern Thailand where there is usually off-season production. In addition, climatic variability affected the fruit yield and fruit quality of mangosteen.
Comfort in autonomous car: mitigating motion sickness by enhancing situation awareness through haptic displays

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Performance Analysis of Nonlinear SFBC OFDM Systems Over TWDP Fading Channel

In this paper, the practical performance of space-frequency block code (SFBC)-based multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in the presence of nonlinear power amplifier with imperfect channel state information (CSI) over two-wave with diffuse power (TWDP) channel is analyzed. In the performance analysis, we first introduce the equivalent single-input single-output (SISO) scalar model with nonlinear power amplifier and then the SISO model is used to construct a model for the nonlinear MIMO-OFDM system. Considering the channel estimation error at the receiver, we study the effects of MMSE channel estimation error on the channel capacity. What is more, based on inter modulation product (IMP) analysis, we study the BER performance of nonlinear SFBC MIMO-OFDM systems, considering M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM). And the effects of channel estimation error on the BER performance are studied. Finally, this paper also derives the closed-form expressions of the system optimal operating point. The numerical results and comparisons are provided for several forms of SFBC MIMO-OFDM. From these analysis results, it is found that the analytical interpretation of the observed behavior in the simulation can be advanced, and the actual analysis of the performance optimization of the nonlinear MIMO-OFDM system is also provided.
A Survey on Obstacles Avoidance Mobile Robot in Static Unknown Environment

Autonomous mobile robots have in recent times gained interest from many researchers. This is due to wide range of mobile robot application. Numerous robots especially in navigation, obstacle avoidance and path following are currently under development. A reliable collision avoidance methodology is needed for effective navigation. Normally robots are fitted with transducers such as ultrasonic sensors, infrared and cameras for detecting environment. Various methods have been established in the past years to resolve navigational problems associated with mobile robots. They include fuzzy logic, potential fields, genetic algorithm, neural network and vision base approaches. Fuzzy logic demonstrates to be an appropriate tool for handling uncertainty that emerge from imprecise knowledge during route finding.
Property Rights, Deregulation, and Entrepreneurial Development in a Transition Economy

ABSTRACT This article investigates the relative roles of formal property rights institutions versus deregulated markets in entrepreneurial development, based on China's market transition. Empirically, it is not yet known which set of institutions matters more for entrepreneurship, particularly in the long run, despite the existence of well-established theoretical arguments for each. Using provincial-level panel data from China's transition economy, this study has the following findings: On average, both formal protection of property rights and deregulated markets have positive effects on entrepreneurial development; yet, as market transition progresses, the effect of formal protection of property rights increases, while that of deregulated markets decreases. These results are robust to both multiple model specifications and an endogeneity test using an instrumental variable approach. Overall, therefore, while both sets of institutions indeed play positive roles in entrepreneurial development, property rights institutions may be more fundamental in the long run.
Harmonic quasi-isometric maps into Gromov hyperbolic ${\rm CAT}(0)$-spaces

We show that for every quasi-isometric map from a Hadamard manifold of pinched negative curvature to a locally compact, Gromov hyperbolic, ${\rm CAT}(0)$-space there exists an energy minimizing harmonic map at finite distance. This harmonic map is moreover Lipschitz. This generalizes a recent result of Benoist-Hulin.