Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaussian Process Based Motion Pattern Recognition with Sequential Local Models

Conventional trajectory-based vehicular traffic analysis approaches work well in simple environments such as a single crossing but they do not scale to more structurally complex environments such as networks of interconnected crossings (e.g., urban road networks). Local trajectory models are necessary to cope with the multi-modality of such structures, which in turn introduces new challenges. These larger and more complex environments increase the occurrences of lack of motion and self-overlaps in observed trajectories which impose further challenges. In this paper we consider the problem of motion pattern recognition in the setting of sequential local motion pattern models. That is, classifying sub-trajectories from observed trajectories in accordance with which motion pattern that best explains it. We introduce a Gaussian process (GP) based modeling approach which outperforms the state-of-the-art GP based motion pattern approaches at this task. We investigate the impact of varying local model overlap and the length of the observed trajectory trace on the classification quality. We further show that introducing a pre-processing step filtering out stops from the training data significantly improves the classification performance. The approach is evaluated using real GPS position data from city buses driving in urban areas.
An EM-Algorithm-Based Approach for Predicting Teacher Candidate Success on the Communication and Literacy Skills Test for Educator Licensure

In 1998, the Department of Education in the State of Massachusetts redefined the requirements for teacher licensure, implementing a series of licensure examinations entitled the Massachusetts Tests for Educator Licensure (MTEL). In response, many Massachusetts colleges and universities now offer preparatory support programs to help teacher candidates pass the MTELs. Little research has been conducted to determine the impact, statistical or otherwise, of those measures on students’ MTEL scores. This paper outlines the development and analysis of a linear model for predicting the scores of teacher candidates at a small liberal arts college on the Communication and Literacy Skills Test, which all K-12 teacher candidates in Massachusetts are required to pass. Although failing test scores are reported numerically by the Massachusetts Department of Education, passing test scores are reported only as a “Pass. ” In this paper, a variation on the EM algorithm is applied to address the problem of missing data. The statistical technique is outlined in detail and is followed by a discussion of the effectiveness of the preparatory sessions. This paper is accessible to readers who have an introductory level background in statistics.
Oligonucleotide hybridization monitored by surface plasmon enhanced fluorescence spectroscopy with bio‐conjugated core/shell quantum dots. Influence of luminescence blinking

The influence of the luminescence blinking of quantum dots on kinetic bio‐sensing measurements is investigated. The hybridization kinetics of labelled DNA sequences with surface‐immobilized DNA probes using surface plasmon enhanced fluorescence spectroscopy is employed to compare the behaviour of quantum‐dot‐ and organic‐chromophore‐labelled DNA targets. The presented experiments show that quantum‐dot labelled targets present apparent complex adsorption kinetics that can lead to inaccurate conclusions. By means of single quantum dot luminescence measurements and Monte‐Carlo simulations, it is demonstrated that the anomalous adsorption kinetics observed with the quantum‐dot labelled targets can be accounted for by the luminescence blinking statistics of the quantum dots. Thus, special attention should be paid when applying quantum dots as fluorescent labels in bio affinity studies in order to avoid wrong interpretations due to the special photo physical behaviour of these semiconducting nanocrystals. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Theoretical formalism for collective electromagnetic response of discrete metamaterial systems

We develop a general formalism to describe the propagation of a near-resonant electromagnetic field in a medium composed of magnetodielectric resonators. As the size and the spatial separation of nanofabricated resonators in a metamaterial array is frequently less than the wavelength, we describe them as discrete scatterers, supporting a single mode of current oscillation represented by a single dynamic variable. We derive a Lagrangian and Hamiltonian formalism for the coupled electromagnetic fields and oscillating currents in the length gauge, obtained by the Power-Zienau-Woolley transformation. The response of each resonator to electromagnetic field is then described by polarization and magnetization densities that, to the lowest order in a multipole expansion, generate electric and magnetic dipole excitations. We derive a closed set of equations for the coherently scattered field and normal mode amplitudes of current oscillations of each resonator both within the rotating wave approximation, in which case the radiative decay rate is much smaller than the resonance frequency, and without such an assumption. The set of equations includes the radiative couplings between a discrete set of resonators mediated by the electromagnetic field, fully incorporating recurrent scattering processes to all orders. By considering an example of a two-dimensional split ring resonator metamaterial array, we show that the system responds cooperatively to near-resonant field, exhibiting collective eigenmodes, resonance frequencies, and radiative linewidths that result from strong radiative interactions between closely-spaced resonators
Machine Learning Challenges with BigData

The developments in the information technology in recent years have led to the collection of massive amounts of data. Big data attracts attention from industry, academia and government as big data technologies allows us to process large volumes of data to get deeper insights for decision making through data analytics. Machine Learning(ML) forms an important component of data analytics because it gets the computers to learn from the trained data and act, and it improves from past experience without being explicitly programmed every time. ML algorithms are used to uncover more fine-grained patterns and make accurate predictions and also they have the ability to improve the performance of other parts of the knowledge discovery. The characteristics of Big data such as volume, variety, velocity, veracity form obstacles to traditional machine learning algorithms for performing data analytics. In this paper we present major challenges that are presented by big data to machine learning such as scalability of the algorithm for large data sets and the ability to be computed in a distributed and real time processing environment. We also review some of the Machine learning methods, frameworks and tools. The identified challenges faced by the ML algorithms pave the way for new opportunities to develop novel algorithms which are adapted for Big Data.
Variation in Life History and Feeding Ecology of the Invasive Eastern Mosquitofish , Gambusia holbrooki Girard , 1859 ( Poeciliidae ) , in a Groundwater-dependent Wetland in Turkey

We studied the variations in life history and feeding ecology of G. holbrooki in Acıgöl, a groundwaterdependent wetland and the only distribution area of the critically endangered Aphanius transgrediens. Fish were sampled from three sites with different ecological characteristics: Site 1 with stable conditions, small size and high fish density; Site 2 with stable conditions, large size and medium fish density; Site 3 with variable conditions, large size and low fish density. The scale readings showed two age groups in males and three in females. The reproduction period was from April to September. More embryos and heavier gonads were found in specimens at Site 3. The overall ratio of fertilised eggs and the absolute and relative fecundities were the lowest at Site 1. Two different trophic positions, plant-based omnivory and animal-based omnivory, were detected. The main food items at Site 2 and Site 3 were adults and larvae of Diptera, as well as zooplankton. At the same time, mosquitofish fed predominantly on plant sources and occasionally on the other ones at Site 1. Significant temporal and ontogenetic differences in feeding were also determined. The highest diet diversity and broadest trophic niche were found at Site 2.
Neuroticism, education and self-assessed health in the general population of the United States

In this study we investigate the interrelation between neuroticism, education, smoking and health. Two lines of research are brought together: one studying the relationship between neuroticism and health and the other studying the association between education and health. As lower educated people more often score high on neuroticism, we study the relationships of education and neuroticism with health simultaneously. Moreover, we hypothesize that smoking behaviour is a common explanatory factor in these associations. A 1996 US general population sample is employed to test the hypotheses. The associations between education and neuroticism on the one hand and self-assessed health on the other hand proved to be substantially smaller when education and neuroticism are mutually controlled for. Yet, the hypothesis that smoking behaviour provides an explanation for educational diﬀerences in health was only supported for men.
Excited-State Dynamics in the Enhanced Green Fluorescent Protein Mutant Probed by Picosecond Time-Resolved Single Photon Counting Spectroscopy

The complex photophysics of the wild-type green fluorescent protein (GFP), one of the most popular fluorescent probes in biology, has been extensively documented in literature. The excited-state dynamics of GFP was explained by means of a model implying excited-state proton transfer (ESPT) and three forms of the chromophore, a protonated A form absorbing at 400 nm and two deprotonated I and B forms absorbing at around 475 nm. We report here a systematic picosecond time-resolved fluorescence study of the enhanced green fluorescent protein (EGFP) variant, carrying the Ser65-Thr and Phe64-Leu mutations. By means of multiple excitation wavelength time-resolved experiments, we were able to distinguish between the fluorescence decay times of the deprotonated I* and B* states (3.4 and 2.7 ns). Spectrally, we found the I form being red shifted in comparison with the B form, both in absorption and in emission. Evidence for an excited-state reaction, namely, proton transfer, is also reported. An additional protonat.
Cointegration, Causality, Error-Correction, and Export-Led growth in six countries: Japan, Philippines, South Korea, Taiwan, United Kingdom and United States

In this paper we have investigated the causal relationship between exports and growth in a bivariate framework using time-series data on six countries. Hsiao's (1979, 1981) version of the Granger causality test is used to find the direction of causation. Our study improves upon previous work by evaluating the time series properties of GDP and exports and supplementing Granger causality tests with a bivariate cointegration te~t and error-correction model. Our fmdings suggest that the evidence in favor of export-promotion policy is weaker than previous statistical studies have indicated. • Assistant Professor and Ph.D candidate, respectively. Department of Economics on Utah State University. All correspondence concerning this paper should be addressed to Chris Fawson at the above address.
Influences of the variation in inflow to East Asia on surface ozone over Japan during 1996–2005

Abstract. Air quality simulations in which the global chemical transport model CHASER and the regional chemical transport model WRF/chem are coupled have been developed to consider the dynamic transport of chemical species across the boundaries of the domain of the regional chemical transport model. The simulation captures the overall seasonal variations of surface ozone, but overestimates its concentration over Japanese populated areas by approximately 20 ppb from summer to early winter. It is deduced that ozone formation around Northeast China and Japan in summer is overestimated in the simulation. On the other hand, the simulation well reproduces the interannual variability and the long-term trend of observed surface ozone over Japan. Sensitivity experiments have been performed to investigate the influence of the variation in inflow to East Asia on the interannual variability and the long-term trend of surface ozone over Japan during 1996–2005. The inflow defined in this paper includes the recirculation of species with sources within the East Asian region as well as the transport of species with sources out of the East Asian region. Results of sensitivity experiments suggest that inflow to East Asia accounts for approximately 30 % of the increasing trend of surface ozone, whereas it has much less influence on the interannual variability of observed surface ozone compared to meteorological processes within East Asia.
Why avoid difficult problems ? Exploring the avoidance behavior within startup motive

Some commonly acknowledged large-scale societal problems include over-population, war, climate change, energy production, lack of electricity and clean water, deforestation, malaria, poverty and famine, and cancer. Entrepreneurs are known as problem-solvers in a market economy. Therefore, why more entrepreneurs are not interested in solving large-scale societal problems? This paper analyzes a discussion thread in an online community specialized in high-technology startups to explore this specific question of entrepreneurial motivation. It concludes that “trivial startups” are a result of particularities of domain-specific expertise, reference points and first-world bias, among other things. In this discussion there is also a value dichotomy in which both sides seem to make sense – on one hand, some founders criticize modern startups for not attempting to solve the societal problems of the world, while others see all startup activity worth undertaking. The end result of the conflict remains unresolved as unpredicted paths of innovation and creation of wealth and employment are properties of trivial applications regardless of whether they solve societal problems directly or not, whereas the opportunity cost of a better society through full focus on societal issues cannot be precisely determined.
Revisiting the Tourism-Led Growth Hypothesis in a Dual Model Using Mwald Granger Causality Analysis

This study reinvestigates the tourism-led growth hypothesis in Taiwan by applying the Johansen co-integration and Modified Wald (MWALD) causality tests to long-term data-from 1958 to 2017. We employ both bivariate (international tourist arrivals and real GDP) and trivariate (international tourist arrivals, international tourist expenditures, and real GDP) models. We find that there is a long-run relationship between the three variables, and that there is a unidirectional Granger causality running from economic growth to international tourist arrivals in both bivariate and trivariate models, supporting the tourism-led growth hypothesis. In addition, the existence of bidirectional Granger causality between the real GDP and international tourist expenditures suggests that policymakers should relax regulations to increase tourism flows to promote economic growth.
Indium clustering in a-plane InGaN quantum wells as evidenced by atom probe tomography

Atom probe tomography (APT) has been used to characterize the distribution of In atoms within non-polar a-plane InGaN quantum wells (QWs) grown on a GaN pseudo-substrate produced using epitaxial lateral overgrowth. Application of the focused ion beam microscope enabled APT needles to be prepared from the low defect density regions of the grown sample. A complementary analysis was also undertaken on QWs having comparable In contents grown on polar c-plane sample pseudo-substrates. Both frequency distribution and modified nearest neighbor analyses indicate a statistically non-randomized In distribution in the a-plane QWs, but a random distribution in the c-plane QWs. This work not only provides insights into the structure of non-polar a-plane QWs but also shows that APT is capable of detecting as-grown nanoscale clustering in InGaN and thus validates the reliability of earlier APT analyses of the In distribution in c-plane InGaN QWs which show no such clustering.
Inequality of Income and Consumption: Measuring the Trends in Inequality from 1985-2010 for the Same Individuals by

Research indicates that during the 1980s economic inequality has increased in the United States (see Cutler and Katz (1991), Johnson and Shipp (1997), Krueger and Perri (2006)). Researchers, however, dispute which resource -income or consumption -should be used to measure economic well-being, and the extent of the change in inequality of well-being (Meyer and Sullivan (2007)). In fact, Meyer and Sullivan (2011) demonstrate that, during the past decade, income inequality has increased, while consumption inequality has decreased. The magnitude of the change in inequality depends not only on the choice of the resource used to measure inequality but also on whose inequality is measured, i.e., whether inequality is measured for individuals or households, or the young or the old (see Slesnick (2001), Meyer and Sullivan (2010), Attanasio et al. (2007)).
MICROFLUIDIC CENTRIFUGO-PNEUMATIC SIPHON ENABLES FAST BLOOD PLASMA EXTRACTION WITH HIGH YIELD AND PURITY

Centrifugal microfluidic plasma separation from whole blood is improved by minimized resuspension of cells. Plasma supernatant is removed by an air pressure mediated coating free siphon. Fluid dynamic characteristics are predicted by numerical simulation on a system level approach enabling reliable separation for a hematocrit range between 20 % and 60 %. Separation of 40 μl of whole blood with 60 % hematocrit resulted in the following characteristics (compared to previous centrifugal microfluidic approach [1]): Separation time of 43 s (120 s), plasma yield of 88 %, cv = 4 % (< 83 %, cv = 6 %) and purity of 99.8 %, cv = 0.1 % (99.5 %).
On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM

Generalized linear mixed models (GLMM) are addressed for inference and prediction in a wide range of different applications providing a powerful scientific tool for the researchers and analysts coming from different fields. At the same time more sources of data are becoming available introducing a variety of hypothetical explanatory variables for these models to be considered. Estimation of posterior model probabilities and selection of an optimal model is thus becoming crucial. We suggest a novel mode jumping MCMC procedure for Bayesian model averaging and model selection in GLMM.
GROUP VIA CALCIUM-INDEPENDENT PHOSPHOLIPASE A2 REGULATES BCL-XL PROTEIN LEVELS IN MICE LUNG

GROUP VIA CALCIUM-INDEPENDENT PHOSPHOLIPASE A2 REGULATES BCL-XL PROTEIN LEVELS IN MICE LUNG By Sang-Jin Nam, B.S. A thesis submitted in partial fulfillment of the requirements for the degree of Masters of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2014 Major Director: Daniel H. Conrad, Ph.D Professor Microbiology and Immunology With previous indication of the Group VIA phospholipase A2 (iPLA2β) enzyme regulating ER-stress induced apoptosis in β-cells by regulating the anti-apoptotic protein Bcl-xL via alternative splicing, our lab postulated iPLA2β to be utilizing a similar mechanism to regulate apoptosis in mice lung. Our previous lab work has shown implications of lung function compromise in iPLA2β mice, and we speculated the cause to be due altered lung architecture stemming from the attenuation of apoptosis. The western blot analysis in this study suggested that iPLA2β is involved in the regulation of Bcl-xL, but the mRNA ratios of the splice variants suggested that alternative splicing is not the mechanism iPLA2β is utilizing for the regulation in our animal models. Additionally, the observation and assessment of the lung morphology of the iPLA2β and wild type mice suggested that iPLA2β does not play an integral role in lung morphology.
Magnetic Resonance Perfusion Imaging in the Diagnosis of High-Grade Glioma Progression and Treatment-Related Changes: A Systematic Review

In patients with high grade gliomas (HGGs), progression after treatment can be difficult to diagnose due to treatment-related effects, which overlap in appearance with tumour progression on conventional magnetic resonance imaging (MRI) sequences. Specialised imaging methods have been studied for this purpose, though most institutions currently use histopathology or clinicoradiological follow-up for diagnosis. This publication aims to review the evidence for perfusion MRI techniques. The databases of Pubmed, MEDLINE, EMBASE and Scopus were searched using combinations of the subject headings high grade glioma and MRI perfusion. 41 articles fulfilled the inclusion criteria. Dynamic Susceptibility Contrast (DSC) MRI was the most extensively studied, with several studies achieving high sensitivities and specificities. Other techniques exhibiting potential include Dynamic Contrast Enhanced (DCE) MRI, Arterial Spin Labelling (ASL). However, these techniques are not widely used or available for clinical practice. Composite measures combining results from multiple techniques tended to achieve higher accuracies. Some publications compared processing software used or looked at machine learning with relative success. An issue common to the literature is the lack of standardisation in the reference standard and acquisition/processing methods. Furthermore, many had small sample sizes, and further consideration needs to be given with regards to timing of imaging, and treatment regimens received in such studies.
International Journal of Advanced Robotic Systems Modular robot system for maintenance tasks in large scientific facilities

Large scientific facilities such as particle accelerators are scenarios that require continuous maintenance and specific type of interventions. The intervening personnel are sometimes required to work exposed to residual radiation. The inclusion of robotic systems into these environmental conditions are being encouraged to increase the availability of the facility and reduce personal radiation doses. However, this scenario presents challenging conditions for robotic systems in terms of structural, equipment and environmental conditions. This paper addresses the design of a modular robotic system as an alternative to conventional robots to overcome the challenges. This work also explores the various capabilities of the design along with its future possibilities. The SMART heterogeneous modular robot systems, prototype and simulation results are presented.