Miracle-dz/UniFAR-FTUs huggingface dataset:
Pixel unmixing for hyperspectral measurement of foliar chemistry in Pacific Northwest coastal forests

Studies for the detection and mapping of variation in foliar chemistry have concentrated on the correlation of wavelength specific reflection and concentrations of foliar pigments and nutrients either through ground-based radiometric measurements or airborne data. The advantage of the former is that the scene components can be effectively controlled so that a relatively simple reflectance model can be constructed and end members extracted. In the case of using airborne data, however, the influence of scene components that mask, or subdue, the reflectance-chemical signal, may dominate. This has led to the development of methodologies for which the various scene components can readily be isolated and accounted. Pixel unmixing to isolate canopy reflectance from other scene components has long been used in the assessment of foliar characteristics and processes. Unfortunately the traditional methods of unmixing rely on distinct spectral signatures from the various scene components. This paper details a method developed to isolate the scene components when they are not spectrally dissimilar.
Öğretmenlerin BİT Entegrasyon Yaklaşımlarının Ölçülmesine Yönelik Ölçek Geliştirme

Bu arastirmada ogrenme ogretme surecine Bilgi ve Iletisim Teknolojileri (BIT) entegrasyon yaklasimlarini belirlemeye yonelik olcek gelistirmek amaclanmistir. Olcek, geleneksel entegrasyon (GE), bilissel yapilandirmacilik (BY) ve Sosyo-kulturel (SK) entegrasyon olmak uzere uc farkli yaklasimi iceren 5’li likert turundedir. 24 madde olarak hazirlanan olcegin kapsam gecerligini belirlemek icin 18 uzman gorusu alinmistir. Olcegin yapi gecerliginin belirlenmesi icin 625 ogretmene uygulama yapilmistir. En cok olabilirlik metodu ile acimlayici faktor analizi yapilmistir. Bu analiz sonucunda oz degeri dusuk, binisiklik gosteren ve birden fazla faktor altinda yer alan 3 ve hicbir faktor altinda yer almayan 1 madde olcekten cikarilmistir. Acimlayici faktor analizi ile belirlenen 3 farkli entegrasyon yaklasimina dayali 20 maddelik olcek yapisinin uygunlugu dogrulayici faktor analizi ile test edilmistir. Analiz sonuclari uyum indekslerinin kabul edilebilir duzeyde oldugunu gostermistir. Analiz sonuclari uc farkli entegrasyon yaklasimini iceren olcegin guvenirlik duzeylerinin yuksek oldugu belirlenmistir.
Comparison of Weights for Meta-Analysis of r and d Under Realistic Conditions

We compared unit, sample size, and inverse variance weighting procedures for estimating the overall mean and random-effects variance component (REVC) in random-effects meta-analysis under realistic conditions for both r and d. Root mean square error and average absolute error of estimation were used to compare weighting schemes via Monte-Carlo simulation. For r, unit weights worked surprisingly well, and sample size weights worked best overall. For d, unit weights worked poorly, and inverse variance weights worked best overall. Discussion focuses on the meta-analyst’s choice of weights, possible explanations for the differences across types of effect size, and implications for meta-analytic inferences in organizational research.
Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring.
Variation and change in Norwegian wh-questions: The role of the complementizer som

In this paper, we consider variation in Verb Second (V2) word order in wh -questions across Norwegian dialects by investigating data from the Nordic Syntax Database (NSD), which consists of acceptability judgments collected at more than 100 locations in Norway. We trace the geographical distribution of the two main variables: phrasal vs. monosyllabic wh -elements (the latter argued to be heads) and subject vs. non-subject questions. In subject questions, non-V2 is realized by inserting the complementizer som in second position instead of the verb. We also discuss the connection between non-V2 and the possibility of inserting the complementizer som under extraction of a wh -subject from an embedded clause, i.e. in that -trace contexts. Based on synchronic data, we propose a diachronic account of the geographical distribution and argue that the development from V2 to non-V2 has started in subject questions, thus allowing us to relate the loss of the V2 requirement to changes in the properties of the complementizer som .
Ambient air pollution and the lungs: what do clinicians need to know?

Educational aims To provide physicians with knowledge of the common ambient air pollutants and their adverse effects on human lungs. To emphasise the fact that neonates, children and adults with pre-existing lung diseases are a susceptible population that need to be protected from the harmful effects of ambient air pollution. To educate physicians about the role of oxidative stress in mediating the harmful effects of ambient air pollution, and the consequences that individuals with inherent defects in antioxidant defence genes may be more vulnerable to the harmful effects. To provide physicians with current knowledge on the available dietary and drug sources of antioxidants which have the potential to enhance lung defence mechanisms against the harmful effects of air pollution. To encourage physicians to act as advocates to help reduce the levels of ambient air pollutants and to conduct research that will not only identify susceptible individuals by conducting genetic studies, but will also help in developing interventions that will reduce the harmful effects of ambient air pollutants. Summary Ozone, oxides of nitrogen, sulphur dioxide and particulate matter are the important ambient air pollutants associated with adverse health effects. A large number of ambient air pollutants have been isolated from the fetal cord blood indicating that babies are exposed to air pollutants even before they are born. Children and the elderly are most susceptible to the harmful effects of ambient air pollutants. High levels of ambient air pollutants are associated with increased incidence or worsening of asthma and increased risk of developing allergic diseases, respiratory symptoms and respiratory tract infections. Mortality rates also increase in individuals with pre-existing cardiorespiratory diseases. The underlying mechanism for the harmful effects is the generation of oxidative stress which induces a strong respiratory as well as systemic inflammatory response. Individuals with genetic defects in enzymes associated with antioxidant defences seem to be particularly vulnerable to the harmful effects of air pollutants. Supplementation with antioxidants such as N-acetyl cysteine and sulforaphene has shown some protective effects in preliminary studies. High doses of vitamin C and vitamin E have shown some benefit in genetically susceptible asthmatic children. Physicians should play an important role in advocating to governments for a reduction in ambient air pollutants using innovative strategies and stricter legislation. Clearly more research is required to better understand intervention strategies to reduce morbidity and mortality due to ambient air pollutants, but in the meantime, the least we can do is to supplement diets with rich sources of antioxidants.
Modeling, Inference and Optimization With Composable Differentiable Procedures

This thesis presents five contributions to machine learning, with themes of differentiability and Bayesian inference. We present Firefly Monte Carlo, an auxiliary variable Markov chain Monte Carlo algorithm that only queries a potentially small subset of data at each iteration yet simulates from the exact posterior distribution. We describe the design and implementation of Autograd, a software package for efficiently computing derivatives of functions written in Python/Numpy using reverse accumulation mode differentiation. Using Autograd, we develop a convolutional neural network that takes arbitrary graphs, such as organic molecules, as input. This generalizes standard molecular feature representations and allows end-to-end adaptation of the feature extraction pipeline to particular tasks. We show how to compute gradients of cross-validation loss with respect to hyperparameters of learning algorithms, with both time and memory efficiency, by chaining gradients backwards through an exactly reversed optimization procedure. Finally, by accounting for the entropy destroyed by optimization, we show that early stopping and ensembling, popular tricks for avoiding overfitting, can be interpreted as variational Bayesian inference.
Boundary Crossings: Cooperative Learning, Collaborative Learning, and Problem-Based Learning

Since the 1960s, there has been growing and sustained interest in small-group learning approaches at the school level and in higher education. A voluminous body of literature in this area addresses theory, research, classroom practice, and faculty development. The approaches most highly represented in the literature are cooperative learning, collaborative learning, and problem-based learning (PBL). In this article, the authors compare and contrast these approaches through answering questions such as the following: What are the unique features of each approach? What do the three approaches have in common? How are they similar, and how are they different?
Design of emergency manoeuvres for automated highway system: obstacle avoidance problem

We analyze the problem of obstacle avoidance in an automated highway system (AHS). For a given scenario (traffic state, obstacle location, etc.), we synthesize the best possible avoidance manoeuvre for each vehicle. Our aim is to obtain a distributed strategy so that the obstacle avoidance manoeuvres can be executed by vehicle based controllers (with some inter-vehicle communication) as opposed to a roadside controller making decisions and communicating it to the individual vehicles.
The numerical analysis of patient-specific human heart towards the design of the next-generation ventricular assist device

ii Acknowledgment . iv Declaration . v List of Publications . vii Table of Content . ix List of Figures . xii List of Tables. xxiii List of Abbreviations. xxiv Chapter 1Introduction . 1 1-1Chapter Overview . 1 1-2Heart Anatomy . 1 1-3Heart Physiology . 5 1-4Phase of Cardiac Cycle . 7 1-5Cardiovascular Disease (CVD) . 14 1-6Current Treatment of CVD. 20 1-7Objectives of the Thesis . 24 1-7-1Aims and Motivations . 24 Numerical Simulation of the Patient-Specific Human Heart . 26 Optimizing and Developing the New Configuration of the VAD Device . 28 1-7-2Thesis Outline . 28 Chapter 2The Current State of VAD Technology and the Optimization of the Swinburne VAD . 32 2-1Chapter Overview . 32 2-2Introduction . 32 2-3Classifying VAD Systems . 34 2-4Description of VADs’ Mechanical Structure . 35 2-5Current State of Available VADs . 40 2-6Limitations of Current VADs . 48 2-7Cardiac Support System (CSS) . 51 2-8Developing Swinburne Wireless VAD System . 57 2-8-1Preliminary Conceptual Design . 58 2-8-2Design of WFE . 61 2-8-3Manufacturing HSE . 69 2-8-4Supplying Power by Wireless Technology . 70 2-8-5Manufacturing by FDM . 76 Chapter 3Literature Review and Background of Patient-Specific LV CFD Simulation 78 3-1Chapter Overview . 78 3-2Introduction . 78 3-3CFD Approaches . 85 3-4Geometry Reconstruction Methods . 88 3-5Boundary Conditions . 89 3-5-1Different Types of Boundary Conditions . 89 3-5-2Incorporating the Valve Leaflets . 96 3-6Patient-Specific Study Subjects . 102 3-6-1Physiological Patient-Specific LV Models . 102
The Acquisition of English Restrictive and Non-restrictive Relative Clauses by L1 Thai Learners

This study examined the acquisition of English restrictive and non-restrictive relative clauses (RRCs and NRRCs) by L1 Thai learners to test whether the Noun Phrase Accessibility Hierarchy (NPAH) (Keenan & Comrie, 1977) and the Perceptual Difficulty Hypothesis (PDH) (Kuno, 1974), both previously confined to RRC data, would be equally applicable to NRRCs. As the distinction between English RRCs and NRRCs does not rely on the factors upon which the two hypotheses are hinged, it was hypothesized that these hypotheses would be equally applicable to both RC types. However, because NRRCs are less common, the acquisition of NRRCs was hypothesized to diverge from that of RRCs. A sentence interpretation task and a grammaticality judgment task were administered to 40 intermediate and advanced L1 Thai undergraduate students and five native controls. The results showed that RRC and NRRC acquisition trajectories confirmed the NPAH, suggesting the NPAH could also be extended to NRRCs. The PDH, however, seemed to apply to the acquisition of RRCs but not NRRCs, possibly because NNRCs’ prototype differs from RRCs’. The results also demonstrated that the learners experienced more difficulty in acquiring NRRCs than RRCs. This asymmetry was attributed to NRRCs’ rarity and lesser degree of prototypicality, including transfer of training and the cognitive factor of overgeneralization (Selinker, 1972).
Performance Comparison of Different Despeckled Filters for Ultrasound Images

Ultrasound (US) is an economical, non-ionizing, transferable, and adaptable imaging technique which have a major role in diagnosing the diseases and in image guided surgery. But due to higher frequency absorbed by tissue and skin it cannot penetrate deeply in comparison to lower frequency. So, it gives degraded quality image by echo signals and we get an inferior quality noisy image with some clinical information loss. There are various filters which despeckle the noisy US image and preserve the clinical details. In this paper, we compare the performance of four despeckle filters: Median filter, Lee Filter, Frost Filter, and Speckle Reducing Anisotropic Diffusion (SRAD) Filter in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) performance index. We have selected two parameters as: window size (3x3, 5x5, 7x7, for all iteration number 10) and number of iteration (20, 30, 40, for all window size 3x3) for performance comparison. Finally, we have seen that SRAD filter is more efficient for removing Speckle noise in comparison to other stated filters, but the performance of median filter is also good enough for speckle noise reduction.
An Evaluation Framework and Instrument for Evaluating e-Assessment Tools

e-Assessment, in the form of tools and systems that deliver and administer multiple choice questions (MCQs), is used increasingly, raising the need for evaluation and validation of such systems. This research uses literature and a series of six empirical action research studies to develop an evaluation framework of categories and criteria called SEAT (Selecting and Evaluating e-Assessment Tools). SEAT was converted to an interactive electronic instrument, e-SEAT, to assist academics in making informed choices when selecting MCQ systems for adoption or evaluating existing ones.
Learning Deep Representations for Scene Labeling with Guided Supervision

Scene labeling is a challenging classification problem where each input image requires a pixel-level prediction map. Recently, deep-learning-based methods have shown their effectiveness on solving this problem. However, we argue that the large intra-class variation provides ambiguous training information and hinders the deep models' ability to learn more discriminative deep feature representations. Unlike existing methods that mainly utilize semantic context for regularizing or smoothing the prediction map, we design novel supervisions from semantic context for learning better deep feature representations. Two types of semantic context, scene names of images and label map statistics of image patches, are exploited to create label hierarchies between the original classes and newly created subclasses as the learning supervisions. Such subclasses show lower intra-class variation, and help CNN detect more meaningful visual patterns and learn more effective deep features. Novel training strategies and network structure that take advantages of such label hierarchies are introduced. Our proposed method is evaluated extensively on four popular datasets, Stanford Background (8 classes), SIFTFlow (33 classes), Barcelona (170 classes) and LM+Sun datasets (232 classes) with 3 different networks structures, and show state-of-the-art performance. The experiments show that our proposed method makes deep models learn more discriminative feature representations without increasing model size or complexity.
Multiple imputation of discrete and continuous data by fully conditional specification

The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in .
Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks

This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face tracking system enables the tracker to run in real-time without any on-line training. This algorithm does not need to change any parameters according to different input video conditions, and the runtime cost will not be affected significantly by an the increase in the number of faces being tracked. In addition, our proposed tracker can overcome most of the generally difficult tracking conditions which include video containing a camera cut, face occlusion, false positive face detection, false negative face detection, e.g. due to faces at the image boundary or faces shown in profile. We use two commonly used metrics to evaluate the performance of our multi-face tracking system demonstrating that our system achieves accurate results. Our multi-face tracker achieves an average runtime cost around 0.035s with GPU acceleration and this runtime cost is close to stable even if the number of tracked faces increases. All the evaluation results and comparisons are tested with four commonly used video data sets.
Digital Commons at Oberlin Digital Commons at Oberlin

A ������� . We study a family of discrete-time recurrent neural network models in which the synaptic connectivity changes slowly with respect to the neuronal dynamics. � e fast (neuronal) dynamics of these models display a wealth of behaviors ranging from simple convergence and oscillation to chaos, and the addition of slow (synaptic) dynamics which mimic the biological mechanisms of learning and memory induces complex multiscale dynamics which render rigorous analysis quite di � cult. Nevertheless, we prove a general result on the interplay of these two dynamical timescales, demarcating a regime of parameter space within which a gradual dampening of chaotic neuronal behavior is induced by a broad class of learning rules.
Enhancing reliability with Latin Square redundancy on desktop grids.

ENHANCING RELIABILITY WITH LA TIN SQUARE REDUNDANCY ON DESKTOP GRIDS Nathan Patrick Johnson April 15, 2010 Computational grids are some of the largest computer systems in existence today. Unfortunately they are also, in many cases, the least reliable. This research examines the use of redundancy with permutation as a method of improving reliability in computational grid applications. Three primary avenues are explored development of a new redundancy model, the Replication and Permutation Paradigm (RPP) for computational grids, development of grid simulation software for testing RPP against other redundancy methods and, finally, running a program on a live grid using RPP. An important part of RPP involves distributing data and tasks across the grid in Latin Square fashion. Two theorems and subsequent proofs regarding Latin Squares are developed. The theorems describe the changing position of symbols between the rows of a standard Latin Square. When a symbol is missing because a column is removed the theorems provide a basis for determining the next row and column where the missing symbol can be found. Interesting in their own right, the theorems have implications for redundancy.