Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for Sparse Functional Data

Methods for Sparse Functional Data Edwin Kam Fai Lei Doctor of Philosophy Graduate Department of Statistical Sciences University of Toronto 2014 The primary aim of this thesis is to study methods for the analysis of sparse functional data. Since this type of data is observed infrequently and irregularly for each subject, even simple descriptive statistics such as the mean and covariance must be reformulated. In the first part of this thesis, we study a related but more challenging problem of recovering the underlying functional trajectories when the subjects are genetically correlated. The key idea is to reconstruct the trajectories by using the Karhunen-Loéve expansion of a random function with a data-driven eigenbasis. In the second part of this thesis, we study effective dimension reduction for regression of a scalar response on a sparse functional predictor. Our proposal estimates the effective dimension reduction space under the presence of sparse functional data, which has the important property that the projection of the functional predictor onto it contains as much information on the response as the functional predictor itself. We derive our estimator’s asymptotic properties and study its finite sample performance. Lastly, we consider extensions of our effective dimension reduction procedure for the classification of sparse functional data.
Identification of Hadronic Tau Lepton Decays at the ATLAS Detector Using Artificial Neural Networks

Tau leptons play an important role in a wide range of physics analyses at the LHC, such as the verification of the Standard Model at the TeV scale or the determination of Higgs boson properties. For the identification of hadronically decaying tau leptons with the ATLAS detector, a sophisticated, multi-variate algorithm is required. This is due to the high production cross section for QCD jets, the dominant background. Artificial neural networks (ANNs) have gained much attention in recent years by winning several pattern recognition contests. In this thesis, a survey of ANNs is given with a focus on developments of the past 20 years. Based on this work, a novel, ANN-based tau identification is presented which is competitive to the current BDT-based approach. The influence of various hyperparameters on the identification is studied and optimized. Both stability and performance are enhanced through formation of ANN ensembles. Additionally, a score-flattening algorithm is presented that is beneficial to physics analyses with no defined working point in terms of signal identification efficiency. Zusammenfassung Tau-Leptonen spielen eine wichtige Rolle in vielen physikalischen Analysen am LHC, u.A. in der Uberprufung des Standardmodells an der TeV-Skala und der Bestimmung von Eigenschaften des Higgs-Bosons. Um hadronisch zerfallende Tau-Leptonen mit dem ATLAS-Detektor zu identifizieren bedarf es komplexer, multivariater Algorithmen. Grund dafur ist der hohe Produktionswirkungsquerschnitt fur QCD-Jets, welche den dominanten Untergrund darstellen. Kunstliche neuronale Netze (KNN) haben in der jungeren Vergangenheit mehrere Wettbewerbe zur Mustererkennung gewonnen und so allgemeine Aufmerksamkeit auf sich gezogen. Diese Arbeit prasentiert eine Ubersicht der Theorie kunstlicher neuronaler Netzwerke mit besonderem Fokus auf die Entwicklungen der letzten 20 Jahre. Darauf basierend wird eine neuartige, KNN-basierte TauIdentifikation vorgestellt, die Ergebnisse vergleichbar zu dem derzeitigen BDT-basierten Ansatz erzielt. Weiterhin erfolgt eine Untersuchung des Einflusses diverser Hyperparameter sowie deren Optimierung. Sowohl die Stabilitat als auch die Leistung der Identifikation wird durch die Bildung von KNN-Ensembles gesteigert. Zusatzlich wird ein Score-Flattening-Algorithmus vorgestellt, der die Ensemble-Ausgabe zugunsten einer vorhersagbaren Signalidentifikationswahrscheinlichkeit transformiert.
Determination of Caffeine and some other Drugs in Cappuccino, Nescafe, Cacao, Coffee samples collected from some Libyan Markets using High Performance Liquid Chromatography (RP-HPLC)

This study aims to determine the caffeine and detection of some other drugs (which has commonly spread and caused suicides in the recent years in El-Bieda city - Libya) in cappuccino, nescafe, cacao and coffee samples using Reversed Phase - High Performance Liquid Chromatography (RP-HPLC). In the presented study, seventeen samples were collected from some Libyan markets. A rapid, simple and reliable extraction method is developed and validated for the determination of caffeine and other drugs in the samples under study using dichloromethan, n-hexane and methanol as the extracting solvents. The method is validated over a wide linear range of 2 – 10 µg/ml with correlation coefficients being consistently greater than 0.999. The minimum caffeine level was observed in the Nesquik (Cacao), Nestle Italiana S.P.A sample (2.9129 µg/ml), while the Nestla (Nescafe), Asbania sample showed the highest caffeine content (598.5315 µg/ml). The RP-HPLC measurements indicate that the results of caffeine concentrations in samples under study are in the range of 2.9129 - 598.5315 µg/ml with an average of 221.8630 µg/ml. The measurements by HPLC methods indicate that the samples under study are free from other drugs: (olanzapine, diazepam and alprazolam). The caffeine content in all the samples analyzed in this study are within the allowable limits set by the US Food and Drugs Administration and documented values.
Research on Futures Price Forecast of WVMD-LSTM Model

The research on futures price prediction is of great significance to society and enterprises. In order to cope with the increasingly complex financial market environment, this paper proposes a new hybrid model, WVMD-LSTM, to improve the accuracy of futures price prediction by capturing the information of multiple dimensions in futures data. A series of single models and mixed models are used to comprehensively compare the forecasting performance of VMD-LSTM model, and the gold futures price is selected to verify the effectiveness of WVMDLSTM in futures price forecasting. The performance of the WVMD-LSTM model is comprehensively evaluated by fitting degree indicators (RMSE, MAE, MAPE, R2) and direction accuracy indicators with economic significance $(\mathrm{D}_{\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{t}})$. The results show that compared with ARIMA, VMD-LSTM, WVMD-LSTM has the best prediction ability.
Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches

We consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution, implementations of joint MVN MI typically assume that categories of discrete variables are probabilistically constructed from continuous values. We use simulations to examine the implications of these assumptions. For each approach, we assess (1) the accuracy of the imputed values; and (2) the accuracy of coefficients and fitted values from a model fit to completed data sets. These simulations consider continuous, binary, ordinal, and unordered-categorical variables. One set of simulations uses multivariate normal data, and one set uses data from the 2008 American National Election Studies. We implement a less restrictive approach than is typical when evaluating methods using simulations in the missing data literature: in each case, missing values are generated by carefully following the conditions necessary for missingness to be “missing at random” (MAR). We find that in these situations conditional MI is more accurate than joint MVN MI whenever the data include categorical variables.
Clinical Meaning of Stromal Tumor Infiltrating Lymphocytes (sTIL) in Early Luminal B Breast Cancer

Simple Summary Stromal tumor infiltrating lymphocytes (sTIL) are a validated predictive and prognostic biomarker in non-luminal breast cancer. Our aim was to evaluate their clinical relevance in luminal (hormone receptor positive, HER2 negative) early breast cancer. Our results show that, although sTIL are associated with a better response to neoadjuvant chemotherapy, they are also associated with worse biological features (proliferation, higher stage) and poorer prognosis in luminal B breast cancer. TIL might improve prognostic stratification and contribute to therapeutic decision-making in the early high-risk setting of luminal B breast cancer. Abstract Luminal breast cancer (BC) is associated with less immune activation, and the significance of stromal lymphocytic infiltration (sTIL) is more uncertain than in other BC subtypes. The aim of this study was to investigate the predictive and prognostic value of sTIL in early luminal BC. The study was performed with an observational design in a prospective cohort of 345 patients with predominantly high-risk luminal (hormone receptor positive, HER2 negative) BC and with luminal B features (n = 286), in which the presence of sTIL was analyzed with validated methods. Median sTIL infiltration was 5% (Q1–Q3 range (IQR), 0–10). We found that sTIL were associated with characteristics of higher biological and clinical aggressiveness (tumor and lymph node proliferation and stage, among others) and that the percentage of sTIL was predictive of pathologic complete response in patients treated with neoadjuvant chemotherapy (OR: 1.05, 95%CI 1.02–1.09, p < 0.001). The inclusion of sTIL (any level of lymphocytic infiltration: sTIL > 0%) in Cox regression multivariable prognostic models was associated with a shorter relapse-free interval (HR: 4.85, 95%CI 1.33–17.65, p = 0.016) and significantly improved its performance. The prognostic impact of sTIL was independent of other clinical and pathological variables and was mainly driven by its relevance in luminal B BC.
On the required Reynolds-number dependence of variational multi-scale Smagorinsky models

A theoretical analysis is presented on the dependence of the standard and variational multi-scale Smagorinsky models on the proximity of the LES fllter width ¢ to the integral length scale of turbulence L on the one hand, and to the Kolmogorov scale · on the other hand. Moreover modiflcations of the models are formulated, which respond better to ¢=· changes. Apart from a priori evaluations of L=¢ and ¢=· efiects, the quality of our proposed modiflcations to the models is further evaluated and corroborated based on LES of decaying homogeneous isotropic turbulence.
Growth performance and blood profile of weaned rabbits fed graded levels of Hevea brasiliensis seed meal as a protein source

A ten-week investigation was undertaken to assess the incorporation of Hevea brasilliensis seed meal (RSM) as an alternative to soyabean meal (SBM) in rabbits' feed. Sixty kit rabbits were distributed at random to four treatments diets and five rabbits / replicate in a completely randomized design (CRD). The diets consist of a control diet (0% RSM) as well as three experimental diets, containing 25%, 50% and 75% RSM. Results showed that RSM affected (P < 0.05) the rabbits' blood profile and performance. The weight gain was similar (P > 0.05) up to 50% SBM replacement, while feed conversion ratio (FCR) were similar (P > 0.05) across the replacement levels. The highest weight gain and best FCR were observed in diets containing 50% and 25% RSM respectively. The highest values of packed cell volume (PCV), haemoglobin (Hb), white blood cells (WBC), mean corpuscular volume (MCV), and mean corpuscular haemoglobin (MCH), were noted in Diet 4 (75% RSM). The total protein increased with an increased level of RSM in the diet, while as RSM in the diet increased, the liver enzymes dropped. In conclusion, RSM could effectively replace 50% SBM as protein ingredient in growing rabbits' diets.
Dextran Methacrylate Reactions with Hydroxyl Radicals and Hydrated Electrons in Water: A Kinetic Study Using Pulse Radiolysis

Dextran methacrylate (Dex-MA) is a biodegradable polysaccharide derivative that can be cross-linked by ionizing radiation. It is therefore considered a potential replacement for synthetic hydrophilic polymers in current radiation technologies used for synthesizing hydrophilic cross-linked polymer structures such as hydrogels, mainly for medical applications. This work is focused on the initial steps of radiation-induced cross-linking polymerization of Dex-MA in water. Rate constants of two major transient water radiolysis products—hydroxyl radicals (•OH) and hydrated electrons (eaq−)—with various samples of Dex-MA (based on 6–500 kDa dextrans of molar degree of substitution or DS with methacrylate groups up to 0.66) as well as non-substituted dextran were determined by pulse radiolysis with spectrophotometric detection. It has been demonstrated that these rate constants depend on both the molecular weight and DS; reasons for these effects are discussed and reaction mechanisms are proposed. Selected spectral data of the transient species formed by •OH- and eaq−-induced reactions are used to support the discussion. The kinetic data obtained in this work and their interpretation are expected to be useful for controlled synthesis of polysaccharide-based hydrogels and nanogels of predefined structure and properties.
Agreement of Dental Students in the Detection of Normal Landmarks When Comparing Digital Lateral Cephalograms and Three-Dimensional Cone Beam Computed Tomography Images

Background: Proper defining of normal landmarks in lateral cephalograms is important for establishing proper orthodontic treatment plan. Objectives: To evaluate the agreement of dental students to identify normal landmarks (NLs) in digital lateral cephalograms and cone beam computed tomography (CBCT) images. Patients and Methods: In this study, lateral cephalograms and CBCT images of 11 orthodontic patients were selected. Three fourthyear dental students were asked to identify 19 NLs after calibrating digital lateral cephalograms and 3D CBCT images. Then, the distances of each landmark from the superior and anterior edges of the images were measured for each observer. Results: The observers’ errors fell within a range of -1.03 to 2.74 in two-dimensional cephalometry and a range of -0.88 to 2.31 in 3D CBCT, showing a 95% limit of agreement. According to the intraclass correlation coefficient (ICC) comparison made by our student observers, only 5% of interobserver assessment in CBCT and 20% in lateral cephalometry showed poor reliability. These same observers showed more agreement when identifying NLs on soft tissue as compared to hard tissue structures and also in detecting NLs located in the midsagittal region rather than on lateral sides in both modalities. Their differences in agreement in detecting midsagittal NLs rather than on lateral sides were statistically significant (P = 0.0001). Conclusions: The training of cephalometric tracing in orthodontic course based on 3D skull models and imaginary methods was deemed successful in increasing the ability of dental students to determine the precise location of NLs, even on CBCT images.
Optimising sampling of fish assemblages on intertidal reefs using remote underwater video

Background Assessing fish assemblages in subtidal and intertidal habitats is challenging due to the structural complexity of many of these systems. Trapping and collecting are regarded as optimal ways to sample these assemblages, but this method is costly and destructive, so researchers also use video techniques. Underwater visual census and baited remote underwater video stations are commonly used to characterise fish communities in these systems. More passive techniques such as remote underwater video (RUV) may be more appropriate for behavioural studies, or for comparing proximal habitats where the broad attraction caused by bait plumes could be an issue. However, data processing for RUVs can be time consuming and create processing bottlenecks. Methods Here, we identified the optimal subsampling method to assess fish assemblages on intertidal oyster reefs using RUV footage and bootstrapping techniques. We quantified how video subsampling effort and method (systematic vs random) affect the accuracy and precision of three different fish assemblage metrics; species richness and two proxies for the total abundance of fish, MaxNT and MeanCountT, which have not been evaluated previously for complex intertidal habitats. Results Results suggest that MaxNT and species richness should be recorded in real time, whereas optimal sampling for MeanCountT is every 60 s. Systematic sampling proved to be more accurate and precise than random sampling. This study provides valuable methodology recommendations which are relevant for the use of RUV to assess fish assemblages in a variety of shallow intertidal habitats.
Physical, mental and healthcare issues of children on the street of Ibadan, Nigeria

Background Most street children studied in lower- and middle-income African countries are without family links. However, the majority of street children are children on the street, living with families during the night and spending their day-time on the streets. The health of this majority group is poorly captured in the literature despite the growing epidemic of child streetism. Aim To explore the health of children on the street of Ibadan using multiple qualitative studies. Setting A street in each of the five urban local government areas of Ibadan Oyo State, Nigeria. Methods Participants comprising of children on the street, parental figures, street shop owners and child-welfare officers were purposively selected and interviewed. Interviews were audio recorded, transcribed, and thematically analyzed. Results Using triangulated data from 53 interviews, the study found that the children on the streets of Ibadan experienced many health challenges. Outstanding are poor carbohydrate-based diet, open defaecation with consequent infections, physical injuries and few deaths from road traffic accidents. Sexual, verbal and substance abuse were common although few children acquired resilience to adversity. The children had poor health-seeking behaviour and resorted to patent medicine dealers or tradomedical practitioners on the streets. Conclusion This study bridged some gaps in the literature regarding the health of children on the streets in Nigeria. The straddling of children between the family and street has cumulative health consequences as depicted in this study. Contribution This research can inform family-level intervention and primary health care plans to forestall the health challenges of children on the streets.
The case for oral histories of neoliberal Africa

ABSTRACT There has been a dearth of oral histories of work and earning a living in Africa, especially during the neoliberal period from the 1980s. Compared to scholarship published more than half a century ago, there seems to be a decline of the use of oral histories to explore the history of the living under capitalism, despite the acceptance of oral histories as an important source and methodology for reconstructing experiences of people. At the same time, the last few decades have seen unprecedented changes across the continent in the working lives of people across generations. Generating substantial bodies of oral historical accounts of working lives would offer invaluable and productive insights into these experiences of change. It would allow a critical analysis of neoliberalism at the incisive level of a person’s biography. This collection aims to address this gap. The introductory article serves three purposes. First, it provides the intellectual background to this collective intervention and discusses the usefulness of oral histories in the reconstruction of economic life under neoliberalism. Second, it provides a historiography of oral history covering debates and developments in anthropology and history. Third, it provides an overview of the seven articles – covering South Africa, Zimbabwe, Uganda, and South Sudan – that constitute this volume. Overall, our opening text is a call for the re-engagement with and use of oral histories to document, understand, and discuss work and life under capitalism.
The Influence of Managerial Ownership, Institutional Ownership and Deferred Tax Burden on Earnings Management in Food and Beverage Companies on the Indonesia Stock Exchange 2017 – 2021

This study aims to analyze the Effect of Managerial Ownership, Institutional Ownership and Deferred Tax Burden on Profit Management in Food and Beverage Companies on the Indonesia Stock Exchange 2017 – 2021. This study used a quantitative approach in the form of financial statements of food and beverage companies listed on the Indonesia Stock Exchange from 2017 to 2021. The sampling technique used in this study used a purposive sampling approach, the number of samples used in this study were 90 samples. The results of this study indicate that managerial ownership has no effect on earnings management, institutional ownership has an effect on earnings management, deferred tax expense has an effect on earnings management, simultaneously the variables managerial ownership, institutional ownership and deferred tax expense have an effect on earnings management. The limitations in this study are that the research object only focuses on food and beverage sector companies that are listed on the Indonesia Stock Exchange from 2017 to 2021. This study uses the dependent variable, namely earnings management and independent variables, namely Managerial Ownership, Institutional Ownership and Deferred Tax Expenses As material for consideration in making decisions related to earnings management and can be used as a reference for companies in determining policies related to financial reports regarding earnings management practices. This Paper is Original Research paper.
PVBLiF: A Pseudo Video-Based Blind Quality Assessment Metric for Light Field Image

Going beyond traditional 2D imaging is not only an emerging trend of imaging technology, but also the key to a more immersive user experience. Light Field Image (LFI) is a typical high-dimensional imaging format, and the quality evaluation of which is very challenging but necessary. In this article, we propose a novel Pseudo Video-based Blind quality assessment metric for Light Field image (PVBLiF). In contrast to most previous Light Field Image Quality Assessment (LF-IQA) metrics, in which different types of 2D representations derived from LFI are used for quality assessment indirectly, our metric exploits a more intuitive 3D representation, named Pseudo Video Block Sequence (PVBS), to evaluate the perceptual quality of LFI. For this purpose, we first divide the LFI into a massive number of non-overlapping PVBSs, which simultaneously contain spatial and angular information of LFI. Then, we propose a novel network (named PVBSNet) based on Convolutional Neural Networks (CNNs) to extract the spatio-angular features of PVBS and further evaluate the PVBS quality. The proposed PVBSNet consists of four stages: multi-information division, intra-feature extraction, cross-feature fusion, and quality regression. Finally, a Saliency- and Variance-guided Pooling (SVPooling) method is presented to integrate all the PVBS quality into the overall quality of LFI. The proposed PVBLiF metric has been extensively evaluated on three widely-used LFI datasets: Win5-LID, NBU-LF1.0, and SHU. Experimental results demonstrate that our proposed PVBLiF metric outperforms state-of-the-art metrics and is capable of highly approximating the performance of human observers.