Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing the CAPM Theory Based on a New Model for Fama-French 25 Portfolio Returns

In this paper, a new model is proposed to empirically test the Capital Asset Pricing Theory. This model is based on the EGARCH-type volatilities in Nelson (1991) and the non-Normal errors of SSAEPD in Zhu and Zinde-Walsh (2009). Is the CAPM theory in Sharpe (1964), Lintner (1965) and Mossin (1966) still alive? Returns of Fama-French 25 stock portfolios (1926-2011) are analyzed. The Maximum Likelihood Estimation Method is used. Likelihood Ratio test (LR) and Kolmogorov-Smirnov test (KS) are used to do model diagnostics. Akaike Information Criterion (AIC) is used for model comparison. Simulation results show the MatLab program is valid. Empirical results show with non-Normal errors and the EGARCH-type volatilities, the CAPM theory is not alive. This new model can capture the skewness, fat-tailness, asymmetric effects and volatility persistence in the data. This new model has better in-sample fit than others. Portfolios with smaller size have larger Beta value.
Early Puberty and Its Relation to Body Mass Index among Schoolgirls in the City of Jeddah, Saudi Arabia

2 Abstract: Puberty is a stage of maturation between childhood and adulthood which is a signal of the onset of the female reproductive cycle. While there is clue that the age of puberty among girls appears to have been decreased in recent decades in different countries around the world, the data concerning the age of puberty for girls is considered scarce in Saudi Arabia. The purpose of this study is to evaluate the current age of puberty among schoolgirls in the city of Jeddah, Saudi Arabia and to find out whether the trend in the rate of fall in pubertal age has stopped or it is continuing. Also, to examine the correlation between age of puberty and the body mass index (BMI). Overweight was defined as higher than or equal to the 85th percentile and obesity as higher than or equal to the 95 percentile using growth charts provided by the Centers for Disease Control th and Prevention (CDC, 2000). A research was conducted on 508 randomly selected female elementary school students from the city of Jeddah. Data of puberty was collected by conducting interviews with these students while at school. Then the researcher performed measuring the height and weight for each student by using the scale and height board that was a standardized scale in Saudi Arabia. The research findings of the study showed that the mean age at puberty for Saudi girls was 10± 0.80 years old. The prevalence rate of obesity and overweight among pubertal girls was 5% and 60.5% respectively. Age of puberty was significantly associated with probability of overweight and obesity with (P = 0.00), (df = 2) and a weak positive correlation (R= 0.26). The pubertal age among Saudi girls is still decreasing that means it follows the secular trend in declining and there is significant association between early puberty and body mass index.
Observational Causal Induction with Small Samples

Causal induction forms the basis for adaptation of animals. It enables diagnosis of the past, judgment in the present, and prediction for the future in uncertain environments. In AI, it is argued that efficient causal inference supports human-like quick learning and decision-making with small amount of data. The purpose of this study is to elucidate how humans judge the causal relationship when given small data. We assume that causal induction has two stages, observational and interventional, according to Hattori and Oaksford. In our experiment, we controlled a factor (symmetry between occurrence and non-occurrence of the causal events) that has been shown to manipulate the cognitive distinction between the two stages. We compared the models that correspond to the two stages, pARIs/DFH and delta-P. It was shown that pARIs, the probability of biconditionals and equivalent to Jaccard index, describes the result best in both conditions.
A Statistical Profiling Model of Long-Term Unemployment Risk in Ireland

This paper develops a statistical profiling model of long-term unemployment risk in Ireland using a combination of administrative data and information gathered from a unique questionnaire that was issued to all jobseekers making a social welfare claim between September and December 2006 who were then tracked for eighteen months. We find that factors such as a recent history of long-term unemployment, advanced age, number of children, relatively low levels of education, literacy/numeracy problems, location in urban areas, lack of personal transport, low rates of recent labour market engagement, spousal earnings and geographic location all significantly impact the likelihood of remaining unemployed for 12 months or more. While the predicted probability distribution for males was found to be relatively normal, the female distribution was bimodal, indicating that larger proportions of females were at risk of falling into long-term unemployment. We find evidence that community based employment schemes for combating long-term unemployment have little effect as participants re-entering the register typically experience extended durations. Finally, we argue that the adoption of an unemployment profiling system will result in both equity and efficiency gains to Public Employment Services.
Multi-tenant Data Migration Strategy in SaaS Platform

On demand migration of tenants' data is a critical technology for elastic load balancing in SaaS application. However, different tenants have different required resource with various SLA requirements. Moreover, previously reported migration techniques are not tenant aware. To study the elastic load balancing of tenants' data, we mainly focus on the problem of when to migrate which tenants' data in a muti-tenant shared DB server and build a new framework that aims to monitor load and minimize migration cost. We establish a demand resource estimation model to decide when to migration and then create a cost model to guide the choice of possible migration tenants. The simulation experimental results show the high effectiveness and the good efficiency of our model.
The Intensity of adoption of Conservation agriculture by smallholder farmers in Zimbabwe

ABSTRACT This article assesses the intensity of technology adoption of conservation agriculture (CA) techniques by smallholder farmers in Zimbabwe. It seeks to understand the drivers of CA adoption intensity in terms of the number of practices implemented using count data analysis. On average, the farmers in our sample adopt five out of eight possible CA practices while only 7.4% use all eight practices in any one year. Practices such as digging planting basins (81.9%), applying manure (73.2%) and timely post-planting weeding (70.1%) are relatively popular, while adoption of crop rotation (22.8%) is comparatively rare. Productivity is positively correlated to the number of techniques used. Farmers adopting all the CA practices are the most productive, with an estimated maize yield of 2.50 tons/ha, compared with a yield of less than 1 tons/ha for those using three techniques or fewer. Results from a Poisson regression indicate that education, agro-ecology, non-governmental input support and extension support have a significant impact on adoption intensity. Subsidised inputs increase the number of components used, although access to those inputs was uneven across regions of Zimbabwe. Further, the number of CA components used in the previous season positively impacts current season adoption intensity, implying that promotions of CA technologies do have a persistent effect, even after those promotions end.
THE ROLE OF OXIDATIVE STRESS IN THE ETIOPATHOGENESIS OF DEPRESSION

In recent years, the role of oxidative stress in the etiopathogenesis of depression has been increasingly discussed. The mechanisms by which stress has a negative effect on the brain are not yet fully understood. Free radicals cause rapid damage to certain cellular macromolecules that may be involved in cytotoxic effects in the central nervous system. The effectiveness of new types of supplementation therapy with antioxidants - vitamins A, E, C, Omega-3 fatty acids, Coenzyme Q10 and Zn are being studied.
Electronic structure of the perturbed blue copper site in nitrite reductase: spectroscopic properties, bonding and implications for the entatic/rack state.

Low-temperature optical absorption, circular dichroism, magnetic circular dichroism, and sulfur K-edge X-ray absorption spectra have been measured for the green “blue” copper center (type 1) in Achromobacter cycloclastes nitrite reductase. Combined with density functional calculations, the results of these spectroscopies have been used to define the extremely “perturbed” electronic structure of this site relative to that of the prototypical “classic” site found in plastocyanin. Experimentally calibrated density functional calculations have been further used to determine the specific geometric distortions which generate the perturbed electronic structure. These studies indicate that the principal electronic structure changes in nitrite reductase, relative to plastocyanin, are a rotation of the Cu dx2-y2 half-filled, highest occupied molecular orbital (HOMO) and an increase in the ligand field strength at the Cu center. The HOMO rotation increases the pseudo-σ interaction and decreases the π interaction of .
Optimization of Conductive Thin Film Epoxy Composites Properties Using Desirability Optimization Methodology

Multiwalled carbon nanotubes (MWCNTs)/epoxy thin film nanocomposites were prepared using spin coating technique. The effects of process parameters such as sonication duration (5–35 min) and filler loadings (1-2 vol%) were studied using the design of experiment (DOE). Full factorial design was used to create the design matrix for the two factors with three-level experimentation, resulting in a total of 9 runs () of experimentation. Response surface methodology (RSM) combined with E.C. Harrington’s desirability function called desirability optimization methodology (DOM) was used to optimize the multiple properties (tensile strength, elastic modulus, elongation at break, thermal conductivity, and electrical conductivity) of MWCNTs/epoxy thin film composites. Based on response surface analysis, quadratic model was developed. Analysis of variance (ANOVA), -squared (-Sq), and normal plot of residuals were applied to determine the accuracy of the models. The range of lower and upper limits was determined in an overlaid contour plot. Desirability function was used to optimize the multiple responses of MWCNTs/epoxy thin film composites. A global solution of 12.88 min sonication and 1.67 vol% filler loadings was obtained to have maximum desired responses with composite desirability of 1.
Twin Registries Moving Forward and Meeting the Future: A Review

Abstract Twin registries have developed as a valuable resource for the study of many aspects of disease and society over the years in many different countries. A number of these registries include large numbers of twins with data collected at varying information levels for twin cohorts over the past several decades. More recent expansion of twin datasets has allowed for the collection of genetic data, together with many other levels of ‘omic’ information along with multiple demographic, physiological, health outcomes and other measures typically used in epidemiologic research. Other twin data sources outside these registries reflect research interests in particular aspects of disease or specific phenotypic assessment. Twin registries have the potential to play a key role in many aspects of the artificial intelligence/machine learning-driven projects of the future and will continue to keep adapting to the changing research landscape.
Designing Urban Media Environments as Cultural Spaces

Many urban media installations neglect the fact that the installations will be used or perceived in a cultural space, leading to a conflict between content and urban setting. To address this shortcoming this paper discusses the importance of considering cultural aspects of public space when introducing urban media interventions into urban life. We propose the notion of urban media environments, which consist of media artefacts, other artefacts, and modes of interaction and perception, to emphasise the relevance of cultural aspects. The paper concludes with five considerations that can lead to a more successful and sustainable strategy for designing media interventions in urban life.
Deep learning for the automatic classification of tissue types in breast biopsies

Breast biopsies are crucial in the process of detec ing a wide range of diseases such as breast cancer. The evaluation of these biopsies is performed by trained pathologists that are often overworked due to the increasing number of pathologies requested. Automatic tumour detection techniques have been developed, achieving very good results. In this work, we propose to classify breast biopsies in all the different types of tissue present in them. The tissue types were identified by hand-labeling them following the indications of an expert pathologist. Afterward, they were trained with diffeerent convolutional neural networks such as GoogleNet, AlexNet, SqueezeNet and DenseNet. Out of these four networks, GoogleNet outperformed all of them achieving 95.4% of accuracy. Finally, we tried to identify why the networks were underperforming while also suggesting how results could be improved.
Policy Instruments and Budgetary Processes: A Reflection on the Deficit Elimination Experience in the Canadian Provinces

Analyses of the deficit elimination experience in provincial governments in Canada in the 1990s show that provincial authorities used similar sets of policy measures to reach the goal of a balanced budget within a short period of time. We look at these measures as policy instruments and we try to make sense of their use through a theoretically informed reflection on the role of information and trust in the budgetary process. The principal-agent theory shows how the Premier and his team circumvented the problems caused by the information monopoly of managers through the implementation of a top-down process for setting budgetary targets and through the decentralisation of operational decision-making. However, this theory is completely silent concerning the use of rhetorical instruments. The convention theory suggests that regulatory and rhetorical measures were combined to influence guardians of the treasury and program advocates in their visions of the budget and thus changed the budgetary process from an incremental process to a fiscal crisis process. The same policy instruments take on a different meaning depending on the theoretical lens that one uses. Their use fosters identical outcomes (in this case, a balanced budget) through different paths. The knowledge of the process through which the use of a given policy instrument might lead to a given outcome is essential if we want to get a better grasp of the side effects of the use of any policy instruments.
1 McCulloch ’ s Derivation of Quantized Inertia from Heisenberg ’ s Uncertainty Principle

In his recent work, physicist Mike McCulloch has derived what he has coined “Quantized Inertia” from Heisenberg’s uncertainty principle. He has published a series of papers indicating that Quantized Inertia can predict everything from galaxy rotations (without relying on the concept of dark matter) to the EM drive; see [1, 2, 3, 4]. Clearly, it is an interesting theory that deserves some attention until proven or disproven. We think McCulloch has several excellent insights, but it is important to understand the fundamental principles from which he has derived his theory. We will comment on the derivation in his work and suggest that it could be interpreted from a di↵erent perspective. Recent developments in mathematical atomism appear to have revealed new concepts concerning the Planck mass, the Plank length, and their link to special relativity, gravity, and even the Heisenberg principle. We wonder if Quantized Inertia is compatible with the atomist view of the world and, if so, how McCulloch’s theory should be interpreted in that light.
Adaptive Estimation of Noise Variance and Matrix Estimation via USVT Algorithm

Consider the problem of denoising a large $m\times n$ matrix. This problem has received widespread attention in recent years. A common approach is to assume that the denoised matrix is of low rank and then apply a thresholding algorithm. The error bound in this case is a function of the unknown rank. Another approach comes from the empirical observation that a low rank matrix may have small nuclear norm and therefore approximating the mean matrix via penalization of its nuclear norm sounds reasonable. In 2015 Sourav Chatterjee \cite{Chatterjee} investigated this problem in the case where the observed entries are a noisy version of a small fraction of the original entries. In his work he introduced the USVT algorithm for matrices with bounded entries (or normally distributed entries with known variance). In this work we want to consider the case that the entries do not necessarily lie in a bounded interval and we don't know their common variance, $\sigma^2$, although then we have to consider only the case that all the entries have been observed. In this work we consider two estimators for $\sigma$ and study their mean square error. Then we use a modified version of the USVT algorithm to give an estimation for the mean value matrix.
Night eating syndrome: a psychiatric disease, a sleep disorder, a delayed circadian eating rhythm, and/or a metabolic condition?

ABSTRACT Introduction: Night Eating Syndrome (NES) refers to an abnormal eating behavior which presents as evening hyperphagia consuming >25% calorie intake and/or nocturnal awaking with food ingestion which occurs ≥2 times per week. Although the syndrome has been described more than seven decades ago, the literature has been growing slowly on its etiology, diagnosis, and treatment. Areas covered: The proposed treatment options for NES are all at a case-study level. Moreover, our understanding of its etiology, comorbidities, and diagnosis is still premature. We performed a literature review in Medline/PubMed to identify all the studies proposing a management plan for NES and summarized all the existing data on its diagnosis and treatment. Expert opinion: To date, none of the proposed treatment options for NES have been promising and long-term data on its efficacy is lacking. The slow growth of evidence on this debilitating but underreported condition may be due to unawareness among clinicians, under-reporting by patients, and unrecognized diagnostic criteria. Objective screening of symptoms during office visits especially for patients at a high-risk for NES will identify more patients suffering from the syndrome.
Audio Extraction Video File Speech / Non-Speech Classifier Audio Stream Speech Segmentation Non-Speech Feature Extraction for AED Audio Event Inference XML Annotation File X

With the ever-growing quantity of multimedia documents uploaded on the Web, automatic description of the audio content of videos can provide very useful information, to index, archive and search multimedia documents. Audio Event Detection (AED), aims to substantially enhance the access to audio in multimedia content. Preliminary experiments with a sound effects corpus showed good results for training models. However, the performance on the real data test set, where there are overlapping audio events and continuous background noise is lower. This work describes the AED framework and methodologies used to build 6 Audio Event detectors, based on statistical machine learning tools (Support Vector Machines). The detectors showed some improvements achieved by adding background noises to the training data, comprised of clean sound effects that are quite different from the real audio events in real life videos and movies. A graphical interface prototype is also presented, that allows browsing a movie by its content and provides an audio event description with time codes.
TEMPORARY FLOODWATER STORAGE VOLUME ESTIMATIONS USING 1-m LiDAR AND 30-m NED DEMS OF THE RED RIVER OF THE NORTH BASIN

Compared here are storage volumes determined for 120 transect locations from 1-m Light Detection and Ranging (LiDAR) and 30-m National Elevation Dataset (NED) Digital Elevation Models (DEMs). The transects represent flat-, moderate-, and high-relief terrain in the Forest River Watershed of the Red River of the North Basin. Results indicate that either DEM yields acceptable storage estimates for terrain with flat to moderate-relief and LiDAR provides the more accurate estimate in areas of high-relief. In general, storage depths derived from LiDAR DEMs are greater than those extracted from NED DEMs, although storage estimates are not significantly different between the two datasets for flat-and moderate-relief areas. Each dataset can be used for storage estimation as a function of the local relief conditions, and certain section characteristics, which provided a basis to explain the patterns of observed discrepancies.
EVIEW / Musculoskeletal imaging adiologically guided percutaneous cryotherapy for oft tissue tumours : A promising treatment

a Adult Diagnostic and Interventional Imaging Department, hôpital Pellegrin, CHU de Bordeaux, place Amélie-Raba-Léon, 33076 Bordeaux, France b Radiology Department, institut Bergonié, 229, cours de l’Argonne, 33076 Bordeaux, France c Radiology Department, hôpital Saint-André, CHU de Bordeaux, 1, place Jean-Burguet, 33076 Bordeaux, France d Radiology Department, hospital Archet-2, CHU de Nice, 151, route Saint-Antoine-de-Ginestière, 06202 Nice, France e Radiology Department, hôpital civil, CHU de Strasbourg, 1, place de l’Hôpital, 67091 Strasbourg, France
Block Edit Errors with Transpositions: Deterministic Document Exchange Protocols and Almost Optimal Binary Codes

Document exchange and error correcting codes are two fundamental problems regarding communications. In the first problem, Alice and Bob each holds a string, and the goal is for Alice to send a short sketch to Bob, so that Bob can recover Alice's string. In the second problem, Alice sends a message with some redundant information to Bob through a channel that can add adversarial errors, and the goal is for Bob to correctly recover the message despite the errors. In a recent work \cite{CJLW18}, the authors constructed explicit deterministic document exchange protocols and binary error correcting codes for edit errors with almost optimal parameters. \ Unfortunately, the constructions in \cite{CJLW18} do not work for other common errors such as block transpositions. In this paper, we generalize the constructions in \cite{CJLW18} to handle a much larger class of errors. These include bursts of insertions and deletions, as well as block transpositions. Specifically, we consider document exchange and error correcting codes where the total number of block insertions, block deletions, and block transpositions is at most $k \leq \alpha n/\log n$ for some constant $0<\alpha<1$. In addition, the total number of bits inserted and deleted by the first two kinds of operations is at most $t \leq \beta n$ for some constant $0<\beta<1$, where $n$ is the length of Alice's string or message. We construct explicit, deterministic document exchange protocols with sketch size $ O( (k \log n +t) \log^2 \frac{n}{k\log n + t} )$ and explicit binary error correcting code with $O(k \log n \log \log \log n+t)$ redundant bits.