Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploration of the positive and negative relationships associated with the development of asthma and atopic disorders in primary school children in Cork

An exploration of the positive and negative relationships associated with the development of asthma and atopic disorders in primary school children in Cork Author(s) Duggan, Eileen Mary Publication date 2013 Original citation Duggan, E. M. 2013. An exploration of the positive and negative relationships associated with the development of asthma and atopic disorders in primary school children in Cork. PhD Thesis, University College Cork. Type of publication Doctoral thesis Rights © 2013, Eileen M. Duggan http://creativecommons.org/licenses/by-nc-nd/3.0/
THE FREQUENCY OF BEIJING GENOTYPE OF MYCOBACTERIUM TUBERCULOSIS ISOLATED FROM TUBERCULOSIS PATIENTS

Background & Aim: Molecular epidemiology is the using of molecular techniques (e.g. Spoligotyping, RFLP VNTR) in order to study bacterial distribution in human populations. The aim of this study was to investigate the prevalence of all genotypes in M. tuberculosis strains typed by spoligotyping and to determine the associated risk factors in patients with different nationalities residing in Iran. Patients & Methods: In this analytic cross-sectional study a total number of 439 patients who referred to NRITLD (a referral tuberculosis center in Iran) were registered during March 21st 2003 to March 21st 2004. The isolated Mycobacterium tuberculosis strains were characterized by performing susceptibility tests against four first-line antituberculosis drugs and were then subjected to spoligotyping characterization. T-test and ch-square test were used for analysis of the data. Results: Spoligotyping of M. tuberculosis strains resulted in 140 different patterns divided into three evolutionary groups(I, II, III). 122(87.1%) of these spoligotype isolates were unique and reported for the first time. The remaining 18(12.8%) spoligotype patterns were previously reported from other geographical regions of the world. Haarlem family was more prevalent than other genotypes. Interestingly, 6.3% of the strains belonged to the Beijing family. MDR(multi drug resistance), double and triple resistance were seen in group I of evolutionary scenario. Antibiotic resistance was higher in those isolated from Afghan patients(P<0.001). Other risk factors such as sex and age were also contributing factors to the disease state. Conclusion: Results showed that multi-drug-resistance was more prevalent in bacteria isolated from Afghan TB patients residing in Iran. In addition, the spread of M. tuberculosis strains belonging to Beijing family among Iranian patients has to be considered seriously. It is also important to undertake studies to identify which factors are the most significant to consider in tuberculosis control program.
Active-targeted Nanotherapy as Smart Cancer Treatment

Drug delivery systems (DDS) can be designed to improve the pharmacological and therapeutic properties of drugs. Targeted drug delivery, sometimes called smart drug delivery, is a method of delivering medication to a patient in a manner that increases the concentration of the medication in infective organs or cells, relative to others. Can‐ cer is one of the major causes of mortality worldwide and innovative methods for can‐ cer therapy are urgently required. Nanoparticles (NPs), by using active targeting strategy, can enhance the intracellular concentration of drugs in cancerous cells while avoiding toxicity in normal cells. Nanoparticles with bioscience are being actively de‐ veloped for in vivo tumor imaging, bimolecular profiling of cancer biomarkers, and targeted drug delivery. The advantages of the targeted release system are the reduction in the frequency of dosages taken by the patient, having a uniform effect of the drug, reduction of drug side effects, and reduced fluctuation in circulating drug levels. In this chapter, we focus on targeted drug delivery systems integrated from nanobiotech‐ nology.
The role of positive and negative social interactions in child custody outcomes: Voices of US women with serious mental illness

In a given year, approximately 26 percent of persons in the United States 18 years and older suffer from a diagnosable mental disorder. About six percent are diagnosed with a serious mental illness and 65 percent of these are mothers. Mothers with serious mental illness are at increased risk of losing custody of their children. This report represents findings from a qualitative study of the relationship between positive and negative social interactions as three such women went through the process of experiencing threats to their children’s safety, relinquishing custody, and working to regain it (or to adjust to a permanent placement). Data collection involved semi-structured interviews. A thematic analysis of data was conducted. Among three conceptual models of social interactions – additive, moderating, and domain specific, the domain-specific model was prominent. Findings are discussed as preliminary to future similar studies involving larger samples. Tentative implications for policy, practice, and research are discussed.
How to play macroscopic quantum game

Quantum games are usually considered as games with strategies defined not by the standard Kolmogorovian probabilistic measure but by the probability amplitude used in quantum physics. The reason for the use of the probability amplitude or "quantum probabilistic measure", is the nondistributive lattice occurring in physical situations with quantum microparticles. In our paper we give examples of getting nondistributive orthomodular lattices in some special macroscopic situations without use of quantum microparticles.
Registry-Dependent Potential for Interfaces of Water with Graphene

An anisotropic interlayer potential that can accurately describe the van der Waals interaction of the water-graphene interface is presented. The force field is benchmarked against the many-body dispersion-corrected density functional theory. The parameterization of ILP yields good agreement with the reference dataset of binding energy curves and sliding potential energy surfaces for various configurations of a water molecule deposited on monolayer graphene, indicating the developed force field enhancing significantly the accuracy in the empirical description of water-graphene interfacial interactions. The water contact angles of monolayer and multilayer graphene extracted from molecular dynamics simulations based on this force field are close to the experimental measurements and predict the hydrophilic nature of graphene. The theoretical approach proposed in this work can be easily extended to mimic the van der Waals interactions between water molecules and various two-dimensional layered materials for studying their wetting properties.
Percepción de equipos directivos y docentes de Educación Física de Secundaria sobre el proceso de implantación y desarrollo de las competencias básicas en la ciudad de Burgos

espanolEl objetivo de este estudio fue analizar la percepcion que tienen los equipos directivos y los docentes de Educacion Fisica de Educacion Secundaria sobre el proceso de implantacion, desarrollo, seguimiento y trabajo de las Competencias Basicas. La muestra utilizada fueron 30 centros educativos (30 miembros de equipos directivos y 30 jefes de departamento de Educacion Fisica), de los cuales un 58.5% eran varones y un 41,5% mujeres, con una media de edad de 44,17 (SD = 6,23). En la metodologia de trabajo se han utilizado procedimientos cuantitativos y cualitativos. Se suministraron dos cuestionarios validados por un grupo de expertos. Ademas se realizo un estudio de caso unico en un centro durante un ano escolar, entrevistando tambien a los docentes y participando en grupos de discusion con los alumnos. La mayoria de los equipos directivos consideraron necesario implantar un proyecto de contribucion a las competencias basicas, pero mostraron ciertas reticencias basadas en: a) el desconocimiento de propuestas, b) la falta de concienciacion entre los docentes, c) la creencia del tiempo excesivo que requiere, d) la falta de informacion coherente y especifica por parte de la Administracion Educativa. Los profesores de Educacion Fisica valoraron positivamente el trabajo de las competencias basicas, relacionando significativamente el aprendizaje obtenido y la coherencia curricular a lo largo del curso. EnglishThe aim of this study was to analyze the development of key competences required by Secondary Physical Education Teachers, their implementation and monitoring by the perception management teams. The sample consisted of 30 high schools (30 members of management teams and 30 heads of department of Physical Education), of which 58.5% were male and 41.5% female, with a mean age of 44.17 (SD = 6.23). In the working methods have been used quantitative and qualitative methods. Two questionnaires validated by an expert group were provided. In addition, a single case study was performed in a center during a school year, also interviewing teachers and participating in discussion groups with students. Most management teams considered necessary to establish a draft contribution to Key Competences, but showed some reluctance based on: a) the lack of proposals, b) the lack of awareness among teachers, c) the belief of the excessive time required, d) the lack of consistent and specific information from the education authority. The PE teachers positively evaluated the work of basic skills, significantly relating curricular learning and consistency obtained along the course.
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference

Modern convolutional neural networks apply the same operations on every pixel in an image. However, not all image regions are equally important. To address this inefficiency, we propose a method to dynamically apply convolutions conditioned on the input image. We introduce a residual block where a small gating branch learns which spatial positions should be evaluated. These discrete gating decisions are trained end-to-end using the Gumbel-Softmax trick, in combination with a sparsity criterion. Our experiments on CIFAR, ImageNet, Food-101 and MPII show that our method has better focus on the region of interest and better accuracy than existing methods, at a lower computational complexity. Moreover, we provide an efficient CUDA implementation of our dynamic convolutions using a gather-scatter approach, achieving a significant improvement in inference speed on MobileNetV2 and ShuffleNetV2. On human pose estimation, a task that is inherently spatially sparse, the processing speed is increased by 60% with no loss in accuracy.
Lorentz symmetry violation in QCD and the frustration of asymptotic freedom

We study the effect of Lorentz symmetry violation (LSV) on the behaviour at high energy of SU(N) gauge theory with quarks in the fundamental representation. The approach is similar to that for QED treated in a previous paper. In contrast to QED, standard Lorentz invariant QCD is asymptotically free. Our aim is to explore the structure of the renormalisation group at high energy and hence weak coupling without requiring the Lorentz symmetry breaking to be small. The simplest type of LSV leaves the theory invariant under a subgroup of the Lorentz group that preserves a (time-like) 4-vector. We examine this case in detail and find that asymptotic freedom is frustrated. That is, at sufficiently high energy the running coupling constant attains a minimum value before increasing again, while the LSV parameter increases without bound.
A priori generalization error for two-layer ReLU neural network through minimum norm solution

We focus on estimating \emph{a priori} generalization error of two-layer ReLU neural networks (NNs) trained by mean squared error, which only depends on initial parameters and the target function, through the following research line. We first estimate \emph{a priori} generalization error of finite-width two-layer ReLU NN with constraint of minimal norm solution, which is proved by \cite{zhang2019type} to be an equivalent solution of a linearized (w.r.t. parameter) finite-width two-layer NN. As the width goes to infinity, the linearized NN converges to the NN in Neural Tangent Kernel (NTK) regime \citep{jacot2018neural}. Thus, we can derive the \emph{a priori} generalization error of two-layer ReLU NN in NTK regime. The distance between NN in a NTK regime and a finite-width NN with gradient training is estimated by \cite{arora2019exact}. Based on the results in \cite{arora2019exact}, our work proves an \emph{a priori} generalization error bound of two-layer ReLU NNs. This estimate uses the intrinsic implicit bias of the minimum norm solution without requiring extra regularity in the loss function. This \emph{a priori} estimate also implies that NN does not suffer from curse of dimensionality, and a small generalization error can be achieved without requiring exponentially large number of neurons. In addition the research line proposed in this paper can also be used to study other properties of the finite-width network, such as the posterior generalization error.
LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning

We describe a policy learning approach to map visual inputs to driving controls that leverages side information on semantics and affordances of objects in the scene from a secondary teacher model. While the teacher receives semantic segmentation and stop "intention" values as inputs and produces an estimate of the driving controls, the primary student model only receives images as inputs, and attempts to imitate the controls while being biased towards the latent representation of the teacher model. The latent representation encodes task-relevant information in the inputs of the teacher model, which are semantic segmentation of the image, and intention values for driving controls in the presence of objects in the scene such as vehicles, pedestrians and traffic lights. Our student model does not attempt to infer semantic segmentation or intention values from its inputs, nor to mimic the output behavior of the teacher. It instead attempts to capture the representation of the teacher inputs that are relevant for driving. Our training does not require laborious annotations such as maps or objects in three dimensions; even the teacher model just requires two-dimensional segmentation and intention values. Moreover, our model runs in real time of 59 FPS. We test our approach on recent simulated and real-world driving datasets, and introduce a more challenging but realistic evaluation protocol that considers a run that reaches the destination successful only if it does not violate common traffic rules.
Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning

Face recognition has been widely studied due to its importance in smart cities applications. However, the case when both training and test images are corrupted is not well solved. To address such a problem, this paper proposes a locality constrained low rank representation and dictionary learning (LCLRRDL) algorithm for robust face recognition. In particular, we present three contributions in the proposed formulation. First, a low-rank representation is introduced to handle the possible contamination of the training as well as test data. Second, a locality constraint is incorporated to acknowledge the intrinsic manifold structure of training data. With the locality constraint term, our scheme induces similar samples to have similar representations. Third, a compact dictionary is learned to handle the problem of corrupted data. The experimental results on two public databases demonstrate the effectiveness of the proposed approach. Matlab code of our proposed LCLRRDL can be downloaded from this https URL.
Correlation distances in social networks

In this work we explore degree assortativity in complex networks, and extend its usual definition beyond that of nearest neighbours. We apply this definition to model networks, and describe a rewiring algorithm that induces assortativity. We compare these results to real networks. Social networks in particular tend to be assortatively mixed by degree in contrast to many other types of complex networks. However, we show here that these positive correlations diminish after one step and in most of the empirical networks analysed. Properties besides degree support this, such as the number of papers in scientific coauthorship networks, with no correlations beyond nearest neighbours. Beyond next-nearest neighbours we also observe a diasassortative tendency for nodes three steps away indicating that nodes at that distance are more likely different than similar.
Hyperbolic Graph Attention Network

Graph neural network (GNN) has shown superior performance in dealing with structured graphs, which has attracted considerable research attention recently. Most of the existing GNNs are designed in euclidean spaces; however, real-world spatial structured data can be non-euclidean surfaces (e.g., hyperbolic spaces). For example, biologists may inspect the geometric shape of a protein surface to determine its interaction with other biomolecules for drug discovery. Although there is growing research on generalizing GNNs to non-euclidean surfaces, the works in these fields are still scarce. In this article, we exploit the graph attention network to learn robust node representations of graphs in hyperbolic spaces. As the gyrovector space framework provides an elegant algebraic formalism for hyperbolic geometry, we utilize this framework to learn the graph representations in hyperbolic spaces. Specifically, we first use the operations defined in the framework to transform the features in a graph; and we exploit the proximity in the product of hyperbolic spaces to model the multi-head attention mechanism in the non-Euclidean setting; afterward, we further devise a parallel strategy using logarithmic and exponential maps to improve the efficiency of our proposed model. The comprehensive experimental results demonstrate the effectiveness of the proposed model, compared with state-of-the-art methods.
Pinning Down the Strong Wilber 1 Bound for Binary Search Trees

The famous dynamic optimality conjecture of Sleator and Tarjan from 1985 conjectures the existence of an $O(1)$-competitive algorithm for binary search trees (BST's). Even the simpler problem of (offline) approximation of the optimal cost of a BST for a given input, that we denote by $OPT$, is still widely open, with the best current algorithm achieving an $O(\log\log n)$-approximation. A major challenge in designing such algorithms is to obtain a tight lower bound on $OPT$ that is algorithm friendly. Although several candidate lower bounds were suggested in the past, such as WB-1 and WB-2 bounds by Wilber, and Independent Rectangles bound by Demaine et al., the only currently known non-trivial approximation algorithm achieves an $O(\log\log n)$ approximation factor by comparing $OPT$ with a weak variant of WB-1, that uses a \emph{fixed} partitioning of the keys. This bound, however, is known to have a gap of $\Omega(\log\log n)$, and therefore it cannot yield better approximation algorithms. To overcome this obstacle, it is natural to consider a stronger variant of WB-1, that maximizes the bound over \emph{all} partitionings of the keys. An interesting question, mentioned by Iacono and by Kozma, is whether the $O(\log\log n)$-approximation can be improved by using this stronger bound. In this paper, we show that the gap between the stronger WB-1 bound and $OPT$ may be as large as $\Omega(\log\log n/\log\log\log n)$. This rules out the hope of obtaining better approximation algorithms via the only known algorithmic approach, combined with the stronger WB-1 bound. We also provide algorithmic results: for any parameter $D$, we present a simple $O(D)$-approximation algorithm with running time $\exp\left(O\left (n^{1/2^D}\log n\right )\right )$. This implies an $O(1)$-approximation algorithm in sub-exponential time.
Topology-Preserving Terrain Simplification

We give necessary and sufficient criteria for elementary operations in a two-dimensional terrain to preserve the persistent homology induced by the height function. These operations are edge flips and removals of interior vertices, re-triangulating the link of the removed vertex. This problem is motivated by topological terrain simplification, which means removing as many critical vertices of a terrain as possible while maintaining geometric closeness to the original surface. Existing methods manage to reduce the maximal possible number of critical vertices, but increase thereby the number of regular vertices. Our method can be used to post-process a simplified terrain, drastically reducing its size and preserving its favorable properties.
Towards An Implementation of the Subset-sum Problem on the IBM Quantum Experience

In seeking out an algorithm to test out the capability of the IBM Quantum Experience quantum computer, we were given a review paper covering various algorithms for solving the subset-sum problem, including both classical and quantum algorithms. The paper went on to present a novel algorithm that beat the previous best algorithm known at the time. The complex nature of the algorithm made it difficult to see a path for implementation on the Quantum Experience machine and the exponential cost - only slightly better than the best classical algorithm - left us looking for a different approach for solving this problem. We present here a new quantum algorithm for solving the subset-sum problem that for many cases should lead to O(poly(n))-time to solution. The work is reminiscent of the verification procedure used in a polynomial-time algorithm for the quantum Arthur-Merlin games presented elsewhere, where the use of a quantum binary search to find a maximum eigenvalue in the final output stage has been adapted to the subset-sum problem as in another paper.
BreakBRD Galaxies. I. Global Properties of Spiral Galaxies with Central Star Formation in Red Disks

We introduce a collection of primarily centrally star-forming galaxies that are selected by disk color to have truncated disk star formation. We show that common explanations for centrally concentrated star formation—low stellar mass, bars, and high-density environments—do not universally apply to this sample. To gain insight into our sample, we compare these galaxies to a parent sample of strongly star-forming galaxies and to a parent sample of galaxies with low specific star formation rates. We find that in star formation and color space from ultraviolet to the infrared these galaxies either fall between the two samples or agree more closely with galaxies with high specific star formation rates. Their morphological characteristics also lie between high and low specific star formation rate galaxies, although their Petrosian radii agree well with that of the low specific star formation rate parent sample. We discuss whether this sample is likely to be quenching or showing an unusual star formation distribution while continuing to grow through star formation. Future detailed studies of these galaxies will give us insights into how the local conditions within a galaxy balance environmental influence to govern the distribution of star formation. In this first paper in a series, we describe the global properties that identify this sample as separate from more average spiral galaxies, and we identify paths forward to explore the underlying causes of their differences.
BitMEX Funding Correlation with Bitcoin Exchange Rate

This paper examines the relationship between Inverse Perpetual Swap contracts, a Bitcoin derivative akin to futures and the margin funding interest rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding rates and goes onto establish a causal relationship between the funding rates and the Bitcoin inverse Perpetual swap contracts based on Granger causality. The paper further dwells into developing a predictive model for funding rates using best-fitted GARCH models. Implications of the results are presented, and funding rates as a predictive tool for gauging the market trend is discussed.