Miracle-dz/UniFAR-FTUs huggingface dataset:
6 Effectiveness of the Regular Implementation of the Mother to Child Transmission Plus ( MTCT-Plus ) Program in Burkina Faso , West Africa

Fabio Buelli1,2,5, Virginio Pietra2,3, Richard Fabian Schumacher2,4, Jacques Simpore3, Salvatore Pignatelli2,3, Francesco Castelli1,2 and the ESTHER-Brescia Study Group 1Institute for Infectious and Tropical Diseases, University of Brescia, Brescia, 2Medicus Mundi Italy, Brescia, 3St. Camille Medical Center, Ouagadougou, 4University Children’s Hospital, Brescia, 5Ph.D. in Appropriate Methods and Technologies for International Development Co-operation, University of Brescia (A. Archetti Fund), 1,2,4,5Italy 3Burkina Faso
CVIA Computational Fluid Dynamics Modeling in Aortic Diseases

Originally, computational fluid dynamics (CFD) was a mechanical engineering method for comprehensive analysis of fluid flow, heat transfer, and associated phenomena with the use of computer-based simulations. Recently, it has become a widely adopted methodology for solving complex problems in many modern engineering fields. The CFD-based approach is used for understanding the relationship between structure and flow by solving systems of partial differential equations to simulate fluid flow. Advances in medical imaging and computational methods provide an opportunity to apply the CFD-based approach in clinical medicine. Cardiovascular imaging is one of the most common medical applications of CFD modeling. Although CFD modeling is essentially studied by applied mathematicians and engineers who are specialists in fluid mechanics, as CFD modeling continues to spread into clinical medicine it is very important for clinicians to understand the basic principles, benefits, limitations, and pitfalls of CFD modeling. In this paper, we present an overview of CFD modeling and its application to aortic diseases, and primarily to aortic aneurysms.
Prevalence of painful and anaemic crises among children with sickle cell anaemia in a tertiary hospital: A crosssectional study

Sickle cell anaemia (SCA) is one of the common causes of morbidity and mortality in children. Most of the hospital admissions among children with SCA in Nigeria are due to painful and anaemic crises. This study aimed to determine the prevalence of painful and anaemic crises among children with homozygous sickle cell disease attending tertiary hospital clinics. Two hundred and seventy-two children with SCA, aged 6 months to 15 years, were consecutively studied. It was a cross sectional study carried out at Ahmadu Bello University Teaching Hospital, Zaria, north-western Nigeria. Their age, sex and socio-economic status was determined through a structured questionnaire administered to the parents/ care-givers. Epi info statistical software was used in analysing the results. A p-value of ≤0.5 was considered statistically significant. Of the 272 children with SCA studied, 185 (68.0%) were in stable state while 87 (32.0%) had SCA crises. Their ages ranges from six (6) months to 15 years with a mean age (± 1 SD) of 6.4 ±3.8 years. Of the 87 subjects with SCA in crisis, 55 had painful crisis while 32 had anaemic crises giving a prevalence of 63.2% and 36.8% respectively. The type of SCA crisis was not affected by age, gender and social status. This study revealed a higher prevalence of painful than anaemic crises among children with SCA attending Ahmadu Bello University Teaching Hospital, Zaria and the type of SCA crises was not associated with age, gender and social class.
Initial appearance of the 184 I 1 e variant in lamivudine-treated patients is caused by the mutational bias of human immunodeficiency virus type 1 reverse transcriptase

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Cyclin D1 Expression in Triple-Negative Breast Cancer with New Treatment Implications

While targeted therapies are available for breast cancer patients whose tumors express ER, PR, or show HER-2 overexpression, no such treatment exists for “triple-negative” cases. As immunohistiochemistry (IHC) expression of Cyclin D1 has been shown concordant with Cyclin D1 gene amplification, we evaluated Cyclin D1 IHC expression in 25 cases of triple-negative invasive ductal carcinoma to clarify its relationship with clinical and pathologic parameters. Twenty-six invasive carcinomas negative for ER, PR, and HER-2 were reviewed. One sarcomatoid carcinoma was omitted leaving 25 invasive ductal carcinomas for this pilot study. Clinical information, including treatment response, was gleaned from patient records. Only one case was known to be BRCA-positive. Tumors were morphologically reviewed and Cyclin D1 IHC applied using BCL-1. Chi square and t-test statistical analyses were performed. Patients were female and ranged in age from 32 to 88 years (m=59). Tumors were in left (n=16) and right breasts (n=9) with tumor size ranging from 0.4 cm up to 7.2 cm (m=2.7cm). Tumors were invasive dustal carcinomas of no special type, Nottingham grade 1 (n=1), 2 (n=10) and 3 (n=14). Eight cases had an in-situ component, high grade (n=7) or low grade (n=1), ranging from 5% to 70% of tumor (m= 23%). All tumors showed Cyclin D1 expression from light and focal staining (1+; n=6; Group 1) to focal intense (2+; n=9) or diffuse intense staining (3+; n=10). The latter two groups were combined for analyses (Group 2). While patients were younger in Group 2, this was not statistically significant. Patients with tumors showing intense BCL-1 staining (Group 2) had larger tumors (p<.05) with more capillary/lymphatic invasion (p<.005) and lymph node metastases (p<.007), and were less likely to respond to treatment (p=.01; see Table 1). Cyclin D1 expression may serve as a marker for more biologically aggressive triple-negative breast cancer. These tumors may respond to targeted therapy that down-regulates Cyclin D1 amplification. Further research with larger sample sizes is needed.
THE IMPACT OF AUDITOR`S OPINION ON EARNINGS MANAGEMENT: EVIDENCE FROM ROMANIA

The aim of this research is to analyze the relation between modified audit opinion and abnormal accruals in the case of listed Romanian entities. In order to investigate the influence of auditor`s opinion on earnings management, a multiple regression was designed. The final sample, after eliminating the financial institutions due to homogeneity considerations, consists of 61 companies listed on Bucharest Stock Exchange – tier I, II and III- through a period of five years, from 2008 until 2012. The dependent variable of the regression which measures the discretionary accruals is represented by abnormal accruals, while the independent variables, namely audit opinion, audit firm size, firm size and current ratio of liquidity, constitute the explanatory variables of the regression which aims to capture properly the impact of auditor`s opinion on reducing earnings management in order to improve the quality of financial reporting process. This research contributes to the existent literature in several ways. First of all, at this point, it is the first article that tackles the issue of auditor`s opinion impact on earnings management in Romania. Second, the results of this study might influence the academic environment by contributing to a better understanding of the theoretical implications that can be adapted into an improved practice for the Romanian listed entities.
Corporate governance quality, incentive factors and voluntary corporate governance disclosures in annual reports of Malaysian publicly listed companies

This paper investigates the relationship between corporate governance quality and voluntary disclosure of corporate governance information for listed companies in Malaysia. The moderating impacts of incentive factors (capital market transactions and stock-based incentives) on this relationship are also examined. Corporate governance quality is measured using a comprehensive index. The empirical evidence of this study is broadly consistent with the notion that high corporate governance quality is positively related to a greater extent of voluntary disclosure. Stock-based compensation significantly influences the relationship between corporate governance quality and voluntary disclosures; however the other incentive factors examined do not appear to influence the relationship.
Moving Average Distance as a Predictor of Equity Returns

The distance between short- and long-run moving averages of prices (MAD) predicts future equity returns in the cross-section. Annualized value-weighted alphas from the accompanying hedge portfolios are around 9%, and the predictability goes beyond momentum, 52-week highs, profitability, and other prominent anomalies. MAD-based investment payoffs survive reasonable trading costs faced by institutions, and are stronger on the long side relative to the short counterpart.
PalmGHG, RSPO greenhouse gas calculator, scientific background

GHG emissions from palm plantations are a major environmental issue in main producing countries. Through its working groups, RSPO developed a GHG calculator, PalmGHG, which can help the producers to monitor the GHG emissions from their supply areas and mill units and establish reduction plans. In 2013, the use of PalmGHG (or an RSPO endorsed equivalent) has been integrated in the revised Principles & Criteria for the Production of Sustainable Palm Oil (P&C 2013), which created an emulation to tackle this GHG issue. This paper provides an overview of the development of PalmGHG and its various versions as well as explains the main characteristics, calculation assumptions and features.
Highly sensitive and high throughput magnetic resonance thermometry using superparamagnetic nanoparticles

Magnetic resonance imaging (MRI) enables non-invasive 3D thermometry during thermal ablation of cancerous tumors. While T 1 or T 2 contrast MRI are relatively insensitive to temperature, techniques with greater temperature sensitivity such as chemical shift or diffusion imaging suffer from motional artifacts and long scan times. We describe an approach for highly sensitive and high throughput MR thermometry that is not susceptible to motional artifacts. We use superparamagnetic iron oxide nanoparticles (SPIONs) to spoil T 2 of water protons. Motional narrowing results in proportionality between T 2 and the diffusion constant, dependent only on the temperature in a specific environment. Our results show, for pure water, the nuclear magnetic resonance (NMR) linewidth and T 2 follow the same temperature dependence as the self-diffusion constant of water. Thus, a T 2 mapping is a diffusion mapping in the presence of SPIONs, and T 2 is a thermometer. For pure water, a T 2 mapping of a 64 x 64 image (voxel size = 0.5 mm x 0.5 mm x 3 mm) in a 9.4 T MRI scanner resulted in a temperature resolution of 0.5 K for a scan time of 2 minutes. This indicates a highly sensitive and high throughput MR thermometry technique potentially useful for monitoring of biological tissues during thermal therapies or for diagnosis.
Predicting the generalization gap in neural networks using topological data analysis

Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods.
Statutory Adjudication in New South Wales: Operational Problems and Potential Improvements

The Australian state of New South Wales (NSW) was the second jurisdiction in the common law world and the first in Australia to introduce statutory adjudication. Although being modelled after the UK statutory adjudication regime, there are significant differences in the NSW regime that make it distinctive. The NSW regime is essentially a neutral certification process conducted by an adjudicator whose role is that of a certifier. However, due to its rapid nature and judicial interventions, there have been some serious problems with regard to the operation of the regime. This paper attempts to identify the operational problems of the NSW regime from neutral (adjudicator) and user (legal representatives in adjudication) viewpoints, allowing for the establishment of potential improvements to the current regime. The first stage involved a review of the relevant literature, parliamentary speeches and consultation documents, cases on statutory adjudication, and expert commentaries. It was followed by qualitative interviews with 11 leading adjudicators and construction lawyers who represent parties in adjudication proceedings. The main problems identified are court involvement, invariability in the quality of adjudicators, rubber stamp approach and the accessibility of the NSW Act. It is established that the introduction of a dual system of adjudication and an adjudication registry could solve most of the problems identified
Learning curves for the multi-class teacher–student perceptron

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher–student perceptron on i.i. d. Gaussian inputs. Both Bayes-optimal (BO) estimation and empirical risk minimisation (ERM) were extensively analysed in this setting. At the same time, a considerable part of modern machine learning practice concerns multi-class classification. Yet, an analogous analysis for the multi-class teacher–student perceptron was missing. In this manuscript we fill this gap by deriving and evaluating asymptotic expressions for the BO and ERM generalisation errors in the high-dimensional regime. For Gaussian teacher, we investigate the performance of ERM with both cross-entropy and square losses, and explore the role of ridge regularisation in approaching Bayes-optimality. In particular, we observe that regularised cross-entropy minimisation yields close-to-optimal accuracy. Instead, for Rademacher teacher we show that a first-order phase transition arises in the BO performance.
Citizenship Status and Remittances: Evidence from German Micro Data

Within the framework of a utility maximization model, this paper examines the remittance behaviour of immigrant households in Germany by focusing on changes in their citizenship status. After controlling for endogeneity and taking into account the censored nature of the remittance act (i.e. only 61% of the household observed made remittances and rarely on an annual basis), evidence is found that the citizenship status significantly affects remittances in the case of immigrants from countries which restrict acquisition of real property by foreigners (e.g. Western Balkans). Giving up the citizenship of the home country has a negative effect on the propensity to remit and the amount remitted, while dual-citizenship has a positive effect. The effect vanishes if the migrant source country opens up its real estate market to foreigners (e.g. Turkey, the twelve new EU member states, and the CIS).
CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training

Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partially due to the lack of effective network structures that can capture both the long-range dependency and high-level semantics of an image. We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level. In each decoder block, global modulation is first applied to perform coarse and semantic-aware structure synthesis, followed by spatial modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios. Extensive experiments are conducted to show that our method significantly outperforms existing methods in both quantitative and qualitative evaluation. Please refer to the project page: \url{https://github.com/htzheng/CM-GAN-Inpainting}.
On the global dynamics of Yang-Mills-Higgs equations

We study solutions to the Yang-Mills-Higgs equations on the maximal Cauchy development of the data given on a ball of radius $R$ in $\mathbb{R}^3$. The energy of the data could be infinite and the solution grows at most inverse polynomially in $R-t$ as $t\rightarrow R$. As applications, we derive pointwise decay estimates for Yang-Mills-Higgs fields in the future of a hyperboloid or in the Minkowski space $\mathbb{R}^{1+3}$ for data bounded in the weighted energy space with weights $|x|^{1+\epsilon}$. Moreover, for the abelian case of Maxwell-Klein-Gordon system, we extend the small data result of Lindblad and Sterbenz to general large data (under same assumptions but without any smallness). The proof is gauge independent and it is based on the framework of Eardley and Moncrief together with the geometric Kirchhoff-Sobolev parametrix constructed by Klainerman and Rodnianski. The new ingredient is a class of weighted energy estimates through backward light cones adapted to the initial data.
Spectral and photometric analysis of the eclipsing binary Aurigae prior to and during the 2009-2011 eclipse ,

A series of 353 red electronic spectra (from three observatories, mostly from 6300 to 6700 A) obtained between 1994 and 2010, and of 171 UBV photometric observations (from two observatories) of the 2010 eclipse, were analyzed in an effort to better understand � Aur, the well-known, but still enigmatic eclipsing binary with the longest known orbital period (∼27 yrs). The main results follow. (1) We attempted to recover a spectrum of the companion by disentangling the observed spectra of theAur binary failed, but we were able to disentangle the spectrum of telluric lines and obtain a mean spectrum of the F-type primary star. The latter was then compared to a grid of synthetic spectra for a number of plausible values of Teff and log g, but a reasonably good match was not found. However, we conclude that the observed spectrum is that of a low gravity star. (2) We examined changes in the complex Hα line profiles over the past 16 years, with particular emphasis on the 2009-2011 eclipse period, by subtracting a mean out-of-eclipse Hα profile (appropriately shifted in radial velocity) from the observed spectra. We find that the dark disk around the unseen companion has an extended "atmosphere" that manifests itself via blueshifted and redshifted Hα "shell" absorptions seen projected against the F star. Significantly, the Hα shell line first appeared three years before first contact of the optical eclipse when the system was not far past maximum separation. (3) Analyses of radial velocities and central intensities of several strong, unblended spectral lines, as well as UBV photometry, demonstrated that these observables showed apparent multiperiodic variability during eclipse. The dominant period of 66. d 21 was common to all the observables, but with different phase shifts between these variables. This result strongly supports our earlier suggestion that the photometric variability seen during eclipse is intrinsic to the F star, and therefore, the idea of a central brightening due to a hole in the disk should be abandoned. Although variability on similar timescales is also seen in the spectrum and in photometry out of eclipse, we were unable to find a coherent periodicity in these data. Nevertheless, these results appear to rule out regular stellar pulsations as the cause of this variability.
CollaChain: A BFT Collaborative Middleware for Decentralized Applications

The sharing economy is centralizing services, leading to misuses of the Internet. We can list growing damages of data hacks, global outages and even the use of data to manipulate their owners. Unfortunately, there is no decentralized web where users can interact peer-to-peer in a secure way. Blockchains incentivize participants to individually validate every transaction and impose their block to the network. As a result, the validation of smart contract requests is computationally intensive while the agreement on a unique state does not make full use of the network. In this paper, we propose Collachain, a new byzantine fault tolerant blockchain compatible with the largest ecosystem of DApps that leverages collaboration. First, the pariticipants executing smart contracts collaborate to validate the transactions, hence halving the number of validations required by modern blockchains (e.g., Ethereum, Libra). Second, the participants in the consensus collaborate to combine their block proposal into a superblock, hence improving throughput as the system grows to hundreds of nodes. In addition, Collachain offers the possibility to its users to interact securely with each other without downloading the blockchain, hence allowing interactions via mobile devices. Collachain is effective at outperforming the Concord and Quorum blockchains and its throughput peaks at 4500 TPS under a Twitter DApp (Decentralized Application) workload. Finally, we demonstrate Collachain's scalability by deploying it on 200 nodes located in 10 countries over 5 continents.
Bounds for Bias-Adjusted Treatment Effect in Linear Econometric Models

In linear econometric models with proportional selection on unobservables, omitted variable bias in estimated treatment eﬀects are real roots of a cubic equation involving estimated parameters from a short and intermediate regression. The roots of the cubic are functions of δ , the degree of selection on unobservables, and R max , the R-squared in a hypothetical long regression that includes the unobservable confounder and all observable controls. In this paper I propose and implement a novel algorithm to compute roots of the cubic equation over relevant regions of the δ - R max plane and use the roots to construct bounding sets for the true treatment eﬀect. The algorithm is based on two well-known mathematical results: (a) the discriminant of the cubic equation can be used to demarcate regions of unique real roots from regions of three real roots, and (b) a small change in the coeﬃcients of a polynomial equation will lead to small change in its roots because the latter are continuous functions of the former. I illustrate my method by applying it to the analysis of maternal behavior on child outcomes.