Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of care and treatment clinics using a four-year retrospective cohort of patients receiving anti-retroviral therapy in Mbeya Region, Tanzania

Evaluations of sub-national anti-retroviral therapy (ART) programmes’ performance are important to support regional programme planning and epidemic response. We constructed a four-year retrospective cohort of clients from 10 care and treatment clinics (CTC) in the Mbeya region of Tanzania using routinely collected data from patients initiating ART between January 1, 2008 and March 31, 2012. Our primary outcomes were mortality rate and lost to follow up. We calculated ART coverage based on regional prevalence estimates and used medical cards, drug registers and quarterly reports to assess data quality. We enrolled 17,813 participants into the cohort. More patients initiated ART at WHO stages 3 (58.9%), and at CD4 cell counts between 50-199 cells/µl (56%). The proportion of patients initiating ARTs at a CD4 counts <50 cells/µl decreased from 40.7% in 2008 to 33.8% in 2012 (p-value <0.001). A total of 10,155 (57.0%) patients were lost to follow up (LTFU) during the 4 years of follow up; 59.2% were female. The overall mortality rate was 17.8 per 1000 person years; the six-month mortality was 6.2 per 1000 person years. The risk of mortality for patients at WHO stage III and IV was more than twice as high (hazard ratio [HR] 2.15; 95% confidence interval [CI], 1.05-4.39 and HR 2.29; 95% CI, 1.39-6.37, respectively) as compared to stage I and II. Those patients initiating ART with CD4 cell count between 50 and 199 cells/µl had significantly lower hazard of death compared to those initiating ART with CD4 cells counts <50 cells/µl (HR 0.51, 95% CI 0.34-0.78). In conclusion, during the study, a high proportion of patients were lost to follow up, and the majority of them were female. Most patients initiated ARTs at lower CD4 count (<200 cells/mm3) and at WHO stages III and IV, and they had higher risk of mortality compared to those initiated at higher CD4 counts and WHO stage I and II. Emphasis needs to be placed on strategies of early diagnosis of HIV infection, prompt initiation of ART, and support for ART adherence to minimise lost to follow up.
Aging Related Bug Prediction using Extreme Learning Machines

Aging-Related Bugs (ARBs) occur in long running systems due to error conditions caused because of accumulation of problems such as memory leakage or unreleased files and locks. Aging-Related Bugs are hard to discover during software testing and also challenging to replicate. Automatic identification and prediction of aging related fault-prone files and classes in an object oriented system can help the software quality assurance team to optimize their testing efforts. In this paper, we present a study on the application of static source code metrics and machine learning techniques to predict aging related bugs. We conduct a series of experiments on publicly available dataset from two large open-source software systems: Linux and MySQL. Class imbalance and high dimensionality are the two main technical challenges in building effective predictors for aging related bugs. We investigate the application of five different feature selection techniques (OneR, Information Gain, Gain Ratio, RELEIF and Symmetric Uncertainty) for dimensionality reduction and SMOTE method to counter the effect of class imbalance in our proposed machine learning based solution approach. We apply Extreme Learning Machines (ELM) with three different kernels (linear, polynomial and RBF) and present experimental results which demonstarte the effectiveness of our approach.
C omovement revisited

Evidence of excessive comovement among stocks following index additions (Barberis, Shleifer, and Wurgler, 2005) and stock splits (Green and Hwang, 2009) challenges traditional finance theory. We show that the bivariate regressions in this literature provide little information about the economic magnitude of excess comovement, with coefficients that are sensitive to unrelated factors. Using robust univariate regressions and matched control samples, almost all evidence of excess comovement disappears. In both examples, the stocks exhibit strong returns prior to the event, akin to momentum winners. We document that winner stocks exhibit increases in betas, generating much of the apparent excess comovement. © 2016 Elsevier B.V. All rights reserved.
MOJAVE. XIII. PARSEC-SCALE AGN JET KINEMATICS ANALYSIS BASED ON 19 YEARS OF VLBA OBSERVATIONS AT 15 GHz

We present 1625 new 15 GHz (2 cm) VLBA images of 295 jets associated with active galactic nuclei (AGNs) from the MOJAVE and 2 cm VLBA surveys, spanning observations between 1994 August 31 and 2013 August 20. For 274 AGNs with at least 5 VLBA epochs, we have analyzed the kinematics of 961 individual bright features in their parsec-scale jets. A total of 122 of these jets have not been previously analyzed by the MOJAVE program. In the case of 451 jet features that had at least 10 epochs, we also examined their kinematics for possible accelerations. At least half of the well-sampled features have non-radial and/or accelerating trajectories, indicating that non-ballistic motion is common in AGN jets. Since it is impossible to extrapolate any accelerations that occurred before our monitoring period, we could only determine reliable ejection dates for of those features that had significant proper motions. The distribution of maximum apparent jet speeds in all 295 AGNs measured by our program to date is peaked below , with very few jets with apparent speeds above . The fastest speed in our survey is , measured in the jet of the quasar PKS 0805−07, and is indicative of a maximum jet Lorentz factor of ∼50 in the parent population. An envelope in the maximum jet speed versus redshift distribution of our sample provides additional evidence of this upper limit to the speeds of radio-emitting regions in parsec-scale AGN jets. The Fermi-LAT-detected gamma-ray AGNs in our sample have, on average, higher jet speeds than non-LAT-detected AGNs, indicating a strong correlation between parsec-scale jet speed and the gamma-ray Doppler boosting factor. We have identified 11 moderate-redshift ( ) AGNs with fast apparent speeds ( ) that are strong candidates for future TeV gamma-ray detection. Of the five gamma-ray loud narrow-lined Seyfert I AGNs in our sample, three show highly superluminal jet motions, while the others have sub-luminal speeds. This indicates that some narrow-lined Seyfert I AGNs possess powerful jets with Lorentz factors in excess of 10, and viewing angles less than , consistent with those of typical BL Lac objects and flat-spectrum radio quasars.
Chapter 1 Growth Hormone Axis in Skeletal Dysplasias

Introduction: Skeletal dysplasias, also termed as osteochondrodysplasias, are a large heterogeneous group of disorders characterized by abnormalities of bone or cartilage growth or texture. They occur due to genetic mutations and their phenotype continues to evolve throughout life. Reduced growth is a common feature. Objective: To evaluate and discuss data about growth and growth hormone axis in patients with the main common skeletal dysplasias, such as achondroplasia, hypochon‐ droplasia, 3M syndrome, and Leri‐Weill syndrome. Design: Evaluate retrospectively the data on growth, final height (FH), height velocity (HV), growth hormone deficiency, and growth hormone response after growth hormone (GH) treatment in patients with these disorders. However, this chapter provides an updated picture of growth hormone axis and endocrinological features in skeletal dysplasia.
An Internet of things resource for rehabilitation

Evolving from a mature body of research titled SoundScapes, this paper reports on a work-in-progress that is investigating the need that is evident for improved information and recommendation infrastructures to assist in healthcare intervention and communication (including apparatus and method). The need is due to increased ICT use and the potentials for positive treatment outcomes from informed decision-making. A challenge is security of sensitive data, however detailing this is beyond the scope of this paper. In line with this need, an evolving cloud-based collaboration infrastructure resource is reported where global networks of professional subscribers will have increasing access to specific profile data and recommendation strategies. The focus on this phase of the work is to network those using (or considering using) ICT in wellness/QOL, rehabilitation and habilitation. The resource is being built from real-world case studies and is envisioned as a developing “feed-in feed-out” entity where evidence-based findings are submitted and extracted via a secured `gateway' by expert professional practitioners. The resource is considered an `Internet of Things' model to also inform and consult families of people diagnosed with impairments or patients undergoing treatment. Recommended intervention apparatus include bespoke/custom systems as well as commercially available systems. `In-action' and `On-action' models for have emerged from the research. This means that therapists adopting ICT into their work have a wealth of prior studies to guide their choices of hardware, software and intervention method and analysis without having to wade through academic or scientific articles, papers and book chapters. Through dissemination of the concept at this early phase of development in establishing the resource, an open invitation for peers and related interested parties to critique, advise, and otherwise contribute toward optimal development and wide impact is offered.
DNA barcoding of Corsican mayflies (Ephemeroptera) with implications on biogeography, systematics and biodiversity

Mayflies (Ephemeroptera) are known to generally present a high degree of insular endemism: half of the 28 species known from Corsica and Sardinia are considered as endemic. We sequenced the DNA barcode (a fragment of the mitochondrial CO1 gene) of 349 specimens from 50 localities in Corsica, Sardinia, continental Europe and North Africa. We reconstructed gene trees of eight genera or species groups representing the main mayfly families. Alternative topologies were built to test if our reconstructions suggested a single or multiple Corsican/ Sardinian colonization event(s) in each genus or species group. A molecular clock calibrated with different evolution rates was used to try to link speciation processes with geological events. Our results confirm the high degree of endemism of Corsican and Sardinian mayflies and the close relationship between these two faunas. Moreover, we have evidence that the mayfly diversity of the two islands is highly underestimated as at least six new putative species occur on the two islands. We demonstrated that the Corsican and Sardinian mayfly fauna reveals a complex history mainly related to geological events. The Messinian Salinity Crisis, which is thought to have reduced marine barriers, thus facilitating gene flow between insular and continental populations, was detected as the most important event in the speciation of most lineages. Vicariance processes related to the split and rotation of the Corso-Sardinian microplate had a minor impact as they involved only two genera with limited dispersal and ecological range. Colonization events posterior to the Messinian Salinity Crisis had only marginal effects as we had indication of recent gene flow only in two clades. With very limited recent gene flow and a high degree of endemism, mayflies from Corsica and Sardinia present all the criteria for conservation prioritization.
Electrophysiological evidence of emotion perception without central attention

The present study examined whether emotion perception requires central attentional resources. Specifically, we used a dual-task paradigm to examine whether people can direct their attention to a face expressing a target emotion even while they are still busy selecting a response to another task. Task 1 required an auditory discrimination (pure tone vs. noise). For Task 2, one happy face and one angry face were presented adjacent to each other. Participants were asked to find the face with a prespecified emotion and indicate its gender (Experiment 1) or location (Experiment 2). The stimulus–onset asynchrony (SOA) between the two tasks was varied. To determine where people were attending, we used an electrophysiological measure of attention known as the N2pc component of the event-related brain potential. The face expressing the target emotion elicited an N2pc effect, indicating attention capture, even when participants were already preoccupied with processing Task 1 (i.e., short SOAs). Thus, it appears that emotion perception can occur even when central attentional resources are unavailable. We also obtained evidence of an attentional bias towards angry faces rather than happy faces.
Human Pose Estimation : Extension and Application

Understanding human appearance in images and videos is one of the most fundamental and explored area in the field. Describing human appearance can be interpreted as a concoction of smaller and more fundamental related aspects like posture, gesture, outlook etc. By doing so we try to grasp holistic sense from semantically lower level information. The utility behind understanding human appearance and related aspects is the industrial demand for applications that involve analyzing humans and their interaction with surroundings. This thesis work tackles two of such related aspects: i. more fundamental problem, human pose estimation and ii. deeper understanding from cloth parsing based on pose estimation. Determining the human body joint locations and configuration is quizzed as human pose estimation problem. In this work we address the problem of human pose estimation for video sequence data type. We exploit the availability of redundant information from redundant data type. The proposed iteratively functioning methodology has the first iteration involving parsing data from a quintessential generic base model. For the following iterations, we run a 3 step pipeline: grabbing confidently positive detections from previous iteration using our novel selection criteria, fine-tuning external-to-base parameters to local distribution by synthesizing exemplars from picked ones, and enforcing the learned information using an updated amalgamation model. The resulting pipeline propagates correctness in temporal neighborhoods of a video sequence. Previous methods that use the same base models have relied more on tracking strategies. From the unbiased experiments conducted, our approach has proven to be much more robust and overall better performing. In the second half, we indulge in determining a more deeper understanding of human aspects i.e. cloth parsing. This involves predicting the cloth types worn by humans and their segmented regions in images. Our work focuses on incorporating robustness to a previously formulated method. Conceivably, determining hot regions for each cloth type is dependent on the underlying pose skeleton of the human, eg. hat will be worn on the head. Hence, availability of pose information is key to cloth parsing problem, but incorrect body part estimations can also simply lead to false cloth detections and segmentations. The previous method uses pose information from pictorial structure based model, whereas we update the formulation to incorporate information from more robust part detectors. The changed model has shown to be performing better at more wild outdoor settings. However, the performance from available and proposed methods is below par and appears non-viable for application purposes. To answer this, we report a set of experiments in which we take different lookouts and report observations.
von Kármán–Howarth Equation for Hall Magnetohydrodynamics: Hybrid Simulations

A dynamical vectorial equation for homogeneous incompressible Hall-magnetohydrodynamic (MHD) turbulence together with the exact scaling law for third-order correlation tensors, analogous to that for the incompressible MHD, is rederived and applied to the results of two-dimensional hybrid simulations of plasma turbulence. At large (MHD) scales the simulations exhibit a clear inertial range where the MHD dynamic law is valid. In the sub-ion range the cascade continues via the Hall term, but the dynamic law derived in the framework of incompressible Hall-MHD equations is obtained only in a low plasma beta simulation. For a higher beta plasma the cascade rate decreases in the sub-ion range and the change becomes more pronounced as the plasma beta increases. This break in the cascade flux can be ascribed to nonthermal (kinetic) features or to others terms in the dynamical equation that are not included in the Hall-MHD incompressible approximation.
Assessing competitiveness: how firm-level data can help. Bruegel Policy Contribution 2011/16, 16 November 2011

As policymakers refocus on growth, the ability to take a firm-level view is key to disentangling the various factors at the root of competitiveness, and thus to designing appropriate policies. • Firm-level data provides critical information for the design of appropriate competitiveness measures that complement traditional macro analysis. • More work remains to be done assembling firm-level information, but the variance of the distribution of firm characteristics already conveys important information in addition to standard averages. • New indicators should be developed to translate the distribution of firm characteristics into indicators of competitiveness designed to capture not only average performance but also the heterogeneity of firm performance. This Policy Contribution builds on ongoing research within EFIGE (www.efige.org), a project to help identify the internal policies needed to improve the external competitiveness of the European Union.
The Nature of Hard X-Ray (3–24 keV) Detected Luminous Infrared Galaxies in the COSMOS Field

We investigate the nature of far-infrared (70 μm) and hard X-ray (3–24 keV) selected galaxies in the COSMOS field detected with both Spitzer and the Nuclear Spectroscopic Telescope Array (NuSTAR). By matching the Spitzer-COSMOS catalog with the NuSTAR-COSMOS catalog, we obtain a sample consisting of a hyperluminous infrared galaxy with , 12 ultraluminous infrared galaxies with , and 10 luminous infrared galaxies with , i.e., 23 Hy/U/LIRGs in total. Using their X-ray hardness ratios, we find that 12 sources are obscured active galactic nuclei (AGNs) with absorption column densities of cm−2, including several Compton-thick ( cm−2) AGN candidates. On the basis of the infrared (60 μm) and intrinsic X-ray luminosities, we examine the relation between star formation (SF) and AGN luminosities of the 23 Hy/U/LIRGs. We find that the correlation is similar to that of the optically selected AGNs reported by Netzer, whereas local, far-infrared selected U/LIRGs show higher SF-to-AGN luminosity ratios than the average of our sample. This result suggests that our Hy/U/LIRGs detected both with Spitzer and NuSTAR are likely situated in a transition epoch between AGN-rising and cold-gas diminishing phases in SF-AGN evolutional sequences. The nature of a Compton-thick AGN candidate newly detected above 8 keV with NuSTAR (ID 245 in Civano et al.) is briefly discussed.
Essays in Empirical Industrial Economics

This thesis consists of two parts, which are connected by an introduction on auction and oligopoly markets and a short discussion about the obtained results at the end. The first part provides a literature review on auctions and analyzes bidders’ behavior in Austrian cattle auctions. The aim is to investigate whether there are differences among bidders and whether bidders take the possibility of buying later into account when bidding for objects. The second part analyzes firms’ strategies in the semiconductor industry. In particular, the strategic effects of learningbydoing and spillovers are considered. Further, the consequences of aggregating firms’ pricing behavior to an industry level pricing equation are empirically investigated. In both parts emphasis is put on the question, whether neglecting asymmetries across market participants and/or neglecting dynamic effects influences the estimated parameters.
CONTROL DESIGN FOR AN OVERACTUATED WHEELED MOBILE ROBOT

In order to simulate road vehicles in a hardware-in-the-loop test setup, TNO has developed a wheeled mobile robot with independently driven and steered wheels. This robot is overactuated, i.e. the number of actuators exceeds the number of spatial degrees of freedom to be controlled. A position controller based on feedback linearization is presented. This controller takes the overactuatedness into account by using the so-called multicycle approach, which essentially regards the robot as a combination of independent unicycles. As a result, the robot is position controlled while the redundant actuators are used to compensate for weight transfer during acceleration and cornering. Copyright © 2006 IFAC.
Dynamic conditional correlation models for realized covariance matrices

New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: one for each realized variance, and one for the realized correlation matrix. The realized correlation model is a scalar dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step, by assuming a Wishart conditional distribution. The model is applicable to large matrices since estimation can be done by the composite likelihood method.
Thesis submission for the degree of doctor scientiarum

A problem in EST clustering is the presence of repeat sequences. To avoid false matches, repeats have to be masked. This can be a time consuming process, and it depends on available repeat libraries. We present a fast and library independent method to eliminate this problem in the process of clustering. We demonstrate that the result is very similar to performing standard repeat masking before clustering.
Steganalysis Algorithm for PNG Images Based on Fuzzy Logic Technique

Embedding a message in media files, also known as steganography, is a common approach to hide secret information. It has been exploited by some criminals to confidentially exchange messages. As a countermeasure, tools have been developed in order to detect hidden information form digital media such as text, image, audio or video files. However the efficiency and performance of previous approaches still have room for improvement. In this research, we focus on algorithm design for better efficiency of hidden message detection from PNG files. We employ three classic AI approaches including neural network, fuzzy logic, and genetic algorithm and evaluate their efficiency and performance in controlled experiments. Finally we introduce our message detection system for PNG files based on LSB approach and present its usability in different case scenarios.