Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Cross-Sectional Surveys in Orange Farm, a Township of South Africa, Revealed a Constant Low Voluntary Medical Male Circumcision Uptake among Adults despite Demand Creation Campaigns and High Acceptability

Background WHO recommends a male circumcision (MC) prevalence rate higher than 80% to have a substantial impact on the HIV-AIDS epidemic in Eastern and Southern Africa. Orange Farm, a township in South Africa, has a free-for-service voluntary medical male circumcision (VMMC) clinic in operation since 2008. Following an intense campaign from 2008 to 2010, MC prevalence rate increased to 55.4% (ANRS-12126). Ongoing and past VMMC campaigns focused on youths, through school talks, and adults at a community level. The main objective of the study was to assess the change in MC prevalence rate among adults aged 18–19 and 18–49 years in the past 5 years. Methods A cross-sectional survey (ANRS-12285) was conducted among a random sample of 522 adult men in 2015. MC status and characteristics of participants were collected through a genital examination and a face-to-face questionnaire. Results MC prevalence rate among young adult men aged 18–19 years increased markedly from 61.2% (95%CI: 57.4% to 65.0%) in 2010 to 87.5% (76.0% to 94.6%) in 2015 (p<0.001). In the same period, among men aged 18–49 years, MC prevalence rate varied slightly from 55.4% (53.6% to 57.1%) to 56.7% (52.4% to 60.9%). In 2015, 84.9% (79.2% to 89.5%) of uncircumcised adult men reported that they were willing to be circumcised. However, we estimated that only 4.6% (11/237; 2.5% to 7.9%) of the uncircumcised men underwent circumcision in 2015, despite 117/185 (63.2%; 95%CI: 56.1% to 69.9%) who reported that they were definitely willing to become circumcised. Conclusions In Orange Farm, VMMC campaigns were successful among the youth and led to a sufficiently high MC prevalence rate to have a substantial impact in the future on the HIV-AIDS epidemic. However, despite high acceptability and a free VMMC service, VMMC campaigns since 2010 have failed to increase MC prevalence rate among adults to above 80%. These campaigns should be revisited.
Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing

A central problem in environmental sensing and monitoring is to classify/label the hotspots in a large-scale environmental field. This paper presents a novel decentralized active robotic exploration (DARE) strategy for probabilistic classification/labeling of hotspots in a Gaussian process (GP)-based field. In contrast to existing state-of-the-art exploration strategies for learning environmental field maps, the time needed to solve the DARE strategy is independent of the map resolution and the number of robots, thus making it practical for in situ, real-time active sampling. Its exploration behavior exhibits an interesting formal trade-off between that of boundary tracking until the hotspot region boundary can be accurately predicted and wide-area coverage to find new boundaries in sparsely sampled areas to be tracked. We provide a theoretical guarantee on the active exploration performance of the DARE strategy: under reasonable conditional independence assumption, we prove that it can optimally achieve two formal cost-minimizing exploration objectives based on the misclassification and entropy criteria. Importantly, this result implies that the uncertainty of labeling the hotspots in a GP-based field is greatest at or close to the hotspot region boundaries. Empirical evaluation on real-world plankton density and temperature field data shows that, subject to limited observations, DARE strategy can achieve more superior classification of hotspots and time efficiency than state-of-the-art active exploration strategies.
Status Quo Maintenance Reconsidered: Changing or Incomplete Preferences?

After reviewing the evidence for status quo maintenance (SQM), I consider how to reconcile SQM with traditional consumer theory. Behavioural economists usually let agents' preferences change as a function of their endowments, treating the same person with different endowments as a set of distinct agents. Many properties of preferences then become immune to empirical test and it becomes impossible to judge whether an agent's decisions make the agent better or worse off. This impedes prediction of when decision rules are likely to change. SQM can alternatively be explained with unchanging preferences if preferences are incomplete. SQM is then consistent with self-interest and there is no reason why it should not persist. Copyright 2004 Royal Economic Society.
A joint SDMA and interference suppression multiuser transmission scheme for millimeter-wave massive MIMO systems

This paper proposes a joint spatial division multiple access (SDMA) and interference suppression multiuser transmission scheme for massive multiple input multiple output (MIMO) systems working at millimeter-wave frequencies. Based on the sparsity of the beamspace channel matrix, we select several beams that capture the channel's main lobe to approximate the original highrank channel. After judging whether the overlap exists among these selected beams, we divide the user set into two subsets, i.e., a non-interference subset or an interference subset. The non-interference subset employs an SDMA based transmission scheme, while the interference subset employs an interference suppression based scheme. This combination takes advantages of both schemes, reduces the feedback amount in frequency division duplex transmission mode, and meanwhile ensures all the users being served on a same time-frequency resource block. Simulation results demonstrate that the proposed joint scheme achieves considerable system sum-rate with a small amount of feedback.
FORMULATION AND EVALUATION OF GASTRORETENTIVE FLOATING DRUG DELIVERY SYSTEM OF METOPROLOL TARTARATE

Drugs that have narrow absorption window in the gastrointestinal tract (GIT) will have poor absorption. For these drugs, gastroretentive drug delivery systems offer the advantage in prolonging the gastric emptying time. Metoprolol tartrate is an antihypertensive drug, which has low elimination half life: 3-4 h. The floating tablets of metoprolol tartrate were prepared to increase the gastric retention and to improve the bioavailability of the drug. Metoprolol tartrate was chosen as a model drug because it is better absorbed in the stomach than the lower gastro intestinal tract. The rapid gastro-intestinal transit could result in incomplete drug release from the drug delivery system above the absorption zone leading to poor bioavailability of the drug. The floating tablets were formulated using HPMC K4M and HPMC K100M as the release retardant polymers, and sodium bicarbonate as the gas generating agent to reduce the floating lag time. The tablets were prepared by direct compression. The formulated tablets were evaluated for weight variation, hardness, friability, swelling index floating lag time, total floating time and dissolution rate in pH 1.2. The floating tablets extended the drug release up to 8 hrs. The drug-polymer interaction was evaluated by fourier transform infrared spectroscopy (FTIR). The FTIR study indicated the lack of drug-polymer interaction.
Applying acoustic array processing to the estimation of the propagation speed of waves in a car exhaust

A parametric method based on spatial filter techniques (beamforming) is proposed to estimate the propagation speed of acoustic waves. The propagation speed estimate is analyzed for the case of narrowband signals and compared to the maximum likelihood estimate (MLE) of the propagation speed. It is shown that for an array of 3 sensors our estimate coincides with the ML estimate but its performance analysis is simpler and its computational cost is much more reduced. The proposed estimate is also applied to the wideband waves propagating along a car exhaust. It is shown that the signal-to-noise ratio and the magnitude of the relative aperture (distance between the array sensors respect to the wavelength) for each frequency could limit the good performance of the speed estimator. Good results have been achieved when these limitations have been taken into account.
DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

Abstract. This papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for ﬁnding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving signiﬁcant hints for classiﬁcation and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods.
Multi-Threaded BLAO* Algorithm

We present a heuristic search algorithm for solving goal based Markov decision processes (MDPs) named Multi-threaded BLAO* (MBLAO*). Hansen and Zilberstein proposed a heuristic search MDP solver named LAO* (Hansen & Zilberstein 2001). Bhuma and Goldsmith extended LAO* to the bidirectional case (Bhuma & Goldsmith 2003) and named their solver BLAO*. Recent experiments on BLAO* (Dai & Goldsmith 2006) discovered that BLAO* outperforms LAO* by restricting the number of Bellman backups. MBLAO* is based on this observation. MBLAO* further restricts the number of backups by searching backward from the goal state, and also from some middle states (states along the most probable path from the start state to the goal state). Our experiments show that MBLAO* is more efficient than BLAO* and other state-of-the-art heuristic search MDP planners.
Laboratory Evaluation of the Dual‐Probe Heat‐Pulse Method for Measuring Soil Water Content

The dual‐probe heat‐pulse (DPHP) method provides a means of estimating volumetric soil water content (θ) and change in volumetric water content (Δθ) from measurements of volumetric heat capacity. The purpose of this investigation was to characterize the accuracy and precision that can be achieved in measuring θ and Δθ with the DPHP method. Tempe pressure cells fitted with DPHP sensors were used to conduct desorption experiments in which DPHP‐based estimates of θ and Δθ were compared with values estimated by the gravimetric method. For water contents corresponding to soil water pressure potentials below −100 kPa, comparisons were made by packing the pressure cells with soil wetted to known water contents. The investigation was conducted with seven soil materials representing a wide range of physical properties for mineral soils. The DPHP sensors slightly overestimated θ at low water contents, but it was shown that the bias could be removed by using an empirical calibration equation, θ = 1.09 θDPHP − 0.045. This relationship appears to be general inasmuch as it was shown to be applicable for all seven soil materials and for water contents ranging from 0.02 to 0.59 m3 m−3 The general calibration equation was also shown to be effective in removing bias in Δθ estimates. Pooled regression analysis (all soil materials) showed that θ can be measured with a root mean square error (RMSE) of 0.022 m3 m−3 Greater precision can be achieved with Δθ measurements (RMSE = 0.012 m3 m−3); however, the results indicated a decrease in precision with increasing magnitude of Δθ.
L. V. Shcherba: a ‘new slant’ on modern foreign languages in the school curriculum?

ABSTRACT In this paper, I offer a critical reflection on the thesis of the general educational value of foreign languages developed by Russian linguist Lev Vladimirovich Shcherba. I do so against the background of current debates on the positioning of foreign languages in the school curriculum in the United Kingdom (UK). I argue that Shcherba's thesis, which was developed almost a century ago, retains its currency and can make an important contribution to the on-going discussion on the value of foreign languages in UK schools. The paper outlines Shcherba's scholarly explorations in general linguistics which underpin his arguments in favour of the inclusion of foreign languages in the basic school curriculum. The conception of language as a system immanently positioned in social experience assigns the foundational role to language in the literacy project. The conscious analytic processing of language phenomena is viewed as an essential pre-condition of literacy, and foreign languages are shown to be instrumental in developing such an analytic capacity of mind. Shcherba's argumentation reflects a comprehensive and interdisciplinary approach, both to foreign language education and to curriculum matters, and merits the attention of language practitioners, educationalists and policy-makers alike.
Geometric Algorithms for Con ict Detection / Resolution inAir Tra c Management 1

We consider the problems of connict detection and resolution in air traac management (ATM) from the perspective of computational geometry and give algorithms for solving these problems eeciently. For con-ict resolution, we propose a simple method that can route multiple aircraft, connict-free, through a cluttered airspace, using a prioritized routing scheme in space-time. Our algorithms have been implemented into a simulation system that tracks a large set of ights, having multiple connicts, and proposes mod-iied routes to resolve them. We report on the preliminary results from an extensive set of experiments that are under way to determine the eeectiveness of our methods.
Influenza Morbidity and Mortality in Elderly Patients Receiving Statins: A Cohort Study

Background Statins possess immunomodulatory properties and have been proposed for reducing morbidity during an influenza pandemic. We sought to evaluate the effect of statins on hospitalizations and deaths related to seasonal influenza outbreaks. Methodology/Principal Findings We conducted a population-based cohort study over 10 influenza seasons (1996 to 2006) using linked administrative databases in Ontario, Canada. We identified all adults older than 65 years who had received an influenza vaccination prior to the start of influenza season and distinguished those also prescribed statins (23%) from those not also prescribed statins (77%). Propensity-based matching, which accounted for each individual's likelihood of receiving a statin, yielded a final cohort of 2,240,638 patients, exactly half of whom received statins. Statins were associated with small protective effects against pneumonia hospitalization (odds ratio [OR] 0.92; 95% CI 0.89–0.95), 30-day pneumonia mortality (0.84; 95% CI 0.77–0.91), and all-cause mortality (0.87; 95% CI 0.84–0.89). These protective effects attenuated substantially after multivariate adjustment and when we excluded multiple observations for each individual, declined over time, differed across propensity score quintiles and risk groups, and were unchanged during post-influenza season periods. The main limitations of this study were the observational study design, the non-specific outcomes, and the lack of information on medications while hospitalized. Conclusions/Significance Statin use is associated with a statistically significant but minimal protective effect against influenza morbidity that can easily be attributed to residual confounding. Public health officials and clinicians should focus on other measures to reduce morbidity and mortality from the next influenza pandemic.
DYNAMIC RANGE AND COMPRESSIVE SENSING ACQUISITION RECEIVERS

Compressive sensing (CS) exploits the sparsity present in many signal environments to reduce the number of measurements needed for digital acquisition and processing. We have previously introduced the concept and feasibility of using CS techniques to build a wideband signal acquisition systems. This paper extends that work to examine such a receiver’s performance as a function of several key design parameters. In particular we show that that the system noise figure is predictably degraded as the subsampling implicit in CS is made more aggressive. Conversely we show that the dynamic range of a CS-based system can be substantially improved as the subsampling factor grows. The ability to control these aspects of performance provides an engineer new degrees of freedom in the design of wideband acquisition systems. A specific practical example, a multi-collector emitter geolocation system, is included to illustrate that point.
Global Growth and Distribution: Are China and India Reshaping the World?

Over the past 20 years, aggregate measures of global inequality have changed little even if significant structural changes have been observed. High growth rates of China and India lifted millions out of poverty, while the stagnation in many African countries caused them to fall behind. Using the World Bank’s LINKAGE global general equilibrium model and the newly developed Global Income Distribution Dynamics (GIDD) tool, this paper assesses the distribution and poverty effects of a scenario where these trends continue in the future. Even by anticipating a deceleration, growth in China and India is a key force behind the expected convergence of per capita incomes at the global level. Millions of Chinese and Indian consumers will enter into a rapidly
Holistics 3.0 for Health

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.
A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.
A Teaching System for Assistance in the Interpretation of Mammograms and the Diagnosis of Breast Cancer

This work proposes the design of a teaching system for training students in the interpretation of mammograms and diagnosis of breast cancer. The proposed system integrates an illustrated tutorial on radiology of the breast, that is, mammography, which uses education techniques to guide the user (doctors, students, or researchers) through various concepts related to the diagnosis of breast cancer. The user can obtain informative text about specific subjects, access a library of bibliographic references, and retrieve cases from a mammographic database that are similar to a query case on hand. The information of each case stored in the mammographic database includes the radiological findings, the clinical history, the life style of the patient, and complementary exams. The breast cancer tutorial is linked to a module that simulates the analysis and diagnosis of a mammogram. The tutorial incorporates tools for helping the user to evaluate his or her knowledge about a specific subject by using the education system or by simulating a diagnosis with appropriate feedback in case of error. The system also makes available digital image processing tools that allow the user to draw the contour of a lesion, the contour of the breast, or identify a cluster of calcifications in a given mammogram. The contours provided by the user are submitted to the mammographic database for evaluation. The teaching system is integrated with AMDI — an Indexed Atlas of Digital Mammograms — that includes case studies, e-learning, and research systems. All the resources are accessible via the Web.
Content-based tag recommendation algorithms for unstructured data

Organisations process a huge amount of documents on a daily basis. Such documents are often stored in an unstructured manner, e.g. HTML, Word or PDF files. The term “unstructured data” refers to information that isn’t stored in a traditional relational database. Such data can contain valuable information, but it is hard to find and organise since the data does not follow a fixed structure. Tagging resources, annotating them with keywords, is a great way to improve findability of resources in a large collection of resources. Tagging is however a human task. This research focuses on the task of tag recommendation, which involves finding and suggesting relevant keywords for documents from their content and relations between users, documents and tag-words. We discuss various types of algorithms and test their performance on three real world datasets. Besides some well known algorithms, we implemented a system proposed by Lipczak et al. which won a major scientific event focussed on tag recommendation. His system outperforms the well known algorithms and returns a maximum F1 score of 0.3127 on the DeliciousT140 dataset. We propose two extensions to his system which respectively achieve a maximum F1 score of 0.3167 and 0.3216.