Miracle-dz/UniFAR-FTUs huggingface dataset:
A Supervised Decision Rule for Multiclass Problems Minimizing a Loss Function

A multiclass learning method which minimizes a loss function is proposed. The loss function is defined by costs associated to the decision options which may include classes, subsets of classes if partial rejection is considered and all classes if total rejection is introduced. A formulation of the general problem is given, a decision rule which is based on the v-1-SVMs trained on each class is defined and a learning method is proposed. This latter optimizes all the v-1-SVM parameters and all the decision rule parameters jointly in order to minimize the loss function. To extend the search space of the v-1-SVM parameters and keep the processing time under control, the v-1-SVM regularization path is derived for each class and used during the learning process. Experimental results on artificial data sets and some benchmark data sets are provided to assess the effectiveness of the approach.
Collective intelligence approach for free software adoption by municipalities

The free technologies adoption by governments is not a new phenomenon. As an innovative contribution, this paper proposes a collective intelligence (CI) approach for improving the free software adoption by small and medium-sized municipalities. To build this CI-approach, the authors have analyzed the dynamic of the communities hosted by Brazilian Public Software (BPS) Portal and the actions related to implementation of software solution in Brazilian municipalities. They started by identifying key variables and their relationship, and mining data from the communities whose software solutions have been successfully implemented in the municipalities according to player reports available in BPS portal. Such as the relationships demonstrate, these players have been appropriated the knowledge related to solutions and created new business to support the modernization of public administration. Based on reports and statistical analysis, the authors could understand both the dynamic of the successful communities and the adoption phenomena by municipalities as a consequence of individual initiatives. The results indicate that a CI-approach based on 5W1H can improve the knowledge sharing and promote the best practice in the free software adoption by small and medium-sized municipalities.
Compact web browsing profiles for click-through rate prediction

In real time advertising we are interested in finding features that improve click-through rate prediction. One source of available information is the bipartite graph of websites previously engaged by identifiable users. In this work, we investigate three different decompositions of such a graph with varying degrees of sparsity in the representations. The decompositions that we consider are SVD, NMF, and IRM. To quantify the utility, we measure the performances of these representations when used as features in a sparse logistic regression model for click-through rate prediction. We recommend the IRM bipartite clustering features as they provide the most compact representation of browsing patterns and yield the best performance.
AUGMENTING SOUND MOSAICING WITH DESCRIPTOR-DRIVEN TRANSFORMATION

We propose a strategy for integrating descriptor-driven transformation into mosaicing sound synthesis, in which samples are selected by taking into account potential distances in the transformed space. Target descriptors consisting of chroma, mel-spaced filter banks, and energy are modeled with respect to windowed bandlimited resampling and mel-spaced filters, and later corrected with gain. These transformations, however simple, allow some adaptation of textural sound material to musical contexts.
SIGNAL AND ANATOMICAL CONSTRAINTS IN ADAPTIVE FILTERING OF FMRI DATA

An adaptive filtering method for fMRI data is presented. The method is related to bilateral filtering, but with a range filter that takes into account local similarities in signal as well as in anatomy. Performance is demonstrated on simulated and real data. It is shown that using both these similarity constraints give better performance than if only one of them is used, and clearly better than standard low-pass filtering.
Are Forward Exchange Rates Rational Forecasts of Future Spot Rates? An Improved Econometric Analysis for the Major Currencies

It has been suggested that prior studies that have puzzlingly found forward rates to be inefficient and biased forecasts of future spot rates may be limited by inadequate statistical methodologies. Using an improved statistical methodology that accounts for both non-stationarity and non-normality in exchange rates, we unfortunately reconfirm that U.S. dollar forward rates for horizons ranging from one to twelve months for the British pound, Japanese yen, Swiss franc, and the German mark over the period 1973–1998 are generally not efficient or rational forecasts of future spot rates. However, as one bright spot, we cannot reject efficiency and rationality for the U.S. dollar forward rate for the Canadian dollar.
Performance Evaluation and Analysis of Direction of Arrival Estimation Using MUSIC,TLS ESPRIT and Pro ESPRIT Algorithms

Source Direction of arrival (DOA) estimation plays an important role in array signal processing, and has a wide range of application. Array signal processing is an important branch in the field of signal processing. In recent years, it has developed dramatically. It can be applied in fields such as radar, communication, sonar, earthquake, exploration, astronomy and biomedicine. Over the past few years, all kinds of algorithms which can be used in DOA estimation have made great achievements, the most classic algorithms among which are Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). In this paper, we will give an overview on performance of the DOA estimation based on MUSIC as well as ESPRIT (both TLS and Pro ESPRIT) algorithm on uniform linear array (ULA) and in the presence of white noise. We will describe what DOA estimation is, and give a mathematical model of DOA estimation for subspace based DOA methods such as MUSIC, TLS ESPRIT and Pro ESPRIT. Then estimate DOA based on the MUSIC, TLS ESPRIT as well as Pro ESPRIT algorithm, with simulations with MATLAB to simulate factors that affect accuracy and resolution of DOA estimation. The performance of these DOA algorithms for a set of input parameters such as number of snapshots, number of array elements, signal-to-noise ratio, angular separation, element spacing are investigated.
Endurance shuttle walking test: responsiveness to salmeterol in COPD

Few studies have shown that the endurance shuttle walking test (ESWT) is responsive to treatment in patients with chronic obstructive pulmonary disease (COPD). This exercise test needs to be further investigated because of its relevance for activity of daily living. The aim of the present study was to evaluate, in patients with COPD, the responsiveness of the ESWT in detecting improvement in walking performance after a single dose of salmeterol. In a randomised, double-blind, placebo-controlled crossover trial, 20 patients with COPD performed two ESWT at 80% of peak capacity 2.5 h after inhaling either a placebo or 50 µg of salmeterol. Cardiorespiratory parameters were monitored during each walking test. Inspiratory capacities and Borg ratings for dyspnoea were obtained every other minute throughout the tests. Compared with placebo, salmeterol produced a significant change in lung function and a significant improvement in walking performance (mean±sd difference in time: 117±20 s; difference in distance: 160±277 m). At isotime (the latest exercise time that was reached on both ESWT), a significant reduction in dyspnoea was observed after bronchodilation. Bronchodilation with salmeterol reduced dyspnoea during walking and improved walking capacity in patients with chronic obstructive pulmonary disease. These findings provide further support for the use of the endurance shuttle walking test as an evaluative tool in chronic obstructive pulmonary disease.
Topological Subset Space Models for Belief

In recent work, Robert Stalnaker proposes a logical framework in which belief is realized as a weakened form of knowledge [28]. Building on Stalnaker’s core insights, we employ topological tools to refine and, we argue, improve on this analysis. The structure of topological subset spaces allows for a natural distinction between what is known and (roughly speaking) what is knowable; we argue that the foundational axioms of Stalnaker’s system rely intuitively on both of these notions. More precisely, we argue that the plausibility of the principles Stalnaker proposes relating knowledge and belief relies on a subtle equivocation between an “evidence-in-hand” conception of knowledge and a weaker “evidence-out-there” notion of what could come to be known. Our analysis leads to a trimodal logic of knowledge, knowability, and belief interpreted in topological subset spaces in which belief is definable in terms of knowledge and knowability. We provide a sound and complete axiomatization for this logic as well as its uni-modal belief fragment. We then consider weaker logics that preserve suitable translations of Stalnaker’s postulates, yet do not allow for any reduction of belief. We propose novel topological semantics for these irreducible notions of belief, generalizing our previous semantics, and provide sound and complete axiomatizations for the corresponding logics.
Aspidosperma (Apocynaceae) plant cytotoxicity and activity towards malaria parasites. Part II: experimental studies with Aspidosperma ramiflorum in vivo and in vitro

Several species of Aspidosperma plants are used to treat diseases in the tropics, including Aspidosperma ramiflorum, which acts against leishmaniasis, an activity that is experimentally confirmed. The species, known as guatambu-yellow, yellow peroba, coffee-peroba andmatiambu, grows in the Atlantic Forest of Brazil in the South to the Southeast regions. Through a guided biofractionation of A. ramiflorum extracts, the plant activity against Plasmodium falciparum was evaluated in vitro for toxicity towards human hepatoma G2 cells, normal monkey kidney cells and nonimmortalised human monocytes isolated from peripheral blood. Six of the seven extracts tested were active at low doses (half-maximal drug inhibitory concentration < 3.8 µg/mL); the aqueous extract was inactive. Overall, the plant extracts and the purified compounds displayed low toxicity in vitro. A nonsoluble extract fraction and one purified alkaloid isositsirikine (compound 5) displayed high selectivity indexes (SI) (= 56 and 113, respectively), whereas compounds 2 and 3 were toxic (SI < 10). The structure, activity and low toxicity of isositsirikine in vitro are described here for the first time in A. ramiflorum, but only the neutral and precipitate plant fractions were tested for activity, which caused up to 53% parasitaemia inhibition of Plasmodium berghei in mice with blood-induced malaria. This plant species is likely to be useful in the further development of an antimalarial drug, but its pharmacological evaluation is still required.
HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

Abstract In this paper, the results of an experiment carried out for exploiting the capabilities of X-band Cosmo-SkyMed data in the monitoring of soil and vegetation characteristics are summarized. SAR data have been collected in two agricultural areas in Italy and compared with ground truth measurements of soil and vegetation parameters. A rather good sensitivity to vegetation features, biomass and the moisture of bare or slightly vegetated soils has been confirmed. On bare surfaces the effect of surface roughness was significant, as expected, due to the high observation frequency. Model simulations have also been performed for better explaining the backscattering response to soil moisture at this frequency. The different backscattering response according to vegetation types, which has already been observed at C-band, has been confirmed at X-band, too. The absorption due to thin vertical stems was observed on wheat crops, whereas sunflower showed a prevalent scattering behavior due to the large circular leaves.
Sparsity analysis of term weighting schemes: Application to Feature Selection

In this paper we revisit the practice of using feature selection for dimensionality and noise reduction. Commonly we score features according to some weighting scheme and then specify that the top N ranked features or top N percents of scored features are to be used for further processing. In text classification, such a selection criteria lead to significantly different sizes of (unique) feature sets across various weighting schemes, if a particular level of performance is to be achieved, for a given learning method. On the other hand the number and the type of features determine the sparsity characteristics of the training and test documents, i.e., the average number of features per document vector. We show that specifying sparsity level, instead of pre-defined number of features per category as the selection criteria, produces comparable average performance over the set of categories. At the same time it has an obvious advantage of providing the means for control of the consumption of computing memory resources. Furthermore, we show that observing sparsity characteristics of selected feature sets, in form of sparsity curves, can be useful in understanding the nature of the feature weighting scheme itself. In particular, we begin to understand the level at which feature specificity, or commonly called ‘rarity’ is incorporated into the term weighting scheme and accounted for in the learning algorithm.
Improving motion and orientation estimation using an equilibrated total least squares approach

This paper outlines the ubiquitous presence of generalized orientation (or subspace) estimation problems in image analysis. We show the potential sources of bias in naive approaches to directional estimation problems, discuss countermeasures against this bias, and point out the direct relation to the total least squares problem. An improved method (using TLS and "equilibration") for a precise direct motion estimation of planar objects (8 parameter motion model, homography estimation) concludes this paper.
APPLICATION OF NEURAL NETWORK FOR FORECASTING OF EXCHANGE RATES AND FOREX TRADING

Expert methods, which widely applied for human decision making, were employed for neural networks. It was developed an exchange rates prediction and trading algorithm with using of experts in- formation processing techniques - Delphi method and prediction compatibility. Proposed algorithm lim- ited to eight experts. Each of experts represented recurrent neural network, Evolino-based Long Short- Term Memory (LSTM) by using of genetic learning algorithm, EVOlution of recurrent systems with LINear Outputs (EVOLINO). Statistical investigation of offered algorithm shows the significantly in- crease of the reliability of prediction. Developed algorithm was applied for trading of historical forex ex- change rates. Obtained test trading results were presented
Parallel Implementation of Expectation-Maximization for Fast Convergence

Latent variable models have become popular tools for modeling data that potentially arise from latent causes. This class of models includes the unstructured Gaussian mixture model (GMM), temporally linked Hidden Markov Model (HMM), and the more complex latent Dirichlet allocation (LDA) [3]. Despite the varying latent variable structures, these models all poses challenging inference problem as the latent variables are not directly observed. Solution techniques are generally only approximate, such as the Expectation-Maximization (EM) algorithm and sampling-based methods such as Gibbs sampling. In most cases, these approaches are computationally intensive. As the data set today becomes ever more gigantic, there is a pressing need for efficiently parallelizing these inference algorithms in multi-core and distributed environment.
Working Paper Number 84 Social Capital and Industrial Transformation

This paper is an exploration of the social capital needs of industrial development. ‘Social capital’ attracts considerable attention in socio-political analysis and we are beginning to see its application to development economics. There has not, as far as I know, been any attempt to apply it to the determinants of successful industrialisation in the developing world. This essay is a preliminary sketch of the concepts rather than a report on research findings or a complete analysis with specific policy recommendations. Nevertheless, it illustrates the value and significance of bringing social capital concepts to bear on specific aspects of development
Compiling queries for high-performance computing

Data-intensive applications motivate the integration of highproductivity query languages with high-performance computing runtimes. We present a technique Compiled parallel pipelines (CPP) for compiling relational query plans to programs suitable for high-performance computing platforms. Rather than compose a sequential query compiler with a high-performance communication library like MPI, we take a holistic approach that leverages the capabilities of parallel languages. For each pipeline in the query plan, CPP generates a parallel partitioned global address space (PGAS) program. This approach affords modular design, and it allows the compiler to reason about whole pipelines that include parallelism and communication. Using PGAS to efficiently execute queries requires designing efficient shared data structures, generating code that avoids extra messages, and mitigating the overhead of an execution model based on fine-grained tasks. We implement our technique as a system called RADISH. Our evaluation shows that CPP is 5.5× faster than compiled iterators on TPC-H queries. To show that RADISH is a practical system for in-memory analytics, we also compare the performance of RADISH on TPC-H with the MPP system DBX and find it to be competitive. Our work takes important first steps integrating query processing and distributed HPC.
Changing attitudes to punishment : public opinion, crime and justice

1. Public Attitudes to Punishment: The Context by Julian V. Roberts and Mike Hough 2. Measuring Attitudes to Sentencing by Loretta J. Stalans 3. Public Opinion and the Nature of Community Penalties by Julian V. Roberts 4. Cross-National Attitudes Towards Punishment by Pat Mayhew and John Kesteren 5. The Evolution of Public Attitudes to Punishment in Western and Eastern Europe by Helmut Kury, Obergfell-Fuchs and Ursula Smartt 6. Public and Judicial Attitudes to Punishment in Switzerland by Andre Kuhn 7. Public Support for Correctional Rehabilitation: Change or Consistency? by Francis T. Cullen, Jennifer A. Pealer, Bonnie S. Fisher, Brandon K. Applegate and Shannon A. Santana 8. Attitudes to Punishment in the US - Punitive and Liberal Opinions by John Doble 9. How Malleable are Attitudes to Crime and Punishment? Findings from a British Deliberative Poll by Mike Hough and Alison Park 10. Improving Public Knowledge about Crime and Criminal Justice by Catriona Mirrlees-Black 11. Strategies for Changing Public Attitudes to Punishment by David Indermaur and Mike Hough 12. Privileging Public Attitudes Towards Sentencing by Rod Morgan
The XMM large scale structure survey: properties and two-point angular correlations of point-like sources

We analyze X-ray sources detected over 4.2 pseudo-contiguous sq. deg. in the 0.5–2 keV and 2–10 keV bands down to fluxes of 2 × 10 −15 and 8 × 10 −15 erg s −1 cm −2 respectively, as part of the XMM-Newton Large Scale Structure Survey. The logN–logS in both bands shows a steep slope at bright fluxes, but agrees well with other determinations below ∼2 × 10 −14 erg s −1 cm −2 . The detected sources resolve close to 30 per cent of the X-ray background in the 2–10 keV band. We study the two-point angular clustering of point sources using nearest neighbours and correlation function statistics and find a weak, positive signal for ∼1130 sources in the 0.5–2 keV band, but no correlation for ∼400 sources in the 2–10 keV band below scales of 100 arcsec. A sub-sample of ∼200 faint sources with hard X-ray count ratios, that is likely to be dominated by obscured AGN, does show a positive signal with the data allowing for a large angular correlation length, but only at the ∼ 2( 3)σ level, based on re-sampling (Poisson) statistics. We discuss possible implications and emphasize the importance of wider, complete surveys in order to fully understand the large scale structure of the X-ray sky.
Impact of Community-Based Lymphedema Management on Perceived Disability among Patients with Lymphatic Filariasis in Orissa State, India

Background Lymphatic filariasis (LF) infects approximately 120 million people worldwide. As many as 40 million have symptoms of LF disease, including lymphedema, elephantiasis, and hydrocele. India constitutes approximately 45% of the world's burden of LF. The Indian NGO Church's Auxiliary for Social Action (CASA) has been conducting a community-based lymphedema management program in Orissa State since 2007 that aims to reduce the morbidity associated with lymphedema and elephantiasis. The objective of this analysis is to evaluate the effects of this program on lymphedema patients' perceived disability. Methodology/Principal Findings For this prospective cohort study, 370 patients ≥14 years of age, who reported lymphedema lasting more than three months in one or both legs, were recruited from villages in the Bolagarh sub-district, Khurda District, Orissa, India. The World Health Organization Disability Assessment Schedule II was administered to participants at baseline (July, 2009), and then at regular intervals through 24 months (July, 2011), to assess patients' perceived disability. Disability scores decreased significantly (p<0.0001) from baseline to 24 months. Multivariable analysis using mixed effects modeling found that employment and time in the program were significantly associated with lower disability scores after two years of program involvement. Older age, female gender, the presence of other chronic health conditions, moderate (Stage 3) or advanced (Stage 4–7) lymphedema, reporting an adenolymphangitis (ADL) episode during the previous 30 days, and the presence of inter-digital lesions were associated with higher disability scores. Patients with moderate or advanced lymphedema experienced greater improvements in perceived disability over time. Patients participating in the program for at least 12 months also reported losing 2.5 fewer work days per month (p<0.001) due to their lymphedema, compared to baseline. Significance These results indicate that community-based lymphedema management programs can reduce disability and prevent days of work lost. These effects were sustained over a 24 month period.