Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive audio-tactile maps for visually impaired people

Visually impaired people face important challenges related to orientation and mobility. Indeed, 56% of visually impaired people in France declared having problems concerning autonomous mobility [10]. These problems often mean that visually impaired people travel less, which influences their personal and professional life and can lead to exclusion from society [28]. Therefore this issue presents a social challenge as well as an important research area. Accessible geographic maps are helpful for acquiring knowledge about a city's or neighborhood's configuration, as well as selecting a route to reach a destination. Traditionally, raised-line paper maps with braille text have been used. These maps have proved to be efficient for the acquisition of spatial knowledge by visually impaired people. Yet, these maps possess significant limitations [37]. For instance, due to the specificities of the tactile sense only a limited amount of information can be displayed on a single map, which dramatically increases the number of maps that are needed. For the same reason, it is difficult to represent specific information such as distances. Finally, braille labels are used for textual descriptions but only a small percentage of the visually impaired population reads braille. In France 15% of blind people are braille readers and only 10% can read and write [10]. In the United States, fewer than 10% of the legally blind people are braille readers and only 10% of blind children actually learn braille [24].
Can prebiotics and probiotics improve therapeutic outcomes for undernourished individuals?

It has become clear in recent years that the human intestinal microbiota plays an important role in maintaining health and thus is an attractive target for clinical interventions. Scientists and clinicians have become increasingly interested in assessing the ability of probiotics and prebiotics to enhance the nutritional status of malnourished children, pregnant women, the elderly, and individuals with non-communicable disease-associated malnutrition. A workshop was held by the International Scientific Association for Probiotics and Prebiotics (ISAPP), drawing on the knowledge of experts from industry, medicine, and academia, with the objective to assess the status of our understanding of the link between the microbiome and under-nutrition, specifically in relation to probiotic and prebiotic treatments for under-nourished individuals. These discussions led to four recommendations: (1) The categories of malnourished individuals need to be differentiated To improve treatment outcomes, subjects should first be categorized based on the cause of malnutrition, additional health-concerns, differences in the gut microbiota, and sociological considerations. (2) Define a baseline “healthy” gut microbiota for each category Altered nutrient requirement (for example, in pregnancy and old age) and individual variation may change what constitutes a healthy gut microbiota for the individual. (3) Perform studies using model systems to test the effectiveness of potential probiotics and prebiotics against these specific categories These should illustrate how certain microbiota profiles can be altered, as members of different categories may respond differently to the same treatment. (4) Perform robust well-designed human studies with probiotics and/or prebiotics, with appropriate, defined primary outcomes and sample size These are critical to show efficacy and understand responder and non-responder outcomes. It is hoped that these recommendations will lead to new approaches that combat malnutrition. This report is the result of discussion during an expert workshop titled “How do the microbiota and probiotics and/or prebiotics influence poor nutritional status? ” held during the 10th Meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) in Cork, Ireland from October 1–3, 2012. The complete list of workshop attendees is shown in Table 1.
Study Of E-Smooth Support Vector Regression And Comparison With E- Support Vector Regression And Potential Support Vector Machines For Prediction For The Antitubercular Activity Of Oxazolines And Oxazoles Derivatives

A new smoothing method for solving ? -support vector regression (? -SVR), tolerating a small error in fitting a given data sets nonlinearly is proposed in this study. Which is a smooth unconstrained optimization reformulation of the traditional linear programming associated with a ? -insensitive support vector regression. We term this redeveloped problem as ? -smooth support vector regression (? -SSVR). The performance and predictive ability of ? -SSVR are investigated and compared with other methods such as LIBSVM (? -SVR) and P-SVM methods. In the present study, two Oxazolines and Oxazoles molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of experiments. Primary experimental results illustrate that our proposed approach improves the regression performance and the learning efficiency. In both studied cases, the predictive ability of the ? - SSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate that ? -SSVR can be used as an alternative powerful modeling method for regression studies. The experimental results show that the presented algorithm ? -SSVR, plays better precisely and effectively than LIBSVMand P-SVM in predicting antitubercular activity.
HOW SIMULATION GAINS ACCEPTANCE AS A MANUFACTURING PRODUCTIVITY IMPROVEMENT TOOL

Simulation is well-recognized among its expert practitioners as a manufacturing productivity improvement tool. Achievements of simulation in reducing operating costs, increasing production quotas, reducing work-in-process inventories, eliminating bottlenecks cost-effectively, achieving efficiency of scheduling procedures, and otherwise improving performance metrics are well-known among simulation experts and well-documented in the technical literature. However, within many companies, simulation is used only in unusual situations and when the need for efficiency improvements has reached a high level of urgency and visibility among managers. This paper describes procedures for upgrading the use of simulation to the status of "norm;" that is, making the exploitation of simulation’s analytical powers a standard, expected step during the design, revision, or operation of a production system.
Translation competence development among learners : a problem-solving perspective

··························································································· 6 Declaration and copyright statement ····························································· 7 Acknowledgements················································································· 8 Chapter
Rising Inequality? Changes in the Distribution of Income and Consumption in the 1980s

This paper examines changes in the distribution of income and consumption in the United States during the 1980s. using data from the Current Population Survey (income) and Consumer Expenditure Survey (consumption). We reach three primary conclusions. First. changes in the distribution of consumption parallel changes in the distribution of income. The lowest quintile of the consumption distribution received 0.9 percentage points less of total consumption in 1988 than in 1980; the corresponding decline for income was 0.6 percentage points. Second. broad conclusions concerning recent changes in the consumption distribution are not very sensitive to the exact choice of a measure of family needs. Under a wide variety of alternative household equivalence scales. there is a widening in the consumption distribution in the 1980s. Third. the usc of consumption measures of well-being in place of measures based on current money income docs change conclusions concerning the extent of poverty in the United Stales. Using the official federal poverty thresholds. we find that the overall consumption poverty rate was three percentage points below the income poverty rate in 1988. Comparisons of the poverty rates of the elderly and the non-elderly are substantially affected by the choice of poverty measure. The consumption poverty rare for the elderly was only 60 percent of the rate for adults and one-third of the rate for children in 1988.
Multi-spectral missing label prediction via restoration using deep residual dictionary learning

Dictionary learning (DL) is one of the popular sparse coding machine learning techniques. In image processing literature, every input image is represented as the sparse linear combination of basis vectors. DL has been shown to have wide applications for image restoration as well as pattern recognition problems. In DL, the input image is factorized into dictionary and sparse codes. This factorization always leaves a residual or approximation error. Very few works in the literature had focused on to leverage the information present in this residual. In this paper, we use residuals within our framework and show that the restoration performance or accurate prediction of missing label in multi-spectral images can be significantly improved over conventional DL based techniques. We initially show that the higher order frequencies are propagated through residuals. Then we show that incorporating this residual in the image restoration methodology can significantly improve the outcomes. Finally, we propose a technique to solve the problem of missing label prediction by using a restoration based deep residual dictionary learning framework.
Notes on Complex Hyperbolic Geometry

3 Isometries 9 3.1 The unitary groups of the first Hermitian form . 9 3.2 The unitary groups of the second Hermitian form . 10 3.3 PU(2, 1) and its action on complex hyperbolic space . 11 3.4 Complex hyperbolic isometries . 13 3.5 Classification of isometries . 15
A New Framework For Crowded Scene Counting Based On Weighted Sum Of Regressors and Human Classifier

Crowd density estimation is an important task in the surveillance camera system, it serves in security, traffic, business etc. At the present, the trend of monitoring is moving from individual to crowd, but traditional counting techniques will be inefficient in this case because of issues such as scale, clutter background and occlusion. Most of the previous methods have focused on modeling work to accurately estimate the density map and thus infer the count. However, with non-human scenes, which have many clouds, trees, houses, seas etc, these models are often confused, resulting in inaccurate count estimates. To overcome this problem, we propose the "Weighted Sum of Regressors and Human Classifier" (WSRHC) method. Our model consists of two main parts: human -- non-human classification and crowd counting estimation. First of all, we built a Human Classifier, which filters out negative sample images (non-human images) before entering into the regressors. Then, the count estimation is based on the regressors. The difference between regressors is the size of the filters. The essence of this method is the count depends on the weighted average of the density map obtained from these regressors. This is to overcome the defects of the previous model, Switching Convolutional Neural Network (Switch-CNN) select the count as the output of one of the regressors. Multi-Column Convolutional Neural Network (MCNN) combines the count and the weight of the Regressors by fixed weights from MCNN, while our approach is adapted for individual images. Our experiments have shown that our method outperform Switch-CNN, MCNN on ShanghaiTech dataset and UCF_CC_50 dataset.
On Totally Split Primes in High-Degree Torsion Fields of Elliptic Curves

Analogously to primes in arithmetic progressions to large moduli, we can study primes that are totally split in extensions of ${\mathbb {Q}}$ of high degree. Motivated by a question of Kowalski we focus on the extensions ${\mathbb {Q}}(E[d])$ obtained by adjoining the coordinates of $d$-torsion points of a non-CM elliptic curve $E/{\mathbb {Q}}$. We show that for almost all integers $d$ there exists a non-CM elliptic curve $E/{\mathbb {Q}}$ and a prime $p<|\text {Gal}({\mathbb {Q}}(E[d])/{\mathbb {Q}})|= d^{4-o(1)}$, which is totally split in ${\mathbb {Q}}(E[d])$. Note that such a prime $p$ is not accounted for by the expected main term in the Chebotarev Density Theorem. Furthermore, we prove that for almost all $d$ that factorize suitably there exists a non-CM elliptic curve $E/{\mathbb {Q}}$ and a prime $p$ with $p^{0.2694} < d$, which is totally split in ${\mathbb {Q}}(E[d])$. To show this we use work of Kowalski to relate the question to the distribution of primes in certain residue classes modulo $d^2$. Hence, the barrier $p < d^4$ is related to the limit in the classical Bombieri–Vinogradov Theorem. To break past this we make use of the assumption that $d$ factorizes conveniently, similarly as in the works on primes in arithmetic progression to large moduli by Bombieri, Friedlander, Fouvry, and Iwaniec, and in the more recent works of Zhang, Polymath, and the author. In contrast to these works we do not require any of the deep exponential sum bounds (i.e., sums of Kloosterman sums or Weil/Deligne bound). Instead, we only require the classical large sieve for multiplicative characters and we apply Harman’s sieve method to obtain a combinatorial decomposition for primes.
The Role of MicroRNAs in Endothelial Progenitor Cell Function

ii ABBREVIATIONS AND DEFINITIONS . iii ACKNOWLEDGEMENTS . vi TABLE OF CONTENTS . vii LIST OF TABLES . xiii LIST OF FIGURES . xiv LIST OF APPENDICES . xvii CHAPTER I INTRODUCTION . 1 1.1 The Endothelium . 2 1.2 Angiogenesis . 3 1.3 Endothelial progenitor cells . 4 1.4 EPCs in cardiovascular injury and repair . 6 1.5 Clinical relevance of EPCs for therapy . 7 1.6 Indirect mechanism of action of EPCs . 9 1.7 MicroRNAs . 12
Potential of Mild Torrefaction for Upgrading the Wood Energy Value of Different Eucalyptus Species

Torrefaction is a promising pre-treatment for improving the fuel quality of biomass. This study examined the effect of a mild torrefaction (from 160 to 230 °C) on the heating value, elemental composition, and thermogravimetric analysis of wood from eight Eucalyptus species (E. botryoides Sm., E. globulus Labill., E. grandis W.Hill ex Maiden, E. maculata (Hook.) K.D. Hill & L.A. S.Johnson, E. propinqua Maiden Deane, E. rudis Endl. , E. saligna Sm. , and E. viminalis Labill. ). The higher heating values (HHV) increased from the initial average of 19 MJ kg−1 to 21 MJ kg−1 in the torrefied samples. The carbon content increased from 48.2% to 53.3% and the C/O ratio from 1.08 to 1.35. The torrefied wood samples showed more thermal stability with a shift of the mass loss peaks to higher temperatures and a higher residual mass at 450 °C of 36%, in comparison to 30% of the untreated samples. Torrefaction concentrated the biomass samples in the more energetic and thermal resistant components and decreased their sulfur and chlorine content, leading to a better combustion performance. The wood of the eight Eucalyptus species had a similar behavior and showed quality improvement. Therefore, torrefaction showed potential as a pre-treatment for eucalyptus biomass fuel improvement.
Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning

Affect detection combined with a system that dynamically responds to a person’s emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human–Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.
Transcriptomic and Metabolomic Profiling Uncovers Response Mechanisms of Alicyclobacillus acidoterrestris DSM 3922T to Acid Stress

Fruit juice spoilage caused by A. acidoterrestris contamination has become a major concern and challenge in the food industry, and this bacterium has been suggested as a target microbe in the design of the pasteurization process. However, the response mechanisms of A. acidoterrestris to acid stress still remain unknown. ABSTRACT Alicyclobacillus acidoterrestris, which has strong acidophilic and heat-resistant properties, can cause spoilage of pasteurized acidic juice. The current study determined the physiological performance of A. acidoterrestris under acidic stress (pH 3.0) for 1 h. Metabolomic analysis was carried out to investigate the metabolic responses of A. acidoterrestris to acid stress, and integrative analysis with transcriptome data was also performed. Acid stress inhibited the growth of A. acidoterrestris and altered its metabolic profiles. In total, 63 differential metabolites, mainly enriched in amino acid metabolism, nucleotide metabolism, and energy metabolism, were identified between acid-stressed cells and the control. Integrated transcriptomic and metabolomic analysis revealed that A. acidoterrestris maintains intracellular pH (pHi) homeostasis by enhancing amino acids decarboxylation, urea hydrolysis, and energy supply, which was verified using real-time quantitative PCR and pHi measurement. Additionally, two-component systems, ABC transporters, and unsaturated fatty acid synthesis also play crucial roles in resisting acid stress. Finally, a model of the responses of A. acidoterrestris to acid stress was proposed. IMPORTANCE Fruit juice spoilage caused by A. acidoterrestris contamination has become a major concern and challenge in the food industry, and this bacterium has been suggested as a target microbe in the design of the pasteurization process. However, the response mechanisms of A. acidoterrestris to acid stress still remain unknown. In this study, integrative transcriptomic, metabolomic, and physiological approaches were used to uncover the global responses of A. acidoterrestris to acid stress for the first time. The obtained results can provide new insights into the acid stress responses of A. acidoterrestris, which will point out future possible directions for the effective control and application of A. acidoterrestris.
Control of bacterial DNA supercoiling

Two DNA topoisomerases control the level of negative supercoiling in bacterial cells. DNA gyrase introduces supercoils, and DNA topoisomerase I prevents super‐coiling from reaching unacceptably high levels. Perturbations of supercoiling are corrected by the substrate preferences of these topoisomerases with respect to DNA topology and by changes in expression of the genes encoding the enzymes. However, super‐coiling changes when the growth environment is altered in ways that also affect cellular energetics. The ratio of [ATP] to [ADP], to which gyrase is sensitive, may be involved in the response of supercoiling to growth conditions. Inside cells, supercoiling is partitioned into two components, superhelical tension and restrained supercoils. Shifts in superhelical tension elicited by nicking or by salt shock do not rapidly change the level of restrained supercoiling. However, a steady‐state change in supercoiling caused by mutation of topA does alter both tension and restrained supercoils. This communication between the two compartments may play a role in the control of supercoiling.
Symbiotic root infections in Medicago truncatula require remorin-mediated receptor stabilization in membrane nanodomains

Significance Pattern recognition receptors control the cellular entry of pathogenic as well as symbiotic microbes. While ligand-induced changes in receptor mobility at the plasma membrane and their localization in membrane nanodomains are general features, the molecular mechanism and the biological relevance of this phenomenon have remained unknown. Here we show that immobilization of the symbiotic cell entry receptor LYK3 in nanodomains requires the presence of actin and two molecular scaffold proteins, FLOT4 and SYMREM1. While FLOT4 forms the initial core structure, infection-induced expression and subsequent physical interaction of SYMREM1 with LYK3 stabilize the activated receptors in membrane nanodomains. This recruitment prevents stimulus-dependent endocytosis and ensures progression of the primary infection thread into root cortical cells. Plant cell infection is tightly controlled by cell surface receptor-like kinases (RLKs). Like other RLKs, the Medicago truncatula entry receptor LYK3 laterally segregates into membrane nanodomains in a stimulus-dependent manner. Although nanodomain localization arises as a generic feature of plant membrane proteins, the molecular mechanisms underlying such dynamic transitions and their functional relevance have remained poorly understood. Here we demonstrate that actin and the flotillin protein FLOT4 form the primary and indispensable core of a specific nanodomain. Infection-dependent induction of the remorin protein and secondary molecular scaffold SYMREM1 results in subsequent recruitment of ligand-activated LYK3 and its stabilization within these membrane subcompartments. Reciprocally, the majority of this LYK3 receptor pool is destabilized at the plasma membrane and undergoes rapid endocytosis in symrem1 mutants on rhizobial inoculation, resulting in premature abortion of host cell infections. These data reveal that receptor recruitment into nanodomains is indispensable for their function during host cell infection.