Miracle-dz/UniFAR-FTUs huggingface dataset:
Histoire évolutive et propagation de la tuberculose à échelle planétaire : vers une approche intégrée combinant la génomique des populations et le typage multi-locus

D’apres un rapport de l’OMS, la tuberculose reste en 2015 l’une des 10 premieres causes de deces a l’echelle mondiale. De ce fait, en matiere de sante, eradiquer la maladie a l’horizon 2030 est un des objectifs majeurs fixes par les Nations Unies. La bacterie responsable de cette infection, Mycobacterium tuberculosis, est un pathogene obligatoire dont l’origine et l’evolution sont intrinsequement liees a celles de son hote principal, Homo sapiens. En effet, les souches actuelles de tuberculose presentent, tout comme l’homme, une forte structure phylogenetique, trace de leur origine geographique. Les pays pauvres et en developpement sont les plus touches par l’epidemie globale, favorisee par des systemes de sante defaillants et une haute prevalence du VIH. Les pays occidentaux ne sont pas epargnes, menaces par l’emergence de souches de plus en plus resistantes aux antibiotiques provenant en grande partie de l’ex URSS. Au cours de cette these, j’analyse l’histoire evolutive, la propagation et l’acquisition de resistances aux antibiotiques de plusieurs epidemies de tuberculose en me basant sur des donnees genetiques et genomiques. Dans un premier temps je m’interesse aux effets d’une campagne nationale de traitements en Asie Centrale sur le developpement de souches multi-resistantes et met egalement en lumiere le role clef de certaines mutations dans le succes des clones presentes. Ainsi cette campagne a ete partiellement mise en echec par la presence de souches pre-resistantes, grâce a la survenue de mutations avant meme la mise en place des traitements antibiotiques. Par la suite je me suis focalise sur un clade particulier de souches multi-resistantes, le clone Russe W148. Je presente sa dispersion geographique et temporelle a travers l’Eurasie et demontre l’importance des mutations compensatoires dans son succes epidemique. De plus, la tuberculose ne touche pas seulement les hommes mais infecte egalement plusieurs autres mammiferes. Afin d’apprehender les contraintes adaptatives accompagnants ces changements d’hotes, j’ai effectue divers tests de selection dans le but d’identifier les genes impliques. Pour finir, nous avons developpe un indice souche specifique, permettant de mesurer le succes epidemique de celles-ci a un niveau individuel. Dans le cadre d’etudes epidemiologiques, cette mesure peut etre croisee avec des informations sur le patient, la souche ou meme socio-economiques.
Clustering using Simhash and Locality Sensitive Hashing in Hadoop – HDFS : An Infrastructure Extension

Apache’s Hadoop, an open source implementation of Google’s Map/Reduce framework, is a software framework for large scale data analysis and storage. For data storage, Hadoop uses Hadoop distributed File System (HDFS) which is an open source implementation of Google File System (GFS). Majority of Map/Reduce applications uses HDFS for storing data. By default, the block placement strategy doesn’t consider the data characteristics into account and places the data blocks randomly based on data node availability. Customized strategies in block placement can improve the performance of HDFS. Most of the applications using HDFS require streaming access to the files and if the related files are stored in the same set of data nodes, the query performance can be increased. This paper discusses a method for clustering documents to the same set of data nodes using the technique of Locality Sensitive Hashing. The method uses the compact bitwise representation of document vectors called fingerprints to increase the data processing speed and performance. A new placement policy is implemented without affecting the default fault tolerant properties of Hadoop and requires only minimal changes to the Hadoop framework. The proposed clustering method and the new placement policy will be beneficial to a large number of applications using Hadoop.
Fully Automated On-Chip Imaging Flow Cytometry System with Disposable Contamination-Free Plastic Re-Cultivation Chip

We have developed a novel imaging cytometry system using a poly(methyl methacrylate (PMMA)) based microfluidic chip. The system was contamination-free, because sample suspensions contacted only with a flammable PMMA chip and no other component of the system. The transparency and low-fluorescence of PMMA was suitable for microscopic imaging of cells flowing through microchannels on the chip. Sample particles flowing through microchannels on the chip were discriminated by an image-recognition unit with a high-speed camera in real time at the rate of 200 event/s, e.g., microparticles 2.5 μm and 3.0 μm in diameter were differentiated with an error rate of less than 2%. Desired cells were separated automatically from other cells by electrophoretic or dielectrophoretic force one by one with a separation efficiency of 90%. Cells in suspension with fluorescent dye were separated using the same kind of microfluidic chip. Sample of 5 μL with 1 × 106 particle/mL was processed within 40 min. Separated cells could be cultured on the microfluidic chip without contamination. The whole operation of sample handling was automated using 3D micropipetting system. These results showed that the novel imaging flow cytometry system is practically applicable for biological research and clinical diagnostics.
A input relaxation measure of congestion in fuzzy data envelopment analysis : A possibility approach

١. Introduction In recent years, fuzzy set theory has been proposed as a way to quantify imprecise and vague data in DEA models (see, e.g., [١۶-١٩]). Traditionally, the coefficients of DEA models, i.e., the data of inputs and outputs of the different DMUs, are assumed to be measured with precision (see, e.g., [٢-۴]). However, as some authors point out (see, e.g., [١٢]), this is not always possible. Fuzzy DEA provides us with a tool to deal with the natural uncertainty inherent to some production processes (see, e.g., [١۶]). Alternatively, chance constrained programming formulations of DEA as stochastic approaches can be used to deal with variations in data (see, e.g., [۵-۶, ١۴-١۶,١٧-١٩]). In this paper, possibility approach which has been used by Dubois , Prade [١٠], is used to develop a-one model approached of congestion, based on relaxed combination of inputs in fuzzy DEA.
Improved fusion method for infrared and visible remote sensing imagery using NSCT

This paper introduces the Nonsubsampled Contourlet Transform (NSCT) into infrared and visible remote sensing image fusion and proposes a novel multi-scale analysis method based on region energy. With better direction selectivity and energy convergence compared to that of wavelet, NSCT is suitable for representing detailed information of remote sensing images. In addition, region energy method is better than coefficient mean value in computing low-frequency coefficients. Firstly, the images are decomposed by NSCT. Then, the low-frequency coefficient is calculated by infrared and visible images region energy and the max absolute value is selected as the high-frequency coefficient. The experimental results of Airborne Visual and Infrared Imaging Spectrometer (AVIRIS) and ASTER images show that the proposed fusion method can get more detail information and achieves better visual quality than discrete wavelet based transform and normal NSCT which uses coefficient mean value in low-frequency.
Preliminary Results on Dynamic Obstacle Avoidance for Powered Wheelchair

The main objective of this paper is to develop a simulator for powered wheelchair which permits to implement different control and motion planning strategies in order to study their performance and to compare them. In simulation the wheelchair can easily be integrated in any desired environment and the algorithm under test can be implemented and tested in this virtual environment. The paper presents firstly a brief description of a general powered wheelchair, then a control algorithm for a powered wheelchair is discussed based on the position of the wheelchair rather than its velocities. A general algorithm for motion planning and obstacle avoidance is briefly talked. This algorithm calculates the next desired position based on the actual and desired final position of the wheelchair and the obstacle position, motion direction and velocity. The next desired position is then fed to the control unit which executes the command. Some simulation results will be finally presented based on existing techniques like the virtual force field technique and the vector field histogram method
SoftRank with Gaussian Processes

We address the problem of learning to rank based on a large feature set and a training set of judged documents for given queries. Recently there has been interest in using IR evaluation metrics to assist in training ranking functions. However, direct optimization of an IR metric such as NDCG with respect to model parameters is difficult because such a metric is non-smooth with respect to document scores. Recently Taylor et al. presented a method called SoftRank which smooths a metric such as NDCG by introducing uncertainty into the scores, thus making it amenable for optimization. In this paper we extend SoftRank by combining it with a Gaussian process (GP) model for the ranking function. The advantage is that the SoftRank smoothing uncertainties are naturally supplied by the GP, reflecting the underlying modelling uncertainty in individual document scores. We can also use these document uncertainties to rank differently, depending on how risky or conservative we want to make the ranking. We test our method on the publicly available LETOR OHSUMED data set and show very competitive results.
Plasma membrane protein trafficking in plant–microbe interactions: a plant cell point of view

In order to ensure their physiological and cellular functions, plasma membrane (PM) proteins must be properly conveyed from their site of synthesis, i.e., the endoplasmic reticulum, to their final destination, the PM, through the secretory pathway. PM protein homeostasis also relies on recycling and/or degradation, two processes that are initiated by endocytosis. Vesicular membrane trafficking events to and from the PM have been shown to be altered when plant cells are exposed to mutualistic or pathogenic microbes. In this review, we will describe the fine-tune regulation of such alterations, and their consequence in PM protein activity. We will consider the formation of intracellular perimicrobial compartments, the PM protein trafficking machinery of the host, and the delivery or retrieval of signaling and transport proteins such as pattern-recognition receptors, producers of reactive oxygen species, and sugar transporters.
A Constraint on the Formation of Dwarf Elliptical Galaxies in the Dense Coma Cluster Core

Deep CCD photometry and positions for a new sample of ≃250 dwarf elliptical (dE) galaxies in the Coma cluster core are analyzed. A significant color gradient is detected in their projected radial distribution, given by Δ(B-R)/Δ(log Rcc) = -0.08 ± 0.02 mag. Calibrating these dE galaxies against Galactic globular clusters yields a corresponding metallicity gradient which goes as Z ∝ Rcc−0.29. Simulations reveal that this gradient in the projected radial distribution corresponds to a true (three-dimensional) radial color gradient which goes as Δ(B-R)/Δ(log r) ≃ -0.20 mag, or Z ∝ r-0.69, over the same radial range. If this radial gradient is a primordial one, it is consistent with a model in which the intracluster gas exerted a significant confinement pressure, impeding the outflow of supernovae-driven metal-rich gas from the young dE galaxies.
SAR automatic target recognition via non-negative matrix approximations

The set of orthogonal eigen-vectors built via principal component analysis (PCA), while very effective for com- pression, can often lead to loss of crucial discriminative information in signals. In this work, we build a new basis set using synthetic aperture radar (SAR) target images via non-negative matrix approximations (NNMAs). Owing to the underlying physics, we expect a non-negative basis and an accompanying non-negative coecient set to be a more accurate generative model for SAR proles than the PCA basis which lacks direct physical interpretation. The NNMA basis vectors while not orthogonal capture discriminative local components of SAR target images. We test the merits of the NNMA basis representation for the problem of automatic target recognition using SAR images with a support vector machine (SVM) classier. Experiments on the benchmark MSTAR database reveal the merits of basis selection techniques that can model imaging physics more closely and can capture inter-class variability, in addition to identifying a trade-off between classication performance and availability of training.
An hp‐adaptive pseudospectral method for solving optimal control problems

An hp‐adaptive pseudospectral method is presented for numerically solving optimal control problems. The method presented in this paper iteratively determines the number of segments, the width of each segment, and the polynomial degree required in each segment in order to obtain a solution to a user‐specified accuracy. Starting with a global pseudospectral approximation for the state, on each iteration the method determines locations for the segment breaks and the polynomial degree in each segment for use on the next iteration. The number of segments and the degree of the polynomial on each segment continue to be updated until a user‐specified tolerance is met. The terminology ‘ hp’ is used because the segment widths (denoted h) and the polynomial degree (denoted p) in each segment are determined simultaneously. It is found that the method developed in this paper leads to higher accuracy solutions with less computational effort and memory than is required in a global pseudospectral method. Consequently, the method makes it possible to solve complex optimal control problems using pseudospectral methods in cases where a global pseudospectral method would be computationally intractable. Finally, the utility of the method is demonstrated on a variety of problems of varying complexity. Copyright © 2010 John Wiley & Sons, Ltd.
Universe in the Classroom: using robotic telescopes in primary schools

Universe in the Classroom is a science engagement programme that provides under-served welsh primary schools with improved teaching methods and innovative tools, including access to robotic telescopes, to modernise and enhance the way science is taught to young children aged 4-11 years. As a result, the programme has engaged with 133 primary schools from 21 of the 22 Welsh counties, 47% of whom receive below average budget per student per year. In addition to the provision of high-quality resources, Universe in the Classroom offers teacher training workshops to improve teachers’ scientific knowledge and confidence, with 75% stating improved confidence using science resources post-training event. The programme has also engaged with of 22,083 schoolchildren, successfully improving their understanding of the Universe and challenging perceptions of scientists, with an additional 10% of girls describing scientists as female after a workshop hosted by our diverse and enthusiastic team of undergraduate role models. Furthermore, the number of complex scientific concepts discussed by students tripled post-workshop. Although we identified several potential deterrents affecting the uptake of robotic telescopes in primary school classrooms, these were addressed by the programme and 25% teachers claim to have used the telescopes in their schools, with an additional 75% stating their school would find a second LCO account useful.
Acoustic vector sensor based reverberant speech separation with probabilistic time-frequency masking

Most existing speech source separation algorithms have been developed for separating sound mixtures acquired by using a conventional microphone array. In contrast, little attention has been paid to the problem of source separation using an acoustic vector sensor (AVS). We propose a new method for the separation of convolutive mixtures by incorporating the intensity vector of the acoustic field, obtained using spatially co-located microphones which carry the direction of arrival (DOA) information. The DOA cues from the intensity vector, together with the frequency bin-wise mixing vector cues, are then used to determine the probability of each time-frequency (T-F) point of the mixture being dominated by a specific source, based on the Gaussian mixture models (GMM), whose parameters are evaluated and refined iteratively using an expectation-maximization (EM) algorithm. Finally, the probability is used to derive the T-F masks for recovering the sources. The proposed method is evaluated in simulated reverberant environments in terms of signal-to-distortion ratio (SDR), giving an average improvement of approximately 1:5 dB as compared with a related T-F mask approach based on a conventional microphone setting.
Conformationally Strained trans-Cyclooctene (sTCO) Enables the Rapid Construction of 18F-PET Probes via Tetrazine Ligation

The bioorthogonal reaction between tetrazines and trans-cyclooctenes is a method for the rapid construction of F-18 probes for PET imaging. Described here is a second generation 18F-labeling system based on a conformationally strained trans-cyclooctene (sTCO)—a dienophile that is approximately 2 orders of magnitude more reactive than conventional TCO dienophiles. Starting from a readily prepared tosylate precursor, an 18F labeled sTCO derivative (18F-sTCO) could be synthesized in 29.3 +/- 5.1% isolated yield and with high specific activity. Tetrazine ligation was carried out with a cyclic RGD-conjugate of a diphenyl-s-tetrazine analogue (RGD-Tz) chosen from a diene class with an excellent combination of fast reactivity and stability both for the diene as well as the Diels-Alder adduct. For both the tetrazine and the sTCO, mini-PEG spacers were included to enhance solubility and improve the in vivo distribution profile of the resulting probe. Extremely fast reactivity (up to 2.86 x 105 M-1s-1 at 25 °C in water) has been observed in kinetic studies in the reaction of sTCO with diphenyl-s-tetrazine derivatives. A kinetic study on sTCO diastereomers in 55:45 MeOH:water showed that the syn-diastereomer displayed slightly faster reactivity than the anti-diastereomer. An 18F-sTCO conjugate with RGD-Tz demonstrated prominent and persistent tumor uptake in vivo with good tumor-to-background contrast. Unlike most radiolabeled RGD peptides, the tumor uptake of this PET agent increased from 5.3 +/- 0.2% ID/g at 1 h post injection (p.i.), to 8.9 +/- 0.5% ID/g at 4 h p.i. , providing evidence for prolonged blood circulation. These findings suggest that tetrazine ligations employing 18F-sTCO should serve as a powerful and general platform for the rapid construction of peptide or protein derived PET agents.
Public Art as a Tool for Environmental Outreach: Insights on the Challenges of Implementation

ABSTRACT Artistic works can be utilized in an environmental-focused educational capacity, particularly when installed in the public realm. While some research on “ecological art” has been conducted in other countries, little attention has been paid to its application in Canada. This article addresses insights derived from in-depth interviews with members of three not-for-profit art organizations in the City of Toronto that are engaged in producing public artworks intended to provoke thought about the environment. Our findings reveal three key impediments to the application of public art as a tool for environmental outreach.
The Glauber dynamics on colourings of a graph with high girth and maximum degree

(MATH) We prove that the Glauber dynamics on the C-colourings of a graph G on n vertices with girth g and maximum degree $\D$ mixes rapidly if (i) $C=q\D$ and $q>q^*$ where $q^*=1.4890. $ is the root of $(1-\rme^{-1/q})^2+q\rme^{-1/q}=1$; and (ii) $\D\geq D\log n$ and $g\geq D\log\D$ for some constant $D=D(q)$. This improves the corresponding result with $q\geq1.763\D$ obtained by Dyer and Frieze [FOCS01] for the same class of graphs.
The Benefits of Estimated Global Information in DHT Load Balancing

Distributed hash tables (DHT) often rely on uniform hashing for balancing the load among their nodes. However, the most overloaded node may still have a load up to O(log N) times higher than the average load. DHTs with support for range queries cannot rely on hashing to fairly balance the system's load since hashing destroys the order of the stored items. Ensuring a fair load distribution is vital to avoid individual nodes becoming overloaded, potentially leading to node crashes or an incentive not to participate in the system. In both scenarios explicit load balancing schemes can help to spread the load more evenly. In this paper, we improve on existing algorithms for item-based active load balancing by relying on approximations of global properties. We show that the algorithms can be made more efficient by incorporating estimates of properties such as the average load and the standard deviation. Our algorithms reduce the network traffic induced by load balancing while achieving a better load balance than standard algorithms. We also show that these improvements can be applied to passive load balancing algorithms. Compared to DHTs without explicit load balancing, both variants are able to reduce the total maintenance traffic, i.e. item movements due to churn and load balancing, by up to 18%. Simultaneously the system is being balanced achieving a better load distribution.
Label propagation in complex video sequences using semi-supervised learning

We propose a novel directed graphical model for label propagation in lengthy and complex video sequences. Given hand-labelled start and end frames of a video sequence, a variational EM based inference strategy propagates either one of several class labels or assigns an unknown class (void) label to each pixel in the video. These labels are used to train a multi-class classifier. The pixel labels estimated by this classifier are injected back into the Bayesian network for another iteration of label inference. The novel aspect of this iterative scheme, as compared to a recent approach [1], is its ability to handle occlusions. This is attributed to a hybrid of generative propagation and discriminative classification in a pseudo time-symmetric video model. The end result is a conservative labelling of the video; large parts of the static scene are labelled into known classes, and a void label is assigned to moving objects and remaining parts of the static scene. These labels can be used as ground truth data to learn the static parts of a scene from videos of it or more generally for semantic video segmentation. We demonstrate the efficacy of the proposed approach using extensive qualitative and quantitative tests over six challenging sequences. We bring out the advantages and drawbacks of our approach, both to encourage its repeatability and motivate future research directions.