dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, the system under test is also capable of inflicting additional load on itself by running the dd command (disk cloning utility), the yes command (output a string repeatedly) while flooding the disturber system and itself (via the loopback interface) with ICMP ping requests. Some tests presented here are referred to as having been done in multi-core and others in single-core operation. Multicore operation means setting up the system under test exactly as shown in Fig. 1. In case of Linux/MRG, the ATM IRQ thread is not only assigned to CPU 2 but its priority is also raised to 99, the maximum priority in Linux. All other IRQ threads are assigned to CPU 1 maintaining their default priority of 50. Single-core operation means that all processes, threads and IRQs are assigned to CPU 1.
V. RECENT LINUX BASED REAL-TIME SYSTEMS This section presents a brief overview of four MARTe and plain Linux based real-time systems installed over the last year in JET. The Vessel Thermal Map (VTM), the WAll Load Limiter System (WALLS), the BetaLi and the Advanced Predictor Of DISruptions (APODIS). All systems run the 4 MARCONI LE 155. Fig. 2. In a multi-core setup with switched ATM synchronisation and simulated stress load the test application's deterministic performance is similar when running on a vanilla and on an MRG kernel. Fig. 3. In a single-core setup with switched ATM synchronisation and simulated stress load the test application's deterministic performance deteriorates more when running on a vanilla than when running on an MRG kernel. 2. 6. 35. 9 Linux kernel from http://www. kernel.
It receives 11 and publishes 2 ATM datagrams in total and has a 10 ms cycle time. Its thread and IRQ affinity distribution The BetaLi system  [27]  was designed to estimate various plasma properties such as the beta (ratio between the kinetic and magnetic pressures) and the plasma's internal inductance. It receives 7 and transmits 1 ATM datagram in total and has a 2 ms cycle time. Its thread and IRQ affinity distribution is listed below and a histogram of the measured cycle time is show in Fig. 5. Core 1 -Linux, MARTe services and all other IRQs Core 2 -ATM IRQ and MARTe threads for receiving datagrams (x7) Core 3-MARTe thread for receiving synchronisation datagram Core 4 -MARTe's real-time thread D. APODIS The APODIS system  [28]  is a classifier based on support vector machines aiming to anticipate the occurrence of a disruptive event so that protective action can take place.
7  and 8 , is far from being considered unacceptable. As a concluding remark it should be noted that the performance discrepancies between the test system of section IV and the online systems synchronising their real-time cycle on ATM is largely due to two factors. First, the absolute jitter of the online source of the synchronisation datagram is < 50 µs while in the test rig is < 5 µs, and second, the online system's datagrams must cross a shared inter-switch link fibre (sharing bandwidth with other traffic) unlike the test rig's setup. This work has demonstrated that user-space applications running under plain Linux can be made to meet timing constraints in a deterministic manner. Four systems recently deployed in the RTDN of the JET tokamak, two of which fundamental protection systems, were shown to conform to real- When compared to the real-time oriented Linux/MRG kernel, the vanilla kernel was shown to be equally deterministic in multi-core configuration.
Tests demonstrated that a real-time OS becomes a requirement when the competition for resources is exceedingly demanding. However, because competition for resources is inversely proportional to the current trend of ever increasing number of cores in present CPUs, real-time OSs may, in many situations, be an avoidable complication. Linux/MRG in particular introduces an overhead in terms of kernel threads that might be considered a bit overwhelming. In single-core operation and under simulated load stress conditions though, the Linux/MRG kernel demonstrated its superiority when compared to a vanilla kernel but, with a relative worst case jitter of 150%, still failed to deliver acceptable performance. All these Linux based user-space applications have proven to be notoriously stable. Having MARTe as a standard greatly benefits maintenance, fault investigation and upgrade activities not only by decreasing effort and time requirements but also because the knowledge can be spread instead of concentrated exclusively in the people responsible for (or even just the developers of) specific applications.
Background: Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures. Results: MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome.
Background Genome-wide Single Nucleotide Polymorphism (SNP) arrays have been widely used over the past few years  [1]. First generations were measuring only genetic variations of Single Nucleotide Polymorphisms, which are single base pair mutations at specific loci. Last generations (e. g. SNP5. 0, SNP6. 0) also include non-polymorphic probes in order to study copy-number variations along the genome in addition to genotyping measures. These arrays are especially used to study the impact of diseases, e. g. cancer, on the human genome. Analyzing data from genome-wide SNP arrays within R requires several packages, e. g. aroma for normalization of Affymetrix® SNP arrays  [2, 3] , changepoint or cghseg for segmentation of copy number profiles  [4] , cghcall for labelling segments  [5] , and glmnet for penalized regressions  [6].
Each package performs a specific task along the whole analysis but none of them is related to the others. Output formats of given packages *Correspondence: quentin. grimonprez@inria. fr 1 MODAL team, Inria Lille-Nord Europe, Villeneuve-d'Ascq, France Full list of author information is available at the end of the article are often not compatible with input formats required by the other, making their use tricky for beginners in R. One main contribution of the MPAgenomics R package is to aggregate these commonly used packages, providing wrappers to inter-relate them automatically. At each step of the analysis a large amount of packages are available to perform normalization, segmentation or marker selection. A careful choice of only a few methods is required to provide an easy-to-use and efficient tool.
Data normalization The normalization process in MPAgenomics contains technical biases correction and copy number and allele B fraction estimation. Following  [8] , allele B fraction refers to the proportion of the total signal coming from allele B. Normalization methods are available for Affymetrix® arrays (10K, 100K, 500K, GenomeWideSNP 5 & 6, and CytoScanHD). The estimation of the total copy number and allele B fraction is made by CRMAv2  [9]  originally implemented in the aroma packages. For studies with matched normal-tumor samples, a better estimation is suggested and implemented for the allele B fraction of the tumoral sample with the TumorBoost method  [8]. The use of aroma packages is difficult for neophytes due to the strict folder architecture it requires and the documentation of the project which is mainly dedicated to experts able to criticize each method proposed and understand details of each procedure.
The PELT method returns a segmentation with a number of segments automatically chosen by the algorithm according to a penalty Kρ log(P) with ρ > 0 a parameter to choose. The choice of the penalty parameter has been raised in  [4]. MPAgenomics suggests an automatic sample-specific choice of ρ chromosome by chromosome (see package vignette for details on the method). In MPAgenomics, the two methods, CGHSEG with the slope heuristic and PELT with our calibration method, are proposed. By default, CGHSEG is used because it is quicker than PELT due to the multiple execution required by the ρ calibration method we propose. The implemented segmentation methods are independently available for both copy number and allele B fraction profiles. In the case of allele B fraction segmentation, only heterozygous SNPs are kept.
The lasso regularization method consists in minimizing g λ : β ∈ R P → g λ (β), where g λ (β) = I i=1 (y i -(Xβ) i ) 2 + λ P p=1 |β p | , with ( Xβ) i = p x i,p β p and λ > 0 controlling the number of non-zero coordinates of β. After minimization, nonzero coefficients β p correspond to influential positions to predict the response. MPAgenomics genomic marker selection drastically improves currently available packages in terms of computation time. With the linear regression model, it efficiently provides the exact solution by using the new R package HDPenReg, which is an optimized implementation of the lars algorithm  [19]  specially dedicated to a huge number of markers. Since the theoretical grounding of Lasso when P >> I relies on a theoretical condition (see  [20] ) that cannot be easily checked in practice, the spike and slab algorithm  [21, 22]  -a three steps algorithm performing filtering, estimation and variable selection -is also provided in MPAgenomics as an alternative.
Discussion MPAgenomics is mainly dedicated to beginners in SNP array analyses. It solves problems commonly encountered by neophytes such as interaction between different packages or specialized documentation dedicated to experts in the field. In addition, MPAgenomics suggests careful and automatic choices of crucial parameters at each part of the analysis. To achieve simplicity of usage, MPAgenomics does not propose all options implemented in the wrapped packages, especially for normalization. However, outputs are generated in such a way that interaction between wrapped packages and MPAgenomics is facilitated. For example, the strict directory structure of aroma packages is built by MPAgenomics. Therefore, advanced users may directly use specific options of aroma to enhance their analysis without renormalizing data from scratch.
Conclusions MPAgenomics provides user-friendly wrappers for normalization and multi-patient analysis of high-throughput genomic data. It offers a guideline for beginners in copynumber variation analysis focusing on proven methods for their effectiveness. MPAgenomics also provides automatic choices of crucial parameters for segmentation and selection of markers. Even though normalization is provided for Affymetrix® arrays, other steps (segmentation, calling, and marker selection) can be applied to normalized data from other DNA arrays and next-generation sequencing data.
An ever growing number of datasets is becoming available for circumstellar disks. Nevertheless, many studies still focus on the interpretation of a single dataset even in the framework of detailed disk modeling. However, full characterization of stellar and circumstellar properties of single objects is often reached only by employing dust and gas diagnostic measurements (photometry and line emission) that cover the entire extent of a protoplanetary disk. In this paper, we aim to investigate the geometrical and chemical properties of the disk around the T Tauri Star (TTS) FT Tauri, by means of a multi-wavelength dataset and consistent dust and gas modeling. Even though the Taurus star forming region and its members are well studied (e. g. Kenyon et al. 1994 , Gullbring et al. 1998 , Luhman et al.
With more sensitive interferometers such as SMA and IRAM/PdBI, FT Tau can serve as an excellent target for more detailed astrochemical studies. The multi-wavelength data used in this work and its data reduction is presented in Sect. 2 and a detailed analysis thereof in Sect. 3. The result of this analysis are then further used in Sect. 4 to build consistent dust and gas models (MCFOST and ProDiMo) for FT Tau. Sect. 5 then discusses the results and the source variability.
Observations and data reduction The data analysed in this paper consist of spectroscopy at optical, NIR, and FIR wavelengths from the Telescopio Nazionale Galileo (TNG), the William Herschel Telescope (WHT), the Nordic Optical Telescope (NOT), the Keck Observatory, and the Herschel Space Observatory. The instrumental settings for the spectroscopic observations are presented in detail in the following sections and summarized in Table  2. Additional data were retrieved from the literature and consist of MIR spectroscopy from Spitzer Space Telescope and of photometry from optical to radio wavelengths (see Table  3  and Fig. 1 ).
TNG/DOLORES observations We present spectroscopic data of FT Tau obtained using the DOLORES spectrograph, the Device Optimized for the LOw RESolution  (Oliva 2004 ) mounted on the TNG (La Palma Observatory). The observations were performed in November 2009 with the VHR-V grism (λ/∆λ = 1527 for a slit width of 1"), covering the wavelength range from 4752 Å to 6698 Å. Three observations (exposure times of 1400, 600, and 200 s) were performed in seeing-limited conditions (FWHM 1. 25"). The DOLORES spectra were reduced using the IRAF software package. The 2-D spectra were flat-fielded, sky-subtracted and wavelength calibrated using the Argon arc lamp. Then, the 1-D spectrum was extracted by integrating over the source spatial profile and corrected for telluric absorption features. No photometric standard observations were taken.
Keck/NIRSPEC observations An M-band high-resolution spectrum of FT Tau was taken with the NIRSPEC, the NIR SPECtrograph  (McLean et al. 1998 ) on the W. M. Keck Observatory. The spectra were obtained in December 2008 using the M-Wide filter and 0. 43" slit providing a resolution of λ/∆λ = 25, 000. The spectra span 4. 67 -5. 05 µm. FT Tau was observed for 16 and 20 minutes in successive exposures. Because of the thermal background in the M-band, the data were observed in an ABBA sequence where the telescope was nodded 12" between the A and B positions. Each frame was flat fielded and scrubbed for hot pixels and cosmic ray hits. The observations were combined as (A-B-B+A)/2 in order to cancel the sky emission. The data were rectified in the spatial direction by fitting a polynomial to the point spread function (PSF) of the star in each column.
8-73. 3 µm and 143. 5-146. 6 µm, with a spectral resolution of 162 km s -1 and 258 km s -1. Data were reduced using HIPE 10. Removal of saturated and bad pixels, chop subtraction, flat-field correction, and mean of two nods were performed by means of the available PACS pipeline. Photometric FIR observations of FT Tau were also taken with Herschel/PACS within the GASPS project. The obtained photometric measurements at 70, 100, and 160 µm are presented in  Howard et al. (2013)  and shown in Table  3.
Summary We have performed analysis and modeling of the SED and emission lines of the TTS FT Tau to fully characterize the stellar, disk, and accretion properties. We reduced and analysed five spectra from optical to FIR wavelengths, taken with the optical Telescopio Nazionale Galileo, the NIR William Herschel Telescope, the NIR Nordic Optical Telescope, the highresolution NIR Keck Observatory, and the FIR Herschel Space Telescope. Additional data were retrieved from the literature and consist of a MIR Spitzer Space Telescope spectrum and of 44 photometric measurements from optical to radio wavelengths. We studied a set of models of the source generated by means of the radiative transfer code MCFOST and the thermo-chemical disk modeling code ProDiMo. We found that FT Tau is a low-mass (0. 3 ± 0.
Acknowledgements. We acknowledge the referee for valuable comments that considerably improved the paper. We thank  Matilde Fernández  and  Victor Terrón  for observing FT Tau and reducing the data at the  Calar Alto Observatory. We really appreciate the helpful discussion about the origin of the variability. We also gratefully thank  Gwendolyn Meeus  for her work in acquiring data with the TNG and  Ilaria Pascucci  and  Veronica Roccatagliata  for reducing the data from Spitzer. This work is supported by the  Swiss National Science Foundation. LP acknowledges the funding from the  FP7   Intra-European Marie Curie Fellowship  ( PIEF-GA-2009-253896 ). IK, WFT, FM, and PW acknowledge funding from an  NWO   MEERVOUD grant and  from the  EU   FP7-2011  under Grant Agreement nr. 284405. FM acknowledges support from the  Millennium Science Initiative (Chilean Ministry of Economy ), through grant Nucleus  P10-022-F.
-Tacking efficiency: due to the presence of three prongs we assumed three times the single track uncertainty (4%). -PID efficiency: for this analysis tight PID cuts are applied for two tracks, the kaon and the proton. We, therefore, assume twice the systematic uncertainty expected for the D 0 analysis, where tight cuts are applied on one track (kaon). -Selection efficiency: this correction has turned out to be of the same order for D 0 , D + and D ⇤+ analyses; we assume that it will be similar for the L c. -Feed-down from L b decays: we assume a systematic uncertainty similar that on D mesons with the current data  [38]. Due to the low signal-to-background ratio, it is unlikely that, even in the high-rate scenario, the feed-down contribution can be estimated using the impact parameter fit technique as planned for D mesons (see previous section).
In these calculations, the v 2 of D + s mesons is predicted to be reduced by 30-40% in the intermediate p T region with respect to that of non-strange D mesons. Also in this case, in order to appreciate this difference, a very precise measurement is required. We provide here an estimation of the statistical significances expected for the v 2 measurements starting from the first preliminary results of signal extraction in the centrality range 20-40% obtained on the 2011 Pb-Pb data sample. In the two scenarios of integrated luminosity (0. 1 and 10 nb 1 ), the significances expected in the p T range 3-5 GeV/c are about 7-8 and 70-80, respectively, for p T bins of 1 GeV/c. The values for the no high-rate case are not sufficient to perform a j-differential analysis and obtain a precise measurement of v 2.
Heavy-flavour Elliptic Flow We have estimated the expected precision on the measurement of v 2 for prompt and secondary D mesons with the upgraded ITS and with an integrated luminosity of 10 nb 1. We have scaled the statistical uncertainties obtained from the simulation studies (Section 2. 1. 1. 1), considering that the significance/event is the same for central (0-20%) and semi-central events (e. g. 30-50%). This feature is observed in the D 0 analysis from 2010 data  [38]. The v 2 extraction can be performed using the raw signal yields in two large intervals of azimuthal angle j with respect to the Event Plane (EP) direction Y EP , determined for each collision  [47] : [ p/4 < Dj < p/4] [ [3p/4 < Dj < 5p/4] (in-plane) and [p/4 < Dj < 3p/4] [ [5p/4 < Dj < 7p/4] (out-of-plane). Given the in-plane and out-of-plane yields, N in and N out , one has v 2 = (p/4) • (N in N out )/(N in + N out ).
N pp ' 3 • 10 3 • N Pb Pb, 0 10%. (GeV/c) T p 0 2 4 In the following, we estimate the required reference statistics for low-p T charm measurements (High-background case)  1. For the case of D 0 ! K p + reconstruction, we have estimated the value of S / p N (significance per event) for pp collisions with the upgraded ITS using the following procedure. -We started from the value of S / p N obtained on pp data at p s = 7 TeV from the 2010 run  [41]. -We assumed that the signal/event is the same with the upgraded ITS. This is justified by the observation that the efficiency is the same with current and upgraded ITS, for the same cut values (see Figure  2. 18 of  [37] ). -We assumed that the background/event is reduced, with the upgraded ITS, in pp by the same factor as in Pb-Pb, i. e. a factor about 5 (see Figure  2.
Indeed, elliptic flow of J/y at LHC energies was predicted within a transport model  [69]. The first measurement at the LHC  [79] , shown in Figure  2. 33, provides a tantalizing hint of a finite elliptic flow of J/y (the significance of non-zero flow for the centrality range 20-60% is 2. 3s in the p T range 2-4 GeV/c). The preliminary ALICE data is consistent with the expectation from transport models, but precision data is needed in order to be able to extract information on the QGP properties and on the amount of J/y produced via regeneration. We note that, at RHIC energies, the preliminary measurement by the STAR collaboration shows a J/y elliptic flow in Au-Au collisions at p s NN = 200 GeV  [80]  consistent with zero. The picture outlined above for the charmonium production, extracted by comparison of data and model predictions, remains to be tested by precision measurements at the LHC which further constrain model calculations.
Inputs for Simulations The following estimates of the performance of the ALICE measurement of J/y and y(2S) in the Central Barrel (|y| <0. 9, measurement done in the e + e channel) and in the Muon Spectrometer (2. 5< y <4. 0, in the µ + µ channel) are based on our current data in Pb-Pb collisions at p s NN = 2. 76 TeV. The increase of the charmonium production cross section at p s NN = 5. 5 TeV will provide a better measurement accuracy than the one estimated in our present study. In the Central Barrel, J/y is currently measured utilizing only the TPC electron identification. The important contribution of the Transition Radiation Detector (TRD) for the electron identification is considered based on its present performance. The measured transverse momentum spectrum  [85]  is also used as input.
The measurement performance at mid-rapidity and at forward rapidities by ALICE have been estimated for integrated luminosities of 1 nb 1 and 10 nb 1 , corresponding to the data sample expected before and after the upgrade, respectively. For the Central Barrel, the impossibility of triggering (for low-p T J/y), enforces the usage of minimum bias data. In this case, the limitations imposed by the current data readout speed are considered, while for the Muon Spectrometer the muon trigger allows an efficient sampling of all interactions. We provide our estimates The centrality dependence of the nuclear modification factor for charmonium states predicted within the transport model. In the plot of the ratios of nuclear modification factors of y(2S) and c c to that of J/y , shown in the lower right panel, the predictions of the statistical model are also included.
J/y Yield and the Nuclear Modification Factor From the data shown in Section 2. 2. 1, it is clear that ALICE will be able to perform a very good measurement of inclusive J/y yields and nuclear modification factor as a function of p T and y with the Muon Spectrometer with the data collected prior to LS2. This is the case also for the Central Barrel where the usage of the complete TRD for electron identification leads to a good accuracy of the measurement up to about 6 GeV/c, see Figure  2. 37. An important capability of the Central Barrel is the measurement in Pb-Pb of J/y from B hadrons decays, covered in the open beauty section, which can only be realized with the full capability of the upgrade.