dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, pitchcontour changes could not be used to segment the stream as this cue was not consistent: only half of the word boundaries were marked by pitch-contour changes. Because some of the syllables appeared in multiple words, transitional probabilities within words ranged from 0. 5 to 1. 0. Transitional probabilities across word boundaries ranged from 0. 1 to 0. 5. The language stream was built by a random concatenation of the five pseudo-words (only constraint: no repetition of the same item twice in a row) and synthesized using Mbrola (http://tcts. fpms. ac. be/synthesis/ mbrola. html). No acoustic cues were inserted at word boundaries. Each word was repeated 94 times in the stream leading to a 5. 5 minute continuous speech stream. In the linguistic test, test items consisted of the five pseudowords used in the exposure phase and five foils synthetized with a flat contour (spoken version).
EEG data acquisition The participants were comfortably seated in a Faraday booth. EEG data were continuously recorded from 32 active Ag-Cl electrodes (Biosemi ActiveTwo system, Amsterdam University) located at standard left and right hemisphere positions over frontal, central, parietal, occipital, and temporal areas (International 10/20 system sites: Fz, Cz, Pz, Oz, Fp1, Fp2, AF3, AF4, F3, F4, C3, C4, P3, P4, P7, P8, Po3, Po4, O1, O2, F7, F8, T7, T8, Fc5, Fc1, Fc2, Fc6, Cp5, Cp1, Cp2, and Cp6). The electrooculogram (EOG) was recorded from Flat-type active electrodes placed 1 cm to the left and right of the external canthi, and from an electrode beneath the right eye. The band-pass was of 0-102. 4 Hz and sampling rate 512 Hz.
ERP analyses Six participants were discarded due to major artifacts, thus yielding to two groups of 10 participants each. Major artifacts were due to excessive environmental noise such as constructions taking place at the floor below the EEG room (4) and low drifts possibly due to sweating  (2). The EEG data, acquired continuously during the exposure phase, were then re-referenced offline to the algebraic average of the left and right mastoids. Signal containing ocular artifacts was corrected using ICA decomposition by removing the component containing the blink  [47]. The full EEG recording was first divided into 4 non-overlapping consecutive time bins of 19200 duration. The EEG was then segmented in epochs of 750 ms starting 50 ms prior to pseudo-words onsets. A 250 to 0 ms baseline zero-mean normalization was applied using Brain Vision Analyzer software (Brain Products, Munich).
This study has been supported by the  French National Research Agency  ( ANR-09-BLAN-0310  to  D. Scho ¨n , http://www. agence-nationale-recherche. fr/). At the time of the submission C. Francois is in a postdoctoral stay at the  University of Barcelona  funded by the FYSSEN foundation (http://www. fondationfyssen. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The previously used touchdown programme was reused for DNA amplification. Electrophoresis genotyping by capillary fractionation was carried out at INRA Clermont-Ferrand (Plateforme Strat egique  INRA, Ibisa 2009 , ISO9001:2008). The profiles obtained for the six isolates were analysed with GENEMAPPER Software Version 4. 0 (Applied Biosystem, Ville-bon-sur-Yvette, France). This led to the selection of 11 polymorphic loci, that is, loci at which two different alleles (Table  S2 ) were identified in the six isolates tested. The physical independence of these 11 loci was checked by their localization within the reference genome (see scaffold names in Table  S2 ). The whole collection was then screened using these 11 polymorphic microsatellite markers. Only a few ascospores were genotyped, with a subset of the markers, as only a few recombination events between parental genotypes were required to show that the ascospores actually resulted from meioses.
Genetic analyses A splitstree was inferred with SPLITSTREE 4 software  (Huson and Bryant 2006 ) (http://www. splitstree. org/). Factorial correspondence analyses (FCA) were performed with GENETIX v4. 05  (Belkhir et al. 1996 (Belkhir et al. -2004)). Linkage disequilibrium among the 11 markers and population differentiation were assessed with GENEPOP on the Web (Raymond and Rousset 1995) (http://genepop. curtin. edu. au/). We used the individual-based Bayesian clustering methods implemented in STRUCTURE 2. 3. 3  (Pritchard et al. 2000)  to infer population structure. STRUCTURE makes use of Markov Chain Monte Carlo (MCMC) simulations to infer the proportion of ancestry of genotypes from K distinct clusters. The underlying algorithms attempt to minimize deviations from Hardy-Weinberg and linkage disequilibria.
At K = 2, the structure identified corresponded precisely to that in the splitstree, with the strains separated according to the presence/absence of Wallaby. The deltaK value confirmed that this split was the strongest structure in the data set (Figure  S3 ). At K = 6, the two clusters defined previously were each split into three well-delimited populations, with some hybrids between populations 2 and 3. Within cluster B, most of the cheese strains (20 of the 32) clustered in population 1, whereas most noncheese strains (10 of the 13) were included in population 3. Moreover, all the cheese strains of population 1 were MAT1-1 strains, whereas population 3 included both mating types, in equal proportions (seven strains of each). Population 2 included mostly MAT1-1 strains from diverse environments (10 MAT1-1 and 1 MAT1-2 strains).
Recombination footprints in populations We then investigated the footprints of sex in our population data set. We first performed a split decomposition analysis, showing recombination events as reticulations. SplitsTree analysis yielded a network, with some reticulations but also with long branches (Fig. 2 ). This pattern is indicative of pervasive clonality with occasional recombination events, within the clusters in particular, and much less frequently between clusters. When we considered only two genetic groups (K = 2), linkage disequilibrium was significant within both clusters, probably due to a Wahlund effect resulting from the genetic structure (Table  S4 ). When six populations were considered, significant linkage disequilibrium was observed only for population 3 within cluster B, but not for the other five populations.
Acknowledgements We thank  Yves Brygoo ,  John Taylor ,  Fabienne Malagnac  and  Ricardo Rodr ıguez de la Vega  for helpful discussions,  Dario de Franceschi  for helping with the micrographs,  Yann Dussert  for helping with the analyses,  Mathilde Delaunay  for technical assistance with microsatellite amplification. This study was funded by the  ERC  starting grant, GenomeFun 309403, awarded to TG and by the ATM Microorganisms-MNHN 2013 to MLV.
In this work a hyperspectral image quality assessment is done to get the optimum range of illumination for venous imaging system. A database of hyperspectral images from 80 subjects has been created and subjects were divided in to four different classes on the basis of their skin tone. In this paper the results of hyper spectral image analyses are presented in function of the skin tone of patients. For each patient, four mean images were constructed by taking mean with a spectral span of 50 nm within near infrared range, i. e. 750-950 nm. Statistical quality measures were used to analyse these images. Conclusion: It is concluded that the wavelength range of 800 to 850 nm serve as the optimum illumination range to get best near infrared venous image quality for each type of skin tone.
A dataset of 80 subjects is made by scanning the forearm of each subject with a hyperspectral camera setup (detailed in next section). As the analysis studies the skin tone, the subjects are separated equally into four different classes based on the luminance measurement of their skin, obtain from a chromameter. To define optimum ranges suitable for illumination, NIR window is divided into four sub-bands (50 nm each) and mean images are formed for each sub-band. These images are analyzed using mean square error (MSE) and universal image quality index (Q) to define the best wavelength ranges where the image quality is highest when compared to the mean reference image.
Projector lamps (halogen) with a constant illumination range from 350 to 2500 nm were used to illuminate the targeted region. Each acquired hyperspectral image is in the form of cube with the spatial resolution of 450 × 1310 and 1040 spectral bands. A complete image of the scene on every single wavelength is acquired and saved in the form of cube (see Figure  2 ). This hyperspectral approach allows us to look deeper into the spectral response of skin tissues and veins against the wavelength of illumination. Spectral DAQ software from Specim® was used to set the camera parameters and data acquisition. The angle for mirror scanner was set in the way that it should scan only the forearm region of the subject, allowing minimizing the data size of each hyperspectral cube for each patient. The scanning time of hyperspectral image for one subject is about 40s.
It depends on the selected scanning range of mirror scanner used with Specim VNIR Image sensor. Our targeted area was the fore arm region of subject, hence around 21°of scanning range (which covers the region from elbow to the tip of subjects fingers) is selected. The distance of mirror scanner is set to be about one meter from the subject's position. The spectral images acquired are in the form of data cubes which are saved in environment for visualizing images (ENVI) compatible formats to be further processed. MATLAB is used to read the raw data cubes using ENVI toolbox. A total of 80 volunteers were recruited for the data acquisition process. Most of these volunteers were students having body mass index (BMI) in healthy range. On the basis of BMI, 51 subjects have classified in healthy range, 7 in underweight, 15 in overweight and 7 in obese category  [15].
Skin tone classification The skin tone is an important factor which affects the veins localization process. People with darker skin are more likely to be affected with a non-visibility of their veins. In order to achieve better results of veins viewing in subjects having different skin tones, we classified the skin into four classes. These classes are: Fair, Light-brown, Dark-brown and Dark. CIE L*a*b values for each subject were obtained with the help of chromameter (Konica Minolta Inc. ,). Data from three different locations for each  forearm per subject were acquired so as to infer a mean value. The chromometer is placed on wrist, central and near elbow region of each subject to get three readings. An average value of luminance (L*) for each subject is then fed into a Fuzzy C-Mean (FCM) algorithm.
Four submean images are created by taking mean of bands in the following ranges: Im: mean 750-950 nm Image-1: mean 750-800 nm Image-2: mean 800-850 nm Image-3: mean 850-900 nm Image-4: mean 900-950 nm From the hyper spectral dataset, 12 subjects from each skin class were selected randomly. With the data from selected subjects, mean reference image called Im and four sub-mean images, named Image1-4, were created for each subject of all four classes. Figure  6 , Figure  7 , Figure  8  and Figure  9  depicts reference images Im and four submean images for a random subject among each skin class. These sub-mean images were then analyzed with the reference of mean reference image Im. The ultimate users of images are human beings. The most trustworthy way of quality assessment of images is subjective analysis.
II. THE BABAR EXPERIMENT This analysis is based on a data sample of 429 fb -1  [14] , corresponding to 471 × 10 6 BB pairs, collected with the BABAR detector at the PEP-II asymmetric-energy e + e -collider at the SLAC National Accelerator Laboratory at center-of-mass energies near and equal to the Υ (4S) mass. The reconstruction efficiency is determined through use of Monte Carlo (MC) simulation, based on the EvtGen  [15]  program for the event generation and the GEANT4  [16]  package for modeling of the detector response. The MC events are generated uniformly in the B 0 →D 0 ΛΛ and B 0 →D 0 Σ 0 Λ phase space. The BABAR detector is described in detail elsewhere  [17, 18]. Charged particle trajectories are measured with a five-layer double-sided silicon vertex tracker and a 40layer drift chamber immersed in a 1.
Study Design Institutional review board approval was obtained and informed consent was waived according to the design of the study. All patients treated for a bAVM in our academic department were prospectively included in a data base. Since May 2008, all patients receiving MR imaging in our department for a bAVM underwent a 4D-MRA. For the purpose of the present study, patients imaged between May 2008 and February 2013 were included if they met the following criteria: 1) they had a bAVM treated at least by 1 of the following modalities (embolization, radiosurgery, or surgery), 2) they were imaged during the follow-up with both 4D-MRA at 3T and DSA, 3) both examinations were performed at least 1 month after the last treatment, 4) both examinations were performed in a time interval less than 1 month and without treatment in between, and 5) image datasets were of sufficient quality.
Conventional Angiography DSA was performed on a biplane angiography unit (Axiom Artis; Siemens, Erlangen, Germany) with a 5-Fr catheter using a femoral artery approach and selective contrast injection of the internal carotid artery, external carotid, and vertebral arteries. A temporal resolution of 3 images per second was used, in standard orthogonal anteroposterior and lateral projections and oblique projections, if necessary. Anteroposterior images had a matrix of 720 ϫ 720 mm 2 and FOV of 32 cm. Lateral projections had a matrix of 960 ϫ 960 mm 2 and FOV of 25 cm. Three-dimensional rotational angiography was not systematically performed and these data were not used in this study. Bolus injections of iodinated contrast material (iodixanol, Visipaque 320; GE Healthcare, Velizy-Villacoublay, France) were 6 mL at a flow rate of 2 mL/s for the external carotid, 8 mL at a flow rate of 4 mL/s for the internal carotid, and 10 mL at a flow rate of 5 mL/s for the vertebral artery using an automated power injector (Angiomat Illumena; Liebel-Flarsheim Medical System, Cincinnati, Ohio).
Image Analysis A first reader (4 years of experience in diagnostic and interventional neuroradiology) evaluated the 4D-MRA overall image quality before image analysis using a practical 5-point grading system formerly used by Hadizadeh et al. 19  Examinations were considered of sufficient quality if the score was Ն3. Examinations with a score lower than 3 were excluded. Two independent readers (11 and Ͼ20 years of experience in diagnostic and interventional and neuroradiology, respectively) reviewed 4D-MRA and DSA examinations randomly. Readers were blinded to baseline and follow-up clinical data and were only aware of the bAVM location and the received treatments. They determined if there was a residual nidus and/or residual venous drainage for each examination. Results for each reader were recorded separately and used to determine interobserver and intermodality agreement.
For studies where the 2 readers disagreed, consensus for each imaging technique was performed by a third reader (Ͼ20 years of experience in diagnostic and interventional neuroradiology), who was blinded from the results of the first readings and used to determine intermodality agreement. A residual bAVM was defined as the presence of a residual venous drainage associated or not with a residual nidus. All MRA and DSA data were transferred to the local PACS and were randomly reviewed on separate workstations using the medical viewer. For 4D-MRA visualization, subtracted data with MIP reconstructions were used. The software enabled the enlargement of regions of interest in any 3D direction, with the possibility of viewing the progress of the contrast injection. For DSA visualization, each stored run was composed of a loop of 6 images, which enabled the readers to appreciate the contrast filling with discrimination of arterial, capillary, and venous phases.
Samples are generated with the CTEQ6L1  [20]  PDF set and the AUET2 tuning of parameters  [21]  for the description of the non-perturbative component of the generated events. In addition, overlaps between samples with heavy-flavour quarks originating from the matrix element and from the parton shower are removed. Events containing b-quarks are reweighted after hadronisation to reproduce b-hadron decay particle multiplicities predicted by the EvtGen package  [22] , to correct mismodelling found in the decay tables of the Herwig generator version used. Alternative Z(→ ℓℓ) samples used for systematic cross-checks are generated with Sherpa 1. 4. 1  [7]. This generator is based on a multi-leg matrix element calculation using the CT10  [23]  PDF set and matched to the parton shower using the CKKW prescription  [24].
Event selection The data analysed were collected by the ATLAS detector in 2011 during stable pp collisions at √ s=7 TeV when all components of the ATLAS detector were fully functioning. Dielectron candidate events were selected with a trigger requiring two electrons with p T > 12 GeV. Dimuon candidate events were selected with a trigger requiring a single muon with p T > 18 GeV. An integrated luminosity of 4. 58±0. 08 fb -1  [35]  was taken with these triggers. The primary interaction vertex (PV) is defined as the vertex with highest p 2 T of ID tracks with p T > 0. 4 GeV associated to it. Candidate events are required to have at least three such associated tracks. Electron candidates are reconstructed by associating a cluster of energy deposits in the EM calorimeter to a well reconstructed ID track, and are required to have E T > 20 GeV and |η| < 2.
CombNNc In the 1-tag event selection, fits are made to the CombNNc distribution as it is found to give the best statistical separation between b-jets and non-b-jets. Templates are derived from MC simulation for all non-multijet contributions. For the multijet background, templates are derived from the respective control regions in each lepton channel after reintroducing the b-tagging requirement as in the baseline selection. As shown in Figure  3   ulation. Fits to data allow the b-and non-b-jet Z+jets yields to float, while backgrounds from sources other than Z+jets are combined into a single template whose normalisation is determined from the sum of their predicted contributions and fixed in the fit. Where a per b-jet yield is measured, all tagged jets are used in the fit; where a per-event yield is measured, only the highest p T tagged jet in an event is used in the fit.
The uncertainties associated with the measurement of these quantities are propagated through the full analysis chain resulting in an uncertainty of around 1% on the final cross-sections. Missing transverse momentum. The calculation of E miss T in each event is repeated for every systematic variation of reconstructed jet and lepton candidates as described above. An additional uncertainty arises from possible differences in data and simulation between the component of E miss T from topological clusters not associated to reconstructed physics objects  [45]. This additional component is propagated through the analysis as an independent uncertainty, and is typically well below 1%. Luminosity. The luminosity scale is determined from a single calibration run taken in May 2011. The associated uncertainty is derived from the calibration analysis itself and from the study of its stability across the 2011 data taking period.
The mcfm predictions are performed using the CT10  [23] , NNPDF2. 3 [54]  and MSTW2008  [55]  PDF sets. The uncertainties associated with the PDF fits to experimental data are propagated appropriately for each PDF set. The dependence on the choice of α S (m(Z)) is assessed by using PDF sets with α S (m(Z)) shifted up and down by the 68% confidence level interval around the default value used in the PDF. For MSTW2008, fits using different b-quark masses are also available. The prediction from mcfm is at the parton-level, so must be corrected for the effects of QED final-state radiation (FSR), hadronisation, underlying event and MPI. The correction for QED FSR is obtained using Photos, interfaced to the Alpgen+Herwig+Jimmy samples used in the data analysis, and evaluated by comparing the cross-sections obtained by applying the selection requirements to leptons before, and after FSR.
The top panels show measured differential cross-sections as filled circles with statistical (inner) and total (outer bar) uncertainties. Overlayed for comparison are the NLO predictions from mcfm and amc@nlo both using the MSTW2008 PDF set. The shaded bands represents the total theoretical uncertainty for mcfm and the uncertainty bands on amc@nlo points represent the dominant theoretical scale uncertainty only. Also overlaid are LO multi-legged predictions for Alpgen+Herwig+Jimmy and Sherpa. The middle panels show the ratio of NLO predictions to data, and the lower panels show the ratio of LO predictions to data. Differential Z+b-jets cross-section measurements from the LHC have been presented using 4. 6 fb -1 of √ s=7 TeV pp collision data recorded by the ATLAS detector in 2011. In total, 12 distributions for Z+ ≥ 1 b-jet and Z+ ≥ 2 b-jets topologies have been investigated and compared to theoretical pQCD calculations.
Software control Sequence writing and reader control is carried out using the Freiberg Instruments software "LexStudio 2" operating under Microsoft Windows TM. A detailed software description is beyond the scope of this contribution, but some relevant aspects are highlighted. LexStudio 2 provides direct access to the hardware components within the reader. This concept allows a high flexibility for individual and complex hardware configurations. Measurement results can be stored as *. csv β-source on/off) or optical stimulation (predefined stimulation power, real stimulation power, count values). For the latter, also the measured stimulation temperature is recorded (also for irradiation, if this is performed at an elevated temperature). All monitored curves are graphically displayed during each measurement.
These L x /T x values exhibit a relative standard deviation (RSD) of 0. 34%, of which 0. 20% can be accounted for by counting statistics. The latter number was derived from the average relative uncertainty of each individual D e value, calculated with the program Analyst (Version 3. 24), based on the equation given in  Galbraith (2002). Further details on the error estimation used in the Analyst are given in  Duller (2007). The remaining uncertainty of 0. 27% ( 2 2 20. 0 34. 0 -) will mainly be the result of instrumental variations. The result of this reproducibility test is shown in Fig. 5. Furthermore, dose recovery tests (DRT) and calibration measurements were carried out, focussing on quartz and green stimulation. Results of these tests can be found in Table  1  and Fig. 6. As material, the Risø calQ for the coarse grain measurements and sample BT379 for the fine grain measurements was used.
Experimental Verification of Pressure Fluctuation 4. 1. Test Rig Setup. Experiments were conducted in a closedloop system composed of two parts: water circulation and data acquisition. The water circulation system supplies the necessary environment for centrifugal pump operation, and the data acquisition system changes all types of physical quantities in different conditions to the corresponding electrical signals by using sensors. The final data are directly discernible after processing. The installation diagram of the test system is shown in Figure  9 (a). The pump, together with the motor, is fixed on a solid base. The meter, torque meter, and static pressure sensors are used to calculate the pump performance. Eight dynamic pressure sensors are used to transduce the transient pressure. The positions of these dynamic pressure sensors are set as the monitoring points, as shown in Figure  9 (b).