dataesr/scientific-paragraphs-categorization huggingface dataset:
Amoxicillin determination All samples were diluted to a concentration of 100 μg/mL with ultrapure water and assayed for amoxicillin concentration using a high-performance liquid chromatography method coupled to UV detection (HPLC-UV). The Elite LaChrom system (VWR, France) included a binary pump (Primaid 1110) used in isocratic mode, a single wavelength ultraviolet detector (L-2400), and an autosampler (L-2200) and was controlled using EZChrom Elite 3. 3 software. Separation was performed using a Nucleosil C8 analytical column (5 μm, 150 x 4. 6 mm, VWR, France). Mobile phase consisted of 20% methanol and 80% ultrapure water; flow rate was set at 1 mL/min and 10 μL of the diluted sample were injected onto the column. Quantification was performed by integration of the peak at a detection wavelength of 225 nm.
The observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event is compatible with the signal hypothesis (ttH) or with one of the background hypotheses. It is a powerful classifying tool requiring high dimensional integral computations. The deployment of our MEM production code on GPU's platform will be described. What follows will focus on the adaptation of the main components of the computations in OpenCL kernels, namely the Magraph matrix element code generator, VEGAS, and LHAPDF. Finally, the gain obtained on GPU's platforms compared with classical CPU's platforms will be assessed.
A second ponderation f (x a ,Q). f (x b ,Q) x a x b S gives the probability that the two incoming proton particles or partons (proton quarks or gluons) interact, in which f (x a , Q) is the Parton Density Function (or PDF) for the parton a. All the parton combinations are described by the Σ p term. Finally, the δ 4 (x a P a + x b P b -ΣP k ) term corresponds to the kinematic constraints between the p-p incoming partons and the outgoing particles considered in the Matrix Element. The final state considered in our analysis: b b, q q pairs, τ h , 2 same sign leptons and 3 undetectable neutrinos ν s. The τ -decays in a hadronic system referred to as τ h is constituted by pions particles π ± (π 0 ) n , π ± π ∓ π ±. together with a ν τ neutrino. The CMS reconstruction software, CMSSW, reconstructs the jets, identifies the b and light quarks, the hadronic τ h , leptons and the Missing Transverse Energy (MET) collecting the transverse energy of undetectable neutrinos.
MEM code implementation Because the MEM is very time consuming, we started our developments with High Perfomance Computing (HPC) in mind, and to avoid large waiting times or elapsed time to obtain the results, we started to build a parallel release MPI-MEM with the MPI library  [15]. The common way to compute high-dimension integrals is to use an adaptive Monte-Carlo algorithm, in this case the commonly-known VEGAS  [12] , implemented in the GSL  [20]. Running the code gave already reasonable waiting time. To analyze a data set of around 2500 events (around 30k integrals to compute), it requires typically 14 hours of computation deployed on 96 cores (6 nodes with 2 Intel Xeon E5-2640 processors, 16 physical cores rates at 2. 6 Ghz), thanks to MPI. This means that the analysis would take 55 days on one core.
OpenCL/CUDA implementation Considering the importance of the portability of our developments, we selected the OpenCL standard  [14]  to handle the GPU's. The parts which have been selected to run on GPU's are the most computationally intensive: the integral evaluations. To achieve this goal we transformed the integration parts from the initial MPI-MEM C++ implementation to the C language to build the GPU kernels. In addition, to minimize memory traffic between the node memory and the GPU memory, all the integrals of a same event are computed in the same GPU. The main components used in the GPU-MPI-MEM implementation are: LHAPDF library: this library takes care of the computation of the PDF function seen in subsection 2. 1. We translated it from Fortran to C and we numerically validated it before implementing it in the integration part.
ROOT utilities: a small subset of ROOT framework  [16]  (such as vector, geometric, Lorentz arithmetic) has been rewritten in the C-kernel language. In addition, basic operations in the ROOT suite, used for instance in the Transfer Functions, have been transformed to C-kernel functions. MadGraph: the Matrix Element (ME) |M i (x)| 2 in the expression (  1 ) is given by the MadGraph5_aMC@NLO framework  [17]. To be efficient on CPU, MadGraph generates C++ code to compute the ME of a given process. We have extended this generative part of Mad-Graph to provide C-sources to be directly integrated into our GPU kernels. MC Integration: the integration computation is the part which is easily parallelized (or vectorized) thanks to the numerous and independent function evaluations  [13]. Nevertheless, the adaptive part of the VEGAS is a little bit more tricky with the GSL implementation.
6 GHz. In addition, each node is equipped with 4 Kepler GK-210 GPU's on 2 NVidia K80 boards. The applications are compiled with gcc 5. 3 compiler with the -O 3 optimization option and, for the GPU's kernels, with nvcc compiler from the CUDA 8. 0 software environment. For all the performance analyses we add a time log at the MPI level to profile the moments when the event processing starts and finishes. The figure  4  (left) shows a excellent speed-up (the ratio between the execution time of one code instance ie one MPI process, divided by the execution time of N instances ie N MPI processes) up to 8 nodes i. e. 32 GPU's. This can be explained by the very small time overhead of the communications and the event scheduling, compared to the average time to process an event. The figure  4  (right) shows that there is no delay between two event processing and that all the dataset is performed in impressive computing time: less than 500 seconds.
Conclusion The MEM is a powerful classification tool used for signal extraction in the ttH analysis. It however requires a significant amount of computations. Its deployment on GPU's platform has remarkably accelerated the computing time. We demonstrate in this paper that there is a high added value in using simultaneously a great number of GPU's for our ttH analysis: 55 days with one core, 14 hours with 96 cores and less than 500 seconds on 32 GPU's platform. Thanks to this computing time speed-up, the analyzers can now run the analysis multiple times. More checks can thus be carried out, and more analyses improvements be tested. This improvement however comes with a price: the OpenCL or CUDA programming paradigm forces to write the code or the kernels which are easy to parallelize/vectorize by the compiler, explaining this important gain.
Nevertheless, it is a tedious and cumbersome task to transform C++ code to C kernels. In the future we will focus on this problem avoiding to penalize the extensibility of this implementation to other models. Using the MadGraph code-generation capability could be a way. Today, the GPU-MPI-MEM version is used to analyze the new data-taken at LHC. In the future with the High-Luminosity LHC upgrade, the computing power required will drastically increase, due to the larger data taking rate and the larger complexity of the events. Our GPU's-based MEM analysis is ready to assimilate this data increase, thus freeing the LHC Computing Grid resources from such heavy computating analyses.
Figure2. The final state considered in our analysis: b b, q q pairs, τ h , 2 same sign leptons and 3 undetectable neutrinos ν s. The τ -decays in a hadronic system referred to as τ h is constituted by pions particles π ± (π 0 ) n , π ± π ∓ π ±. together with a ν τ neutrino. The CMS reconstruction software, CMSSW, reconstructs the jets, identifies the b and light quarks, the hadronic τ h , leptons and the Missing Transverse Energy (MET) collecting the transverse energy of undetectable neutrinos.
g. , Pan-STARRS and LSST. We calculate predicted quasar yields for the assumed values of the rate of decline of the QLF beyond z = 6. If the decline of the QLF accelerates beyond z = 6, with k = -0. 92, Euclid should nevertheless find over 100 quasars with 7. 0 < z < 7. 5, and ∼ 25 quasars beyond the current record of z = 7. 5, including ∼ 8 beyond z = 8. 0. The first Euclid quasars at z > 7. 5 should be found in the DR1 data release, expected in 2024. It will be possible to determine the bright-end slope of the QLF, 7 < z < 8, M 1450 < -25, using 8 m class telescopes to confirm candidates, but follow-up with JWST or E-ELT will be required to measure the faint-end slope. Contamination of the candidate lists is predicted to be modest even at J AB ∼ 23. The precision with which k can be determined over 7 < z < 8 depends on the value of k, but assuming k = -0.
At higher redshifts (z 6. 5) searches for quasars must be undertaken in the near-infrared (NIR), as the signature Lyα break shifts redwards of the optical z band. The first quasar found at z > 6. 5 was the z = 7. 08 quasar ULAS J1120+0641  (Mortlock et al. 2011) , discovered in the UKIDSS Large Area  Survey (LAS). This is one of five quasars now known at z > 7. Discovered more recently, ULAS J1342+0928, z = 7. 54  (Bañados et al. 2018) , also located in the UKIDSS LAS, is the most distant quasar currently known. Yang et al. (2019)  discovered four z > 6. 5 quasars, including one object with z = 7. 02, using photometric data from the Dark Energy Survey (DES), the VISTA Hemisphere Survey (VHS) and the Wide-field Infrared Survey Explorer (WISE). Wang et al. (2018b)  recently published the first broad-absorption line quasar at z > 7 using photometric data from the Dark Energy Spectroscopic Instrument Legacy Survey (DELS), Pan-STARRS1 and WISE.
(2015)  (but for a different interpretation of the same data, see  Parsa et al. 2018). The prospects for finding many more bright z > 7 quasars in the short term, using existing datasets, are poor nevertheless. The main reason for this is simply that z > 7 quasars are very rare: for example, assuming k = -0. 72 is applicable at z > 6, the results of  Jiang et al. (2016)  imply there are only ∼ 200 redshift 7 < z < 9 quasars brighter than J AB = 22 over the whole sky. In the redshift interval 7 < z < 9, Lyα lies in the Y or J band. To discriminate against contaminants requires one or more bands redward of the Lyα band, so optical surveys, including those stretching to the Y (or y) band, such as DES and (in the future) LSST, are not competitive on their own. Multiband, deep, widefield, near-infrared surveys, combined with deep optical data are ideal.
Existing near-infrared datasets such as the LAS, VHS, and VIKING have been thoroughly searched, but do not survey a sufficient volume to yield significant numbers of bright sources. Selection of z > 7 quasars is hampered by contamination from intervening populations: late M stars, and L and T dwarfs (hereafter MLTs); and early-type galaxies at z = 1 -2, which we also refer to as 'ellipticals' in this work. These populations are far more common than, and have similar NIR colours to, the target quasars (e. g. Hewett et al. 2006). Consequently, colour-selected samples of fainter candidates become swamped by contaminating populations, especially as quasar searches move to lower S/N to maximise the number of discoveries. The launch of Euclid, currently planned for Q2 2022, should prove to be a landmark in high-redshift quasar studies.
To this end, we have developed better models of the contaminating populations, and we also explore more powerful selection methods which allow us to go fainter. We also consider the impact of deep ground-based z-band optical data on the predicted numbers. The aim of this paper is to accurately model the high-redshift quasar selection process, and make robust predictions of the Euclid quasar yield, appropriate for the Euclid Reference Survey (ERS) currently defined in  Scaramella et al. (in prep. ). We compare selection using either Euclid or z-band optical data, focusing in particular on the overwhelming contamination from MLTs and early-type galaxies. The paper is structured as follows. We summarise the data that will be available to us, both from Euclid and from complementary ground-based surveys, in Sect.
Data In Sect. 2. 1 we give a brief technical overview of the Euclid wide survey (see  Laureijs et al. 2011 and Scaramella et al. , in prep. , for further details). The search for high-redshift quasars is enhanced with deep data in the z band, and we summarise complementary ground-based optical data in Sect. 2. 2. The areas and depths of the Euclid and ground-based data are summarised in Table  1. Since the ground-based data have not yet been secured, in this paper two scenarios are considered: i) the case where Euclid data are the only resource available, for which we consider optical data from the visual instrument, in a wide filter (R + I + Z) which for brevity we label O; and ii) where we replace Euclid optical data with complementary ground-based z-band data.
The discrimination against MLT dwarfs and early-type galaxies will not be affected at this level of reddening since it is primarily set by the contrast at the Lyman break, which is barely changed. The Euclid deep fields will ultimately reach two magnitudes fainter than the wide survey in OYJH; however, they are unlikely to prove such a useful resource for z > 7 quasar searches, since they will cover a total area of just 40 deg 2  (Scaramella et al. in prep. ). Without any consideration of the completeness of a potential search for quasars in the deep survey, the  Jiang et al. (2016)  QLF (k = -0. 72) implies 12 sources with 7 < z < 9 in the magnitude range J = 24 -26, i. e. , in the deep survey but not the wide survey. As shown in Sect. 4. 1, this is a factor of 10 -20 lower than the predicted yield of J 23 quasars from the wide survey, depending on the choice of optical data used to select candidates.
2017 ). We therefore concentrate on these two ground-based resources. The exact crossover areas between Euclid and the groundbased surveys, and the target z-band depths are still to be finalised, so we have made a set of working assumptions for the purposes of this paper, which are summarised in Table  1. We additionally indicate the assumed crossover area in Fig. 2. The adopted 5 σ depth for LSST, z = 24. 9, is based on one year of data (following the start of operations scheduled for 2022), assuming 20 zenith observations of each source with a singlevisit 5 σ depth of z = 23. 3 (Ivezić et al. 2008). The proposed LSST crossover area is composed of three separate surveys: the LSST main survey covering -62 • < δ < 2 • , and northern (2 • < δ < 30 • ) and southern (-90 • < δ < -62 • ) extensions, across which the final depths will differ  (Rhodes et al.
2017) ; however, for the sake of simplicity, we assume a uniform LSST depth. For Pan-STARRS we assume the planned depth at the time of Euclid DR3 (2029), which is anticipated to be z = 24. 5 at S/N = 5. The Pan-STARRS and LSST z filter curves are extremely similar, and the resulting z -Y colours are essentially identical. Differences in the selection functions for the two surveys are driven by the different depths in z. Although not considered further here, we note the possibility of using z-band data from other sources in the future, e. g. , DES, which will cover 5000 deg 2 of sky, almost entirely in the southern celestial hemisphere, in common with LSST. DES is ultimately expected to reach a 5 σ depth of z ∼ 24 (e. g. Morganson et al. 2018) , i. e. , around 1 mag shallower than LSST; however, DES data will be available considerably sooner, with the final release (DR2) expected August 2020.
(2017). The methods are based on improved population models for the key contaminants: MLT dwarf types; and compact early-type galaxies. Both methods require model colours for each population. The BMC method additionally requires a model for the surface density of each source as a function of apparent magnitude. We present the population models in Sect. 3. 2. In this work we assume that MLTs and early-type galaxies are the only relevant contaminating populations for the selection of high-redshift quasars in Euclid. In Sect. 5. 4 we consider this assumption further, by analysing deep COSMOS data  (Laigle et al. 2016 ). We do not see evidence for further populations that need be considered for high-redshift quasar searches with Euclid.
The individual weights for each population are calculated as follows: W t (d) = Σ t (θ t ) p (d | θ t , t) dθ t , (3) where θ t is the set of parameters describing a single population. The two terms in the integral in Eq. (  3 ) are respectively the surface density function, and a Gaussian likelihood function based on model colours, which is written in terms of linear fluxes. Explicitly, the full likelihood function is given by p (d | θ t , t) = N b b=1 1 √ 2π σb exp        - 1 2 fb -f b (θ t ) σb 2        , (4) where the data in each of the N b bands b is of the form fb ± σb , and f b (θ t ) is the true flux in band b of an object of type t described by the parameters θ t. From the above definition of the individual population weights, which incorporates both the prior weighting and likelihood, it follows that the ratio of any pair of population probabilities (P q , P g , P s ; cf.
The standard line strength has rest frame equivalent width EW C iv = 39. 1 Å and UV continuum slope, defined by the ratio of f λ at rest frame 1315 Å and 2225 Å, f 1315 / f 2225 = 1. 0. Since we are only interested in redshifts z > 7, we assume that all flux blueward of Lyα is absorbed for all sources that we simulate, except that we include a near zone of size 3 Mpc (proper). The results are insensitive to the choice of near-zone size. In the actual search of the Euclid data we will adopt a set of quasar spectral types, characterised by variations in the continuum (redder/bluer), and variations in the equivalent width of the reference C iv emission line, keeping all emission line ratios fixed. The surface density term (i. e. , the prior) will be divided in proportion to the expected numbers, based on our knowledge of quasars at lower redshifts.
Acknowledgements. We would like to acknowledge the anonymous A&A referee, who provided useful and detailed comments on this manuscript. The Euclid internal referees were  Francisco Castander  and  Johan Fynbo , whose reports led to several improvements in the presentation of this paper. We are also grateful to several members of the  Euclid Primeval Universe Science Working Group , who provided comprehensive comments and suggestions for this work, which have led to significant changes in the analysis and discussion:  Rebecca Bowler ;  Peter Capak ;  Pratika Dayal ;  Andrea Ferrara ;  Kari Helgason ; and  Michael Strauss. We would additionally like to thank  Konrad Kuijken  and  Jean-Charles Cuillandre  for their advice on Pan-STARRS. The Euclid Consortium acknowledges the  European Space Agency  and the support of a number of agencies and institutes that have supported the development of Euclid.
Statistical Analyses Data are expressed as mean ± standard deviation (SD), and were initially tested for normal distribution using the Shapiro-Wilk W-test. All statistical analyses were performed using the open-access statistical package jamovi  [21] , and the level of significance set at p < 0. 05. A repeated-measures (RM) design was chosen for this study to capture the range of physical abilities in the participant sample. One-way, repeated measures ANOVAs with Bonferroni post hoc were used to assess the effect of mode (rest, W6, AG, AG W ) on HR% and EE. Two-way, repeated measures ANOVAs with Bonferroni post hoc tests were used to assess the effect of game level, added weight, and their interaction, on HR% and EE. Pairwise comparisons between AG and AG W (game duration, number of actions performed, time spent playing at HR% and EE above W6 values) and between W6 and AG and W6 and AG W were tested for significant differences using Student's t-test (using HR% and EE at maximum level attained).
When the result is considered good? In the short-term follow-up, the outcome was considered good when patients or their parents (caregivers) reported an improvement of symptoms. Good long-term outcome was considered if the patient was evaluated as Bimproved^according to CCOS score. Due to a small study group, data and basic descriptive statistics were summarized and analyzed with the use of Microsoft Office 2016, Excel software, and Statistica 13. 0 (StatSoft).
Physical Analysis The geometrical features (diameter, length, width and circularity) and surface colour (expressed in CIEL*a*b* model, where L* -lightness, a* -greenness/redness, b* -blueness/yellowness) of cranberries were measured using digital image analysis (DIA) according to the method described. The images were acquired by a high resolution, low-noise CCD (charge-coupled device) Nikon DXM-1200 colour camera (Nikon Inc. , Melville, USA) and analysed by LUCIA v. 4. 8 software (Laboratory Imaging, Prague, Czech Republic). The frame grabber was at a resolution of 1280 × 1024 pixels. The light source was a Kaiser RB 5004 HF -High Frequency Daylight Copy Light set with 4 × 36 W fluorescent light tubes (colour temperature about 5400 K) (Kaiser Fototechnik GmbH & Co. KG, Buchen, Germany). Before analyses, the calibration to a standard white reflective plate was done.
Statistical Analysis The obtained data were analysed statistically using Statistica 12. 0 PL software (StatSoft, Kraków, Poland). A one-way analysis of variance (ANOVA) with the Tukey test was used. Pearson's correlation coefficients were also determined to establish the relationship between phenolics and colour as well as antioxidant and antitrypsin activities. The statistical significance was set at the 5% level.
Ruse and Rakcejeva  [10]  reported that the fruits of the five large-fruited cranberry cultivars (Steven, Bergman, Ben Lear, Pilgrim and Early Black) contained more anthocyanins than the fruits of wild-grown cranberry fruits. Pappas and Schaich  [8]  noted that anthocyanins in cranberry fruits are located mainly in the peels. Therefore, in our study the content of these compounds in fruit peels was determined. As can be seen from the data included in Table  2 , the studied fruits of particular cranberry cultivars were characterised by a large diversity in terms of the amount of anthocyanins in the peels. The content of these compounds in 100 g fw of fruit peels ranged from 114. 4 (Pilgrim cultivar) to 270. 3 mg (Bergman cultivar). In turn, 100 g fw of fruit peels of wild-grown cranberry contained 136.
Statistical Analysis We used the R statistical package (www. rproject. org) for statistical analyses  [49]. Differences in meniscus healing rates were assessed through analysis of a contingency table using Fisher's exact test. All categorical data were analysed using Fisher's exact test. The VAS score, KOOS, WOMAC and IKDC score were analysed using the two-tailed Mann-Whitney U test or unpaired t-test (after assessment for parametric or non-parametric distribution using the Shapiro-Wilk test)  [50]. Arthroscopy-free survival was analysed using Kaplan Meyer plot and log-rank testing for statistical significance. Results were considered statistically significant at a P-value < 0. 05. Sample size was calculated for the primary outcome (meniscus healing), with a two-tailed significance level at alpha = 0.