dataesr/scientific-paragraphs-categorization huggingface dataset:
Data was collected up until two weeks after the course completed to allow for learners who had fallen behind in the course schedule. An optional pre-course survey was sent to all course joiners, which 1067 people completed, followed by a post-course survey which 139 people completed. Surveys comprised a combination of Likert scale, multiple choice and open questions, delivered via the SurveyMonkey platform, and were developed from previous research on the use of open educational resources  [30].
During weeks 3 and 4 different aspects of smart city initiatives are explained and learners collect ideas for their projects. At the end of week 3 they asked to identify what types of data may be helpful in solving their problem and how they would collect it. There were 99 comments, 41% were replies, and once again there was a lot of discussion about transport/mobility data sets but also data about social care, the rented housing sector, water, energy, local food and homelessness. Weeks 5 and 6 teaches learners how to prototype and plan their project which includes completing the Smart Cities Business Model Canvas. The level of discussion in the relevant steps was low (63 comments), with only 21 people sharing their completed business model canvas. This was not completely unexpected, the MOOC covers a lot of material in a short time period and co-creation involves working with stakeholders so project development may take longer than the course length.
To address this we could reduce the number of discussion and Padlet steps and see what impact this has on course completion. Condensing the scope and length of the course might also help, as shorter courses tend to have higher completion rates  [31]. We also plan to launch the course on OpenLearn, the OU's free online learning platform, and a future study will explore if the drop-out rates are lower when there is no pressure on the start and finish date. Despite the low completion rate, satisfaction was high in learners completing the course and their comments provide us with a rich picture of an international citizens' views on smart cities. We plan further analysis of the comments using data analytics. Our results support the existing literature that suggests MOOCs can support citizens to learn about smart cities, helping them to develop new skills, competencies and ways of thinking  [17, 18] , but identifies the fact that the impact could be limited if there is a high drop-out rate.
VI. CONCLUSION The aim of this paper was to explore how a MOOC can be used to engage citizens in learning about smart cities and teach them how to co-create a smart cities project in their community. Evaluation of data collected from the first presentation of the Smart Cities MOOC suggests it was successful at attracting a diverse international audience who want to learn about smart cities and engages them in collaborative problem solving. Unfortunately the major weakness is the low completion rate, typical of MOOCs, which could limit the wider social impact. Despite this satisfaction was high in learners completing the course, with valuable insights for the ICT4S community on designing educational materials. We are using learner feedback to improve future presentations and think that the educational materials could be adapted to meet the specific learning needs of smart city initiatives.
Acknowledgement This work was supported in part by the  Academy of Finland  through its  Centres of Excellence Programme  ( 2012-2017 ) under Project No. 251748  and  Aalto Energy Efficiency Research Programme. We acknowledge the computational resources provided by  Aalto Science-IT  project,  CSC-IT and Structure and Dynamics Group of NIP University of the Philippines. The structural models in Figure 1 were rendered using VMD. 53.
They affect also the performance of coverage of waste disposal and decrease the strength of earth embankments  [3]   [4]. The formation and evolution of desiccation cracks have been studied by many authors experimentally and numerically. Some numerical simulations have been carried out by discrete element method and others by finite difference method. The objective of this research is to study the evolution of suction and strain fields during desiccation, as well as the propagation of desiccation cracks with taking into account the interaction between them. The simulation is carried out by using the finite element (FEM) 2D code Porofis  [5]. This code allows modelling hydro-mechanical coupling and fluid mass exchanges between fracture/matrix, fracture/fracture, and fracture/boundary. The hydro-mechanical coupling in this code is realized by an iterative process passing from the hydraulic to the mechanical problem and vice versa (weak numerical coupling).
Hydro-mechanical coupling Eq. (  11 ) and Eq. (  14 ) are the base of hydraulic simulation in the code Porofis. The suction which is a result in the hydraulic problem is introduced after in the mechanical model to find the stress and strain fields. The opening of cracks calculated in the mechanical problem plays the role to update the cracks hydraulic conductivity like Poiseuille's law. The hydro-mechanical coupling in the code is carried out as showing in the Figure  3 : E 2016 and 13 mm of thickness in slurry state is prepared and let in chamber condition with temperature 19. 5° ( 0. 3  °C) and relative humidity 37 %( 4  %). By using the 2D profile laser technique, the characteristics soil in drying are observed (volume change, water loss…). Especially, the evolution crack network's morphology (thickness, depth) is observed in the vertical plane.
Conclusions This work presents an approach for desiccation cracking simulation talking into account the hydro-mechanical coupling: flow and deformation in presence of cracks by using the FEM code Porofis. The simulation including the cohesive joints shows that this approach allows simulating two phases of desiccation process: the consolidation one without cracking and the cracks development one. In addition, the simulation allows observing the evolution of soil parameters during drying as: suction, degree of saturation, settlement and cracks opening. The comparison of the numerical simulation with the experimental data  [8]  allowed evaluating the model. This work showed that the cohesive fracture model is a good tool to simulate the evolution of desiccation cracks. Study of spacing and depth of cracks during drying will be investigated in future work in order to better understand the mechanisms of desiccation cracking.
In contrast to previous studies, MUSE observations are not limited to isolated stars but promise to encompass the complete stellar population of a cluster in a single observation and therefore to bridge the gap to photometric studies. In this paper, we discuss MUSE observations of the globular cluster NGC 6397 which is an extremely metal-poor, corecollapsed globular cluster with a moderate stellar mass. Thanks to being one of the closest clusters with a distance of only 2. 4 kpc  (Harris 1996) , it has been subject of spectroscopic and photometric studies. This makes it a perfect object for testing the capabilities of MUSE in the analysis of globular cluster populations. The paper is structured as follows. In Sect. 2 the observations and the basic data reduction will be discussed, following with the source extraction in Sect.
The individual exposure times were always short, ≤ 60 s, to avoid saturation of the brightest cluster giants. In total, we obtained 127 exposures with a total integration time of 95 min. Including overheads, the observations took about 6 h. The seeing during the observations was a bit variable, but never worse than ∼ 1. However, the observations of the central part of the cluster were carried out under excellent seeing conditions of ∼ 0. 6. The data reduction was done using the official MUSE pipeline (in versions 0. 18. 1, 0. 92, and 1. 0) written by  Weilbacher et al. (in prep. , see also  Weilbacher et al. 2012). It performs the basic reduction steps -bias subtraction, flat fielding, and wavelength calibration -on an IFU-per-IFU basis. In the next step, the data of each IFU was transformed into spatially calibrated coordinates before the data from all 24 IFUs were combined into a single pixtable, a FITS-file containing for each CCD pixel information about its flux and uncertainty, wavelength, and spatial position inside the field of view.
Extraction of stellar spectra To obtain clean spectra in a crowded region such as a globular cluster, sophisticated analysis techniques are required. We developed a code to perform source deblending via PSF fitting on IFS data, described in detail in  Kamann et al. (2013). It relies on the existence of a photometric reference catalogue, including relative positions and brightnesses for the stars in the field of view. For NGC 6397, we used the catalogue prepared by  Anderson et al. (2008)  as part of the ACS survey of Galactic globular clusters  (Sarajedini et al. 2007). Our MUSE mosaic extends beyond the ACS observations in this cluster (see Fig. 1 ), therefore we had to obtain positions and brightnesses of stars there by other means. To this aim, we created broadband images from the datacubes of these fields and analysed them with daophot  (Stetson 1987).
Afterwards, we identified the stars that exist in the ACS catalogue, used those to calibrate the magnitudes measured with daophot, and added the missing stars to the catalogue. A possible shortcoming of this approach is that our deblending code works best with a reference catalogue obtained at higher spatial resolution while the broadband images obviously have the same resolution as the IFS data. However, the areas in NGC 6397 without coverage by the ACS catalogue are not that densely crowded. Therefore we do not expect a significant impact on the results of the spectrum extraction. For details on the method we refer the reader to  Kamann et al. (2013) , here follows a brief summary of the methodology. The extraction of the stellar spectra is performed in several steps. The analysis is started from an initial guess of the PSF in the MUSE data, which we model as an analytic Moffat profile with up to four free parameters -the FWHM, the kurtosis β, the ellipticity e, and the position angle θ, all of which can be wavelength dependent.
By using a simple polynomial for the interpolation, the increase in computation time as compared to a fit without tellurics is almost negligible. A&A proofs: manuscript no. ngc6397_methods A kappa-sigma clipping at 3σ (indicated by the overplotted ellipse) on this data is used for determining membership of stars to the cluster. An example for the Fraunhofer A band found in a typical G subgiant from our sample is shown in Fig. 6. As one can see, the residuals are of the order of a few per cent of the flux, which is of the same order as the noise at a S/N of about 100. Marked in the residuals with arrows are the positions of of two K i absorption lines from the interstellar medium (ISM). A detailed discussion on ISM and diffuse interstellar bands (DIBs) will be presented in another part of this series  (Wendt et al.
Note that all horizontal branch stars are missing in the HRD, since we currently do not have matching templates for fitting their spectra. A comparison of our photometrically obtained surface gravities for single stars with values from the literature is plotted in Fig. 10. Since most of the presented studies -some of them with a rather large sample -concentrated on outer fields of the cluster, we could match only a small number of stars to our data. The matching itself has been done by comparing RA/Dec coordinates and magnitudes. Although using the FLAMES spectrograph at the VLT, all the discussed studies determined T eff and log g photometrically. Lovisi et al. (2012)  used a method similar to the isochrone comparison that we use for our initial guesses, deriving values for T eff and log g from theoretical stellar models from the BaSTI database  (Pietrinferni et al.
Following on our earlier work on leading-logarithmic (LL R ) resummations for the properties of jets with a small radius, R, we here examine the phenomenological considerations for the inclusive jet spectrum. We discuss how to match the NLO predictions with small-R resummation. As part of the study we propose a new, physically-inspired prescription for fixed-order predictions and their uncertainties. We investigate the R-dependent part of the next-to-next-to-leading order (NNLO) corrections, which is found to be substantial, and comment on the implications for scale choices in inclusive jet calculations. We also examine hadronisation corrections, identifying potential limitations of earlier analytical work with regards to their p t -dependence. Finally we assemble these different elements in order to compare matched (N)NLO+LL R predictions to data from ALICE and ATLAS, finding improved consistency for the R-dependence of the results relative to NLO predictions.
NLO+LL R matched results Fig. 4  shows the inclusive jet cross section integrated from 100 GeV to 1992 GeV (the full range covered by the ATLAS data  [26] ), as a function of R, normalised to the leading order result. The left-hand plot shows the standard NLO result (light blue band), the "NLOmult. " result of Eq. (3. 5) (green band) and the NLO+LL R matched result of Eq. (3. 4) (orange band). To illustrate the issue of cancellation of scale dependence discussed in section 3. 2, the scale uncertainty here has been obtained within a prescription in which the We adopt the standard convention of independent µ R = { 1 2 , 1, 2}µ 0 and µ F = { 1 2 , 1, 2}µ 0 variations around a central scale µ 0 , with the additional condition 1  2 ≤ µ R /µ F ≤ 2. Our choice for µ 0 is discussed below. One sees that in each of the 3 bands, there is an R value for which the scale uncertainty comes close to vanishing, roughly R = 0.
Comparisons to data Having formulated and studied the perturbative and non-perturbative contributions to the inclusive jet spectrum, we now consider comparisons with data. The purpose of this section is to highlight the relative sizes of different physical effects as compared to the precision of the data. We will compare our predictions to the two datasets that have the smallest R values: that from ALICE at centre-of-mass energy √ s = 2. 76 TeV with R = 0. 2 and 0. 4  [13]  and that from ATLAS at √ s = 7 TeV with R = 0. 4 and 0. 6  [26]. 9 All our results are obtained with CT10 NLO PDFs. This is the case also for our LO and NNLO R results. For the latter, since NNLO R does not correspond to full NNLO, it is justifiable to use NLO PDFs. 10  One should also be aware that most modern PDF sets include inclusive jet-data in their fit.
However such simulations additionally show a non-trivial p t dependence that is absent from simple analytical estimates. Accordingly we decided to rely just on Monte Carlo simulations to evaluate non-perturbative corrections. We compared our results to data from the ALICE and ATLAS collaborations in section 6. For the smallest available R value of 0. 2, both the NNLO R and the LL R corrections beyond NNLO R play important roles and at the lower end of ALICE's p t range, the effect of NNLO R corrections was almost 50%, while further LL R corrections mattered at the 20% level. For R = 0. 4, NNLO R corrections still mattered, typically at the 10-30% level, depending on the p t. However LL R resummation then brought little additional change. Overall, for the ALICE data and the forward ATLAS data, NNLO R +LL R brought somewhat better agreement than NLO, while for central rapidities, the ATLAS data were substantially above the NNLO R +LL R predictions.
It will be important to revisit the pattern of agreement once the full NNLO corrections are known, taking into account also aspects such as correlated experimental systematic uncertainties and PDF uncertainties. Where the NNLO R and NNLO R +LL R predictions clearly make the most difference is for reproducing the R-dependence of the cross sections. For the inclusive spectrum plots, once one goes to NNLO R +LL R , the picture that emerges is consistent across different values of R. That was not the case at NLO. This is visible also in the ratios of cross sections at different R values. In particular, for the reasonably precise ATLAS data, NNLO R and NNLO+LL R are in much better agreement with the data than the NLObased predictions. For the ALICE data, the uncertainties are such that it is harder to make a definitive statement.
Nevertheless NNLO+LL R performs well and notably better than plain NNLO R. Overall, the substantial size of subleading R-enhanced terms in the NNLO corrections also motivates studies of small-R resummation beyond LL R accuracy and of small-R higher order effects in other jet observables. A final comment concerns long-term prospects. We have seen here that the availability of data at multiple R values provides a powerful handle to cross-check theoretical predictions. As the field moves towards ever higher precision, with improved theoretical predictions and reduced experimental systematic uncertainties, cross checks at multiple R values will, we believe, become increasingly important. In this respect, we strongly encourage measurements at three different radii. Small radii, R 0. 2-0. 3, are particularly sensitive to hadronisation effects; large radii, R 0.
Acknowledgements MD and GPS are grateful to the  MITP  for hospitality and support during part of this work. We would all like to thank  Matteo Cacciari  for numerous useful discussions and comments throughout this work. We are grateful to  Simone Alioli  and  Emanuele Re  for assistance and cross-checks with POWHEG and to them and the remaining authors of Ref. [43] for discussions about the results. We thank  Peter Skands  for discussions about the nonperturbative effects. We also thank  Jan Kretzschmar ,  Mark Sutton  and  Pavel Starovoitov  for discussions about ATLAS's non-perturbative correction factors and the first of them for discussions about the determination of uncertainties on ratios of cross sections at different radius values. Finally we are grateful to an anonymous referee for constructive comments on the structure of the paper.
MD's work is supported in part by the  Lancaster-Manchester-Sheffield Consortium for Fundamental Physics  under  STFC  grant  ST/L000520/1. FD is supported by the  ILP LABEX  ( ANR-10-LABX-63 ) supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. GPS and GS's work is supported by  ERC  advanced grant Higgs@LHC. GS's work is also supported by the  Paris-Saclay IDEX.
To minimize animal suffering, animals where under continuous observation 24h per day during the whole experiment duration by 2-people shifts every 8h, immediately sacrificed whenever diagnosed with CM+ signs and handled according to recommended regulations. At the same time, 24 B10. D2 mice which received either PBS or uninfected C57BL/6 Red Blood Cells (RBC) were sacrificed to constitute the control group (CTR). Parasitemia was measured on Giemsa-stained thin blood smears from day 4 p-i for all infected mice. All individuals included in this study displayed parasite positive staining from day 4 until ECM development, with 1 to 20% of parasitized RBCs (data not shown).
D2 mice, they were not analysed. TRBV16 was excluded from this study afterwards due to technical problem with the corresponding primer. PCR products were loaded on a 36-well ABI373 or 96-well ABI377 automated sequencer (Applied Biosystems, Foster city, CA) and separated according to their nucleotide length forming a profile of peaks for each primer combination, spaced by 3 nucleotides as expected for in-frame transcripts. Each peak corresponds to a CDR3 length. The Immunoscope software  [24]  was used to obtain peak area and nucleotide length and CDR3 profile displays from sequencer raw data. IMGT nomenclature has been used for TRBV genes  [30].
CDR3 Spectratype Analysis We used the ISEApeaks software package (2000-2002 Institut Pasteur, Paris, France) to extract, smooth, manage and analyse the data  [25, 26]. For each CDR3 length profile, the peak distribution is calculated as the percentage of each peak, obtained by dividing its area by the total area of all peaks within the profile. Briefly, for each TRBV-TRBC combination analysed, a reference repertoire is computed as the average CDR3 peak distribution of the samples belonging from the control group. Then, the distance between the peak distribution of each sample and that of the reference repertoire is calculated for each TRBV-TRBC combination. This distance, named DBV-BC, is the perturbation index reflecting the perturbation of the repertoire against a reference repertoire  [31, 32].
Since perturbation score distributions are often skewed, we log-transformed them for statistical analysis. Then, for each sample of each experimental group, the average of the DBV-BC (μDBV-BC) perturbations of all the TRBV-TRBC combinations is calculated, reflecting the global perturbation of the repertoire. DBV-BC perturbations indices range from 0 (identical profiles) to 100 (completely different profiles). To deal with possible missing values while keeping statistical power for unbiased conclusions, we applied multiple imputation as proposed by Rubin et al. [33] :  (1). Missing data were imputed randomly 1000 times to produce 1000 complete datasets using predictive mean matching (ppm) method  [34, 35] ;  (2). On each of 1000 complete datasets, linear regressions were used to model the relationship between perturbation scores and day post-infection for each TRBV; (3).
Significantly enriched clusters identified by GSEA are called signatures. To test the predictive power of significantly enriched signatures, random forest models were trained on our datasets and cross-validated on an independent dataset  (24). Missing datasets were imputed 100 times to produce 100 complete datasets using the above imputation process. A random forest model was trained on each of the 100 complete datasets with 100 trees each (intermediate models) and combined within a single model (final model). This model was tested on a test dataset. For cross-validation, random forest model was trained on the test dataset and tested on the training dataset. Prediction accuracy was set as the good prediction rate based on the confusion matrix (S1 Fig).
The control uninfected group was composed by 24 mice half of which received PBS or 1. 10 6 non-parasitized RBCs. Spleen, blood and brain were harvested as described in Material and Methods, leucocytes were isolated and following RNA extraction and cDNA synthesis, we performed the combined CDR3 spectratyping technique and ISEApeaks strategy in order to evaluate the perturbation of the repertoire. Perturbation score was calculated against the average repertoire of the spleen control uninfected group as the polyclonal reference repertoire for all the groups (see Material and Methods and  [26, 31] ). Perturbation score distributions were log-transformed and multiple imputation was applied to correct for bias due to missing data. Descriptive analyses were performed on the average of 1000 complete datasets.
Progressive but Massive Modifications of Spleen and Blood Repertoires during PbA Infection We further analysed the perturbation for each TRBV-TRBC combinations accounting for a total of 2079 variables. As previously, we used a multiple imputation process (see Material and Methods). 1000 complete datasets were generated and linear regressions were fitted for each TRBV-TRBC combination to compare perturbation score in each day p-i to uninfected group. Regression coefficients estimated over complete datasets were pooled to produce a unique coefficient and its confidence interval. Using such information, t-tests were performed to compare each infected group to the CTR in the corresponding organ. Results are shown in the Table  1. Except for TRBV14, all other TRBVs are deeply altered. In the spleen, significant alterations were observed at day 5 p-i for 8 out of 20 TRBVs to which 7 other TRBVs were added at day 6 p-i.