dataesr/scientific-paragraphs-categorization huggingface dataset:
The HW definitions are given in the dataset overview paper  (Langguth et al. , 2023) , and are part of the guidelines that were given to the human annotators of the data. Despite the definitions being well-written, annotators had to regularly discuss their understanding of the categories, suggesting the difficulty of the task at hand 4. The EG definitions are generated with GPT-3 from the training set, by providing GPT-3 with 25 examples of tweets mentioning a given conspiracy theory and 25 examples of tweets not related to the conspiracy theory. We use 5 different random seeds to randomly select the example tweets 5  , resulting in 45 definitions generated in total. We then ask the model to come up with a short textual description that could separate the sets of tweets. In this setting, we do not provide the textual label of the conspiracy theory, but we only give example tweets to the model.
, 2020). This is a smaller issue in our case, as we do not train such models from scratch; rather, we fine-tune them on relatively small datasets or use models for inference in zero-shot settings. ease, sterilization, or other forms of population control. The tweets in the first set suggest that some individuals believe that the COVID-19 pandemic and the development of vaccines are part of a larger depopulation agenda orchestrated by individuals such as Bill Gates. These beliefs are not present in the second set of tweets, which focus on other topics related to COVID-19 such as its impact on mental health, the effectiveness of vaccines, and political responses to the pandemic.
Acknowledgements This work has been partially supported by  CHIST-ERA  within the  CIMPLE  project ( CHIST-ERA-19-XAI-003 ) and by  ANR  within the  ECLADATTA  project ( ANR-22-CE23-0020 ). The work at the  Universitat Politècnica de València  is carried out in the framework of the  XAI-DisInfodemics research project on eXplainable AI for disinformation and conspiracy detection during infodemics  (Grant  PLEC2021-007681 ), funded by  MCIN/AEI/ 10. 13039/501100011033  and by  European Union NextGeneration EU/PRTR.
Selections were performed for the five tumors, as illustrated for patient 1 in Figure  3C , for 12 ROIs in the tumoral zone or peritumoral zone for primary and recurrent paired tumors. The ROIs analyzed for the 5 tumors can be find in Figure  S1. Quantification of the antigens was performed on the five patients in 12 different tumoral or peritumoral ROIs, using the QuPath v0. 3. 2 software. The distribution of key immune markers in the different ROIs from the center of the tumor to the periphery, corresponding to macroscopically normal tissue in primary and recurrent tumors from patient 1, are represented in Figure  3C. The distribution of key markers (immune or cancer) appeared to be extremely variable among the patients examined (Figure  3D ). In the primary tumors, patient 5 appears to be "colder" than the other patients.
Acknowledgments: We acknowledge the technical collaboration of  NanoString Technologies Inc. , 530  Fairview Avenue North, Seattle ,  WA 98109 ,  USA. IBISA for the DSP analyses and the  MicroPICell facility (Biogenouest) , as a member of the national infrastructure  France-Bioimaging , supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We thank the biological resource center for biobanking ( CHU Nantes, Hôtel Dieu, Tumorothèque, F-44093 Nantes, France ).
Laser Processing Workstation A linearly polarised, 450 fs, λ = 1030 nm, laser source delivering a maximum average power P = 310 W with maximum repetition rate RR = 13 MHz (Tangor, by Amplitude Laser, Pessac, France) was utilised to generate nanometric laser-induced periodic surface structures (LIPSS) on the surface of a 200 µm thick, 304 stainless-steel coil (see Figure  1 ). The coil width was 40 cm. Estimations of the surface roughness value S a were obtained by means of the software ConfoMap ST 8. 2 after surface profile acquisitions carried out with a ×50 optical profilometer (Smart Proof 5, by Zeiss, Göttingen, Grmany). The laser beam was magnified by a factor of 2, then deflected by a hybrid polygonal galvanometric scanning head (UHSS-II-15, by Raylase, Wessling, Germany) and focused through an F-theta lens with focal length f = 650 mm.
The generalized equation of the cost function is expressed as: max 𝝆 * 𝑉 (𝜓, 𝜃, 𝑧, 𝝆) s. t. cond(𝑱 𝑑ℎ ) < 𝑘 max (12) where  max is the maximum condition number as a constraint to remain in the dexterous workspace. The workspace volume is obtained from Algorithm 1 as follows: 𝑉 = 𝑛 ∑ 𝑖=0 𝑚 ∑ 𝑗=0 ( 𝛥𝜖 2 √ 𝜓 2 𝑖,𝑗 + 𝜃 2 𝑖,𝑗 𝛥𝑧 𝑖 ) (13) Finally, the pattern search algorithm of Matlab ® was used to isolate the optimal value from a set of data points within the given search domain to obtain the maximum dexterous volume. Pattern search is a nongradient optimization method that uses a search pattern around the existing points. This method was adopted because it ensures global convergence. H. Nigatu.
Image volumes were imported in a DICOM format onto an Imagys workstation (Keosys). Lymphoma lesions were identified visually as areas of increased uptake outside areas of physiological uptake. The CT fusion display was used to ensure that the metabolic volumes did not spill over into anatomical structures. The TMTV was obtained by summing the metabolic volumes of all individual nodal and extranodal lesions, using a semi-automated artificial-intelligencebased tool for the segmentation of lymphoma lesions using the PAIRE software v1. 0. 0. 39.
Statistical analysis Receiver-operating characteristic (ROC) and X-tile analysis  42  analysis were performed to determine optimal cut-off values for survival predictions. Briefly, a ROC analysis was performed to determine the optimal cut-off value for each continuous variable (ctDNA and TMTV) for stratifying the population according to binary progression-free survival (PFS) patient status (0 = no progression vs. 1 = progression) and for achieving the best compromise between sensitivity and specificity (Youden index = Se + Sp-1). The X-tile software  40  was then used for biomarker assessment and outcome-based cut-off optimization, and Kaplan-Meier analyses of survival were performed for each continuous variable (ctDNA and TMTV), taking into account the time of progression and time of censoring, in addition to the binary classification of PFS.
Basin-averaged landslide intensity To quantify the intensity of landsliding in drainage basins upstream of the gauging stations, we used landslide volumes in the inventory of  Marc et al. (2018). In this study,  Marc et al. (2018)  mapped the areas of Morakot-induced landslide scars A L in Formosat 2 aerial imagery at 8-m multispectral (2-m panchromatic) resolution, and estimated landslide volume V L with Equation 6  (Larsen et al. , 2010). V L = cA p L (6) In the landslide catalogue in  Marc et al. (2018) , the calculation of V L included corrections for amalgamated landslide polygons  (Marc et al. 2018) , in which it is assumed that each landslide has an elliptical shape and a mean width calculated with the formula proposed and validated by  Marc et al. (2018). These calculations also involved estimating scar area using a mean length to width ratio derived from a global database of 277 landslide scars with volumes ranging from 1000 m 3 to 1 km 3  (Domej et al.
Estimations of  sensible heat flux at sea from sonic anemometers are extremely noisy, resulting in large dispersion between datasets (Fig. 3 ). Measurements of the latent heat flux is done by gas analyzers, which are very sensitive to rain, high humidity rates at sea, sea spray and pollutants. All of this results in highly scattered values, even in the 5-20 m s -1 wind speed range (Fig. 4 ). However, surface heat transfer play a central role in TC intensification (e. g. Emanuel, 2018)  and correctly representing it for strong winds in NWP models is a key step towards a better forecast of TC intensity. Besides, heat transfer plays a central role on modulating the climate-scale dynamics (in particular in the intertropical band) and can also control local processes even at low winds (e. g. Redelsperger et al.
It is also based on the Charnock relationship with a dependency of the Charnock parameter on the wave age for wind speeds between 7 and 22 m s -1 , corresponding to the growth of wind sea,  (Janssen, 2004) , α = Aχ B (6) where χ = c p /U 10 is the wave age and A and B are polynomial functions of U 10 (see Appendix A1 for more details). For wind speeds less than 22 m s -1 , the WASP transfer coefficients closely follow those derived by  Edson et al. (2013) , using a very large and carefully screened dataset. We do not pretend here to improve much the state of the art of turbulent fluxes at sea that can be used for wind-wave coupling, but rather to design a tool that can be used with every atmospheric model coupled with SURFEX v8. 1, producing realistic wind stress and heat fluxes at every wind speed.
In addition, the drag coefficient varies as a function of the wave age for a given wind speed in the moderate-to strong-wind range where wave growth is the major process absorbing the wind energy. The next section presents the principle used for building the new parameterization, the observations used to check the mean values of the transfer coefficients for a given wind speed, and the dependency of the drag coefficient on wage age. These options are discussed with respect to the literature and information from various datasets. Section 3 presents the four case studies that were used to validate this parameterization. Some conclusions are given in Sect. 4.
The stability functions ψ are defined as in  Beljaars and Holtslag (1991)  with the modifications of  Fairall et al. (2003)  concerning the free convection conditions (see appendix A2 for their full definition). WASP is intended to be used either in a coupled mode through the SURFEX v8. 1 -OASIS3-MCT coupling interface  (Voldoire et al. , 2017)  or in a forced mode using outputs of a wave model. But using WASP without wave information is also possible. For the latter use, transfer coefficients are functions of the wind speed corresponding to the mean value taken in coupled mode with a well-developed wind sea ("mean values" hereafter), to ensure that the coupled-mode variability corresponds actually to the wave effect. Section 2. 1 presents the datasets used to derive these mean values (as explained in Sect.
2. 2. 2). Turbulent fluxes and transfer coefficients are usually derived from in situ measurements recorded using high-frequency sensors (sonic anemometers and gas analyzers) with either the eddy-covariance (EC) or the inertial-dissipative (ID) methods. While obtaining reliable estimates using the ID method is easier and more straightforward, it implies strong assumptions on the surface-layer structure, which restrict its use. In this study and for wind conditions up to 25 m s -1 , we use only carefully checked datasets from measurements at 5 m asl or above computed using the EC method. Thanks to the effort of the observing community, a large number of such datasets exist and many of them were already used by  Edson et al. (2013)  for deriving the wind stress The screening consists in evaluating the symmetry of the binned distributions and whether they correspond rather to normal or log-normal laws.
Depending on the results, outliers more than 4 standard deviations from the mean values were removed. Other historical datasets available in the literature (see Table  B2 ) have been used for the range of wind speed up to 30 m s -1. Direct EC measurements in strong winds are scarce and usually made airborne at height between 30 and 500 m asl  (Black et al. , 2007; Vickery et al. , 2009; Cook and Renfrew, 2015). For extreme winds between 30 and 60 m s -1 , only very few observations are available, especially for the heat transfer coefficients. Some of them are derived from profiles of dropsondes  (Powell and Ginis, 2006) , mostly computed indirectly from the effect of the wind stress on the oceanic surface layer, which is more easily sampled than the atmospheric boundary layer in extreme conditions  (Jarosz et al.
This function is fitted on the data with weighting based on the uncertainties published with the data (average values and standard deviation are computed with weights equal to the inverse of the variance of the individual datasets). The root mean square of the residuals on C d is 3. 35 ± 0. 32 × 10 -4. For the wind speed range above 45 m s -1 , we consider a constant drag coefficient in the continuity of the previous wind speed range, with a value 1. 56 × 10 -3. The weighted average of the published datasets for the drag coefficient in this wind range is 1. 66 ± 0. 24 × 10 -3 , compatible with the value chosen for this constant part of the drag coefficient.
Heat fluxes The principle retained here for building the heat flux transfer coefficient is very similar to the one of the COARE 3. 0  (Fairall et al. , 2003)  parameterization. It is clear from Eq. (  7 ) that the neutral transfer coefficients both for sensible and latent heat fluxes depend only on the roughness lengths z 0x and z 0. The values of the neutral transfer coefficients for turbulent heat C hN and C eN corresponds to those of the COARE 3. 0 parameterization. Then, Eq. 7 is inverted to obtain the value of z 0x , z 0 being obtained in WASP as explained in Sect. 2. 2. In the following, we use datasets of available observations to evaluate these parameters for wind speed in the range 0 to 60 m s -1. These observations are grouped in direct, EC measurements between 0 and 21 m s -1 for C hN , 0 and 19 m s -1 for C eN , and less direct measurements for higher wind speed, available as mean values with estimates of uncertainties for a given wind range or in binned form (see Fig  3  and 4 ).
Latent heat flux The direct EC observations have a weighted mean of 1. 159 ± 0. 034 × 10 -3 for C eN and the high wind or less direct observations (in the range 11-60 m s -1 ) a weighted mean of 1. 155 ± 0. 012 × 10 -3. All together, the whole dataset gives a weighted mean of 1. 156 ± 0. 011 × 10 -3 , even closer to the constant value of C eN in WASP than for C hN. The mean difference and standard deviation between WASP and the binned values of this dataset are 1. 3 × 10 -4 ± 3. 4 × 10 -5.
Direct comparison An offline test was performed to assess the differences between the current version of ECUME used in the Météo-France NWP and climate runs, and WASP. The SURFEX v8. 1 model was used to compute the friction velocity and turbulent heat fluxes with either the ECUME or WASP scheme on the same dataset corresponding of observed atmospheric parameters, SST and wave parameters H s and T p. This dataset consists in more than 53 000 hourly in situ measurements at the Lion buoy, located in the Gulf of Lion, between December 2001 and February 2014. They represent a large range of atmospheric conditions (Fig. 6 ) with wind up to 25 m s -1 , air temperature between 5 and 28 °C, relative humidity down to 40 % and wave age (C p /U 10 ) as low as 0. 4 due to strong wind and short fetch in mistral conditions.
https://doi. org/10. 5194/gmd-2023-58 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. Code and data availability. The new developed code will be included in the next official version of SURFEX v9. 0. The complete code of SURFEX v8. 1 including WASP and the WASP subroutines is available at https://doi. org/10. 5281/zenodo. 4557378 (Bouin, 2021); the data used to tune the transfer coefficients are available by contacting C. W. Fairall (NOAA Physical Science Division) or J. B. Edson (U. Conn. Marine Sciences). The offical release of SURFEX v8. 1 offline is available at https://www. umr-cnrm. fr/surfex/spip. php?rubrique141. The ARPEGE-Climat model is only available to registered users for research purposes. The access to the AROME code is ruled by the first Memorandum of Understanding of the ACCORD consortium (http://www.
accord-nwp. org). For non-commercial research purposes, AROME can be distributed upon signature of a licence agreement (see http://www. accord-nwp. org/?ACCORD-MoU-2021-2025 for conditions). The modifications of the ARPEGE and AROME codes including WASP and used for the case studies of this paper are stored at https://doi. org/10. 7910/DVN/WXZ1FO. The output parameters of the simulations used to validate WASP (Figures  8 to 19 ) and the data of the Lion buoy used to compare the wave effects in Figure  5  are available at https://doi. org/10. 5281/zenodo. 6783319. The SAR product was obtained from Ifremer/Cyclobs and produced with SAR wind processor co-developped by IFREMER and CLS and can be accessed here: https://cyclobs. ifremer. fr/app/archive/2022/SI/sh082022 (last access on 30/06/2022).
direct EC observations have a mean value of 1. 388 ± 0. 044 × 10 -3 for C hN and the high wind or less direct observations (in the range 11-60 m s -1 ) a weighted mean of 1. 081 ± 0. 020 × 10 -3. The mean values are computed as weighted means using the standard deviations of different groups of observations as weights. All together, the whole dataset gives a weighted 10 https://doi. org/10. 5194/gmd-2023-58 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. mean of 1. 143 ± 0. 021 × 10 -3 , very close to the constant value of C hN in WASP. The mean difference and standard deviation between WASP and the binned values of this dataset are 2. 1 × 10 -4 ± 3. 5 × 10 -5.
doi. org/10. 5194/gmd-2023-58 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License. Table B1. Eddy covariance datasets used for fitting the neutral transfer coefficients in wind speed range 0-20 m s -1. All these measurements except CBLAST low (RP FLIP) were made onboard ships and most of them were used for the COARE 3. 5 parameterization for wind stress Edson et al. doi. org/10. 5194/gmd-2023-58 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License.
A search is presented for the Higgs boson decay to a pair of electrons (e + e -) in proton-proton collisions at √ s = 13 TeV. The data set was collected with the CMS experiment at the LHC between 2016 and 2018, corresponding to an integrated luminosity of 138 fb -1. The analysis uses event categories targeting Higgs boson production via gluon fusion and vector boson fusion. The observed upper limit on the Higgs boson branching fraction to an electron pair is 3. 0 × 10 -4 (3. 0 × 10 -4 expected) at the 95% confidence level, which is the most stringent limit on this branching fraction to date.
Data and simulated events This analysis uses pp collision data corresponding to an integrated luminosity of 138 fb -1 , of which 36. 3, 41. 5, and 59. 8 fb -1 were collected during 2016, 2017, and 2018, respectively. A Monte Carlo (MC) simulated sample is generated for each of the ggH, VBF, VH, and ttH signal processes using Mad-Graph5_amc@nlo v2. 6. 5  [30]  at next-to-leading order (NLO) accuracy in perturbative quantum chromodynamics (QCD). For the ggH process, up to three additional partons are considered in the matrix element calculation; for VBF production, up to one additional parton is considered, excluding the quark-initiated jets produced in the leading order process. In addition, events from ggH production are weighted to match the predictions of the nnlops program  [31] , as a function of the Higgs boson transverse momentum and the number of jets in the event.
The right plot shows the distribution of the output score of the VBF BDT in a control region around the Z boson mass. The simulated samples include both DY and EW Z → e + e -events. The combined impact of the statistical and systematic uncertainties in simulation is shown by the red shaded band, where the systematic component includes uncertainties in the jet energy scale and resolution corrections, alongside the electron energy scale corrections. Uncertainties in the efficiency of electron identification, reconstruction, and trigger selection are also included, as well as the uncertainty on the integrated luminosity, presented in Section 7. Good agreement is observed between the DY simulation (filled histogram) and data (black markers), within the phase space in which the analysis categories are constructed.
5, and varying both in the same direction simultaneously. The impacts of the signal acceptance uncertainties are evaluated keeping the overall normalization of each signal process constant, and are at largest (for the ggH production mode) 10%. Agreement is compared between the DY simulation (filled histogram) and data (black points). The combined impact of the statistical and systematic uncertainties in simulation is shown by the red shaded band, where the sources contributing to the systematic component are identical to those included in Fig. 1. Residual differences between data and simulation are smaller than the theory uncertainties in the ggH cross section and event kinematics, which are included in the final maximum likelihood fit. • PDF uncertainties: these account for the uncertainty due to imperfect knowledge of the composition of the proton, that affects which partons are most likely to initiate high energy events.
Context. The recent Gaia third data release contains a homogeneous analysis of millions of high-quality Radial Velocity Spectrometer (RVS) stellar spectra by the GSP-Spec module. This led to the estimation of millions of individual chemical abundances and allows us to chemically map the Milky Way. The published GSP-Spec abundances include three heavy elements produced by neutron-captures in stellar interiors: Ce, Zr, and Nd. Aims. We study the Galactic content in cerium based on these Gaia/RVS data and discuss the chemical evolution of this element. Methods. We used a sample of about 30 000 local thermal equilibrium Ce abundances, selected after applying different combinations of GSP-Spec flags. Based on the Gaia DR3 astrometric data and radial velocities, we explore the cerium content in the Milky Way and, in particular, in its halo and disc components.
Introduction Our understanding of the Milky Way has made a great leap forwards through the different data releases of the Gaia mission. The third release (Gaia Collaboration 2023b) consists of a major and unique step because it includes a large variety of new data products, including, in particular, an extensive characterisation of the Gaia sources. In this context, the module called general stellar parametrizer from spectroscopy (GSP-Spec hereafter; see Gaia Collaboration 2023a) has estimated atmospheric parameters (effective temperature T eff , surface gravity log(g), global metallicity [M/H], and abundances of α-elements with respect to iron [α/Fe]) as well as individual chemical abundances of up to a dozen elements 1 for about 5. 6 million stars that have been observed by the Radial Velocity Spectrometer (RVS hereafter;  Cropper et al.
Gaia GSP-Spec local thermal equilibrium cerium abundances First, we briefly recall that the chemical analysis of the Gaia-RVS spectra (R ∼ 11 500) was performed by the GSP-Spec module  (Recio-Blanco et al. 2023 ) through the GAUGUIN algorithm  (Bijaoui 2012; Recio-Blanco et al. 2016 ) and a specific grid of synthetic spectra covering the whole stellar atmospheric parameters space and with varying Ce abundances. Briefly, to derive chemical abundances, GAUGUIN builds a reference grid spectrum (1D) from this large 5D cerium grid that is interpolated at the atmospheric parameters of the analysed star. These atmospheric parameters are provided by the MatisseGauguin method (see Sect. 6 of  Recio-Blanco et al. (2023)  for more details). The observed spectrum of the analysed star is normalised, and a second normalisation around the line is then performed to readjust the continuum locally  (Santos-Peral et al.