dataesr/scientific-paragraphs-categorization huggingface dataset:
Contours We describe the delineation of primary tumors for all cases in the dataset, used as ground truth for the segmentation and VOIs for one of the outcome prediction tasks. As described below, some cases had original delineations from radiotherapy which were re-contoured to obtain target contours that are as close as possible to the true tumor boundaries. Since no guidelines were available for contouring true HNC tumoral volumes on PET/unenhanced CT, we defined them to reduce inter-observer variability by reaching a consensus on the best approach to adopt. Definition 3. 1 (GTVp Primary Tumor Delineation Guidelines). Oropharyngeal lesions are contoured on PET/CT using information from PET and unenhanced CT acquisitions. The contouring includes the entire edges of the morphologic anomaly as depicted on unenhanced CT (mainly visualized as a mass effect) and the corresponding hypermetabolic volume, using PET acquisition, unenhanced CT and PET/CT fusion visualizations based on automatic co-registration.
The 71 cases were divided between the three experts and each delineation was then cross-checked by all three of them. This re-delineation was performed in a centralized fashion with the MIM software, and the verification of the contours was made possible by the MIM Cloud platform. 5  Overall, although different strategies were used to create the initial contours, these were controlled and corrected to strictly follow the unified guidelines introduced in Definition 3. 1.
Patient outcome Clinical information regarding patients was collected in each clinical center along with the PET/CT images. Such clinical variables included age, gender, T, N and M stage and clinical staging (7th or V. Andrearczyk et al. 8th edition depending on the center), as well as tobacco and alcohol consumption, performance status, HPV status and therapy modalities (i. e. , radiotherapy only or chemoradiotherapy) when available. Information regarding the chosen endpoint to predict (see the section below), i. e. , censoring and time-to-event between PET/CT scan and event (in days) was provided as well (for the training data only).
Challenge tasks Task 1 -primary tumor segmentation. The first task is similar to the first edition with a larger dataset, namely the automatic segmentation of GTVp from the PET/CT images. To perform well in this task, algorithms should accurately segment test primary tumors. Task 2-3 -outcome prediction. The new task of outcome prediction was added for this second edition of the challenge. It relied on the same training and testing dataset as the segmentation (Task 1). This design allowed challengers to exploit their segmentation results from Task 1, although this was not mandatory. Outcome prediction was divided into two different tasks and associated submissions and rankings, depending on whether the reference expert contours (the ground truth of Task 1) were exploited by the method or not.
The C-index on CHUV was much lower (0. 648) compared to the value of 0. 727 obtained on the CHUP test. This better prediction for CHUP patients can be explained by the fact that some CHUP patients were included in the training set, whereas all CHUV patients were held out for the test set. This was observed even though the survival profiles of CHUP patients were significantly different (a higher rate of events and shorter PFS overall) compared to all five other centers (thus, also compared to the CHUV set in the testing data). The lower generalization of performance on the CHUV dataset is therefore more likely explained by differences in PET/CT image properties than on survival profiles.
Discussions Task 1 -segmentation. Task 1 on GTVp segmentation was conducted for the second time after HECKTOR 2020. This year, data from a new center (CHUP) was added. Some of the trends observed in the first edition were confirmed with this larger dataset and increased participation. The winner method  (Xie and Peng, 2022)  was similar to the previous year's winning approach  (Iantsen et al. , 2021). Following the general trend in medical image segmentation, DL methods based on U-Net models were mostly used in the challenge, as reported in Table  B. 7. Model ensembling, as well as data preprocessing and augmentation, seem to have played an important role in achieving top-ranking results. Note that, as reported in Table  3 , ensembles of participants' results outperformed the top-1 performance.
Therefore it is particularly relevant to evaluate the prognostic power of image-based AI models in order to identify outcome beyond TNM, knowing that several image-and basic clinical-based prognostic biomarkers were reported in the literature  (Castelli et al. , 2019; dit Deprez et al. , 2022; Morand et al. , 2018). It is worth noting that a negative finding for SUVmax was also reported  (Patel et al. , 2021). Tasks 2 and 3 attracted a promising number of submissions, with varied pipeline and algorithm developments. One of the most interesting findings was the comparison between the three main categories of approaches, namely a first relying on clinical factors only, and two relying on the available PET/CT images, with the use of either DL or more classical radiomics modeling approaches. Despite the relatively limited training dataset size (224 patients) compared to other computer vision tasks where DL has established itself as the state of the art thanks to the availability of thousands or even millions of images for training, the challengers relying on DL methods tended to slightly outperform the ones relying on radiomics approaches using handcrafted features selected and combined through classical ML techniques.
Indeed, DL approaches took four of the five first places in the ranking. Another important finding is that, despite their best efforts, the challengers failed to improve the predictive performance by a large margin thanks to the use of the PET/CT images (C-indices ranging from 0. 65 to 0. 72), compared to a basic model relying on clinical factors alone (C-index of ≈0. 64-66). Half of the challengers even achieved lower performance, hinting at a lack of generalizability of their trained model when applied to the test set. It remains to be investigated whether further improving the C-index is possible with more appropriate or novel methodological developments, and/or if this can be achieved only by making a larger training dataset available, which will be the case in the 2022 HECKTOR edition (489 patients for training instead of 224).
A third important observation is that the dataset we used in HECK-TOR 2021 offered an important challenge, related to its multicentric nature. Indeed, PET/CT images came from six different centers (five in the training set, two in the test set, one of which was also present in the training set) with five different scanner models from the three main vendors (Philips, GE, Siemens), associated with various acquisition settings and reconstruction parameters. However, none of the teams implemented explicit harmonization techniques (of the images or the features) beyond classical algorithms such as image interpolation, rescaling and normalization. Further harmonization (of images and/or features) might have helped get more generalizable models, especially in the case of standard radiomics modeling relying on handcrafted features, for which the impact of imaging characteristics is now well documented.
With a C-index > 0. 7 the algorithms showed potential capabilities to model the progression of tumors in internal and external data. Surprisingly, the best results were also obtained without the use of ground truth tumor delineations, opening the door to large-scale studies without the need for costly manual annotations. As future work, the third edition of HECKTOR at MICCAI 2022 will feature a larger dataset with additional centers. In order to reach fully-automatic pipelines, bounding-boxes locating the oropharyngeal regions will no longer be provided. Besides, the segmentation task will combine primary tumor and metastatic lymph nodes segmentation due to their prognostic value for the final outcome prediction.
8-s rotation time, slice thickness 3. 75 mm) was performed from the base of the skull to the mid-thigh. PET scanning was performed immediately after acquisition of the CT. Images were acquired from the base of the skull to the mid-thigh (3 min/bed position). PET images were reconstructed by using an orderedsubset expectation maximization iterative reconstruction (OSEM) (two iterations, 28 subsets) and an iterative fully 3D (DiscoveryST). CT data were used for attenuation calculation. CHUP: PET/CT acquisition began after 6 h of fasting and 60 ±5 min after injection of 3 MBq/kg of 18F-FDG (421 ±98 MBq, range 220-695 MBq). Non-contrast-enhanced, non-respiratory gated (free breathing) CT images were acquired for attenuation correction (120 kVp, Care Dose ® current modulation system) with an in-plane resolution of 0.
Acknowledgments We thank all participants for their contributions in the HECK-TOR 2021 challenge. We thank  Siemens Healthineers 11 Switzerland , Aquilab 12 and Bioemtech 13 for kindly sponsoring Tasks 1, 2 and 3, respectively, with a prize of 500€each. We thank MIM software 14 for kindly providing licenses to coordinate VOI contouring in PET/CT images. This work was also partially supported by the  Swiss National Science Foundation (SNSF , grant  205320_179069 ), the  Swiss Personalized Health Network (SPHN , via the  IMAGINE and QA4IQI projects)  and the  Hasler Foundation, Switzerland.
Evaluation data Blowing snow flux data is available for 3 stations in the Grandes Rousses test zone. One of these is the Col du Lac Blanc observatory where long-term monitoring of blowing snow fluxes and of various atmospheric forcings have been performed  (Guyomarc'h et al. , 2019). In particular, a vertical profile of Snow Particle Counters (SPC)  (Sato et al. , 1993)  recording blowing snow fluxes at four different heights, is located at a particularly wind-exposed location. Because of frequent snowfall and ablation events, the height of these sensors varies significantly with time. Consequently, a vertically integrated flux between 0. 2 and 1. 2 m is provided from these four measurements from 01 December to 01 April since 2010  (Guyomarc'h et al. , 2019). The shapes of vertical profile concentration are classified into different categories: 'no flux' when no flux was recorded, 'inconsistent' when flux at the different height was physically inconsistent, 'power-law' when the concentration profile fits with a power-law or 'mean' when the flux depends weakly on height.
2. 3. 5 ). We use these data to evaluate blowing snow occurrence and fluxes. The other sites are the Huez (FHUE) and Chambon (FCMB) stations from the ISAW network, which data were already used in blowing snow studies  (Vionnet et al. , 2018; He and Ohara, 2017). They are equipped with snow height, temperature, wind measurements and Flowcapt sensors  (Chritin et al. (1999) ), which record integrated blowing snow from 0 to 2m above the ground surface. Trouvilliez et al. (2015)  indicate that Flowcapt sensors of different generations give similar results with respect to SPC if a threshold value higher than 1 g m -2 s -1 is taken. However, they can be partially buried under snow depending on snow height and their reliability in terms of estimated flux is still debated in the literature  (Cierco et al.
HSS varies between -1 and +1, +1 for a perfect agreement and 0 for a random forecast. These scores were first applied to the 4 model configurations previously described and to new runs of the Sytron system to cover the same evaluation period and share the same code version of SURFEX-Crocus. For Sytron, as in  Vionnet et al. (2018)  (although not mentioned in the original publication), the occurrence of blowing snow is considered detected if a non-zero flux is simulated on at least one of the 8 slope aspects. The occurrence scores are then applied to the 2D simulation outputs for the 2018-2019 season, driven by simulated wind fields as described in Sect. 3. 1. The evaluation is carried out at Col du Lac Blanc station and at both ISAW stations. Each station was associated with the closest grid point from its location.
Evaluation of blowing snow fluxes Integrated blowing snow fluxes between z min = 0. 2m and z max = 1. 2m, called Q t,int were evaluated against SPC data. Modelled flux is computed at each time step as : Q t,int = zmax zmin q susp (z)dz = c r z r u * k(1 -γ) [( z max z r ) -γ+1 (log( z max z 0 ) - 1 1 -γ ) -( z min z r ) -γ+1 (log( z min z 0 ) - 1 1 -γ )] (34) Note that the blowing snow flux below 20 cm in height can not be accounted for in this evaluation although it may represent a significant contribution to the total flux, especially for low or moderate wind speeds. Observed data are available with a 10-minute time step. For evaluation purposes, model output and SPC fluxes were first averaged hourly. As the observed fluxes cannot be used when precipitating particles prevail, periods when at least one 'mean' concentration profile was observed were removed.
Considering the other missing data, 4947 hours of data are considered in the evaluation. To assess the ability of SnowPappus to capture the long-term magnitude of wind-induced snow transport, monthly averages of simulated fluxes were compared to the observed ones, keeping only the months when at least 15 days of valid observed data are available. Hourly data were also classified distinguishing two cases : (i) days with snowfall according to SAFRAN reanalysis and (ii) days with no snowfall. Observation-derived wind friction velocity was also classified in 20 equally distant wind speed intervals (interval widths are about 0. 064 ms -1 ). Averaged observed and modelled flux by category and wind speed step were computed and compared. Less than 20 hours of data were available by wind steps for Wind speeds of more than 0.
13. At worst, SnowPappus is using 60% of the total Snow3L time for 60 threads. For this configuration, most of the SnowPappus computing time is spent in MPI communications (Fig. 13 ) with other parts of the SnowPappus routine executing in negligible time. Indeed, classic SURFEX computing routines benefit greatly from parallelization with simulations points being computed independently of one another. In SnowPappus however, we create a dependency on the neighbourhood simulation point. Logically, increasing the number of subdomains increases the number of blocking MPI communication. This can lead to an increase in MPI communication and waiting time, which become the limiting factor at high thread numbers. MPI communication time depends on the amount of data to communicate between threads, itself depending on domain shape, whereas waiting time depends on the equity of work sharing among computing threads.
, 2018). As explained in Sect. 2. 2. 3, the SnowPappus blowing snow occurrence detection algorithm was also adapted from VI13 with a modification concerning treatment of blowing snow events with concurrent snowfall. We evaluated blowing snow occurrence in SnowPappus, Sytron and VI13 on the 2010-2020 period, reproducing the methods described in  Vionnet et al. (2018)  in order to get comparable results. All three methods are coded within the current version of SURFEX (v9). We interestingly obtained similar results as  Vionnet et al. (2013). Moreover, we also evaluated Sytron and VI13 within the current version of SURFEX (v9) with the same dataset, and obtained similar results. All three methods give reasonable but perfectible detection scores with a large false alarm rate, even if  (Vionnet et al.
We could retrieve the original simulation outputs of  Vionnet et al. (2018)  and applied our evaluation process to these data (see code availability). We obtained results very close to our own Sytron run with the original data. Consequently, hypothesis (iii) seems the most likely. Beyond this reproducibility issue, we can state that our model performs similarly to previously tested models in terms of blowing snow occurrence. In terms of fluxes, evaluation of the monthly averaged blowing snow fluxes integrated between 0. 2 and 1. 2 m was performed with different parameterizations of the terminal fall speed v f. It shows SnowPappus is able to simulate satisfactory average fluxes at Col du Lac Blanc if v f is adequately calibrated. However, simulated fluxes suffer from a high standard deviation of the error, leading to a low correlation between observed and simulated data.
It emphasizes that a well-calibrated constant threshold wind speed performs as well or even slightly better than those parameterizations. However, the above statements must be taken with caution as our study suffers important limitations. Indeed, only blowing snow fluxes integrated between 0. 2 to 1. 2 m were evaluated, which tells nothing about what happens in the saltation layer and in the saltation-suspension interface. Moreover, evaluations and calibrations on flux and occurrence were applied to only one site, with particular climate and environmental conditions. The calibrations of d m and of the constant threshold wind speed may be over-calibrated to this site and thus not directly valid or optimal at other sites. The microstructure-dependent parameterizations of the threshold wind speed from Guyomarc'h and Mérindol (1998) might transfer better to other topographic and climatic conditions but the lack of extended data to evaluate directly this process does not allow it to be affirmative.
Acknowledgements. This work was conducted using mainly  Meteo France  office and computing ressource. Main fundings are  SENSASS  project funded by the  Region Auvergne Rhône-Alpes (France)  and  CDSN PhD  grant. We thank  Hervé Bellot  for providing Blowing snow flux data at col du  Lac Blanc , as well as  Yannick Deliot ,  Hugo Merzisen  and  Isabelle Gouttevin  who provided advice and technical information about their use and about Col du Lac Blanc experimental site in general. We also thank  Rafife Nheili  for her help and participation in coding the MPI parallelization routine. Generally, we thank the whole  Centre d'Etude de la Neige modelling team , for multiple fruitful discussions and diverse technical support. CNRM /CEN and LECA are part of LabEx OSUG@2020.
We test the hypothesis that starburst galaxies are the sources of ultra-high energy cosmic rays and high-energy neutrinos. The computation of interactions of ultra-high energy cosmic rays in the starburst environment as well as in the propagation to the Earth is made using a modified version of the Monte Carlo code SimProp, where hadronic processes are implemented for the first time. Taking into account a star-formationrate distribution of sources, the fluxes of ultra-high energy cosmic rays and high-energy neutrinos are computed and compared with observations, and the explored parameter space for the source characteristics is discussed. We find that, depending on the density of the gas in the source environment, spallation reactions could hide the outcome in neutrinos from photo-hadronic interactions in the source environment and in extra-galactic space.
In this work, we assume that SBNi are capable of powering UHECRs and, for the first time, we use a sourcepropagation model to derive the UHECR and high-energy neutrino fluxes from these sources. We develop an extension of the Monte Carlo code SimProp [see 18] to simulate the behavior of UHECRs and study the multimessenger implications in terms of associated High Energy (HE) neutrino flux, focusing on the role of the hadronic and photohadronic interactions in these environments.
Comparison to experimental data The purpose of this study is to investigate the claim that SBGs are UHECR's sources. This is done by comparing experimental data to the CR flux as it has been affected by interactions in the SBNi and extra-galactic propagation. We adopt a measurement of the energy spectrum in log 10 (E/eV) bins of 0. 1 width from 17. 8 eV to 20. 2 eV, obtained with the data collected over 15 years with the surface detector array of the Pierre Auger Observatory  [28]. As for the depth of the shower maxima X max distributions  [29] , we consider log 10 (E/eV) bins of 0. 1 from 17. 8 eV to 19. 6 eV, plus one additional larger bin containing events with energies above 10 19. 6 eV; each X max distribution is binned in intervals of 20 g cm -2. Following  [26] , we use the deviance D = -2ln(L/L sat ) as estimator of the agreement of our parametric model to data, where L is our model and L sat is a model that perfectly describes the data.
Additionally, we compute the neutrino flux related to the transport of UHECRs and demonstrate how this enhances the constraining power of our model. This could enable us to consider whether a set of source parameters can adequately describe the UHECR data with-out exceeding the measured neutrino fluxes. In particular, we show that the UHECRs accelerators would contribute significantly to the neutrino flux measured by IceCube at energy 10 2 TeV if SBGs were hosting them. We observe that the expected neutrino flux from sources dominates the cosmogenic one. Additionally, we demonstrate that hadronic interactions might be essential for explaining the observed neutrino flux and deserve further research in source environments , since they could be able to hide the outcome in neutrinos from photo-hadronic interactions in the source environment and in the extra-galactic space.
Therefore, in the near future, a complementary search for the origins of UHECRs could be guided by the identification of steady PeV neutrinos emitters. We find that the data may be precisely described within the investigated parameter space if the UHECR nuclei are injected with a hard spectrum. Here, we emphasize that even if the conventional injection ∼ E -2 , typical of the diffusive shock acceleration, is not preferred, it is not necessarily impossible for such a mechanism to accelerate UHECRs. In fact, it is possible to obtain hard spectra in the context of diffusive shock acceleration under a number of core SBG conditions, such as multiple shocks, converging flows, particle re-acceleration, or transport conditions in the acceleration region that are different from those in the rest of the SBG.
The results that are being given here were obtained under the assumption that just one heavy nuclear species had been injected. The analysis of scenarios in which many masses are injected, each with a distinct relative composition that might also soften the spectral index discovered at the injection, goes beyond the scope of this work and is left for further research. Nonetheless, when the injected masses are heavier than silicon nuclei, the expected scenario fails. We specifically tested the iron nuclei at injection scenario and found that the data description significantly worsens. This is due to two factors: 1) The expected composition at Earth is too heavy compared to the observational data from the Pierre Auger Observatory; 2) The maximum rigidity of the iron nuclei is determined by comparing the expected and measured UHECR spectra; and 3) The maximum energy of the nucleons from the disintegration (1/A of the maximum energy of nuclei) is too low to adequately describe the data at the energy of the ankle.
The ability of UHECR data to constrain increases as the measured neutrino flow is ex- The assumption that all similar sources are distributed throughout the universe has, however, simply been taken into consideration up to a redshift of z = 6 in this work. Instead of the UHECR fluxes, which are predicted to originate close to z = 1, this hypothesis has a greater impact on the predicted neutrino fluxes. Additionally, it is anticipated that using luminosity functions as opposed to a single prototype will reduce and broaden neutrino fluxes. Future developments of this work will involve the production and propagation of photons within SBGs, as additional multi-wavelength restrictions may be discovered, particularly in hard X-rays, where electrons and positron pairs are predicted to emit through synchrotron on the strong magnetic fields typical of SBGs.