dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset To evaluate the proposed task, we constructed a dataset named DS-Few 1 , based on two widely-used DS datasets. The first one was originally built by aligning New York Times corpus with Wikidata 2. Following the construction method of FewRel, we grouped the instances according to their semantic relations 3 , and obtained the basic version of DS-Few, consisting of 87 relations with 192, 142 instances (61, 361 entity pairs) in total 4. We used 60, 10, and 17 relations for training, validation, and testing, respectively. For further evaluation, we built an alternative version of DS-Few by employing as test set another DS dataset-NYT10  (Riedel et al. , 2010). Akin to the aforementioned procedure, it was first grouped, and then, we filtered out the sentences that literally appeared in the training set, but retaining the clusters even if the corresponding relations appear in the training set.
Eventually, we got a test of 20 relations (10 are seen in the training set) 1 A download link to the data will be provided in the final version. 2 https://github. com/thunlp/PathNRE 3 In this study, clusters with <15 instance bags were discarded, because in the 10shot settings, at least 15 unique instance bags are necessary-10 as support instance bags and 5 as queries. 4 For ease of comparison, FewRel is of 100 relations and 70,000 instances. with 142, 424 instances in total. In summary, the two versions represent scenarios that are both possible in real life, i. e. , a relation may be seen or not during training.
We tested all models five times, and the average results are reported. For fair comparison, we evaluated all models at instance bag level  (Jiang et al. , 2018) , i. e. , to predict a relation for an instance bag concerning the same entity pair. For the competing approaches that do not work at instance bag level, e. g. , Proto  (Gao et al. , 2019) , we trained them at instance level, and chose the instance with the highest confidence score in the instance bag as query in testing. (3) on two datasets, the corresponding accuracy of different models is quite similar, demonstrating that FSRC models perform similarly on recognizing old relations and fresh relations.
Results on FewRel Dataset The two versions of test sets of DS-Few are constructed automatically by distant supervision. To show the performance of few-shot relation classification models on the human labeled data, we also tested our proposed AMProto and all competing methods on FewRel dataset. Specifically, we used the train set of DS-Few to train these models, and the best epochs on the validation set are picked for testing. In our experiments, we tested all few-shot relation classification models on the public train set of FewRel which contains 64 relations. We tested all models on the train set of FewRel due to two reasons: (1) the test set of FewRel is not publicly available; (2) the train set of FewRel contains more relations than the test set (containing 20 relations), which is more challenging for few-shot relation classification models.
Due to the presence of large unresolved anodic waves, raw voltammograms could not be treated based on specific peak, and therefore, signal intensity data were obtained as areas under the curve (AUC) in the range 0-1200 mV, accounting for the total current passed during the analysis. Strong and highly significant correlations were found for both total phenolics and total tannins when total AUC of linear sweep votammograms was considered (Figure  2 ), with regression equations of y = 0. 0002x -183. 58 for total phenolics and y = 0. 0001x -155. 52.
Due to the presence of large unresolved anodic waves, raw voltammograms could not be treated based on specific peak, and therefore, signal intensity data were obtained as areas under the curve (AUC) in the range 0-1200 mV, accounting for the total current passed during the analysis. Strong and highly significant correlations were found for both total phenolics and total tannins when total AUC of linear sweep votammograms was considered (Figure  2 ), with regression equations of y = 0. 0002x -183. 58 for total phenolics and y = 0. 0001x -155. 52 for total tannins respectively. Previous studies have indicated that AUC of either total raw voltammograms or subsections thereof are well correlated with wine values for total phenolics  [20]  as well as with total phenolics or total tannins values of commercial tannin powders  [11, 21].
To this point, it is worth mentioning that, although in this study the concentrations of tannins measured were relatively low compared to, for example, red wines, our data indicate that the method proposed shows a linear response for addition of phenolic (pure catechin) up to 2 g/L, so it can be adapted to different scenarios including the analysis of red wine samples treated with oak alternatives. Voltammetric analyses of the different OAs extracts reflected the complex phenolic composition of OAs. However, although some differences could be observed between linear sweep voltammograms of chips compared to staves, the spectra were rather convoluted and extraction of specific characteristic features was difficult. Raw votammograms were indeed characterized by a highly convoluted signal consisting of two largely unresolved anodic waves.
, 2015; Pankratova et al. , 2019). In the present work, we analyse data from the SWERUS-C3 campaign aboard a ship in the Arctic Ocean during summer 2014  (Thornton et al. , 2016a). Such short-term mobile campaigns are necessary for complementing the limited number of longterm fixed, mostly coastal stations currently available. In particular, oceanic campaigns are expected to provide information not only on oceanic sources but also on land sources located upwind. However, CH 4 from various sources is being mixed during the atmospheric transport of the air masses, which makes it difficult to separate them without resorting to numerical modelling  (Berchet et al. , 2016). Atmospheric inversions merge together observations, numerical modelling and emission datasets to attribute the observed variability in CH 4 concentrations to emitting regions and thus optimize the CH 4 budget.
(Model 0010, FGGA-24EP, Mountain View, California, USA). Air inlets were located at 9, 15, 20 and 35 m above the sea surface; air was pulled through all inlets continuously and analysed from one inlet at a time for 2 min before switching to the next inlet. Data were filtered using wind speed and direction to avoid contamination from the ship exhaust. As no local sources influenced our measurements, concentrations are similar at all levels. We concatenate measurements from all inlets indifferently for our study. The spectrometer was calibrated every 2 h using two synthetic air target gases; the target gases themselves were calibrated before, during and after the cruise to two NOAA Earth System Research Laboratory-certified standards for CH 4. The reported precision was 0. 5 ppb. Further details on the campaign conditions and instrument configuration are available in  Thornton et al.
The chemical sink of CH 4 by OH radicals is explicitly computed in CHIMERE using pre-computed fixed OH fields from the chemical model LMDz-INCA (Interaction with Chemistry and Aerosols;  Hauglustaine et al. , 2004; Folberth et al. , 2006). CHIMERE runs use the following input data streams: (i) meteorological fields downloaded from the European Centre for Medium Range Weather Forecasts (https://www. ecmwf. int) at 0. 5 • resolution every 3 h, (ii) anthropogenic emissions aggregated at the CHIMERE resolution from the EDGARv4. 3. 2 database at 0. 1 • horizontal resolution  (Crippa et al. , 2016) , (iii) wetland emissions interpolated from the model ORCHIDEE at 0. 5 • horizontal resolution  (Ringeval et al. , 2010) , (iv) boundary CH 4 concentration fields extracted from the general circulation model LMDz (these global simulations include both the chemical sinks of OH and chlorine as well as their impact on the isotopic ratios, and Cl and OH fields are prescribed offline from the chemical model LMDz-INCA) and (v) and isotopic signatures of the different sources chosen from  Sherwood et al.
) from the East Siberian Arctic Shelf (ESAS) as well as from air masses coming from the sides and roof of our domain of simulations are optimized in the system. Apart from the ESAS, emissions are spatially differentiated into 23 geographical regions (see Fig. 1 ). Contributions from different regions and sectors are differentiated by computing so-called response functions by region, emission type and boundary side. That is to say, we carry out individual CHIMERE chemistry-transport simulations for every region, every type of emission and every side of the domain, with all the other emissions and boundary conditions being switched off, resulting in an ensemble of 98 response functions (23 regions × 4 sectors + ESAS + 4 sides + top). The simulated isotopic final composition y(t) at every given time step t when an observation is available is retrieved by scaling relative contributions according to assumed source signatures (or original average composition for boundary conditions) as follows: y(t) = r∈regions s∈sectors α r,s (t) × δ r,s (t), (2) with r and s varying over all available regions and sectors respectively; α t r,s (0 < α t r,s < 1) is the relative contribution of the sector s from region r at time t, and δ t r,s is the signature (in ‰) of the sector s from region r at time t.
Conclusions Observations of total atmospheric methane and isotopic ratio were carried out in summer 2014 in the Arctic Ocean during the SWERUS-C3 campaign aboard the Swedish icebreaker Oden. A unique continuous dataset of 45 d of atmospheric isotopic ratios over the Arctic Ocean is available from this campaign. Consistent with other campaigns in the region collecting flasks, the synoptic variability in atmospheric isotopic ratios in the Arctic is very strong, at ∼ 2 ‰, largely above observation error. Using forward simulations, we confirmed that the assumption of uniform isotopic signatures to represent emission sectors is invalid in the Arctic areas dominated by natural sources. We also exhibited the strong dependency of atmospheric isotopic ratios on largescale changes in air mass origin (lateral boundaries of our simulation domain, corresponding to mid-latitude and lowlatitude air masses; top boundaries corresponding to lowerstratosphere air masses).
Based on a simplified inversion framework, the SWERUS-C3 data were used to infer isotopic source signatures of the Arctic regions and emission sectors. Due to the limited number of available observations and the important distance between sources and observations, our system was not able to provide any significant constraints on anthropogenic emissions and could optimize signatures from the ESAS and wetlands near the Arctic Ocean shores only. Wetland and oceanic ESAS source signatures were found to span a very wide range with a multimodal distribution for wetlands. The inversion also indicated that CH 4 emissions from the ESAS are composed of a mixture of dominant thermogenic methane, complemented by some biogenic methane. Overall, only a strong spatial and temporal variability in emission signatures and in stratospheric isotopic ratios can explain the variability in observations.
Therefore, our study points towards necessary improvements in simulating the first-order transport and chemistry of methane and its isotopes to reproduce large-scale hemispheric features, especially stratosphere to troposphere exchanges. This makes it necessary to improve (i) the quality of continuous isotopic measurements to capture the synoptic signal with even higher confidence, (ii) numerical chemistry-transport models so that the uncertainties in the first-order processes are at least 1 order of magnitude smaller than the regional signal, which is not the case in our study, and (iii) the mapping of isotopic emission signatures used as priors in inversions as initiated by  Ganesan et al. (2018). Data availability. All observational data used in this work are publicly available on the Bolin Centre Database (https://bolin.
su. se/ data/?s=SWERUS-C3, last access: 30 March 2020) alongside complementary data during the SWERUS-C3 campaign. They are divided into three separate sub-datasets: (i) atmospheric measurements for total CH 4 during the first leg of the SWERUS-C3 campaign (https://bolin. su. se/data/thornton-2016; Thornton et al. , 2020a) , (ii) atmospheric measurements for total CH 4 during the second leg of the campaign (https://bolin. su. se/data/swerus-2014-ghg; Thornton et al. , 2020b) , and (iii) carbon isotopic ratio in atmospheric methane (https://bolin. su. se/data/swerus-2014-d13c; Thornton et al. , 2020c). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-3987-2020-supplement. Author contributions. AB, TT and IP designed the simulation experiments.
Acknowledgements. We thank the crew of  I/B Oden , who made the SWERUS-C3 expedition possible. SWERUS-C3 funding was provided by the  Knut and Alice Wallenberg Foundation ,  Vetenskapsrådet (Swedish Research Council) ,  Stockholm University, Swedish Polar Research Secretariat , and the  Bolin Centre for Climate Research. δ 13 C-CH 4 observations from SWERUS-C3 campaign will be posted to the  Bolin Centre database  at https://bolin. su. se/data/. This work has been supported by the  Swedish Research Council VR  through a  Franco-Swedish  project called  IZOMET-FS: "Distinguishing Arctic CH 4 sources  to the atmosphere using inverse analysis of high-frequency CH 4 , δ 13 C-CH 4 and  CH 3 D measurements " (grant no. VR 2014-6584 ). The study extensively relies on the meteorological data provided by the ECMWF.
Data collection The procedure of patient level data collection has been described in detail previously. [1]  In brief, we searched all studies reporting cases of Lemierre syndrome published between January 2000 and August 2017. We searched the following sources without restriction of language or study design: MEDLINE (via PubMed), Embase (via Ovid), the Cochrane Library and the grey literature, which comprises all relevant research results not reported in the traditional academic publishing and distribution channels. In addition, we searched the references of retrieved articles. We identified data for extraction based on the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual patient data (PRISMA-IPD) statement, which was adapted to the current setting. [17]  The study protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO (CRD42016052572).
Two models were tested: one adjusted for age, sex and anticoagulation status at the time of the complication, and one additionally adjusted for additional potential confounders. These variables were selected based on the following criteria: (1) clinical relevance, (2) absence of potential reverse (temporal and causal) association with the outcome and (3) number and distribution of the outcomes recorded. We conducted a sensitivity analysis by repeating the analysis of early complications only in the case series contributing at least five patients with complete in-hospital follow-up and an additional analysis focusing on typical Lemierre syndrome. Data management was done in SPSS, version 23. 0 (SPSS Inc. Chicago, Illinois). Data analysis was conducted with R version 3. 5. 1 (The R Foundation for Statistical Computing, 2018).
Study selection Data were extracted or obtained by the authors of 540 studies identified in the literature based on our predefined criteria for Lemierre syndrome. [1]  Of these, 480 were single case reports, 48 series of up to 5 cases, 16 series of 6 to 20 cases and 2 series of more than 20 cases (N = 23 and N = 33). [20, 21]  Additional details are reported in the Supporting Information. A total of 712 patients were included in the present study and used for analysis of baseline characteristics and initial clinical presentation. Of these patients, 60 were described in publications that did not provide follow-up information and only focused on the clinical picture before or at the time of diagnosis, such as educational cases in differential diagnosis or imaging. This left 652 patients with hospital follow-up data up to discharge or beyond; their data were used for analysis of treatment, in-hospital and early complications, and long-term complications.
Over the course of the period considered, the fatality rate appeared to decrease from 5. 9% in 2000-05 to 2. 3% in 2012-16. Whether this represents an actual improvement or only reflects differences in patient characteristics over time cannot be extrapolated from our data. We acknowledge limitations of our study. First, because our data were collected through a systematic review of the published and grey literature and are based on small cases series or case reports, they are subject to selection and publication bias. As a consequence, our analysis represents an accurate snapshot of current global observations and practice, but cannot serve to draw firm conclusions on the optimal diagnostic and therapeutic measures in the management of Lemierre syndrome. However, we made the best efforts to measure and limit the potential impact of some biases, especially publication bias, in the data collection procedure and the selection of the inhospital follow-up cohort, and we conducted a sensitivity analysis in the largest cases series that showed rates of early complications, including death, similar to those found in the primary analysis.
Acknowledgements The work of  Luca Valerio ,  Stavros V. Konstantinides  and  Stefano Barco  is supported by the  German Federal Ministry of Education and Research  ( BMBF 01EO1003  and  01EO1503 ). Luca Valerio  received the ' Marco Brockhaus scientific award for Thrombosis Research in Paediatrics ' from the Gesellschaft f€ ur Thrombose-und H€ amostaseforschung (GTH) to promote and financially support the research project 'Clinical presentation, treatment and course of Lemierre syndrome and bacterial-associated venous thromboembolism in children and adolescents'.
paper, we propose (1) an approach to calibrate the readout time of rolling shutter camera, (2) a two-step method to correct the image distortion introduced by this effect. The two-step method makes assumption that during exposure, the change of camera orientation is negligible with respect to the change of camera position, which is often the case when camera is fixed on a stabilized mount. Firstly, the camera velocity is estimated from the results of an initial bundle block adjustment; then, one camera pose per scan-line of the image sensor is recovered and image observations are corrected rectified. To evaluate the performance of the proposed method, four datasets of block and corridor configurations are acquired with the DJI Mavic 2 Pro and its original Hasselbald L1D-20c camera. The proposed method is implemented in MicMac, a free, opensource photogrammetric software; comparisons are carried out with other two.
This assumption is adopted for the following reasons: (1) the simulation results in  Zhou et al. (2019)  confirm this assumption; (2) the camera rotation is often diminished by for instance, a stabilized mount, as in our experiment; (3) we don't really have access to angular information in our experiment. The camera translational velocity is estimated with time information in image metadata and camera positions issued from an initial bundle block adjustment; the camera positions can also be extracted when GNSS data are available. Afterwards, the camera position at exposure of each line is recovered and the observations in image space are corrected rectified accordingly. This method is implemented in MicMac, a free, open-source photogrammetric software  (Rupnik et al. (2017) ). To evaluate the performance of the proposed method, four datasets of block and corridor configurations are processed without and with the rolling shutter correction.
A standard photogrammetric pipeline is performed to obtain an initial solution. It consists of identifying common feature points between overlapping images and recovering their poses firstly in a relative coordinate system. The geo-referencing and a second bundle block adjustment in absolute scale are carried out with the help of ground control points (GCPs). Step 2: calculate image sensor velocity. In our case with DJI Mavic 2 Pro, the time related information of the exposure is recorded in the metadata of each image. An initial solution of the camera position and orientation is issued after step 1. The velocity of one image is considered as the ratio between the displacement and the time interval of its two adjacent images. Note that the time related information in the metadata of one image is often rounded in second.
Therefore, there is no much interest in applying a more sophisticated approach for the estimation of image sensor velocity. We observed that the DJI Mavic 2 Pro gives erroneous GNSS measurements and the issue has not been fixed until after the acquisitions are conducted 3. Therefore, the GNSS data are not explored. In the case where correct GNSS measurements are available, the camera velocity can be estimated more rigorously with the help of time information in image metadata and camera positions extracted from GNSS data; the Step 1 can then be skipped. Step 3: recover camera position at exposure. For image i, its camera position C i = [C i,x , C i,y , C i,z ] is acknowledged with Step 1. In the case where the moving direction of the drone and the that of the rolling shutter are opposite (as in Figure  4 ), the acquired images are dilated compared to images without rolling shutter effect the ones obtained with global shutter.
Data processing The same data processing pipeline is performed on the original and the corrected dataset. For the corrected dataset, the tie points and the image measurements are modified as explained in Section 5 while the rest remains unchanged with respect to the original dataset. Firstly, a bundle block adjustment is performed with the tie points as input to recover the camera poses in a relative coordinate system. Afterwards, GCPs are used to geo-reference the 3D model and to perform a second bundle block adjustment in an absolute reference frame. It is worth noting that each time there is one set of points that participate in the bundle block adjustment as GCPs, the other set is used as CPs to evaluate the accuracy of the issued results. Then the same procedure is carried out with GCPs and CPs being swapped.
It allows to verify the consistency of the obtained results and to eliminate the occasionality of GCP distribution. To summarize, for one dataset, the original and the corrected observations are explored; two camera models are employed for processing; two combinations of GCPs/CPs are used. Therefore, each dataset is processed in 8 configurations. The notation of the configurations is given in Table  3 : and "10p" for 8-parameter and 10-parameter camera models; "GCP1" and "GCP2" for point set 1 and 2 as GCPs. Original Corrected 8-param GCP set 1 o-8p-GCP1 c-8p-GCP1 GCP set 2 o-8p-GCP2 c-8p-GCP2 10-param GCP set 1 o-10p-GCP1 c-10p-GCP1 GCP set 2 o-10p-GCP2 c-10p-GCP2 We choose four datasets to be processed: 1. nadir-30m, block configuration 2. nadir-90m, block configuration 3. nadir-30m + nadir-90m + oblique-60m, block configuration.