dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge the help provided by  Jelena Čuklina  at  ETH Zurich ,  Vincent Noël  at  Institut Curie ,  Annika Meert  at  BSC  and  Aurélien Naldi  at  INRIA Saclay. The authors acknowledge the technical expertise and assistance provided by the  Spanish Supercomputing Network (Red Española de Supercomputación) , as well as the computer resources used: the  LaPalma Supercomputer , located at the  Instituto de Astrofísica de Canarias  and MareNostrum4, located at the  Barcelona Supercomputing Center. This work has been partially supported by the  European Commission  under the  PrECISE  project ( H2020-PHC-668858 ), the  INFORE  project ( H2020-ICT-825070 ) and the  PerMedCoE  ( H2020-ICT-951773 ).
This accounts for a wider range of cognitive phenomena. For instance,  Portrat et al. (2016)  implemented a supplementary searching mechanism in long-term memory within TBRS* to model the maintenance and recall of chunks in WM. More recently,  Lemaire and Portrat (2018)  proposed a hybrid version of TBRS* that included an interference mechanism. This accounted for several interference effects, such as item-distractor similarity effects  (Oberauer, Farrell, et al. , 2012) , which the original TBRS* model is unable to simulate. Despite these new refinements, the TBRS* model does not account for the presence of semantic effects in WM. In this study, we integrated the core assumptions made by interactive activation models of language processing to model the semantic relatedness effect in TBRS* 1 , by considering that items are directly activated in long-term memory.
Due to this large number of pairs of words, the participants were provided with only 250 pairs to judge, and were nonetheless free to stop the survey at any moment. Final data collection indicated that each pair was judged 12,659 times on average. A Bayesian independent samples T-Test (see the statistical analysis section below) confirmed that the a priori defined related and unrelated pairs did differ in term of semantic relatedness judgment, this difference being associated with decisive evidence (M = 4. 463, SD =. 5, and M =. 427, SD =. 601, for related and unrelated pairs, respectively, BF10 = 9. 809e+387). The immediate serial recall task was composed of sixty trials in total, twenty for each experimental condition (T1, T2 and NT). To avoid stimulus list effects, we generated 36 different versions of the lists to be remembered.
e. the model including all main effects and all possible interactions). The BF value for each term is then assessed by directly comparing the full model against the same model, but by dropping the term under investigation. To minimize error of model estimation, the number of Monte Carlo simulations generated was set to Niterations = 100,000. For some critical contrasts of interest, we also report the 95% Bayesian Credible Intervals using the highest density intervals of the sampled posterior distribution of the model under investigation (Niterations = 100,000). All analyses were performed using the BayesFactor package  (Morey & Rouder, 2014)  implemented in R (R Development Core Team, 2008) using the default wide Cauchy prior distribution of r = √2 2. On each graph we report the 95% Confidence Intervals for each mean.
5) = 0. 95). However, to model some variability, it is not the value R which is used but rather the outcome of a random draw from a normal distribution centered at R, with a standard deviation of 1. This term is called. Implementation details of this procedure can be found in the Matlab code associated to  Oberauer and Lewandowsky (2011)   then re-encoded using the same principles as in Eq. 2. Refreshing occurs through rapid switching between memoranda. In the original TBRS* model, each refreshing attempt lasts 80 ms, which is close to the empirical estimation of 50 ms  (Vergauwe & Cowan, 2015). Retrieval & recall. Before being recalled and/or refreshed, an item first needs to be retrieved. Retrieval is performed by cueing the weight matrix using positional markers: (5)   = ∑       =1 +  ℎ ~(0, ) In this formula, the activation   of item  is the sum of all the weights that connect item  to the positional unit  that correspond to the current position.
5 s. After selection and recall of an item, response suppression is applied to the weight matrix using Hebbian anti-learning: (6) ∆  = -. When applying Hebbian anti-learning, the   vector retrieved from Eq. 5 is used. In the case of an omission, no response suppression is applied. Refreshing schedule. There are controversies as regards the refreshing schedule that participants may use during maintenance  (Vergauwe et al. , 2016). For instance, participants could refresh items cumulatively (1, 2 -1, 2, 3 -1, 2, 3, 4…) just like one rehearses verbal information  (Tan & Ward, 2008) , but they may use other schedules as well. In an extensive investigation of the TBRS* model,  Lemaire et al. (2018)  tested the ability of several refreshing schedules to fit 3 different datasets. Among the different schedules investigated, they found that a Least Activated First schedule provided the best fit and outperformed the cumulative refreshing one.
For illustrative purposes, we used median raw recall latencies when plotting the results (see Figure  9 ), as log-transformed values are likely to be poorly informative from a psychological perspective. Influence of recall latencies on recall performance. The analyses detailed above show that positions 1, 2 and 3 in the T1 condition were associated overall with shorter recall latencies, which also shorten the time elapsed when recalling the items that directly followed the semantic triplets. However, these analyses do not inform us whether faster recall predicted recall performance over the items that followed the semantic triplets. If so, are recall latencies alone responsible for the proactive benefit of the semantic triplet, or rather do they have an additive effect with the semantic influence during maintenance? To explore these questions, we performed a Mixed Effect Bayesian ANOVA using the brms R package  (Bürkner, 2017).
II. SYSTEM UNDER STUDY The studied system consists of two RESs, SRG-WECS and PV. These two RESs are connected to the system at a common coupling bus. This common coupling bus is connected to the grid through two transformers and two transmission lines. STATCOM is connected to the system at PCC to improve the system performance, as depicted in Fig. 3. The rating of the WECS is 24. 8 kW. The PV system is 100 kW and composed of 100 string; each has five series-connected modules with relevant data listed in the Appendix.
IV. RESULTS AND DISCUSSIONS The MATLAB/SIMULINK TM is used for simulating the hybrid model integrated into the grid and the STATCOM. The WOA and PSO are used for the near-optimal scheduling of the two PI controller parameters for driving the STAT-COM. Therefore, the overall system dynamic performance is enhanced during any abnormal operating conditions, including the PCC's undesired three-phase faults. STATCOM. The size of the STATCOM is 48 kVA. The convergence of the objective function, J introduced in (8) when using PSO and WOA is indicated in Fig. 10. The PI controllers parameters using PSO and WOA are given in Table  2 , while the three-phase fault to ground is applied to the system at PCC between 2 and 2. 25s. The PCC voltage with and without STATCOM is depicted in Fig. 11-a. Without STATCOM, the PCC voltage ranges out of the continuous operating zone (for both Spain and US codes) that will call the RES, including WECS and PV to be disconnected from the grid.
• Immersion in a sonic and visual environment; • Integration between real components and a simulated environment; • Management of driver information. The equipment includes Alstom Citadis Dualis desk, a six-degrees-of-freedom motion system, OKSimRail software, five screens providing a 225° view, three eye trackers, cameras, a 6. 1 audio system, and scripting, synthetized into a dynamic platform by means of measurements, such as systems capturing movements, physiological measurement sensors, etc.
• Immersion in a sonic and visual environment; • Integration between real components and a simulated environment; • Management of driver information. Machines 2021, 9, x FOR PEER REVIEW 5 of 16  The equipment includes Alstom Citadis Dualis desk, a six-degrees-of-freedom motion system, OKSimRail software, five screens providing a 225 • view, three eye trackers, cameras, a 6. 1 audio system, and scripting, synthetized into a dynamic platform by means of measurements, such as systems capturing movements, physiological measurement sensors, etc. SPICA RAIL, by University of Technology of Compiegne  [6] , is a supervision platform to recreate real accident scenarios in the laboratory in order to analyze human behaviors and decisions in such situations. It is able to analyze and simulate the entire network by integrating traffic control functions.
Integrated Simulators MISSRAIL ® and INNORAIL are tools developed entirely by the Université Polytechnique Hauts-de-France in Valenciennes  [15] [16] [17]. They include four main modules (Figure  6 ): (1) railway infrastructure design, (2) route design module, (3) driving cab simulation module, 4) control-command room simulation module. A version with an immersive helmet is also available. It is a Client/Server application which is capable of connecting different users at the same time. Several configurations are possible: wired, wireless, portable, fixed. Moreover, MISSRAIL ® is able to design various automated driving assistance tools (e. g. , eco-driving assistance, collision avoidance system, cruise control and vigilance control system) and accident scenarios combining pedestrians, trains and cars (Figure  7 )  [17].
A great deal of research has reported on the utility of using physiological, behavioral, auditory and vehicle data to detect the mental state of the driver, such as presence/sleep, drowsiness or cognitive workload, with considerable accuracy  [27] [28] [29]. Diverse parameters can provide information on targeted mental states: physiological measures include signals such as ElectroEncephaloGram (EEG) data, ElectroDermal Activity (EDA), and heart rate and heart rate variability. Behavioral measures include aspects such as headdirection, head-movement, gaze-direction, pose of the superior part of the body, gazedispersion, blinking, saccades, PERCLOS, pupil-size, eyelid movement, postural adjustment and nonself-centered gestures. Such data may be combined in multimodal approaches with information on vehicle activity and auditory information.
A great deal of research has reported on the utility of using physiological, behavioral, auditory and vehicle data to detect the mental state of the driver, such as presence/sleep, drowsiness or cognitive workload, with considerable accuracy  [27] [28] [29]. Diverse parameters can provide information on targeted mental states: physiological measures include signals such as ElectroEncephaloGram (EEG) data, ElectroDermal Activity (EDA), and heart rate and heart rate variability. Behavioral measures include aspects such as head-direction, head-movement, gaze-direction, pose of the superior part of the body, gaze-dispersion, blinking, saccades, PERCLOS, pupil-size, eyelid movement, postural adjustment and nonself-centered gestures. Such data may be combined in multimodal approaches with information on vehicle activity and auditory information.
In GOODS-South and COSMOS we modeled the source morphology in the available CANDELS HST F814W images corresponding to the Skelton et al. (  2014 ) photometry. For the two HUDF parallel fields we based the models on the HST F160W images to ensure full coverage of the 2 × 2 MUSE pointings using the corresponding CANDELS and 3D-HST imaging and to ensure access to morphological priors from the Skelton et al. (  2014 ) catalog for the modeling of the HST images. For extractions in the HUDF region we based the spa-2 https://github. com/kasperschmidt/TDOSE tial models on the source catalog by  Rafelski et al. (2015)  and the version 1. 5 HST F775W images from the Hubble Legacy Field (HLF 3  ;  Illingworth et al. 2016). No additional exposures were added to the F775W images for the recent HLF version 2.
Hence, the final sample of emission line sources with 1. 5 < z < 6. 4 from MUSE-Wide, the UDF mosaic and UDF10 analyzed in the remainder of this paper amounts to 2052 unique objects of which 1997 are LAEs. The thick green histogram in Figure  2  shows the redshift distribution of these objects compared to the independent MUSE-Deep UDF mosaic and UDF10 catalog presented by  Inami et al. (2017, thin   breadth of data and supporting discussions of individual objects in the remainder of the paper are shown in Appendix E.
Searching for UV emission lines in 1D spectra Knowing the (approximate) redshift for each of the 2052 unique objects, we searched the TDOSE spectra for signs of rest-frame UV emission lines through template matching using the publicly available Python software for Finding Emission Lines in Spectra (FELIS 4 described in Appendix A). In short, FELIS matches a set of predefined emission line (doublet) templates independently to each spectrum via cross-correlation and χ 2 minimization. By cross-correlating each template (T ) with the spectrum around the expected location of the UV emission line, the template flux scaling that minimizes the disagreement with the data is estimated (α in Equation A. 4). Calculating the S/N of the best match of the cross-correlations provides an estimate for (S/N) T ,max for each template and its flux scaling.
The matched emission line (doublet) template with the highest (S/N) max then provides the best match to the considered part of the observed spectrum overall. We refer to this best-match S/N as the "FE-LIS S/N" of the detected emission lines in the remainder of this 4 https://github. com/kasperschmidt/FELIS, Schmidt (2021)  work. Here we focus our search on the rest-frame UV emission lines red-wards of Lyα listed in Table  2. All emission line template matches were performed independently allowing for individual velocity offsets with respect to the LSDCat lead line of the spectra, which provides the selection redshift as described in Section 3. The lead line redshifts (and the targeted UV lines) are not guaranteed to be at systemic redshift, especially for objects where the lead line is the resonant C iv or Lyα line, which can be offset from systemic by hundreds of km s -1 , as indicated by the location of the vertical gray lines based on the LSDCat lead line redshift shown in Figure  3.
Hence, if the redshift evolution is insignificant (no indication of a redshift dependence is seen for the data in the redshift range probed by the MUSE samples) these correlations can be used to predict likely emission line fluxes for the fainter UV emission features based on C iii. This will be useful for targeting these fainter lines in the EoR where only the brightest UV lines (Lyα and in some cases C iii and C iv) have been recovered with current facilities. If we instead consider the individual components of the UV emission line doublets, we find that the flux ratios F(N v 1 )/F(N v 2 ) and F(O iii] 1 )/F(O iii] 2 ) are both generally < 1, Article number, page 9 of 54 A&A proofs: manuscript no. uves-arxivversion F([Si iii])/F(Si iii]) and F([C iii])/F(C iii]) are > 1. The mean, median and standard deviation of these four flux ratios are (0.
There is no indication that the z > 1 data would be particularly biased and only contain systems with low gas-phase abundances. In several cases the line emitters were detected in broader representative samples of galaxies. In the case of the MUSE sources studied here, the parent sample consists of line-emitters (mostly LAEs) from an unbiased untargeted emission line search in the full data cubes. The z ≈ 0 observations from the literature do on average have slightly higher gas-phase abundances and EW 0 (C iii) than the measurements at higher redshift. However, as the EW 0 (C iii) color coding in Figure  18  shows, these galaxies predominantly have strong C iii emission which might cause a selection bias rather than illustrating an underlying intrinsic correlation between EW 0 (C iii) and 12+log 10 (O/H).
Conclusions We have presented a comprehensive search for rest-frame UV emission lines in a parent sample of 2052 emission line galaxies detected with the MUSE integral field spectrograph in the COSMOS, GOODS-South, and HUDF as part of the MUSE-Wide and MUSE-Deep GTO surveys (Section 2). The studied objects were selected to have redshifts above 1. 5 based on the emission feature(s) identified during the untargeted emission line search in the MUSE data cubes. This resulted in 1997 LAEs at z > 2. 9 and 55 objects in the so-called MUSE redshift desert at 1. 5 < z < 2. 9 where neither [O ii] λ3726 + [O ii] λ3729 nor Lyα are available for source redshift identification (Section 3). For each of the objects in the parent sample, we extracted 1D spectra from the 3D MUSE data cubes optimized in both flux and S/N using the software TDOSE (Section 4).
Acknowledgements. We would like to thank  Charlotte Mason  for useful discussions and for providing the data for the curves shown in Figure 13 and  Dawn Erb  for providing the observational data for the comparison sample studied by Steidel et al. (2014), also shown in Figure 13. This work has been supported by the  BMBF  grant  05A14BAC  and we acknowledge support by the  Competitive Fund of the Leibniz Association  through grant  SAW-2015-AIP-2. AF acknowledges the support from grant  PRIN MIUR2017-20173ML3WW_001. JS acknowledges the support from  Vici grant   639. 043. 409from the  Dutch Research Council (NWO). GM received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  MARACAS -DLV-896778. This paper is based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  094.
To illustrate these results, one solves the system (3. 12) numerically by developing a Matlab code using a discretization technique based on the formula (3. 7). Choosing a value for α greater than α * , for example, α = 0. 9, one compares the solution of (3. 12) in terms of classical fractional operator and its solution in terms of the fractional operator with sliding fixed memory length L = 30. The two trajectories are start from the same initial point X 0 = (2. 64, 4. 88), belonging to the attracting limit cycle. The results are shown in Fig.
, 2019) and RACMO2 (van Wessem et al. , 2018)  ERA-I-driven simulations (later MAR-ERA-I and RACMO2-ERA-I respectively in the text) and in situ data (for temperature only) from the SCAR READER database  (Turner et al. , 2004) , as in  Beaumet et al. (2019a). The significance of the differences is assessed using double-Table  1. Summary of the period, sea surface conditions, greenhouse gas concentration and reference historical simulation for each ARPEGE future projections and historical simulation presented in this study. AC, OC and AOC acronyms stand for atmospheric and/or oceanic correction. ARP-AMIP, ARP-MIR-21-OC and ARP-NOR-21-OC are also described in  Beaumet et al. (2019a). sided t tests as in  Beaumet et al. (2019a). The limitations and potential improvement of the method are also addressed.
The potentially large effect on the Antarctic ice sheet of the differences in snow accumulation and surface climate changes suggested in this study should be explored using surface forcings coming from atmospherecorrected projections to drive ice dynamics or ocean and ice shelf interaction impact studies. Data availability. Historical run and future projections presented in this study are available on the Antarctic CORDEX grid at a daily time step for atmospheric surface temperature (mean, min and max), precipitation, snowfall, surface snowmelt, surface runoff and surface snow sublimation at the following address: https://doi. org/10. 5281/zenodo. 4059193 (Beaumet et al. , 2020). Data and metadata mostly follow CORDEX recommendations. Atmospheric variables (temperature, wind specific humidity, geopotential and surface pressure) over 17 constant-pressure levels at a 6-hourly resolution are available for the Southern Hemisphere upon request.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-15-3615-2021-supplement. Author contributions. AA prepared the ARPEGE version and scripts that were used to produce the ARPEGE runs used in this study. MD performed the sensitivity tests and ran the bias-corrected simulations. JB ran the non-corrected reference runs, analyzed the output, and prepared the manuscript and the figures. CA provided MAR outputs and useful scripts for the interpolation and comparhttps://doi. org/10. 5194/tc-15-3615-2021 The Cryosphere, 15, 3615-3635, 2021 isons of MAR, RACMO2 and ARPEGE output. GK read and corrected the manuscript many times. All the authors have provided complete reviews and relevant remarks for the improvement of the manuscript. Competing interests.
Acknowledgements. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups participating in CMIP5 for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provided coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. The  Centre National de Recherches Météorologiques  ( Météo-France, CNRS ) and associated colleagues are warmly thanked for providing resources and help to run ARPEGE model. We also thank the  Scientific Committee on Antarctic Research (SCAR)  and the  British Antarctic Survey  for the availability of the SCAR READER database.
We kindly acknowledge  Michiel van den Broeke  for providing RACMO2 data and helpful discussions. Finally, we want to thank the two anonymous reviewers for their constructive remarks and the editor for helping in improving the quality of this paper. Financial support. This publication was supported by  PROTECT. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 869304. This is PROTECT contribution number  21. Review statement. This paper was edited by  Masashi Niwano  and reviewed by two anonymous referees.
METHODS: We used individual patient data from 6 randomized controlled trials within the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration to validate the original model. Then, we updated the model and performed a second validation with data from the observational MR CLEAN Registry. Primary outcome was functional independence (defined as modified Rankin Scale score 0-2) 3 months after stroke. Treatment benefit was defined as the difference between the probability of functional independence with and without EVT. Discriminative performance was evaluated using a concordance (C) statistic.
B enefit of endovascular treatment (EVT) for ischemic stroke due to a proximal intracranial occlusion in the anterior circulation varies considerably among patients because of differences in prognostic factors and heterogeneity of treatment effect. Previously, we have developed the MR PREDICTS decision tool with data from the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). This prediction model combines eleven patient and imaging characteristics at baseline to estimate outcome and treatment benefit in individual patients. 1, 2 MR PREDICTS is meant to support clinicians in decision making for EVT, so as to treat patients who are most likely to benefit from EVT and avoid futile treatment. A previous external validation with data from the Interventional Management of Stroke III trial showed moderate discriminative ability.