dataesr/scientific-paragraphs-categorization huggingface dataset:
AOD, AE and fine-mode contribution The seasonal aerosol classification based on the first graphical method described in Sect. 3 (AE 440-870 vs. AOD 440 ) is presented in Fig. 2  for Ersa and in Fig. 3  for Palma. The graphs are made with the whole daily AOD dataset. Seasonal mean values of AOD, AE and δAE are given in Table 1. The relationship between daily AE 440-870 and AOD 440 shows three principal features visible at both sites: (1) a wide range of AE 440-870 [0; 2. 5] year-round, (2) a narrower range of AE 440-870 [1; 2] at high AOD 440 (> 0. 4) especially marked in summer at Ersa and in autumn at Palma and (3) a narrow range of AE 440-870 [0, 0. 5], also at high AOD 440 (> 0. 4), especially marked in summer, autumn and spring at Ersa and in summer and autumn at Palma. The first feature indicates a wide range in particle size with higher inter-season variations at Ersa (yearly AE 440-870 = 1.
The most interesting differences are a greater number of high-AOD dust events (AOD 440 > 0. 6, coarse-mode cluster) in spring compared to summer and conversely a greater number of high-AOD pollution events (AOD 440 > 0. 4, fine-mode cluster) in summer compared to spring. In the 5-year study period 2011-2015 Ersa has at least 3 full years of data, while Palma has more sparse data. Before starting with the monthly analysis, the representativeness of Palma data is checked with Ersa data by taking the subset of Ersa data coincident in time with those of Palma (which are comprised of the period August 2011-December 2013). In Fig. 6a  the monthly means of this restricted dataset (black bullets) are superimposed on the monthly means of the whole dataset (red bullets). In all cases the monthly means of the August 2011-December 2013 dataset are within the monthly variability of the whole dataset.
In summer and autumn, the representativeness of the August 2011-December 2013 dataset is good: the difference between both datasets is lower than 0. 01. The highest differences, 0. 02-0. 03, are reached during the period March-May. In the Palma restricted dataset only spring 2013 contributes to the spring mean. Curiously during that spring no moderate to large AODs (AOD 440 > 0. 4) were observed (see Fig. 5d ). This result may produce the underestimation in AOD of the August 2011-December 2013 dataset compared to the whole dataset observed at Ersa in March and April and suggests that Palma monthly means during those months may also be underestimated. Given the restriction of the Palma dataset, the discussion of the Palma spring means has to be taken cautiously in the following. The monthly mean AOD 440 shows a clear annual cycle at Ersa and Palma (Fig.
, 2007). The summer mean AOD 440 (± standard deviation) is 0. 19 ± 0. 10 and 0. 25 ± 0. 13 at Ersa and Palma, respectively, while the winter averages are equals (0. 08). In order to see the contribution of the fine-mode particles we plot the fine-mode AOD 440 , AOD f 440 , in Fig. 6b. Except for 2 months (March and April) the annual cycles at both Ersa and Palma are similar in shape and magnitude. Similar maxima are found in summer (0. 14 ± 0. 09 at Ersa and 0. 13 ± 0. 07 at Palma). In March and April AOD f 440 is more than double at Ersa than at Palma. In addition to the possible underestimation of the Palma dataset in spring (see two paragraphs above), the maps of AOD per aerosol type from  Barnaba and Gobbi (2004)  suggest a contribution of aerosols of continental origin already in spring over Corsica and not before summer over the Balearic Islands.
AAOD, absorption Ångström exponent (AAE) and refractive index Besides aerosol amount and size, other important aerosol properties are those related to their absorbing ability. It must be kept in mind that AERONET level 2. 0 inversion products linked to the aerosol absorption properties like the AAOD, the AAE and the refractive index are performed with the following restrictions: 50 < SZA < 80 • and AOD 440 > 0. 4. To gain an insight into the seasonal frequency, intensity and aerosol type under such restrictions, we show in Fig. 8  the (δAE, AE) plots only for those AERONET level 2. 0 daily inversion products in our dataset that meet those criteria. In this subset, there is in general at least twice more data available in summer than in the other seasons. The plots for both sites show without ambiguity that such restrictions lead to only two types of aerosols: mineral dust corresponding to the coarse-mode cluster (δAE < 0.
(2) The range of values of AAE provides useful information on shortwave absorption produced by different types of aerosols, namely black carbon (BC), organic carbonaceous matter and mineral dust  (Russell et al. , 2010). However, recently  Mallet et al. (2013)  highlighted the difficulties in attributing AAE values higher than 1 over the Mediterranean, the value for pure BC, to organic species (and/or mineral dust) or to coated BC since they all produce AAE > 1  (Lack and Cappa, 2010). The seasonal variations of the spectral dependency of the AAOD are shown in Fig. 9a. Seasonal mean values are given in Table  1. At each site the spectra are shown for the whole dataset (All) and for strong mineral dust (MD) and strong pollution (Pol) cases determined with the classification obtained from Fig.
At Palma the highest values of AAOD 440 (AAE), 0. 061 (2. 37), occur in autumn (Table  2 ) and correspond to an intense mineral dust outbreak. The summertime average of AAOD 440 at Ersa (0. 019) measured over the whole dataset is within the error bar of the value of 0. 020 found by  Mishra et al. (2014)  at the same site from a larger dataset of AERONET observations. It is, however, lower than the average value given in  Mallet et al. (2013)  for the western Mediterranean Basin calculated at sites characterized mostly as urban and dusty, which could indicate that they considered more dusty sites than urban ones in the computing of their basin average. The mineral dust AAOD spectra at both sites are similar in magnitude and shape to the measurements made during PRIDE (Puerto Rico Dust Experiment, 2000; aerosols: Saharan dust) and ACE-Asia (Aerosol Characterization Experiment-Asia, 2001; aerosols: Asian dust, urban and industrialized)  (Bergstrom et al.
During autumn and spring at Ersa and autumn at Palma, the seasonal mean of SSA λ calculated with the whole dataset (MD + pollution) increases from 440 to 675 nm and decreases afterwards. This behavior is representative of a combination of both MD and pollution. During summer at Palma the SSA spectra (calculated with the whole dataset) are very similar to that of MD (76 % of the dataset corresponds to mineral dust; see Table  2 ). MD and pollution SSA 440 vary in the range 0. 89-0. 94 and 0. 97-0. 98, respectively. For comparison,  Denjean et al. (2016)  found SSA at 530 nm ranging from 0. 90 to 1. 00 in layers of different aerosol types in the western Mediterranean Basin during the ChArMEx summer 2013 field campaign. Inter-season variations are more pronounced for MD than for pollution. As a consequence of higher MD AAOD 440 in autumn, MD SSA 440 is smaller in autumn than in summer.
40. The values of ARF and ARFE are given for 50 < SZA < 60. Seasonal variation of the spectra of (a) the single scattering albedo (SSA) and (b) the asymmetry factor (g) during (1) summer, (2) autumn and (3) spring, derived from AERONET level 2. 0 daily inversion products available for the period 2011-2015. The legend in plot (a3) applies for all plots (a) and (b). SSA is retrieved with the following restrictions: 50 < solar zenith angle < 80 • and an aerosol optical depth at 440 nm (AOD 440 ) > 0. 4. The error bars represent the standard deviation. The seasonal mean is represented for the whole dataset (All) and separately for strong mineral dust (MD) and strong pollution (Pol) cases determined with the classification obtained from Fig. 8  (see first paragraph of Sect. 5. 3). tween 0. 71 and 0.
2-3 % w. r. t. the instantaneous ARF). Detailed information on the radiative transfer module used by the operational AERONET inversion algorithm can be found in  García et al. (2011 García et al. ( , 2012a, b), b). García et al. (2008)  made an intensive validation of AERONET estimations of fluxes and radiative forcings using ground-based measurements from solar databases at nine stations worldwide but AERONET estimations of the aerosol direct radiative forcing are little used in the literature. Cachorro et al. (2008)  used the AERONET ARF estimations to study the impact of an extremely strong desert dust intrusion over the Iberian Peninsula. Derimian et al. (2008)  used the AERONET estimates of the ARF for mineral dust mixed with biomass burning and for pure mineral dust at M'Bour, Senegal, and tested the impact of neglecting aerosol nonsphericity on radiative effect calculations.
Comparison of AERONET radiative fluxes with ground-based and satellite data A comparison of AERONET estimations of the two following solar fluxes that are the most critical for aerosol forcing calculations is performed. Firstly, the solar downward flux at the surface, F ↓ BOA : we perform a comparison between AERONET estimations and pyranometer measurements, using the Barcelona AERONET/SolRad-Net (Solar Radiation Network) site, which is the closest site to our study area in the western Mediterranean Basin where collocated AERONET and solar flux measurements are available. All SolRad-Net data were downloaded from the SolRad-Net web page at http: //solrad-net. gsfc. nasa. gov/. The period with coincident measurements is May 2009-October 2014. The pyranometer is a Kipp and Zonen CMP21 sensor that provides a measurement of the total solar flux in the range 0.
3-2. 8 µm every 2 min. Coincident AERONET and pyranometer measurement times are restricted to ±1 min. We use SolRad-Net level 1. 5 data which are cleared of any operational problem. The manufacturer accuracy (2 %) and the sensor drift (< 1 %) yield an overall accuracy on the order of 3 %. Secondly, the solar upward flux at the TOA, F ↑ TOA : we perform a comparison between AERONET estimations and CERES (Clouds and the Earth's Radiant Energy System) satellite measurements at Ersa, Palma and Alborán. We use CERES Single Scanner Footprint (SSF) level 2 products, namely the shortwave (0-5 µm) upward flux at the TOA given for a spatial resolution equivalent to its instantaneous footprint (nadir resolution 20 km equivalent diameter). All CERES data were downloaded from the CERES subsetting and browsing web page at https://ceres-tool.
larc. nasa. gov/ord-tool/products?CERESProducts=SSFlevel2. CERES www. atmos-chem-phys. net/16/12177/2016/ Atmos. Chem. Phys. , 16, 12177-12203, 2016 TOA shortwave fluxes are derived from CERES radiance measurements using angular distribution models described by  Loeb et al. (2005)  and  Kato and Loeb (2005). Measurements from CERES/Aqua and CERES/Terra are used indistinctively. We screen CERES data spatially by accounting only for the pixels in which one of the ground sites falls and by temporally allowing a time difference of ±15 min. The time of overpass of both CERES/Aqua and CERES/Terra over the three sites varies in the range of 10:00-14:00 UT. The CERES/Terra instantaneous shortwave TOA flux uncertainties are estimated to be 13. 5 W m -2 for all-sky conditions (CERES, 2016). According to  Loeb et al.
(2007)  CERES/Aqua TOA flux errors are similar to those of CERES/Terra. Because of the CERES overpass time range (10:00-14:00 UT), the SZA restriction for AERONET level 2. 0 data (50 < SZA < 80 (2) Second, because the three sites are in coastline regions, CERES pixels contain information from both land and water. The first issue is due to the different techniques used by both AERONET sun photometers and CERES which make the air mass volumes sampled by both instruments quite different. The second one is in general not problematic, except at given periods of the year and at given hours of the day when the sun glint produces a significant increment of the upward fluxes in the direction of the spaceborne sensor. Both cases result in an increase of CERES upward fluxes at the TOA. To discard those cases, we resort to two more products of CERES SSF level 2 files: (1) the cloud fraction derived from MODIS radiances using the algorithms described by  Minnis et al.
In all three sites only a small number of cases correspond to a fine-mode cluster (η > 70 %; 0. 10 < r f < 0. 15 µm). At Ersa most of the  ), (f) the Ångström exponent AE 440-870 , (g) the aerosol radiative forcing (ARF) and (h) the aerosol radiative forcing efficiency (ARFE); (i) the columnar size distribution and (j) spectra of the asymmetry factor (g) averaged over the whole period. The data are from AERONET level 2. 0 inversion products during the period August to December 2011. BOA and TOA stand for bottom and top of the atmosphere, respectively. The numbers of points in the plots (g) and (h) (not indicated in the plots for the sake of clarity) are 123, 133 and 101 for Ersa, Palma and Alborán, respectively. The color code is the same in all figures (d)-(j): red, green and blue for Ersa, Palma and Alborán, respectively.
, 2011)  and/or from ship emissions  (Valenzuela et al. , 2015). The emissions of both types of aerosols have been quantified in the Bay of Algeciras by  Pandolfi et al. (2011). However, without further information on the aerosol properties at Alborán, this cannot be confirmed from our dataset. The aerosol direct radiative forcing (Fig. 13g ) at the BOA shows an increasing (in absolute value) NE-SW gradient. At the TOA, ARF is higher (in absolute value) at Alborán but it is similar at Ersa and Palma. Alborán measurements can be compared to ARF of dust in Granada (140 km N-NW of Alborán) from  Valenzuela et al. (2012b) , who found annual means of ARF BOA (ARF TOA ) at SZA = 55 ± 5 • for African desert dust events of approximately -50 (-20) W m -2. The difference at the TOA between Alborán and the other two sites may come from low aerosol absorption properties at Alborán (AAOD 440 < 0.
Data availability The data contained in this manuscript can be accessed and freely downloaded from the following sources: http://aeronet. gsfc. nasa. gov/ for AERONET AOD and inversion products (Goddard Space Flight Center/NASA, 2016a). http://solrad-net. gsfc. nasa. gov/ for SolRad-Net data (Goddard Space Flight Center/NASA, 2016b). https://ceres-tool. larc. nasa. gov/ord-tool/products? CERESProducts=SSFlevel2 for CERES SSF level 2 products (Langley Research Center/NASA, 2016).
• ) -2014ts many measur-2014s that coincide in time but are for SZA < 50 •. Consequently the use of AERONET level 2. 0 data provides very few points for comparison. Thus we select AERONET level 1. 5 data with 40 < SZA < 80 • and check that the cases with 40 < SZA < 50 • represent ∼ 33 % of the total. The periods with available AERONET level 1. 5 data are2008-2014 at Ersa, 2011-2014  at Palma and 2011-2012at Alborán. We have to deal with two more issues to further filter CERES data points. (1) First, sometimes CERES pixels are affected by clouds when at the coincident time AERONET is not.
The monthly means of the corrected AERONET level 2. 0 instantaneous solar ARF and ARFE are shown in Fig. 12at both the BOA and TOA. By plotting the whole dataset of ARF and ARFE as a function of SZA we observe that both quantities remain approximately constant independently of SZA. However, as SZA increases, the slant path increases and it is logical to expect a decrease of the ARF/ARFE related to the decrease in solar radiation reaching the Earth. This effect has been observed on instantaneous ARFE observations by diSarra et al. (2008) and Di Biagio et al. (.
Acknowledgements. This study was performed in the framework of work package on aerosol-radiation-climate interactions of the coordinated program ChArMEx (the  Chemistry-Aerosol Mediterranean Experiment ; http://charmex. lsce. ipsl. fr). It was also supported by the  ACTRIS (Aerosols, Clouds , and  Trace Gases Research Infrastructure Network) Research Infrastructure  Project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 654169  and previously under grant agreement no. 262254  in the 7th  Framework Programme  (FP7/2007-2013); by the  Spanish Ministry of Economy and Competitivity  (project  TEC2012-34575  and  TEC2015-63832-P ) and of  Science and Innovation  (project  UNPC10-4E-442 ) and  EFRD (European Fund for Regional Development) ; by the  Department of Economy and Knowledge of the Catalan autonomous government  (grant  2014 SGR 583 ); and by the Andalusia regional government through projects  P12-RNM-2409  and  P10-RNM-6299.
The monthly means of the corrected AERONET level 2. 0 instantaneous solar ARF and ARFE are shown in Fig. 12at both the BOA and TOA. By plotting the whole dataset of ARF and ARFE as a function of SZA we observe that both quantities remain approximately constant independently of SZA. However, as SZA increases, the slant path increases and it is logical to expect a decrease of the ARF/ARFE related to the decrease in solar radiation reaching the Earth. This effect has been observed on instantaneous ARFE observations by diSarra et al. (2008) and Di Biagio et al. ( ARFE c TOA = 0. 88 • ARFE AER TOA. (12) 6. 2 Solar direct radiative forcing and forcing efficiency: monthly variations at Ersa and Palma.
001) with our preliminary measures of point-to-point distances using the Vincenty formulae performed on 10 data sets. Therefore, we used the internet-based calculations of the formulae as a simple and generalisable procedure to obtain point-to-point distances. Point-to-point speed was subsequently calculated using the ratio of point-to-point distances and time (one data point every 5 s) between each datum. Preliminary calculations revealed that GPS devices did not discriminate for speeds slower than 1 km h -1 (0. 28 m s -1 or 1. 39 m in 5 s) based on the typical error in speed in a static position (drift, when a device will record speed values due to the non geo-synchronous nature of the constellation of satellites). At the other end of the speed spectrum, it was considered that speeds higher than 20 km h -1 (5.
Elevation changes We used previously published procedures  (Kerhervé, Millet & Solomon, 2015)  to alleviate the inaccuracies in GPS-based elevation  (Townshend, Worringham & Stewart, 2008). The online utility (GPS Visualizer; http://www. gpsvisualizer. com) was used to match the recorded geographical positions (determined by latitude and longitude) to a DEM elevation datum from the National Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) database. As the resolution of DEM is relatively low for the study of human locomotion (90 m, only the SRTM3 was available where the current study was conducted), the procedure can create a series of steps when increasing or decreasing elevation every time a contour line is crossed. Likewise, it can also mean that actual changes in elevation are not detected when data points were recorded inside the same contour line.
Variables and statistical analysis The identification of relevant data was performed for each participant using official race results, GPS time-stamps and variations of speed (increase or decrease) indicating changes in position at the start and finish lines. Speed and gradient values were computed as a function of relative distance for each participant, where total individual distance represents 100% of the distance completed for the 173-km, and 60% for the 103-km event participants. Relative distance was used instead of the actual distance values because of the difference in total distances across participants. The dynamics of speed were determined relative to each participant's average speed over the entire event (100%) in order to increase the relevance of inter-individual comparisons.
We then used independent t-tests to evaluate whether differences in relative speed existed between the 173-km and 103-km participants in the same categories. Due to varying lengths of data sets (173-km and 103-km distances), we investigated the dynamics of speed within each gradient category using a one-way ANOVA on ranks (Kruskal-Wallis test) and a pairwise multiple comparison (Dunn's method) when required, to determine and locate potentially significant differences between sections of relative distance. The relationship between the level of performance (individual average speed) and the variability of speed (coefficient of variation of speed), the magnitude of speed loss (slope of the linear regression of speed over the entire event) and the total time stopped (assumed to correspond to resting, eating, clothing and gear change, toilet, other), were assessed using correlations.
RESULTS The datasets from four participants were not included in the analysis, due to either not completing the event, or discrepancies between official results and GPS data (difference in finish time greater than 5%). A total of 15 GPS datasets were used for analysis (see Fig. 1 ): ten participants completed the 173-km distance averaging 32. 9 ± 3. 6 h (range 26. 5-36. 3 h), with a mean total distance of 173. 0 ± 4. 0 km (5. 68 ± 1. 51 km h -1 ), and five participants completed the 103-km distance averaging 18. 9 ± 2. 3 h (range 15. 6-21. 1 h) with a mean total distance of 101. 9 ± 2. 3 km (5. 91 ± 1. 48 km h -1 ). Weather conditions on race day were dry (no precipitations), the temperatures ranged 14. 9-22. 8 • C at the start point, 10. 2-29. 1 • C at the 103-km checkpoint, and 11. 4-26.
Data Availability The following information was supplied regarding data availability: Datasets, R code and results are available at Figshare: Kerherve H, Cole-Hunter T, Wiegand AN, Solomon S. 2016. Pacing during an ultramarathon running event in hilly terrain. Figshare: https://dx. doi. org/10. 6084/m9. figshare. 3369790. v1.
Furthermore, as learning progresses, the neurons' responses rapidly accelerates. These responses can then be fed to a classifier to do a categorization task. In this study, we show that such an approach strongly outperforms state-of-the-art computer vision algorithms on view-invariant object recognition benchmark tasks including 3D-Object  [25, 26]  and ETH-80  [27]  datasets. These datasets contain natural and unsegmented images, where objects have large variations in scale, viewpoint, and tilt, which makes their recognition hard  [28] , and probably out of reach for most of the other bioinspired models  [29, 30]. Yet our algorithm generalizes surprisingly well, even when "simple classifiers" are used, because STDP naturally extracts features that are class specific. This point was further confirmed using mutual information  [31]  and representational dissimilarity matrix (RDM)  [32].
Dataset and Experimental Setup To study the robustness of our model with respect to different transformations such as scale and viewpoint, we evaluated it on the 3D-Object and ETH-80 datasets. The 3D-Object is provided by Savarese et al. at CVGLab, Stanford University  [25]. This dataset contains 10 different object classes: bicycle, car, cellphone, head, iron, monitor, mouse, shoe, stapler, and toaster. There are about 10 different instances for each object class. The object instances are photographed in about 72 different conditions: eight view angles, three distances (scales), and three different tilts. The images are not segmented and the objects are located in different backgrounds (the background changes even for different conditions of the same object instance). Figure  2  presents some examples of objects in this dataset.