dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Initially, 27 younger adults and 36 older adults were recruited for this experiment. However, only 22 of the 27 younger adults (81%) and 22 of the 36 older adults (61%) met the criteria of having a minimal range of 70% of trials with valid gaze information. Loss of eye-tracking data could occur in cases of excessive blinks or sudden changes in body position. In older adults, eye-tracking was more complicated because of eyeglasses, droopy eyelids or watery eyes  (Isaacowitz, 2006). Therefore, participants for this study finally included 22 younger adults (20-29 years, 12 females, M = 22. 18, SD = 1. 60) and 22 healthy and independently living older adults (60-79 years, 11 females, M = 70. 4, SD = 7. 0). Demographic information for these two groups, including age, years of education and sex ratio, is presented in Table  1.
Stimuli and Apparatus Stimuli consisted of pictures of facial expressions that were obtained from the FACES database  (Ebner et al. , 2010). This database was chosen because the stimuli have a high resolution, homogeneous color, and provide good examples of universal emotion categories with a high accuracy of labeling. The faces of 8 models (with an equal number of young and older, male, and female faces) expressing 5 different facial expressions, namely "joy, " "anger, " "fear, " "sadness, " "disgust, " and "neutral" 2  , constituted the set of 48 stimuli used in the experiment. All stimuli, presented over a black background, were 10 cm high by 8 cm wide and subtended a vertical visual angle α of 10. 4 • at a viewing distance of 55 cm (Figure  1A ). The average luminance of the stimuli was 3.
Procedure Participants were tested individually, and after providing informed consent, they completed demographic, cognitive, affective, and visual acuity measures. Then, they were seated at approximately 55 cm from the eye-tracker's computer screen and the experimental session began with a 9-point eye-tracking calibration grid. Participants were asked to avoid large head movements as much as possible, but there were no other physical constraints. Each trial started with a 0. 5 s fixation-cross in the middle of the screen, indicating that the stimulus would appear. The target stimulus was presented for 2 s (time during which eyetracking data were recorded), and participants were instructed to look at the face in a natural manner. After 2 s, 6 emotional labels appeared at the bottom of the computer screen, and participants were asked to click on the label that best described the emotion presented.
There was no time limit, and the labels were visible until the participant responded. Then, a 0. 7 s black screen appeared, indicating that the participants should rest, and the next trial began. The order of stimuli presentation was pseudo-randomized, with the restriction that the same emotion or the same actor could not appear two consecutive times. Stimulus presentation and response collection (i. e. , accuracy) were controlled using E-Prime presentation software (Psychology Software Tools, Pittsburgh, PA). Sessions lasted approximately 45-60 min.
Averaged Fixations Analysis Firstly, to provide average statistics about gaze behavior during the target face presentation, the mean number of fixations (i. e. , fixation count) and the total fixation duration (in seconds) within the AOIs were calculated using Tobii Studio software. Blinks and saccades were excluded from the analysis. The data were entered into an overall analysis of variance (ANOVA), with age group (younger adults, older adults) as between-subjects factor, and AOIs (upper-face and lower-face areas) and emotion ("joy, " "neutral, " "fear, " "anger, " "sadness, " and "disgust") as withinsubjects factor.
Dynamic Analysis Average fixation statistics have been widely used in the literature to characterize gaze behavior. However, they fail to describe important spatio-temporal dynamics, such as the spatial distribution and time course of fixations. To further explore gaze behavior dynamics in younger vs. older adults, a deeper analysis of eye movement data was performed in a second step. Concretely, we computed the percentage of participants from a given age group looking at each AOI for each time instant t i of the 2 s stimuli watching. As a result, we built aggregated dynamic behavior time-series, which are double timelines (one per AOI: upper-face and lower-face) using a graded color-scale to codify the percentage of participants looking at each AOI across time. The time-series were obtained by aggregating the information per type of emotional stimuli and age group.
Emotion Identification Data Overall, both younger and older adults obtain high scores on the emotion identification task. The mean percentage of correct responses of younger adults was 96. 50% and that of older adults reached 90. 53%. While the few confusions by younger adults were evenly distributed among positive and negative emotions, older adults clearly tended to mix-up negative emotions. For instance, the emotional categories most often confused by older adults were "fear" (confused with "anger" in 9. 66% of cases) and "anger" (confused with "disgust" in 5. 11% and with "sadness" in 4. 55% of cases). The "neutral" emotion was never confused with "joy, " but was confused with "sadness" (3. 41% of the cases) and "anger" (2. 84% of cases). To avoid such biases, the unbiased hit rate (Hu) was preferred.
Pattern Level Figure  7  shows the result of Isomap projection and clustering of dataset gaze vectors, globally (a) and per basic emotion (b). Each point in the 2D space corresponds to one of the 2,112 trials and is colored in dark blue or dark red, depending on whether it was performed by a younger or older adult, respectively. The boundaries of the two clusters obtained are shaded in light blue ("cluster 1") and light red ("cluster 2"). Table  2  summarizes the percentage of trial points that belong to each cluster, depending on the age of participants and the type of emotional stimuli shown. Clustering reveals that 92% of older adults' instances belong to the same cluster ("cluster 2"), while younger adults' trials are equally divided between the two clusters (48% in "cluster 1" and 52% in "cluster 2").
g. ,  Calder et al. , 2003; Circelli et al. , 2013). Finally, some studies found impairments for all the three emotions of "fear, " "anger, " and "sadness"  (Wong et al. , 2005; Grainger et al. , 2015). The fact that older adults showed better expression identification for "joy" and "disgust" is consistent with other reports  (Ruffman et al. , 2008). It should be noted that "joy" was the only positive emotion, so participants were able to make the correct choice as soon as they recognized a smile, and its identification was near ceiling in both younger and older adults. Thus, using only a few basic emotional categories allowed participants to choose their response based essentially on exclusion rules, which is less likely to be the case in real life  (Frank and Stennett, 2001). In this study we focused on basic emotions, but a possible way to avoid this limitation would have been to use an alternative facial expression database (e.
Discussion During the SemEval2013 opinion mining task, the best system achieved a F 1 -measure of 0. 68  (Nakov et al. , 2013). (Andreevskaia and Bergler, 2008)  are a reference in stance detection: on the topic "Firefox VS Internet Explorer", their hybrid classifier achieved F 1 = 0. 66. On another dataset,  (Hasan and Ng, 2013 ) reached a 0. 75 accuracy. These results are good, but not excellent: the stance detection task is indeed very difficult. Moreover, during annotation, the inter-annotator agreement 11 is often quite low, which means that even if the stance detection procedure performs well on the testing data, it is still debatable whether the results truly represent the actual stances. We believe that stance detection on tweets process could benefit from disambiguation techniques.
However, due to the difficulties of social media contents, we cannot rely only on well-established resources. Disambiguation itself also presents great challenges: (Perez-Tellez et al. , 2010) reached a F 1 -measure of 0. 74 on some ambiguous company names. Nevertheless, we believe that word sense disambiguation has great potential and including such a step in stance detection can have a positive impact. Contextonymy appears to have many advantages that makes it suitable to use for disambiguation. Hence, the aim of this study is to develop a method to extract a database of contextosets from tweets, and then exploit this 11 On an annotation task, each sample is labeled by different persons to check if they agree; measures such as Kohen's kappa enables evaluation of their agreement. database to disambiguate the senses of tweets in order to improve stance detection.
By filtering away the edges that have small values, i. e. whenever β e < β threshold , we get rid of the unimportant associations and only retain what is important for our contextosets. Contextoset Extraction. We chose to extract the contextosets using a method proposed by  (Palla et al. , 2005). They outline a way to obtain k-cliques, that is, communities derived from overlapping cliques. This seems to suit our problem particularly well as maximal cliques provide inadequate contextosets because of the tendency to form many, almost identical sets of words that specify the same context. k-cliques improve the contextosets as they merge cliques that share many of the same words. We used the k clique communities implementation of  (Palla et al. , 2005)  method in the NetworkX  (Hagberg et al. , 2008)  python package.
Introduction Automatic Natural Language Generation Software aims to express information stored in a knowledge database as a Natural Language text. The information at the source typically consists of a series of simple atomic statements, such as "John owns a house", "Mary lives in Manchester", "Peter is Ann's cousin". These statements are usually stored in a knowledge database or ontology in a formal notation such as Prolog (e. g. "Own(John,House)") or XML. Translating each elementary statement into an isolated English sentence is straightforward; the difficulty arises when one tries to process a complex set of statements to generate a text that feels "natural": an entity that is mentioned several times might then have to be referred to by a pronoun, a possessive determiner (e. g. He is her cousin), or an anaphoric term (e.
Introduction With respect to solar electricity generation the need of temporally and spatially resolved long-term solar radiation databases has been increasing in recent years. The surface solar irradiance (SSI), also known as the downwelling shortwave irradiance at surface, is defined as the power received from the sun on a horizontal unit surface at ground level. Under concern here is the SSI integrated over the whole solar spectrum, i. e. between 0. 3 µm and 4 µm, called total or broadband SSI. The global SSI is the sum of the direct SSI from the direction of the sun, also called the beam SSI, and the diffuse SSI from the  Rigollier et al. , 2004; Heliosat-3). The principles of the Heliosat method describe the case when a cloud exhibits a larger reflectance than the ground. Consequently, the appearance of a cloud in the field of view of the satellite sensor should result in an increase of the perceived signal: the cloud appears brighter than the ground.
The latter are preparing the operational provision of global aerosol properties analyses and forecasts together with physically consistent total column content in water vapour and ozone  (Benedetti et al. , 2011; Kaiser et al. , 2012; Peuch et al. , 2009). Such 3-h resolved information has not been available so far from any operational numerical weather prediction (NWP) centre. This paper describes the methodology chosen, its practical implementation, its strengths, its current weaknesses and restrictions. It also provides a validation against ground measurements. The concept of Heliosat-4 is discussed in Section 2. Section 3 presents an overview of the clear-sky model estimating the SSI under cloudless skies, hereafter called clear-sky conditions. The model for cloudy sky and its input data are described in Section 4 and its implementation in Section 5.
Actually, the McClear abaci comprise the clearness index KT c for ρ g equal to 0, 0. 1, and 0. 9, and the direct clearness index KT Bc for ρ g = 0. A specific method is used for the interpolation of the ground albedo  (Lefèvre et al. , 2013). The choice of using only three different values of ρ g (0, 0. 1 and 0. 9) reduces the size of LUTS, but also guarantees the best performance for the full albedo range from 0 to 1. Based on the input data availability in CAMS, Mc-Clear delivers time-series of SSI for any place in the world and any instant starting from 2004. Its inputs are: • θ S as provided by the fast algorithm for the solar position SG2 (Blanc and Wald, 2012); • three parameters describing the bidirectional reflectance distribution function (BRDF) in the Moderate-resolution Imaging Spectroradiometer (MODIS) database  (Schaaf et al.
, 2002)  from which ρ g can be computed. The worldwide climatological monthly means of these three parameters as derived by  Blanc et al. (2014b)  from the MODIS BRDF/Albedo model parameters product MCD43C1 and MCD43C2 data have been used. This dataset has a spatial resolution of 0. 05 °; • the altitude of the ground level, given by the user, or taken from the SRTM data set  (Farr et al. , 2007)  if available or the GTOPO30 data set  (Gesch and Larson, 1996) ; • the elevation of the CAMS cell above the ground; • the AOD at 550 nm from CAMS, and the Ångström coefficient computed from the aerosol optical depths at 550 nm and 1240 nm as provided in CAMS; • the aerosol type  (Hess et al. , 1998). CAMS delivers also partial optical depths at 550 nm for dust, organic, sea salt, sulphate, and black carbon aerosol species which are input to the algorithm of  Lefèvre et al.
More precisely,  Oumbe (2009)  and  Qu (2013)  have shown by using the RTM libRadtran that changes in cloud liquid/ice water content and droplet effective radius have limited impact on the estimated SSI. In the specific case of CAMS, very limited information on the cloud liquid/ice water content and effective droplet radius is available up to now for the complete database starting in 2004. Considering the unavailability of the data and their limited impact, typical values are selected. They are set to 0. 005 g m -3 and 20 µm for ice clouds and 1. 0 g m -3 and 10 µm for water clouds  (Oumbe et al. , 2014). Oumbe et al. (2014)  established Eq. (  2. 3) by a methodology based on Monte-Carlo statistical analysis. Seven typical geometric positions of water clouds and three of ice clouds were tested in combination with 20 000 physically-consistent -i.
Additionally, APOLLO/SEV contains an updated correction database for masking regional peculiarities as e. g. cold currents and thermal fronts in the ocean, deserts with airborne dust, or known bright surfaces as dry lakes, etc. Since the received radiation in the 0. 6 µm channel of SEVIRI is subject to only a small amount of water vapour absorption, it can be similarly used for derivation of τ cloud as in previous APOLLO schemes. A check if results of APOLLO/SEV are reasonable has been done Cloud coverage, i. e. the fraction of a pixel covered by a cloud, expressed in percent, is derived for each level and the thin class of clouds separately. The cloud coverage is calculated at daytime by the relationship between the reflectance measured at 0. 6 µm and 0. 8 µm and the average reflectances for fully covered and cloud-free pixels in a region of 58 × 58 pixels centred on the pixel under concern.
(  5. 3)-(5. 6) largely outperforms any linear interpolation made with more fitting points. The ground albedo ρ g is computed in the same way as in  Lefèvre et al. (2013). Blanc et al. (2014b) created a worldwide climatological complete database containing monthly means of the three BRDF parameters, called f iso, f vol, and f geo  (Schaaf et al. , 2002). This database is available at http://www. oie. mines-paristech. fr/Valorisation/ Outils/AlbedoSol/. In any operational use of the Heliosat-4 approach one may want to use the most recent BRDF maps in order to take year-to-year variations of the most recent data period into account. Lefevre et al. (  2013 ) discussed the two approaches (near-realtime and climatology) in the case of McClear. They reported that when compared to ground-based measurements of the SSI, both approaches gave the same results with very small differences not taking into account the gaps.
Data sets for validation Data sets for validation were collected for 13 stations of the Baseline Surface Radiation Network (BSRN) representing a variety of climates in the field of view of MSG (BSRN snapshot 2014-06 DOI:10. 1594/ PANGAEA. 833428). Table 1 lists these stations, ordered by decreasing latitude. Lerwick, Toravere, Lindenberg, Cabauw, Camborne, Palaiseau, Payerne, and to a lesser extent Cener, experience marine climate with mild winters, constantly moist, noted Cbf in the classification of  Trewartha (1954). Carpentras belongs to the Provence region in France, where the sky is often clear and clean. It experiences a climate with mild winters with limited but intense episodes of rain and dry summer (Csa, Mediterranean). Izana and De Aar are located both in high altitude and experience semi-arid climate (BSk).
clouds presented in the afternoon around 14:00 UT are well detected in this example. Figure  10  shows another example encountered in summertime in Sede Boqer. The very strong temporal variation of the BSRN observations of B N suggests the presence of scattered clouds. On the opposite, the lower sub-plot (labelled COD) shows that according to APOLLO/SEV data, it was a cloud-free day. As a consequence, Heliosat-4 predicts clear sky irradiance and therefore overestimates B N. Visual interpretation of a colour composite MODIS image centred on Sede Boqer around 09:10 UT indicates small-size cumulus in the northern region (Fig. 11 ). Sede Boqer is at the edge of the cloudy zone and it is hard to tell if its sky were cloudy or not at this very instant. Nevertheless, it supports the fact that rapid variations in B N observed in BSRN data may result from this field of clouds.
The cloud masking and the retrieval of COD should be enhanced following the feedback given from the solar irradiance evaluation. This work is ongoing  (Klüser et al. , 2015)  and APOLLO_NG (Next Generation) is making use of a probabilistic cloud masking and treats cloud effective radius explicitly. APOLLO_NG is currently evaluated with respect to Heliosat-4 irradiances and with respect to CREW (Cloud Retrieval Evaluation Workshop) datasets. If successful, APOLLO_NG will be implemented in Heliosat-4 and a major reprocessing of cloud properties is foreseen. The retrieval of the cloud properties when a pixel is not fully covered by clouds should be improved. In case of clear sky, uncertainties in the retrieval of aerosol properties for desert stations become important. The underestimation of the aerosol optical depth during dust events in or near desert regions may be significant.
It introduces a strong positive bias for the estimation of the direct SSI. A systematic overestimation of the aerosol optical depth in non-dust-storm periods for desert stations may cause an underestimation of the direct SSI. Besides these improvements on the quality of the inputs, the Heliosat-4 model can be improved to better account for the vertically developed clouds by considering them as two-phase clouds. This could be done by computing additional abaci. A correction on circumsolar irradiance should be also applied when comparing to ground-based measurements which is expected to largely reduce the bias of D and B N especially for cirrus clouds. The Heliosat-4 method with inputs from CAMS, MODIS and APOLLO/SEV from DLR is available as a Web service, i. e. an application that can be invoked via the Web and using always the best-available input data and algorithm version.
Acknowledgements The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. 218793  ( MACC project ) and no. 283576  ( MACC-II  project) and from the  European Union 's  Horizon 2020 Programme  ( H2020/2014-2020 ) under grant agreement no. 633081  ( MACC-III  project). The  MACC  ( Monitoring Atmospheric Composition and Climate) projects are preparatory projects for Europe's Copernicus  programme. The authors thank all ground station operators of the BSRN network for their valuable measurements and the  Alfred-Wegener Institute  for hosting the BSRN website. The authors thank the team developing libRadtran (http://www. libradtran. org).
Acknowledgements EAC was partially supported by the  US National Science Foundation  ( DMS 1223137 ,  1120888 ). GL was partially supported by the  Spanish Ministry of Economy and Competitiveness , Grant  MTM2015-67304-P  and  FEDER ,  EU. NV was partially supported by the  French Agence Nationale de la Recherche  ( ANR 2011 BS01 010 01  projet Calibration).
received support from  EPSRC  ( EP/G043434/1 ),  ANR  project  GraphEn  ( ANR-15-CE40-0009 ), the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement n. 267959 and the  Research Council of Norway  (the project CLASSIS). An extended abstract of it appeared in the proceedings of SWAT 2016 [12].
During the past two years there has been enormous experimental progress, with ground breaking analyses from LHCb, ATLAS, CMS, BaBar, and Belle. The general picture that emerges is one of overall agreement with SM expectations. However, there are several observables for which measurements are in some tension with the SM. Confirmation of these discrepancies would point to the existence of new massive particles that are clearly within the reach of ATLAS and CMS and which would very likely introduce novel non-CKM like flavour changing interactions. The most notable anomalies appear in B → K ( * ) and B → D ( * ) ν decays. The upcoming high luminosity B-factory Belle II is about to come online, promising important cross checks of these effects  [3]. Moreover, the NA62  [4]  and KOTO  [5, 6]  experiments are also taking data and will offer precise measurements of the extremely rare kaon decays K + → π + ν ν and K L → π 0 ν ν, whose branching ratios can be calculated with incredible theoretical accuracy.
The experiment makes use of a tightly collimated neutral beam (average momentum 2 GeV) and compact, hermetic detector. From a brief pilot run in 2013, KOTO obtained the limit BR(K 0 L → π 0 ν ν) < 5. 1 × 10 -8 (90% CL)  [6]. The experiment ran for many months in 2015-2016, with various improvements to reduce the background. From the preliminary analysis of 10% of the data collected in 2015, KOTO has reached a single-event BR sensitivity of 5. 9×10 -9 , with background levels still under evaluation. With the entire 2015-2016 data set, KOTO should be able to push the single-event sensitivity to below the Grossman-Nir bound. By the end of 2015, the beam power reached 42 kW; it is expected to increase to 100 kW by 2018. A series of upgrades to the experiment are also in progress, including an additional layer for the barrel calorimeter (tested in 2016) and front/back readout for the forward CsI calorimeter to provide additional neutron rejection.