dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Theo Aspert  and  Youlian Goulev  for support on the cell-tracking microfluidics setup. Work in the R. S. laboratory was supported by the  DFG  through  SFB 1064  ( project-ID 213249687 ) and  SFB 1309  (project -ID 325871075), Marie Skłodowska-Curie Innovative Training Network (ITN)  project " Cell2Cell " ( H2020-MSCA-ITN-20219  grant no. 860675 ), as well as  AmPro program  ( ZT0026 ) and the  Helmholtz Gesellschaft. The A. K. laboratory is co-funded by the  European Regional Development Fund  and the  Republic of Cyprus  through the  Research & Innovation Foundation  (projects:  EXCELLENCE/1216/0215  and  EXCELLENCE/0918/0105 ). G. C. was supported by grant no. ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Purpose -Recent figures show that users are discontinuing their usage of TripAdvisor, the leading user-generated content (UGC) platform in the tourism sector. Hence, it is relevant to study the factors that influence travelers' continued use of TripAdvisor. Design/methodology/approach -We have integrated constructs from the Technology Acceptance Model, Information Systems (IS) Continuance Model, and eWOM literature. We used PLS-SEM (smartPLS V. 3. 2. 8) to test our hypotheses using data from 297 users of TripAdvisor recruited through Prolific. Findings -Findings reveal that perceived ease of use, online consumer review (OCR) credibility, and OCR usefulness have a positive impact on customer satisfaction, which ultimately leads to continuance intention of UGC platforms. Customer satisfaction mediates the effect of the independent variables on continuance intention.
Data Collection An online questionnaire, preliminary pilot-tested with 25 regular users of OCRs among the social networks of the researchers, was used to collect data. Following the pilot test, an email with a summary of the research and conditions for participation, including a hyperlink to the online survey, was sent to a Prolific panel of TripAdvisor users. The only sample selection criterion was that they had used TripAdvisor in the last month for seeking tourism-related information. TripAdvisor was chosen as it is the largest travel community, and it has been widely used in studies on travelers' behavior (e. g. Ayeh et al. , 2013; Filieri et al. , 2015; Yu et al. , 2017). Prolific is one of the fastest-growing platforms for the recruitment of online participants to research projects. Prolific's interface is user-friendly, reasonably priced, and explicitly informs participants that they are recruited for participation in research  (Palan and Schittera, 2018).
Context. Clouds are ubiquitous in exoplanet atmospheres and represent a challenge for the model interpretation of their spectra. Complex cloud models are too numerically costly for generating a large number of spectra, while more efficient models may be too strongly simplified. Aims. We aim to constrain the atmospheric properties of the directly imaged planet HR 8799e with a free retrieval approach. Methods. We use our radiative transfer code petitRADTRANS for generating spectra, which we couple to the PyMultiNest tool. We added the effect of multiple scattering which is important for treating clouds. Two cloud model parameterizations are tested: the first incorporates the mixing and settling of condensates, the second simply parameterizes the functional form of the opacity. Results. In mock retrievals, using an inadequate cloud model may result in atmospheres that are more isothermal and less cloudy than the input.
The units for the parameters are the same as the ones used for Table  1. f SPHERE and f GPI are the scaling factors retrieved for the SPHERE and GPI data, respectively. (a) and (b): please see Section 4. 2 for a definition of P phot and T connect. It holds that Xi = X i 0 /X i eq , where the latter quantity has been defined in Table  1. Table  3. As in Gravity Collaboration et al. (  2020 ), we fitted multiplicative scaling factors f SPHERE and f GPI to account for systematic biases in the flux normalization of these datasets. The T connect quantity referenced in the prior range of T 3 is the uppermost temperature of the 'photospheric' layer, and was calculated by setting τ = 0. 1 in Equation  2. Like the priors for T 2 and T 1 , this ensures a temperature profile that is monotonically decreasing with altitude, also see Section 2.
Hence also the logarithm of the planet mass is constrained with a symmetric, uni-modal peak, but the distribution of the mass itself is skewed towards lower masses, with large mass uncertainties due to the large uncertainty on log(g). The atmosphere is clearly affected by disequilibrium chemistry, with a large quench pressure of log(P quench /1 bar) = 1. 35 +0. 50  -0. 56. We find that disabling quenching at the best-fit parameters leads to strong CH 4 absorption features in the spectrum, which are inconsistent with the data. Moreover, the scaling value retrieved for SPHERE is consistent with one, 1. 01 +0. 08  -0. 07. The scaling retrieved for GPI is significantly smaller than 1, namely 0. 90 +0. 06 -0. 05. Only when applying this scaling on GPI do the SPHERE and GPI datasets agree in their overlapping region, see Figure  2.
(2018). Considering the apparent lack of comparison brown dwarfs that resemble the HR8799 planets, it is important to remember that the earlier results in the literature are highly heterogeneous in terms of the available data (and models) that were used to arrive at a given conclusion. High quality spectra are important to truly unlock the planetary characteristics. For example, additional SPHERE spectroscopy obtained by  Zurlo et al. (2016)  allowed  Bonnefoy et al. (2016)  to show that especially HR8799de can be well fit with low-gravity cloudy brown dwarfs of the late L spectral type. For HR8799bc the picture is less clear, and  Bonnefoy et al. (2016)  find that good comparison objects can only be identified when reddening T-dwarf spectra with iron or silicate extinction. Given the abundance of literature reporting on the cloudiness of the HR 8799 planets, our finding that HR 8799e is cloudy therefore does not come out of the blue (sky).
Disequilibrium chemistry Disequilibrium chemistry has also been reported in the HR8799 planets by a variety of studies. For planets such as HR 8799bcde, disequilibrium chemistry means that CO is more and CH 4 is less abundant then predicted from chemical equilibrium, due to atmospheric mixing overruling the chemical reactions in the upper atmosphere. Using OSIRIS H and K band spectra at low resolution,  Barman et al. (2011)  report on HR8799b exhibiting weaker CH 4 absorption than expected, and that disequilibrium chemistry may be at play in order to decrease the CH 4 abundance. Similar findings were also reported for the medium-resolution OSIRIS data for planets b and c, where CH 4 was not detectable in c  (Konopacky et al. 2013; Barman et al. 2015). Madhusudhan et al. (2011a)  found that they had to neglect the 3.
3 µm band containing too strong CH 4 absorption when fitting their chemical equilibrium models to the HR8799bc data obtained by  Currie et al. (2011)  (there was only an upper limit available for d in the 3. 3 µm band). Marley et al. (2012)  reported disequilibrium chemistry to be important for HR8799bcd. Skemer et al. (2014)  report on disequilibrium chemistry for HR8799cd, based on NIR and MIR (narrow and broad band) photometry. Based on photometry,  Currie et al. (2014)  report the need for disequilibrium in planets b and c, but less strongly for d and e. Lavie et al. (2017)  presented the first free retrieval analysis of the HR8799 planets and found that bc require disequilibrium chemistry, but not planets de. It is important to note, however, that no K-band spectroscopy was available for planet e, and that the K-band seemed heavily affected by systematics for planet d.
C/O deserves a more detailed discussion. Only one other study considered the atmospheric C/O. The retrieved values range from 0 to 0. 60 +0. 07 -0. 08 , the latter being the value we derive with petitRADTRANS in this study. As discussed in Section 4. 5, the Exo-REM fit runs into the grid boundaries for both C/O and [Fe/H], making it difficult for us to assess whether this trend to low C/O ratios is actually merited by the data or whether this parameter is used to copmensate for the too stringent cloud description. The value reported by  Lavie et al. (2017)  (C/O = 0) suffers from the fact that K-band spectroscopy was not available at the time of their study, such that the CO abundance could not be determined. They only report an upper limit for the atmospheric C/H value, and their best-fit spectrum does not show any CO absorption in the K-band, which we detect in the GRAVITY data.
Retrieval analyses could be aided by longer wavelength data in the mid-IR, however, which could reveal the spectral features of cloud particles at ∼10 micron  (Cushing et al. 2006 ). We ran our retrieval setup on archival GPI, SPHERE, and partially new GRAVITY data for the directly imaged planet HR 8799e, using Cloud Model 1. Applying such data-driven, free retrievals for directly imaged planets becomes possible with our high S/N observations, especially GRAVITY's K band spectra at a spectral resolution of R = 500. In addition, observations in the K band can probe H 2 O, CH 4 and CO features and are therefore crucial for constraining atmospheric disequilibrium chemistry and the atmospheric C/O. We are able to fit the observations well, and confirm a cloudy atmosphere dominated by disequilibrium chemistry.
Thus, such comparison retrieval studies offer additional insight regarding the real parameter uncertainties, including the modeling choices. On the observational side, additional data in the mid-IR, ideally spectroscopy, is necessary to explore the properties of clouds further. This is because the L', 3. 3 µm and [4. 05] band may encode information about a heterogeneous cloud cover. Excitingly, the outer planets (HR 8799bcd) will be studied in the mid-IR, using NIRSpec IFU spectroscopy with the JWST 11. HR 8799bcde will also be studied with photometry in the mid-IR with JWST 12. ESO's ERIS 13 instrument, to be mounted at the VLT, is a promising option, as well as KPIC, to be mounted on Keck  II (Mawet et al. 2018 ). ESO's imminent CRIRES+ instrument may offer the possibility to study the HR 8799 planets at high spectral resolution, where the S/N of the planetary flux measurement could be boosted using the cross-correlation method (e.
Acknowledgements. We would like to thank  Joanna Barstow  for a thorough referee report, which greatly improved the quality of this paper. We also thank the A&A editor,  Emmanuel Lellouch , for additional comments. P. M. thanks  M. Line ,  J. Zalesky , and  M. Min  for insightful discussions. P. M. acknowledges support from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 832428. T. S. acknowledges the support from the  ETH Zurich Postdoctoral Fellowship Program. G. -D. M. acknowledges the support of the  DFG priority program SPP 1992 "Exploring the Diversity of Extrasolar Planets " ( KU 2849/7-1 ) and from the  Swiss National Science Foundation  under grant  BSSGI0 155816  "PlanetsInTime". Part of this work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation.
A. V. and. G. O. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 757561 ). I. S. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No  694513. P. G. was supported by  Fundac  ¸ão para a Ciência e a Tecnologia, with grants reference  UIDB/00099/2020  and  SFRH/BSAB/142940/2018. T. M. acknowledges support by the grant from the Government of the Russian Federation  075-15-2019-1875 , "Study of stars with exoplanets". D. S. acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ). A. Z. acknowledges support fromthe  FONDECYT Iniciación en investigación  project number  11190837.
2. Simplified geological map of the study area (from http://dati. lazio. it/catalog/it/dataset/carta-geologica-informatizzata-regione-lazio-25000). Legend: (1) anthropic debris, (2) coastal and marsh sands and recent dunes, (3) calcareous marls and clays, (4) flysch, (5) clays with chalks, (6) landslide de posits, (7) alluvial deposits, (8) lavas, (9) travertines,  (10)  sandy deposits, and (11) sandy deposits of marine facies. The Holocene deposits are represented by travertines, slope debris, alluvial, weathering deposits, gravels, and sandy beaches  [22]. The neotectonic of this sector of the Tyrrhenian coast of Italy is marked by the elevation of the MIS 5. 5 marine terraces that show stability and a weak uplift of the inland sector for the last 124 years  [23, 24] , which is related to magmatic injections under the Vulsini and Sabatini volcanic complexes  [24].
To scale the aerial images, we used a dual-frequency geodetic Global Positioning System Real Time Kinematic (GPS/RTK) receiver STONEX S900A ® to measure the coordinates of a set of reference Ground Control Points (GCPs) falling in the investigated areas. GCPs positions were estimated in real time by the RTK technique with about 1 to 2 cm of accuracy, with respect to the reference GPS station TOLF. The latter is part of the GNSS network of the Istituto Nazionale di Geofisica e Vulcanologia (INGV)  [26]  and also pertains to the Leica SmartNet ItalPoS network (https://hxgnsmartnet. com/en-gb/) for real-time positioning services. We used the Agisoft PhotoScan ® software package (http://www. agisoft. com) based on the Structure-from-Motion photogrammetry technique  [27]  to process the acquired georeferenced images.
The analysis included: (1) camera alignment with image position and orientation, (2) generation of a dense points cloud, and (3) generation of an orthophoto covering a land surface of 0. 226 km 2 with a ground resolution of 1. 94 cm/pixel as well as the DSM creation in the WGS84-UTM32 coordinate system. The obtained ortho-rectified images (orthophotos) and digital elevation models were also managed by Global Mapper ® and ESRI ArcGis ® software to create cross sections, slope maps, surfaces, and coastline positions as well as calculate the dimension of the potential flooded areas. The extracted Digital Surface Model has a resolution of 15. 5 cm/pixel and a point density of 41. 5 points/m 2  As targets for GCPs measured by GPS/RTK, we used a set of i) natural markers belonging to fixed structures (e.
226 km 2 with a ground resolution of 1. 94 cm/pixel as well as the DSM creation in the WGS84-UTM32 coordinate system. The obtained ortho-rectified images (orthophotos) and digital elevation models were also managed by Global Mapper ® and ESRI ArcGis ® software to create cross sections, slope maps, surfaces, and coastline positions as well as calculate the dimension of the potential flooded areas. The extracted Digital Surface Model has a resolution of 15. 5 cm/pixel and a point density of 41. 5 points/m 2  As targets for GCPs measured by GPS/RTK, we used a set of i) natural markers belonging to fixed structures (e. g. , the center of manholes, wall and sidewalk corners, small structures, or large stones), and ii) mobile targets for the time of the flight, such as thin metal crosses of 60 × 60 cm of size, deployed in the investigated area.
These can be related to storm surge events that occur only a few hours in a year, when water is pushed from the sea onto the land due to a temporary decrease in atmospheric pressure and wind. We preferred to adopt this local vertical datum instead of the value of the geoid elevation for the Italian region provided by the International Service for the Geoid-ISG-ITG2009 (http://www. isgeoid. polimi. it/Geoid/Europe/Italy/ITG2009_g. html) [31]  since it is an independent and more accurate elevation datum. The International Service for the Geoid (ISG) estimates a geoid height in the Italian Geoid (ITG) 2009 for the coastline of Pyrgi at 48. 319 m. This elevation corresponds to the contour line equal to zero in the Italian height reference frame. The DSM height reference frame is 0. 42 m, as estimated by GPS/RTK data at a GCP located at the sea level along the coastline.
The International Service for the Geoid (ISG) estimates a geoid height in the Italian Geoid (ITG) 2009 for the coastline of Pyrgi at 48. 319 m. This elevation corresponds to the contour line equal to zero in the Italian height reference frame. The DSM height reference frame is 0. 42 m, as estimated by GPS/RTK data at a GCP located at the sea level along the coastline. Its elevation was corrected for the tidal range at the time of the surveys. The reference mean sea level estimated by the tidal analysis provided a local mean sea level with an uncertainty of ±11. 8 cm.
Vertical Land Motion (VLM) at Pyrgi The current rate of VLM at Pyrgi was estimated by the analysis of the available GPS data collected at the nearest GNSS stations of TOLF, belonging the INGV Rete Integrata Nazionale GPS (RING) network (DOI:10. 13127/RING) and MAR8, belonging to the Topcon-NetGeo network (http://www. netgeo. it). These stations, which are located at about 6. 5 km and 0. 3 km of distance from the study site, respectively (Figure  1 ), have a robust time series that span for 2004-2019 for TOLF (15. 23 years) and 2012-2019 for MAR8 (7. 38 years) (Figure  6 ).
Vertical Land Motion (VLM) at Pyrgi The current rate of VLM at Pyrgi was estimated by the analysis of the available GPS data collected at the nearest GNSS stations of TOLF, belonging the INGV Rete Integrata Nazionale GPS (RING) network (DOI:10. 13127/RING) and MAR8, belonging to the Topcon-NetGeo network (http://www. netgeo. it). These stations, which are located at about 6. 5 km and 0. 3 km of distance from the study site, respectively (Figure  1 ), have a robust time series that span for 2004-2019 for TOLF (15. 23 years) and 2012-2019 for MAR8 (7. 38 years) (Figure  6 ). GPS data analysis has been carried out following the procedures already described in Reference [32] and updated in Reference  [33]  by adopting a three-step procedure using the GAMIT/GLOBK V10. 7  [34]  and QOCA software.
Acknowledgments: The  SAVEMEDCOASTS  Project, funded by the  EU  (Agreement Number:  ECHO/SUB/2016/742473/PREV16 , coordinator  Marco Anzidei ) and  Istituto Nazionale di Geofisica e Vulcanologia  partially supported this study. We are thankful to  Vincenzo Sepe  of the  INGV , for his contribution during preliminary GPS/RTK surveys.
Motivation: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach. Results: Here, we describe coolpup. py-a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions.
TADs are suggested to constrain enhancer-promoter communication in some cases  (Franke et al. , 2016; Lupiañez et al. , 2015; Williamson et al. , 2019)  and might be related to genome stability  (Canela et al. , 2017 (Canela et al. , , 2019)) , while some loops have been suggested to correspond to enhancer-promoter contacts  (Rao et al. , 2014). In addition, distal polycomb sites can be brought together in 'loops'  (Joshi et al. , 2015; McLaughlin et al. , 2019). To our knowledge, currently the only robust method to identify loops de novo requires very deep Hi-C libraries, on the order of over a billion Hi-C contacts  (Rao et al. , 2014). This means that the vast majority of Hi-C datasets cannot be used to identify loops. However, they can be used to quantify the average loop strength (i. e.
enrichment of contacts in those loops relative to their local background). To do this, one can average (or 'pile up') all areas of the Hi-C maps containing loops, that have been annotated in a highdepth dataset  (Rao et al. , 2014). This idea is very similar to 'average/aggregate profiles' used, for example, in chromatin immunoprecipitation and sequencing (ChIP-seq) analysis to quantify signal in a subset of regions, except in Hi-C this is for a 2D matrix instead of a linear track. The same approach can, of course, be applied directly to the data where the loops were annotated for quantification of their average prominence. Apart from quantifying the strength of known features, the same analysis can be used to investigate whether certain regions, defined for example based on ChIP-seq peaks, tend to interact with each other on average above background.
, 2019; Kruse et al. , 2019; McLaughlin et al. , 2019; Nora et al. , 2017; Rao et al. , 2017; Rowley et al. , 2019; Schwarzer et al. , 2017). A visual interactive tool to semi-manually classify and pile-up predefined regions has also been developed  (Lekschas et al. , 2018). However, no single computational tool can perform all the various kinds of pile-up analyses that have been used in the literature, including local and rescaled (features of different size or shape are averaged, e. g. average TADs) and off-diagonal (e. g. average loops) pile-ups with different normalization strategies (Table  1 ). At the same time, performing detailed analysis of Hi-C data remains difficult for non-specialists due to the absence of easy to use tools. Here, we present a unified command-line interface tool written in Python to pile-up Hi-C data stored in the widely used and versatile.
cool format  (Abdennur and Mirny, 2019) (coolpup. py). A simple script for plotting the output of coolpup. py is provided in the package (plotpup. py), although for higher flexibility, we suggest directly using matplotlib or another library. We have extensively applied this tool to investigate the role of Polycomb group proteins in 3D genome organization of mouse ES cells  (Boyle et al. , 2019; McLaughlin et al. , 2019). Here, we have applied coolpup. py to published data to investigate the effect of different normalization strategies on the resulting pile-ups, and to replicate published results to verify coolpup. py's algorithm. We also present a novel variation of the pile-up approach implemented in coolpup. py that retains some of the locus-specific information and would allow more detailed statistical analysis of looping interactions in Hi-C data.
Sources of datasets and data analysis As a proof of principal, we applied coolpup. py to publicly available Hi-C data  (Bonev et al. , 2017; Nora et al. , 2017)  using distiller (https://github. com/mirnylab/distiller-nf) to obtain. cool files filtered with a map quality (mapq) of !30. We used these data at 5 kb resolution. In addition. cool files for single-nucleus Hi-C (snHi-C), together with coordinates of loops and TADs used in the original publication (kindly shared by Hugo Brandão)  (Gassler et al. , 2017; Rao et al. , 2014) , were re-analysed at 10 kb resolution (without balancing and with coverage normalization and 10 random shifts). We also used single-cell Hi-C data for mouse ES cells grown in serum from  Nagano et al. (2017)  (. cool files were kindly shared by Aleksandra Galitsyna) at 5 kb resolution.