dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to tackle all previously seen PAs, a current trend is to combine multiple cues (see Section 2. 5). However, due to the abovementioned challenges in the dataset creation as well as the technological advances that ill-intentioned users can access to deploy increasingly sophisticated attacks, the PAD method might have to detect PAs that were not included in its training dataset. This problem, called "Unknown attack" previously, is especially challenging. Beyond the current methods that try and use zero/few-shot learning approaches to tackle this problem, the question of learning features that are representative enough of "real" faces, so that they can discriminate between genuine faces and any kind of PAs under any type of capture condition, is still an open issue. This issue, for which some researchers have recently proposed solutions based on domain adaptation, will very probably raise a lot of attention from researchers in the coming years, especially with the emergence of ever more sophisticated DeepFakes.
As detailed in Figure  1  on page 3, obfuscation attacks may entail (possibly extreme) facial makeup or occluding significant portions of the face using scarves, sunglasses, face masks, hats, etc. In some cases, the person deploying obfuscation attack may also use tricks that are usually used for impersonation attacks, e. g. , by using a mask showing the face of a noncriminal. As well as impersonation attacks, obfuscation attacks also include previously unseen attacks/unknown attacks. The detection of unseen/unknown obfuscation attacks is still an open issue which needs to be further studied. To the best of our knowledge, so far, the only dataset containing examples of obfuscation attacks is SiW-M. This dataset has been introduced in  [87] , where the authors have shown the effectiveness of extreme makeup for facial obfuscation.
We have also gathered, summarized and detailed the most relevant information about a dozen of the most widespread public datasets for facial PAD. Using these datasets as benchmarks, we have extensively compared different types of facial PAD methods using common experimental protocol and evaluation metrics. This comparative evaluation allows us to point out which types of approaches are most effective, depending on the type of PA. More specifically, according to our investigation, texture-based methods which are also the most widely used PAD methods, and especially dynamic texture-based methods, are able to detect almost all types of PAs. Furthermore, the methods based on texture features learned using deep learning have significantly improved the state-of-the-art facial PAD performances compared to methods based on hand-crafted texture features.
However, in general, high-quality 3D mask attacks are still a great challenge for texture-based approaches. On the other hand, liveness-based methods or 3D geometric-based methods can achieve relatively better generalization capabilities, even though they are still vulnerable to video replay attacks or complex illumination conditions. Multiple cues-based methods, by leveraging different cues for facial PAD, are in general more effective for detecting various PAs. Nevertheless, the computational complexity of multiple-cues based methods is an issue that needs to be considered for real-time applications. Partly because of the complexity of the facial PAD problem, the huge variability in the possible attacks and the lack of dataset that contains enough samples with sufficient variability, all current approaches are still limited in terms of generalization.
The face angle was only limited to 300 degrees. Imposters stood within a range of 0. 3 to 1. 0 m from the camera. The RealSense SR300 camera was used to capture the RGB, depth and Infrared (IR) images. The database is divided into three subsets for training, validation and testing. In total, there are 300 subjects and 6300 videos (2100 for each modality) in the training set, 100 subjects and 2100 videos.
UAD [76] MSU-USSA [66] 2015 404 2016 1140 Caucasian 44% Asian 53% African 3% Diverse set (from web faces database from the [183]) 7× video replays Printed photos Photo display 3× video replays 808/16,268 (V) 1140/9120 (V) 7 display devices White paper (11 × 8. 5 paper) Macbook (2080 × 1800) Nexus 5 (1920 × 1080) Tablet (1920 × 1200) Frontal No Frontal Yes 6 different cameras (no moible phone) (1366 × 768) Nexus 5 (front: 1280 × 960) (rear: 3264 × 2448) iPhone 6 (rear: 1920 × 1080) 6 different cameras (no moible phone) (1366 × 768) Same as MSU-RAFS Cameras used to capture celebrities' photos are unknown.
Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index.
A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period.
Proxy records provide indirect estimates of past local or regional climate, derived from natural archives coming for instance from sediment cores, speleothems, ice cores or tree rings. According to its nature, each proxy record has a specific temporal resolution, from years to millennia, and can cover a specific period: from hundreds to millions of years. New proxy records are continuously gathered, extending the available datasets and allowing paleoclimatologists to build increasingly consistent reconstructions  (PAGES 2K Consortium, 2013 , 2017). Based on the assumption that climate modes such as the NAO affect climate conditions in different locations, some studies have used regression-based methods on temperature and drought-sensitive proxy records to reconstruct the variability of these modes over the last thousand years.
In this paper, we present the computer tool ClimIndRec (Climate Index Reconstruction) version 1. 0, which includes multiple statistical approaches, for reconstructing climate modes indices. It is based on four regression methods: PCR  (Hotelling, 1957) , partial least squares (PLS;  Wold et al. , 1984) , elastic net (Enet;  Zou and Hastie, 2005)  and random forest (RF;  Breiman, 2001). It communicates with a large proxy database that contains various types of proxy records distributed worldwide, which are sensitive to different climate variables. ClimIndRec is thus designed to reconstruct the past variability of different climate modes (Fig. 1 ). It should be stressed that ClimIndRec will only be useful with climate indices for which there are enough proxy records represent- ing their regional climate imprints, and that have the appropriate time resolution to capture their preferred timescale of variability.
2. 3. 2. It gives a score estimating the model ability to reconstruct the climate index using the first-seen data of proxies. This procedure is called the "hold-out" approach  (McCornack, 1959). The scores obtained for different regression methods for a given training/testing sample might be impacted by the specific sampling. This is overcome by repeating the hold-out approach several times where years of observations between the training and the testing samples are shuffled. An ensemble of scores is obtained, yielding an evaluation of the methods' ability to reconstruct the climate index. The most robust regression model is the one that has the highest scores, as it means this is the most accurate at reconstructing the climate index using the first-seen data of proxies. This most robust regression method is then applied to the whole learning period to build a final model and infer the paleo-variations of the climate index from proxy records.
Data The assessment of the proposed reconstruction techniques is investigated for the NAO index, as it is probably the mode of variability that has been observed for the longest time period. This index is indeed relatively simple to calculate from the SLP time series as it only requires two locations with instrumental records: one within the centre of action of the Azores anticyclone (typically Gibraltar) and one within the Icelandic Low (typically Reykjavik). The reference NAO index is then calculated as the normalized SLP difference between these two locations. We here use the  Jones et al. (1997)  index spanning the whole historical period since 1856. In terms of proxies, we use the state-of-the-art PAGES 2k database (PAGES 2K Consortium, 2017) in its latest 2017 version (hereafter P2k2017).
Proxy records with resolutions lower than annual were removed. Even if these proxy records could be interpolated to a finer temporal scale and used for the reconstruction, their use is not recommended as the interpolated time series will present high auto-correlation coefficients, which could inflate the correlations with the NAO and thus their weight in the final reconstruction, potentially leading to spurious results  (Hanhijarvi et al. , 2013). We added 44 annually resolved proxy records used in  Ortega et al. (2015)  and not present in P2k2017 (see Table  S1 ). We end up with a database of 554 well-verified and worldwide-distributed annually resolved proxy records.
Methodological sources of uncertainty in the reconstruction We apply ClimIndRec with the four methods presented above to the reconstruction of the NAO. In the following, each reconstruction is obtained by averaging R individual reconstructions performed for R training/testing splits. R depends on the size of the testing samples relative to the size of the learning period as we perform block-style splits of the data to produce training and testing samples (Sect. 2. 1 and 2. 2). Here, we set the relative length of the training splits as 80 % of the learning period. NSCE scores are thus produced and stored in a vector of R elements. This vector will thus be used as a quality metric to characterize the methodological uncertainty in the reconstruction. The following actions were undertaken to minimize the reconstruction uncertainty identified in Sect.
2. 1, and estimate its sensitivity: 1. pre-selecting the most relevant proxy records, 2. selecting the best learning period. These two steps are described below, before assessing the reconstruction itself. [1000, 1970] ] and the learning period T =  [[1856, 1970] ] encompassing 110 available proxy records with n = 115. Figure  5  shows that RF method, particularly useful for larger datasets, is more efficient using the proxy records correlated at the 80 % confidence level with med(S NSCE ) = 0. 15 (med is the median function), even if using proxy records uncorrelated with the NAO or not located in regions affected by NAO variations. On the other hand, the three other regression methods are more adapted when the finest proxy selection (95 %) is applied, as highlighted by  Ortega et al. (2015)  www.
Discussion and conclusion Discussion, caveats and outlooks The results presented above regarding the NAO have all been obtained using ClimIndRec. Indeed, they require advanced programming and statistical knowledge to ensure a good estimation of the reliability of the reconstruction performed. This is possible because ClimIndRec offers an integrated package through which parameters and methods can be efficiently tested and compared, together with reliable validation metrics such as the NSCE. Nevertheless, the methodology proposed in ClimIndRec could be further improved in different ways. ClimIndRec does not deal with missing data in proxy records. This implies selecting exclusively the proxy records that entirely cover the reconstruction period, which thus excludes some existing proxy records.
(2015)  reconstruction but remains significantly correlated with it (r = 0. 49; p < 0. 01, over the period 1073-1855). We have shown that for Enet, PLS and particularly PCR, which is frequently used in paleoclimatology, selecting proxy records with a strong correlation with the index to be reconstructed over the training periods is a good way to improve the NSCE scores, and hence it allows more reliable reconstructions (Sect. 3. 1. 1). Contrarily, RF gives more reliable reconstructions using the proxy records significantly correlated at the 80 % confidence level with the NAO (Sect. 3. 1. 1). This may be due to the fact that it has been mainly developed for large datasets  (Breiman, 2001). For both cases, gathering new proxy records to the 554 available proxy records collected, may be a reliable source of reconstruction improvement.
The inclusion of new NAO-sensitive proxy records in the future may thus lead to better reconstructions. ClimIn-dRec should allow one to easily perform such new reconstructions. Code and data availability. ClimIndRec's code and the proxy records database are available at https://github. com/SimMiche/ ClimIndRec (last access: 29 September 2019), and at the following Zenodo link: https://doi. org/10. 5281/zenodo. 3464293 (Michel, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-841-2020-supplement. Author contributions. SM integrally coded CliMorRec and used it to produce the results of this study. SM was the main author of the manuscript, including figure production. DS contributed to develop the main features of ClimIndRec and supervised the manuscript writing throughout the process.
Acknowledgements. To develop the statistical tool and analyse its outputs, this study benefited from the  IPSL Prodiguer-Ciclad facility , supported by  CNRS ,  UPMC Labex L-IPSL. Finally, this study used the PAGES 2k database version 2. 0, available online and supported by the  PAGES group. Financial support. This research has been partly funded by the  Université de Bordeaux. It is also funded by the  LEFE-IMAGO  project  VADEMECUM. Didier Swingedouw  is supported by the  European Commission, H2020 Research Infrastructures (Blue-Action  (grant no. 727852 ) and  EUCP  (grant no. 776613 )). Review statement. This paper was edited by  Lauren Gregoire  and reviewed by three anonymous referees.
This paper is intended to give a comprehensive overview of the current status and developments of the Paris Observatory STARK-B, MOLAT and SESAM databases which can be interrogated thanks to interoperability tools. The STARK-B database provides shifting and broadening parameters of different atomic and ionic transitions due to impacts with charged particles (the so-called Stark broadening) for different temperatures and densities. The spectroscopic MOLAT and SESAM databases provide the wavelengths, the oscillator strengths or Einstein spontaneous emission coefficients of H 2 , CO and isotopologues molecules.
Introduction Astrophysical environments can only be probed remotely thanks to particle or photon messengers collected through space or ground-based telescopes. The analysis of the recorded spectra is expected to allow for derivation of the physical conditions of the observed regions. However, this can only be achieved if the fundamental basic microscopic processes involving atoms, ions, molecules, electrons, etc. , are well enough identified and understood. We describe in this review the efforts developed at the Paris Observatory to provide complete and relevant spectral information dedicated to astrophysical and laboratory plasmas and colder molecular environments through online databases. Section 2 reports the STARK-B database providing the shifting and broadening parameters of different atomic and ionic transitions due to electron impact, referred to as Stark broadening, for different temperatures and densities.
We present in Section 3 the spectroscopic MOLAT and SESAM databases which provide the wavelengths and the transition strengths of different molecules. These achievements have greatly benefited from the interoperability tools developed within the VAMDC project which will be described in another paper of this issue by M. L. Dubernet and coworkers. The integration process of SESAM and STARK-B databases into the VAMDC architecture and the software tools that were used are described in Section 4.
The access to such data via online databases has become indispensable. In Astrophysics, the interest for databases emerged at the IAU XXIInd General Assembly (1994), where a Joint discussion "Atomic Databases, New power Source for Astronomy" was organized by W. L. Wiese  [1]. In fact, since the eighties, Sahal-Bréchot, Dimitrijević and coworkers have been updating and operating at a large scale the numerical code that was created by Sahal-Bréchot several years earlier for calculating Stark broadening and shifting of isolated lines in the impact complete collision approximation of neutral and ionized elements. Though this broadening mechanism is usually called "Stark" broadening, it is a matter of collisions with electrons and ions and not of Stark effect. This code, named SCP, is based on the impact semi-classical-perturbation approximation  [2].
Thanks to this collaborative work, which is currently being continued, more than 150 papers, concerning 125 neutral and ionized elements broadened and shifted by collisions with electrons and ions, have been published. In addition, the development of the Virtual Observatory, due to the great amount of data which needed to be connected, increased in the beginning of this new century. Interoperable e-infrastructures were urgently needed. This request enabled the creation of databases at the Paris Observatory. Then LERMA 1 laboratory gave us a support to put all the results of our numerous tables in a database named STARK-B  [6]. A precursor, named BELDATA, was foreseen by AOB (Astronomical Observatory of Belgrade)  [7]. Then, STARK-B started at Paris Observatory, with S. Sahal-Bréchot and M.
S. Dimitrijević, who developed the database with the support of LERMA, as part of the databases of the Paris Observatory. STARK-B opened on line at fall 2008 with a link to the Serbian Virtual Observatory  [8]. Currently, S. Sahal-Bréchot and M. S. Dimitrijević are the scientists co-responsible for the database, and N. Moreau is responsible for all the technical aspects. STARK-B is in free access  [6]  and is also accessible through the thumbnail of the Scientific databases of the website of Paris Observatory  [9] , in addition to MOLAT  [10]. In 2009, the European project VAMDC (Virtual Atomic and Molecular Data Centre) [11], was born, and this helped us a lot. STARK-B became a node of VAMDC. VAMDC is a secure, documented, flexible, interoperable platform based on e-science, which permits optimized search and exchange of atomic and molecular data.
Description of STARK-B Database Since 2010, approximately every two years the database and its detailed content has been presented in several workshops and conferences. We will only give a brief outline in the following. The homepage of STARK-B (http://stark-b. obspm. fr) has several menus. "Introduction" summarizes the theoretical methods and approximations, and provides references to the basic papers. "Data Description" describes the data that are in the files. "Access to the Data" provides a graphical interface with the periodic table with access for getting to the required data. The user is guided throughout the query toward the desired element; the required ionization degree; and the required lines, densities, temperatures and colliding perturbers. Concerning SCP data, colliding perturbers are electrons, and most often protons; He + and He ++ ions, which interest stellar physics; and frequently, other positive ions interesting for laboratory or technological plasmas.
For MSE data, only widths (and sometimes shifts) due to electron collisions exist. The user can make a query by domain of wavelengths or by transitions. The data model (in particular the identification of the levels of the line transitions) follows the VAMDC standards, in order to allow interoperability with other atomic databases included in VAMDC. So the transitions are defined by configurations, terms and J-values, but different couplings can be taken into account. Then a table displaying the widths and the shifts is generated. According to the theory, when LS coupling is valid, the data are given for multiplets only and not for the individual fine structure lines. A number of warnings (validity of impact approximation, of isolated line approximation, of complete collision approximation) are included in the tables.
It gives the fitting coefficients with the temperature, which can also be downloaded in ASCII and in VOTable format, and introduced in the computer codes for stellar modeling. Newly added datasets and revised datasets are displayed in the "Data History" menu. The "Contact" menu is intended for further inquiries and user support. Almost 2000 queries have been performed on the STARK-B website over 2018 and 2019. The access query through VAMDC is minor (less than one hundred per year).
MOLAT and SESAM The MOLAT  [10]  and SESAM  [17]  databases provide atomic and molecular data computed or gathered by members of the Paris Observatory and some other spectroscopic databases. The consulting and possible downloads are fully accessible without any restriction. The focus is principally on UV electronic spectroscopy, which reflects the availability of the 10 m VUV spectrograph on the Meudon site of the Paris Observatory and the work performed on the LURE synchrotron radiation facility on the Orsay Campus of Paris University. These experimental studies were closely linked to several spatial missions: the 1200-1500 Å spectral range was indeed available with the International UV Explorer (IUE) and the Hubble Space Telescope (HST) with the Goddard High Resolution Spectrograph (GHRS).
MOLAT The MOLAT database was set up about thirty years ago. The deliveries are essentially ascii files with proper references. Nevertheless, some data are also displayed in PDF format. We focus here on the data which have been subsequently translated in the XML language via the XSAMS protocol to comply with interoperability requirements. They correspond to VUV Spectra of H 2 and its isotopologues and CO. The specificity and we think the interest of the deliveries are the joined information of spectral transition wavenumbers/wavelengths and the corresponding oscillator strengths or Einstein emission transition probabilities. Most spectroscopic experimental papers report accurate transition wavenumbers which are subsequently interpreted in terms of Hamiltonian properties with derivation of term energy values.