dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, for our typical datasets the planetary CCFs drift by less than σ from one observation to the next, such that the exponential functions in Eq. 15 partially overlap. This means that the individual exponential contributions cannot be distinguished in the shape of the function G(v), which tends to value 1 when v runs in the velocity range encompassed by the set v p,i , and tends to 0 as v runs out of this range. The average CCF in Eq. 14 can now be used to normalize the CCF of the individual spectra (Eq. 12), obtaining:  16 ) r (v) thus represents the amplitude of any Doppler-shifted signal with respect to the continuum of the average CCF. r (v) = CC F (v) CC F (v) = (1 -)CC F o (v) + CC F o (v -v p ) (1 -)CC F o (v) + δ + A G(v). ( In Fig. 1  we plot Eq. 16 assuming Eq. 6 with σ=10.
e. the contrast of the planetary signal against the continuum is reduced by a factor 1 -G(v p ) with respect to r max (v p ). For the datasets we analyze in this work, following the definition in Eq. 15 we have G(v p ) 10 -2 -10 -1 , i. e. the planetary signal is reduced by 10% at most. From a different perspective, the effect of G(v p ) in Fig. 5  is to lower the continuum of the r (v) function in the velocity range spanned by the planet, such to decrease the strength of the planetary signal. Incidentally, we remark that this effect was ignored by  Martins et al. (2013)  and it may explain why they could not retrieve exactly the same signal which they injected in their simulations. Moreover, the decrease in the continuum level is present in the examples shown by  Borra & Deschatelets (2018) , but the authors do not discuss its origins and effects.
Observations and data reduction We observed the 51 Peg system as part of the GAPS program for the TNG (PI G. Micela,  Covino et al. 2013 ) in GIARPS mode  (Claudi et al. 2017) , which allows simultaneous coverage of the optical and near-infrared spectral bands. In this work we only analyze the optical spectra provided by the HARPS-N instrument  (Cosentino et al. 2012) , a collection of two sets of observations in the nights of 26 and 27 of July 2017, for a total of 159 spectra. The infrared spectra are currently under analysis and they will be discussed in a future publication. We complement our dataset with publicly available data, taken with the same purpose of measuring the light reflected by the HJ in the system. The full list of dataset is in Table  1. The dataset of program 091. C-0271 has been analyzed by  Martins et al.
As we will discuss in the following, our data analysis requires that many spectra are taken within the same night of observation. For this reason, we do not analyze the full library of spectra from the HARPS archive, as they were sparsely collected across different nights (see the itemized list above for the dates spanned by each program). We only select the 39 (out of 91) spectra taken on the night of 2013-09-20 for program 091. C-0271 and the 48 (out of 218) spectra taken on the night 2018-10-21 for program 101. C-0106. These two subsets are the largest collections of back-to-back spectra provided by the two programs. The remaining spectra have been collected occasionally on different dates and we use them only for the refinement of the orbital solution (Sect. 4), not for the extraction of the reflected spectrum (Sect.
Data analysis To enhance the detectability of the stellar light reflected by 51 Peg b, we use the CCF technique described in Sect. 2. In the computation of the CCF, we do not use the full wavelength coverage of the spectra (∼3800-6900 Å), but we operate the following cuts referred to the stellar rest frame: • we discard the spectral range λ<4500 Å, because the bluest echelle orders are the noisiest ones and accurate continuum normalization cannot be achieved; • we discard the range λ>6700 Å as it is heavily contaminated by saturated telluric absorption by O 2 that cannot be corrected accurately by MOLECFIT; • the HARPS spectra do not cover a wavelength window of ∼100 Å around 5300 Å. To make the datasets comparable with each other, we cut the 5250-5350 Å wavelength range in all the spectra in Table  1 , as it is the shortest cut which excludes the blind range in all HARPS spectra; • in Sect.
Analysis of simulated datasets Before analyzing the data, we run a few simulations to test the robustness of the method. The first step is thus to simulate datasets where we inject a known signal. To this purpose, for each night of observations, we use the corresponding average spectrum as a model template and we generate the simulated spectra assuming the same orbital phases sampled during the night. We inject the planetary spectrum assuming the ephemeris in Sect. 4, together with K p =132 km/s, max = 10 -4 and i = 80 • (consistently with Borra & Deschatelets 2018), and computing the planetary spectrum, velocities and phase functions according to Eqs. 1-5. To each simulated spectrum, we finally add random noise using the noise model we mention in Sect. 3. In Fig. 5  we plot an example of the simulation, using the dataset of date 2017-07-27 in Table  1 : the individual CCF and the average CC F are identical within noise, such that the planetary signal cannot be discerned.
However, in the general case of a back-scattering atmosphere any alternative to Eq. 4 is a function which monotonically increases towards superior conjunction. Using different formulations of the phase function will still put more emphasis on the spectra taken closer to superior conjunction, and will introduce second order corrections to the final result. Secondly, we exclude all the spectra taken too close to superior conjunction. Referring to Fig. 1 , we exclude the range 0. 48 < φ < 0. 52, because in this phase interval the planetary and stellar CCFs are blended and thus cannot be separated. This rejection criterion excludes 5, 43 and 40 spectra taken on  2015 -10-27, 2016 -10-12 and 2016-10-29 respectively (Table 1)-10-29 respectively (Table 1). As an example, we apply this approach to the same simulated datasets discussed so far (date 2017-07-27).
clearly shows that the planetary signal peaks close to the assumed K p =132 km/s. As expected, we also find that random noise in the continuum of r (K p ) has scaled down approximately by the square root of the number of spectra. We further improve the detection of the planetary signal by extending our approach to the full dataset: after exclusion of the 88 spectra close to superior conjunction, 411 spectra are left in total and random noise is further decreased by a factor of ∼2.
Analysis of real datasets The simulation discussed so far proves that our method is able to robustly extract the planetary signal claimed by  Martins et al. (2015)  and  Borra & Deschatelets (2018). We now want to replicate their detection using the large set of spectra we have collected (Table  1 ). The result of our analysis is shown in Fig. 7. The most striking evidence is that we do not find any signal at the expected K p 132 km/s above noise. For the sake of comparison with  Martins et al. (2015)  and  Borra & Deschatelets (2018) , in Fig. 7  we also plot the result of our method applied only to the dataset taken on 2013-09-30. Also in this case we do not find any evident signal above noise, which is now larger because we have restricted the analysis to a smaller set of spectra. We remark that, despite all the approaches converge in using the CCF of the spectra, several differences are in place.
8. As a final test, given that we do not find any signature of the planetary reflected spectrum, we assume that the stellar spectra are not contaminated by the planet, and we inject in the spectra a fake planetary spectrum assuming K p = 132 km/s and max = 10 -4 , as done for the simulations discussed earlier in this section. We find that the such a signal would be clearly detectable above noise in all the datasets in Table  1  and even better in the joint analysis (right panel in Fig. 7 ). In particular, the amplitude of the planetary signal would be ∼ 3. 6 and ∼ 22 times larger than noise when we analyze the night 2013-09-30 and the full dataset respectively.
We also described how we reduce the data in order to optimize the extraction of the planetary signal. We check that our method and data reduction are robust enough to allow the detection of the signal claimed by  Martins et al. (2015)  and  Borra & Deschatelets (2018). However, we do not find any evidence of the reflected planetary spectrum. Including our re-analysis, there are thus two firm detections of the planetary reflected spectrum in the optical and two null detections. The two positive detections are obtained using similar techniques on the same dataset. The null detection by Di  Marcantonio et al. (2019)  is obtained on the same data but with a completely different mathematical approach, while our result is an extension of the CCF technique to a larger dataset. This opens the possibility that the detected signals may be caused by pitfalls of the technique coupled with the characteristics of the analyzed dataset.
As a matter of fact, once the planet-tostar flux ratio max in Eq. 5 is fixed, there is an inverse proportionality between the geometric albedo A g and the square of the planetary radius R p. Based on observational evidence,  Angerhausen et al. (2015)  provide a typical value of A g = 0. 1 for the planetary albedo, which translates in a radius of ∼ 3. 9 R Jup. Combining this predicted radius with the mass estimates provided by  Brogi et al. (2013)  and  Birkby et al. (2017) , we obtain that the bulk density expected for 51 Peg b is 0. 01 g/cm 3 , which puts 51 Peg b beyond the sample of H/He dominated extremely-low density planets  (Laughlin 2018). This is a plausible yet unlikely scenario: for example, to date in the Exoplanet Orbit Database  (Han et al. 2014 ) there is only one HJ (HAT-P-65 b,  Hartman et al.
Acknowledgements. This research has made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets. org. GSc acknowledges his niece MMa for delighting her proud uncle during the writing of this paper. GSc, FBo, GBr, IPa and GPi acknowledge the funding support from Ital-Article number, page 12 of 13 G. Scandariato et al. : The  GAPS Programme: No detection of reflected light from 51 Peg  b.
We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers.
The primary goal of our work-similar to that of past work on IEEE VIS papers  [15] , keywords  [16] , or EuroVis papers  [39] -is to extend the corpora of data we can use for studies of the visualization field. Our primary contribution is a dataset we call VIS30K (Fig. 1 ). It contains images and tables from 30 years (1990-2019) of the IEEE VIS conference, spanning all tracks: Vis, InfoVis, SciVis, and VAST. IEEE VIS is the longest-running and largest conference focusing on visualization and its images reflect the evolution of the field. Our primary data sources include IEEE Xplore, conference CDs, and hard copies of the conference proceedings from which we obtain the images in their best possible quality. In addition to images, we include tables as special form of data organization that can be informative to the community.
Our dataset can serve many purposes. It enables researchers to study the visual evolution of the field from an objective, image-centric point of view. It assists teaching about visualization by providing fast visual access to refereed research images and contributions. It also can serve as a data source for researchers in other fields such as computer vision or machine learning. And, finally, it supports visualization researchers when browsing and discovering new work. Collecting these figures and tables was challenging. We optimized data quality with a hybrid solution. We first extracted figures via convolutional neural networks (CNNs), followed by expert curation. This way we ensure reliable data (i. e. , completeness and image locations/dimensions), while at the same time requiring a manageable amount of manual cleaning and verification.
RELATED WORK Previous work from three areas inspired our own. The first surveys past work and offers visual access to past publications. A second group collects and analyzes metadata derived from visualization research papers. The third group relates to the CNNbased extraction algorithms we employed for our data extraction. Visual Collections of Visualization Research. We are not the first to attempt a visual overview of the visualization field. Yet past work generally focuses on specific subareas of the research, each of which provides an overview of work on the subtopic rather than a comprehensive and browsable image database. For instance, some work has focused on providing references and representative images of specific data or layout techniques: Schulz's 300 tree-layout methods  [35] , Kucher and Kerren's more than 470 text visualizations  [21] , Aigner et al.
's over 100 temporal data visualizations  [1] , and Kehrer and Hauser's multivariate and multifaced data of more than 160 images  [18]. Others examine specific visualization applications, e. g. , Kerren et al. 's biological data  [19] , Kucher et al. 's sentiment analysis  [22] , Chatzimparmpas and Jusufi's trustworthy machine learning  [4] , and Diehl et al. 's VisGuides  [9]  on advice and recommendations on visual design. In contrast to these focused perspectives, VIS30K provides a broader coverage of all 30 years of IEEE VIS. Our downloadable dataset comprises all the images contained in each paper, rather than just a few samples per publication or approach. The work most closely related to ours is Deng et al. 's VisImages collection of IEEE InfoVis and IEEE VAST images  [8]  and Zeng et al.
's VISstory  [44]. Both sets of authors plan to release their datasets but only provide a subset of our data. VISstory only covers data from 2009-2018, while VisImages does not include IEEE Vis and SciVis paper images. Our work also differs in its approach to quality control. We rely on expert input to check the capture of all images, while VisImages uses crowd-sourcing. VISstory only tests a subset of images for quality. Similar to these tools, we provide a web-based tool to explore the image data although focus on different aspects. VisImages categorizes image content in addition to metadata and VISstory focuses on a paper rather than image-centered views where each paper is encoded as a ring with sectors standing for individual images. Meta-Analysis of Visualization Publications. Another direction of research centers on meta-analyses of the visualization field, without focusing on visual content.
Lam et al. [23] , e. g. , established seven empirical evaluation scenarios by analyzing 850 papers appearing in the 'information visualization' subcommunity of IEEE VIS. Isenberg et al. [17]  later extended this historical analysis of evaluation practices to all tracks of IEEE VIS in a systematic review of 581 papers. Isenberg et al. [16]  further collected IEEE VIS paper keywords to derive visualization topics from a metadata collection of IEEE VIS publications  [15]. We make use of metadata from this collection in our work to gather paper PDFs prior to automatic extraction. Conceptually, our new VIS30K extends this line of work by leveraging new image-based extraction methods  [27]  and search tools to make the figure and table data accessible. CNN-based Extraction Algorithms. Using data-driven CNN algorithms to train classifiers to extract figures and tables is becoming increasingly popular  [6] ,  [31] ,  [37].
Terms IEEE VIS. Over its history, IEEE VIS has undergone a number of name changes ( Fig. 2 ). It started out in 1990 as IEEE Visualization (Vis), then added IEEE InfoVis in 1995 followed by IEEE VAST in 2006. In 2008-2012, all three venues were jointly called IEEE VisWeek, and since 2013 the blanket name IEEE VIS has been used. From 2013 onward, the IEEE Vis ceased to exist, replaced by the IEEE Scientific Visualization (SciVis) conference. Here we use VIS to refer to all four venues: Vis, InfoVis, VAST, and SciVis, for the entire time period covered by our dataset. Figures and Tables. We refer to a figure as a container for graphical representations. These representations can be images, screenshots of visualization techniques, user interfaces, photos, diagrams, and others. We classify algorithms, pseudocode, and equations as textual content and thus do not include them in VIN.
Choosing Figures and Tables Scholarly articles are often structured based on a template and are properly referenced, yet authors use varying approaches to generate figures and tables and to embed them in their papers (Fig. 3 ). These varying practices required us to make decisions about which types of visual representations to include and exclude in our database: High Variation in Composition of Figures and Tables. Authors often treat algorithms, pseudocode, and tables as figures with figure numbers. In our data collection, we separated algorithms and pseudocode from figures and tagged tables and figures separately. While both pseudocode and algorithms are important scientific content in papers, they generally consist of text and are not the forms of visual data representations we target with VIS30K.
Occasionally, authors placed figures and tables wrapped within the text flow without captions or figure/table numbers. We collected such figures and tables nonetheless but excluded small, often repetitive word-scale visualizations and word-scale graphics such as those in Blascheck et al. 's work  [2]. In our dataset, we list tables that contain primarily text but sometimes also small inline images (e. g. , Fig. 3(f )) as tables. We include other column-row representations such as heatmap matrices and table lenses (e. g. , Fig. 3(g) ) that use a primarily graphical encoding as figures. Handling of Subfigures and Subcaptions. A figure can be composed of multiple images or be a combination of images and text. Such composite figures are common in visualization papers (e. g. , Fig. 3 ). We initially hope to dissemble these composite figures into subfigures, but ultimately choose not to due to their variable degree of separability.
Training Data Preparation: Pseudo-Papers The essential part of our approach is to treat training data as a composition of individual document elements, where the goals are (1) to record bounding boxes for each of the labels and components in a PDF image to produce high-quality labels for the training data; and (2) to synthesize appearance to reduce the differences between the training data and the real papers to some extent. Image and Text Corpora. To reduce the number of training images needed and to increase training data diversity, we used image collections from Borkin et al. 's MASSVIS dataset  [3]  and from the SciVis memorability data by Li and Chen  [26]. Early papers are often black and white and may contain salt-andpepper image noise. This variation in image appearance (brightness, contrast) can reduce our CNN's image detection accuracy.
In the prediction stage, we downloaded the paper PDFs by following the links in the VisPubData database  [15]. We excluded short papers, posters, panels, and keynote files, so our collection comprised 2,916 full papers for the years 1990-2019. We first converted these PDFs to PNG pixel images using the convert command with dpi = 300 and pixel resolution up to 2353 × 3213. This conversion was necessary to capture all images in their cameraready rendered visual form in the paper PDF, including scanned pages from early years, vector images, pixel graphics, simple text versions, and any combinations thereof. We then fed these images into the CNNs to extract figures, tables, captions, etc. For each  paper page, we thus produced the bounding boxes of the 17 classes (6 textual content, 11 figures/tables).
Fine-Grained Recognition and Data Validation Fine-grained recognition refers to the task of distinguishing very similar categories or correcting the results to obtain the ground truth. CNN results can lead to errors or imprecision in image detection and localization  [31]. We obtained the final cohort by manually cleaning the CNNs' predictions using a collaborative annotation tool with two interfaces: one enables us to edit the content of the dataset and the other provides an overview of the data collection by year (see supplementary materials Sec. B for these two interfaces). We used the first interface to examine all pages in our dataset and check the labeling to remove, add, move, and resize the machine-generated bounding boxes as needed. After this individual pass, the first two authors of this paper used the second interface to verify the results for the entire dataset.
Through this process we cleaned 26,776 figures and 2,913 tables from all 30 years of the IEEE Visualization conference, as described in Sec. 3. Using the manually cleaned data as the ground truth, we evaluated our CNN-based labeling following the evaluation metrics of PDFFigure2. 0  [6] , using intersection over union (IoU = area o f overlap/area o f union) as 0. 8. We found the overall recall of our CNN-based extraction approach on the VIS30K images to be 0. 84, with precision 0. 94 and F1 score 0. 89. For this analysis we only used the "image" label in our dataset because our training phase images came from two limited datasets-they did not capture the full range of images in visualization papers. Nonetheless, we analyzed the entire dataset, including the early years with their low-quality images for which other algorithms would fail.
VISIMAGENAVIGATOR (VIN): EXPLORING FIG-URES AND TABLES IN THE LITERATURE We posit that our ability to extract data must be accompanied by the community's ability to use, further classify, manage, and reason about the content of the figures and tables. Our second contribution is thus the design and implementation of VISImageNavigator (VIN; see Fig. 6  and visimagenavigator. github. io), a lightweight online browser to view and query the dataset and its metadata. VIN can be used to explore VIS figures and tables, VIS publication venues, keywords, and authors over time.
Fig. 6. Our VISImageNavigator (VIN) interface and search engine. We arranged figures and tables using (a) a "brick wall" layout, (b) a timeline view, or (c) a paper list layout We color-coded the images with frames based on the conference types. Users can query the database, by terms (using authors' keywords or terms in titles and abstracts), by image type (figure, table, or both), by conference category (Vis, SciVis, InfoVis, VAST), or by year. A click on an image displays article details including authors and a hyperlink to the full PDF in IEEE's digital library. visualization techniques have been developed? and what are the techniques in domains such as "brain imaging" and "quantum physics"? (cf. see Fig. 10  of the supplemental material Sect. D). To answer this question using VIN, the user can query the term 'brain' to reveal diverse advances in showing tomography in the nineties, tensor lines, as well as metaphorical maps.