dataesr/scientific-paragraphs-categorization huggingface dataset:
They concluded that hyperspectral imaging could become an efficient tool for the recognition of figures, coloring matter and state of conservation. The objective of this study is to show what kind of useful additional information VNIR HSI imaging can bring in the case of rock painting on yellow-reddish oxidized sandstone walls measured in shaded shelters, compared to standard RGB cameras. In particular we are focusing on the capacity of HSI to detect invisible/barely visible figures, to extract the pigment distribution from the rock texture pattern and to separate the different paint layers. 'Visual simulations' using only the visible range of the HSI data is out of the scope of this paper. The images we provide are B&W or 'false color' synthetic images to best display to the eye the 'invisible' and mathematically transformed spectral information contained in HSI data.
DStretch® process from classical RGB camera Different types of stretching algorithms (decorrelation stretch (DS), photographic stretch, saturation stretch, …) and transformation algorithms (PCA, ICA, MNF) may be applied to RGB images to enhance the information it contains. Some comparisons have been already made between DS and PCA, showing that the later was slightly more efficient  [63]. We made some tests of all these algorithms on several of the images presented below and finally decided to use only decorrelation stretch throughout this study by using the DStretch® software, the only algorithm/software widely used as a reference tool by prehistoric archaeologists. Thanks to its flexibility in use and its optimization to the specific problem of rock painting it provides among the best results when faint paints are present.
Data aquisition using HSI Specim IQ camera Field captures of rock paintings were performed with a SPECIM IQ (Fig. 4 ), an HSI system operating in the VNIR, which is based on an internal line scanner process, i. e. , push broom principle. It covers the whole wavelength range 400-1000 nm at a spectral resolution of 7 nm. Its characteristics are summarized in Table  1   [64]. A 1. 2 Megapixels RGB context image with slightly better resolution fully including the HSI field of view is also recorded simultaneously with the 'viewfinder' camera. This context image is taken from virtually the same point of view (2. 5 cm above the HSI lens) and with exactly the same illumination. It will be used, raw and processed with DStretch©, to compare with the HSI results. As common for any hyperspectral measurement, a suitable lighting providing a continuous spectrum over the wavelength range of interest is required.
Context image: To overlay the spectral and viewfinder camera images their parallax is corrected either automatically or manually. 4. Integration time: Then, the integration time is adjusted (in the range 1-500 ms). The viewfinder camera image provides an evaluation for the integration time of the spectral camera but a manual optimization was always made in order to use 80-90% of the capacity of the detector while being cautious about saturation, especially in the white reference target. 5. Recording: After these initial adjustment steps, the image recording process is triggered. At first, a dark reference image representing the sensor background noise and read noise, without incoming light is recorded automatically. Then, the line scanner starts the actual data acquisition. 6. Validation and reference selection: after the full 512 × 512 pixels image is scanned, the focus and possible saturation or under-exposition are checked in the data validation view with a synthetic RGB image derived from the hyperspectral image cube and minimum and maximum intensity pixel histograms.
If the image is validated, part of the white area of the Spectralon ® reference target (typically 2000-8000 pixels depending on camera-wall distance) is then selected by thresholding the intensity of the image followed by a pixel connectivity algorithm. 7. Calibration: After removal of the dark background the calibration process converts the image cube in 'reflectance factor' unit by dividing the spectra of all pixels by an average spectrum of the selected area of the Spectralon ® reference. All the data are automatically stored, the raw and calibrated hyperspectral data cubes, the dark and white reference spectra, an RGB preview of the HSI data and the RGB context image.
Hyperspectral data analysis A set of hyperspectral reflectance data obtained on various sites have been analyzed using different standard tools available in the standard ENVI® hyperspectral image software (Version 5. 5, L3HARRIS GEOSPATIAL TM ). The results have been compared to the simultaneously recorded context RGB image (with only 20% better spatial resolution, but identical lighting and viewing geometry) as well with very high-resolution images of the same scenes recorded by the different cameras (listed in 2. 2) and analyzed with DStretch®. Several unsupervised mathematical transformations aimed at extracting and packing the information contained in hyperspectral image cubes have been tested and compared: • Principal Components Analysis (PCA), with covariance matrix • Independent Components Analysis (ICA), with LogCosh contrast function and 2D spatial coherence sorting • Minimum Noise Fraction Transform, (MNF), with noise statistics from whole image.
They are called 'rotations' as they aim at changing the base on which the dataset is projected in order to reduce the dimensionality of the useful data (initially 204 spectral information planes, one at each wavelength) and to segregate the noise in the data. Their working hypotheses on the initial content of the data are however different and the recently developed ICA  [65]  is expected to be the most efficient transformation to separate different layers of information, i. e. different types of paintings as well as rock composition and texture. This transformation is generally used for 'blind source separation', with no apriori information on the mixing. It assumes non-Gaussian distribution of the independent information sources, which is typical of natural hyperspectral datasets, and uses highorder statistics to reveal interesting but faint features or covering only a small portion of the image.
Removing the noisiest channels should improves the detectivity of subtle painting that otherwise may remain buried in noise. The first five spectral channels in the deep blue (< 410 nm) and the 923-1000 nm very near infrared range receive very little light and are removed from this analysis. The hyperspectral data cube is thus restricted to the 410-920 nm range, i. e. 172 spectral channels (instead of 204). Tests on a few data sets showed that both these spectral and spatial filtering, commonly used in the analysis of space exploration hyperspectral images  [65] , significantly improved the quality of the results in particular in terms of noise segregation. Fig. 5. Statistics on the spectra of the reference target (~2700 pixels) with mean (black), mean +/standard deviation (green), min and max values (red) showing the increasing noise below 410 nm and above 920 nm.
Only the central part of the spectrum (dashed lines: 410-920 nm) with standard deviation < 5% is kept for the hyperspectral data analysis. Fig. 6  presents the comparison between the different synthetic results obtained from the PCA, ICA and MNF mathematical transformations for one of our case studies (panel #2, see part "Panel #2: Separation between a complex wall and pigments"). The different transformations applied on the HSI data, because of the numerous spectral channels and extended range, not only efficiently separates the large-scale effects of the rock texture from the painting patterns but also removes a large fraction of the 'noise' in the components containing the pigment and rock information. This 'noise' might be a purely instrumental noise but it can be also high spatial frequency fluctuations of the reflected signal due to the microscopic texture of the rock inducing random local illumination and reflection angles, micro-shadows as well as color variations at the pixel scale.
Panel #1 : simple scene In Figs 7 and 8, a typical simple case is presented: a single scene (panel #1) probably painted with a single pigment on a relatively smooth and homogeneous rock wall. Indeed, the ICA transformation of the hyperspectral image concentrates most of the painting information in a single component with only minor local variations depicted in two other components. Those components add little information, just a slightly different hue of pigment in the upper dog, which is confirmed by comparing average spectra of the different figures. When combined in a false color RGB image and compared to the RGB context image processed with DStretch®, it becomes clear that the hyperspectral data allows a better extraction of the spatial distribution of the pigment. Indeed, the main ICA component has a much higher contrast with less background noise than the corresponding component in the DStretched image.
We then tested the physically-based spectral classification algorithm Sample Angle Mapper (SAM) on the original spectra of the hyperspectral data cube and on both the MNF or ICA transformations. For the transformations we only used the first significant components (13 for MNF, 8 for ICA) containing information on pigments and rock wall and removed all the noise components. We then used the 'Pixel Purity Index' (PPI) tool in ENVI to find the most spectrally pure (extreme) pixels in the hyperspectral image and the n-D Visualizer to manually select 6 groups of these end-member pixels. The supervised SAM algorithm, using an n-D angle to match pixels to the selected endmember spectra, is then run on the corresponding data and its 'maximum angle threshold' parameter optimized (to 1. 0 radian) to classify most of the pixels of the pigments and the rock wall in one of the 6 classes.
The results presented in Fig. 12  show that, as generally stated, the classification of the pigments (3 classes) on the MNF transformation (Fig. 12b ) seems more efficient to remove noise than the one run on the ICA (Fig. 12a ), but it also misses some faint painting especially in the 'blue' class. Increasing the value of the angle parameter would help include these faint paintings but will also dramatically increase the amount of noise (from wall) in the pigment classes. We should also note that the classification directly performed on the hyperspectral data cube (not shown) is not able to correctly extract most of the pigments for this scene painted on a complex wall. A transformation to concentrate the information and segregate most of the noise is thus really necessary before any classification.
Panel #3: Highlighting invisible/barely-visible figures on complex rock wall Another interesting example is a strongly oxidized brown-red wall with barely visible traces of red pigment on top of the image (panel #3, Fig. 13 ). The DStretch® (YUV) processing of the context RGB image allowed us to confirm the presence of several figures at the top of the image and possibly a bovine in the middle-left. The ICA analysis of the HSI image clearly displays these figures in components #2 and #7, but components #5 and #4 uncovered in a very clear way a few large anthropomorphic figures belonging to another layer of painting that is unobserved by eye. These figures remained undetected on the context RGB image despite a whole set of analysis attempts using various options of DStretch®, as well as different types of stretching algorithms (Photographic stretch, Saturation stretch, Decorrelation Stretch), transformation algorithms (PCA, ICA, MNF) and anomaly detection algorithms (RXD, UTD, RXD-UTD) available in ENVI software.
Only the 'elongated head' of the main anthropomorph can be barely recognized a posteriori in some of these transformations. With the ICA of the HSI data the different types of rock texture and composition are also well separated in components #1, #3 and #6. Even with a high-resolution camera image (60 Mega-pixels) covering part of the hyperspectral dataset and processed with DStretch® (IDS) or the other algorithms we can hardly recognize some of the 'anthropomorphs' elements, even knowing where they should be located in this stretched image (Fig. 14 ). They only have a very slightly different orange hue in this stretched image compared to the surrounding oxidized rock with strongly variable hues. A comparison of end-member spectra of the three main ICA components of panel #3 and of the rock wall (Fig.
24b ) most of its information being concentrated in ICA component #1. The PCA transformation is found to be slightly less efficient to segregate the graffiti information in this hyperspectral dataset (Fig. 24f ), and a MNF transformation did not separate efficiently enough painting, rock and graffiti to be useful in that case. DStretch® applied on the RGB context image is also completely unable to remove the Graffiti (Fig. 24c ). At the opposite it has the tendency to highlight them to the detriment of the other colors.
Discussion Even with a spectral range limited to 410-920 nm, mainly due to measurements of shaded walls indirectly illuminated by sunlight scattered from the sky and reflected by the surrounding landscape, the analysis of the hyperspectral data cubes provided many improvements over DStretched RGB camera images. The main advantages of hyperspectral imagery for the study of Neolithic paintings can be listed as follow. First, based on ICA analysis which concentrates the useful information in 8 to 12 components, with 3 to 7 concerning pigments, we could better separate different physical contributions to the image which are generally mixed in the three channels of a standard camera, including: • the pigments and the underlying rock, • the composition (mostly Fe-bearing minerals) and the color of the pigments, • the different paint layers and their juxtaposition.
While ICA is relatively efficient in separating different types of pigments used (independent components), it did not provide by itself composition information. This can be obtained from the end-member spectra of the components by comparing them with laboratory spectra of well characterized pigments or minerals  [50]. However, with the VNIR range we can only provide information on the Fe-bearing minerals. SWIR would be necessary to get info on some of the other minerals, such as kaolinite, carbonate and sulphates, constituting the paintings. We could detect faint paintings, which were undetected by the naked eye and even with high resolution camera images post-processed with DStretch®. In the various cases studied we found different reasons that led the ICA analysis of hyperspectral data to highlight new figures.
Now, with a hyperspectral data cube recorded with a VNIR HSI instrument we can better remove the wall texture as well as the natural noise and extract the distribution of the various pigments with a greater contrast. When the color contrast of a figure with the surrounding rock, or figures, becomes poor or statistically null, due to the progressive alteration of both paint and rock, an HSI can reveal new figures by capitalizing both on its strong ability to extract the distribution of the different pigment types, and on the new discriminating information contained in the very near infrared part of the spectrum. Another important result is that measurement of shaded walls is not a major problem, mostly slightly reducing the VNIR spectral range and increasing measurement time, but possibly positively reducing the natural high spatial frequency noise linked to rock and pigment micro-textures that otherwise strongly disturb the extraction of pigmented pixels.
There is also a growing number of compact commercial 'snapshot' (or quasi-snapshot) acquisition solutions, with 8 to up to a hundred spectral channels, but generally still with a small image format and sometimes with too large minimum focusing distances for archaeolog-ical use (currently mostly developed for UAV). They should in the future reduce the preparation and acquisition time of hyperspectral measurements close to that of a normal camera. When these instruments will be mature and will have overcome some of their restrictive characteristics, they will be the perfect and powerful camera in remote area. The last limitation is the data processing, as ICA is a time-consuming convergence algorithm and involves also a visual selection of the relevant components, which may be more than 3, to be displayed in B&W or RGB.
Currently, unlike DStretch®, this processing cannot be performed on site, but the development of dedicated plugins can be considered. However, back from the field, data analysis with ICA can be efficiently computed with dedicated software (such as ENVI) on powerful computers. It requires a little bit more skills to preselect the best wavelength range and some ICA parameters, but some of these steps may be automatized to get the best results for rock art. With the coupled use of hyperspectral images and ICA algorithms the gains in detectivity and separability between pigments and with rock wall texture is such that it should make the extraction of faint or barely-visible painting easier, faster and more reproducible than with camera, even with the current limitations of the instruments. In the following, we suggest a photographic strategy for remote and hard-to-reach area in 3 steps: Step 1: identify the paintings -use of RGB cameras (e.
Step 2: systematic recording of the paintings of whole panels -use of a high performance VNIR HSI instrument (1500-3000 pixels swath) to easily cover large scenes at high spatial resolution -use lighting lamps for shaded walls (but energy consuming) to reduce acquisition time and benefit from the full very near infrared range, up to 1000nm. -complement measurements with a portable VNIR-SWIR point spectrometer to record typical and end-member spectra of paint and rock wall over the full spectral range (but may need on-site HSI data preprocessing to locate them accurately) for pigment and rock composition determination. Step 3: mapping of the pigment composition and rock taphonomy -in addition, use a SWIR HSI instrument (1000-2500 nm) to improve the pigment separability and map pigment composition and rock taphonomy (mineral identification).
The spectra obtained in the absorbance mode with 128 scans and a spectral resolution of 4 cm -1 were acquired within the 4000 to 375 cm -1 range. The OPUS/Mentor program (version 6. 5) was used to record and analyze the spectra. Identification of bone composition was accomplished by comparing the major characteristics of the acquired spectra between untreated and laser-treated surfaces, as well as with those from the published literature that have made use of comparable methods and materials. The chemical composition of the outermost bone surface was determined using an Xray photoelectron spectrometer (Kratos AXIS Supra XPS, monochromatic Al K X-ray source with 225 W: 8 mA/15 kV energy). With a starting pressure of 10 -9 Torr and a measurement area of 700 µm × 300 µm, the photoelectron signal for the complete survey spectrum was collected.
The pass energy values for each step were as follows: (i) Wide: 160 eV/1000 meV; (ii) Regions: 20 eV/100 meV. To minimize sample charging effects, all samples were pretreated using a combination of electron and argon ion gun neutralizer systems (Ar + 500 eV). The bone sample was analyzed at a depth of roughly 5 nm (3~10 nm) in many areas. The hybrid-slot lens mode was used to collect spectra for the broad survey scan and particular places of interest, resulting in a spot analysis area of about 700 mm × 300 mm. Analyses were conducted before and after 300 s of Ar + etching. The bone surfaces were examined using a 230× magnification handheld microscope (Dino-Lite Edge) connected to a computer via USB. The final image was recorded using the Dino Capture 2. 0 operating software. A thermal imaging camera (Therma Cam P25, Teledyne FLIR Systems, USA) was utilized to record an increase in the bone surface average temperature while it was irradiated.
Acknowledgments: The Atapuerca research project is financed by Ministerio de Ciencia, Innovación y Universidades, grant no. PID2021-122355NB-C31/MCIN/AEI/10. 13039/501100011033/ FED ER. UE. Fieldwork at the Atapuerca sites is funded by the Junta de Castilla y León and the Fundación Atapuerca. The archaeological materials presented in this work were made available by the Laboratory of Human Evolution of the University of Burgos, in close collaboration with the "Colección Museística de Castilla y León" of the Junta de Castilla y León, and Museo de la Evolución Humana (Burgos); we acknowledge Juan Luis Arsuaga for the permit to analyze the sample. The use of Servicio General de Apoyo a la Investigación and the National Facility ELECMI ICTS, node "Laboratorio de Microscopías Avanzadas" at the University of Zaragoza is acknowledged.
Information sources, search strategy, and eligibility criteria Three electronic databases were consulted for this review: Scopus, Psyc'INFO, and Pubmed. The Scopus and Psyc'INFO databases were used because of the large number of available publications they include and because they list studies on dialogue, and Pubmed was used to search for possible studies on patients with neurological or psychiatric pathology. We limited our selection to studies published in English scientific journals with no time limitation. The keywords used in the search included terms associated with the concept of common ground and terms reflecting dialogue. We adapted the Boolean operators and truncators to the specificities of each database: "common ground" OR "audience 1 3 design" OR "lexical entrainment" OR "joint effort*" OR "shared information*" OR "referencing process" AND conversation OR "communication partner*" OR "collaborative interaction*" OR "collaborative dialogue" OR "referential communication" OR "language production" OR "language comprehension" 2  OR "matching task.
Study selection Database searches were performed in October 2021 and were updated in September 2022. The first selection step was performed independently by the first author and seven other examiners (Master's degree students) paired in three different groups. This first step was based on the titles and abstracts of each record yielded by the literature search after all duplicates had been removed by the first author. According to the eligibility criteria presented above, irrelevant studies were excluded. Then, after the first selection step, the same investigators read the full text of the remaining articles. For the latter, the same eligibility criteria were used for the inclusion/exclusion of the articles. During both steps of the selection process, any disagreements were resolved by discussion to find consensus.
Out of the 825 studies initially retained, 23 publications finally met the eligibility criteria (Fig. 1 ). It should be noted that three studies  (Lysander & Horton, 2012; Moreau et al. , 2015 Moreau et al. , , 2016)) , which did not emerge from the research in the databases because they did not include any of our keywords about common ground but which met the eligibility criteria, were added manually.
Data extraction and analysis For each selected study, the following relevant data were extracted independently by the same investigators using a data extraction table. Firstly, we identified the different types of measures of common ground used in the relevant studies and then we applied the classification, i. e. , the two different types of linguistic measures of common ground proposed in the Introduction. When the measure was directly related to the referential content of the discourse (i. e. , derived from the analysis of the type of articles or the lexicon used), it was classified as a reference marker. On the other hand, when the measure took root at a more macro level of discourse, reflecting the shape of the discourse as a whole, it was classified as a discourse-shaping indicator. Then, we listed the characteristics of the populations (age, gender, normal healthy adults vs.