dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank the country and people of The Bahamas for allowing continuous research in their beautiful waters and to local fishers who provided countless hours of local knowledge of where and how to fish, local traps, and conch slop swaps for Guinness that were essential for collecting many of the fishes within this dataset. We would like to thank the country of  French Polynesia  and the people of Mo'orea for allowing the continual study of their marine ecosystems and to local community members, notably Moana at the corner shop, for helping us identify locations and methodologies for the collection of many species within this dataset. We would like to thank  Craig Layman ,  Amy Rosemond , Friends of the Environment,  Darlene Haines ,  Jim Richard ,  Richard Appaldo , and  Nathalie Giroire  for their tremendous support without which the research in The Bahamas could not have been accomplished.
We would like to thank  Tom Maddox  of the  Analytical Chemistry Lab , the  University of George  who was instrumental in chemical analysis mentorship and processing. We would like to thank  Mark Hunter ,  Sean Geary , and members of the  Allgeier lab , and three reviewers for thoughtful comments that greatly improved the manuscript. We would like to thank the Mo'orea Coral Reef Ecosystem LTER, funded by the  US National Science Foundation  ( OCE-1637396  and earlier awards). Support for this study was provided by  Lucille  and  David Packard Fellowship  and  National Science Foundation OCE # 1948622  to  JEA, Mo'orea Coral Reef LTER  ( OCE-1637396 , and previous grants), and  National Science Foundation   OCE-1547952  to D. E. B.
Water was also filtered onto membrane filters to collect host cell biomass for lipid and protein enzymatic rates (see below for details). All filters were snap-frozen in liquid nitrogen and stored at -80 °C until processed. Seawater samples were kept in the dark at in situ temperature prior to processing in the lab on the R/V Atlantis. Biomarker definitions are the same as in Fig. 8. OxPC PC -1 also used additional data from the 1% light depth due to some samples being lost in transit. See Supplementary Fig. 13  for raw parameter data from these stations. Exact p values be found in Source Data file. Water was also collected at six depths (mostly within the upper mixed layer and extending down to 150 m) during 'core casts' conducted later the same morning and sampled for phytoplankton, virus, TEP and DOC concentrations.
HPLC pigment-based community analysis. Between 1 and 2 L of whole seawater samples from Niskin bottles and from the flow-through system were collected via filtration onto 25 mm GF/F filters (Whatman®). Filters were immediately stored in liquid nitrogen after filtration and were kept in liquid nitrogen or at -80 °C until sample analysis. High performance liquid chromatography was performed at the NASA Goddard Space Flight Center, following predetermined quality assurance and quality control protocols  80, 81. Empirical Orthogonal Function analysis (using the pca functions) and network-based community detection analysis (using the modularity_und. m function) were performed in MATLAB R2020a. The mean ratios of accessory pigments to total ChlA in each community were then used to determine the taxonomic significance of the community  62.
1. 0 in R, which performs a dendrogram analysis based on the optimized Hamiltonian path length to find which variables covaried together. Data were pretreated with the normalize function, which subtracts the minimum value and divides by the maximum value to bring each distribution to a scale of 0 to 1. Degree of stratification and mixed layer depths. The stratification index or degree of stratification is based on set threshold levels averaged over the upper 300 m water column. Brunt-Väisälä or buoyancy frequency (N 2 ; expressed in s -1 ), was calculated as: N 2 ¼ ðÀgÞ=ρ ∂ρ=∂z ð4Þ where z is depth (m), ρ is the potential density of seawater (kg m -3 ) and g is the gravitational acceleration (9. 8 m s -2 ). Satellite-derived mixed layer depths were obtained by 2D interpolation of ~9 km MLD data centered on coordinates from GPS taken while sampling.
Kruskal-Wallis was chosen throughout this analysis due to a lack of sample size homogeneity across bloom phases and stations. When significant differences were found, a Dunn test with Holm p value adjustment was performed as a multiple comparisons test between the bloom phases. The general relationship between stratification and phytoplankton biovolume, cell, TEP, virus, and DOC and DOC SA concentration was shown using a LOESS analysis of best fit. The degree of vertical stratification and enrichment of phytoplankton, TEP, virus and DOC and DOC SA concentrations were assessed by fitting a general additive model (GAM) to each variable. Model optimization was done by fitting each variable to depth and determining the optimum number of knots and smoothing spline type (mgcv package version 1. 8-31 in R) using the Akaike Information Criterion (AIC) as a guide for best fit.
Acknowledgements This work was made possible by  NASA 's  Earth Science Program  in support of the  North Atlantic Aerosol and Marine Ecosystem Study  ( 15-RRNES15-0011  and  0NSSC18K1563  to K. D. B. ;  NNX15AF30G  to M. J. B. ), as well as with support from the  National Science Foundation  ( OIA-2021032  to K. D. B. ,  OCE-157943  to C. A. C. , and  OCE-1756254  to B. A. S. V. M. ), the  Gordon and Betty Moore Foundation  (Award#  3789  to K. G. V. B. ), and  NASA 's  Future Investigators in Space Science and Technology program (FINESST ; grant # 826380  to K. D. B. ; graduate support to BD). We thank the captain and crew of the  R/V Atlantis  for their tireless efforts and roles in helping to collect field samples and data during NAAMES. We also thank  Kim Thamatrakoln ,  Chana Kranzler , and  Austin Grubb  for insightful conversations that helped guide this study, as well as  Jo Hunter  for technical assistance with lipid analyses.
Data availability Raw datasets of measured environmental parameters during the NAAMES cruises are accessible in the NASA SeaBass archive (https://seabass. gsfc. nasa. gov). Float profiler data are available from the University of Maine Float Explorer website for all NAAMES cruises (http://misclab. umeoce. maine. edu/floats/, accessed 9/9/2020). The physiological, environmental, and extracellular data generated in this study have been deposited in the Zenodo database under accession code 10. 5281/zenodo. 5512903. Source data are provided with this paper.
Data To help constrain the atmospheric injection into the seismic signal, InSight measures multiple environmental parameters continuously at a high sampling rate and sensitivity: wind speed and direction, temperature, pressure, and the vector magnetic field, using APSS  (Banfield et al. , 2018). The TWINS sensors measure winds facing in opposite directions in the east and west directions, and stand at slightly different heights (of less than 10 cm) due to InSight's tilt in its landed position on Homestead hollow  (Banfield et al. , 2020). A detailed study of the characteristics and meteorological phenomena observed from InSight's atmospheric science instrument data are described in  Banfield et al. (2020). Data are recorded continuously on board the lander at 1 sps (samples per second) for the wind and at 20 sps for pressure with the higher rate selected to capture transient excursions caused by passing vortices.
Data are downlinked to Earth at selectable frequencies depending on the available bandwidth: 0. 1 sps or 1 sps for the wind sensor and between 2 sps and 20 sps for the pressure sensor. Seismic data are recorded continuously and returned at 20 sps for the VBB. The SP records continuously at 100 sps, but also downlinks at 20 sps. Downlink requests for specific events of interest allow small time windows of pressure, wind and SP data to be transmitted at the higher recorded rates. This study focuses on the continuous data  (InSight Mars SEIS Data Service, 2019b). This allows for longer durations of uninterrupted data, permitting a deeper investigation into the atmospheric coupling measured by SEIS. Examples of some of the longest duration of uninterrupted 100 sps data are also investigated to demonstrate the broadband characteristics of atmospheric injection into the system and any variability in the seasonal evolution.
Hence, it is impossible to provide analog testing that would elucidate injections at the levels we have observed with InSight, and assumptions of linearity would be required to extrapolate from Earth testing to the mission environment. The approach outlined in this work is instead to use the data itself to reveal the effect of these hidden variables as a time-dependent coupling between the environmental and seismic signals. We use the observed comodulation of the environmental and seismic signals to estimate the power transferred from the environment to SEIS. The method of moments applied here allows this coupling to be quantified within related regions even in the absence of an injective function which would provide a phase relationship between the signals to allow the estimation of a transfer function.
Acknowledgments The authors would like to thank the  Editor-in-Chief Prof. Laurent Montesi ,  Adam Ringler  and another anonymous reviewer who provided thorough and helpful reviews that have improved this manuscript. We would also like to thank  Andrea Christophi  for constructive criticism of the manuscript. We acknowledge  NASA, UK Space Agency, CNES , their partner agencies and  Institutions (UKSA, IC, SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, MPS-MPG ) and the flight operations team at  JPL , SISMOC, MSDS, IRIS-DMC, and  PDS  for acquiring and providing InSight data, including SEED SEIS data.
Data Availability Statement SEIS raw data are in "InSight Mars SEIS Data Service together with IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC" (2019) with 10. 18715/SEIS. INSIGHT. XB_2016. The seismic and atmospheric measurement data presented here are also publicly available in InSight Mars SEIS Data Service (2019a) SEIS Bundle with 10. 17189/1517570, APSS TWINS Bundle (Mora, 2019b) with 10. 17189/1518950 and APSS PS Bundle (Mora, 2019a) with 10. 17189/1518939 through the Planetary Data System (PDS) Geosciences node, but also through the Incorporated Research Institutions for Seismology (IRIS) Data Management Center under network code XB and through the Data center of Institut de Physique du Globe, Paris (http:// seis-insight. eu). The InSight seismic event catalog (InSight Marsquake Service, 2020) is available from the IPGP Datacenter and IRIS-DMC.
Codes for processing comodulation as used in this analysis have been put on GitHub available at https://github. com/ConstantinosCharalambous/comodulation in Charalambous (2020a) with 10. 5281/zenodo. 4292720. Processed data and sample data sets to run the code is available in Charalambous (2020b) with 10. 18715/ipgp. 2020. Khw87roo. French coauthors acknowledge the support of CNES and of Agence Nationale de la Recherche under grant ANR-19-CE31-0008-08. This is InSight Contribution Number 170.
Results The CPROPS (CloudPROPertieS) software  (Rosolowsky & Leroy 2006 ) was used to identify and measure the properties (size, flux, velocity dispersion) of molecular cloud associations (GMAs) in the 2. 7 datacube. The CPROPS program first assigns contiguous regions of the datacube to individual clouds and then computes the cloud properties (flux, radius, and velocity width) from the identified emission. The algorithm ignores clouds smaller than a resolution element and does not decompose clouds smaller than two resolution elements. We used the modified CLUMPFIND 2 algorithm (ECLUMP) and required a peak of at least 1. 5 σ in every distinct cloud and at least two channels. The CPROPS decomposition was used to produce the moment maps which are presented in Fig. 2. As a consistency check, we cleaned the datacube with a velocity channel width of 6.
Conclusions The Taffy system is composed of two massive spiral galaxies which had a head-on collision about 20 Myr ago. We present new high-resolution (∼ 2. 7 ) CO(1-0) observations with the Plateau de Bure Interferometer. An rms of ∼ 5 mJy in a 6. 5 km s -1 channel was reached by our observations. The CPROPS software  (Rosolowsky & Leroy 2006 ) was used to identify and measure the properties of giant molecular cloud associations (GMAs). The detected CO luminosity of the Taffy system is L CO,tot = 4. 8 × 10 9 K km s -1 pc 2. We divided the CO intensity map into disk and bridge regions (Fig. A. 2). The CO luminosity of the bridge is L CO,bridge = 1. 2 × 10 9 K km s -1 pc 2 , 25 % of the total CO luminosity. Assuming a Galactic N (H 2 )/I CO conversion factor for the galactic disks and a third of this value for the bridge gas, we obtain H 2 masses of M H2,tot = 1.
Acknowledgements. We would like to thank the  IRAM  staff for the help observing the Taffy system with the PdBI. Based on observations made with the  NASA/ESA Hubble Space Telescope , and obtained from the  Hubble Legacy Archive , which is a collaboration between the  Space Telescope Science Institute (STScI/NASA) , the  Space Telescope European Coordinating Facility  ( ST-ECF/ESA ) and the  Canadian Astronomy Data Centre  ( CADC/NRC/CSA ). We thank  Dominique Aubert  for the creation of the 3D views.
The main challenge shared by these methods is the construction of adequate training datasets ensuring good results on new real-world examples. As first suggested by  [3] , noise residuals can provide substantial cues for detecting forgeries. Indeed, the initial Poisson noise  [23]  is transformed by multiple operations specific to each image formation process  [24] , leading to the final JPEG image. Hence, detecting noise inconsistencies is a rich source of forgery evidence. The use of noise residuals has evolved over time. Early methods  [25, 26]  directly search inconsistencies in this residual whereas more recent algorithms use it as an input for further feature extraction  [27, 28]. Accurately estimating the residual noise traces after the complex set of transformations of the camera's processing chain is the main challenge of this class of algorithms.
More recently, the same authors presented a novel CNN-based method for noise residual extraction  [28]. A similar approach can be found in  [46]. On the other hand, Zhou et al. [47]  proposed a two-stream CNN, one for the detection of tampering artifacts and the other to leverage noise features. Deep learning-based methods are more general than previously described ones. A major limitation of these methods is that they require large training datasets, which are not always available. Furthermore, their performance generally remains dataset dependent.
3. For each scale and each color channel, estimate the global NLF of the image and compare it to NLF computed at each macroblock. We are interested in the percentage of histogram bins below the global curve. 4. To obtain the final result of the algorithm, the heatmaps obtained at each of the scales are combined. Please refer to Algorithm 1 for a detailed pseudo-code description. The actual source code is available at (accessed on 31 May 2021) https://github. com/marigardella/PB_ Forgery_Detection, together with the instructions and requirements to run the method. Further implementation details are given in Appendix C. f I c s ← noise curve estimation for I c s using  [29]  as described in A. 8: H c ← zeros(M x × M y ). 9: for each macroblock in macroblocks_list do 10: M c s ← get macroblock in scale s and channel c.
Datasets All experiments were conducted on the CG-1050 database  [49]  which contains four datasets, each one corresponding to a different forgery technique: colorization, copy-move, splicing and retouching. The total number of forged images is 1050. This database is varied in nature, including images captured in 10 different places. The size of the images is 3456 × 4608 or 4608 × 3456 pixels. The database includes both RGB and grayscale images, all of which are JPEG-compressed. The estimated JPEG-quality  [50]  for each dataset is shown in Table  1. Forgery masks were constructed by computing the absolute difference between the original image and the forged one in each channel. To avoid pixels whose values had changed due to global manipulations rather than tampering, the difference from one image to another was thresholded.
Relevance of the Multi-Scale Approach We first examined the pertinence of a multi-scale scheme. For this purpose, we computed the results obtained when considering one single scale S 0 (I) (which would correspond to the input image), using two scales S 0 (I) and S 1 (I), and using three scales S 0 (I), S 1 (I) and S 2 (I). The scores obtained for each of these settings are shown in Table  2. We can observe that using multiple scales leads to better results compared to a single one. Indeed, in all four datasets, the scores obtained by PB2 and PB3 are better than those obtained by PB1 for the three metrics. Regarding the number of scales yielding a better performance, the use of three scales obtains the best scores for the retouching, colorization and splicing datasets, whereas the use of two scales achieves a better performance in the copy-move dataset.
However, the results obtained for the copy-move dataset are poor for the three variants of the method, and furthermore, they have very similar scores. We conclude that the use of three scales, S 0 (I), S 1 (I) and S 2 (I), gives the best performance among the evaluated alternatives. In fact, given that JPEG-compression is applied in 8 × 8 blocks without overlap, it is at S 2 that the most accurate noise estimation is achieved since we are able to capture noise contained in lower frequencies, which is less affected by the quantization of the DCT coefficients.
Comparison with State-of-the-Art Methods In order to assess the performance of our method, we compared the results obtained on the CG-1050 dataset with those delivered by state-of-the-art noise-based methods: Splicebuster  [27] , Noiseprint  [28] , Mahdian  [25] , Pan  [26] , Zeng  [36] , Zhu  [45]  and Median  [52]. For each algorithm, we used a publicly available implementation  [53]. Table  3  lists all the evaluated methods as well as their reference article and the link to the source code used for the comparison. Table  3. State-of-the-art methods used for the comparison as well as their reference and link to source code.
Source Code The obtained results are given in Table  4. We observe that Splicebuster outperforms the rest of the methods in the retouching and splicing datasets regardless of the metric. Our method ranks first for colorization attacks for all the three metrics considered. This forgery technique shows the relevance of considering noise curves instead of single noise levels. Indeed, when changing the color in a region of the image, noise levels are not necessarily perturbed. However, those noise levels will not be consistent with the new intensity but with the original. Estimating noise curves as the proposed method does enables detecting this kind of inconsistency which only appears when considering intensity-dependent noise models. Regarding the copy-move dataset, Splicebuster delivers the best results when considering the F1 and IoU scores.
However, our approach obtains the best MCC score. The average ranking shows that Splicebuster outperforms the rest of the methods when considering both the F1 and IoU scores, followed by our method. Nevertheless, our method achieves the best average ranking when considering the MCC score, followed by Splicebuster. Noiseprint stands out as the third best performing method for the IoU and F1 scores. It even ranks second for retouching and copy-move attacks when considering these scores. However, it shows a poor performance for the colorization dataset. This can be explained by the fact that the camera signature is left unchanged when performing this kind of manipulation. The Pan and Mahdian methods are middle-ranked, showing better results when considering the MCC score. Finally, Median, Zeng and Zhu show the worst performance of all the considered methods regardless of the metric considered.
All of the evaluated methods have different resolutions which may affect their performance when forgeries are too small. To analyze the effects of the size of the forgeries, we computed the average score as a function of the forgery size. Figure  5  shows the average score obtained by each method when setting different lower bounds for the forgery size in each of the datasets considered. The results suggest that our method outperforms the state-of-the-art approaches when considering large forgeries in all the datasets regardless of the considered score. The fact that it does not perform that well when considering small manipulations is a direct consequence of the size of the macroblocks. Indeed, for our method to provide reliable detection, the tampered region should be at least of the size of one of the tested macroblocks.
However, noise-based methods require accurately dealing with the changes induced by the successive steps of the camera processing chain. In the present paper, we proposed a method that can correctly deal with the complex noise residuals observable in the JPEG image. The proposed method implements a multiscale approach which has shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. Our comparative results show that our method outperforms state-of-the-art approaches when evaluating the results with the MCC score. For colorization attacks, our method performs best, regardless of the metric. In addition, when the size of the forgeries is large enough, our method shows the best performance in all the datasets, for all three considered metrics. Nevertheless, the proposed method has its own limitations, mainly related to too-small and too-large forgeries.
Funding: This work was supported by the  Paris Region Ph. D. grant from  Région Île-de-France , the  International Fact-Checking Network (IFCN)  and  Agence France Presse (AFP)  through the  Enhancing Visual Forensics  ( Envisu4 ) project, the  DGA  Defals challenge n°ANR- 16-DEFA-0004-01 ,  MENRT  and  Fondation Mathématique Jacques Hadamard. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: Images from the CG-1050 database  [49]  were used in this article. The full database and documentation can be downloaded from https://data. mendeley. com/datasets/ dk84bmnyw9/2 (accessed on 31 May 2021). The source codes for Splicebuster  [27]  and Noiseprint  [28]  methods are available at http://www. grip. unina. it/research/83-multimedia_forensics (accessed on 31 May 2021). The source codes for Pan  [26] , Mahdian  [25] , Median  [52]  and Zeng  [36]  algorithms are available at https://github. com/MKLab-ITI/image-forensics/blob/master/matlab_toolbox (accessed on 31 May 2021). The implementation of the Zhu  [45]  method is available at https: //github. com/marigardella/Zhu_2018 (accessed on 31 May 2021). Finally, the source code for the proposed method is available at https://github.
This may therefore decrease the given dose to the healthy surrounding tissues compared to standard RT by photon. In recent years, more facilities have been delivering PT which is now available in three centres in France with the recent opening of the Caen hadrontherapy centre (2018) beside Nice  (1991, upgraded in 2016)  and Orsay near Paris  (1991, upgraded in 2010). Administrative medical databases (AMDB) are massive repositories of collected healthcare data for various purposes with a constant and often on-going collection process  [4]. They often encompass very large population and frequently the whole nation, ensuring high statistical power without biases related to the representativity of a sample. They can be used to conduct epidemiological studies and evaluate medical practices  [5].