dataesr/scientific-paragraphs-categorization huggingface dataset:
2008 , Robinson et al. 2011). Antarctic coastal areas, in particular, are rarely accessed and documented due to logistical constraints, access being for example impossible during the austral winter due to sea ice cover  (De Broyer et al. 2014). The availability of species absence records is also a limiting factor to modelling performances and model calibrations  (Brotons et al. 2004, Wisz and Guisan 2009). Models are usually based on a limited number of presence-only records and limited number of sampling sites, which are both spatially aggregated in the vicinity of scientific stations, where access is frequent and datasets from different seasons, have been compiled over decades and even beyond  (De Broyer et al. 2014 , Guillaumot et al. 2018a , Fabri-Ruiz et al. 2019 , Guillaumot et al. 2019).
Studied species and environmental descriptors The distribution of six sea star species (Asteroidea: Echinodermata) was studied (Table  1 ). The six species, Acodontaster hodgsoni  (Bell, 1908) , Bathybiaster loripes (Sladen, 1889), Glabraster antarctica (Smith, 1876), Labidiaster annulatus Sladen, 1889, Odontaster validus  Koehler, 1906 and Psilaster charcoti (Koehler, 1906)  are abundant and common in benthic communities in the Southern Ocean. The biology, ecology and distribution of these species have been extensively studied and are relatively well documented  (McClintock et al. 2008 , Mah and Blake 2012 , Lawrence 2013). Presence-only records were compiled from a recently updated database, thoroughly scrutinised with the World Register of Marine Species (WoRMS Editorial Board 2016), to delete potential discrepancies, update taxonomy and correct for georeferencing errors  (Moreau et al.
2018). Models were generated for the different species using 298-851 presence-only records, and projected at different depth ranges (Table  1 ). The distributions of these presence-only records are contrasting between species (Appendix 1), with A. hodgsoni, B. loripes and G. antarctica having an Antarctic and sub-Antarctic distribution, with an important number of data available for B. loripes and G. antarctica but less data for A. hodgsoni (respectively 591, 851 and 298 presence-only records). Labidiaster annulatus has a distribution mainly gathered in the sub-Antarctic region with few data available (375 presence-only records). Odontaster validus and P. charcoti are mainly present on the coasts of the Antarctic shelf. Environmental descriptors were selected from the dataset provided at https://data.
MESS calculation The MESS was measured using the dismo R package  (Hijmans et al. 2017)  and following the guidelines provided in  Elith et al. (2010). Pixels for which at least one environmental descriptor has a value that is outside the range of environmental values defined by presence-only records (calibration range) were considered to be extrapolation (i. e when MESS gets negative values, Appendix 4). The proportion of extrapolation areas (i. e. the proportion of cells defined as extrapolations over the total projection area) was calculated and compared between species. On SDM projection maps, extrapolated pixels were displayed in black. Environmental parameters responsible for extrapolation were estimated by modifying the code provided in  Elith et al. (2010). Detailed R scripts are available at https://github.
Influence of the number and distribution of presence-only records on extrapolation The proportion of extrapolation areas may vary with presence-only sampling effort. In order to study the influence of the number and distribution of these presence-only records on the proportion of extrapolation areas, two analyses were performed. First, several SDMs were generated with different numbers of presence-only records, following the chronological addition of new presence-only records through time, from 1980 to 2016. Second, SDMs generated with 10-100% (10% increments, so 10 subsets) of the entire presence-only dataset were compared. In this analysis, in contrast to the previous one, presence-only records are randomly sampled among the datasets available. In these two analyses, SDMs were projected on the environmental space limited by the maximum depth defined for each species (Table  1 ), 100 model replicates were generated and averaged in each case and spatial autocorrelation (SAC) was estimated to assess the influence of presence-only records aggregation on modelling performances.
The decrease of extrapolation with the addition of presence-only records was tested by running, for each species a series of models with different subsets of presence-only records randomly sampled from the total dataset. One hundred model replicates were progressively run with 10-100% of the total dataset and proportions of extrapolation areas were computed accordingly (Fig. 3 , Table  S7 ). Results confirm that the addition of presence-only records strongly reduces proportions of extrapolation areas. Proportions of extrapolation areas also vary between species models as a function of depth. Low proportions of extrapolation areas are obtained in models run for deep species and large datasets (e. g. 8. 2% for 591 records in B. loripes and 23. 9% for records in G. antarctica). In contrast, models run for shallower species show higher proportions of extrapolation areas (40.
Modelling performances and extrapolation SDMs were generated for Southern Ocean sea star species, with contrasting distributions and different numbers of presence-only records available (Table  1 , Appendix 1). Overall, species presence-only records are spatially concentrated in the most accessible and visited areas of the Southern Ocean. Most of the sea star samples were collected close to the coasts of the Western Antarctic Peninsula, the Ross Sea and sub-Antarctic Islands such as the Kerguelen Islands. Consequently, high spatial autocorrelation values were computed, for L. annulatus and O. validus in particular (Table  S6 ). Overall, models all show good performances (Table  2 ), the spatial cross-validation procedure ensuring a relevant evaluation of modelling performances when using spatially aggregated data  (Muscarella et al.
How can we reduce model extrapolation? Enriching SDMs with knowledge of species ecology One objective of this work was to provide some methods to mitigate the effect of extrapolation on model uncertainties. Our results show clear contrasts between models generated for "deep" and "shallow" species, with lower proportions of extrapolation areas computed for deep species models (29. 1 and 15. 73% respectively for B. loripes and G. antarctica). The model generated for P. charcoti departs from this general scheme, with extrapolation reaching 67. 9% of the projection area. This is due to the strong spatial aggregation of records and the small presence-only record dataset available in deeper habitats. Depth is indeed responsible for 58. 1% of the extrapolation for P. charcoti (Appendix 5). Indeed, the erroneous characterization of species occupied space, due to an incomplete sampling, has been identified as a significant source of bias in SDM predictions  (Hortal et al.
How can we reduce extrapolation? Improving sampling effort Increased sampling effort over enlarged areas allows the production of larger datasets from which many records can be used to generate reliable models with reduced extrapolation areas. In this study,   S6 ); (C) Proportion of grid-cell pixels of the projection area that are extrapolations (%). The maximal number of presence-only records present in Table  2  may not be reached here because some collection dates remain unknown. Fig. 3. Boxplot diagrams representing the decrease of proportions of extrapolation areas (in % of the total projection area) with addition of presence-only records used to generate model replicates (in % of data available, see Tables  1  and S7 ), for: (a) Acodontaster hodgsoni, (b) Bathybiaster loripes, (c) Glabraster antarctica, (d) Labidiaster annulatus, (e) Odontaster validus, (f) Psilaster charcoti.
For each box, mean values (blue dots) and outliers (black dots) are shown for the 100 model replicates. Some boxes are missing for low percentages of presence-only records (10-30%, corresponding to close or less than 100 presence-only records) that do not allow models to be generated. proportions of extrapolation areas proportionally decreased when increased numbers of presence-only records were used to generate models. The occurrence datasets were significantly augmented between 1980 and 2016, with a number of presence-only records multiplied by 1. 9-3. 3 times according to the studied species, which allowed reduction of model extrapolation from 10. 2 to 30. 7% according to the species (Fig. 2 , Table  S6 ). However, results suggest that about twice the number of presence-only records actually available would be necessary to reduce extrapolation down to a "satisfactory" threshold of 10% of the projection area (Table  3 ).
Generating reliable and stable models using a sufficient number of presence-only records is essential. In this study, some models could not be run when the number of presence-only records was too low (approaching 150 presence-only records or less) compared to the broad extent of the projection area and the spatial aggregation of these data (Table  S7 ). Considering that the spatial cross-validation procedure splits the initial dataset into training and test data, and that at each step, 75% of these training data are randomly sampled by BRT to iterately create a model tree (and generate stochasticity in the procedure), the final number of presence-only records available to describe the presence data -environment relationship becomes too low (around 37. 5% of the initial number of presence-only records).
Acknowledgements This work was supported by a " Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture" (FRIA)  and " Bourse fondation de la mer " grants to  C. Guillaumot. This is contribution no. 46  to the vERSO project (www. versoproject. be), funded by the  Belgian Science Policy Office (BELSPO , contract n •  BR/132/A1/vERSO ). Research was also financed by the " Refugia and Ecosystem Tolerance in the Southern Ocean " project (RECTO;  BR/154/ A1/RECTO ) funded by the  Belgian Science Policy Office (BELSPO) , this study being contribution number  23.
, CA, USA) using a paired-end 150 bp reads strategy. One lane of HiSeqX5 flow cell was used per sample specific library in order to reach an average sequencing depth of 30x for each sequenced individual. Sequence quality parameters have been assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ file for each sample. FastQ sequences were aligned on human genome hg37 using the BWA-mem program  (Li & Durbin, 2009). Variant calling was performed using the GATK HaplotypeCaller (GenomeAnalysisTK-v3. 3-0, https://software. broadinstitute. org/gatk/documentation/article. php?id=4148) tool followed by recalibration. Single nucleotide variants (SNVs) that succeeded the "PASS" filter were then annotated using Annovar  (Wang et al, 2010).
To comply with the autosomal dominant mode of inheritance of the observed PSD, we selected as candidate culprit PSD causing mutation any rare variant that has either never been reported in public database or at a very low allele frequency (<1‰) and that were present at the heterozygous states in the 5 whole genome sequenced patients but not present in 2 healthy relatives. Interrogated public databases were dbSNP, GnomAD (https://gnomad. broadinstitute. org/) Ensembl (https://www. ensembl. org/index. html), 1000 genomes (https://www. internationalgenome. org/), ExAC (http://exac. broadinstitute. org/) and FrEx (http://lysine. univ-brest. fr/FrExAC/).
The c. -39T allele is expected to produce a shorter protein of 51 amino acids with no homology to wild-type Protein S. However, we were not able to detect it through our WB experiments. This prevented us from assessing whether the resulting protein is not stable, or not produced because of a possible degradation of the new mRNA by the nonsense mediated decay. Unfortunately, we were not able to collect mRNA materials from patients which also prevented us from investigating this hypothesis. Importantly, none of the popular bioinformatics tools used to annotate variants such as PolyPhen, Sift, CADD  (Rentzsch et al, 2019) , TraP  (Gelfman et al, 2017)  or RegulomeDB  (Boyle et al, 2012)  were able to predict any deleteriousness for this mutation while the PreTIS software  (Reuter et al, 2016)  dedicated to 5'UTR variants predicted a "moderate" impact of the mutation.
Wolf (de Wolf et al, 2006) (Supplemental Table2). P C R a m p l i f i c a t i o n o f g e n o m i c D N A w a s p e r fo rm e d u s i n g P h u s i o n ™ G r e e n H o t S t a r t I I H i g h F i d e l i t y D N A P o l y m e r a s e with GC Buffer in presence of 10% of DMSO (ThermoFisher). Big dye sequencing chemistry was used to sequence the PCR products in both directions using the ABI 3500xL Genetic analyser (Applied Biosystems, Foster City, CA) and the sequence were analyzed by the SeqScape Software (Applied Biosystems, Foster City, CA). Plasmid constructsTo assess the impact of the PROS1 c. -39C>T variation, several expression vectors were investigated(Fig 2B). The human PROS1 cDNA (GenBank NM_000313. 4) was PCR-amplified from HepG2 cDNA and cloned into pcDNA3. 1/myc-His(-) plasmid (Invitrogen).
ACKNOWLEDGMENTS O. S and M. R were financially supported by the GENMED Laboratory of Excellence on Medical Genomics [ANR-10-LABX-0013]. DA T was supported by the «EPIDEMIOM-VTE» Senior Chair from the Initiative of Excellence of the University of Bordeaux. This work was partially supported by the GENMED Laboratory of Excellence on Medical Genomics (ANR-10-LABX-0013) and the French Clinical Research Infrastructure Network on Venous Thrombo-Embolism (F-CRIN INNOVTE), two research programs managed by the National Research Agency (ANR) as part of the French Investment for the Future. Clinical investigations of patients and of their relatives were conducted by SLM, CM, YH and JC. Whole sequencing was performed by CB, AB and RO. MR was in charge of the bioinformatics analysis of the whole genome sequenced data.
The idea behind the data pre-processing technique is that the effect of the gain function can be represented in the transformed dataset by amplifying the impact of the guesses in proportion to their reward. For example, consider a pair p, q in D  , and assume that the reward for the guess  is p, q " 5, while for another guess  1 is p 1 , q " 1. Then in the transformed dataset D 1  1 this pair will contribute with 5 copies of p, q and only 1 copy of p 1 , q. The transformation is described in Algorithm 1. Note that in general it causes an expansion of the original dataset. Estimation of. Given D  , we construct the set D 1  1 of pairs p, q according to Algorithm 1. Then, we use D 1   1 to train a classifier  ‹  1 , using an algorithm that approximates the ideal Bayes classifier. As proved below,  ‹  1 gives the same mapping Y Ñ W as the optimal empirical rule  ‹  on D  (cfr.
Pros and cons of the two methods The fundamental advantage of data pre-processing is that it allows to estimate   from just samples of the system, without even blackbox access. In contrast to channel pre-processing, however, this method is particularly sensitive to the values of the gain function. Large gain values will increase the size of D 1  1 , with consequent increase of the computational cost for estimating the -vulnerability. Moreover, if  takes real values then we need to apply the technique described in Appendix C. 3, which can lead to a large increase of the dataset as well. In contrast, the channel pre-processing method has the advantage of controlling the size of the training set, but it can be applied only when it is possible to interact with the channel by providing input and collecting output.
EVALUATION In this section we evaluate our approach to the estimation of vulnerability. We consider four different scenarios: (1) X is a set of (synthetic) numeric data, the channel  consists of geometric noise, and  is the multiple guesses gain function, representing an adversary that is allowed to make several attempts to discover the secret. (2) X is a set of locations from the Gowalla dataset  [1] ,  is the optimal noise of Shokri et al. [37] , and  is one of the functions used to evaluate the privacy loss in  [37] , namely a function anti-monotonic on the distance, representing the idea that the more the adversary's guess is close to the target (i. e. , the real location), the more he gains. (3) X is the Cleveland heart disease dataset  [24] ,  is a differentially private (DP) mechanism  [25, 26] , and  assigns higher values to worse heart conditions, modeling an adversary that aims at discovering whether a patient is at risk (for instance, to deny his application for health insurance).
For the validation part, since the "best"  function has been already found and therefore we do not need any preprocessing anymore, "equivalent size" just means same size. But what does it mean, in the case of training sets built with different pre-processing methods? Assume that we have a set of data D  coming from a third party collector (recall that  represents the size of the set), and let D 1   1 be the result of the data pre-processing on D. Now, let D 2  2 be the dataset obtained drawing samples according to the channel pre-processing method. Should we impose  2 "  or  2 "  1 ? We argue that the right choice is the first one, because the amount of "real" data collected is. Indeed, D 1  1 is generated synthetically from D  and cannot contain more information about  than D  , despite its larger size.
To amend this problem, we modify the k-NN algorithm in the following way: instead of looking for neighbors among all the  1 samples, we only consider a subset of  ď  1 samples, where each value  only appears once. After the logpq neighbors have been detected among the  samples, we select  according to a majority vote over the  1 tuples p, q created through the remapping. The distance on which the notion of neighbor is based depends on the experiments. We have considered the standard distance among numbers in the first and fourth experiments, the Euclidean distance in the second one, and the Manhattan distance in the third one, which is a stand choice for DP. Concerning the ANN models, their specifics are in Appendix D. Note that, for the sake of fairness, we use the same architecture for both pre-processing methods, although we adapt number of epochs and batch size to the particular dataset we are dealing with.
Experiment 2: location privacy In this section we estimate the -vulnerability of a typical system for location privacy protection. We use data from the open Gowalla dataset  [1] , which contains the coordinates of users' check-ins. In particular, we consider a square region in San Francisco, USA, centered in (latitude, longitude) = (37. 755, ´122. 440), and with 5Km long sides. In this area Gowalla contains 35162 check-ins. We discretize the region in 400 cells of 250m long side, and we assume that the adversary's goal is to discover the cell of a check-in. The frequency of the Gowalla check-ins per cell is represented by the heat-map in Figure  5. From these frequencies we can directly estimate the distribution representing the prior of the secrets  [27]. The channel  that we consider here is the optimal obfuscation mechanism proposed in  [37]  to protect location privacy under a utility constraint.
Specifically, our  is illustrated in Figure  6 , where the central cell represents the real location. For a generic "guess" cell , the number written in  represent p, q. 3  In this experiment we consider training set sizes of 100, 1k and 10K samples respectively. After applying the data pre-processing transformation, the size of the resulting datasets is approximately 18 times that of the original one. This was to be expected, since the sum of the values of  in Figure  6  is 20. Note that this sum and the increase factor in the dataset do not necessarily coincide, because the latter is also influenced by the prior and by the mechanism. 3 Formally,  is defined as p, q " tp expp´p, q{qqs, where  " 4 is the maximal gain,  " 0. 95 is a normalization coefficient to control the skewness of the exponential,  is the euclidean distance and  " 250 is the length of the cells' side.
Experiment 3: differential privacy In this section we consider a popular application of DP: individual data protection in medical datasets from which we wish to extract some statistics via counting queries. It is well known that the release of exact information from the database, even if it is only the result of statistical computation on the aggregated data, can leak sensitive information about the individuals. The solution proposed by DP is to obfuscate the released information with carefully crafted noise that obeys certain properties. The goal is to make it difficult to detect whether a certain individual is in the database or not. In other words, two adjacent datasets (i. e. , datasets that differ only for the presence of one individual) should have almost the same likelihood to produce a certain observable result.
In our experiment, we consider the Cleveland heart disease dataset  [24]  which consist of 303 records of patients with a medical heart condition. Each condition is labeled by an integer number indicating the severity: from 0, representing a healthy patient, to 4, representing a patient whose life is at risk. We assume that the hospital publishes the histogram of the patients' records, i. e. , the number of occurrences for each label. To protect the patients' privacy, the hospital sanitizes the histogram by adding geometric noise (a typical DP mechanism) to each label's count. More precisely, if the count of a label is  1 , the probability that the corresponding published number is  2 is defined by the distribution in  (32) , where  and  are replaced by  1 and  2 respectively, and  is 1. Note that  1 , the real count, is an integer between 0 and 303, while its noisy version  2 ranges on all integers.
As for the value of , in this experiment we set it to 1. The secrets space X is set to be a set of two elements: the full dataset, and the dataset with one record less. These are adjacent in the sense of DP, and, as customary in DP, we assume that the record on which the two databases differ is the target of the adversary. The observables space Y is the set of the 5-tuples produces by the noisy counts of the 5 labels. W is set to be the same as X. We assume that the adversary is especially interested in finding out whether the patient has a serious condition. The function  reflects this preference by assigning higher value to higher labels. Specifically, we set W " X and p, q " $ ' & ' % 0, if 𝑤 ‰ 𝑥 1, if 𝑤 " 𝑥 ^𝑥 P t0, 1, 2u 2, if 𝑤 " 𝑥 ^𝑥 P t3, 4u, (34) For the estimation, we consider 10K, 30K and 50K samples for the training sets, and 50K samples for the validation set.
C. 3 Data pre-processing when 𝑔 is not integer Approximating  so that it only takes values P Q ě0 allows us to represent each gain as a quotient of two integers, namely Numeratorp , q {Denominator p , q. Let us also define 𝐾 def " lcm 𝑤𝑥 pDenominatorp𝐺 𝑤,𝑥 qq, ( 75 ) where lcmp¨q is the least common multiple. Multiplying  by  gives the integer version of the gain matrix that can replace the original one. It is clear that the calculation of the least common multiplier, as well as the increase in the amount of data produced during the dataset building using a gain matrix forced to be integer, might constitute a relevant computational burden.