dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: The Inspire Program was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175), the European Regional Development Fund (ERDF) (Project number: MP0022856), ARS «Agence Régional de Santé d'Occitanie» and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN, Pfizer, Pierre-Fabre, Fondation Avenir Cogfrail Grant. Potential Conflicts of Interest: The authors declare that they have no conflict of interest for the present paper. Acknowledgements: The authors thank all the health professionals participating in the INSPIRE ICOPE CARE Program especially Cendrine Blazy and Dr Marie Dominique Medou from Occitania Regional Health Agency and all the members of the Gerontopole "Hospital of the walls" care unit (Augusto S, Bouchon L, Cazes MC, Da Costa F, Poly M, Vaysset S) and all the members of the Occitania Territorial Teams of Aging and Prevention of Dependency.
A minimum vertical eddy diffusion (KZZ) is set to 0. 1 m 2 s -1 and a minimum Planetary Boundary Layer (PBL) height of 20 m is applied to avoid unrealistic concentrations. For the meteorology, the WRF 3. 7. 1 version is used. The WRF simulation has been nudged to National Centers for Environmental Prediction (NCEP) final analysis meteorological fields Global Forecast System (GFS) at 1 × 1 •. and 6-hourly resolution at the coarsest model initial and domain boundaries (ds083. 2 dataset,  [31] ). For the reference simulation performed in this study, REF01, the main WRF settings are summarized in Table 1  [32] [33] [34] [35] [36] [37]. The choice for the Planetary Boundary Layer (PBL) scheme YSU is supported by  [38]  and proposed as a "best practice" by the WRF development team as well as in  [39].
Overall, the use of YSU exhibits good performances but as shown by  [40]  there is no universal good schemes, and a part of this study will be to test other configurations in this peculiar region. For high resolution simulations the common practice is to avoid to use a parameterization of the Cumulus cu_physics=0. The PBL computed by WRF is directly used in CHIMERE with a minimum value of 20 m to avoid unrealistic overshoot on the computation of concentrations. For the anthropogenic emissions, the main pollutant's emission values (PPM2. 5, PPMcoarse, NOx, NH3, CO, NMVOC, SO2) are from EMEP (Co-operative program for monitoring and evaluation of long range transmission of air pollutants in Europe) program  [41]. For France, the emission inventory benefits from a spatial improvement using 1 × 1 km 2 proxies for the main activity sectors.
Period Several pollution episodes occurred in France in November and December 2013 affecting the whole country and particularly over the Northern and Eastern France. Over the Alps, the weather conditions were often very stable with frequent High Pressure systems developing from North of France to Central Europe. As usual in these situations, Low Pressure systems over the north of Italy and the Mediterranean basin can develop and some precipitations were recorded on short periods over the Alps, for instance in the Grenoble area: Some synoptic maps of the meteorological situation are provided in Supplementary Material Figure  S1  for these key dates. The official air quality forecasting system PREV'AIR  [10]  archives the observed and modeled dataset and is accessible at www. prevair. org. Several episodes were recorded from 28 Novermber to 19 December, this work focusing on the 27-30 Novermber 2013 (as EPI) and 11-18 December 2013 (as EPII) episodes.
5, NO, NO 2 , O 3 , SO 2 ). The chemical composition of the PM10 fraction is available on a daily basis for a limited number of stations (Passy, Chamonix, Marnaz and Grenoble) from the DECOMBIO program  [53, 54]  and the CARA program. The dust concentrations are estimated multiplying by a factor of 8 the Calcium concentrations and the Organic Matter by applying a factor of 1. 6 from Organic Carbon concentrations  [55]. The list of stations is presented in Table  4  and the full list in supplementary material Tables  S1-S3. All sites are urban, periurban or rural background stations. Usual errors statistics used for the analysis are computed by formula reported in Appendix A.
Evaluation of the Meteorology Gridded data from the E-OBS v20. 0e dataset  [59]  are used to evaluate the quality of the meteorological simulation for the 2 m temperature (T2). This dataset is provided at 0. 1 • resolution over Europe. Only EUR01 and ALP0033 are evaluated and compared over the ALP0033 area to estimate the added value of a higher resolution. The E-OBS dataset is remapped with NCO  [60]  over the simulation domains to perform the grid to grid error statistics. Error statistics displayed in Figure  2  show a general underestimation of the mean temperature with a negative bias of -1. 05 K and -0. 82 K with spatial correlations of 0. 75 and 0. 70 on average for EUR01 and ALP0033 domains, respectively. A better bias drives an improved RMSE between APL0033 and EUR01 which are respectively 0.
Chemical Compounds Concentrations Tables  6  and 7  provide an overall evaluation with consistent datasets respectively for the two target domains ARV0011 and GRE0011 for the reference simulation REF01. In order to characterize the effect of the resolution, stations used are those present in each domain only. Results show that a finer resolution does not necessary provide the best error statistics for the PM but for NO 2 there is an obvious added value particularly in the Arve Valley. Nitrate concentrations are on average overestimated particularly at high resolution while sulfate concentrations are underestimated but clearly improved when increasing the resolution. EC and OM benefit from an increase of resolution but OM remains largely underestimated particularly in the Arve Valley. As expected an increased resolution provides higher concentrations close to the sources.
Acknowledgments: The authors acknowledge Laurent Létinois (INERIS/LCSQA) for extracting the official air quality data from the GEOD'AIR database (available on request at https://www. lcsqa. org/fr/les-donneesnationales-de-qualite-de-lair). Synoptic meteorological data (MESONET network) is available at http://www. weather. uwyo. edu/surface/meteorogram/europe. shtml. Meteorological data at Grenoble Le Versoud are available at http://www. meteociel. com. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www. uerra. eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www. ecad. eu). The PM10 chemistry and the vertical temperature profiles in the Arve Valley were acquired during the DECOMBIO project funded by Ademe and Primequal (1362C0028) with the dedicated efforts of F Chevier, J Allard, and C Coulaud.
The PM10 chemistry data for Grenoble is funded by the Program CARA and internal fundings at IGE. Many thanks to numerous people at IGE for the chemical analyses of the Arve and Grenoble series, and to many people at Atmo AuRA for the collection of these samples. We acknowledge the use of imagery from the NASA Worldview application (https://worldview. earthdata. nasa. gov), part of the NASA Earth Observing System Data and Information System (EOSDIS). For the NASA Earth Observations of MODIS water vapor, special thanks and acknowledgments go to David Herring, Jesse Allen, Jacques Descloitres, Gene Feldman, Chelle Gentemann, Mark Gray, Goran Halusa, Jackie Kendall, Norman Kuring, Rebecca Lindsey, Chris Lynnes, Alex McClung, Bill Ridgway, Holli Riebeek, Jeff Schmaltz, Michon Scott, Rob Simmon, Allan Smith, Reto Stockli, Ross Swick, Stephanie Schollaert Uz, Tom Whittaker and Wenli Yang for generously sharing their time, expertise, imagery, and data.
In a more recent study  [7] , Suchacka and Chodak constructed a new approach to analyze historical data obtained from a real online bookstore. The proposed approach is based on association rule discovery in customer sessions and aims to evaluate the purchase probability in an online session. In  [8] , the author proposed a system that identifies the website component that has the highest business impact on visitors. To build such a system, a data set based on the Google Analytics tracking code  [9]  has been created. Moreover, naïve Bayes and multilayer perceptron classifiers have been explored for classification. In  [1] , authors set up a real-time user behavior analysis system for virtual shopping environment which is made up of two modules. In the first module, the purchasing intention of the visitor is predicted using aggregated pageview data kept track during the visit along with some session and user information.
Data Description As mentioned earlier, we reposed on the same dataset used in  [1]  but we only keep the part pertaining to session and user information. Features related to the selected data are depicted in Table  1. As reported in  [1] , features belong to 12330 sessions and the data were constituted so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Moreover, among the 12330 sessions in the dataset, 84. 5% (10422) were negative class samples that did not finalize a transaction and the rest (1908) were positive class samples ending with purchasing  [1]. Based in Table  1 , we can notice that, except the "Day" feature, all the rest of features are categorical. In order to homogenize the features types, we used a binning process in which the "Day" variable is discretized to 5 discrete levels  [10].
At each step, when the best attribute is chosen, only a small subset of attributes from the dataset is considered. Considering only the subset of attributes chosen, we then compute the best attribute as in Classification And Regression Trees (CART)  [20]. Each tree is built to its maximum depth and no pruning procedure is applied after the tree has been fully built. Lastly, to compute the predicted class for a sample, the predictions of the ensemble of decision trees are aggregated through majority voting.
Experiments and Results To conduct experiments, data described in Sect. 3. 2 are fed to naïve Bayes, C4. 5 decision tree and random forest classifiers using 70% of dataset for training and the rest for validation  [21, 22]. Moreover, to ensure statistical significance, this procedure is repeated 100 times with random training/validation partitions. Table  2  presents the average accuracy, sensitivity (true positive rate), specificity (true negative rate) and F1 Score for each classifier. The results show that naïve Bayes classifier yields the highest accuracy rate on test data. However, a class imbalance problem emerges  [23]  since, for each classifier, the sensitivity is much lower than specificity (sensitivity is even null for naïve Bayes classifier). Indeed, it is clear that each classification technique tends to label the test samples as the majority class (negative class).
This class imbalance problem is a natural situation for the addressed problem since most of the online visits do not end with purchasing  [24]. However, this issue should be properly handled since correctly identifying directed buying visits which are represented with positive class in the dataset is as crucial as identifying negative class samples. To deal with class imbalance problem, we considered the use of the widely used technique to balance the training set before the learning phase, namely the "Synthetic Minority Oversampling Technique" (SMOTE) methodology  [25]. To do that, 30% of the data set consisting of 12330 samples is first excluded for testing and the oversampling SMOTE method is applied to the remaining 70% of the samples. The results obtained on the balanced dataset are reported in Table  3.
Background Keloids are pathological scars that are characterized histologically by an overwhelming aggregation of fibroblasts and collagen type I within the inflammatory reticular dermis  [1]. Their clinical characteristics include continuous growth and invasion into the neighboring healthy skin beyond the original wound boundary via an erythematous and pruritic leading edge. They also show a strong tendency to recur when they are surgically excised in the absence of adjuvant therapies. Despite the fact that keloids are common throughout the world, their epidemiology has not been adequately investigated. In this chapter, we will summarize the limited epidemiological data on keloids that exists to date. Most of these data are from English language papers that are listed in PubMed. Below, we will describe what is known about  (1)  the demographic distribution of keloids, mainly on the geographical regions and ethnic races;  (2)  the internal genetic factors that shape keloid rates; and (3) the external environmental factors that influence keloid epidemiology.
Objectives The objectives of this review are to investigate the evidence related to the genetic basis and its association with keloid disease. A comprehensive literature search was performed using PubMed, Google Scholar, CNKI and Embase databases, by applying combinations of relevant MeSH (Medical Subject Headings) words as title. The key search terms included "Keloid, HLA immunogenetics, Linkage, and Large scale population SNP analysis. " The appropriate keywords included "Keloid, RNA Sequence analysis, Microarray, Micro RNA, Methylation, Mutation, Epigenetics, and FISH. " All retrieved records were compiled in the study for comprehensive review and evaluated based on significance, methodology, evidence, and reproducibility (. Fig. 8. 1).
Moreover respective gene network analysis, revealed four common hierarchical regulatory networks, consisting of four key regulators, (i) PENK (ii) PRKG2, (iii) pryoxamide (iv) tributyrin, and total 19 intermediate regulatory molecules. This analysis highlighted the involvement of regulatory networks in keloid phenotype development  [36, 45]  and with the development of this study approach in recent research since last five years, methylome of keloid have been characterized as most hypo-methylated rather than hyper-methylated  [45]. List of hyper-methylated genes includes CACNB2, ACTR3C, PAQR4, SLCO2B1, C1orf109, LRRC61, AHDC1, FYCO1, CMKLR1 and CCDC34 as compared to hypo-methylated group of genes, which are GHDC, DENND1C, MX2, ANKRD11, SCML4, GALNT3, IFFO1, WIPF1, PPP1R13L and CFH. Recently, further analysis was carried out using bioinformatics approach by applying Ingenuity Pathway Analysis (IPA) software on data set, obtained from keloid samples, revealed some key pathways shows significant association with keloids.
Van der Wal et al. performed a study where the reliability, validity, and feasibility of narrow-band reflectance spectrophotometry and tristimulus reflectance colorimetry on normal skin and scar tissue were studied  [5]. All three devices included in this study, Mexameter (narrow band spectrophotometer) and Colorimeter (tristimulus reflectance) (both from Courage & Khazaka Electronic GmbH, Cologne, Germany) and DSM II ColorMeter (narrow band spectrophotometer) (Cortex Technology, Hadsund, Denmark), obtained reliable results after a single measurement, performed by one observer. Some aspects of these measurements need to be considered when using skin color data for therapeutic evaluation: Skin color is generally adapted to seasonal differences and is likely to be different on various anatomical regions of the body.
Therefore, measurements of skin or scar lesions on specific locations need to be compared to the relevant normal skin of a nearby or contralateral comparative anatomical region. For narrow band spectrophotometric measurements, this needs to be performed by subtraction of the data of the normal skin from the lesional data. Furthermore, for the narrow band spectrophotometers, some caution is needed in interpretation of the data from highly pigmented skin: There is some influence on the erythema index by the melanin index, in that the erythema index apparently increases with increasing melanin index. Also, the melanin index is influenced by the oxygen saturation level of hemoglobin, since reduced hemoglobin absorbs more red light than oxyhemoglobin. For example, lowering the arm of a subject to be measured will lower the oxygenation, and give an increase in both erythema and melanin indices.
The Mexameter MX 18 (Courage and Khazaka electronic GmbH, Köln, Germany) uses 16 light-emitting diodes that emit light at three wavelengths (green: 568 nm; red 660 nm; infrared 880 nm), where after the reflected light is measured by a receiver unit. Erythema is measured by the green and red wavelengths, whereas melanin is determined by the red and infrared wavelengths. Readings are expressed between 0 and 1000, corresponding to white and black respectively. Both tools are examined in several clinical studies and provide reliable data on scars  [10]. The most common reliability parameter examined is the intraclass correlation coefficient (ICC) for ratings performed by several observers (i. e. , interrater reliability). Erythema measured by the Mexameter shows an ICC between 0. 82 and 0. 97 and for the DSM II ColorMeter an ICC of 0.
Practical recommendations for improving patient safety are judicious use of fluence (especially in darker skin types), use of pre-and post-cooling regimes, single pass, and avoiding stacking pulses, intentionally or otherwise  [8]. Multimodality treatment to one area within the same sitting is indeed possible, given the mastery of the underlying principles, correct understanding of the disease extent, the background skin type, and the patient's potential for healing. However, it significantly increases the risk for adverse events and therefore best avoided, except where mandated by individual risk-benefit considerations and availability of experts with appropriate experience. The use of test-patching and initiation of treatment in non-cosmetically conspicuous areas are advocated. In addition, a readily available database of previous patient-specific settings allows setting optimization to be delivered based on previous successes or complications.
In diabetic foot ulcers, its efficacy and its safety were demonstrated  [2]. In addition, some other clinical trials are ongoing using a medicinal product comprised of viable allogeneic dermal fibroblasts suspended in HypoThermosol®-FRS for remodeling scar contractures from burn patients and from dystrophic epidermolysis bullosa patients. Fibroblasts are from neonatal foreskin, cryopreserved, thawed, and expanded in culture under good manufacturing practice at Intercytex Ltd. , UK (7 ClinicalTrials. gov, Identifier: NCT01564407 [burn patients], ISRCTN67757229 [recessive dystrophic epidermolysis bullosa patients]).
This study suggests that MSCs, even if eventually rejected quickly after injection, transplanted with fibroblasts normalize matrix regeneration during wound healing. This promising study provides insight into allogeneic cell therapies as a viable method for antifibrotic treatment and demonstrates that even transiently engrafted cells can have a long-term impact via matrix modulation and effect on other tissue cells. One of the first randomized, controlled clinical trial will study the safety and efficacy of MSCs in skin scar for the treatment of Cesarean section  [8]. Eligible patients received transdermal hydrogel MSCs once a day for six consecutive days. Their outcomes should be available soon (7 ClinicalTrials. gov, NCT02772289).
When it is not clear from the context, we will specify using d GW,p (d X , d Y , µ X , ν Y ). The resulting coupling tends to associate pairs of points with similar distances within each pair (see Figure  6 ). The Gromov-Wasserstein distance allows for the comparison of measures over different ground spaces and defines a metric over the space of all mm-spaces quotiented by the isomoprhisms (see Definitions 4 and 5). More precisely, it vanishes if the two mm-spaces are isomorphic. This distance has been used in the context of relational data e. g. , in shape comparison  [17, 22] , deep metric alignment  [23] , generative modelling  [24]  or to align single-cell multi-omics datasets  [25].
Only FGW retrieves a transport matrix respecting both feature and structure. Example on two simple images. We extract a 28 × 28 image from the MNIST dataset and generate a second one through translation or mirroring of the digit in the original image. We use pixel gray levels of the pixels as the features, and the structure is defined as the city-block distance on the pixel coordinate grid. We use equal weights for all of the pixels in the image. Figure  11  shows the different couplings obtained when only considering either the features, the structure only or both information. FGW aligns the pixels of the digits, recovering the correct order of the pixels, while both Wassertein and Gromov-Wasserstein distances fail at providing a meaningful transportation map. Note that in the Wasserstein and Gromov-Wasserstein case, the distances are equal to 0, whereas FGW manages to spot that the two images are different.
A two-dimensional (2D) embedding is computed from a FGW distance matrix between a number of examples in this dataset with multidimensional scaling (MDS)  [43]  in Figure  12 (top). One can clearly see that the representation with a reasonable α value in the center is the most discriminant one. This can be better understood by looking as the OT matrices between the classes. Figure  12  (bottom) illustrates the behavior of FGW on one pair of examples when going from Wasserstein to Gromov-Wasserstein. The black line depicts the matching provided by the transport matrix and one can clearly see that while Wasserstein on the left assigns samples completely independently to their temporal position, the Gromov-Wasserstein on the right tends to align perfectly the samples (note that it could have reversed exactly the alignment with the same loss), but discards the values in the signal.
Graph-Structured Data Classification In this section, we adress the question of training a classifier for graph data and evaluate the FGW distance used in a kernel with SVM. Learning problem and datasets. We consider 12 widely used benchmark datasets divided into 3 groups. BZR, COX2 [44], PROTEINS, ENZYMES  [45] , CUNEIFORM  [46]  and SYNTHETIC  [47]  are vector attributed graphs. MUTAG  [48] , PTC-MR  [49] , and NCI1  [50]  contain graphs with discrete attributes derived from small molecules. IMDB-B, IMDB-M  [51]  contain unlabeled graphs derived from social networks. All of the datasets are available in  [52]. Experimental setup. Regarding the feature distance matrix M AB between node features, when dealing with real valued vector attributed graphs, we consider the 2 distance between the labels of the vertices.
However, these comparisons are not fair because of the high variance (on most datasets) w. r. t. the folds chosen for training and testing. This is why, in our experiments, the nested cross validation is performed on the same folds for training and testing for all methods  [59]. In the result, Tables  1 2 3 , we write in bold the best score for each dataset and we add a (*) when the best score does not yield to a significative improvement (based on a Wilcoxon signed rank test on the test scores) compared to the second best one. Note that, because of their small sizes, we repeat the experiments 50 times for MUTAG and PTC-MR datasets. For all methods using SVM, we cross validate the parameter C ∈ {10 -7 , 10 -6 ,. , 10 7 }. The range of the WL parameter H is {0, 1. , 10}, and we also compute this kernel with H fixed at 2, 4.