dataesr/scientific-paragraphs-categorization huggingface dataset:
AC K N OW L E D G E M E N T S SY was supported by a  Science and Technology Facilities Council (STFC)  studentship. The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University  -A * MIDEX, a  French 'Investissements d'Avenir' programme  ( AMX-20-CE-02 -DARKUNI ). AFR acknowledges funds through the  program Ramon y Cajal  ( RYC-2018-025210 ) of the  Spanish Ministry of Science and Innovation. IFAE  is partially funded by the  CERCA program  of the  Generalitat de Catalunya. IPR was supported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Actions  grant agreement No. 754510. This research is supported by the Director,  Office of Science, Office of High Energy Physics of the U.
| Data acquisition and analyses We used homologous landmarks and semilandmarks placed on threedimensional surfaces to describe the morphology. Digitization and landmark definition were performed following published protocols (Appendix S2;  Harbers, Neaux, et al. , 2020; Neaux, Blanc, Ortiz, Locatelli, Laurens, et al. , 2021; Neaux et al. , 2020). We performed all the analyses in the R environment (R Core Team, 2019). Coordinates were aligned using a generalized Procrustes superimposition  (Rohlf & Slice, 1990) , implemented in the procSym function of the package "Morpho"  (Schlager & Jefferis, 2020). We chose to work on form, that is, the combination of size and shape  (Dryden & Mardia, 1998) , rather than shape as form is a more comprehensive description of an object than shape alone in the context of ontogenetic studies  (Mitteroecker et al.
ACKNOWLEDGMENTS This study was funded by  ANR , through the  Domexp  project ( ANR-13-JSH3-0003-01 ), the  LabEx   ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir "  ANR-11-IDEX-0004-02 , program Emergence  SU-19-3-EMRG-02 ,  Muséum National d'Histoire Naturelle (Paris) , and the  CNRS INEE Institute. The authors are most grateful to the staff of the  Réserve Zoologique de la Haute-Touche (Christophe Audureau ,  Jérémy Bernard ,  Christophe Jubert ,  Sandrine Laloux ,  Emmanuel Marechal ,  Régis Rabier ,  Patrick Roux , and  Colin Vion)  for their help during the setup of the experimental structures, the care they provided to the experimental specimens and during the data acquisition. They would like to thank the  CIRE platform at INRAE  ( Hans Adriensen ,  Frédéric Elbout ,  Christian Moussu , and  Luc Perrigouard ).
Application to Tecator Data In this paragraph, we apply the GNFPLSIM model for Tecator data, popularly known in the functional data analysis. These data can be downloaded in the following link http://lib. stat. cmu. edu/datasets/tecator (accessed on 1 March 2022). For more details, check Ferraty and Vieu  [12]. Given 215 finely chopped pieces of meat, (see Figure  5 ) Tecator's data contain their corresponding fat contents (Y i , i = 1,. , 215), near-infrared absorbance spectra (Z i , i = 1,. , 215) observed on 100 equal wavelengths in the range 850-1050 nm, the protein X 1,i and the moisture content X 2,i. We try to predict the fat content of the finely chopped meat samples. The following figure shows the absorbance curves. We divide the sample randomly into sub-samples: the training I 1 of size 160 and the test I 2 of size 55.
MdlS was funded by the ANR grants AGIRA ANR-16-CE40-0022 and Noncommutative analysis on groups and quantum groups ANR-19-CE40-0002-01. (1) if ξ i denotes the orthogonal projection on H i , then i a i ξξ i 2 < 9ε, (2) every operator b ∈ B(H) which commutes with each a i preserves each H i. In other words, the condition i a i ξ 2 > 1-ε implies that: (1) (a 1 ,. , a n ) is 9ε-close on ξ to a PVM (p 1 ,. , p n ) (2) which preserves the symmetries of the original POVM. This theorem generalizes to infinite dimension and slightly strengthens a result from  [4]  (see also  [8, Lemma 19] ), which proves a form of this theorem with finite dimensional Hilbert spaces. More precisely, we can rephrase  [4, Theorem 5. 2]  as follows: if H = H A ⊗H B is a tensor product of finite dimensional Hilbert spaces and the a i are of the form A i ⊗ id B , then the theorem holds, but with 9ε in (1) (which is essentially optimal, see Remark 1.
3. Mutual diffusion coefficient in saliva. (A) A schematic of the unidirectional drying setup used to measure concentration gradients building up upon drying saliva at controlled RH. (B) Typical concentration profiles obtained at RH = 0% at two different times, rescaled by the space/time variable x = distance/time (1/2). (C) Fickian mutual diffusion coefficients obtained numerically from the two above concentration profiles. The orange line represents the best fit of these datasets, which is used in the following for calculations. Mutual Diffusion Coefficient in Drying Saliva. Water evaporation from pure water droplets is a well-described process that is controlled by water transport in the gas phase. In contrast, water evaporation from an aqueous solution of nonvolatile solutes, such as saliva, is a more complex process.
Acknowledgments The first author is a member of the  Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM)  and is partially supported by the research project Evolutive and stationary Partial Differential Equations with a focus on bio-mathematics, funded by  Fondazione di Sardegna  ( 2019 ).
19 and we give the proofs of its consequences presented above. In section 3, we give a quantitative version of the Moser trick. This part is deterministic. In section 4, we give the proof of Theorem 1. 19. Aknowledgments. The author would like to thank Denis Auroux, Vincent Beffara, Sylvain Courte, Laura Monk, Alejandro Rivera, and Jean-Yves Welschinger for valuable discussions. The research leading to these results has received funding from the French Agence nationale de la recherche, ANR-15CE40-0007-01 and from the European Research Council project ALKAGE, contract 670846 from Sept. 2015.
Investigating the transition between educational levels is one of the main themes for the future of mathematics education. In particular, the transition from secondary school to STEM degrees is problematic for the widespread students' difficulties and significant for the implications that it has on students' futures. Knowing and understanding the past is key to imagine the future of a research field. For this reason, this paper reports a systematic review of the literature on the secondary-tertiary transition in Mathematics Education from 2008 to 2021. We constructed two corpuses: one from the proceedings of three international conferences in mathematics education (PME, ICME, and INDRUM) and the other from peer reviewed research papers and book chapters returned by the databases ERIC and Google Scholar.
Related to these two steps, we differentiate between the two searches we conducted: one focused on conference proceedings, the other on refereed journal papers and book chapters. For both searches, the starting year was 2008 and the ending was October 2021. However, the database and, in part, the automatic search criteria were different between the two searches. According to our focus, we included in this review studies describing the STT to university mathematics of students enrolled in STEM degrees. These studies may involve pre-service secondary mathematics teachers in those countries where they are taught mathematics in the same lectures of students enrolled in a STEM degree. However, we consider the STT for pre-service teachers (including teachers for primary and lower secondary education) as a special case of transition to tertiary mathematics deserving, in our view, a separate discussion, not addressed in this paper.
We developed a specific search for conference proceedings since international conferences are likely to be a forum to share ideas which are then developed in journal papers or book chapters, and results emerging from conference proceedings often anticipate trends in the development of research themes. The first choice we made was related to which conference's proceedings to include in the review. We decided to include two of the most popular (by number of participants) international conferences in mathematics education: the International Congress on Mathematical Education (ICME) and the Conference of the International Group for Table  1  The three steps for a literature review  (Green et al. , 2006)  Step 1: Selection of the topic Step 2: Sources of information Step 3: Selection criteria Why the topic is relevant? Database Automatic search criteria Why a literature review about the topic is needed? Starting and ending year of the search Selection criteria used to include or exclude in the review a study selected by the automatic criteria the Psychology of Mathematics Education (PME).
The database for the construction of this first corpus therefore consisted of the published proceedings of ICME, PME, and INDRUM since 2008. We identified all the contributions including the key term 'transition' in the title, abstract, or text (automatic search criterion). We then considered only contributions where the occurrence was related to the secondary-tertiary transition and with STT as the significant focus of the paper (not mentioned as a secondary topic). For the construction of the second corpus (which includes journal papers and book chapters), we first considered what search criteria to use, as this is a crucial step for every literature review. Gusenbauer and Haddaway (2020)  recognised three main quality criteria for literature searches: completeness (identifying all the most significant resources about the topic), transparency, and reproducibility.
Through the analysis of the systematic search qualities of 28 academic search engines, the authors suggested to conduct literature reviews using at least two different search engines, according to their quality with respect the three criteria mentioned above. Regarding Google Scholar,  Gusenbauer and Haddaway (2020)  observe that this search engine has several limitations in terms of transparency and reproducibility of searches; however, 'it is considered a suitable supplementary source of evidence (including on grey literature)  ' (p. 196)  for systematic literature reviews. Based on the above considerations, we used two databases: ERIC (Education Resources Information Center, https:// eric. ed. gov), the online database of education research promoted by the US Department of Education, and Google Scholar (https:// schol ar.
google. com). The use of Google Scholar in our review appeared important to bring to the fore contributions by researchers who may not have access to the main journals and conferences of our community (for example due to the high cost of conference fees and travel). We also followed  Haddaway et al. 's (2015)  advice to focus on the first 200 to 300 results returned by Google Scholar for systematic reviews as supplementary source of evidence. Since the databases were not specific to mathematics education, we used a strict automatic search criterion. We used 'transition AND mathematics AND school AND university' as search terms. We observed a posteriori that the inclusion of the word 'university' in the Google Scholar search was not useful: Google Scholar searches the entire document -unlike ERIC which searches only the title, the abstract, and the keywords -and therefore the word university featured in all documents where the names of authors and their institutions were included.
At the end of this first phase, following the recommendation of  Haddaway et al. (2015) , we considered the corpus produced by the union of the 252 results produced from ERIC and the first 300 results by relevance from Google Scholar taking care to eliminate repetition of entries between the two sets of results since, as expected, there was a significant overlap. The selection criteria used to include a study identified using the automatic search criteria were the same for both databases. Assuming the definition of STT given in the Encyclopaedia of Mathematics Education as 'the process experienced by students leaving secondary school and entering different kinds of postsecondary institutions: universities, engineering schools, etc. '  (Gueudet & Thomas, 2020, p. 762) , we selected the contributions describing a transitional process, considering a 'before' and an 'after' this process.
These data highlight an issue of underrepresentation in the PME conferences concerning the discussion about STT. Moreover, 16 (62%) out of 26 reports are by researchers from German universities. This regional predominance is also reflected in the research methodologies: the quantitative approach is prevalent in these empirical studies (62. 5% versus 29% for the qualitative approach and 8% for the mixed one), and samples are usually large, notwithstanding the presence of three interesting case studies. Several reports focus on the identification of significant correlations between academic success/failure and other variables. This aim is related to a clear definition of the variables involved, to the development of instruments for measuring these variables and the students' success or failure, and to the adoption of statistical models (the more frequently adopted being the Rasch model).
On the other hand, it points to two significant issues. First, most data are collected through online surveys: this choice is 'not neutral'. Psychologist have discussed the impact of computer versus paper-pencil survey in collecting self-reports  (Bates & Cox, 2008)  and, in mathematics education, recent publications address the impact in students' performance in online versus pen and paper tasks  (Lemmo, 2021). Second, the participants of these studies are almost exclusively volunteers, and we need to consider and discuss the limitations related to this. The effects of a sample of volunteers appear to be particularly significant in the analysis of failure in STT, where students who drop out or are about to do so are often the focus of research. The volunteer sampling in the STT may create a bias towards higher achieving students  (Vollstedt et al.
Acknowledgements. We would like to thank the referees for their careful readings and comments on an earlier version of this paper. Funding. SC acknowledges the support of the  UK Engineering and Physical Sciences Research Council (EPSRC)  grant  EP/T004290/1. KJ acknowledges the support of the  Swedish Research Council (VR)  and grant  KAW 2015. 0270  of the  Knut and Alice Wallenberg Foundation.
Acknowledgments H(f ) = R 3 f log f dv. The Fermi-Dirac entropy is introduced as S ε (f ) = -ε -1 R 3 εf log(εf ) + (1 -εf ) log(1 -εf ) dv = -ε -1 H(ε f ) + H(1 -εf ). (2. 1) The Fermi-Dirac relative entropy is defined as follows: given nonnegative f, g ∈ L 1  2 (R 3 ) with 0 f ε -1 and 0 g ε -1 , set H ε (f |g) = -S ε (f ) + S ε (g). For the Fermi-Dirac relative entropy, a two-sided Csiszár-Kullback inequality holds true (see  [24, Theorem 3] ). There exists C > 0 (depending only on ε and g L 1 2 ) such that g -M ε 2 L 1 2 R 3 g(v)dv H ε (g|M ε ) C g -M ε L 1 2. (2. 2) The long time behaviour of the solutions of the equation will be studied using the classical method consisting in comparing the relative entropy with the entropy production. In our case, the entropy production is defined as D ε (g) := - R 3 Q(f ) log f (v) -log(1 -εf (v)) dv.
Study variables Data variables included selected characteristics of managers and HCWs: locality (Conakry or countryside), gender (male or female), age (in years), duration of professional experience (in years), occupation (nurse/midwife, medical doctor, health technician, student/intern, laboratory staff, or other staff), type of facility/service (health centre, hospital, or management service), and facility unit (e. g. , Maternity or Surgery). Variables related to the local environment (e. g. facility) preparedness includ-ed: received infection prevention and control (IPC) supplies, duration of supplies being sufficient, the existence of handwashing devices at facility/service entry, water supply, or availability of an Epidemic Response Task Force. Variables related to facility-level capacities to detect, isolate, refer, and manage suspected COVID-19 cases included: received guidance or instructions related to COVID-19, triage system at facility entrance, training of HCWs, the main source of information, needs for training and priority areas, sample collection protocol and capacity, space to manage suspected cases, equipment, tracing and notification system, transportation capacity, quarantine/isolation capacity, and availability of Integrated Disease Surveillance and Response (IDSR) Technical Guidelines.
Data analysis Data were exported from the Kobo platform as an MS-Excel file and imported for analysis into Stata 15 Software (Stata Corporation, College Station, TX, USA). Descriptive statistics (proportions and means with standard deviations) were presented for each sub-sample (Managers and HCWs). These proportions were then compared according to the two survey localities (Conakry and countryside) and by health sector (public and private) using the Pearson's Chi-squared test or the Fischer exact test when applicable with a significance level set at p<0. 05.
Using the United Nations Comtrade database, we perform the Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020 comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms -the PageRank, the CheiRank and the reduced Google matrix -take into account the multiplicity of the WTN links providing new insights on the international trade comparing to the usual import-export analysis. These algorithms establish new rankings and trade balances of countries and products considering every countries on equal grounds, independently of their wealth, and every products on the basis of their relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occur for the year 2020 highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis.
With this aim, we perform the Google matrix analysis of the World Trade Network (WTN) constructed from the UN Comtrade database  [7]  for the years 2018, 2019, and 2020. The WTN concerns N p = 10 types of commercial products (see Tab. I) traded between N c = 194 world countries. After the pioneering work of Brin and Page  [8] , the invented PageRank algorithm, based implicitly on the Google matrix construction, found broad applications for the World Wide Web (WWW) search engines  [8, 9]. In the last decades, the human society created a variety of complex networks which the main properties are described, for example, in  [10]. The applications of Google matrix algorithms to various directed networks include: university web sites and Wikipedia networks  [11, 12] , Linux Kernel networks  [13, 14] , networks of protein-protein interactions  [15] [16] [17]   Commodities and transactions not classified elsewhere in the SITC TABLE I.
IV. DISCUSSION In summary, on the basis of the UN Comtrade database gathering data of the international trade  [7] , we contructed the multiproduct WTN for the years 2018 to 2020. We presented its Google matrix analysis based on the PageRank algorithm  [8, 9]  and other extended algorithms described in  [19, 20, 22, 24, 25]. The PageRank-CheiRank approach allows to obtain a more detailed and advanced description of the international trade, in comparison to the usual import-export analysis, since it takes into account the complex multiplicity of the trade transactions between the countries. Thus, the PageRank-CheiRank method takes properly into account the robustness and the diversity of the trade connections of a given pair (country, product) highlighting the network influence of specific countries which is not so visible from the standard import and export description.
A prospective Poisson space-time scan statistic  [3, 4]  implemented in SaTScan  [3, 5]  is used to detect clusters of COVID-19 as Desjardins et al. did in early 2020 at the county level in the U. S. [6]. Clusters are computed at the communal and the sub-communal/administrative district level defined by the French National Institute of Statistics and Economic Studies (INSEE). For each geographic region, we consider the population according to the 2016 population census. The maps from the two first cases are used for the third use case, combined with open data available for administrative districts in France  [7]  in terms of population density, demography descriptors (age groups, gender distribution), income, education, social vulnerability, etc.
We can state for example  Yu and Jones (1998) ,  Fan et al. (1996) ,  Berlinet et al. (1998a,b) ,  Honda (2000a,b)  and  Plancade (2013). For recent references see  Benziadi et al. (2016) ,  Chowdhury et al. (2018) ,  Al-Awadhi et al. (2019) ,  Chikr-Elmezouar et al. (2019)  and  Slaoui and Khardani (2020). Over the past decade, data streams have become an increasingly important area of research. Some of the most common data streams include Internet packet data, Twitter activity, Facebook newsfeed, credit card transactions and more recently COVID-19 epidemic data. In these situations, the data arrives regularly so that it is impossible to store it in a traditional database. In such a context, it is very interesting to build a recursive multivariate conditional cumulative distribution estimator that does not need to store all the data in memory and that can be easily updated to handle the online data.
The basic target of this paper is to provide a non-parametric strategy to recursively estimate the CCDF. The paper is organized as follows. In section 2 we introduce our method for the estimation procedure of CCDF. The main results of our recursive estimator are displayed in section 3. Section 4 exhibits the asymptotic properties of non-recursive Nadaraya-Watson's estimator. A second generation plug-in scheme for constructing data-driven bandwidth selection procedures is elaborated in section 5. Section 6 highlights the performances of our estimator on simulated data as well as a real dataset. Finally, the conclusion is drawn in section 7.
6. 3 Real Datasets: In this section, our focal point is to examine two real datasets Models, namely the Insurance Company Benchmark (COIL 2000) dataset as well as the French Hospital Data of COVID-19. 6. 3. 1 Application 1: The Insurance Company Benchmark  (COIL 2000)  dataset The  (COIL 2000)  dataset is found in data. world website https://data. world/uci/insurance-company-benchmark-coil-2000). As far as our application is concerned, we shall consider the following two models: • Model 1: X corresponds to the sociodemographic data attribute number 16 and Y stands for the whole 5822 observations of customer records. • Model 2: X corresponds to the sociodemographic 5-dimensional data attributes number 6,8,11,12 and 13 and Y stands for the whole 5822 observations of customer records. x=0 x=1   (0, 0, 0, 0, 0).
Application 2: French Hospital Data of COVID19 The French Hospital data of the COVID-19 epidemic are extracted from https://www. data. gouv. fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/. The Santé publique France's mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 epidemic, Santé publique France has taken in charge monitoring and understanding the dynamics of the epidemic, anticipating the dierent scenarios and implementing actions to prevent and limit the transmission of this virus on the national territory.