dataesr/scientific-paragraphs-categorization huggingface dataset:
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT Similarity measurements. N-dimensional distance measurements are calculated, using the n selected variables, between all pairs of samples belonging to the same class providing an intrasimilarity vector. Then, similarities for all possible pairs of samples belonging to different classes are calculated providing an inter-similarity vector. These two vectors are used for the AUC estimation. Selection of the first variable. The purpose being to maximize the AUC estimate, the first variable to be selected is the one with the highest individual AUC value. Thus, the algorithm first estimates for each variable an individual AUC value and sorts the variables in decreasing order according to their AUC estimate. The first selected variable is the first column of this sorted data matrix.
Cross-validation procedure All chemometric analyses were performed with Matlab R2016b. PLS-DA functions from the SAISIR toolbox  [23]  were used on the datasets both with and without a prior variable selection procedure. Even if in some cases very few variables were selected, cross-validation PLS-DA was still used for comparison purposes because it is a discrimination method of reference. The goal is to show that the variable selection method based on the AUC criterion succeeds in highlight variables carrying information about the question at hand as efficiently as the bcoefficient vectors of PLS-DA, while substantially reducing the dimensionality of the data. The predictability of the models was evaluated through 100 hold out double cross-validation PLS-DA. The classes in the datasets being balanced and because there was no preference for either TPR or TNR, the TPR as a percentage of correct classification was used as a measure of classification performances.
Results and discussion The discussion hereafter is systematically organized by comparing the results from the double cross-validation PLS-DA applied to the datasets using all variables and the variables selected by the three methods. The global results for each property in each dataset are presented in a table. Each table shows the value of the parameters optimizing the percentage of correct classification (TPR): the increment and the correlation threshold for the AUC based method, the number of variables selected and the number of LVs extracted to build the final model, along with the predictability performances of the respective models. The final values of the parameters were chosen as the median value over all the values used in the 100 external validation steps and the Median Absolute Deviation (MAD) was used to mathematically describe the dispersion around the median.
Fresh meat samples As explained in the section 3. 1. 1, the fresh meat dataset is characterized by the property Species. The metric used in the AUC based method for the calculation of the similarities between Comment citer ce document : De Figueiredo, M. , Cordella, C. , Jouan-Rimbaud Bouveresse, D. , Archer, X. , Bégué, Rutledge, D. (2018). A variable selection method for multiclass classification problems using two-class ROC analysis. Chemometrics and Intelligent Laboratory Systems. , DOI : 10. 1016/j. chemolab. 2018. 04. 005.
Conclusion Within the framework of this paper, datasets with a relatively small dimensionality were chosen in order to provide easily interpretable illustrations of the results. In fact, high-dimensional datasets are difficult to display graphically in a comprehensive manner if the operator wants to show more than correct classification rates. This point also explains why the results with and without variable selection are not that different. PLS-DA performs very well on its own for these datasets. However, it is well known that PLS-DA performances decrease with the increase of data dimensionality. In that sense, what is important to emphasize here is the ability of the variable selection methods to correctly identify spectroscopic domains carrying information about the discrimination problem at hand, while keeping maximal the predictability of the subsequent models built.
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT in the forward step is always dependent on the previously selected variables. In the end, it is not the individual AUC value of the variables that matters but the way the variables interact with each other to discriminate the classes. Finally, the method presented in this article has been tested on two datasets and the results compared with two other high-performance variable selection methods. PLS-DA was applied to the datasets with and without a prior variable selection. Results have shown that a proper variable selection can substantially reduce the dimensionality of the data, while maintaining the predictability performances of the models compared with the PLS-DA models without variable selection. The three variable selection methods have shown different behaviors in the variable selection process but all have shown themselves to be efficient for the problems at hand.
investigated the potential of several well-known variable selection methods, including ReliefF. The authors have shown that the use of ReliefF on several Comment citer ce document : De Figueiredo, M. , Cordella, C. , Jouan-Rimbaud Bouveresse, D. , Archer, X. , Bégué, Rutledge, D. (2018). A variable selection method for multiclass classification problems using two-class ROC analysis. Chemometrics and Intelligent Laboratory Systems. , DOI : 10. 1016/j. chemolab. 2018. 04. 005 M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT datasets resulted in better performances compared to other variable selection methods, even more sophisticated ones like support vector machine based methods. The function used to perform ReliefF was the built-in function of Matlab.
Validation We compared the following strategies for asking the first k questions to a newcomer. Random Sample k questions from the item bank at random. Uncertainty Sample the k questions for which the probability that the learner will answer them correctly is closest to 0. 5 (which means, questions of average difficulty). This method is known as uncertainty sampling in the context of active learning  (Settles 2010). InitialD Sample k questions according to a determinantal point process, as described above. For our experiments, we could use the following datasets. TIMSS Trends in International Mathematics and Science Study (TIMSS) organizes a standardized test in mathematics. The collected data is freely available on their website for researchers, in SPSS and SAS formats. This dataset comes from the 2003 edition of TIMSS.
It is a binary matrix of size 757 × 23 that stores the results of 757 learners from 8th graders over 23 questions in mathematics. The qmatrix was specified by experts from TIMSS and has 13 knowledge components that are described in  Su et al. (2013). It was available in the R package CDM  (Robitzsch et al. 2014). Fraction This dataset contains the results of 536 middle school students over 20 questions about fraction subtraction. Items and the corresponding q-matrix over 8 knowledge components are described in DeCarlo (2010).
Discussion On every dataset we tried, InitialD performed better, and with a narrower confidence interval, than the other strategies. On the TIMSS dataset, the Random strategy performs well compared to a normal adaptive test. It may be because the test is already well balanced, so random questions have high probability to be diverse. If the number of questions to ask (k), the number of questions in the bank (n) and the number of measured knowledge components (K) are low, it is possible to simulate every subset of k questions over n. However, in practice, question banks on platforms will be large, so InitialD's complexity, O(nk 3 ) after a preprocessing step in O(n 3 ), will be an advantage. In this paper, we tested our method on datasets of up to 23 questions, but the exact determinantal point process sampling algorithm has already been tried on databases of thousands of items  (Kulesza and Taskar 2012).
Acknowledgments This work is supported by the  Paris-Saclay Institut de la Société Numérique  funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Data analysis Following the outbreak, paper contact tracing forms were requested from all County Health Teams. Forms were received from six counties and the data were entered into a Microsoft Access database. Data were analyzed using Microsoft Access and Epi Info. Each form was considered a unique contact record and unit of analysis, though it was possible for individual contacts to be monitored more than once if re-exposed. Source cases were identified using unique case identifiers, name, age, county, and district. To assess the coverage of contact tracing, or the percentage of cases for which contacts were monitored, we calculated the ratio of source cases in the database to the total number of suspected, probable, and confirmed EVD cases in MOH situation reports for the same counties using the closest approximate dates  [24, 25, 26, 27, 28].
Contacts who presented with symptoms could be referred for medical evaluation without meeting EVD case definitions; hence, we use the terminology "potential cases". We calculated the positive predictive value (PPV) defined as the proportion of traced contacts-excluding those with dropped and unknown outcomes-who were potential cases. Sensitivity was defined as the ratio of potential cases identified during monitoring to the number of new cases in situation reports in the same counties  [24, 25, 26, 27, 28]. This analysis assumes all potential cases were infected with EVD, and that all source cases and potential cases in the database were included in the total counts from situation reports. Therefore, to the extent that these assumptions are overstated, the calculations serve as upper limit estimates.
Ethical considerations This assessment is included under Johns Hopkins School of Public Health Institutional Review Board no. 6296 with DHP as principal investigator. A letter of agreement was signed with the Liberia MOH concerning the publication of contact tracing analyses. This assessment used retrospective data collected for public health surveillance purposes so informed consent was deemed unnecessary according to the U. S. Common Rule. We followed the Declaration of Helsinki, aiming to provide assurance that the rights, integrity, and confidentiality of participants were protected.
This is greater than expected, especially compared to other examples in West Africa where approximately 69% to 78% of cases were not being traced prior to case identification  [29, 30]. This measure is dependent upon the level of contact tracing coverage, and based on our database, though admittedly not comprehensive, coverage only accounted for a maximum of one-quarter of all EVD cases reported for these six counties. While this ratio is aligned with similar findings in two Guinea prefectures (32% and 39%) and in Sierra Leone (19%)  [29, 30] , it is possible that contact tracing was not initiated for up to three-quarters of the remaining EVD cases in Liberia, potentially due to a combination of factors discussed below. Potential cases were more likely to be identified in rural districts and early in the epidemic, despite intensified efforts as the epidemic progressed.
During future outbreaks, clear and comprehensive protocols need to be initiated early in the epidemic and reinforced throughout implementation. Furthermore, if resources are limited, inclusion criteria could prioritize contacts with multiple exposures, and/or those with household contact or direct contact with the body or bodily fluids. Triggers for identifying potential cases could include contacts reporting multiple symptoms types, fever, nausea, and weakness. Finally, community perceptions, stigma, and mistrust reportedly led to challenges in obtaining complete and reliable information, to delays or an inability to trace contacts due to evasion, and even to violence  [5, 33]. Underreporting of symptoms due to fear or due to feverreducing drugs may explain why relatively few symptoms were captured in our database.
Likewise, we could not conclude whether symptoms reported amongst potential cases evidenced EVD infection. Our data primarily represented contact monitoring, as we did not have a comprehensive contact listing. Finally, EVD case counts from situation reports were unavailable to stratify coverage and sensitivity by urban-rural districts and/or phase, and these aggregated totals could not be linked to our individual-level database. Our findings suggest that despite the unprecedented scale of contact tracing for EVD in Liberia, there were limitations in its ability to detect new cases, especially in urban areas and during the peak case load. Since contact tracing remains a critical intervention for controlling outbreaks, we suggest rigorous implementation early in the outbreak and focusing on four key areas to optimize its performance within similar contexts: (1) strengthening integrated surveillance and electronic data systems, (2) decentralizing management of multidisciplinary teams for improved coordination and oversight, (3) instituting and reinforcing clear and comprehensive protocols, and (4) adapting community-led strategies to foster cooperation, trust, and ownership.
Acknowledgments We would like to acknowledge the  Liberia MOH  staff, county health teams, contact tracing teams, and partner organizations who participated in the implementation of contact tracing and data collection. We would also like to thank  Mosoka Fallah  and  Luke Bawo  (Liberia MOH);  Terrence Lo and E. Kainne Dokubo (CDC) ; and  Marc Lipsitch  ( Harvard University ) for their review.
The various strategies for calibrating the hyperparameter γ are tested on 3 different intensity functions (non null over [0, T ], with T = 8): λ 0,1 (t) = 4 1l [0,3] (t) + 2 1l [3,8] (t) λ 0,2 (t) = e -0. 4t λ 0,3 (t) = cos -1 Φ(t)1l [0,3] (t) -( 1 6 cos -1 Φ(3)t - 3 2 cos -1 Φ(3))1l [3,8] (t) where Φ is the probability function of the standard normal distribution. For each intensity λ 0,1 , λ 0,2 and λ 0,3 , we simulate 3 datasets corresponding to n = 500, 1000, 2000 respectively. The histograms of the 9 datasets with the true corresponding normalized intensities λ 0,i are plotted on Figure  1. In the following we denote by D i n the dataset associated with n and intensity λ 0,i. For each dataset, we adopt the 3 exposed strategies to calibrate γ. The posterior distributions are sampled using 30000 iterations of which are removed a burn-in period of 15000 iterations.
Indeed, the simulation of the λ conditionally to the other variables involves an accept-reject (AR) step. For some particular values of γ (small values of γ), we observe that the acceptance rate of the AR step can be dramatically low, automatically inflating the execution time of the algorithm, this phenomenon occurring randomly. The computational times (CpT) are summarized in Table  1 , which also provides for each of the 9 datasets the number of points (N T ), the γ computed using formula (4. 9) and to be compared with the targeted value Ψ -1 (E theo ), the perturbation factor ρ used in the fixed γ strategy and the standard deviation of the prior distribution of γ σ γ (the prior mean being γ) used in the two hierarchical approaches. The second hierarchical prior distribution (last columns of Table  1 ) corresponds to a prior distribution more concentrated around the empirical value γ.
On Figure  2  (respectively 3 and 4), we plot for each strategy and each dataset the posterior median of λ 1 (respectively λ 2 and λ 3 ) together with a pointwise credible interval using the 0. 1% and 0. 9% empirical quantiles obtained from the posterior simulation. Table  2  gives the distances between the estimated normalized intensities λ i and the true λ i , for each dataset and each prior setting. On λ 0,1 (which a simple 2-steps function), the 4 strategies lead to the same quality of estimation (in term of losses / distances between the functions of interest). In this case, it is thus interesting to have a look at the computational time in Table  1. We notice that for a small γ 0 or for a diffuse prior distribution on γ (so possibly generating small values of γ) the computational time explodes.
0246 0. 0238 0. 0207 0. 0921 * 0. 0817 * 0. 0549 * 0. 1382 0. 0596 0. 0606 Distances between the estimated and the true density for all datasets and all strategies. Distance L 1 in horizontal block one, distance L 2 in horizontal block two, distance • ∞ in horizontal block three.
Acknowledgements The first author wants to thank the  Laboratoire de Mathématiques Pures et Appliquées at Université du Littoral Côte d'Opale as well as FRIAS  for hospitality during research visits in the spring of 2016 and 2018, respectively. During the preparation of this paper, he has been partially supported by the  DFG-Collaborative Research Center SFB/TR 45  " Periods, moduli spaces and arithmetic of algebraic varieties ". The second author gratefully acknowledges the hospitality of the mathematical departments of the universities of  Duisburg-Essen  and Freiburg as well as of the FRIAS during the spring of 2016, 2017 and 2018. Both authors would like to thank  Peter Heinzner  for fruitful and interesting discussions.
However applications now consider, for example, the design of aircrafts  (Chung and Alonso 2002) , the prediction of the mechanical properties of nanomaterials  (Yan et al. 2012) , the optimization of supply chain networks  (Dixit et al. 2016) , the construction of financial term-structures  (Cousin et al. 2016) , the modeling of social systems  (Oliveira et al. 2013) , and in all cases the quantification of the uncertainty. It is therefore not surprising to see in Table  29. 1 that the number of articles on kriging (word "kriging" or "cokriging" present in the title) published by the journals of the Scopus database doubles decade after decade. The situation is slightly different for the three journals published by the IAMG: Mathematical Geosciences (formerly Journal of the International Association for Mathematical Geology, then Mathematical Geology), Computers & Geosciences, and Natural Resources Research; indeed, IAMG journals played a major role in the dissemination of the geostatistical literature in English in the first decades, but have now to share this role with the journals of the new application domains.
Conclusion We have shown the long way from Krige's regression, which took account of two average sample grades (a local one and a global one) to avoid bias in the estimation of a panel, to present applications of kriging, which can deal with few data (e. g. , a limited number of computer experiments in applications to DACE) as well as several hundred thousand data (remote sensing, seismic). We have seen the large diversity of application domains of kriging, so that is it probable that many users do not know the origin of the word: this is the price of success. We also gave a look at current research to enable a global application of kriging to large data sets, with the requirement to also benefit from nonstationary random function models. Much work remains necessary to transform them in standard methods applicable to a large variety of situations but, in view of the large community of researchers and developers in this area, no doubt that it will be done.
Acknowledgements The first author is partially supported by the  French Agence Nationale de la Recherche  ( ANR   GeoLie  project  ANR-15-CE40-0012 ). The second author acknowledges the  MIUR Excellence Department Project  awarded to the  Department of Mathematics, University of Rome "Tor Vergata" ,  CUP E83C18000100006. The authors also would like to thank the anonymous referees for their useful comments and suggestions to improve the presentation of this article.
6 , 0. 7 , 0. 8 , 0. 9, depicted for obstacle configuration (E) and testing configuration 5 × 5, are seen to shrink to a small neighbourhood of x as Ĵ6 approaches J min The measurement residuals u B e -u obs e , which carry the useful information for obstacle identification, are severely affected by even small perturbations of u obs e since their magnitude is much smaller than that of the measurement u obs , especially at the lower frequency kd = 1. Estimations R est and x est have been computed for cases (S), (E) and (B), using the same sampling grid G, for all testing configurations and frequencies. The relative errors on R est and offsets |x est -x| achieved are shown in Table  3  for noise level σ = 0. 02. On comparing these results with those for errorfree data (Table  2 ), the accuracy deterioration remains moderate in most cases with kd = 2 or kd = 5, while being unacceptable for the lower frequency kd = 1.
Methods This retrospective analysis was carried out in the infectious diseases departments of two university hospitals (Bichat Claude Bernard and Pitié-Salpêtrière) in Paris, France. Both departments are part of the Paris/Ile de France regional plan for the management of contagious emerging infectious diseases in adults. They serve as referral centers for emerging infectious diseases in the Paris area and have isolation wards and dedicated rooms with anterooms and negative pressure. We enrolled patients who had been admitted to these two wards after being classified as possible cases of MERS-CoV infection, as defined by the WHO epidemiological bulletins  [21]. In hospitalized patients, epidemiological data were collected: demographic characteristics, travel history, purpose of the travel, contact with animals or sick people and inpatient or outpatient visits during the travel.
Microbiological documentation None of the patients hospitalized for suspected MERS-CoV inhibition was found to be positive on MERS-CoV PCR. A microbiological etiology was identified in 70 (75. 4%) patients (Table  3 ). A viral infection was documented in 47 patients (50. 3%), including 26 (37%) with human rhinovirus (HRV) and 25 (35. 6%) influenza virus. For the latter, each type of virus was identified in approximately half of the cases. A bacterial infection was documented in 22 patients (23. 6%), the most common etiologies being Legionella pneumophila and Streptococcus pneumoniae in 10 patients (31. 8%) each. Patients with Legionnaires' disease (LD) had more chronic cardiac disease and were more immunosuppressed than other patients (data not shown). Finally, those with LD had a longer duration of hospital management (11 days vs.
Data and Experimental Process The data used in the present study consisted of daily closing prices collected between January 2010 and December 2016 for four stock indexes from developed countries: S&P500, DJI, Nikkei225, and CAC40. The daily close prices were converted to daily log-returns. We took the first differences of the natural logarithm of the daily prices. For an observed price P t , the corresponding one-day log-return on day t was defined by: r t = ln P t P t-1. Table  1  contains descriptive statistics for the sample return of the four considered stock indexes. We observed that the returns were negatively skewed and characterized by heavy tails since the kurtoses were significantly greater than 3. The series had a distribution with tails that were significantly fatter than those of normal distribution.