dataesr/scientific-paragraphs-categorization huggingface dataset:
The surveys were dated, edited and coded as returned and the overall statistical descriptive analysis was completed using SPSS. Differences in response rates of early versus late respondents were assessed as per  Armstrong and Overton (1977)  and no differences were found. A check was also completed for multivariate outliers using SPSS Regression (Mahalanobis Distance) but none were located. One of the main findings from the survey indicated that price and gift token were considered the most effective sales promotion tools in promoting re-engagement intentions of the lapsed member market and this was further tested in terms of true actual purchase behavior in Study B. Study B sought to systematically manipulate re-engagement incentives including price discount, gift token and no incentive (control group) through random assignment.
The study was set in a real world context and did not just test stated intentions as in Study A, but true behaviors through the use of a field experiment (n 5 300). Anderson and Simester (2011, p. 102 ) maintain that "companies can gain more from field experiments than from analyzing historical transactions due to the greater accuracy which can be gained from the analysis of data generated in field experiments. " The sample for this study drew upon the same health club that was used for Study A with a sample size of 300 randomly chosen recently lapsed members who discontinued membership within the preceding six months which were randomly assigned to one of three experimental groups; Group 1 was used as the control or baseline group and was not offered any incentive to re-engage; Group 2 was offered a price reduction; Group 3 was offered a gift token.
Incentivizing health club members relationship between previous behavioral loyalty and expressed future behavioral loyalty. Previous purchase behavior was measured by length of membership in years from the health club database. It was assumed that lapsed members who had not joined another club since leaving would also be more likely to re-engage, this information was captured as a single measure item. Situational constraints were captured in the survey as a single measure item using a version of model of switching behavior developed by  Keaveney (1995)  and  Alexandris and Carroll (1999). Respondents were asked to list their top reason for discontinuing membership of the club and this was analyzed as part of hypothesis seven (H7A) and included price and nonprice reasons for leaving the club.
Study B -Measurement items and scales Re-engagement behavior and promotion incentives (H1B) was measured as true actual purchase behavior using a field experiment in Study B. Re-engagement was measured on the basis of internal membership records in the health club with an objective yes/no effectiveness measure related to the question whether or not the customer renewed membership within the last six months so as to provide consistency in the reporting of the results. Recency of lapse (H3B) was captured as an objective measure from the time the membership lapsed on the club database system and after one month was checked to see who returned. 6. 1 Research findings from Study B 6. 1. 1 Sample -Study B. The sample for this study drew upon the same health club that was used for Study A with a sample size of 300 randomly chosen lapsed members who discontinued membership within the preceding six months randomly assigned to one of three experimental groups.
001). This supports hypothesis (1B) and confirms that offering an incentive is better than offering no incentive and that price is more effective than gift token in re-engaging the lapsed member market. In Study B, most of the hypotheses were tested through a logistic regression, with actual rejoining behavior as dependent variable and the information obtained from the club's database as independent variables. The results of this regression are outlined in Table  11  Appendix A and will be discussed for each variable independently. In this analysis, the use of a price incentive was entered as a dummy variable in the equation. Consistent with the chi-square analyses reported beforehand, this variable was found to influence the likelihood to rejoin the club, with lapsed members more likely to rejoin when exposed to a price incentive than otherwise (the incentive accounted for between 15.
Joining fees appear to be only effective in encouraging members to remain as members up to six months so health club providers should consider the value of joining fees in extending the term of the membership. Although loyalty, duration, age, gender and income were found not to be significant in this study, satisfaction, frequency and recency of lapse were significant. Therefore, health clubs should strive to keep members satisfied, monitor and increase frequency of attendance with creative programming/competitions and commence the re-engagement process prior to the membership expiry so as to maximize re-engagement and customer retention using price discount as an incentive. Exceptional customer service and management of the health club will also help alleviate customer disengagement. An accurate membership database is essential and must be managed effectively in order to maximize return from the lapsed member market.
Constraints  (10)  enforce the workstation operating time to remain within the cycle time C T , for all opened workstations, jointly with at least a predefined probability 1 -α. Finally, set  (11)  represents the possible values of the decision variables. In the next section, lower and upper bounding schemes will be defined in order to transform conraints (  8 ) and  (10)  and optimally solve problem (I) with quadratically constrained program solvers, such as CPLEX.
Numerical Illustration Models (LMinI) and (UMinI) as well as the nonlinear optimizations and numerical integrations are implemented in Linux using C++ on a PC with 8×CPU 2. 80 GHz and 32 Go RAM. Nonlinear optimizations are done with ALGLIB, numerical integatons with Gauss-Legendre quadrature while models (LMinI) and (UMinI) are solved using CPLEX 12. 6. All are applied to a remanufacturing industrial case product: a Knorr-Bremse EBS 1 Channel Module. It represents a real case study in the automotive part remanufacturing sector. Such a product is composed of at least 45 components  [8]. Tables  1  and 2  and Figure  3  summarize the obtained optimization results for different values of R e , ∈ L. Columns '(stat,hstat)' and 'time(s)' in Tables  1  and 2  indicate, respectively, the number of opened workstations of the disassembly system, the number of hazardous workstations, if there is any, and the resolution time in seconds.
The air passed through a glass pipette with an inner diameter of 1 mm. The distance between the exit of the pipette and the skin surface was optimized to 10 mm. A linear laser was used to measure the vertical displacement of the skin during the test with a sampling frequency of 8 kHz, as shown in Fig. 1(b ). The laser line was composed of 800 sensors over a length of 7 mm. Finally, a turntable was placed on the head of the laser to rotate both the laser and the glass pipette together around the pipette axis. The turntable permitted to perform the measurements in different directions (angular orientation) and in the tangent plane of the skin surface (Fig. 1 (b) ). A software application was developed using LabviewTM language (National Instruments) to control the system. For each impact test, the air flux pressure was set to 1 bar for 4 ms and 3 tests were performed for each measurement.
Statistical analyses MATLAB software was used for statistical data analysis. The data are considered statistically significant if the p-value is lower than the significance level defined (0. 05 in our case). In our study, we used ANOVA to see whether we had statistically significant differences as a function of age and directions of measurement. Spearman correlation analyses were conducted to check whether the mechanical properties of the skin correlated to the topographical ones. The Spearman's rank correlation coefficient is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. A correlation coefficient higher than 0. 8 indicates a strong positive correlation while a correlation lower than À 0.
Test procedure The mean pressure in the test rig is set and the mean flow rate is adjusted by manipulating the flow restrictor. Then, the white noise signal is applied to the high-frequency response servo-valve and adjusted by an equalizer in order to have a good signal-to-noise ratio in all the frequency range. After checking to be on a stabilize condition for the mean pressure, the pressure ripples and the oil temperature, these different physical quantities are acquired in the temporal domain. In the case of a symmetrical component, only one measurement is needed but if the component is not symmetrical, the measurements have to be also achieved by returning the component on the test rig in order to acquire new data where pressure ripples are applied at the other port of the component.
Principle A rigid pipe model has been in AMESim © taking into account fluid born noise in a temporal domain modelling software. As the hydroacoustic characteristics are described in the frequency domain and do not take into account the steady-state of the pressure and flow ripples, two major issues have been encountered which are: -The transition from the frequency to the temporal domain -The modelling taking into account the steady-state and the dynamic pressure and flow rate. As a matter of fact, the AMESim © software is not able to process frequency data during a simulation, that's why this data has to be transformed into the temporal domain to parameterize the hydroacoustic part of the rigid pipe model. This transition is explained in the following chapter. The steady-state is performed using the hydraulic component of a rigid pipe available in the hydraulic library in AMESim ©.
In our case, the equation (  24 ) has been simplified by neglecting the coefficients d vf and e by setting them to zero. This method is implemented in a Matlab © function called "rationalfit". The outputs of this function are the values of the residue r m and the pole a m of each first order rational function. To fit anti-resonance of the frequency response f(s), this function can give complex poles and residues. When it is the case, this function always gives a couple of first order rational function which one has its residue and pole as the conjugate of the other. In order to avoid all first order rational function with complex numbers, each pair of functions with complex numbers is transformed into a second order rational function. 6 Comparison between the measurements and the simulation results for fluid born noise in a rigid pipe The admittance matrix terms are computed according to the mathematical model for wave propagation in a rigid pipe and transformed into the temporal domain thanks to the rational approximation method called "Vector fitting".
The order of approximation was set to 18. Each rational function of the admittance matrix terms obtained by the transition from the frequency domain to temporal domain is inserted in the AMESim © model of a rigid pipe. The inputs of the AMESim © model are the pressure at its ports. In this comparison, the pressure measurements in temporal domain obtained on a test rig were inserted (see Fig. 11 ) and, therefore, the flow ripples were computed according to it. In Figure  12  are the inlet and outlet pressure and flow ripples obtained by the simulation. The simulation results in temporal domain for the flow ripples at both ports in the AMESim © model are then processed in Matlab © in order to perform a spectral analysis of these flow ripples. Then the modules and phases obtained by measurement and by simulation are compared.
Conclusion The results obtained with the test rig for the rigid pipe characterization, whose the mathematical model for wave propagation is well known, have been found as sufficiently accurate to validate the test rig. This allows forecasting the characterization of other passive hydraulic components and particularly for the characterization of the interaction between components. The comparison of the numerical model of a rigid pipe according to measurements done with the test rig has shown a very good correlation which validates especially the transition from the frequency to the temporal domain, necessary to simulate the hydroacoustic behaviour in the temporal simulation software AMESim ©. Admittance matrix term at the row i and column j (m 3 Pa -1 s -1 ) Z Impedance matrix (Pa s m -3 ) Z ij Impedance matrix term at the row i and column j (Pa s m -3 ) du Phase precision of the Fourier analyser°n Oil kinematic viscosity (m 2 s -1 ) r Oil density (kg m -3 ) v Angular velocity (rad s -1 ).
Methods implementation. CovReport is implemented as a standalone Java application. It can run on any platform where Java Runtime (JRE) is installed (Windows, Mac OS and Linux). The supported Java version is 8. The application is using open source external libraries (JARs) which are embedded into the main executable JAR. The following external dependencies are used: • Apache PdfBox (https://pdfbox. apache. org), • Apache CLI (http://commons. apache. org/proper/commons-cli), • JarClassLoader (http://www. jdotsoft. com/JarClassLoader. php).
The command line options are: -i,--input <arg> input file -n,--name <arg> patient name -c,--config <arg> config file (optional) The input file containing per-exon coverage information is a tab delimited CSV file. All entries in the file can be quoted per CSV file standard or can be without quotes. There should be no special characters in the input file. The following columns are expected: RefSeqName, GeneSymbol, Exon, Size, Mean Depth, SD Depth, Coverage 1×, Coverage 5×, Coverage 10×, Coverage 20×, Coverage 30×. An example of an input file is provided in the Supplementary data (also available at http://www. jdotsoft. com/CovReport/44genes-stat_coverage_exon. txt). The input file can be easily generated by the Coverage Module of VarAFT tool 2 (https://varaft. eu/) that uses BEDTools  3, 4  to calculate exon-level coverage.
However, results of any other coverage calculating pipeline can be converted into the appropriate format and used as CovReport input. The command line version of CovReport allows integrating this visualization tool as a step in an automated diagnostic sequence analysis pipeline. The application user interface is in English, but the generated PDF file is in the current locale language. English and French are supported in the current distribution, with the possibility to add other languages by the user. The text included in the output pdf report can also be customized, making it possible to adopt CovReport in any diagnostic setting. Detailed instructions for downloading and running CovReport are described at http://www. jdotsoft. com/ CovReport. php. Exome sequencing of NA12878. DNA for the reference sample NA12878 was obtained from the Coriell Institute for Medical Research Repository (Coriell Institute, Camden, NJ, USA).
Whole Exome Sequencing (WES) was performed by the Genomics and Bioinformatics Platform (GBiM) from the U 1251/Marseille Medical Genetics facility, using the NimbleGen SeqCap EZ MedExome kit (total design size 47 Mb) according to the manufacturer's protocol (Roche Sequencing Solutions, Madison, USA). The SeqCap EZ MedExome kit targets the entire human exome with enhanced coverage of exons from medically relevant genes in Mendelian diseases. Enriched fragment libraries were sequenced on the Illumina NextSeq 500 platform (Illumina, San Diego, CA, USA) using a 150 bp paired-end sequencing protocol. Raw data were mapped to the built of the human genome (hg19) by using BWA 0. 7. 5.
Results CovReport allows one-glance overview of exon coverage for a diagnostic sequencing test by generating a concise easy-to-understand report in the PDF format. Intuitive interface and absence of complex installation steps makes CovReport easy to apply by users without special bioinformatics training. CovReport can also be launched by command line allowing integration into any bioinformatics pipeline. Several features make CovReport especially useful in a diagnostic setting. First, the application runs on a local computer allowing complete data security. Second, the patient's name from the previous analysis is automatically reset upon entering the new coverage file in order to avoid identity errors. Third, the format, content and the language of the report can be easily customized by the laboratory, adapting to any diagnostic setting.
Use case CovReport has been used for all diagnostic gene panel sequencing tests at the Laboratory of Molecular Genetics at the Timone Hospital (Marseille, France) since June 2018. The reports generated are directly annexed to the diagnostic results of various sequencing panels. Moreover, the coverage reports produced by CovReport are also routinely used for technical validation of the test since gene coverage statistics for the panel are integrated in the output. To demonstrate the performance of CovReport, we used exome sequencing data for NA12878 to visualize the coverage of 40 genes on the extended Limb-Girdle Muscular Dystrophy (LGMD) gene list defined by the French National Network for Rare Neuromuscular Diseases 5. Coverage module of VarAFT 5 was used to obtain exon coverage for these genes.
The default options were used to generate the report using CovReport with Show gene transcript and Show statistics options activated. As seen from Fig. 1 , coverage was visualized for 43 transcripts corresponding to 40 genes. Most of the genes were covered at 100%. The advantage of using Show gene transcript option is clear from this example, since TTN (NM_133379) is covered at 100%, while TTN (NM_001267550) has several exons with lower coverage. Indeed, NM_133379 isoform is much shorter, as exons 50-219 of NM_001267550 are not included in this transcript. Six exons had no regions with above 20x threshold coverage (black exons). Several other exons had suboptimal coverage in regions corresponding to more than 10% of their length (dark gray). If similar coverage of these genes is obtained after a diagnostic sequencing for a patient affected with LGMD, the prescribing clinician will take into account the presentation of the disease in order to CovReport in order to visualize the sequencing coverage of 40 genes on LGMD (Limb Girdle Muscular Dystrophies) panel using exome sequencing data for the standard DNA sample NA12878.
Discussion The importance of coverage data is well established in genetic diagnostics. Indeed, most sequencing reports provide some information about coverage of target regions. However, these data are usually presented as an overall gene panel coverage or as an average-per-gene coverage. To our knowledge, no exon-level coverage data are provided with diagnostic reports. However, the distribution of pathogenic variants in a given gene is not homogeneous, as the disease-causing amino acid changes are often clustered in functionally important regions of the protein. Thus, information about coverage of the regions where disease-causing variants are concentrated is much more informative than an overall average gene coverage  6. CovReport highlights individual exons with suboptimal coverage, facilitating the interpretation of the sequencing data.
Since the arrival of high-throughput sequencing technology, several tools have been developed for calculating sequencing coverage, such as BEDTools genomecov  3, 4  , Sambamba 7 and Mosdepth 8. Diagnostic laboratories use one or combination of these tools as part of their standard sequencing analysis pipelines. However, the optimal way to visualize and evaluate the obtained coverage data is not universally defined. Several tools have been designed to evaluate the coverage of target regions after short-read sequencing in a diagnostic setting  2, 9, 10. While these tools are useful to monitor quality control of runs and samples during technical validation of sequencing tests, unlike CovReport, they do not generate exon-level coverage visualization that can be directly annexed to the diagnostic report.
Acknowledgements We would like to thank the initial users of  CovReport at the Laboratory of Molecular Genetics at the Timone Hospital, Marseille (Caroline Lacoste ,  Véronique Blanc ,  Christope Pecheux  and  Amandine Boyer ) as well as  Henri Pégeot at the Laboratory of Molecular Genetics, Montpellier, France  for the valuable comments and suggestions for improvement. We would also like to thank the  Genomics&Bioinformatics Platform at MMG  for providing the exome sequencing data for NA12878.
) Under the serverworker framework, distributed computing systems with straggling nodes have for example been studied in  [15] -  [25] , which focused on high-dimensional matrix-by-matrix or matrix-by-vector multiplications, and in  [26] -  [30] , which proposed codes for gradient computing. Fewer works studied MapReduce systems with straggling nodes (hereafter referred to as straggling systems) which are more relevant for the present article. Specifically, Li et al. [31]  proposed to incorporate MDS codes into the CDC scheme to cope with straggler nodes. Their construction however works only when the map functions accomplish matrix-by-vector multiplications. Improved constructions were proposed by Zhang et al. by choosing the parameters of MDS code and CDC scheme separately in a more flexible way  [32] , but also their techniques are applicable only for map functions that are matrix-by-matrix multiplications.
P. prunorum' using complementary statistical approaches that combine genetic data and geographical distances. Our study was conducted in three French growing regions [Pyrénées-Orientales (PO), Bas-Rhône (BR) and Valence (VA)] with different agroecological conditions and strongly impacted by the disease. There, we intensively sampled the three main hosts (psyllids, wild and cultivated Prunus) of the pathogen.
The eigenvalue plot illustrates the percentage of variance explained by the different axes. (b) Contributions of rows and columns of the contingency table to the first two dimensions of the analysis. The red line represents the expected row or column contributions if the contributions were uniform. Any row or column above this threshold is a major contributor to the corresponding axis. Factorial map (a) and graphs illustrating the contributions of rows and columns (b) were generated with statistical software R 3. 4. 0  53 , respectively with coa function in the ade4 package  54  and factoextra package  55. Then, the figure was finalized with Photoshop CS6 (https://www. adobe. com/products/photoshop. html). For VA, the significant genetic proximity extended up to 35 km for the orchards, only up to 12 km for the bush samples, and the psyllid samples were genetically closer within 4 km (Figs.
5 ). The values of diversity index D' are in red on the y-axis. Graphs were generated using the statistical software R 3. 4. 0  53 , and the figure was finalized with Photoshop CS6 (https://www. adobe. com/products/ photoshop. html). differs very significantly between the three regions (Fig. 4b ), which might imply that there are few or no natural connections (i. e. , psyllid dispersal) between the three major French regions where stone fruit trees are cultivated. One of the crucial unknowns in the different scenarios illustrated in Fig. 1  and Supplementary Fig. S1  was the scale of vector dispersal between pathogen acquisition and inoculation. Our study shows that pathogen genotypes are more similar than expected by chance (i. e. , sites are epidemiologically connected) within a radius of 50 km or less (Fig.