dataesr/scientific-paragraphs-categorization huggingface dataset:
The Methodology Workflow Before applying the proposed methodology, the initial event log should be preprocessed. A mandatory step is to construct a matrix whose rows will be the cases and columns the cases/ characteristics (unless such data are available as a distinct dataset). Then we need to determine the number of clusters that we expect to assign the cases to. In the proposed version, we select to choose the number of clusters that minimizes the Bayesian Information Criterion (BIC) of the clustering model. After we have selected the optimal number of Clusters, we solve the latent class model using  [25]  and derive the cluster membership of cases. This information is appended to both datasets (the event log and the cases matrix). The next phase involves filtering the initial Event Log on the clustering membership, getting a sub-Log per cluster and discovering a Petri Net per sub-Log.
Our algorithm is agnostic with respect to the discovery technique. Having obtained a set of Petri Nets (one per cluster), we apply the model similarity algorithm to get a measure of models dissimilarity. Such a measure allows us to focus on the factors that cause really significant differences in the process flow. The similarity metric is calculated pairwise for all the Petri Nets. The algorithm proceeds by selecting the most dissimilar ones. Then, following a reverse logic, we move back to the clusters that produced these dissimilar graphs, and filter the dataset of characteristics (cases matrix) on only the cases that are members of these clusters. On the new, filtered dataset, we apply a decision tree technique treating the cluster membership as the dependent variable. Again, the decision tree technique to be applied is irrelevant for this algorithm, however we propose the techniques described in Section 3.
95 (5% was allowed to noise, i. e. , random assignment). ─ 5 variables to averagely affect the flow. For 'quasi-important' variables the probability to affect the branching decision was set to 50%. ─ 5 random variables that follow a categorical distribution with a dominant level (50% to 90%) and two complementary levels that share the rest probability. ─ 5 random variables that are uniformly distributed. The first, naive approach that one can employ to check how characteristics affect the flow is to test the connections between trace variants and characteristics. We tested this approach by treating characteristics as factors and variants as the responses in a multinomial classification model. Since the number of variants in the benchmark dataset is rather big (169), decision trees perform really badly.
Moreover, because of the number of categories, it is practically impossible to apply any technique that subsets the datasets (e. g. random forests). Therefore, we applied another variable selection technique, capable for multiclass classification, the sparse group lasso, as proposed by  [27]. The results were really poor, in the sense that the only non-zero feature that was selected was always the intercept, in other words, no feature was marked as important. This is a clear indication that the knowledge about variants (traces) similarity (that is missing from this naive approach) is an important piece. Next, we applied a number of trace clustering techniques, as implemented by ProM 5. 2 (the Trace Clustering plugin). In particular, we applied the Agglomerative Hierarchical Clustering, the k-means and the EM algorithm using the Euclidean, the Jaccard and the Hamming distances.
An Application with Real Data In order to assess the proposed methodology, we applied it to a real life event log of a Dutch academic hospital  [28] , originally intended for use in the first Business Process Intelligence Contest (BPIC 2011). This log contains data for 1143 patients. Each case is a patient of a Gynaecology department. We select this log because a) it is freely available and ii) because it contains many characteristics for each case. We preprocessed the dataset like we describe below. Since the time window of the data spans a period larger than two years, it is likely than some patients visit the hospital many times. In this work, if the same patient does not visit the hospital for one week, for her future visits, she is considered as a different case (this concept is also followed by  [29] ).
Then, we eliminated cases that contain just one event. Moreover, each event may be related to many diagnosis and treatment codes. We consider each code to be a distinct variable. All variables of this kind are binary variables (true if that diagnosis/treatment code was noted, false otherwise). Next, the original dataset is an event log, but in our work, we care also about the case-wise characteristics. Therefore, if there is at least one event that is described as "urgent" during the case lifecycle, we describe the case as "urgent" as well. Finally, we exploit the different resources involved within a case to create two variables that indicate the starting department and the most frequently visited one. After pre-processing the data, we end up with a dataset containing 72 characteristics for 4640 cases that account for 147888 events.
In other words, patients belonging to clusters 2 and 8 have the most different behavior with respect to their flow in the process. The most interesting challenge now is to discover the reasons for this differentiation. Therefore, we return to the cases dataset and we filter it so that only cases of the two clusters remain. Following the technique presented in section Error! Reference source not found. , we set the cluster membership as the dependent variable and we grow a Random Forest. The first conclusion that we can draw is that the single most important characteristics is the department that the patient is visiting. Although this seems trivial, there are some interesting insights that we can get. For instance, cases of cluster 2 are visiting most frequently the Obstetrics & Gynaecology clinic (with a 99.
Simulation and numerical results We validate the proposed RM models through numerical simulations performed using CPLEX 11. 2 solver on a computer running under Linux 64-bit and with an Intel Xeon 5160 CPU, 3GHz, 16GB. We use two types of networks (linear and star) and different scenarios for the demand profile. We keep the networks and time horizons relatively small to allow acceptable computing time for solutions of the mathematical models (\1 min). Again, we leave more sophisticated solution methods for future research. The purpose in these experiments is to give some insights on the benefits that could be obtained with the proposed booking and RM system. We design the linear and star networks as follows: Linear network. We introduce four consecutive cities, A, B, C, D. We define two cyclic services s 1 and s 2 , connecting A to C and C to D, respectively.
DATA SOURCES, ITEMS AND SCALES Until now, data on the learning demands of jobs, employee learning dispositions, job satisfaction and well-being have not been available in the same data set. However, the Skills and Employment Survey 2012 (SES2012) offers a rare opportunity to examine the interconnections. The survey collected responses from working adults in Britain, interviewed in their own homes for around 1 hour. The sample was drawn using random probability principles subject to stratification based on local unemployment rates and the percentage of household heads in non-manual occupations. Interviews were carried out with those who had done at least 1 hour of paid work in the week immediately prior to the survey. Where necessary, a random number grid 2 was used to select one respondent per address.
The response rate was 49%. To maintain quality control, call back procedures were used and the fieldwork closely monitored. Data collection was carried out by GfK NOP, a well-respected market research company. The survey is the sixth in a series of nationally representative sample surveys of individuals in employment aged 20-65 years old, although SES2012 is the only one that contains data on the features of interest for this article. A total of 3,200 working adults took part in the survey, although in this article, we focus on the 2,810 employee respondents -the remainder were self-employed. A weight was computed to take into account the differential probabilities of sample selection, the over-sampling of certain areas and small response rate variations between some groups (technical appendix in  Felstead et al.
, 2015). All of the descriptive analyses that follow are weighted accordingly. It should also be noted that the data are cross-sectional, and so we are only able to test the strength of correlations not causal relationships. To operationalise the nature of the learning demands of the job, we derive an index measuring the extent to which jobs are expansive or restrictive. The 'job learning demands index' is based on six survey questions -the higher the score the more expansive the learning and vice versa. Inevitably, in all surveys, questions are asked of individuals, and so they are subjective in terms of their source. However, in this survey, the responses they gave were objective in that respondents were asked to report on objective features of the job. Only for analytical purposes do we refer to such responses as 'low' or 'high', such terms were not used in the survey itself.
Analysis of climate suitability for wine production Climate suitability was assessed in a previous phase of the study based on multiannual averages  of ten climate variables characterizing the Husi wine growing region  (Irimia et al. , 2014). Each climate variable was mapped, and its values classified and ranked with 5 to 10 points, according to their suitability for wine production. Then the maps were combined in a GIS database, were georeferenced and the average of ranking points was computed at the pixel level (30 x 30 m). The average of ranking points (a. r. p. ) expresses climate suitability and varies between 5 and 10 with the following significance: 5 a. r. p. = climate suitability for white table wines, sparkling wines and wines for distillates; 6 a. r. p. = climate suitability for white table wines, sparkling wines, and wines for distillates, as well as for quality white wines in very suitable years, in terms of climate; 7 a.
If bacteria, already used in the wastewater plant, could metabolize the coagulant itself, then less sludge could be generated. AQUA + TECH is able to leverage an expanded technical base for a detailed LCA study through the collaboration, which is part of a SME-PhD program supported by EU FP7. An interesting caveat of making water treatment chemicals from waste streams is that the fi nal product has high levels of water. Therefore, the extraction and transformation must take place near the waste stream and the fi nal product can only be transported economically about 300 km. A key driver of the project is AQUA + TECH's vision of a zero-discharge facility. Therefore, any aqueous streams used in production have to be either re-used or recycled with minimum cost and effort, back into the process.
Conclusions and Research Needs The case studies represent a snapshot in time, so it is not possible to draw signifi cant conclusions regarding the sequence of skill-building suggested in the LCM CMM. However, the programs SDA had conducted with its network of companies to incrementally develop compliance, clean production and environmental management systems were seen to be signifi cant factors in the success of their pilots and were consistent with the suggested sequence of LCM CMM. It seems there is a logical order for acquiring LCM skills that is path dependent, but also a need to embed the lower level capabilities in more developed patterns of action  (Hart 1995 ). Thus, Ediouro Grafi ca focused on wastes, but used the data in an LCA study. Freudenberg and NEHSU developed KPIs for supply chain management, but again within a LCM frame.
Acknowledgements and Disclaimer The views expressed do not necessarily represent the decision or the stated policy of the  United Nations Environment Program. Funding from the  European Commission  is kindly acknowledged for the case studies presented in Sect. 2. 2. 1. Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
The Unified Model Visual Elements A UML profile is a package that belongs to the extension mechanism. This package is stereotyped <<profile>> and the extension mechanism is allowed thanks to UML characteristics. In fact, the latter serves as foundation for building domain specific languages by specifying stereotypes, which introduce new language primitives by subtyping UML core types and tagged values, which represent new properties of these primitives. Model elements are assigned to such types by labeling them with the corresponding stereotypes. The main modeling element of our system is the class stereotyped <<community>> used to represent a Web services community as a collection of Web services related to the same area of interest. A community is identified by a stereotype <<ID-parameter>> representing an abstract definition of the data used to identify a community.
UML profile realization A UML profile is a package belonging to the extension mechanism. This package is stereotyped "Profile" and is written in XML. In StarUML platform we added an approach called 'CSW Approach' and a UML profile for Web services community composed of three models or diagrams are: CSWClassDiagram and CSWUeCaseDiagram and CSWSequenceDiagram. Indeed, we created two XML files, the first one is used to define the approach and the second one is used to create the profile. When we start StarUML, the 'New Project by approach' appears to select the desired approach. Our proposed approach called 'CSW Approach' is integrated in the platform. Consequently, it can be selected as all the approaches of the platform (figure  3 ).
Collection of data The study began in December 2008 with all resource persons that are involved in the project and was conducted by three researchers. At first, it was set, with the network administration, work methodology. Thereby, we have agreed on the phases of the study, meeting dates and the various key people for the project. The works carried out to complete the study were as follows: 1) Documentary research: The research team has analyzed all documents relating to the case study, the history and the work tools of the network. This step was useful in the sense that it should prevent the degree of mastery of the research subject the whole techniques of project management. It was also interesting to determine what kind of monitoring and evaluation methods were in place. 2) Semi-structured interviews: Several semi-structured interviews were conducted at several levels.
! Our primary empirical materials have been collected from five sources. First, we conducted thirteen one hour semi-structured interviews, between 2010 and 2011, of people working for the main carmakers and suppliers operating in France. Some employees were directly in charge of CSR programs but we also interviewed some employees less related to CSR policy (like purchasing managers and one R&D top manager). The interviews were designed to capture the employees' perception of the implementation of socially responsible practices in the automotive industry in general, and more specifically along the supply chain. We thus built up a database of verbatim information. Second, inspired by the methodology used by  Shinkle and Spencer (2012)  analysing the sustainability reports of carmakers, we read and extracted data verbatim from the sustainability reports of the three main French suppliers  (Valeo, Faurecia, Plastic Omnium)  and carmakers (Renault, Peugeot-Citroën).
Since the early days of the ETRIA community, the efforts of presenting new TRIZ related contents have been focused in two directions: development of TRIZ and novel applications of TRIZ. As a result of 14 years of TRIZ Future Conferences, the ETRIA database now contains a wide variety of papers, prepared by TRIZ enthusiasts from academia, industry and consultancy. Both the number of the scientific contributions to the ETRIA conferences, as well as the scientific level of these contributions has increased steadily. Within the scientific community, TRIZ is more and more becoming a well-known and accepted methodology that facilitates directed innovation. A dominant tendency in many international academic communities is to measure the significance and the dynamism of the research in a given field or of a particular researcher.
Next-generation sequencing (NGS) technologies promise progress in this area by overcoming some of the technical difficulties associated with the complexity of MHC genotyping and by allowing cost-effective processing of large datasets (reviewed in Babik 2010;  Huchard and Pechouskova 2014; Koboldt et al. 2013; Lighten et al. 2014a). Here, we investigated MHC variation (allelic richness and divergence) and patterns of selection of two highly polymorphic MHC class II genes, DRB and DQB, in the endangered Madame Berthe's mouse lemur, Microcebus berthae (B1ab. iiii, Andriaholinirina et al. 2014) , by genotyping a total of 100 individuals sampled over 9 years in three study areas. This world's smallest primate (ca. 30 g) is endemic to the dry forests of the Menabe region in western Madagascar  (Schmid and Kappeler 1994; Ganzhorn et al.
Assessment of minimum sequencing depth and genotyping reliability Based on the estimated number of loci, we proceeded with a final screening step to evaluate the sequencing depth necessary for accurate genotyping using the program 'Negative Multinomial' (http://www. lirmm. fr/~caraux/Bioinformatics/ NegativeMultinomial/) developed by  Galan et al. (2010). Amplicons that did not return a sufficient amount of reads based on this estimate were re-genotyped. The efficiency of allele-sorting was then evaluated through two steps. First, assuming that artefacts originated during a single PCR reaction or introduced by a genotyping mistake would not occur independently in many amplicons, we investigated the relationship between the MPAF of each allele and the number of amplicons possessing this allele.
Assessment of minimum sequencing depth and genotyping reliability Based on our conclusions of no loci duplication, we used the probabilistic model  (Galan et al. 2010 ) and estimated that a minimum of 18 reads were required for reliable genotyping of each given locus with a confidence level of 0. 95. All amplicons with <18 reads (DRB 26; DQB 5) were regenotyped and replaced in the dataset, except of three amplicons in DRB that did not return >18 reads in the second genotyping attempt. Additionally, 23 amplicons for DRB and 27 for DQB were genotyped in replicates to estimate genotyping reliability, and all of them showed a perfect reproducibility of assigned genotypes. The correlation between the MPAF of each allele and the number of amplicons possessing this allele was significant before allele sorting in both loci (Pearson's correlation, DRB n=321 alleles, r=0.
Sequence analysis and phylogeny reconstruction From the total of 17 Mibe-DRB and 22 Mibe-DQB sequences found in 96 and 98 individuals of M. berthae, respectively, none have been described previously. Accession numbers of these alleles as well as the full nucleotide sequence of alleles occurring in only one individual are given in Appendix (Table  3 ). Average nucleotide divergence (number of differences) between sequences was comparably high in both loci (mean±SD; DRB 17. 07±2. 44; DQB 14. 44±2. 16). Among Mibe-DRB sequences, we found 47 (29 %) variable nucleotide sites and 35 (21 %) among Mibe-DQB sequences. Each nucleotide sequence of both loci translated into a unique amino acid sequence and the absence of stop codons suggests that all sequences can encode functional proteins. Amino acid sequences revealed 23 (43 %; DRB) and 23 (41 %; DQB) variable sites out of 54 and 56 sites (see below), and comparable amino acid divergence was found in both loci (mean±SD; DRB 11.
73±2. 0, DQB 10. 76±1. 82). The reconstruction of evolutionary relationships between amino acid sequences of both species revealed two distinct loci-specific clusters, with the exception of three Mibe-DQB sequences (Mibe-DQB*017, *016 and *007), that clustered separately among DRB sequences of both M. berthae and M. murinus (Fig. 1 ). These three Mibe sequences were retrieved independently at least in two and up to 11 individual amplicons across different sequencing runs, and their presence and loci identity were confirmed by replicating from one to three amplicons possessing affected sequences. Moreover, these three sequences clustered with other DQB sequences when including only Mibe sequences in the analysis (data not shown). There was no clear separation between sequences of the two species in either locus (Fig.
Additionally, these sequences occurred within multiple individuals and their presence was confirmed by replication, which excluded the possibility of sequencing run-specific amplification mismatch. Such paraphyly has been also reported previously in M. murinus by  Huchard et al. (2012)  and may result from a combined effect of tight physical linkage, shared origin and high functional similarity between the two loci. In contrast to 17 Mibe-DRB and 22 Mibe-DQB sequences obtained by this study from the total of ca. 100 individuals sampled over 9 years in three study areas, 59 Mimu-DRB and 58 Mimu-DQB originated from 664 individuals of M. murinus sampled over a comparable period of time within a single study area  (Huchard et al. 2012). Allelic richness (number of distinct alleles) for a given sampling effort (number of sampled individuals) that was estimated through re-sampling procedure in dataset of both species revealed the average number of distinct alleles detected for a given sampling effort to be ca.
Acknowledgments We would like to thank all people who have contributed to the collection and processing of DNA samples, with a special thanks to the Kirindy field assistants, notably  Bruno Tsiverimana ,  Tiana Andrianjanahary ,  Jean-Claude de Beroboka  and  Mamy Razafindrasamba , as well as to  Christina Glaschke. We also thank the  Département de Biologie Animale, Université d'Antananarivo , the  CAFF of the Direction des Eaux et Forêts  and the  CNFEREF Morondava  for granting a research permit and to  Prof. Lutz Walter  and  Dr. Christian Roos  for use of the GS Junior sequencer and  Nico Westphal  for providing technical help. This research was funded by a  SAW  project of  Leibniz Graduate School  for the  Foundations of Primate Social Behaviour  issued by Wissenschaftsgemeinschaft Gottfried.
Funding This study was funded by a  SAW  project of  Leibniz Graduate School  for the  Foundations of Primate Social Behaviour  issued by  Wissenschaftsgemeinschaft Gottfried Wilhelm Leibniz e. V. ( SAW-2011-DPZ-4 ), and field work was supported by the  DPZ  and  DFG  ( Ka 1082/10-1&2 ).
Trimmed filtered reads were analyzed using QuRe software to reconstruct viral variants, which were subject to phylogenetic analysis. HIV population genetics at each time point was analyzed using measures of divergence and diversity based on pairwise distance, and selection pressure was inferred using dN/dS tests. Results: There was no correlation between pre-ART viral load and number of sequence reads (P=0. 54). There was a positive correlation between number of reconstructed viral lineages and pre-ART viral load (P=0. 01) but no correlation with the number of reads (P=0. 44). Phylogenetic analysis revealed that HIV diversity was similar between time points despite several years of ART (pre-ART range 0. 006-0. 04, post-ART range 0. 003-0. 03; mean pre-ART vs. post-ART: 0. 014 vs. 0. 015 repeated measures t-test P=0.