dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-eruptive magma storage conditions To estimate the magmatic temperature of the Pepom tephras, we employ the clinopyroxene-melt thermometer of  Masotta et al. (2013)  that is specifically calibrated for use with alkaline compositions. Pyroxene-melt Fe-Mg Kd values vary in total between 0. 22 and 0. 27 with an overall average 0. 24 when using glass and bulk rock compositions as the nominal liquid, with the bulk rock typically having a slightly higher Kd value. These values are somewhat lower than the equilibrium Kd value of 0. 27 +/-0. 03 proposed by  Putirka (2008) , a feature  Masotta et al. (2013)  suggest may reflect a lack of alkaline compositions in the original dataset used to derive the  Putirka (2008)  thermometer. Rather, we take the trachytic experimental Kd values of  Masotta et al.
This filter was employed iteratively until all individual values fell within 2 standard deviations. For the hygrometer, the assumed temperature input is unimportant, with a 50 • C difference in input temperature resulting in a c. 0. 6 wt% difference in resultant water content. For each Pepom deposit we take the temperature derived from clinopyroxene-melt thermometry (above) as input. Overall water contents range from 2. 3 to 6. 4 wt% with the lowest values coming predictably from the units with the least evolved, highest temperature character, the P2-P5 package. The standard deviations of the P2-P5 package are notably larger (0. 84-1. 78 wt%) than the other Pepom tephras (0. 21-0. 75 wt%). In general, the higher the Or component of the feldspar (coupled with a given liquid) the higher the water content.
Acknowledgements This work was supported by generous funding from the  Swiss National Science Foundation  ( SNF 200020_197040  to BE). Financial support for fieldwork for AE came from the Grubenmann-Burri funds from ETH Zürich. We are very grateful for this aid. We thank  Sherissa Roopnarain  for help with the O-isotope analyses. We thank  Christoph Beier  and  Alessio Pontesilli  for their constructive and thoughtful reviews and  Jim Gardner  for editorial handling.
For a more technical example, suppose that we want to allow users to query a database in limited ways, while keeping a certain fact p secret. One way to do this is to allow queries only about domain Q. We can then verify that p remains unknown after the answer to the query is given by checking that [Q](¬Kp ∧ ¬K¬p) holds. More generally, when modelling dynamics of a multi-agent system it is often the case that the vocabulary is finite. In particular, often only a finite number of atomic propositions are considered relevant for each given subtask of a problem to solve, where this vocabulary might vary between subtasks. In such cases, FSAPAL might be more suitable modelling tools than 'generic' APAL. For example, consider distributed systems wherein agents may communicate about their own local state value  [29].
Similarly, in gossip protocols  [28] , the protocols wherein agents merely exchange their secrets are less powerful than those wherein they are permitted to exchange other information, such as who they previously communicated with  [18, 25]. In SCAPAL we also restrict announcements to some domain of discourse, but instead of adding this domain as a parameter to the operator, we consider a propositional variable p to be in the domain of discourse if it occurs inside the scope of the announcement operator. This is not always a good idea; in the database query example above, the secret p should definitely not be in the domain Q of queries. The assumption that variables are relevant because they appear in the scope does make sense in a conversational context, however, if we are debating the truth of ϕ, then announcements regarding the truth of any variable that occurs in ϕ are clearly relevant.
In pocket format and offering an alpha digital content, the edition resembles a novel, which nevertheless resists ordinary reading. The artist restores the documentary dimension by the print slipped into the book and a text in English mentioned on the last page. This text is a reprise of the captions mentioned in the databases from which the images come: the Australian War Memorial for the first one and the German Federal Archives for the second one. The artist thus creates a game of disappearance and appearance of the documentary dimension of these photographs. Although he assures us that he did not know Hurley's story before making the book, 27  his gesture resonates with those of the Australian photographer and the latter's perplexity regarding the documentary power of a "simple photograph".
Background and Objectives: Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. Methods: We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers.
11, 12  More reliable biomarkers are needed to better differentiate between individuals with central hypersomnolence disorders, specifically in those without cataplexy. ] [15] [16] [17]  In this study, we used an unsupervised machine learning approach, agglomerative hierarchical clustering, to identify clusters of clinically distinct central hypersomnolence disorders. 9] [20]  The EU-NN is an association of 21 leading European sleep centers that launched the first prospective European web-based database for narcolepsy and idiopathic hypersomnia. One main goal of the EU-NN database is to identify new biomarkers specific to central hypersomnolence disorders and to improve definitions and understanding of its subtypes. The comprehensiveness of variables and large number of individuals across different European countries provide the opportunity to implement unsupervised machine learning algorithms in an unprecedented fashion and allows for comprehensive data-driven insights into the different phenotypes of central hypersomnolence disorders.
EU-NN database Records of 1078 adults and adolescents (≥ 13 years old) with central hypersomnolence disorders from 21 European sleep centers were included. In line with diagnostic ICSD-3 recommendations, only data of individuals unmedicated at the time of evaluation (including polysomnography and MSLT) was used. In total, 97 variables were input into the hierarchical clustering (eAppendix 2). Variables were assessed by sleep experts (e. g. , symptom presence), objectively assessed (e. g. , sleep tests, hypocretin and HLA-DQB1*0602 positivity) or self-reported through questionnaires (Epworth sleepiness scale and fatigue severity score). Except for the questionnaire results that were fully patient-rated, other subjective variables were entered by the clinician after the clinical interviews. For the database preprocessing steps we refer to eAppendix 3.
Number of clusters The number of clusters was determined combining two techniques. We first calculated multiple standard clustering evaluation metrics (i. e. , clustering quality scores) that describe how well the clustering algorithm performs with different numbers of clusters (eAppendix 3). These metrics are normally high when individuals are similar to others in its cluster and distinct from the individuals in other clusters. The main aim of this study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy as current diagnostic criteria struggle most with this subpopulation. We therefore also focused on subgrouping of individuals without cataplexy by visual inspection of the clustering steps of the full dataset from 15 to two clusters to better understand how people without cataplexy were subdivided and when these clusters were merged.
Differentiating variables Two methods were used to quantify differentiating variables between clusters. First, we used a resampling technique to test how different the clustering means were from the entire EU-NN database. We then also formally compared clusters dominated by individuals without cataplexy on all variables. A resampling technique was implemented to test whether the clustering results deviated from the entire EU-NN database. The resampling technique enabled us to deduce the extent to which the means barcodes were different from what would be expected if the same number of clusters with the same sizes were randomly drawn from the entire EU-NN database. We generated 10,000 such draws and calculated the mean and standard deviation of each draw per cluster per variable. For each variable, we divided the difference between the resampled mean and corresponding original clustering mean by the standard deviation of the resampled means.
This was done per variable per cluster and visualized as the "significances barcodes". Only values with more than 25 observations were displayed. The significances barcodes better represent how substantially different the means barcodes are from the entire EU-NN database. We post-hoc statistically compared the resulting clusters with a substantial proportion of individuals without cataplexy on all included variables using Mann-Whitney tests. If the variables contained count data, chi-square tests were used instead. Corrected p-values below 0. 05 were reported, after multiple comparisons correction using the Benjamini-Hochberg procedure to decrease the false discovery rate to 0. 05.
EU-NN database The database included 1078 individuals, of whom 108 were adolescents (between 13 and 18 years old) and 970 were adults. There were 489 females and 589 males. Cataplexy was present in 724 and absent in 353. In line with ICSD-3 criteria, 752 people were diagnosed as narcolepsy type 1 (646 definite, 51 probable, 33 possible and 33 unknown diagnostic certainty), 200 as narcolepsy type 2 (132 definite, 49 probable, 10 possible and 9 unknown certainty) and 126 as idiopathic hypersomnia (83 definite, 32 probable, 6 possible and 5 unknown certainty). For an overview of the number of inclusions per center and ethical approval see eAppendix 4. The clustering algorithm and the researchers were blinded for the current diagnosis and inclusion center until the analyses were finished.
Clustering algorithm & number of clusters The clustering evaluation metrics did not clearly favor any single number of clusters below 15 (eAppendix 5). This suggests that individuals in the EU-NN database are not organized according to a single number of archetypes. This means subdivision can still result in distinct clusters, but in presence of individuals closely bordering different clusters. The number of clusters was therefore based on subgrouping of people without cataplexy, as data-driven subdivision of these individuals was our main aim. A simple model with a small total number of clusters was preferred. Visual inspection of the clustering steps from 13 to 7 clusters revealed that changes only occurred in clusters with people with cataplexy and that people without cataplexy were consistently divided in the same two clusters.
We previously applied supervised machine learning to classify people with narcolepsy type 1 and narcolepsy type 2 in the EU-NN database and longer weekend-week sleep length difference was a more important deterministic parameter for narcolepsy type 2 than for type 1, further highlighting its potential to become a new clinical biomarker. 20 It is important to keep in mind that also chronic sleep deprivation and sometimes circadian disturbances may show a similar difference. These disorders are however, currently, not included in our database. Extended 32hour polysomnography recordings  6, 40  and/or actigraphy assessment of weekend-week sleep length differences during consecutive weekends may solve to find all potential causes for hypersomnolence without cataplexy. 39, 41   ur analyses highlight the potential of adding quality of awakening variables and weekend-week sleep length difference as sensitive new clinical biomarkers for future diagnostic criteria as they are potentially more reliable than exclusively using overall mean sleep duration.
Cluster 7 is the smallest cluster among all with poor distinctness and clustering reproducibility (eAppendix 1), and could therefore reflect an inhomogeneous group of borderline phenotypes with limited clinical relevance. The strong differences between clusters in the significances barcodes (Figure  2 ) and the substantially better goodness of fit in the original clustering compared with the randomly generated datasets (eAppendix 1) emphasize the possibility of identifying distinct subgroups within the larger central hypersomnolence population. Lack of a clear clustering evaluation metrics peak suggests that independent of the number of subgroups, there will always be people with central hypersomnolence that are difficult to categorize, especially when strict cutoffs are used (e. g. , during the MSLT).
This could provide important insights in pre-stages of narcolepsy and data-driven treatment regimens for newly diagnosed individuals. More frequent HLA-DQB1*0602 positivity and lower hypocretin-1 levels were, for example, seen in cluster 6 than in cluster 5, suggesting a pathophysiological nature of hypersomnolence complaints closer to narcolepsy type 1. Individuals within cluster 6 may be more likely to show disease progression and should therefore be closely monitored for development of cataplexy. A major strength of the EU-NN database is the harmonized prospective data acquisition protocol, resulting in a true Pan-European collaboration with minimal inclusion-site-specific biases. This is supported by the uniform distribution of inclusion sites over the clusters (Figure  3B ). We included both adolescents and adults to best incorporate different stages of disease in our Copyright © 2022 The Author(s).
Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. clustering analyses. Post-hoc testing indicated a similar distribution of age at evaluation over different clusters. Children younger than 13 years old were however, currently, not included and should be studied in light of our proposed clusters in future studies. Even though the EU-NN database covers most important hypersomnolence-related aspects, incomplete availability of nonmandatory variables related to vigilance, cognitive functioning and mood has hindered their full integration into our clustering analyses. Future studies should focus on these variables in relation to our proposed subgroups. Our analyses cannot be considered fully unbiased as agglomerative hierarchical clustering algorithms require manual input of variable weightings.
We tried to overcome this issue by carefully designing the analysis strategy with pre-determined weighting and grouping of variables, potentially to give every asset of central hypersomnolence disorders a fair chance of influencing the clustering. Post-hoc testing to determine the influence of clustering settings by resampling with random subsets of the entire database (eAppendix 1) showed that the EU-NN database is adequately sized with solid cluster (and biomarker) reproducibility for people without cataplexy. Separate post-hoc analyses were performed to test whether our methodological choices could have influenced the results. Clustering was repeated three times by respectively excluding those with cataplexy and/or hypocretin deficiency (<110 pg/mL), with uniform weightings for all variables, and by recoding the polysomnography REM latency variable to polysomnography SOREMP presence.
All three analyses demonstrated similar grouping and differentiating variables for subgrouping of those without cataplexy as in the full dataset (sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference). These robustness checks further validate that the differentiating variables for those without cataplexy are reproducible. Within the EU-NN database, no data entries are available for people with other conditions that could lead to daytime sleepiness complaints, such as chronic sleep deprivation, myalgic encephalomyelitis/chronic fatigue syndrome and circadian rhythm disorders. These disorders could function as control groups in future clustering analyses on central hypersomnolence disorders to test the specificity of our results. Narcolepsy type 1 could also be considered a control group with a distinct cataplectic phenotype.
The fact that the clustering algorithm recognized narcolepsy type 1 as separate clusters while we were blinded for the current Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. diagnosis, provides an important argument that the algorithm is able to identify distinct, clinically relevant, subgroups. Future studies should focus on external validation of our clustering results in a substantially sized independent dataset and prove internal validation by standardized follow-up data. Both approaches will validate the clinical impact of our clustering results by assessing how cluster assignments relate to clinical decisions like treatment planning, prognosis and mechanisms of disease. We report an exceptionally sized quantitative subgroup assessment in people with central hypersomnolence disorders using the full range of clinical and diagnostic variables.
In addition, H2S also constitutes a by-product of the oil refinery industry, where it is usually treated via the Claus process  [8]. As a result, the significant presence of H2S in these raw materials has brought about a renewed interest in its combustion chemistry, which has been matter of extensive studies in the latest two decades. Moreover, past works have shown that chemical interactions between H2S and hydrogen  [9] , as well as H2S and hydrocarbons  [10, 11] , modify the reactivity of the fuel mixture. Due to safety issues, the experimental characterization of H2S combustion has often been difficult to perform, and until recently literature has not been particularly rich in dedicated datasets. The earliest experiments concerned its laminar flame speed at atmospheric pressure  [12] [13] [14] [15]  and speciation in laminar premixed flames  [16, 17].
They were followed by Zhou et al. [26, 34] , who extended such an analysis to oxidation, still leveraging fundamental calculations  [35] [36] [37] [38]. Bongartz and Ghoniem  [39]  developed a detailed chemical reaction mechanism for the oxy-fuel combustion of sour gas, thus including CH4 chemistry and related interactions with sulfur species. To this purpose, they used the Zhou oxidation mechanism  [26]  to perform optimization, targeted to an experimental dataset made up of laminar flame speeds and flow reactor speciation. They found out that optimization alone was not sufficient to ensure accuracy in all the analyzed datasets, as matching the different experiments was competitive among each other. Therefore, they highlighted the need of more accurate experimental and theoretical data on sulfur kinetics.
No data on H2S pyrolysis were found below ∼900 K, and as highlighted by Karan et al. [22] , the extrapolation of the rate constants obtained via high-temperature data might result in significant deviations at lower temperatures. For oxidation, too, expanding the experimental database at lower temperatures, with variable oxygen amounts, would allow to shed more light on the reaction pathways governing oxidation kinetics, which can then be matter of further theoretical research. In such scenario, this work aims at providing a more comprehensive insight on the pyrolysis and oxidation kinetics of H2S at atmospheric pressure, under diluted conditions and over a wider range of temperatures, equivalence ratios and residence times. In order to cover such an operating space, three different experimental configurations were used, respectively a Jet-Stirred Reactor (JSR) and two Flow Reactors (FRs).
Some experiments have been repeated twice providing an excellent reproducibility of the data (see Figure  S11 ). The second setup consisted of a flow reactor (identified as FR1) with an inner diameter of 4 mm and a length of 100 cm. It was located horizontally in an electrically heated oven (Carbolite Gero, working up to ∼2000 K). An R-type thermocouple was used to measure temperature profiles (provided in the Supplementary Material). The isothermal reaction zone was located between 36 and 58 cm with a uniform temperature profile (±30 K). For each experiment, different residence times were studied and fixed constant to about 0. 1 and 0. 25 s, with initial fuel mole fraction of 500 ppm (thus the flow rates were adjusted for each experiment in order to keep a constant residence time). Flow rate conditions are also provided in the Supplementary Material.
Theoretical methodologies The rate constants for the H-atom abstractions by H, OH and O2 from H2S and of the recombination between SH and HO2 were computed theoretically using EStokTP automatized routines  [53]  (available at https://github. com/EStokTP/EStokTP), which rely on Gaussian G09  [54]  and Molpro 2021  [55]  for electronic structure calculations, and MESS  [56]  (available at https://github. com/Auto-Mech/MESS) for Master Equation (ME) simulations. Geometry optimization and frequency calculations of the stationary points of the Potential Energy Surfaces (PESs) were conducted at DFT level using B2PLYPD3 functional  [57]  and aug-cc-pVTZ basis set  [58]. For H-atom abstraction reactions, also entrance and exit Van der Waals complexes were determined. The energies of all stationary points were refined at CCSD(T)/aug-cc-pVTZ level.
In addition, a complete study of such PES requires considering the competition between S(3P) + H2 = H2S and S(3P) + H2 = SH + H, both occurring on the triplet PES. All channels are also expected to show a significant pressure dependence. Therefore, theoretical studies on R1 are pretty scarce. To the authors knowledge, the only available theoretical works were performed by Shiina et al. [20, 76]. Figure  2  compares the kinetic rates proposed for R1 by different authors  [18] [19] [20] 22, 76] , as well as the few available experimental data  [18, 19, 22]. They differ even by 4 to 5 orders of magnitude, especially at low temperatures (∼1000 K). Moreover, except for the work of Karan et al. [22] , most data were obtained at very high temperatures. In this work, the kinetic rate proposed in this last work  [22]  was chosen, as the result of an optimization among the different experimental datasets, and as shown in Figure  2 , it was proven the most accurate in reconciling low-and high-temperature data.
The of Wang et al. [69]  was used instead for abstraction by O, while H-abstractions by H (R9) and OH (R10) were calculated as described in Section 3. 1. For HO2, the reverse rate (R11) was adopted, in competition with R12 providing instead HSOH + OH  [34]. Regarding sulfur-containing species, abstractions by SO were taken from the estimations of Zhou et al. [26] , included in their oxidation model, while for reaction with S (R15), the experimental data by Shiina et al. [20]  were fitted through a three-parameters Arrhenius rate, and supposing a branching ratio equal to 90% of the channel providing two SH radicals. For some of the reactions (R6, R7, R8, R9, R15, R20), small modifications of the kinetic parameters were performed (still within their estimated uncertainty) for an improved agreement with the experimental data (the datasets obtained in this work as well as those available in literature).