dataesr/scientific-paragraphs-categorization huggingface dataset:
This analysis of GeoSentinel surveillance data has several limitations. Given the specialized nature of the sites that comprise the surveillance system, these data may not be representative of all travellers with malaria. GeoSentinel is not population-based, so malaria rates and risks cannot be determined. GeoSentinel does not routinely collect information regarding malaria chemoprophylaxis, including the medication taken and compliance; as such, chemoprophylaxis appropriateness or traveller adherence or identify the specific reasons why these travellers became infected cannot be assessed. Nevertheless, given the low proportion of receipt of pre-travel counseling among travellers diagnosed with malaria in this analysis, preparation for travel to malaria-endemic regions was likely inadequate.
Severe malaria may not be adequately captured in GeoSentinel, as not all sites have in-patient treatment capabilities and diagnosis relies on clinical identification and provider discretion. Similarly, death is not well-recorded through GeoSentinel surveillance, reflected in the absence of deaths reported among patients with cerebral malaria and the absence of species determination. Data are collected from a single time point, and may not capture a later death, therefore a case fatality rate cannot be calculated. More than 400 travellers did not have Plasmodium species information available in GeoSentinel; the entry of a specific Plasmodium species was not required prior to 2017, resulting in confirmed malaria without a Plasmodium species diagnosis. Also, the lack of diagnostic methodology information collected in Geo-Sentinel does not allow for independent validation of species diagnoses or mixed-species identifications; although microscopy is the gold standard for malaria diagnosis in most centres  [1] , mixed-species infections may be overlooked or incorrectly characterized  [29, 30].
Methods We evaluated all septic shock patients admitted to our intensive care unit (ICU) at the University Hospital of the Pontificia Universidad Católica de Chile during the 18-month period from July 2013 to December 2014. These patients were managed according to a local algorithm  [9]  and entered into a prospective dataset. The Institutional Review Board of our Institution approved this study and waived the need of an informed consent because of the retrospective observational nature of the study (CEC-MEDUC;  2016).
Study population Patients ≥18 years old were included if they presented: (a) septic shock according to sepsis-3 Definitions (requirement of vasopressors to sustain MAP ≥65 mmHg and hyperlactatemia >2 mmol/l after initial fluid resuscitation)  [1] ; (b) concomitant values for ScvO 2 , P(cv-a)CO 2 and CRT; (c) full commitment for resuscitation; and (d) complete follow-up in the dataset and medical charts until hospital discharge.
The graphs in these instances have been generated from spatial data for the design of infrastructure networks. We restrict ourselves to the ten smallest instances from this dataset ("viennai-simple"), which already exceed the size of previous instances significantly. The new dataset is referred to as VIENNA and made available online at https://msinnl. github. io/pages/sstp. html (detailed results for each instance are also availabe). The average characteristics of all benchmark instances are listed in Table  1. For each dataset except VIENNA, |K | ∈ {5, 10, 20, 50, 75, 100, 150, 200, 250, 300, 400, 500, 750, 1000}. For VIENNA, only instances with up to 50 scenarios have been created due to their large size. For performance plots instances are aggregated into a group of small instances SMALL := {K100, P100, LIN01-10, WRP} and a group of large-scale instances LARGE := {VIENNA}.
The corresponding optimality gaps are within few percent, explaining the effectiveness of f 1 , which profits from the availability of tight bounds. However, the effects achieved by f 2 are also considerable, as they manage to eliminate up to 15% of all variables. Moreover, on dataset VIENNA these tests manage to even outperform f 1. Even in the worst case, already after the initialization phase at least 10% of all variables can be eliminated on average. f 1 [%] f 2 [%] g [%] t [s] Fixed [%] f 1 [%] f 2 [%] g [%] The effectiveness of the variable fixing is further amplified during the subgradient procedure. Furthermore, the framework manages to decrease the remaining optimality gap to less than 2% on average. The most impressive results are achieved on dataset P100, where on average 82% of all variables are eliminated.
Even on dataset LIN01-10, on which the largest average optimality gap remains (1. 81%), almost 30% of all variables can be eliminated. Between the initialization and subgradient phase, the amount of variables fixed by f 1 is more than doubled in most cases. The only dataset seemingly resistant to the bound-based variable fixing f 1 is WRP, on which even after reducing the gap from over 2% to almost 0. 3% during the subgradient phase, only few additional variables can be fixed. Figure  9  demonstrates the influence of the employed variable fixing w. r. t. the overall performance. We compare the optimality gaps at the end of the subgradient procedure without fixing (setting DL described above), and with fixing enabled (setting DLR). The results on SMALL instances show that the proposed tests are effective.
Moreover, the heuristic approach is outperformed by both DLRB 3 and B 2. On dataset WRP, DLRB 3 computes slightly better bounds on average and also better primal solution. However, the average solution time is slightly higher than B 2 , and as a consequence in some cases less instances can be solved to optimality within the time limit. This property is mainly a consequence of the larger number of variables of model (SDC FB  3 ), a burden that outweights its gain on this specific type of dataset. As already noted in Sect. 4. 2, despite the availability of very tight bounds, our reduced-cost-based variable fixing tests are not capable of eliminating many variables on these instances. Here, a more light-weight modeling approach appears more beneficial when the main aim is to solve instances to optimality.
Finally, we can observe that the heuristic approach H manages to outperform both DLRB 3 and B 2 with respect to solution quality on instances with at least |K | = 200. However, on average a significant amount of time is required to reach this quality. Table  6  reports results on dataset VIENNA. As this large-scale dataset has been newly introduced in this article with the aim of further testing the limits of our methods, unfortunately no results for the heuristic by Hokama et al. are available. We can observe that indeed, these instances are more challenging than the previously existing datasets, as no instance could be solved to optimality by any of the tested methods. The obtained results show that on average DLRB 3 significantly outperforms B 2 both with respect to optimality gaps and the quality of primal solutions.
Acknowledgements Open access funding provided by  Austrian Science Fund (FWF). M. Leitner  and  I. Ljubić  have been supported by the  Vienna Science and Technology Fund (WWTF)  through project  ICT15-014. M. Sinnl was supported by the  Austrian Research Fund (FWF , Project P  26755-N19 ). M. Luipersbeck acknowledges the support of the  University of Vienna  through the  uni:docs fellowship programme. 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,.
Discussion We note that the baseline and the simple pairwise combinations have a high performance because they already capture arguments' semantic types successfully. This also lies behind previous success on the analogy dataset. Moreover, the nature of semantic spaces and of semantic datasets is such that they typically contain close or quasi-identical variants for the same phenomenon, that the baselines identify easily. The additive input-output combination shows promising results, especially when it comes to capturing the structure: in the clustering setting with 9 clusters, it identifies 8 classes out of 9 in the standard. This indicates a good potential in differentiating between relation types, especially because the standard is conceived in a way that it contains strongly related classes.
Identification of dental procedures For each participant we identified dental procedures in the SNIIRAM database. According to the respective codes (see list in supplementary material), dental procedures were classified as invasive when they required manipulation of the gingival or periapical region of the teeth or perforation of the oral mucosa (excluding local anaesthetic injection), as recommended by 2015 European guidelines; these invasive procedures require antibiotic prophylaxis. Other dental procedures were classified as non-invasive. Two dentists, blinded to study results, determined the correspondence between codes and classification.
Identification of antibiotic prophylaxis As antibiotic prophylaxis before invasive dental procedure was not recorded in the SNIIRAM database, we considered antibiotic prophylaxis preceding the dental procedure when the participants had an antibiotic treatment (see supplementary file for list) active against oral streptococci dispensed by pharmacists in the 21 days before the dental procedure, irrespective of whether the procedure was invasive or non-invasive. This 21 day interval was based on a previous survey conducted among 585 dentists, which showed that 84% of practitioners prescribed antibiotic treatment during the two weeks before the dental procedure. 33 finition of exposure Initially we classified exposure into two categories according to whether the dental procedure was invasive or non-invasive.
We used two models according to the different exposure categories (two or four). As the temporal trend of dental procedures remained stable throughout the control periods, we did not perform any case-time control analyses. For all analyses we considered non-exposure to be the reference group. Interaction tests using the formula of Altman and Bland  35  were calculated to estimate the interaction effect between invasive and non-invasive dental procedures; we considered P<0. 05 to be statistically significant. Statistical analyses were performed with SAS 9. 4 software (SAS Institute, Cary, NC).
37 he fact that the cumulative non-exposure periods were much longer than the exposure periods (248 544 person years v 36 490 person years, ie, seven times higher) strongly suggests that most cases of infective endocarditis are due to everyday life bacteraemia, which is consistent with the results of the case crossover study, reporting a history of dental procedures in the three months preceding infective endocarditis in only a small number of participants. This cohort shows that the incidence of oral streptococcal infective endocarditis during the three months after an invasive dental procedure was higher, but not statistically significantly higher, in participants not receiving the recommended antibiotic prophylaxis. However, the analysis performed in this cohort is based on pooled data derived from a heterogeneous population of patients in terms of use of dental care (measured in our database) and certainly in terms of oral hygiene and dental status (not measured in our database), which represent a potential source of bias  9  in the non-exposure periods.
Similarly, in a recent case crossover study based on Taiwan's national health insurance programme, one study reported that dental procedures did not increase the risk of infective endocarditis. 31 However, only 125 patients with infective endocarditis with valvular heart disease (proportion of patients with prosthetic heart valves not specified) were included in the analysis and no data were available concerning the microorganisms responsible for infective endocarditis. The small numbers of cases with streptococcal infective endocarditis in these two studies limited the statistical power of the analyses. In our case crossover analysis we found a low rate of invasive dental during the three months preceding oral streptococcal infective endocarditis (5. 1%, but consistent with the literature) showing that most cases of streptococcal infective endocarditis were not associated with recent dental procedures.
Notably, this case crossover analysis was not affected by recall bias, as dental procedures were retrieved from the SNIIRAM database. In a recently published case-control study performed in patients with infective endocarditis admitted to hospital in six French tertiary care hospitals (regardless of predisposing heart conditions), we showed that 16. 9% of participants with definite streptococcal infective endocarditis had undergone dental procedures during the preceding three months. 9 lthough the case crossover analysis revealed a statistically significant association between invasive dental procedures and oral streptococcal infective endocarditis among participants with prosthetic heart valves, there is no good evidence to support a different effect between invasive and non-invasive dental procedures probably as a result of lack of power (P=0.
Limitations of this study Both the cohort and the case crossover study present several limitations. Firstly, they are subject to the limitations related to the use of administrative databases, which may include inaccurate coding of the participant's clinical diagnosis and procedures, with clinical information limited to conditions and treatments defined by ICD-10 codes. Secondly, identification of the causal microorganism was also subject to limitations, as no specific ICD-10 codes are available to identify oral streptococci, and the diagnosis of infective endocarditis was not confirmed by using modified Duke criteria. However, ICD-10 codes were validated by referring to clinical records in selected participants, which showed high positive predictive values for the codes used. Finally, no information is available on adherence to antibiotic treatment after dispensing by the pharmacy.
This characterisation should show that biobinders' behaviour is suitable for being used in asphalt mixtures and to some extent, similar to that of bitumen or bituminous binders  (Pouget & Loup, 2013). Within this framework, the objective of this paper is to show the linear viscoelastic properties of biobinders used in high-RA content mixtures in order to provide a further step for their characterisation and use. For this purpose, two biobinders and a reference one were rheologically tested at binder and mixture level including 50% RA. Data was fitted with mechanistic models to obtain and compare their linear viscoelastic properties.
Conclusions This paper shows the linear viscoelastic properties of binders and mixtures with 50% RA and biobinders. Binders were studied by means of DSR while mixtures were tested in two-point bending mode. Rheological data from both tests was analysed and modelled using 2S2P1D and Huet-Sayegh models. The following conclusions can be drawn:  Biobinders studied are thermo-rheologically simple materials which allow their rheological characterisation by means of the same techniques used for conventional binders.  Biobinders used in this investigation showed to act as rejuvenating agents over the RA binder (decreasing its complex shear modulus and increasing its phase angle) as well as allowing obtaining high-modulus asphalt mixtures with high RA content.  Biobinders have an important effect on mixtures behaviour.
Acknowledgements The research presented in this paper was carried out as part of the  Marie Curie Initial Training Network (ITN ) action, FP7-PEOPLE-2013-ITN and  BioRePavation. These  projects have received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement number  607524  and  31109806. 0002respectively. BioRePavation is co-funded by Funding Partners of The  ERA-NET Plus Infravation  and the  European Commission.
Stimulus presentation, feedback, and response recording were controlled and acquired using Matlab. Responses were given on a standard keyboard. In the A/B condition, the following question was displayed in the top-middle of the screen in black font "X or Y?" where X and Y were replaced by category labels informing the participants that they should respond using one of these two categories in this trial. The participants responded "A" using the "d" key, "B" using the "k" key, "C" using the "x" key, and "D" using the "m" key (sticker labeled). In the Yes/No condition, the following question was displayed in the top-middle of the screen in black font "Is this a 'X'?" where X was replaced by a category label informing the participants of the target category for this trial. The participants responded "yes" using the "d" key and "no" using the "k" key (sticker labeled).
Kumari et al. 5  consider performance of Linux Random Number Generator (RNG) in virtualized environments and as if emulated hardware can provide sufficient entropy sources for the RNG. They show that, as expected, hosts have higher entropy sources available and generate entropy at a higher rate. Further, an adversary can find the output of the host RNG by controlling the guest by generating high amounts of disk activity on the guest itself. For many years, silicon Physical Unclonable Functions (PUFs) have been seen as a promising and innovative security technology. Today, Static Random-Access Memory (SRAM)-based PUFs are no more a promise but a reality which offers mature roots of trust for commercial products. PUFs can be found in devices ranging from tiny sensors and microcontrollers to high-performance Field-Programmable Gate Arrays (FPGAs) and secure elements.
The asymptotic slope of IAR increases with the exponent (a) under the power-law correlation-distance function, and the pace of convergence to its universal asymptotic slope (that is, 1) increases with the decay rate (1/L) under the exponential function. Lastly, if, for any practical reason, one wants to describe IAR as a linear function on a log-log scale (as is often done for SAR  23  ), the slope will decrease with local correlation (r 1 ) and increase with either the exponential decay rate (1/L) or the power-law exponent (a) (Supplementary Fig. 1 ). Application of IAR to dataset on primary productivity. We combined field  28, 29  and remote sensing 30 data to derive IARs of primary productivity over the period 1985-2014. On a log-log scale, IARs exhibited triphasic curves from plot to continental scales (Fig.
2 ). Finally, note that spatial synchrony in the remote sensing data was much higher than that in the field data (Fig. 2b, c ). This is because spatial synchrony depends not only on distance, but also on grain size. As grain size increases, spatial synchrony increases (see Supplementary Note 2). Application of IAR to dataset on North American bird biomass. We also used data from the North American Breeding Bird Survey  31  to investigate IARs of total community biomass and individual species biomass. We selected 406 routes that had no missing records during the period 1990-2010 and that are located east of 100 o W (see Methods). We used the number of survey routes as a surrogate for sampled area. The invariability of total community biomass increased quasilinearly with the number of routes, with a slope of 0.
Under the exponential function, the asymptotic slope (z asym ) equals 1. Under the power-law function, we have: z asym ¼ a 2 ao2 1 a ! 2 ð8Þ Data sources. Two data sets were used to study the IARs of primary productivity. The first included two 50-hectare tropical forest plots in Barro Colorado Island (BCI), Panama  28  , and in Pasoh, Malaysia  29. Both were established in 1980s and have since been surveyed 6-7 times. In each census, all woody stems with a diameter at breast height larger than 1 cm were identified, tagged and mapped. The aboveground biomass of each stem was estimated using an allometric equation  52. Annual primary productivity was then estimated, for each of the 10 Â 10 m 2 subplots, as the total growth of biomass between two censuses divided by the length of the period (usually 5 years).
The procedure was repeated 500 times. For the satellite-based NPP data, IARs were constructed for each of the five continents, with similar procedures as in BCI and Pasoh (Supplementary Fig. 6 ). Because the original data were based on a resolution of 0. 00833 degree, surface area differed between grids at high and low latitudes. We thus converted the longitude-latitude distance to Euclidean distance, and calculated the area for each grid. The IARs of bird biomass were constructed similarly as above. However, the area in this case was not real area, but the sampled area or the number of routes. We first calculated the IAR of total community biomass. By taking different routes as the starting route, we obtained 406 replicates. In each replicate, we started with one route and increased the number of routes to 2, 4, 8, y, 256, 406 by including the closest routes.
We also derived IARs for 121 bird species that were recorded at least twice during 1990-2010 in at least 100 routes, with similar procedures as for total community biomass. For both data sets, we calculated the temporal correlation for all pairs of grids (for plant data) or routes (for bird data). We grouped these pairs (of grids or routes) into different categories according to their distance, and computed the average correlation for each distance category. For computational reasons, the calculation for the Modis NPP data was based on a grid size of 1 o Â 1 o ; thus, the distance between two neighbouring grid was about 100 km around the equator and smaller around the poles. Note that the calculation for the two tropical forest plots was based on a grid size of 10 Â 10 m 2 , much smaller than that for the Modis-NPP data.
In Supplementary Note 2, we showed that, given the distance, spatial synchrony increased with grid size. This explained why the spatial synchrony of the Modis NPP data was much larger than that of the forest plot data (Fig. 2b, c ). B. Haegeman and J. Kolasa for comments on earlier versions of the manuscript. We also thank the Smithsonian Tropical Research Institute for making the BCI data sets publicly available and the thousands of U. S. and Canadian participants who annually perform and coordinate the North American Breeding Bird Survey. This work was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41) and by the BIOSTASES Advanced Grant, funded by the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement No 666971).
Data availability. The data that support the findings of this study are available on request from the BCI Forest Dynamics Plot data set (http://ctfs. si. edu/webatlas/ datasets/bci/), the Pasoh Forest Dynamics Plot data set (http://www. ctfs. si. edu/site/ pasoh/), the MODIS GPP/NPP Project (MOD17) (http://www. ntsg. umt. edu/project/mod17) and the North American Breeding Bird Survey (https:// www. pwrc. usgs. gov/bbs/).
d. to the mean distance spread and to test for a significant interaction of treatment with the mean (likelihood ratio test). Model predictions for the s. d. were transformed to variances for display and comparison with experimental results. Data from the dispersal assay (number of beetles dispersed away from the origin patch out of the number starting in the origin patch) were analysed using a generalized linear mixed effects model with a binomial distribution and a logit link function for the probability that a beetle disperses away from the origin patch. Fixed effects included population density starting in the origin patch, location of population origin (core, edge or shuffled) and the interaction of density and location. Random intercepts were modelled for each landscape nested within temporal block.