dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Evaluation on the KITTI dataset The NDT RC is evaluated on the KITTI dataset as well. The resulted odometry translation and rotation errors are computed by using the code provided with the dataset  [29]. The details can be found in the original publication  [30]. To summarize, the errors in rotation and translation are computed over segments of varying lengths (from 100 m to 800 m by increment of 100 m) and then averaged over all segments in all sequences. There are 11 sequences in this dataset. The NDT RC is compared to the D2D NDT. The D2D NDT is configured with a cell size of 0. 5 m. The error obtained with this configuration is inferior to the error reported in the work of Zaganidis, Magnusson, Duckett, et al. [11] , which validates this choice of parameters. A NDT variation has been proposed with good results on the KITTI dataset: the SE-NDT  [11] , described in the introduction.
Since the SE-NDT has been evaluated on the KITTI dataset by using the standard error measures, the results are comparable to ours. Thus, the results of the SE-NDT paper  [11]  are printed here. The runtime is not reported directly since the comparison does not appear to be valid. Indeed, in their paper, the SE-NDT algorithm takes 2. 82 s while the D2D NDT takes 4. 15 s. In our case, the D2D NDT, with the same set of parameters, runs in less than   [11]  2. 60 0. 2 See comments D2D NDT  [25]  3. 65 0. 208 499 LOAM  [20]  0. 88 -<1000 0. 5 s. Such a difference might be explained by hardware or implementation differences, but this is impossible to validate without access to SE-NDT implementation. For this reason, it was chosen not to report a potentially misleading runtime result. The results are compiled into Table  V.
It is nonetheless fair to compare ourself to those methods since they are applicable to the same scenarios. Indeed, the recentring algorithm makes it possible to move the map with the robot. NDT OM for instance, could not be applied directly to the KITTI dataset because the map would not fit into memory. The current top performing laser based method on the KITTI odometry dataset is the LOAM method  [20] , which achieves 0. 88 % of translation error. The drawback is the runtime, since to achieve those precise results, the slow mapping step was run with each new scan. We can also note that this method uses 2 CPU threads, while NDT RC is entirely monothreaded.
The influence of the size of the map  kept in memory is examined as well. Those parameters are all evaluated for different cell sizes. The Ford dataset  [21]  is used. Then, the the KITTI dataset  [18]  is used to evaluate the influence of the LIDAR point cloud density on the precision and runtime of NDT RC. In both cases, the testing conditions are described in Section IV. Note that the influence of d thrc is not examined since this parameters only affects how often the map is recentered. If set accordingly with the speed of the vehicle and the size of the map, it should have no influence on the precision of the algorithm and as shown in Table  III , a neglectible influence on the runtime. Fig. 3  shows the variation of the ATE and runtime with the value of α and β. α is an indication of the confidence that the cell is empty when a ray goes through it, while β quantifies the probability that a cell is occupied when a point is detected inside it.
The runtime is barely affected by the size of the map in this dataset, meaning that the number of occupied cells at the edge of the map is small since their disappearance does not lead to a significant reduction in runtime. Finally, it is interesting to look at the influence of the density of the input point cloud on the quality of the results. The KITTI dataset  [18]  is used to perform this investigation. It is important to note that the Velodyne HDL-64E LIDAR used in the KITTI datasets is adapted to automotive applications, but not to small UAVs or robots: its weight is around 13 kg and it consumes approximately 50 W of power. A more realistic sensor for small UAVs would be the Velodyne VLP-16 lite  [31]  which weighs 590 g and draws around 8 W of power. The drawback is that the VLP-16 has only 16 vertical beams, compared to the 64 of the HDL-64E, with a vertical resolution going from 0.
VI. CONCLUSION In this paper, NDT RC, a recentering algorithm applied to the NDT OM framework is proposed. This algorithm introduces a novel recentering mechanism that allows unlimited movement of the robot, therefore enabling usage on larger datasets. A test of NDT RC on two publicly available datasets shows that the recentering algorithm allows to reduce both the registration error and the runtime when compared to other NDT based methods. NDT RC achieves an RMS ATE of 1. 49 m on the Ford dataset and a translation error of 1. 54 % on the KITTI dataset, with a runtime below 250 ms. A sensitivity analysis is carried out to show the influence of parameters and point cloud density over the algorithm, both in terms of precision and runtime. This analysis shows how the runtime is mainly influenced by the map resolution, and allow a unique set of parameters to be set for both the Ford and KITTI dataset.
(2013). It is available in the Global Forest Change dataset at the Earth Engine as the outcome variable (https://earthenginepartners. appspot. com/science-2013-global-for eston). Following  Kinda and Thiombiano (2021) , the dataset is based on Landsat satellite images from 2000 to 2019 and are available at a spatial resolution of 20 m. The "loss of forest cover" is the change from a forested to a non-forested state over time. Conversely, "forest cover gain" reflects a complete shift from a nonforest to a forest state. The "forest loss year" disaggregates the total forest loss at the annual scale. The dataset comes primarily from the University of Maryland and was recently published and made freely available by  Hansen et al. (2013). This dataset remains a potentially valuable source of forest cover information, although it has been criticized for its inaccuracies in distinguishing between forests and plantations at the local level  (Tropek et al.
Therefore, we monitor the endogeneity of the following factors: total extractive rents in percent of GDP (oil, gas, mineral), GDP per capita growth, commodity prices, foreign direct investment (FDI), population density, climate shocks, industry value-added, forest rents, governance indicators such as control of corruption, government effectiveness, rule of law, voice and accountability, political stability, and non-violence. It is impossible to control for unobserved factors that may affect the likelihood of joining EITI. However, the control variables allow us to consider known sources of bias. These data come mainly from the datasets of the World Development Indicators (WDI), the International Monetary Fund (IMF), and World Governance Indicators (WGI). Following  Lujala (2018) , our primary selection equation consists of three structural factors influencing EITI implementation and deforestation: internal motivation, internal capacities, and external pressure, such as from development agencies and organizations  (Lujala, 2018).
Mean rainfall shock, which is defined as the deviation of the mean annual rainfall from its long-term trend (mean rainfall from 1901 to 2016), is a data point extracted from the University of East Anglia's Climate Research Unit (CRU) database. This climate variable is thought to control the profitability of agriculture and natural determinants of forest cover loss. Countries with low precipitation levels tend to experience extreme temperatures. This can be detrimental to plants and lead to the loss of forest cover. High climate variability may also lead policymakers to tighten environmental standards and reduce deforestation. Thus, an ambiguous effect of rainfall shock on deforestation can be expected.
Acknowledgments The Authors are grateful to two anonymous referees,  Grégoire Rota-Graziosi ,  Fouzi Mourji ,  Simone Bertoli , and  Tony Addison , for their helpful comments on earlier drafts. This work was supported by the  Agence Nationale de la Recherche of the French government  through the program " Investissements d'avenir' '  ANR-10-LABX-14-01 ". The ANR was not involved in the study design, data collection, analysis and interpretation, writing, and the decision to submit the article for publication.
Sample To test our hypotheses, we used six pooled cross-sectional datasets from the Business Structure database known as the Business Register and the UK Innovation Survey (UKIS) from the period 2002-2014. We also used annual business survey data to analyse the total factor productivity (TFP) calculation as part of the robustness check. Although the two datasets were pooled together and constructed from two different sources, they are matchable. Firstly, we collected and matched six consecutive UKIS waves  (UKIS 4 2002 -04, UKIS 5 2004 -06, UKIS 6 2006 -08, UKIS 7 2008 -10, UKIS 8 2010 -12 and UKIS 9 2012-14). Each wave was conducted every second year by the Office of National Statistics (ONS) in the United Kingdom (UK) was included in this study on behalf of the Department of Business Innovation and Skills (BIS).
Secondly, we matched the Business Structure Database (BSD) data for years  2002, 2004, 2006, 2008, 2010 and 2012  to see the correspondence to CIS survey waves with the data from BSD which was taken for the initial year of the UKIS period. The BSD is a version of the Inter-Departmental Business Register intended for research use, and takes full account of changes in firm legal status, ownership (foreign or national firm), alliance information (whether the firm belongs to a larger enterprise network), exports, turnover, employment, industry at the 5-digit level and firm location by postcode. The BSD is the key sampling frame for UK business statistics and is maintained and developed by the Business Registers Unit (BRU) within the ONS. Given the availability of data in the UK Innovation survey and BSD, we analysed them and created two samples.
This means that firms with lower levels of productivity (<70th percentile) are unable to benefit from international knowledge collaborations therefore supporting H3b. As we differentiate international knowledge sourcing across European partners and partners in the rest of the world, we found that returns to knowledge collaboration for Europe start at the lower levels of productivity, in the 70th percentile, whilst most productive firms can org/10. 5255/UKDA-SN-6697-9. Further citation:  UK Innovation Survey, 1994 -2016; Business Structure Database, 1997 -2017; Annual Business Survey, 2008 -2014.
We controlled for firm age and size and found that the relationship between firm age and innovation output is U-shaped for firms with low Note: reference category for legal status is Company (limited liability company), industry (mining), city-region (Newcastle). Instead of industry dummies in this estimation employment (in logs is used). Robust standard errors are in parenthesis. The coefficients of the tobit and logit regressions are the marginal effect of the independent variable on the probability of Incoming spillover, knowledge collaboration, ceteris paribus. For dummy variables, it is the effect of a discrete change from 0 to 1. Significance level: * p < 0. 05; ** p < 0. 01, *** p < 0. 001. Source:  UK Innovation Survey, 1994 -2016; Business Structure Database, 1997 -2017; Annual Business Survey, 2008 -2014.
Robustness check In previous analysis (Tables  2  and 3 ), sales per employee was used to measure labour productivity in our analysis. This is a crude measure, specifically for capital intensive firms. These firms have few employees and thus seem highly productive, even when they are not. As part of a robustness check, we used total factor productivity as a measure of productivity and estimated (2) with the results provided in Table  4. Our dependent variable is a binary variable and equals one if a firm introduces new products and services, and zero otherwise. The data for this estimation was matched to the Innovation Survey from the Annual Business Survey and included four variables: total turnover, full time employment, value of total capital investment acquisitions and total purchases of goods, energy, materials and services.
The coefficients of the regressions (1-3) are the marginal effect of the independent variable on the knowledge collaboration rescaled variable, ceteris paribus. The coefficients of the regression (4) are the marginal effect of the independent variable on the Incoming knowledge spillover, ceteris paribus. For dummy variables, it is the effect of a discrete change from 0 to 1. Significance level: * p < 0. 05; ** p < 0. 01, *** p < 0. 001. Source:  UK Innovation Survey, 1994 -2016; Business Structure Database, 1997 -2017.
First stage probit regression used for constructing the predicted values of knowledge collaboration with regional, national, European and international partners for Tables 2-4 to validate and consolidate our findings. (continued ) Regions Innovative sales sample % Product innovator sample % UKIS5 (2007) 2425 11. 17 6264 21. 02 UKIS6 (2009) 1454 6. 70 4734 15. 88 UKIS7 (2011) 2773 12. 78 2853 9. 57 UKIS8 (2013) 1174 5. 41 1509 5. 06 UKIS9 (2015) 1319 6. 08 1891 6. 34 Total observations 21,702 100. 00 29,805 100. 00 Source: UK Innovation Survey, 1994-2016; Business Structure Database, 1997-2017. Appendix A3. Dependent variable Model Collaboration regional industry ϱ 2 (instrument) CollaborationRegional (1) 4. 283*** (0. 27) CRediT authorship contribution statement Collaborationnational CollaborationEurope CollaborationRest of the world David B.
Acknowledgments I am indebted to many colleagues and friends who contributed to this project Acknowledgments Earlier versions of this paper were presented in Vienna and Paris. Thanks to  Sebastjan Vörös  and  Matteo Mossio  for their invitations and comments and to  Arantza Etxeberria ,  Alejandro Fábregas Tejeda ,  Phillip Honenberger , and  Andrea Gambarotto  for further suggestions. Acknowledgments Special thanks are due, firstly, to the " Georges Canguilhem Seminar " organized by the  Centre for the History of Philosophy and Continental Philosophy (Ghent University, Belgium)  in cooperation with  Charles Wolfe  ( Université Toulouse II Jean Jaurès, France ). This seminar was the birthplace of most of our basic ideas for this chapter. Secondly, to  Andrea Gambarotto , for having invited us to present an early version of this chapter to the "Life  and Cognition Seminar " organized by the  Institut supérieur de philosophie (Université Catholique de Louvain, Belgium).
In addition, there undoubtedly remain defects not yet appreciated because we all -author and commentators -remain blind to them. Acknowledgments I would like to thank  Argyris Arnellos ,  Charbel El- hani,  Giuseppe Longo ,  Carlos Sonnenschein ,  Ana Soto ,  Charles Wolfe , and more broadly, the audience of the Paris workshop: Organisation as a Theoretical Principle for the  Life Sciences , for helpful comments and discussions on an earlier version of this chapter. I also thank the editor of this volume,  Matteo Mossio , for his numerous advices, comments and suggestions, which considerably helped improve my thoughts on this topic. Finally, I gratefully acknowledge the financial support of the  Alexander von Humboldt Foundation. Acknowledgments  James Griesemer ,  Andrea Loettgers ,  James DiFrisco ,  Rick Gawne ,  Jannie Hofmeyr , and  Denis Walsh  provided extremely useful comments on earlier versions of this manuscript.
813873 ) from the  European Commission. We would like to thank  Cliff Hooker  and  Juli Peretó , as well as the editor of the book,  Matteo Mossio , for carefully reading a first manuscript of this contribution and providing critical feedback that certainly helped us improve it. Acknowledgments This study was funded by the  Basque Government  (Project:  IT1228-19 for LB ),  Ministerio de Ciencia, Innovación, Spain  (research project  PID2019-104576GB-I00  for LB and DS and " Ramon y Cajal" Programme   RYC-2016-19798  for LB), and the  John Templeton Foundation  (Project  62220   -Subproject "Directedness in the physiology, ecology, and evolution of holobiont systems " -for DS and LB). The revisions were done during LB's  Visiting Fellowship  at the  Center for Philosophy of   Science of the University of Pittsburgh  (Spring Term 2022).
Acknowledgments This work was conducted as part of the research project entitled " Building bridges between natural and social sciences  through the prism of a  theory of organisms " during AMS tenure as a Fellow of the  Institute for Advanced Studies of Nantes, France. Additional sup-Acknowledgments We are indebted to  Alan Love ,  Matteo Mossio ,  Gaëlle Pontarotti , and an anonymous reviewer for their comments on a previous version of the manuscript, which importantly contributed to its improvement. The work on this chapter has been supported by funding from the  John Templeton Foundation  (# 62220 ) within the cohort program "Agency,  Directionality & Function ," coordinated by  Alan   Love  ( University of Minnesota, USA ). We are also indebted to  CNPq  (proc. n o  465767/2014-1 ),  CAPES  (proc.
These IVs were used for testing the causal effect of IPF as the exposure and on RA as the outcome. SNPs were excluded from the analyses if they were not present in the relevant outcome dataset and no suitable proxy (linkage disequilibrium r 2 >0. 8) could be found. For palindromic SNPs (i. e. , A/T, C/G SNPs), non-palindromic proxies (r 2 >0. 8) were selected. For correlated SNPs (r 2 >0. 01, determined using 1000 Genomes Project EUR population using LDlink  (24) ), the SNP with the least significant association with the exposure was excluded. As the use of "weak" instruments can bias the results of MR, SNPs with an F-statistic < 10 were excluded, where the F-statistic represents a measure of instrument strength  (25, 26).
Methods of Literature Search We searched PubMed (https://pubmed. ncbi. nlm. nih. gov/) without limits on the year or language of publication, including papers indexed in that database up to and including 1 August 2022. We used the search terms ("recurrent" OR "postoperative" OR "post-operative" OR "post-surgical" OR "postsurgical" OR "secondary") AND "trichiasis". The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.
Articles were independently screened for eligibility in two stage. First stage by the titles and (where available) the abstracts of the search output. Second stage, full-text versions of papers selected by either reviewer were obtained for more detailed scrutiny. Data extraction and study quality evaluation were performed independently by the same two reviewers. Reference lists of full-text papers were hand-searched for additional pertinent published research. All potentially relevant non-English publications were translated into English. Disclaimer: ABK and AWS are staff members of the World Health Organization.
Using a 50-year dataset and Multivariate Autoregressive State Space models, we estimated hyaena and prey densities over four time periods contrasted in carrion availability. Hyaena and prey population dynamics had similar trends within periods (increased prey density is generally accompanied by an increased hyaena density) and these trends differed across periods (increases in some periods and decreases in other). These results are consistent with an effect of management measures on prey and hyaena population dynamics. We rather hypothesise a predominance of bottom-up mechanisms, although some subtle patterns could be interpreted as consequences of changes in predation pressures following increases or decreases in scavenging opportunity. We discuss why signals of prey and predator population dynamics decoupling are less likely to be observed in system with a high diversity of prey such as African savannas and why inputs of mega-carcasses as pulsed resources hardly impacted top-down relationships on the long run.
0001 to the whole dataset, which corresponds to one order of magnitude lower than the smallest value of the data. We used data on hyaena and its prey. For prey, we pooled all (males, females, adults, sub-adults and juveniles) preferred and common prey species of hyaena in Hwange National Park  (Périquet et al. 2015) ('prey' hereafter). This includes plains zebra (Equus quagga), greater kudu (Tragelaphus strepsiceros), sable (Hippotragus niger), roan (Hippotragus equinus), blue wildebeest (Connochaetes taurinus), waterbuck (Kobus ellipsiprymnus), impala (Aepyceros melampus), and warthog (Phacochoerus africanus).
In part 1, at month 12, a similar proportion of patients receiving CVC or placebo achieved the primary endpoint (22. 3% vs 25. 5%; odds ratio, 0. 84; 95% confidence interval, 0. 63-1. 10; P ¼. 21) and complete resolution of steatohepatitis without worsening of fibrosis (23. 0% vs 27. 2%; P [. 21). The safety profile was generally comparable across treatment groups. CONCLUSIONS: This study did not demonstrate the efficacy of CVC for treating liver fibrosis assessed by histology in adults with NASH; however, CVC was safe and well tolerated in patients with NASH and liver fibrosis. (ClinicalTrials. gov, Number: NCT03028740).
Study Design The study design and rationale, including sample size determination, randomization, and blinding details, have been reported elsewhere. 15 Briefly, the AURORA study (NCT03028740) was a phase III, randomized, doubleblind, placebo-controlled study, conducted in 2 parts to assess a histological surrogate endpoint (part 1) and long-term clinical outcomes (part 2). Patients were randomized 2:1 to receive CVC 150 mg QD or placebo for 12 months (part 1) and up to 60 months (part 2). Patients in part 1, plus those newly randomized in part 2, were combined for analysis and presented as the full study cohort. The study was performed at 454 international sites and in accordance with the Declaration of Helsinki, International Conference on Harmonization E6 Good Clinical Practice guidelines, and local/national regulations.
Statistical Analyses Efficacy analyses are reported for the modified intention-to-treat population. For the modified intentionto-treat analysis, liver biopsies at month 12 were included if the biopsy was performed at any time between 6 and 15 months while the patients were in the study. Missing liver biopsy data were treated in 2 ways. For patients missing the month 12 visit liver biopsies or who did not have readable liver biopsies at the month 12 visit assessments (due to COVID-19 or for reasons unrelated to study drug or study procedures), the biopsy data were considered missing at random and multiple imputation was used to impute the unobserved liver biopsy data. For reasons related to the study drug or study procedures, or those with missing readable liver biopsies due to the data cutoff date used for interim analysis, the biopsy data were considered missing not at random and were considered as nonresponders (those who did not improve in fibrosis stage).
Acknowledgments AbbVie is committed to responsible data sharing regarding the clinical studies we sponsor. This includes access to anonymized, individual and study-level data (analysis data sets), as well as other information (eg, protocols and Clinical Study Reports), as long as the studies are not part of an ongoing or planned regulatory submission. This includes requests for clinical study data for unlicensed products and indications. This clinical study data can be requested by any qualified researchers who engage in rigorous, independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan and execution of a Data Sharing Agreement. Data requests can be submitted at any time and the data will be accessible for 12 months, with possible extensions considered.