dataesr/scientific-paragraphs-categorization huggingface dataset:
e. time and position information) will be considered as the number of users increases as well as the duration of the reporting. We will then consider anonymizing the data at the export phase (for analysis) with clusters of trajectories  [34]. Guidelines are based on the assumption that patients regularly use their treatment and that recommendations are not tested with real-life data. Moreover, for many questions, recommendations are uncertain. Next-generation guidelines will need to use anonymized real-life data optimally retrieved using mobile technology to fill the current gaps. The results of this paper will then be used for guideline development.
Acknowledgements The MASK group was listed as an author. It should be searchable through individual Pubmed records. MASK Study group:  J Bousquet  1,2 , I Agache 3 ,  R Almeida 4  ,  R Angles 5  ,  I Annesi-Maesano 6  ,  JM Anto 7-10  ,  S Arnavielhe 11  ,  E Asayag 12  ,  E Bacci 13  ,  C Bachert 14  ,  I Baiardini 15  ,  I Baroni 16  , BA Barreto 17 ,  X Basagana 7  ,  A Bedbrook.
Data sets We use data collected by the SocioPatterns collaboration in various contexts. These data were gathered using wearable sensors able to detect face-to-face close range proximity (1. 5 m) of participants wearing the sensors on their chests. In addition, the sensors broadcast a signal that can be received by RFID readers located in the environment. In open space, each reader can receive signals from sensors situated within a range of ∼ 30 m, while the actual reception range in a building depends on its specific structure and on the nature of its walls, floors and ceilings. Each reader thus defines a coarse spatial area and the sensors' signals can be followed when the individuals carrying them change area. For each sensor, we define its "spatial location" at each time as the set of readers receiving its broadcasted signal at this time, and we define two individuals to be in co-presence if they share the same spatial location, i.
In these cases, the overall structure of networks aggregated over a certain time window can be summarised, in addition to usual quantities such as the density, the clustering coefficient or the degree distribution, by contact (resp. co-presence) matrices that give the fraction of pairs of individuals of different groups who have been in contact (resp. in co-presence). Moreover, temporal features of interest include the distributions of durations of contact or co-presence events, of the time elapsed between successive events, of the numbers and aggregated durations of such events between pairs of individuals (the latter quantity yields a natural definition of the weight of a link between individuals in the aggregated network). We will show in the main text the results corresponding to the InVS15 data set, and we refer to the Supplementary Information for the results obtained with the other data sets.
For each instance of each sampling method, we thus build the resulting surrogate aggregated contact network and rank nodes according to each centrality measure. We then compute the Jaccard similarity index between the top N % nodes in the real contact network and in the surrogate one. We plot in Fig. 7  the median similiarity with the 90 % confidence interval, as a function of N , for the InVS15 case (see SI for the other cases). 28. 4 (0. 360) 42. 6 (0. 559) 15. 8 (0. 117) For each dataset we compute the maximum coreness, and report between parenthesis the Jaccard index between the k-core of the contact network and the k-core in the original and sampled co-presence data (results are averaged over 100 realisations for each sampling method).
Figures  8  and 9  summarize our results for the InVS15 dataset (see SI for the figures obtained with the other datasets). In terms of the evaluation of the impact of a spreading process, results are context dependent. The simulations performed on the surrogate data obtained with the first method generally lead to an overestimation of the epidemic risk, except for the hospital data. When using the second sampling method, we obtain a good estimation of the risk for the conference, school and highschool data but an underestimation for offices and hospital data. The third method on the other hand leads to a correct estimation for the offices and hospital data but an underestimation for the school and highschool and an overestimation for the conference. We show in Fig. 9  the impact of the various vaccination strategies, quantified through the ratio of the probabilities of large outbreaks with and without vaccination, as well as the ratio between the median sizes of these large outbreaks.
Acknowledgements This study was supported by the  National Natural Science Foundation of China (NSFC)  Grants No. 41630965  and  91428310  and the  State Oceanic Administration of China  Grant No. GASI-GEOGE-04  to  Zhimin Jian , the  NSFC  Grants No. 41606061  to  Weijia Fan  and No. 41606049  to  Haowen Dang , the  Fundamental Research Funds for the Central Universities  No. 101201705  and the  NSFC  Grant No. 41606045  to  Yue Wang. We are grateful to  Dr. Mahyar Mohtadi  for his helpful comments on our study. We also sincerely thank the scientists and crew of the  R/V Marion Dufresne  for their hard working in sample and data collection during the IMAGES IV cruise in 1998.
, spatial interpolation and generalized additive modeling (GAM). Both methods converge on the need to distinguish two focal areas of the feature CFNM. The first one, the Central Focal Area (CFA), is the most prominent of the two and spans the east of West Africa and parts of Central Africa, largely coinciding with  Dryer's (2009)  core area of VO&VNeg languages reproduced in Figure  1. The second one, the Western Focal Area (WFA), is less prominent and is restricted to West Africa. The two focal areas are separated by a major discontinuity around Ghana, Togo and Benin. In Section 5. 2, I call onto other types of data to better calibrate the results of the spatial analysis produced in Section 5. 1 and to identify the historical core of the CFA. Finally, Section 5. 3 addresses the distribution of optional and/or restricted CFNMs in Africa, with a particular focus on the spread of CFNMs among Bantu languages to the south of the CFA, primarily in the Congo River corridor and the north of the Democratic Republic of Congo.
That these spurious prominence regions in the Sahara are absent in Figure  8  is just due to the way the sample data points happen to be distributed on the southern fringes of the Sahara and the bandwidth value chosen for kernel smoothing. 12 Finally, care should be exercised when interpreting the region of high prominence (as reflected by its darker shading) within the CFA in southern Chad and the Central African Republic in Figure  8. Although it is tempting to interpret it as the core or hotbed of the CFA, as discussed in Section 5. 2, a different interpretation may be more appropriate. In this respect, note that this region of high prominence is absent in Figure  9 , which uses a different spatial interpolation method. Although spatial interpolation is a valuable visualization tool, it does not produce quantifiable results.
A statistical tool well-adapted for spatial analysis that can do that is provided by generalized additive models (GAM), 13 for instance, as implemented in the mgcv package for R  (Wood 2015). There are different ways to code our response variable, viz. , the type of the feature CFNM. One option would be to code it as an ordered categorical variable. The big disadvantage of this option would be that it does not reflect the importance of the divide between the absence and presence of CFNMs in the language and the certain hierarchy between the six values of the feature CFNM of Table  1. For this reason, I will not use this coding. The results it produces are actually largely comparable to the other two options that we are going to consider, although less clear-cut. The latter two options both involve pseudo-numeric values assigned to the six values of the feature CFNM.
Acknowledgement: This work is situated within the project  LC2 Areal phenomena in Northern Sub-Saharan Africa of the Labex EFL  (the  Investissements d'Avenir program , overseen by the  French National Research Agency ; reference:  ANR-10-LABX-0083 ). Special thanks with respect to the present paper are due to  Mark Van de Velde. I am grateful to the referees and the editors for their constructive criticism. Last but not least, my sincere gratitude goes to  Johan van der Auwera , who first sparked my interest in the study of negation. Johan got me started in linguistic typology and has always been a great example to me in many respects.
How is the evolution and molecular epidemiology of DENV measured and estimated? Sequencing of DENV RNA from human sera or vector specimens is the first step of contemporary DENV molecular epidemiology analyses  [40]. The relatively short genomic length of DENV has facilitated the feasibility of DENV next-generation sequencing (NGS) methods and publically accessible DENV whole genome repositories such as GenBank and ViPR have rapidly grown in the last two decades  [41, 42]. For instance, a recent curation of DENV full env genes and full genomes from GenBank yielded 11728 and 4111 sequences respectively, representing at least 104 countries across 73 years  [43]. The quality of such sequence data may vary, therefore requiring multiple steps of quality control, such as ensuring a plausible number of mutations are present based on the date of the sequence, before final use in analysis  [44].
Phylogenetic analyses can also include geospatial information ('phylogeography') to examine the spread of DENV populations across space  [51, 52]. 'Phylodynamics' investigates DENV evolution in the context of immunological and ecological pressures  [45, 53]. These non-genomic data are often referred to as 'meta-data', and like sequence data, the quality and/or completeness of such meta-data provided in GenBank and in semi-curated databases such as the Virus Pathogen Resource and Virus Variation may vary  [54, 55]. One crucial type of data which is most often lacking is host serological data (see section 8. 5 for implications). More recently, Bayesian phylogenetic methods using software such as Bayesian Evolutionary Analysis Sampling Trees (BEAST) have become widely available  [56]. Bayesian methods can incorporate complex evolutionary models, including those that describe population dynamics, variation in rates of molecular evolution and migration through space.
Negative (purifying) selection pressure As with other RNA viruses, dengue viruses are commonly found to be under strong purifying selection  [13, 105]. Interestingly, this has been noted on an equivalent magnitude in DENV datasets comprising continental-wide pandemic datasets in comparison to focal epidemics in a single country  [106]. This is consistent with a substantial amount of negative selection occurring as the virus alternates between vector and vertebrate hosts  [20]. While strong purifying selection has been hypothesized to contribute to lineage extinction and replacement, this has was not explicitly demonstrated in one study which examined the role of negative selection in lineage shift or within-lineage variant extinction  [107]. Purifying selection has also been demonstrated to operate on within-human variant populations but less so on variant populations within vectors  [22].
Selection pressure analyses on ancestral populations have not shown consistent, pervasive adaptive evolution associated with the emergence of the four DENV serotypes (discussed in  [63] ). However, episodic positive selection pressure at the divergence of major lineages within DENV-2, DENV-3 and DENV-4  [100, 116, 117] , and prior work by Twiddy et al, did indicate weak positive selection in deep branches defining these serotypes, suggesting that adaptive evolution likely plays some role in shaping the diversity of major DENV lineages. However, a caveat in interpreting these findings is that estimates of selection pressure differ strikingly between methods (some of which are markedly more conservative than others), and are often subject to the composition of the sequence dataset with regards to whether statistically significant selection pressure is detected  [67, 118].
One particular area that merits further study is how population immunity profiles may shape the dispersal of DENV on a local, regional and international scale by unifying spatially and temporally explicit population-level serological and genomic data. The major barrier to this line of critical research is the lack of well characterized population level DENV serological datasets that are standardized across multiple populations within and across geographic locations. Cross-cohort active surveillance studies across multiple locales could perhaps partly redress this problem  [171]. More comprehensively, there have been recent calls for a 'global immunological observatory' for communicable diseases, founded upon a 'World Serum Bank'  [172].
Evolutionary approaches may in turn then be used to determine how well conserved candidate peptides are across global DENV populations by the use of entropy plots (Fig  1). Conversely, key CD4+ and CD8+ epitopes which overlap in both natural and Phase I vaccinated populations have been interpreted in the context of the global diversity of DENV populations represented in the public protein sequence databases. Moderate conservancy of these key epitopes across global DENV populations has been shown, thereby indicating putative protection among various DENV endemic regions well before Phase II and Phase III vaccine efficacy studies commence  [220, 221]. One important consideration with translating such evolutionary analyses into vaccine design is that sites of positive selection are commonly found on internal DENV proteins  [145, 222, 223].
Current limitations and future directions in the research and application of DENV evolution 12. 1. Redressing the paucity of African DENV genome data The current understanding of the molecular epidemiology and evolution of DENV is significantly limited by a major deficit of sequence data from Sub Saharan Africa, with less than 1% of all global DENV envelope sequence data coming from African countries (authors' observation from an NCBI GenBank search for all DENV full envelope sequence data [41]). Redressing this lack of sequence data is a priority as Africa has possibly played a key role in the early emergence of DENV with human cases described since the late 1800s, and even the term 'dengue' is believed to have origins in the Swahili language  [230]. While DENV surveillance in Africa has been historically limited compared to other DENV endemic regions, recent DENV global cartography efforts have shown evidence of DENV circulation in Angola, Burkina Faso, Gabon, Kenya, Mali and Nigeria  [19] , and severe human dengue infections have been documented in Senegal  [20, 231].
Prospective active surveillance studies are critical to capture such sub-populations, but such cohort studies are often resource-intensive. Even among symptomatic cases captured by conventional surveillance, the limited success in sequencing DENV from DENV-4 (authors' observation) or lowerviremia cases inherently skews the representation of DENV genome databases. While biases are prevalent across many fields of observational biomedical research, it is striking that the practice of viral phylogenetic analysis, a complex interdisciplinary field with many 'traps'  [154, 243]  does not have a consensus statement on study reporting to guide the quality of phylogenetic analysis and interpretation for DENV and indeed other viral pathogens. This is in contrast to other well-established study reporting checklists for a range of other research fields  [244] [245] [246] [247].
Most of web sites adopt a Captcha system to distinguish malicious software from humans. This paper proposes an attack on a recent interactive image-based Captcha scheme, called CaptchaStar. The CaptchaStar was designed to be more secure and user friendly than existing solutions, however, as we show in this paper, it fails to meet these goals Nevertheless, the presented attack is very efficient, with a success rate for the attacker of 96% on the on-line version proposed by the authors. Moreover, the modification of CaptchaStar parameters (as noise addition) does not prevent our attack.
More sophisticated text-based Captchas have been proposed, as for example hollow Captchas, 3D text-based Captchas or animated text-based Captchas (as NuCaptcha  (Xu et al. , 2012) ). The first attack on text-based Captchas was proposed by  Mori and Malik in 2003 (Mori and Malik, 2003). This attack works with matching using shape context of characters and a database of image of known objects. The success rate was 92% on EZ-Gimpy (a single word) and 33% on Gimpy (three words in an image). The following year, Chellapilla and Simard use machine learning techniques (and segmentation) to break six other text-based Captchas  (Chellapilla and Simard, 2004). It includes Captchas used by Ticketmaster, Yahoo V2 and Google/Gmail, with success rates of 45. 7% for Yahoo V2 and 4. 9% for Google. Yan and El Ahmad broke a large set of text-based Captchas using very simple techniques (as the pixel count by letters), or more evaluated techniques in several papers  (Yan and Ahmad, 2007a; Yan and Ahmad, 2007b; Yan and Ahmad, 2009; Ahmad et al.
Image-based Captchas and Variants The failure of secure text-based Captchas encouraged the design of other types of Captcha, as proposed by  (Chew and Tygar, 2004)  in 2004. In this case, the user is asked to link a word with an image (with variations in different schemes). Nevertheless, most of them were not more secure than previous schemes. One of most known image-based scheme is Asirra (Animal Species Image Recognition for Restricting Access), described in  (Elson et al. , 2007)  and also defined as an HIP (Human Interactive Proof). In this system, users are asked to identify dogs and cats in a set of random images. Nevertheless, the system was not secure and Asirra has been closed by Microsoft in 2014. The image database should be (very) large and the number of possible responses, related to the image(s) should be sufficiently high, in order to avoid a random response.
Resiliency to Automated Attacks CaptchaStar authors claimed that their solution showed promising results against all the studied attacks, comparable or even better than the state of the art. CaptchaStar defeats classic attacks as indirect attacks, database exhaustion or leak of database, random choice, and pure relay attacks. Only the stream relay attacks that is the more powerful one, is a real threat to this Captcha, but it is difficult to set up The authors also investigated the resiliency of their Captcha against machine learning. The best parameters for their machine learning technique lead to a success rate of 78. 1% with a computation time of 421 seconds. CaptchaStar authors also proposed some heuristics to automatically solve the Captcha, e. g. looking at the dispersion of the stars or the distance between the most distant stars.
These results are obtained by executing the JavaScript code of the attack with Chromium 51. 0. 2704. 79 on Ubuntu 14. 04 (64bit). The attack was executed on 1, 000 challenges. Recalling that, having more than 1% of success rate is sufficient to consider a Captcha as broken. One can note that, the obtained success rate by the automated attack is greater than the one obtained by the user study (91%), and the execution time of the attack is lower than the solving time of a human (≈ 20s) for T 2. The few challenges resisting to the proposed attack are those containing figure with an important amount of stars or figures with a blank zone. Only few cases of these figures lead to a failure. Figure  3  shows two challenges resisting to the proposed attack. The solution found by the attack is near from the solution of the challenge.
Attack Implementation The grid uses a Canvas, an HTML5 object, from this object it is easy to extract the RGBA values for each pixel using JavaScript. Pixels are black or white, so using the RGBA values it is easy to transform it into a matrix of 0 and 1. When the cursor moves, the stars position are updated on the client side, with a JavaScript code. Therefore, it is possible to move the stars with a program using JavaScript calls. The challenge generation and verification are done with a PHP script on the server side. There is no guarantee that the parameters ( , n max , n s , and 2 ) used for our attack are optimal. It is explained by two reasons. First, for a sake of simplicity, we have run our experiments using JavaScript but the execution time is not optimal with this language. In-creasing the value of 2 and n s increases the accuracy of the attack, and so the success rate, but also the execution time.
Using another programming language, as the C language, and make the computation of the scores in parallel, can strongly reduce the execution time. There exists, some solution as Selenium driver, to interact between JavaScript and other languages. Second, tuning the parameters gives only the best success rate for one implementation of CaptchaStar. Optimal parameters of the attack clearly depend on parameters of CaptchaStar implementation. The objective is to demonstrate that CaptchaStar is not ready for real use, by establishing a proof of concept of the attack. Finally, in an optimal version of the attack, the parameters could be chosen dynamically depending the number of stars of the challenge.
It has to be stressed, though, that the datasets used to estimate the global Gini coefficient have typically been based on household surveys, and these most likely underestimate the magnitude of top incomes  (Milanovic, 2016). 2 A reviewer notes that the overall Gini index may be seen as a weighted sum of subgroup Ginis plus a measure of between group inequality plus a term which reflects the extent to which lower income groups overlap with higher income groups  (Mookherjee and Shorrocks 1982, Anderson et al. 2018). When the overlap between country's income is small, as is arguably the case for the real world distribution, the global index will tend to be higher than individual country-level indexes. Economics: The Open-Access,  Open-Assessment E-Journal 12 (2018-63)  Global Solutions Papers www.
Overcoming data constraints is key to identifying robust empirical relationships between multidimensional living standards and policies, as well as to ensure the responsiveness of outcomes to policy intervention. Indicators of social cohesion are possibly even more in short supply, partly because of the under-theorization of the concept, and partly for lack of empirical measurement (for an exception, see Bertelsmann Foundation and  Eurofund, 2014). Against this background, it appears highly desirable to improve the depth, breadth, timeliness and international comparability of datasets and statistical instruments underpinning the measurement of social and economic indicators; and to develop analytical capacity to measure both inclusive growth and social cohesion patterns and understand their policy drivers, especially among G20 emerging economies.
Huynh-Feldt corrections for degrees of freedom and Holm-Bonferroni corrections for multiple measures were used where appropriate. Mean pitch difference analysis. To assess how mean pitch drove judgements of dominance/trustworthiness, we computed the mean pitch difference in each pair of voices. For each listener, we divided these measures into 15 equal sets and computed the response probability for dominant/trustworthy in each task. We fitted psychometric functions to the data of each listener using logistic functions: f(x) = (a0 + b0. /(1 + exp(-(x -x0) * s0)) and the nonlinear least-squares method of the Matlab "fit" function. Lower and upper bounds for the regression parameters [a0, b0, s0] were [-20, -30, -10] and  [20, 30, 10] , respectively; the initial condition was  [1, 0, 20]. The different conditions (listener and stimuli gender) were compared by using two 2 × 2 [SubGender × StimGender] ANOVAs (one per task) on the slope values (s0) from these regressions.