dataesr/scientific-paragraphs-categorization huggingface dataset:
Item s representation A social FS consists of resources items, the users profiles and the histories which memorizes the interactions of the users (ratings) about items recommended. We exploited the meta-data of the Dublin core vocabulary as being a standardization description of items, the attributes values of the vocabulary allowed us to calculate the degree of similarity between items and group them into communities. Dublin Core vocabulary. Dublin Core DC (http://dublincore. org) is a set of simple and effective elements to describe a wide variety of web resources, the standard version of this format includes 15 elements of which semantics has been established by an international consensus coming from various disciplines recommended by W3C. These elements are gathered in three categories those which describe the contents (Cover, Description, Type, Relation, Source, Subject) and those which describe the individual properties (Collaborator, Creator, Editor, Rights) and others for instantiations (Date, Format, Identifier, Language), the current version is known as 1.
Datasets For experimental tests we exploited two sets of data: • Book -Crossing dataset (http://www. informatik. unifreiburg. de/ cziegler/ BX/), a free download dataset for ends of research collected by Cai-Nicolas Zeigler in 2004 from the famous Amazone. com site. The dataset constitutes of 278858 users producing 1149780 votes for 271379 books. • f oaf P ub dataset (http://ebiquity. umbc. edu/resource/), is a set of data extracted from FOAF files collected during the year 2004, includes 7118 FOAF documents collected from 2044 sites and distributed under the Creative Commons license (v2. 0). This set has allowed us to import FOAF properties by SPARQL queries to determine the similarity sim f. Our empirical tests require the deployment of a parser to extract FOAF and DC properties through the SPARQL engine of the framework jena 2-6-4 (https://jena.
Relevance metrics To evaluate the method presented in this article, we held a special metric and widely used in the FS, it is MAE, and two other metrics, recall and precision of information retrieval field  [16, 9]. • M AE: Mean Absolute Error, calculating the mean absolute difference between predictions p i retained by the system and the real evaluations e i given by users. This measure is simple to implement and directly interpretable. M AE = N i=1 |p i -e i | N • P recision: it is the ratio between the number of relevant items returned by the system and the total number of items returned. P = N pr N r • Recall: it is the ratio between the number of relevant items returned by the system and the total number of existing relevant items in the database. R = N pr N p These metrics respectively measures the error, the effectiveness and the quality of FS.
Results and discussion In this section, we discuss the experimental results obtained, for that, we divide the dataset size in two parts, one having a proportion of 80% has dedicated for training phase and the other of proportion of a 20% has dedicated for test phase. From Figure  2 , the curves show that the MAE error is minimal in the neigh- bourhood range [25-45] and important in outside of this range, it means that as the number of neighbours is less than 25 so there are not enough neighbours to calculate the similarity which lowers the prediction quality, unlike the other side, or the number of neighbours exceeds 45, there are sufficient neighbours, but less similar which degrades prediction quality , this explains that between 25 and 45 there are enough better similar neighbours. Also we observe that the DC curve illustrates a slightly favourable result compared to the FOAF curve, as the items are identified and enriched by descriptions and meta-data with certain stability better than valorising links and subjective opinions between a user s networks.
The best result is obtained in Hybrid curve, or the error is reduced to 0. 68 for a neighbourhood size of 35, this favourable result is argued by exploiting items implicit information s and estimating attributes of user profiles and links between them such as see also or know properties, which form a social network on the web and therefore a rich database that reduces the MAE, in addition, taking into account the opinions of users through their notes with respect to the items recommended what leads to a profitable collaboration. Two conclusions can be drawn the benefit of this additional data mass reduces the effect of sparsity as a problem moderating filtering systems, and adequately addresses the cold start problem for a new item. Moreover, the URI clause for the unique resource identification in rdf documents lowers the effect of scalability.
In order to alleviate the limitations of collaborative filtering systems, we have presented in this paper, a hybrid model based on the FOAF formalism to better appreciate and enrich user profiles via social networks and information networks. The weighted classification that we have defined for the representation yield more adaptable and flexible profiles and still better adjustable, which alleviate the sparsity problem. On the other hand, the use of DC elements to describe items in a standard way leads to the good development of communities and overcome the problem of cold start for a new resource. The notable progress in the results founded by the formal use of meta-data to describe the valued resources and links with a standard and unified structure. Moreover, the union of similarities adopted for the recommendation is considered a balance between using different data sources and therefore increased the quality of prediction.
Data collection Data were collected over three to four consecutive days, typically from a Wednesday or Thursday to Saturday, so that sampling took place in both weekday and weekend regimes, and before and after the 'canteen' days when prisoners can purchase tobacco or other personal goods (typically Fridays). A researcher trained in the use of air quality monitoring and surveying, with the help of a prison service headquarters staff member, placed the SidePak monitors in static locations on wing landings and in prisoners' cells, or attached the monitor to wingbased prison staff to collect personal exposure data during parts of their work shifts. Fixed locations on wing landings were chosen to cover the range of wing designs and function. Monitors were placed as discreetly as possible to avoid disturbing prisoners' normal behaviour, though wing officers knew where monitors were placed and for how long.
The device was usually placed half way down the wing, above head height and away from open outside doors, windows, or cooking equipment. The monitor keypads were locked during sampling. We collected samples on each day for as long as the researcher was allowed access to the wing, and subject to limitations of battery life and in the case of personal monitoring, staff shift patterns. The gentle buzz emitted from the SidePak monitors could not be heard above the surrounding environmental noise during personal and wing sampling. Data on the layout of the wing, prisoner roll count and lock/unlock times were recorded. Prisoners who inquired were informed that we were measuring air quality. Wing officers were asked to identify smoking and non-smoking prisoners who were suitable to have a SidePak monitor placed in their cell, and these prisoners were then approached by the researcher who explained the study, answered questions and requested written consent.
Given consent, the SidePak monitor was generally placed on a shelf or desk at around waist height in the cell. Data on each cell location, the number of prisoners in the cell, their smoking status and the style of the cell window were recorded. Due to the gentle buzz the SidePak monitor makes whilst sampling it was placed in a cool box surrounded by foam padding. Data were typically collected for a few hours over a morning or afternoon period. Prison Officers working in the prisons were contacted by email in advance of the study visit, or by word of mouth at the time the monitors were placed on wings or in cells, and invited to volunteer to wear a monitor for personal sampling. All who volunteered were given an explanation of the study and asked to provide written consent. We recruited both current smokers and nonsmokers.
Data analysis Since the SidePak monitors were usually turned on and off just before and after being placed in the sampling sites we discarded the first and last five minutes of each data record. Each set of sampling data was downloaded from the monitor using Trackpro 4. 6. 1 software, and transferred to a Microsoft Excel spreadsheet with the corresponding location, cell and staff member data. We then used STATA 13 to generate descriptive statistics including arithmetic means, 95 % confidence intervals, standard deviations, ranges and times of maximum values, and to estimate the proportion of time in which the PM 2. 5 concentration exceeded World Health Organisation (WHO) 24 h mean PM 2. 5 upper limit of 25 μg/ m 3  [14]  for each dataset. Although PM 2. 5 data distributions were skewed, we present arithmetic as well as geometric mean figures since the former are used by the WHO to define upper limits.
Wing landings A total of 48 datasets were collected from 30 different smoking and non-smoking landing locations. Thirtyeight locations were sampled exclusively during the daytime period, and ten were sampled into the night time. The average period over which data were collected was 6. 5 (Standard Deviation (SD) 2. 0) hours. Arithmetic mean PM 2. 5 in the 48 data sets was 40. 08 μg/m 3 , and ranged from 0 to 1124 μg/m 3. Mean PM 2. 5 concentrations were significantly higher on landings where smoking was permitted in cells than non-smoking wing landings (43. 87 μg/m 3 and 5. 90 μg/m 3 respectively, p < 0. 001). Of the 42 datasets from smoking locations, 18 landings spent over half of the sampling time over the WHO 24 h mean upper guidance limit of 25 μg/m 3 (14). In the three prisons with a single canteen day (one prison was excluded from the analysis because its canteen delivery spanned two-three days, therefore no pre-canteen data were available), PM 2.
Prison cells All 13 cells sampled were located on wings where smoking was permitted in cells, and five of the cells sampled had occupants who smoked. The average time for which data were collected was 4. 88 h (SD 1. 76) and the arithmetic mean of the 13 datasets was 103. 10 μg/m 3. High concentrations of PM 2. 5 were recorded in the five smokers' cells with means ranging from 62. 31 to 434. 74 μg/m 3 , and in all cases exceeded the WHO limit of 25 μg/m 3 as a 24 h mean  (14)  for over 60 % of the sampling time. The arithmetic mean PM 2. 5 concentration in smoking cells (226. 16 μg/m 3 ) were significantly higher than in nonsmoking cells (16. 98 μg/m 3 , p < 0. 001). Figure  3  shows concentrations of PM 2. 5 recorded in a single cell where the occupant smoked. The prisoner reported smoking four hand-rolled cigarettes during the sampling period.
Staff members Of the 22 staff members who volunteered for personal monitoring, 21 were prison officers and one a healthcare assistant. All were based on wings where smoking was permitted in cells and had prisoner contact. Twenty-one staff members were monitored during a daytime shift and one on a night shift. The average period of data collection was 4. 18 h. The arithmetic mean PM 2. 5 concentration to which participants were exposed was 23. 51 μg/m 3. Figure  5  shows concentrations of PM 2. 5 sampled from a single prison officer during a morning shift alongside their self-reported timed outline of locations and duties during sampling. The location report for this individual suggested that higher exposure levels tended to occur during periods spent on the wing landings, a finding that was evident in records from all other staff members.
We carried out much of our sampling during the summer months when natural ventilation to the wings and cells through open windows and doors would have been greater than during the winter months, potentially causing our findings to underestimate average pollution levels over the longer term. Safe locations for the SidePak monitors were limited, but we tried to collect data from a broad selection of settings. Since we were obliged to answer questions from staff members and prisoners who enquired about the monitoring, our measurements were not carried out blind. However, whilst it is possible that prisoners or staff changed their  behaviour in response to being monitored, we think that is unlikely to have occurred to any appreciable degree over the course of our measurements. Our maximum sampling time was determined by a battery life of around 9 h, though in practice we were also constrained by restrictions on the times that we could leave and collect the monitors.
5 of 93. 11 μg/m 3  [16]. The arithmetic mean reported for all smoking wing landing datasets in this study is less than half (arithmetic mean 43. 87 μg/m 3 ) of that reported in North Carolina, even though they report a 65 % prisoner smoking prevalence which, anecdotally, is broadly similar to that in England. Twelve datasets were collected from smoking locations in North Carolina (compared with 42 in this study) and the average time for data collection was 1. 28 h (compared to 6. 66 h in this study). Another study, conducted in prisons in New Zealand  [17]  recorded PM 2. 5 concentrations before a smoke-free policy was introduced, and produced a geometric mean before the policy of 6. 58 μg/m 3. Although much lower than the geometric mean recorded across smoking locations in this study (35.
57 μg/m 3 ) the authors acknowledge that the representativeness of their findings was constrained by their decision, out of fears that the monitors would be tampered with, not to sample air in common areas used by prisoners. Samples were therefore taken only from the 'staff base' , and did not reflect levels elsewhere in the prison. Research evidence summarised by the WHO and others suggests that there is no safe level of exposure to SHS  [1, 14]. Data collected from staff members gave an insight into locations where exposures to PM 2. 5 were highest, and these included the wing landing, and at the doorway and inside a prisoner's cell. Taken together, these findings can offer some guidance as to the types of wings or duties where staff members are exposed to the highest levels of SHS and therefore where protection from SHS is particularly needed.
Using the weighted histogram analysis method (WHAM) the unbiased density of states (and thus the corresponding free energy profile) can be iteratively reconstructed from the histograms. 46 We apply Umbrella Sampling using a harmonic energy bias with a spring constant of 0. 12 eV Å À2 (1. 95 N m À1 ). We use 60 sampling windows with a spacing of 0. 75 Å. The sampling windows are located in the domain y A [À22. 125, 22. 125], with y = 0 being at the center of the channel. The spring stiffness in combination with the width of the sampling windows results in partially overlapping histograms, which is needed for the reconstruction of the unbiased profile. The system is equilibrated for 200 ps after the bias potential is imposed. We subsequently collect data over the next 500 ps. We confirm that the equilibration time and the sampling time are sufficiently long.
5. 7(d). All of the previous results show that, at least at a qualitative level, the asymptotic model captures many important characteristics of multi-dimensional detonations. Next, we investigate how quantitatively close the predictions are to the solutions of the reactive Euler equations. The numerical simulations of the reactive Euler equations were carried out using PyCLAW  [18] , which provides, among other things, a Python wrapper for the classic routines in CLAWPACK. The software requires the user to provide a Riemann solver. We use a Roe-linearized Riemann solver with a Harten-Hyman entropy fix  [21]. The classic package of PyCLAW employs a second-order finite volume algorithm with a fractional step method for the time evolution  [21]. For the numerical simulations, the reactive Euler equations were non-dimensionalized in the conventional form by the ambient state with velocities scaled by u a = p a /ρ a , spatial variables scaled by the half-reaction length, x 1/2 , and time by u a /x 1/2.
Acknowledgements L. F. and A. K. gratefully acknowledge research support by  King Abdullah University of Science and Technology (KAUST). The research by  R. R. Rosales  was partially supported by  NSF  grants  DMS-1007967 ,  DMS-1115278 ,  DMS-1318942 , and by  KAUST  during his research visit in November 2013. L. F. would like to thank  Slava Korneev  and  David Ketcheson  for their help with numerical computations.
Statistical Analysis Data were analyzed using the GraphPad Prism 6 software (GraphPad Software Inc. , San Diego USA). When necessary, groups were compared using the Mann-Whitney rank sum test. Differences with a P-value of <0. 05 were considered statistically significant. The level of significance is depicted as * P < 0. 05; * * P < 0. 01. Data are presented as means ± standard error of mean (SEM).
Materials and Methods Participants 9615 children were recruited in primary school (CM1 and CM2 in France) in the frame of the French 6 Cities Study (6C Study) according to a protocol described in a previous study  [8]. The sample was taken from all pupils in the 401 relevant classes from 108 schools randomly selected in the six French communities (Bordeaux, Clermont-Ferrand, Creteil, Marseille, Strasbourg and Reims), which were chosen for the contrast in their air quality. 7781 questionnaires have been collected. A total of 5221 children (54. 3%), for whom complete data on ETS exposure and at least one of the two outcome variables (emotional symptoms or conduct problems) were available, have been included in the present study.
Statistical analysis The characteristics of our study population (N = 5221) were compared to the sample of children without complete data (N = 2560), by using Chi-square tests. We also compared these characteristics in children according to their emotional symptoms and conduct problems, using Kruskal-Wallis tests. In the unadjusted models, a total of 5077 children were included in the analyses of emotional symptoms and of 5126 children in the analyses of conduct problems. We used a multinomial logistic regression model to analyze the association between behavioral problems and ETS exposure  [10]. The dependent variables (emotional symptoms and conduct problems) were classified in three categories (normal, borderline and abnormal). Results are presented as odds ratios (OR) and 95% confidence intervals (95% CI).
Characteristics of the study population Children were included in the analyses if their parents had participated and answered questions about smoking behavior and completed the SDQ (5221 children). These 5221 children differ in many characteristics from children with missing data (N = 2560), including children age, parents' educational level, asthma, siblings, single parenthood, preterm birth, origin and city (Table  1 ). They were aged 10. 8 +/-0. 8 in mean. 2619 children of our study population (50%) were girls (Table  1 ). Table  2  presents the characteristics of the sample of 5221 children of our study according to emotional symptoms and conduct problems.
Only another study found similar results but it used a different rating scale and the sample was smaller than ours (N = 230)  [14]. This study used data from a community based, longitudinal investigation examining the relation between childhood's exposure to ETS and later emotional symptoms and conduct problems. Exposure to parental smoking increased the risk for their children to develop emotional symptoms and conduct problems. This relationship could not be explained by a number of psychosocial risk factors like demographic variables (parental gender, mother's age at child's birth, child gender, education level), parental intra-personal attributes (depression or unconventionality), and parental child-rearing practices (affection toward child). Child behavior problems were measured using items from the Child Behavior Checklist (CBCL) of Achenbach.
MethodS This was a population based, observational study using data on 11 353 cases of NTD not associated with chromosomal anomalies, including 4162 cases of anencephaly and 5776 cases of spina bifida from 28 EUROCAT (European Surveillance of Congenital Anomalies) registries covering approximately 12. 5 million births in 19 countries between 1991 and 2011. The main outcome measures were total and live birth prevalence of NTD, as well as anencephaly and spina bifida, with time trends analysed using random effects Poisson regression models to account for heterogeneities across registries and splines to model non-linear time trends.
WhAT ThIS STudy AddS The prevalence of NTD has not decreased in Europe despite longstanding recommendations aimed at promoting peri-conceptional folic acid supplementation and the existence of voluntary folic acid fortification Policies for mandatory fortification of food staples with folic acid should be considered as an important and more effective means for prevention of NTD in Europe recent but preliminary analysis of the pooled data on the prevalence of NTD in the European Surveillance of Congenital Anomalies (EUROCAT) network suggested that their overall prevalence may have slightly decreased in the period 2004-08. 5 ost population based congenital anomaly registries in Europe belong to the EUROCAT network (www. eurocat-network. eu/), with a common database. In 2009 EUROCAT published a special report on NTD, 9 which showed that important barriers continue to exist for successful implementation of the recommendations for folic acid supplementation.
data sources Since 1980, the EUROCAT central database has held individual anonymised records of cases of congenital anomaly occurring for full member registries and aggregate data for associate member registries, including live births, fetal deaths from 20 weeks gestational age, and termination of pregnancy for fetal anomaly. Information on each of the registries, including methods of case ascertainment and local procedures regarding ethics approval for the registries' activities and their collaborations with EUROCAT, are available on the EUROCAT website (www. eurocat-network. eu/ABOUTUS/Member-Registries/MembersAndRegistryDescriptions/AllMembers) and in the publication by Greenlees et al. 13  All registries use ICD-9 (international classification of diseases, 9th revision) or ICD-10 with BPA extension to code up to nine syndrome or malformation codes for each case.
All cases that were not associated with a chromosomal anomaly and had a diagnostic code corresponding to a neural tube defect (ICD-9 740 -742 and ICD-10 Q00, Q01, Q05) were included for full and associate EUROCAT registries that could provide data from both the 1990s and the 2000s; most registries provided data for the entire, or almost the entire, time period between 1991 and 2011 (table  1 ). We extracted data on 11 353 cases of NTD, including 4162 cases of anencephaly (ICD-9 740 and ICD-10 Q00), 5776 cases of spina bifida (ICD-9 741 and ICD-10 Q05), and 1415 cases of encephalocele (ICD-9 7420 and ICD-10 Q01), from 28 registries in 19 countries covering approximately 12. 5 million births, in October 2013 for the analysis of time trends in total and live birth prevalence of NTD between 1991 and 2011.
We used random effects models to take into account any heterogeneity that may exist across the registries. 16 17 In the Poisson models, we used linear splines (or "piece-wise exponential models") for modelling the time trends in the prevalence of NTD; we used splines with five equally spaced knots based on the quintiles of the distribution of the time period to estimate separately the annual trends for the following time periods: 1991-95, 1995-99, 1999-2003, 2003-07, and 2007-11. We used Stata software (versions 11 and 13) for all analyses.