dataesr/scientific-paragraphs-categorization huggingface dataset:
More in detail, we can rescale the proposed functional form in each class b by defining the variable x b = k/c b , yielding p b (x b ) 1 β = (1 + x b ) -1. (2) In the presence of a single exponent β characterizing the system, as shown in Figure  1  [I-K], all empirical curves do collapse on the reference function (1+x) -1. The data collapse however is not occurring in the case of the MPN dataset. In the latter we find a more heterogeneous scenario in which different nodes' classes are characterized by different values of β b and c b , see Figure  1  L. In the Supplementary Online Material we provide further evidence for the evidence of a single or distirbuted value of β in different datasets.
Its implicit expression is given in the SI. A first general result concerns the evolution in time of the average degree k(a, t) of nodes belonging to a given activity class that follows the scaling laws k(a, t) ∝ (at) 1 1+β. ( 6 ) The growth of the system is thus modulated by the parameter β that sets the strength of the reinforcement process in the process ruling the establishment of new social ties. In the limit case β = 0 the growth would be linear. Indeed, the reinforcement of previously activated ties would be zero and nodes would keep connecting randomly to other vertices, thus increasing indefinitely their social circle. In the opposite limit β → ∞ each node would invest is social capital on just one single connection, i. e. the first established. In the six datasets described by a single β value, we observe the range 0.
Asymptotic theory for networks with distributed β As we already mentioned, in the M P N dataset we find the evolution of social ties described by a distribution of β rather than a single value of it. This observation points to a more heterogeneous distribu-tion of social attitudes with respect to the other six analyzed datasets. Arguably, such tendency might be driven by the different functions phone calls serve enabling us to communicate with relatives, friends or rather to companies, clients etc. The need to introduce different values of β in the system complicates the model beyond analytical tractability (see SI for details). Nevertheless, we find that the leading term of the evolving average degree can be described by introducing a simplified model, in which the nodes of the system feature different values of β and undergo a simplified dynamics (see SI for further information) that neglects, for every node, the effects of links established by others.
Notwithstanding these limitations, the modeling framework we propose pave the way to a deeper understanding of the emergence and evolution of social ties. The agreement between the analytical predictions and observed behaviors in seven real datasets, describing different types of social interactions, are encouraging steps in this direction. Finally, our results are a starting point for the development of predictive tools able to forecast the growth and evolution of social systems based not just of regression models or simplified toy models but on a more rigorous analysis of ego-network dynamics.
Asymptotic solution of the ME for distributed β i values The solution of Eq. (  4 ) found in Eq. (  5 ) holds if the system feature a single value of β. As already discussed in the MPN dataset we find multiple values of β ranging from a minimum value, βmin to a maximum one β max. To find a prediction of the long time behavior of such a system, let us propose a simplified model in which we focus on a single agent whose parameters are a i , β i and c i. In this simplified version the agent can only call other nodes in the network, i. e. we neglect the contribution coming from the incoming calls). In this approximation we have to solve a modified version of Eq. (  3 ), obtained by discarding all the terms containing the activity a j of the nodes j = i. By repeating the same procedure above, we get to the continuum limit that reads: ∂Pi(k, t) ∂t = -ai ci k β i ∂Pi(k, t) ∂k - 1 2 whose solution is similar to Eq.
e. KS d (x min ) = max xi≥xmin |S(x i ) -P (x i )|. Once all the distances are computed we determine x min as the values at which the minimum distance is recorded, i. e. x min = min x KS d (x) (see SI and  [39]  for details). Once we compute all the parameters for all the functional forms analyzed we compare them with the likelihood ratio test R combined with the p-value that gives the statistical significance of R (see SI for details). The result of this procedure gives us the best candidate for the F (a) for each dataset. We find that a truncated power law is the best candidate for all the APS datasets together with the MPN one. The only exception is the TMN that displays a Log-Normal distribution of activity (see Fig. 1  and SI for details). After we estimate the functional form and the parameters of the activity distribution F (a), Eq.
The selected PDF are, from top to bottom: power law, stretched exponential (Stret. Exp. ), power law with cutoff (Trunc. PL) and the Log-Normal distribution. in the formation of r&d networks. Scientific reports, 4, 2014. The APS dataset contains the five co-authorship networks of five journals of the American P hysical S ociety, i. e. , Physical Review A, B, D, E and Letters (L). The various datasets contains the data referring to all the issues of the single journals from their first issue up to a certain edition, specifically: Each dataset is composed by several files (one per month). Each file has as many lines as the number of papers published in that month. Finally, each line contains the IDs of the authors of the specific paper. For instance, the typical head of a file is: The data are cleaned so as to not take into account the papers with a single author.
S1. 2 Twitter Mention Network The dataset of Twitter is composed by 273 daily files covering the period between January the 1 st to September the 30 th 2008. The dataset contains the so called fire-hose, i. e. , all the 16, 329, 466 citations done by all the 536, 210 users in the given period. The nodes in the network are connected via 2, 620, 764 edges. Each file contains the daily events with the structure: Citer_ID_00 Cited_ID_00 # Event 0 Citer_ID_01 Cited_ID_01 # Event 1 Citer_ID_02 Cited_ID_02 # Event 2. This dataset is not cleaned, as we have all the events that happened on the platform in the selected period. When analyzing this dataset we define the user's activity a i as the number of citation made by i, i. e. the number of events actually engaged by the node i.
S1. 3 Mobile Phone Network The dataset of the M obile P hone C alls (MPC ) is composed by a single file containing the 1, 949, 624, 446 time ordered events with 1 second resolution covering the period between January and July of 2008 for 6, 779, 063 users of a single operator with 20% market share in an undisclosed European country. The dataset contains all the events from and toward users of the company (so that even the calls from non-company users to company users and vice-versa are taken into account). As a result, we have 33, 160, 589 nodes (of which 6, 779, 063 are users of the selected company) that are connected via 92, 784, 825 edges. We split the huge list of events in 98 files (each of them containing more or less the same number of events) for computing convenience. Each file contains events with the structure: When analyzing this dataset we define the user's activity a i as the number of calls done by the node, i.
S2. 1 Activity distribution and the nodes binning For the datasets presented in Section S1 we first evaluate, for each node i, the total number u i of events engaged by the node itself. For instance u i is the number of calls made by the node i in the MPC dataset or the number of citations done by i in the Twitter dataset. We then define the node activity a i as the ratio between the i-th node's number of events and the total number of events observed in the dataset, i. e. a i = u i /u tot where u tot = j u j. Thus, a i falls in the range a i ∈ [ , 1. 0) with = min i (u i )/u tot. We then introduce and compute the activity distribution F (a). In Fig. S1  we show the resulting activity distribution for each analyzed dataset, while in Table  (1)  we show the best candidate functional form for the F (a) distribution of each dataset.
The latter is estimated using the methods found in  [39]. In particular, we compare the goodness of fit on the F (a) distribution of the functional forms found in Table  [1]  of the main paper, i. e. power-law, truncated power-law, stretched exponential and log-normal distribution. The procedure for each dataset and each functional form reads as follows: -we fit the F (a) taking into account all the nodes featuring a i ≥ x min , where x min is the lower bound of the distribution. The fit is performed using the maximum likelihood estimators (MLE) that return the optimal values of the parameters; -once the optimal parameters are found we compute the Kolmogorov-Smirnov distance (KD d (x min )) between the analytical and experimental complementary cumulative distribution function (CDF); -we then apply this procedure for different x min and set the a min = min xmin KS d (x min ) lower bound value as the one that minimizes the KS d.
TMN and (G) MPN (blue points). We also show the best candidate fit of the F (a) distribution (blue solid lines) featuring the functional form and parameters found in Table  (1). In all the plots we show the data and fit ranging from the lower bound xmin to the 99. 9% of the measured data, thus excluding from the visible area the top 0. 1% of the activity values (see Table  (1)  for the lower bound details). Tab. 1: The candidate functional form of the activity distribution for each analyzed dataset, the evaluated parameters (see Table  [1]  in the main for the analytical expressions), the Kolmgorov-Smirnov distance KS d , the percent % of nodes in the dataset that have activity ai ≥ amin and the normalized log-likelihood L. In the parameters we include amin that is the value of the activity that minimizes the KS distance.
Dataset Distribution We then repeat this procedure for all the functional forms of the F (a) and we then compare them with the likelihood ratio test R combined with the p-value that gives the statistical significance of R  [39, 40]. The result of this procedure gives us the best candidate for the F (a) for each dataset as shown in Table  (1). We find that a truncated power law is the best candidate for all the APS datasets together with the MPN one. On the other hand, in the TMN we find a log-normal distribution as the best candidate for the dataset. Our datasets provide evidence that nodes within the same activity class (i. e. node with similar values of activity a i ) can feature very different memory behavior. In particular agents with large activity may connect to very few different nodes (strong reinforcement) or establish new links at almost every step (weak reinforcement).
In this case a single β opt cannot fit all the curves and we have to consider a multi-β model where each class b features a different optimal value of β, β opt (b). In Fig. S3  we present the rescaled p b (k) curves for the PRA, PRD, PRE, PRL, TMN and MPC datasets. In the first five cases we show the rescaled curves obtained by substituting k → k/c b and then plotting p b (k) → p b (k) 1/βopt. As one can see, the curves nicely collapse on the reference curve (1 + k) -1. In the MPC case we show instead the original curves, each one fitted with its own β opt (b). The latter parameter falls in the 1. 2 β opt (b) 3. 0 interval for most of the curves as we also show in Fig. S2. To quantitatively define the β opt parameter, let us define the total mean square deviation χ 2 (β) as χ 2 (β) = N b b=1 χ 2 b (β) , (S5) where N b is the total number of curves, i.
e. the number of activity-degree bins b. Then, for the single exponent case, the function χ 2 (β) allows to define β opt as: In the multi-β case instead, we compute the different values of the exponent β opt (b) found in the system by grouping the memory classes b accordingly to their final degree as shown in Fig. S2. The optimal value of β opt (b) is found to be minimum for the bins featuring a large final degree, i. e. β min ≡ β opt ∼ 1. 2, which, as we will show in Section S3. 2. 3, is the exponent driving the evolution of the network. β opt = min β (χ 2 (β)). ( S6 ) (a) (b) (c) To corroborate the results just outlined, we show in Fig. S4  the box plot of the β opt (b) distribution for different groups of nodes classes b grouped by their final degree. We note that the APS and TWT datasets are well approximated by a single β opt as the distribution of β opt (b) within each sub-group of nodes is compatible with the global optimal value β opt.
On the other hand, in the MPN case we see that the large final-degree classes have their β opt (b) distribution centered around a smaller value of β min ∼ 1. 2. As already anticipated, this value will lead the asymptotic growth of the system as we will show in Section S3. 2. 3. As a last remark we present in Fig. S5 (a, b ) the measured distribution of the constant c(b) for the MPN and TMN datasets. We show the distribution for all the nodes in the network and for each activity class a, i. e. the group of nodes featuring similar activity. The values of this constants are distributed but peaked around an average value. Moreover, the distribution of the c(b) parameter within each activity closely follows the global one. The distribution of the social attitude c(b) then appears to be a global, activity independent feature of the nodes in the system.
S3. 2. 3 The multi-β case As shown in Section S2 a single value of the reinforcement exponent β is found to fit most of the p b (k) curves in both the APS and TMN datasets, while for M P N a single value of β cannot fit all the p b (k) curves for each activity-degree class b at once. For this reason we further develop the model, letting each node i to feature three parameters: the activity a i and the reinforcement constant c i together with the exponent β i of the underlying reinforcement process. Since the model with three parameters per node is difficult to handle, we apply some approximations in order to get analytical insight. In particular, we work in simplified single-agent framework, where we focus on a single agent that can only connect to other nodes and never get called. Within this approximation the master equation for the node i reads: P i (k, t + 1) = a i p(k -1)P i (k -1, t) + P i (k, t) [a i (1 -p(k)) + (1 -a i )].
1970 to Dec. 2006 and contains 36,880 papers written by 34,093 authors and connected by 100,683 edges. The PRB dataset refers to the Jan. 1970 to Dec. 2007 period and con-tains 104,047 papers published by 84,367 authors which are connected by 416,048 links. The PRD datasets covers the same period as the PRB one and it is composed by 33,376 papers, 21,202 authors and 60,033 edges. The PRE dataset refers to the Jan. 1993 to Dec. 2006 period with 24,204 papers published by 28,188 authors connected by 68,029 edges. Finally, the PRL dataset contains all the 66,422 papers published between Jan. 1960 to Dec. 2006 and written by 78,763 authors forming 299,017 edges.
e. to the optimal value for the class b. (c) The box plot showing the global distribution of the constant c(b) in all the datasets analyzed. The height of the box corresponds to the lower and upper quartile values of the distribution and the horizontal solid line corresponds to the distributions median, while the dashed lines indicates the average value for each range of final degree. The whiskers extend from the box to values that are within 1. 5x the quartile range. (d) In this table we report all the values of the reinforcement exponent βopt and the average reinforcement constant c(b). For the MPN case we report the βopt = βmin and the constant values are evaluated for each nodes class b using its optimal value of β, βopt(b).
1970 to Dec. 2006 and contains 36,880 papers written by 34,093 authors and connected by 100,683 edges. The PRB dataset refers to the Jan. 1970 to Dec. 2007 period and con-tains 104,047 papers published by 84,367 authors which are connected by 416,048 links. The PRD datasets covers the same period as the PRB one and it is composed by 33,376 papers, 21,202 authors and 60,033 edges. The PRE dataset refers to the Jan. 1993 to Dec. 2006 period with 24,204 papers published by 28,188 authors connected by 68,029 edges. Finally, the PRL dataset contains all the 66,422 papers published between Jan. 1960 to Dec. 2006 and written by 78,763 authors forming 299,017 edges. • One network dataset describing Twitter mentions (TMN), exchanged by users from January to September 2008. The network has 536,210 nodes performing about 160M events and connected by 2.
In addition, participants provided information on job, food and drink, tobacco consumption and hands wash during the sampling period through a questionnaire. Results Urine samples were collected from 90 cashiers and 44 controls. Free and total BPA were detected in all samples. The median urinary total BPA concentration was 3. 54 µg/L (2. 89 µg/g creatinine) for controls and 8. 92 µg/L (6. 76 µg/g creatinine) for cashiers. For the free BPA, the 1 3 BPA is used as a developer in thermal papers, which include a variety of uses including cash register and credit card receipts, self-adhesive labels, etc. Data provided by experiments on animals suggest that BPA could represent a risk for human and animal health because of its oestrogenic properties  (Vom Saal and Hughes 2005; CERHR 2008 ). It has also been demonstrated that BPA interacts with other endocrine receptors, such as thyroid hormone receptors, for example  (Diamanti-Kandarakis et al.
Statistical analyses Descriptive and inferential statistical analyses were performed using Stata 13. 0 software. The mixed linear regression model was used to test the effects of different variables on urinary BPA (according to its different expressions: free or total forms, adjusted or not by creatinine). This type of model allowed us to take into account data non-independence (urine samples from same subjects, themselves from the same company) by integrating a subject random effect and a company random effect. A logarithmic transformation was applied to the data on urinary BPA in its different forms. The questionnaires were analyzed for the food and drink consumption. A Pearson's Chi-squared test was performed to determine whether the control group and cashiers had similar food and drink habits.
2010). And finally, the main BPA conjugated form (BPA-monoglucuronide or BPAG) has been shown to be unstable under certain conditions  (Ye et al. 2007 ). Hydrolysis of BPAG in BPA during sample conservation and processing must be avoided during the determination of free BPA. In this study, the urine samples were thus obtained under controlled conditions by our laboratory team in each company and rapidly frozen at -20 °C after collection until analysis. The stability of BPA forms in urine was checked. The free BPA remains stable and the conjugated forms were not hydrolyzed to BPA up to 24 h at room temperature (data not shown). However, we made sure that urine samples were kept the shortest time possible at room temperature. To minimize the BPA background contribution, all solvents, vials, containers, pipettes were checked for BPA contamination.
Unfortunately, no data for the French general population are currently available to ensure that our controls, which were from the same companies as cashiers, were representative of the French general population. It is generally recognized that diet is the predominant source of BPA exposure for humans. As the objective of this study was to investigate whether frequent contact with thermal paper is associated with an increase in urinary BPA excretion, attention was required to avoid bias from food and drink habits. Detailed information were collected through a questionnaire on diet but also smoking and doit-yourself habits. As there was no difference between our control group and cashiers concerning these factors, it was relevant to compare the cashiers to our non-occupationally exposed group for BPA excretion.
Acknowledgments The authors would like to thank all participants, occupational physicians and companies who took part to this study. We would like to recognize the assistance of  D. Lafon ,  N. Bertrand ,  C. Aubry ,  S. Malard  from  Institut National de Recherche et de Sécurité  and the assistance of  C. Beausoleil  from  Agence Nationale de Sécurité Sanitaire Alimentation, Environnement, Travail. We would like to acknowledge  F. Denis ,  P. Marsan  and the  Biomonitoring team  who help with sample collections and analysis.
(  2 ), we have weighted single-event expressions in Eqs. (  3 ) and (  4 ) with weights M(M-1) and M(M-1)(M-2)(M-3), respectively  [29]. The dataset used in this analysis was obtained with the ALICE detector  [38, 39]. It consists of minimumbias Pb-Pb collisions recorded during 2010 LHC Pb-Pb run at √ s NN = 2. 76 TeV. With the default event and track selection criteria described below, we have obtained in total about 1. 8 × 10 5 events per 1% centrality bin width. All individual systematic variations were combined in quadrature to obtain the final uncertainty. The centrality was determined with the V0 detector  [40] [41] [42]. As a part of systematic checks centrality was determined independently with the Time Projection Chamber (TPC)  [43]  and the Silicon Pixel Detector (SPD)  [44, 45] , which have slightly worse resolution  [42].
Since January 2012, a national observatory whose purpose is to study low flows (Observatoire National Des Etiages, ONDE) has been implemented by the National Agency for Water and Aquatic Environments 2 (ONEMA). This observatory has a dual purpose: building a solid database of knowledge on summer low flows and developing tools to manage drought crises. In France, the south and southwestern regions are currently the most sensitive regions to droughts, while the north and northwestern regions generally do not require long-lasting water bans. However, even in a catchment located in northwestern France, as is the case for the Vilaine catchment, although it rarely suffers from severe droughts compared to what is observed in the southern part of the country, it can happen that water managers have to face episodic problems of imbalance between water availability and demand.
Enhance the Knowledge of the Water-Related Impacts of Climate Change in the Specific Vilaine Catchment The task force mentioned in the preceding section would collect all the data related to the impacts of climate change on the territory of the Vilaine. These data should be used to identify and, if possible, quantify drought issues related to climate change and translation impacts in terms of water availability as well as their consequences for current and potential future water uses. On the basis of this state-of-the-art data collection, the task force should also promote knowledge of climate change's impacts on the territory by undertaking specific studies targeting the main water uses of the catchment. Three main water sectors could be investigated: (i) agriculture, (ii) drinking water supply and (iii) tourism (including boating for the Arzal dam).
Aeromagnetic and gravity data have proven to be among the most effective methods for mapping deeply buried basin/basement interfaces. However, the data interpretation generally suffers from ambiguities, due to the non-uniqueness of the gravity and magnetic signatures. Here, we tie the gravity and magnetic signatures with a petrophysical characterization of the lithologies outcropping around the French Paris Basin. Our methodology investigates the lithology and structure of its hidden Variscan substratum at the junction between the Armorican Massif and Massif Central. Our approach is based on the combination of potential field data, magnetic susceptibilities measured in the field, density values of sample rocks and information documented in boreholes, in order to propose a new interpretative geological map of the buried substratum of the Paris Basin.