dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the two referees  P. Roudil  and  F. Haslinger  for their reviews and advice, and the Editor  A. Armigliato  for his careful proof-reading. The magnitude M wp parameters of the Pc used in this paper were provided to the authors courtesy of  Barry Hirshorn of the Pacific Tsunami Warning Center. This work has been funded by the  Italian Flagship Project RITMARE , by the  EU   FP7  project  NERA  ( 262330 ), and by project  ASTARTE  ( Assessment, Strategy And Risk Reduction for Tsunamis in Europe )  FP7-ENV2013 6. 4-3 , grant  603839. The Early-est software is being further developed in the framework of the agreement between Italian DPC and INGV, annex B2 (2015). Figures are produced using GMT (Wessel and Smith, 1995) and python matplotlib plotting library. We used broadband seismograms provided by the  IRIS DMC , the  NEIC-USGS , the  IPGP Data Center , the  GFZ Seismological Data Archive , the  USGS  and the  INGV and MedNet seismic networks.
Introduction In the last few years we have witnessed important progress towards the understanding of the Milky Way bulge. The availability of large photometric and spectroscopic datasets from ongoing surveys are finally allowing us to obtain a wider view of the Bulge properties, even expanding our knowledge to the innermost regions previously not accessible. The bar in the inner regions of the Galaxy was first suggested by  de Vaucouleurs (1964)  and its main structural properties have been investigated in detail since then  (Blitz & Spergel 1991; Stanek et al. 1994; Dwek et al. 1995; Babusiaux & Gilmore  Based on observations taken with ESO telescopes at the La Silla Paranal Observatory under programme IDs 187. B-909(A) and 71. B-0196. 2005;  Rattenbury et al. 2007; Gonzalez et al. 2011, and references therein).
As a consequence, the comparison of [α/Fe] values in Bulge stars   (2011 ), Johnson et al. (2013 ), and Gonzalez et al. (2011)  found a similarity between the [α/Fe] abundance ratio of Bulge and thick disc giant stars. The origin of this discrepancy has been pointed to a systematic difference when analysing the abundances of dwarf stars from the disc and Bulge giants  (Meléndez et al. 2008). In support of this scenario, the microlensed dwarfs from the Bulge have shown the same similarity in the α-element enhancement to those of the thick disc dwarfs  (Bensby et al. 2011). Therefore, the usage of well-calibrated measurements on homogenous datasets are fundamental for these kinds of studies. A few high-resolution studies have looked at a few fields located at relatively similar latitude between each other (e.
GIBS high-resolution spectra The Giraffe Inner Bulge Survey (GIBS) is a survey of ∼ 6,500 RC stars in the Milky Way bulge observed with the GIRAFFE spectrograph of the FLAMES instrument  (Pasquini et al. 2000)  at the ESO Very Large Telescope (VLT). The aim of the GIBS survey is to investigate the metallicity and radial velocity distribution of Bulge stars across different fields, spread over a large area of the inner Galactic bulge (-10 • ≤ l ≤ +10 • and -10 • ≤ b ≤ +4 • ). A detailed description of the target selection and data products of the program is given in Paper I. In this article, we focus on the analysis of the high-resolution spectra of 400 RC stars located in four fields at Galactic latitude b ∼ -3. 5 • for which we provide a brief description summary. Target stars for the spectroscopic observations were selected from the VVV multi-band photometric catalogues  (Gonzalez et al.
In  Zoccali et al. (2008)  and  Gonzalez et al. (2011)  this process was done manually, in the sense that the parameters were modified by hand to minimise the corresponding slopes. This manual procedure was found to produce some systematics, particularly in the high metallicity regime  (Hill et al. 2011). To correct for this issue and bring our datasets into a common baseline, we have now improved our method into an automatic procedure, similar to the one used in  (Hill et al. 2011 ) but using the GALA code  (Mucciarelli et al. 2013)  to spectroscopically refine the first guess photometric parameters. Note that another difference with respect to the aforementioned studies is that for this work we have used ATLAS9 model atmospheres  (Castelli & Kurucz 2004). This is because GALA already includes a dynamic call to the ATLAS9 code so that whenever GALA needs to investigate a new set of atmo-1 http://www.
44 dex. A fixed value of M * = 0. 8M has been adopted similarly to  Zoccali et al. (2008)  and  Hill et al. (2011). Microturbulence velocity and global metallicity are set to 1. 5 and 0. 0, respectively, as a first step. These values are used to obtain a first guess ATLAS9 stellar model atmosphere and are subsequently refined spectroscopically by GALA using the equivalent widths of isolated Fe lines obtained by means of DAOSPEC  (Stetson & Pancino 2008). After feeding GALA with the photometric stellar surface parameters and the corresponding model atmosphere, the code iteratively searches for spectroscopic effective temperatures and microturbulence velocity by imposing excitation equilibrium and the null slope of iron abundance versus equivalent width of the Fe lines. During each iteration, a new model atmosphere is generated by GALA using the refined stellar parameters.
In our sample, this computation results in a mean metallicity error of σ [Fe/H] =0. 20 ± 0. 07 dex. However, note that the errors in [Fe/H] can reach up to 0. 4 dex for the most metal-rich stars but can be lower than 0. 1 dex for metalpoor stars and are mostly driven by T eff uncertainties. These errors are consistent with those reported in  Hill et al. (2011)  and  Zoccali et al. (2008)  for similar datasets and are also in agreement with the differences seen in Fig. 2. Furthermore, the larger errors in stellar parameters towards the high metallicity regime are expected. Extensive analysis of errors in derivation of stellar parameters was presented in Smiljanic et al. (  2014 ) in the context of the Gaia-ESO survey and showed how the errors become larger at high metallicities due to the increase of line blends.
Alpha element abundances In order to measure α-element abundances for all the target stars we adopt the same procedure and line lists used in  Gonzalez et al. (2011). As a first step, the corresponding ATLAS9 model atmosphere  (Castelli & Kurucz 2004 ) is generated using the stellar parameters found by GALA for each star. This model is then fed to  MOOG (v. Feb2013, Sneden 1973) , in order to generate a synthetic spectrum. The synthetic spectrum is iteratively compared with the observed one after a local normalisation of the continuum, by varying only the elemental abundance of interest in each iteration until the best fitting abundance if found. Based on similar dataset -same setup, resolution and similar signal-to-noise -  Gonzalez et al. (2011)  showed that among all the α-elements measurable in the observed spectral range Mg has the smallest scatter.
Also, in this study we are restricted to inner Bulge fields in which we have a narrow target selection box in the colour-magnitude diagram. For this reason, the contamination by the thin/thick disc and halo is negligible here (components D, F, and E in  Ness et al. 2013a). It is worth stressing that here the use of two Gaussian distributions is not necessarily meant to describe the actual shape of the metallicity distribution of each of the two components. It represents the attempt to perform the best possible parametrisation of the observed global metallicity distribution, so we can therefore investigate how its shape changes across the fields. We perform a least-squares Gaussian fit to the global metallicity distribution using the IDL routine XGAUSSFIT  (Lindler 2001). The code automatically fits the main Gaussian component, and additional components can be then included by specifying an initial guess for the mean, peak, and sigma value.
Acknowledgements. We are grateful for the useful comments received from an anonymous referee. We warmly thank the  ESO Paranal Observatory  staff for performing the observations for this programme. MZ and DM acknowledge funding from the  BASAL CATA  through grant  PFB-06 , and the  Chilean Ministry of Economy  through  ICM grant  to the  Millennium Instritute of Astrophysics. MZ acknowledge support by  Proyecto Fondecyt Regular   1150345. DM acknowledge support by  FONDECYT  No. 1130196.
5 ν aA )(ν AA + 0. 5 ν aA ). Visualization An intuitive first step in analyzing the transient behavior of a Markov chain model is a diagnostic visualization of the transition matrix. By summarizing results in an accessible way, the resulting diagram may ideally also provide a basis for direct biological interpretation. With one exception (landscape plot), all the following visualization methods are available using the functions histogrid, histo3d and networkplot (with its support function percolation) in the mamoth module; an example for the runtime of each method is given in Additional file 1.
In contrast, the property that one-step transitions are possible between all states is deliberately given up. The sparse approximation algorithm is available as the appromatrix function in the mamoth module. Both the efficiency, i. e. the density or memory use of the resulting matrix, and the bias vary according to the value of s and the distribution of values in the original matrix. If s is low or the probability distribution in the column is far from uniform, more values will be discarded (compare Fig. 3 ). An appropriate value for s has to be determined heuristically by testing successively increasing values, up to the point where the bias due to the approximation no longer interferes with the interpretability of the model results. The sum of the differences between the entries of the approximate and original matrices has a theoretical upper limit of (1s) • |S|, but the effect of this perturba- tion on the model output may be more complex.
Conclusion We described and evaluated a set of tools, implemented in the Python module mamoth, for working with staterich Markov chain models in population genetics. These tools ease the interpretation of model behavior by providing diagnostic visualizations of transition matrices, and allow substituting dense transition matrices with a sparse counterpart by applying an iterative approximation algorithm that is independent of model symmetry. Thus, our methods permit an advanced analysis of increasingly complex Markov chain models in population genetics, without giving up their space and time discrete structure. They may therefore contribute e. g. to the study of the population genetic consequences of partially clonal reproduction.
Availability and requirements The methods we described can be easily implemented in any scientific programming environment; we provide sample code for Python for all methods which do not rely on the specific state definitions of our model example. Project name: mamoth. Project home page: http://www6. rennes. inra. fr/ igepp_eng/Productions/Software Operating system(s): Platform independent. Programming language: Python. Other requirements: Python 2. 7 or 3. 4 and higher, extension modules numpy/scipy, matplotlib and networkx (  [44] [45] [46] ). License: GNU public license, version 2 (GPL2). Any restrictions to use by non-academics: see GPL2 license.
Acknowledgements We thank  Jürgen Angst ,  Sophie Arnaud-Haond ,  Sina Brunsch ,  Florent Malrieu ,  Romuald Rouger  and  François Timon  for constructive discussions, and all reviewers for their helpful criticism. This study is part of the  CLONIX  project ( ANR-11-BSV7-007 ) financed by the  French National Research Agency. Katja Reichel  receives a PhD grant by the  Région Bretagne and the division "Plant Health and Environment" of the French National Institute of Agricultural Research (INRA) , and would like to thank her supervisors  Solenn Stoeckel  and  Jean-Christophe Simon.
Of course, the observed correlations do not reveal the presence nor direction of causality, but at the very least these studies indicate a strong relation between communication patterns and socio-economic development, which can be leveraged for the construction of real-time indicators. The plurality of the mentioned studies focus on developed countries, perhaps due to the availability of extensive economic data. It is thus unclear whether existing findings can be generalized to developing nations. Here we investigate whether mobile communication data can fill the gaps in economic data of low-income countries, which may allow researchers and governments to study regional economies using existing mobile infrastructure. Leveraging the mobile phone usage dataset provided by the France Telecom-Orange as a part of the Data for Development challenge in , we study how the structure of a nation's social communication networks relates to its economic development, focusing on Côte d'Ivoire.
Network construction and terminology We construct mobile communication networks between locations. Let A = {a  , a  ,. , a n } denotes all antennae, R = {r  , r  ,. , r m } denotes all regions, D = {d  , d  ,. , d l } denotes all departments, and P = {p  , p  ,. , p k } denotes all development poles in Côte d'Ivoire. As we mentioned before, |A| = ,, |R| = , |D| =  and |P| = . We construct two types of networks based on calls and trajectories on four different scales: antennae, departments, regions, and poles. Call networks: The Dataset  contains the number (and duration) of calls between any pair of antennae for every hour during a five month period. We construct three types of networks: () G n a = (A, N a ) is a directed weighted network where N a = {n(a i , a j )} denotes the total number of calls between a i and a j ; () G d a = (A, D a ) is also a directed weighted network where D a = {d(a i , a j )} denotes the total duration of calls between a i and a j.
Mobility networks: The Dataset  contains individual movement trajectories, approximated by the geographic location of the cell phone antennae during calls. We define a movement trajectory of a user u i during a period of time as a sequence of antennae that his/her mobile phone connected to: S(u i ) = {a s  , a s  ,. , a s n }. Then for each pair (a s i , a s i+ ) we build an edge from a s i to a s i+ if a s i = a s i+ and use w(a s i , a s i+ ) to count the frequency of such movements in S(u i ). Finally, we obtain a weighted directed trajectory record network G t a = (A, T a ), where T a = u∈U w(a s i , a s i+ ) denotes the collective trajectories of all users U = {u  , u  ,. , u z }. There may be overlapping users, but it does not likely affect our collective trajectory, since , users are randomly sampled for each fortnight period during the whole five month period.
Discussion We analyze the mobile phone call logs and human mobility traces from a large-scale mobile dataset collected in Côte d'Ivoire with the aim to further understand the country's economic development. First, we develop several network indicators from call and mobility networks, and compare them to economic indicators. We found that CallRank informs us about the importance of regions, and the relative frequency of initiating calls to other areas (i. e. outgoing call ratio) consistently correlates with local economic statistics like low poverty rate and high annual income. Our research implies that features derived from mobile phone data may be useful in measuring and predicting economic development, thereby complementing scarce economic statistics in developing countries. Second, we identify regional communities from mobile communication and mobility graphs.
poor areas of the North and West. 2 Materials and methods 2. 1 Dataset rich ones than expected. A rich-club is distributed across the South and South-West areas of Côte d'Ivoire, which mainly communicate with each other, and not as much as expected with other Our call log data is provided by France Telecom-Orange through the Data for Develop- ment (DD) Challenge b in . Orange has about five million customers in Côte d'Ivoire, which is about one third of its mobile subscriptions, a significant sample of mobile users in Côte d'Ivoire. The data consists of anonymous metadata pertaining to .  billion calls and SMS exchanges that took place from December ,  to April , . In this study, we use two mobile phone datasets including the hourly antenna-to-antenna traffic and in- dividual trajectories for , customers for two weeks with a spatial resolution down to the individual antenna.
In addition, we use the geographical information (latitudes and longitudes) of the antennae and sub-prefectures. The dataset pertains to calls between , cell towers that are distributed across  de- partments (see Figure  (left); the color in the graph has no special meaning). Based on regional economic levels, the entire nation is divided into  development poles: Centre- North, Centre-West, North-East, North, West, South, South-West, Centre, Centre-East, and North-West []. Figure  (right) shows a poverty map at the level of development poles, with darker colors indicating higher levels of poverty. As shown, economic development • We identify regional communities based on mobile communication activities, and compare them to administrative boundaries. At the country level, we find that adjacent regions form communities, as found in previous studies [-].
Results A diagram of the allosteric model of calmodulin with hemiconcerted conformational transitions is shown in Figure  4. Our model was built in three steps. First, we parameterised a model of TR2C, (isolated C-lobe), then parameterised a model of TR1C (isolated N-lobe), and finally the two submodels were merged into a model of intact calmodulin. (see Methods). The model of intact calmodulin (in SBML format) was deposited in BioModels Database  [47]  and assigned the identifier MODEL1405060000.
Choice of experimental data for parameter fitting and validation Several published steady-state titration curves described how calmodulin and TR2C fractional saturation at varying concentrations of free calcium change in the presence of different peptides that mimic the consensus motif of several in vivo binding partners of calmodulin. Published datasets were digitised using the freely available PlotDigitizer program. The datasets of Bayley et al. [7]  and Shea et al. [12]  were of particular interest because they reported the affinity for the peptides both in the absence and at saturating concentrations of calcium. In an allosteric perspective, these data provide estimates of the intrinsic affinity of the peptide for the T-state (predominant in calcium-free calmodulin) and Rstate (predominant in calcium-saturated calmodulin).
These affinities were thus known quantities in our model. The properties of the peptides used in the above mentioned experiments are summarised in Table  1. It must be noted that none of the experimental datasets in the literature reported error bars or standard deviations for the measured datapoints, even when the datapoints were averages of measurements performed in triplicate  [12]. In reality, all plotted datapoints are affected by uncertainty both on the x (free calcium concentration) and y axis (saturation) although free calcium concentration is usually known to high precision  [6].
Fitting procedure and choice of constraints on the parameter space When choosing the initial conditions for the fitting procedure we assumed that our model must account for a wide range of observed behaviors. An initial exploratory study on different possible parameterizations was performed using the analytical MWC model described in the previous paragraphs, using custom Python scripts. These preliminary results were used to formulate additional constraints on the parametric space that the computational model was allowed to sample fitted to the experimental data describing the saturation curve of TR2C in the presence of targets. In the MWC framework, the apparent ligand affinity is produced by a mixture of two distinct populations of molecules, in the R-state and T-state. Target peptides shift the saturation curve by differentially stabilising the R and T state.
With the addition of targets, combinatorial explosion implies that the number of equations in the kinetic model quickly increases to more than one thousand. Manual modification of such models and inclusion of additional targets would be error-prone and labour-intensive. On the other hand, the interaction rules underlying the model are simple, and the complexity is purely combinatorial, which means the model lends itself to be generated iteratively. Taking advantage of the existing libSBML library, we wrote custom Python scripts to automatically generate allosteric models of calmodulin in the presence of targets, in the SBML format. SBML is natively supported by COPASI, thus making the setup process of different kinetic models both faster and more reliable.
Parameters for the model of intact calmodulin The generic names "tbp" and "rbp" were used to desctibe peptides that exhibit a tighter binding for the T and R state, respectively. The reactions of the model of intact calmodulin contains over 500 reversible reactions (or more if additional targets are included), therefore they are not listed here for brevity. The SBML model can however be retrived from Biomodels Database (model ID: MODEL1405060000).
Then, we use some scripts that we developed to calculate the connectivity probability. For urban scenario we use formula of connectivity probability (  9 ) and  (10)  proposed in  [22]. Due to lack of space, the formula for a highway scenario is not described in this paper; but the reader can refer to  [30]  for more details. 𝑝𝑢 = 1 𝑖𝑓 0 ≤ 𝐿 < 𝑟 -𝜆𝑒 -𝜆𝑟 𝐿 -𝑖𝑟 𝑖 𝑖! 𝐿 𝑟 𝑖=0 -𝑒 -𝜆𝑟 -𝜆𝑒 -𝜆𝑟 𝐿 -𝑖 + 1 𝑟 𝑖 𝑖! 𝑖𝑓 𝐿 ≥ 𝑟 𝐿 𝑟 -1 𝑖=0 (9) 𝑝𝑙 = -𝜆𝑒 -𝜆𝑟 𝐿 -𝑖 -1 𝑟 𝑖 𝑖! 𝐿 𝑟 +1 𝑖=0 -𝑒 -𝜆𝑟 -𝜆𝑒 -𝜆𝑟 𝐿 -𝑖𝑟 𝑖 𝑖! 𝐿 𝑟 𝑖=0 ( 10 ) Where pu is the probability that there is a sequence of connected nodes in the road segment, pl is the sufficient condition to have a distance between two consecutive vehicles at most equal to the radio range r. is the vehicle arrival rate and L is the road segment length. After that, we choose the segment with lower connectivity probability as best candidate locations.
V. RESULTS AND PERFORMANCE We develop automatic scripts in order to generate different maps, traffic scenarios and to extract density and speed information in each road segment using traffic simulator  [29] , awk, shell and xml. The density changes over the time and from one road segment to another. Then, we develop Dijkstra and genetic algorithms and the different adaptations inside evolutionary framework proposed in  [33]  using c++. We run several tests with different scenarios for both low and high density. We carry out all the performance tests on virtual machine with Intel i5 64Bits processor and 3 Gb. of memory. In Table  II  we present the parameters used in our tests and we summarize the optimization results in Table  III , IV and V. To evaluate our solution's performances, we choose three criteria: (i) the delivery delay required to send messages from any road segment toward the nearest RSUs, (ii) the number of necessary RSUs to guarantee applications delay requirements and (iii) the computation performance in term of number of iterations to find best solutions.