dataesr/scientific-paragraphs-categorization huggingface dataset:
PCA was first performed to reduce noise and dimensionality. Each movement was subsequently represented by the fourteen first principal components of the PCA out of 53 components (representing all together about 93% of the variance), to which the total duration of the movement was added. Multiclass categorization was implemented in two steps: a series of two subsequent SVM classifiers with linear kernel was applied for automatic categorization of movements: the first SVM classifier discriminated slow forward swims vs. turns and escapes, and if necessary a second SVM discriminated between a routine turn and an escape. We used two distinct datasets from WT 5-7 dpf larvae, one for learning the three maneuver types (five videos, n = 201 movements) and one for testing their recognition (three videos, n = 189 movements).
ACKNOWLEDGMENTS We thank  Dr. Stanley Durleman ,  Dr. Lionel Moisan ,  Dr. Carlos Pantoja , and  Claire Oldfield  for critical reading of the manuscript. We thank  Dr. Olivier Colliot , and  Drs. Stanley Durleman  and  Lionel Moisan  again for advice on machine learning, tracking, and Markov chain analysis. We thank  Caroline Vidal ,  Philippe Domineaux , and  Antoine Mongay  for data server access. We thank  Laura Garnero ,  Audrey Baradel ,  Lydia Djenoune ,  Victor Adenis ,  Devika Nair , and  Maxime Gaudin  for technical assistance and fish care. We thank  Dr. Herwig Baier  for kindly sharing the atoh7 mutant line. This work received critical support from the  Institut du Cerveau et de la Moelle Epinière (ICM/CR) , the  Ecole des Neurosciences de Paris (ENP) , the  Fondation Bettencourt Schueller ,  Mr.
Pierre Belle , the  City of Paris Emergence program , the  ATIP-AVENIR program  from  INSERM  and  CNRS , the  Fyssen Foundation , the  International Reintegration Grant  from  Marie   Curie Actions Framework Program 6 , and the  European Research Council (ERC)  starter grant " OptoLoco. " Jenna R. Sternberg  is affiliated with the graduate program of the  Ecole des Neurosciences de Paris (ENP)  associated with the ED3C. Olivier Mirat is funded by a  DIGITEO PhD fellowship  associated with the  Ecole Doctorale Interdisciplinaire Européenne Frontières  du vivant ED474 program doctoral Bettencourt.
Then, the muonic component of the vertical showers induced by protons of 1, 10 and 100 TeV, simulated with GEANT4, is Published by Copernicus Publications on behalf of the European Geosciences Union. compared with the muonic component of similar showers simulated with CORSIKA  (Heck et al. , 1998). CORSIKA is a dedicated software for the simulation of atmospheric showers over a very large energy spectrum, widely used in cosmic ray experiments.
Simulation of air showers using GEANT4 GEANT4 is a widely used simulation software, designed to simulate the interaction of particles with matter in a fully customizable environment. The advantage of this program in the context of muon tomography is the possibility to describe in the same simulation framework the development of air showers, the interactions of the muons in the volcano, and finally  their detection. The full program chain is currently in development within the TOMUVOL collaboration, and we focus here on the part dealing with the development of air showers. The present simulation setup consists of an atmosphere modelised with 1960 spherical layers of air, with temperature and density set according to the mid-latitude winter model  (Kneizys et al. , 1996). This data model is also currently used in CORSIKA.
geosci-instrum-method-data-syst. net/2/11/2013/ Geosci. Instrum. Method. Data Syst. , 2, 11-15, 2013 10 TeV incident protons is given in Fig. 1  for all the different production channels active in our physics list. As a very general observation, one can see that the relative contributions from the different mother particles are compatible with the description of the formation of the atmospheric muons flux made in the previous section. Before extending the simulation to the generation of a cosmic ray flux which includes all the nuclear species and covering a wide energy range and the full sky aperture, a more accurate check of the generated muon fluxes is needed. In order to do so, the code was tested by comparing its results with those from another simulation software, CORSIKA.
Comparison with CORSIKA results CORSIKA is a program specifically designed for the simulation of air showers, commonly used in cosmic ray physics, maintained and improved for decades as new experimental data on air showers was obtained. Therefore, the results provided by this program will be considered as a reference in this study. In order to validate our GEANT4 code with respect to the simulations of CORSIKA, the atmospheric muons fluxes recorded at an altitude of 870 m (a. s. l. ) in both programs were compared. The GEANT4 data which have been used corresponds to vertical proton primary particles of energies 1, 10 and 100 TeV. In CORSIKA we simulated a realistic cosmic ray spectrum, over the full energy and zenith angle ranges and including also Helium nuclei. To have a meaningful comparison with the GEANT4 simulation, we restricted the CORSIKA sample to proton-initiated showers with zenith angles less than 10 • and energies within 10 % of the corresponding energy of the GEANT4 showers.
Note that a cut on the energy of the muons, E µ > 10 GeV, was applied, so the effects of the Earth's magnetic field, not yet included in our GEANT4 code but present in CORSIKA, could be neglected. The muons at z = 870 m were characterised by their energy and their spatial and arrival time distributions within the shower. The energy spectra of the muons (the number of entries in the histogram is normalised to the total number of muons present in the showers) are shown in Fig. 2  for both GEANT4 simulation and CORSIKA, and the agreement is good. For the spatial distributions, the mean centre of gravity of all the muons in the shower at the z = 870 m plane was subtracted from the position of each individual muon, such that the normalised spatial distributions shown in Fig. 3  are centred on zero.
Again, there is a reasonable agreement between the GEANT4 and CORSIKA simulations, though CORSIKA displays a slight tendency towards wider high energy showers. The analysis concerning the arrival time profiles of the muons obtained from the two softwares is still ongoing. Indeed, the distributions obtained in CORSIKA and GEANT4 after performing our analysis are not in agreement with each other and are both showing unexpected features. This effect needs to be investigated because the number of accidental coincidences induced by the showers in the muon telescope depends on the time distribution of the particles reaching the detection level.
Conclusions The first step toward the Monte Carlo study of the atmospheric background in muon tomography is the simulation of the air showers. A choice has been made in TOMUVOL to develop such a program in the framework of the GEANT4 simulation code, since the next stage of this work should involve the simulation of the detection of the particles in the telescope, which GEANT4 is designed for. In order to validate the part of the code related to the shower simulation, the muon fluxes at an altitude of 870 m produced by vertical 1, 10 and 100 TeV primary protons were compared to those from the well-tested CORSIKA air shower simulation code. We found that the lateral and energy profiles of the muons were in relatively good agreement in both codes. The arrival time distributions of the muons that we obtained from GEANT4 and CORSIKA were not in agreement, and the analysis to determine the origin of this effect is still ongoing.
Back Close Full hillslope hydrological models preferential flow is commonly incorporated as enhanced vertical infiltration, rapid slope-parallel flow on the bedrock surface or modification of the saturated permeability function  (Bogaard, 2002; Beckers and Alila, 2004; Kosugi et al. , 2004; Mulungu et al. , 2005; Zehe and Bl öschl, 2004; Zhang et al. , 2006)  without accounting for spatial and temporal variation of the preferential flow paths characteristics. Weiler and McDonnell (2007)  stressed that incorporation of the spatially dynamic nature of preferential flow systems for conceptualization and parameterization of the effect of lateral preferential flow on hillslope hydrology is one of the greatest challenge. In 1999, Van Beek and Van Asch proposed a spatially distributed physically based model coupling hydrological and stability dynamics, developed in the PCRaster environmental modelling software package.
The use of meta-language of PCRaster GIS package provides an expedient way to include and change spatially distributed hydrological and geotechnical parameters. In the subsequent development of the STAR-WARS model  (Van Beek, 2002) , fissure flow was introduced in a simpler manner, allowing a fraction of the surface detention, equal the volume of free pore space (i. e. fissures), to bypass the unsaturated matrix and directly recharge the groundwater. In 2005, Malet et al. applied the STARWARS model to the Super-Sauze landslide using the simple bypass flow scheme representing only shallow bypassing flow without fissure -matrix interaction. Krzeminska et al. (2012b)  included more detailed representation of fissure flow in STARWARS model. Following a dual-permeability approach  (Gerke, 2006; Šim ůnek et al.
Meteorological data The meteorological data (rain intensity, air temperature, incoming short wave radiation and relative humidity), observed at 0. 8 km distance from the landslide, were used to perform model runs. A snowmelt routine based on the degree-day approach was applied. A temperature threshold (T s ) was used to discriminate rainfall from snow fall and a critical temperature (T m ), above which snowmelt occur, was used to govern the melt Figures.
Social coding enables a different experience of software development as the activities and interests of one developer are easily advertized to other developers. Developers can thus track the activities relevant to various projects in one umbrella site. Such a major change in collaborative software development makes an investigation of networkings on social coding sites valuable. Furthermore, project hosting platforms promoting this development paradigm have been thriving, among which GitHub has arguably gained the most momentum. In this paper, we contribute to the body of knowledge on social coding by investigating the network structure of social coding in GitHub. We collect 100,000 projects and 30,000 developers from GitHub, construct developer-developer and project-project relationship graphs, and compute various characteristics of the graphs.
I. INTRODUCTION Recently, developers have witnessed the emergence of platforms for social coding, such as GitHub  1  and Altassian BitBucket  2. These platforms offer unique experiences to developers: they can broadcast their activities and/or listen to the activities of others; they can also investigate and leverage activities occurring in a variety of projects in one umbrella site. The current momentum of social coding sites provides an opportunity for research on the impact of programmer networking in software projects. Recently, Dabbish et al. have investigated, through a series of interviews, the impact of transparency in GitHub  [3]. Such studies are important as they help us to better understand the phenomenon of social coding. A good understanding of the characteristics of GitHub can indeed help researchers and practitioners to gain more of the insights that are needed to design better tools for supporting social coders.
II. GITHUB: A SOCIAL CODING SITE GitHub is a social coding site that uses Git 3 as its distributed revision control and source code management system. It implements a social network where developers are enabled to broadcast their activities to others who are interested and have subscribed to them. GitHub currently hosts over three million projects maintained by over one million registered developers. A given developer can participate in multiple projects and each project may have more than one developer. The GitHub social coding site is a developerfriendly environment integrating many functionalities, including wiki, issue tracking, and code review. Within GitHub, there are pages for developers and pages for projects. An example of a GitHub page 4 related to the user kemitche (Keith Mitchell).
This page includes information on kemitche's repositories (i. e. , projects) and his recent public activities, such as committing code to a repository, opening an issue report, etc. , which are seldom easily visible in other development environments. The page also shows several statistics that are often used on social networking sites, such as the number of other developers following him, the number of projects he is watching, etc. Such transparency is an interesting feature of GitHub and other social coding sites. 3 http://git-scm. com/ 4 https://github. com/kemitche 1 III. METHODOLOGY In this section we describe our methodology for constructing a sample network from GitHub. We also introduce the statistics and the PageRank algorithm that we use for analyzing the network.
This set of projects is typically of a small size. We then just compare the input project with all projects in the set. In a developer-developer network, each node represents a given developer in our dataset. The corresponding graph contains an edge between two vertices when the corresponding developers work together in at least one common project. Similarly to the project-project graph, we associate a weight to each edge taking into account the number of projects where the two relevant developers work together. To build the developer-developer network, we proceed with the same methodology as for the project-project network.
C. PageRank Introduced by Brin and Page, the PageRank algorithm for weighting web pages importance based on their links has gained popularity driven by its use in the Google search engine  [2]. PageRank works in many iterations. In the initial iteration, the algorithm assigns the same PageRank score to all web pages. Then subsequent iterations update these scores: the score of a page p is distributed to the pages that p links to; each linked page receive 1  |Lp| of the score, where L p is the set of pages that p links to. The PageRank score of a web page p at iteration i can thus be computed by the following equation: P R(p, i) = 1 -r T + r q∈Kp P R(q, i -1) |L q | In this equation, r represents the probability that a web surfer would continue to surf (a. k. a. the damping factor), T is the number of web pages in the database of the search engine, K p is the set of web pages that link to p, and L q is the set of web pages that q links to.
A. Dataset and Research Questions Our dataset is comprised of 100,000 projects collected from GitHub using its API  5. From these projects we have sampled 30,000 developers using GitHub's API. Using this dataset, we intend to answer the following research questions: RQ1 How strong are the relationships among projects? RQ2 How strong are the relationships among the developers? RQ3 Which projects are the most influential? RQ4 Which developers are the most influential?.
B. Project-Project Relationship To answer the first research question, we proceed in two steps: first, we compute the number of edges in the projectproject graph. We have found 1,161,522 edges, meaning that 1,161,522 pairs of projects share at least one common developer. Second, we compute the degree of each node, i. e. , the number of edges incident to this node in the projectproject network. Figure  1  shows the degree distribution across the 100,000 projects of our dataset. We find that Figure  1 , which shows a long tail, follows a power law distribution as is the case in most natural processes  [8]. Finally, we measure the diameter of the largest connected component and the average shortest path between sampled project nodes. The diameter is 9 and the average shortest path, 3. 7. These numbers are lower than the findings reported for many real networks  [6] , implying that project networks are actually more interconnected than human networks.
G. Threats to Validity In this preliminary study, we only study a sample of projects and developers in GitHub. In our setting, analyzing the entire dataset is by far too expensive in terms of computing time, memory cost, and hard disk costs. This might cause a bias in the results. Nevertheless, we believe that this threat is limited as we consider a sizeable sample of 100,000 projects. In the future, we plan to mitigate this threat further by including more projects and developers. Another threat to validity is that we consider two developers to be linked as long as they are involved in the same project. We do not differentiate when they work on the project, whether they work on the same code base, and whether they actually work together. Nonetheless, our findings offer initial interesting insights on social coding and its impacts.
VI. CONCLUSION AND FUTURE WORK We have performed an empirical study on a a popular social coding site: GitHub. In this study, we extract information about 100,000 projects from GitHub and analyze both the project-project network and the developer-developer network. Our evaluation results show that distribution graphs in the project-project network generally follows a power law or long tail phenomenon while the developer-developer network generally does not. Nevertheless, we have shown that social coding indeed improves collaboration among developers: this conclusion can be inferred from the small value of the average shortest path in the largest community of developers. In the future, we wish to allocate more resources into extracting and computing statistics for a larger network of millions of developers contributing to a more extensive set of projects in GitHub.
The third and last part analyzes the experimental results obtained by the LOS/NLOS separation algorithm based on the previous constrained solution. The efficiency of the road constraints in this process will be demonstrated. Finally, we will discuss the impact of the separation algorithm and its interest with respect to the localization problem, before and after a final map-aiding, and we will compare this algorithm to a more usual satellite selection based on signal-to-noise ratio (SNR). These comparisons are done from data registered in a real experiment in the center of Paris (France). The duration of this experiment is about 10 minutes and the total length of the circuit is about 2 km in an archetypal European urban center.
Presentation of the NLOS Detection Method The core of the method consists in checking whether a satellite that generates a signal received by the GNSS receiver can be directly viewed (LOS) or not (NLOS) by the receiver antenna. With this aim in mind, one needs the predicted position of the receiver, the predicted positions of the satellites, and the map of the environment in the same reference frame. A 3D city model of the environment is available in a Geographical Information System (GIS) embedded software from BeNomad company  [12] , SIVNav SDK TM (Software Development Kit), which is dedicated to augmented reality, 3D map rendering and vision for robotics. It contains a geometrical description of buildings, roads (actually streets) and sidewalks. The 3D elements that are in the vicinity of the receiver localization are extracted from the database, and a virtual image is returned.
Extraction and image computation are basic functions of the GIS. The availability of this software, on the CityVIP platform, has motivated the use of a virtual image. Geographical data are provided by the French National Geographical Institute (IGN), in the national French plane projection (Lambert 93) plus mean sea level (MSL) altitude (further called: the GIS reference frame). Their accuracy is: 5 cm horizontally and 25 cm vertically in Paris  [13, 14]. The method starts with the computation of a virtual image of each satellite, with a virtual camera located at the antenna center, oriented with the azimuth of the considered satellite, with tilt angles (roll and pitch) set to zero (Figure  1 ). An important parameter of the virtual camera is its focal distance. From an initial value, this is iteratively reduced until the sky is visible on top of the frontal building.
The position of the user is in fact the most critical point in the process. In a first step of this research  [5] , we used our Reference Trajectory Measurement (MRT)  [15]  system to produce the accurate position that feeds our NLOS algorithm. The results we obtained and published were very promising (Figure  2 ), but there was no error on the estimation of the position of the user that might bring an error in the NLOS detection itself, with the risk of a possible divergence. Here, the position of the antenna will come from a CityVIP positioning sub-system that combines data from dead-reckoning sensors and GNSS, under the 3D map constraints. Figure  2  shows the impact of ground-truth-based NLOS detection and exclusion (for 2 receivers U-blox LEA-4T and LEA-6T and for both least square (LS) and extended Kalman filter (EKF) solutions): solid lines (corresponding to NLOS filtered solutions) are always closer to the y-axis than dashed lines (non-filtered solutions), which proves the efficiency of the LOS/NLOS separation algorithm.
Localization Method In the CityVIP project, a localization task has been introduced in order to continuously update an estimation of the configuration of the vehicle in a 3D world together with an accuracy statement. This estimation is iteratively computed by combining 3 data sources (Figure  4 ), each one with a different time scale: 1. GPS localization is obtained by a positioning algorithm at the update rate of the receiver (4 Hz). A complete localization may be scarce in environments with poor satellite visibility, 2. odometric data are generated by the wheel speed sensors of the ABS (Anti-Blocking System). When some data become available, the configuration is updated by using Equation (  5 ), 3. geographic data, 3D polylines modelling the road network, are given upon request but require a map-matching procedure prior to using them for localization.
Experimental Set-Up and Test Data For validation purpose, we use data that have been recorded in an experiment carried out for the final demonstration of the cityVIP project in September 2011 in Paris. The data were collected on 13 September 2011, and two circuits, both with two laps, were performed at respectively 11:48 AM and 12:08 PM (local time). The first lap was used by the CityVIP partners in charge of several image processing localization tasks for their machine learning; afterwards, the second and next laps could be used for evaluation purpose. The distance travelled per lap is around 1 kilometer. The urban environment in the vicinity of the 12 th district city hall is very dense, with high Haussmann style buildings. The experimental vehicle of IFSTTAR/MACS (Monitoring, Assessment, Computational Sciences) is shown in Figure  5.
It has been used for the final demonstration of the CityVIP project in September 2011 and was equipped with: • a CAN (Controller Area Network) bus connection (for the odometry), • a low-cost automotive GPS receiver LEA-6T from U-blox (for raw data and NMEA GGA  [23] , Global Positioning System Fix Data, and GSV, Satellites in view, sequences at 4 Hz) and its patch antenna, • the MRT (Reference Trajectory Measurement) dedicated specific equipment, LANDINS of the IXSEA society, from which the reference trajectory of the present experiment is issued. Its accuracy is about 10 centimeters, • a Marlin video camera (not used here). Data were logged in real-time using the Aroccam multithread software architecture  [24]  and processed off-line in "replay" mode. The minimum system requirements to perform these tests are: a multi-core processor (quad core at 2.