dataesr/scientific-paragraphs-categorization huggingface dataset:
Of course, since all these discussions are to some extent model dependent, they require confirmation by experiments or direct numerical simulation. An important phenomenon that is not found from linear stability analysis either is the formation of layers in the Ledoux stable case. With a non-linear analysis Proctor (1981) was able to show that in a region limited by 1 ≤ R sc ρ ≤ Le -1/2 (2. 7) stable layering has to occur in the semi-convective regime (the upper limit is only approximate). The initial layering process is based on local breaking of gravity waves, often associated with the so-called Kato instability. Fig. 1  shows an advanced stage of layer formation in a simulation of semi-convection with the ANTARES code. A more detailed description of the simulation of such layers with this code is given below in Section 3.
ANTARES The ANTARES code  (Muthsam et al. , 2010 ) is a multipurpose simulation program to solve the hydrodynamical conservation laws numerically in one, two, and three spatial dimensions. An initial state of the system can be evolved in time given various types of boundary conditions. The spatial discretization of advection operators (and in some cases also of pressure gradient operators) is based on the weighted essentially nonoscillatory scheme of 5th order (WENO5) proposed by  Jiang and Shu (1996) , optionally with Marquina flux splitting  (Donat and Marquina, 1996). Diffusion terms are discretized in a compatible way  (Happenhofer et al. , 2013). Time integration is performed using strong stability preserving Runge-Kutta methods  (Shu and Osher, 1988, Kraaijevanger, 1991 , see also  Kupka et al.
, 2012). The code is fully parallelized following the MPI (message passing interface) paradigm (see  Muthsam et al. , 2010  for further details). With this framework implementation of an explicit time integration scheme for the Navier-Stokes equation augmented by an equation for the time evolution of concentration and the conservation laws for mass and energy, i. e. the system (2. 1), was readily possible  (Zaussinger, 2010). As mentioned in Section 2. 1 the Boussinesq approximation is useful for basic explorations of doublediffusive convection. This holds in particular for comparisons with oceanographic cases. In Zaussinger (2010) ANTARES was hence extended to solve the dynamical equations (2. 2)-(2. 3) or, in practice, (2. 3)-(2. 4). This setup differs from the compressible case by requiring the solution of a Poisson equation for the pressure fluctuations which ensures the incompressibility constraint (2.
MITgcm The Massachusetts Institute of Technology general circulation model (MITgcm) is a general purpose gridpoint algorithm that solves the Boussinesq form of the Navier-Stokes equations for an incompressible fluid, here fully non-hydrostatic, with a spatial finite-volume discretization on a curvilinear computational grid (in the present context on a three-dimensional Cartesian grid). The model algorithm is described in  Marshall et al. (1997) ; for online documentation and access to the model code, see MITgcm  Group (2012). The code supports multi-threading and MPI for parallelization and also vector-cpu architectures; it has been shown to scale for order(10 3 ) CPUs  (Hill et al. , 2007; Losch et al. , 2014). Here, we use the MPI parallelization. The MITgcm was originally built for large scale oceanographic and atmospheric applications, but the robust numerics and the non-hydrostatic extension of the solution algorithm (a pressure correction method) allows simulations of small scales with very high grid resolution (e.
Acknowledgements This research has been supported by the  DFG  within the projects " Modelling of diffusive and double-diffusive convection ", projects  KU 1954/3-1  and  LO 1143/3-1 in SPP 1276/1  and  KU 1954/3-2  and  LO 1143/3-2  in  SPP 1276/2  within the interdisciplinary MetStröm project. F. Zaussinger  has also been supported by the  Austrian Science Fund FWF  through project  P20973  (project leader H. J. Muthsam). F. Kupka is grateful for support by the  FWF  through projects  P21742  and  P25229  (project leader). We thank  A. Weiss ,  MPI for Astrophysics, Garching , for providing a stellar evolution model used in this research.
The brain is a large-scale complex network often referred to as the "connectome". Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources.
Overview The main elements of EEGNET are: The data. This file represents either the scalp EEG data or the reconstructed sources. The default file format is the '. mat'. It should be a 3 dimensional matrix (Nc x Ns x Nt) where N c , N s and N t are the channels (brain regions in the case of sources file) number, the sample size and the number of trials (N t is considered 1 for data averaged over trials), respectively. When solving the inverse problem and for visualizing the network at scalp level, the electrode location file is required. In the current EEGNET version, both the. xyz and. mat formats are supported. Some basic preprocessing features are available in the current version of EEGNET. The imported data can be firstly visualized. These signals can be then filtered using a Finite Impulse Response (FIR) linear filter before computing the function connectivity (FC) matrices.
This feature allows users to choose the frequency band where to compute the FC. The data can also resampled by changing the sampling frequency. This feature can be essential in some cases where users attempt to reduce the size of the data. The preprocessing tool allows user also to specify the baseline from the visualized data. This baseline is essential for computing/normalizing the post vs. pre stimulus connectivity for instance or to compute the noise covariance matrix when solving the inverse problem. EEGNET provides also the possibility of computing the time-frequency representation of the data. In the current version, the complex Morlet wavelet is used as it was shown to provide a good compromise between time and frequency resolution  [40] [41] [42]. This time frequency maps can be shown trial by trial in the case of multi-trial data.
The M/EEG data are imported (256 dense EEG signals is this example). The functional connectivity is then computed directly between the scalp signals. Network measures can be extracted from the adjacency matrix and the scalp network can be visualized in an interactive way. On the other hand, the original M/EEG data can be used to estimate the brain sources by solving the inverse problem. Functional connectivity measures can be applied on the reconstructed sources. Graph measures can also be computed and the correspondent cortex network can be visualized. Node's size and color can be used to encode any chosen network measures (their strength for instance) as well as the edges (their weight for instance). doi:10. 1371/journal. pone. 0138297. g001 To characterize the obtained networks, graph theory based analysis was widely used and proved its high performance and usefulness  [31].
Visualization Scalp level. When the user is only interested in scalp networks, the EEG data should be firstly loaded. The functional connectivity is then computed among scalp signals, according to a pairwise procedure. For visualizing the network and computing the network measures, the channel file is required. Users can also directly import their connectivity matrices (computed elsewhere) to compute the network measures and visualizing the network. The channels position is a file with four columns, the first for the node number or label, the next three for x, y and z positions (an example of channel location file is contained within in the Examples folder included in the downloaded EEGNET). This part supports the static and dynamic option. Cortex level. To compute the brain networks at source level from M/EEG data, the inverse problem must be solved.
Technical details EEGNET was developed using MATLAB as programming language with a user-friendly GUI under 64 bit Windows 7 environment, this toolbox has been successfully tested on different operating systems with MATLAB installed, including Windows 7, Linux and Mac OS under 64-bit versions. To facilitate the first use of EEGNET, tutorial and user manual documents are available in the download webpage that also provides the user with some examples for scalp and source networks. It is worth mentioning that EEGNET depends on other software tools. Some of these tools are written in MATLAB such as the BCT toolbox  [31]. Preferably, the cortical surfaces and the scout files may be generated using Freesurfer  [54]  and checked/visualized using Brainstorm  [30].
Discussion To identify networks from M/EEG data, at least four tools are required from loading/preprocessing the EEG data, solving the inverse problem, computing the functional connectivity, computing the network measures to then visualizing the identified networks in interactive way. However, researchers always look for reducing the number of tools they use to accomplish a complete data processing. As a network identification/visualization tool, EEGNET achieves most of these functions. In addition, the interactive analysis/visualization is a crucial part of scientific research. The easy visualization of data can inspire novel hypotheses, help researchers to quickly evaluate their results, and allow for significant quality control. EEGNET has novel interactive visualization features not available in existing software packages for visualization of the connectome such as the ability to interactively threshold networks based on the network measures for instance.
Approaches for data collecting EEGNET provides all the steps from loading the EEG signals to the identification of the brain networks. However different files are needed to accomplish these steps. Here we show some suggestions to how obtain these files. A. The cortical surface. This file is very crucial to the analysis/visualization of the cortex level networks. Using the structural MRI of the participant (or template), FreeSurfer  [54]  can be used to compute all the different cortical parcellation with the correspondent different atlases. Performing all the cortical reconstruction steps, including subcortical segmentation for both hemispheres may take about ~16h for each MRI. The Destrieux and Desikan reconstructions atlases  [45, 46]  can be generated automatically using FreeSurfer and divide the cortical surface into parcels based on macroscopic sulcal and gyral profiles.
), 2D/ 3D connectivity visualization nor network measures (via BCT, based on graph theory). In the latest version of brainstorm, some connectivity measures (with circular visualization) were included. However, it does not implement EEG scalp connectivity visualization or EEG source connectivity 3D visualization. Moreover, network measures were not included in brainstorm. Freesurfer is open-source software widely used by the Neuroimaging community. It was not integrated with EEGNET but can be optionally used to get the cortical surface from MRI in the case where subject-specific data is necessary. The corollary is that users interested in scalp EEG connectivity do not need to use Freesurfer. Users interested in the EEG connectivity at the source level will need to load their own cortical surface or use the template provided in EEGNET.
ii. Connectivity measures. The two tools include totally different family of connectivity measures. The current version of EEGNET provides four different functional connectivity measures, the cross-correlation, the mean phase coherence (MPC), the mutual information (MI) and the Phase Locking Value (PLV) while eConnectome offers methods of functional connectivity based on the multivariate autoregressive mode (mainly Directed transfer function DTF and adaptive DTF). iii. Visualization. EEGNET also provides the possibility of visualizing the network based on the computed metrics. Therefore, the visualization is very different between eConnectome and EEGNET. In EEGNET, the nodes and edges can encode the network measures providing the user with direct and intuitive representation of graph features.
Limitations and future directions Further ways for software improvements may include the use of new visualization approaches or improve the existing ones. For instance, EEGNET will be updated to visualize modular partitions of networks, allowing for comparisons to well-studied brain networks (e. g. , default mode network). The circular view of the brain network used in different tools such as CVU will be also included in EEGNET. Analyzing the dynamics of the identified networks is an important direction of future work, by including algorithms for functional connectivity states for instance  [47]. The current version of EEGNET does not provide all preprocessing features. Different preprocessing modules will be included in the next version of EEGNET such as bad channel/trials and artifact removal  [69].
This work was supported by  AZM  and  SAADE Association (Tripoli, Lebanon)  and the  Rennes University Hospital  ( COREC  Project named conneXion,  2012-14 ). The work has also received a French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the " Investing for the Future" program  under reference  ANR-10-LABX-07-01.
g. from different sensors, models or approaches) so this idea is not new in itself. For example,  Jung et al. (2006)  developed a fuzzy agreement scoring method to determine the synergies between global land cover products for modelling the carbon cycle while  Fritz et al. (2011b)  employed this synergy concept in combination with expert knowledge to rank land cover products in order to combine them into a single cropland map of Africa. Iwao (2011)  integrated the GLC2000, MODIS and the older University of Maryland (UMD) land cover product using a simple majority voting approach and validated the resulting map with data from the Degrees of Confluence project. However, the resulting improvements in accuracy were not statistically significant. More recently,  Yu et al. (2013a)  used a decision tree to combine two 30 m cropland products with a 250 m cropland probability layer to produce a global cropland mask.
, 2012; See et al. , 2014a)  and then used in subsequent research, e. g. Fritz et al. (2013a)  used data from the first campaign to downgrade estimates of land availability for biofuels. This database, which represents a valuable source of data for both training and validation of land cover, continues to grow, with more than 4. 5 million samples collected recently on the presence of cropland using a game version of Geo-Wiki  (See et al. , 2014b). Initial attempts were undertaken by  Comber et al. (2013)  to map the areas of highest correspondence between the Geo-Wiki crowdsourced data and the GLC2000, MODIS and GlobCover for one land cover type, i. e. tree cover, for a section of western Africa. The authors employed crowdsourced data from the first Geo-Wiki competition  (Perger et al. 2012 ) and geographically weighted regression (GWR), which is a spatial extension to linear regression in which the coefficients of the regression equation are able to vary across space, which captures any effects due to location  (Brunsdon et al.
, 1998). The aim of this paper is to extend the research of  Comber et al. (2013)  in a number of ways. Firstly, we apply the method globally to all land cover types using larger amounts of crowdsourced data from four campaigns, including one campaign focussed on data collection in areas where all global land cover maps disagree with one another. Secondly, we implement a second approach in which the method is only applied to those areas where there is complete disagreement between land cover products, taking agreement between two or more land cover products as the land cover type at all other locations. Finally, we use an independent crowdsourced dataset to validate the products, using the sampling scheme of the global validation dataset created by  Zhao et al. (2014)  as the basis for collecting data via Geo-Wiki.
Global Land Cover Maps Three global land cover datasets have been used in the creation of the hybrid land cover map. The first is the GLC2000, which was developed by the Joint Research Centre (JRC) of the European Commission using 14 months of SPOT Vegetation data  (Mayaux et al. , 2006). This mapping effort was divided into regional windows with research teams around the world contributing to this effort. Maps created at the regional level were then harmonised to create a single global product. The exercise was intended to provide baseline information for the environmental year 2000 as a one-off land cover mapping exercise. The GLC2000 has the coarsest resolution of 1km at the equator. The MODIS global land cover product is developed by Boston University using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite.
Crowdsourced Data from Google Earth for Training and Validation Geo-Wiki was originally developed as a way of displaying global land cover maps (GLC2000, MODIS and GlobCover) and layers of spatial land cover disagreement on top of Google Earth. Volunteers were then asked to indicate the quality of the different land cover maps based on comparing the land cover type at a given pixel with the land cover visible from Google Earth  (Fritz et al. , 2009). Since then Geo-Wiki has been used in a number of crowdsourcing campaigns where we have moved away from determining the quality of particular global land cover maps to collecting land cover information from selected samples on the Earth's surface. Figure  1  provides a screenshot from the Geo-Wiki system from the second campaign, which shows a sample pixel drawn on top of Google Earth along with the types of information that we ask volunteers to collect, e.
The spatial distribution of these two training datasets is shown in Figure  2. <Insert Figure  2  here> To collect data at the same locations as the validation data assembled for the Chinese 30 m land cover map  (Zhao et al. , 2014)  Combined with the Wilderness dataset to validate both hybrid maps More details of the validation data are provided in section 2. 4 but the samples from the two competitions (Table  2 ) were purposely chosen to align with the location of the global validation dataset developed by  Zhao et al. (2014)  in their evaluation of the Chinese 30-m global land cover product.
, 2002). Here we used logistic regression to calculate the probability of correspondence between the validation data and the global datasets at each pixel of a 300 m grid according to:   ) ( log ) 1 ( , i i v u i b it y P   (2) where ) 1 (  i y P is the probability that a given global land cover product defines the same land cover type as the validation data at each location i; logit is a logistic regression;   i i v u , is the two-dimensional vector of location i; and   i i v u b , is the intercept. The optimal size of the window in terms of how many validation samples to use in each local instance of logistic regression is first calculated based on the overall number of points and their spatial distribution. The observed outcome is a probability associated with each global land cover product where the highest probability determines which land cover product to choose.
Map Validation In order to estimate the quality of the two hybrid products and to determine if they have achieved a measureable improvement over the existing products (i. e. GLC2000, MODIS and Globcover), it is necessary to validate all of the datasets against a common sample dataset. The validation data set used here was developed from three different sources in an effort to ensure the highest quality. The first was an external dataset that was developed specifically for the validation of the Chinese 30 m global land cover map  (Gong et al. , 2013; Zhao et al. , 2014) , which contains 38,664 sample units globally based on Landsat and Google Earth image interpretation. The sample design utilised equal-area stratified random sampling, which partitioned the global land area into approximately 7,000 equal-area hexagons, inside of which 5 random samples were chosen from each one.
This data set represents the land cover at an x,y location on the Earth's surface. The x,y locations of the validation data from this exercise were then used as the sample that was provided to volunteers in two different Geo-Wiki crowdsourcing campaigns, where volunteers were asked to indicate the percentage of land cover in a 1 km pixel (the pixel was centred on the x,y location). The samples from the two different Geo-Wiki crowdsourcing campaigns were then compared with one another where there was more than one answer provided at each sample pixel. Only those pixels where there was agreement between the two competitions were then kept. Finally, we selected only homogeneous pixels where the land cover type agreed with the x,y location class. This reduced the final validation dataset to a total of 5,096 1km 2 pixels.
Owing to the steps outlined above, a potential bias is present in the remaining sample, i. e. oversampling in the tree cover class. Although this can increase overall accuracies, we are actually more interested in relative performance so we will only judge the products in relation to one another rather than in terms of absolute performance. We have applied the standard validation test of a cross-tabulation matrix with both hybrid products and the three existing products, using the above mentioned validation sample dataset. Initially we converted the observed sample matrix into an estimated unbiased population matrix which represents the entire study area  (Pontius and Millones, 2011). From this we calculated two measures, i. e. the quantity disagreement, q, and the allocation disagreement, a, calculated as follows for each land cover type, g:       J j gj J i ig g x x q 1 1 (2)              gg J j gj gg J i ig g p x p x a 1 1 , min * 2 (3) where J is the total number of categories, xij are the elements of a standard confusion matrix, i refers to the map being evaluated and j is the reference data, in this case the crowdsourced validation data from Geo-Wiki.
Results This study has resulted in the first global hybrid land cover maps obtained with the aid of crowdsourced training data. Figure  4  shows the land cover map chosen at each location based on the highest probability calculated by the GWR for Hybrid Map 1. Probability is assigned to the three datasets as: GLC2000 (33%), MODIS (41%) and GlobCover (25%). While the resulting map is quite heterogeneous, global patterns nonetheless emerge. GLC2000, together with GlobCover, have the highest probability in the far north. This could be explained by the fact that there are missing MODIS data in the northern high latitudes, which can lead to degradation in the performance of the classification algorithm  (Friedl et al. , 2010). The continental US is ranked highest by MODIS. Based on the results demonstrated in Figure  4 , two hybrid maps were derived (Figures  5  and 6 ).