dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION In this article we have presented Mozaik, a Python-based integrated workflow framework for spiking neuronal simulations. Much of the effort in detailed modeling projects in computational neuroscience is in managing the workflow and moving data and metadata between different simulation, analysis and visualization tools. Mozaik eliminates much of this effort by providing a framework that takes care of most of these low-level details, leaving the modeler to focus on the high-level concepts of models and experiments. Mozaik leverages several broadly used neuroscience Python tools, thus increasing interoperability between current and future tools as well as increasing the possibility of replacing in future some of the Mozaik components with dedicated tools. Long-term, conditional on the continued convergence of neuroscience tools around common standards, the goal is for Mozaik to serve only as a glue between existing dedicated tools.
This lack of standard limits the ease of integration of current neuroscience tools, including Mozaik, as even though tools are able to exchange raw data thanks to common data formats such as those provided by the Neo package, the interpretation of metadata has to still be reconciled on an individual basis. It is the belief of the authors that creation of a common standard capturing the semantics of metadata is the next key technological advance that is required in the neuroscience community to achieve sufficient integration between the various tools. As is clear from its architecture, Mozaik is a package that will be continuously developed and extended, and great care was put into its design to allow for ease of such incremental development. Specifically, the typical user will probably develop their own model components, stimuli, experimental protocols, analysis and visualization methods.
We hope that users will contribute these additions back to the core Mozaik project, which will thus be able to provide a growing library of such resources. Furthermore, even the core API is likely to be changed in future as some parts of Mozaik will be taken over by dedicated tools, or forked out into independent packages. An example of such planned future changes is the replacement of the Mozaik datastore with dedicated database software, or linking of the Mozaik stimulus interface to one of the existing Python-based stimulation packages used in biological experiments. Finally, one of our short-term plans is the addition of a dedicated GUI for the analysis and visualization steps of the workflow and/or integration of Mozaik with one of the analysis and visualization libraries that are taking shape in the field.
This paper presents an integrated approach to conduct a scenario-based volcanic risk assessment on a variety of exposed assets, such as residential buildings, cultivated areas, network infrastructures or individual strategic buildings. The focus is put on the simulation of scenarios, based on deterministic adverse event input, which are applied to the case study of an effusive eruption on the Mount Cameroon volcano, resulting in the damage estimation of the assets located in the area. The work is based on the recent advances in the field of seismic risk. A software for systemic risk scenario analysis developed within the FP7 project SYNER-G has been adapted to address the issue of volcanic risk. Most significant improvements include the addition of vulnerability models adapted to each kind of exposed element and the possibility to quantify the successive potential damages inflicted by a sequence of adverse events (e.
, 2008) , if a systemic analysis is carried out (i. e. functionality loss assessment of various systems of exposed elements). On the other hand, RiskScape Volcano offers the possibility of carrying out either fully probabilistic risk assessment with event trees (e. g. Neri et al. , 2008; Marzocchi et al. , 2004)  or scenario-based risk assessment, which are commonly used by local authorities for decision-making or mitigation. This software incorporates hazard and vulnerability models for a wide range of volcanic phenomena and the focus is put on critical infrastructures such as lifeline networks or strategic facilities, with less emphasis on residential buildings.
Objective of this study Indeed, in the case of Mount Cameroon, the issue of assessing the potential consequences of drastic adverse events may be less relevant than examining how their succession might affect people and infrastructures, how the crisis can be managed and finally how much reconstruction may cost  (Thierry et al. , 2008). In light of this previous work, the objective of our study is to explore the potential and limitations of risk scenario softwares in supporting disaster risk management at Mount Cameroon, in the scope of the MIAVITA project (7th Framework Programme). To this end, we designed and adapted an object-oriented based software initially developed for seismic risk scenario designs  (Franchin et al. , 2011; Cavalieri et al. , 2012; Franchin and Cavalieri, 2013)  and extended its capabilities to take account of existing damage functions in the field of risk assessment (Sect.
These important structures are treated as single objects, as opposed to regular residential buildings, and their attributes include information about their relative importance before, during and after the crisis. Similarly to the hazard input, a GIS-format map for each type of exposed element is imported into the toolbox environment. In the case of built or cultivated areas, data are projected on a mesh grid composed of a series of cells; a refinement algorithm has been developed by  Cavalieri et al. (2012)  in order to generate variable-sized cells, smaller cells being concentrated around the borders of the polygons. The projection of attributes such as population density or building typologies into each cell is carried out by pondering the respective area of each census polygon within the cell; this means that the attributes are represented and averaged as a proportion of the overall cell size.
Data inventory This case study benefits from a previous study on Mount Cameroon, conducted by  Thierry et al. (2008)  in the frame of the GRINP project  (Thierry et al. , 2006). Extensive inventory field work as well as the use of GIS databases made available by the Ministry of Industry, Mines and Technological Development of Cameroon (MINIMIDT) have led to the identification of the following systems, which are considered in the scenario implementation (see Appendix B): -Built areas: three main structural typologies have been identified (i. e. 62. 8 % of T 1: wooden houses with metal sheet roofs; 33. 1 % of T 2: reinforced-concrete or cinder-block masonry buildings with metal sheet roofs supported by wooden frames; 4. 1 % of T 3: clay brick masonry buildings with metal sheet roofs), but no data are available on the specific proportions of these typologies within each built-area polygon.
This information only exists at the global level and therefore the same proportions are applied to all built areas, as a very rough approximation. Moreover, no information on the number of buildings has been gathered, and therefore the buildings (and the associated losses) are merely represented as percentages of the total built area. Finally, a dataset with the population amount within each built-area polygon is available, allowing us to compute specific population densities (see Fig. 7 ). -Cultivated areas with crop types: they are represented as crop polygons, each one being assigned a specific type (i. e. subsistence farming or industrial plantations producing bananas, heating wood, hevea trees, palm trees or tea). The repartition of different crop types around Mount Cameroon is represented in Fig.
8. -Water supply system: pipelines, water catchments and storage tanks are represented in a GIS database. Demand nodes are also assigned to the end of each network branch that feeds a built area. -Electric power network: medium-voltage power lines and electric substations are modelled. -Road network: only the primary paved road segments are considered, as well as bridges. Some traffic analysis zones (TAZ) are assigned to some nodes that are located in built areas, thus leaving the opportunity to estimate accessibility loss through the computation of origin-destination paths  (Franchin et al. , 2011). -Critical facilities: the locations of strategic buildings are identified and three types are considered (i. e. heathcare centres, decision centres, and law enforcement buildings).
Acknowledgements. The MIAVITA and SYNER-G projects are financed by the  European Commission  under the 7th  Framework Programme for Research and Technological Development, Area "Environment", Activity 6. 1  " Climate Change, Pollution and Risks ". Regarding the first version of the toolbox for seismic risk assessment, the authors gratefully acknowledge the support from the research groups from  University of Roma -La Sapienza  ( Paolo Franchin  and  Francesco Cavalieri ) and  Karlsruhe Institute of Technology (Bijan Khazai ). Finally, the authors are grateful to  Olivier Sedan  (BRGM) and  Richard Thevenot  ( ESGT ) for actively participating in defining the requirements for the Mount Cameroun application. Edited by: A. Costa Reviewed by:  A. Felpeto ,  S. Jenkins , and  G. Zuccaro.
iop. org/JNE/10/046019/mmedia)). The analogue response of each sensor had an intensity proportional to the indentation level, with a high signal-to-noise ratio (50 dB), and a Gaussian-shaped profile with a peak amplitude of 200 ± 3. 3 (mean ± std) femtoFarads (fF) and a width of 2. 5 ± 0. 044 mm. The acquisition frequency of each capacitive pad was 20 Hz. The dataset of analogue responses collected through these experiments served as a basis for developing a simulated version of the artificial touch sensor. A family of 24 noisy Gaussian kernels sampled the input space based on the spatial location distribution of the real array. Each kernel modelled the response profile of an individual sensor, with white noise added to the amplitude and width of the response, 2. 5 fF and 0. 1 mm respectively.
Acknowledgments This work was granted by the  EC  Project  SENSOPAC  (no. IST-028056-IP ), the  UPMC  Project Emergence 2011 (no. EME1114 ) and the  Délégation Générale de l'Armement (DGA). The authors thank the  Consorzio interuniversitario per le Applicazioni di Supercalcolo Per Università e Ricerca (CASPUR Consortium , www. caspur. it)for providing the high performance computing facilities.
The consistency of our method is described in Section 3. In Section 4, we present the results of a series of simulations, in which we used our method to consider several different models and compare the results with those produced by the k-means algorithm, MCLUST and DBSCAN. Using a synthetic data set, we also compared the tree structures produced by CART (using the training sample, with the labels) and CUBT, considering the same sample in each case without the labels. A set of real data is analysed in Section 5, and our concluding remarks are made in Section 6. All proofs are given in the Appendix.
Clustering a la CART We begin by establishing some notation. Let X ∈ R p be a random p-dimensional real vector with coordinates X(j), j = 1,. , p, such that E(∥X∥ 2 ) < ∞. The data consist in n random independent and identically distributed realisations of X, Ξ = {X 1 ,. , X n }. For the population version the space is R p , while for the empirical version the space is Ξ. We denote the nodes of the tree by t. Each node t determines a subset of R p , t ⊂ R p. We assign the whole space to the root node. Even though our procedure uses the same general approach as CART in many respects, two main differences should be stressed. First, because we use unsupervised classification, only information about the observations without labels is available. Thus the splitting criterion cannot make use of the labels, as it can in CART.
For the empirical version of the algorithm we replace α t by αt and ∆(t, j, a) by ∆(t, j, a) = R(t)-R(t l )-R(t r ), and denote by minsize = [τ n] (the minimum size of a node). minsize and mindev are tuning parameters that must be supplied by the user. The uniqueness problem also appears on the empirical version, and it can be treated as in the population algorithm. When the algorithm stops, a label is assigned to each leaf (terminal node), and we then call the actual tree the maximal tree. At this point, we have obtained a partition of the space and in consequence a partition of the data set, in which each leaf is associated with a cluster. Ideally, this tree has at least the same number of clusters as the population, although in practice it may have too many clusters, and then an agglomerative stage must be applied as in CART.
A comparison between CART and CUBT In the following simple example, we compare the tree structure obtained by CART using the complete training sample (observations plus the group labels) with that obtained by CUBT considering only the training sample without the labels. We generated three sub-populations in a two-dimensional variable space. The underlying distribution of the vector X =(X 1 , X 2 ) is a bivariate normal distribution in which the variables X 1 and X 2 are independent, with their distributions for the three groups being given by X 1 ∼ N (0, 0. 03) , N (2, 0. 03) , N (1, 0. 25), X 2 ∼ N (0, 0. 25) , N (1, 0. 25) , N (2. 5, 0. 03). The data were then rotated through π/4. One of the difficulties was that the optimal partitions are not parallel to the axis. Figure ?? shows the partitions obtained using CART and CUBT, for a single sample of size 300.
A real data example. European Jobs In the following example, the data set describes the percentage of workers employed in different sectors of economic activity for a number of European countries in the year 1979 (this data set can be obtained on the website http://lib. stat. cmu. edu/DASL/Datafiles /EuropeanJobs. html). The categories are: agriculture (A), mining (M), manufacturing (MA), power supplies industries (P), construction (C), service industries (SI), finance (F), social and personal services (S) and transportation and communication (T). It is important to note that these data were collected during the cold war. The aim was to allocate the observations to different clusters, but the number of clusters is unknown. We must therefore study the data structure for a range of different numbers of clusters.
Concluding Remarks We have herein presented a new clustering method called CUBT which shares some ideas with the well known classification and regression trees, defining clusters in terms of binary rules over the original variables. Like CART, our method may be very attractive and useful in a number of practical applications. Because the tree structure makes use of the original variables, it helps to determine those variables that are important in the conformation of the clusters. Moreover, the tree allows the classification of new observations. In our approach, a binary tree is obtained in three stages. In the first stage, the sample is split into two sub-samples, thereby reducing the heterogeneity of the data within the new sub-samples according to the objective function R(•). The procedure is then applied recursively to each sub-sample.
Results for the generalized Petersen graphs Our goal is to study the "phase diagram" of the Q-state Potts model on the generalized Petersen graphs G(nk, k) in the (Q, v) plane. From a physical point of view, this is the most natural approach to the problem. We will focus on the antiferromagnetic/unphysical region v < 0, as we are interested in studying a possible BK phase in this model. To study the partition-function zeros in this plane, we took several lines: Q = -pv, v = -pQ (with p = 1, 2, 3), and v = -1, -2,. , -6. The line v = -Q (resp. v = -1) corresponds to the flow-polynomial (resp. chromatic-polynomial) subspace. Along these lines, the two-variable partition function Z G(nk,k) (Q, v) reduces to a single-variable polynomial with integer coefficients. We then find its zeros by using the program MPSolve by Bini and Fiorentino  [14, 15].
Acknowledgments We thank  Gordon Royle  for suggesting us to study the generalized Petersen graphs, and  Alan Sokal  for many illuminating discussions. Both authors also thank the  Isaac Newton Institute for Mathematical Sciences, University of Cambridge , for hospitality during the programme on Combinatorics and  Statistical Mechanics  ( January-June 2008 ), where this project started. J. S. also warmly thanks the  École Normale Supérieure  for hospitality in June 2009, June 2011, and December 2012. The research of J. L. J. was supported in part by the  Agence Nationale de la Recherche  (grant  ANR-10-BLAN-0414: DIME ) and the  Institut Universitaire de France. The research of J. S. was supported in part by  Spanish MINECO  grants  FIS2012-34379  and  MTM2011-24097 , and by  US National Science Foundation  grant  PHY-0424082.
The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim.
Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.
The efficiency of the algorithm (which does not require to compute the state transition graph) makes this tool particularly useful when dealing with large logical models. However, other means are needed to assess the reachability of the stable states from specific initial states, or yet to identify cyclic attractors. Deeper dynamical analyses imply the computation of the state transition graph. GINsim user can define a set of initial states and an updating strategy; the software then computes the state transition graph, highlighting stable states and cyclic attractors. GINsim further eases the definition of perturbations, which are simulated by forcing the level of a subset of components to fixed values (or value intervals). For instance, in the Boolean case, we can reproduce a loss-of-function by setting a component to 0, whatever the levels of its regulators, whereas a gain-of-function can be simulated by forcing the corresponding component to 1.
Model reduction To deal with large models, GINsim enables their reduction by ''hiding'' selected components  [63]. In practice, the user selects the components to hide, and the software hides each of them iteratively, while recomputing the logical rules of their targets. Provided that no functional regulatory circuit is eliminated in the process, this reduction preserves all attractors. For example, the stable states are all conserved, in the sense that each stable state of the reduced model is the projection (on the reduced state space) of a stable state of the original model  [63]. This tool is particularly useful when the high dimensionality impedes the computation of the full STG.
MAPK reaction map Based on published data, we have built and annotated a comprehensive reaction map using CellDesigner (supplementary Dataset S1). This map encompasses 232 species (proteins, genes, complexes, other molecules) and 167 reactions involved in the three most extensively documented MAPK cascades (ERK, JNK, p38). The CellDesigner version of the map (XML format) is provided as supplementary Dataset S2. The MAPK map has been further integrated into the Atlas of Cancer Signalling Networks developed by the group of Emmanuel Barillot at Institut Curie in Paris (https://acsn. curie. fr), where it can be explored using a web browser. Our reaction map takes into account signals propagating from different major stimuli, such as growth factors, cytokines, stress, which lead to the activation of MAPKs and their downstream targets.
We considered information concerning different human and mouse cell types, implying that the MAPK map should be considered as generic. Indeed, at this stage, information is lacking to build a detailed map based exclusively on data for a specific cell type. However, we selected biological events explicitly considered to be cell type independent. When applicable, information concerning cell types is provided through links to relevant database entries (mainly PubMed). Because the precision of the information retrieved from the literature varies, our map represents different pathways with different levels of details. For instance, we could find detailed information about the scaffold proteins intervening in the ERK cascade and on the sub-cellular localisation of the corresponding protein complexes; in contrast, such information is still largely lacking for the JNK and p38 cascades.
Altogether, we defined three different reduced model versions, whose component lists are reported in supplementary Table  S3. These definitions are enclosed in the comprehensive model file (supplementary Dataset S4) and enable the generation of reduced versions according to simulation needs. Figure  3  shows the regulatory graph corresponding to one reduced version, namely, ''red1''. Supplementary Dataset S3 lists the simulations performed for each reduced version. The three model reductions were found equivalent in terms of attractors and main dynamical properties. Briefly, reduction ''red1'' was used to obtain the results discussed in the sections ''Coherence with well established bladder cancer deregulations'' and ''Predictions generated with the MAPK logical model'', while reductions ''red2'' and ''red3'' were used to obtain the results discussed in the section ''Coherence with additional cancer-related facts''.
In practice, the entire process from reaction map construction to model simulations is iterative, requiring several rounds of literature searches and in silico experiments. Whenever the model disagreed with established facts, we went back to the literature to seek complementary information and refined our modelling hypotheses. The reaction map and the logical model where systematically and consistently completed with relevant information during this process. Coherence with well established bladder cancer deregulations. As we build our model around the comparison between EGFR over-expression and FGFR3 activating mutation in bladder cancer, our first simulations were dedicated to the assessment of the model behaviour in these circumstances. Figure  4a  reports a simplified view of the model dynamics following EGFR over-expression, obtained by setting EGFR to 1 throughout the simulation, in the absence of additional stimuli (see supplementary Dataset S3 for precise simulation settings).