dataesr/scientific-paragraphs-categorization huggingface dataset:
Many of these variables are also available during simulations via an on-screen chart recorder. Users can choose to log the fluxes of each ionic species through each of the different transporters individually at the two membranes. All data are logged in *. csv format, which is readable by most spreadsheet programs. For a first review of the OnGuard output, importing and plotting the data using Microsoft Excel is ideal. For more detailed analysis and for generating publication-quality graphs, we use the Systat software package SigmaPlot. Interpreting the outputs then becomes a matter of interrogating the model variables. Just as in vivo, changes in each of these variables -including the various solute concentrations, membrane voltages, cytosolic-free [Ca 2+ ] and pH, as well as the rates of ion and solute flux through each of the transportersarise through interactions between the transporters, metabolism and associated buffering characteristics.
S1  of  Wang et al. (2012) ]. OnGuard predicts a substantial inactivation of the inwardrectifying K + current and activation of the outward-rectifying K + current in slac1. The current-voltage curves for each of these currents [see Fig. 4C  and D of  Wang et al. (2012) ] are accessible for export from OnGuard. Simply pause the simulation at the same relative time of day when running with the control, and then with the slac1 parameters, use the 'Modelling' drop-down menu and select 'Dump I:V Curve(s) to Text File' to export these data before importing and plotting either using Excel or another graphical software package. The reduced current at the free-running plasma membrane voltage accounts for the slower K + uptake, despite the effect in hyperpolarizing the membrane of slac1 elimination.
A summary analysis is provided with each of the subsequent figures; further details will be found in  Chen et al. (2012). The full set of model parameters and initializing variables are listed in  Wang et al. (2012)  and are available with the OnGuard software at www. psrg. org. uk. Shown are (A) stomatal aperture, turgor pressure and total guard cell volume, and (B) plasma membrane and tonoplast voltage. then select the 'Transporters' tab. Choose the '4-state Slayman Hx1 in the Plasma Membrane drop-down list, then click 'Modify' immediately below the list entry. [This transporter corresponds to the H + -ATPase as described by a 4-state carrier cycle (visible in the editing window) with reaction constant values that are drawn from analyses of the Chara and Vicia guard cell H + -ATPases  (Blatt, 1987; Blatt et al.
Clearly, these problems can only be addressed satisfactorily through quantitative mathematical modeling, such as the modeling illustrated here. We anticipate that the HoTSig libraries, on which the OnGuard software is built, will find applications in exploring many other cell systems for which there is kinetic detail of transport sufficient to develop truly predictive models. The modular construction of the HoTSig libraries  (Hills et al. , 2012)  means that solute content, volume and turgor can be 'bolted on' to any phenomenological descriptors for virtually any plant cell type.
The nodes of the graph are associated with the overlaps of words from H. The edges are associated to the prefix and suffix relations between overlaps. An originality of our data structure is that pattern H need not be explicitly represented in nodes or leaves. The algorithm relies on the Cartesian product of the overlap graph and the graph of HMM states; this approach is analogous to the automaton approach from JBCB 4: 553-569. The gain in size of SUFPREF data structure leads to significant improvements in space and time complexity compared to existent algorithms. The algorithm SufPref was implemented as a C++ program; the program can be used both as Web-server and a stand alone program for Linux and Windows. The program interface admits special formats to describe probability models of various types (HMM, Bernoulli, Markov); a pattern can be described with a list of words, a PSSM, a degenerate pattern or a word and a number of mismatches.
Notation. x x (x ⊂ x) means that x is a suffix (proper suffix) of x; x x (x ≺ x) means that x is a prefix (proper prefix) of x. The elements of OV (H) that are proper prefixes (respectively suffixes) of a given word are totally ordered. The empty string is the minimal element. The maximal elements are crucial for our algorithms and data structures. ( ) 1 Let P(H) denote the set of all equivalence classes on H. Example 2. Consider the pattern H from the previous example.
, S. Within the traversal we store the probabilities of D-sets related to the nodes on the path from the root of OvGraph to a current node w, i. e. the nodes x from OverlapPrefix(w), in the temporary arrays TempDProb(x, q) of the size S; the size of the data related to a node x on the path is O(|Q| × S), see sub-section "Main loop" below. Then update of auxiliary information stored in nodes of OvGraph, namely, probabilities of R-sets, is performed by a bottom-up traversal of OvGraph using right edges. Computation on the inductive equations relies on a generic procedure, analogous to the forward algorithm for HMM  [40] , see also  [5].
Updatable probabilities related to nodes of overlap graph At the beginning of the n-th stage, for each pair w, q , where w ∈ OV (H) and q ∈ AllState(w) we store a (mlen(w)) × S matrix RProbs(w, q), where RProbs(w, q)[i] [s] = Prob(R(l, s, w), q); l ∈ [k(n, w), n -1] ; s = 1,. , S; i = l mod (m -len(w)). The probabilities were computed at the previous stages. And the values in the matrices are updated at the end of the n-th stage. At the preprocessing stage, we compute the probabilities for n = 1,. , m; s = 1,. , S and q ∈ AllState(w) according to the formulas: Prob(R(m, 1, w), q) = Prob(H(w), q); if n < m or (n = m and s > 1), Prob(R(n, s, w), q) = 0. The global data unrelated to overlap graph Besides the data related to nodes of OvGraph we store the following data. • Transition probabilities.
This parameter describes the "density" of an HMM; the smaller N Trans , the smaller the complexities of the algorithm. The factor λ(G) was studied as a function of N Trans and the number of states N States in used HMMs. We have performed 96 = 4 × 24 series of experiments, 100 experiments in each series. In all series we have used following input data: • the pattern is defined by a PSSM for transcription factor FOXA2 from the database HOCOMOCO  [47]  and cut-off 5. 89 that corresponds to roughly 0. 03% of all words of length 12; • number of occurrences is 10; • text length is 1000. Thus, a series differs from the others only with the used HMMs. Each series is determined by the number N State of states in the HMMs, and the number N Trans , see above. The value N State ranges from 2 to 25, therefore 24 values of N State were considered.
Comparison with the existing algorithms The algorithm SUFPREF implements a P-value computation for HMM and achieves the theoretical complexity of the best algorithms for P-value computation. Notably, the complexities of SUFPREF are consistent with the complexities of algorithms based on finite automata. Our optimization of the data structure provides an improvement for the constant factor. A comparison of the number of nodes of OvGraph and the number of states of a minimal automaton for a given pattern is given in paper  [37]. It was observed in  [46]  that an average number of overlaps (nodes of OvGraph) for random patterns generated according to Bernoulli models is proportional to the number of words in the patterns and is independent of the length of the words. For Bernoulli and first order Markov model cases, we have compared program SUFPREF with the implementation of program AHOPRO  [31].
The program AHOPRO admits only Bernoulli and first order Markov models. The P-values were computed with the following input parameters: (  1  A pattern presented by PSSM and cut-off consists of all words whose score according to PSSM is greater than the cut-off. The cut-offs were precalculated such that the numbers of words matching the PSSM and a cut-off are equal to the fractions of all 12 (14)-mers in range from 0. 00001 to 0. 003. The fractions correspond to the fractions of words in patterns using for transcription factor binding sites (TFBS) prediction. The experiments were performed using a quad-core Intel Core i5 system running at 2. 67 GHz (only one core used) with 8 GB RAM and dual-disk stripped swap partition. Both programs AHOPRO and SUFPREF were compiled using the GCC 4. 5 tool chain for the 64-bit Linux target.
To measure running time and maximum sizes of memory during the program's runs we used POSIX's "getrusage()" function twice: before and after processing to measure data size excluding program code itself. We have slightly modified the source code of AHOPRO to call this function before and after main program execution. We consider the matrices PSSM from the database HOCOMOCO  [47]  describing binding sites of lengths 12 and 14 in human genome for transcription factors FOXA2 and E2F1; the matrices are given in [Additional file 6]. Observe that the P-values computed for both probability models are the same, when the other parameters are identical. The results of the experiments for PSSM-based patterns of length 12 are presented in Tables  1  and 2. The results for other patterns are given in [Additional file  7].
Usage of P-values for TFBS prediction The majority of methods for TFBS prediction firstly search for genome regions with high number of occurrences of a pattern corresponding to needed TFBS. Then the candidate regions have to be chosen following proper criterion of statistical significance  [48, 49]. We have compared predictive abilities of methods using criteria based on P-values for different probability models and a method using criterion based on a number of occurrences. The experiments were performed with human transcription factor FOXA2. We have considered several patterns based on the PSSM of length 12 from the database HOCOMOCO  [47]  and different cut-offs. The best results were obtained for the cut-off 5. 89; about 0. 0003 of all words of length 12 match the PSSM with this cut-off.
The pattern H that is discussed below consists of all words having score exceeding the cut-off and their reverse-complemented words. We have considered the test set of 1800 genome regions of length from 200 to 400; the set consists of 900 "positive" regions and 900 "negative" ones. The positive regions were taken from the database ENCODE  [50]. We have chosen top 900 regions related to human transcription factor FOXA2 having length from 200 to 400 b. p. in accordance with their quality (Signal value). The length distribution of regions is almost uniform; all the regions belong to Top 1000 of the FOXA2-related regions according to their Signal value. The negative regions presumably do not bind FOXA2. They were taken from random places of the first chromosome of human genome, the length of negative regions by construction are uniformly distributed from 200 to 400 b.
A further reduction to the reachable vertices leads to extra improvement of time and space complexity. Despite the fact that Bernoulli and Markov models can be treated as special HMMs, it is worth implementing specialized and optimized versions of software for these models. Indeed, paper  [37]  can be viewed as a meta version of SUFPREF. The peculiarity of the implementation of Markov models of higher orders will be presented in a separate paper. The implementation of the algorithm SUFPREF was compared with the program AHOPRO for a Bernoulli model and a first order Markov model. The comparison shows that, for a majority of cases, our algorithm is faster than AHOPRO in more than ten times for the Bernoulli model and in more than five times for the Markov model. The greatest advantage of SUFPREF is to decrease the needs in space.
Acknowledgements This work was supported by  INRIA  associated team MIGEC,  French-Russian  grant  CARNAGE  and  14-01-93106  from  RFBR. Evgenia Furletova ,  Mikhail Roytberg  and  Victor Yakovlev  were supported by grants  08-01-92496-NCNIL-a ,  09-04-01053 -a,  12-04-00944-a ,  14-01-93106-NCNILa  from  RFBR  and contract  07. 514. 11. 4004  within the  Russian Federation research program   2011-1. 4-514-008-009. Evgenia Furletova also was supported by the grant  14-04-32220-mol_a  from  RFBR.
The program FeynRules is a Mathematica package developed to facilitate the implementation of new physics theories into high-energy physics tools. Starting from a minimal set of information such as the model gauge symmetries, its particle content, parameters and Lagrangian, FeynRules provides all necessary routines to extract automatically from the Lagrangian (that can also be computed semi-automatically for supersymmetric theories) the associated Feynman rules. These can be further exported to several Monte Carlo event generators through dedicated interfaces, as well as translated into a Python library, under the so-called UFO model format, agnostic of the model complexity, especially in terms of Lorentz and/or color structures appearing in the vertices or of number of external legs. In this work, we briefly report on the most recent new features that have been added to FeynRules, including full support for spin-3 2 fermions, a new module allowing for the automated diagonalization of the particle spectrum and a new set of routines dedicated to decay width calculations.
While these tools are ready to be used to simulate any SM process, the situation is however different for BSMs where these models are not yet implemented in the Monte Carlo event generators. The FeynRules package  [3, 4, 5, 6, 7, 8, 9]  has been created with the aim of facilitating this task. Starting by providing basic model information such as its underlying gauge symmetries, field content, parameters and Lagrangian (that can also be semiautomatically derived for supersymmetric theories), FeynRules then extracts all the interaction vertices from the model Lagrangian and exports the associated Feynman rules, through dedicated interfaces, to various high-energy physics tools. Such interfaces exist for the CalcHep  [10, 11] , FeynArts  [12] , MadGraph  [13, 14, 15, 16, 17, 18] , Sherpa  [19, 20]  and Whizard  [21, 22]  programs.
Moreover, the model can also be translated to a Python library under the Universal FeynRules Output (UFO) format  [23]. The UFO has been developed to overcome all restrictions imposed by previously designed model formats for Monte Carlo programs. It indeed allows for arbitrary color and/or Lorentz structures in the interaction vertices and any number of external particles. In this paper, we briefly review some of the new functionalities that will be included in the new version of the package, FeynRules 2. 0, that can already be downloaded from the website http://feynrules. irmp. ucl. ac. be. We first describe the FeynRules program in section 2. Next, we detail the support for spin- 3  2 fields in section 3 and a new module allowing for automated spectrum generation in section 4. Section 5 is dedicated to automated decay width calculations and we finally summarize our work in section 6.
The FeynRules package The FeynRules program is a Mathematica package allowing for extracting Feynman rules from a quantum field theory Lagrangian. Additionaly to the improvements described in this paper, the latest version of FeynRules comes with a set of tools directly connecting the model definition in FeynRules to a series of Monte Carlo programs. Furthermore, it is also possible to store the model information in the form of a Python library, under the so-called Universal FeynRules output (UFO) format. This takes advantage of the object-oriented nature of the Python language and its ability to be interfaced with any other program. Proper FeynRules model files follow a syntax inspired by the FeynArts model format and encompass information about the model gauge symmetries, field content and parameters.
From the input of the Lagrangian describing the dynamics of the model particles, possibly given in a very compact form by means of quantities such as gauge covariant derivatives, (super)field strengh tensors or even user-defined functions, the FeynRules package is able to extract all the Feynman rules associated with the underlying interaction vertices. The latter can then be exported, together with all the relevant model information to several Monte Carlo and symbolic tools via dedicated interfaces. In addition, supersymmetric models can be naturally and easily implemented in a superspace module shipped with the code allowing to declare chiral and vector superfields as any other field and containing a set of methods dedicated to superspace computations. All model publicly available can be found in the database located in the FeynRules website.
Spin-3/2 Rarita-Schwinger fields It is of critical importance for phenomenological purposes to have tools able to handle any new physics models, no matter the structure of the interactions or the particle content. In particular, an automated treatment, starting at the level of FeynRules, for spin-3 2 particles was still missing until very recently. The version 2. 0 of FeynRules fills this gap. The user can now use two new particle classes RW and R to declare two-and four-component spin- 3  2 fields, respectively. In parallel, the UFO conventions have been updated so that they account for the arbitrariness associated with spin- 3  2 propagators, and recent versions of both Monte Carlo event generators CalcHep and MadGraph 5 support spin- 3  2 fields and their interactions  [9]. The superspace module of FeynRules  [6]  has also been upgraded, and computations related to goldstino and gravitino fields are now simplified within the FeynRules framework.
The interactions of the gravitino G and the goldstino χ fields are then given by L χ = i 2 χσ µ ∂ µ χ -∂ µ χσ µ χ + 1 2v 1 χ•∂ µ Jµ + ψ1 •∂ µ Jµ , L G = 1 2 ǫ µνρσ (ψ Gµ σ ρ ∂ σ ψ Gν + ∂ σ ψ Gν σ ρ ψ Gµ ) + N ψ Gµ •J µ + ψ Gµ • Jµ , that can be implemented in FeynRules following standard techniques. The supercurrent J µ α can be derived in FeynRules by calling, in a Mathematica session, the function SuperCurrent[lv,lc,lw,sp,mu] which takes as arguments the Lagrangians lv associated with the gauge sector of the theory, lc associated with its chiral sector and the superpotential Lagrangian lw. Moreover, the supercurrent spin and Lorentz indices are given by sp and mu. As it was shown in Ref. [6] , the different pieces of the supersymmetric Lagrangian can be straightforwardly obtained by means of standard methods of the superspace module of FeynRules.
Automated spectrum generation When implementing in a given Monte Carlo tool a specific model, it is important to pay special attention to the numerical values of the various model parameters. This usually requires to compute and diagonalize mass matrices associated with the different gauge-eigenstates of the model, which often mix. In practice, such calculations cannot be solved analytically and it is necessary to rely on a numerical code. This issue has implied the development of a new module of FeynRules allowing, on the one hand, for the analytical extraction of all the model mass matrices from the Lagrangian, and, on the other hand, to generate a self-contained C++ package, dubbed ASperGe 1  , diagonalizing the mass spectrum and outputting both masses and mixing matrices under a Les Houches compliant format  [8].
WriteASperGe[lagr] and consists in a set of model-dependent and model-independent files stored in a directory whose name is made up from the model name and the suffix _MD. The model-dependent files consist of C++ commands allowing to define the various parameters of the model and the mass matrices to be diagonalized. In contrast, the model-independent files contain all necessary routines allowing ASperGe to compute both the mass eigenvalues and the rotation matrices from the mass matrices on the basis of the QR reduction algorithm as implemented in Gsl. In addition, these files also include methods to store these results in a Les Houches compliant format. Finally, the numerical code can be compiled and executed by either typing RunASperGe[] in the Mathematica session or executing in a shell, after moving to the dicrectory where it is stored, make;.
Automatic calculation of decay widths A careful design of a benchmark scenario for a given model requires, in addition to providing a numerical value for the model external parameters and the calculation of the mixing matrices necessary to diagonalize the mass spectrum, the computation of the particle total widths. Furthermore, the latter must in general be given together with the decay table of all the unstable particles. To this aim, the version 2. 0 of FeynRules includes a series of functions dedicated to width calculations  [24]. The advantages of using FeynRules for such a purpose are twofold. First, the new module calculates the analytical formulas associated to all tree-level 1 → 2 processes, so that the results are independent of any specific benchmark point. Second, the calculations can be performed once for a given model and reused every time the benchmark scenario is modified.
Conclusion The constant and fast evolution in new physics model building activities has triggered the development of programs such as the Mathematica package FeynRules, which aims to facilitate the development of BSM models and their implementation within Monte Carlo tools. The soon-to-be-released 2. 0 version of FeynRules contains several important new features with respect to the previous release, in addition to the already available superspace module, interfaces to the CalcHep, FeynArts, MadGraph, Sherpa and Whizard programs and possibility to translate the full model under the form of a Python library, the so-called UFO format. Among the major new functionalities, we have presented in this report a brief description of two modules, a first one dedicated to the automated computation of decay widths and branching ratios and a second one addressing the extraction of the model mass matrices and their diagonalization.
Acknowledgments We are grateful to the organizers of the ACAT2013 conference for arranging such a nice event. This work has been partially supported by the  Theory-LHC France initiative of the CNRS/IN2P3  and the  French ANR   12 JS05 002 01 BATS@LHC. A. A. has been supported by a  Ph. D. fellowship of the French Ministry for Education and Research. Ce. D. was supported in part by the  U. S. Department of Energy  under Contract No. DE-FG02-13ER42001  and by the  NSF  grant  PHY-0757889 ; N. D. C. was supported in part by the  LHC-TI  under  U. S. National Science Foundation , grant  NSF-PHY-0705682 , by  PITT PACC , and by the  U. S. Department of Energy  under grant No. DE-FG02-95ER40896  and C. D. was supported by the  ERC  grant " IterQCD ".
Introduction Public protection and disaster relief (PPDR) organizations play a major role in disaster preparedness and recovery, assisting the response to emergency events. Typically, first responders that include law enforcement, firefighters, emergency medical personnel, and other organizations are among the first on the scene of an emergency. In large natural disasters, military organizations can also support the response to the disaster. PPDR organizations are increasingly reliant on network infrastructures and services (e. g. , video streaming, picture download, remote database applications) to perform their operational duties. Disaster management includes a set of tasks to reduce the impact and injury to individuals, mitigate the impact of the crisis on assets, provide support to injured civilians, and accelerate the time to recover the conditions of stability before the crisis.