dataesr/scientific-paragraphs-categorization huggingface dataset:
Oceanogr. , 24:6, 508-520. variables. According to these results, overfishing of bluefin tuna can be alleviated and swordfish can be targeted more efficiently in the northwest Atlantic by avoiding fishing in anticyclonic eddies and in nearshore waters and by using more light sticks and fishing at night. Although dynamic management based on oceanographic features is difficult to implement, this technique has been used or proposed in other regions and fisheries  (Hobday and Hartmann, 2006; Howell et al. , 2008; Teo and Block, 2010). To propose a solid oceanography-based management plan for selecting catch among our focal species in the northwest Atlantic fishery, further studies on the responses of these species to mesoscale oceanographic features are needed. Our research calls for studies that compare the different effects of eddies in different regions within our study area using different statistical approaches, different biological datasets, or a combination of both.
ACKNOWLEDGEMENTS This research was supported by  NASA  grant  NNX08AK73G. A. M. B. was partly supported by the  NF-UBC Nereus Program. The altimeter products used in this study were produced by  Ssalto/Duacs  and distributed by AVISO, with support from  CNES  (http://www. aviso. oceanobs. com/duacs/). The authors thank the fishers and  NOAA SEESC  for providing logbook data and  Connie Kot  for processing and sharing logbook data. We wish to thank  Edward Farrell  for sharing his knowledge in applying ZINB models to fishery data. Helpful comments to an earlier version of this manuscript from four anonymous reviewers significantly improved this study. The authors also thank  Jie-Sheng Tan-Soo  for statistical advice and  Tina Praprotnik  for writing advice.
We introduce Rosetta, a program allowing for the translation between different bases of effective field theory operators. We present the main functions of the program and provide an example of usage. One of the Lagrangians which Rosetta can translate into has been implemented into FeynRules, which allows Rosetta to be interfaced into various high-energy physics programs such as Monte Carlo event generators. In addition to popular bases choices, such as the Warsaw and Strongly Interacting Light Higgs bases already implemented in the program, we also detail how to add new operator bases into the Rosetta package. In this way, phenomenological studies using an effective field theory framework can be straightforwardly performed.
In contrast, the Warsaw and SILH basis Lagrangians are directly written in terms of the SM gauge-eigenstates, ∆L (gauge) = i c i Λ 2 O i (G a µ , W i µ , Bµ, Φ, QL, uR, dR, LL, eR) , (2) and are manifestly symmetric under the electroweak symmetry group. We have implemented the mass basis Lagrangian ∆L (mass) into FeynRules  [18]   2  and tuned the output format of Rosetta so that the translation maps an EFT Lagrangian given in a specific basis to ∆L (mass) and generates an output file that is compatible with the FeynRules implementation. As a consequence, any high-energy physics tool (and in particular any Monte Carlo event generator) that is interfaced to Feyn-Rules can be employed within the context of any EFT basis of operators that is included in Rosetta. With the advent of automated next-to-leading order (NLO) accurate Monte Carlo event generation software, it is important that Rosetta remains flexible enough to eventually provide compatibility with this new generation of tools.
As long as the input format respects the conventions sketched in Section 2. 2 and that are inspired by the Supersymmetry Les Houches Accord (SLHA)  [27, 28] , the user may define his/her own map to the BSMC coefficients (or to any other basis implementation) and proceed with event generation using the accompanying FeynRules implementation. This highlights one of the key features of Rosetta, the possibility to easily define one's own input basis and directly use it in the context of many programs via the translation functionality of Rosetta. The strength of this approach is that it is much simpler than developing from scratch new modules for existing tools in the context of a new basis. To this end, Rosetta not only enables the translation of an EFT basis into the BSMC Lagrangian, but also allows for translations into any of the other bases included in the package, i.
Getting started with Rosetta The latest release of Rosetta can be obtained from http://rosetta. hepforge. org The package contains a Python executable named translate, an information file named README and two directories, a first folder (named Cards) collecting example input files and a second folder (named Rosetta) including the source code of Rosetta. The executable takes as input an SLHA-style parameter file with the coefficients of the dimension-six operators associated with a particular basis. Information on the format of such an input file can be found in Section 2. 2. The execution of the translate script from a shell yields the generation of an output parameter file where all parameters are this time the coefficients of the dimension-six operators associated with a specified new basis, the default choice being the BSMC Lagrangian.
Their ordering follows their order of appearance in the LHCHXSWG proposal. The imaginary parts of all parameters are stored in corresponding blocks whose names are prefixed with the IM tag. The Rosetta package contains built-in methods for dealing with an SLHA-like structure, and these methods have all been implemented in the Rosetta/SLHA. py file. When an input file is read, the parser included in the SLHA. py file recognizes the existing BLOCK and DECAY structures of the input file and stores them as instances of the SLHA. NamedBlock, SLHA. NamedMatrix and of the SLHA. Decay classes. These are dictionary-like objects that can be assigned, indexed and iterated over as regular Python dictionaries. An SLHA. Named-Block object reflects the information embedded in an SLHA block so that it possesses a name attribute and stores values associated with integer keys as well as a mapping from the integer keys to the parameter names.
Structure of Rosetta Rosetta is a Python package containing the implementation of a Basis class equipped with several utility functions for reading, processing and writing SLHA-style parameter files. Working implementations of bases are derived from this class and only require a small amount of information specifying the block structure of the EFT parameters, the required SM inputs and a series of translation functions to other existing basis implementations. In order to be able to define a new basis class, we describe in this section the properties of the Basis objects. The Basis class has a number of intrinsic data members that should be defined in order to get a working implementation of an EFT basis. These consist of the independent, required inputs and required masses attributes already mentioned in Section 2.
Card class that can be accessed via the card member of the basis class is created and populated with the information provided as input. The content of the mandatory MASS and SMINPUTS blocks is exported to data members of the basis class named mass and inputs that can then be used for accessing the SM parameters, while the CKM matrix is stored into the ckm container of the basis class. In the Rosetta framework, the EFT operator coefficients are implemented as elements of the relevant basis class and can be accessed via standard Python methods. For instance, all the coefficients associated with a basis object named MyBasis could be printed, together with their values, by coding for k, v in MyBasis. items(): print k, v In addition, a direct accessor to each EFT operator coefficient is created from its name, which facilitates the implementation of the translation functions that in general extensively reference individual parameter values.
This however assumes that there are no duplicate parameter names in the SLHA-like input file, which nevertheless leads to a program exception. There are hence multiple ways to access a given parameter. For example, a parameter A stored as the third element of a block MyBlock that is part of the definition of a basis MyBasis could be equally accessed as MyBasis['A'] MyBasis. card['A'] MyBasis. card. blocks['MyBlock']['A'] MyBasis. card. blocks['MyBlock'][3] In the lines above, the parameter A is respectively accessed from the MyBasis object, from the SLHA. Card instance associated with the current basis and from the SLHA. NamedBlock object associated with the MyBlock block (using either the parameter name or the counter as an index).
Second, the independent, blocks and flavored attributes of the class define the input parameters of the basis and their desired SLHA-like structure, while the required inputs and required masses lists are specified according to the needs of the translation functions that are planned to be implemented. One can also specify a dependent attribute to explicitly define a particular parameter as dependent. For instance, the following code refers to the implementa- This snippet of code specifies the declaration of the basis class MyBasis whose unique string identifier is given by mybasis. The independent parameters to be read from an input SLHAlike file are defined to be A, B, one and two and are assumed to be organized into the two SLHA blocks LETTERS and NUMBERS. A flavored matrix, MYxMAT, is also present and deemed to be an independent input parameter except for its  (3, 3)  component that is explicitly included within the dependent attribute of the basis class.
Interface to eHDecay In order to calculate dimension-six operator contributions to the Higgs boson width and branching ratios, Rosetta includes an interface to the eHDecay program  [11]. It can be switched on by executing the translate script with the eHDecay option (see Section 2. 1). In order to use this feature, the path to a local installation of eHDecay on the user system should be specified in the Rosetta/config. txt file, next to the eHDECAY dir keyword, and a (possibly indirect) translation linking the basis of interest to the SILH basis should exist. If so, the translation will be performed, eHDecay will be run internally and an SLHA decay block for the Higgs boson will be appended to the output file. Since the SILH basis description adopted in eHDecay assumes the MFV paradigm, an additional layer of translation is internally performed by Rosetta to render its internal SILH basis implementation MFV-compliant.
The relations among the different Wilson coefficients that hold in the context of the Higgs, SILH and Warsaw bases of independent operators have been implemented into Rosetta so that they are preserved when a setup is exported to the BSMC Lagrangian by the program. This scheme has the strength to be easily generalizable to study different setups providing a description of the Higgs boson properties, such as those with a non-linearly realized electroweak symmetry or including higher-dimensional operators beyond dimension six. In the future, we believe that translations from one basis to another will allow for broadening the scope and the use of past calculations very relevant for precision Higgs physics. Along these lines, higher-order calculations in QCD performed in the BSMC Lagrangian  [31] [32] [33]  could be used within any given EFT language, and the renormalization group running of the Wilson coefficients, that has been calculated in the SILH basis  [34, 35]  and in the Warsaw bases  [3, 21, 22] , could be exported to different bases too.
Acknowledgments The authors are grateful to  Fabio Maltoni  for lively interactions during all phases of this project and would also like to thank  Christophe Grojean ,  Alex Pomarol  and  Michael Trott  for useful comments. This work has been partially supported by the  FP7 Marie Curie Initial Training Network MCnetITN  ( PITN-GA-2012-315877 ), by the  Belgian Federal Science Policy Office  through the  Interuniversity Attraction Pole   P7/37 , by the  Strategic Research Program 'High Energy Physics ' and the  Research Council of the Vrije Universiteit Brussel , by the  Swiss National Science Foundation  under the  Ambizione grant   PZ00P2 136932 , and by the  Theory-LHC-France initiative of the CNRS (INP  and  IN2P3 ).
Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with crosscovariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matérn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.
Introduction Spatial data very frequently have more than one component. In meteorology, for instance, temperature, rainfall and pressure are measured at the same locations at predefined instances in time. Similarly, in order to estimate the spatial distribution of rainfall, data from rain gauges are combined with radar data. In economy, to name a further example, price developments of goods are spatio-temporal data consisting of various components at each instant of time and at each market. Although these kinds of data are ubiquitous, multivariate spatial models are largely under-While spBayes mainly focuses on spatial regression models, multivator allows for inference and prediction of multivariate data based on emulator techniques  (Oakley 1999). The package gstat provides several tools for prediction and model fitting and also allows for multi-Gaussian sequential simulation.
However, to the best of our knowledge, the package RandomFields  (Schlather et al. 2015)  is by now unique in its variety of tools and models for multivariate spatial processes. The paper is organized as follows. In Section 2, a summary of the theoretical background is given. Section 3 discusses principles for analyzing and simulating multivariate random fields. Section 4 offers an overview over multivariate and vector-valued covariance functions that are implemented in RandomFields. Section 5 reconsiders the data analysis by  Gneiting et al. (2010). Section 6 concludes with a brief summary of other features of RandomFields that are already available or are intended to be implemented.
The method has been extended by  Dietrich and Newsam (1996)  and  Chan and Wood (1999)  to the multivariate case. In RandomFields, the Cholesky decomposition and the circulant embedding method suggested by  Dietrich and Newsam (1996)  are implemented also in the multivariate case. Furthermore, an extended version of Matheron's turning bands method  (Matheron 1973)  for the multivariate case is implemented. Various methods exist to estimate the parameters of an intrinsically stationary random field. Least squares methods are based on the variogram cloud or a binned version of it, and do not require further assumptions on the fields  (Cressie 1993). For example, the package gstat relies mainly on these methods. The second often used method is the maximum likelihood (ML) method which relies on the additional important assumption of the Gaussianity of the field.
Various strategies exist. In geoR, initial starting values can be created by various methods and are then passed as an argument to the fitting procedure. Others seem to use fixed values only (fields). Data based initial values are considered by CompRandFld using a weighted moment estimator and by gstat extracting values from the variogram cloud. Usually, the packages allow for an additional starting value given by the user. RandomFields estimates variances and scales very roughly from the data and uses fixed values for other parameters to perform several variants of the least squares fit. Afterwards it uses the best parameter set of the least squares fits as initial values for the ML estimation. Similar to the packages CompRandFld and fields, the package RandomFields recognizes sparse matrices and uses the package spam  (Furrer and Sain 2010)  for calculating the likelihood.
Being also mathematically based on the inverse of the covariance matrix, spatial prediction methods face similar problems as the ML approach  (Chilès and Delfiner 1999). Work-arounds that share the same idea with the composite likelihood approach, such as the neighbourhood kriging (De Marsily 1984), have been suggested. Many packages exist that deal with kriging, for instance, constrainedKriging  (Hofer and Papritz 2011) , DiceKriging (Roustant, Ginsbourger, and Deville 2012) and geoR. The package gstat offers an enormous amount of variants for kriging, including multivariate kriging for linear models of coregionalization  (Goulard and Voltz 1992). RandomFields allows for basic kriging tasks, such as simple kriging, ordinary kriging and universal kriging, on a large variety of models. A last, also well-known difficulty appears with earth data as only few packages like sspline (Xie 2013) handle spherical data.
Concluding remarks Here, the multivariate features of RandomFields version 3. 0 have been presented. Besides the extension of the tool box for analyzing and simulating random fields, a fundamental goal of version 3. 0 of RandomFields has been the improvement of the accessibility of the package. To this end, the number of functions and the number of visible arguments have been reduced, at the same time as the functionality of the package has been extended. Models are now defined as functions, and even the "tilde" definition (~) of models can be used, in accordance with the formula specification of linear models. The number of manual pages has been largely increased, offering a separate manual page for each model. Further, several summary pages are included, and the documentation of more advanced features is shifted to secondary pages.
Currently, RandomFields offers more than 35 simple models and more than 30 functionals operating on simple models to construct more complex ones. Besides multivariate models also space-time models are included. RandomFields offers a modular construction system that allows for any combination of models, functionals operating on models, and functions, whenever the combination is mathematically sound. Validity checks of the model are performed by RandomFields. The inference on Gaussian random fields will be extended towards mixed models with a spatial Gaussian component. The package will also advance into the analysis and the modeling of non-Gaussian fields, in particular max-stable random fields. In Section 5 all the bivariate data have been co-located. This is certainly not the case for many data sets.
Acknowledgments The project has been financially supported by the  German Science Foundation (DFG) ,  Research Training Groups 1644  " Scaling problems in Statistics" and 1023 "Identification in Mathematical Models ", and by  Volkswagen Stiftung  within the project ' WEX-MOP '. The authors are grateful to  Ulrike Ober  for suggestions on the paper. The authors are much obliged to  Paulo Ribeiro ,  Edzer Pebesma  and an unknown referee for many valuable comments that helped to significantly improve both the package and the paper. Being the maintainer of RandomFields,  M. Schlather  is also indebted to  Daphne Boecker ,  Katharina Burmeister ,  Sebastian Engelke ,  Sebastian Groß ,  Juliane Manitz , and  Johannes Martini  for their contributions to version 3. 0 of the package.
However, very few applications regarding the spatial resilience of SEE systems have been published  [27] , particularly in the context of coral reefs. In this study, we map the spatial resilience of SEE systems associated with coral reefs across a large region (~1200 km length) at the highest spatial resolution possible (1 arcsec, approximately 30 m). All of the Japanese islands with shallow coral reefs (i. e. , Nansei archipelago, Figure  1 ) are considered. Our study focuses on a suite of 19 spatialized endogenous, exogenous stress-inducing and stress-mitigating factors derived from freely available spaceborne and waterborne observations, model output, historical databases and census data. Because of the uncertainty inherent in the knowledge of society-coral-environment interactions, we transform factor layers to fuzzy logic measures  [28]  and assemble them into single socio-economic and single ecological resilience metrics.
Socio-Economic Factors The Nansei archipelago is particularly suitable for studying socio-economic resilience in a coastal context given the high population density in coastal areas (185-615 inhab•km -2 , [30]) and the high frequency of both tropical cyclones (TCs) and earthquake-driven tsunamis  [31, 32] , which greatly strain coastal systems. We used historical databases, spaceborne data and census data to create spatial layers that represent endogenous, exogenous oceano-climatic and elevation factors (Table  1 ).
Oceano-Climatic Stress Exogenous Factors TC-and earthquake-induced surges, also called typhoons and tsunamis in Southeast Asia, respectively, were the two main socio-economic stressors in our study region  [33, 34]. The TC layers were derived from the tropical cyclone best-track data provided by the Japan Meteorological Agency (http://www. digital-typhoon. org/). This database contains the date and time, central pressure (hPa), and latitude and longitude (y, x with 6 arcmin spatial resolution) measured every 6 hours for each recorded typhoon since 1951. We developed an algorithm to create a layer of TC influences for a specific period. A suite of temporary sub-layers was created based on the 1 arcsec linear triangulation of the 1° buffer area around each sample (hPa, y, x) using IDL-ENVI (Exelis Visual Information Solutions, Boulder, CO, USA).
Then, temporary sub-layers were inverted to change the buffer into the influence layer before being summed for a specific period of interest. As a proxy for an acute and chronic climatic stressor, a TC-influence layer was computed for each year from 2002 to 2012 (160 ± 49 sub-layers) and for the 1951-2010 period (5796 sub-layers), respectively. The tsunami layers were produced from the global historical tsunami database maintained by the National Geophysical Data Center (http://www. ngdc. noaa. gov/hazard/tsu_db. shtml). The array features the year, primary magnitude, and latitude and longitude (y, x with 6 arcmin spatial resolution) of every tsunami recorded since 2000 BC. We attributed a magnitude of 7 to the historical events deprived of any values (i. e. , 10 out of 67) insofar as its database identifies major tsunami events.
Habitat Class Drag Coefficient (Cd) Large anomalies in seawater temperature lead to coral bleaching and frequently to coral death  [36] [37] [38]. Sea surface temperature (SST) data were used to compute an indicator developed and validated as a heating rate index. The indicator basically corresponds to the ratio between the degrees Celsius above the average over the period of interest (high anomaly) and the number of weeks experiencing positive Poor water clarity due to runoff may be very harmful to coral reefs by depriving them of sunlight and covering them with sedimentation  [42]. Applying the computation principles of the heating rate index to the water clarity (hereafter, turbidity), we calculated the ratio between the positive anomalies and the number of weeks experiencing positive turbidity anomalies.