dataesr/scientific-paragraphs-categorization huggingface dataset:
IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain). D. C. and C. D. wish to acknowledge the financial contributions of the School of Physics, University College Dublin, Ireland, and IPAG, Grenoble, France. We gratefully acknowledge the GEMINI Helpdesk team, in particular Emma Hogan and Marie Lemoine-Busserolle, for all support in ensuring the most accurate data calibration. We also thank Tracy Beck for her kind assistance in understanding the GEMINI pipeline.
We present a new technical simulator for the eLISA mission, based on state space modeling techniques and developed in MATLAB. This simulator computes the coordinate and velocity over time of each body involved in the constellation, i. e. the spacecraft and its test masses, taking into account the different disturbances and actuations. This allows studying the contribution of instrumental noises and system imperfections on the residual acceleration applied on the TMs, the latter reflecting the performance of the achieved free-fall along the sensitive axis. A preliminary version of the results is presented.
The simulator is also constructed in such a way that the configuration of the system can easily evolve. For example, testing configurations with one test mass per S/C, spherical test masses, improved sensors and actuators can be studied. A quantitative comparison between a two-arm configuration (eLISA) and three-arm one (LISA) is also easy to implement. Once the impact of each subsystem has been adapted to the level of the best knowledge available, the residual acceleration achieved for each system configuration can be transferred to data analysis programs, such as LISACode  [3] , in order to quantify the scientific performances of the future mission.
Acknowledgments E. P. and H. I would like to acknowledge the financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ) and the  French space agency CNES  for its financial support during these studies. The authors would like to thank the LISA and LISAPathfinder teams at  AEI  and  Prof. Stefano Vitale  for some very fruitful discussions.
The dust-collecting substrates exposed at comet 67P/Churyumov-Gerasimenko are designed to catch dust grains, impacting at low speed, with a high probability  (Hornung, 2014). The dust grain size is an a priori unknown factor. Dust smaller than visible by the optical microscope, Cosiscope, will be detectable by the TOF-SIMS instrument. Therefore we need to establish methods by which we can identify a grain on a visually seemingly empty background. The optimum size of the dust grains to be measured would be the same size as the beam diameter. The purpose of this presentation is to investigate how well we can identify a set of selected dust grains, from the substrate background, using a few different sets of data and applying multivariate statistical analysis enhanced with the relatively recent random projection method  (Bingham and Mannila, 2001) , and developed for our purpose  (Varmuza et al.
In mathematical terms we can express these statements as follows. We may regard each measurement as a row vector d. The dimensionality is determined by the number N of parameters measured. The complete set of data is then given by the matrix D. It consists of M measurements, rows, each N dimensional vectors d. A linear dimensionality reduction then consists of a projection of D using a transformation matrix R giving a transformed data set X with dimension N × K as X M×K = D M×N • R N×K. Random projection is suitable to map even a very high dimensional data set into a reduced dimension which still retains the most important features of the original data but is small enough to be numerically tractable. The selection of transformation matrix is of critical importance. If we by ad hoc reasoning decide that only certain lines in the mass spectrum are important, we actually choose a very limiting simple form of projection.
Therefore an advanced kind of information extraction is utilized in a robust manner  (Candes, 2006). We can actually compute the PCA components reliably by first projecting the data set by RP and then apply standard PCA. When the number of samples, i. e. spectra, grows, the accuracy of the PCA method decreases. For this case, clustering approaches for classification can be shown to perform better than simple PCA  (Varmuza and Filzmoser, 2009). We describe a set of calibration measurements of 19 minerals measured by the instrument. A separate paper,  (Krüger, 2014) , will discuss the sample preparation and details of the manual spectral evaluation. In this paper we are interested in constructing a method which would accurately and rapidly relate a new measurement to previously measured spectra.
Database A database provides fast and easy access to the data. Three different instrument models are accessible, namely the laboratory reference model, the flight model at the comet and a second laboratory reference model in France. More than a hundred thousand spectra are available. A wide variety of substances, both organic and inorganic, have been measured in positive and negative ion modes. For the purpose of this study we use data from the database covering the substrates and minerals as shown in Table  1. The samples studied in this presentation are listed in Table 2. Calibration experiments validating the measurements of organics are also described separately in Le  Roy (2014).
Because the RP reduces the dimensionality greatly, the PCA becomes much faster. The speed depends primarily on the dimensionality. The reduction of it therefore speeds up the repetitive computations required for the cross validation. The process is difficult when full spectra are considered because of the high dimensionality. The selection of random number sequences used in the RP has a small and insignificant influence on the results, as long as the same projection matrix is used for the entire data set studied. The transformation matrix establishes an approximately orthonormal coordinate system in which the data is represented. The clustering can be directly computed from the RP projected data set but alternatively also be made by other methods, which we do not discuss here any further. We finally estimate the number of valid signal carrying components using the method of  Nadler (2008).
The Cohen κ is similar to within a few % and the classification results essentially identical. The compute time is one to several orders of magnitude lower with the RP providing results in only a few minutes. Furthermore the RP seems to yield a computationally more stable solution. The classification and cross validation for the full data set using the RP method for computations is shown in Table 4. The number of PC values exceeding the noise level is about 90 for the peak-list data set. Decreasing the RP dimension to correspond to the data set in Table  4  only slightly changes the κ, decreasing it by less than 1 %. The corresponding score plot for the first two principal components is shown in Fig. 10. It represents the heart of the instrument performance. The shape is due to the fact that we view a point set (spectra) distributed on a unit sphere.
Acknowledgements. COSIMA was built by a consortium led by the  Max-Planck-Institut für Extraterrestrische Physik, Garching, Germany in collaboration with Laboratoire de Physique et Chimie de l'Environnement, Orléans, France, Institut d'Astrophysique Spatiale, CNRS/INSU  and  Université Paris Sud, Orsay, France ,  Finnish Meteorological Institute, Helsinki, Finland, Universität Wuppertal, Wuppertal, Germany, von Hoerner und Sulger GmbH, Schwetzingen, Germany, Universität der Bundeswehr, Neubiberg, Germany, Institut für Physik, Forschungszentrum Seibersdorf, Seibersdorf, Austria, Institut für Weltraumforschung, Österreichische Akademie der Wissenschaften, Graz, Austria  and is lead by the  Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany. The support of the national funding agencies of  Germany (DLR) ,  France (CNES) ,  Austria and Finland  and the  ESA Technical Directorate  is gratefully acknowledged.
Resources to mine the large amount of protein structures available today are necessary to better understand how amino acid variations are compatible with conformation preservation, to assist protein design, engineering and, further, the development of biologic therapeutic compounds. BCSearch is a versatile service to efficiently mine large collections of protein structures. It relies on a new approach based on a Binet-Cauchy kernel that is more discriminative than the widely used root mean square deviation criterion. It has statistics independent of size even for short fragments, and is fast. The systematic mining of large collections of structures such as the complete SCOPe protein structural classification or comprehensive subsets of the Protein Data Bank can be performed in few minutes.
INTRODUCTION The large amount of protein structures available, thanks to the efforts of the structural genomics, now constitutes a valuable resource to analyze in depth the impact of amino acid sequence variation on protein conformation. Efficient and large-scale mining of the structures available offers promising perspectives to assist protein engineering and design. It also meets the increasing interest of pharmaceuti-cal industry to develop new biologic entities including large peptides, recombinant proteins, antibodies, immunoconjugates or synthetic vaccines to cite some  (1). Indeed, beyond the analysis and the classification of complete protein domains, the focus is progressively moving to a more local level of structure analysis. The present number of entries of the Protein Data Bank (PDB)  (2) , over 100 000 protein structures, corresponds to several tens of millions of protein fragments of short size (10-20 amino acids) amino acids.
Finally, TopMatch (8) that can generate several alignments between a query and a target protein structure has been recently updated as TopSearch  (9). For the comparison of non-sequential motifs, much more complex and slower algorithms have been proposed. These include Rasmot-3D (13), SPRITE and ASSAM  (14)  or ProSMoS  (15) , to cite some. Another important application of structural fragment mining is knowledge-based loop modeling. It implies the search for fragments matching geometric boundary conditions in subsets of the PDB or SCOPe  (16). For that purpose, some online services are available at this time. Arch-PRED  (17)  uses secondary structures flanking the missing loop, their relative orientation and the number of missing residues to identify candidate loop conformations. Super-Looper (18) mines the Loop In Protein (LIP) database  (19) , a comprehensive loop database containing all protein seg-Nucleic Acids Research, 2015, Vol.
Both provide comparable measures between close structures. But, since the RMSD averages the distances between atoms, the mediumrange and even low-range RMSDs do not imply significative conformation similarity. On the contrary, the BC-score characterizes more precisely global shape similarity. Combined with distortion rate, it allows better discrimination among medium RMSD range hits  (23). Therefore, contrary to the RMSD, the BC-score can be efficiently used to search for fragments in structure databases with a certain amount of flexibility while discarding spurious fragments which cannot be structurally aligned with the query.
Data sets BCSearch can mine large collections of protein structures. Presently, two collections of structure have been considered. The first corresponds to the complete collections of structural domains of SCOPe version 2. 04 (over 190 000 domains in total)  (16) , for which it is possible to specify any level of the hierarchy using the class. fold. superfamilly. familly scheme--e. g. g. 3 for Toxic hairpinKnottins, g. 3. 3 for cyclotides. In order to make possible analyses on a subset of structures at high resolution only, a second collection denoted as PDB corresponds to a subset of the PDB corresponding to structures resolved using X-ray diffraction, at resolutions better than 1. 6, 1. 8, 2. 0, 2. 2 or 2. 5 Å, and with an R-value less than 0. 25 or 1. 0, as defined by the pisces server  (25).
Input As input, BCSearch requires a structure, and in some cases some sequence information. Structures can be uploaded as PDB-formatted files or searched in repositories given a PDB or an SCOPe identifier. For fragment search and mirror search, a sequence specifying the part of the query structure to use can be input. For loop search, the complete sequence must be provided. Missing parts of the protein are automatically detected by comparing the sequence to that of the gaped structure. The collection of structures to mine can correspond to subsets of either the SCOP databank or the PDB at different resolutions. The PDB and SCOP collections can be filtered depending on sequence identity--90, 70, 50 or 30%. Cutoff values can also be set for the BC-score and the rigidity score.
Output Results page of the BCSearch services but BCSpecificity-Search return all the hits in a csv file and an interactive table ordered by BCscore, truncated to the best 1000 hits to preserve interactivity. For each match, the data reported are: the name of the query, the name of the hit (PDB or SCOP ID), starting and ending residues number of the query and of the match, BCscore, rigidity value, P-value, RMSD and the sequence of the match. The P-value and the RMSD are calculated from the query and any hit for BCFragSearch and BCMirrorSearch, and between the flanking regions of the query and the match for BCLoopSearch. When relevant, a sequence logo is also provided. It depicts the sequence variability among the hits. A visualization panel is available thanks to the PV--JavaScript Protein Viewer (http://biasmv.
Fragment mining Figure  2  illustrates a BCFragSearch run applied to the search for fragments similar to the fragment Cys15-Gly37 from the human zinc finger protein (PDB: 2EMJ) that belongs to the superfamily of the beta-beta-alpha-zinc fingers (SCOPe g. 37. 1). The search was performed against the SCOPe collection at 100% sequence identity, using the default search values of 0. 95 and 1. for the BC-score and rigidity, respectively. Twenty eight hits were identified over the 280 proteins of the superfamily. The logo representation of the corresponding sequences clearly shows the C2H2 motif specific of the zinc binding motif. Importantly, similar fragments in the remaining members of the superfamily do have indels in the fragment, highlighting the stringency of the BC-score, which detects such events.
Candidate loop search To illustrate the BCLoopSearch service, we start from the known complex between the glycoprotein Ib alpha and the von Willebrand factor (PDB: 1M10). 3A ). Starting from the unbound conformation, we removed residues 226-242 of the moving loop. We then performed a BCLoopSearch against the complete SCOPe collection -100% sequence identity, using BC-score and rigidity cutoff values of 0. 95 and 1. 0, respectively. We obtained 20 different conformations. Not only the bound conformation but also 19 other conformations cover a range of RMSD from 0. 9 to 17. 1 Å (see Figure  3B ). The closest conformation to the bound conformation, excluding itself, deviates by 0. 9 Å and is from a ternary complex between von Willebrand factor, glycoprotein Ib alpha and botrocetin (PDB: 1U0N).
BCSpecificitySearch The porcine beta trypsin (PDB: 1QQU) is associated with the SCOPe fold b. 47. Using BCSpecificitySearch, it is possible to ask what fragments of the structure are specific of the fold, searching occurrences of fragments in the b class, but discarding the protein domains of the b. 47 fold (5352 protein domains). We have used a fragment size of 9, against the corresponding SCOPe subset filtered at 90% sequence identity, BC-score and rigidity cut-off values of 0. 95 and 1, respectively. Figure  4 , left shows the sites associated with specificity scores greater than 0. 995, i. e. associated with less than 0. 5% of matches. It is striking that these sites define a patch on the structure. Interestingly, Figure  4 , right shows that this patch corresponds to the patch in interaction with the soybean trypsin inhibitor (PDB: 1AVX).
Measure of dominant frequency variability We extracted dominant frequency for each call of the entire night recording, using Avisoft Saslab software. Call dominant frequency was here defined as the frequency with the highest energy, in a frequency range between 1. 8 and 3 kHz (Fig  1 ). To calculate it, mean power spectra were automatically computed for each call with a Fast Fourier Transform of 256 points and a 16 kHz sampling rate. These parameters yielded frequency values with an accuracy of 62. 5Hz. Such a value corresponds to about 2. 5% of the mean dominant frequency value in our species. Note that the between male CV calculated in H. arborea for dominant frequency is about from 5. 14% to 8. 20%  [32] ,  [33]. Also note that in Hyla ebraccata, the hylid with the most accurate perception for which data is available in literature, female discrimination threshold for dominant frequency is about 8.
Test of different sampling methods to assess frequency variability We developed a program SONIO (http://sites. google. com/site/sonioprogram/) to test different sampling methods on Avisoft outputs. Each method was defined by two parameters: the number of subsamples to randomly draw from the entire night record and the subsample window size (in consecutive calls or in seconds). Once the method defined, the program randomly drew the subsamples and computed the mean value of dominant frequency, its estimated SD and estimated CV value. This sampling was repeated 1,000 times and the program computed a CV ratio (estimated CV value / total CV computed over the entire night) representing the fraction of the total variability captured. The sampling methods mimicked different possible decisions of an experimenter deciding a priori to adapt or not the record duration to male calling activity.
Dominant frequency: a honest acoustic parameter? Given that female choice and male decisions during contests are based on very small samples (e. g. for female choice:  [44] ; for male contests:  [45] ; for sneaker strategy:  [46] ) the sudden discontinuities observed in the present study may lead to a poor estimation of male quality through dominant frequency. This estimation may be different depending on time at which the congener receives the signal raising the issue of the honesty of this call property. On average, we measured a DF variability of 116 Hz in our dataset but the difference during the course of the night can be as strong as 470 Hz. Can dominant frequency still be considered as an index signal (sensu Maynard-Smith and Harper  [47] )? In the literature, dominant frequency is usually considered an index signal as many empirical studies have shown a negative correlation between call dominant frequency and individual mass, both at interspecific  [21]  and intraspecific level (reviews in  [22] ,  [23] ).
Each of these tools has its own input language based on some programming language, or use some sentences in a programming language, or is, simply, a programming language like Java, C or C++. Therefore, those practitioners who want to simulate their models using such tools must have, at least, basic programming skills or they need a programmer to implement their models. Generally, each of these input languages is different, hindering the interoperability between simulation tools. For instance, a simulation model described using PowerDEVS cannot be simulated in JDEVS (or at least not in a straightforward way). On the other hand, the model described in PowerDEVS models essentially the same system that would be modeled in JDEVS. Hence, it would be desirable to be able to describe the abstract model as is seen by the expert and then automatically translate it to one or more input languages.
The former is given by means of examples and its EBNF (Extended Backus Naur Form), the usual way to formally describe grammars, while the latter is given in terms of the code that must be generated in the input language of two M&S tools, DEVS-Suite and PowerDEVS. These rules completely define the multi-target compiler, turning its implementation in a rather direct task (using standard compiler implementation techniques). To extend CML-DEVS to other target languages, it is only necessary to provide and implement the corresponding set of translation rules. The choice of these particular tools is relatively arbitrary. Since these tools have different input languages (C++ and Java) the intention is to show how a CML-DEVS model can be automatically translated into different simulators languages.
For instance, the external and internal transition functions consist of sequences of expressions (expr), that are not further explained except for a few examples. Also, no reference to the elapsed time, e, is made in the external transition function. Finally, in this paper we present the translation rules that allow the implementation of a multi-target compiler for the input language of two different simulators. This does not seem to have be presented by Hong and Kim. Mittal and Douglass  [18]  present a domain specific language, based on Finite Deterministic DEVS (FDDEVS)  [19] , for Parallel DEVS as a component of the revised DEVSML framework (DEVSML 2. 0). It also intends to be used as an abstract representation of DEVS models (among other things). To define DEVS models with some fancy features like code assistance and run-time model validation, they integrate into Eclipse a DEVSML editor using the Xtext framework and the EBNF grammar of their language.
Initial values for simulations CML-DEVS provides, also, an easy way to define initial values for the state variables. The structure is quite simple: simulate ModelName from assignments end simulate where the assignments have the same form as those already described before and are used, precisely, to assign values to the state variables. Although this issue does not concern to the modeling phase, but rather to the simulation of the model, we think that it is helpful (and almost mandatory) to provide this alternative in CML-DEVS. As mentioned before, the modeler should not modify the code already translated into the simulation language. Thereby, he or she can set these initial values without dealing with such code.
Translation of CML-DEVS models into simulation models The intention of this section is to show that it is possible to automatically translate a CML-DEVS model into different simulation models (in different languages). We think that it is an important contribution for the M&S community to be able to describe abstractly a DEVS model and, then, automatically translate it into the language of the desired simulation tool. As an example, we show how to translate CML-DEVS models into PowerDEVS and DEVS-Suite models. This translation task is responsibility of the multi-target compiler, shown in Fig. 1. The compiler can generate code in different target languages. The abstract syntax tree (AST) generated by the lexical analyzer has all the information about the model in a structured form. Then the AST is processed by a semantic analyzer, responsible for checking the correctness of the model, for example, not attempting to compare a set with text (type checking).