dataesr/scientific-paragraphs-categorization huggingface dataset:
It operates at  Ku (13. 575 GHz)   In the followings, ranges used to derive altimeter heights and backscattering coefficients are the ones processed with the Ice-1 retracking algorithm  (Wingham et al. , 1986). Previous studies showed that Ice-1-derived altimetry heights are the more suitable for hydrological studies in terms of accuracy of water levels and availability of the data (e. g. ,  Frappart et al. , 2006a,b; Santos da Silva et al. , 2010)  among the commonly available retracked data present in the GDRs. They were also used in previous studies for characterizing land surfaces properties  (Fatras et al. , 2012 (Fatras et al. , , 2015)) , showing a strong linear relationship with SSM  (Fatras et al. , 2012). Besides, they are present in the SARAL, ENVISAT and Jason-2 GDRs in the high-frequency mode.
TRMM rainfall The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite (a Japan-US satellite launched in November 1997) Precipitation Analysis (TMPA) 3B42 v7 daily rainfall data, with a spatial resolution of 0. 25°Â 0. 25°, are used from January 2003 to December 2011. This dataset is produced by combining several data products  (Huffman et al. , 2007 (Huffman et al. , , 2010))   (Schneider et al. , 2011). It is available on the Goddard Earth Sciences Data and Information Services Center (GES DISC) website: http://daac. gscf. nasa. gov.
AMSR-E soil moisture The Advanced Microwave Scanning Radiometer (AMSR-E) from the Japanese space agency onboard the Aqua (EOS PM-1) satellite is dedicated to the observation of climate and hydrology. It is a multi-frequency dual-polarized microwave radiometer operating at C-and X-bands that detects faint microwave emissions from the Earth's surface and atmosphere. The VUA-NASA Land Retriever Parameter Model (LPRM) is a forward radiative transfer model that used passive microwave remote sensing data to retrieve SSM  (Owe et al. , 2008). Available products cover the period June 2002-October 2011. In this study, we only consider C-band level 3 LPRM VUA (Vrije Universiteit of Amsterdam) products, which are available online (http://www. falw. vu/~jeur/lprm/) for the descending orbit, corresponding to night-time measurements (equator crossing time: 1:30 am) at a spatial resolution of 0.
The propagation corrections applied to the range are derived from the Global Ionospheric Maps (GIM) and Era Interim model outputs from the European Centre Medium-Range Weather Forecasts (ECMWF) for the ionosphere and the dry and wet troposphere range delays respectively. A virtual station is defined as a given intersection of an orbit groundtrack and a water body (i. e. , lake, reservoir river channel, floodplain or wetland). There, the variations of the altimeter height h from one cycle to the other can be associated to changes in water level. In this study, we used the Multi-mission Altimetry Processing Software (MAPS) that allows a refined selection of the valid altimetry data to build virtual stations  (Frappart et al. , in press ) in the Inner Niger Delta. Data processing is composed of three main steps: a coarse delineation of the virtual stations using Google Earth, a refined selection of the valid altimetry data, and a computation of the water level time-series.
Non-flooded tropical evergreen forest Radar altimetry backscattering coefficients present a quite stable behavior over non flooded tropical evergreen forest (Fig. 8 ). Their average values decrease from $17 dB at Ka-band to $4 dB at Ku-band using Jason-2 data. The difference in backscattering is $3 dB between C and Ku bands using Jason-2 data and $0. 5 dB between S and Ku bands using ENVISAT data (Tables  2  and 3 ). A large variability of r 0 is observed using ENVISAT data from June 2002 to March 2007 (cycles 6-56). Then the variability of r 0 is very similar to that of Jason-2. A larger variability is also observed at Ka-band (std = 2. 94 dB compared to std = 0. 80 dB at Ku-band using Jason-2 data, see Table  2 ), with a time series presenting higher values in April and from November to January, during the wet seasons of the Congo Basin.
Acknowledgements C. Fatras  is supported by a  CNES/ONERA  PhD grant. This work was supported by the  Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/actions-sur-projets/pntsprogramme-national-de-teledetection-spatiale),grant n°P  NTS-2012-03. We thank two anonymous Reviewers for their helpful comments that helped us in improving the quality of this study.
The initial model temperature is 37ºC, except in the electrode tip which is 22ºC, imposed as a consequence of the saline irrigation. The electrical (σ) and thermal conductivity (k) of cardiac tissue are temperature-dependent and are defined by piecewise functions as in  [8]. Electrical, thermal and velocity boundary conditions are applied to the model. We consider a constant power ablation mode of 35 W for 30 s  [2] , which is the usual ablation mode for open-irrigated electrodes in RFCA. We implemented a proportional-integral control algorithm using MATLAB (MathWorks, Natick, MA, USA) and the applied voltage was modulated to maintain the delivering power within 3% of the target. Therefore, a voltage boundary condition is applied at the active electrode surface. On all the outer surfaces of the model, except the bottom surface, a null electrical flux is imposed.
The voltage on the bottom surface is set to 0 V to model the dispersive electrode. For the thermal boundary conditions, a null thermal flux is used on the symmetry plane and a constant temperature of 37ºC is fixed on the outer model surfaces. For the velocity boundary conditions, an inlet velocity boundary condition is applied on the left surface of the fluid volume to impose a blood flow velocity of 0. 1 m s -1  [2]. An outlet boundary condition of zero pressure is fixed on the right surface of the fluid volume. The saline irrigation flow is taken into account by an inlet velocity condition into the blood region. A no slip condition is applied on the upper surfaces of the fluid volume, at the symmetry plane and at the tissue-blood and electrode-blood interfaces. The model is based on a coupled electric-thermal-flow problem, which is solved numerically using the Finite Element Method (FEM) with COMSOL Multiphysics software (COMSOL, Burlington, MA, USA).
Acknowledgements This work was supported in part by the  Basque Government  through the  BERC 2014-2017 program  and by  Spanish Ministry of Economy and Competitiveness MINECO  through the  BCAM Severo Ochoa excellence accreditation   SEV-2013-0323 , and also by the  Spanish "Plan Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad " under Grant  TEC2014-52383-C3-R (TEC2014-52383-C3-1-R ).
Thus, the phenotypic characteristics of this strain support the claim that it belongs to Pantoea septica. Matrix-assisted laser-desorption/ionization time-offlight mass spectrometry protein analysis was performed using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany), as previously reported  [9]. The scores previously established by Bruker Daltonics, used to validate or invalidate identification compared to the instrument database, were applied. Briefly, a score ≥ 2 for a species with a Table  1  Classification and general features of Pantoea septica strain FF5 according to the MIGS recommendations  [12]  MIGS ID Property Term Evidence code a Classification Domain: Bacteria TAS  [24]  Phylum: Proteobacteria TAS  [25, 26]  Class: Gammaproteobacteria TAS  [26, 27]  Order: Enterobacteriales TAS  [28]  Family: Enterobacteriaceae TAS  [4, 28, 29]  Genus: Pantoea TAS  [  Evidence codes -IDA: Inferred from Direct Assay; TAS: Traceable Author Statement (i.
0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the spectra of 6252 bacterial spectra. Spectra were compared with the Bruker database that contained spectra from the ten validly named Pantoea species. The spectra obtained were similar to those of P. septica. A score of 2. 3 was obtained for strain FF5 supporting the identification of P. septica. Its reference mass spectrum was added to our database (Fig. 2 ).
Genome project history Pantoea septica strain FF5 was selected for sequencing because no genome of P. septica has previously been described. Besides, this strain is part of a study aiming to characterize the skin flora of healthy Senegalese people. It is the 17 th genome of Pantoea species to be sequenced and the first genome within P. septica. The GenBank accession number is CCAQ000000000 and it consists of 4 scaffolds and 37 contigs. Table  2  shows the project information and its association with MIGS version 2. 0 compliance  [11]. Associated MIGS records are detailed in Additional file 1: Table  S1.
Genome sequencing and assembly The genomic DNA of Pantoea septica was sequenced using MiSeq Technology (Illumina Inc, San Diego, CA, USA) with the 2 applications: paired-end and mate-pair. The rpoB sequences were aligned using MUSCLE  [31] , and the phylogenetic tree was inferred using the Maximum Likelihood method with Kimura 2-parameter model from MEGA software. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1,000 times to generate a majority consensus tree. The scale bar represents a rate of substitution per site of 0. 02 The paired-end and mate-pair strategies were barcoded in order to be mixed respectively with 10 other genomic projects prepared with the Nextera XT DNA sample prep kit (Illumina) and 11 other projects with the Nextera Mate-Pair sample prep kit (Illumina).
Genome annotation Open Reading Frames prediction was performed using Prodigal  [12]  with default parameters. We removed the predicted ORFs if they spanned a sequencing gap region. Functional assessment of protein sequences was performed by comparing them with sequences in the GenBank  [13]  and Clusters of Orthologous Groups (COG) databases using BLASTP. tRNAs, rRNAs, signal peptides and transmembrane helices were identified using tRNAscan-SE 1. 21  [14] , RNAmmer  [15] , SignalP  [16]  and TMHMM  [17]  respectively. Artemis  [18]  was used for data management whereas DNA Plotter  [19]  was used for visualization of genomic features. In-house perl and bash scripts were used to automate these routine tasks. ORFans were sequences with no homology in a given database i. e. in a nonredundant (nr) or identified if their BLASTP E-value was lower than 1e-03 for alignment lengths greater than 80 amino acids.
If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. PHAST was used to identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids  [20]. To estimate the nucleotide sequence similarity at the genome level between P. septica and another 7 members of the genus of Pantoea and 4 members of the genus Enterobacter, we determined the AGIOS parameter as follows: orthologous proteins were detected using the Proteinortho software (with the parameters following: E-value 1e-5, 30 % identity, 50 % coverage and algebraic connectivity of 50 %)  [21]  and genomes compared two by two. After fetching the corresponding nucleotide sequences of orthologous proteins for each pair of genomes, we determined the mean percentage of nucleotide sequence identity using the Needleman-Wunsch global alignment algorithm.
This paper describes our contribution in Opinion Target Extraction OTE and Sentiment Polarity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of features including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submissions. A Logistic Regression model with a weighting schema of positive and negative labels have been used for sentiment polarity; several groups of features (lexical, syntactic, semantic, lexicon and Z score) are extracted. Our submission for Sentiment Polarity is ranked third over ten submissions on the restaurant data set, third over thirteen on the laptops data set, but the first over eleven on the hotel data set that is out-of-domain set.
4-Z score Features Z score can distinguish the importance of each term in each class, their performances have been proved  (Hamdan et al. , 2014a). We assume as in the mentioned work that the term frequencies are following the multi-nomial distribution. Thus, Z score can be seen as a standardization of the term frequency using multi-nomial distribution. We compute the Z score for each term ti in a class C j (t ij ) by calculating its term relative frequency tf r ij in a particular class C j , as well as the mean (mean i ) which is the term probability over the whole corpus multiplied by n j the number of terms in the class C j , and standard deviation (sd i ) of term ti according to the underlying corpus (see Eq. 1). We tested different threshold for choosing the words which have higher Z score, we found 3 is the best one for restaurant data and 4 for laptop data.
Experiments In addition to the restaurant data set presented in tabel 1, two other data sets statistics are presented in table 3 (Laptops data which consists of training and testing data sets while the Hotel test set is out of domain set that was provided to test our model on new domain without having training data). We trained a L1-regularized Logistic regression classifier implemented in LIBLINEAR, which has given good results in several papers  (Hamdan et al. , 2015b)   (Hamdan et al. , 2015a). The classifier is trained on the training data set using the previous features with the three polarities (positive, negative, and neutral) as labels. A weighting schema is adapted for each class, we use the weighting option -wi which enables a use of different cost parameter C for different classes.
Since the training data is unbalanced, this weighting schema adjusts the probability of each label. Thus, we tuned the classifier in adjusting the cost parameter C of Logistic Regression, weight wpos of positive class and weight wneg of negative class. We used the 1/10 of training data set for tuning the three parameters in the two data sets (Restaurant, Laptop), all combinations of C in range 0. 1 to to 4 by step 0. 1, wpos in range 1 to 8 by step 0. 1, wneg in range 1 to 8 by step 0. 1 are tested. The combination C=0. 3, textitwpos=1. 2, wneg=1. 9 have been chosen for the restaurant set and C=0. 2 wpos=2. 1 wneg=1. 9 for the laptops set.
Data Reviews   4  shows the results of our system on the three data sets. It should note that we use the trained classifier on restaurant data set for predicting the polarity in the Hotel test set the out-of-domain set. Our system outperforms the baseline over the three data set. The gain is of 11. 95%, 7. 9%, 14. 16% in restaurant, laptop, hotel reviews respectively. The baseline of Hotels is the majority baseline while the other baselines are provide by the organizers which use a trained SVM on the BOW features and the category name feature in each data set. Our system is ranked third over ten submissions in the restaurant data set, third over thirteen in the laptops set, and the first over eleven in the hotel set.
Conclusion and future work We have built two systems for opinion target extraction of restaurant data set, and sentiment polarity analysis for three data sets (restaurant and laptops) and one out-of-domain set (hotel). We have used supervised tagger for OTE, trained a CRF model with several features. A Logistic regression classifier is used for sentiment polarity where we adopted a weighting schema in each domain and applied the same classifier and weighting schema trained on restaurant set on the Hotel test set. In future work, we will focus on using parsing tree for determining the context of OTE instead of the syntactic method. And play with other types of features for the two subtasks OTE and Sentiment Polarity.
Pattern matching is an interesting feature which is often used during the development of meta programs. With it, it is simpler to describe specific situations over the input language, simplifying the implementation of this kind of programs. Many meta programming languages (specialized languages for the development of meta programs) and libraries are based on expressive forms of pattern matching (Haskell  [15] , Scala  [28] , TOM  [3] , ASF+SDF  [6] , Maude  [8] , StrategoXT  [7] , ELAN  [4]  and Rascal  [18, 19] ). The challenge we address is: given a grammar which describes syntactically correct inputs, to generate a minimal amount of inputs which will cover the meta program. Our contribution is the notion of pattern coverage which links grammar coverage to conditional coverage in meta programs which use pattern matching.
To evaluate and compare the pattern coverage criterion we used the mutation technique  [11]  to evaluate the generated test sets. As result of pattern coverage use we could observe a reduction on the test set size and the preservation of the quality level for the pattern coverage test set. The remainder of this article is organized as follows. Section 2 presents the basic theoretical foundation used to the work presentation. Section 3 define the pattern coverage criterion and present the test data generation algorithm. The Section 4 present the evaluation process and the results obtained during the process. Related work is discussed in Section 5, followed by concluding remarks in Section 6.
These tools N. Instruction Description 1 int x := 3; Typed pattern, the x variable is bound to value 3. 2 if ( add (l , r ) := add ( var ( " x " ) , var ( " y " ))) println ( l + " ," r ); Constructor pattern, the identifier add is the constructor and the variables l and r are bound to values var("x") and var("b"), respectively. 3 for (/ str x := add ( var ( " x " ) , var ( " y " ))) println ( x ); Deep matching pattern, the matching can happen on any level of the term. Table  1. Example of some pattern-based instructions in Rascal. basically receive a context-free grammar as input and some additional restrictions and generate a set of test cases (language sentences). These restrictions are necessary to limit the size of the resulting set, since, in general, the language associated to the input grammar is infinite.
Pattern Matching Mechanism Pattern matching is a programming language feature. It is common to implement meta programs using pattern matching because it facilitates the description of interesting cases over the object language and dubs as a de-structuring variable binding mechanism. Formally, the pattern matching problem is, given two terms s and l, determine if there is a substitution σ, such that σ(l) = s  [2]. The substitution itself, if it exists, represents the binding of variables which can be used by the program after the match. Table  1  depicts a number of pattern types as used in the Rascal language  [18]. Each line shows a value on the right and a pattern on the left of the match := operator. A most basic form of pattern, the congruence pattern, is an arbitrarily nested expression using only constructors and open variables.
Using the for loop the programmer can iterate over all possible bindings of x, namely "x" and "y". During this section, we will use a meta program (presented on Listing 1. 1) as example to illustrate the concepts introduced on the section. This example is a typical implementation for the Cyclomatic Complexity (CC)  [25]  algorithm. The implementation of CC is coded in Rascal and was used by Landman  [22]  to calculate and analyze the correlation between the CC and source lines of code on a large corpus of Java code. For the purpose of the current paper this code is assumed to be correct. The visit statement generates a recursive traversal and each case represents a pattern to detect in the traversed tree, and after the colon (:) an action to perform when it matches (increasing a counter). Most of the cases define simple patterns which identify a particular node type, but two are more interesting, filtering only infix expressions with either && or || as operators.
, infix(_,"||",_) }. Since in Rascal functions are dispatched by patterns, the formal parameter Statement impl is also considered a pattern, namely matching only abstract syntax trees of type Statement. The set TR = P m , and to cover TR the test data must include the set of java statements that match the corresponding patterns. For example, the pattern \if(_,_) would be covered by a program including the statement "if (true) {}", and an input which covers also the patterns for the Boolean operators would be "if (true && false || true) { }".
Otherwise, if all all tests set run without any failure, the mutant is alive and the random test case generation is deemed ineffective. For this last case there are two possibilities: the test set is indeed not good enough to trigger the mutant or the mutant is accidentally semantically equivalent to the original program. At the end, the percent of mutants killed by the test set is called mutation score. The mutation process used in the evaluation is implemented in Rascal for Rascal. The set of mutation operators (Table  2 ) introduces bugs randomly, simulating programmers forgetting cases, making errors in patterns and making errors in the code that is triggered after a pattern is matched. Rascal is a patternoriented language, where patterns govern both data (binding) and control flow (conditionals) dependency.
Patterns occur in the conditions of all structured control-flow statements and the parameters of functions (dynamic dispatch). Common Rascal programming errors are forgetting to update a pattern when a language has changed, accidentally overlapping patterns for which the code is then never or always executed, and writing overly restrictive patterns accidentally. The mutation operators are designed to highlight code which is executed conditionally under a pattern, in order to make observable whether and how quickly the test set can trigger code which depends on (possibly buggy) patterns. To evaluate the efficiency, we measure the number of test cases necessary to kill 100% of the mutants.
The Pico language is a small educational programming language with a Rascal implementation  8. This initial evaluation would also detect bugs in either our implementations or our evaluation methods. After this we continue with our evaluation on the Java language. Table  3   3. Grammar statistics for Java and Pico used in the evaluation. To generate production coverage test data we used the LGen tool  [27]. For both languages we generated the test data and after that ran the software under test, the cyclomatic complexity algorithms, to create the expected result and produce the complete test set.
We expect future investigation on larger meta programs to produce more specific patterns (which does not incur an overhead since pattern extraction is linear in the size of the meta program). More specific patterns will likely lead to larger but not more test cases. We hypothesize that pattern-based testing will still outperform exhaustive grammar-based testing in the number of test cases, while we need to find out experimentally what the mutation scores will show. The mutation score is influenced by the internal complexity of the meta program which may use other kinds of predicates and queries next to pattern matching to guide control flow. More complex meta programs will also require us to extend the set of mutation operators to generate representative mutations. Our future target is a group of type analysis and type inference tools for Rascal, Java and PHP which we wish to test exhaustively.