dataesr/scientific-paragraphs-categorization huggingface dataset:
In the last case, a bi-directional matching of lexemes and aliases between Wikipedia and Prolexbase is not always a good strategy. For instance, the redirects in Example (2) are former names ('Napoleon Square', 'Warka Square') of a square ('Warsaw Uprising Square in Warsaw'). Recall that in Prolexbase such variants are not considered as aliases but refer to different pivots (linked by the diachronic synonymy relation). Finally, we give a penalty if e shares the lexeme with the existing pivot p but either their URL or their type differ (line 18). The distance function is used to compare an incoming Wikipedia entry e with each pivot existing in Prolexbase (lines 4-6). For each of the three languages we get a sorted list of pivots which are similar to e (line 7). The three resulting lists are then merged (line 8) by taking two factors into account: (i) the rank of a pivot in each of the three lists, (ii) its membership in the intersections of these lists.
If no similar pivot was found in any language then a new pivot is proposed (line 9-10). The actual implementation of this algorithm does not scan all existing pivots for each incoming entry e. The entry is directly compared, instead, to the existing lexemes and aliases in the given language, which is optimal if data are indexed. For instance, if we admit that the database engine running Prolexbase implements indexes on B-trees, and that l, p and a denote the worst-case length of a candidate pivot list, the number of the existing prolexemes and of the existing aliases, respectively, the complexity of our algorithm is of O(log p + log a + l). In practice, p was up to four times higher than a, and l was no higher than 10. The candidate pivot searching algorithm proved not to be a bottleneck of our procedure.
As a result, both the Wikipedia and the GeoNames entries were suggested as new Prolexbase entries with two different pivots. This problem occurred most often for regions (European administrative division units) extracted from GeoNames, many of which were cited in the holonym country's language only. During the human validation, proper Polish, English and French equivalents were to be found manually for such names, which made the whole procedure highly time-consuming. Therefore, those region names that were hard to identify manually were left for a further stage of the project.
Prolexemes in the three languages are proposed together with their Wikipedia URLs (which are usually new to Prolexbase). Some aliases, like Wieczne Miasto ('Eternal City') in Table  1 , can be transformed into new pivots. Missing relations as well as derivations can be added manually, and the proposed inflected forms of the Polish prolexeme can be corrected or validated. [ 207 ] evaluation In order to estimate both the quality of the data integration process and the usability of the human validation interface, samples of Wikipedia entries of three different types were selected: celebrity, work and city, containing 500 entries each. A lexicographer was to process these samples type by type in the GUI, collect the statistics about wrongly proposed pivots and count the time spent on each sample.
2011). However, since Prolexbase models both semantic and morphological relations among proper names, we expect the benefit from this resource to be most visible in NLP applications dedicated to morphologically rich languages. The first estimation of this benefit has been performed for Nerf 10  , a named entity recognition tool based on linear-chain conditional random fields. Nerf recognizes tree-like NE structures, i. e. , containing recursively nested NEs. We used the named entity level of the manually annotated 1-million word National Corpus of Polish, NKJP  (Przepiórkowski et al. , 2012)  divided into 10 parts of a roughly equal number of sentences. In each fold of the 10-fold cross validation Nerf was trained once with no external resources (setting A), and once with the list of Polish Prolexbase instances and their types (setting B).
related work Before ProlexFeeder was created, Prolexbase population had been performed mostly manually  (Tran et al. 2005). Uniqueness of pivots was supported by a rather straightforward method based on a prolexeme match alone. Lists of entries and attributes were crafted in spreadsheet files which were then automatically inserted to Prolexbase provided that pivot identifiers appeared in them explicitly. Data were manually looked up in traditional dictionaries, lists and Internet sources. Inflected forms were generated via external tools. The complexity of the model hardly allowed the users to work in this way on more than one language or more than one type at a time. As a result, Prolexbase contained initially mainly French data. ProlexFeeder largely facilitates the lexicographer's work in that most data are automatically fetched, pivot uniqueness relies on more elaborate multilingual checks, entry validation is supported by automatic Prolexbase lookup, and inflected forms are automatically generated.
Prolexbase can be compared to EuroWordNet (EWN)  (Vossen 1998)  and to the Universal Wordnet (UWN) (Melo and Weikum 2009), although both of them are general-purpose wordnets with no particular interest in named entities. All three resources distinguish a language-independent and a language-specific layer. Language-  [ 211 ]  independent entities, i. e. Interlingual Index Records (ILIRs) in EWN and pivots in Prolexbase, provide translation of lexemes in languagespecific layers (but ILIRs unlike pivots form an unstructured list of meanings). UWN, conversely, provides direct translation links between terms in different languages. The main specificity of Prolexbase w. r. t. EWN and UWN is that proper names are concepts in Prolexbase while they are instances in EWN and UWN. Thus, adding a new proper name to Prolexbase implies enlarging its conceptual hierarchy, which does not seem possible e.
Like Freebase 13 but unlike others we manually validate all preprocessed data. Most important, we aim at a limited size but high quality, manually validated resource explicitly dedicated to natural language processing and focused on proper names. Thus, we are the only ones to: • consider proper names as concepts of our ontology, which results in non-standard instance-to-concept matching, • describe the full inflection paradigms for the retrieved names (notably for Polish being a highly inflected language), • associate names not only with their variants but with derivations as well. We also inherit Prolexbase's novel idea of synonymy in which a (diachronic, diaphasic or diastratic) change in the point of view on an entity yields a different although synonymous entity (note that e. g. in WordNet synonyms belong to the same synset and thus refer to the same entity).
This fact enables a higher quality of proper name translation in that a synonym of a certain type is straightforwardly linked to its equivalent of the same type in another language. Last but not least, ProlexFeeder seems to be the only approach in which the problem of a proper integration of previously existing and newly extracted data (notably by avoiding duplicates) is explicitly addressed. Thus, we truly propose an enrichment of a pre-existing proper name model rather than its extraction from scratch. Wikipedia is one of the main sources of data for ontology creation and enrichment in the methods discussed above. An opposed point of view is represented within the Text Analysis Conference 14 (TAC) by the Knowledge Base Population 15 (KBP) task. In particular, its 2011 mono-lingual (English) and cross-lingual (Chinese-English 16 ) 13 We have not found any information about the proportion of truly manually validated Freebase data (as opposed to the initial seeding data, whose validation method is unclear).
conclusions We have described resources, methods and tools used for an automated enrichment of Prolexbase, a fine-grained high-quality multilingual lexical semantic resource of proper names. Three languages,  [ 216 ]  Polish, English and French, were studied. The initial data contained mainly French names. New data were extracted mainly from Wikipedia and partly from GeoNames, and their integration with Prolexbase was based on a manual mapping of the three corresponding typologies. Attention was paid to establishing the degree of popularity of names, represented by their automatically pre-calculated frequency value, based in particular on Wikipedia hits of the corresponding entries. The morphological description of Polish names was supported by automatic inflection tools. The results of these preprocessing tasks were fed to ProlexFeeder, which contains two main modules: the pivot mapping, which automatically finds the proper insertion point for a new entry, and the graphical lexicographer's interface, which enables a manual correction and validation of data.
Recent development in applications such as coreference resolution, corpus annotation and parsing show that enhancement in lexicon/grammar interface is needed with respect to MWEs. For instance, the multi-level annotated National Corpus of Polish represents both named entities and syntactic groups as trees  (Przepiórkowski et al. 2012). Human or automatic annotation of such a corpus can greatly benefit from a rich linguistic resource of proper names such as Prolexbase. However, multi-word names contained in such as resource should possibly already be described as trees that could be reproduced over the relevant occurrences in the corpus. At least two kinds of trees are needed: (i) syntactic parse trees, (ii) semantic trees whose nodes are names embedded in the given name (e. g. [[Wydział Teologii]   Finally, linking Prolexbase to other knowledge bases such as DBpedia or YAGO would combine the Semantic Web modelling benefits with advanced natural-language processing-oriented features and allow interlinking Prolexbase with many other data sets.
Migration Analysis: Optical Flow and Affine Flow The migratory behaviors of CNCC populations captured in time-lapse movies (Figure  1A  A' for example) were quantified using the Optical Flow (OF) image processing method. OF analysis was performed using the algorithm described in  [16]  that combines global and local fitting approaches. OF procedures were executed in Matlab R2011b (MathWorks, Natick, MA, USA) using a freely available code (http://people. csail. mit. edu/celiu/ OpticalFlow/) that implements the Bruhn/Weickert/Schno ¨rr equations  [23]. An additional Matlab code was written for further analysis and plotting of data. OF finds the motion vectors Dx (x,y) ,Dy (x,y) that best describe the differences in intensity between two consecutive images; such that: I x,y,t ð Þ~I xzDx (x,y) ,yzDy (x,y) ,tzDt À Á where I(x,y,t)is the intensity of the pixel located at (x,y)in the frame corresponding to time t , and Dt is the time interval between two frames.
Global analysis of cell movements. Using OF, we analyzed movies of CNCC migration patterns over time in control and EtOH treated embryos. The CNCC migration can be described by lateral and vertical pixel displacements, with observed angle and magnitude of movements resulting in a vector field in which color intensity represents movement magnitude and a color hue represents the movement's direction (Fig. 2 ). The movement analysis (performed on the whole sequence) is shown for three time points: 0, 3 and 6 hours, as well as a sum of the whole sequence data (Fig. 2A8, 2B8, 2C8 ). In the control embryos (Fig. 2A5-7 ), horizontal cell movements were observed in the two bilateral groups of cells moving laterally and in opposite directions, one side moving left (Fig. 2A8 , green-blue colors) and the other moving right (Fig.
CNCC Migration as a Population We were able to characterize the migration of CNCC populations in the living embryos and use EtOH as a representative environmental toxicant to better understand detrimental effects on CNCC migration. The analysis of migration on a population-wide basis was very important because it has been shown that CNCC migrate ''collectively'' and that cooperation between individual cells contributes to the overall directionality of the group  [1]. The majority of the available imaging methods use single cell trajectories that are measured and can be accumulated to ''create'' a population. The results obtained with these approaches can be biased, are labor intensive, and the results often lose important information on dynamics of cell populations during migration. We adapted the image processing method ''Optical Flow''  [16, 23]  which can be easily executed in a Matlab platform and yields several quantifiable and straightforward parameters of group migratory behaviors, allowing us to quantify environmentally induced defects in CNCC migration.
This work was supported by  NIHR01HD50820 (KEW) ,  ICM  Proyecto  P09-022 -F (PO, KEW, KB),  FONDECYT 1111046  ( KEW ),  ANR-47 CONICYT (PO, TV) ,  FONDECYT 1130862  (PO),  ACT-1113  (PO), and  CONICYT Doctoral Fellowship (KB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Abstract. This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment response during flash flood events may provide new and valuable insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset.
MARINE flash flood model The modeling approach is the distributed model MARINE for flash flood forecasting  (Roux et al. , 2011)  with a subsurface transfer module. The predominant factor determining the formation of runoff is represented by the topography: slope and downhill directions. MARINE runs on a fixed time step and is structured into three main modules (Fig. 1 ). The first module allows separating the precipitation into surface runoff and infiltration using the Green and Ampt model; the second module represents subsurface downhill flow with an approximation of the Darcy's law and the standard TOP-MODEL transmissivity profile  (Beven and Kirkby, 1979)  and the third one the overland flow (over hillslopes and in the drainage network): the transfer function component allows routing the rainfall excess to the catchment outlet using the kinematic wave approximation.
Selected validation flash flood events for sensitivity analysis Monitoring flash floods remains a hard exercise  (Borga et al. , 2008)   ment since 2002 (resp. 1994). In this paper an interesting set of 10 validation events is used. This constitutes a large validation dataset given the scarcity of data about flash flood events in general. Validation event hydrographs with distinct shapes represent contrasted hydrological responses to different resonances between rainfall spatio-temporal distribution and catchment physiographic properties, in other words a catchment's spatial and temporal dampening effect (cf. -and a 50 yr return period extreme event (8 September 2002 at Anduze). In addition to classical normalized least-squares criterion, L NP (Table  5 ) considers features characterizing the flood peak (discharge value and time to peak)  (Roux et al.
R 2 of first-order metamodel are above 0. 93 and indicate a good convergence of the method. Event-averaged standard error on S i s is 0. 03 and the following parameter ranking C Z > C KSS > K D1 > K D2 > C K is obtained for the whole catchment flood dataset. According to results displayed in (Table  7 ), soil profile storage capacity controlled by parameter C Z is the most important input factor for 8 of the 10 events considered. Soil storage capacity has a large impact on soil saturation dynamics and thus runoff generation mechanisms. Relation   between soil profile storage capacity and flood event magnitude seems nonmonotonous according to parameters sensitivities (Tables  4  and 7 ). Previous global sensitivity analysis studies already show that the model response is sensitive to C Z  (Bessière, 2008; Roux et al.
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts.
Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.
This complex task involves segmenting various structures from anatomical MRIs, numerical solution of the electromagnetic forward problem, signal denoising, a solution to the ill-posed electromagnetic inverse problem, and appropriate statistical control. This complexity not only constitutes methodological challenges to MEG investigators, but also offers a great deal of flexibility in data analysis. To successfully process M/EEG data, a comprehensive and well-documented analysis pipeline is therefore required. MNE-Python is a sub-project of the more general academic software package MNE  (Gramfort et al. , 2013a) , whose goal is to implement and provide a set of algorithms allowing users to assemble complete data analysis pipelines that encompass most phases of M/EEG data processing. Several of such software packages for M/EEG data processing exist, including Brainstorm  (Tadel et al.
, 2011) , EEGLAB  [Delorme and Makeig (2004)  and  Delorme et al. (2011) ], FieldTrip  (Oostenveld et al. , 2011) , NutMeg  (Dalal et al. , 2011)  and SPM  (Litvak et al. , 2011). These other packages are implemented in MATLAB, with some dependencies on external packages such as OpenMEEG  (Gramfort et al. , 2010b)  for boundary element method (BEM) forward modeling or NeuroFEM for volume based finite element method (FEM)  (Wolters et al. , 2007)  forward modeling. Many analysis methods are common to all these packages, yet MNE-Python offers some unique capabilities, in a coherent package facilitating the combination of standard and advanced techniques in a single environment described below. While MNE-Python is designed to integrate with packages within the Python community, it also seamlessly interfaces with the other components of the MNE suite (and other M/EEG analysis tools) because it uses the same Neuromag FIF file format, with consistent analysis steps and compatible intermediate files.
MNE-Python and the related MNE-Matlab sub-package that ship with MNE are both open source and distributed under the new BSD license, a. k. a 3-clause BSD, allowing their use in free as well as commercial software. The MNE-Python code is the most recent addition to the MNE suite. After an intensive collaborative software development effort, MNE-Python now provides a large number of additional features, such as time-frequency analysis, non-parametric statistics, connectivity estimation, independent component analysis (ICA), and decoding, a. k. a. multivariate pattern analysis (MVPA) or simply supervised learning, each of which is readily integrated into the standard MNE analysis pipeline. This comprehensive and still growing set of features available in the MNE-Python package is made possible by a group of dedicated contributors coming from multiple institutions, countries, and research areas of expertise who collaborate closely.
(2011)  and  Buitinck et al. (2013) ] is required for decoding tasks; and the Python Data Analysis Library (Pandas) is used for interfacing with spreadsheet table oriented data processing tools as often used in econometrics and behavioral sciences. Mayavi, Scikit-Learn and Pandas are only required by a small subset of the code, and are therefore considered optional dependencies. Besides these general libraries, MNE-Python has some other optional dependencies on neuroimaging packages such as Nibabel for reading and writing volume data (MRI, fMRI). The online documentation of MNE is generated with Sphinx http://sphinx-doc. org. At present, MNE-Python contains more than 44,000 lines of Python code with around 22,000 lines of comments, contributed by a total of 35 persons. In this paper, we describe the MNE-Python package in detail, starting from the standard analysis pipeline to more advanced usage.
THE MNE-PYTHON STANDARD WORKFLOW FOR M/EEG DATA ANALYSIS This section describes the standard analysis pipeline of MNE-Python. First, we discuss sample datasets that are available for working with MNE-Python. They allow readers to follow along with the workflow and examples in this manuscript. We then present the core Python structures employed in such an analysis, and use these to go from raw data preprocessing to the most commonly used linear inverse methods. The full script corresponding to the steps described below is available at the end of this section in Table  1.
SAMPLE DATASETS The MNE software package provides a sample dataset consisting of recordings from one subject with combined MEG and EEG conducted at the Martinos Center of Massachusetts General Hospital. These data were acquired with a Neuromag VectorView MEG system (Elekta Oy, Helsinki, Finland) with 306 sensors arranged in 102 triplets, each comprising two orthogonal planar gradiometers and one magnetometer. EEG was recorded simultaneously with 60 electrodes. In the experiment, auditory stimuli (delivered monaurally to the left or right ear) and visual stimuli (shown in the left or right visual hemifield) were presented in a random sequence with a stimulus-onset asynchrony (SOA) of 750 ms. To control for subject's attention, a smiley face was presented intermittently and the subject was asked to press a button upon its appearance.
These data are used in the MNE-Python package and in this manuscript for illustration purposes. Small samples from these data are also used in the MNE-Python test suite which guarantees reproducibility of results across systems and environments, as well as the absence of regression when new code is contributed. This sample dataset can also serve as a standard validation dataset for M/EEG methods, hence favoring reproducibility of results. For the same purpose, MNE-Python facilitates easy access to the MEGSIM datasets  (Aine et al. , 2012)  that include both experimental and simulated MEG data. operating procedure by offering convenient objects that facilitate data transformation. Continuous raw data are stored in instances of the Raw class. MNE-Python supports reading raw data from various file formats e.