dataesr/scientific-paragraphs-categorization huggingface dataset:
Building a Model of Aesthetic Perception Below, we discuss model building by means of Eureqa Desktop. Eureqa is a tool that uses a recent breakthrough in machine learning to unpick intrinsic relationships within complex data and explains them as simple mathematical formulas  (Schmidt and Lipson, 2009). When the target expressions are defined by Equations (8-10), the basic, trigonometric and exponential functions are selected in the formula building blocks of Eureqa Desktop. In detail, the basic functions include addition, subtraction, multiplication, division and the constant operation. The trigonometric functions include sine, cosine and tangent functions. The exponential functions include exponential, natural logarithmic, factorial, power and square-root functions. Before model building, the 10 selected features and emotion values were smoothed, outliers removed and normalized with the default algorithms embedded in Eureqa Desktop.
ACKNOWLEDGMENTS We would like to thank the anonymous reviewers and the handling associate editor for their insightful comments. This project was supported by the  "National Natural Science Foundation of China " [Grant number  61203364 ]. The second author also acknowledges support of the Austrian  COMET-K2  programme of the  Linz Center of Mechatronics (LCM) , which is funded by the  Austrian federal government  and the federal state of Upper Austria. We thank  Dr. Honglin Xu  and  Dr. Xueyan Wei  for their help with the algorithm design and the psychological experiment. This publication reflects only the authors' views.
A final wash was performed at 4 °C with distilled water to remove excess of buffer salts. Then, brain sections were dried overnight at room temperature and stored until counting. Before being used for image acquisition and data analysis, slides containing brain sections were exposed under tritiumsensitive phosphor screens in the dark for 10 days at -20 °C. Images were then captured with a computer-controlled Cyclone phosphorimaging scanner using the OptiQuant acquisition and analysis software (Packard Instrument Company, Meriden, CT, USA). Optical densities expressed as digital light units per mm 2 over [ 3 H] standard spot were measured. Specific binding was determined by subtracting nonspecific binding from total binding. Saturation binding data were fitted according to a one-site binding (hyperbola) model using Graph Pad prism (Graph Pad Prism 4.
2 MPa absolute (2 atm) argon (100 vol %) or nitrogen (100 vol%; controls) for 1 h. Then, the vials were decompressed, filtered using a filtermate Perkin-Elmer (Waltham, MA, USA), placed in 24-well plates previously coated with 0. 5% polyethylenimine and left to dry for one additional hour at 50 °C before being added with 100 μl scintillant to allow radioactivity counting. Specific binding was determined by subtracting nonspecific binding from total binding. Saturation binding data were analyzed as described above. Changes induced by argon in Bmax and Kd values were calculated and expressed as percentage from controls. The ratio of Bmax to Kd was calculated and used to estimate the activity of the mu-opioid neurotransmission in the presence of nitrogen or argon.
Data presentation and statistical analysis Data were expressed as the median and quartiles values, and analyzed using nonparametric statistical methods. Following ad hoc analysis of variance, between-group comparisons and within-group comparisons were performed using the Mann-Whitney U-test and Wilcoxon signedrank t-test, respectively. Statistical significance was set at P ⩽ 0. 05. N7758) were purchased from Sigma-Aldrich (Illkirch, France). Bacitracin was purchased from MP biomedicals (Santa Ana, CA, USA), and [ 3 H]DAMGO (66 Ci mmol -1 ) from Amersham Biosciences (Buckinghamshire, UK). Oxygen, nitrogen and argon of medicinal grade were purchased from Air Liquide (Paris, France). Gas mixtures composed of 75 vol% nitrogen+25 vol% oxygen or 75 vol% argon+25 vol% oxygen were obtained using calibrated gas flowmeters and gas analyzers.
(b) As assessed postmortem immediately after being challenged with amphetamine, rats pretreated with repeated administration of saline solution (SA) or amphetamine (AA) had increased mu-receptor activity in the nucleus accumbens (as estimated by the ratio of Bmax to Kd) compared with rats pretreated and challenged with saline solution (SS). Opioid mu-receptor activity in control rats pretreated and challenged with amphetamine was taken as a 100% value. Data are expressed as the median value ± 25th-75th percentiles; n = 7-8 per condition. *P o0. 005 and **P o0. 001 vs SS; # P o0. 001 vs SA. AA, pretreatment+challenge with amphetamine; SA, pretreatment saline+challenge amphetamine; SS, pretreatment+challenge with saline. 23] 34  In contrast with these gases, argon has dual effects on tissue-plasminogen activator,  35  show no action on the N-methyl-D-aspartate receptor,  36  and has facilitating effects on the GABA-A and benzodiazepine receptors.
fMRI Data Pre-Processing The fMRI data were analyzed using SPM8 software (Wellcome Department of Cognitive Neurology, London, UK). For the functional analysis (pushing task), the first four volumes of all EPI series were excluded from the analysis to allow the magnetisation to approach a dynamic equilibrium. Data processing started with slice time correction and realignment of the EPI datasets. A mean image for all EPI volumes was created, to which individual volumes were spatially realigned by means of rigid body transformations. The structural image was co-registered with the mean image of the EPI series. Then the structural image was normalized to the Montreal Neurological Institute (MNI) template (resampling voxel size of 3 × 3 × 3 mm), and the normalization parameters were applied to the EPI images to ensure an anatomically informed normalization.
Participants showing head motion above 3. 5 mm of maximal translation (in any direction of x, y, or z) and 2. 0 • of maximal rotation during scanning would have been excluded (this was not the case for any participant). A spatial filter of 8 mm full-width at half maximum (FWHM) was used. Low-frequency drifts in the time domain were removed by modeling the time series for each voxel by a set of discrete cosine functions to which a cut-off of 128 s was applied. For the resting state analysis, the first five volumes were discarded to allow the magnetisation to approach a dynamic equilibrium, and for the subjects to get used to the scanner noise. Part of the data pre-processing, including slice timing, head motion correction (a least squares approach and a 6-parameter spatial transformation) and spatial normalization to the MNI template, were conducted using the Data Processing Assistant for Resting State fMRI toolbox (DPARSF; Chao-Gan and Yu-Feng, 2010).
fMRI Data Analysis Brain activity during the pushing tasks was analyzed at the first (within-subject) level using regressors for left and right pushing blocks. Each block (20 s duration) was convolved with a hemodynamic response function and head movement parameters were included in the design matrix. We were interested in the contrast comparing left and right pushing (functional localizer) as well as left pushing compared to rest (functional localizer and pushing task during the experiment). Resting state activity was analyzed in terms of regional homogeneity. Based on the fact that fMRI activity is typically spatially clustered  (Tononi et al. , 1998) , this analysis approach determines voxels at which BOLD fluctuates in synchrony with its neighboring voxels  (Zang et al. , 2004; Wu et al.
, 2007). The analysis was performed with the toolbox DPARSF (Chao-Gan and Yu-Feng, 2010), using Kendall's coefficient of concordance of the time series of a given voxel with those of its nearest 26 neighbors. ReHo was calculated within a brain-mask, which was obtained by removing the tissues outside the brain using the software MRIcron (https://www. nitrc. org/projects/mricron). The resulting images were entered into a series of one-sample T-tests at the second (between-subject) level. For the functional localizer task, the resulting SPMs (left-push > right-push, leftpush > rest) were thresholded at p < 0. 05 with familiy-wise error correction (FWE). For the pairwise comparisons of resting state activity (ReHo) and functional activations in the pushing task before and after the intervention, the resulting statistical maps (post-local > baseline, post-global > baseline, post-local > post-global, post-global > post-local) were thresholded at p < 0.
Acknowledgments We thank  Gabriele Faust ,  Nadine Taube ,  Sebastian Schröder ,  Nina Lisofsky ,  Margarete Oberländer ,  Ilka Popp ,  Katharina Voigt ,  Katharina Wermuth ,  Anika Werner , and  Manuel Zellhöfer  for their support with this study. FS is financed by  ERC Advanced  Grant  323674  "FEEL".
Specifically, there were seven healthy pairs, six discordant and four concordant pairs for lifetime DSM-IV diagnoses. In addition, on the day of blood sampling, current psychiatric symptoms were evaluated using the Brief Symptom Inventory (BSI). 33, 34 The BSI is a self-administered 46-item screening instrument aimed at identifying the experience of psychopathological symptoms during the last 30 days. It is composed by six subscales (depression, phobic anxiety, paranoid ideation, obsession-compulsion, somatization and hostility) conceived for use in both clinical and nonclinical samples. Items are rated on a five-point scale of distress, according to self-perception of symptoms. Descriptive data from the current sample are summarized in Table  1. Overall, there were four concordant, six discordant and seven healthy MZ twin pairs (Table  1 ).
Methylation data The Illumina Infinium HumanMethylation450 (450 K) BeadChip  35, 36  was employed with peripheral blood DNA samples for all 34 participants. Specifically, by genotyping sodium bisulfite-treated DNA, DNA methylation is assayed by this platform at 4450,000 CpG sites across the genome at single-base resolution; next, bisulfite-converted DNA undergoes wholegenome amplification, before being fragmented and hybridized to microarray probes. The DNA methylation fraction of each CpG site is estimated as β = M/(M+U+α); M and U stand for methylated and unmethylated fluorescence intensities, and α is an arbitrary offset applied to stabilize β-values with low intensities.
1| threshold. Of note, as shown in the Results section, CpG probes with an intrapair Δβ ≥ |0. 1| are highly infrequent in this data set, suggesting this may be a proper threshold. Then, after the previous step, all 4450,000 CpG sites are examined to determine which of them show variability only within concordant, discordant or healthy pairs; they are later examined via pathway analysis to evaluate the potential biological relevance of their stochastic disruptions. Pathway analysis. Relevant sets of genes from whole-genome analyses indicated in the Results section were uploaded to Cytoscape (version 3. 0. 2)  41  , using Reactome FI Cytoscape Plugin 4,  42  network version 2013, to obtain data on underlying reactions, pathways and biological processes. All the outcome assessment steps (that is, statistical analyses) described in this section were conducted by an investigator (AC-P) who did not participate in sample collection, clinical evaluation, zygosity determination or DNA methylation measurement and data pre-processing.
ACKNOWLEDGMENTS Supported by the  Spanish   SAF2008-05674-C03-01 ,  European Twins Study Network on Schizophrenia Research Training Network  (grant number EUTwinsS;  MRTN-CT-2006-035987 ), the  Catalan   2014SGR1636  and the  Ministry of Science and Innovation  ( PIM2010ERN-00642 ) in frame of ERA-NET NEURON. AC-P was funded by  CONACyT (Mexico). We are indebted to  X Goldberg  and  S Alemany  for their collaboration in sample collection, and to  J Ignacio Martín-Subero  for his critical reading of the manuscript. We are indebted to the Genotype service at  Centro Nacional de Investigaciones Oncologicas (CNIO Madrid, Spain)  for performing array hybridization and providing technical support.
MEG RECORDINGS Brain activity was recorded with a 306-channel Neuromag system (Elekta-Neuromag Oy; Helsinki, Finland) in a magnetically shielded room (Euroshield Oy, Eura, Finland) at NeuroSpin (CEA, DSV/I 2 BM, France). The MEG device includes two orthogonal planar gradiometers and one magnetometer per sensor unit for a total of 204 planar gradiometers and 102 magnetometers. Prior to each experimental run, the position of the participant's head in the MEG dewar was measured by feeding the HPI coils with distinctive currents prior to actual brain measurements. The ECG and EOG (horizontal and vertical) were simultaneously recorded for artifact corrections and trial rejections (see pre-processing). Data were acquired with a sampling frequency of 1 kHz, low-pass filtered at 330 Hz and high-pass-filtered at 0.
ANATOMICAL MRI AND MEG-MRI COREGISTRATION Anatomical T1-weighted MRIs were obtained for each participant with a 3T MRI scanner (Siemens) with 1 × 1 × 1. 1 mm resolution. Digitized anatomical landmarks, HPI and head shape information were used for proper realignment of MEG data with each individual' MRI. The coregistration was performed using both mrilab and mne_analyze.
MEG DATA PREPROCESSING Data were pre-processed in two steps. First, magnetic interferences originating outside of the MEG helmet were suppressed by using Signal Space Separation  (Taulu and Simola, 2006)  provided by MaxFilter (Elekta-Neuromag Oy; Helsinki, Finland). The median head position of each participant over the three experimental runs was used as reference for the other two runs. In the majority of cases, the second run was the reference run. Second, PCA was performed to remove components accounting for ECG and EOG variance using Graph (Elekta-Neuromag Oy; Helsinki, Finland). The average cardiac and blink artifacts were computed on the basis of ECG and EOG recordings. Components were manually checked for each sensor type (gradiometers and magnetometers) and saved as separate matrices (for detailed procedure, see: http://www.
SOURCE RECONSTRUCTION AND DATA PROCESSING Data were processed using in-house MNE-python pipelines elaborated on existing procedures (http://mne-tools. github. com/ mne-python-intro/, RRID: SciRes_000118). Continuous data were segmented into 1 s epochs centered on auditory stimulus onsets from -400 to 600 ms post-auditory stimulus onset. Baseline correction was applied from -400 to -250 ms before the auditory onset (i. e. , -400 to -30 ms with respect to the visual onset for DEV stimuli). Epochs were averaged per condition of interest and source reconstructed on the whole cortex (dSPM) to provide the temporal course of source estimates. For time-frequency analysis, a Morlet wavelet transform was used on single trial source estimates from 2 to 120 Hz. Data were decimated three times (i. e. , new sampling frequency of 333 Hz) and computed as a percentage change from baseline  (Kiebel et al.
STATISTICAL ANALYSIS Statistical analyses performed in sensor space used FieldTrip routines and analyses in source estimates used MNE-python. Both analyses used cluster-level permutation tests temporally or spatiotemporally  (Maris and Oostenveld, 2007; Oostenveld et al. , 2011). The number of permutations applied was 1024. For source estimates, an epsilon value of 0. 1 was added in order to correct for spurious and transient variance shifts leading to clusters splitting. Detailed examples of the code can be found here: http://martinos. org/mne/auto_examples/stats/plot_cluster_stats_spatio_temporal. html#example-stats-plot-cluster-stats-spatio-temporal-py. All significant results are reported for t values of 3. 13 and corrected p values <0. 01 unless otherwise specified (for instance, lower t = 2.
ACKNOWLEDGMENTS This work was supported by a  Marie Curie   IRG-249222 , an  ERC-YStG-263584  and an  ANR10JCJC-1904  to Virginie van Wassenhove. We thank  Marco Buiatti  and  Leila Rogeau  for their daily assistance in the  MEG lab, NeuroSpin UNIACT  for their help in recruiting and preparing participants, and Alexandre.
In this article, we present the Brazilian Portuguese Lexicon, a new word-based corpus for psycholinguistic and computational linguistic research in Brazilian Portuguese. We describe the corpus development, the specific characteristics on the internet site and database for user access. We also perform distributional analyses of the corpus and comparisons to other current databases. Our main objective was to provide a large, reliable, and useful word-based corpus with a dynamic, easy-to-use, and intuitive interface with free internet access for word and word-criteria searches. We used the Núcleo Interinstitucional de Linguística Computacional's corpus as the basic data source and developed the Brazilian Portuguese Lexicon by deriving and adding metalinguistic and psycholinguistic information about Brazilian Portuguese words.
We obtained a final corpus with more than 30 million word tokens, 215 thousand word types and 25 categories of information about each word. This corpus was made available on the internet via a free-access site with two search engines: a simple search and a complex search. The simple engine basically searches for a list of words, while the complex engine accepts all types of criteria in the corpus categories. The output result presents all entries found in the corpus with the criteria specified in the input search and can be downloaded as a. csv file. We created a module in the results that delivers basic statistics about each search. The Brazilian Portuguese Lexicon also provides a pseudoword engine and specific tools for linguistic and statistical analysis. Therefore, the Brazilian Portuguese Lexicon is a convenient instrument for stimulus search, selection, control, and manipulation in psycholinguistic experiments, as also it is a powerful database for computational linguistics research and language modeling related to lexicon distribution, functioning, and behavior.
Introduction Word-based corpora are extremely important in providing metalinguistic, psycholinguistic, and statistical information about lexicons. They are used for selecting, controlling, and manipulating words in psycholinguistic experiments  [1] [2] [3] [4]. Furthermore, word-based corpora provide rich databases of language behavior and lexicon distribution for computational linguistics Lexicon will be helpful and useful for searching, controlling, manipulating, and selecting stimuli in psycholinguistic experiments  [2, 34] , for computational linguistic analysis  [5, 35] , and for language modeling  [7, 8, 36, 37] , thus filling a gap in Brazilian Portuguese research, the lexicon, and language description and information. We identified the two largest and most representative corpora of Brazilian Portuguese at the Linguateca: the Corpus Brasileiro <http://corpusbrasileiro.
linguateca. pt/acesso/NILCsaocarlos. html/>, mainly newspapers (45. 02%) and journals (42. 07%) but also essays (3. 65%), letters (1. 87%), legal documents (1. 77%), encyclopedias (1. 54%), magazines (1. 48%), literature (1. 38%), and didactic (1. 22%) sources <http://www. linguateca. pt/acesso/desc_corpus. php?corpus=SAOCARLOS/> as shown in Fig 1. The NILC's source origins are a remarkable collection of written text consisting primarily of the Folha de São Paulo's newspaper database, many other journalistic sources, and a variety of different textual genres  [38]. Regarding its size, Brysbaert and New (2009, p. 980)  [11]  stated that "[. ] for most practical purposes, a corpus of 16-30 million words suffices for reliable word frequency norms. In particular, there is no evidence that a corpus of 3 billion words is much better than a corpus of 30 million words"; the same finding was robustly replicated in the SUBTLEX-NL  [13]  and SUBTLEX-UK  [12].
The Brazilian Portuguese Lexicon was developed by processing the data from the NILC available in the Linguateca using R software version 3. 1. 2 (R Foundation for Statistical Computing, Vienna, Austria)  [39]  <http://www. r-project. org/>, 'vwr'  [40]  <http://cran. r-project. org/web/packages/vwr/index. html/>, 'languageR'  [41]  <http://cran. r-project. org/web/ packages/languageR/index. html/>, and 'psych'  [42]  <http://cran. r-project. org/web/packages/ psych/index. html/> R packages. After establishing the corpus, we imported it to a MySQL database on a free internet server <http://www. biz. nf/> and constructed the Brazilian Portuguese Lexicon internet site using the programming languages HTML, Java, and CSS for the visual interface; and MySQL and PHP for the logical functions and interface between the user and the corpus.
In the next sections, in materials and methods, we explain the data acquisition, organization, and computation, as well as the lexicon construction, and the lexicon information. Afterwards, we describe the internet site, the search engines, the search results, the site resources, and the Creative Commons License. In the results section, we present the general framework of the Brazilian Portuguese Lexicon, the lexicon distributions, and comparisons to other corpora. Then, in the discussion section, we discuss these results in detail, and end with final remarks, limitations, and further developments of the Brazilian Portuguese Lexicon.
Lexicon information A number of columns that contain psycholinguistic and metalinguistic information about each Brazilian Portuguese word in the corpus were computed and added to the database. Table  2  summarizes all 25 columns that were created and describes the information they contain. Orthography column presents the orthographic form of each word in the corpus (e. g. , cachorro, 'dog'). Grammatical category shows the grammatical category to which the word belongs, according to the first seven rows in Table  3  (e. g. , cachorro = nom). Grammatical information provides all grammatical information for each entry (i. e. , gender, number, class, mood, tense, and person), according to the conventions listed in Table  3  (e. g. , cachorro = m, s [masculine, singular])  [21, 22, 43, 44, 48].
Internet site The Brazilian Portuguese Lexicon was conceived as an easy, fast, and accessible Brazilian Portuguese word-based corpus with psycholinguistic, metalinguistic, and statistical information for everyone. We successfully fulfilled three very important criteria in developing the Brazilian Portuguese Lexicon: 1) we created a large, reliable, and complete database with information about Brazilian Portuguese words and lexicon, 2) we developed an instinctive and friendly interface between the user and the corpus, and 3) we offered free internet access, downloading of the full database, and specific search exporting. To create the internet site, we imported to a MySQL database the whole corpus from a. txt file in which each lexical entry was placed on a different row and each column had a lexical information.
The final corpus with 25 columns of information about the words and 215,175 rows with different entries (a 49 MB file) was hosted on a free server that fulfilled our requirements (i. e. , <http://www. biz. nf/> offers: 250 MB space, 5 GB data transfer, 100 MB MySQL database, PHP 4/5, MySQL 5, POP3/SMTP webmail, FTP access, free host and free domain). We acquired the specific domain <http://www. lexicodoportugues. com/>, at which the Brazilian Portuguese Lexicon corpus is accessible, and linked it to our server. Then, we developed the internet page using the algorithm provided in S3 Text. For the search engines and the results interface, we established some conventions that should be used when searching and interpreting the results. Table  3  presents the grammatical categories (first seven rows) and grammatical information conventions  [4].