dataesr/scientific-paragraphs-categorization huggingface dataset:
2-D subspace spanned by two vectors 1 v and 2 v for which a 90 ° rotation transforms 1 v to 2 v and 2 v to 1 v -(here, designated 'ROT'). This is spanned by: (i) θ θ = -   , all else 0 1 1 0,0,0,0, ,0, ,0,0 2 2          -           (ii) θ θ = -   , all else 0 1 1 0,0,0,0,0, ,0, ,0 2 2          -           4. 1-D subspace in which a diagonal mirror negates coordinates (here, designated "DII"). This is spanned by: (i) | β β - = -, all else 0 1 1 , ,0,0,0,0,0,0,0 2 2          -           We compute the normalized principal axes { } Once all components have been assigned to symmetry classes, we rank-order components within each class. This resulted in unambiguous pairing between natural image dataset and psychophysics in all but one pair of components in three image analyses (out of a total of 9 components for each of 31 separate image analyses).
In those analyses (image analyses N = 2, R = 48, 64, 128 in the van Hateren database), there were two nearly-degenerate SYM components in the image dataset; we paired these components with the psychophysics data by maximizing their overlap. To compare between natural image and psychophysics analyses, we compute the fractional contribution ( ) ( ) ( ) ( ) ( ) | \ / [ , , , ] i i i i i f f f f f β β θ α - =  of sets of coordinates to each principal component, where the components of ( ) i f  are given by: ( |- | - 2 2 = + i i i f β β β ξ ξ ) ( ) ( ) ( ) ( ) \ / \ / 2 2 = + i i i f β β β ξ ξ (0. 4) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 2 2 2 2 = + + + i i i i i f θ θ θ θ θ ξ ξ ξ ξ     (0. 5) ( ) ( ) ( ) 2 = i i f α α ξ (0. 6) (0. 3) ( ) ( ) ( ) ( ) ( ) | \ / 1 i i i i f f f f β β θ α -+ + + = for each normalized component ( ) i ξ .
Analysis variants for Penn Natural Image Database In Figures  1  and 3 , we reported results using image analyses with varying values of the block-average factor N (N = 2, 4) and patch size R (R = 32, 48, 64). In Figure  1 -figure supplement 3, we show that the relative variation in different image statistics (first shown in Figure  1E ) is not an artifact of our image analysis pipeline, as the pattern of variation is destroyed if white-noise image patches are instead used. In Figures 3-figure supplement 5-3-figure supplement 8, we show that the comparison between natural image and psychophysical analyses is consistent across a wider range of image preprocessing parameters: N = 2,  4, 8, 12, 16, 20 and R = 32, 48, 64, 80, 128. Note that sampling limitations restrict some combinations of N and R (e.
Comparison with van Hateren Database All analyses reported in Results and shown in Figures  1  and 3  were performed on a set of images from the UPenn Natural Image Database  (Tkačik et al. , 2011). Here, we extend our analyses to a set of 2300 images from the van Hateren image database (van Hateren and van der Schaaf, 1998), using the same set of parameters used to analyze images from the UPenn database, with block-average factors  N = 2, 4, 8, 12, 16, 20 and patch sizes R = 32, 48, 64, 80, 128. Note that we are able to perform a larger number of analyses (specific combinations of N and R) than was performed using the Penn database, as we have a larger selection of images and therefore do not face the same sampling limitations. Figures 3-figure. This shuffling, applied to the psychophysical data, significantly decreases the overlap between image analyses and psychophysical data.
Data analysis and simulation We analyzed both the model-level and parameter-level of analysis in moving windows of 50 trials (451 window frames for the 500 trials in total). This window-size was chosen based on a tradeoff between the reduced power in smaller window sizes and loss of temporal resolution in larger ones. Rates of acquisition were calculated for 0. 33, 0. 5, 0. 66, and 0. 99 threshold probabilities of selection based on the posterior probabilities obtained from the Model level of analysis.
We may not be able to pinpoint the exact switch point but with our current analysis and by testing multiple thresholds, we can generate a consistent and reliable measure for the model acquisition rate of each model and participant. In addition to selecting the simplest, most-viable function, the simpler functions also enjoyed faster acquisition rates for different switch combinations (Figure  4 ). Therefore, simpler functions have an advantage both in the extent of selection and also in their rates of selection. Both these independent measures suggest that the learning of simpler models is preferred over that of equally viable complex models. The Occam's razor principle states that when multiple models are equally capable of explaining a dataset, the simplest one should be selected. Recent studies have revealed that humans exhibit Occam's razor-like parsimony in perceptual and sensorimotor learning tasks  (Gershman, 2013; Genewein and Braun, 2014).
ACKNOWLEDGMENT We would like to thank  Daniel Braun ,  Loes van Dam , and  Jacqueline Fulvio  for insightful discussions. Funding for this research was provided by the  European Community 's  Seventh Framework Programme   FP7/2007-2013  (grant agreement number  214728-2 ), and by the  VICI grant  ( 453-08-004 ) from the  Netherlands Organization for Scientific Research  grants.
Wu and Thierry (2011)  presented Chinese-English participants with pairs of pictures and asked them to judge whether the labels of the two pictures rhymed in L2 English (Experiment 1) or shared a character in L1 Chinese (Experiment 2). The stimuli were manipulated such that some of the picture pairs overlapped in one language (e. g. rhymed in L2 English), while others overlapped in the other language (e. g. character overlap in L1 Chinese). EEG data was recorded throughout the experiment to examine the neurocognitive indices of cross-language lexical access. Consistent with the standard N400 priming effect  (Kutas and Hillyard, 1984) , when asked to evaluate L1 Chinese overlap, participants found picture pairs whose labels overlapped in Chinese easier to process relative to unrelated picture pairs.
Production task Following the main experiment, participants also completed a production task, where they were asked to label a series of 60 images aloud in both German and English. Half of the participants labeled the images in German first, while the other half labeled in English first. Stimuli used in the production task were the prime images from the within-and between-language conditions. The answers provided by the participants were automatically recorded via a microphone, time-locked to the appearance of the image on-screen. Production data was analyzed offline to determine whether participants labeled the prime images from these conditions with the label we had chosen for each picture. If the label provided by a participant was different from the chosen label for an image, then the trial containing this image was removed from the main experiment.
EEG RECORDING AND ANALYSIS Electrophysiological data was recorded using the Biosemi Active Two Amplifier system at a sampling rate of 2048 Hz from 32 Ag/AgCl electrodes placed according to the 10-20 convention. Electrode offsets were kept at less than 25 μV. Electroencephalogram was re-referenced offline to the averaged mastoid reference. EEG data was then filtered off-line using a 0. 1 Hz high-pass forward filter and a 20 Hz low-pass, zero-phase shift filter. Averaging and artifact rejection were carried out using the BESA software (Version 5. 3). Blink and movement artifacts were automatically rejected using a 100 Hz amplitude cut-off across all electrodes. Epochs were defined from -200 to 1000 ms from the onset of the auditory target word. We then analyzed the data in 50 ms time windows (from 0 to 1000 ms) to determine the onset and offset of significant differences between conditions.
Based on this analysis, and the known onset of the N400  (Kutas and Hillyard, 1984) , we focused our analyses on the time window between 300 and 400 ms (the N400 window;  Desroches et al. , 2009). Average ERP waveforms were quantified by computing mean amplitudes per subject, electrode and condition in the selected time windows. ERP waveforms were baseline corrected by subtracting the mean amplitude for the baseline time window (-200 to 0 ms) from the selected time window. For purposes of data reduction, a selection of electrode locations was entered into data analysis, 16 electrodes divided into four regions and four lateral columns: frontal (F7, F3, F4, F8), fronto-central (FC5, FC1, FC2, FC6), central (T7, C3, C4, T8), and centroparietal (CP5, CP1, CP2, CP6). Our analysis was based on specific planned comparisons between related conditions (identical, within-and between-language conditions) and the unrelated condition instead of a general condition effect; we therefore do not report the omnibus ANOVA (see  Abelson and Prentice, 1997).
The model is freely available as a MATLAB toolbox  (Leman et al. , 2001, see www. ipem. ugent. be/Toolbox), allows starting the simulations from the auditory signal and has been already used in several studies investigating tonal structure processing (e. g. ,  Koelsch et al. , 2007; Marmel et al. , 2010). In the next sections, we first describe the auditory model in more detail. We then assess the ability of this model to account for behavioral and neurophysiological data of studies investigating the processing of chord sequences. We then consider the studies reporting interfering effects between simultaneous musical and linguistic syntactic processing, and we address in the discussion section, the simulation of some developmental data, as well as intercultural comparisons. The overall set of simulation is summarized in Table  1 , quoting the main outcomes of studies and simulations.
This component takes neuronal firing probabilities as input and generates summary autocorrelation patterns as output. The processing details of the pitch processing can be checked in the MATLAB routines of the Toolbox (www. ipem. ugent. be/Toolbox). A tutorial has been proposed by  Leman et al. (2001). To illustrate the behavior of the pitch periodicity analysis, let us consider the example of a stimulus with frequency components of 600, 800, and 1000 Hz (see  Van Noorden, 1982). These frequencies are processed by three different auditory channels of the peripheral auditory system. The channel corresponding to 600 Hz will show periods at multiples of 1/600 (e. g. ,  1. 66, 3. 33, 5, 6. 66, 8. 33 ms) , the channel corresponding to 800 Hz will show periods at multiples of 1/800 (1. 25, 2. 50, 3.
Simulations were carried out for all stimuli used in these studies. Original MIDI files from the published studies were used to generate sound files with a piano timbre. In all simulations, chord duration was 600 ms, and the target chord sounded for 1200 ms. Figure  4  reports the mean differences between the TC of the related and less-related target chords as a function of the memory parameters. Statistics were performed on the TC values of all target chords from the related and unrelated conditions. The ASTM model accounts for the data on the harmonic priming effects of  Bigand and Pineau (1997)  and  Pineau and Bigand (1997 ; Figure  4B ). The model also accounts for the data of the priming effects reported by  Tillmann and Bigand (2001)  with scrambled chord sequences (Figures  4C, D ).
Data and statistical analyses Individual dim light melatonin onset (DLMO) and amplitude were calculated respectively as the 25% upward crossing and maximal secretion of the smoothed melatonin profiles (locally weighted smoothing LOESS method, span = 0. 5). The 25% threshold was calculated for each subject from his/her profile obtained during the dark condition of the study (between 20:00-04:00). DLMOs were compared within subjects across session and between subjects using a two-way ANOVA for repeated measures (time, subject). DLMO were compared between young and aged groups using a two-way ANOVA for repeated measures (time, age groups). Melatonin suppression scores were determined by the following formula: Mel Supp~{((01 : 30 h mel pg=ml ½ ) {(00 : 30 h mel pg=ml ½ ))=(00 : 30 h mel pg=ml ½ ) Melatonin suppression values were then normalized to controladjusted (CA) suppression scores by subtracting the control (no light) condition percent suppression scores for each subject from that same subject's light exposure score (Figure  2 ): Mel Supp CA~Mel Supp Light Exposure Session ð Þ {Mel Supp Control Session ð Þ This technique accounts for the normal individual rise or fall in plasma melatonin levels with respect to the light-induced changes  [45, 46].
RPN was funded by '' Ministere de l'Enseignement Superieur et de la Recherche Francais'' and ''Federation des Aveugles et Handicapes Visuels de France ''. CC was funded by  Fondation de France  ( Berthe Fouassier  grant). This work was supported by grants from  FP6-EUCLOCK ,  ANR-09-MNPS-040 ,  ANR 12-TECS-0013-01 ,  GIS-Vieillissement, Rhone-Alpes Cible, Rhone-Alpes Cluster HVN , and  Retina France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
STIMULI Original stimuli were recorded three times each in a sound studio at the Voice Neurocognition Laboratory (http://vnl. psy. gla. ac. uk/). Three males and three females voiced the phonemes /ta/ and /pa/ and stimuli with the clearest separation between the consonant and the vowel (as seen on spectrograms) were selected. Stimuli were then manipulated using STRAIGHT  (Kawahara, 2003 (Kawahara, , 2006) )  running under Matlab®. STRAIGHT performs an instantaneous pitch-adaptive spectral smoothing in each stimulus for separation of contributions to the voice signal arising from the glottal source vs. supra-laryngeal filtering. The algorithm decomposes a voice stimulus into five parameters: f0, frequency, time, spectro-temporal density and aperiodicity. Stimuli are then synthesized and each parameter can therefore be manipulated and combined across stimuli independently of one another.
DATA PROCESSING For each subject, condition, continuum, and morphing step, the 6 scores and RT were collected and cleaned for outliers. S-outliers (deviant from the absolute median distance among all pairs;  Rousseeuw and Croux, 1993)  were detected from the RTs, and, if any were present, they were removed from the data (both from the scores and RT-8. 6% of the data). The mean score (percentage female/ta) and mean RT were then computed. The procedure was iteratively repeated for each stimulus (i. e. , 18 subjects, 3 conditions, 2 morphs, 11 steps). From the mean percentages of female/ta responses per continuum, a cumulative Weilbull function was fitted in Matlab® using unconstrained nonlinear minimization and the point of subjective equality (PSE: 50% male-female or /pa/-/ta/) was computed.
Both categories, however, included male and female voices and, across participants, different PSE values were obtained and different voice stimuli were used. By comparing the average acoustic properties of perceived /pa/ vs. perceived /ta/ sounds across participants, it was possible to reveal which acoustic features distinguished the two phonemes. Finally, since the same stimuli were used for both tasks, differences in which acoustic properties distinguished malefemale from /pa/-/ta/ stimuli should reveal which features were diagnostic to the task at hand. Using the Praat software  (Boersm and Weenink, 2009) , the fundamental frequency (mean f0) and Harmonic to Noise Ratio (HNR) of each sound was obtained. In addition, the consonants (/p/ or /t/) and vowel (/a/) were analyzed separately to obtain the mean f1, f2, f3, and f4 formant positions.
Analysis of the stimuli using the praat software  (Boersm and Weenink, 2009)  showed no significant difference of VOT between male-/pa/ and female-/ta/ (mean VOT male-/pa/ 55 ± 14 ms vs. mean VOT female-/ta/ 54 ± 14 ms; p = 0. 21) or between male-/ta/ and female-/pa/ (mean VOT male-/ta/ 54 ± 14 ms vs. mean VOT female-/pa/ 56 ± 15 ms; p = 0. 26). The main difference between these two consonants was therefore the place of articulation, perceived as the formant dispersion and this is what we observed in the reverse correlation analysis. However, in contradiction with the hypothesis that the same phonemic cues are used for voice and speech, we observed that only the vowel was important for gender categorization (which was observed in all three conditions). This difference shows that different acoustic features are diagnostic for the task at hand  (Schyns, 1998; Schyns et al.
Tissue Bath Setup Longitudinal strips were attached to force transducers using silk sutures. They were incubated in Krebs solution in 25 mL organ baths, equilibrated with 95% O 2 and 5% CO 2 and maintained at 37. 5°C. Isometric strip tension was recorded through an acquisition unit (MP30, BioPac, Goleta, CA, USA) connected to a computer database. After mounting, each strip was allowed to equilibrate with a basal tension between 0. 20 g and 0. 25 g for at least 2 to 3 hours. During this time, the harvested segments were washed regularly.
The SIAM (suicide intervention assisted by messages) software that we developed aims to simplify the inclusion of the patients and to have an optimized text sending. We assessed the technical feasibility and acceptability of this device in a pilot study  [10]. Text messages provide several characteristics of existing monitoring strategies for suicide attempt repeaters. Using our software, we personalized the messages as recommended by Motto  [4]  and reached patients earlier than with a letter. Our software program allows us to readily personalize each message according to the required level of care. We clearly identify care services as the senders of the messages, as would have been done in a letter. Call-back numbers proposed in our messages are available 24 h per day and 7 days per week, in line with the contact strategy proposed by Evans et al.
Study design The study is a 2-year multicentric randomized controlled trial conducted by the Brest University Hospital, France. Participants are adults discharged after self-harm, recruited in Brest, Lille, Rennes, Nantes, Tours, Vannes, and Saint-Nazaire. Participants will be recruited over a 12month period. The study is a randomized controlled trial registered on Clinical Trials Registry (ClinicalTrials. gov; number: NCT02106949). It was authorized by ANSM (French Health Ministry) and approved by the Northwest IV Ethical Committee for the Protection of Patients.
Randomisation/intervention Random allocation to the intervention group and the control group will be carried out by an independent statistician and will be stratified by center study using blocked randomization and computer generated random numbers. This will be conducted using an online randomization system. This will be concealed from the research team (psychiatrist, nurse). The statistician will notify the study coordinator regarding the group allocation. Text messages will be sent by SIAM program, designed for the pilot study. Once the patient data is entered, the program will generate four independent text messages containing patient's name, doctor's name and monitoring schedule from the date of inclusion. At any time the patient data, date of sending the text message and message content could be modified.
Acknowledgements The authors would like to acknowledge  Professor Michel Walter  for his contribution to the study design,  Zarrin Alavi  for her contribution in writing this manuscript, and  Catherine Mesmeur  for her contribution during the pilot study. Thanks to Lanestel and  Michel Gravey  for developing the SMS monitoring software system. This study is funded by a grant from the  Fondation de France  and  CRITT santé Bretagne.
DATA RECORDING Actual and mental trials were recorded using the Biopac MP150 (Biopac System, CA, USA) and an electronic stopwatch (temporal resolution 1 ms). The Biopac served for the recording of muscle activation patterns during mental trials and the electronic stopwatch for the recording of movement duration. Before an actual or a mental trial, participants placed the pencil with their right or left arm on the center of the ST and remained motionless. They also held the electronic stopwatch in their left hand (when pointing with the right arm) or right hand (when pointing with the left arm). After a variable temporal interval (between 2 and 3 s), the experimenter started the trial acquisition. After 1 s (S1), an auditory cue was given as the starting signal for each actual or mental trial.
Only otoneurological examination (performed before CI surgery) and a questionnaire (done at the moment of the postural investigation) were available. Oto-neurological examination had revealed no vestibular loss with the caloric test. Data from the questionnaire appreciating the possible involvement of CI surgeryinduced vestibular damages on the long term showed that few of the CI patients complained of vertigo and postural instability with or without vision, during head movements, in supermarkets (3/16), and with their prosthesis off (2/16). Most of the CI patients reported not being subject to motion sickness (14/16) (Table  1 ).
Postural performance during quiet standing Displacements of the CoP were used to measure the limits of stability and the postural performance of the subjects. This traditional approach has been complemented by a more accurate non-linear analysis of CoP displacements using the wavelet transformation. The wavelet analysis software (PosturoPro, Framiral, Cannes) provides a time-frequency chart of body sway and a 3D representation of body sway under both static and dynamic conditions (see  Lacour et al. , 2008). This method gives access to the changes in the frequency components of body sway with time, the third dimension calculated as the decimal logarithm of the spectral power being given on the 3D map by a color code. Postural performance in static conditions was evaluated through a postural instability index (PII) derived from the wavelet plots (cf.
50 Hz stimulus frequency of platform translation. These data collected in more challenging conditions confirm the crucial role of vision described before in the CI patients under quiet standing. Prosthesis off had no significant effects on the dynamic postural performance of the CI patients, while dual-tasking reduced significantly the spectral power density peak (1. 6 × 10 11 ) compared with the single postural task (2. 7 × 10 11 ). More interesting are the different strategies of posture control observed when the visual cues were suppressed. Control subjects selected a head on trunk strategy, the so-called strapdown strategy described by  Horak and Nashner (1986)  and characterized by a rigid whole body in space. The gain at the three segmental levels tested (head, hip, and knee) was close to unity.
Between 1991 and 2009, 16 participants died and 4 were too ill or disabled to answer. The overall response rate to the TEMPO questionnaire was 44. 5% (n51,103), which is comparable to response rates of other mental health surveys in France  (Alonso et al. , 2004). Leading reasons for non-participation were non-transmission of the questionnaire by the parent (34. 4%) or the youth's lack of interest (28. 5%). Compared to 2009 respondents, non-respondents were older, more likely to have parents who were divorced, and had low socioeconomic background but did not vary with regard to their parents or their own overall psychological characteristics. In 1991, data on participating children were collected via parental reports. In 1999, data were collected via parental reports (n51,268) and youth self-reports (n51,148).