dataesr/scientific-paragraphs-categorization huggingface dataset:
For isolated sign synthesis, AnimGen takes as input the SiGML notation  [47]  of a sign and translates it into low-level parameters for the animation based on inverse kinematic controllers. For utterance synthesis, the concatenative method is preferred: the signs are assembled to form utterances. Some inflections can be added to the isolated signs to take the context into account: the location, direction, gaze and facial expressions of signs can be changed. However those modifications can only be applied at a sign level creating a possible over-synchronization. The AnimGen module was used internationally in numerous works from 2001 until today  [21, 48, 74, 75, 113, [116] [117] [118] [119]. Notably, Hanke et al. [117]  whose work focuses on the study of timing differences on the SL channels, used AnimGen to test their hypothesis on the synchronicity between the hand configuration with respect to the hand location.
In more recent work, JASigning constituted the basis of Ebling's animation module to test her machine translation system  [21]. The AnimGen module is suited for simple translation tasks as it allows the creation of new signs and utterances. Indeed, it only depends on a sign representation ( SiGML ) and not on annotated data. However, the motions generated lack the naturalness of human motion. In addition, it depends on a concatenative synthesis module to create utterances: simultaneous mechanisms cannot be captured by AnimGen and the inflections that can be added to the signs are limited by SiGML.
SignCom and Sign3D The avatars SignCom  [83]  and Sign3D  [28]  aim to animate French Sign Language avatars with natural and realistic movements from captured human data. They rely on two databases: (i) a raw motion database in which the captured motion is stored as a hierarchical structure (the skeleton) with a set of transformations applied to each joint, and (ii) a semantic database that serves as a mapping between the gloss level annotations and the movements contained in the first database. The Sign3D system operates at a sign/gloss level. Following a stereotyped syntactic scheme, it allows to precisely synthesize novel utterances by replacing signs or groups of signs. The building of utterances is done following an interactive graphical language  [28]. SignCom is a multichannel system that operates on parallel channels (lower body, torso, arms, hands, head, face, and eyes) corresponding to phonological tracks  [83].
The channels are traversed in a tree-like manner through a scripting language that activates animation engines adapted to the different parts of the body. Interpolation, blending and fading in/out operations ensure co-occurrence between channels. For the synthesis of isolated signs, the appropriate sign is retrieved from the movement database. To do this, the semantic database makes it possible to find the movement(s) labelled with the desired gloss. If several movements correspond to the gloss, a choice can be made using various criteria: length of the time segment, profile of the movement, etc. The sign can be edited if necessary with a temporal inversion of the signal to change [TAKE] to [GIVE] or [LIKE] to [DO NOT LIKE]. As in most data-based systems, the synthesis of novel signs remains limited by the content of the MoCap database.
Concerning the content of the corpus, two main trends are often confronted. In Duarte's Ph. D. thesis  [123]  , they are named breadth and depth. Breadth consists in capturing a large number of different signs to try to cover the whole language, while depth consists in capturing a limited set of signs numerous times with many variations (for example, by varying the location of the sign in space). As sign languages do not contain a finite number of signs due to mechanisms such as proforms or role-shifts, and because capturing data is costly, covering all the possible signs in all the possible contexts (the breadth solution) is not a viable design solution. Moreover, the motion data, after being captured, is not directly workable. In the case of passive MoCap, the signer whose motion are recorded is covered by numerous markers (sometimes more than 100).
In addition, new sign language utterances are hard to generate from a limited set of motions in the database and context-based variation in the captured motions is not easy to synthesize which is a problem considering the great iconicity and variability of signed languages. Machine learning methods associated to MoCap data are a promising way to reduce the limitations of data-driven techniques. In the near future, with the advent of deep learning, captured motion data could be replaced by video data, thus facilitating the generation of SL content. Sign representations can be the basis for precise, flexible and fast generation of movements allowing for real-time animation. However, the exploitation of sign representations often requires the prior manual intervention of transcribers to perform the mapping from a specific gloss to the lower-level sign representation.
This kind of synthesis is based on a database built offline and often relies on a sequence of glosses to retrieve the relevant motions. The transitions between the motions are smoothened using signal processing functions like blending or interpolation, and modifiers of the original signs are often added to add prosodic cues or to take into account the context of the sign. Concatenative synthesis is an inherently sequential technique that can be seen as contradictory to sign languages philosophy; however, concatenating chunks of motions along the temporal axis are not the only option of concatenative synthesis. Little work has been dedicated to the concatenation of smaller motions on the different channels of sign language (hand configuration, motion, orientation, facial expression, etc. ) and this could be the focus of future research.
Currently, data-driven utterance synthesis systems rely on concatenative synthesis to achieve three linguistic purposes: (i) replacing signs or groups of signs within an utterance, (ii) replacing phonetic or phonological components and in this way modifying the grammatical or semantic aspects of the utterance, or (iii) altering prosody in the produced sign language utterances  [84]. Articulatory synthesis aims to build sentences on-the-fly based on a sign or gesture specification. The animation can be computed from biocontrol or inverse kinematics models. This approach can sometimes lead to real-time animation as no data has to be retrieved from a pre-built database. The benefits and drawbacks of articulatory and concatenative approaches are oddly complementary. On the one side, the articulatory techniques allow for a real-time generation of novel utterances but are poorly accepted by the Deaf community due to their inexpressive and robotic motions.
Neural oscillations are thought to provide a cyclic time frame for orchestrating brain computations. Following this assumption, midfrontal theta oscillations have recently been proposed to temporally organize brain computations during conflict processing. Using a multivariate analysis approach, we show that brain-behavior relationships during conflict tasks are modulated according to the phase of ongoing endogenous midfrontal theta oscillations recorded by scalp EEG. We found reproducible results in two independent datasets, using two different conflict tasks: brain-behavior relationships (correlation between reaction time and theta power) were theta phasedependent in a subject-specific manner, and these "behaviorally optimal" theta phases were also associated with fronto-parietal cross-frequency dynamics emerging as theta phase-locked beta power bursts.
We found that theta power-reaction time correlations are maximal at specific theta phase regions that were characteristic to each individual, and that predicted the timing of betaband (~20 Hz) bursts in a fronto-parietal network, as revealed by multivariate phase-amplitude coupling analyses. Contrary to our hypothesis, these theta phase-dependent effects were not specific to conditions that maximized response conflict, but rather reflected the temporal organization of a more general mechanism likely related to response execution occurring during the conflict tasks we used. All of the key findings were replicated in an independent dataset  (Gulbinaite et al. , 2014). The two datasets were acquired using different conflict tasks (Eriksen flanker task and Simon task; total N ¼ 62) in two different research centers, supporting the interpretation that these findings reflect general neural signatures of cognitive control over behavior.
Participants 30 students from the University of Amsterdam volunteered in study 1  (Cohen, 2015). Two datasets were excluded because of excessive EEG artifacts. 34 students from the University of Groningen participated in study 2  (Gulbinaite et al. , 2014). In both studies, participants had normal or corrected-to-normal vision. The experiments were conducted in accordance with the declaration of Helsinki and with approval of local ethics committees. Written informed consent was obtained from all participants prior to the experiments. Study 1 used a modified version of the Eriksen flanker task  (Appelbaum et al. , 2011)  in which participants had to respond to a central letter while ignoring surrounding letters that acted as distractors. The strength of response conflict was manipulated in three different types of trials (Fig.
EEG acquisition and preprocessing In study 1, EEG data were recorded at 512 Hz from 64 electrodes placed according to the international 10-20 system. Data were referenced offline to linked-earlobes, high-pass filtered at 0. 5 Hz, and then epoched from À1 s to 1. 5 s locked on stimulus onset. Additional electrodes were used on both earlobes and also to acquire EMG data from both thumbs' flexor pollicis brevis. EMG data were used to identify small activation of the thumb muscle prior to the response. When trials contained such activation on the side of the wrong response prior to a correct one, they were labeled "mixed correct" (sometimes referred to as partial errors; van den  Wildenberg et al. , 2010). The identification of mixed correct trials was based on changes over time in EMG variance.
The complete description of the identification algorithm can be found in  Cohen and van Gaal (2014). Given that mixed correct trials are assumed to have the maximal amount of response conflict, their identification was done to label the trials in which control was most strongly implemented. All trials were visually inspected and those containing EMG or other clear artifacts were excluded. In study 2, EEG data were acquired from 62 channels at 500 Hz. The electrodes were placed according to a modified version of the 10-10 system (electrodes FT7, PO7, O1, FT8, PO8, and O2 were placed 10% below standard and electrodes F1, F2, CP1, CP2, FT7, and FT8 were not measured). Data were referenced off-line to the mastoids, high-pass filtered at 0. 5 Hz, and then epoched from À1. 5 s to 2 s locked on stimulus onset.
Two artifact rejection steps were carried out. The first consisted in the manual rejection of trials containing clear muscle or blink artifacts and the second was an independent component analysis resulting in the subtraction of components containing eye-blinks or noise. After artifact rejection, a surface Laplacian spatial filter was applied to the data to attenuate spatial smearing as a result of volume conduction (this was done in  Gulbinaite et al. , 2014 , to facilitate connectivity analyses). For both studies, the epoch length was ~2 theta cycles longer than the selected window of interest (from 300 to 1200 ms) and visual inspection of time-frequency plots did not suggest any contamination of the selected time window by the edge artifacts. In both datasets, the ERP was subtracted from the data (separately per channel and per condition) prior to any other analysis to avoid stimulusrelated phase resetting.
Spatial filtering of the data All signal analyses were performed in Matlab (The Mathworks, USA, version 2016a) using custom written code based on published equation  (Cohen, 2014b ; matlab code is available at : https://github. com/ jduprez). Because of our a priori hypotheses regarding theta-band activity, we isolated midfrontal theta using optimal spatial filters that maximized power in the theta band. It is important to note that this approach to spatial filtering is based on statistical criteria (channel covariance matrices) and is not designed for inference about potential cortical generators. We limit our interpretations to topographical distributions to facilitate embedding in the literature. Construction of the spatial filter involves finding a set of channel weights that maximally distinguishes theta-band from broadband multichannel activity as measured through covariance matrices  (Nikulin et al.
Within subject phase-resolved power-RT correlation analyses In order to test whether power-RT correlations significantly varied according to phase, we fitted the correlation values to sine waves for each subject and condition. These analyses were done using R (version 3. 4. 2; R Development Core Team, 2017) and the {minpack. lme} package for nonlinear least square fitting  (Elzhov et al. , 2016). Data were fitted by applying the nlsLM function to the model: ρ~A*sin (2*π*F*phasebinþθ), where ρ is the correlation coefficient, and A, F and θ, are the amplitude, frequency, and phase parameters to estimate, respectively. Parameter estimates were then used in a harmonic addition of a sine and a cosine (which is equivalent to a single sine wave allowing for a phase shift) to enter in a linear model following: ρ ~(Asin(2πF*phasebinþ θ) þ Acos(2πF*phasebinþ θ)).
For the S matrix, the same filtering was applied but we extracted the data around the empirically determined phase of maximum theta power-RT correlation. We identified each occurrence of the preferred theta phase in each trial (between 300 and 1200 ms), then extracted the data from 1 / 8 of a theta cycle before that phase to 1 / 8 of a cycle after that phase (the exact number of ms depended on subject-specific theta frequency); the covariance matrices from these time windows were averaged to create the S covariance matrix. The spatial filter with the largest generalized eigenvalue was selected to define the component. In this case, rather than isolating narrowband theta dynamics, GED served the purpose of enhancing the signal's characteristics in higher frequencies occurring around the phase of maximum theta power-RT correlation.
For each iteration, all the data after a randomly-defined relative theta phase (between the 10th and the 40th bins) were shifted and a difference map between the phaseshifted map and the original unshifted map was calculated. This operation was carried out 10,000 times, generating a distribution of difference maps under the null hypothesis. The average real data map was z-scored using the mean and the standard deviation of the difference maps. The resulting map was then thresholded at p < 0. 001. Multiple comparisons were addressed by the means of cluster-based correction so that any cluster equal or larger than 99. 9% of the distribution of null hypothesis cluster sizes was considered significant. Power data from the biggest significant cluster was then extracted and compared at the group level between conflict conditions using a one-way ANOVA.
Control analyses Although all the results argue in favor of the hypothesis that theta phase influences brain-behavior relationships, we wanted to test whether the effect we found was specific to the theta band and whether the effect was reproducible within each dataset individually (cross-validation). We chose to test phase-resolved brain-behavior correlations in the beta band, since beta suppression is also typically observed around RT in conflict tasks (see Fig. 3B  and E ) and since beta activity has been shown to relate to RT and errors  (Doyle et al. , 2005; Torrecillos et al. , 2015).
8 Hz and -4π/7 for study 2. Similar to the first analysis, power extracted from those clusters did not show any significant change according to the strength of conflict (Fig. 6C2  and F2 ; study 1: F (4, 135) ¼ 0. 48, p ¼ 0. 7; study 2: F (4, 165) ¼ 0. 87, p ¼ 0. 5). As a whole, both the cross-validation approach and the replication in the 2 independent datasets strongly suggest that there was a specific increase in power in the beta band around -π/5 relative to the phase of maximum power-RT correlation that was not specific to the strength of conflict but rather generally involved in response execution.
In particular, the relevant theta phase was associated with power bursts in the beta band. This pattern was consistent across both datasets and was robust to two distinct statistical evaluation methods (permutation testing and cross-validation). Beta oscillatory activity has consistently been associated with motor control  (Kilavik et al. , 2013; Neuper et al. , 2006) , notably because of the exaggerated beta activity in Parkinson's disease  (Oswal et al. , 2013). Motor-related beta is centered around the motor cortex and emerges as short-lived burst that are suggested to underlie response execution  (Feingold et al. , 2015). This suggests that the theta-phase-locked beta power bursts we found around motor areas relate to response execution. Moreover, we did not detect condition-specific modulations of multivariate phase-amplitude coupling.
1, provided by Herschel Science Center, producing the images using its "naive" map-making procedure and destriper module (Herschel Science Ground Segment Consortium 2011). Differently, PACS data were reduced with HIPE v10. 1 and images were obtained using UNIMAP Version 6. 4. 1  (Piazzo et al. 2015)  for the map making and destriping phase. The pixel size of the maps has been set to 3 at 70 µm, 4 at 160 µm, 6 at 250 µm, 10 at 350 µm, and 14 at 500 µm, respectively. The FWHM beam sizes are 8. 4 , 13. 5 , 18. 2 , 24. 9 and 36. 3 for the maps at 70 µm, 160 µm, 250 µm, 350 µm, and 500 µm, respectively 2 The Herschel maps have an arbitrary zero-level intensity, so the intensity in each pixel is known except for a constant term. This offset was evaluated assuming a dust model exploiting Planck and IRAS data, using a method similar to the one described by  Bernard et al.
SOURCE DETECTION AND CLASSIFICATION A dense core is a roundish structure that can be considered as the final result of cloud fragmentation, and whose gravi- tational collapse could give rise to a star or a multiple star system. Based on this concept, we generate a catalogue of starless cores for all the star-forming regions of the Gould Belt observed by Herschel. To ensure uniformity with other HGBS works, this catalogue is built following the procedure already described by  Könyves et al. (2015)  and  Marsh et al. (2016)  as a two-step process: Step 1: Detection. Source extraction is carried out by the multi-wavelength, multi-scale software getsources  (Men'shchikov et al. 2012 ) which simultaneously analyzes the Herschel images between 70 µm and 500 µm, as well as the "high-resolution column density" and the "temperature corrected" maps (Sect.
A second set of getsources source extractions is performed to trace the presence of YSOs/protostars. In this process, at the detection stage only the PACS 70 µm data are used. Point-like 70 µm emission properly traces the internal luminosity, and therefore the presence itself, of a protostar (e. g. Dunham et al. 2008). We eliminated contamination due to galaxies and other non-YSOs by looking for those sources in Simbad database within 2 from the centroid coordinates. At the end of this process, we count 124 of those objects, named proto-stellar cores. Although proto-stellar cores are contained in the catalogue, they are not analyzed in this work, but will be discussed separately in a subsequent paper. Step 2: Classification. The raw source list outputted by getsources in the region studied here, is then filtered to select only dense starless cores, by applying the following criteria: (i) column density detection significance higher than 5 in the high-resolution column density map, where significance Table  3.
5 times the band (at this stage, namely the last one based on constraints on detection parameters, the candidate sources remain 1081); (vii) the source is not spatially coincident within 6 of a known IR source of at least one of the following catalogues: Spitzer Space Telescope (SST, 137 matches found), Widefield Infrared Survey Explorer (WISE, 149 matches found) and Simbad Database (6 matches found) for further infrared observations; (viii) the source is not spatially coincident within 6 with a known galaxy, i. e. , a comparison with NASA Extragalactic Database (NED) found no matches; (ix) the source appears real on visual inspection of the images between 160 µm and 500 µm. In this respect, 15% of the dense starless sources were visually rejected. The resulting 833 starless cores are contained in the catalog available in the online version of this paper, and from the HGBS website (see the footnote 2 in Sect.
Estimation of Core Masses & Temperatures Similarly to the approach described in Sect. 3, the core masses and the temperatures can be estimated by fitting the modified black-body function (Eq. 2) to their SED obtained through the getsources software. In the case of temperature and column density maps, the SEDs are composed by single and unsubtracted pixel fluxes, while in the case of source fluxes they are background-subtracted and integrated over the entire source area. This different treatment of the background, which changes with wavelength, can lead to different estimates of core temperature and mass, whether they are derived from the source SED, or taken from temperature and column density maps (cf. , e. g. ,  Kirk et al. 2013). Furthermore, we notice that actually the structure of a quiescent core might be more accurately modelled as a series of dust layers, the outermost of which is the warmest, heated by external radiation, and the innermost the coldest  (Roy et al.
ACKNOWLEDGEMENTS SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). PACS has been developed by a consortium of institutes led by  MPE (Germany)  and includ-ing UVIE (Austria); KUL,  CSL ,  IMEC (Belgium) ;  CEA, OAMP (France) ;  MPIA (Germany) ;  IFSI, OAP/AOT ,  OAA/CAISMI, LENS, SISSA (Italy) ;  IAC (Spain).
This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI (Italy) , and  CICT/MCT (Spain). D. A. acknowledges support by  FCT/MCTES  through national funds (PIDDAC) by the grants  UID/FIS/04434/2019 & UIDB/04434/2020. The computational needs of this project were supported by the GENESIS cluster, which was funded by the project  PRIN-INAF 2016 The Cradle of Life -GENESIS-SKA (General Conditions in Early Planetary Systems  for the rise of life with SKA). This work has received support from the  European Research Council  under the European Union's  Seventh Framework Program  ( ERC Advanced Grant  Agreement no. 291294 -"ORISTARS" ) and from the  French national programs of CNRS/INSU  on stellar and ISM physics (PNPS and PCMI).