dataesr/scientific-paragraphs-categorization huggingface dataset:
This is normal: even if we can demonstrate that a new approach is very interesting, changing technology is always a high cost decision, and ensuring the continuity of the data, analysis and student training is a real challenge. We decided to develop some tools in order to display data stored in ontologies as a virtual relational database in order to have a simplified view of the stored data. These tools can provide a static point of view on these ontologies and allow simple manipulations similar to those possible on a relational database. The first one is an editor that displays classes, instances and properties present in an ABox (a set of assertional axioms in an ontology) as if the data were structured by a table in a relational database (see Figure  11 ). By reading the ABox, the editor is able to display the classes present in the ontology, all their instances and offers a simple and dynamic way to display data properties as normal fields of these instances and the possibility to modify them (also, as we are in the ontology context, to add or remove fields).
In its simplest form, it allows for representing concepts (class), instances (individual), attributes (data properties) and relations (object properties). The ontology construction in OWL, symmetric to the Java taxonomy, cannot be produced automatically. Each concept of the ontology has been constructed with a concern for the representation of accurate knowledge from a particular point of view: measurement. The same point of view presides over the elaboration of the Java taxonomy, but software engineering constraints involve differences in the two hierarchies of concepts. For each ontology concept, a procedural attachment method has been developed with OWLAPI. As each concept present in the ontology has a homologous class in the Java tree, each individual of the ontology can produce a Java instance and can benefit from its computational capabilities.
Chronology X equals y UgualeA x is contemporary to y Post-hole equal for typology and function. The definition of these concepts given in Table  5  is based on a previous work of the Florentine team when defining the database named PetraData  [73]. This is the point of view chosen for the future work that we plan to develop on the use of duration for US. Even if only considered an 'intellectual challenge', we are studying the possibility to control from the beginning the periodization of the archaeological stratigraphy. In fact, after creating the matrix, the archaeologists' second step is to build a coherent periodization that considers all the known elements. We are currently testing this system ensure better control of the entire pipeline.
Conclusions and Future Work This paper focuses on the main goals obtained by the collaboration between medieval archaeologists from the University of Florence (Italy) and ICT researchers from CNRS LIS laboratory of Marseille (France). From the beginning, this fifteenth collaboration is built on a mutual exchange of knowledge. Our first common field of interest is survey, and our main purpose is to add knowledge to it. The future of this collaboration can be expressed in this way: first of all, the changing paradigm and producing new tools mean deep changes in the way we work. We change both how photogrammetry is used and how the observed data are managed; as new photogrammetric tools are developed and traditional relational databases are replaced by ontologies, new tools become available to manage and infer data.
The first step was to obtain a 3D GIS, and we worked very hard to make it possible  [25]. Now, our group is starting to work on the field of ontologies, especially to manage the US and to add knowledge to the survey  [74]. Even if there are some theoretical works on the ontologies, until now, not many archaeologists have studied and tested ontologies to manage heterogeneous data. To do this, it will be possible to replace the database, which is a very important tool, but often too rigid. The intensive use of ontologies to manage both photogrammetric geometry and archaeological data will allow us to first check the consistency of digitized data and then infer new relationships between US using an inference engine, such as Pellet, directly from the ontology. For example, we are currently working on producing Harris relations using known physical relationships between US, and then, we will be able to check and produce new temporal relationships between US according to Allen's formalism.
We plan to investigate the ASP approach for several reasons. ASP is a unified formalism for both representing and implementing the consistency checking problem expressed by a logic program that is very close to natural language. We already used this formalism when dealing with the revision of geographical information within the framework of the European project REVIGIS (Uncertain knowledge, maintenance and revision in geographic information systems, ID: 27781, 1998-1999)  [77]. More precisely, we compared the CSP, SAT and ASP approaches. CSP focuses on the direct resolution of inconsistencies by means of propagation mechanisms, while SAT and ASP concentrate on the identification of inconsistencies. However, an experimental study on a real-world application benchmark showed that ASP gave better results than SAT  [78].
Acknowledgments: The  University of Florence (UniFI)  and the  French National Center for Scientific Research (CNRS)  have worked together for more than 15 years in the framework of the  ARPENTEUR  project (an  Architectural PhotogrammEtry Network Tool  for Education and Research http://www. arpenteur. org),which has been developed since 1997 in Marseille, France, by the  CNRS. The ontology aspect of this work is developed in the context of the  GROPLAN  project (http://www. groplan. eu),partially funded by the  French National Agency for Research (ANR). The authors wish to thank  Yaaqoub Semlali  for the development of the ontological editor during his internship at the CNRS.
e. , until 05/20/2016, excluding school holidays. • Non-inclusion criteria: We did not enroll subjects who were participating in a specific social skills group (e. g. , language pragmatics) or were using different methods. • Exclusion criteria: We excluded children who had stopped participation. We analyzed the data of all ASD children from 9 to 11 years old who were treated in our day hospital and were participating in our implicit social skills group without sampling because of the small population size.
The efficacy of the social skills training groups was evaluated to show an increase in EQ and SEP scores and a decrease in CARS scores. 2. 2. 6 Data entry. An Excel file was created to collect before-and after-intervention data. The children's data were anonymized and identified only using a digital code, including the inclusion rank and the first letter of their name and surname (for example, 01EJ). The data were checked by someone apart from the person who input the data.
This group relied on blended implicit and explicit learning. The authors created special software, "Junior Detective Computer Game. " They explained that they used a computer game because computers are a highly effective teaching medium for children with ASD. In this game, the children with ASD had to be "detectives" and decode the suspects' thoughts and feelings. For this game, they benefited from virtual theoretical learning (explicit learning) and learning by exposure to virtual social situations (implicit learning). Next, they participated in a real social group to facilitate generalization. This group improved the social skills of the children  [70]. The specific nature of our group was that it was exclusively implicit, using games that had been transformed into cooperative games, which should allow for even better generalization.
The socalled steadystate evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signaltonoise ratio of the narrowband steadystate response in the frequency and timefrequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to many noninvasively placed electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response.
page 3 The steadystate evoked potentials (SSEPs) are rhythmic neural responses to rhythmic sensory stimulation. It has many applications in vision, cognitive, and clinical neuroscience research, as well as brain computer interfaces (for reviews, see  Norcia et al. , 2015; Vialatte et al. , 2010). Our focus here concerns a novel SSEP data analysis method that is aimed to increase utility of SSEPs in vision and cognitive neuroscience research. The traditional data analysis approach in SSEP research involves examining power spectra or narrowband activity from a single or several EEG electrodes (or MEG sensors) that show maximal response to rhythmic stimulation ("bestelectrode" approach; e. g. Fuchs et al. , 2008). This seemingly simple and straightforward approach has several data analysis issues, which we detail below.
When exploring various analysis procedures, we found that none is particularly wellsuited to resolve these issues. However, a combination of existing temporal and spatial filtering methods outperforms "bestelectrode(s)" approach both in simulations and in real data. The purpose of this paper is to present this SSEP data analysis approach, termed rhythmic entrainment source separation (RESS), and provide specific recommendations for SSEP experiment design and data analysis. RESS is a combination of several existing methods, principally including those from Nikulin et al. (Nikulin et al. , 2011 ) , Chevinge et al. (de Cheveigné and Arzounian, 2015; de Cheveigné and Parra, 2014)  , as well as others  (Dmochowski et al. , 2015; Särelä and Valpola, 2005). The following list summarizes data analysis challenges in SSEP research, and ways that our RESS procedure resolves those challenges.
, 2014). We show that RESS can be used to subtract SSEPrelated activity from the data while leaving endogenous activity relatively intact (or at least, more intact compared to a notch filter). 4) Time series analysis of the timecourse of the SSEP. If the amplitude of the SSEP indexes attention differences across conditions, it is reasonable to expect that timevarying fluctuations in SSEP amplitude might reflect timevarying fluctuations in attention, which can be extracted using narrow bandpass filtering  (Andersen and Müller, 2010). However, verynarrowband filtering obliterates fine temporal dynamics, because temporal nonstationarities are represented by sidelobes of the frequency spectrum, and not by the peak at the flicker frequency. We show that by applying a spatial filter to nontemporally filtered or weakly filtered data allows accurate reconstruction of the time course of SSEP amplitude fluctuations.
5) Separating SSEPs for multiple simultaneous frequencies. Different flicker frequencies are often presented simultaneously in order to track attention to multiple items. Providing the spatiotemporal dynamics of the SSEPs are distinct, the RESS procedure can isolate activity from multiple simultaneous flicker frequencies, even those separated by 1 Hz. 6) Singletrial analyses. The RESS method is optimized for withinsubject crosstrial analyses, while most SSEP analysis methods are optimized for trialaverage analyses (or are sometimes strictly limited to trialaverage results). Indeed, the RESS method does not require multitrial designs. This means that the onset phase of the flicker can differ on each trial, which increases experiment and analysis flexibility. Finally, we stress that the RESS procedure is not a "blackbox toolbox" that should be applied to data without thinking.
Overview of RESS page 6 The general approach of using "guided" (as opposed to blind) source separation techniques in multivariate signals has a decadesold tradition. In this sense, the method we developed and present here is not a new technique per se, but instead, is a blend of several existing techniques that are optimized for SSEP data analysis. The procedure is outlined in Fig. 1. Matlab code to implement and modify the method, as well as to run the simulation and apply to sample EEG data, are provided online Data and scripts are currently available at mikexcohen. com/ress. zip (eventually, this zip will be moved to data. donders. ru. nl).
Simulated data Simulations are advantageous because the ground truth is known. On the other hand, simulations often fail to represent characteristics of real data. Our solution to this is to use an empirical EEG dataset as "realistic noise," and add a sine wave page 10 as the simulated SSEP. The empirical data were taken from an eyesopen rest recording in an adult human volunteer (120 seconds). The SSEP was simulated as an amplitudemodulated sine wave that was added to a dipole in the brain, and then projected onto the scalp electrodes. The purpose of the amplitude modulation was to simulate timevarying fluctuations in attention. The overall goals of the simulation were to examine how the analysis parameters affect the static (power spectrum of the data) and dynamic (timevarying fluctuations of power at the flicker frequency) SSEP characteristics.
Empirical data We collected empirical data from one human volunteer (author RG) using a variety of experimental conditions designed to assess the utility of RESS for SSEP data analysis. Data were acquired from only one subject as a proofofprinciple demonstration of the method. The experiment involved recording EEG activity from 64 electrodes (BioSemi equipment; see biosemi. com for hardware details), sampled at 1024 Hz. Visual stimulation was delivered by a custombuilt setup containing: Fixation LED light placed at the level of the eyes, and two LED arrays placed in the lower visual field with an eccentricity of 6. 5 visual angle relative to the fixation LED. Auditory stimulation was delivered via headphones. There was no cognitive task required other than maintaining attention. Additional descriptions of various stimulation conditions are provided in the Results section.
Component subtraction Given research interests that involve studying interactions between SSEPs and the spontaneous EEG activity  (Birca et al. , 2006; Mast and Victor, 1991)  , it would be ideal to separate the two signals. In practice, a perfect separation seems unlikely. In part this is because the rhythmic stimulation interacts with ongoing endogenous oscillations and produces some entrainment of neural oscillations, page 11 particularly when the stimulation frequency matches an endogenous neural oscillation frequency  (Antal and Herrmann, 2016; Notbohm et al. , 2016). Nonetheless, attempted separation of SSEP and endogenous oscillatory activity can be implemented by removing the largest RESS component(s) from the raw data. Although it is intuitive to conceptualize subtracting a component from data, in fact, it is implemented by reconstructing the data using N1 components.
We estimated anatomical localization of the components by adapting a procedure developed for obtaining distributed localization of independent components  (Hild and Nagarajan, 2009)  , which mainly involves correlating the forward model of the RESS component with a leadfield matrix developed for the EEG montage. To increase sensitivity, our leadfield contained three cardinal orientations per voxel, and correlation magnitude was taken as the length of the projection of the correlation onto the dipole orientation space. The leadfield was created from a standard MRI and BEM model, using the Matlab Brainstorm toolbox  (Tadel et al. , 2011)  , which in turn utilizes algorithms developed by openmeeg  (Gramfort et al. , 2010). Because the components are a weighted combination of all electrodes, thresholding the anatomical distribution based on inferential statistical values makes little sense.
Quantifying the static and dynamic SSEP We use the term "static SSEP" to refer to the power spectrum of the data (extracted via the fast Fourier transform) recorded over a period of time. In this sense, "static" indicates that we extract no information about temporal dynamics. The static SSEP is computed as SNR, which we define as the ratio between power at the SSEP frequency, to power at the average of neighboring frequencies (+/ 2 Hz, excluding +/. 5 Hz around the SSEP frequency). Converting to SNR units has several advantages, including accounting for 1/f power spectral shifts, robustness to electrode montage choices, and comparability across individuals, groups, and page 13 methods (EEG, MEG, LFP). In figures we show the SNR spectrum, which is the SNR computed at each frequency. We use the term "dynamic SSEP" to refer to timevarying fluctuations of power at the SSEP frequency.
SNR computed from the best electrode increased with SSVEP frequency as the EEG signal power decreased, reaching a maximum of around 1200 for high frequencies and strong source power. Although this would normally be considered a large value for SSVEP SNR, it is an order of magnitude smaller than that obtained from RESS in the same dataset. We next sought to determine whether the RESS and bestelectrode methods could recover the timecourse of the SSVEP. This is not as trivial as it initially may seem: Although the sensory stimulation may be extremely narrowband and frequency and amplitudestationary, the brain's response to it has timevarying amplitude and frequency characteristics, and these nonstationarities manifest as sidelobes in the Fourier power spectrum (Fig. S1 ). Thus, in order to reconstruct timevarying amplitude fluctuations in the SSVEP, the filter should be wider than the stimulation frequency.
But on the other hand, wideband filtering allows nonSSVEPrelated activity to contaminate the time course. RESS performs favorably in this regard: Because the RESS filter suppresses SSEPunrelated activity, the spatial filter can be applied to the unfiltered data, not to the narrowband filtered data used for eigendecomposition. This allows for page 16 higher temporal precision of the RESS component. The correlation between the reconstructed SSVEP time course and the original simulated SSVEP depended on the amount of noise in the simulation, but generally was above r=. 6 and was as high as r=. 98 with minimal noise (Fig. 2b ). The optimal filter FWHM in the simulations shown in Fig. 2  was around 4 Hz FWHM. The maximum depends in part on the spectral characteristics of the simulated SSEP magnitude, but in general we find an invertedUshaped relationship such that very narrow and very wide filters are suboptimal.
Empirical EEG data Simulations are important to demonstrate feasibility of a method, but confirmation in empirical data provides an irreplaceable examination of how a method performs under realistic conditions. We collected empirical data from one human volunteer as proofofprinciple demonstration using several different rhythmic stimulation conditions that are commonly applied in SSEP cognitive neuroscience research, or that provide difficulties for traditional SSEP data analyses. Visual stimulation using spatially overlapping stimuli. Centrally positioned two circles of LED lights were placed at eye level and flickered at 10 Hz and at 17 Hz 17 Hz (and their harmonics) are clear in the electrode data, RESS produced SNRs around 23 times larger than SNRs of the electrode data (Fig. 3a ). The increased SNR for RESS is partly a biased measure: The spatial filter is specifically designed to maximize power at the center frequency while suppressing activity at neighboring frequencies.
We stress that RESS should not be used as an automated ("blind") procedure that is applied without consideration. Instead, one must approach the data carefully and optimize the parameters based on the goals of the analyses, details of the experiment design, and assumptions about the underlying neurocognitive temporal dynamics. Simulations and proofofprinciple empirical data clearly demonstrate the advantages of RESS over the bestelectrode picking procedure prevailing in the SSEP literature. Indeed, even when RESS provides only marginal improvement over the bestelectrode approach in terms of SNR (this can happen with large stimuli and long presentation times), RESS still avoids the issue of having to select specific electrodes, which may differ by individual, flicker frequency, and page 29 experiment condition.
For example, muscle artifacts will contaminate a 25Hz SSEP, but because EMG noise has a relatively broad frequency spectrum, computing the R matrix from 23 Hz and 27 Hz will effectively suppress EMG activity. On the other hand, if time course analyses will be performed, neighboring frequencies should not be too strongly suppressed. In this case, we recommend using wider FWHM at neighboring frequencies compared to the peak frequency (e. g. ,. 5 Hz for the peak and 2 Hz for the neighbors). For the spatial filter to remain valid, the spatial location and the temporal frequency of the entraining stimulus must be constant. RESS is not suitable, for example, if a flickering visual is moving around the monitor, not is it suitable for frequency chirps (timevarying frequency changes). Concerning data cleaning and preprocessing, we recommend removing trials or sections of data that contain excessive artifacts, but not subtracting independent components.
If complex eigenvectors are obtained, the neighboring frequencies that make the R matrix can be moved a bit further away and/or their FWHM can be made broader. In general, it is ideal to have a single set of parameters for all frequencies. However, we have observed that over a large range of frequencies (e. g. , 3 Hz to 80 Hz), a single set of filtering parameters is unlikely to be suitable. For example, R matrix frequencies might need to be further apart from the SSEP frequency at lower frequencies and closer to the SSEP frequency at higher frequencies. Piloting with a subset of the data will help determine appropriate parameter ranges. However, it is important that parameters (and the spatial filters) are matched across experiment conditions to prevent biases. page 33.
In our case, the filter was a sine wave at the stimulation frequency. JD produced acceptable results that were often comparable with RESS (results not shown). However, JD has two disadvantages with respect to SSEP. First, the temporal bias filter is a stationary sinusoid, whereas the brain's response to a rhythmic stimulus is neither perfectly stationary nor is it perfectly sinusoidal. Second, JD is not designed to suppress power at neighboring frequencies, meaning it captures a mixture of SSEP and page 34 endogenous activity at the same frequency. Again, this is not a criticism of JD in general; instead, it reflects that a specific experimental method like SSEP requires a correspondingly specific data analysis technique. There are myriad other spatial filters that have been and could be applied to SSEP data.