dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; 2016a; 2016b; Bogdanova et al. , 2013). Isotope data are used to shed light into the sources of the initial melts. Published data for the Ukrainian AMCG complexes are limited and signify a predominantly crustal source of the melts, with limited input of juvenile mantle material  (Shumlyanskyy et al. , 2006). However, isotope data for the silicic rocks was hitherto absent and ideas about relationships of basic and silicic members of the Ukrainian AMCG complexes in terms of their origin were highly speculative. In this paper we present extensive U-Pb geochronological and Hf isotope data on the two Palaeoproterozoic AMCG complexes located in the Ukrainian shield and sampled by the authors over the last decade. Our primary aim is to constrain the secular relationships between the main rock suites that compose the AMCG association and to shed light on their origins.
For the SIMS analysis the c. 1065 Ma Geostandard 91500 reference zircon  (Wiedenbeck et al. 1995)  was mounted along with the samples. The SIMS U-Th-Pb analyses were carried out using a large geometry Cameca IMS 1270 instrument at the Nordsim facility, Swedish Museum of Natural History, Stockholm. The analytical method follows that described by  Whitehouse and Kamber (2005). The following procedures were fully automated: (a) pre-sputtering with a 25 mm raster for 120 s; (b) centring of the secondary ion beam in the 4000 mm field aperture; (c) mass calibration optimization; and (d) optimization of the secondary beam energy distribution. Data reduction employed Excel ® macros developed by M. J. Whitehouse. The LA-ICP-MS techniques for U, Th, and Pb isotope analysis of zircon at the Museum für Mineralogie und Geologie (Senckenberg Museum of Natural History, Dresden), use a Thermo-Scientific Element 2 XR sector field ICP-MS coupled to a New Wave  Excimer Laser System.
Each analysis consisted of a 15 s background acquisition followed by a 30 s data acquisition, using a laser spot-size of 25 µm. Raw data were corrected for background, common Pb, laser induced elemental fractionation, instrumental mass discrimination, and time-dependant elemental fractionation of Pb/Th and Pb/U using an Excel ® spreadsheet program developed by Axel Gerdes. U and Pb contents and Th/U ratios were calculated relative to the GJ-1 zircon standard and are accurate to approximately 10%. In all methods described above the common lead correction, when needed, was done using the measured 204 Pb signal and the modern (i. e. 0 Ma) Pb isotope composition  (Stacey and Kramers, 1975). The reported discordance refers to the position of data points in relation to the concordia curve in conventional 207 Pb/ 235 U-206 Pb/ 238 U space.
Decay constants used follow the recommendations of  Steiger and Jäger (1977). Age calculations were performed using the routines of ISOPLOT 4. 15  (Ludwig, 2012). To ensure that applied methods of U-Pb dating produce comparable results, one sample of granite (sample 05-C1, Susly quarry) was analysed by all three methods. Results of dating (2059 ± 5 Ma) are identical within error. Hf isotope analyses were performed at Bristol University, UK. The data were acquired with a Thermo-Scientific Neptune multicollector inductively coupled plasma mass spectrometer (ICP-MS) coupled to a New Wave 193 nm ArF laser ablation sampling system operating at 4 Hz and using a 50 mm spot size over a 60 s ablation period. The Yb isotope compositions of  Segal et al. (2003)  were adopted for interference corrections which are done by determination of the Yb mass bias by measuring 173 Yb/ 171 Yb during the run, and calculating 176 Yb/ 177 Hf using the natural 176 Yb/ 171 Yb ratio.
Two fractions, with 2-3 baddeleyite grains in each, and one single-grain fraction were analysed by TIMS for U-Pb isotopes. All the results are concordant and very consistent (Table  3 , Fig. 5 ) and yield an age of 1789. 8 ± 1. 5 Ma (lower intercept anchored to 0 Ma). In addition, our intention was also to expand the geochronological dataset for silicic rocks of the KPC, which had previously lacked adequate attention. Two of the granite samples were collected at the extreme northern part of the KPC, close to the contact with country rocks; these granites were recovered by drilling under the thin sedimentary cover. Zircons from sample 503/105 are euhedral and elongate, with well-developed prismatic and di-pyramidal facets; crystals are transparent to semi-transparent, pinkish and commonly loaded with numerous solid and fluid inclusions.
Acknowledgments This research gained financial support granted by the  Royal Society, UK  ( 2006/R4 IJP ), and the  Swedish Institute, Sweden. We are indebted to  O. Shestopalova  who kindly provided zircons separated from a xenolith hosted by an anorthosite. Prof. Jean-Clair Duchesne  is appreciated for his valuable discussion and suggestions. The paper has benefitted from constructive comments from  Lewis D. Ashwal ,  Tom Andersen , and an anonymous reviewer. This paper is NORDSIM contribution number  484 , and publication no. 62  of the  Large Igneous Provinces -Supercontinent Reconstruction -Resource Exploration Project  ( CAMIRO  Project  08E03 , and  NSERC   CRDPJ 419503-11 ) (www. supercontinent. org,www. camiro. org/exploration/ongoing-projects),and a contribution to IGCP 648.
Recordings of human interaction data can be organized into temporal representations with different affordances. We use audio data of a learning-related discussion analyzed for its low-level emotional indicators and divided into four phases, each characterized by an overarching emotion. After arguing for the relevance of emotion to learning, we examine this original analysis with the help of three different representations, transforming the data between them in order to connect micro-and macro-levels of analysis and give meaning to these connections. The first is a FRIEZE representation showing the temporal distribution of the lowlevel indicators of emotion as well as the phases. The second is an epistemic network analysis with an aggregated representation that shows how the pattern of associations among indicators of emotion differs between phases.
Context of Study Our corpus -called Jaune Fluo (Fluorescent Yellow) -was originally analyzed in the French Orféo project  (Quignard et al. , 2016)  in order to observe the emotional character of verbal sequences and to analyze how this character emerges or declines through verbal, non-verbal, and interactional mechanisms  (Plantin, Doury, & Traverso, 2000). The corpus -part of Meal Conversations Amongst Students -is in French and is available from the CLAPI database. 1 It concerns a conversation between two students, M and J, during their lunch. J narrates her experience of one of her teachers giving graded work back to her while criticizing her in front of her peers.
Methodology In this section, we describe how we first harnessed previously transcribed and analyzed audio data from another project that had been annotated using methods from interactional linguistics and put into the FRIEZE timeline (see below) where annotations were visible. Annotations were carried out on both a micro-and macro-level, in both cases characterizing phenomena with an emotional colouring. We relate how this data was used as input to an epistemic network analysis and how in light of the results we obtained, we returned to the timeline as well as to the transcribed audio data in order to interpret our findings. It is important to note that timescales and representations are related, but they are separate dimensions. Timescales are part of the initial problem space. Data are coded in terms of timescales, on both the micro-level, in terms of words or events (e.
g. , pauses, interjections) and on the macro-level in terms of phases that have been given a characterization. On the other hand, this same data is represented in different ways, depending on the tool used. FRIEZE chooses to use rectangles to represent both micro-words/events and macro-phases on a timeline. In this way, one can see which micro-words/events make up which macro-phase. ENA process micro-words/events and macro-phases differently. It produces one network for each phase and can also produce a network that illustrates how one phase differs from another in terms of how the micro-words/events characterize them.
Harnessing previously analyzed data The original analysis of the Jaune Fluo corpus -carried out by linguists -was divided into four successive phases using a collaborative, grounded analysis approach. First, each of the nine researchers involved was asked to analyze with his/her own method and research habits the corpus excerpt and explain how (s)he would divide the entire interaction in terms of phases that could be given an emotional colouring. These research habits involved studying and annotating a transcription that had been done using a set of conventions designed to render visible particular forms of human interaction. Each phase that was proposed had to be justified by giving the set of indicators or clues by which the emotional colouring could be assessed. Second, researchers held a data session to share their respective analyses.
Here then, we propose to do just that; we pick up this project in its third phase at the stage of the annotated excerpt and we investigate which low-level emotional indicators and indicator patterns are the most relevant with respect to the four phases that were given emotional colouring. To that end, we more specifically focused the indicator set on phenomena that are simple to identify, and that can theoretically be recognized automatically. These are also indicators that closely overlap with the transcription conventions in  Polo, Lund, Plantin, and Niccolai (2016). They are shown in Table  2 , below. The annotation had been carried out in the Orféo project on a time-aligned transcript in which each word has a determined position in time (starting and ending time). Since the indicators are attributed to words, we extracted a large table from the annotation software Praat  (Boersma & Weenink, 2017 ) that had the position and extension in time of all the indicators.
If they don't include interaction, they could indicate a high excitability in the speaker. Because of the segmentation of data by time slices rather than utterances, we did not distinguish between speakers while running the models in ENA. Thus, we cannot evaluate these hypotheses unless we return to the FRIEZE representations and the original transcript. However, even if we had distinguished among speakers, we still need to return to the FRIEZE representation in order to find interactional meaning.
Returning to the FRIEZE Representation At this stage, we created another new analytic artifact where each sequence in which indicator pairs occur is put into boxed form. Figure  4  shows us where these associations of indicators occur within two of the phases: fear and pride. This now gives us a means to return to the original data and, having located instances that characterize each phase, re-examine the evidence and re-anchor our interpretation of these representation-driven findings. Specifically, the FRIEZE representation lets us return directly to the audio data at each point in time to review the linguistic and cognitive markers of interest.
Perspectives In terms of perspectives, having established the interest of our methodology, we are currently performing similar analyses on data from a serious games context where students must work together using knowledge about mechanics and electronics in order to explore the terraforming of other planets. In addition to indicators of emotion, we will also analyze the competencies mobilized, and we will do so in relation to the pedagogical activity phases. We frame this as another instantiation of our method, but this time, we will perform two types of micro-level analyses. We will still use low-level indicators of emotion, but we will add low-level indicators of the competencies mobilized as students take on the savoir-faire of mechanics and electronics professions. Then, we will relate both of these low-level indicators to the specific pedagogical activity phases.
the  Investissements d'Avenir program  ( ANR-11-IDEX-0007 ) of the French government, operated by the  National Research Agency (ANR). This work was also funded in part by the  United States National Science Foundation  ( DRL-0918409 ,  DRL-0946372 ,  DRL-1247262 ,  DRL-1418288 ,  DUE-0919347 ,  DUE-1225885 ,  EEC-1232656 ,  EEC-1340402 ,  REC-0347000 ), the  MacArthur Foundation , the  Spencer Foundation , the  Wisconsin Alumni Research Foundation , and the  Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin -Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, co-operating institutions, or other individuals.
This work is devoted to the calculation of the composition for a monoatomic plasma (argon in the case presented) for which the assumption of thermal equilibrium is not realized. The plasma composition is obtained from a CR model, taking into account free electrons and a great number of electronic levels of Ar atoms and Ar + ions. This model is based on a large set of transition probabilities and reaction rate coefficients for radiative processes (spontaneous emission and radiative recombination) and on an extended database of direct and reverse reaction rate coefficients for collisional processes (excitation/de-excitation and ionization/recombination mechanisms). Assuming Maxwellian energy distribution functions for electrons and heavy chemical species, detailed balance equations are determined for all kind of reactions in the frame of the micro reversibility principle.
[5]  and Al-Mamun et al. [6]  concerning respectively argon, air, Ar-N 2 , Ar-H 2 and Ar-CO 2 -H 2 radio-frequency inductively coupled plasma (ICP) torches, Ghorui et al. [7]  for the study of the oxygen flow inside the nozzle of a metal cutting device, Boselli et al. [8]  concerning a direct current (DC) welding arc in argon, Trelles et al. [9] , Bartosiewicz at al. [10]  and Kaminska et al. [11]  for the study of a DC plasma torch in argon, Baeva and Uhrlandt  [12]  and Freton et al. [13]  concerning free-burning arcs in argon, Colombo et al. [14]  for a 3D transient model of a twin torch system, Park et al. [15]  for a DC transferred arc in argon and Benilov and Naidis  [16]  for the study of a wall-stabilized DC arc discharge. The implementation of these multi-T models is based upon 2T thermodynamic and transport property databases (as a function of T e and of the ratio θ = T e /T h ) and the first and unavoidable step in obtaining these 2T properties is the calculation of the plasma composition.
The fundamental theoretical concepts necessary for the calculation of 2T transport coefficients are expounded in the works of Rat  [17] , Rat et al. [18, 19] , Devoto  [20]  and Bonnefoi  [21]. 2T thermodynamic and transport property databases are also available for several gases or mixtures such as argon  [18, 19] , lithium  [22] , N 2  [23, 24] , O 2  [25] , SF 6  [26] , N 2 -O 2  [27] , carbon-oxygen  [28] , N 2 -H 2  [29] , argon-helium  [30] , Ar-H 2  [29, 31, 32] , argon-copper  [33]  and Ar-H 2 -He  [34]. These studies are founded upon solid theoretical bases with the exception of the calculation of the composition. Indeed, there is no consensus in the literature and the theory allowing the computation of composition for plasmas in thermal non-equilibrium conditions is not yet clearly established.
Indeed, compared to the two other techniques, this kind of approach allows the avoidance of the simplifying assumptions associated with the internal excitation modes (electronic, vibrational and rotational). On the other hand, CR models are more complex to develop because they require the implementation of an extended and complete cross sections or reaction rate coefficients database for all inelastic collisional processes between the chemical species of the plasma. In the present work, a detailed CR model taking into account a great number of electronic levels of Ar atoms and Ar + ions is proposed to determine the composition of argon plasma in thermal non-equilibrium conditions. In order to obtain an accurate result, the two following points corresponding to basic initial assumptions for the calculation are crucial: -the dependence of the direct reaction rate coefficients as a function of temperatures T e and T h ; -the use of accurate detailed balance relations for the calculation of reverse rate coefficients.
Energy diagram Three types of species are considered in this CR model: Ar atoms, Ar + ions and electrons. The energy diagram of argon used in this study is taken directly from the NIST database  [73]. Thus, 379 electronic levels have been selected for Ar. Concerning Ar + , only the first seven electronic levels are considered. Thus, all possible transitions between states until 32. 2 eV above the ground state of Ar can then be taken into account: this is compatible with the values considered in the present work for the heavy and electron kinetic temperatures (i. e. T e and T h lower than 15 000 K). Density effects on the ionization potential of energy levels are not considered here. The plasma studied can therefore be assumed as totally ideal. Each considered electronic level of Ar and Ar + can interact through different elementary processes, detailed in the following section.
Radiative processes Ar and Ar + can produce radiation by means of spontaneous emission processes Ar j Aji --→ Ar i<j + hν (4) Ar + n Anm ---→ Ar + m<n + hν (5) The Einstein coefficients A ji (s -1 ) from the NIST database [73] have been directly used. In total, 423 lines have been taken into account for argon, and 3 lines for Ar +. Radiative recombination processes of the type Ar + m + e -k RR m→i (Te) ------→ Ar i + hν (6) have also been taken into account. The cross-sections due to Zel'dovich and Raizer  [74] , assuming a hydrogen-like behavior for the atom Ar on its excited state i after recombination, have been used and integrated under the Maxwell-Boltzmann distribution for the kinetic energy ε of electrons. The rate coefficient for radiative recombination is finally given by [64] k RR m→i (T e ) = 8k B T e πm e 32πa 2 0 3 √ 3 × 137 3 E H ion k B T e 2 × E m -E i E m 3/2 ξ 1 (a) × e a (7) where a 0 is the first Bohr radius, E H ion the ionization energy of the hydrogen atom.
These rate coefficients are then interpolated under the modified Arrhenius law: k A or + i→j or m (T h ) = A(i, j or m)T α(i,j or m) h ×e -B(i,j or m)/T h. ( 24 ) Note: For heavy particle impact forbidden transitions, there is no cross section formulation proposed by Drawin and Emard and, to our knowledge, no form proposed in the literature. As a consequence, for forbidden transitions, we have used the same formulation as for allowed transitions, i. e. relation  (20) , provided that an oscillator strength for the considered forbidden transition is available in the NIST database  [73]. Other heavy particle impact forbidden transitions are not taken into consideration.
Conclusion A CR model has been developed to determine the composition of argon plasma in non-thermal equilibrium conditions. This CR model takes into account a large number of atomic levels of neutral argon (the 379 levels available in the NIST database  [73] ) and the first 7 states of Ar + ion up to 32. 2 eV above the ground state of Ar. This selection of electronic levels for Ar and Ar + is coherent with the study of the plasma composition for temperatures T e and T h lower than 15 000 K. The 2T plasma composition is obtained from the resolution of a non-linear system of equations constituted of 387 conservation equations (i. e. a balance equation for each considered electronic level and for electrons). This system of equations is dependent on the forward and backward reaction rate coefficients of inelastic collisions occurring in the plasma (excitation and ionization through electron and heavy particle impact in the present work).
Speech enhancement and automatic speech recognition (ASR) are most often evaluated in matched (or multi-condition) settings where the acoustic conditions of the training data match (or cover) those of the test data. Few studies have systematically assessed the impact of acoustic mismatches between training and test data, especially concerning recent speech enhancement and state-of-the-art ASR techniques. In this article, we study this issue in the context of the CHiME-3 dataset, which consists of sentences spoken by talkers situated in challenging noisy environments recorded using a 6-channel tablet based microphone array. We provide a critical analysis of the results published on this dataset for various signal enhancement, feature extraction, and ASR backend techniques and perform a number of new experiments in order to separately assess the impact of different noise environments, different numbers and positions of microphones, or simulated vs.
real data on speech enhancement and ASR performance. We show that, with the exception of minimum variance distortionless response (MVDR) beamforming, most algorithms perform consistently on real and simulated data and can benefit from training on simulated data. We also find that training on different noise environments and different microphones barely affects the ASR performance, especially when several environments are present in the training data: only the number of microphones has a significant impact. Based on these results, we introduce the CHiME-4 Speech Separation and Recognition Challenge, which revisits the CHiME-3 dataset and makes it more challenging by reducing the number of microphones available for testing.
, 2014; Xu et al. , 2014; Weninger et al. , 2015) , which require a larger amount of training data not limited to the immediate context. Chen et al. (2015)  and  Kim and Smaragdis (2015)  considered the problem of adapting DNN based enhancement to unseen test conditions, but their experiments were conducted on small, simulated datasets and evaluated in terms of enhancement metrics. Regarding ASR, the variation of the word error rate (WER) as a function of the SNR was studied in several evaluation challenges, e. g. ,  (Hirsch and Pearce, 2000; Barker et al. , 2013). The adaptation of DNN acoustic models to specific acoustic conditions has been investigated, e. g. ,  (Seltzer et al. , 2013; Karanasou et al. , 2014) , however it has been evaluated in multi-condition settings rather than actual mismatched conditions.
Suspicion about simulated data is common in the speech processing community, due for instance to the misleadingly high performance of direction-of-arrival based adaptive beamformers on simulated data compared to real data  (Kumatani et al. , 2012). Fortunately, this case against simulation does not arise for all techniques: most modern enhancement and ASR techniques can benefit from data augmentation and simulation  (Kanda et al. , 2013; Brutti and Matassoni, 2016). Few existing datasets involve both real and simulated data. In the REVERB dataset  (Kinoshita et al. , 2013) , the speaker distances for real and simulated data differ, which does not allow fair comparison. The CHiME-3 dataset  (Barker et al. , 2015)  provides a data simulation tool which aims to reproduce the characteristics of real data for training and twinned real and simulated data pairs for development and testing.