dataesr/scientific-paragraphs-categorization huggingface dataset:
Trials containing artifacts or/and incorrect responses were not included in the average ERPs. This led to an average rejection rate of 10. 5% of all trials (4. 2% due to artifact rejection; 6. 3% due to incorrect responses). A minimum of 32 trials were included for each condition in the average ERP data from each participant (mean of the averaged number trials per condition across participants: M = 46, SD = 4. 8). ERPs were averaged separately for each of the experimental conditions, each of the subjects, and each of the electrode sites. As our main interest was on the earliest latency of the wordfrequency effect (N170), we first identified the electrode sites with the maximum grand averaged N170 amplitude (125-225 ms) across all conditions. This led to the selection of two channel groups encompassing left occipital-temporal (T7, CP5, P3, P7 and O1) and the corresponding right occipital-temporal electrodes (T8, CP6, P4, P8 and O2).
We performed onset latency analyses on the ERP data over these electrodes to assess the effects of word-frequency and its interaction with script on the N170 and further on. To correct for multiple comparisons, we used the Mass Univariate ERP toolbox  (Groppe et al. , 2011). To assess the interaction between word-frequency and script, running t-tests were applied at every sampling point (4 ms) for the 125-650 ms epoch on 2 x 2 level interactions that included Word-Frequency (High, Low) and either Script-easy (Printed, Easy handwritten) or Script-difficult (Printed, Difficult handwritten) by inputting the difference of the difference waves in the Mass Univariate ERP toolbox (see Gutierrez-Sigut et al. , 2019 for a similar procedure). To address simple comparisons, we then performed the running t-tests between low-and high word-frequency conditions for each script.
Behavioral results In the latency analyses, we excluded the RTs shorter than 250 ms (0 observations in word trials; 1 observation in nonword trials) and the incorrect responses (6. 3% of word trials and 7. 5% of nonword trials). The mean RTs and accuracy rates per condition are displayed in Table  2. For the inferential analyses, we created generalized linear mixed effects in R with the lmer package  (Bates et al. , 2015). The fixed factors were Word-Frequency (encoded as -0. 5 and 0. 5 for the high-and low-frequency words, respectively) and Script (where the printed format was the reference for the easy handwritten and difficult handwritten script). We chose the most complex model that converged in terms of random effects intercepts and slopes. In the model based on latency data, we chose the Gamma distribution to avoid the interpretive issues of non-linear transformations in factorial designs.
1, 0. 3, 0. 5, 0. 7, and 0. 9 quantiles, respectively), thus replicating earlier research  (Perea et al. , 2016). Critically, as shown in Fig. 1B  for the difficult handwritten condition, the word-frequency effect not only changed across quantiles with a steeper slope, but it was also larger in the first quantiles  (38, 57, 72, 92 , and 111 at the 0. 1, 0. 3, 0. 5, 0. 7, and 0. 9 quantiles, respectively). This finding suggests that word-frequency already taps onto an encoding stage of processing when the stimuli appear in the difficult handwritten script. In sum, while these results suggest an additive pattern of script and word-frequency with regard to the easy handwritten manipulation, an interactive pattern between script and word-frequency was obtained in the difficult handwritten manipulation.
EEG results Fig. 2  shows the ERP waves for the script and word-frequency comparisons in the occipital electrodes (O1, O2). The ERPs of all conditions showed an initial positive potential peaking around 100 ms, followed by a larger negativity ranging between 125 and 225 ms (N170). Next, a negativity peaking around 250 ms is followed by a larger negativity peaking around 400 ms (N400). Importantly, a word- Note: For the nonword stimuli, the mean RTs and error rates were: Printed: 699 ms (6. 2%); Easy Handwritten: 747 ms (8. 7%); Difficult Handwritten: 769 ms (6. 3%). Fig. 1. Group RT distributions in the script and wordfrequency manipulations in word stimuli. Script: printed, easy handwritten (easy HW), difficult handwritten (difficult HW); word-frequency: high frequency (HF), low frequency (LF).
For clarity, the comparison of printed vs. easy handwritten script is shown in A, while the comparison of printed vs. difficult handwritten script is shown in B. Each point represents the average RT quantiles (. 1,. 3, 0. 5, 0. 7, and 0. 9) in each condition. These values were obtained by computing the quantiles for each participant and subsequently averaging the obtained values for each quantile over the participants (see  Vincent, 1912). frequency effect is present in the difficult handwritten condition, with larger N170 amplitudes for the high-compared to the low-frequency condition. Secondly, word-frequency effects are evident from approximately 350 ms in the printed script condition (larger negative amplitudes for the low compared to the high-frequency conditions). More specifically, the word-frequency effect was larger in the printed than in both easy and difficult handwritten conditions (see Figs.
2  and 3 ). This same pattern was also found in the LPC component (450-650 ms). The t-test analyses (see Fig. 4 ) show robust differences regarding the time-course and size of the word-frequency effect across the three scripts. As shown in Fig. 4 -panel A, the interaction between wordfrequency and script (printed vs. easy handwritten) was only significant in the 400-470 ms time window. In contrast, Fig. 4 -panel B shows that the interaction between word-frequency and script (printed vs. difficult handwritten) was significant in the N170 time window (145-160 ms), in the 224-270 ms interval, and in the 388-512 ms interval. Below, we describe the results of simple comparisons: Word-Frequency and Script (printed vs. easy handwritten). The interaction between Word-Frequency and Script (printed vs.
easy handwritten) was only significant in the 400-470 ms time-window over electrodes P3 and P4. Although simple comparisons revealed that the printed and the easy handwritten conditions showed wordfrequency effects in this interval (larger negative amplitudes were obtained for low-frequency words compared to the high-frequency words), word-frequency effects were significantly larger in the printed than in the easy handwritten condition, as shown in Fig. 3. Word-Frequency and Script (printed vs. difficult handwritten). Firstly, simple comparisons revealed word-frequency effects in the difficult handwritten condition (but not in the printed condition) over O1, O2, CP6 and P4 starting at around 150 ms post-stimuli (see the blue marks in Panel B: difficult handwritten script), with larger negative amplitudes for the high-frequency words condition compared to the low-frequency words condition.
Secondly, for the next significant interaction obtained in the 224-270 interval, simple comparisons revealed that word-frequency effects in the printed script condition (but not in the difficult handwritten condition) were present over T7, CP5, P3 and P7, with lowfrequency words eliciting larger negative amplitudes than highfrequency words (see Fig. 3 ). Thirdly, for the significant interaction obtained in the 388-512 ms interval (N400), simple comparisons revealed that word-frequency effects were present in the printed script condition (but not in the difficult handwritten condition) over T7, CP5, P3 and O1 starting at 388 ms until 424 ms: larger negative amplitudes were obtained for low-frequency words compared to the high-frequency words. In the following interval (424-512 ms) the printed and the difficult handwritten conditions showed word-frequency effects.
2 ) is consistent with prior literature in the field (see  Chauncey et al. , 2008). The apparent difference between the printed and the handwritten conditions suggests an enhanced effort when mapping letter features onto abstract letter representations for the handwritten conditions. Notably, this pattern is different to the early ERP responses to printed words, where letter shape is preserved, thus providing a better matching with the prototypical letter representations. (close to) pristine (printed words and easy handwritten), perceptual encoding operates in a feedforward fashion and the recruitment of top-down mechanisms of perception is delayed. The differences regarding the latency of the word-frequency effect in the difficult handwritten condition in the N400 window can be interpreted in terms of difficult handwritten script being less efficiently computed at a lexical-semantic level (see  Grainger and Holcomb, 2009).
Acknowledgements This research work has been carried out under the ELSAT 2020 project supported by the  European Union  with the  European Regional Development Fund , the  French State  and the  Hauts de France Region Council. The authors gratefully acknowledge the support of these institutions. The present research work has also been carried out in the context of the  LIA (International Associate Laboratory , now  International Research Project) ROI-TML (Operational Research and Computer Science in Transportation, Mobility and Logistics) between LAMIH UMR 8201 (France)  and  CIRRELT (Canada). The authors gratefully acknowledge the support of these laboratories and related institutions.
Specification of the research objectives and search protocol Identifying the research objectives helps set the conceptual boundaries of the review  (Behera, Bala, and Dhir 2019). Accordingly, the current SLR has identified five distinct research objectives commensurate with the research questions discussed previously. These are: (a) investigate the descriptive dimensions of research evidence on Big Data application in OSCM, (b) distil key research themes, (c) identify research gaps, (d) set a future research agenda, and (e) develop a conceptual framework to measure varied aspects of Big Data application in OSCM. With regard to the search protocol, we first identified the databases for the literature search. To ensure that this study covered multiple research areas of OSCM, two widely used digital databases, Scopus and Web of Science (WoS), were used to search for relevant literature and to select the appropriate studies, as suggested by scholars  (Dhir et al.
Data collection Using Scopus and WoS as our databases, we conducted our search using the selected keyword combinations of 'Big Data' AND 'supply chain' OR 'operations management' OR 'operations' on October 23, 2020. The search yielded an initial tally of 7,646 and 3,025 probable studies from Scopus and WoS, respectively. After applying the specified inclusion criteria, we shortlisted 2,148 (Scopus) and 1,788 (WoS) studies for further consideration. Next, we applied the exclusion criteria to eliminate duplicate studies as well as those lacking immediate congruence with the topic at hand, resulting in a joint pool of 150 studies to take forward. The specified quality evaluation criteria were then applied to generate a quality score, which helped us identify 114 congruent studies that satisfied these criteria.
Concerning participants' demographic profile, the selected samples included respondents with undergraduate to doctoral qualifications, experience ranging from fewer than five years to more than 15 years, and ages ranging from younger than 30 years to older than 50. However, most of these studies did not report their gender ratio, while those that did had a largely male-skewed sample. Concerning the research methods employed, the selected studies utilised a wide variety of methods, as presented in Figure  5 , with quantitative (n = 66) and qualitative (n = 27) being the most popular ones. In regard to data analysis methods, however, Figure  6  reveals that thematic analysis (n = 31) and SEM (structural equation modelling) (n = 24) were the most commonly used. Furthermore, the selected studies utilised twelve theories, as presented in Table  1 , clearly indicating a dearth of theory-based research, with only 39 studies invoking theories to propose their conceptual model.
Finally, to ensure inter-rater reliability and present a consensus view, the three researchers discussed the individually delineated codes and worked on settling the differences. As the selected studies had clearly discussed their findings related to Big Data application in OSCM, there were no divergent views about the eight emergent themes identified. The debate largely centred, instead, on determining the most coherent presentation order. Finally, we invited two professors from the operations management area with expertise in Big Data application to review the identified themes. Upon receiving their feedback, the eight themes were finalised, as presented in the mind map in Figure  8.
Data collection methods To begin with, several of the reviewed studies utilised cross-sectional survey data to test the proposed hypotheses  (Chen, Preston, and Swink 2015; Bag 2017; Papado poulos et al. 2017; Dubey et al. 2018; Jeble et al. 2018; Mandal 2018b ). As cross-sectional data collected from self-report instruments on one occasion from homogenous participants may suffer from issues of respondent bias and generalizability, there is a need for greater empirical studies employing other research designs, such as longitudinal surveys, which will help address these issues.
Conclusion The present study conducted an SLR aiming to synthesise the existing literature on Big Data application in OSCM. Toward this end, we searched two digital databases, Scopus and WoS, to identify studies for inclusion in the review. To ensure a robust selection of studies, we specified and applied stringent inclusion, exclusion, and quality evaluation criteria to select 116 congruent studies. We then proposed and addressed four research questions (RQs). In our response to RQ1, we presented the research profile of the selected studies to offer a bird's eye view of the research design and publication details. We addressed RQ2 by undertaking a content analysis of the studies to extract eight themes that make the extant findings discernable and meaningful. To address RQ3, we critically analysed the research profile and themes to reveal gaps in these findings.
In 2017, he won the Best Paper Award by The Academy of Management Journal and by The Electronic Markets: The International Journal on Networked Business. He has been serving as a member on the editorial board of five international journals. Moreover, he is a CompTIA RFID+ Certified Professional, and the Academic Co-Founder of RFID Academia. Apart from teaching and conducting research, He leads the newly created Artificial Intelligence & Business Analytics Cluster. In one of his areas of research, he has been recently recognised as the most influential scholar in big data analytics and enterprises based on the number of published articles and the number of citations [1] , and among the 2% of the most influential scholars in the world based on the Mendeley database that includes 100,000 top-scientists.
We present here the first solution to this important problem which, in addition to its own interest in the theory of MB integrals, can potentially lead to many new results in the fields mentioned above. A Mathematica implementation of our method is given in the Supplemental Material to this paper, along with important specific examples of application of our method. The code is used, among others, to obtain the first evaluation of two highly nontrivial resonant cases in QFT: the hexagon and double box conformal Feynman integrals with unit propagator powers (see  [53]  for the nonresonant generic propagator powers cases). The method-The type of series representations that can be derived from Eq. (  1 ) strongly depends on the Ndimensional vector ∆ = i a i e ij b j f j. If ∆ is null, which is the case we focus on in the present work, this corresponds to a degenerate situation  [66, 67]  where there exist several convergent series representations for the MB integral, converging in different regions of the x parameter space.
It gave, in less than two minutes of CPU time, a series representation consisting of 26 terms for the hexagon  [53]. In contrast the MBsums Mathematica package of  [28]  gives a hardy usable linear combination of 112368 terms in over 12 hours on the same computer. We have also used our code to derive the first series representations of the hexagon and double box in the highly nontrivial resonant case of unit propagator powers.
Friot, S. Ghosh There is no need to explicitly call the Multivari-ateResidues package, which is directly called by our package internally. One then inputs Eq. (  2 ) as follows: In[3]:= MBRepOut = MBRep[Gamma[c]/(Gamma[a]Gamma[b 1 ] Gamma[b 2 ]), {z 1 , z 2 }, {-u 1 , -u 2 }, {{-z 1 , -z 2 , a + z 1 + z 2 , b 1 + z 1 , b 2 +z 2 }, {c + z 1 + z 2 }}]; Then one proceeds with the finding of the different features of the MB integral and, e. g, of the first two series representations by calling: Other examples: the Hexagon and Double Box conformal Feynman integrals with unit propagator powers-These highly nontrivial resonant cases can be handled with our code, but the resulting expressions are too lengthy to be given here. Two particular series representations, built from respectively 64 and 140 series of nine variables, are however calculated and explicitly shown in the provided Examples.
nb notebook. The corresponding master series allowed us to easily find values of the nine variables that could be used to check the numerical matching between these series representations and the Feynman parameterizations of the hexagon and double box. A second check has been to numerically verify that the differential equation that links the hexagon to the double box is satisfied. The latter check has been performed at a 71 decimal places level, far below the size of the smallest series of each series representation, which guarantees that the contributions of all series of the series representations derived in the notebook have been tested. These examples provide another nontrivial test of the MBConicHulls package. Note that although in principle the code can give all of the many possible series representations of the hexagon and double box, its present version cannot do it in a decent time.
We next group these three denominator singular factors in G as (f 1 , f 2 ), using Eq. (B3). Each N -combination (i 1 , i 2 ) on the RHS of Eq. (B3) must satisfy the conditions that it must be an element of the set S and its singular factors {s i1 , s i2 } must belong to the set G. Therefore, we have (f 1 , f 2 ) = S 1,3 (s 1 , s 3 ) + S 1,5 (s 1 , s 5 ) = -(s 1 , s 3 ) -(s 1 , s 5 ) = -(s 1 , s 3 s 5 ) = ((z 1 + z 2 )z 2 , -z 1 ) (B7) where we have used the rules in Eq. (B1) and Eq. (B2). Finally, using the MultivariateResidues package with (f 1 , f 2 ) in Eq. (B7) we obtain the logarithmic result: S 1 = - √ π ∞ n1,n2=0 log(-u 2 ) -ψ(-3/2 -n 2 ) + ψ(1 + n 2 ) Γ(-3/2 -n 2 )Γ(1 + n 2 ) × u 1 u 2 n1 - 1 u 2 n2+2 = - √ π u 1 -u 2 ∞ n2=0 log(-u 2 ) -ψ(-3/2 -n 2 ) + ψ(1 + n 2 ) Γ(-3/2 -n 2 )Γ(1 + n 2 ) × - 1 u 2 n2+1 (B8) • Set 2: Poles at (n 1 , -1 -n 2 ) of  (1, 5).
1994 ) and optical  (Stirpe et al. 1994)  reverberation results have been obtained. NGC 3783 is also one of the AGN that clearly demonstrated the virial relationship between emission line lag and width  (Onken & Peterson 2002). Through reverberation mapping, the black hole mass of NGC 3783 has been estimated to 1 We adopt a distance of 38. 5 Mpc based on the Tully-Fisher (TF) relation and reported on the NASA Extragalactic Database (NED) which is significantly smaller than the luminosity or angular size distance inferred from the redshift (47-48 Mpc) and due to the peculiar velocity of the galaxy. This distance though could potentially be affected by AGN contamination and thus underestimated. However,  Crook et al. (2007)  found NGC 3783 to reside in a group of four galaxies at a distance of 37 Mpc and  Kourkchi & Tully (2017)  found NGC 3783 to reside in a group of nine galaxies at a distance of 42 Mpc.
( , 2004) )  concluded from polarisation data that the polar axis is at -45 •. However, there are several issues that make this conclusion uncertain: NGC 3783 does not show the expected position angle rotation across the Hα line profile, the depolarisation in the line core may partially be due to the narrow lines, and the polarisation peak in the line wing is not unique to this specific model. Lira et al. (2020)  also noted that NGC 3783 has unusual polarisation characteristics, with the position angle showing a more M-like profile possibly indicative of a radially outflowing scatterer. Further, both the mid-IR and polarisation measurements could be biased towards the edges of the outflow cones if this is the location of most of the mass in the cones and has been hinted at recently through observations of Circinus (e.
Analysis of the H-and K-band variability by  Lira et al. (2011)  revealed a 70 day lag with respect to optical continuum, indicative of a radius of 0. 06 pc for the hot dust distribution. The size measured from K-band interferometry is very comparable at 0. 1 pc  (Weigelt et al. 2012; Gravity Collaboration et al. 2020b). The new data we report here present a new look at this innermost region of NGC 3783. This work adopts the following parameters for a ΛCDM cosmology: Ω m = 0. 308, Ω Λ = 0. 692, and H 0 = 67. 8 km s -1 Mpc -1 (Planck  Collaboration et al. 2016). Using this cosmology and our adopted distance of 38. 5 Mpc, 1 pc subtends 5. 36 mas on sky and 1 µas corresponds to 0. 22 light days.
SINFONI We observed NGC 3783 on April 20, 2019 using SINFONI with adaptive optics in service mode at the VLT  (Eisenhauer et al. 2003; Bonnet et al. 2004 ). The observations were obtained at the 25 mas pix -1 scale and the K grating (R 4000). A total of 6 exposures of 100s were taken in a dithering object-sky-object sequence. Data were reduced with our standard pipeline and wavelength calibration scheme (SPRED,  Schreiber et al. 2004; Abuter et al. 2006). Sky frames were subtracted from the images to correct for instrumental and atmospheric background. We then reconstruct 12. 5 mas pix -1 images with a spectrum at each pixel, applying flat-fielding, bad-pixel, distortion, and cosmic-ray hit corrections. The wavelength calibration is done using emission line lamps and is further tuned on the atmospheric OH lines in the raw frames.
Thus, we fit a line profile to the calibrator's absorption in the IRAS 09149-6206 spectrum from each night and used these fits to remove the stellar Brγ feature from the NGC 3783 spectra. The spectra from each of the two nights were then averaged together, weighted by their uncertainties to produce the final GRAVITY flux spectrum. Fig. 3  shows the final GRAVITY mean spectrum for both Brγ and [Ca viii]. With higher spectral resolution and S/N, the SINFONI data also provide a high quality K-band spectrum that can be compared with the GRAVITY spectrum. We extracted a nuclear spectrum using a circular aperture centred on the peak of the continuum and a diameter of 9 pixels that matches the PSF FWHM of the smoothed cube. The region around the Brγ and [Ca viii] lines were then normalised to a local fit of the continuum.
1994; Onken & Peterson 2002; Bentz et al. 2020 ) indicating gas at smaller radii than the R min measured here. At large radii, both observations and models suggest a maximum radius of the BLR at the dust sublimation radius  (Laor & Draine 1993; Netzer & Laor 1993; Korista et al. 1997; Baskin et al. 2014; Schnorr-Müller et al. 2016a; Suganuma et al. 2006 ) which for NGC 3783 should occur around 80 light-days. Our best fit model only contains < 1% of clouds at radii larger than this so indeed for NGC 3783 it does seem the model BLR is confined within the dust sublimation radius. As a test of the sensitivity of the model to the inner and outer radii, we further decided to fit the data with a model with fixed R min = 4 light-days and R max = 80 light-days. We find that by fixing these radii we can still achieve a fit nearly as good as our fiducial one.