dataesr/scientific-paragraphs-categorization huggingface dataset:
As an example, the segmentation procedure detected a region on chromosome 14 spanning from 14,1Mb to 14,4Mb that includes the MC1R locus, a gene associated with coat color. This gene was also identified in  Rochus et al. (2018) , but the length of the reported region was 6 times bigger, spanning from 13,5Mb to 15,2Mb. Similarly, a large genomic region located on chromosome 6 was reported in  Fariello et al. (2014)  (33. 2 to 41Mb using 50K genotypes) and in  Rochus et al. (2018)  (22. 8 to 48. 6Mb using the present dataset). This region includes several likely candidate genes: ABCG2  (36, 55Mb)  and NCAPG/LCORL  (37, (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) 33/ 37, (36) (37) 45Mb)  that are respectively associated with milk production, growth and height related traits in several species.
1000 Genome Dataset To illustrate the ability of our procedure to cope with large datasets, we considered the 1000 Genome Dataset  Consortium et al. (2010) , focusing on the detection of selection sweeps across 2 population subsets: the admixed American populations (CLM, MXL, PEL, PUR) and the European populations (CEU, TSI, FIN, GBR, IBS). We considered 4 chromosomes  (1, 2, 15, and 20)    non-informative for the populations under studies (i. e. with a maximum weight D(m) over all pairwise comparisons lower than 0. 001) and were discarded before the analysis. For a given pairwise comparison regions with an F ST level higher than 3 times the baseline F ST were declared under selection, and regions under selection in at least 10 pairwise comparisons (among 20) were reported. This last threshold corresponds to the theoretical number of detections one would expect when at least 2 populations of the smallest subset exhibit a selection pattern compared to the populations of the biggest subset.
Discussion In this paper we implemented a segmentation approach coupled with a moment based F ST estimator to detect genomic regions under positive selection. Our approach allows for pairwise comparisons of populations that can be combined to test for selection across sets of populations. The procedure runs within seconds even for large genome datasets with millions of SNPs, as illustrated on the 1000 Genome application, and is implemented in the fst4pg R package available on the CRAN repository. The applications highlighted the presence of regions under selection that clearly do not reduce to a single SNP, bringing out the need for procedures that do not rely on a single-marker detection. A popular solution to identify regions from a single-marker detection is to combine marker statitics through a region/window-based procedure.
Regarding the Sheep dataset, our procedure proved to be able to retrieve many of the regions known to be under selection with greater precision in terms of genomic region delimitation compared to previous studies. Although the procedure is based on F ST and weight estimates that simply rely on the allelic frequency at each marker, it was able to detect regions already known for their haplotypic heterogeneity. This was illustrated by e. g. the detection of the RXFP2 and MC1R genes, which have been previously investigated and are both characterized by multiple adaptive alleles segregating in different breeds of sheep. A major contribution of our approach is to provide a clear genomic landscape of the F ST distribution throughout the chromosome. This landscape could not be observed from the pointwise marker estimate representation due to the high variability observed across markers (see Fig  1  and 2 ).
The effective response of a cross-section is obtained from the finite element method, whose tabulated results are used to derive an approximate effective energy density to be used in the macroscale, structural-level calculations, which exploit the discrete rod code UtoBeams (UtoBeams, 2020). More refined approaches based on, e. g. , kriging or machine-learning to embed the effective material model in the UtoBeams library are potential avenues to improve the efficiency of our formulation. Alternatively, a switch from pre-tabulation and interpolation to on-the-fly computations, where the effective energy in the vicinity of specific strain values is determined by the loading path of the macroscale problem is another direction to consider. We have shown that, in all examples considered here, truncating the asymptotic expansion at leading order (resulting in a local effective energy density) is sufficient, while problems involving significant size effects or localization along individual struts may necessitate higherorder expansions leading to effective strain-gradient structural models, which can be included in our numerical framework.
Introduction In this supplementary material, we provide: • details about datasets used in experiments; • comparison with other recent methods which were designed for memory-free class-incremental learning (MFCIL); • implementation details for all tested approaches; • extended results on the negative examples ratios; • supplementary results for the use of a ratio between positives and negatives; • working FeTrIL code for CIFAR-100  [6]  with T = 10 states.
Datasets details Dataset #Train #Test µ(Train) σ(Train) CIFAR-100  [6]  50,000 10,000 500. 0 0. 0 TinyImageNet  [7]  100,000 The datasets used in evaluation are designed for visual classification tasks. Their main statistics are in Table  1. Since the actual test subsets are not provided by the organizers of the ImageNet LSVRC competition, we follow common practice in incremental learning  [8, 3, 5]  and use the original validation subsets for the test phase.
Implementation details When implementations of compared methods were available, we first tested them using the protocol and datasets from the original paper to make sure that we reproduced their results. We then used the authors' optimal parameters to test these methods in our evaluation setting. Note that for sake of fairness, all baselines were run using both training and validation sets (from Table  1 ). A ResNet-18 model  [4]  and an SGD optimizer with momentum = 0. 9 are used for all methods. We explicitly list the learning parameters of each method hereafter:.
Training the incremental models: Here, we describe the hyper-parameters used to train the methods which were retrained in Table  1  of the main paper. • LUCIR  [5]   Table  2. Comparison of SDC  [11]  with FeTrIL 1 using the evaluation protocol for two supplemenary datasets used in  [11]. Best results in bold. CIL Method ImageNet50 ImageNet100 T = 5 T = 20 ABD  [10]  (ICCV'21) 71. 5 12. 1 FeTrIL 1 89. 0 39. 0 10 at epochs 30 and 60. The method-specific parameters are the same as those from the original paper  [5]  and can also be found once we release the codes and configuration files. • DeeSIL  [1]  -the initial model is the same one used for FeTrIL. The training of linear classifier is also done using the same parameters.
Comparison to other recent MFCIL methods In Table  2 , we compare FeTrIL SDC  [11]  using the evaluation protocol and datasets from  [11]. Half of the datasets are assigned to the initial state and the rest of classes are split evenly among the remaining states. Following  [11] , the training of the initial FeTrILmodel for CUB200 and Flower102 datasets is initialized with a pretrained ILSVRC model. We do the same here to facilitate comparison with the original paper. The results from Table  2  indicate that FeTrIL 1 is clearly better than SDC  [11]  in all tested configurations. The better stability of FeTrIL results with the increase of the number of CIL states observed in the main submission is also confirmed for CUB200, Flower102, the three medium-scale datasets used in  [11]. In Table  3 , we present results obtained with FeTrIL and Always Be Dreaming (ABD)  [10]  a recent method which combines distillation and image inversion to address MF-CIL.
Effect of a positives-to-negatives ratio In addition to Figure  4  of the main submission, we present in Figure  1  the behavior of FeTrIL 1 when we approximate the negative example pool with different ratios r for CIFAR-100 and TinyImageNet. These results confirm the observations made in the main paper since the accuracy trends observed when using a positives-to-negatives ratio is similar for all three datasets. This highlights the possibility to accelerate the training of FeTrIL with very limited accuracy loss. Generally speaking, no EFCIL method can ensure a class separability comparable to that provided by standard learning with all images of all classes available simultaneously. The objective is to find a good balance between the stability and the plasticity of EFCIL representations. The experiments from the main paper show that, while imperfect, the combination of features and of pseudo-features used in FeTrIL 1 provides better performance compared to methods which update the model using variants of knowledge distillation and more complicated class prototypes.
MATB-II The Multi-Attribute Task Battery (MATB-II, see Figure  1 ) is software designed by NASA to provide activities to study workload. MATB-II is used to design training scenarios  [28]  to study relations between cognitive control modes and mental workload  [29]  and for real-time estimation of the mental workload with machine learning  [30]. The main feature of this tool is the simultaneous performance of multiple tasks: • System monitoring (SYSMON): the user has to respond to the absence of the green light and to the presence of the red light, and has to monitor the four moving pointer dials for deviation from the midpoint. • Communications (COMM): this task presents pre-recorded auditory messages to the user, who has to determine which messages are addressed to NASA-504 and who has to respond by selecting the appropriate radio and frequency.
Apparatus To carry out the experiment, two unobstructed floor spaces of at least 2 × 2 m, two VRready computers connected together either via the Internet or via a Local Area Network, two immersive Head Mounted Displays (we used a HTC Vive Cosmos for the confederate and an Oculus Quest 2 for the participants) were required. The immersive technology difference had no effect on the user experience, as both HMDs offered the same interaction capabilities and the same feedback from the virtual environment. However, what is important is that all the participants had the same HMD, the Oculus Quest 2, and the confederate always had the same HTC Vive Cosmos. This did not induce variability in the experiment, so it is asserted to not have affected the results. The experiment itself was run by the MATB-II-CVE application: Custom software was developed for this study (see the data availability section for accessing it, see Figure  5 ) using the Unity 2018 game engine, the SteamVR Virtual Reality framework for VR tracking, and Photon, a network framework.
Protocol We first gave detailed information and explanations to the participants. Then, we equipped them with an immersive headset and its controllers and started the initial training phase. For each condition, participants went through an additional training phase and a ten-minute task scenario, collaborating with the confederate. At the end of the experiment, participants were asked to answer a questionnaire to gather demographic data and subjective evaluation of the proposed indicators. The experiment lasted forty-five minutes for each participant. There were three training phases: the initial training phase and two condition training phases. During the initial training, the participant could experience the completion of each task one after another: once the RESMAN, the training stopped when the participant clearly said that they understood the functioning of all the pumps; the SYSMON task twice; the TRACK task once; the COMM task twice; all messages not addressed to NASA-504 were ignored in the count.
The participant could ask to retry as many tasks as they wanted until ready to begin a 10-min scenario. Before each condition, the tasks triggered once each, one after another in the same order as the initial training phase. For the NoData condition, the participants were asked to complete the task as they triggered; this was followed by the simultaneous activation of all tasks at once, and the participant and the confederate completed their task together. For the AggData condition, the participants were asked to watch the indicators as the task went on while the confederate gave a detailed explanation of them. This was followed by the simultaneous activation of all tasks at once where the participant and the confederate completed their task together. During the experiment, the participant and the confederate had to complete two task scenarios on the MATB-II interface.
Hypothesis We formulated two hypotheses about the user's task status indicators: H1-people would prefer instantaneous information conveyed by icons to a history constructed from the progression bars in terms of activity feedback. This could only be measured by data gathered via the questionnaire. H2-people would more easily detect that a collaborator needs help with AggData compared to NoData. This could be measured by data gathered via the questionnaire and by counting the number of times participants will help the confederate (help measures).
Results We first present objective data, followed by subjective data. Each participant went through two conditions, so we have 2 × 20 = 40 data points. We checked for normality of the distribution with Shapiro-Wilk tests among measures, and we did not have normal distributions, so we used Wilcoxon tests to check if there were significant differences in results between the two conditions. We did not observe any significant difference between the two conditions for the objective data, neither expected help (p = 0. 90) nor unnecessary help (p = 0. 47). An explanation would be that the participant feared not being able to help the confederate with NoData. They were checking if the confederate was in need of help every few seconds at the risk of their own tasks. Concerning subjective data, participants evaluated bars and icons on two aspects: frequency of use and a score, both on a [0-4] scale.
We observed significant differences between the frequency of use (p = 0. 004) and score (p = 0. 007) of bars and icons (Figure  8 ). Participants also evaluated their ability to detect that their collaborator needs help and their ability to help a collaborator needing help with the two conditions, all with a binary response (yes or no). We observed a significant difference in participants' perception of their ability to detect the need for help (p = 0. 03). Thirteen participants felt that they were able to detect when their collaborator needed help with AggData (compared to only seven for NoData). The difference in participants' perception of their ability to help (p = 0. 08) was not significant (see Figure  9 ).
Acknowledgments: We would like to thank  Fabrice Poirier  for his quick introduction to statistical analysis;  Pierre Bégout ,  Nicolas Delcombel , and  Anthony David  for their help with Unity development;  Damien Bouchabou  and  Benjamin Sommers  for their help with the network's architecture;  Jerome Kerdreux  for his help with finding a place and some equipment to run the experiment; and all participants, especially those who told their friends and family about the study.
Funding: This research was supported by the  Centre National de la Recherche Scientifique (CNRS)  with international PhD funding, and by the  University of South Australia (UniSA)  through a  PhD scholarship. This work was also supported by  French government  funding managed by the  National Research Agency  under the  Investments for the Future program  ( PIA ) grant  ANR-21-ESRE-0030  (CONTINUUM).
Data Availability Statement: The data presented in this study are openly available on GitHub at https://github. com/ThomasRinnert/MATB-II_CollabVR, accessed on 12 December 2022. The MATB-II-CVE application can be found as compiled Desktop and VR windows executables or as a Unity 2018 project in this repository. The measures acquired during the user study can be found in the same repository as "XP1_Results. csv".
3 and 2. 1 M , as well as metallicities lower than Z = 0. 0004 (corresponding to an iron abundance lower than ∼-1. 6 dex, for Z = 0. 0152; see Caputo 1998;  Marconi et al. 2004, for details). Similarly to RR Lyrae stars, the ACEPs can pulsate in the fundamental or first overtone modes and in both cases show well-defined PL and PW relations, especially in the Large Magellanic Cloud (LMC;  Ripepi et al. 2014; Soszyński et al. 2015a). Great advances in the study of variable sources have been made thanks to the Gaia mission (Gaia Collaboration 2016a) and its subsequent data releases  (DR1, DR2 and EDR3, Gaia Collaboration 2016b , 2018 , 2021a; Riello et al. 2021). Indeed, the multi-epoch nature of Gaia observations makes the satellite a very powerful tool to identify, characterise, and classify many different classes of variable stars across the whole Hertzsprung-Russell diagram (HRD; see Gaia Collaboration 2019).
In Gaia DR1, time-series photometry in the G-band and parameters derived from the G light curves were released for a small number of objects in and around the LMC, including 599 Cepheids of all types and 2595 RR Lyrae stars  (Clementini et al. 2016, hereafter Paper I). In 2018, Gaia DR2 released more than 550 000 variable sources belonging to a variety of different classes (see  Holl et al. 2018 ), including about 9500 Cepheids of all types and about 140 000 RR Lyrae stars  (Clementini et al. 2019, hereafter Paper II). In December 2020, Gaia Early Data Release 3 (EDR3; Gaia Collaboration 2021a) published average photometry, parallaxes, and proper motions but no time-series data. Epoch data are now made available with Gaia Data Release 3 (DR3, see Gaia Collaboration 2023a), providing multiband time-series photometry for nearly 12 million variable sources (see  Eyer et al.
2023). The Specific Objects Study (SOS) Cep&RRL pipeline (SOS Cep&RRL pipeline hereafter) was developed to validate and characterise Cepheids and RR Lyrae stars observed by Gaia. The pipeline has been described in detail in Papers I and II, to which we refer the interested reader. The general properties of the entire sample of variable objects released in Gaia DR3 are discussed in  Eyer et al. (2023) , which also describes the chain of subsequent steps carried out in the general variability analysis before the SOS Cep&RRL processing of the data (see also  Holl et al. 2018). In this paper, we describe the properties of the Cepheids for which time-series data are released in DR3 -along with their characteristic parameters -and that populate the vari_cepheid catalogue, which is part of the data release.
After all these steps, we remained with the number of stars listed in the last column of Table  2. Linear PL and PW relations were derived from them using the python LtsFit package  (Cappellari et al. 2013 ), which has a robust outlier-removal procedure. 2. M 31 and M 33. Given the faint apparent magnitude of the Cepheids in these distant galaxies, for reference we adopted the PL and PW relations that we calculated for the LMC (see above) -which have the lowest scatter -and simply re-scaled the zero points to take into account the difference in distance moduli between the LMC and M 31/M 33. For the latter, we adopted µ M 31 = 24. 40 mag (the typical value for the M 31 globular clusters, see  Perina et al. 2009 ) and µ M 33 = 24. 57 mag  (Conn et al. 2012 ). However, a different choice for the distance moduli of M 31 and M 33 would not affect our analysis and results, as we used rather large magnitude intervals (up to 0.
Separation of known or suspected Cepheids in the literature. From the whole sample of Cepheid candidates, we separated sources that are known or suspected Cepheids of all types in the literature. This was done for each of the five subregions defined in Table  1. This first step was necessary to avoid filtering out possible good objects in the following cutting steps. The majority of the literature Cepheids were then validated by visual inspection as described in Sect. 4. For the known Cepheids in the LMC and SMC, we retained those mentioned in Sect. 2. 2, while to the All Sky known Cepheids we added all the objects classified as Cepheids as of February 2021 in the SIMBAD database (available at CDS, Centre de Donnés astronomiques de Strasbourg,  Wenger et al. 2000). For M 31 and M 33, we adopted the samples by  Kodric et al.
Multi-mode Cepheids DCEPs in the All Sky sample that, during visual inspection, were suspected to be multi-mode pulsators were further investigated by analysing their light curves with software external to the SOS Cep&RRL pipeline. In particular, we used the Period04 package  (Lenz & Breger 2005)  for a first selection of the most promising candidates and to determine their periodicities. We then used a custom program written in Python to carry out the non-linear fitting with truncated Fourier series, the prewhitening of the first periodicity, and then the fitting of all periodicities together. In close similarity with the SOS Cep&RRL pipeline, we finally determined the period uncertainties with a bootstrap procedure. The re-processing led to the identification of 109 DCEP_MULTI variables in addition to the 86 DCEP_MULTI for which the SOS Cep&RRL pipeline provided the correct classification.
Comparison with the literature, which is discussed in more detail in Sect. 6. 1, along with a cross match with the SIMBAD database 5   (Wenger et al. 2000)  allowed us to calculate the number of Cepheids of any type already known in the literature, the number that are classified as variables but of non-Cepheid type, and the number of new discoveries. The result of this exercise is reported in the last line of Fig. 1. Petersen diagram for confirmed DCEP_MULTI objects published in the Gaia DR3 catalogue (red filled circles) and for additional DCEP_MULTI objects detected in the re-processing of the data (blue filled circles). P L and P S represent the longest and shortest pulsation periods of the multi-mode object. Labels show the typical location of the different multi-mode pulsation combinations identified in these sources.
Sampling cadence of the fullframe images was initially 30 min in years 1-2, and was lowered to 10 min in the first extended mission (years 3-4). The spatial resolution of TESS is limited to 21 px -1. Therefore, although it is capable of reaching the brighter Cepheids in the LMC and SMC, the images suffer from severe crowding and blending  (Plachy et al. 2021). To avoid that, we only looked at Galactic Cepheids in this study. We cross-matched the Gaia coordinates with the sector coverage using the Web TESS Viewing Tool 10  and then queried the TESS Quick Look Pipeline (QLP) database for light curves  (Kunimoto et al. 2021 ;  For reference, red and blue dots show the PW for the same DCEP_F and DCEP_1O samples displayed in Fig. 18. Huang et al. 2020a,b). The pre-processed QLP light curves have a faint limit of T = 15 mag, which is equivalent to the same G RP magnitude, and are produced primarily for searches of exoplanet transits.
As a consequence, not all Cepheid candidates have good QLP light curves. Therefore, we also extracted photometry from the full-frame images with the eleanor software, which is capable of both pixel aperture and PSF photometry and post-processing of the light curves via regression against a systematic error model or via principal component analysis  (Feinstein et al. 2019 ). We then selected the best light curves from the QLP and the four eleanor results (raw, corrected, PCA-corrected, and PSF photometry), and applied further corrections: sigma clipping to remove outliers and detrending to remove residual slow variations. For the trend removal, we used the method described by  Bódi et al. (2022). Briefly, the algorithm searches for the dominant periodicity in the light curve, computes the phase dispersion of the folded data, and then fits a polynomial to the data by minimising against the phase dispersion.