dataesr/scientific-paragraphs-categorization huggingface dataset:
Global modelling We performed a joint RV and photometric analysis with the Juliet (Espinoza, Kossakowski & Brahm 2019 ) python package, which is a versatile code wrapped around the Batman  (Kreidberg 2015 )  for light-curve modelling and radvel  (Fulton et al. 2018  ) for RV analysis. Our data set consists of five HARPS RVs and a total of 13 616 photometric data points from NGTS, TESS, and SAAO. Since each instrument has its own precision, and work under distinct environmental conditions, each data set encapsulates noise differently, thus requiring a proper modelling so that planetary properties are optimally deriv ed. F or that reason, we included Gaussian processes (GP) in the noise model to account for correlated noise in the light curves, where each instrument was modelled by an approximate Matern Kernel.
89 ± 0. 1 d and A rot = 10 ± 1 ppt. In order to provide further confidence on our P rot estimation, we attempted to measure the activity index log 10 R HK and projected rotational velocity vsini, yet we were unable to estimate such parameters due to the spectrum low ( < 10) S/N. Fig. 9  (top panel) shows the phase-folded light curve to the period at maximum power of the LS periodogram (centre panel) from one bootstrap realization with its corresponding sinusoidal model, while the rotation period from the ACF (bottom panel) was computed with the astroML package based on the Edelson and Krolik method  (Edelson & Krolik 1988 ) , where we adjusted an underdamped Simple Harmonic Oscillator (uSHO) to the data in order to extract the period. The LS method is available through the astropy package based on  VanderPlas & Ivezic ( 2015 ).
Finally, we visually checked the periods presenting moderately high LS power, and ruled out the ones below and near one day, which are likely associated to either instrumental noise or poor observing conditions. Yet, the ∼14. 2 d signal near the rotation period cannot be associated to neither half the Lunar cycle of 14. 8 d nor other nonastrophysical signals. Therefore, we followed the same procedure described abo v e to model the 14. 2 d signal, and found the bestfitting period and amplitude of 14. 18 ± 0. 13 d and 9. 27 ± 0. 98 ppt, respectively, and associate it to a possible NGTS-21 differential rotation. The stellar rotation period we derived was independently confirmed with the RoTo 6  code  (Briegal et al. 2022a ) , which extracts stellar rotation periods automatically using LS, generalized ACF and GP, thus providing further confidence on our reported measurement.
Based on the implementation of the mind monitoring service, we conducted an actual experimental evaluation of the collected answers, feedback messages, and the mind monitoring service itself with Software Quality Requirements and Evaluation (SQuaRE)  [54, 55]. As the main result, the response rate for six in eight elderly subjects was above 90%, and the average for 19 younger subjects was around 80%. Meanwhile, the collected response messages reflected the important events in their real lives, such as a lot of stress from COVID-19 due to social activities limited by the curfew. Through the questionnaires with the SQuaRE, most of the subjects were able to reflect on their own state using the implemented service, and interacting with the chatbot was not laborious, nor difficult to answer questions in terms of frequency.
Upon receiving every answer from the elderly person, the proposed service stores the answer in our time-series database. Since the seven questions are covered in a week, the proposed service evaluates the collected answers on a weekly basis, and sends feedback based on the result. This section first presents a method of scoring the answers for evaluating the mental states. We then describe a method of generating weekly feedback for promoting spontaneous self-care. The challenge here is how to quantify (i. e. , score) the collected answers. We consulted a clinical expert to consider how a third party (e. g. , doctor, caregiver, family) tries to assess the target person's condition from his/her answer. As a result, we found that there are typically three steps. The first step is to check if the answer is positive or negative, which is the direct objective of the question.
During the experiment, if the subject feels any burden, the person can quit answering questions from the chatbot at any time. In addition, we explained to the subjects that the logs of their interactions with the chatbot would be stored in our private database, and would not be disclosed to any third party except the experiment administrators. The experiment was approved by the research ethics committee of Graduate School of System Informatics, Kobe University (No. R01-02). Written informed consent was obtained from subjects for publication of this paper and accompanying images. Based on the data collected in the experiment, we evaluate the proposed mind sensing service from the following viewpoints: (1) responsiveness and continuity; (2) findings from mental state data. (3) Investigation of quality in use.
Findings from Mental State Data Our next interest was to see if the collected mental state data really reflect the minds of elderly people. Through 14 months of the experiment, the service obtained a large amount of time-series data. Using Data Visualizer, we first investigated the time-series data of each subject. Then, we conducted interviews with some elderly subjects to ask what happened by showing the visualized data. Figure  6  and 7 show graphs of the mental state scores of two elderly subjects, subject A (male in 70s) and subject D (female in 70s), in 2020. In the graph, the vertical axis represents the average score value and the horizontal axis represents months. The blue, yellow, and green lines represent the scores of physicality, mentality, and sociality, respectively. In Figure  6 , we can see that subject A's scores of each perspective are generally positive.
I especially can't remember what happened on the weekend. Male in 20s 4. 2. 3. Investigation of Quality in Use Our final interest is to see if the proposed mind monitoring service is satisfactory for users including not only the elderly but also young people. We believe that there is a need to analyze the experimented service using objective measures. In particular, the evaluation from the user's viewpoints (e. g. , the usability of the subjects) is indispensable. For this, we conducted a "quality evaluation during use" to evaluate the operation and usability of the system during use. More specifically, we evaluated the proposed service using the international software quality standard, SQuaRE (Systems and software engineering Systems and software Quality Requirements and Evaluation, ISO/IEC 25000 Series)  [55].
Acknowledgments: This research was partially supported by  JSPS KAKENHI  Grant Numbers  JP19H01138 ,  JP18H03242 ,  JP18H03342 ,  JP19H04154 ,  JP19K02973 ,  JP20K11059 ,  JP20H04014 ,  JP20H05706  and  Tateishi Science and Technology Foundation (C)  (No. 2207004 ).
Benchmarks We validate the FEFD/FDFD ORAS solver against four benchmarks (Table  1 ). We limit ourselves to isotropic acoustic media with a constant density equal to 1. However, the conclusions of these tests should apply to visco acoustic media with heterogeneous density. All of the λ-adaptive FDFD simulations are performed with a discretization rule of four grid point per minimum wavelength, while the FEFD simulations are performed on h-adaptive tetrahedral mesh with four grid point per local wavelength, unless stated otherwise. The unstructured tetrahedral meshes are generated with the Mmg re-meshing software  (Dapogny et al. , 2014).
Discussion We propose a forward engine to perform 3D frequency-domain FWI of datasets collected by long-offset seabed acquisitions implemented with sparse layout of ocean bottom nodes. In particular, we design carefully the experimental setup allowing us to achieve the best compromise between computational efficiency and simulation accuracy, the latter being quantitatively controlled by the solutions of the CBS method  (Osnabrugge et al. , 2016). We also compare two discretization schemes based on the recently-proposed 27-point wavelength-adaptive finite-difference (FDFD) method on regular Cartesian grid  (Aghamiry et al. , 2021)  and an h-adaptive finite-element method (FEFD) on unstructured tetrahedral mesh.
Acknowledgments This study was granted access to the HPC resources of SIGAMM (http://crimson. oca. eu) and  CINES/IDRIS  under the allocation 0596 made by  GENCI. This study was partially funded by the WIND consortium (https://www. geoazur. fr/WIND)sponsored by  Chevron ,  Shell  and  Total. The finite-difference time-domain simulation of the discussion section has been performed by  L. Combe  (Geoazur) with the GeoInv3D fwk software. We thank the  MUMPS team (P. Amestoy (Mumps Technologies, ENS Lyon) ,  A. Buttari  ( CNRS, IRIT ),  J. -Y. L'Excellent  ( Mumps Technologies, ENS Lyon ),  T. Mary  ( Sorbonne Université ,  CNRS ,  LIP6 ) and  C. Puglisi  ( Mumps Technologies, ENS Lyon) ) for proving the MUMPS solver (https://mumps-consortium. org) and for fruitful discussions.
We finally reanalyzed four real data sets on vectors and/or their parasite: the tick Ixodes scapularis in North America  (De Meeûs et al. , 2021) ; Glossina palpalis palpalis, vector of African trypnosomiasis in Côte d'Ivoire  (Berté, De Meeus et al. , 2019) ; the snail Galba truncatula and the fluke it transmits, Fasciola hepatica in France  (Correa, De Meeûs et al. , 2017) ; and Trypanosoma brucei gambiense, the agent of sleeping sickness in Guinea and Côte d'Ivoire  (Koffi, De Meeûs et al. , 2009). On these datasets, we checked if more loci with stuttering could be diagnosed, cured the loci with suspicion of stuttering, following the technique proposed recently  (De Meeûs et al. , 2021)  to verify if some conclusions could be changed.
We finally reanalyzed four real data sets on vectors and/or their parasite: the tick Ixodes scapularis in North America  (De Meeûs et al. , 2021) ; Glossina palpalis palpalis, vector of African trypnosomiasis in Côte d'Ivoire  (Berté et al. , 2019) ; the snail Galba truncatula and the fluke it transmits, Fasciola hepatica in France  (Correa et al. , 2017) ; and Trypanosoma brucei gambiense, the agent of sleeping sickness in Guinea and Côte d'Ivoire  (Koffi et al. , 2009). On these datasets, we checked if more loci with stuttering could be diagnosed, cured the loci with suspicion of stuttering, following the technique proposed recently  (De Meeûs et al. , 2021)  to verify if some conclusions could be changed.
0001 that followed a mixed model with 70% of mutations following a stepwise mutation model (SMM) and 30% following a KAM model. The maximum possible number of alleles was K=20. Each simulation started with maximum variability and was run for 10,000 generations. At the end of each simulation, 20 individuals (10 males and 10 females in dioecious populations), when N≥20, were randomly sampled in 10 subpopulations. As can be seen with the real datasets reanalyzed in the present work, such a sampling design approximately represents what is classically obtained for most parasites or vectors studies. Simulations of monoecious populations with 30% of selfing allowed checking the interaction of stuttering detection in inbred populations with a high expected heterozygote deficit (here 18%). A subset of simulations with n=100, N=100 (same values for other parameters as above) but with 100% clonal propagation, was finally undertaken, to fit with diploid clonal pathogens as trypanosomes  (Koffi et al.
Detection of stuttering and testing with MicroChecker All datasets (raw and with stuttering) were analyzed with MicroChecker with 10,000 randomizations. All loci were considered as mononucleotidic, as simulated by Easypop. Stuttering was detected when the observed heterozygosity for alleles with one repeat difference was below the 95% confidence interval (95%CI) for random mating expectation. This was observed from the graphic outputs of MicroChecker (Figure  1 ). For each locus, we summed the number of times MicroChecker found a significant heterozygote deficit probably due to stuttering over the 10 subsamples. We compared this quantity with the expected 5% under the null hypothesis with a one sided exact binomial test with R (R-Core-Team, 2020) (command "binom. test"). The alternative hypothesis was that there are more than 5% significant tests.
Then, we needed to compute the observed frequency of such heterozygotes. For this, we copied the raw data (one allele per column) in a spreadsheet. Let us assume that the first allele of the first locus of the first individual was in cell B2 and the last allele of the last locus of the first individual was in cell AO2. In cell AQ2 we typed: =IF(ABS(B2-C2)=1,1,0). Please, note again that for dinucleotidic loci, the difference in size would be two, and it should be one and two for imperfect dinucleotidic loci. We then copied this command in all remaining cells corresponding to the rest of the dataset. In the cell AQ202 (below the last line of the data), to compute the total of observed heterozygous individuals for alleles with one repeat difference, we typed: =SUM(AQ2:AQ201). A template, for the first simulation, is available in the spreadsheet file "TestStutterDioeciousNoStutter-n1000N100-1-FstatRes.
Statistical comparisons of method performances Performance of tests were compared with the Fisher exact test with R-commander package  (Fox, 2005; Fox, 2007)  for R. We also undertook generalized linear mixed models with the package lme4  (Bates, Maechler et al. , 2015)  of R to explain the number of times a test appeared significant. We used a Poisson distribution with a log link. The models were of the form NSig~n+N+Mating+Stuttering+Mating:Stuttering+(1|Rep) where NSig was the number of loci that outputted a significant stuttering test or the number of locus pairs that appeared in significant LD, n is the total number of subpopulations, N was the size of subpopulations, Mating was the mating system (either pangamic dioecy or Monoecy with 30% selfing), Stuttering was either 0 (no stuttering) or 10 (10% stuttering), ":" stood for interaction between two variables, and (1|Rep) was the random effect of replicates.
With stuttering (H1), the number of significant tests reached 7% and 24% in dioecious populations for MicroChecker and alternative methods respectively (5% and 17% respectively with Benjamini and Hochberg). This reached 14% and 47% respectively with 30% selfing (10% and 38% respectively with Benjamini and Hochberg). Figure  2 : Statistical comparisons, within each kind of simulation, between MicroChecker and the alternative method (New method), between reproductive systems and, within the alternative method, between dataset without or with 10% stuttering to detect stuttering under the null hypothesis with no stuttering or with 10% of alleles affected, and for different system of mating (dioecious pangamy or 30% of selfing). Significant differences (all p-values<0. 0001) are indicated by different letters and correspond to Fisher exact test comparing number of significant (S) and not significant (NS) tests (before Benjamini and Hochberg correction) between the two methods.
Asterisks stand for comparisons, within a single parameter set, between Microchecker and the alternative method. To this respect, the p-value within dioecious pangamic (Pangamy) populations with 0% selfing was p-value=0. 2494. Figure  2 : Statistical comparisons, within each kind of simulation, between MicroChecker and the alternative method (New method), between reproductive systems and, within the alternative method, between dataset without or with 10% stuttering to detect stuttering under the null hypothesis with no stuttering or with 10% of alleles affected, and for different system of mating (dioecious pangamy or 30% of selfing). Significant differences (all p-values<0. 0001) are indicated by different letters and correspond to Fisher exact test comparing number of significant (S) and not significant (NS) tests (before Benjamini and Hochberg correction) between the two methods.
The effect of stuttering on LD was never significant, whatever the mating system (all p-values>0. 388). This could be expected since in our simulations, stuttering was not correlated between loci. In real datasets, however, it may occur that stuttering happen in samples with issues (poor preservation, mutations affecting the zone of primers' anchorage, low DNA concentration). In that case, several loci of the same individuals will be affected together, then producing fake significant LDs, as was observed for the tick I. scapularis  (De Meeûs et al. , 2021).
Real datasets For I. scapularis from Western USA, MicroChecker test only found one locus with significant stuttering out of nine (i. e. 11%), after exact binomial tests and Benjamini and Hochberg correction. Alternatively, the new method developed here found three loci out of nine loci with a significant stuttering (33%). Cured data provided results in agreement with a pangamic reproductive strategy, as was already observed by the authors  (De Meeûs et al. , 2021). For Glossina palpalis palpalis in Côte d'Ivoire no binomial tests provided a significant result with MicroChecker statistics. For the new method presented here, two loci (B3 and XB110) out of seven (22. 22%) displayed a significant stuttering. Cured data set was obtained by pooling alleles following De Meeûs et al (2019)'s rules  (De Meeûs et al.
Conclusions The new method developed here appeared at least three times more efficient (and often much more) than MicroChecker. Moreover, the use of spreadsheet programs makes its portability universal for any microcomputer. In dioecious pangamic populations, like ticks and tsetse flies, detection works well and cure improves population genetics parameter estimates but not perfectly so, which means that, for instance, FIS and FST will still be slightly overestimated in datasets cured for stuttering. So, whenever possible, removal of affected loci may help to shift such estimates towards (slightly) more accurate values. In monoecious selfers, detection works well and cure works very well, providing other confounding factors as null alleles do not interfere, in which case avoiding stuttering cure and correct for null alleles appear more appropriate.
Facial caricature is the art of drawing faces in an exaggerated way to convey emotions such as humor or sarcasm. Automatic caricaturization has been explored both in the 2D and 3D domain. In this paper, we propose two novel approaches to automatically caricaturize input facial scans, filling gaps in the literature in terms of user-control, caricature style transfer, and exploring the use of deep learning for 3D mesh caricaturization. The first approach is a gradient-based differential deformation approach with data driven stylization. It is a combination of two deformation processes: facial curvature and proportions exaggeration. The second approach is a GAN for unpaired face-scan-to-3D-caricature translation. We leverage existing facial and caricature datasets, along with recent domain-to-domain translation methods and 3D convolutional operators, to learn to caricaturize 3D facial scans in an unsupervised way.
The main goal of this paper is to propose and evaluate novel methods for the automatic generation of 3D caricatures from real 3D facial scans, first with a rule-based method, in order to keep tunable and interpretable parameters, and a deep learning method, to leverage real caricature data and hence generate caricatures closer to real ones. The main hypotheses we wish to address in this paper are: H1: the specialization of generic exaggeration methods for human faces should allow to produce convincing caricatures. To this end, we adapted the generic method proposed by  Sela et al. (2015)  in order to generate caricatures by exaggerating facial features from a 3D face scan (see Figure  1 ). This method has two main stages, one based on a curvature EDFM (Exaggerating the Difference From the Mean), and another based on a nearestneighbors search in a 3D caricature dataset, to apply the proportion exaggeration.
In the same spirit,  Fujiwara et al. (2002)  developed a piece of software named PICASSO for automatic 3D caricature generation. They used a set of feature points to generate simplified 3D faces before performing EDFM. EDFM was also used by  Blanz and Vetter (1999)  in an application example of their morphable model. They learn a principal component analysis (PCA) space from 200 3D textured faces. Their system allows caricature generation by increasing the distance to the statistical mean in terms of geometry and texture. Statistical dispersion has been taken into account by  Mo et al. (2004)  who showed that features should be emphasized proportionally to their standard deviation to preserve likeness. Chen et al. (2006)  created 3D caricatures by fusing 2D caricatures generated using EDFM from different views.
In the same vein, but only in the 3D domain,  Zhou et al. (2016)  regressed a set of locally linear mappings from sparse exemplars of 3D faces and their corresponding 3D caricature. As far as we know,  Clarke et al. (2011)  are the only authors that proposed a physics-oriented caricature method. They capture the artistic style of 2D caricatures by learning a pseudo stress-strain model which describes physical properties of virtual materials. All these data-driven approaches are based on paired datasets which require the work of 2D or 3D artists. Such datasets are costly to produce, therefore techniques of this kind are hardly applicable. Unsupervised learning based methods learn how to caricature from unpaired face and caricature exemplars. Chen et al. (2001)  and  Liang et al. (2002)  generated 2D caricatures by learning a nonlinear mapping between photos and corresponding caricatures made by artists.
Derived from the image synthesis literature, where they have been used for unpaired one-to-one translation  (Liu et al. , 2017; Taigman et al. , 2017; Yi et al. , 2017; Zhu et al. , 2017) , or unpaired many-to-many translation  (Huang et al. , 2018b; Liu et al. , 2019; Choi et al. , 2020) , Generative Adversarial Networks (GANs) have also shown impressive results on mesh synthesis and mesh-to-mesh translation  (Goodfellow et al. , 2014). Other approaches achieve 2D stylization using 3D priors and a differentiable renderer  (Wang et al. (2021). ) Cao et al. (2019)  proposed a photo to 2D caricature translation framework CariGANs based on a large dataset of over 6,000 labeled 2D caricatures  (Huo et al. , 2018) , and two GANs, namely CariGeoGAN for geometry exaggeration using landmark warping, and CariStyGAN for stylization.
Datasets Realistic 3D faces were sampled from the LSFM dataset  (Booth et al. , 2016)  which contains nearly 10k distinct 3D faces. In order to have textured meshes, we completed this set with 300 in-house 3D face scans. Their topologies are unified through automatic facial landmarking and geometry fitting  (Danieau et al. , 2019). To build our 3D caricatured mesh dataset, we run the 2D to 3D caricature inference method of  Cai et al. (2021)  on the WebCaricature dataset  (Huo et al. , 2018) , which enables to extract the 3D caricatured face mesh from each 2D image. The WebCaricature dataset contains over 6k 2D caricatures. When Cai's algorithm did not successfully estimate the faces, due to extreme drawing composition (quick sketch, incomplete drawings, drafts, cubism etc. ) the generated output remains the same default caricature mesh.