dataesr/scientific-paragraphs-categorization huggingface dataset:
9) while the large versions of other models were the ones whose probability distribution included more cases with lower label probability. The models were overall more confident with correct predictions, and only very slightly less confident (with a few labels closer to 0. 4 -0. 6, but still the majority above > 0. 9) for wrong predictions. Our findings on classifying duration were similar to the ones on telicity, with the models performing overall better on this classification task despite the dataset being smaller. The BERT models were the most successful ones, achieving accuracy of up to 0. 96, however all models achieved accuracy of > 0. 93. The effect of the use of the verb position information is not apparent in this classification task, since we notice an improvement or deterioration of 0.
Qualitative analysis As mentioned, we also created our own annotated datasets of telicity and duration, in order to study aspectual properties beyond the scope of classification metrics. We took a closer look at the correct and incorrect predictions of the models, in order to determine which cases were easier or more difficult for models to classify. For the sake of brevity, we are presenting only a few examples of successes and failures; our goal was to manually examine the strengths and weaknessess of the models in difficult and conflicting cases of classification, hence the smaller qualitative datasets and the presentation of the most interesting examples. For telicity, overall, models were quite successful in classifying the sentences of our qualitative dataset. For example, all models were able to identify that sentences with statements are atelic, such as Cork floats on water.
; even though the action should have a perceived ending, the continuous tense and the presence of the adverb always render this sentence atelic). Moving to our minimal pairs of telic-atelic sentences, we observe that, in most cases, most models are able to classify correctly a sentence based both on the verb action and the context; I drank the whole bottle. and I drank juice. were correctly classified as telic and atelic respectively, despite of the presence of the same verb and tense. However, in our qualitative dataset, we noticed that the sentence The cat drank all the milk. was incorrectly classified as atelic by all the models. Another interesting mistake we noticed was the classification of the pair The boy is eating an apple. and The boy is eating apples. as both atelic; in the former sentence, the action is telic for pragmatic reasons (one apple that will be finished), but the tense is continuous.
Unseen verbs In our training and test datasets, there was a large variety of verb-head words, which allowed us to test the classification success on sentences where the verb has not been observed by the model. For telicity, 267 verb forms which were the head of their phrase were not "seen" by the model in the training set (and 146 of them were not split in subwords), and for duration, 117 verbs (and 80 intact). We tested which of the corresponding sentences were marked incorrectly, and the models' average probability of the assigned label. Overall, few sentences were labeled incorrectly (see results in Table  10 ), with labels of either category for both classification tasks. This suggests that the context plays an important role for the models' choices, even when the verb form has not been observed by the model.
Discussion Transformer models were quite successful in the classification tasks, outperforming our baselines to a large extent, and they proved to be quite successful even without fine-tuning. Contextual embeddings proved to be an efficient way to encode the aspectual information of a verb and its interaction with its context, and this knowledge is probably already learned in the pretraining process. The superior performance of the duration classification with fine-tuned models did raise a question: from our datasets, most stative questions came from the Friedrich dataset and most durative sentences from the Captions dataset; did the models learn to classify duration or to identify the different corpora? With our qualitative analysis on two languages, we can conclude that the models are indeed able to classify duration and were successful because of the little overlap between stative and durative verbs and contexts.
However, the models struggled with sentences for which world knowledge is crucial, which is a known issue  (Rogers et al. , 2021). From our experiment with verb tenses and prepositional phrases, we noticed that perfect and continuous tenses are beneficial to classification by the models, and leading a sentence with a prepositional phrase of time sometimes improved predictions. However, conflicting context will almost always confuse the models. In addition, our findings on the French datasets showed that, even with our lower-performing models, the syntactic and semantic choices that a language makes in expressing aspect did affect the models' capabilities of classifying aspect. The differences in classification errors and successes that we observed, between the qualitative datasets of the two languages, may also indicate that there is a different way in which languages are semantically represented by transformer models, even with different model architectures.
Conclusion In this study, we conducted several experiments that test the capability of transformer models to grasp aspectual categories, viz. telicity and duration. We tested this capability using a binary classification setting. Using two annotated datasets for telicity and duration  (Friedrich and Gateva, 2017; Alikhani and Stone, 2019) , we fine-tuned transformer models of different architectures and in two languages and found that transformer models were very successful on the classification of aspect even when trained on small datasets. Providing the verb position as additional information improved performance in both telicity and duration classification for English. The pretrained transformer models also possess knowledge of aspect even without finetuning (when looking at layerwise contextual word embeddings).
Acknowledgements This work has been funded by  CNRS  ( 80|PRIME-2019  project  MoDiCLI ). Experiments were carried out with the OSIRIM platform 5 which is administered by  IRIT  and supported by  CNRS , the  Region Midi-Pyrénées , the  French Government , and  ERDF. We would like to thank our reviewers for their insightful comments and suggestions.
By employing a dual-objective spectral imaging configuration compatible with live cell imaging combined with dedicated computation tools, we demonstrate simultaneous 3D single particle localization and tracking of multiple distinct species over large field of views to be feasible without compromising spatio-temporal resolution. The dispersive element introduced into the second optical path induces a spectrally dependent displacement, which we used to analytically separate up to five different fluorescent species of single emitters based on their emission spectra. We used commercially available microscope bodies aligned one on top of the other, offering biologists with a very ergonomic and flexible instrument covering a broad range of SMLM applications. Finally, we developed a powerful freely available software, called PALMTracer, which allows to quantitatively assess 3D + t + λ SMLM data.
49, Nikon); ii) to determine the spectral signature of the detected fluorophores using a second high NA objective (×60, Water Dipping NA1, Nikon), without compromising the localization performances (Figure  1B ). The choice of using commercial microscopes is to benefit from existing hardware (filter cube, objective turret, TIRF illumination module, Perfect Focus System), motorization, software control, and stability for longterm acquisitions. It also provides a user-friendly environment that biologists are familiar with. The whole 4Pi microscope is caged in a custom plexiglass enclosure heated at 37 °C for live cell experiments (Life Imaging System) and driven by the MetaMorph software (Molecular Devices). The conventional vertical architecture enables using standard live sample holders, simplifying live sample preparation protocols and imaging.
Single Molecules Localization Analysis We have developed a complete analysis software solution, called PALMTracer, to analyze and represent multi-dimensional (x, y, z, t, λ) SMLM data. It is developed as a plugin of MetaMorph software, to be used either directly on the microscope during the acquisition and provide rapid user-feedback, or in off-line mode to analyze the data post-acquisition. It allows standard 2D and 3D single molecule localization and tracking as well as advanced spectral analysis, integrating quantitative analysis of diffusive properties with various visualizations. It is the result of 10 years of developments with biologist end-users  (Rossier et al. , 2012; Nair et al. , 2013; Garcia et al. , 2015; Chamma et al. , 2016; Beghin et al. , 2017; Floderer et al. , 2018; Jullié et al.
Such a large mean error stems mainly from pairing errors due to over-localization obtained by systematic multi-Gaussian fitting. Analyzing the same dataset using the spectrally-informed multi-Gaussian fitting resulted a mean fit error of 56 nm, a 4-times improvement that enables more accurate SPT of two spatially overlapping emitters. This difference is explained by the elongation of the astigmatism-based point-spread function with respect to the axial position of the emitters (Figure  4B -iii).
Multiple 3D QDot Tracking of Synaptic Receptors on Living Neurons Finally, we tracked simultaneously several membrane proteins implicated in synaptic transmission and neurodegenerative disorders on live mixed hippocampal neuron cultures. We probed the same protein (D1R) with secondary antibodies tagged with 2 different quantum dots species (Qdot655 and Qdot705), and the Ephrin B2 receptor with Qdot605 (Figure  3B ). Multicolor data were acquired using our spectral microscope and analyzed using spectrally-informed multi-Gaussian fitting and tracking, from which we could extract Qdots trajectories in 3D for each species. Spectrally-informed multi-Gaussian fitting allowed extracting 1. 5 more localizations and twice more trajectories, mostly occurring when localized molecules are at a distance below 200 nm (Figure  4C ).
DISCUSSION We described a powerful and versatile multidimensional singlemolecule localization microscopy workflow. It is composed of a mix of commercially available hardware and software with custom freely available analysis software named PALMTracer. It integrates a spectrally-displaced single molecule localization process and a spectrally-informed multi-Gaussian fitting method allowing to precisely determine the emission wavelength of each localized molecule, and separate overlapping single-molecule signals with high reliability. As spectral determination is performed by collecting and analysing the emitted photons through a second microscope objective, it allows state of the art 3D single molecule localization and tracking to be performed, as well as simultaneous spectral analysis without compromising the spatio-temporal resolution and the field of view.
ACKNOWLEDGMENTS We would like to thank  Ralph Jungmann ,  Maximilian Strauss  and  Thomas Schlichthärle  for providing us the DNA-PAINT labelling kits as well as  Olivier Thoumine  and  Ingrid Chamma  for providing us the N-CAM-AP DNA plasmids. We would also like to thank  Philippe Rideau  from  Nikon France  for his support, as well as MIFOBIO and the  GDR Imabio. We finally would like to thank the  Cell Biology Facility , especially  Emeline Verdier  and  L. Groc  team's technical staff, for cellular tool productions and general cell biology activity management.
FUNDING This work was supported by the  FranceBioImaging  infrastructure  ANR-10-INBS-04 ,  ANR NANOPLANSYN  and the  LabEx BRAIN  to  J-BS , the  ANR  soLIVE  16-CE11-0015-01  to  RG and Human Frontier Science Program   RGP0019/2016  to GS, JD, and LG.
+271 -172 Smoothing factor, β U(0. 2, 1. 5) 0. 9 ± 0. 2 0. 7  samples. The results of our analysis are illustrated in Fig. 12  where we present the B ℓ observed data and the best-fit GP model. 3  shows the priors and best-fit parameters calculated by the medians and their 0. 16 and 0. 84 quantiles uncertainties. We performed the same analysis on both APERO and LE B ℓ data, which gives consistent GP parameters within 1σ. The APERO data are noisier, with an RMS of residuals of 2. 3 G, while LE data give an RMS of 1. 4 G. However, we adopted the star rotation period obtained with the APERO data, which has a posterior distribution that is more tightly constrained than in the LE data. In Fig. 13 , we present the B ℓ data phasefolded to the best-fit star rotation period of P rot = 35. 7 days, where we highlight the different rotation cycles using different colors.
We investigated if the periodicity found in the B ℓ data is also present in the TESS photometry data. Here, we considered the TESS flux data subtracted by the best-fit transit model using the parameters in Table  6  and binned by weighted average with a bin size of 0. 1 days. Figure  14  shows the GLS periodogram with a highest power at a period of 10. 9 days and two other smaller peaks at 6. 2 days and 17. 2 days. The rotation period of near its harmonics. However, as illustrated in the bottom panel of Fig. 14 , the TESS data phase-folded with P rot = 35. 7 days show that different rotation cycles present some agreement in their overlapping features. This suggests that the surface of TOI-1759 has several small spots rather than large spots at some specific longitudes that would generate a simpler oscillatory modulation in the TESS light curve.
The local Stokes I profiles are truncated at ±15 km s -1 from line center and modeled with a Voigt function defined by a Gaussian and Lorentzian width of 1. 2 and 5. 1 km s -1 , respectively. As central wavelength and Landé factor, we use the normalization values of 1750 nm and 1. 2. We adopted the linear limb darkening coefficient reported in Table  6  and let the spherical harmonics expansion reach the fifth degree in l, as higher degrees are unnecessary given the low value of the projected velocity  (Morin et al. 2008). The observed and modeled Stokes V profiles are shown in Fig. 16. The two maps of surface magnetic flux are shown in Fig. 17  and the map characteristics obtained from both APERO and LE datasets are reported in Table  4. Assuming solid body rotation, P rot = 35. 7 days, v eq sin(i) = 0.
84 km s -1 , and stellar inclination = 80 deg (instead of 90 deg to prevent mirroring effects), we are able to fit the Stokes V profiles down to a reduced chisquare level of χ 2 ∼ 0. 9 for LE and χ 2 r ∼ 0. 4 for APERO. The latter indicates that APERO overestimates the error bars of its LSD profiles. This problem will be addressed in future works, therefore, we adopt here the ZDI results obtained from the LE profiles. The magnetic topology is predominantly poloidal (99% of the magnetic energy) with the axisymmetric component decreasing from 75 ± 5% in 2020 to 54 ± 5% in 2021. We also observed a change in the tilt of the dipole field from 22 ± 5 deg in 2020 to 46 ± 5 deg in 2021. Our data also suggest a 20% increase in the intensity of the dipole component between 2020 and 2021; otherwise, the intensity of the mean magnetic field remained constant: the mean (B m ) and the dipole (B dip ) field strengths are 17 ± 3 G (2020) and 18 ± 4 G (2021), and -23 ± 5 G (2020) and -27 ± 5 G (2021), respectively.
For comparison, the APERO data set gives no substantial change in the field strengths between the two seasons; however, the field strengths are about 1. 5-2. 0 times lower than the LE results, with an agreement within 2σ. This slight disagreement indicates that the different noise characteristics and normalization of the LSD profiles can probably bias the measurements of the field strengths. To summarize, the magnetic topology of TOI-1759 is characterized by a weak (<20 G) and predominantly poloidal field, whose main axis of symmetry shows a variable inclination with time. This agrees with previous results on similar stars, for example Gl 205, whose spectral type, rotation periods, and magnetic properties are very similar to those of TOI-1759  (Hébrard et al. 2016). We show the radial (left panels), azimuthal (middle panels), and meridional (right panels) components of the magnetic field.
Planet characterization We modeled the TESS photometry data with a baseline GP component to account for stellar activity as described in Sect. 4. 3 multiplied by a transit model calculated using the BATMAN toolkit by  Kreidberg (2015). The SPIRou RV data are modeled by the orbital reflex motion of the star caused by the planet, given by a Keplerian velocity as described in the Sect. 4 of  Martioli et al. (2010). To perform a Bayesian MCMC joint analysis of the photometry and RV data sets, we build a global likelihood function that can be evaluated at each iteration of the MCMC sampler. Our likelihood function is given by the sum of the logarithm of the likelihood (log-likelihood) of the prior probability on the data and on the planet parameters plus the posterior probability of the trial models conditioned to the data.
2 and 0. 2 ± 0. 2, respectively. However, the BIC values obtained for a circular orbit, BIC CCF = 19 896 and BIC LBL = 19617, are lower than those obtained for a noncircular orbit, BIC CCF = 19 917 and BIC LBL = 19 645. This means that the information provided by the data does not justify increasing the number of model parameters, so we adopt a circular orbit from now on. To investigate if an activity related signal modulates our RV data, we fitted a QP GP activity component to the RV data subtracted by the best-fit orbit model obtained above, where we assume a prior for the rotation period of P rot = N(35. 65, 0. 17) days, as obtained in Sect. 4. 3, and the same priors shown in Table  3  for the other GP parameters. Then we subtract this GP model from the original RVs and perform again a joint fit of the photometry and RV data.
In Appendix C, we present the results of this analysis, where we show the MCMC samples and posterior distributions for the fit parameters and the RV data and each component of the best-fit model. Figure  18  shows the RV data and the best-fit orbit models phase-folded to the orbital period and with t 0 being the time of transit (T c ). We notice that both orbits are in phase with the TESS transits. The BIC also improves with respect to the solution without a planet for all data sets, showing a consistent preference in favor of the orbit model. The Appendix D presents a periodogram analysis for the CCF and LBL data, both showing coherent peaks in the orbital period of P = 18. 85 days. These peaks become more relevant when subtracting the GP model. As an additional test, we performed a joint analysis of the photometry, RV, and Bℓ data including the transit model, the RV orbit models and the GP activity model, simultaneously.
The GP activity component improves the final dispersion of the residuals and the BIC for both data sets. Our MCMC analysis assumes flat priors for the velocity semi-amplitude, K p = U(0, 100) m s -1 ; therefore, a null detection should result in a flat probability distribution toward zero. However, as illustrated in Figs. C. 1 and C. 2, K p has a well-constrained posterior distribution at the upper and lower bounds for both data sets, defining an important constraint for the mass of TOI-1759 b. With LBL+GP we obtained the lowest BIC value, with a velocity semi-amplitude of K p = 2. 3 ± 0. 7 m s -1 and a RMS of residuals of 4. 6 m s -1. Therefore, the final fit parameters we adopt are those obtained by the LBL RVs with GP activity model. The best-fit system parameters and derived quantities are summarized in Table  6.
Acknowledgements. We acknowledge funding from the  French National Research Agency (ANR)  under contract number  ANR18CE310019  (SPlaSH). This work is supported by the  ANR  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the  "Origin of Life" project of the Grenoble-Alpes University. T. V. and C. C. acknowledge funding from the  Technologies for Exo-Planetary Science (TEPS)   CREATE program  and from the  Fonds de Recherche du Québec -Nature  et technologies. J. F. D. acknowledges funding from the  European Research Council (ERC)  under the  H2020 research & innovation programme  (grant agreements 740651 New Worlds). A. A. V. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
Thus, when the fitness value is close to zero one may consider, at least in principle, that the closed-form expression that models the target data has been found. To illustrate the operating principles of SR, we consider as reference the Nguyen's fifth benchmark function  [27]  f T (x) = sin(x 2 ) cos(x) -1, (1) which in the current example serves also to generate the target data depicted with blue crosses in Fig. 2(b ). Throughout this work we employ a GP-based SR numerical implementation known as GPLearn. This is an open source library coded in Python  [28]  that provides a closed-form expression in LISP format  [29]  sub(mul(sin(mul(X0, X0)), cos(X0)), div(X0, X0)) that can be written as f SR (X0) = sin(X0 2 ) cos(X0) -X0/X0, (3) and has a tree representation, as illustrated in Fig. 2 (a).
It is noteworthy to say that, in the context of GP, the length of an expression is defined as the number of functional and terminal nodes in the tree. Consequently, it is not necessarily related with the number of terms in the expression retrieved. In the current example, the length is 11 and not 2 as one might intuitively think. Also, it is evident that the term X0/X0 in the expression (3), known as intron, can be written as 1. In Fig. 2 (b) we compare the target data y T = f T (x) with the data y SR = f SR (x), depicted with a solid red line, computed by means of the expression retrieved. Although it was not necessary for this simple example, in certain cases the closed-form expressions found require further simplification. This can be achieved by means of Sympy, another open source Python library devoted to symbolic calculus  [30].
We thus combine all available EDC water isotope measurements to generate a high-resolution (11 cm) dataset for the past 800 kyr. A frequency decomposition of the most complete δ 18 O record and a simple assessment of the possible influence of diffusion on the measured profile shows that the variability at the multidecadal to multi-centennial timescale is higher during glacial than during interglacial periods and higher during early interglacial isotopic maxima than during the Holocene. This analysis shows as well that during interglacial periods character-ized by a temperature optimum at the beginning, the multicentennial variability is strongest over this temperature optimum.
Measurement techniques and coherency of the dataset Several analytical techniques have been used to measure δ 18 O and δD in the EDC1 and EDC2 ice cores (Tables  1  and 2 ). Initial analytical techniques included a uranium reduction method for δD  (Vaughn et al. , 1988 ) and a CO 2 -H 2 O equilibrium method for δ 18 O  (Meyer et al. , 2000) , while the most recent method used to determine δ 18 O and δD on the EDC2 ice core is cavity ring-down spectroscopy (CRDS)  (Kerstel and Gianfrani, 2008; Busch and Busch, 1999). When uranium reduction was replaced by CRDS measurements at the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), an extensive series of comparisons were performed which showed that there was excellent agreement between the two methods within the uncertainty ranges of the instruments.
The analytical precisions for the methods are comparable, with 2σ values ranging between 1 and 1. 4 ‰ for δD and between 0. 1 and 0. 4 ‰ for δ 18 O (Table  2 ). Figure  1  displays the full high-resolution (11 cm) datasets for δ 18 O and δD of water over the EDC ice core.