dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, Figure  3d  shows the ripple orientation distribution obtained from the SEM images using the ImageJ software  [32]. Note that the use of SEM instead of AFM in this case is justified to probe large areas of the sample. In the graph, the main orientation of the ripples was fixed at 0 in order to better compare the three distributions (we have included the one of the sample irradiated at 2•10 17 cm -2 , but in this case, due to the small amplitude and size of the ripples, we analyzed the corresponding AFM image). The full width at half maximum (FWHM) is initially close to 22 and this value remains similar for 10 18 cm -2 , which decreases to 17. 7 for the highest fluence. Thus, these data suggest that the orientation of the ripples tends to become more homogeneous with increasing the ion fluence.
Funding: This research is supported by  MINECO  ( CTQ2017-84309-C2-2-R ) and  Comunidad de Madrid  (NANOAVANSENS ref. S2018/NMT-4349 ) projects, and the  Ramón y Cajal program  ( RYC-2015-18047 ). This research is also funded by  FEDER  funds through the  POR Lisboa, COMPETE 2020 Programme and National funds  through  FCT -Portuguese Foundation for Science and Technology (FCT)  under the projects  UID/CTM/50025/2019 ,  POCI-01-0145-FEDER-028011 & LISBOA-01-0145-FEDER-029666.
In section "3 Current RT Topics and Models: Heritage Studies, Discussions, and PACE Updates, " we focus on scattering in the ocean ("3. 1 Ocean Body" section), by the ocean surface ("3. 2 Ocean Surface" section), in the atmosphere ("3. 3 Atmosphere" section), and by the entire AOS ("3. 4 AOS models" section). In each of these subsections, we provide a brief overview of heritage work, followed by (when applicable) updated work performed by our team. For updated work, we differentiate between forward (FWD) RT studies that focus mainly on sensitivity analyses and/or simulating data sets, and inverse (INV) RT studies that involve also retrieval of PACE mission science products.
However, due to their limited coverage of water types and/or averaging over data sets, they cannot replicate the variability in bidirectional scattering by marine particulates seen in laboratory (e. g. ,  Volten et al. , 1998; Witowski et al. , 1998)  or ocean  (Mobley et al. , 2002; Sullivan and Twardowski, 2009; Zhang et al. , 2011; Twardowski et al. , 2012)  measurements. In addition, the Petzold volume scattering function data appear to be affected by an error such as stray light reflections in the near backward, which becomes prominent/obvious in his clear water dataset which do not agree with theory, other β measurements, or satisfy closure with simulated apparent optical properties (i. e. , properties that depend on the ambient light field). The work by  Sullivan and Twardowski (2009)  represents another example of Class A models.
The free parameters (i. e. , eigenvalues) for the D-P II model are the backscattering coefficient b b,p at λ 0 in Eq. 1; the Chlorophyll a concentration [Chla] in Eq. 2; and the absorption coefficient a cdm at λ 0 = 443 nm in Eq. 3. The prescribed and [Chla]-driven parameters (i. e. , eigenvectors) for this hydrosol model are the power law exponent k in Eq. 1, the specific phytoplankton absorption coefficient âph (λ) in Eq. 2, and the spectral decay constant α cdm for absorption by non-algae and dissolved matter in Eq. 3. The set of parameterizations chosen for b b,p (λ), a ph (λ), and a cdm (λ) is close to the default configuration (DC) used for the generalized inherent optical properties (GIOP) software database that was developed by  Werdell et al. (2013a). The exception is the [Chla]-driven exponent k for b b,p in Eq.
1, which is taken from  Morel and Maritorena (2001)  instead of retrieving it from waterleaving radiance measurements as in  Lee et al. (2002). The D-P II model further adopts the same value of 0. 018 nm -1 for α cdm in Eq. 3 as was done for the GIOP-DC database. The RT quantities ω and β for the D-P II model are computed in the same manner as for the D-P model  (Chowdhary et al. , 2006 (Chowdhary et al. , , 2012) )  except for (1) choosing Eqs 1-6 for the non-water absorption and scattering coefficients, and (2) using the ratio b b,p / bp from Eq. 1 for the particulate scattering coefficient b p. To deploy the D-P II hydrosol model for analyses of airand spaceborne polarimetric observations over oceans, rigorous underwater light computations were performed with the adding RT method to obtain reflection matrices of diffuse waterleaving radiance (that is, radiance that excludes the contribution of skylight reflected off the ocean surface alone).
PACE updates (FWD RT studies): synthetic data sets linking IOPs and Rrs The performance of IOP inversion algorithms for PACE-like data will be evaluated by comparing the IOP retrievals with colocated independent IOP measurement sets (cf. "1. 1 The PACE Mission" section). However, IOP measurement sets may not always be available for such performance evaluations. In addition, there are always measurement uncertainties which prevent conclusive evaluation of IOP inversion algorithms. To address these cases, the PACE Science Team created a synthetic dataset containing IOPs and remote-sensing reflectance Rrs. The overall scheme follows that adopted by the IOCCG Report 5  (IOCCG-OCAG, 2003; IOCCG, 2006) , which was summarized in "Models, parameters, and approaches that are used to generate wide range of absorption and backscattering spectra, " but here the spectral range is expanded (now 350-800 nm), along with wider variations of phytoplankton absorption spectra.
Because Hydrolight  (Mobley and Sundman, 2013)  was used for the generation of Rrs from IOPs, the critical step for this synthetic dataset was the creation of reasonable IOPs spectra for both oceanic and coastal waters. Specifically and briefly, as articulated in  IOCCG-OCAG (2003) , the absorption (i. e. , a) and backscattering (i. e. , b b ) coefficients, the two key component IOPs for Rrs, were modeled as a (λ) = a w (λ) + a ph (λ) + a dm (λ) + a y (λ) (7a) b b (λ) = b b,w (λ) + b b,ph (λ) + b b,dm (λ) (7b) Values of a w (λ) were taken from combinations of  Lee et al. (2015)  (350-550 nm range),  Pope and Fry (1997)  (555-725 nm range), and  Smith and Baker (1981)  (730-800 nm range). From more than 4000 measured a ph (λ) spectra (spanning 350-800 nm at 5 nm steps), 720 a ph (λ) spectra were selected and divided into twelve groups with a ph (440) ranging between ∼0.
Hasekamp et al. (2011)  and  Russell et al. (2014)  use a particle volume-weighted approach to obtain an effective refractive index for an external mixture of a fine and a coarse mode aerosol. However, the validity and limits of this approach remain to be investigated for external mixtures. Alternatively, one may just simply want to flag the presence of externally mixed multiple aerosol types. The multi-parameter classification method described by  Russell et al. (2014)  for a dominating single aerosol type offers a possible venue for such tasks. Regardless of the aerosol mixture type, identifying the individual components will require an extensive database of their complex refractive indices that covers the full spectral range of UV-SWIR. The PACE mission will continue to need well-defined characterization of aerosol optical properties for RT calculations, but will require those models to cover the expanded spectral range and spectral density of OCI.
Through the era of the Earth Observing System (EOS) that began with the launch of the Terra satellite, aerosol models for RT and remote sensing have become anchored in the statistics from the aerosol properties retrieved from the ground-based remote sensing measurements of the Aerosol Robotic Network (AERONET;  Holben et al. , 1998; Dubovik and King, 2000; Dubovik et al. , 2000). The AERONET database provides a wealth of statistics of retrieved aerosol optical properties that cover a wide range of conditions, although the open ocean is not well sampled. This allows linking variability in optical properties to measurable or retrievable parameters, such as aerosol optical depth, or more recently to relative humidity  (Ahmad et al. , 2010). Furthermore, representations of aerosols in RT are moving away from assumptions of sphericity and Mie theory  (Meng et al.
Direct measurements of scattering matrix elements depend on discrete laser wavelengths that inadequately span the spectral range of future space sensors. However, recently a custom instrument was used with laser wavelengths at 375 nm and 405 nm to characterize phase matrix elements of biomass burning aerosol in the deep blue and near-UV  (Manfred et al. , 2018) , an important step forward toward providing the input to RT modeling in the PACE era. The recent work shown above is just a sample of the increased capability of laboratory and in situ field measurements of aerosol particle properties now reaching a mature stage. As deployment of these new measurement systems increases and the database of particle characterization grows, we can use these detailed measurements to guide the development of new aerosol models for RT that will span OCI's broad spectrum and represent new aerosol types.
Similarly, the Oxygen A-and B-bands (at ∼760 nm and ∼687 nm, respectively) can also be challenging to utilize for ocean color retrievals due to the presence of scattering aerosols near the boundary layer of the atmosphere. Thus, a Beer's-Lambert compensation of the water vapor and Oxygen bands, without consideration of the absorption-scattering coupling effects, the overlapping absorption of different gas species, and the inhomogeneous path, could lead to significant errors  (Bouffiès et al. , 1997; Dubuisson et al. , 2004; Ibrahim et al. , 2018). The first challenge toward addressing the impact of absorption by H 2 O and O 2 on OCI's hyperspectral data is to quantify this impact. This, in turn, requires the use of RT methods that can properly handle all absorbing gases within OCI's spectral coverage.
( , 2004) )  for a large set of environmental conditions. Another possibility is to generate atmospheric correction algorithms (i. e. , look-up tables, or LUTs) or gas retrieval algorithms with RT codes that inherently takes in consideration the strong gases absorption coupling with scattering; however, that could dramatically increase the dimension and complexity of the algorithm LUTs. A trade-off study would be necessary for such assessment in order to provide an optimal utilization to operationally capable algorithm for the PACE mission. The second challenge toward addressing the impact of H 2 O and O 2 absorption on OCI's hyperspectral data is to retrieve and compensate for the CWV transmittance. In the EOS era, NIR data have been utilized as an alternative to thermal IR data to retrieve CWV over land and ocean  (Chesters et al.
The base line of the model is the RT model developed by  Zhai et al. (2010 Zhai et al. ( , 2015 Zhai et al. ( , 2017a)). Gas absorption from water vapor, oxygen, ozone, nitrogen dioxide, methane, and carbon dioxide are included. First, hyperspectral lookup tables of gas absorption coefficients have been built using the atmospheric radiative transfer simulator (ARTS)  (Buehler et al. , 2011)  based on the HITRAN (HIgh Resolution TRANsmission molecular database) 2012 database  (Rothman et al. , 2013)  for water vapor, oxygen, carbon dioxide, and methane. Absorptions by ozone and nitrogen dioxide are included separately using measurements of  Daumont et al. (1992)  and  Bogumil et al. (2003). For each measurement channel, we choose several representative gas absorption optical depths, and run the full RT that couples gas absorption and scattering.
CONCLUDING REMARKS This paper summarizes research frontiers on RT in coupled ocean and atmosphere systems to enable new ocean-atmosphere science and specifically to support the PACE mission development. RT is an essential tool in remote sensing of atmosphere and ocean components, and in furthering our understanding of the coupled system. It is used to facilitate designing new retrieval algorithms; providing validation datasets for satellite data products; and performing sensitivity to different components of the Earth system. In anticipation of the advanced remote sensing capabilities of the instruments onboard the PACE spacecraft, the PACE Science Team has performed novel FWD RT and INV RT studies on different components of the atmosphereocean system. This work included examining the representation of hydrosols, aerosols, gases, surface whitecaps and sunglint in these models.
This benchmark has exceeded any previous dataset for the coupled ocean and atmosphere system. Despite the advances described above, gaps remain in RT topics that require continued research in the near future. Several topics were identified by the PACE Science Team: • For RT of light in the ocean, these topics are (i) better understanding of the role of absorption by dissolved inorganics in the UV (FWD RT studies); (ii) better understanding of variability in backscattering shape of VSF (FWD and INV RT studies); (iii) developing particulate models that combine non-sphericity and inhomogeneity (FWD and INV RT studies); (iv) accounting for temperature and salinity dependence of Raman scattering and emission (FWD RT studies). • For ocean surface reflection, outstanding topics are (i) impact of anisotropic surface roughness on the reflection of diffuse skylight (FWD RT studies); (ii) polarization properties of whitecaps in the UV-SWIR (FWD RT studies).
Statistical Analysis SPSS 21 statistic software was applied for histochemical data analysis. Statistical analyses were established at two-factor level; the first corresponded to treatments (B. amyloliquefaciens I3, T. harzianum A, Bion 50 WG, SA, MeJa, and negative control) and the second factor indicated the growth stages (germination, cotyledons, leaf production, rosette, and flowering). ANOVA analysis was applied for analysis of mean data variation, while the Duncan test was used for the comparison of means at p = 0. 05. The value of β-glucuronidase activity was transformed to square roots for data analysis. The applied factors were treatments (B. amyloliquefaciens I3, T. harzianum A, Bion 50 WG, SA, MeJa, and negative control), growth stage (flowering, silique ripening), and vegetative organs (roots, leaves, and floral stems).
amyloliquefaciens I3, T. harzianum A, Bion 50 WG, SA, MeJa, and negative control), growth stage (flowering, silique ripening), and vegetative organs (roots, leaves, and floral stems). Variance analysis was utilized to study the variation of averages in the amount of β-glucuronidase activity using SPSS 21. Means were compared with the Duncan test at p = 0. 05.
Acknowledgments: We thank the  Ministry of National Education, Vocational Training, Higher Education, and Scientific Research of the Kingdom of Morocco  for funding the Arimnet research project entitled "Bacplant", and we also thank the  National Center for Scientific and Technical Research (Morocco)  for its financial support within the framework of the excellence research grant program. We also thank the financial support from the  French ANR  ( Agence National de la Recherche ) project  LabCom ,  ESTIM , and the  ERASMUS funding for Agrocampus-Ouest and Agronomic and Veterinary Institute Hassan II, French-Moroccan collaboration.
We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-ofthe-art  (Al Saied et al. , 2018).
One of the main tasks that constitute MWE processing is the automatic identification of MWEs in running text which can be used as a preprocessing step for parsing or machine translation. MWE identification can be seen as a sequence labeling task similar to named entity recognition (NER): A system receives sequences of tokens as input and outputs the same sequences with annotation labels added to it  (Constant et al. , 2017). As in NER, most parts of the sequence will belong to the negative class, that is, the majority of words is not part of an MWE. However, certain issues that occur when dealing with NEs and MWEs are much more prevalent in case of the latter. Especially with respect to discontinuity. In the PARSEME 1. 0 corpus  (Savary et al. , 2018) , wich comprises datasets of 18 languages, only three of them have a continuity rate of over 80% when it comes to VMWEs.
Encoding Our methodology relies fundamentally on the idea of encoding MWE occurrences as tree labellings. More precisely, given a sentence and the corresponding dependency tree, the set of MWE occurrences present therein is encoded as a labelling of dependency arcs and nodes. A machine learning method is used to model the probabilities of different labellings, which it learns based on a training dataset of encoded MWE annotations. MWE identification requires a reverse procedure of decoding a given labelling to the set of MWE occurrences. MWE Occurrence. Each MWE occurrence is represented as a set of tokens in a particular sentence. We are thus not concerned with determining the category of a MWE occurrence (in our experiments we train one model per MWE category).
Basic Encoding In the basic encoding scheme, we assume that elements of a single MWE occurrence are connected by dependency arcs. The set of MWE occurrences in a sentence with a given dependency tree is encoded as a single labelling function E : E → B defined over the set of arcs E ⊂ V × V of the tree. Encoding. E (v, w) := 1 for a given arc (v, w) ∈ E iff both v and w belong to a single Dali im cynk , że nie ma co wychodzić Gave them zink , that no has what leave Figure  1 : Example of extended encoding applied to a tree fragment with two Polish idioms, dać komuś cynk 'give someone a tip' and nie ma co [wychodzić] 'it is not worth [leaving]', adjacent in the dependency tree. The nodes and arcs labelled with 1 are marked in bold. The example (simplified) comes from the Polish dataset of the PARSEME Shared Task 1.
Dataset All the experiments were run on the German (DE), French (FR), and Polish (PL) datasets of the edition 1. 1 of the PARSEME corpus  (Ramisch et al. , 2018b). This highly multilingual corpus was created in the context of a shared task on the automatic identification of VMWEs and consists of annotated datasets of 20 different languages. The individual datasets are collections of sentences which were, among other things, tokenized, part-of-speech (POS) tagged, lemmatized, and enriched with dependency information. While FR contains solely manual dependency annotations, the dependencies in DE were annotated partly manually and partly automatically. Besides automatic annotations, PL includes dependencies that were converted from a manually annotated constituency treebank. The VMWE annotation comprises the identification of the words that belong to a VMWE instance, as well as the categorization of the identified instances.
Pre-processing The first pre-processing step we used in our experiments involved removing (multiword) tokens, 6 Around 16 hours were required to train a single global model on the 220465 tokens of the Polish dataset using four cores of a 2. 40GHz Xeon E5-2630 machine. 7 IAVs were an experimental PARSEME category and a lot of languages in the PARSEME corpus do not cover them. such as the contraction du of de le 'of the' in French, from consideration. 8 In the PARSEME datasets, only the expanded forms (i. e. , de le rather than du) are annotated at the level of dependency structures and VMWEs. The second pre-processing operation consisted in adding a dummy root node (with special POS and dependency relation values) to each dependency structure, to enforce that it is actually a tree (as required by the global model, see  Sec.
3. 3. 3). This was particularly important for the German dataset, in which some of the dependency structures did not satisfy this property. The two previous steps are carried out automatically by our VMWE identification systems. However, we also performed one full-fledged preprocessing operation of adding the missing lemmas 9  in the French test set. Even though having no impact on the results of the proposed systems, this step was necessary to obtain reliable comparison with the benchmark system (see Sec. 4. 3), configured to use lemma information in case of French.
Conveniently, this requires no changes in the structure of the algorithm itself. This is similar to how the inside algorithm can be extended with its outside counterpart automatically, using automatic differentiation  (Eisner, 2016). Optimization. We used stochastic gradient descent (SGD) to train the models for the individual datasets and VMWE categories. We used mini- Table  2 : General results per language and system on the test data. batches of size 30 and the training length of 60 epochs. We did not apply drop-out. We used the Adam variant (Kingma and Ba, 2015) of the SGD algorithm with the default parameters: initial stepsize α 0 = 0. 001, the exponential decay rates β 1 = 0. 9 and β 2 = 0. 999, and = 10 -8. We additionally used a gradually decreasing stepsize α = α 0 ×τ τ +t , where t ∈ [0, 60] is the epoch number (fractional) and τ = 15.
Evaluation Results We evaluated the two implemented systems and the benchmark (ATILF) system on the development and the test parts of the German (DE), French (FR), and Polish (PL) PARSEME datasets. 12 We trained one local and three global models per language and per VMWE category. Training separate models for different categories allows to partially handle the issue of overlapping VMWE instances, as well as to simplify the architecture (no need to encode the categories in terms of tree labellings). The benchmark system predicts all the categories in one pass. We used the official evaluation script 13 provided by the shared task organizers to calculate all the scores. 12 In contrast to several systems participating in the PARSEME shared task 1. 1, we didn't use the development parts for training.
This is important in that the development set can contain VMWEs unseen in the training part. 13 Available at https://gitlab. com/parseme/ sharedtask-data/tree/master/1. 1/bin/. As mentioned above, we trained three global models per language and per VMWE category. One model was obtained using constrained training and two models using unconstrained training (see  Sec. 3. 3. 3). We observed that the results between different training runs can differ significantly. For instance, the LVC. full identification Fscores differed by almost 3% on the FR development set between the two unconstrained models. We therefore used all three models (for each language and VMWE category) to calculate ensemble node scores (see Eq. 4) and ensemble arc scores (see Eq. 5) by simply summing up the corresponding scores coming from the three models.
Conclusions and Future Work In this paper we propose a neural, graph-based VMWE identification method relying on the idea that VMWE annotations can be represented as labellings of dependency trees. Couching the task of VMWE identification as syntax-driven labelling allows to transparently handle the issue of discontinuity, a challenging property of VMWEs which makes sequential models poorly adapted for this task. Relying on neural scoring and pre-trained word embeddings, on the other hand, facilitates identifying unseen VMWEs. While the idea of applying parsing-based (in particular, graph-based) methods to VMWE identification is not novel, we show that combining it with a neural scoring component and supplying the system with pre-trained word embeddings allows to achieve overall results on par with state-of-the-art on all three PARSEME datasets we experimented with (German, French, and Polish), and to surpass it as far as handling discontinuous and unseen VMWEs is concerned.