dataesr/scientific-paragraphs-categorization huggingface dataset:
The phase of the pixels can be modified by offsetting the grating patterns on each macropixel. We use a grating period of two pixels, giving access to only two levels of phase modulation, 0 and π. We can then create three complex amplitude states; 0, 1 and -1. Sequences of patterns are generated and sent to the control board of the DMD where they are stored in the on-board memory. The sequence is then displayed at a 1kHz frame-rate on the DMD, which triggers the acquisition of the frames on the camera. A tutorial on the Lee holograms is made available on our website:  [38]. This modulation procedure has been implemented in the Python module SLMlayout  [39]  and the interface control of the DMD was done using the Python module ALP4lib  [40]. We developed, share, and maintain both packages. The complex output field is measured using an off-axis holographic technique  [35].
ACKNOWLEDGEMENTS The authors kindly thank  Arthur Goetschy  and  Esben R. Andresen  for fruitful discussions. M. W. M, J. R. and S. M. P acknowledge the  French Agence Nationale pour la Recherche  (grant No. ANR-16-CE25-0008-01  MOLOTOF and grant No. ANR-20-CE24-0016 MUPHTA ) and the  Labex WIFI  ( ANR-10-LABX-24 ,  ANR-10-IDEX-0001-02 PSL* ). Y. B. is supported by the  Zuckerman STEM Leadership Program. Y. B. and S. M. P acknowledge the  France-Israel grant  ( PRC1672 ) supported by the  Israel's Ministry of Science and Technology and the France's Centre National de la Recherche Scientifique (CNRS).
The primary goal of the General Data Protection Regulation (GDPR) is to regulate the rights and duties of citizens and organizations over personal data protection. Implementing the GDPR is recently gaining much importance for legal reasoning and compliance checking purposes. In this work, we aim to capture the basics of GDPR in a well-founded legal domain modular ontology named OPPD (Ontology for the Protection of Personal Data). Ontology-Driven Conceptual Modeling (ODCM), ontology layering, modularization, and reuse processes are applied. These processes aim to support the ontology engineer in overcoming the complexity of the legal knowledge and developing an ontology model faithful to reality. ODCM is used for grounding OPPD in the Unified Foundational Ontology (UFO). Ontology modularization and layering aim to simplify the ontology building process.
Background: UFO and UFO-L This section outlines UFO  [21]  and UFO-L  [23]  as our study's background. UFO is a foundational ontology that employs results from formal ontology, cognitive psychology, linguistics, and philosophical logic. It is composed of three main layers: UFO-A  [28]  (ontology of substances), UFO-B  [30]  (ontology of events), and UFO-C  [28]  (ontology of social entities). UFO has been employed in the design of the ontologically well-founded conceptual modeling language OntoUML  [21, 31]. OntoUML uses the ontological constraints of UFO as modeling primitives and is specified above the UML2. 0 meta-model  [21]. We referred to UFO as a foundation since it comprises a rich theory of relations and complex relational properties absent in other foundational ontologies  [32]. UFO has been successfully applied in many domains ranging from natural science to social domains  [33].
Ontology Validation and Evaluation In this section, we validate OPPD by transforming the reference model into an operational ontology. The ontology environment OLED  [40]  provides the transformation by generating the OWL code. The code generator maps OntoUML classes, associations, and attributes to OWL classes, object properties, and data properties. It considers generalization sets and their disjointness properties plus model cardinalities. The code generator transforms to SWRL 5  rules the domain constraints and cardinalities and the transitivity of material and parthood relations. The resulted operational ontology is manageable in ontology editors such as Protégé (see Figure  8  for an example). OPPD's consistency is verified using HermiT, an OWL2 inference engine  6. The ontology, under construction, contains 89 classes, 83 subClassOf relations, 56 equivalent classes, 234 object properties, and 14 disjoint axioms.
Using SWI-Prolog  [44]  and Thea 8 , OPPD, represented as OWL abstract syntax terms, is converted into a logic program. The mapping process implements Description Logic Programs (DLP)  [45]. For instance, OPPD's axioms (A) are converted to the Prolog program (B). Furthermore, the generated logic program will be used for encoding logic rules representing the procedural aspect of GDPR legal rules. For instance, the subsequent set of rules is proposed to represent Art. 82. 1. (Figure  5 ). In this context, the isomorphism principle, stated by Bench-Capon  [46] , is followed to create a well-defined correspondence between the rules in the formal model and the rules in natural language. (1) Right_Duty_to_Compensation(Z) :-Controller(X), Data_Subject(Y), Personal_Data(P), Infringement_of_GDPR(I), involved_in(X,I), Personal_Data_Damage(D), triggers(I,D), suffers_from(Y,D), depends_on(D,P), Legal_Relator(Z), grounds(D,Z).
Related Work As related to this study, two main preferences are defined, the legal core ontologies and the domain ontologies representing the GDPR norms. Therefore, LKIF-Core  [24]  is selected being the latest validated legal core ontology preceding UFO-L. Besides, two domain ontologies are considered, PrOnto  [12]  as a legal domain ontology covering the GDPR norms and concepts and GConsent that represents the consent concept based on the GDPR  [11]. LKIF-Core  [24]  is a legal core ontology implemented in OWL  9. It is composed of 15 modules categorized into three main categories, each of which describes a set of closely related concepts from both legal and commonsense domains: (i) Abstract Concepts define the place, mereology, time, and spacetime; (ii) Basic Concepts define concepts around process, role, action, and expression; (iii) Legal Concepts define legal action, legal role, and norm.
LKIF-Core considers the representation of normative knowledge which is the basis of the normative reasoning in the AI and Law research domain. It provides interpretations for the terms obligation, prohibition and permission  [47]. By reusing LKIF-Core, the representation of the hierarchical structure of basic legal concepts is maintained. However, the main drawback is the lack of interpretation of the procedural aspect of provisions. This deficiency is admitted in the literature that most ontologies did not have an adequate solution for legal procedures mainly because of the difficulty to find a language to express knowledge in a declarative way  [48]. PrOnto  [12]  is a legal ontology developed for modeling the GDPR concepts and norms. Its main goal is to support legal reasoning and compliance checking by employing defeasible logic theory.
PrOnto is developed using the MeLOn methodology, which iterates over ten steps. To resume, we outline five essential steps that are commonly addressed in ontology engineering approaches (as described by the authors in  [12] ): Describe the ontology goal; Reuse existing ontologies, design patterns, or domain vocabularies; Use usable tools (e. g. , tables, UML diagrams, and the Graffoo tool); Refine and optimize the ontology with the help of an ontology expert that manually adds the axioms; Ontology evaluation. In PrOnto, concepts such as Agent and Role and relations such as plays are reused from LKIF-Core to represent legal roles (e. g. , Controller) aiming to model obligations and rights in the GDPR (e. g. , Right to Data Portability). PrOnto is composed of different modules: (i) data and documents, (ii) agents and roles, (iii) processing purposes and legal bases; (iv) data processing and workflow, risk management, and (v) legal rules and deontic operators.
GConsent  [11]  is an OWL2-DL ontology for representing information associated with consent, specifically, the given aspect of consent i. e. , consent provided by the data subject. For building GConsent, the "Ontology Development 101" methodology  [49]  is used by applying the following phases: Gather information about consent from GDPR, articles, academic papers; Create use-cases and competency questions based on collected information; Create ontology to express information about use-cases; Ontology evaluation. The core concepts defined in GConsent are Consent, Data Subject, Personal Data, Purpose, Processing, and Status. To conclude, in PrOnto and GConsent, the representation of legal concepts and relations is established. However, an explicit description of legal procedures is not supported.
II A), independent-particle shell model (Sect. II B) and natural orbitals shell model (Sect. II C). Given that neutrino collaborations have presented the semi-inclusive experimental data as function of different variables, in Sec. III we define the three sets of observables employed in experimental analyses and give their expressions as function of the momentum and angles of the particles detected in the final state, i. e. , a muon and a proton in all cases considered in this work. Next, we present the comparison of our theoretical results with the available semi-inclusive data from T2K (Sect. IV A), MINERνA (Sect. IV B) and MicroBooNE (Sect. IV C). Finally, in Sect. V we summarize our conclusions.
C. MicroBooNE In the previous sections we have compared and analyzed experimental data of muon neutrinos on 12 C published by the T2K and MINERνA collaborations. The MicroBooNE collaboration has also published CC0π1p flux-integrated differential cross sections of muon neutrinos on 40 Ar  [16, 17]  as functions of the final particle momenta and angles. Next we present results compared with two sets of experimental measurements published by MicroBooNE. The kinematic constraints imposed in the two measurements are summarized in Table  III. MicroBooNE A ) and  [17]  (MicroBooNE B). Here, ∆θ l,N is the opening angle defined as the angle between the muon and the proton and δpT the momentum imbalance in the transverse plane defined in Eq. (  22 ). In Fig. 14  we show single differential ν µ -40 Ar cross sections as function of muon momentum and scattering angle (left panels) and as function of proton momentum and polar angle (right panels) for two nuclear models, RFG and IPSM, compared with data shown in  [16].
ACKNOWLEDGMENTS This work was partially supported by the  Spanish Ministerio de Ciencia, Innovación y Universidades  and  ERDF (European Regional Development Fund)  under contracts  FIS2017-88410-P , by the  Junta de Andalucía  (grants No. FQM160  and  SOMM17/6105/UGR ) and by  University of Tokyo ICRR's Inter-University Research Program   FY2020  and  FY2021. M. B. B. acknowledges support by the  INFN  under project  Iniziativa Specifica  and the  University of Turin  under Project  BARM-RILO-20. J. M. F. P. acknowledges support from a fellowship from the  Ministerio de Ciencia, Innovación y Universidades. Program FPI (Spain). G. D. M. acknowledges support from the  European Unions Horizon 2020  research and innovation programme under the  Marie Sklodowska-Curie  grant agreement No. 839481.
SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking.
Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set focusing on performance at the coast. It will also present the results of a study assessing regional tidal models. Aim is to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and tested, and a processing scheme will be implemented to generate global coastal zone and river discharge data sets.
-Validation datasets Tidal harmonic constituents derived from altimetry along-track data and crossover points (TP/J1/J2, CryoSat-2 in the Arctic) Tidal harmonic constituents derived from tide gauge time series Assessment of tidal models in coastal regions 4 -Global mean vector differences 5 -Regional mean vector differences Vector difference calculated for each tidal component, between each model and each validation dataset, reduced to the continental shelves. The RSS scores provide overall statistics on the models performance. ! Globally, FES2014b performs best. The analysis of the global and regional models in a regional approach, considering 20 shelf regions, provides more contrasted results. " Where available, the recent regional models provide the most accurate results " The global models perform differently depending on the regions.
Data Analysis The voltage signals were amplified and filtered with a 120 dB/decade, six-pole anti-aliasing active Bessel filter  [61] [62] [63] [64] [65] [66] [67]. We used a custom DAQ software package to save on disk the data stream acquired through a 18 bit analog-to-digital board with a sampling frequency of 2 kHz  [68]. A derivative trigger was applied to the data to identify thermal pulses; the trigger parameters were tuned for each detector according to its noise and the shape of the thermal pulse response. Moreover, a random trigger was set every 60 s, to sample detector baselines without any signal. For each trigger, we constructed a 5-s-long time window for both LDs and Li 2 100 MoO 4 crystals (from now on, LMO). The triggered data were then processed offline via a dedicated analysis chain, which was adapted from a C++ based analysis framework developed for CUORE  [69] , CUPID-0  [70]  and their predecessors  [71].
Results Among the 8 LMO crystals of the presented setup, we noticed that 2 of them (LMO-2, LMO-8) were not functioning when we reached base temperature, due to broken electric contacts. We will present the analysis of the data acquired for the other 6 LMO detectors and related LDs. The detector response depends on the operating temperature: a lower temperature decreases the thermal capacitance and enhances the NTD-Ge response, generally leading to a better signal-to-noise ratio. For this work, we operated the detectors at 18 mK, with working NTD-Ge resistances of 10-50 MΩ for the LMO detectors. Due to technical problems related to the test cryogenic facility, it was not possible to operate the detector at ∼10 mK (working resistance of hundreds of MΩ), where we expect the best performance. We measured a signal amplitude ranging from 30 to 120 µV/MeV (mean: ∼50 µV/MeV), and a baseline energy resolution of 1.
Summing the LY of the two LDs facing an LMO detector, indeed, can help improve the discrimination capabilities in case of the bare crystals, where the detected scintillation light is poor and so is the single detector LY. The measurement of the LY was used to validate a Monte Carlo simulation of the scintillation light based on the Litrani software  [77]. We reconstructed the geometry of the detector, using the optical properties of lithium molibdate crystals  [78] , its scintillation spectrum  [79]  and scintillation decay time at cryogenic temperatures  [80]. The optical properties of the coated germanium disk and the reflecting foil were taken from  [81]. The only missing parameter for the simulation was the absolute number of photons emitted by LMO at 18 mK. To our knowledge, this value is not reported in literature and would require dedicated measurements.
Acknowledgements The CUPID Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; by the  European Research Council (ERC)  under the  European Union   Horizon 2020 program  ( H2020/2014-2020 ) with the  ERC Advanced  Grant No. 742345  ( ERC-2016-ADG , project  CROSS ) and the  Marie Sklodowska-Curie Grant  Agreement No. 754496 ; by the  Italian Ministry of University and Research (MIUR)  through the  grant Progetti di ricerca di Rilevante Interesse Nazionale  ( PRIN 2017 , grant No. 2017FJZMCJ ); by the  US National Science Foundation  under Grant Nos. NSF-PHY-1401832 ,  NSF-PHY-1614611 , and  NSF-PHY-1913374. This material is also based upon work supported by the  US Department of Energy (DOE) Office of Science  under Contract Nos.
DE-AC02-05CH11231  and  DE-AC02-06CH11357 ; and by the  DOE Office of Science, Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551 ,  DE-SC0011091 ,  DE-SC0012654 ,  DE-SC0019316 ,  DE-SC0019368 , and  DE-SC0020423. This work was also supported by the  Russian Science Foundation  under grant No. 18-12-00003  and the  National Research Foundation of Ukraine  under Grant No. 2020. 02/0011. This research used resources of the  National Energy Research Scientific Computing Center (NERSC). This work makes use of both the DIANA data analysis and APOLLO data acquisition software packages, which were developed by the  CUORI-CINO, CUORE ,  LUCIFER  and  CUPID-0  Collaborations.
In this article, we intend to popularize this technique by describing all its facets in the framework of optical microscopy, namely the working principle, its implementation on a microscope and the theory of image formation, using simple pictures. Also, we provide and comment an algorithm of interferogram processing, written in Matlab. Then, following the new extension of the technique for microscopy and nanophotonics applications, and the deviation from what the technique was initially invented for, we propose to revisit the description of the technique, in particular by discussing the terminology, insisting more on a grating-shadow description rather than a quadriwave process, and proposing an alternative appellation, namely "grating shadow phase microscopy" or "grating-assisted phase microscopy".
Wavefront gradient integration The final step consists in a very fundamental mathematical task: retrieving a 2D scalar field from its vectorial gradient (Fig. 11 ). Several algorithms are freely availabe, some of them written in Matlab. For instance, the Matlab package from John D'Errico does a good job. Although this wavefront retrieval postprocessing works perfectly fine in 99% of cases, it may not yield proper reconstruction for three reasons: (i) The wavelength is far from the nominal wavelength the grating has been designed for. Indeed, the π phase shift of the grating are produced by local substrate etching, the depth of which equals to wavelength to ensure a phase shift of π. The use of a different wavelength from the nominal wavelength does not create any bias, since QLSI is achromatic.
Acknowledgments The author wishes to thank  Daniel Andrén ,  Rodrigo Gutiérrez-Cuevas  and  Miguel A. Alonso  for helpful discussions, and  Ljiljana Durdevic  for providing the U2OS cell image of Figure 1h. This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 772725 , project  HiPhore ).
However, linguist and automated speech researchers today use analysis pipelines that are either Python or shell scripts. To this day, no open-source implementation allows for the γ-agreement to be computed in a programmatical way, and researchers that are already proficient in Python and willing to automate their work might be hindered by the graphical nature of the original Java implementation. Moreover, the original γ-agreement algorithm has several parameters that are determinant in its computation and cannot be configured as of now. For this reason, it would greatly benefit the speech and linguistic scientific community if a fully open-source Python implementation of the original algorithm was available -this is what we are making availble here. We have made sure that our implementation has several key features: • It is comparatively as fast as the original implementation, taking about 10s to compute a high-confidence γ-agreement measure on a middle-range processor.
• The pygamma-agreement package is modular and users can easily extend one of the modules without the burden of rebuilding an optimized code base from scratch (e. g. , Users can easily add a new dissimilarity measure). • Our code allows for fine-grained constructions of an annotation Continuum and an advanced configurability of the γ-agreement's different parameters. • We also made sure the interpretability of the input (i. e. , the annotation continuum) and the output (i. e. , the alignments) data structures are straightforward, as both can be visualized in a Jupyter Notebook (see Figure  1 ). • Finally, we support most of the commonly used data formats and analysis pipelines in the speech and linguistic fields.
The pygamma-agreement Package The pygamma-agreement package provides users with two ways to compute the γ-agreement for a corpus of annotations. The first is to use the package's Python API. import pygamma_agreement as pa continuum = pa. Continuum. from_csv("data/PaulAlexSuzann. csv") dissimilarity = pa. CombinedCategoricalDissimilarity(categories=list(continuum. cate gamma_results = continuum. compute_gamma(dissimilarity, precision_level=0. 02) print(f"Gamma is {gamma_results. gamma}") The most important primitives from our API (the Continuum Figure  1  and Alignment Figure  2  classes) can be displayed using the matplotlib. pyplot backend if the user is working in a Jupyter notebook. The second is a command-line application that can be invoked directly from the shell, for those who prefer to use shell scripts for corpus processing: pygamma-agreement corpus/*.
, 2020). We made sure that it is robustly tested using the widely-adopted pytest testing framework. We also made sure that pygamma-agreement's outputs matched both the theoretical values from the original paper and those of the Java freeware. Travis CI is used to run tests to ensure package quality is maintained and most of the code is type-hinted and has descriptive docstrings, both of which can be leveraged by IDEs to ease the use of the API. We provide a user documentation as well as an example Jupyter notebook in the package's repository. Additionally, we have used and tested pygamma-agreement in conjunction with the development of our own custom-built annotation platform, Seshat  (Titeux et al. , 2020). In Table  1 , we present two use cases for our implementation of the γ-agreement measure on two corpora.