dataesr/scientific-paragraphs-categorization huggingface dataset:
Gradience and phylogenetic comparative methods Using a gradient measure of word order allows us to model cross-linguistic variation and word order change more accurately because of the continuous nature of the resulting variables. From a statistical perspective, this is not problematic at all; rather than using the chi-squared test, logistic regression, or discrete phylogenetic comparative methods, we can use (generalized) linear mixed-effect models and continuous phylogenetic comparative methods. The following presents a case study on the phylogenetic signal of entropy of 24 different word orders taken from  Levshina (2019)  in Indo-European. Levshina (2019) calculated entropy using Universal Dependency treebanks  (Zeman et al. 2020 ). The dataset is available as Dataset8. txt in the associated OSF directory.
Acknowledgements: The authors would like to thank the participants and audience at the 2020 SLE workshop "New Approaches to  Word Order ", where this collaboration was first conceived. In addition, the authors are grateful to  Calle Börstell ,  Josef Fruehwald ,  Ingo Feldhausen , the two anonymous reviewers, and our editor  Volker Gast  for their helpful feedback on previous versions of this paper. Natalia Levshina 's work on this project was funded by the  Netherlands Organisation for Scientific Research (NWO)  under Gravitation grant Language in Interaction ( 024. 001. 006 ). Marc Allassonnière-Tang  is thankful for the support of the  French National Research Agency  ( ANR-20-CE27-0021 ). The contribution by  Chiara Naccarato ,  Anastasia Panova  and  Natalia Stoynova  was supported by the  Ministry of Science and Higher Education of the Russian Federation  within Agreement No  075-15-2020-793.
1 and 2. 2, and the interface between quantitative morphology and distributional semantics in Sections 2. 3 and 2. 4. Section 3 outlines the experimental protocol: we train separate linear models for several morphological processes, predicting the frequency of a form in the target cell from various types of information. Section 4 reports the results of two comparable experiments on datasets of different sizes. We end with a general summary of our findings and future perspectives for this work in Section 5.
The conceptualization of the inflection-derivation distinction is of importance beyond theoretical morphology. Take as an example the use of morphological language data in computational linguistics: large resources such as UniMorph  (Kirov et al. 2016 (Kirov et al. , 2018;; McCarthy et al. 2020)  have been extensively used to make typological generalisations about the world's languages, to test linguistic hypotheses on a diverse language sample, and to evaluate the performance of language processing models, among other things. Decisions made about the Uni-Morph tagset and the possible shape of the UniMorph paradigms are dependent on decisions made by editors of the Wiktionary pages for the languages in the resource -deciding where to draw the line between inflection and derivation (or whether to draw a line at all) for an individual language has cascading consequences on all of the uses made of data from UniMorph.
3 The median frequency ratio varies independently of the inflection-derivation divide, 3 Frequencies are taken from the FRCOW corpus (Schäfer and Bildhauer 2012; Schäfer 2015); derivational relations are extracted from the Démonette database (Hathout and Namer 2014), while inflectional relations are extracted from the GLÀFF inflectional lexicon  (Hathout et al. 2014). Only pairs of words which both have non-zero frequency in the corpus and each have no homograph documented in the GLÀFF are taken into account. [ 202 ] with the derivational relation standing between the two inflectional relations. This is not really surprising, as the frequency of inflectional paradigm cells is known to be subject to considerable variation. What is of interest to us is the spread of variation in frequency ratios for each morphological relationship, which we can assess by examining the ratio between the first and ninth deciles.
This coefficient corresponds to the proportion of the variation in the targets not explained by a model. A model with a lower RSE will be more accurate in its predictions than a model with a higher RSE. In more precise terms, an RSE of x would indicate that predictions with a standard deviation below 1 ought to be accurate to ±x. This measure was chosen because it is well-suited both for comparing prediction accuracy for models of the same process with different predictors, and for comparing accuracy of the same type of model trained on datasets of different sizes. Therefore RSE is better equipped for comparing model fit both within and between relations than possible alternatives such as R 2 or AIC/BIC. [ 215 ].
7. The threshold was chosen based on claims in the literature about which formal contrasts have similar semantics, for formal contrasts on which such discussion is available. As a result of this grouping, the 34 processes under examination correspond to 8 paradigmatic relations. Table  5  indicates which processes ended up grouped together, and provides a mnemonic label for each of the groups. 11 Among others, all these databases are currently being integrated into Démonette version 2 as part of the Demonext project  (Namer et al. 2019). Unfortunately the enlarged database was not yet available when the present research was conducted. As one of the goals of this research is to compare the effect that different types of predictors have on model accuracy, we wish to train all models for a single paradigmatic relation on the same set of data points.
For the same reason that we imposed the 50-token threshold on the reference form, we impose the same threshold on all other distributional vectors we employ in finding word forms, or in the models themselves. If a data point fulfils all conditions above, it will be included in the dataset for models of type D. If, after this filtering, the relation still  [ 218 ]  has more than 900 data points available, we fit all four model types to this same set of filtered data points. For inflection, we also exclude cells such as the past subjunctive and the simple past, which are out of current use or restricted to a specific style of discourse. Usage in these cells is inherently biased for reasons orthogonal to the inflection-derivation debate, introducing noise into any generalizations about how usage in these cells relates to that of a reference form, since the causes for variability would be different.
These filtering conditions leave us with three deverbal derivational relations (verb → agent noun; verb → action noun; verb → agent noun; verb → adjective), one nominal inflection relation (singular noun → plural noun), and inflectional relations between the infinitive and 15 other verbal paradigm cells. Note that the dataset includes no clear instance of contextual inflection; in particular, because we use the infinitive as the reference form for verbs, the reference and target forms never differ by agreement only.
In a language such as French (or English), where a single form is recruited for the expression of TAM and voice, this entails seeing the past participle as a syncretic form with two discrete functions of a perfect vs. passive participle, with distinct morphological and syntactic prop- erties  (Aronoff 1994; Abeillé and Godard 2002). Under this view, each of our four past participle datasets is in fact composed of aggregate data corresponding to two distinct but homophonous paradigm cells, one of which is higher than the other on the inflection-derivation continuum. How does this relate to the contrast between RSEs for models of the masculine singular vs. other forms of the participle? As it happens, person and number agreement with the subject is systematic and obligatory for passive and absolute uses of participles, while it is rare for perfect uses.
ACKNOWLEDGMENTS A previous version of this study was presented at the second Paradigmo workshop ( Université Bordeaux Montaigne/Online , June 2021). We thank the audience at the workshop, as well as anonymous reviewers both for the workshop and for this journal, for insightful comments and suggestions. This work was partially supported by three public grants overseen by the  French National Research Agency (ANR) : the  Démonext  project (reference:  ANR 17-CE23-0005 ),  Idex Lorraine Université d'Excellence  (reference:  ANR-15-IDEX-0004 ), and  Labex  EFL (reference:  ANR-10-LABX-0083 ), through which it contributes to  Idex Université de Paris  ( ANR-18-IDEX-0001 ). Finally, we acknowledge the support by the  Fo-Tran  project, funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  n°771113 ).
Tribout 2010) databases, and combining it with inflectional information from the GLÀFF lexicon (Hathout et al. 2014), itself derived from French entries in the francophone wiktionary. 11 4. 1. 1 Data Selection Our initial dataset was constructed by compiling information on French (base, derivative) pairs documented in the Démonette (Hathout and Namer 2014), Denom (Strnadová 2014), Mordan (Koehl 2012), and Converts (.
Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many areas. These devices (Edge devices or "Things" in IoT) are heterogeneous sensors, cameras and even robots performing sometimes certain tasks locally. Fog Computing (or Fog robotics) optimizes the management of these tasks, offering data management mechanisms (computation and storage) closer to the data source. Nevertheless, many aspects remain closed to Fog computing environments like resource needs estimation in such environments. Indeed, such a topic remains a critical challenge, as it falls under either solving very complex optimization problems or comparing hypothetical scenarios very time consuming and/or expensive for deployment in a real environment. To help on this challenge we built SERFRI, an approach to estimate the resource needs in Fog robotics environments based on simulation.
This application was made using a Reachy humanoid robot  (Mick et al. , 2019)  that captured 100 images (at the rate of 7 images per second) that were processed locally or on remote resources, to determine whether there was a human face or not. Then by the fiftieth request, the local deployment becomes in average 50s slower than the remote one. Based on the fact that industrial robotics applications often consist of millions of requests and based on the benefits of the Fog Computing (in terms of latency and power computing), it is then really more advantageous to deploy such applications on Fog-based environments. Fog Computing offers a large computing and storage capacity near data sources (robots in this case, where we talk about Fog Robotics Platform (FRPs)  (Gudi et al. , 2017) ). This is a recent approach that solves the latency problem present in native Cloud environments, for long-term and real-time applications (as in the case of industrial robotics).
The architecture of Fog computing consists of three main layers: an IoT layer which contains connected data sources (such as smartphones, sensors, robots, etc. ) sending requests (periodic, asynchronous and heterogeneous in the case of robotics applications), a Fog layer which contains resources (static/dynamic and heterogeneous/homogeneous) near the data sources and a Cloud layer which contains static resources far the data sources. We are interested in two main problems related to such platforms: (i) indeed, in general, setting up certain hypothetical scenarios of Fog platforms is not possible at the real scale (this is sometimes due to high price of industrial robots and the financial cost of reserving or using a certain number of resources); (ii) moreover, the global makespan 1 of the platform often decreases with the number of resources increasing while the financial cost of using these resources does not have a known and specific evolution a pri-1 global queries' completion time ori.
Deployment of Robotic Ecosystems on Large Scale Distributed Systems Many works present the deployment of robotic ecosystems on large-scale distributed systems in various paradigms as Service Oriented Architecture (SOA), Cloud Computing, Edge Computing, Fog Computing and peer-to-peer. Cloud and SOA  (Papazoglou and Van Den Heuvel, 2007 ) offer a high level of security, large computing capacities and interoperability. An initial type of deployment to virtualize robotic hardware and software resources and offer them as services on the Cloud via the web is more and more topical. For example, RobotWeb  (Koubaa, 2014 ) is a SOAP-based web service middleware-based system that binds robot computing resources as services and publishes them to end users. In  (Koubaa, 2014) , the robots run on the Robotic Operating System (ROS), as it provides a hardware abstraction that facilitates application development.
Fog Resource Needs Estimation for IoT Applications Fog Computing brings a recent approach to data management, resource management is very little discussed in Fog-based environments. Previous studies are limited to Cloud and Edge scenarios only. Reference  (Aazam and Huh, 2015)  presents a service oriented resource management model for IoT devices, through Fog, which can help in efficient, effective, and fair management of resources. Their work mainly focused on customer type and device based resource estimation and pricing, but they do not take into account IoT device capabilities, like CPU and memory in their model. Reference  (Garcia et al. , 2018)  analyzes and estimates the real Fog computational capacity in the city of Barcelona, which is one of the world's most popular smart cities. Thus, they do not focus on specific applications or specific types of applications like robotics applications.
Reference  (Kattepur et al. , 2017)  mainly deals with the estimation of the execution times of robotics applications in heterogeneous Fog environments. They implement their approach on applications pro-cessing images and videos. However, they do not address the estimation of Fog resource requirements, moreover the model does not take into account the CPU degradation of Fog resources. This work primarily discusses the Fog resource requirements of robotic devices of the Edge layer, since this kind of deployment is a compromise for the majority of the comparison criteria (good latency, better security, good data management) in Table  1. It differs from existing works about multi-objectives optimization in that it does not focus on resource utilization optimization but on resource needs estimation (whose use can be further optimized).
Simulation Settings Even though there are a very large number of simulators in Fog based environments, there are some limitations on the most known of them, as they are based on other simulators that are limited in terms of network communication models for example. To circumvent this limitation, we used SimGrid which is very powerful for simulations in many types of LSDS and in Cloud and Grid computing in particular. SimGrid has an accurate network communication model, which helps make the simulation realistic  (Velho and Legrand, 2010). It has been used in hundreds of projects and helps in both distributed applications and LSDS evaluation. Moreover, there is a very large community around him since it represents more than twenty years of code improvement. A SimGrid study consists of: • The description of the platform concerned: defining the zones of the platform studied, the entities (hosts) which composed them as well as a description of the specifications of these hosts and the connections between them.
This can be done through an XML file or an assembly of code instructions, built from the following parameters: resource specifications {FN j } n j=1 , latency (lt) and bandwidth (bw) of the connection between the IoT layer and the Fog layer; • The description of the application: a set of functions or events that must occur during the execution of a deployment scenario. In SimGrid, they are represented by Classes or Event Functions; • The deployment: which specifies which host executes which event. It can be expressed through an XML file or an assembly of code instructions, built taking into account the workflow described above (Sending -Computing -Receiving) executed by the corresponding hosts (robot -Fog resources -robot). Our simulation is therefore likened to a simulate() function which finally takes as parameters: {FN j } n j=1 , lt, bw, the requests sending period t and the function T in Equation (  4 )).
Simulation Scalability Evaluation Equation (4) (Section 4. 2) presents a generic function for estimating the completion time of a i-order request on a j-order Fog resource. We said in Section 4. 3 that this function can be similar to a mathematical formula, machine learning model or mapping object and so on in our simulation (For each platform setting). In our use case, we built machine learning models trying to find the optimal correspondence between the data (e i , d i ) and t i j according to the platform setting. We thus first collected execution traces of the human face detection service on 400 images for each configuration of the Fog layer (1, 2, 3, 4 and 5 chetemi nodes). Then a clustering algorithm (KMeans in our case) was applied to 280 random occurrences of the couple (e i , d i ) in the dataset containing the 400 starting couples.
Fog Resource Needs Estimation To implement SERFRI on the use case presented in Section 2, we consider that Fog instances have similar characteristics to OVHCloud instances 8 designed for data analysis and data-science uses: r2-15, r2-30, r2-60 , r2-120 r2-240. We simulate the platform scenario made-up of : 2, 4, 8, 16, 32 and 64 resources for each of OVHCloud instance types considered. We present first the simulation settings, then the pareto optimization and finally the SERFRI steps: (i) phitransformation 9 , (ii) sigma-transformation 10 and result expression.
Ions were detected by Roentdek 80 mm MCP and HEXanode detector, recording ion flight times (TOFs) and radial hit positions, which were combined into a coincidence dataset together with the electron energy. Combining the ion hit positions on the detector with the deviations in the ion flight times, we could derive 3D momenta for each ion and hence obtain kinetic energy release (KER) for each dissociation event leading to the NH 2 + fragment. The ions emitted in all directions (4p) were collected using the pulsed extraction field, which is applied after detection of each electron during a defined time window. In addition, non-coincident ''random'' triggers for ion extraction were generated during data acquisition and added to the dataset. These ''randomly'' generated ions create a statistical background, which was used in the analysis for the subtraction of false coincidences from the Auger electron-ion coincidence (PEPICO) maps and KER distributions.
Acknowledgements We acknowledge SOLEIL Synchrotron in France for time on the PLEIADES beamline under Proposals No. 20171496  and  20201704. We are grateful to SOLEIL staff, in particular to  C. Nicolas , for smoothly running the facility. O. T. acknowledges financial support from the  French Agence Nationale de la Recherche (ANR)  through the  MUSTACHE  project ( ANR-18-CE30-0015 ).
The latest data release from the ACT CMB experiment (in combination with previous WMAP data) shows evidence for an Early Dark Energy component at more than 3 standard deviations. The same conclusion has been recently shown to hold when temperature data from the Planck experiment limited to intermediate angular scales (l ≤ 650) are included while it vanishes when the full Planck dataset is considered. However, it has been shown that the full Planck dataset exhibits an anomalous lensing component and a preference for a closed universe at the level of three standard deviations. It is therefore of utmost importance to investigate if these anomalies could anti-correlate with an early dark energy component and hide its presence during the process of parameter extraction. Here we demonstrate that extended parameters choices as curvature, equation of state of dark energy and lensing amplitude A L have no impact on the Planck constraints on EDE.
, 2021b; Schöneberg et al. , 2021). Among them, one of the most promising solutions is given by an Early Dark Energy component  (Karwal and Kamionkowski, 2016; Poulin et al. , 2019; Smith et al. , 2020). This component does not only solve the discrepancy between Planck and luminosity distance data, but also accommodates Baryon Acoustic Oscillation data (that, combined with Planck, are also in disagreement with low redshift SN-Ia data). When Planck, BAO and SN-Ia data are combined, the best fit with an EDE component is improved by χ 2 ∼ -15 with respect to CDM, showing a clear preference for EDE from this combined dataset  (Smith et al. , 2020). The Planck dataset alone, however does not provide any statistically significant indication for EDE  (Hill et al. , 2020). CMB angular spectra are able to probe an EDE component in the cleanest way since they constrain directly most of the physics around recombination.
The lack of any direct indication from Planck for an EDE component, therefore, does not offer much support to this hypothesis. Recent analyses of the ACT-DR4 CMB experiment, however, have shown an indication for an EDE component at the level of 2 standard deviations  (Hill et al. , 2021). Even more recently, it has been shown that the combination of the ACT-DR4 with Planck temperature data at small and intermediate angular scales ( < 650) shows an indication for EDE at more than 3 standard deviations while there is no evidence for EDE when the full Planck dataset is considered  (La Posta et al. , 2021; Smith et al. , 2022). It is however well known in the literature that the full Planck angular spectra show also anomalies at the level of three standard deviations (see discussion in  Aghanim et al.
It is therefore of utmost importance, especially in light of the recent ACT-DR4+Planck results, to investigate if current anomalies in the Planck CMB angular spectra could also anti-correlate with an EDE component. If the anomaly is indeed due to experimental systematic effects, this could mask the presence of EDE in the Planck data and place too strong constraints as in the case of neutrino masses. This is therefore the goal of our paper: to clarify if the indication of a lensing anomaly or a positive curvature in the Planck data could mask the presence of an EDE component as identified by ACT-DR4. Our work is structured as follows: in the next section we will briefly review the EDE model (2. 1) considered and our analysis method (2. 2), and in section 3 we present our results. In Section 4 we finally present our conclusions.
Results Let us first consider a variation in the curvature parameter K in the framework of the EDE scenario. As already shown in the literature, the Planck angular spectra data prefers a closed universe at the level of about 3 standard deviations. The same indication is not present in the ACT data  (Aiola et al. , 2020) , while this dataset prefers an EDE component also at the level of 3 standard deviations. The main question we like to answer here is therefore if the curvature anomaly present in the Planck data could affect the Planck constraint on EDE, masking a possible detection. We answer to this question by looking at the parameters constraints reported in Table  I  and in Fig. 1. As we can see there is no significant correlation between the K and f EDE parameters. The indication for a closed universe from Planck data persists when EDE is considered.