dataesr/scientific-paragraphs-categorization huggingface dataset:
Error Analysis We calculated the uncertainty of the neutral density measurement by estimating and propagating the error in the near-Earth XMM counts (C SWCX ) obtained from the soft X-ray background removal process and the error in the potential reaction rate (Q) from the model-data magnetosheath boundary mismatch. As described in Section 5. 1, we obtained C SWCX by subtracting the instrumental background (C inst ), sky background (C sky ), and heliospheric background (C helio ) from the total XMM background counts (C bgd ). 𝐶𝐶𝑆𝑆𝑆𝑆 𝐶𝐶𝑆𝑆 = 𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏 -𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 -𝐶𝐶𝑖𝑖𝑠𝑠𝑠𝑠 -𝐶𝐶ℎ𝑒𝑒𝑒𝑒𝑖𝑖𝑒𝑒 (11) We calculated the error in C SWCX by propagating Poisson errors in each component: 𝐸𝐸𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆 = √ 𝐸𝐸 2 𝑏𝑏𝑏𝑏𝑏𝑏 + 𝐸𝐸 2 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝐸𝐸 2 𝑖𝑖𝑠𝑠𝑠𝑠 + 𝐸𝐸 2 ℎ𝑒𝑒𝑒𝑒𝑖𝑖𝑒𝑒 (12) We estimated the relative error (E SWCX /C SWCX ) for the 12 November 2008 event at 18%.
The soft X-ray emission of this event mostly comes from the magnetosheath (see the XMM LOS in Figure  3a ), and therefore depends on the magnetosheath plasma flux and the magnetosheath thickness along the LOS. The model-data comparison in Figure  4  shows that OpenGGCM reasonably reproduces the magnetosheath plasma flux. However, OpenGGCM shows a wider magnetosheath than the THEMIS observation. During this event, THEMIS B observes multiple magnetopause crossings, from 00:30 UT until it fully enters the magnetosheath at 01:30 UT. At ∼12:40 UT, THEMIS crosses the bow shock and enters the upstream solar wind. However, the simulated spacecraft in OpenGGCM crosses the magnetopause around 00:20 UT, earlier than THEMIS B does, and stays still in the magnetosheath at 12:40 UT when THEMIS B went out to the solar wind.
OpenGGCM provides an approximate calculation of the Earth's magnetospheric system under a single-fluid MHD theory, so it is understandable if it does not reproduce all the magnetopause crossings observed in THEMIS B. The temporary motion of the magnetosheath boundaries does not significantly affect the calculation of Q because such impact is smoothed out during the 3-hr observations of the soft X-ray emission. The important factor is whether our model can catch the average magnetosheath boundaries correctly. The model-data comparison in Figure  4  shows a thicker magnetosheath than that in the THEMIS B observations. This thicker magnetosheath in the MHD model over-estimates Q due to its longer integration path, and subsequently underestimates exosphere density (N 0 ) in Equation  10. The error in the estimate of Q caused by the model-data boundary mismatch must be addressed.
(  2010 ) occurred during solar minimum, whereas the XMM cases in CC2019 occurred near solar maximum. However, Fuselier et al. (  2020 ) derived a solar maximum neutral density of 11-18 cm -3 from an IBEX event on 4 November 2015, that was not very different from the solar minimum density of 4-11 cm -3 from five IBEX events in 2008 and 2009. They concluded that the dayside outer exosphere is weakly dependent on the solar cycle, although they cautiously pointed out that the F10. 7 solar irradiance index of their solar maximum event is 110 solar flux units (sfu), lower than the 144. 4 sfu and 205. 8 sfu of the two solar maximum events of CC2019. The large density discrepancy between the XMM of our result and that of the IBEX studies still exists, partly due to the inherent difference between the two datasets and partly due to the different density derivation techniques used.
We found that our density estimate, 36. 8 ± 11. 7 cm -3 , are still higher than 4-17 cm -3 of  Fuselier et al. (2010); Fuselier et al. (2020)  but comparable to other geocorona studies, 24-46 cm -3 of  Zoennchen et al. (2015)  and the 41 cm -3 of  Baliukin et al. (2019) , although these geocoronal studies may have had large uncertainties due to their observational difficulties above 8 R E geocentric distances. Additionally, our exospheric density estimate at 10 R E subsolar location is higher than the 25 cm -3 , adopted by the SMILE modeling working group for the near-Earth SWCX prediction  (Branduardi-Raymont et al. , 2018). Although the direct comparison of the XMM and IBEX densities has been troublesome, comparison of XMM observations are still meaningful because it clears some ambiguities caused by the different datasets and the different density derivation techniques.
Data Availability Statement WIND SWE and MFI data are available at https://cdaweb. gsfc. nasa. gov/index. html website and potential SWCX event list and OpenGGCM results are available at https://github. com/jjung11/XMM2021. HEASARC tooklit can be found via https://heasarc. gsfc. nasa. gov/ftools/ (we used HEASoft version 6. 25), XMM Science Analysis System can be found at https://www. cosmos. esa. int/web/xmm-newton/sas. XMM observations data can be downloaded from http://nxsa. esac. esa. int/nxsa-web/#home. XSPEC is available at https://heasarc. gsfc. nasa. gov/ xanadu/xspec/ The effective area of the XMM MOS cameras is documented at https://xmm-tools. cosmos. esa. int/ external/xmm_user_support/documentation/uhb/epicfilters. html.
Despite the close relationship between planetary science and plasma physics, few advanced numerical tools allow to bridge the two topics. The code Menura proposes a breakthrough towards the self-consistent modelling of these overlapping field, in a novel 2-step approach allowing for the global simulation of the interaction between a fully turbulent solar wind and various bodies of the solar system. This article introduces the new code and its 2-step global algorithm, illustrated by a first example: the interaction between a turbulent solar wind and a comet.
-The way incoming turbulence is processed by planetary plasma boundaries sudden change of spatial and temporal scales, change of spectral properties, existence of a memory of turbulence downstream magnetospheric boundaries. Indirectly, because of the high numerical resolution required to properly simulate plasma turbulence, this numerical experiment will provide an exploration of the various obstacles with the same high resolution in both turbulent and laminar runs, resolutions that have rarely been reached for planetary simulations, except for Earth's magnetosphere. Menura, the new code presented in this publication, splits the numerical modelling of the interaction into two steps. Step 1 is a decaying turbulence simulation, in which electromagnetic energies initially injected at the large spatial scales of the simulation box cascades towards smaller scales.
Step 2 uses the output of Step 1 to introduce an obstacle moving through this turbulent solar wind. The code is written in c++ and uses CUDA APIs for running its solver exclusively on Graphics Processing Units (GPUs). Section 2 introduces the solver, which is tested against classical plasma phenomena in Section 3. Sections 4 and 5 tackle the first and second step of the new numerical modelling approach, illustrating the decaying turbulence phase, and introducing the algorithm for combining the output of Step 1 together with the modelling of an obstacle (Step 2). Section 6 presents the first global result of Menura, providing a glimpse of the potential of this numerical approach, and introducing the forthcoming studies. Menura source code is open source, available under the GNU General Public License license.
Physical tests In this section, the code is tested against well-known, collisionless plasma processes, and their solutions given by the linear full kinetic solver  WHAMP Rönnmark (1982). We first explore MHD scales, simulating Alfvénic and magnetosonic modes. We use a 2-dimensional spatial domain with one preferential dimension chosen as x. A sum of six cosine modes in the component of the magnetic field along the x-direction direction are initialised, corresponding to the first six harmonics of this periodic box. The amplitude of these modes is 0. 05 times the background magnetic field B 0 , which is taken either along (Alfvén mode) or across (magnetosonic mode) the propagation direction x. Data are recorded along time and along the main spatial dimension x, resulting in the 2D field B(x, t).
Acknowledgements. E. Behar  acknowledges support from  Swedish National Research Council , Grant  2019-06289. This research was conducted using computational resources provided by the  Swedish National Infrastructure for Computing (SNIC) , Project  SNIC 2020/5-290  and  SNIC 2021/22-24  at the  High Performance Computing Center North  ( HPC2N ),  Umeå University, Sweden. This work was granted access to the  HPC resources of IDRIS  under the allocation 2021-AP010412309 made by  GENCI. Work at  LPC2E  and Lagrange was partly funded by  CNES. S. F. acknowledges supports from the  Swedish Research Council (VR)  grant  2018-03454  and  SNSA  grant  115/18.
Neuropsychological tests and questionnaires Participants were asked to perform subtests of the WAIS (WM subtest: forward, backward, and ascending digit spans, arithmetic; Processing Speed subtest: codes, symbols). They also had to fill-in self-evaluation questionnaires about their attention (Adult Self-Report Scale, Schweitzer et al. 2001 -going from 0 to 30, high scores indicating attentional difficulties), and their anxiety and depression level (Hospital Anxiety and Depression Scale,  Zigmond, Snaith, 1983). This allowed us to prevent from a major impact of anxiety or depression issues on cognitive processes, as the score of all participants was contained in the normal range. Demographic and neuropsychological data are summarized in Table  1. Material 96 four-note-long melodies were created using pseudo-random combinations of eightharmonic synthetic tones from the C major scale, spanning two octaves between 110 Hz and 440 Hz.
Funding and acknowledgements This work was performed within the framework of the  LABEX CELYA  ( ANR-10-LABX-0060 ) and the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French ANR. We would like to thank  Camille Chantreau ,  Jérémie Ginzburg , and  Agathe Pralus-Durand  for their help with piloting and collecting data. None of the data or materials for the experiments reported here is available.
The CAEclust Python package implements an original deep spectral clustering in an ensemble framework. Recently, strategies combining classical clustering approaches and deep autoencoders have been proposed, but their effectiveness is impeded by deep network hyperparameters settings. We alleviate this issue with a consensus solution that hinges on the fusion of multiple deep autoencoder representations and spectral clustering. CAEclust offers an efficient merging of encodings by using the landmarks strategy and demonstrates its effectiveness on benchmark data. CAEclust enables to reproduce our experiments and explore novel datasets.
Introduction and Related Theory In the context of unsupervised learning, recent studies improved classical clustering approaches by relying on deep networks for dimensionality reduction, as such networks can automatically learn important features from data  [2, 11, 20, 26]. In particular, deep autoencoders (DAE) propose a new data representation or encoding using an unsupervised learning. Yet, the effectiveness and ease-touse of existing DAE clustering strategies are impeded by their sensitivity to hyperparameters. First, network weights initialization adds randomness to the results. Pretraining helps to mitigate this issue, but is computationally intensive. Finding the correct deep architecture is another hurdle. In almost all recent papers on deep clustering, architectures are fine-tuned for each benchmark dataset and lack of technical rationales.
Specifically, the architecture is usually derived by cross-validation on a validation set for a grid of hyperparameters  [26, 2, 10]. Such determination is infeasible in practice for unsupervised clustering. Other studies reuse architectures that were historically proposed for the considered dataset, without any technical explanations. As an example, the DAE architecture used in  [24, 7, 25, 5] , which contains an encoder having 3 fully connected layers with 500-500-2000 units and an encoding size of 10, is taken from  [21] , which is inspired by  [16]  and  [8]. The CAEclust package relies on our recent work  [1]  and practically addresses the above mentioned issues with a consensus optimization that reaches better predictive performance and more relevant clusterings than single data representation strategies.
The solution of (  1 ) is to set the matrix B ∈ R n×k equal to the k eigenvectors corresponding to the largest k eigenvalues of S. After renormalization of each row of B, a k-means assigns each datapoint x i of X to the cluster that the row b i of B is assigned to. An advantage of the spectral clustering is that it performs well on arbitrary shaped clusters, by contrast with several other clustering algorithms (e. g. k-means). However, this approach has also difficulties to handle large-scale datasets. This is the consequence of the high complexity of the graph Laplacian construction and the eigen-decomposition. Recently, the Landmark-based Spectral Clustering (LSC )  [4]  (or AnchorGraph  [12] ) strategy offered a scalable spectral clustering. In particular, LSC allows to efficiently construct the graph Laplacian and compute the eigen-decomposition.
Deep Autoencoders An autoencoder  [9]  is a neural network that implements an unsupervised learning algorithm in which the parameters are learned in such a way that the output values tend to copy the input training sample. The internal hidden layer of an autoencoder can be used to represent the input in a lower dimensional space by capturing the most salient features. We can decompose an autoencoder in two parts, namely an encoder f θ , followed by a decoder g ψ. The first part provides the encoding Y of the input dataset by computing a feature vector y i = f θ (x i ) for each input training sample. Then, the encoding is transformed back to its original representation by the decoder part, following xi = g ψ (y i ). The sets of parameters for the encoder f θ and the decoder g ψ are learned simultaneously during the reconstruction task while minimizing the loss, referred to as J given by J AE (θ, ψ) = n i=1 L(x i , g ψ (f θ (x i ))), (2) where L is a cost function for measuring the divergence between the input training sample and the reconstructed data.
Software Architecture and Functionalities We summarize the package usage in Figure  2  and detail below CAEclust functionalities. [io] dataLoader. py loads a dataset. CAEclust proposes by default two well-known benchmark images datasets, namely USPS and MNIST. dataLoader. py is needed at line 1 of Algorithm 1. [daeNet] daeEncoding. py sets a DAE with general parameters (e. g. optimizer function, number of epochs, batch size, encoding dimension), and any layer number and width. The method deep_ae() generates the encoded data. The package proposes to generate the encodings either in serial (serial_encodings. py) or in parallel (parallel_encodings. py). daeEncoding. py is needed at lines 2 and 4 of Algorithm 1. [clustering] ensClust. py computes the ensemble sparse affinity matrix (Equation (  4 )) through the method ensemble_approximate_affinity() (Algorithm 1, line 5).
The deep consensus clustering is then obtained from the shared space B (Equation (8), Equation (  7 ) & Proposition 2. 1) using the sparse_SVD() method (Algorithm 1, line 6). [evaluation] evaluation. py provides 2D and 3D visualizations of the shared space B with commonSpace_plot(). These visualizations are also proposed with the UMAP  [13]  transformation. When studying benchmark datasets with ground truth labels, allMetrics() function can compute three informative metrics (accuracy, NMI  [19] , and ARI  [18] ). Furthermore, compare_metrics() provides an html summary table to easily compare the CAEclust results with other clustering algorithms.
Evaluations The CAEclust ensemble package is evaluated on USPS and MNIST. The sets of encodings are made with different DAE initializations (Init. ), epoch numbers (Ep. ) or structures (Struct. ). We trained fully connected autoencoders with an encoder f θ of three hidden layers of size 500, 750 or 1000 for MNIST and USPS, as suggested by Bengio et al. [3] , in all possible orders. The decoder part g ψ mirrors the encoder stage f θ. The encoding dimension e corresponds to the number of expected classes, which is 10. We consider landmarks numbers within 100 and 1, 000 by step of 100. The accuracy reported for DAE-LSC corresponds to an average over 50 replicates (10 replicates on each of the 5 encodings per DAE structure), over all epoch and landmark numbers. The simple use of LSC on encoded data already improves the LSC performance.
Indeed, we found with LSC an average accuracy of 68. 55 ± 2. 25 and 77. 20 ± 1. 49 on the original MNIST and USPS datasets respectively, while the use of a single encoding enables to reach an average accuracy between 87. 06 ± 8. 27 and 91. 54 ± 3. 06 for MNIST ([500-750-1000] and [1000-500-750] DAE hidden layers) and between 79. 72 ± 6. 21 and 83. 47 ± 7. 40 for USPS ([750-500-1000] and [500-1000-750] DAE hidden layers). Yet, finding a priori the most appropriate DAE structure remains a challenging task. The accuracy may also vary for different landmark and epoch numbers. The ensemble strategy provided by CAEclust offers a straightforward way to alleviate these issues and avoid the fine tuning of the DAE hyperparameters. As can be seen from Table  2 , CAEclust provides higher clustering accuracy as compared to the use of a single encoding.
ensemble, CAEclust also offers a better accuracy than other approaches. Beyond the DAEs hyperparameters, CAEclust also depends on the number of landmarks that are used for the LSC clustering. As can be seen in  [12, 2] , the anchor graph strategy reduces the error rate as the number of landmarks increases. As an example, the experiments from  [12]  demonstrate that the error rate of such an approach reaches a plateau above 800 landmarks on the USPS dataset. In  [2] , experimental results show that the higher the number of landmarks, the better results for MNIST. Our experiments tend to confirm these behaviors, as can be seen in Figure  4 , which provides the accuracy of CAEclust on MNIST and USPS, for different DAE architectures trained during 50 epochs. MNIST, 50 epochs USPS, 50 epochs As complementary experiments, we investigate an ensemble strategy of CAEclust on the number of landmarks.
Baseline Evaluations The notebook Baseline_evaluations first loads the dataset with dataLoader. py. Then, it performs k-means and LSC clusterings on the original data. For LSC, the required CAEclust functions are single_approximate_affinity() followed by sparse_SVD(). We then set several DAEs with different structures to encode data using daeEncoding. py. Finally, we perform k-means and LSC on each encoding, evaluate the results with allMetrics() and display them using compare_metrics(). Figure  5  1 provides the evaluation metrics for k-means and LSC clusterings on single deep representations obtained with various autoencoders structures. As can be seen, the use of a single encoding usually improves the clustering results, both for k-means (Figure  5 , a & b) and LSC (Figure  5, c  & d ).
Ensemble Evaluations The notebook Ensemble_evaluations first loads the dataset with dataLoader. py. Then, it encodes the data with various hyperparameters using daeEncoding. py (either in serial or in parallel). To compute the ensemble sparse affinity matrix Z, we use ensemble_approximate_affinity(). The clustering is obtained from the common shared space using sparse_SVD(). We can compare the different ensemble strategies based on the metrics of allMetrics() and display them using compare_metrics(). Figure  7  2 provides the evaluations of different CAEclust ensemble strategies on MNIST and USPS. As can be seen, all the ensemble strategies (initialization, epoch and structure) enable to reach high accuracy, NMI and ARI. 1 Tables are images extracted from the outputs of the CAEclust Baseline_evaluations tutorial notebooks.
Conclusions We developed a Python package CAEclust for deep spectral clustering in an ensemble framework. CAEclust is capable of learning a consensus solution that hinges on the fusion of multiple deep autoencoder representations and spectral clustering. It is an effective and user friendly package that proposes parallelization to overcome the computation time issue. Future extensions of this work will be dedicated to the challenging text data, which is by nature unstructured, highly dimensional and sparse. All in all, CAEclust alleviates the hyperparameters settings issue that usually impedes the efficiency of deep network based clustering techniques, and offers an efficient merging of encodings by using the landmarks strategy.
Acknowledgments We thank  Amine Ferdjaoui  ( Université Paris Cité, Centre Borelli UMR9010, Paris, France ) for his valuable technical assistance in the design and implementation of the CAEclust IPOL demo. This work was supported by a grant overseen by the  French National Research Agency (ANR)  ( ANR-19-CE23-0002 ). It also received the labelling of  Cap Digital and EuroBiomed/Cancer-Bio-Santé  competitiveness clusters.
Facing that, demonstrations by Deliveroo couriers took place in 2017 and 2018 against brutal price changes in France (shift from hourly remuneration to payment per trip). A call for a strike by deliverers was launched in France during the last FIFA World Cup (2018). Activist groups of couriers have been the main drivers of resistance in France, mainly around Paris and Nantes. Couriers have also been represented by traditional unions, in particular SUD (Solidaires Unitaires Démocratiques) and CGT (Confédération Générale du Travail), especially in Bordeaux, Dijon, and Lyon, other French big cities  (Vandaele 2020). In this context, Coopcycle was developed in France to offer platform software to any local cooperative who would like to benefit from a platform. Forty-one cooperatives, two in Canada, and 39 in Europe (nineteen in France), currently use the software (CoopCycle).
' An initiative launched in 2017 by the French association 'Coop des Communs' (Plateformes En Communs 2020) to federate cooperative platform projects that bring social and solidarity economy and the 'commons' together. The source code of CoopCycle is available on GitHub, but its commercial use is reserved for cooperative companies. The license of the software (Coopyleft) is available only to structures that adopt a cooperative model, employ their riders with a traditional contract or through a wage portage company, and that meet the definition of social and security economy as stipulated by the national law of the country in which the platform operates  (Chagny 2019 ). In the Coopcycle organisation, different statuses are possible, as the state of salaried employees is not possible to apply in all countries, neither desired by all riders.
, 2019). The earth system modelling (ESM) community uses LULCC model scenarios, along with dynamic vegetation models, to understand interactions between different components of the earth system in the past  (Gilgen et al. , 2019; He et al. , 2014; Hibbard et al. , 2010; Smith et al. , 2016). Disagreement between LULCC scenarios suggests that their evaluation is needed using independent, empirical datasets  (Gaillard et al. , 2010a). Pollen-based reconstruction of past land cover represents probably the best empirical data for this purpose as fossil pollen is a direct proxy for past vegetation, and fossil pollen records are ubiquitous across the continent of Europe  (Gaillard et al. , 2010a (Gaillard et al. , , 2018)). The landscape reconstruction algorithm (LRA) with its two models Regional Estimates of VEgetation Abundance from Large Sites (RE-VEALS)  (Sugita, 2007a)  and LOcal Vegetation Estimates (LOVE)  (Sugita, 2007b)  is the only current land-cover reconstruction approach based on pollen data that effectively reduces the biases caused by the non-linear pollen-vegetation relationship due to differences in sedimentary archives, basin size, inter-taxonomic differences in pollen productivity and dispersal characteristics, and spatial scales.