dataesr/scientific-paragraphs-categorization huggingface dataset:
Perplexity A sentence is scored using the average perplexity of GPT-2  (Radford et al. , 2019)  across all words. QUESTEVAL text-QuestEval corresponds to the baseline presented in Section 3. 1, using textual QUESTEVAL to compare evaluated descriptions to their references. In contrast, data-QuestEval corresponds to the multimodal version we propose in Section 3. 2, comparing the evaluated description to its structured source. For all experiments, unless stated otherwise, we used multimodal-QG/QA models trained on the synthetic corpus corresponding to the evaluation data (e. g. synthetic WebNLG for evaluation on WebNLG). Finally, OOD-data-QuestEval was trained on a synthetic E2E dataset  (Dušek et al. , 2020)  to assess the impact of out-of-domain training.
Datasets We evaluate our metric on WebNLG  (Shimorina et al. , 2018)  and WikiBio  (Lebret et al. , 2016). The WebNLG data consists of sets of RDF triples and corresponding descriptions in 16 DBPedia categories (e. g. , Airport, or Food). Authors of WebNLG provided a set of 2, 000 English descriptions generated by 10 different systems and rated by human annotators on a 3-level Likert scale on three dimensions: Fluency -does the text sound fluent and natural?, Grammar -is the text grammatically correct?, and Semantic -does the text correctly represent the meaning in the data? The WikiBio data consists of biographies paired with the corresponding infoboxes extracted from Wikipedia biography articles. WikiBio tables are one order of magnitude larger than tables in WebNLG (see Table  3  in supplementary material), and training instances have been built automatically from online sources, resulting in very noisy reference texts  (Dhingra et al.
, 2021). This can explain why no metric exhibits a significant correlation for Fluency. On Semantic, the only metric that correlates significantly is QUESTEVAL, indicating the effectiveness of our proposed method to evaluate DTG. We stress that Semantic is one of the most important dimensions to measure: current models have shown to be fluent but hallucinating  (Lee et al. , 2018). Finally, we observe on both datasets that QUESTEVAL performs better using the source than the reference. We hypothesize that some references fail to give accurate descriptions of the tables, which might explain the lower performance (more details in Appendix D of the supplementary materials). On the QG/QA potential QUESTEVAL directly depends on the performances of its QG/QA models. While not the focus of this study, recent works  (Chen et al.
, 2020; Nan et al. , 2021)  on multimodal-QA have shown great promise and could lead to further improvement for data-QuestEval. In a larger view, research in Question Answering has been very prolific, and further improvements, either on tables or texts, will undoubtedly lead to improvements in the quality of QUESTEVAL's evaluation. We note that in some way, the problematic of evaluating text is removed from the Data-To-Text task, and moved to the QA field, where it is arguably better defined. On cross-domain adaptation How would QUESTEVAL perform if its multimodal-QG/QA components were to be trained on another synthetic dataset, such as E2E  (Dušek et al. , 2020) ? In Table  1 , we observe that the results of OOD-data-QuestEval on WebNLG are only slightly lower than those of QUESTEVAL in-domain, indicating that our approach generalize on similar domains.
In contrast, results on WikiBio are not conclusive, highlighting that significant variations on structure and domain leads to dramatic decrease of performances across tasks. An interpretable metric As noted by  Rebuffel et al. (2021) , the commonly used metrics compared in this work provide sentence-level information, which can be hard to decipher: for instance, given a 0. 5 PARENT score, which part of the predic-tion is incorrect? In contrast, QUESTEVAL scores are directly related to its QA's answers for a set of questions generated by its QG. As such, it provides fine-grained, interpretable explanations of its score, via the analysis of the QG's questions and QA's answers (or lack thereof). Table  2  showcases an example of tabular data with two evaluated descriptions, generated questions, and the predicted answers.
This emphasizes the explainability of QUESTEVAL, an interesting property that we plan to further explore in future work. On Multilingual extensions Experiments of this paper are performed on English corpora. However, in the light of successful QG/QA approaches in different languages, we believe that our work will generalize well to other languages. In particular, languages in which ngram-based metrics perform poorly due to the language's structure (e. g. Korean  (Lee et al. , 2020) ) would benefit the most from our approach).
Conclusion In this work, we proposed an efficient method to evaluate in a reference-less multimodal setup of Data-to-Text Generation. To train Data-QuestEval on different modalities, we propose a methodology to create synthetic corpora of (data, question, answer) triples from any Data-to-Text Generation task. We show that our approach outperforms several existing metrics w. r. t. human judgement on two standard Data-to-Text Generation benchmarks, and performs reasonably well when trained on a different domain. While QG/QA modeling per se is out of the scope of this paper, further improvements could be obtained thanks to advances in multimodal QA, a field particularly prolific  (Chen et al. , 2020; Herzig et al. , 2020; Nan et al. , 2021). In future works, we plan to study how QG/QA systems perform on more complex and abstractive datasets.
This is relies to know the maximum deformation of the model which on the ratio of the displacement of extremum points by the initial height of the structure. Furthermore, the viewable maximum stress of the structure is analyzed in comparison with the ultimate tensile strength. These evaluation para-meters are visible in Table  1  and there are respectively noted υ, F max , max ∈ and σ max. For this study, a 3D model of ZPR structure with snapping mechanism was chosen. First, a CAD model of this new structure was made with the software PTC Creo. The built model is completely parametric, driven by relations and parameters which is means that the geometric parameters can be changed and the CAD model will be regenerated automatically. The structure is composed of 8 external symmetric snapping beams and also 4 internal symmetric snapping beams in two levels.
EXPERIMENTAL PLANNING This section relates to Table  1  where ν, F max , max ∈ and σ max are respectively the Poisson ratio average, the maximum traction force, the maximum strain and the maximum stress of the structure during only the snapping behavior. The geometrical parameters spotlighted in Table  1  are illustrated in Figure  3. The parameters T1, T2 and L are expressed in milli-meters, F max in Newtons and σ max in Mega-Pascals. To exploit the simulation results prescribed by the DoE, the software Minitab was used. A regression of each evaluation parameter was built. This, to express the evaluation parameters depending on the action parameters (the geometrical parameters). First, the regression was realized with all the full quadratic terms. Then, only the significant terms were retained relying on the p-value (Fisher's test).
Acknowledgements The authors acknowledge  Dr. Arghya Bhowmik  for valuable discussions and funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  957189  ( BIG-MAP ) and No  957213  ( BATTERY2030PLUS ). T. V. also acknowledges the  Novo Nordisk Foundation  (SURE # NNF19OC0057822 ) and K. E. acknowledges the  Knut and Alice Wallenberg Foundation  ( INTELL 139501042 ) and the  Swedish Foundation for Strategic Research (SSF).
As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it.
The aim of this paper is to present an evolution of this widely used Local Relief Model method, allowing the automatic adaptation of the filtering size according to natural relief, producing a single-model, which makes simpler, faster, more efficient, and more reliable detection of target features in large datasets with variegated topography. This Self-AdaptIve LOcal Relief Enhancer (SAILORE) automatically uses the best filter configuration, allowing the detection of all the types of anthropogenic remains, independently of the global relief context. In this paper, we first present in the material and method section the study site and the LiDAR dataset, containing various types of anthropogenic structures coupled with a high variability of natural reliefs. Then, we present the LRM principles, the SAILORE approach and the details of the algorithm.
Airbone Laser Scanning Data and Derived Dataset In order to test the performance of the proposed approach, a high-resolution LiDAR dataset covering a large zone with various relief configurations and with a high concentra-tion of archaeological remains in various conservation status was used. Its acquisition was carried out as part of a multi-disciplinary program called AYPONA and focused around the basaltic plateau of Corent (Auvergne, France)  [24]. It covers an area with heterogeneous relief, such as the floodplain of the Allier river, terraced slopes managed for vine growing, and quarries for basalt extraction  [25]. The plateau summit is an exceptional archaeological site, with a continuum of human settlements from the Neolithic to the late Middle Ages periods  [26, 27]. A wide variety of anthropogenic landforms can then be detected throughout various relief configurations and with a more or less visible trace in the field.
Testing the Performance of the SAILORE Approach In order to compare the performance of SAILORE approach vs. conventional LRM, we applied both filtering algorithms to the available LiDAR dataset (see Section 2. 1). For the LRM, we used 3 different settings for the filtering window size (5, 15, and 30 m), corresponding to the optimal configurations for high, medium, and low slopes, respectively. Then, we selected 2 comparison windows, including several typical terrain types: flat areas under cultivation with a few agricultural structures and archaeological remains, terraced steep slopes, and intermediate slopes with buildings (see Figures  7 8 9 ). Finally, we carried out a visual comparison of the ability of all the processed images to detect the same features in both windows.
Discussion and Conclusions The comparison between the LRM with different settings and the SAILORE algorithms showed clearly that the latter combines the benefits of the others in a single image, simplifying the processing and visualization of DEMs for archaeological and/or geomorphological purposes. The first interest of this approach, beyond making the synthesis between the results of LRM with different filtering buffer sizes, is to be sure that for each visualized area, the filtering was conducted with the adapted kernel size and that the elements that appear actually correspond to potential objects of interest. This is especially relevant for large LiDAR datasets with diverse natural relief, which is the most standard situation. Obviously, the manual adaptation of the kernel size to each field configuration is possible in the case of LRM, but it requires a certain experience of the user, in particular in terms of interpretation of the results.
This mode of operation is very practical for inexperienced users but can perhaps appear reductive for experienced users who might wish to test different steps of variation of the kernel size (variation of 5 in 5 instead of 10 in 10 pixels for example). Indeed, this is an empirical choice, justified by the tests we have performed showing that a step of 10 was the best compromise between the accuracy of the result and computation time. That said, perhaps some applications might require more precision. The evolution towards a version of SAILORE allowing the choice of this parameter is envisaged, but the priority remains to deploy this algorithm in the form of a toolbox on other GIS software in order to make this tool accessible to the greatest number. Moreover, the ArcGIS model is modifiable, and can, therefore, be adapted by experienced users if needed.
Furthermore, we have employed the python based pipeline PyStoch  [70]  to perform the analyses on folded data reported in this paper. We do not find evidence for gravitational waves in any of the three analyses and hence set direction-dependent upper limits on the gravitational-wave emission. Though stringent upper limits on the anisotropic GWB have been obtained in the past  [54, 55, 71] , our new constraints improve upon existing limits by a factor of ≥ 2. 0. This paper is structured as follows: Sec II presents the GWB model adopted in our analyses, and the search methods used. Section III describes the datasets used in the searches and briefly explains the data processing. Results from all three analyses are presented in Section IV. Finally, we conclude with the interpretation of our results in Sec V.
Since the Galactic Center is at a lower declination, the effect of the Earth's motion becomes significant and hence we combine more frequency bins. To perform these three analyses, cross-correlation data from each baseline is folded into one sidereal day by taking advantage of a temporal symmetry of the observations induced by the Earth's daily rotation about its axis. We therefore reduce the computational cost of this search by a factor equal to the total number of days of observation  [69]. For the NBR and BBR analyses, the folded data are analyzed by Python-based pipeline, PyStoch  [70] , which takes advantage of the compactness of the folded data and the standardization and optimizations of the wellknown HEALPix (Hierarchical Equal Area isoLatitude Pixelation) Python package  [81]  to reduce the computational cost and memory requirements by a factor of a few compared to past analyses.
B. Spherical harmonics analysis The sky maps obtained in the SHD analysis are presented in Fig. 3 , while a summary of the results is in Table  II. The maps presented in Fig. 3  are obtained by integrating over all available datasets (O1, O2 and O3) and running a combined analysis over the three baselines HL, HV, LV (O1 and O2 analyze only the HL baseline). However, sensitivity maps for the individual baselines are still useful to show how multiple baselines yield different anisotropy in its sensitivity, and are shown in Fig. 8  in the Appendix. In Fig. 3 , each column represents a different value of α and the top row shows the SNR maps while the bottom row shows 95% confidence level upper limit maps. According to the p-values in Table  II , the SNR sky maps are consistent with Gaussian noise; hence we place upper limits on the normalized gravitational-wave energy density.
Similar to BBR, the p-values in Table  II  are calculated from the maximum SNR distribution computed numerically by simulating many realizations of the dirty map. Table II also gives the range of upper limits in each sky map for combined data from LIGO-Virgo's three observing runs, as well as that from LIGO's O1+O2 analysis alone for comparison. The Bayesian upper limits on the energy density spectrum have been derived based on posterior samples of P m after marginalizing over the calibration uncertainties (see Ref. [91]  for more details on how we treat calibration uncertainties).
Gravitational waves from Scorpius X-1 have been constrained using modelbased cross correlation and hidden Markov Models using data from the first two Advanced LIGO/Virgo runs  [62, 63, 95, 96]. The upper limits reported for Scorpius X-1 from continuous-wave searches  [62, 95, 96]  using LIGO/Virgo O1 and O2 data are comparable to or better than the limits we obtained in our analysis. The limits from continuous-wave searches are expected to further improve with LIGO/Virgo O3 data. The improvements in the modeled continuous-wave searches come at the expense of higher computational cost. Compared to the continuous-wave searches  [62, 95, 96] , the unmodeled radiometer analysis reported in this paper is computationally inexpensive and also covers a larger frequency band  (20-1726 Hz)  than  [95, 96].
Acknowledgments The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Netherlands Organization for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Cells were let at least 1-2h before the beginning of the experiment. Images were taken using an inverted Zeiss Axio Observer Z1 or a Zeiss Axiovert. The focus was maintained using a software custom algorithm developed on MatLab. Fluorescence images were taken using a LED light (CoolLed, LumenCor). The objectives used were a 63x N. A. 1. 4 objective (with Zeiss Axio Observer Z1) or a 40x N. A. 1. 4 objective (Zeiss Axiovert) and an EM-CCD Luca-R camera (Andor). Multi-Position imaging was enabled by an automated stage (up to 80 positions). GFP was excited at XX nm after passing through an excitation filter () and fluorescence was then collected using an emission filter (). For all experiments, one image was acquired every 10 or 15 min except for the determination of the SRX1pr-GFP-deg expression.
Acknowledgments We thank  Michel Toledano  for fruitful discussions,  Sophie Quintin  and  Sandrine Morlot  for careful reading of the manuscript. This work was supported by the  Fondation pour la Recherche Médicale  (FRM, B. J, and G. C. ), the  Agence Nationale pour la Recherche  (T. A. and G. C. ), the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
83 and 0. 37 (0. 83 to -0. 24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset 1. Introduction.
The role of epiphreatic conduits on the hydraulics and flow velocity was found to be significant for many KHS. This specific behavior cannot be simulated with the usual hydrogeological software packages. For this reason, pipe-flow modelling (e. g. the SWMM software developed by US EPA and other urban drainage software) have increasingly been used in modeling karst hydrology:  Campbell and Sullivan (2002) ;  Peterson and Wicks (2006) ;  Wu et al. (2008) ;  Gill et al. (2013) ;  Chen and Goldscheider (2014) ;  Jeannin et al. (2015) ;  Kaufmann et al. (2016) ;  Malard (2018) ;  Vuilleumier et al. (2019) ;  Morrissey et al. (2020); Schuler et al. (2020). These models enable flow to be simulated in a complex network of pipes with various diameters, roughness, and cross-sectional shapes. If this approach is efficient to link observed heads and discharge rates, it is not adequate to link precipitation to discharge hydrograph.
One neuron calculates two values: (i) the weighted sum of its input vector with its parameters, and (ii) its output, which transforms the weighted sum in a scalar value using predefined functions (e. g. linear or sigmoid), known as activation functions. The graded, nonlinear response of sigmoid activation functions enables the ANN to capture nonlinear relationships within the training data. At this stage, it is important to notice that ANN models have no predefined function before training. The goal of the training is to build both the "function" and to calculate its "parameters". For this purpose, a database representing all the kinds of behaviors is necessary. Usually, to represent the behavior with good quality, the necessary length of the database depends on the behaviors to be considered.
When the database is continuous with a small time-step (1 h), as in this study, several behaviors at different time scales take place; thus the database must be sufficiently long to allow the model catching the underlying phenomena: 15 years should be necessary  (Artigue et al. , 2012; Coutouis et al. , 2016). In this study only 4. 5 years in two different periods of time were available. It is thus important to notice that the database used in this work is not sufficient for ANN approaches. Results shown hereafter can thus be considered as a proof of feasibility. The various approaches (models) used in this study are summarized hereafter. As these techniques are diverse and not commonly used in karst modelling further details are given as supplementary material. All neural network models designed in this work used hourly precipitation and temperature, daily evapotranspiration data resampled at hourly time step, and discharge data at an hourly time step.
Recurrent deep multilayer perceptron (ANN/rec_MLP) The chosen multilayer perceptron is a recurrent neural network (RNN) with one hidden layer of n h hidden neurons and one output neuron. The hidden layer neurons are nonlinear and apply a sigmoid function. The output neuron is linear; its output is equal to the weighted sum of its inputs. This specific architecture displays the two properties of universal approximation and parsimony, the latter of which is due to the nonlinearity relative to model inputs and parameters  (Barron, 1993). The Universal approximation  (Hornik et al. , 1989)  means that the model is able to identify any differentiable function, provided the existence of a database representing the behavior to identify. For further details on this architecture, the interested reader is referred to  Dreyfus (2005).
The karst spring discharge is simulated by using a drain. The parameters needed by the model are described in Fig. 2. MODFLOW-CFPv2, in short CFPv2 (Fig. 3 ), is a flow and transport discrete-continuum model which combines CAVE heat and solute transport routines  (Liedl et al. , 2003)  with MODFLOW2005-CFP Mode1 flow code  (Shoemaker et al. , 2008) , considering some additional improvements  (Reimann et al. , 2018). In CFPv2 the aquifer encloses a network of conduits with turbulent/laminar flow embedded within a matrix of lower permeability (porous medium). Recharge can be split between diffuse recharge through the low permeability porous medium, infiltration through the epikarst reservoir (e. g. ,  Chang et al. , 2019) , and direct infiltration into conduits and their associated fast-response  reservoirs, namely CADS (see  Kavousi et al.