dataesr/scientific-paragraphs-categorization huggingface dataset:
RSAT source code on GitHub and Docker container The RSAT source code, under AGPL-3. 0 open-source license, has been transferred to GitHub, to stimulate community-wise participation in its development: https: //github. com/rsa-tools. Additional RSAT documentation is available there as well. A Docker container has been built to analyze the promoters of coexpressed genes in plants (  27 ): https://github. com/eead-csic-compbio/ coexpression motif discovery.
CONCLUSIONS Compared to alternative programs, RSAT is unique for its wide range of functionalities, extensive motifs collections and >10 000 supported organisms from all kingdoms. The main alternatives are the MEME suite  (31) , which mainly focuses on motif analyses, and HOMER  (32) , which primarily focuses on motif discovery. Deep-learning methods are more focused in discovering context-specific TFBS, whereas RSAT aims at providing a complete environment for motif analysis. We aim for RSAT to be usable in combination with other programs (including MEME and HOMER); RSAT thus offers several file format conversion utility tools (convert-matrix, convert-backgroundmodels, convert-features,. ). After 20 years of existence, RSAT remains one of the most used tools in regulatory genomics. Looking forward, we aim at (i) continuing to enhance the suite in particular to cope with the challenges posed by single cell technologies in terms of data analysis efficiency, and (ii) continuing to ensure long-term maintenance, with packaging in conda, a non-plant docker container and continuous integration on GitHub.
ACKNOWLEDGEMENTS RSAT is managed by an international team ( France, Mexico, Spain, Norway ). We are particularly thankful to the colleagues who help us installing and maintaining RSAT servers:  Victor del Moral Chavez ,  Alfredo José Hernández Alvarez  ( Centro de Ciencias Genomicas, Cuernavaca, Mexico ),  Luis Alberto Aguilar Bautista  and  Jair Garcia Sotelo  ( Laboratorio Nacional de Visualizaci ón Científica Avanzada, Mexico ),  Aurora Martín  ( Estaci ón Experimental de Aula dei, Zaragoza, Spain ), along with the  ABIMS platform in Roscoff, France  and the mutualized task force of the  Institut Francais de Bioinformatique. We also thank  Olivier Sand  and  Matthieu Defrance  for regularly answering RSAT-related questions. We thank  Ieva Rauluseviciute  for finding and reporting bugs in RSAT programs.
FUNDING Institut Universitaire de France  (to M. T. C. , N. T. T. N. );  ANR  [ ANR-14-CE11-0006-01  to M. T. C. , N. T. T. N. ];  ITMO Cancer  [ 20CM114-00  to D. T. , W. S. G. ];  CONA-CYT [11311  to A. M. R. , M. P. G. ];  Universidad Nacional Aut ónoma de México PAPIIT (UNAM)  [IA203021];  Spanish State Research Agency, EUROPEAN REGIONAL DEVELOPMENT FUND (FEDER)  [ AGL2017-83358-R  to N. K. ;  AEI/FEDER , UE];  Gobierno de Arag ón  [ A08 17R ,  A09 20R , Phd Contract to N. K. ];  PRIMA  [ PCI2019-103526  to B. C. M. ;  Programaci ón Conjunta Internacional, Programa Estatal de l+D+i Orientada a los Retos de la Sociedad]; Erasmus+  (to N. K. );  Norwegian Research Council  [ 288404  to J. A. C. M. ];  Institut Francais de Bioinformatique (IFB)  [ ANR-11-INBS-0013 ];  SEP-CONACYT-ECOS-ANUIES [291235  to A.
DATA AVAILABILITY RSAT public servers are accessible from the RSAT portal at http://www. rsat. eu/. RSAT Web servers can be freely accessed by all users without login requirement. For bioinformatician users, RSAT is accessible (i) as a command-line suite for installation on a local server or on a computer cloud, from its source code https://github. com/rsa-tools, or (ii) via the REST API web programmatic access. RSAT is part of the Service.
ParamRepro stored all parameters of the normal distribution used to introduce individual variability on the clutch size (CS),  ParamRepro also stored the value of the hatching success (HS), which was set constant at 75% according to our lab observations (reanalyzed data of the results presented in  Pan et al. , 2016). Two additional non-located agents were used to represent the environment and the experimenter. The environment agent (Envi) refers to the overall environment, including temperature (fixed at 28°C) and salinity. The experimenter agent (Scientist) recorded the age of the female at the time of death and the number of eggs and nauplii produced throughout the simulation.
Statistical Analysis To confirm the proper implementation of the model, we compared the results obtained by et al. (  2016 ) with the model outputs for the same conditions. The Kolmogorov-Smirnov test confirmed the heteroscedasticity of the data. We, therefore, used the Wilcoxon rank-sum test (aka Wilcoxon Ttest, WT) to compare the model results to the experimental results for each tested condition. All statistical tests were conducted at the 95% confidence level. These analyses were performed using the Matlab software (Mathworks Inc. , Version, 7. 5).
ACKNOWLEDGMENTS This paper is a contribution to the  International Associated Laboratory between Universitéde Lille  and the  National Taiwan Ocean University  (IAL MULTIFAQUA). The Mobidyc platform (version  V2. 22 ), including codes and tutorials, is freely available in French and English for non-commercial use at https://mobidyc. cnrs. fr/index. php?title=English_summary. We express our gratitude to  Dr. Guillaume Drillet  for his comments on a previous version of the manuscript. This study is dedicated to the memory of  V. Ginot  for his exceptional work in creating and promoting the  Mobidyc  project.
FUNDING This work was supported by the research grant from the  Faculty of Science of Shizuoka University  to GD. This work is a contribution to the research projects supported by  Taiwanese Ministry of Science and Technology; Council of Agriculture , Executive Yuan; and  Ministry of Education  ( 109-2636-M-019-001  and  110-2636-M-019-001 ;  110AS-1. 3. 2-ST-aO  and  111AS-1. 3. 2-ST-aU ; Higher Education Sprout Project  to the  CEO, NTOU)  to  Y-JP.
Cosmological experiments often employ Bayesian workflows to derive constraints on cosmological and astrophysical parameters from their data. It has been shown that these constraints can be combined across different probes, such as Planck and the Dark Energy Survey, and that this can be a valuable exercise to improve our understanding of the universe and quantify tension between multiple experiments. However, these experiments are typically plagued by differing systematics, instrumental effects, and contaminating signals, which we collectively refer to as 'nuisance' components, which have to be modelled alongside target signals of interest. This leads to high dimensional parameter spaces, especially when combining data sets, with 20 dimensions of which only ∼5 correspond to key physical quantities.
We present a means by which to combine constraints from different data sets in a computationally efficient manner by generating rapid, reusable, and reliable marginal probability density estimators, giving us access to nuisance-free likelihoods. This is possible through the unique combination of nested sampling, which gives us access to Bayesian evidence, and the marginal Bayesian statistics code MARGARINE. Our method is lossless in the signal parameters, resulting in the same posterior distributions as would be found from a full nested sampling run over all nuisance parameters, and typically quicker than evaluating full likelihoods. We demonstrate our approach by applying it to the combination of posteriors from the Dark Energy Survey and Planck.
The problem is compounded when combining different data sets with different models for common nuisance components and different systematics or instrumental effects that have to be modelled. In this work, we demonstrate that we can use density estimators, such as kernel density estimators  [6, 7]  and masked autoregressive flows  [8] , to rapidly calculate reliable and reusable representations of marginal probability densities and marginal Bayesian summary statistics for key signal or cosmological parameters. This gives us access to the nuisance-free likelihood functions and allows us to combine parameter constraints from different data sets in a computationally efficient manner given marginal posterior samples from the different experiments. We use the publicly available code (https://github.
com/htjb/margarine (accessed on 26 October 2022)) MARGARINE  [9]  to generate density estimators. In Section 2, we mathematically demonstrate that the application of MARGARINE to the problem of combining the marginal posteriors from two data sets is equivalent to running a full nested sampling run including all 'nuisance' parameters. We define in this section the nuisance-free likelihood. Section 3 briefly discusses the methodology behind MARGARINE with reference to a previously published work  [9]. Finally, we show the results of combining samples from DES and Planck in Section 4 and conclude in Section 5.
Notation Given a likelihood L(Θ) ≡ P(D|Θ, M) representing the probability of data D given some model M with parameters Θ, Bayesian inference proceeds by defining a prior π(Θ) ≡ P(Θ|M), and then through Bayes theorem computing a posterior distribution P (Θ) ≡ P(Θ|D, M) for the purposes of parameter estimation and an evidence Z ≡ P(D|M) in order to perform model comparison. In our notation, we suppress model dependence, but where we wish to refer to the likelihoods derived from different datasets, we denote this with a subscript so for example L A (Θ) ≡ P(D A |Θ, M), and L B (Θ) ≡ P(D B |Θ, M). In our setting, the parameter vector is split into two sub-vectors Θ = (θ, α), where θ are parameters of scientific interest, and α are nuisance parameters, including for the purposes of data analysis.
The nuisance-free likelihood Equation (  4 ) is straightforward to compute in our framework since we (uniquely) have NS-computed evidence Z combined with MARGARINE trained distributions P (θ) and π(θ)  [9]  (see  Section 3). We now explain why Equation (  4 ) is a useful definition Theorem 1. Let L A (θ, α A ) and L B (θ, α B ) be two likelihoods with distinct datasets, each with their own nuisance parameters. The nuisance-free likelihoods L A (θ), L B (θ) form a lossless compression in θ. This means that we can recover the same (marginal) inference in combination that we would have made when performing a combined analysis with all nuisance parameters: L A (θ, α A )L B (θ, α B )π AB (θ, α A , α B ) = P AB (θ, α A , α B )Z AB , (5) ⇒ L A (θ)L B (θ)π(θ) = P AB (θ)Z AB , (6) if their respective priors π A (θ, α A ) and π B (θ, α B ) satisfy the marginal consistency relations: π(θ) = π A (θ, α A )dα A = π B (θ, α B )dα B , (7) π AB (θ, α A , α B )dα A = π B (θ, α B ), π AB (θ, α A , α B )dα B = π A (θ, α A ).
Discussion Equation (  5 ) represents Bayes theorem for the combined likelihood of both datasets L AB (θ, α A , α B ) = L A (θ, α A )L B (θ, α B ), using the combined prior π AB (θ, α A , α B ). We assume the combined prior is marginally consistent, Equations (  7 ) and  (8) , which is reasonable, merely demanding that the priors are identical in the parameter spaces where they overlap. In practice, this would usually be achieved by assuming separability between signal and nuisance parameter spaces π(θ, α) = π(θ)π(α), but Equations (  7 ) and (  8 ) are a slightly less restrictive requirement and therefore more general. The upshot of this is that if you have performed inference for two datasets separately, such that you are able to compute the nuisance-free likelihoods with MARGARINE, you may discard the nuisance parameters for the next set of analyses when you combine the datasets.
Methods MARGARINE was first introduced in  [9]  and uses density estimation to approximate probability distributions such as P (θ) and π(θ) given sets of representative samples. The code was developed initially to calculate marginal Kullback-Leibler (KL) divergences  [10]  and Bayesian model dimensionalities (BMD)  [11]  however as discussed in Section 2 it can be used to calculate the nuisance-free likelihoods. This in turn means that we can use MARGARINE alongside an implementation of the nested sampling algorithm to sample the product L A (θ)L B (θ). In this manner, MARGARINE allows us to combine constraints on common parameters across different data sets. We refer the reader to  [9]  for a complete discussion of how MARGARINE works; however, we discuss briefly the density estimation here.
We note that the re-weighting described above relies on the calculations of the two prior log probabilities for which we use MARGARINE, and currently do not have an estimate of the error for. As a result, the error in the combined evidence, Z B , is given by the error in Z A from the nested samples and is likely underestimated. Using MARGARINE  [9]  we can also derive the combined KL divergence, also reported in Figure  3 , which we find is consistent with the result in the literature of D = 6. 17 ± 0. 36  [11]. Similarly, we derive marginal KL divergences for the DES and Planck cosmological parameters using MARGARINE. A full discussion of the implications of combining the two data sets for our understanding of cosmology can be found in the literature (e. g. ,  [11, 14] ). By reducing the number of parameters that need to be sampled, we significantly reduce the nested sampling runtime.
Conclusions In the paper, we demonstrated the consistency between combining constraints from different experiments in a marginal framework using density estimators and the code MARGARINE with a full nested sampling run over all parameters, including those describing 'nuisance' components of the data. We have shown this consistency mathematically and with a cosmological example. For the combination of Planck and DES, we found Bayesian evidence and KL divergence that were consistent with previous results  [11, 14]. The analysis in this paper is only possible because (a) we were able to estimate densities in the (much smaller) cosmological parameter space θ using MARGARINE, and (b) because we have evidence, Z, from our original nested sampling runs. It is this unique combination that allowed us to compress away or discard nuisance parameters once they have been used.
We note also that working in the marginal space results in a compression that is lossless in information on θ as it recovers an identical marginal posterior and total evidence as is found during a full Bayesian inference. Finally, through the nuisance-free likelihood we can significantly reduce the dimensionality of our problems and since it is faster to emulate a likelihood rather than analytically evaluate, MARGARINE offers a much more computationally efficient path to combined Bayesian analysis. In principle, our work paves the way for the development of a publicly available library of cosmological density estimators modelled with MARGARINE that can be combined with new data sets using the proposed method in a more efficient manner than currently implemented techniques. However, the work has implications outside of cosmology in any field where multiple experiments probe different aspects of the same physics.
L'Alpe d'Huez is a ski resort in the French Alps, and it is also famous among cyclists for the number of bends in the road to the ski resort. It is a good location to evaluate the capabilities of map generalisation tools, as the surroundings contain urban, rural and mountainous areas, and it was chosen 15 years ago as one of the four datasets to benchmark map generalisation software  (Stoter et al. , 2009). The EuroSDR benchmark used data from IGN France, the French National Mapping Agency (NMA). At that time, open science policies were not popular in NMAs, but now they release their dataset with open licenses, so it is a good opportunity to create an open benchmark for topographic map generalisation. Although NMA have been using automated generalisation for some years now to produce their topographic maps  (Duchêne et al.
, 2014) , there are still many issues of map generalisation that automated solutions cannot handle. And usually, NMAs decide to lower the cartographic quality of the map, until better generalisation techniques are proposed. This is why we think it is still useful to propose a benchmark for NMA-oriented topographic map generalisation, to foster the development of new techniques. The dataset proposed for the benchmark is not the exact same one used in the EuroSDR project  (Stoter et al. , 2009) , as a few layers have been trimmed (Figure  1 ). It contains the usual important layers for map generalisation (buildings, roads, rivers), basic topographic layers (contour lines, forests, lakes), and layers that might require specific procedures (ski lifts, tower points, embankment lines). The goal of this benchmark is to generalise the dataset for a 1:50k scale map.
The symbols for the target 1:50k scale map are described in a table file, using the same template as the EuroSDR project. The projection used is "NTF Lambert II Etendu", EPSG 9801. The target scale and the style description are not enough to describe what the dataset should look like at the 1:50k scale, and we propose to use generalisation constraints  (Beard, 1991)  to describe what the target map should look like. The constraints are described in a table file that follows the structure of the constraints proposed during the EuroSDR benchmark  (Stoter et al. , 2009). Table  1  shows a small extract of these constraints, with two constraints on the area of buildings: these two constraints make sure buildings are large enough at the target scale (IGN-1-4), and that buildings that are already large do not change too much (IGN-1-5).
IGN-1-4 EuroSDR In order to use this dataset as a benchmark for topographic map generalisation, there should be a way to evaluate how much the generalised map satisfies all the constraints provided in the benchmark. For each constraint, we have to compute the satisfaction for all the constrained objects in the map. According to the literature on constraints-based evaluation  (Bard, 2004) , we need (1) a function that measures the current value of the constrained property (e. g. the area in the constraint IGN-1-4 from Table  1 ), and (2) a function that derives a satisfaction value from the current value and a goal for the constraint (area > 0. 2 map mm² is the goal for constraint IGN-1-4 in Table  1 ). Satisfaction values are modelled with a qualitative scale of 8 values from "Unacceptable" to "Perfect"  (Touya, 2012).
A code to compute these constraint monitors, i. e. the entities that measure the current value and the satisfaction of a constraint on one map object  (Touya, 2012) , is available in the open CartAGen platform  (Touya et al. , 2019). However, to make the evaluation easier, we plan to release a QGIS plugin that computes the constraint satisfactions automatically, given the shapefiles of the generalised data. Knowing that a given constraint on a given road object is better satisfied with a generalisation process A compared to another process B, does not guarantee that process A is globally better than process B, as we have to summarize all the constraints satisfactions. Even if the satisfaction mean can be a good proxy of the global generalisation quality, we propose to use aggregation techniques inspired from social welfare  (Touya, 2012)  as they capture with more subtlety the differences between two distributions of constraint satisfactions.
Importance values are provided in the constraints table, as some constraints are more important to fully satisfy than others, and these importance values are used to weight the aggregation of the constraints. There are lots of constraints and layers in this dataset, some very common, and some more anecdotal (e. g. the preservation of patterns of groups of lakes). Generalisation processes/software able to deal with all of these constraints should be quite rare, but it should not prevent researchers and developers to use this dataset to benchmark their generalisation process/software, on a subset of the constraints. For instance, a generalisation process that only generalises buildings could use this dataset and only consider the constraints on buildings to evaluate the results. Maybe different sub-benchmarks could be derived from this dataset, to foster the use of the dataset to benchmark these generalisation processes limited to a few layers of the map.
g. ,  Ginski et al. 2021; Pineda et al. 2020; Alves et al. 2019). This is where the MPG -IRAM observing program "PROtostars & DIsks: Global Evolution" (PRODIGE, CO-PIs: P. Caselli, Th. Henning) comes in: this program is designed as a coherent study of the physical and chemical properties of young protostellar systems, targeting 32 Class 0/I sources and 8 Class II T Tauri protoplanetary disks. One of its goals is to search for material flowing into Class 0 and I sources and investigate the mass budget during these phases. PRODIGE observations are done with the IRAM NOrthern Extended Millimetre Array (NOEMA), located at the Plateau de Bure in the French Alps. This program takes advantage of the PolyFix correlator to make an unbiased survey of molecular lines, thus allowing for the search of streamers in multiple chemical tracers.
NOEMA observations The observations are obtained with NOEMA and are part of the MPG-IRAM Observing Program PRODIGE (Project ID L19MB). In this program, we use the Band 3 receiver and the new PolyFix correlator, tuned with a local-oscillator (LO) frequency of 226. 5 GHz. PolyFix provides a full 16 GHz of bandwidth at coarse spectral resolution (2 MHz channel width) and is divided into 4 units (LSB Outer, LSB Inner, USB Inner and USB Outer). Simultaneously, we place 39 high spectral resolu-  Notes. ( *) This is a characteristic radius at which there is a significant drop in the dust flux exponential profile, a proxy for the radius. Observations of Per-emb-50 were conducted in two separate periods for each antenna configuration. The C configuration data were observed during 2019 December 29th and 2021 January 5th.
The D configuration observations were taken in 2020 August 6th and September 7th and 8th. The maximum recoverable scale (MRS) for our data is 16. 9 at 220 GHz, approximately 5000 au at the distance of Per-emb-50. We calibrate the data using the standard observatory pipeline in the GILDAS (Grenoble Image and Line Data Analysis Software) package CLIC (Continuum and Line Interferometer Calibration). We use 3C84 and 3C454. 3 as bandpass calibrators. Phase and amplitude calibration sources are 0333+321 and 0322+222, and observations for these calibrators were taken every 20 min. LKHA101 and MWC349 are used as flux calibrators. The uncertainty in flux density is 10%. The continuum is bright enough to allow for self calibration. Only for the continuum image used in this work, self calibration is performed iteratively with solution intervals of 300 s, 135 s, and 45 s.
Protostellar mass and velocity The integrated intensity image of C 18 O (2 -1) is shown in Fig. 3  Left. We refer to C 18 O (2 -1) as C 18 O from now on, unless oth-erwise stated. The C 18 O observations show the most extended emission of our NOEMA observations and have a similar velocity range as SO, between -1 and 14 km s -1. This molecule's emission closest to the protostar allows us to determine the protostar's velocity and mass. We produce the position-velocity (PV) diagram for C 18 O along the major axis of the disk in Per-emb-50 found by  Segura-Cox et al. (2016)  (170 • counter-clockwise from North, see Fig. 3 ). We use the astropy package pvextractor  (Ginsburg et al. 2016 ) to obtain the PV diagram along a path centered on the protostar spanning a total length of 2400 au. We averaged all emission along the path that is within 1 width.