dataesr/scientific-paragraphs-categorization huggingface dataset:
For this purpose, one needs to define a dissimilarity measure in the graph with the support of the ANR "Investissements d'avenir" program ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and grant ESIGMA ANR-17-CE23-0010. space, in order to constitute the minimal requirement to implement simple machine learning algorithms like the k-nearest neighbors. The most used dissimilarity measure between graphs is the graph edit distance (GED)  [10, 26]. The GED of two graphs G 1 and G 2 can be seen as the minimal amount of distortion required to transform G 1 into G 2. This distortion is encoded by a set of edit operations whose sequence constitutes an edit path. These edit operations include nodes and edges substitutions, removals, and insertions. Depending on the context, each edit operation e included in an edit path γ is associated with a non-negative cost c(e).
Thus, the cost edit function may be different depending on the data encoded by the graph and the one to predict. Generally, they are fixed a priori by an expert of the domain, and are provided with the datasets. However, these predefined costs are not optimal for any prediction tasks, in the same spirit as the no free lunch theorems for machine learning and statistical inference. In addition, these costs may have a great influence on both the prediction performance and the computational time required to compute the graph edit distance. In  [9] , the authors show that a particular set of edit costs may reduce the problem of computing graph edit distance to well-known problems in graphs like (sub)graph isomorphism or finding the maximum common subgraph of a pair of graphs. This point shows again the importance of the underlying cost function when computing a graph edit distance.
Related Works As stated in the introduction, the choice of edit costs has a major impact on the computation of graph edit distance, and thus on the performance associated with the prediction task. The first approach to design these costs is to set them manually, based on the knowledge on a given dataset/task (when such knowledge is available). This strategy leads, for instance, to the classical edit cost functions associated with the IAM dataset  [24]. However, it is interesting to challenge these predefined settings and experiment how they can improve the prediction performance. In order to fit a particular targeted property to predict, tuning the edit costs and thus the GED can be seen as a subproblem of metric learning. Metric learning consists in learning a dissimilarity (or similarity) measure given a training set composed of data instances and associated targeted properties.
In  [25] , the authors propose to learn edit costs using a stochastic approach. This method shows a performance improvement, hence demonstrating the interest to tune edit costs; it is however restricted to strings. Another strategy is based on a probabilistic approach  [19] [20] [21]. By providing a probabilistic formulation for the common edition of two graphs, an Expectation-Maximization algorithm is used to derive weights applied to each edit operation. The tuning is then evaluated in an unsupervised manner. In  [20] , the strategy consists in modifying the label space associated with nodes and edges such that edit operations occurring more often will be associated to lower edit costs. Conversely, higher values will be associated with edit operations occurring less often. The learning process was validated on two datasets.
However, this approach is computationally too expensive when dealing with general graphs  [4]. In  [4] , the authors propose an interesting way to evaluate whether a distance is a "good" one. This criterion is based on the following concept: This principle is then derived to define an objective function to optimize. The matrix encoding the edit costs minimizing this objective function is then used to compute edit distances. However, this approach has only been adapted to strings and trees, but not to general graphs. a similarity function is ( , γ, τ ) -good if a 1 - Another set of methods that address the problem of learning edit costs for GED is proposed in  [13, 14]. These methods propose to optimize edit costs to maximize a ground truth mapping between nodes of graphs. This framework requires thus a ground truth mapping, which is not available on many datasets like chemoinformatics.
Problem formulation In this section, we propose an optimization procedure to learn edit costs in the context of regression tasks. Consider a dataset G of N graphs such that each graph G k = (V k , E k ), for k = 1, 2,. N , where V k represents the set of nodes of G k labeled by a function f v : V → L v , and E k encodes the set of edges of G k , namely e ij = (v i , v j ) ∈ E k iff an edge connects nodes v i and v j in G k. The graph edit distance between two graphs is defined as the minimal cost associated to an optimal edit path. Given two graphs G 1 and G 2 , an edit path between them is defined as a sequence of edit operations transforming G 1 into G 2. An edit operation e can correspond to a node substitution e = (v i → v j ), deletion e = (v i → ε) or insertion e = (ε → v j ). Similarly, for edges, we have (e ij → e ab ), (e ij → ε), and (ε → e ab ).
Experiments We conducted experiments 6  on two well-known datasets in chemoinformatics, both composed of molecules and their boiling points. The first dataset is composed of 150 alkanes  [11]. An alkane is an acyclic molecule solely composed of carbons and hydrogens. A common representation of such data consists in implicitly encoding hydrogen atoms using the valency of carbon atoms. Such an encoding scheme allows to represent alkanes as acyclic unlabeled graphs. The second dataset is composed of 185 acyclic molecules  [12]. In contrast with the previous dataset, these molecules contain several hetero atoms and are thus represented as acyclic labeled graphs. To evaluate the predictive power of different settings of edit costs, we used a k-nearest-neighbors regression  [2]  model, where k is the number of the neighbors considered to predict a property.
The performances are estimated on ten different random splits. For each split, a test set representing 10% of the graphs in the dataset is randomly selected and used to measure the performance of the prediction. The remaining 90% are used to optimize the edit costs and the value of k, where k is optimized through a 5-fold cross-validation (CV) procedure over the The proposed optimization procedure is compared to two other edit costs settings: a random set of edit costs and a predefined cost setting as given in  [1] ; the latter is the so-called expert costs. Tables in Fig. 1  show the average root mean squared errors (RMSE) obtained for each cost settings over the 10 splits, estimated on the training set and on the test set. The ± sign gives the 95% confidence interval computed over the 10 repetitions.
Figures show a different representation of the same results with error bars modeling the 95% confidence interval. As expected, a clear and significant gain in accuracy is obtained when using fitted costs on the two datasets. These promising results confirm the hypothesis that ad-hoc edit costs may help the graph edit distance catch better targeted properties that are associated to a graph, and thus improve the prediction accuracy while still operating in the graph space. The fitted values of edit costs are summarized in Table  1. From these results, we can observe that insertion and deletion costs are almost similar, hence showing the symmetry of these operations. Also, one can observe that deletion and insertion costs are more important than substitution costs, which shows that the number of atoms is more important than the atom itself.
Conclusion and future work In this paper, we introduced a new principle to define optimal graph edit costs of a GED for a given regression task. Based on this principle, we defined the optimization problem of fitting the edit costs to a particular metric, measured for instance on a targeted property to predict. An alternated optimization strategy was proposed to solve this optimization problem. The conducted experiments on two well-known datasets showed that the optimization process leads to a GED with a better predictive power compared to other methods. All these observations confirm that the proposed method helps to fit edit costs and outperforms other methods. There are still several challenges to address in future work. First, a clear and complete comparison to other methods cited in the introduction and related works will be established.
Conclusion Interest in metabolic surgery for diabetes mellitus is growing from year to year. Particularly, when searching for the key expression of "ileal transposition," more than 300 publications are found in the Pubmed (US National Library of Medicine National Institutes of Health) database. The growth trend of publications has an upward exponential dependence. Modern bariatric procedures, such as RYGB, BPD/DS and transit bipartition, which involve the mechanism of accelerated intake of undigested nutrients to the terminal segments of the small intestine, are the most effective in the correction of morbid obesity and DM. Changes in the anatomy of the gastrointestinal tract during bariatric surgery mediate changes in hormonal regulation that contribute to the remission of diabetes, which requires further study by experimental operations.
3 The diverse range of projects presented in this issue reveal a range of 'link-making' figures, moments and places. First, the figures of mediators who, with one foot in research and the other in a different sphere, are able to move between the two and translate questions and knowledge. The PhD student involved in developing the SERAC database of mountain accident and incident reports, in training to become both a researcher and a high mountain guide (Vanpoulle et al. ), is a typical example of such a figure. Link-making moments and places, such as alpine refuges, also bring researchers and other actors together; the Lautaret Alpine Garden emerges from the issue as a particular 'hot spot'  (Parker & Hackett, 2012)  for links between research and society  (Clivaz et al. ; Vialette et al. ; Ronsin).
LIGAND DATABASES: COARSE-GRAINING THE LIGANDS The very first step to develop a Martini drug design pipeline is to create curated and validated databases containing hundreds to thousands of small-molecule models. This CG database needs to include molecular moieties usually found in drugs, such as halogens, heterocycles, and sulfamides. Alternatively, the databases of low-molecular-weight molecules (∼150 Da) can also be created for fragment-based drug discovery campaigns  (Rognan, 2012). Parameters for molecules/fragments of pharmaceutical interest need to be validated by comparison between CG, AA and, if available, experimental data for a subset of relevant target systems. Once validated, all the models will be made available via the open-access Martini Database (MAD) web server 1. The initial CG databases are also the foundation to develop and calibrate automatic tools to generate parameters for new CG models.
Such automatic tools should perform AA to CG mapping [as performed by auto-martini  (Bereau and Kremer, 2015) ], bead assignment (i. e. , the choice of the CG interaction parameters), and determination of the bonded parameters [as PyCGTOOL  (Graham et al. , 2017)  or Swarm-CG (Empereur-mot et al. , 2020)], allowing further coverage of chemical space. The creation of accurate databases and integration of automatic tools is currently one of the main bottlenecks hampering high-throughput screening with Martini.
For instance, lipid membrane composition may affect kinetic rates and (un)binding constants in GPCRs  (Vauquelin, 2010; Sykes et al. , 2014 Sykes et al. , , 2019;; Yuan et al. , 2018)  by altering ligand partitioning to the membrane where the target protein is located. Atomistic MD simulations of such complex systems are computationally very costly, while they are already within reach with Martini  (Marrink et al. , 2019). Combining "standard" docking algorithms with Martini provides a computationally cheap alternative to all-atom docking. As recently demonstrated by HADDOCK  (Honorato et al. , 2019; Roel-Touris et al. , 2019) , docking with Martini can be one order of magnitude faster than atomistic docking. This would allow to routinely explore very large ligand datasets  (Lyu et al. , 2019)  or even to use massive docking with grids covering the whole the protein, or exploring multiple proteins/conformations at the same time.
FURTHER CONSIDERATIONS AND DISCUSSION We described a new vision of high-throughput drug screening based on Martini CG models. Although most of the recent efforts in new drug design approaches focused on artificial intelligence (AI), the development of new methods covering gaps in standard approaches is equally important. Machine learning and other AI approaches have great advantages when tackling problems with enough experimental data to be used as training dataset. In situations where this is not the case, physics-based approaches (such as CG molecular dynamics) can perform better. In particular, structural databases of transmembrane proteins are still limited. The same is also true for databases that include dynamic information, which can be important to elucidate hidden allosteric pockets, to properly model fit-induced ligand binding process or to determine ligand association/dissociation pathways.
FUNDING VL acknowledges the support from the  European Research Council  ( ERC Consolidator Grant "CoMMBi "), the  Swiss National Science Foundation  (Project No. 200021_163281 ), the  Italian   MIUR-PRIN 2017  ( 2017FJZZRC ), and the  Swiss National Supercomputing Centre (CSCS). SM acknowledges funding from the  ERC  through an  Advanced grant  " COMP-MICR-CROW-MEM. " LM acknowledges the  Institut National de la Santé et de la Recherche Medicale (INSERM)  and the  Agence Nationale de la Recherche (ANR)  for funding (Grant Nos. ANR-17-CE11-0003  and  ANR-20-CE13-0030-03 ).
Global conservation policy and action have largely neglected protecting and monitoring genetic diversity-one of the three main pillars of biodiversity. Genetic diversity (diversity within species) underlies species' adaptation and survival, ecosystem resilience, and societal innovation. The low priority given to genetic diversity has largely been due to knowledge gaps in key areas, including the importance of genetic diversity and the trends in genetic diversity change; the perceived high expense and low availability and the scattered nature of genetic data; and complicated concepts and information that are inaccessible to policymakers. However, numerous recent advances in knowledge, technology, databases, practice, and capacity have now set the stage for better integration of genetic diversity in policy instruments and conservation efforts.
Monitoring genetic diversity directly can help detect this vulnerability. Although DNA-based monitoring will not be globally routine for some time, numerous countries already assess and monitor genetic change directly, in an increasingly standardized fashion, and are making data available for global use  (Bruford et al. 2017 , Posledovich et al. 2021 ). Since the 1980s, geneticists have contributed more than 2 billion DNA sequences to the searchable database GenBank (www. ncbi. nlm. nih. gov/genbank/statistics), and the cost of DNA sequencing continues to decline. DNA sequencing can now often be outsourced by sending biological samples to companies who efficiently perform laboratory and bioinformatic procedures (when sending samples internationally, adherence to legislation and consideration of access and benefits is necessary).
2020). There is, of course, room for improvement, and large-scale projects (genome-scale data across many populations of dozens to hundreds of species), such as the California Conservation Genomics Project and FORGENIUS, will further demonstrate the importance of documented, standardized workflows. Metadata standards-for instance, those based on Darwin Core  (Wieczorek et al. 2012 )-applied to data repositories such as the Genomics Observatory Metadatabase (GEOME; see also supplement S2) mean that genetic data sets can be searched, aggregated, filtered, and analyzed across space and time. Also, the 2011 Joint Data Archiving Policy mandated publicly available, archived genetic data sets for numerous major journals  (Fairbairn 2011 , Whitlock 2011) , whereas numerous groups have created searchable archives with thousands of DNA data sets, sometimes with environmental or trait data (see supplement S2).
These advances can make genetic monitoring routine, in the way that large databases of species' occurrence (e. g. , the Global Biodiversity Information Facility, GBIF) help monitor and predict change in species distributions (figure  3 ). Geneticists and data scientists can and do use these resources to provide information on genetic diversity to policymakers  (Hollingsworth et al. 2020)  or identify potential partnerships nationally or internationally for generating new data. Meanwhile, the issue of access and benefits of genetic diversity data use are increasingly recognized, including in the policies of major journals  (Marden et al. 2021).
g. , feathers, scat) and that have some genomic resources (sequenced genomes or exomes or an SNP chip), allowing a faster start-up and increasing informativeness and affordability. We do not advocate particular DNA markers, because the field is changing rapidly and most of today's markers will be compatible with the future of whole genome sequencing. Species should be chosen in consultation with managers to ensure that genetic data can address their needs and to coordinate with existing species and habitat monitoring programs. Finally, these considerations apply whether a program is choosing species to monitor with genetic data or proxies, although if the program uses proxies, species choice may also consider how much data exists in national or global occurrence record databases (e. g. , GBIF, the European Forest Genetic Resources Programme, forestry inventories).
Acknowledgments We thank anonymous reviewers and the editor for helpful comments. This article is based on work from  COST Action G-BiKE CA 18134 , supported by  COST (European Cooperation in Science and Technology). We acknowledge logistical support from the  Group on Earth Observations Biodiversity Observation Network. CK was supported by grant no. 2019-67012-29733  and project accession no. 1019405  from the  USDA National Institute of Food and Agriculture. PG acknowledges the structural support of the Flemish government. LL was funded by the  Swedish Research Council  and  SRC Formas. SH and MPG were supported by the  Institute of Museum and Library Services  (grants no. This article does not represent the official finding or policy of any government or institution. This article has been peer reviewed and approved for publication consistent with  USGS Fundamental Science Practices  (https://pubs.
N c estimates exist for thousands of populations in the LPI, and RL assessments. Also, agencies, NGOs, and museums often hold data on N c estimates. Scientific literature also has numerous N c and N e estimates. N e estimates will increase rapidly as genomic data becomes widely affordable and accessible. Support many large populations (N e > 500) and baseline connectivity among populations. Action is taken to increase size or connectivity among populations above 500. With small N e , rapid genetic erosion occurs and remaining diversity is at risk. Stability by 2030, increasing by 2050. populations within species or proportion of populations remaining is available in existing databases (LPI, GBIF, museums, RL assessments, stock assessments and government reports). Protect all populations that remain now.
It is achievable. Restore populations to baseline when possible. Populations often contain adaptations to local environments. Maintaining populations maintains genetic diversity. Aim for no loss by 2030, restoration by 2050. publications is available on scholarly databases or Google Scholar. Information on genetic data sets is available on data portals (GenBank, GEOME, Dryad; see Supplemental S2). Genetic experts can offer guidance on suitable data availability. Invest in research and knowledge sharing. The number of populations investigated genetically is rapidly increasing. Knowledge of genetic diversity informs conservation action, and supports indicators 1 and 2. Capacity in place by 2030. Downloaded from https://academic. oup. com/bioscience/advance-article/doi/10. 1093/biosci/biab054/6278470 by guest on 27 May 2021 Abbreviations: N e , effective population size; LPI, Living Planet Index; RL, Red List.
Acknowledgments. The authors would like to thank  Lucas Chesnel  and  Mahran Rihani  for fruitful discussions on T-coercivity technics and for suggesting the idea used in (4. 6) to cope with the fact that affine functions are not preserved by the extension operator. This paper was completed during the visit of the third author at  Institut Elie Cartan de Lorraine , whose warm hospitality is gratefully acknowledged.
Acknowledgements. This paper is a contribution of the  ERC Advanced Research Grant RHEOLITH  (grant agreement no. 290864 ) of the  Institut Universitaire de France  and  Labex VOLTAIRE. The paper benefited from the positive suggestions of  Antonio Castro  and  Andrea Brogi , who reviewed the first draft. Financial support. This paper is a contribution of the  ERC Advanced Research Grant RHEOLITH  (grant agreement no. 290864 ) of the  Institut Universitaire de France  and  Labex VOLTAIRE. Review statement. This paper was edited by  Mark Allen  and reviewed by  Antonio Castro  and  Andrea Brogi.
This study aims to deliver a better understanding of photoproduction of the hyperon resonance, Λ(1520). The framework of this paper is the following. Section II presents a brief summary of previous experimental and theoretical studies on the photoproduction of the Λ(1520) hyperon. Section III introduces the experimental set up that provided the data for this study. Section IV outlines the details of the event selection, simulation, and yield extraction procedures for cross sections in Sections IV A, IV B, and IV C. The measured cross sections are displayed in Section V. Section VI gives an account of the systematic uncertainties for this study. The comparison of the results with the theoretical predictions are discussed and our conclusions are provided in Section VII.
III. EXPERIMENTAL SETUP A. The CLAS Detector The data set used in this analysis of Λ(1520) photoproduction is taken from the g12 experiment performed by the CLAS Collaboration at the Thomas Jefferson National Accelerator Facility (TJNAF). Located in Newport News, Virginia, Jefferson Lab houses four experimental halls, namely, A, B, C, D, and the Continuous Electron Beam Accelerator Facility (CEBAF). The CLAS detector was installed inside the Hall B and was decommissioned in 2012. The design of the CLAS detector was based on a toroidal magnetic field that had the ability to measure charged particles with good momentum resolution, provide geometrical coverage of charged particles over a large laboratory angular range, and keep a magnetic field-free region around the target to allow the use of dynamically polarized targets  [14].
B. g12-Run The data set used in this analysis was acquired from the CLAS g12 experiment that was performed in the summer of 2008. This CLAS experiment was a highluminosity, high-energy, real-photon run. This run used an electron beam current of 60-65 nA that produced bremsstrahlung photons. The photons continued forward towards the 40 cm LH 2 (liquid hydrogen) target. The photon energy for the run was up to 5. 7 GeV. Details of the g12 experiment, the running conditions, and the formulated standard procedures for the data analysis can be found on The g12 Analysis Procedure, Statistics, and Systematics document  [15].