dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, combining one intensity and one timing feature improves performance considerably, up to 65%. This result can be understood by mapping the error done by individual features in space (Supplementary Figure  S6 ), showing that intensity and timing features are complementary, i. e. intensity features perform well in locations where timing features perform poorly. We next seek to clarify whether the results depend on space. To this end we compose five different dataset, a to e, obtained by extracting odor snapshots from horizontal planes at source height (b), above the source (c to e), and below the source (a) (Figure  5a ). From a to e, sparsity increases and intensity decreases (Figure  5b ) simply because closer to the boundary, the air slows down and the odor accumulates. By analyzing performance across these dataset, we find that ranking of individual features shifts considerably.
The two intensity features outperform all timing features when the dataset is not very sparse (dataset a-b, Figure  5c, d   ranking of individual features shifts with height, pairing one intensity and one timing feature remains the most successful strategy across all heights (Figure  5c, d ). In contrast, combining all five features contributes little improvement (Figure  5c-d  and Supplementary Figure  S7 ). Let us now focus on the plane at source height and separate locations based on their distance from the source. We assemble a distal dataset and a proximal dataset, composed of points that are further and closer than 2330η from the source respectively (Figure  6a ). The odor is more intense and more sparse closer to the source and it becomes more dilute and less sparse with distance from the source (Figure  6b ).
Materials and Methods Direct numerical simulations of turbulent odor plumes. To reproduce a realistic odor landscape and generate the dataset showed in Figure  1 , we solve the Navier-Stokes Eq. (  1 ) and the advection-diffusion equation for passive odor transport Eq. (  2 ) at all relevant scales of motion from the smallest turbulent eddies (Kolmogorov scale η) to the integral scale (L > 600η), using Direct numerical simulations (DNS): ∂tu + u • ∇u = - 1 ρ ∇P + ν∇ 2 u ∇ • u = 0 [1] ∂tθ + u • ∇θ = κ θ ∇ 2 θ + q [2] where u is the velocity field, ρ is the fluid density, P is pressure, ν is the fluid kinematic viscosity, θ is the odor concentration, κ θ is its diffusivity and q an odor source. We simulate a turbulent channel flow with a concentrated odor source and an obstacle that generates turbulence by customizing the open-source software Nek5000  (42)  developed at Argonne National Laboratory, Illinois.
Nek5000 employs a spectral element method (SEM) (43) (44) based on Legendre polynomials for discretization  (45) , and a 4th order Runge-Kutta scheme for time marching. The code is written in fortran77 and C and it uses MPI for parallelization. The three dimensional channel is divided in E = 160 000 discrete elements: 200 × 40 × 20 (number of elements in length × width × height); within each element the solution is expanded in 8th grade tensor-product polynomials so that the domain is effectively discretized in 81 920 000 elements. The average spatial resolution is equal in each direction ∆x ≈ 4η. A cylindrical cap of height = 160η is added on the ground; the cylinder spans the entire width of the channel. The mesh is adapted to fit the cylinder. Fluid flows from left to right and the obstacle generates turbulence in the channel, in particular the height of the cylinder tunes the velocity fluctuations.
Dataset. To compose the dataset for regression we first extract twodimensional snapshots of odor at fixed height from the 3D simulation. Each snapshot from the simulation has dimensions 1600 × 320 (number of points in the downwind direction × crosswind direction). The initial evolution up to 300 τη is excluded from the analysis as odor has not yet reached a stationary state. At stationary state we save 2700 frames at frequency ω = 1/τη per simulation. Thus at each spatial location we have the entire time evolution composed of 2700 time points at regular intervals of τη. We partition each simulation in fragments with M snapshots (duration M τη). Most simulations are shown for M = 100, thus for each spatial location we have 27 time series of the same duration (except for results leading to Figure  3a , where we vary memory from 10τη to 250τη resulting in 270 to 10 time series per location respectively).
The threshold c thr used for binarization is adaptive i. e. c thr = 0. 5 c|c > 0 , where the average is computed over each time series separately. The threshold thus varies from c thr = 0. 5c 0 at the source to c thr = 10 -6 c 0 at the farthest edges of the cone, where c 0 is the concentration at the source. The choice of an adaptive threshold was suggested in  (20). The precise value of the relative threshold has little effect on the results as shown in Figure  S9 , left. Fixed thresholds were tested and discarded because results depend sensibly on the threshold and the optimal threshold varies with the dataset in non-trivial ways (Figure  S9 , right). Finally, adaptive thresholds that are defined based on purely local information appears more plausible for a biological system that has no information on the intensity of the source.
ACKNOWLEDGMENTS. This work was supported by the  Air Force Office of Scientific Research  under award number  FA8655-20-1-7028 ; by the  French government , through the  UCA JEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  under reference number # ANR-15-IDEX-01 ; by the project  DynCellPol  ( ANR-19-CE13-0004 ); by  CNRS PICS  " 2FORECAST " and by the  Thomas Jefferson Fund a program  of  FACE. The authors are grateful to the OPAL infrastructure from  Université Côte d'Azur  and the  Université Côte d'Azur's Center for High-Performance Computing  for providing resources and support. N. M. and N. R. are thankful for the support of  Instituto Nazionale di Fisica Nucleare (INFN) Scientific Initiative SFT: Statistical Field Theory, Low-Dimensional Systems ,  Integrable Models  and Applications.
It also associates, to each modelled molecule and reaction, the physical and kinetic parameters needed to setup the simulation; for the study proposed in this paper, they are limited to the molecular weights (automatically retrieved from online databases, such as UniProt 22 and ChEBI 23 ) and the turnover numbers. A generalisation of the XML input and its construction, along with the actual file generated for running the simulations, is provided in the Supplementary Information. By importing the reactions of the SMBL file as the input of our agent-based simulations, we excluded all those for which the Smallbone2013 model does not provide the enzymatic concentrations. Our simulator can actually handle this kind of reactions, since we can model them in terms of their bulk effects; however, for the aim of observing the global behaviour of glycolysis as resulting from the the local molecular interactions, introducing any bulk reaction would perturb the environment and hide the absence of actual interactions among the molecules, making the ABM close to a standard kinetic model.
Each of these articles is summarized below Machine learning approaches based on large experimental datasets allow the behavior of some complex biological systems to be predicted. As we know, the rational design of RNA enzyme (ribozyme) activity is challenging, and many ribozyme-based systems are only engineered or improved by random mutagenesis and selection (in vitro evolution). Moreover, extensive pairwise and higher-order epistasis prevent straightforward prediction of the effect of multiple mutations that is needed for rational design. Beck et al. used high-throughput experimental data from variants of a self-cleaving ribozyme to train a predictive model through machine learning approaches. Using only sequence and activity data, they showed that a machine learning approach can be used for RNA design even for RNA molecules with unknown structures.
This work is very important because self-cleaving ribozymes can be used to engineer control of gene expression thanks to their capacity to alter RNA processing and stability. Beck et al. unveiled negative epistasis in the RNA data suggesting that additional information, eg. thermodynamic stability of helices, might be necessary for increasing accuracy when predicting effects of long-range distance mutations. This will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts. Machine learning also enables the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e. g. , from genomic, transcriptomic and metagenomic analyses. DiMucci et al. have developed a suite of algorithms, named BowSaw, that takes into account different combinations of variables also called "rules".
They applied this approach to study the role of the gut microbiome in Crohn's disease. They found a previously unreported combination of microbial taxa that is broadly and precisely associated with Crohn's disease samples. This is an important work whose current implementation shows that BowSaw can be applied to other datasets and used to uncover patterns associated with other diseases. This new suit of algorithms utilizes variable interactions in a trained Random Forest (RF) model in order to extract multiple candidate explanatory rules. Applying BowSaw to a study on the role of the gut microbiome in Crohn's disease, DiMucci et al. shows that it can find a previously unreported combination of microbial taxa that is broadly and precisely associated with Crohn's disease samples in the data set: they unveil higher-order interactions in complex phenotypes.
This article presents a framework called Theseus as a conceptual and operational answer that leverages the Semantic Web languages and technologies to represent GD and their evolution over time, as knowledge graphs (KGs). The main contributions of this article are as follows: a presentation of these three challenges in the context of the Semantic Web; a detailed overview of the architecture of the Theseus framework; and a case study that illustrates the different processing steps achieved by Theseus on a real dataset encompassing two versions of the administrative divisions of Switzerland. The remainder of the article is organized as follows: in Section 2, we present the underlying challenges we address. We show how existing approaches answer them, but only partially. In Section 3, the general architecture of the Theseus framework is described.
| CHALLENGES AND RELATED WORKS The general idea behind the Theseus framework is to immerse semantic descriptions of evolving GD in the LOD Web, as KGs. A KG is a network of several linked datasets and ontologies that are relevant to a specific domain and on which a reasoner can be applied to infer new facts  (Ehrlinger & Wöß, 2016). We adopt Semantic Web technologies to benefit from the syntactic and semantic interoperability between systems exchanging GD. These technologies allow for the dissemination of data in a format that suits with the open data directives, and in accordance with the FAIR guideline principles for data management: to make data FAIR. As a consequence, it becomes possible to exploit the global and distributed KGs provided by the LOD Cloud to ease the understanding of territorial evolution.
| Representing geographical divisions Official statistical institutes are increasingly publishing open and linked statistical data on the Web. For instance, using the Open Data Europe Portal, one can discover several statistical datasets published as LOD. 1 Regarding the spatial dimension of such statistics, statistical institutes use official nomenclatures, called Territorial Statistical Nomenclatures (TSNs) to compile statistics on a study territory (see an abstract representation of a TSN on Figure  1 ). A TSN provides, for a given period, a catalog of nested territorial levels, each one being composed of several TUs. For instance, for England, nested levels are regions, counties, districts, etc. Using such standardized GD, statistical institutes and other public or private organizations ensure the spatial comparability of the statistics they collect.
When it comes to the representation of these TSN in the LOD Web, as underlined in  Bernard (2019) , one can observe a proliferation of unaligned vocabularies. Indeed, most of the time, each statistical institute (whether it is national, regional, or local) creates its own ontology for describing the way the space under its jurisdiction is organized. For instance, the Territorio Ontology 2 of the Italian National Institute of Statistics describes the Italian territory, the Geography Ontology 3 of the Scottish government describes the Scottish territory, while the ontology of the Ordnance Survey of Ireland 4 (Debruyne et al. , 2017) lists the administrative areas of Ireland. All these ontologies are non-generic vocabularies, which enables a description of the targeted territory only. The most thorough initiative is probably that of the United Kingdom's Office for National Statistics (ONS), which proposes vocabularies 5 to represent geographic areas for statistical data publishing purposes.
In this approach, a certain level of abstraction is to be underlined since the terms used to describe TUs are generic enough to apply to other TUs than those belonging to the UK. However, none of these vocabularies accounts for the inherent hierarchical structure of a TSN and its levels. For instance, it is necessary to add new concepts to the Geography 6 ontology of the UK's ONS to describe how a given territory is divided on several levels into TUs and sub-TUs, forming a hierarchy. To conclude, none of these initiatives emerging from statistical institutes offers a sufficient level of abstraction to semantically describe the hierarchical structure of any TSN. Considering standard vocabularies, or vocabularies that take a more abstract approach for the description of both statistical data and their spatial dimension, the W3C RDF Data Cube (QB) ontology 7 appears as an essential vocabulary.
This W3C recommendation is widely used for describing statistics as LOD. Based on the ISO SDMX standard, QB provides systems with interoperability capabilities for exchanging and sharing statistical data and metadata between organizations  (Cyganiak & Reynolds, 2014). In particular, the QB extension for spatiotemporal components, QB4ST 8  (Atkinson, 2017)  provides canonical terms to define the spatial and temporal dimensions of data. Using this vocabulary, one can describe a territorial hierarchy using two concepts qb4st:RefArea and qb4st:subdivides. 9 After having defined reference areas using the QB4ST terms, one can describe RDF Data Cube observations whose spatial dimension points toward these reference areas. However, the QB4ST vocabulary does not include terms dedicated to the temporal coverage of the reference areas.
In a previous work  (Plumejeaud et al. , 2011) , we introduced a relational database model that provides a sufficient level of abstraction to semantically describe any TSN. While this model is taken as a relevant basis for our objective, it must be adapted, enhanced, and upgraded toward the principles of FAIR data for an immersion in the Semantic Web, as Open and Linked Data schema. In particular, this requires the creation of unique identifiers (URIs in the LOD) for each resource representing what a TU has been during its life. From a conceptual point of view, the challenge is the following: we have to represent the existence (the life) of a TU present in different versions of a TSN (called TSN versions) and likely to undergo one or several changes between two successive versions of this TSN.
Each concept in the SONADUS ontology is linked either to the SNAP or to the SPAN concept of the BFO ontology. SNAP are entities existing at some given instant in time (e. g. , a person), while SPAN are processes (e. g. , the life of a person) or events (e. g. , the birth of a person). Two other initiatives from statistical agencies also address the COSP problem and are worth mentioning. First, the United Kingdom ONS proposes a vocabulary called the ONS Boundary Change Ontology 11 to represent the evolution of geographical areas in the context of statistical data publication. This ontology adopts a certain level of abstraction that allows the description of other territories' changes than those of the UK. However, none of the concepts to be found in the ONS Boundary Change Ontology allows for naming changes that impact several TUs at the same time (e.
g. , the merge of two TUs). Concepts describing change events are few and are limited to the terms "Recoding change" and "Boundary change. " Second, the vocabularies of the Statistical Bureau of Japan  (Yamamoto et al. , 2017)  represent geographical areas and their changes, also in the context of statistical data publication. Although the description of changes in municipalities is handled, it is limited to a literal where one would have expected a richer event representations, in the form of LOD resources. Thus, changes in the structure of a TSN are rarely described, and if so, this description relies on a vocabulary adapted only to the peculiarities of the observed territory. Also, when changes are described, the descriptions are made on a TU-by-TU basis, which means without grouping changes that occur during a single event (e.
| Detecting territorial changes In the previous subsections, we have emphasized the need to describe territorial changes with a rich and sufficiently generic vocabulary. With regard to the large number of TUs potentially contained in a TSN, solutions to automate or semi-automate the detection and description of these changes are required. The two approaches from  Kauppinen et al. (2008)  and  Lopez-Pellicer et al. (2012)  presented above implement a whole software framework from data collection to their restitution. However, regarding the creation of KGs based on the proposed ontological models, the process of change detection is not automated. Indeed, to populate the ontological models, in both approaches, a handwritten dictionary listing all area changes is first established and then transformed into RDF.
This is a major drawback as it is time-consuming to manually list the changes observed in an entire nomenclature. Regarding the automatic detection of changes, conflation algorithms, which face data comparison issues but to resolve conflicts, are good candidates. Indeed, conflation algorithms try to combine several information from overlapping sources to retain accurate data, minimize redundancy, and reconcile data conflicts  (Longley et al. , 2005; Shekhar & Xiong, 2007). The main step in the conflation algorithm performs feature matching. This involves the identification of features in multiple datasets that represent the same entity in reality  (Li & Goodchild, 2011). In our case, we also need to identify, between several TSN versions, the features (TUs) that represent the same entity or not (if TUs have changed so much that their identity is not preserved).
Then, similar to conflation algorithms, we have to define a similarity measure to be used as a criterion for matching TUs through TSN versions. But, contrary to conflation algorithms, that most of the time are used to create one resulting dataset that merge data from different sources, we need to preserve all the information from each data source and to describe the changes between them. Among existing conflation algorithms in the GIS domain, the ones from  Hastings (2008)  and  McKenzie et al. (2014)  take a multi-attribute approach to perform points of interest or digital gazetteers conflation. This multi-attribute approach has never been implemented to manage TSN versions conflation. This approach is of great interest since it allows one to address the complexity of TUs geographical objects.
| Overview Theseus is a framework designed and developed to manage the entire life cycle of evolving TSNs data: from the acquisition of data to their modeling, transformation and publication as KGs on the Web. The Theseus Framework takes as input several geospatial files (Esri shapefile format 12 ), one shapefile for each version of a TSN, and automates the detection and the semantic description of changes from one version to its subsequent version(s). Theseus encapsulates two ontologies we have designed to describe TSNs (TSN ontology) and their evolution over time (TSN-Change Ontology)  (Bernard, Villanova-Oliver, et al. , 2018). Figure  3  shows the main concepts of the TSN-Change ontology that describes territorial changes. The concept Change on the top of Figure  3  has two sub-typologies of concepts: StructureChange and FeatureChange.
| Step 1: Specifying and modeling a TSN and its versions This step consists of acquiring shapefile data representing a TSN and its versions, and in transforming each of them in a preformatted TSN-shapefile format we have specified so that the framework can rely on a homogeneous representation of the versions to be processed. This TSN-shapefile format is aligned on the TSN ontology. In other words, the parsing of the TSN-shapefiles allows for and eases the identification and instantiation of the TSN ontology's concepts. To guide the user in the description of a TSN, its structures (levels), geometries, and versions, we have designed and implemented a pre-processing application, called Theseus Prep. Step by step, the user is invited to upload the initial shapefiles and to inform the mapping between the attributes table of these shapefiles and the attributes tables of the TSN-shapefile format.
Once filled, the TSN Shapefile attributes table reflects the main concepts of the TSN ontology  (nomenclature, version, territory, level, unit, etc) , the nested levels and their TUs, as well as the versions of the TSN to be considered. This Theseus Prep module has been developed in Python (using the PyShape and GeoPandas libraries). Using the Theseus Knowledge Graph Creator Graphical User Interface (GUI), the user integrates first, in a Post-GIS database, the TSN-shapefiles prepared by the Theseus Prep Module. Then, the TSN Semantic Annotator transforms data from this PostGIS database into RDF triples, using the TSN ontological concepts for the description of each feature. The TSN Semantic Annotator has been developed in Java, using the Apache Jena and Apache Jena GeoSPARQL libraries. 13 The result comes as a data graph containing the list of all versioned TUs.
It detects similarities and changes between two successive versions of a TSN, achieving a traversal level by level. Walking through the two input versions of TSN, temporary matrices that store knowledge about the TUs similarities and changes are created, before building the output RDF graph. The heart of the algorithm consists of several similarity measures implemented as distance calculations between two TSN versions to determine whether a TU keeps its identity unchanged throughout the two versions, even if it undergoes some changes (in its boundaries, name, etc. ). For instance, regarding the name of a TU, a Levenshtein distance test is applied to determine whether the string value associated with the attribute has changed and if so, to which degree. Concerning the geometry of a TU, using the PostGIS database, the algorithm computes an areal distance test, as conceptually described in Bel Adj Ali and Vauglin (1999), to determine how much the geometries of the two versions of the nomenclature intersect for each TU.