dataesr/scientific-paragraphs-categorization huggingface dataset:
Robustness against Scale Modification We report below the robustness performance regarding scaling effects. Figure  12  provides the results obtained on dataset TSS. The proposed cfCNN leads to better results when the bounding box scale reduces compared to its original size. However, SVM has a slight advantage over the cfCNN when the bounding box scale increases. It appears that the identification score is always higher for the cfCNN compared to the other architectures. Again, the cfCNN(fc) identification scores are below the cfCNN with GAP. One can also note that when the bounding box size is reduced (i. e. , with a scale factor below 1), some parts of the targets may be masked. Thus, these results may also be interpreted in terms of robustness to masking effects.
Simulated target MIWR signatures and 3-D models of the targets have been refined to provide a more realistic simulation dataset. More clearly, we took into account 2-dimensional heat transfers for each surface based on its composition. This results in smoother temperature gradients. DS3 is composed of the same simulated scenes and targets as in dataset DS2, using target MWIR signatures recalibrated using real MIWR images. Temperature measurements on real vehicles were also used to validate or rectify physical models used in dataset DS2. As such, this dataset is the most realistic one among the three simulation datasets.
In addition to the six classes represented in dataset DS1, two other truck classes have been appended (referenced as t2 and t3). Simulated target MIWR signatures and 3-D models of the targets have been refined to provide a more realistic simulation dataset. More clearly, we took into account 2-dimensional heat transfers for each surface based on its composition. This results in smoother temperature gradients. DS3 is composed of the same simulated scenes and targets as in dataset DS2, using target MWIR signatures recalibrated using real MIWR images. Temperature measurements on real vehicles were also used to validate or rectify physical models used in dataset DS2. As such, this dataset is the most realistic one among the three simulation datasets.
The morphology of marine and lacustrine terraces has been largely used to measure past sea-and lake-level positions and estimate vertical deformation in a wealth of studies focused on climate and tectonic processes. To obtain accurate morphometric assessments of terrace morphology we present TerraceM-2, an improved version of our Matlab R graphic-user interface that provides new methodologies for morphometric analyses as well as landscape evolution and fault-dislocation modeling. The new version includes novel routines to map the elevation and spatial distribution of terraces, to model their formation and evolution, and to estimate fault-slip rates from terrace deformation patterns. TerraceM-2 has significantly improves its processing speed and mapping capabilities, and includes separate functions for developing customized workflows beyond the graphic-user interface.
TerraceM has been designed to close this gap by providing open-source tools to interactively map and model the formation of marine and lacustrine wave-cut terraces using high-resolution topography and Matlab R programming. So far, TerraceM has been used for diverse purposes in different coastal settings worldwide (e. g. ,  Melnick, 2016; Benjelloun, 2017; Jara-Muñoz et al. , 2017; Melnick et al. , 2017 Melnick et al. , , 2019;; Pedoja et al. , 2018; Bilbao-Lasa et al. , 2019; de Gelder et al. , 2019; Jara-Munþoz et al. , 2019; Racano et al. , 2019). These studies include research topics such as coastal dynamics, paleo-geography, paleo-climate, neotectonics, and earthquake geology, highlighting the broad spectrum of applications and the advantages of applying integrated joint mapping and modeling techniques in the Earth sciences.
TerraceM was originally designed as a Matlab R Graphical User Interface (GUI) to assess marine terrace elevations using high-resolution topography. The new version "TerraceM-2" goes further, including new modeling tools and processing functions inside user-friendly GUIs. Furthermore, TerraceM-2 is based on a novel programmatic architecture improving the computational efficiency and processing speed (see Table  1 ). Topotoolbox functions  (Schwanghart and Scherler, 2014)  are nested in TerraceM-2 to perform basic analyses, such as swath profile extraction, calculation between topographic parameters, and DEM visualization. The TerraceM-2 architecture is based on independent functions, whose outputs are stored as data structures, enabling memory efficiency, and processing greater number of swath profiles and larger DEMs, at least eleven times faster than the previous release (see speed test in SP1 and Supplementary Figure  1SP ).
The three new TerraceM-2 modules are devoted to mapping marine and lacustrine terraces, estimating metrics of past sea-level positions, exploring their formation and degradation mechanisms, and using these metrics to estimate deformation processes. The shoreline-angle mapping tool in the previous version  (Jara-Muñoz et al. , 2016)  has been complemented with an interface to compute surface classification models, which allow mapping marine and lacustrine terraces based on the distribution of morphometric parameters extracted from the topography. Additionally, TerraceM-2 includes a new interface to model terraces formation using a Landscape Evolution Model, which simulates the effect of sea-erosion and vertical deformations on the development of wave-cut terraced topography. Furthermore, the spatial distribution of terrace elevations and surface uplift estimated from past sea-or lakelevel metrics can be compared with fault dislocation models to derive fault slip rates.
The integration of TerraceM-2 interfaces and routines, in addition to user-friendly GUIs, optimizes the time required for mapping and modeling marine and lacustrine terraces for inexperienced users in Matlab R scripting. However, the TerraceM-2 architecture comprises independent functions allowing experienced users to create customized workflows. Taken together, we envisage that the evolving technological advances and the increased accessibility of high-resolution topography, in addition to the advanced morphometric and modeling methods in TerraceM-2, will promote studies on Quaternary sea-and lake-level changes and neotectonics. In light of the impacts of global sea-level change and seismic activity at plate-margin settings, such studies are crucial, particularly regarding the adequate assessment of hazards associated with seismicity and sea-level change, and a combination thereof.
CONCEPTUAL FRAMEWORK AND NEW MODULES IN TERRACEM-2 Here we present several concepts to define the morphogenetic characteristics of marine and lacustrine terraces, which are essential to understand the functionalities of TerraceM-2. For instance, the term coastal terraces refer to marine and lacustrine wave-cut terraces, generated by the effects wave erosion. In contrast to wave-built marine or lacustrine terraces formed by the accumulation of beach deposits (e. g. ,  Dietz, 1963; Jara-Muñoz and Melnick, 2015) , these terraces are purely erosional and may be covered by a thin veneer of sediments. The term coastal-terrace sequence denotes several coastal terraces that occur at successively higher positions above a reference datum, i. e. , sea-level or lake level. The terraces form during rather high water levels and may form staircase-like sequences if the climatically controlled water-level changes are superposed to vertical crustal movements (e.
Thus, the shoreline angle represents a geodetic marker that can be used to estimate relative water-level changes and vertical deformation (e. g. ,  Lajoie, 1986; Burbank and Anderson, 2011; Jara-Muñoz et al. , 2016). TerraceM-2 uses these geomorphic relationships and provides quantitative methodologies to analyze the metrics of coastal terraces by assessing their morphological elements and by modeling the topographic arrangement of coastal terrace sequences. TerraceM-2 comprises three modules, (a) Maptools (MAP) (b) Landscape evolution model (LEM), and (c) Dislocation Modeling (DIM), designed to be used both as independent and complementary GUI's (Figure  1 ). Various ancillary tools are available for data analysis and interpretation as well, each module include an information interface that displays verbose operations to guide the user through the GUI's workflow, the World Catalog of Coastal Sequences (WCCS, Figure  1 ) comprising, wave-cut and wave-built marine terraces, coral reefs, and strandlines from the last high-stands  (MIS 5, MIS 7, and MIS 9).
TerraceM-2 is designed to run in Mac R , Linux R , and Windows R computers using Matlab R 2017 or later (see Table  1 ), minimal RAM memory required is 2GB and more than 5 GB free disk space, which are also the minimal requirements to run Matlab R. The inputs and outputs of each TerraceM-2 module are based on data structures (Supplementary Figures  2SP, 3SP ) allowing flexible organization and exchange between the different modules. TerraceM-2 inputs are: topography in raster format (geotiff or Arcgrid ascii), and polygon(s) in shapefile format that may be created in any GIS platform (for example QGIS or ArcGIS) containing rectangular boxes defining the location of swath profiles. Inputs are loaded and processed in the MAP module, and these results may be used as inputs for the LEM and DIM modules facilitating data exchange between the different modules.
TerraceM-2 LEM also includes the option to find the best fit by minimizing the area between model and observations. To perform the analyses, we developed the "LEM iteration and fit interface, " which allows an interactive selection of the modeling parameters and quick analysis of profiles (Figure  5 ). The best-fit models can be exported as graphic (. pdf) and Matlab R data (. mat) files. The LEM model and LEM fit functions can be called independently from command line to develop custom workflows (see SP2. 2 and Supplementary Figure  6SP ).
EXAMPLES Three examples are provided to illustrate the capabilities of TerraceM-2 modules and also the use of topographic data with different spatial resolution. The spatial resolution ranges from very-high (0. 5 m/px) for drone surveys in the Dead Sea, high (2 m/px) for airborne Light Detection and Ranging (LiDAR) surveys in California, and moderate  (12-m)  for radar topography such as TanDEM-X along the coast of Peru. These need for moderate, high, or very-high resolution data will depend on the spatial scale of the geomorphic features of interest and the analysis method, which will be illustrated in the following case studies.
The ages of the lacustrine shorelines at both sides of the Dead Sea range between 20 and 30 ka  (Ghazleh and Kempe, 2009; Jara-Munþoz et al. , 2019) , and record the progressive drop of the lake during the Late Pleistocene. We used the TerraceM-2 MAP module to estimate the amount of permanent deformation accumulated by the DSF, comparing the distribution of lacustrine shorelines at both flanks of the Dead Sea. Lacustrine shorelines along the Dead Sea are characterized by a smooth topographic expression that complicates the mapping of shoreline angles using topographic swath profiles. Therefore, instead of using swath profiles we studied the metrics of lacustrine shorelines using the SCM that can be adjusted to enable the distinction between slight changes of slope, differentiating shoreline paleo-platforms of different levels (Figures  9A, B ).
• User experience: The intuitive TerraceM-2 interface design includes interactive message windows to guide the user through the different modules. • Integration: TerraceM-2 modules are designed to exchange data between them allowing to easily integrate the mapping and modeling capabilities. • Efficiency: TerraceM-2 improved its processing speed at least eleven times in comparison with the previous release of TerraceM. Limitations of TerraceM-2: • Objective: TerraceM-2 is designed for mapping only marine and lacustrine terraces. However, the SCM interface may be used to map any planar landform, such as peneplains or fluvial terraces associated with both aggradation or erosion. • Inputs: TerraceM-2 is designed to work with highresolution topography. However, the resolution will • depend on the spatial scale of the targeted geomorphic features.
We present three examples that rely on data at different resolutions to illustrate the use of TerraceM-2 to analyze geomorphic features with different spatial scales. • Software platform: TerraceM-2 is open source but runs under Matlab R 2017 or later, which is a paid software. However, MathWorks R provides free Matlab R licenses to students and licenses for educational and research institutions. We favor Matlab R above other open-source programming software because of its versatility with GIS data. • Efficiency: TerraceM-2 can be used in a normal desktop or notebook computer running Windows R , Linux R , or Mac R operational systems. However, its efficiency depends of the technical characteristics of each computer (e. g. , amount of memory or processor speed). We present three examples to highlight the new features in TerraceM-2: lacustrine shoreline mapping using very-highresolution drone topography (0.
ACKNOWLEDGMENTS We thank  Peru Bilbao ,  Simone Racano ,  José-Miguel Martínez ,  Gino de Gelder , and  Sedigheh Farahi  for valuable suggestions and bug reports that helped improving TerraceM-2, and  Marco Meschis  for insights on the synchronous correlation method. The TerraceM-2 codes and scripts are included in the Supporting Information and available at www. terracem. com. We would also like to thank  Prof. Amotz Agnon  and  Yaniv Darvasi  from the  Institute of Earth Sciences of the Hebrew University of Jerusalem  that surveyed the drone topography in Israel and introduced the authors on the geology and geomorphology of the  Dead Sea.
FUNDING This study was supported by the  Deutsche Forschungsgemeinschaft (DFG) , the Project " LIFE: Linking Lake-level variations, surface deformation and seismogenesis in the Dead Sea " Grant  JA 2860/1-1  funded by  DFG , Grant  STR 373/30-1 , project  ME 3157/4-1  funded by  DFG , and the  Millennium Science Initiative (ICM)  of the Chilean government through Grant  NC160025  " Millennium Nucleus the Seismic Cycle along Subduction Zones, Cyclo, " National Fund for Scientific and Technological Development (FONDECYT)  grants  1150321 ,  1181479 , and  1190258. MS was supported by the  German Israeli Foundation (GIF)  (Grant No. I-1280-301. 8/2014 ).
Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL's benchmark models and provide a way to calculate the SHiP's sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.
(4. 6) We see that it is sufficient to know 9 functions of the HNL mass -N α (M N ), P linear decay,α (M N ) and det,α (M N ) -to determine the number of detected events for any combination of the mixing angles. To determine these numbers we ran 9 Monte Carlo simulations for each mass. We first ran 3 simulations with vectors -→ U 2 = (x, 0, 0), -→ U 2 = (0, x, 0), -→ U 2 = (0, 0, x), where x is any sufficiently small number such that l decay l det. We then ran a set of 6 non-physical simulations, where a particle is produced solely via channel α and decays solely through the channel β = α. Using results of these simulations we extract N α , P α and det,α values that allow us to generate the expected number of detected events for any values of masses and couplings. The results are available at Zenodo platform  [91]  with instructions for reading the file and generating sensitivity curves at different confidence levels.
Conclusion Using a detailed Monte Carlo simulation of HNL production in decays of charm and beauty mesons, and of the detector response to the signal generated by a decaying HNL, we calculated the sensitivity of the SHiP experiment to HNLs, updating the results presented in the Technical Proposal  [2]. In particular, we assess the potential impact of HNL production from B c mesons decay, showing its influence on the extent of the probed HNL mass range. We take into account cascade production of B and D mesons as well as revised estimates of branching ratios of HNL production and decay, and we extend our calculation to masses below ∼ 500 MeV, where SHiP has a potential to fully explore the allowed region. Finally, we present our results as a publicly available dataset, providing a model-independent way to calculate the SHiP sensitivity for any pattern of HNL flavour mixings.
Acknowledgments The SHiP Collaboration wishes to thank the  Castaldo company (Naples, Italy)  for their contribution to the development studies of the decay vessel. The support from the  National Research Foundation of Korea  with grant numbers of  2018R1A2B2007757 ,  2018R1D1A3B07050649 ,  2018R1D1A1B07050701 ,  2017R1D1A1B03036042 ,  2017R1A6A3A01075752 ,  2016R1A2B4012302 , and 2016R1A6A3A11930680 is acknowledged. The support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (GA No  694896 ) is acknowledged. The support from the  Russian Foundation for Basic Research (RFBR)  and the support from the  TAEK of Turkey  are acknowledged.
Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that.
Background With the decreasing cost of genome sequencing, rice molecular geneticists, breeders, and diversity researchers are increasingly adopting genotyping technologies as routine components in their workflows, generating large datasets of genotyping and genome sequence information. Concurrently international consortia have made re-sequencing or high-density genotyping data from representative diversity collections publicly available. These include but are not limited to the medium-depth (15-20× coverage) re-sequencing data of the 3,010 accessions from the 3K Rice Genome (3K RG) Project (∼1-2 million single-nucleotide polymorphisms [SNPs] per accession)  [1, 2]  and the 700,000 SNP Affymetrix array data for the 1,445 accessions of the High Density Rice Array (HDRA) germplasm collections  [3].
The corresponding accessions are available at non-profit prices from the Genetic Resource Center of the International Rice Research Institute (IRRI) for phenotyping, allowing subsequent genome-wide association studies (GWAS) to be performed. Analysis of such datasets is a challenge to rice researchers owing to (i) the fairly large data matrix and the compute-intensive algorithms that require specialized computing infrastructure (a fairly large RAM, powerful central processing unit  [CPU] , and large disk space), and (ii) the relative difficulty in using open source/free software tools for analysis, which are commonly provided without graphical user interface and require proper installation in a Linux operating system environment. On the computational side, public web resources with specialized tools already exist and are maintained at different institutions.
The Rice SNP-Seek database  [4, 5] , largely developed and hosted by IRRI, contains phenotypic, genotypic, and passport information for >4,400 rice accessions from large-scale rice diversity projects such as the 3K RG and the HDRA collections. SNP-Seek  [6]  currently contains phenotype data for 70 different morphological and agronomic traits and stores SNPs and small insertions and deletions (indels) discovered by mapping the 3K RG accessions to 4 published rice draft genome assemblies, collectively resulting in the discovery of ∼11 million new SNPs and ∼0. 5 million new indels. While SNP-Seek focused on delivery of prior analyzed content rather than providing an analysis platform, Gigwa  [7, 8] , hosted at the South Green portal  [9, 10] , is a scalable and user-friendly web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data from next-generation sequencing (NGS) experiments.
Gigwa allows for filtering of genomic and genotyping data from NGS analyses based not only on variant features, including functional annotations, but also on genotype patterns to explore the structure of genomes in an evolutionary context for a better understanding of the ecological adaptation of organisms. Gramene  [11]  is a curated, open source, integrated data resource for comparative functional genomics in crops and model plant species that, among other species, includes rice. Data and analysis tools are available as portals at the Gramene site  [12]. In these resources mentioned, the analysis methodologies are custom-built by the respective projects. There are other freely available web-based bioinformatics and breeding informatics software tools, optimized for plant species other than rice, including Araport  [13]  for Arabidopsis, Cassavabase  [14]  for cassava, and The Triticeae Toolbox (T3  [15] ) for wheat and barley.
While these tools are very useful, they are species/crop-specific and custom-built for the specialized requirements of their respective communities (such as project datasets), making adoption in rice challenging for ≥2 reasons: (i) the need to produce curated rice datasets that work seamlessly with the software system (e. g. , genome-browser-ready data, curated genes, published quantitative trait loci from bi-parental crosses and GWAS and markers associated to traits), and (ii) the need for a dedicated software development team to customize the application for rice-specific data and analyses. The ability of software to automate repetitive analysis task is attractive for data analysts, and the public sharing of the analytical methodology (as opposed to just the raw data and the results) enhances reproducibility and is being supported by academic communities of practice such as FORCE11  [16].
Many research groups working with NGS data have a high demand for computing infrastructure, and their complex analyses often comprise several steps using different software tools (pipeline). The deployment of these different software tools is a big challenge to small institutions without dedicated scientific computing support staff. There is no single solution to address these challenges. Our approach to help overcome them is the integration of a range of these different bioinformatics tools into the Galaxy bioinformatics system. Galaxy  [17]  is a web-based analysis workbench and workflow management system initiated at Penn State University. It includes a collection of software packages that can be operated via a web browser on a public server. Galaxy is a mature community effort, supported by various high-powered institutions, is relatively easy to deploy and maintain, and is thus well-suited to serve low-and moderately resourced institutions such as IRRI.