dataesr/scientific-paragraphs-categorization huggingface dataset:
Maintainability Generally, high-level development environment, i. e. , MATLAB or C/C++, can add to the maintainability of the design by reducing the size of the source code. The proposed design flow of this work allows a high abstraction environment for FPGA designs. Figure  12  shows the size estimation of the source code for the different implementations. We can see that there is no significant size gap between the matlab and original_c versions. Meanwhile, debugged_c and optimized_c necessitate more code for algorithm description. This is because in debugged_c, the HLS-unamenable C library functions need to be re-specified manually, while optimized_c requires more code to configure the optimization directives or to manage the registers for the proposed optimization methods. However, it should be noted that this rise is completely acceptable compared to the intensity of RTL code.
The code intensity ratio between the hand-written RTL and HLS-generated implementations, R code , can be quantified as follows: R code = L RTL L HLS  (33)  where L RTL and L HLS (= 186) refer to the line number of the manual register-transfer level and source C code. L RTL can be expressed by p × N RTL. gate , in which p is the ratio between the line number of hand coded RTL and its NAND-equivalent gate count, N RTL. gate. According to the findings of Wakabayashi Kazutoshi  [30] , a 1M-gate design requires 300 K lines of RTL code approximately, which yields p = 0. 3 lines per gate. The case study of Homsirikamol and Gaj  [39]  demonstrates that the area ratio between the HLS-generated and hand-written register-transfer level code, q, varies from 0. 78-1. 2 times within the devices of Xilinx, so we compute N RTL.
To do this, the algorithm will be re-analyzed to perform a highly efficient task scheduling. Additionally, the HLS-based design flow results in a high intensity Verilog code for the design of this paper, and our experiments demonstrate that the changes of data formats may produce small and acceptable impacts on the image segmentation results. Therefore, analyzing the basic architecture of the design in the register-transfer level will help to understand how it works and may give considerable improvements both in terms of clock speed and logic resources required.
Natural phenomena in mountains such as rockfalls cause severe damage to exposed population and assets. Numerical modelling is widely used to assess the hazard level (combination of phenomenon's intensity and frequency). This paper describes how innovative methods and tools are used to assess both thematic (related to the characteristics of the rockfalls) and spatial (related to the local topography) data uncertainties considering rockfall propagation. Those uncertainties are propagated in rockfall simulation models using classical Monte Carlo (probabilistic) and hybrid (possibilistic) approaches. Uncertainties related to the altimetric information, through Digital Elevation Model (DEM), are modelled by random fields using the new ModTer software, that can produce terrain simulations. This paper compares both uncertainty propagation approaches, taking into account spatialized uncertain variables, and proposes a sensitivity analysis describing the contribution of DEM variability on the global uncertainty.
2015). In trajectography analysis, terrain topography has also an essential role and its uncertainty has to be considered in numerical modelling. The ModTer project aims first to assess spatial uncertainty related to available elevation data and, secondly, to analyse the influence of this uncertainty on global simulation results. A ModTer software has been developed  (Crimier et al. 2016)  to build DEM uncertainty models. A specific framework has been build to propagate both DEM uncertainty, modelled by the software, and thematic uncertainty (Figure  4 ). This paper aims first to generalise the comparison of Monte Carlo and Hybrid uncertainty propagation results described in  Dupouy et al. (2015) , by including DEM variability as modelled by ModTer, and by focusing on spatialized variables of interest.
It also provides a sensitivity analysis to evaluate the influence of DEM variability over the output uncertainty. The present first section introduces the context and needs. The second part recalls the main tools and method, and more specifically the possibilistic, so-called Hybrid propagation method, and the main outputs of the new ModTer software. It also details the description of the analysis. The third section presents the results of Monte Carlo and Hybrid propagation works, and sensitivity analysis. Finally the discussion analyses the results and concludes about perspectives.
Rockfall simulation model: Rockyfor3D The rock propagation simulations are conducted using the code Rockyfor3D. This well-known and widely used tool was developed in Irstea Grenoble conjointly with the Ecorisq research fondation  (Dorren et al. 2006 , Bourrier et al. 2009 , Dorren 2015). Rockyfor3D allows simulating the 3D propagation of the rocks as a succession of free flights through the air and rebounds on the soil. The rock propagation is modelled through a rasterized Digital Terrain Model which can be seen as a matrix in which each cell value represents the mean terrain elevation of the square cell. The set up of a rockfall simulation series requires defining a set of parameters related with the slope surface, the rocks and the release conditions (rock volume, departure location and initial falling height, in particular).
Probabilistic modelling of DEM uncertainty: ModTer The ModTer software  (Crimier et al. 2016 ) aims to build a random field to model altimetric uncertainty, from a given operational input Digital Elevation Model. The field's parameters are set from statistical learning, using morphometric characteristics of the input DEM, and the errors between the input DEM and a set of available, reference altimetric data (Lidar, photogrammetry. ). Once the model is build, the software is able to export a sample of output DEM, stemming from the random field. Dupouy et al. (2015)  aimed to apply and compare both Monte-Carlo and Hybrid approaches to propagate uncertainty and imprecision in the context of rockfall protective structure design. Nevertheless, this first study only focused on propagating uncertainty from thematic, scalar variables.
Background: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes. Results: Against commonly-held views, we have found that pose generation error has generally a small impact on the accuracy of binding affinity prediction. This is also true for large pose generation errors and it is not only observed with machine-learning scoring functions, but also with classical scoring functions such as AutoDock Vina. Furthermore, we propose a procedure to correct a substantial part of this error which consists of calibrating the scoring functions with re-docked, rather than co-crystallised, poses.
Background Molecular docking tools are routinely utilised to predict the binding pose as well as the binding affinity of a ligand, usually a small organic molecule, bound to a target protein of interest. On one hand, the predicted pose suggests putative intermolecular interactions that can be helpful to understand the mechanism of protein-ligand binding. On the other hand, the predicted affinity prioritizes strong-binding ligands over weak-binding ones from a large library of compounds to evaluate. A typical docking program implements a sampling algorithm to generate possible binding poses and a scoring function to estimate their binding affinity. The former operation is known as pose generation, and the latter is known as scoring. For example, modern docking tools such as AutoDock Vina  [1]  and idock  [2]  are currently capable of generating near-native poses with a redocking success rate of over 50 % on three diverse benchmarks  [2].
cse. cuhk. edu. hk/idock, for large-scale docking-based prospective virtual screening. Furthermore, recent study  [9]  has investigated the benefit of training machine-learning scoring functions with low-quality structural and interaction data. In prospective structure-based virtual screening  [2] , scoring of the docked poses of a molecule is required because the experimentally determined pose is not available in most cases. Therefore, accurate prediction of binding affinity of docked poses, rather than co-crystallised poses, is required for ranking compounds from screening libraries. Pose generation error is typically measured by comparing the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein (Fig. 1 ). The impact of this error on binding affinity prediction is yet to be systematically analysed across diverse protein-ligand complexes.
035579 w 2 = -0. 005156 w 3 = 0. 840245 w 4 = -0. 035069 w 5 = -0. 587439 w 6 = 0. 05846 (3) e k is the predicted free energy of binding reported by the Vina software when scoring the kth docked pose. e k,inter and e k,intra are the inter-molecular and intra-molecular contributions, respectively, which have both the same functional form described in Eq. 2 but are summed over different atom pairs. The values for the six weights were calculated by OLS (Ordinary Least Squares) using a nonlinear optimisation algorithm as it has been the case in related force-field scoring functions  [10] , although this process was not fully disclosed in the original publication  [1]. N rot is the calculated number of rotatable bonds. The predicted free energy of binding in kcal/mol units was converted into pK d units with pK d = -0.
PDBbind v2013 blind benchmark We also adopted the PDBbind v2013 blind benchmark  [6] , a recently proposed new benchmark mimicking a blind test to provide a more realistic validation than the PDBbind v2007 benchmark. Its test set is composed of all the complexes in the PDBbind v2013 refined set that were not in the v2012 refined set, i. e. those 382 complexes that were newly added in the v2013 release. Its training set is simply the v2012 refined set, which contains 2897 complexes. By construction, this benchmark can be regarded as a blind test in that only data available until a certain year is used to build the scoring function that will be used to predict the binding affinity of future complexes as if these had not yet been measured. Consequently, the test set and training set do not overlap.
Here we denote this version of RF::VinaElem as RF-Score-v4 Fig. 3  Box plots of performance of the four models trained on crystal and docked poses and tested also on crystal and docked poses of the PDBbind v2013 blind benchmark. The same notations are applied here as in Fig. 2  specifically for the purpose of binding affinity prediction given a docked pose from Vina. Importantly, since Vina and RF::Vina used the same features and were trained on the same data, RF::Vina performed much better in predicting binding affinity than the widely-used Vina software while having the same applicability domain.
Training with more complexes on docked poses still improves predictive performance In a previous study  [6] , with the help of the PDBbind v2013 blind benchmark, we showed that training RF models with larger datasets greatly improved their predictive performance on scoring crystal poses, while the performance of MLR::Vina nearly stayed flat. Here we observe similar results when the models were trained on docked poses and tested also on docked poses. As shown in Fig. 4 , when more complexes were used for model training, RF::VinaElem consistently increased its predictive accuracy in terms of RMSE, SD, Rp and Rs. In contrast, for MLR::Vina, its accuracy improvement obtained from larger training sets was just marginal, if not negligible. The performance gap between MLR::Vina and RF::VinaElem is not only substantial, but grows as more data is available for training, thus increasing the importance of employing RF in scoring function development.
Although our presented schemes 3 to 6 did not succeed in increasing predictive performance, further analysis of the influence of the number of poses on the error in binding affinity prediction might lead to better performance. Another contribution of this study is the release of free software implementing RF::VinaElem trained on docked poses, named RF-Score-v4, so that it can be directly used by the large number of Vina users (poses generated from other docking programs can also be re-scored by our software once these are converted to AutoDock's pdbqt format). With this purpose, we have trained the best.
As an alternative, DNA barcoding-a 658-bp fragment of the mitochondrial gene cytochrome c oxidase I (COI)-which has been widely used for more than a decade, shows a high correspondence with taxonomical identification, particularly for distinguishing arthropod species, e. g. [12] [13] [14] [15] [16] [17]. This method has two advantages compared to the morphospecies approach: (1) two or more people can unambiguously assign individuals to species without requiring any taxonomic expertise; and (2) a species identification can be obtained if a barcode sequence is compared to sequences of conspecifics deposited in a database for which the taxonomic name is already known (i. e. DNA barcode library,  [18] ). DNA barcoding has been applied to Lepidoptera diversity since the beginning of the barcoding initiative, with several campaigns in different parts of the world  [19].
The purpose of our project was to study elevational diversity patterns in a large remnant of this forest in southern Brazil, and compare those results with available data from other regions  [41]. Additionally, during the course of our study almost all species found were barcoded and the resulting sequences were entered into the database of the Barcode of Life Data Systems-BOLD Systems-a worldwide database of DNA barcodes covering animals and other species  [19]. The current project represents one of the largest inventories of moths using DNA barcoding in the Neotropical region. Neotropical noctuoid species assemblages are highly diverse  [42, 43] , and thus the completion of a census of species, even in small areas, is highly challenging. Here, we sampled extensively over a two year period an area in the Atlantic Forest to produce a fast, though incomplete, census of arctiine moths whose species diversity was assessed using two approaches: (1) a superficial separation into morphospecies upon arrival in the laboratory and (2) separation into species units through the analysis of DNA barcodes.
Species delimitation methods For the initial delimitation of species boundaries we chose the two fastest methods available: (1) we sorted the species in a superficial morphology-based method (Initial Morphological Assessment, from now on IMA) and (2) we delimited molecular operational taxonomic units (MOTUs) defined according to the Barcode Index Number system (from now on BIN) as implemented in BOLD. In a second step, we (1) reassessed morphology in cases where the IMA and BINs did not match (Morphological Reassessment, from now on MRA), and (2) we also reassessed MOTUs using two alternative automated species delimitation methods (i. e. 2% threshold and Automatic Barcode Gap Discovery (ABGD)). These new results were then compared to the IMA and the MOTUs defined by BINs (Fig  1 ). The IMA was done along a two-year sampling period and all specimens were deposited at "Museu de Zoologia Adão José Cardoso" of "Universidade Estadual de Campinas", Brazil.
The samples and the data were shipped periodically from March, 2010, to December, 2011. All sequences used in this work were generated by the CCDB following standard protocols for extraction, PCR amplification, product checking and sequencing described in the CCDB web site  [50]. We built a database (identified by its project code LEMMZ) hosted online by the Barcode of Life Data systems (BOLD)  [19] , where each specimen is represented by a record combining taxonomic information, collection data, sequence data (including trace files), as well as digital images in dorsal and ventral views. The Barcode Index Number is an interim taxonomic system used by the BOLD Systems, based on refined single linkage algorithm (RESL), to cluster DNA barcode sequence data into MOTUs  [51]. This system has been proved to be highly efficient to cluster barcode sequences of large datasets of Lepidoptera species and outperforms all available methods based on different algorithms  [51].
Once a barcode sequence enters the BOLD database it is automatically assigned to a BIN, which may contain similar sequences available in the database  [52]. We therefore compiled the taxonomic names within each BIN obtained exclusively from the specimens we found in our study area in the Atlantic Forest whose barcode sequences are deposited in the BOLD database. The names of the sequences of other studies that eventually may have fall into BINs shared with our sequences were disregarded. The MRA was restricted to the conflicting results between IMA and BINs. Those cases of agreement between IMA and BINs species delimitations were treated as well-delimited species and were posteriorly computed as being MRA species. Three basic outcomes may arise when the IMA species do not match BINs: an IMA species splits into two BINs (splits); two IMA species merge into the same BIN (merges); and two individuals from the same IMA species split into different BINs and at least one of them merges with another individual from a different IMA species (mixtures)-see Fig 1.
In most of cases the comparisons were made with males but females were used in a few cases. Only visual morphological characteristics were used and no morphometric analysis was done. After the morphological reassessment based on BINs was concluded the species delimitations were reorganized accordingly and the taxonomic identifications were improved based on comparisons made with vouchers deposited in the Vitor O. Becker collection  [54]. The specimens were compared based exclusively on habitus since vouchers were not allowed to be dissected. To distinguish the putative cryptic species delimited in this study the letters MMZ and a number were added after the species name (e. g. Cosmosoma auge sp. MMZ01, Eucereon rosa sp. MMZ02). Finally, we used all information provided by our integrative species delimitation method and the new taxonomical data to update the online database.
Comparisons between different approaches A total of 302 MOTUs were delimited under a 2% approach with the same set of sequences used to delimit BINs (Table  1 ), representing an increase in the species richness of the initial Table  1. Pairwise comparisons of the shared number of species of an assemblage of Tiger Moths recorded along an elevational gradient in southern Brazilian Atlantic Forest delimited with five different species delimitation methods. IMA: Initial Morphological Assessment; MRA: Morphological Reassessment; BINs: Barcode Index Numbers; 2% threshold approach; ABGD: Automatic Barcode Gap Discovery. MOTUs, BINs and S are generically referred as to species. The ABGD approach revealed that a total of 304 MOTUs were found with prior interspecific divergence of 0. 0215 (Fig 5 ), representing an increase in the species richness of the initial morphological assessment of nearly 6.
Discussion This project (Lepidoptera of Serra do Mar-LEMMZ) is among the ten largest projects in BOLD with respect to the number of sequences of arctiine moths. It accounts for approximately 75% of DNA barcode sequences of this taxon recorded from Brazil (data accessed in June 15 th 2015). Two factors were important in obtaining a large volume of high quality sequences. First, fresh specimens were used preventing DNA degradation and second, the optimized protocols developed by the CCDB  [50, 57]  resulted in a large amount of DNA extracts and high quality PCR products. Although the current protocols allow DNA extraction of 30 or more years old museum specimens, the sequences frequently do not exceed 500 bp and might not be able to differentiate species in large assemblages  [13, 24]. However, in more than 96% of the sequenced specimens in our project the sequences were 658 bp long and virtually all species could be distinguished.
Therefore, our empirical evidence supports the use of the BIN system for automated species delimitation of Arctiinae moths in local inventories. In the last decade, Internet-based projects has allowed access to Lepidoptera biodiversity information and one of the most famous example is the "Area de Conservación Guanacaste-ACG" project, in Costa Rica  [29, 30]. In this biodiversity inventory, almost 200,000 caterpillars of 2,810 morphospecies originated from the several habitats within ACG were reared and all life stages and host plants documented in an online database  [79]. This project also was a pioneer in evaluating DNA barcoding as a tool to reveal hidden diversity in the Lepidoptera and it is the largest in the BOLD Systems in number of sequences (data accessed in June 15 th 2015). Similarly to our project, the ACG project revealed several cases of putative cryptic species and sexual dimorphism when the cases of splits, merges and mixtures were closely analyzed.
Another important aspect of the ACG inventory was the possibility to correlate barcode clustering with habitat, behavior and immature stages morphology. Indeed, the ACG inventory has been ongoing for 50 years and more than 150 members of taxasphere (including 50 professional taxonomists) contributed to its development  [29, 30]  while ours is a much smaller project that employed two taxonomists. Nevertheless, the information here reported will certainly be useful to the development of a database similar to that of the ACG project that may be used not only by biodiversity scientists working in our study area but also in other regions of the Atlantic Forest. Although our project is far from representing a comprehensive library of DNA barcodes, it is expected to be useful to ecological studies and the understanding of insect-plant relationship in the Atlantic Forest.
Based on this library many species of arctiines of the Atlantic Forest can be reliably identified and the host-plant could be linked to the adult if a single caterpillar found feeding in a tree is barcoded, as ecemplified elsewhere  [29, 32]. Also, the diet analysis of the insectivorous predators can be assessed by barcoding the stomach contents of bats, birds, small mammals, frogs, lizards and other predators  [36]. Indeed, the Bold Systems database has been used successfully to reveal the diet of bats in Canada  [80]  and Finland  [81]. Therefore, we believe that our contribution to the library of DNA barcodes in arctiine moths will be of great help for the development of many other biological studies in the Atlantic forest in the future. The success of biodiversity inventories of moths and other hyperdiverse arthropod taxa rely strongly on comparisons with specimens deposited in natural history collections, and thus taxonomy must be very accurate.
Cells in different shades of brown: conflicting results between BINs and the remaining molecular species delimitation methods. IMA and MRA species delimitation methods may or may not correspond to each other (see rearrangements into MRA species). The species names may differ because taxonomy was improved after the morphological reassessment (see Material and Methods section). (XLSX) S2 Appendix. Accession number of the sequences used in this study. (XLS).