dataesr/scientific-paragraphs-categorization huggingface dataset:
txt") Depending on your computer this may take a considerable amount of time. • Now determine your bisulfite conversion. Run: bisulfite <-lapply(data, slot, "quality. conversion") conversion <-100*as. numeric(lapply(bisulfite, mean, na. rm=TRUE)) • The resulting vector of numbers is the average bisulfite conversion per well in the 384 plate. The value denoted should be <2 per measurement. Higher values may denote a sample with an incomplete bisulfite conversion or some amplification of unconverted DNA. For some samples no assessment (NA) can be made. In most cases a high background signal around the conversion control fragment is the cause. The order of samples is the same as the order in the EpiTYPER R Analysis software. Please note that this assessment of bisulfite conversion is a qualitative measure, not an exact measure, of the bisulfite conversion.
using four sequential commands from NCBI Entrez Programming Utilities (E-Utilities). This redundant procedure was defined in order to guarantee retrieval of the main chromosome of complete genomes exclusively. The first command allows retrieval of the species-specific bioproject: http://eutils. ncbi. nlm. nih. gov/entrez/eutils/esearch. fcgi? db¼bioproject&term¼  [speciesname]  The second command permits to examine the 'Sequencing_-Status' flag for completeness: http://eutils. ncbi. nlm. nih. gov/entrez/eutils/esummary. fcgi? db¼bioproject&id¼  [bioproject]  The third command retrieves the unique and chromosomespecific GenBank Identification (GI) number: http://eutils. ncbi. nlm. nih. gov/entrez/eutils/esearch. fcgi? db¼nuccore&term¼  [bioproject]  The fourth command retrieves locally the organism-specific data file in GenBank format: http://eutils.
ncbi. nlm. nih. gov/entrez/eutils/efetch. fcgi? db¼nucleotide&id¼[GI]&rettype¼gbwithparts The Thermococcales protein sequences were extracted in Fasta format from these GenBank files using an in-house c# parsing script retaining only the actual amino acid sequence and the unique genomic identification number (GI). All proteins were merged into a single database which was converted to binary format using the NCBI executable 'makeblastdb'. The same script generated a separate indexed file where each individual protein was represented using the following fields: ORF genomic orientation, ORF starting and ending coordinates, gene name, unique protein GI identifier, protein function and source organism name.
Thermococcales origin of replication prediction Replication origin predictions with GC skew or Z-curve methods were performed using software Ori-Finder 2 available at http:// tubic. tju. edu. cn/Ori-Finder2/ [9]. In a second predictive method, we used the mini-ORBs sequences identified in Pyrococcus abyssi by Matsunaga et al. [10]  as a matrix for oriC prediction using FITBAR available at http://archaea. u-psud. fr/fitbar [11]. In this case, the search algorithm parameters were log-odds PSSM, with a local Markov Model to compute the p-value of the newly predicted ORB site and the investigation was made in intergenic regions only. We have considered as putative replication origin, intergenic regions where more than 4 mini-ORBs can be predicted using FITBAR, with p-values < 0. 005. These results were compared to those obtained with Ori-Finder 2 using as ORBs sequences, the three motifs predicted for Thermococcaceae.
Core genome procedure The core genome procedure was conducted as follows. We designed a c# script to construct protein orthologous groups by non-redundant bi-directional BLASTs. Every BLAST score was normalized to the alignment of query and hit proteins to themselves. Proteins showing normalized bi-directional BLASTs > 30% were considered orthologous as recommended by Lerat et al. [16]. A c# script was designed to query the orthologous groups and define the core genome which consists of all protein genes present at least once in the whole dataset. A 'single core' dataset was derived for this core genome by excluding orthologous classes containing more than a single representative per genome.
Thermococcales genomic dataset At the time of writing, 21 Thermococcales genomes have been completely sequenced and annotated. They are publicly available at the NCBI repository and consist of 13 Thermococcus, 7 Pyrococcus and 1 Palaeococcus (Table  1 ). Thermococcales carry a single ~2 Mb chromosome and encode an average of 2100 proteins. Evolutionary relationships among the various species are illustrated by a phylogenetic tree of their 16S ribosomal RNA genes (Fig. 1 ). Genomic sequences were retrieved as described in Materials and Methods. The comparative genomic analysis presented here is based on this entire dataset. The first step of this analysis consisted in the identification of chromosomal landmarks such as the origin and terminus of DNA replication followed in a second step by the comparison of the protein content at the genomic level.
Several archaeal species such as S. solfataricus, Sulfolobus acidocaldarius, Haloferax volcanii and Aeropyrum pernix possess multiple oriC per chromosome  [23,27e29]. Multiple chromosomal replication origins might have arisen by capture of viral or plasmidic replication origins and their respective associated initiator factor  [21]. On the other hand, single origins were found in Methanothermobacter thermautotrophicus  [24]  and mapped precisely in the Thermococcales genus Pyrococcus  [22, 30]. In order to compare our genomic dataset, it was fundamental to identify a common and unique genomic feature shared by all 21 Thermococcales genomes under study. Since the origin of replication was shown to be unique in these genomes, we proceeded with a computational prediction of their respective locations.
All Thermococcales encode a unique Cdc6 gene except Thermococcus sp. CL1 which encodes a second putative Cdc6-related protein encoded by gene CL1_0695. Using the published archaeal mini-ORB sequences  [10] , the web service FITBAR  [11]  was used to build consensus sequence and detect its occurrences genome wide, as described in Materials and Methods. A unique oriC could be detected unambiguously in all Thermococcales from the dataset with a p-value < 0. 005 (Table  2  and Suppl. Fig. S2 ). No putative ORB sequence could be found near the second Cdc6-related gene of Thermococcus sp. CL1 and this observation is in agreement with Ori-Finder 2 predictions (data not shown). The association between oriC and Cdc6 was found in all genomes except Thermococcus litoralis and Thermococcus sibiricus where the oriC-Cdc6 distance is respectively 453 kb and 349 kb.
The efficiency of the archaeal XerA/dif system has been demonstrated in vitro  [14]. By sequence homology search, XerA orthologs were found in single copy in all Thermococcales (data not shown). In order to identify dif sites in our dataset, we followed the same methodology used for oriC, as described above. The biological dif sites proposed by Cortes et al. [14]  were used to build a consensus for genome wide searching using FITBAR  [11]. Bona fide unique dif sites could be identified for 19 genomes out of 21 (Table  2  and Suppl. Fig. S2 ). The dif site position of Pyrococcus sp. NA2 and Pyrococcus sp. ST04 were estimated to be opposite from their respective predicted oriC.
Core genome Early chromosomal alignments demonstrated the high level of recombinations and rearrangements in Thermococcales genomes  [6]. These observations indicate that these genomes evolve rapidly which might suggest that their genetic content is also highly variable among species. In order to quantify this genomic drift, we submitted our dataset to a recursive systematic comparison of the predicted protein sequences they encode. Each Thermococcales genome encodes an average of 2100 proteins. All the corresponding sequences were compared as described in Material and Methods in order to rank them into orthologous groups. These groups could then be queried to extract common proteins, defined as 'core genome' as well as species-specific or genus-specific proteins and their combinations (Fig.
It was shown that S. solfataricus and S. acidocaldarius are equipped with three origins or replication surrounded by a higher density of core or essential genes; furthermore, these same regions are more highly expressed  [36]. These reports prompted us to investigate the genomic architecture of the Euryarchaeota Thermococcales. For each genome in the dataset, we constructed a detailed physical map indicating the position of each gene. We have used our oriC and dif sites predictions to determine the polarity of each gene respective to the orientation of the replication forks (Fig. 3  and  Suppl. Fig. S2 ). These maps could be used to calculate the proportion of genes whose transcription is collinear with the orientation of DNA replication. Out of the 19 genomes where dif could be predicted, 16 display a higher proportion of genes encoded on the leading strand (Suppl.
64 98 C Aquatic [64] Thermococcus barophilus MP PRJNA54733 315229765 2257 2. 01 41. 76 85 C Aquatic [65] Thermococcus eurythermalis strain A501 PRJNA251677 700302025 2183 2. 12 53. 47 85 C Aquatic [66] Thermococcus gammatolerans EJ3 PRJNA59389 240102057 2210 2. 05 53. 56 88 C Aquatic [67] Thermococcus guaymasensis DSM11113 PRJNA230529 744793172 2170 1. 92 52. 86 88 C Aquatic Zhang,X. et al. , 2015 Thermococcus kodakarensis KOD1 PRJNA58225 57639935 2358 2. 09 52. 00 85 C Aquatic [68] Thermococcus litoralis DSM 5473 PRJNA82997 530547444 2575 2. 22 43. 09 83 C Aquatic [69] Thermococcus nautili strain 30-1 PRJNA237737 589908590 2288 1. 97 54. 84 87. 5 C Aquatic [70] Thermococcus onnurineus NA1 PRJNA59043 212223144 2026 1. 85 51. 27 80 C Terrestrial [71] Thermococcus sibiricus MM 739 PRJNA59399 242397997 2107 1.
85 40. 20 78 C Oil [72] Thermococcus sp. 4557 PRJNA70841 341581088 2181 2. 01 56. 08 ND Aquatic [73] Thermococcus sp. AM4 PRJNA54735 350525682 2279 2. 08 54. 78 80 C Aquatic [74] Thermococcus sp. CL1 PRJNA168259/PRJNA167371 390960176 2090 1. 95 55. 82 85 C Aquatic [75] Thermococcus sp. ES1 PRJNA230233 573023865 2090 1. 95 40. 30 82 C Aquatic [76].
g. grid size and time steps). The experimental data is made of the measurements to which the model predictions are to be compared (e. g. mass loss rate, surface temperature and in-depth temperature profile). Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/firesaf The goal of a pyrolysis model is to be able to predict the evolution of the pyrolysis behaviour. In practice, the change of experimental conditions can influence the controlling mechanisms  [4, 5] , implying the necessity of adding more mechanisms (simple models are often valid only for specific experimental conditions) or to compensate the simplified mechanisms by considering effective properties, which become dependent of the experimental procedure  [3, 6]. The first models for the prediction of ignition were simple analytical expressions based on heat conduction.
The applicability of this methodology is therefore limited in practice. The number of experiments can be reduced by exploiting the database available in the literature. However, the variability of the results can be significant  [3]. As an example, Kashiwagi et al. [9]  have demonstrated that two materials supplied by different manufacturers, but sold under the same trade name, can present large differences in their pyrolysis behaviour. This material variability cannot be ignored and it is expected to increase with the use of new technologies such as fire retardant and nanocomposites. The mentioned difficulties associated with the calibration process inspired the concept of inverse modelling. In this case, the experimental data become entirely integrated in the calibration process and an optimization routine is used to quantify the best set of parameters which explain the observed pyrolysis behaviour (i.
e. multivariable curve fitting). The most used experimental data for model calibration have been the mass loss rate and the surface temperature  [10] [11] [12]. The optimization technique used is function of the number of variables and their interactions. In the past, only the few most uncertain parameters (i. e. the kinetics parameters) were generally used as potentiometers  [13]. However, sophisticated mathematical procedures have been developed to increase the number of parameters optimized simultaneously (e. g. Genetic Algorithm (GA)  [10, 14]  or Shuffled Complex Evolution (SCE)  [11] ). Lautenberger and Fernandez-Pello  [12]  have recently investigated the influence that the choice of algorithm can have on the optimized parameters. They generated using their code GPYRO a set of synthetic data (mass loss rate and surfaces temperature) and tried with different algorithms to find back the set of input parameters.
Reference model Lautenberger and Fernandez-Pello  [10]  developed their pyrolysis model in the software GPYRO and performed an inverse modelling analysis with the all set of experimental data provided by Kashiwagi and Ohlemiller  [21]. The model solves kinetics, heat and mass transfers in a 1D domain. Their best predictions for the experiments studied in this work are also shown in Fig. 1. The simulated mass loss rate follows closely the experimental measurements (within experimental errors). However, in the early (o80 s) and final (4160 s) stages, the mass loss rate is underpredicted. In the case of the surface temperature, the predictions capture even better the experimental behaviour. The model used by Lautenberger and Fernandez-Pello  [10] , called reference model in the rest of this study, is based on mass, species and energy balances for the condensed phase.
Model taxonomy In this study, the software GPYRO is used to develop the models with different levels of complexity. The most complex model, called M 1 , is identical to the reference model with the exception that the pressure evolution inside the condensed phase is considered negligible (i. e. gas phase momentum equation not solved). Lautenberger et al. [27]  used a similar level of complexity for the pyrolysis behaviour of fibre reinforced polymer. The other models, ranked from M 2 to M 5 (decreasing level of complexity), correspond to simplifications of M 1. The total number of parameters evolves from 30 to 3 between M 1 and M 5. M 2 assumes that among the mechanisms included in M 1 , the predominant ones are related to the chemical decomposition of the solid sample. The evolution of the material properties with temperature is therefore considered negligible ( 0 γ = φ ) and the energy is integrally ( 1 ε = ) absorbed at the surface (κ → ∞).
Optimization process The process of inverse modelling has been performed in this study with a genetic algorithm (GA) implemented in GPYRO. This technique, based on Darwinian evolution, enables to optimize several parameters for non-linear models. Details on the principle and its implementation in GPYRO are available in the user and technical reference guides of the software  [26]. For each model, a GA with a population of 250 individuals has been run in order to obtain the best set of parameters which predicts the measured mass loss rate and the surface temperature evolution presented in Fig. 1. The process of optimization has been performed over 500 generations, except for M 1 which was judged more ambitious and for which the process was performed over 1000 simulations. The fitness, f sim exp.
g. BRIEF and SKB directly compute the binary descriptor pattern by means of intensity comparisons or thresholding), require less storage, and can be matched very efficiently using the Hamming distance. Overview of the stereo video feature detection system with two SKB cores and one matching unit. The 'Teddy' image shown here is from the dataset provided by  [20]. In this work, we consider the calculation of a sparse disparity map from stereo video, as this is a crucial ingredient for certain video processing methods such as automatic stereo-to-multiview conversion  [22]. Our system implements the SKB algorithm  [26] , as it provides competitive results in the restricted setting of stereo vision and is amenable to efficient hardware implementations. We present a hardware architecture of the whole feature extraction and matching system (a system overview is shown in Figure  1 ).
Interest Point Detection Similar to the original SKB implementation, we use a variant of the simple Difference of Boxes (DoB) filter to detect interest points in the image. However, our system builds upon the original DoB version of CenSurE  [2]  instead of the modified SUSurE DoB  [9]  that is used in the SKB paper. The CenSurE detector basically performs a pixel-dense scan on all scales, whereas the SUSurE detector uses a scan line sparsification to leave out pixel positions which are not likely to lead to an extremal filter response. The SUSurE detector is about 3× faster than the CenSurE in software  [9] , but it exhibits a data dependent, irregular flow which inhibits parallel processing of different filter scales. The DoB filter is a simplified Laplacian of Gaussian (LoG) filter and its response is given by the subtraction of the pixel sums within two quadratic boxes with side length 2n + 1 and 4n + 1.
The precision is defined as the percentage of false matches, and the recall is the ratio of the number of correct matches and the number of existing true matches. The dataset  [11]  provides the ground truth in the form of accurate depth maps. We use these maps in order to transform interest points from one view to the other to check for true correspondences. In this evaluation, we always used view1 and view5 of the scenes. The recision-recall curves are averages over the whole test set.
Acknowledgements This work was supported by the  European Commission  within the  Seventh Framework Programme through IA-ENSAR  (contract No. RII3-CT-2010-262010 ) and  GSI Helmholtzzentrum für Schweri-onenforschung GmbH, Darmstadt, Germany , and the  United States National Science Foundation  (Grants Number  PHY-1068192  and  PHY-1419765 ).
The second one is the architecture of the embedded system, where it is introduced as a fast and simple object processor (OP) optimized for the domain of mobile AR. Finally, the OP implements a new pixel rendering method (incremental pixel shader (IPS)), which is implemented in hardware and takes full advantage of OpenGL ES light model. A GP+OP(s) complete architecture is described and prototyped on field programmable gate-array (FPGA). It includes hardware/software partitioning based on the analysis of application requirements and ergonomics.
These rely on hardware/software design methodologies and recent high-performance FPGAs. These FPGAs are often power-hungry; however, the roadmap of FPGAs is clearly focused on this power issue with the aim to address the embedded system market. Recent hybrid ARM/FPGA architectures such a Xilinx/Zynq open new perspectives. On-chip memory capacity is also a key issue where significant progress has been made. For example, the Artix Xilinx low-power, low-cost family embeds up to 12 Mbits of block RAM. Regarding GPUs on FPGAs, Xylon has added a 3D graphics module to the Logibrick library available under license in an early access version for evaluation. The architecture relies on a three-stage pipeline. This solution is a simplified version of the usual graphics pipeline and is designed for general purpose OpenGL embedded system (ES) applications.
SoC and development board We have implemented a first version of the application on a ST-Ericsson Snowball board, which includes the A9500 SoC (see Figure  3 ). It is a GPP+GPU architecture that implements a double cortex A9 with a flexible clock frequency between 200 MHz and 1. 0 GHz and a GPU Mali-400 MP. It also includes a wireless communication chip that provides Bluetooth (BT), WiFi, and GPS. Head tracking can be performed with the MEMS available on the board, which are an accelerometer/magnetometer 3-axis (LSM303DLHC from ST) and a gyroscope 3-axis (L3G4200D from ST). The GPS data are supposed to be available from the boat or user throughout the lowbandwidth wireless (BT) connection, so the GPS and the WIFI are switched off.
OS and software The mobile OS is Android; it offers a large amount of application programming interfaces (APIs) that simplify GPU programming with OpenGL ES 2. 0 API  [12]  and allows to control and acquire MEMS data (sen-sorManager) to get position data from a GPS (Loca-tionListener) and some managers to handle wireless connectivity (BT, WiFi). As a proof of portability, we have also implemented the application on a Galaxy S3 smartphone, which is based on the same GPU. In AR applications, the main functionality is the graphic service and the system has to draw 2D or 3D objects with different colors and textures. The second service is the attitude computation or head tracking, and this step is needed to place right objects in right orientation on the display. A special task with MEMS is implemented and it is mainly based on a standard 7states extended Kalman filter  [13] ; we come back on this type of filter in Section 5.
Design methodology Considering the mobile AR with distant and simple objects, the project objectives and the optimization opportunities, the first step was the analytical estimation of performances. The second one was a projection on various architecture models on Xilinx FPGA that led to hardware (HW)/software (SW) partitioning decisions. The next step was the specification of the heterogeneous architecture and the coding with a hardware-description language (VHDL) at register-transfer level (RTL). We will see in Section 7 that hardware implementations are necessary. In the next section, we detail the final heterogeneous architecture model.
Multiple OP heterogeneous architecture The system architecture is fully specified and tested at a cycle level, and the VHDL implementation is completed and synthesized; moreover, HW/Linux interfaces have been specified. The architecture, obtained after specialization and hardware/software partitioning, is described in Figure  11. The result is compact and flexible. The architecture is mainly built around a softcore, which is a MicroBlaze (MB) in this study, running Linux (Petalinux) to simplify the access to standard peripherals (I2C, UART, Ethernet) used for network access and communications with sensors. According to the specific AR context, we made the choice to consider positioning and graphics at the object level. So the processor is enhanced with some instances of a new accelerator called object processor (OP) that handles object positioning and drawing.
When new objects must to be loaded according to the user environment, the Linux solution can provide the required secure network access (SSL), and in this case, only write operations are physically implemented and no program running on the CPU can read local memory data. If the chip is considered as a secure area, then this solution is efficient since no sensitive data are stored on external memory except the configuration that may be stored in on a encrypted flash memory  [24]. H-MPSoC reconfiguration. The architecture is such that multiple OPs can run in parallel since each of them works with a given collection of objects. The CPU and OPs are quite independent, since they rely on shared memory communications, so that the CPU can feed the OP local memories with new position data. So the architecture can be dynamically and partially reconfigured according to the number of objects and real-time constraints.