dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, since we had good quality, long (400 bp) sequences, we set the minimum high quality bases depth to 2x per genotype. This means that the script filtered out all variant positions that did not have at least 2 genotypes, each with 2 different base calls with a minimum phred score of 20 and within good quality context (5 bases on each side with a minimum phred score of 15). By applying these filters a first set of putative SNPs was defined. Since we used fixed lines, a second filter was applied to keep only positions for which each accession was strictly homozygous independently of phred score. Finally, due to the high error rate of 454 sequencing on homopolymers, all indels were excluded from the final selection defined as robust SNPs. Further annotation was done on contigs containing at least one robust SNP using Blast2GO  [64].
DNA extraction and genotyping DNA was extracted from leaf tissue using a CTAB method as described by Rogers and Bendich  [65]. DNA was quantified with the Quant-iT™ PicoGreen® Assay (Invitrogen, Carlsbad, USA), using the Appliskan multiplate reader (Thermo Scientific, Courtaboeuf, France). DNA concentrations were adjusted to 50 ng/μL for each sample. For each assay, five plates of 96 samples containing 50 μL of genomic DNA normalized to 50 ng/μL were provided for genotyping using the "GoldenGate Genotyping Assay for VeraCode Manual Protocol" (Illumina Inc. , San Diego, USA)  [66]. The automatic allele calling for each locus was accomplished using the Genome Studio software (Illumina Inc. , San Diego, USA). The homozygous and heterozygous clusters were checked visually and they were manually edited when necessary.
Composite genetic map construction Markers that were used from different published  [33, 38, 39, 46, 47, [67] [68] [69] [70]  or unpublished [Mohamadi et al. , in preparation] maps and 1360 from our 1920 SNP markers were added to constitute a combined genotyping matrix for the four Recombinant Inbred Line (RIL) mapping populations. The 1/1 segregation ratio of each marker within each population was checked using a Chisquare test (P > 0. 01 and P > 0. 001). Genetic linkage analyses were performed using the "group" commands of CarthaGene software  [71] , with a minimum LOD score threshold of 3. 0 and a recombination frequency < 0. 3. Marker order was refined using the "annealing 100 100 0. 1 0. 9" command of CarthaGene software. The Kosambi function was used to calculate centiMorgan (cM) distances between markers.
Statistical analyses A statistical approach was used to describe the relationship between accessions. Marker polymorphism information content (PIC) was calculated with Powermarker V3. 25  [73]. In order to get a representation of the genetic structure of the 92 pea accession collection (Additional file 4: Table  S1 ), an analysis based on the 1538 newly generated polymorphic SNPs was performed with DARwin5 software  [74]. The dissimilarity matrix generated using the Rogers-Tanimoto method with 10,000 bootstraps was used for factorial analysis and for construction of the Ward hierarchical clustering tree  [75]. The same software and parameters were used using a transposed matrix to structure the 1538 SNP set with the 92 pea accessions (data not shown).
Acknowledgements This work was initiated and largely supported by  SOFIPROTEOL  under the  FASO (Le Fonds d'Action Stratégique des Oléoprotéagineux) project  " PEAPOL ", to answer the increasing demand from French breeders for a massive development of markers in pea, allowing a breakthrough in MAS. We acknowledge the contribution of French pea breeders for supplying cultivar seeds, and especially of  Anne-Marie Bochard  for helpful discussions in creating the diversity panel. We greatly acknowledge  Jeroen Wilmer  for critical review of the manuscript.
Related Work One way to design multi-mode systems is to use designers knowledge and experience to identify similar patterns in different modes and to handcraft multi-mode architectures. However this is incompatible with the time to market constraint. Hence multi-mode design needs to be automated. Works on the automation of the design process can be divided into two categories. One uses algorithmic specifications of different configurations to generate an RTL description of a multi-mode system while the second uses RTL descriptions to generate a netlist of it. In  [1, 2] , HLS-based approaches to automate the design are proposed. The data flow graphs (DFGs) of the multiple modes are merged into a single graph after each DFG is scheduled separately. Then, the resource binding is performed using the maximal weighted bipartite matching algorithm presented in  [3].
Resource sharing is done independently from the way of coding the RTL hardware description. This gives the freedom to use any RTL design to generate an mASIC with efficient resource sharing without changing a single line of code. Even though these applications are passed through a software flow which may seem complex, this methodology can be integrated easily into a logic synthesis tool. The FPGA architecture used for mASIC is the same as the architecture used for ASIF. It is shown in Figure  3. It is a VPR-style (Versatile Place and Route  [11] ) mesh-based architecture that contains CLBs, I/Os, and hard blocks (HBs) that are arranged on a two-dimensional grid. In order to incorporate HBs in a mesh-based architecture, the size of HBs is quantified with size of the smallest block of the architecture, that is, CLBs.
Through this process, the synthesis tool performs logic optimizations on multiplexers inserted in the customization stage. The main goal is to improve the efficiency of the synthesis tool by shaping the input circuit. Later, mASIC can be implemented as an ASIC or in an FPGA. When the code in Listing 1 is given to a synthesis tool, it will be optimized. Synthesis tool propagates all constants in the circuit. In this particular case, constants in the "instMux SRAM 1" are propagated. As a result, synthesis tool removes this multiplexer and replaces "cmd mux routing 1" by "s netlist. " So the multiplexer which is in the routing channel will be controlled directly by the input of the circuit. This is illustrated in Figure  9(b). mASIC contains SRAMs also in logic resources (LUTs in CLBs) and these SRAMs have to be customized as well.
(ii) It is a brute force technique. Thus, it has a high function cost: (!)  for each LUT on the initial architecture, where  is the number of LUT inputs and  is the number of netlists mapped on the LUT. In this section we explore the impact of the LUT input pin reordering technique on the total area. MCNC benchmarks shown in Table  1  are used in the experiments of this technique. We had already implemented mASICs with different LUT size and analyzed the constantmultiplexer ratio in CLBs in Figure  17. Here, we apply the LUT input pin reordering technique to mASICs which contain 2, 3, 4, and 5 netlists (mASIC-2, mASIC-3, mASIC-4, and mASIC-5). However, due to high function cost we could not retrieve the results for LUT-5 based mASIC-4 and mASIC-5 in a reasonable time. An SRAM is replaced by a constant when all bitstreams of different netlists, which are using this particular SRAM, program it with the same value.
Three specimens of the Spanish population were snap frozen in the field in liquid nitrogen, serving as a field control (FC). Once in the laboratory, individuals were acclimated for one week in aquaria, with mineral water and some vegetation taken from the place of origin. Specimens were kept at room temperature (RT, always below 25uC), which was considered the control for these experiments, and with a natural day/night cycle. Specimens were fed ad-libitum on frozen red Chironomidae larva from commercial sources (sold as fish food). After a week, an equal number of specimens were randomly allocated to each of three treatments for 12 h: 4uC, RT, and 27uC. This is within the range of temperatures the species are likely to experience under natural conditions (WorldClim_2. 5 m database). After the treatment, specimens were snap frozen in liquid nitrogen, separated into three samples of 2-3 specimens for each temperature treatment and stored at 280uC.
2D-DIGE image analysis and statistical quantification of relative protein abundance were performed with Progenesis SameSpots v4. 0 (Nonlinear Dynamics, Newcastle, UK). This software allows detecting, quantifying and matching of spots between gels after normalization to the internal standard. Statistically significant differences in protein expression between groups (temperature treatments) were tested with one-way ANOVA. To correct for multiple tests we used the false discovery rate correction (FDR) as implemented in SameSpots (q,0. 05).
Statistical analyses Gel images were aligned with reference to the internal standard, normalized, and the protein spots verified. For each experiment, pairwise comparisons of the expression level between replicated samples and between treatments were done, selecting those spots which indicated a significant difference according to the ANOVA analysis at P,0. 05, P,0. 01 and P,0. 001 levels. The analyses included: 1) experimental variation due to technical error; 2) variation between replicated samples, in order to detect variability due to individual differences (genetic background, sex, age, physiological state); 3) variation between treatments within the same population; and 4) variation between treatments across populations. To determine the variation due to technical reasons, for each experiment the five images of the internal standard were compared using the 'single stain per gel' option in SameSpots software to detect false positives.
We also computed the coefficient of variation (CV) among the spot volumes across the different replicas, averaged for all spots (CV = SD/mean 6100;  [24] ). To assess the variability between replicated samples or treatments within the same population we calculated the distribution of the differences in expression of the same protein spot for each comparison, generating a matrix of protein expression data which showed significantly different level of expression at the selected P-level. With these matrixes we did a hierarchical cluster analysis using the Euclidean distance and Ward's amalgamation method (see  [1]  for comparison). We used the single available field control to have an estimate of the changes introduced by the acclimation process, with a standardised food supply. For a global comparison of treatments across populations we performed an ANOVA analysis for those protein spots exhibiting significant differences of expression level in the pairwise comparison between treatments within the same population.
5-2 Hz) of the 8 most intense precursor ions with an intensity threshold for fragmentation of 5000 counts and using a dynamic exclusion time of 0. 5 min. All spectra were acquired on the range 100-2200 Da. LC-MSMS data was analysed using the Data Analysis 4. 0 software (Bruker). Proteins were identified using Mascot (Matrix Science, London UK) by search on the NCBI database limiting the search to Other Metazoa (13,577,271 sequences; 4,662,347,403 residues). MS/MS spectra were searched with a precursor mass tolerance of 10 ppm, fragment tolerance of 0. 04 Da, trypsin specificity with a maximum of 2 missed cleavages, cysteine carbamidomethylation set as fixed modification and methionine oxidation as variable modification.
Acknowledgments We thank  Nard Bennas  and  Sumia Fahd  for assistance on the field trip in Morocco and  Pedro Abellan ,  Andres Milla  ´n and  Josefa Velasco  for their help to collect the specimens of the Murcia population. We also thank  Nuria Colome  ´and  Joan-Josep Bech  for assistance in the preparation and analyses of the DIGE experiments,  D. T. Bilton  for data on the thermal tolerance of the Moroccan population, discussion of the results and linguistic revision, and three anonymous referees for comments on the manuscript. The  Proteomics Laboratory at VHIO  is a member of the  ProteoRed-ISCIII network  of the  Instituto de Salud Carlos III.
Most studies are still based on the well-known and widely accepted Generalized Extreme Value (GEV) distribution. GEV distribution allows to extrapolate return levels -i. e. level of a variable which will be reached only once in average during the corresponding return period -from observed dataset. The first formalism issued was univariate. Considering so, studies are quickly limited while dealing on questioning that requires spatial dependence of a phenomenon. For instance, let us consider a study of the long-term long-shore sediments transport. And let define the joint probability of having the significant wave heights of four littoral sites exceeding their 20 years return level in a month. This quantity must be computed to assess the long-shore transport. Assuming the independence of the extreme events across sites may lead to a bias in the estimation of this quantity.
A 52-YEAR WAVE HINDCAST A well-fitted statistical model and therefore the quality of its extrapolation rely on the availability of a representative data set. This becomes a real constraint as far as we consider extreme events. Such events are rare by definition and direct observations are often scarce, both in time and space. It is particularly the case for our area of interest the Gulf of Lions, see Figure  1. Although well known for their abrupt changes, the storm-waves in that area are monitored by only four surface buoys. Obviously the chance to miss extreme waves is high and information to analyse the full area is scarce. Moreover the longest time series available is less than a decade, which is unsatisfying to extrapolate to several decades. One can use altimeters data sets as an alternative, but their major drawback is the non-regularity of satellites tracks through time and space around the globe.
Output dataset We generate hourly waves features at each node -47086 in total -of the computational grid for the 52 year period, 1961-2012. Only considering the Gulf of Lions area, it represents 3954 wet sites. A validation step is performed to measure the quality of the hindcast. The simulated signals (e. g. , significant wave height, wave direction, wave period, etc. ) are compared to records at the four littoral surface buoys presented above. The results are satisfying, even if some of the extreme events are slightly smoothed out against the observed signals. A short investigation shows that this bias for extreme events often results from an actual bias of the wind input fields. In the sequel we consider the produced data as real observations. We focus our study on the extreme waves in the Gulf of Lions by stochastically modelling the extreme significant wave heights (Hs).
The signal is carefully analysed regarding the spatial dependence behaviour. The inference of such modelling is hard to afford as soon as the number of sites gets important. Therefore we have to decrease the number of sites considered. A Latin Hypercube Sampling (LHS) method is used to randomly select 100 nodes of the computational grid, with respect to a good spatial representation. Only wet sites of the grid are considered. If two sites are close to a distance inferior to 1 km, only one of those sites is conserved in the dataset. This scheme leads us to analyse 97 sites presented in Figure  3. We restrict our methodology to those 97 sites. However, one may investigate the sensitivity of the approach by increasing the number of sites considered or move their location or both. Such sensitivity analysis would be a valuable add, but remains out of scope for this document.
, ψ p ) of parameters maximizing the pairwise likelihood, which in log-form is L(Ψ) = n i=1 k<l log f (z ik , z il ; Ψ), (11) where f (• , • ) is the bivariate density of the unit Fréchet max-stable process. Under suitable conditions, the maximum composite likelihood estimator Ψ has a limiting normal distribution as n → +∞, with mean Ψ and a covariance matrix estimable by H( Ψ) -1 J( Ψ)H( Ψ) -1. The observed information matrix H and the squared score statistic matrix J are respectively defined by H( Ψ) = - n i=1 k<l ∂ 2 log f (z ik , z il ; Ψ) ∂ Ψ∂ ΨT and (  12 ) J( Ψ) = n i=1 k<l ∂ log f (z ik , z il ; Ψ) ∂ Ψ ∂ log f (z ik , z il ; Ψ) ∂ ΨT. ( 13 ) Once all models are fitted, the best one must be designated. Akaike Information Criterion (AIC) is a standard measure of the quality of a given model for a dataset.
, Z(x ω ) > r t ) = P(Z(x k ) > r t ), x k ∈ Ω = t -1 , by definition, while for the total independence it is defined as P(Z(x) > r t (x), x ∈ Ω) = P(Z(x 1 ) > r t ) ×. × P(Z(x ω ) > r t ) = P(Z(x) > r t ) ω = t -ω , with ω the number of sites considered. From several set of sites Ω i ⊂ χ, we compare the joint probability of exceedance P(Z(x) > r t (x), x ∈ Ω i ) computed from 1) the theoretical full dependence case; 2) the theoretical total independent case; 3) the observations -i. e. hindcast dataset -and 4) the best fitted max-stable model through the simulations of 5000 processes. The results are given in Figure  8. Generally speaking the max-stable model outperforms the other theoretical cases for any set of sites and therefore whatever the distance considered. When considering littoral sites altogether, the model achieve to represent joint probabilities relatively close to the dependent case, as outlined by the observations.
ACKNOWLEDGEMENTS This work was performed within the context of both LITTOCMS and MISTRAL-LITTORAL projects. It also takes place in the GLADYS initiative -see www. gladys-littoral. org. The work was partially supported by  ANR-McSim and GICC-Miraccle  projects. Some computations were also realized with the support of  HPC@LR , a  Center of Competence in High-Performance Computing  from the  Languedoc-Roussillon region , funded by the  Languedoc-Roussillon region , the Europe and the  Université Montpellier 2 Sciences et Techniques. The  HPC@LR Center  is equiped with an IBM hybrid Supercomputer.
One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time.
iCub PLATFORM AND INTERACTION ARCHITECTURE The platform that we used is the iCub, furnished by the FP6 EU consortium RobotCub (see Figure  2 ). The iCub  (Metta et al. , 2010)  is a 53 DOF humanoid robot built by the Italian Institute of Technology (IIT) with the size of a three and a half year-old child. We use YARP  (Metta et al. , 2006)  as the robotic middleware with the Biomimetic Architecture for Situated Social Intelligence Systems (BASSIS architecture) built for the FP7 Experimental and Functional Android Assistant project  (Petit et al. , 2013). The Supervisor module is implemented with the CSLU RAD Toolkit  (Sutton et al. , 1998)  Rapid Application Development for spoken language interaction. It uses the Festival system for speech synthesis  (Taylor et al. , 1998)  and Sphinx II for spoken language recognition  (Huang et al.
, 1993). The Supervisor provides a dialog management capability built as a finite-state system. This capability allows the user to guide the robot into the different states of behavior, but is distinct from the neural language model, described below. The Supervisor/Manager orchestrates the communication and exchange of data between speech recognition and synthesis, the neural models for language comprehension and generation, and the robot perception and action systems. The ability of the iCub to perceive physical objects and their manipulation in the context of action performance and description is provided by the ReacTable, which detects objects on a translucid table based on detection of fiducial markers on the object bases, using an infra-red camera  (Bencina et al. , 2005). The ReacTable thus provides data on the type and position of objects on the table with high precision.
The meaning output dimension is 36 (=6 × 3 × 2): 6 semantic words that each could have three possible thematic role assignment (predicate, agent or location), for each of up to maximum 2 verbs. Post-processing. To determine the meaning specified in the output, the activity of the output at the last time step is thresholded. For each SW, we take the role that has the maximum activation (if there are several). Each semantic word in the FIFO stack is then bound with its corresponding role(s). The full predicative meaning is then obtained and written in the output data file in order to be processed by the Supervisor module, and then used to command the robot.
g. , passive voice). The full input information could be represented as <SW1_Predicate-left>, <SW2_Object-drums>, and <SW3_Agent-toy>. The system must find a construction that allows this mapping of SWs to thematic roles. SW#_θ: Semantic Word # has thematic role θ, with # the relative position in the sentence among all Semantic Words. Preprocessing. The model is given the meaning and the sentence type desired (canonical or non-canonical) by the Supervisor module. This information is converted in the corresponding coded meaning, as described in Figure  4. The semantic words of the meaning are stored in the FIFO memory.
INTEGRATED SYSTEM The system operates in real-time in a human-robot interaction. Figure  5  shows how the communication between modules is performed. Again, the system can operate in "action performer" (AP) and in "scene description" (SD) tasks, and the Supervisor module allows the user to specify which of these tasks will be used. The Supervisor interacts with the human through spoken language to determine if he wants to run the system in train mode-to teach the robot new <meaning, sentence> pairings-or in test modeto use the corpus of pairings already learned by the robot. Thus,   there are two tasks (AP or SD), each of which can be run in two execution modes (train or test). Details for AP and SD tasks are provided in the next section. Now we briefly describe train and test modes. In train mode, the Supervisor incrementally generates one of the two training data files depending on the task (AP or SD).
EXPERIMENTS We now illustrate in detail how the system works in two distinct modes: training and testing for the AP and SD tasks. An overview is provided in Table  1. In both tasks, meanings are expressed in a predicate-argument form: for instance put(toy, left) (for Action Performing task; see Figure  3 ), or left(toy, drums) (for the Scene Description task; see Figure  4 ). During training, meaning is produced by transforming the events and relative position of objects into the respective action and scene meanings. This is achieved by analyzing the change in object positions on the ReacTable (in order to get scene meanings) and by interrogating the program generating random robot action (for action meanings). Spoken sentences are transformed from a speech record into a list of words (using the Sphinx II recognizer) and paired with the associated meaning to populate the training database.