dataesr/scientific-paragraphs-categorization huggingface dataset:
This module includes an xml file, which contains traces left by all learners in the Moodle log file and also contains the datalib file. The module uses the same Moodle database. The datalib file of Moodle platform has been modified in order to be able to record and save all traces of learners connected to the Moodle platform. The following figure shows the datalib file. In the next section, we present our Model based on AUML methodology. AUML or agent UML is a support notation for agent-oriented Multi-agents systems development. It consists in using the UML modeling language and extending it in order to represent agents, their behavior and interactions among them.
D. Distances curves between the target and previous traces The following figure displays the distances curves between the target and previous traces in order to shows the distance between them, these curves are generated in real times starting from the results of retrieval phase. These curves display also the history of these distances. For Tutor, the distances curves present very important information about the change of the distances database. The Tutor will be able to take her decision and to choose the trace most similar to the target trace. Several researchers have focused on classical versus dynamic CBR architectures where target case are static versus dynamic, but all these systems have been used static CBR cycle. Consequently, the Incremental Dynamic CBR approach has been proposed as an appropriate alternative, which have demonstrated its efficacy.
Dynamic Static REBECAS  [18] , AuRA  [16]  , SAPED  [2] , CASEP2  [33] , SBR  [4]  CICLMAN  [28] , RoBoCats  [20]  , S-MAS  [24]  MCBR  [17] , CBR-TEAM  [26]  Dynamic Dynamic IDCBR-MAS VIII. CONCLUSION AND FUTURE WORK Our system allows connecting and comparing the current situation (target trace) to past situation (previous traces) that are stored in a database. The continuous analysis of information coming from the environment (learner's traces) makes it possible to suggest to various actors (learners and tutor) possible evolutions of the current situation. The Multi-Agents architecture that we propose is based on three layers of agents with a pyramidal relation. The lower layer allows building a representation of the target case. The second layer implements a dynamic process: search for past situations similar to the current one.
The BrainScaleS hardware system A very efficient way of interconnecting multiple VLSI (Very Large Scale Integration) modules is offered by socalled wafer-scale integration. This implies the realization of both the modules in question and their communication infrastructure on the same silicon wafer, the latter being done in a separate, post-processing step. The BrainScaleS wafer-scale hardware  Schemmel et al (2010)  uses this process to achieve a high communication bandwidth between individual neuromorphic cores on a wafer, thereby allowing a highly flexible connection topology of the emulated network. Together with the large available parameter space for neurons and synapses, this creates a neuromorphic architecture that is comparable in flexibility with standard simulation software. At the same time, it provides a powerful alternative to software simulators by avoiding the abovementioned computational bottleneck, in particular owing to the fact that the emulation duration does not scale with the size of the emulated network, since individual netowrk components operate, inherently, in parallel.
Neuromorphic testbench and investigated distortion mechanisms In this section we introduce the BrainScaleS neuromorphic wafer-scale hardware system and its executable system specification, henceforth called the ESS, as the testbench for our studies. The system's hardware and software components are only described on an abstract level, while highlighting the mechanisms responsible for distortions of the emulated networks. Finally, we identify the three most relevant causes of distortion as being synapse loss, synaptic weight noise and non-configurable axonal delays. 2. 1 The BrainScaleS wafer-scale hardware Fig. 1  shows a 3D-rendered image of the BrainScaleS wafer-scale hardware system: the 8 inch silicon wafer contains 196 608 neurons and 44 million plastic synapses implemented in mixed-signal VLSI circuitry.
Software framework The utilized software stack  Brüderle et al (2011)  allows the user to define a network description and maps it to a hardware configuration. The network definition is accomplished by using PyNN  Davison et al (2008) , a simulator-independent API (Application Programming Interface) to describe spiking neural network models. It can interface to several simulation platforms such as  NEURON Hines et al (2009)  or  NEST Eppler et al (2008)  as well as to neuromorphic hardware platforms  Brüderle et al (2009) ;  Galluppi et al (2010). The mapping process  Ehrlich et al (2010) ;  Brüderle et al (2011)  translates the PyNN description of the neural network structure, as well as its neuron and synapse models and parameters, in several steps into a neuromorphic device configuration.
This translation is constrained by the architecture of the device and its available resources. The first step of the mapping process is to allocate static structural neural network elements to particular neuromorphic components during the so-called placement. Subsequently, a routing step is executed for establishing connections in between the placed components. During the final parameter transformation step, all parameters of the network components (neurons and synapses) are translated into hardware parameters. First, the model parameters are transformed to the voltage and time domain of the hardware, taking into account the acceleration and the voltage range of 0 V to 1. 8 V Millner et al  (2010). Second, previously obtained calibration data is used to reduce mismatches between ideal neuromorphic circuitry behavior and real analogue signal hardware behavior.
Investigated distortion mechanisms Reviewing the hardware and software components of the BrainScaleS wafer-scale system (Sec. 2. 1 and 2. 2) leaves us with a number of mechanisms that can affect or impede the emulation of neural network models: neuron and synapse models are cast into silicon and can not be altered after chip production limited ranges for neuron and synapse parameters discretized and shared parameters limited number of neurons and synapses restricted connectivity synapse loss due to non-optimal algorithms for NPhard mapping parameter variations due to transistor level mismatch and limited re-write precision non-configurable pulse delays and jitter limited bandwidth for stimulation and recording of spikes It is clear that, for all of the above distortion mechanisms, it is possible to find a corner case where network dynamics are influenced strongly.
To this end, we have chosen three very different network models, each of which highlights crucial aspects of the biology-to-hardware mapping procedure and poses unique challenges for the hardware implementation. In order to facilitate the comparison between simulations of the original model and their hardware implementation, all experimental setups were implemented in PyNN, running the same set of instructions on either simulation back-end. For each of our benchmark models we define a set of specific well-quantifiable functionality criteria. These criteria are measured in software simulations of the ideal, i. e. , undistorted network, which is then further referenced as the "original". Assuming that the broad range of hardware-specific distortion mechanisms affects various network parameters, their impact on these measures are investigated in software simulations and various changes to the model structure are proposed in order to recover the original functionality.
Architecture Self-sustained states in spiking neural networks are known to be exquisitely sensitive to the correlation dynamics generated by recurrent activity  Kumar et al (2008) ; El  Boustani and Destexhe (2009). Because of this sensitivity, a model of self-sustained activity within the asynchronous-irregular regime can provide a strong comparison between hardware and software platforms, by requiring the hardware network to reproduce the low firing, weakly correlated, and highly irregular dynamics of this state. Notably, it is often observed that this activity regime provides a good match to the dynamics observed experimentally in the awake, activated cortex  Destexhe and Pare (1999) ;  Brunel (2000) ;  Destexhe et al (2003). Additionally, one can note that the selfsustained activity regime provides an interesting test of the BrainScaleS hardware system, as in this state, the model network is not driven by external Poisson input, but has dynamics dominated by internally generated noise  Destexhe and Contreras (2006) , beyond the initial brief Poisson stimulation to a small percentage of the network.
If no transition point could be identified, the transition was fixed at at either 25 or 0 stimulated MCs, depending on whether all success ratios were above or below 0. 5. When there were no valid attempts for a certain delay/stimulus pair, its success probability estimate was replaced by the median of all valid activation attempts for that particular time delay ∆T (this only occurred sporadically in ESS and PYR population size scaling with less than 15 PYR cells per MC). After identifying the transition point for every time delay ∆T , intermediate values were interpolated linearly. Finally, the interpolated values were Gaussfiltered (µ = 0. 25 × step size for ∆T in the dataset) to better approximate the true 0. 5 iso-probability contour.
Acknowledgments We would like to thank  Eric Müller  for his invaluable help with the software infrastructure;  Jens Kremkow  for his support with the synfire chain model;  Mitja Kleider ,  Christoph Koke ,  Dominik Schmidt  and  Sebastian Schmitt  for providing measurements from the  Brain-ScaleS  neuromorphic system, as well as the necessary framework. This research was supported by  EU  grant # 269921  ( BrainScaleS ) and the  Manfred Stärk Foundation.
Data Availability The authors confirm that all data underlying the findings are fully available without restriction. The three benchmark models, the performed simulations, as well as the analysis and compensation methods are fully described in the manuscript and the supporting information. For the original L2/3 network with detailed neuron and synapse models, we provide the complete simulation data at: http://brainscales. kip. uni-heidelberg. de/ largePublicContent/plos_one_2014_fit_data. tar. gz The executable system specification of the BrainScaleS wafer-scale neuromorphic hardware as used for the simulations in this article is provided on a Linux live-system available at: http://brainscales. kip. uni-heidelberg. de/ largePublicContent/plos_one_2014_ess_live_system. iso.
Core collection MSTRAT software (v 4. 1) developed by  [49]  used the Mmethod proposed by  [50]  and allowed the construction of core collections that maximize the number of observed alleles in the SSR data set. We defined a core-collection from the metapopulation of 3,000 individuals using MStrat software and the 500 SSRs. This core-collection (Call) consisted in 1,000 individuals, including the founders of all breeding populations; it was built to represent the genetic diversity of the entire meta-population (all) with minimal redundancy (which is the aim concept of corecollection building). In each replicate of the domestication step, five core collections of 1,000 individuals were designed and ranked first by the number of SSR alleles captured; core-collections exhibiting the same allelic richness (determined by the total number of alleles represented) were then ranked using Shannon's index as second criterion.
Estimation of diversity indices Diversity indices, such as genetic variance estimates, the level of differentiation in quantitative trait (Q ST ) following  [51] , and Fstatistics following  [52]  for each pair of populations and for all types of markers, were calculated with quantiNemo. To calculate unbiased heterozygosity and compare it to published data  [27]  on highly polymorphic SSR markers, we selected all SSR with more than 10 alleles per locus at the end of the domestication step. Data analysis was performed using the ''Excel Microsatellite Toolkit''  [53]. We also calculated allele frequency for each SNP and QTL locus, in order to filter out rare SNPs with minor allele frequency (MAF) below 5% that would have biased association tests.
Population structure and relatedness Population structure was calculated on the 3,000 individuals using 500 SSR with STRUCTURE software version 2. 3. 3  [54]  accessed through Bioportal  [55]. We used an admixture model varying the ancestral number of population (K) from two to five, in order to identify the best K level of population subdivision. Within STRUCTURE, we allowed an iterative process with a burn-in phase of 15,000 iterations and a sampling phase of 15,000 replicates. Five replicates of each assumed K level subdivision were compared to estimate group assignation stability. Outputs were visualized and interpreted with Structure Harvester web v0. 6. 93  [56]. The optimal group number was chosen based on the estimated 'log probability of data'. Realized relationship matrix (RRM;  [57]  was calculated using R  [58]  using all filtered SNPs (MAF.
Linkage disequilibrium LD measures were performed with the R package LDcorSV  [59]  which corrects for the bias due to population structure and relatedness (r 2 SV ). LD was measured in two different positions: in neutral genomic regions and around each QTL. In neutral positions, mean and median values of r 2 were calculated between each pair of SNP within five arbitrarily chosen windows of 600 kb. Around QTLs, r 2 was calculated between the QTL locus and all SNP located within 300 kb. We used the Hill and Weir formula  [60]  for describing the decay of r 2  SV and we characterized LD by the distance corresponding to a r 2  SV value of 0. 2.
Genome-wide association GWAS were performed using the multi-locus mixed-model (mlmm) approach  [61] , including the population structure as fixed covariant in the mixed model. This R script implements a forward-backward stepwise approach to include significant effects in the mixed model, while re-estimating the variance components of the model at each step. We ran mlmm on the meta-population of 3,000 individuals and on the core-collection with a random polygenic term, with a variance proportional to the estimated RRM and a fixed population structure term (three groups) consisting in ancestry fractions estimated by Structure software. We also ran mlmm on each sub-population with a random polygenic term only. Maximal number of forward steps was set to 25. For model selection we chose the multiple-Bonferroni (mBonf) criterion, selecting the largest model in which all cofactors have a P-value below a Bonferroni-corrected threshold (we used a threshold of 0.
We performed BLR analysis with the R package BLR 1. 3  [63]. The lambda parameter was set as random, sampled from a gamma distribution with rate = 0. 0001 and shape = 0. 53  [65]. The initial value of l 0 was calculated using the heritability rules given in  [20] : l 0 ~2 Ã n {1 Ã P n i~1 P m j~1 X 2 ij Ã 1{h 2 ð Þ h 2 where h 2 is the narrow-sense heritability, n is the number of individuals, m is the number of SNPs and X is the matrix of genotypes. s 2 e were chosen from the prior x {2 (v e ,S P m j~1 X 2 ij Ã h 2. We allowed an iterative process with a burn-in phase of 10,000 iterations and a sampling phase of 40,000 replicates. In marker-assisted RR (cofRR) we combined RR-BLUP with the effects of markers previously detected with mlmm. Effects of significant markers and population structure were estimated as described for cof method and remaining SNPs were used in a RR model as described earlier.
Test on pine data The method cofRR was tested on a real data set of loblolly pine described in  [66]  using a 10-fold cross-validation schema. Data consisted of 926 individuals genotyped with 4,853 SNPs and phenotyped for 17 traits. Information about population structure was not available. For the analysis, markers with more than 20% of missing data were removed in both training and validation sets. For the remaining loci, missing genotypes were imputed with the mean. In the training set, we applied a filtering of 5% on minor allele frequency (MAF. 0. 05). Kinship matrix (RRM) was calculated as described above. GWAS were performed using mlmm approach setting the maximal number of forward steps to 10. To limit the detection of false-associated cofactors, we choose the extended Bayesian information criterion (EBIC  [67] ) for model selection, which is more stringent than the multiple Bonferroni criterion  [61].
Q ST was measured as an index of phenotypic distances between each pair of simulated sub-population. Q ST values were always higher for selected traits than for neutral ones (Figure  S3  in Information S1). Overall Q ST values reflected F ST values with the TE population diverging more from the other two populations. However, since no published data on Q ST are available yet, we were unable to compare our data with actual observations. In conclusion, the simulated populations matched observed data reasonably well. We thus considered that the demographic scenario was able to generate pertinent genotypic and phenotypic data allowing further GWA studies and the building of GS models.
Simulated data Because high density SNP markers (over 20 K) are still unavailable in grape, we have used simulations in order to test both GWAS and GS. Three populations of 1,000 individuals were simulated in order to reflect real data  [22] : three genetic pools of high heterozygosity (He = 0. 74) but with relatively low differentiation (F ST values of up to 0. 07). The simulation of genomes and causative mechanisms (genetic architecture) in different species is complex. There are many different forms of genomic variability, a wide variety of plausible demographic and evolutionary histories, as well as considerable uncertainty about how mutation and recombination rates vary and about the mode and distribution of gene action  [68]. We chose a forward-simulation strategy and developed a complex demographic scenario based on historical information, which was implemented using quantiNemo software  [46].
Through GWAS, an average of 5. 9 to 30% of the QTLs could be identified, the best results being obtained for simple non-structured traits. Genomic estimated breeding values (GEBV) were calculated using the same data set. Predictions for simple traits within population were always more accurate, with a very high accuracy of 0. 9, while accuracy dropped to 0. 2 for complex trait and betweenpopulation predictions. Accuracy also depended on the pairs of populations in relation with the mean phenotypic differences between the training and candidate populations. The highest prediction accuracy (up to 0. 9) was obtained using the combined GWAS-GS model (cofRR). Finally, for grapevine breeding or for other important economic crops with the same characteristics, we recommend using the combined prediction model with a core-collection as training population.
Some are generic and provide many algorithms, while others are more specialized. This includes not only simulators  [54] , but also packages for graphical depiction and analysis of networks  [43, 55, 56] , and databases of reactions and kinetic parameters  [57] , to name but a few examples. In some cases these packages can even work in an integrated way, such as in the Systems Biology Workbench (SBW) suite  [58]. Another area of cell physiology where mathematical modeling has been used to describe complex molecular-scale dynamics is the study of ion channels. Molecules (both large and small) move back and forth across a cell membrane, to ensure that conditions for homeostasis are met  [42]. Some molecules are small enough (and soluble to lipids) to diffuse across the membrane, while others require energy, working against electrochemical gradients between the outside and the inside of the cell  [42].
GROMOS force fields The GROMOS (Groningen Molecular Simulation) force fields are united-atom force fields that that were developed in conjunction with the software package of the same name to facilitate research efforts in the field of biomolecular simulation in a university environment  [125]. Its functional form varies from Equation (31) in the dihedral term, and differs from other force fields in its goal of reproducing enthalpies of hydration and solvation. The initial GROMOS force field (A-version) was developed for applications to aqueous or apolar solutions of proteins, nucleotides and sugars. However, a gas phase version (B-version) for the simulation of isolated molecules is also available  [125]. Important versions of the GROMOS force fields include GROMOS 43A1  [126]  (improved treatment of lipid bilayers), GROMOS 45A3  [127]  (relevant to lipid membranes and micelles) and GROMOS 53A5 and 53A6  [128]  (recommended for the simulation of biomolecules in explicit water).
Molecular dynamics methods Examining the dynamics of a system at an atomic scale requires beginning with a model having atomic-level resolution. For biological macromolecules, this may be experimentally obtained from nuclear magnetic resonance (NMR) spectroscopy or X-ray crystallographic data. Although electron microscopy data does not provide structures with atomic resolution, this data can be combined with structural data from NMR or crystallography to obtain a high-resolution structure. NMR, crystallographic and electron microscopy structures of bio-macromolecules can be downloaded from the Protein Data Bank (http:// www. pdb. org). In the absence of experimental data, homology modeling may be used to generate the 3D structure of a protein (the target) using its amino acid sequence and an experimentally-available 3D structure of a homologous protein (the template).
Acceleration of quantum mechanics with graphical processing units Graphics processing units (GPUs) have led to substantial accelerations in highperformance computing, with significant advancements for costly QM calculations. GPU-accelerated code has been developed for the HF method  [223] , correlated ab initio methods  [223] [224] [225] , semi-empirical methods  [226, 227] , and DFT  [228]. In fact, GPU acceleration is now implemented in many quantum chemistry programs. NVI-DIA reports the following programs as having GPU support: ABINIT, BigDFT, CP2K, GAMESS-US, GAMESS-UK, GPAW, LATTE, MOLCAS, MOPAC2012, NWChem, OCtopus, PEtot, Q-Chem, QMCPACK, Quantum Espresso, TeraChem, and VASP  [208]. However, the most extensive use of GPUs in a QM code  [229] [230] [231] [232] [233] [234]  has been implemented in TeraChem, an electronic-structure package specifically designed for use with GPUs.