dataesr/scientific-paragraphs-categorization huggingface dataset:
(  8 )) in linear time. Indeed, x * is computed by a modified version of the LD algorithm, which applies to a new tree obtained from the input tree R by collapsing the branches corresponding to the active set C. Then, * can also be calculated in linear time (Online Appendix). The time complexity of QPD is O(f ×n), where f is the number of iterations needed to reach the optimal solution, and n is the number of taxa. f depends on the data and the chosen starting point. We use here the LD algorithm, initializing C with the violated constraints (t i < t a(i) ) in the LD solution, which are combined to obtain a feasible point. In our experiments (described below), QPD performs 3 iterations on average with simulated trees of 110 taxa, and 69 iterations with an H1N1 influenza data set of 891 taxa.
Methods for Comparison These simulated data were used to assess the performance of our two methods (LD and QPD) and of three other methods: RTT, Langley-Fitch (LF*), and BEAST (BSMC with a SMC model and BRMC with a relaxed clock): • For LD and QPD, if the tree is rooted, the program uses the given tree; the methods are then denoted as LD* and QPD*, and we use the "variance" option (WLS). Otherwise, the root position is estimated and the methods are simply denoted as LD and QPD. • For RTT, we re-implemented the linear regression method, which takes both rooted and unrooted trees as input. Given unrooted trees, it estimates the position of the root by minimizing the sum of squared residues. Given rooted trees, the method is a standard regression and is denoted as RTT*. For dozens of data sets, we checked that our implementation gives the same result as Path-O-Gen v1.
, Yanez R. , personal communication). Moreover, other runs of BEAST were carried out to assess the accuracy of internal node date estimations. We then used the true rooted tree topology (otherwise date comparisons are meaningless), and forced it to be constant in BEAST, so that only the branch lengths were reestimated, just as with PhyML (see above). The length of the MCMC chain was set to 10×10 6 generations, with a burn-in of 10% and a sampling every 10×10 3 generations. In all of our analyses, we used meanRate estimator for rate estimations with BRMC, since it was more accurate than ucld. mean, and clock. rate with BSMC; treeModel. rootHeight was used to estimate the root date with both BSMC and BRMC. BEAST xml and log files with the 800 simulated data sets are available at http://www. atgc-montpellier.
Results The detailed results of all tested methods using above criteria are available from our web site http://www. atgc-montpellier. fr/LSD/ and in the Online Appendix. Distance-based dating methods have negligible computing times with these data (∼0. 1 seconds or less, even with unrooted trees where the root position has to be searched among all edges), except LF*, which is still fast but requires a few seconds with rooted trees. In contrast, BEAST requires a few hours with a SMC, and a dozen hours with a relaxed clock. For a fair comparison, we also have to account for tree building, as BEAST infers both the tree and the dates. However, PhyML is much faster, requiring 8 min for the largest 110-taxon trees. The computing time difference between distancebased approaches and BEAST is thus very large (see Online Appendix Supplementary Table  S1  for details), but does not correspond to gains in estimation accuracy, as discussed below.
Most results in these simulations were expected. Among distance-based methods, LF* has the most realistic (Poisson) model and obtains the best results; LD* and QPD* use a simplified (normal) version of the LF* model, and their results are not as good as those of LF*, although the difference is not significant in most cases; RTT* is the worst distance method, as expected since its model is too simple and does not account for the fact that the root-to-tip paths are highly correlated  (Drummond et al. 2003b ). The main surprise comes from the results of BEAST, expected to be the best due to its sophisticated model, being identical or very close to the data model, but in fact the results on the data sets used here do not suggest this. However, results in Figure  2  have to be interpreted with care: first, BEAST in our experiments does not use an outgroup as the best distance approaches do, and thus should be compared to the direct methods (LD, QPD, and RTT); second, BEAST shows a substantial bias with rate estimation that remains to be explained, but performs well (Fig.
(1)-(  4 ), and notably the variance term). However, this simplification was not used with BEAST, which handles such data due to its coalescent, population genetics model. To run our dating algorithms, we first have to infer a phylogenetic tree. Two methods were used, as in our simulation study, with different speed/accuracy tradeoff: (i) a fast distance-based method, namely FastME with the SPR option and distances estimated by DNADIST under F84+ (the parameter was set to 1. 0, as in other experiments); (ii) a more accurate but slower ML method, namely PhyML with SPR option and GTR+I+ 4. For both methods we analyzed the ingroup sequences only and considered both the outgroupbased rooted tree, and the unrooted tree obtained by root removal. To compute confidence intervals we used the bootstrap method with 100 replicates generated with SEQBOOT from the PHYLIP package.
Two independent MCMC chains were used per model, with a minimum of 250 million generations each, sampling every 10,000 generations. The first 25 million generations in each run were discarded as burn-in, and the Highest Posterior Density statistics for each parameter were calculated over a posterior sample of 1000 states using Tracer 1. 5. Moreover, as we observed a strong discrepancy between BEAST and the other methods regarding substitution rate estimations (see below), we also launched BEAST with the cleaned data set where identical sequences were grouped (891 taxa), and using the PhyML rooted tree topology which was kept constant all along the computations, solely sampling the branch lengths and model parameters. Such use of BEAST seems to be rather uncommon, but corresponds to the way a number of other dating programs proceed, for example, PAML  (Rannala and Yang 2007).
However, the main fact here is that distance-based and BEAST rate estimates (obtained from the complete data set, while optimizing the tree topology) widely differ (∼ 3. 0×10 -3 and ∼ 6. 5×10 -3 , respectively, with non-overlapping confidence and credibility intervals). With simulated data we found that BEAST with the specified priors and options may overestimate the substitution rate (Online Appendix Supplementary Tables  S2  and S3 ). We also observed similar discrepancies between both approaches on other biological data sets (results not shown). However, the gap here was so large that we ran BEAST with the cleaned data set and the fixed PhyML rooted tree topology that was used with other approaches. Then, BEAST rate estimates (BSMC* and BRMC* in Fig. 3a ) became much closer to the others, being still somewhat faster (∼ 3.
5×10 -3 instead of ∼ 3. 0×10 -3 ) but with mostly overlapping intervals. BEAST (combined with TREEANNOTATOR) infers a tree where ∼2% of the temporal precedence constraints are violated with the complete data set, while with the cleaned data set and the fixed PhyML rooted tree, all constraints are satisfied. The reasons for these findings are still unclear. One explanation could be that with such a large data set (> 1000 sequences) BEAST has difficulty in converging on a reasonable rooted tree topology, notably because it does not use any outgroup to root the tree. Such calculations in a Bayesian setting could simply be too heavy, thus supporting the use of simpler PAMLlike approaches for estimating dates and rates from fixed rooted tree topologies. Paradoxically, no such gap is observed for the date of the MRCA (Fig.
3b ): the best distance-based methods, namely QPD* and LF*, find nearly the same point estimates and confidence intervals as BEAST used in a standard free-topology way, at least with a strict molecular clock (BSMC), that is, end of 2008. This date is compatible with  Rambaut and Holmes (2009) ,  Lemey et al. (2009)  and  Hedges et al. (2013)  studies, but slightly older, as expected due to our larger data set incorporating more ancient strains. When using a fixed rooted tree topology, BEAST tMRCA becomes clearly older by 1 year or so, especially with a relaxed clock (BRMC*). However the discrepancy with distance methods involves only the MRCA and a few basal nodes (2 nodes with difference > 6 months between QPD* and BSMC*, and 22 with BRMC*), while for most of the nodes the dates are highly similar (Pearson correlation coefficient with all node dates: QPD*/BSMC* ≈ 0.
This is clearly impossible and shows the advantage of incorporating temporal constraints, as in QPD and QPD*. With this data set, the solution of LD has ∼7% of branches such that the descendant node is older by 1 month or more than its parent (∼1. 5% when the time difference is larger than 2 months, and ∼0. 5% with 3 months). To summarize, while the best distance-based methods (QPD* and LF*, used with FastME) are considerably faster than BEAST (especially QPD*, with negligible computing times), their dating results are quite similar. Regarding substitution rate estimation, we observe a large discrepancy between distance-based methods and BEAST, when used in the usual way estimating all parameters, including the tree topology and its root. However, with the fixed rooted tree topology, BEAST estimates of the substitution rate become similar to those of distance-based approaches.
Globally, we did not observe any obvious limitation of our algorithms compared to BEAST, with simulated as well as real data sets. Moreover, our results clearly show the importance of having an accurate root position, a difficult goal when no outgroup is available and with relaxed (realistic) molecular clock. Our algorithms require (quasi)linear computing times with rooted trees, as a function of n, the number of leaves. With unrooted trees, the computing time is (nearly) quadratic in n. This is obtained with complex algorithms, exploiting the closeness between least-squares and linear algebra; we also exploit the tree structure which makes it possible to design fast recursive procedures. This speed is important for current applications of phylogenetics. In  Mourad et al. (2015) , we analyzed a tree containing ∼24,000 dated HIV strains; running QPD* required ∼30 min on a standard desktop, while LF from r8s did not return any result after 2 weeks of computation.
ACKNOWLEDGEMENTS Thanks to  Tanja Stadler ,  Jessica Hedge ,  Gabriel Leventhal ,  Raphaël Mourad ,  Alexei Drummond , and  Romina Yanez  for help with BEAST and testing LSD on large data sets, and to  David Bryant ,  Mike Sanderson ,  Lars Jermin , and  Frank Anderson  for their comments that greatly helped us to improve the first version of this paper.
Also, firm sizes and their growth exhibit patterns which match empirical regularities. The model is agent-based and therefore allowing for enough flexibility to incorporate multiple scenarios about stock market changes. In this paper there will be three variations of the model carried out by computer simulation using the software Wolfram Mathematica. Two of the three model extensions comply with the original paper as it is a special case of the extended model. Therefore, the original model will serve as a benchmark case. The third variation is only slightly different and exhibits similar dynamics. The first influence is that the way credit is granted is depending on the stock prices. When banks estimate the creditworthiness of borrowers they also refer to the stock price as piece of information.
C. Wilcoxon Signed Rank Test In order to check whether the results are robust, a Wilcoxon Signed Rank test is conducted. The Wilcoxon Signed Rank test is performed because it allows for testing samples where a normal distribution cannot be assumed and where the variance is unknown. It is a non parametric test. The following procedure is as described by  Sheskin (2011). Basically, the test is whether the medians of two sample populations (data sets) are likely to be the same at a certain level of significance. If so, the two sample populations can be assumed to be drawn from the same distribution. Some assumptions are essential for the test: 1. The observed data either constitute a random sample of N independent pairs of items. 2. The observed data are measured at a higher level than the ordinal scale.
insert traces in the appropriate places and assign the appropriate formulas to relative pronouns and clitics. 5 All steps are done by a single Prolog tree transformation, then verified and corrected manually (either by writing an ad hoc tree transformation script or by manually editing the output, then verifying that the result remains a valid derivation). Since the FTB annotation makes 5 Subject clitics are treated as normal np subjects. Object clitics, such as the object clitic "l" in "Marie l'aime" (Marie him-clitic loves, Marie loves him) are assigned the formula (np\s)/((np\s)/◊ 1 □ 1 np) following the analysis of  Moot and Retoré (2006). By assigning these higher-order formulas to the clitics, we can assign a normal transitive verb formula to "aime" (loves). Only the reflexive clitic "se" and the clitic "y" in the construction "il y a" (there is/are) are treated as arguments of the verb (with formulas cl se and cl y , respectively).
[ 235 ]  the distinction between modifiers and arguments only for certain categories (sentences, infinitive phrases, present participle phrases, but not past participle phrases or noun phrases), this information is not explicitly annotated for many major categories (the extraction script treats these cases as modifiers for noun phrases and as arguments for other categories, such as past participle phrases). In addition, all forms of the verb "être" (to be) with a past participle as argument have been manually changed to passive whenever this was a passive construction. 6 n Step 4, which harmonizes the annotation of coordinations, many simple coordinations are treated correctly by the extraction script. Special care has been taken of the punctuation symbols, which in many cases are manually given a coordination-like formula assignment, and of gapping, which must be treated manually as well (the treatment of gapping is presented in detail in Appendix B.
4). Finally, relative pronouns are treated by the extraction script as arguments of the immediately following verb, which is correct in many cases but needs to be manually verified for all occurrences. In sum, after a pass of the extraction script, many constructions are manually verified and corrected. To give an indication of the amount of manual cleanup done: simply running the Prolog script on the treebank results in a lexicon with 5,240 distinct formulas assigned to the words of the lexicon  (Moot 2010b ) (note that this is without a distinction between passives and past participles), but after cleanup there are 1,101. From Section 4. 1 to Section 4. 5, we will treat each of the stages of the extraction algorithm in turn.
Verb clusters As discussed in Section 3, verb clusters (which include clitics and some adverbs) and the arguments of verbs are sisters in the FTB annotation trees. While this wasn't a problem for the simple cases treated in the previous section, this becomes problematic in the case of a complex verbal group. Figure  6  shows an example corresponding to sentence (4) (Figure  1   In a categorial setting, we obtain a much simpler analysis if the VN arguments are arguments of the embedded verbs instead: in the current case, we'd like the infinitival group to be the argument of the past participle "pu" (past participle of the verb "pouvoir", can). At the bottom of Figure  6  we see the rightward branching structure which results from the corpus transformation. Note also how the adverb "déjà" (already) is assigned the VP-modifier formula (np\s x )/(np\s x ) which is parametric for the type of sentence (in essence, this is a formula with an implicit first-order quantifier ranging over the different sentence types, see The extraction script automatically rebrackets the verb clusters as indicated above and treats any arguments of the verb cluster as arguments of the final verb in the cluster.
To give an idea of how effective this strategy is in the current context and with the reduced lexicon of 761 formulas: using the supertagger of  Clark and Curran (2004)   de (np\s di )/(np\s i ) [Lex] s' cl r [Lex] attaquer likely formula to each word, 90. 6% of the words are assigned the correct formula. When assigning each word all formulas with probability greater than 1% of the most likely supertag (for an average of 2. 3 formulas per word), the supertagger assigns the correct formula to 98. 4% of all words (for the FTB section of the TLGbank, using ten-fold crossvalidation). Supertagging does not solve the problem of data sparseness: for the supertagger, formulas which are seen only once or twice in the training data are not fundamentally different from formulas which do not occur at all.
To give an indication that even the current parser implementation performs well: the only other parsing statistics I've seen for the Itipy corpus are given by Nguyen (2012), who reports that a total of 18. 5% of the sentences in the Itipy corpus were successfully parsed using an off-the-shelf parser. 7. 2 http://richardmoot. github. io/TLGbank/ An unfortunate exception to this is the main part of the Typelogical Treebank itself: being a derived work of the French Treebank, it is available only to those who have a license for the original treebank (contact to author for access to the private Git). The Sequoia part of the treebank and the models derived from the complete treebank are freely available, however. [ 250 ] conclusion We have shown how the French Treebank has been semi-automatically transformed into a set of derivations in multimodal type-logical grammars: the TLGbank.
acknowledgments I would like to thank Michael Moortgat and Noémie-Fleur Sandillon-Rezer for our work together on similar grammar extraction tasks. In addition, Michael Moortgat's insights on how to design multimodal type-logical grammars have deeply influenced all aspects of the design of the current treebank. I also thank Yannick Parmentier and Denys Duchier for organizing the ESSLLI 2013 workshop in Düsseldorf where I presented this material and all workshop participants for their feedback. I would also like to thank the anonymous referees for their many useful comments. I, of course, take full responsibility for any remaining errors.
4. The sinusoidal excitation varies from f e = 0 to 5 Hz with amplitudes varying from A = 0. 5 to 3. 5 V pp , corresponding to a vertical displacements ranging from h e = 0. 5 mm to 3. 5 mm. The pendula are driven near the volumetric resonant frequency and its double to allow the development of surface modes during the mechanical excitation through parametric instability. The motion of pendula has been recorded from the top with a video camera. The data processing has been performed with the software Image J and the plug-in MJ Track. This plug-in enable to track the motion of each pendulum and thus to determine the distance R i (t) between the center of the ring and a pendulum at a fixed  time t. Applying a spatial fast Fourier transform on R i (t), the amplitude of each vibration mode of order n can be obtained.
ACKNOWLEDGMENTS Thanks to  Jean-Marc Grégoire  and  Jean-Yves Tartu  for their fruitful help in the design of the experimental setup. Jennifer Chaline  was the recipient of a PhD fellowship from the  Région Centre (France). Financial support from the  University Francois-Rabelais doctoral school of Tours  was granted to finance a research stay on this topic at the  Universidad Politecnica de Valencia (Spain). A. Mehrem is recipient of a research fellowship from  Generalitat Valenciana  ( Santiago Grisolia program ).
Tribological Tests under Controlled Environment Friction experiments were performed with a linear reciprocating pin-on-flat tribometer placed inside an high vacuum (HV) chamber and allowing gas introduction, which has been extensively described previously  19. The calibration of the optical sensors was performed using standard weights for the normal force and a pulley for the tangential force. The friction coefficient is calculated as the ratio of the tangential force and the applied normal load. A time-averaging data-reduction method is applied for processing the friction data by averaging the data acquired during one sliding cycle. To avoid end-point effects, only the data corresponding to a slider position between 1/8 and 7/8 of the stroke length were considered. The standard deviation of the values acquired during this period is also evaluated.
Analysis of a-C:H:Si:O after Tribotesting The wear tracks and the contact region on the pin were analysed by scanning electron microscopy (ESEM-FEG XL30, FEI-Philips, UK) with energy dispersive X-ray spectroscopy (EDX, SDD Xmax 50 mm 2 , Oxford Instruments, UK). The EDX analyses were performed using a 5 kV acceleration voltage. A Zygo NewView TM 6300 scanning white-light interferometer (SWLI) (Zygo Corporation, Middlefield, CT, USA) was used for characterizing the wear rate of the a-C:H:Si:O films. All measurements were performed with a 20X objective (vertical resolution < 1 nm). A Matlab ¨ (MathWorks ¨, Natick, MA) script was developed for processing the profilometry data. To determine the worn volume, the images were first leveled and then the volume removed upon sliding was computed. Raman spectra were acquired using a near-field scanning Raman spectrometer (NT-MDT NTGRA TM Spectra Upright, NT-MDT Co.
Acknowledgements This material is based upon work supported by the  National Science Foundation  under Grant No. DMR-1107642  and by the  Agence Nationale de la Recherche  under grant No. ANR-11-NS09-01  through the  Materials World Network program. F. M. acknowledges support from the  Marie Curie   International Outgoing Fellowship for Career Development  within the 7 th European  Community Framework Program  under contract no. PIOF-GA-2012-328776. K. K acknowledges  RŽgion Rh™ne-Alpes  and  University de Lyon  for their fellowship support through  CMIRA and PALSE program  respectively. J. B. M. , F. M. and R. W. C. acknowledge support from the  Advanced Storage Technology Consortium ASTC  (grant  2011-012 ). The authors would like to thank  Dr. C. Jaye  and  Dr. D. A. Fischer  for the kind assistance with the NEXAFS measurements at the  National Synchrotron Light Source.
Sequence alignment and processing To enable comparable analysis of high-throughput sequencing data sets, we used a uniform alignment and preprocessing pipeline. Reads were sequentially aligned using Bowtie 2 v. 2. 0. 5  (Langmead and Salzberg 2012). All reads mapping to human rRNA and tRNA sequences were filtered out. The remaining reads were aligned to APPRIS principal transcripts (release 12)  (Rodriguez et al. 2013)  from the GENCODE mRNA annotation v. 15  (Harrow et al. 2012). For all transcript level analyses, reads that map only to coding regions were used. For details, see Supplemental Methods.
Ribosome profiling sample identity verification The cell line identity for all ribosomal profiling libraries were verified by comparing empirically generated genotype calls to the reference genotypes. Specifically, we utilized SAMtools mpileup utility in combination with BCFtools  (Li et al. 2009 ) to generate genotype calls from the ribosomal profiling read alignments. Finally, a custom Perl script was used to compare the number of perfect matches between empirically called genotypes and the reference genotype that was available from the HapMap and the 1000 Genomes Project (The International HapMap 3 Consortium 2010; The 1000 Genomes Project Consortium 2012). For details, see Supplemental Methods. We included all variant calls provided by both release and pilot data sets without additional score or source filtering.