dataesr/scientific-paragraphs-categorization huggingface dataset:
SoC Overview An overview of the CCNV1-A1 chip is shown in Figure  1. The chip consists of a three-core microcontroller featuring a rich set of peripherals, a 512 kiB SRAM (static random-access memory) and a 768 kiB eFlash. An overview of the CCNV1-A1 chip is shown in Figure  1. The chip consists of a three-core microcontroller featuring a rich set of peripherals, a 512 kiB SRAM (static random-access memory) and a 768 kiB eFlash. The peripherals associated with the GNSS receiver include two 256-point FFT (fast Fourier transform) cores, as well as a dedicated module supporting signal acquisition (see Figure  1 ; GNSS Controller). The microcontroller peripherals include a number of communications interfaces (UARTs, SPIs, I2C, CAN, 1WIRE) and GPIO (general-purpose input/output), as well as timers, a watchdog and a battery backed-up RTC (real-time clock) domain.
System-on-Chip Verification A complete dual-frequency single-chip GNSS receiver including radio was implemented in a 110 nm CMOS technology (see Figures  24  and 25 ). A two-wideband of 52 MHz high gain RF AFE of 131 dB with NF 2. 3 dB was integrated with a triple-core 32-bit RISC navigation and application processor with user accessible eFlash. The radio part includes the LNAs, active baluns, mixers, ADPLLs and IF with I/Q ADCs. The total chip area is 34 mm 2. The chip used has been manufactured and is currently in the measurement phase (see Figure  26 ). The manufactured SoC has been successfully used to acquire the GNSS signals using an in-house developed antenna (see Figure  27 ). The acquisition time used was 1 ms and the SNR (signal-to-noise ratio) obtained was 16. 4 dB. The CCNV1_A1 was also used to develop positioning software.
A further computational perspective posits that humans learn sequences as recurrent models, that is, by decoding relevant features from former adjacent items in the string  [23] [24] [25]. Overall, contrary to human experiments in which subjects typically see a few dozen examples, all considered computational models have been trained with large datasets, and sometimes with significant overlap between training and test sets, making it difficult to draw any substantial comparisons with human cognition. Implicit and explicit learning. Another important aspect investigated in the experimental AGL literature regards the distinction between implicit and explicit learning. There are aspects of knowledge for which we have little conscious introspection, such as language  26. Experimental evidence has suggested that AGL occurs implicitly, that is in an automatic and nonintentional way  [27] [28] [29].
This result is in accordance with the assumption that working memory has restrictions on the number of items (i. e. hypotheses) explicitly accessible at any given time  39, 40  , thus limiting abilities to process complicated rules (e. g. based on long-distance dependencies). On the contrary, implicit processes possibly rely on different mechanisms, capable of dealing with larger amount of information at the cost of reduced flexibility  [41] [42] [43]. Purpose of the study. In this study we tested 4 grammars spanning over 3 Chomsky's hierarchy levels. Both human participants and artificial neural networks were trained and tested on datasets generated from those grammars. Importantly, we aimed to use comparable amounts of training for humans and artificial neural networks, which were composed of fully-connected layers, whose parameters were trained via backpropagation (through-time in case of recurrent networks).
Materials and methods Artificial grammar datasets. We performed 4 experiments with different artificial grammars (Fig. 1A ), each composed of the same amount of correct and incorrect sequences. According to the Chomsky hierarchy introduced above, two grammars were regular, referred in the following as grammar A and grammar B, one was context free and one was context sensitive. In the regular grammars, the sequences' length ranged from 2 (grammar A) or 3 (grammar B) to an arbitrary maximum length of 12 items. Longer sequences were discarded. Grammar A's vocabulary was composed of 4 letters, while grammar B counted 5 letters. The dataset from context free and context sensitive grammars (respectively type II and type I of the Chomsky hierarchy) were composed following an approach based on symmetry  10, 13.
Artificial neural networks. Experimental design. The neural network design was composed of two parts: a first parameter search, and a subsequent comparison with human behavior. Both were implemented using the Keras library  48  , back-ended in Tensorflow  49. Altogether, we trained feedforward and recurrent architectures, each composed of a series of fully connected layers. All networks were trained to classify the sequences as correct or wrong, employing the same dataset (i. e. 4 grammars) and the same amount of trials as in the human experiments. Regarding the parameter search, we aimed at determining the parameters whereby each architecture scored closest to human performance. We tested a range of networks, varying the number of layers and the learning rate, defining a 2-dimensional space (see Fig.
Data analysis. In the human experiments, we recorded accuracy and reaction times at each trial. Both measures were averaged over blocks and tested by means of Bayesian ANOVA  55, 56. Each analysis provides a Bayes Factor (BF) for each independent factor (e. g. BLOCK). As widely accepted  57  , we considered BF above 3 as substantial evidence in favor of the alternative hypothesis, over 20 as strong evidence, and BFs beyond 100 correspond to very strong evidence. Conversely, a BF below 0. 3 suggests lack of effects  58, 59. In all our analyses we reported BFs and estimate's error.
e. Gated Recurrent Units or Long-Short Term Memory recurrent networks, but also residual or convolutional networks). However, an exploratory analysis using LSTM on the current dataset (maximum 500 examples, trained over 1 epoch) using the same parameters as the recurrent networks suggested that in our study (i. e. on these grammars and using these parameters) potentially more training examples would be necessary to properly train a gated version of our recurrent networks than the number of examples used by human subjects (see supplementary figure  A5 ). On the other hand, a complete search in the parameters space (i. e. learning rate, number of layers/neurons) would may still provide a gated recurrent network (e. g. LSTM) with the ability to perform the task with the same limited number of training examples.
Related work on artificial grammars and neural networks. In our simulations, we contrasted the performance of feedforward and recurrent architectures in AGL tasks. The two architectures are substantially different, and represent distinct functional processes. Feedforward networks allow the information to flow one way from input to output. In feedforward architectures the sequence is processed at once, with a single sweep. A very similar approach is the competitive chunking network  21  , in which the whole sequence is processed as one to identify hierarchical, nested patterns (i. e. the chunks). In this model several layers can be involved in the process before classification occurs  68. However, our results suggest that FF networks having only 2 layers provide the best classification in AGL tasks within our experimental constraints (with a limited training dataset, comparable to human learning).
Interestingly, when trained over larger dataset (~ 10,000 sequences) we observed that recurrent network still perform better than feedforward ones on regular grammars (on both GA and GB, FF ~ 0. 85%, RR ~ 0. 95%-not shown), but the difference reduces in our Context-Free and Context-Sensitive grammars (both architectures on both grammars ~ 75%not shown). In addition, differently than previous studies on artificial grammar learning in humans, we adopted an experimental design in which training and testing occurred at the same time, allowing a direct comparison of learning dynamics (i. e. , learning curves) between humans and artificial neural networks. Previous studies had compared recurrent networks and a chunking model (similar to a feedforward architecture, see paragraph "Related work on artificial grammars and neural networks") with human behavior, but with opposite results  23, 86.
Acknowledgements This work was funded by an  ERC   Consolidator Grant  P-CYCLES number  614244  to RV, and a  CRCNS ANR-NSF  Grant "OsciDeep" to RV, and an  ANITI (Artificial and Natural Intelligence Toulouse Institute ,  ANR-19-PI3A-0004 ) Research Chairs to RV. We are grateful to  Jacob Martin  for fruitful discussion about the Chomsky's hierarchy. Parts of this work were done while RV was visiting the  Simons Institute for the Theory of Computing.
The supernatant was discarded, the pellet washed with 300 µl of cold 70% ethanol, and the pellet dried in a SpinVac (Savant Thermo Fisher Scientific) for 20 min. To eliminate trace contaminates, the RNA was resuspended in 400 µl high salt buffer and re-ethanol precipitated at -20°C. The pellet was recovered, washed and dried. The final pellet was resuspended in 20 mM Tris-base, pH 7. 5, 0. 1 mM EDTA and stored at -20°C until needed. The concentration was determined using an absorbance at 260 nm of 32 µg/ml/cm, which was based on the calculated values of Oligo 7 software (Molecular Biology Insights, Inc, Colorado Springs, CO).
We used the Ded1-K192A (GAT) mutant in motif I as a negative control as it had no detectable ATPase activity  (46). Reactions were started by adding 1 mM final of ATP and taking aliquots over the time course. The reactions were stopped by making the solutions approximately 60 mM final in EDTA. The Malachite green solution was added as previously described  (46)  and the absorption measured with a Tecan NanoQuant Infinite M200Pro microtiter plate reader at 630 nm. Enzymatic reaction velocities were determined by a linear regression fit over the initial linear phase of the reaction with five data points over a time course of 45 min using optimized protein concentrations. We used a serial dilution of Phosphate Phosphorous Standard for IC (Fluka Sigma-Aldrich) for each experiment to determine the corresponding phosphate concentration from the absorption.
6. 9 software (Bio-Rad). Rodney Rothstein for the G49 and G50 yeast strains, and Claude Thermes, Yves Daubenton, Erwin  Van   (13). In brief, 0. 5 ml fractions were collected starting from the top of the gradient and subjected to nano-liquid-chromatography electron-spray mass spectrometry analysis b ENO2, SSA2 and FBA1 are reference proteins; they showed the strongest signals in the fraction at the top of gradient. c Fractions correspond to those shown in Supplemental Figure  S7   6, 7 ---a Data was collected and analyzed as previously described  (13). Equivalent fractions as shown in Table  1  were subjected to IgG-Ded1 pull-downs with protein-A Sepharose beads and subjected to mass spectrometry analysis. b ENO2, SSA2 and FBA1 are reference proteins; they showed the strongest signals in Table  1.
kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP14 TH_4306 pSRP14::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SEC65 TH_6981 pSEC65::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP68 TH_3180 pSRP68::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP72 TH_3294 pSRP72::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP101 TH_7193 pSRP101::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon GFP-SRP14 BY4741 MATa his3∆1 leu2∆0 met15∆0 ura3∆0 SRP14-GFP::HIS3MX6 Life Technologies RDM 50-94C MATα sec62-1, ura3-52, leu2-3,112, his4, suc+/-MATa ura3-1trp1-1 leu2-3,112 his3-11,15 can1-100 RAD5 ADE2 trp1-1::DsRedExpress2-HDEL-TRP This study Escherichia coli Rosetta (DE3) 70954 F-ompT hsdSB(rB-mB-) gal dcm Novagen DH5α DH5α F -φ80lacZΔM15 Δ(lacZYA-argF)U169 recA1 endA1 hsdR17(r K -, m K + ) phoA supE44 λ -thi-1 gyrA96 relA1 New England Biolabs Purified His-tagged recombinant proteins.
ACKNOWLEDGMENTS We thank  Caroline Lacoux  and  Stéphanie Ørum  for helpful discussions,  Martin R Pool  for the Sec65 IgG and for the pMW295 and pMW299 SRP plasmids,  Benjamin Glick  for the  KAR2-RFP_YIPlac204 ,  Jiří Hašek  for the pFA6a-mCherry-NatNT2 plasmid,  Michael Lisby  and.
FUNDING This work was supported by  Centre National de la Recherche Scientifique ;  HeliDEAD grant  [grant number  ANR-13-BSV8-0009-01  to NKT] from the  Agence Nationale de la Recherche ; and  Initiative d'Excellence program  from the  French State grant DYNAMO [grant number   ANR-11-LABX-0011-01 ]. Funding for open access charge:  Centre National de la Recherche Scientifique.
Supplementary Figure 1. 1 kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP14 TH_4306 pSRP14::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SEC65 TH_6981 pSEC65::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP68 TH_3180 pSRP68::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP72 TH_3294 pSRP72::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon TET-SRP101 TH_7193 pSRP101::kanR-tet07-TATA URA3::CMV-tTA MATa his3-1 leu2-0 met15-0 Dharmacon GFP-SRP14 BY4741 MATa his3∆1 leu2∆0 met15∆0 ura3∆0 SRP14-GFP::HIS3MX6 Life Technologies RDM 50-94C MATα sec62-1, ura3-52, leu2-3,112, his4, suc+/-MATa ura3-1trp1-1 leu2-3,112 his3-11,15 can1-100 RAD5 ADE2 trp1-1::DsRedExpress2-HDEL-TRP This study Escherichia coli Rosetta (DE3) 70954 F-ompT hsdSB(rB-mB-) gal dcm Novagen DH5α DH5α F -φ80lacZΔM15 Δ(lacZYA-argF)U169 recA1 endA1 hsdR17(r K -, m K + ) phoA supE44 λ -thi-1 gyrA96 relA1 New England Biolabs Purified His-tagged recombinant proteins.
The selection of the independent variables of the DIC processing parameters and their variation ranges was based on the data issued from the literature and the accumulated 30-year know-how of our research team. Additionally, some preliminary experiments were carried out in this objective. The dependent variables chosen as responses were the following: oil yield Y 0 , time to reach the same yields as cracked Cr soybeans do in 160 min, starting accessibility δYs, and effective diffusivity Deff. To set the response surface methodology (RSM) to perform the statistical study of the data issued from our experiments, Statgraphic software has been able to define the Analyses Of Variance (ANOVA) and p-value of 5% probability level (p<0. 05), and display on Pareto charts to highlight the impacts of independent variables on each response.
Statistical analysis of oil yields To express and evaluate the kinetic parameters of solvent oil extraction (yields, Deff, and δYs), versus the DIC texturing parameters such as the saturated dry steam pressure and processing time, we used Statgraphics software in analyzing the effect plots, estimating the surface response, defining the variance, and fitting the empirical equation of the experimental results. We also systematically introduced the normalized Pareto diagrams. It is a convenient way to possibly recognize the significant effects of the linear, quadratic, and interaction DIC parameters P and t. The statistical results are represented in Figure  5. In the case of oil yields, it confirmed that P and t had the highest significant effects with a p-value of 0. 05. Similarly, Eq. 6 gives the second-order empirical regression model of the extraction yields Yoil (expressed in g oil/g db) for cracked soybeans, versus the DIC processing parameters; P in MPa and t in s with R 2 =81.
models. SB and VC supervised the work. This work was funded by the  European Research Council  ( NOVEL  project, grant # 648321 ). The authors also thank the  Région Hauts-de-France , and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development  for their financial support. Data accessibility statement: no data to be archived 1.
A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998-2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset.
Analysing spatio-temporal variations in LSMs Unpublished analysis within the CLINF project has identified specific variables whose spatio-temporal behaviour is correlated with CSI incidence; these include vegetation activity (here represented by net primary production, NPP), soil moisture, soil surface temperature, snow cover, precipitation and land cover. This section concerns analysis of how predictions of such variables differ between LSMs. For a given variable, say NPP, the data simulated by an LSM can be arranged as a two-way spatial × temporal table, where the spatial dimension has as many entries as latitudelongitude positions, and the temporal dimension represents monthly values for each year over the period studied. For our dataset, the historical data simulations from December 1997 to December 2013 have 193 monthly entries over the selected   if  we include all the variables given above.
Since the LSMs provide NPP for each PFT, the PFT dimension could also be added, but this is not done here. Analysing such structured datasets to understand spatial, temporal and between-model variations can be challenging when there are long-tail distributions (as is the case in our dataset: see Fig. 1 , which shows the histogram of NPP values in the combined historical datasets simulated by the four LSMs) which preclude the use of classical geostatistical methods, and due to their multivariate nature. For two-way tables, singular value decomposition (SVD) is a powerful tool to extract associations of variables and patterns within data, e. g. clusters and trends. The SVD of a data matrix finds pairs of vectors (components) that successively extract decreasing fractions of the variation in the data and are uncorrelated with previous pairs.
The LSMs also exhibit different distributions (see Table  3 ): notably CLM5 has 35 % of its NPP values in the first decile of the reference distribution, while OR_HL and JULES have very few values in this decile, and the decile with peak occupancy is different for all four LSMs. However, all the LSMs place around 10 % of their NPP estimates in each of the higher deciles (70 % to 100 %). These distributional differences tell us nothing about the spatio-temporal differences between the LSMs, and for that we use the decomposition provided by the R package PTAk  (Leibovici, 2010)  of which the first 10 terms are displayed in Fig. 2. This describes the hierarchical and nested decomposition of the sum of squares of X into PTs and associated PTs. Each row corresponds to a PT, identified by a label and number, -no-, and its singular value; e.
1. The squares of the LSM weights are in the ratio 1 : 1. 29 : 1. 19 : 1. 24, while the values of the original sum of squares of NPP (see Table  2 ) are in the ratio 1 : 1. 37 : 1. 25 : 1. 29. Hence the first PT correctly picks up the ordering of the variability amongst the LSMs, but not its full value, since it is effectively a smoothing of the dataset. 2. The spatio-temporal maps for the individual LSMs capture 92. 0 %, 86. 5 %, 87. 3 % and 93. 1 % of the original variability of OR_MICT, OR_HL, CLM5 and JULES respectively. Hence each one is a reasonable approximation to the original LSM simulation, particularly for OR_MICT and JULES. 3. The mean NPP represented by vs111 is 1. 834 × 10 -8 kg m -2 s -1 , which is very close to that of the mean of X (1. 824 × 10 -8 kg m -2 s -1 ), though the individual NPP spatio-temporal maps for each LSM track the original mean NPP less closely (+3.
44 × 10 -8 kg m -2 s -1 in December 2006, which will again occur for OR_HL and at the same position as the overall maximum NPP. Results from Table  2  and this first PT makes the important point that a single spatio-temporal pattern does well at cap-turing the NPP from the four LSMs. Whilst this expresses a common trend between the LSM, their weight similarity is up to 14 % differences, showing a variation in intensity from one to another. Despite similar photosynthesis modules in most LSMs, parameter settings, such as the choice of PFTs together with different climate datasets (GCM; see Table  1 ) and settings in other modules, induce these variations. The subsequent PTs provide a series of corrections to this common pattern, expressing LSM specificities, such as how the PFTs are parameterized.
(  3 ), captures 0. 42 % of the variability and principally improves the fit to the variability captured by CLM5 and, to a lesser extent, JULES. The summation of all 10 PTs that each represent at least 0. 1 % of the variability captures 98. 4 % of the variability in X and between 97. 4 % and 99. 0 % of the variability in the individual LSMs (last line of Table  2 ). Overall, this analysis shows that a single optimal spatiotemporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a reasonably good approximation for all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern is a spatial correction that principally applies to OR_HL and significantly improves the fit of the approximation to this LSM, with only small improvements for the other LSMs.
These various levels of inter-annual variability linked to the effects (i. e. month pattern, spatial and LSM differences) already described are to modulate the uncertainties associated with LSM choice. Our analysis of the impact of the choice of GCM on the simulations of NPP was restricted to runs with JULES out to 2100 driven by 34 different GCMs. This showed that a single spatio-temporal pattern captured over 99 % of the variability of NPP in the combined dataset for climate change scenarios, leading to either 1. 5 or 2. 0 • C atmospheric warming, and that none of the GCM weightings differed by more than 3 % from uniform weighting (maximum difference of 6 %). The temporal pattern showed increases in NPP up to the 2070s, with small decreases thereafter. Although this analysis was only carried out for JULES, there is no reason to expect significantly different findings for the other LSMs, since they all use a form of the Farquhar photosynthesis model to derive gross primary production, of which some fraction is allocated to NPP.
Acknowledgements. This work was carried out under NordForsk funding to CLINF, a  Nordic Centre of Excellence (NCoE)  led by  Professor Birgitta Evengård  (https://www. nordforsk. org/en/programmes-andprojects/projects/climate-change-effects-on-the-epidemiology-ofinfectious-diseases-and-the-impacts-on-northern-societies-clinf,last access: 2  March 2020 ). Maria Val Martin  was supported by the  Leverhulme Trust  through a  Leverhulme Research Centre Award  ( RC-2015-029 ). Financial support. This research has been supported by the  Nord-Forsk  (grant no. 76413 ). Review statement. This paper was edited by  Alexey V. Eliseev  and reviewed by two anonymous referees.