dataesr/scientific-paragraphs-categorization huggingface dataset:
PoS(CORFU2015)071 Readout via Flex-Prints for the Mu3e Experiment Sebastian Dittmeier  sensors is depicted in Figures  8  and 9. On the top layer the power for the sensors is distributed. The common signals and the high voltage are routed in a bus structure. On the left end of the layer the signals are foreseen to be connected to another flex-print or PCB to connect the module to the frontend readout board. All sensor specific signals like data output are routed on the bottom layer. All signals have their corresponding contact pads on this layer to allow to use the LTU SpTAB technology  [18]  to connect the flex-print to the MUPIX chip. The second option is a multi-layer laminate approach (see Figure  10 ). Power and ground will be distributed using two 12 µm aluminium and 25 µm Kapton R laminates which can be produced in-house.
C2 timate of fluxes based on SIF data. We revised portions of the text in the introduction section to make sure the novelty of our approach is clearly stated. Third, the authors used MPI-BGC product as a training dataset while testing the product against FLUXNET data. As MPI-BGC product was trained against FLUXNET dataset, the approach is self-correlated. Why not evaluating the product against independent datasets from MPI-BGC? E. g. water balance derived ET in basin scale. Re: That is not exactly correct. We train our algorithm against a target dataset which is derived from three products (including MPI-BGC) by using the Triple Collocation method and assigning a priori weights to every product in each pixel. This means that our target dataset has collective information from all three products and not just MPI-BGC.
Therefore, we acknowledge that there is some information carried from FLUXNET tower data in MPI-BGC to our training process. However, the degree of self-correlation is mainly true for the comparison of FLUXNET-MTE with the tower estimates but not for the WECANN estimate. Moreover, given that FLUXNET-MTE uses flux tower estimates for the retrieval we would have expected this product to be the better one when compared to local eddy covariance data. In fact, we show the opposite. WECANN informed by direct remote sensing observations typically outperforms FLUXNET-MTE, especially in terms of seasonal cycle, further emphasizing the information content provided by remote sensing data.
Specific comments: P6: why only 21 FLUXNET sites were used? More than 150 sites data are open to public Re: We had selected this 21 sites to represent a range of climatic conditions along a geographical gradient for validation of our retrieval. Presenting evaluation metrics and temporal time series for 150 sites would lengthen the manuscript and make it hard to read. However, in the revised manuscript we will present summary statistics from comparison of WECANN retrievals against a much larger number of tower data from the FLUXNET 2015 dataset in the Appendix.
P10: The authors compared WECANN to FLUXNET-MTE, ECMWF, GLEAM and MODIS-GPP which were the training data for WECANN. I feel there should be self-correlation, so I am curious whether this is a reasonable approach. Re: We feel that there is a misconception here. We did not compare WECANN to these products. Indeed, we are comparing WECANN and the other products against FLUXNET individual tower observations and reporting the performance of each one of them. We show that WECANN has a better performance while it is trained on the collective set of data from these products, and we believe this is an advantage of our approach. P10 L8: I know there are few eddy flux tower data in India, so FLUXNET-MTE might involve higher uncertainty. However, this is the same situation for WE-CANN as it used FLUXNET-MTE and others, which are all uncertain as training dataset.
Re: It is true that there are few towers in India, but our retrieval does not rely on regional C7 towers to estimate surface fluxes. We train an artificial neural network algorithm using the three training products that is mentioned in the manuscript for all the pixels. That means we use the information from all the pixels over the globe to train one retrieval algorithm. This algorithm uses remote sensing observation at each point in time and space to retrieve surface fluxes. Therefore, lack of FLUXNET towers in any part of the globe would not impact the accuracy of WECANN retrievals, while this can be an issue for products that upscale tower-based observations to estimate fluxes across the globe. P10 L4: Be quantitative. Report bias. Re: Thanks for noting this. The point we have raised in this line (on the spread of scatter plots) can be quantitatively compared using the RMSD value that are provided in the figures.
As the authors well recognized, I feel it is odd to compare 1 degree WE-CANN to several hundred meters in flux towers. All discussion from this comparison seems too subjective. I think "validation" of 0. 5-degree product is unlikely possible. As your products are too coarse, I would recommend evaluating at larger scales. For example, look interannual variability of global GPP (PgC yr-1), ET (W m-2), and H (W m-2) and compare to atmospheric inversion estimates. Test whether your product could capture big climate extreme events such as Russian heatwave, Texas drought etc. Compare to other existing global land surface products which were not used as input/training dataset in WECANN.
P12- : : As the authors well recognized, I feel it is odd to compare 1 degree WE-CANN to several hundred meters in flux towers. All discussion from this comparison seems too subjective. I think "validation" of 0. 5-degree product is unlikely possible. As your products are too coarse, I would recommend evaluating at larger scales. For example, look interannual variability of global GPP (PgC yr-1), ET (W m-2), and H (W m-2) and compare to atmospheric inversion estimates. Test whether your product could capture big climate extreme events such as Russian heatwave, Texas drought etc. Compare to other existing global land surface products which were not used as input/training dataset in WECANN.
How to complement MCTS ? MCTS progressively weights by self-play several possible evolutions of the game. However, additional knowledge can substantially enhance the learning process. A brief presentation of the MCTS process along with its dynamic is presented in Section 2. 1. Section 2. 2 reviews the main enhancement in the current programs based on MCTS and Section 2. 3 details the underlying data structure. An extensive presentation of MCTS and its enhancements is beyond the scope of this paper, we invite the reader to refer to a more detailed survey 3.
Overall view of additional data structures The policies select the action to perform based on the knowledge available for the system. The data-structure supporting this knowledge has a high influence on how this knowledge may be re-exploited. Current programs based on MCTS handle different kinds of knowledge. In the present paper, we differentiate the knowledge learned in the search tree from the additional knowledge considered in the enhanced policies. The latter can be applied a The only exception is the second-order history heuristic  15  which considers also the previous one A Self-Acquiring Knowledge Process for MCTS 5 to different situations contrary to the node's knowledge which is specific to a single game state. In this section, the existing complementary data-structures for MCTS are reviewed for both kinds of knowledge.
Background History Reply Forest We propose to build up an independent data-structure to complement the Monte Carlo tree and allow transversal knowledge memorizing. In order to improve the roll-out reliability, we choose to exploit the self-acquired and long-term data to influence the roll-out policy. Moreover the purpose of this contribution is to produce a policy that improves the learning process using knowledge that is already gathered in the search tree. That is why, in our A Self-Acquiring Knowledge Process for MCTS 7 proposal, the complementary data structure is updated according to the current sequence played during the descent phase and the simulation outcome (Winning, Loosing). The complementary data structure presented here accumulates small temporal patterns of increasing size, thanks to a forest data structure.
Ability to play local patterns In this section, we focus on the ability to play roll-out patterns for the selected approaches (pure random, Fuego and BHRF). Figure  9  states that the BHRF roll-out plays more than four times more local patterns than the pure random one. One can understand that BHRF can acquire relevant patterns that helps the general MCTS process to understand the necessity to play locally. Without these self-acquired patterns, a pure random approach misses the pattern opportunities which considerably decreases the program competitiveness. In Figure  10 , one can note that expert Fuego roll-out plays even more local patterns. In fact, Fuego plays local patterns in a more systematic way, and cannot consider the rele-vance of a pattern move. From our point of view, this is the counterpart of myopic a-priori patterns.
Combining BHRF with expert knowledge In the previous section, we have shown that BHRF produces local moves which do not follow expert spatial patterns in a systematic way. However the temporal patterns memorized by BHRF may, even though, complement sequence-like policies. In this section, we choose to involve expert knowledge heuristics for BHRF roll-out as a second choice. When no move is selected by BHRF, the standard rules originating from Fuego roll-out policy are applied. BHRF competes then with Fuego set to the best of its ability. In Figure  11 , we show that BHRF with a soft-UCT selection process outperforms Fuego by 11% when we use BHRF data moderately ( = 15). The number of simulation was set to 30k in order to accumulate substantial data about the game. A low value involves more exploration through the general MCTS process.
Although this method may be of interest in other research fields, we will hereafter focus on spatial and landscape genetics, where pairwise metrics are widely used but flexible visualisation tools are still lacking for these measures (see  Miller 2005; Vandergast et al. 2010 , Etherington 2011 , Petkova et al. 2016). Although new methods, such as spatially-explicit clustering, are increasingly used in spatial genetics to describe genetic variation and identify barriers to gene flow  (Guillot et al. 2009 ), pairwise genetic measures, such as distance, remain appealing as: i) they can be computed between individuals or populations, ii) they facilitate handling massive datasets, which are increasing with the advance in high-throughput sequencing (Duforet-Frebourg and Blum 2014) and, iii) their regression against geographic distances may be informative with respect to isolation-by-distance (IBD), i.
In this general context, MAPI provides an approach to i) visualise pairwise genetic measures without confounding effects resulting from IBD, ii) test for spatial genetic discontinuity through a nonparametric randomisation procedure and, iii) explore relationships between observed genetic patterns and environmental heterogeneity, notably by using MAPI results for further statistical analyses. In this work, we assessed the efficiency of MAPI to accurately detect spatial discontinuity in pairwise genetic metrics by applying permutation tests on controlled simulated datasets of panmictic populations and populations under isolation-by-distance, including scenarios of separation by a linear barrier to gene flow. We also appraised the potential of MAPI in a landscape genetics framework by analysing genotypes simulated under landscape constraints with various spatial configurations of favourable and unfavourable habitats.
We used a Bayesian conditional autoregressive model to analyse the relationships between MAPI results and landscape variables. To illustrate MAPI we also re-analysed published data from three biological models: 1) microsatellite genotypes from a rodent population under IBD, 2) DNA sequences from a plant virus exhibiting spatial genetic discontinuities and, 3) microsatellites and landscape data from both a forest specialist and a generalist species of ground beetles. Finally, we used controlled and observed datasets to explore MAPI sensitivity to parameter setting and sampling scheme.
Data output. Cell-specific information, such as their geometry and associated m w and p-values, are stored as a spatial PostgreSQL table which can be exported as a shapefile or text file and imported into GIS software or the software R using the rgdal package. These data can be used for further statistical analyses of relationships between genetic measures and landscape variables. Visualisation. On the final graphical output (Figure  2D ), the observed cell values (m w ) are displayed using a colour scale. Cells with significant extreme values after FDR correction are visualised as black contours mapped as an additional layer. When significant cells are spatially connected, only the perimeter of the polygon they form is represented. Vector or raster maps, satellite imagery or teledetection products can be inserted below the MAPI layers for landscape interpretation.
This article is protected by copyright. All rights reserved. Analyses of simulated test datasets with MAPI were performed using a grid of hexagonal cells defined by setting the parameter to 0. 5 and 0. 25 for regular and irregular samplings, respectively (Table  1 ). The network was built using the genetic distance a r (Rousset 2000), an error radius for sample location of 0. 01 and ellipses with an eccentricity value of 0. 975. Cells with extreme high m w values were detected using 1,000 permutations of the sample locations and a significance level of 0. 05 for computing significance for each cell. As the exact location of the barrier was known, we computed the proportion of the barrier covered by cells with significantly higher values.
Simulated data in spatial genetics This article is protected by copyright. All rights reserved. (hereafter referred to as Coverage). We also computed the proportion of the study area covered by cells with significantly higher values having no contact with the simulated barrier, which may result from stochasticity, edge effects or IBD (hereafter referred to as Unexpected). We also investigated the sensitivity of MAPI to the eccentricity value (0. 900, 0. 975 and 0. 999) and number of cells (setting the parameter to 0. 15, 0. 25, 0. 5 and 0. 75). To this aim, we used a subset of datasets simulated under both large regular sampling and small irregular sampling (see section 2. 1 in the Appendix to identify test datasets).
Simulated data in landscape genetics Using a forward-in-time (individual-based) simulation algorithm, we simulated datasets of 10 microsatellite genotypes for diploid individuals distributed in a landscape raster of about 50x50 cells. Three landscape models, which consider two habitats with contrasted carrying capacity (20 and 2 for the favourable and unfavourable habitat, respectively) were analysed (20 simulated datasets each): (1) a spatial transition from the favourable to unfavourable habitat with a high level of interpenetration (landscape test datasets 1; Gradient); (2) fragmentation of the favourable habitat in small areas isolated by a prevalent unfavourable habitat (landscape test datasets 2; Fragmentation); (3) a random distribution of the favourable and unfavourable habitats over the study area (landscape test datasets 3; Random) (see Figure  4  to visualise the simulated landscapes).
For the latter, the detection of the habitat effect is unexpected since the spatial scale of autocorrelation in habitat is very small. Details on simulation algorithms, parameter settings and basic measures of genetic variation can be found in sections 2. This article is protected by copyright. All rights reserved. number of individuals was randomly sampled from each square of a 3x3 grid encompassing the landscape raster. Analyses of simulated datasets with MAPI were performed using grids constituted of squared cells that matched the landscape raster pixels (side length=1), the genetic distance a r  (Rousset 2000) , an error radius for sample location of 0. 5 to consider that individuals can be anywhere within a cell, ellipses with an eccentricity of 0. 975 and a minimal distance between samples of 0.
Biological datasets To illustrate MAPI behaviour in the presence of IBD, we analysed microsatellite genotypes from a rodent population that was previously characterised as a single genetic unit, only structured by IBD  (Gauffre et al. 2008) , using the clustering method GENELAND  (Guillot et al. 2005). MAPI efficiency in detecting genetic discontinuities is further illustrated using DNA sequences from different strains of plant virus. These data were previously analysed  (Desbiez et al. 2009; Joannon et al. 2010 ) using phylogenetic analyses, the clustering method SAMOVA  (Dupanloup et al. 2002 ) and the maximum difference Monmonier's algorithm  (Monmonier 1973). Finally, MAPI was applied to genetic distances computed from microsatellite genotypes of two species of forest ground beetles with contrasted level of habitat specialisation (i.
Sensitivity to isolation-by-distance Simulation test datasets. MAPI did not detect unexpected areas of genetic discontinuity under strict IBD, regardless of its strength (see test datasets 1 in Table  2 ; Figures  3E, 3I ). When models combine IBD and barrier effects, MAPI still did not detect unexpected areas of genetic discontinuity, i. e. which did not overlap with the simulated barrier (see test datasets 3 in Table  2 ; Figures  3F-H.
, 3J-L). Under This article is protected by copyright. All rights reserved. large regular sampling, the average percentage over replicates of unexpected discontinuous areas was less than 0. 15% whatever the simulation setting considered (see 'Unexpected' in Table  2 ), with a maximal percentage across the 625 simulated datasets of 1. 3%. Under small irregular sampling, the percentage of unexpected discontinuous areas was still minute, with an average lower than 0. 53% whatever the simulation setting (Table  2 ) and a maximum across the 625 simulated datasets of 5. 8%. Thus, gaps between samples did not drastically increase the proportion of unexpected significant discontinuous areas, even under strong IBD (g=0. 25, slope = 0. 055). The single exception was the combination of strong IBD and genotype datasets of 100 loci, for which the small and irregular sampling led to a more significant (but still relatively low) increase in the percentage of unexpected discontinuous areas, with an average over replicates of 2.
Detection of a barrier to gene flow Simulation test datasets. We found that MAPI was efficient in detecting strong to moderate barriers to gene flow, i. e. N e m≤1; F ST ≥0. 1 with a barrier coverage ≥ 75% (see test datasets 2 in Table  2 ; Figures  3C-3D ). When permeability increased (N e m>1; F ST <0. 1), the method lost its accuracy to detect the barrier, i. e. the proportion of undetected barriers became large (see test datasets 2 in Table  2 ; Figure  3B ). However, our simulations showed that IBD increased the power of the method to detect the linear barrier, especially for high levels of gene flow. For example, for N e m=2. 5, even This article is protected by copyright. All rights reserved. weak IBD (g=0. 750; slope=0. 005) increased the barrier coverage from 7% to 79%. As a result, MAPI performed well in identifying barriers to gene flow given that N e m<10 (see test datasets 3 in Table  2 ;.
Biological dataset. In line with previous results published on the plant virus sequence dataset, MAPI identified a major area of genetic discontinuity bisecting the study area from North to South (see section 3. 2 in the Appendix). Sensitivity to parameter settings. The eccentricity and number of cells have little effect on the detection of unexpected significant areas (i. e. Unexpected in Figure  5 ). The worst situation (i. e. 3%) occurred when using very narrow ellipses (e=0. 999; see Figure  S1  of the Appendix) combined with a very high number of cells (i. e. β = 0. 15). The combination of narrow ellipses (e=0. 999), which result in a lower number of ellipses intercepting each cell, and a very low number of cells (i. e. β = 0. 75) decreased MAPI efficiency to detect a barrier to gene flow (i.
Assessment of landscape effects Simulation test datasets. As expected, when the two habitats were distributed randomly, there was no obvious correspondence between the spatial variation in m w and landscape cell values (Figure  4 ; see also Figure  S4  in the Appendix to visualise examples of MAPI graphical outputs). This was confirmed by the results of the Bayesian conditional autoregressive model which showed that most of the posterior probabilities for a habitat effect were not significant (Table  3  and Figure  S5  in the Appendix). A difference between habitats was, however, still detected from a few simulated test datasets, especially when using random sampling. For the gradient and fragmented landscapes, MAPI graphical outputs showed a relative convergence between variation in m w cell values and spatial habitat configuration (Figure  4  and Figure  S4  in the Appendix).
Accordingly, the intercept was significantly less for the favourable habitat (∝ ) than for the unfavourable habitat (∝ ) for all datasets regardless of the sampling scheme and size for the fragmented landscape and for most of the datasets when large (500 genotypes) and random or gridded samplings were used for the gradient landscape (Table  3 ). When the sampling size was smaller (200 genotypes) the proportion of significant posterior probabilities for ∝ < ∝ decreased of 25 and 50% for the random and gridded samplings, respectively. Interestingly, under the gradient configuration, the balanced sampling led to very poor results and even, in a few cases, to false positives (i. e. ∝ < ∝ ) (Table  3  and Figure.
Accepted Article Accepted Article This article is protected by copyright. All rights reserved. Biological dataset. On ground beetle microsatellite dataset, MAPI analyses supported previously published conclusions by identifying a significant area of genetic discontinuity corresponding to a large open field for the forest specialist only (Figure  6 ). When using m w cell values in the Bayesian conditional autoregressive model with the proportion of trees as an explanatory variable, we found a significant negative relationship only for the forest specialist (posterior probability=1). This means that, on average, between-individual genetic distances were lower within highly forested areas. No significant pattern was found for the generalist (see section 3. 3 in the Appendix).
These highly discontinuous areas were, however, small and generally located on the border of the study area. As for many spatial exploratory analyses, regular individual-based samplings are more likely to provide reliable and interpretable results  (Oyler-McCance et al. 2013; Balkenhol et al. 2015). In landscape genetics, assessing the effects of environmental features on spatial patterns of genetic variation often requires going beyond the detection of barriers to gene flow. In this context, our controlled simulations and ground beetle datasets  (Brouat et al. 2003)  showed that overlaying MAPI graphical outputs on landscape layers may provide information on which environmental variables are potential candidates to explain observed genetic patterns (see Figure  4  and Figure  S9  in the Appendix).