dataesr/scientific-paragraphs-categorization huggingface dataset:
MetHyd MetHyd is the meteo-hydrological pre-processor for hydrometeorological data of VSD+ to calculate daily evapotranspiration, soil moisture, precipitation surplus and parameters related to N processes  (Bonten et al. , 2016). MetHyd reads daily data on temperature, precipitation and radiation, or, alternatively, derives daily inputs from monthly data. MetHyd input includes information on soil properties such as bulk density, the content of clay, sand and organic C. Also soil hydraulic properties (soil water content at saturation, field capacity, wilting point and at hydraulic tension of -1 bar) can be given as input to MetHyd, or derived in MetHyd from given soil properties. We  used MetHyd to calculate annual values of soil moisture (Theta, m 3 m - 3 ), precipitation surplus (percol, m yr -1 ), as well as soil-and temperature dependent coefficients for mineralization, nitrification and denitrification (rf_ miR , rf_ nit , rf_ denit ), used as input to VSD+ to modify the turnover rates of organic matter.
e. , end of observed weather data to 2100, average values of the latter part (from 2000) of the observation period were used. MetHyd was not calibrated to match observed runoff at the sites. GrowUp was used with information on forest region, tree species and N deposition. Additional data on litter fall biomass and N concentrations were available (e. g. ,  Fabbio and Amorini, 2002; Andreassen et al. , 2002; Janssens et al. , 2002; Neirynck et al. , 2008; Ukonmaanaho et al. , 2008; Bertini et al. , 2011; Kobler et al. , 2015). Also, more general area and species specific yield tables were utilized; e. g. , for the Finnish and the Polish sites, information derived from the EFISCEN inventory database was used  (Schelhaas et al. , 2006). For each site, observations of soil BS and C:N ratio, soil solution pH and soil solution concentrations [SO 4 2-], [NO 3 -], [NH 4 + ], [Bc 2+ ] were available for different time periods  (Neirynck et al.
Calibration The graphical user interface of VSD+ Studio provides an automatic calibration routine, which utilizes a Bayesian approach. The probability distribution of the parameter vector is updated based on an initially assumed distribution and a dataset for verification of the model results. A Markov chain Monte Carlo method is used to perform the calibration  (Reinds et al. , 2008; Bonten et al. , 2016). We applied the automatic calibration routine together with manual adjustment of the parameters. The final values for the calibrated parameters were chosen by taking account of both the results of the automatic calibration and visual inspection of the overall performance of the model. The following VSD+ parameters were calibrated at most sites: cation exchange (K_AlBc, K_HBc), the cation weathering rates (Ca, Mg, K, Na), the initial C pool; and the initial C:N ratio.
To the EURO-CORDEX RCMs, a number of different bias adjustment schemes in combination with different calibration data sets have been applied, as indicated in Supplementary Table  A2. At the time of data retrieval in April 2017, overall 69 different combinations of RCP -GCM -RCMbias adjustment method and calibration dataset were available from the ESGF data nodes. Out of these, we selected a set of 12 combinations per RCP4. 5 and RCP8. 5, for which model outputs of air temperature, precipitation and radiation for both RCPs were available (see Supplementary Table  A2  for an overview of datasets used). Note that only air temperature and precipitation were bias adjusted. Our 12-member subset per RCP of the overall ensemble takes into account data availability and samples the overall spread of the climate change signals of the ensemble.
Their results indicate a temperature increase that is most pronounced in southern and (north-)eastern Europe (cf. "SE" and "FI" sites in Fig. 4 ) and an annual precipitation decrease in southern (c. f. "IT" sites in Fig. 4 ) and an increase in eastern and northern Europe. Site specific time series of 2 m air temperature, global radiation and precipitation from control simulations and climate projections were extracted from the overall 24 selected ensemble members at daily resolution at the nearest neighbour grid point to the actual site location. Because the actual altitude of a site may not match with the altitude of the closest RCM grid element, the 2 m air temperature was height corrected using a hypsometric lapse rate of 0. 65 K/100 m before temporal averaging was applied. For the climate change simulations at the 26 sites MetHyd was used with monthly temperature, precipitation and radiation data according to the 12 climate change projections representing RCP4.
To address these and related questions, we examined how growth rate scales with body size in a genetically diverse population of Arabidopsis thaliana accessions (Dataset S1), a species that exhibits three orders of magnitude in plant dry mass  (10)  and occurs in a wide range of contrasting environments  (17). We provide evidence indicating that scaling variation is maintained by an adaptive trade-off between alternative environments. We show that this variation has a polygenic basis, and that there is genetic correlation between allometry and local climate.
001), with both negatively correlated with plant lifespan (P < 0. 001) (Fig. 1C  and Dataset S2). Previous studies have shown that variation in A. thaliana growth allometry is positively correlated with the carbon assimilation rate and nutrient concentration, but negatively correlated with lifespan  (10). Thus, variation of growth allometry in A. thaliana connects life history variation to the strategies for leaf resource use. At one end of the distribution are high scaling exponents, representative of "live fast/die young" strategies that maximize resource capture (i. e. , high RGR and carbon assimilation rate) at the expense of plant lifespan and final size. At the other end are low scaling exponents, representative of "live slow/die old" strategies that maximize the retention (i. e. , thick leaves with low nutrient concentration and long lifespan) rather than the acquisition of resources.
We then examined the correlations between the scaling exponent and 21 climatic variables, which include 19 Bioclim variables (www. worldclim. org/bioclim), as well as the estimated mean annual potential evapotranspiration (PET, in millimeters) and the aridity index  (19)  at the geographic origin of the accessions. Consistent with the idea that resource-acquisitive plants (i. e. , early-flowering/ fast-growing ecotypes) are more adapted to hotter and drier regions, the scaling exponent was positively correlated with the mean annual temperature measured at the collection point of the accessions (Fig. 2A  and Dataset S2). The strongest correlations were with the maximum temperature of the warmest month and the mean temperature of the warmest quarter (r = 0. 30 and 0. 28, respectively) (Fig. 2B  and Dataset S2).
Inversely, the scaling exponent was negatively correlated with precipitation, specifically with precipitation during the driest quarter (Fig. 2C ), precipitation seasonality, and the aridity index (Dataset S2). In contrast, it was not correlated with the altitude at the collection point. Using stepwise regression, we found that 13 climatic variables explained >27% of the allometric variation. Four of these variables are related to summer climate and two are related to winter climate. The strongest effects were estimated for annual mean temperature, isothermality, and mean summer temperature. Modeling the geographic distribution of scaling exponent with the 13 topcorrelated climatic variables as predictors showed that intermediate exponents are more common in temperate regions (Fig. 2D ), while extreme exponents are favored under more stressful conditions (e.
62; P < 0. 001) (Dataset S2). A polynomial regression of relative fitness using fruit number (standardized by the population mean over the scaling exponent) returned a significant negative second-order coefficient (y = 1. 00 + 4. 23x -4. 06x 2 ; P < 0. 001 for all coefficients), i. e. , an estimate of quadratic selection gradient, jγj, that might be indicative of stabilizing selection for the allometric exponent under benign conditions  (20). Conversely, deviation from 3/4 scaling was positively correlated with survival under severe drought (r = 0. 16, P < 0. 05; measured in ref. 21  across 210 common accessions) (Dataset S2) and negatively correlated with growth reduction under moderate drought (r = -0. 26, P < 0. 05; measured in ref. 22  across 60 common accessions) (Dataset S2). However, neither stressresistance trait was correlated with the scaling exponent itself.
After multiple-testing correction, a total of 8,250 single nucleotide polymorphisms (SNPs) out of 1,793,606 tested were significantly associated with at least one phenotypic trait or climatic variable (26) (Dataset S3). Only six SNPs were significantly associated with the scaling exponent [false discovery rate (FDR) <0. 05]. Five of these six SNPs were located in the same region on chromosome 2 (Fig. 4A ) and were associated with the maximum temperature of the warmest month (Fig. 4B ). Three SNPs were also significantly associated with the mean annual temperature and the mean temperature of the coldest month (Dataset S4). The same genomic region showed a strong association with precipitation during the driest month (Fig. 4C ), although the six SNPs associated with scaling variation did not reach the significance threshold for this climatic variable (FDR >0.
05). In contrast, no significant SNPs were shared between RGR, lifespan, fruit number or rosette dry mass, and the climatic variables (Dataset S4), suggesting that a genetic association between traits and climate is relatively rare. One SNP among the five associated with both the scaling exponent and the maximum temperature of the warmest month was located in the U-box protein gene PUB4 [At2g23140; minor allele frequency (MAF) = 6. 1%] (Fig. 4A ). As E3 ubiquitin ligases, U-box proteins are involved in protein turnover, a key regulatory component of plant responses to abiotic stresses  (27). PUB4 plays a notable role in a quality control pathway that removes damaged chloroplasts  (28). Two other SNPs were located in the nearby cytochrome P450 gene CYP81D6 (At2g23220), 40 kb from PUB4 (r 2 = 0.
63). CYP450s catalyze the production of diverse secondary metabolites involved in biotic and abiotic stress responses  (29). The remaining two SNPs were also linked to PUB4 and CYP81D6 but affected noncoding sequences. We note that the PUB4 polymorphisms account for only approximately 1% of the genetic variance in the scaling exponent. Because broad-sense heritability was H 2 >95%, many other loci are expected to contribute to allometric variation, potentially reducing the power of classical GWA to detect SNPs significantly associated with the scaling exponent. For instance, given the strong correlation between the scaling exponent and plant lifespan (Dataset S2), we expected to find that many flowering time genes would be significantly associated with allometry; however, no SNP reached the significance threshold for lifespan in our analysis (FDR >0.
05), and thus we do not have evidence identifying flowering time genes as predictive of allometric variation. This might be due to overcorrection for population structure or to the high number of SNPs involved in phenotypic variation among accessions. Indeed, a strong correction for population structure might be inappropriate if many genes across the entire genome contribute to the phenotype in question. To account for the potentially complex genetic architecture of traits, we ran Bayesian sparse linear mixed models (BSLMMs) implemented in GEMMA  (30). BSLMM models two hyperparameters: a basal effect, α i , that captures the fact that many SNPs contribute to the phenotype, and an extra effect, β i , that captures the fact that not all SNPs contribute equally. SNP effects, which can be estimated as the sum of α i and β i  (30) , were strongly correlated between the scaling exponent and all climatic variables except annual temperature range (Dataset S5).
As expected, correlations between SNP effects on scaling exponent and climate were strongest for mean annual temperature and temperature and precipitation during summer (Dataset S5). Consistent with the measurement of broad-sense heritability (H 2 ), "chip heritability" (a proxy for narrow-sense heritability, h 2 , measured with GWA) was very high for the scaling exponent (h 2 = 0. 87 vs. H 2 = 0. 95) (Table  S1 ), suggesting that most of the phenotypic variance can be explained by the additive effects of SNPs controlling allometric variation. Gene Ontology (GO) analysis (  31 ) of the top 1% of genes affecting the scaling exponent revealed enrichment in genes with catalytic activity and genes related to carbohydrate metabolism, postembryonic development, posttranslational protein modification, and response to abiotic stimulus (Fig.
Materials and Methods Published Data. For stress resistance analysis, we used published data from two studies on the response of A. thaliana natural accessions to drought, including one study in which 210 accessions shared with our study were subjected to severe, lethal drought and survival was estimated for all accessions  (21) , and another in which 60 shared accessions were subjected to 7 d of nonlethal drought and fresh weight was measured  (22). We also reanalyzed previously published phenotypic data  (10, 23)  from a population of 120 Ler-2 × Cvi recombinant inbred lines (RILs)  (49)  grown under water deficit and high temperature. Climatic data consisted of 19 bioclimatic variables (www. worldclim. org/ bioclim) with a 2. 5 arc-min resolution for the 1950-2000 CE period, plus the mean annual PET (in millimeters) and annual aridity index downloaded from www.
cgiar-csi. org/data/global-aridity-and-pet-database (19). Monthly averages were calculated with 30 arc-s (ca. 1 km). Additional details are provided in SI Materials and Methods. Plant Material and Growth. We selected 451 natural accessions of A. thaliana from the 1001 Genomes Project (17) (1001genomes. org/) (Dataset S1). Seeds were obtained from parents propagated under similar conditions in the greenhouse. Four replicates of each accession were grown, with one replicate each sown on four consecutive days. Two replicates per accession were harvested as 16-d-old seedlings for dissection, imaging, and weighing, and two other replicates were cultivated until the end of the life cycle (i. e. , until fruit ripening) for trait measurement. Plants were cultivated in hydroponic culture on rockwool.
Statistical Analyses. All statistical analyses except genomic analyses were performed in R  (52). The coefficients of correlation (and their associated P values) reported between phenotypic traits and climatic variables were the Pearson's product moment coefficients obtained with the function cor. test in R. The effect of population structure on the scaling exponent was tested with ANOVA, using the nine genetic groups identified in the 1001 Genomes Project dataset (1001genomes. github. io/admixture-map/) after removing admixed accessions  (17). Broad-sense heritability (H 2 ) was measured as the proportion of variance explained by genotype (Vg) over total variance (Vg + Ve) in a linear mixed model fitted with the lme4 R package, such as H 2 = Vg/ (Vg + Ve). Similarly, Q st was measured as the amount of variance in phenotypes explained by genetic group membership.
As for H 2 , we used linear mixed model in the lme4 package in R to fit traits against genetic groups (nine genetic groups after removal of admixed accessions). Genetic Analyses. Conventional GWA studies were performed with easyGWAS (25) (https://easygwas. ethz. ch/). We used 1,793,606 SNPs with an MAF >0. 05 to compute the realized relationship kernel from the full sequence of the accessions (1001genomes. org/). Association analyses were performed with EMMAX  (53). For polygenic GWA, we used the BSLMM implemented in GEMMA  (30). GO analysis was performed online using AgriGO (bioinfo. cau. edu. cn/agriGO/)  (31)  and REVIGO (revigo. irb. hr/) (54). Before F st calculation, genetic groups in the 1001 Genomes Project collection were defined by ADMIXTURE clustering (17, 55) (1001genomes. github.
ACKNOWLEDGMENTS. We thank  Rebecca Schwab  and  Justine Bresson  for their help preparing and performing the experiments, along with members of the D. W. and  Burbano labs  for their comments on previous versions of the manuscript. This work was supported by the  sabbatical fellowship program  from  Colegio de Postgraduados de Montecillo  (to O. J. A. -G. ), a  National Science Foundation Advancing Theory in Biology and Macrosystems award  (to B. J. E. ), a  CNRS Associate Research Fellowship  (to B. J. E. ), grants from the  European Research Council (ERC-StG-CONSTRAINTS , to C. V. , and  ERC-AdG-IMMUNEMESIS , to D. W. ), and funding from the  Max Planck Society  (F. V. , M. E. -A. , G. W. , and D. W. ).
Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO -Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO -Human Language).
We validate the importance of latent context by showing that our model performs well both on simple queries without temporal words and more complex queries requiring temporal reasoning. Moreover, our formulation is generic and unifies approaches in  Hendricks et al. (2017)  and  Gao et al. (2017) , allowing us to ablate model component choices, as well as which kind of video context is best for localizing moments described with temporal language. Though datasets used for moment localization in video  (Hendricks et al. , 2017; Regneri et al. , 2013; Sigurdsson et al. , 2016)  include temporal language, as we will show, there is not enough temporal language to effectively train and evaluate models. We seek to extensively study this aspect, particularly with respect to temporal prepositions  (Pratt-Hartmann, 2004).
Thus, we collect the TEMPOral reasoning in video and language (TEMPO) dataset which builds off the recently collected DiDeMo dataset  (Hendricks et al. , 2017). The dataset consists of two parts: a dataset with real videos and sentences created with a template model (TEMPO -Template Language (TL)), and a dataset with real videos and newly collected user-provided temporal annotations (TEMPO -Human Language (HL)). Considering template sentences allows us to create a large dataset of sentences quickly for study of temporal language in a controlled setting. The human language data then allows us to see these trends transfer to more complex human-language queries. For data collection, we focus on the most common temporal referring words naturally occurring in language-and-video datasets. Our contributions are twofold.
(i) We are the first to study models for temporal language in video moment retrieval with natural language queries. To this end, we introduce TEMPO which includes examples of how humans use temporal language to refer to video moments. (ii) We propose MLLC for moment localization which treats video context as a latent variable and unifies prior approaches for moment localization. Our model outperforms prior work on TEMPO-TL and TEMPO-HL as well as the original DiDeMo dataset.
(  2017 ) uses global context by averaging features over an entire input video, and  Gao et al. (2017)  incorporates features adjacent to the proposed video segment. We argue that to do proper temporal reasoning, pre-determined, query independent context features may not cover all possible temporal relations. Thus, we propose to model the context as a latent variable, allowing our method to learn which context moments to consider as a function of the video and importantly, the query. Both  Gao et al. (2017) and Hendricks et al. (2017)  collect data to test their models;  Gao et al. (2017)  considers the Charades  (Sigurdsson et al. , 2016)  and TACoS  (Regneri et al. , 2013)  datasets. While TACoS includes localized sentences, Charades only has sentences and activity detection localizations, so a semi-automatic method is used to align action detection annotations to visual descriptions in Charades.
Hendricks et al. (2017)  collected the Distinct Describable Moment (DiDeMo) dataset, which consists of Flickr  (Thomee et al. , 2016)  videos with localized referring expressions. Both Charades and DiDeMo contain a large set of diverse videos (approximately 10,000 videos each). We chose to base TEMPO on DiDeMo because it contains more clip/sentence pairs (40,000 vs. 13,000), and is focused on general videos which we believe is an interesting and useful scenario, rather than being restricted to indoor activities. Temporal Language. Prior work on temporal language processing has considered building explicit logical frameworks to process temporal prepositions like "during" or "until"  (Pratt-Hartmann (2004) ,  Konur (2008) ). We do not derive a particular temporal logic, but rather learn to understand temporal language in a data driven fashion.
, 2017; Hu et al. , 2017; Peyre et al. , 2017; Plummer et al. , 2017). Our approach is analogous to this in the temporal domain; we hope to localize moments in videos. CLEVR, a synthetic visual question answering (VQA) dataset  (Johnson et al. , 2016) , was created to allow researchers to systematically study the ability of models to perform complex reasoning. Our dataset is partially motivated by the success of CLEVR to enable researchers to study reasoning abilities of different models in a controlled setting. In contrast to CLEVR we consider a more diverse visual input in the form of real videos. In the video domain, the TGIF-QA  (Jang et al. , 2017)  and Mario-QA  (Mun et al. , 2016)  datasets provide opportunities to study temporal reasoning for the task of VQA. The TGIF-QA dataset considers three types of temporal questions: before/after questions, repetition count, and determining a repeating action.
Each question is accompanied by multiple choice answers. Videos we consider are much longer (25-30s as opposed to an average of 3. 1s) which makes the use of temporal reasoning much more important. The MarioQA dataset is an additional VQA dataset de- Score Visual Feature Embedding (f V ) Similarity (f s ) Input Query: The girl talks before she bends down. signed to gauge temporal reasoning of VQA systems. Both TGIF-QA and MarioQA datasets include template-based natural language queries. In this paper, we consider synthetic queries similar to TGIF-QA and MarioQA, but also include human language queries. In addition, unlike the MarioQA dataset, that consists of synthetic data constructed from gameplay videos, our dataset consists of real visual inputs, and includes temporal grounding of natural language phrases.