dataesr/scientific-paragraphs-categorization huggingface dataset:
Scalability In this part of our experimentation, we investigate the performance of the individual methods for applications at scales larger than those targeted by prior work (see Section 2). The competing methods tested in this section are the MRkNN-CoP algorithm  [3]  and the RdNN-Tree  [51]. We evaluate the performance of the individual methods on choices of k ∈ {10, 50} and report the mean performance of a sample of 100 queries drawn uniformly at random from within the data points of the data set. The dataset used in this second batch of experiments is described as follows. The Imagenet dataset  [38]  consists of 1,281,167 images collected from flickr and other image databases  3. Every image was labeled with the absence or presence of objects from 1000 categories. For each image, 4096 high-level features were extracted using a 19-layer deep convolutional network.
Note that the RdNN-Tree is based on the R * -tree index structure, whose performance is known to severely deteriorate for data with representational dimension of roughly 20 or more  [47]. This significantly affects the preprocessing costs of the RdNN-Tree. To illustrate this effect we investigate the rate at which the performance of the competing methods diminishes when considering subsamples of various sizes drawn from Imagenet. We downsized the full dataset by uniform random selection to subsets of 100,000 (Imagenet100), 250,000 (Imagenet250) and 500,000 (Ima-genet500) objects. For all experiments on these sets, we limit our comparison to those methods whose preprocessing computation time is less than one week (168 hours).
Comparison to Prior Work In Figures  3 4 5 6 we present the result of our comparison of RDT and RDT+ with their competitors TPL, MRkNN-CoP, SFT and the RdNN-Tree. The figures show the respective results for k ∈ {10, 50, 100} on the Sequoia, FCT, ALOI and MNIST datasets. On all these sets, among the exact methods, the query times of TPL are not competitive with MRkNNCoP and the RdNN-Tree, particularly for larger values of k; however, TPL had the smallest preprocessing cost of all methods studied. At the other end of the spectrum, on most of the execution runs, the RdNN-Tree and MRkNNCoP achieved the smallest query times. However, this performance comes at an enormous cost in preprocessing time, many orders of magnitude higher than those of the heuristic methods studied. The RdNN-Tree also has the deficiency that a new tree must be constructed for each value of k tested.
Of the three heuristic methods, in terms of tradeoff between speed and accuracy, RDT+ strictly outperforms RDT, which in turn outperforms SFT, over all but the smallest datasets studied. On the FCT dataset, SFT has a slight edge over RDT+ and RDT for certain values of k. This can be explained by the extremely fast kNN query support provided by the Cover Tree. While the computational overheads due to their lazy accept and lazy reject mechanism reduce the competitiveness of RDT+ and RDT for the smaller datasets, for the larger sets these refinement strategies give them a distinct advantange over SFT. However, for these relatively small-scale datasets, the improvements in query response time are within one order of magnitude for any given level of recall. The mean recall rates achieved by RDT+, RDT and SFT grow monotonically with the choices of the respective parameters t and α; unsurprisingly, as the numbers of candidates generated in the filter phase of all three algorithms is increasing.
As observed in Section 7. 2, the conflicting influences between increased costs of maintaining pruning information and lowered numbers of kNN verifications can be expected to lead to fluctuations in the overall query execution times. In fact, we observe such execution time fluctuations to varying extents across all the performance curves in Figures  3 4 5 6. When the choice of the reverse neighbor rank k is low and the dataset is very small (Sequoia),the heuristic methods outperform their exact competitors as recall rates approach 100%. For experiments in which k is high, the query performance of MRkNNCoP becomes quite competitive across all datasets, outperforming RDT and RDT+ easily on some. For the sets of medium scale (FCT, ALOI, MNIST ), we observe that the advantages of MRkNNCoP diminish, as it is generally outperformed by RDT+ for the sets of lower intrinsic dimensionality (ALOI and FCT).
This trend can be explained by the advantage that MRkNNCoP gains by using precomputed upper bounds on the k-nearest neighbor distances of the data points, allowing earlier pruning of potential search paths in the underlying M -tree index. Figures  3 4 5 6 indicate that automatic determination of the value of the scale parameter t is effective, particularly when the datasets are small. For the variants of RDT+ in which estimators of intrinsic dimensionality are used to determine the value of the scale parameter t, this estimation is shown to be effective, particularly for the estimators of correlation dimension (Takens, GP ), and particularly when intrinsic dimensionality of the dataset is relatively small (ALOI, FCT ). For the MNIST dataset, the intrinsic dimension is overestimated by MLE, leading to high query times with virtually    1 ).
Lazy Acceptance and Lazy Rejection We next examine the efficacy of the acceptance and rejection mechanisms within RDT and RDT+, which are based on the outcome of local distance computations. Figure  7  shows the proportion of candidate points that are treated by either of the aforementioned techniques. Throughout the range of values of t, the proportion of candidates accepted due to Assertion 2 remains relatively small (yet still significant). For low values of t, when the number of processed candidates is low, we see that the explicit verification mechanism accounts for a relatively large proportion of the cases. However, for larger choices of the scale parameter t, as the search expands further into the dataset and more candidates are discovered, RDT is given more opportunities to apply Assertion 1 to eliminate true negatives.
The lazy reject mechanism, which represents a fundamental difference between RDT and SFT, incurs only constant-time overheads. With this in mind, the significant performance improvements of RDT over SFT can be explained by the ob- RDT+ in terms of their query performance. However, as the sample size is increased, the precomputation time for both methods rises and quickly becomes prohibitive -from approximately 60 hours for Imagenet250 to more than two weeks for Imagenet500. For this reason, MRkNNCoP and the RdNN-Tree were excluded from the experimental comparison for Imagenet500 and the full Imagenet dataset. For the full set we are unable to compute true positives in reasonable time by any method. Therefore we show the query time of RDT+ for the value t = 10, for which we expect (based on the results for the smaller samples) an average recall rate of roughly 0.
If this number of candidates is the same, then the two methods would return the same query result set. However, whereas SFT expresses this target number only as a multiple (α) of k, our method is sensitive to the distribution of distances encountered as measured from the query point q. As shown in Theorem 1, our algorithm is guaranteed to find all reverse k-nearest neighbor distances within a distance threshold that is a function of k, t, and the forward (k+1)-nearest neighbor distance from q. We also developed a heuristic variant RDT+ that is capable of handling data of large size and high dimensionality. To our knowledge, RDT+ is the first method able to efficiently handle datasets of this scale. The estimators of intrinsic dimensionality described in Section 6 allow the automatic determination of the scale parameter t at a useful level of efficiency.
Water in the side channels in the Mekong delta was strongly impacted by inputs from the extensive shrimp farming ponds. The 25 values of pCO 2 , CH 4 , %O 2 , δ 13 C-DIC indicated intense organic matter degradation that was partly mediated by sulfate reduction (presumably in sediments), as indicated by the slope of TA and DIC co-variations. The δ 13 C-POC variations also indicated intense phytoplankton growth in the side channels, presumably due to nutrient enrichment related to the shrimp farming ponds. A dataset in the mangrove creeks of 30 the Ca Mau province (part of the Mekong delta) was also acquired in April 2004 and October 2004. These data extended the range of variability of pCO 2 and %O 2 with more extreme values than in the Mekong delta (Bến Tre), with maxima and minima of.
386°N 144. 656°E), located in the Pacific Ocean, approximately at the same latitude as Mekong delta. For CH 4 , a constant atmospheric value of 1. 8 ppm was used. The Henry constant of CO 2 and CH 4 was computed from salinity and temperature according to  Weiss (1974)  and  Yamamoto et al. (1976) , respectively, 30 and the Schmid number for CO 2 and CH 4 was computed from temperature according to  Wanninkhof (1992). The air-water CO 2 and CH 4 values were area-averaged and scaled to the surface of the three estuarine branches using surface areas derived from satellite images with Google Earth. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-444 Manuscript under review for journal Biogeosciences Discussion started: 24 October 2017 c Author(s) 2017. CC BY 4. 0 License.
The pCO 2 values from the extensive dataset in the Mekong River at Tan Chau (~100 km upstream of the delta) ranged between 390 and 4861 ppm and averaged 1325 ppm  (Li et al. , 2013) , encompassing the pCO 2 values we obtained in 10 the freshwater part of the delta. The high pCO 2 values in freshwaters in December   (Reynolds and Descy, 1996) , as also observed in other tropical rivers (for example  Descy et al. , 2017). F M M F M F F 13 F M M 13 M F M 13 M F 13 F 13 DIC Sal DIC Sal ) DIC DIC ( Sal DIC C DIC Sal DIC C DIC Sal ) DIC C DIC DIC C DIC ( Sal DIC C                  (3) Phytoplankton development during low water was also reported in the Upper Mekong River (confluence with the Tonle Sap River) by  Ellis et al. (2012) , based on elemental 20 and lignin analyses. The impact of biological activity on CO 2 dynamics in the uppermost freshwater part of the estuary, was confirmed by δ 13 C-DIC values that were higher in April 2004 compared to December 2003 and October 2004.
Indeed, pCO 2 was positively related to freshwater discharge, while %O 2 and δ 13 C-DIC were negatively related to freshwater discharge (Fig. 4 ), as also shown in other tropical 25 rivers such as the Oubangui  (Bouillon et al. , 2012; 2014). The dataset in the Mekong River at Tan Chau reported by  Li et al. (2013) , shows a similar seasonal pattern, with lower pCO 2 values during low water (March-May) and higher pCO 2 values during high water (October-December). In April 2004, there was a marked increase of pCO 2 from the most up-stream stations (salinity 0) to the stations located at 60 km from 30 Vĩnh Long (corresponding roughly to a salinity of 2). This increase of pCO 2 was mirrored by a general decrease of %O 2 , suggesting enhanced organic matter degradation in the oligohaline estuarine region, typical of estuarine environments (for example,  Morris et al.
(2014)  have shown based on an extensive water chemistry dataset that carbonate rock weathering largely dominates silicate weathering in the Lower 30 Mekong River, and this seems to be also the case in the Upper Mekong River  (Manaka et al. , 2015). TA in freshwater was also correlated to Na + but with a slope of 0. 5, lower than expected from the weathering of Albite (NaAlSi 3 O 8 ; HCO 3 -:Na + = 1:1), and to K + but with a slope of 14, higher than expected from the weathering of Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-444 Manuscript under review for journal Biogeosciences Discussion started: 24 October 2017 c Author(s) 2017. CC BY 4. 0 License. microcline (K-Feldspar, KAlSi 3 O 8 , HCO 3 -:K + = 1:1). Weathering of calcite alone would not account for all of the TA, but this would be the case for a mixture of weathering of calcite and dolomite (Fig.
Comparison with the Ca Mau mangrove creeks The Ca Mau peninsula accounts for the largest proportion of mangrove forests in the Mekong Delta system. Data were gathered in two mangrove creek networks 5 (Tam Giang and Kiên Vàng) allowing the comparison with data in the three estuarine channels of the Mekong delta (My Tho, Ham Luong and Co Chien) and associated side channels (hereafter referred to as Bến Tre Mekong delta, based on the name of the Province), where the bordering mangroves forests have been cleared for the implementation of shrimp farming ponds. Data comparison is limited to the April and 10 October 2004 cruises (Fig. 9 ). pCO 2 was negatively related to %O 2 in Ca Mau creeks and the Bến Tre Mekong delta owing to organic matter degradation as confirmed by the positive relation between Δδ 13 C-DIC and %O 2.
Acknowledgements. We are extremely grateful to the  Research Institute for Aquaculture N°2  ( Ho Chi Minh City ) and the  Bến Tre Fishery Department  for logistical support during the collection of samples. Freshwater discharge data were kindly provided by  Nguyen Hong Quang  from the  Vietnam National Satellite Center. This work was funded by the  Fonds National de la Recherche Scientifique (FNRS)  10 ( 1. 5. 066. 03 ). AVB is a senior research associate at the FNRS.
Results Structural annotation of LBD genes, phylogenetic analysis, and nomenclature. Genes that were previously identified as LATERAL ORGAN BOUNDARIES DOMAIN in the grapevine genome  30  were used to performed sequence comparison analyses with BLASTX, either against the most up to date gene predictions from CRIBI V1 and V2, the NCBI refseq (remapped on the 12Xv2 of the genome assembly) and the VCOST (on the 12Xv2 of the genome assembly). Analyses were also performed directly against the reference genome sequence with TBLASTX to check whether any potential gene could have been missed by these predictions. By using these approaches, we identified 50 genome regions that shared homology with at least one of the genes. Gene models were curated using the data collected from gene structure comparisons using different databases as well as the available inflorescence and flower RNAseq data from the laboratory (data not shown).
Cis-acting regulatory elements in promoter regions. Analysis of cis-regulatory elements in the VviLBD promoter regions was performed using the PlantCARE (Supplementary Fig. S4 ; Supplementary Table  S2 ) and PlantPAN databases (Supplementary Fig. S5 ; Supplementary Table  S3 ). In addition to the core cis-elements, including the TATA box and CAAT box motifs presented in all promoter regions (data not shown), several regulatory motifs were identified and are associated with light regulation (BOX I, BOX 4, ACE, MRE), low temperature and heat stress responses (LTR, HSE), defence and stress responses (e. g. TC-rich repeats), hormonal Figure  3. Grapevine LBD genes orthology against plant species with sequenced genomes. Green color represents one-to-one orthologs in the species (ortholog one-to-one = best match in the species that has the grapevine deduced protein as the best match in grapevine); white color represents no one-to-one homology match, and black color represents no match in the species.
6 ). Plant Ontology (PO) was attributed when a gene was clearly expressed in a given tissue. (ii) co-expression analysis was preformed based on the same original data using relative values of expression of all genes, centered on the average expression (Supplementary Table  S5 ). The main objectives of this analysis were to determine expression patterns and to identify genes that were following the same pattern as the LBD genes and that could be under the same regulatory elements, or under the regulation of the LBD gene itself. The results presented in Table  2  revealed that twelve genes showed a correlation with other genes with a Pearson Correlation Coefficient (PCC) threshold of 0. 2. Finding the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function  38  , but the PCC that was chosen was very stringent.
Methods Identification of LBD genes. Genes previously identified as encoding LOB domain proteins in Grimplet et al. 30  were blasted (blastp and tblastn) against the grapevine genome 12Xv. 2 (https://urgi. versailles. inra. fr/Species/ Vitis/Data-Sequences/Genome-sequences), the non-redundant list of genes in Grimplet et al. 30  , the NCBI refseq (both remapped on the 12Xv2 of the genome assembly) and the COST annotation gene set available at the ORCAE website (http://bioinformatics. psb. ugent. be/orcae/). Results from different analyses were manually crosschecked to identify new potential loci corresponding to LBD genes in the grapevine genome. The UGene software  74  was used to design the gene models on the grapevine genome and test their structure. Gene structure analysis. The potential coding DNA sequences (CDS) were blasted (blastx) against the NCBI public database to compare the structures with other known LBD genes in other species and the NCBI Refseq predictions of the grapevine genes.
Promoter analysis. Promoter cis-acting regulatory elements within 1. 5 kb of the upstream sequence from the ATG initial codon of each grapevine LBD gene were analyzed with PlantCARE software  75  (http://bioinformatics. psb. ugent. be/webtools/plantcare/html/). Analysis of transcription factor binding sites (TFBSs) of the 3 kb upstream sequence region of the initial codon was also performed using the Plant Promoter Analysis Navigator (PlantPAN) software  76  (http://plantpan2. itps. ncku. edu. tw/).
The bootstrap consensus tree inferred from 100 replicates was taken to represent the evolutionary history of the taxa analyzed  80. Branches corresponding to partitions reproduced in less than 30% of bootstrap replicates were collapsed. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The coding data was translated assuming a standard genetic code table. All positions with less than 95% site coverage were eliminated. The genes were named according to Grimplet and co-workers  32  based on the distance homology with Arabidopsis genes. The alignment file between Arabidopsis and grapevine sequences was uploaded to the Jalview and UGene software for manual adjustment of the alignment and manual motif editing.
Motifs were flagged and labelled for the grapevine genes; additional motifs of high homology were also identified (at least 50% homology within the members of the subfamily on at least 10 amino acids) among grapevine sequences. Expression analysis. Expression data were retrieved from 3 different microarray platforms (Affymetrix Genchip (16k probesets) GrapeGen (21k probesets), Vitis Nimblegen array (29k probesets) and from our in-house RNAseq projects. Data normalization was performed on all the array of each platform (RMA normalization). After retrieving the values for the probesets corresponding to each gene, the values for the 3 or 4 replicates of the same condition were averaged to obtain a total of up to 256 conditions (organ, cultivar, treatment, platform) for the genes present in all platform.
Heat maps were performed with the ComplexHeatmap R package (https://github. com/jokergoo/ComplexHeatmap). Sequence comparison among diverse plant species. We performed a sequence comparison using the LBD genes from 33 plant species (Arabidopsis thaliana, Brassica rapa, Carica papaya, Theobroma cacao, Gossypium raimondii, Eucalyptus grandis, Citrus clementina, Manihot esculenta, Ricinus communis, Populus trichocarpa, Linum usitatissimum, Malus domestica, Pyrus bretschneideri, Prunus persica, Fragaria vesca, Cicer arietinum, Glycine max, Medicago truncatula, Citrullus lanatus, Cucumis sativus, Solanum lycopersicum, Utricularia gibba, Nelumbo nucifera, Hordeum vulgare, Triticum aestivum, Oryza sativa subsp. indica, Phyllostachys heterocycla, Sorghum bicolor, Zea mays, Musa acuminata, Phoenix dactylifera, Picea abies) retrieved at http://planttfdb.
Acknowledgements Funding was provided by the  Portuguese Foundation for Science and Technology  ( PEst-OE/BIA/UI4046/2014 ,  PD/BD/114385/2016 , FCTInvestigator  IF/00169/2015 ). JG was supported by the  Ramon y Cajal program  ( RYC-2011-07791 ) and the  AGL2014-59171-R project  from the  Spanish MINECO. PA-R was supported by a  Portuguese fellowship  ( SFRH/BPD/72070/2010 ) and an  Australian Research Council  grant ( LP130100347 ). Additionally, we would like to thank to  Dr. Michael Considine  for his support in the enrichment of cis-regulatory elements analysis.
, 2014; Castellazzi et al. , 2016; Long et al. , 2016; Nanteza et al. , 2016)  where timeseries records of other individual freshwater stores are available (Eq. 1). TWS t = GWS t + ISS t + SWS t + SMS t (1) GRACE-derived TWS derive from monthly gravitational fields which can be represented as spherical harmonic coefficients that are noisy as depicted in north-south elongated linear features or "stripes" on monthly global gravity maps  (Swenson and Wahr, 2006; Wang et al. , 2016). Post-processing of GRACE SH data is therefore required. The most popular GRACE products are NASA's GRCTellus land gravity solutions (i. e. spherical harmonics based CSR, JPL, and GFZ), which require scaling factors to recover spatially smoothed TWS signals  (Swenson and Wahr, 2006; Landerer and Swenson, 2012). Additionally, NASA's new monthly gridded GRACE product, Mass Concentration blocks (i.
e. SWS, SMS, GWS, and ISS). Indeed, a major source of uncertainty in the attribution of GRACE TWS derives from the continued reliance on modelled SMS derived from LSMs (i. e. CLM, NOAH, VIC, and MOSAIC) under the Global Land Data Assimilation System or GLDAS  (Rodell et al. , 2004 ) and remote-sensing products  (Shamsudduha et al. , 2012; Khandu et al. , 2016). Further, analyses of GRACE-derived GWS often assume SWS is limited  (Kim et al. , 2009) , yet studies in the humid tropics and engineered systems challenge this assumption, showing that it can overestimate GWS  (Shamsudduha et al. , 2012; Longuevergne et al. , 2013). Robust estimates of GWS from GRACE gravity signals have, to date, been developed in locations where SWS is well constrained by in situ observations and groundwater is used intensively for irrigation so that GWS comprises a significant (> 10 %) proportion of TWS  (Leblanc et al.
, 2009; Famiglietti et al. , 2011; Shamsudduha et al. , 2012; Scanlon et al. , 2015). In sub-Saharan Africa, intensive groundwater withdrawals are restricted to a limited number of locations (e. g. irrigation schemes, cities) and constrained by low-storage, low-transmissivity aquifers in the deeply weathered crystalline rocks that underlie ∼ 40 % of this region  (MacDonald et al. , 2012) , including the Upper Nile Basin (Fig. 1 ). Consequently, the ability of low-resolution GRACE gravity signals to trace GWS in these hard-rock environments is unclear. A recent study  (Nanteza et al. , 2016)  applies NASA's GRCTellus (CSR GRACE) data over large basin areas (> 300 000 km 2 ) of eastern Africa and argues that GWS can be estimated with sufficient reliability to characterize regional groundwater systems after accounting for SWS by satellite altimetry and SMS data from the GLDAS LSM ensemble  (Rodell et al.