dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Ede Rancz ,  Michael Ha ¨usser ,  Gen Ohtsuki ,  Christian Hansel ,  João Couto  (the data shared by the above-mentioned people were used in the manuscript for validation of the simulation results),  Bruce Bean ,  Indira Raman  (the data shared by the above-mentioned people were used to constrain ionic current properties),  Matthew Nolan ,  Edward Zagha ,  Bernardo Rudy , and  Paul Mathews  for sharing related experimental data for us to understand PC properties. This work was supported by the  Okinawa Institute of Science and Technology Graduate University.
The highest decrease was observed for tannins (61%), followed by oxalates with (58%) and polyphenols (48%). The main discrepancies were observed with the data published by Chukwuma et al. (2010)  [43] , but for tannins, phytates and polyphenols, the contents were 100-fold below those observed in other tubers or in chickpeas  [44] [45] [46]. The composition analysis shows that trans-ferulic acid, vanillic acid, vanillin and trans-cinnamic acid are the main phenolic compounds in tiger nut oils  [38]. Moreover, the antioxidant activity of tiger nut polyphenols was investigated in comparison with flavonoids from a chemoprotective perspective. A dietary supplementation with 25% of whole tiger nut showed moderate renal and hepatoprotective properties against acrylamide-induced toxicity in rats  [47].
The 18S rRNA gene sequences generated in this study were deposited in the NCBI GenBank database under accession numbers MG760572, MG818163-MG818165, and MG818835-MG818856. Sequence alignments including our new cloned and isolate-derived sequences and previously published sequences representative of the known diversity of Cercozoa and Endomyxa were generated using MAFFT v. 7 (eins-i algorithm)  (Katoh and Standley 2013)  and masked to omit ambiguously aligned positions. Phylogenetic analyses were performed on the CIPRES Science Gateway  (Miller et al. 2010). Maximum likelihood analyses were performed with the program RaxML v 8  (Stamatakis et al. 2008; Stamatakis 2014)  and Bayesian analyses using MrBayes v 3. 2. 5  (Ronquist et al. 2012). Two separate MC 3 runs with randomly generated starting trees were carried out for 5 million generations each with one cold and three heated chains.
In parallel, preliminary phylogenetic analysis of the full 18S rRNA gene sequences of flagellates isolated from a reservoir and wetland in Vietnam (morphologically described below) showed them to also be related to 'core' Aquavolonida, some of them with sequences highly similar to clone Dsoil. All of these relatives to 'core' Aquavolonida would be missed by the primers used in PCR strategy I. We therefore designed PCR strategy II to target more broadly the diversity of Aquavolonida, and applied these primers to the same habitat types tested with PCR strategy I (Table  1 ). Figure  1  shows a Bayesian phylogenetic tree of the diversity of Aquavolonida 18S rRNA gene phylotypes identified with PCR strategy II, using tremulids and NC12 as outgroups. 'Core' Aquavolonida, which comprises all previously identified sequences from GenBank, all our cloned sequences from freshwater planktonic samples, and a few cloned sequences from freshwater sediments and river biofilm, form the bulk of Aquavolonida diversity and are here labeled lineage Aquavolonida NC10-A.
The NC10-A radiation is subdivided into three phylogenetically distinct subgroups Ai to Aiii; Ai is the most diverse both among GenBank sequences and our clone libraries. No morphological information is available for any of them; however, Ai is the subgroup targeted by our FISH probes (see below). Below 'core' Aquavolonida, we identified a grade of three earlier-branching lineages (B-D) that by contrast were not identified in freshwater planktonic samples. Aquavolonida NC10-B comprises most of our isolated strains, and the two new species described in this study (see below). This lineage was detected in soils, freshwater sediments, and river biofilm. The single, divergent sequence in Aquavolonida NC10-C is another isolated strain, a gliding flagellate with a similar gross morphology and feeding mode to the new species, to be formally described elsewhere.
ACKNOWLEDGMENTS This work was supported by grants from the  Russian Foundation for Basic  to DT and APM and in the framework of the state assignment of  FASO Russia  (theme no. AAAA-A18-118012690098-5 ), from the  Natural Sciences and Engineering Research Council of Canada  ( 227301 ) to PJK, and  NERC  grants  NE/H009426/1  (to DB and CB),  NE/P00251X/ 1  and  NE/H000887/1  (to DB). RF was supported by  Grantin-Aid  funding to The  Natural History Museum. CB is grateful to the  International Society of Protistologists  and the  Gordon and Betty Moore Foundation for current funding  (grant  GBMF5257 / UniEuk  project, www. unieuk. org),and was also supported by the ' Investissements d'Avenir' program OCEANOMICS  ( ANR-11-BTBR-0008 ) awarded by the  French Government via Agence Nationale de la Recherche.
Standard PCR and qPCR procedures PCRs were performed with a GeneAmp® PCR 2700 system (Applied Biosystems, Barcelona, Spain). ARGs from the control strains were amplified by conventional PCR, cloned in pGEM-T Easy vectors (pGEM®-T-Easy Vector, Promega, Barcelona, Spain) to generate the constructs and verified by sequencing (Table  2 ). The constructs were used to generate the standard curves. Real-time qPCR assays for bla TEM , bla CTX-M-1 -group, bla CTX-M-9 -group ,mecA, armA, qnrA group, qnrS group and sul1 were performed as previously described  [9, 12, 20, 21]. The bla OXA-48 gene qPCR assay (Table  2 ) was designed with the Primer Express Software version 3. 0 (Applied Biosystems). The gene was amplified using specific primers (Table  2 ) from the sequence of bla OXA-48 harbored in the K.
Funding Brown-Jaque et al. , 2017 This study was supported by  Plan Nacional de I+D+i and Instituto de Salud Carlos III, Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Economía y Competitividad ,  Spanish Network for Research in Infectious Diseases  ( REIPI RD12/0015 ) -co-financed by  European Development Regional Fund "A way to achieve Europe" ERDF ; the  Fondo de Investigación Sanitaria  (grant  PI13/00329 ); and the  Generalitat de Catalunya  ( 2014SGR0073 ). MABJ has a grant from  COLCIENCIAS  ( Republic of Colombia ). PE is funded by the  Instituto de Salud Carlos III "Sara Borrell " contract number  CD15/00017.
Image features We use the ImageNet dataset  (Russakovsky et al. , 2015) , including the fall Ima-1 http://mattmahoney. net/dc/textdata geNet 2011 release  (Deng et al. , 2009). It contains 14, 188, 125 images organized according to 21, 842 synsets of WordNet  (Fellbaum, 1998). Each synset contains 600 images on average. To extract image features, we rely on the Caffe toolkit  (Jia et al. , 2014)  and use the GoogLeNet convolutional neural nets  (Szegedy et al. , 2015)  pre-trained on the 1000 synsets of ILSVRC 2012. The 1024-dimensional activation of the pooling units (before the softmax layer) are then taken as our image features. Visual representation of words For each word in the vocabulary, we recover all the synsets that it belongs to using the WordNet interface of the NLTK module (Python)  (Bird et al.
, 2009). We then remove the synsets not covered by our Im-ageNet dataset. This results in 9,713 words, out of the 50,000 words in the vocabulary. For each visual word, we randomly draw 1,000 distinct images in ImageNet. If the number of images for a word is less than 1,000, we increase the coverage using images belonging to the hypernyms of the considered word's synsets, as in  (Kiela and Bottou, 2014). We then take the average of these features as the word's visual representation x. Hyper-parameter setting For all models, we set the dimension of linguistic and visual embeddings, e and z, to 100, following many previous works. In our model, the encoder/decoder neural networks are implemented as one-hidden-layer neural nets with 500 hidden units each. The dimensions of the inputs and the outputs of the decoder neural networks are 100 and 1, 024 respectively (1, 024 and 100 for the encoder).
Datasets #word pairs MEN  (Bruni et al. , 2014)  3000  EN-MC (Miller and Charles, 1991)  31 EN-RG  (Rubenstein and Goodenough, 1965)  65 SimLex  (Hill et al. , 2015)  999 MTurk  (Radinsky et al. , 2011; Halawi et al. , 2012)  287 WORDSIM  (Finkelstein et al. , 2001)  350 REL  (Agirre et al. , 2009)  150 SIM  (Agirre et al. , 2009)  200 SEMSIM  (Silberer and Lapata, 2014)  5494 VISSIM  (Silberer and Lapata, 2014)  5494   (Lazaridou et al. , 2015) , § :  (Collell et al. , 2017)  4. 2 Task 1: Word Similarity Datasets Word similarity is a common type of evaluation task for measuring the effectiveness of word embeddings. To this end, we retain 10 benchmark datasets consisting of pairs of words associated with similarity scores given by human judges. Table  1  summarizes their basic properties. There are different types of similarities being assessed.
SEMSIM, SimLex, SIM, EN-RG and EN-MC focus on semantic or taxonomic similarity -e. g. CAR is similar to AUTOMOBILE. MEN, REL and MTurk consider general relatedness -e. g. CAR is related to GARAGE. VISSIM is about visual similarity -e. g. GOOSE looks like SWAN. Note that SIM and REL are the similarity and relatedness subsets of the full WORDSIM dataset  (Finkelstein et al. , 2001)  respectively. VISSIM contains the same word pairs as SEMSIM. Competing models We benchmark our model PIXIE against several strong uni-and multi-modal models listed below: • SGNS: Skip-Gram with Negative Sampling  (Mikolov et al. , 2013). Without the visual component, PIXIE reduces to SGNS. We can thus assess the impact of the perceptual information by comparing PIXIE to SGNS. • VAE: Vatiational Auto-Encoder  (Kingma and Welling, 2013) , which corresponds to the visualspecific component of PIXIE.
Main results The results across different datasets are shown in Table  2. We perform evaluations under two settings: by considering (i) word similarity between visual words only and (ii) between all words (column 100% in Table  2 ). For the models CNN, VAE and their concatenation with SGNS embeddings, the latter setting is not applicable. The two last rows correspond to the multimodal embeddings inferred from our model. In particular, PIXIE + (resp. PIXIE ⊕ ) represents the multimodal embeddings built using Eq. (10) (resp. Eq. (  11 )). Overall, we note that PIXIE ⊕ offers the best performance in almost all situations. This provides strong empirical support for the proposed model. Below, we discuss the above results in more depth to better understand them and characterize the circumstances in which our model performs better.
How relevant is our formulation? Except PIXIE and V-SGNS, most of the multimodal competing methods rely on independently pre-computed linguistic embeddings. As Table  2  shows, PIXIE and V-SGNS are often the best performing multimodal models, which provides empirical evidence that accounting for perceptual information while learning word embeddings from text is beneficial. Moreover, the superior performance of PIXIE ⊕ over V-SGNS suggests that our model does a better job at combining perception and language to learn word representations. Joint learning is beneficial PIXIE ⊕ outperforms VAE⊕SGNS in almost all cases, which demonstrates the importance of joint learning. Where does our approach perform better? On datasets that focus on semantic/taxonomic similarity, our approach dominates all other methods.
On datasets focusing on general relatedness, our approach obtains mixed results. While dominating other approaches on MEN, it tends to perform worst than SGNS on MTurk and REL (under the 100% setting). One possible explanation is that general relatedness tends to focus more on "extrapolating" from one word to another word (such as SWAN is related to LAKE), while our approach better models more concrete relationships (such as SWAN is related to GOOSE). The low performance of CNN and VAE confirms this hypothesis. On the VISSIM dataset focusing on visual similarity, both CNN⊕SGNS and VAE⊕SGNS perform the best, strongly suggesting that visual and linguistic data are complementary. Our approach comes very close to these two methods. Note that our learning objective is to jointly explain visual features and word-context co-occurrences.
Thus, two visually similar words, which never occur within the same context, could be mapped into slightly different directions in the latent space. Visual Propagation Here we wish to evaluate the ability of our model to propagate perceptual information to words lacking visual features. To  * ) ). We then perform evaluations, under the two settings, on the datasets of Table  1  considering only pairs composed of words in the above subset of 2K words. As baselines for this experiment, we consider SGNS and the multimodal approaches which can propagate perceptual information, namely V-SGNS and IV-SGNS, as well as their outputs when the 2K visual features are ignored (denoted by V-SGNS ( * ) , and IV-SGNS ( * ) ). The results are given in Table  3. We observe that PIXIE ⊕ ( * ) outperforms SGNS in almost all cases.
Qualitative analysis Table  5  displays several qualitative examples of word similarity. We have selected 4 words: goose, brave, birthstone and savagery. The first two have visual feature representations in our training dataset and the last two do not. Furthermore, for each case, we chose one concrete and one abstract word. 4 For each word, we identify their nearest neighbors in the embedding space. For the visual words, there is a noticeable difference between our method and others. For instance, for word goose, SGNS expresses more "general" relatedness and returns other animals like pig or shark, while our approach is more specific and tends to give visually similar neighbors by focusing on goose looks-like birds. V-SGNS's result is somewhat in between. On the abstract word brave, we observe that PIXIE ⊕ tends to select more explicit embodiments of the adjective brave than SGNS and V-SGNS.
Moving towards the non-visual words, we do not seem to find a consistent discrepancy pattern between V-SGNS and PIXIE ⊕ , though, as for visual words, both methods seem to select more explicit exemplars compared to SGNS. For instance, for the abstract word savagery, both multimodal approaches suggest cannibals and zombies. We now study the usefulness of the learned word embeddings for the tasks of image and caption retrieval. Our hypothesis is that multimodal word embeddings will perform better for downstream tasks involving multimodal information. Experimental setup We use the Flickr30K dataset  (Young et al. , 2014)  containing 31,000 images and 155,000 sentences (5 captions per image). The sentences describe the images. The task is to identify the best sentence describing an image or to identify the best image depicting a sentence.
Acknowledgements This work was partially supported by the virtual lab  Inria@SiliconValley ,  ANR  Grant  GRASP  No. ANR-16-CE33-0011-01 , and a grant from  CPER Nord-Pas de Calais/FEDER DATA Advanced data  science and technologies 2015-2020,  NSF   IIS-1065243 ,  1451412 ,  1513966/ 1632803/1833137 ,  1208500 ,  CCF-1139148 , a  Google Research Award , an Alfred P. Sloan Research Fellowship, gifts from  Facebook  and  Netflix , and ARO#  W911NF-12-1-0241  and  W911NF-15-1-0484. We thank anonymous reviewers for their suggestions and comments.
C. 2 User preferences-based stream prioritisation Each path between the IUG and the ING should be analysed to learn the real-time status in terms of latency, bandwidth, and packet loss rate, so that the decision on link selection can be facilitated. Benefiting from the MPTCP architecture, the link estimation can be performed by regularly collecting the information from multiple TCP connections. In order to evaluate the maximum TCP bandwidth, the internal variables of the TCP control block are evaluated. These include the amount of unacknowledged data in the TCP send-buffer and the number of consecutive duplicate ACKs (DUPACKs) that have been received. When three DUPACKs are received, the TCP host deduces that the link is congested and reduces the size of its window. At this point, the link estimation module indicates the maximum bandwidth a TCP connection can achieve.
In parallel, the currently used bandwidth for each connection is estimated at every RTT interval by low-pass filtering the rate of returning ACK packets from a peer. When an ACK is received by the source, it conveys the information that a certain amount of data corresponding to a specific transmitted packet has been delivered to the destination. Averaging the delivered data count over time yields a good estimation of the bandwidth currently used by the source. When DUPACKs reach the source, they should also count toward the bandwidth estimate, and a new estimate should be computed right after their reception. Besides, the amount of sent UDP packets is also considered. Each sub-flow is estimated individually, and the sum of all estimations provides an accurate bandwidth estimate for each tunnel.
C. 3 Link selection A novel QoS-aware link selection method called Path Selection based on Object Length (PSBOL) has been defined that considers the application traffic requirements and the realtime status of each link. At the beginning, the link selection process analyses the TCP object size, where an object is typically an Application Protocol Data Unit (APDU). The algorithm assumes that for objects that are longer than a certain threshold, the priority should be given to the high bandwidth links for reducing the overall transmission time. For short objects on the other hand, it should be preferred to select the low-latency links. The discovery of the objects' boundaries is based on an algorithm that computes the Inter-Arrival Time (IAT) between the packets. Based on this process, the size of each object is computed on the fly.
B. Subjective tests Four applications have been used to measure the QoE in the presence of IUG and ING. These are: video streaming in high definition, browsing, video chat, and gaming. Video streaming and browsing include TCP data traffic while the video chat and gaming applications include UDP traffic too, as they are relatively more sensitive to latency. The tests have been performed in an isolated network to prevent the influence of any Internet-associated issues that are beyond our control. Thus, all applications are hosted by the application server integrated to the test-bed, as shown in Figure  3. For the video streaming application, several test videos were generated at an average bitrate of 8 Mbit/s using H. 264/AVC and audio encoded at 0. 5 Mbit/s and kept in an Apache web server.
ACKNOWLEDGEMENT This work has been supported by the  BATS Project (Broadband Access via Integrated Terrestrial & Satellite Systems ) funded by the  European Commission's Seventh Framework Programme  under contract no 317533. The authors would like to acknowledge the members of the project consortium for their contributions to the presented work, and especially  Dr Hongfei Du  and  Dr Argyrios Kyrgiazos  for their help in setting up the test-bed.
Acknowledgements This research is conducted through collaboration between the  University of Valenciennes  and the  National Engineering School of Sfax. This collaboration is jointly financed in the frame of the Utique CMCU programme. The present research work has also been supported by the  Nord-Pasde-Calais region , the  European Community , the Regional EPJ Web of Conferences 183, 02009 (2018) https://doi. org/10. 1051/epjconf/201818302009DYMAT 2018  Delegation for Research and Technology , the  French National Research Agency , and by the  Ministry of Higher Education and Research. The authors gratefully acknowledge the support of these institutions.
Canopy Spectral Data Canopy hyperspectral reflectances were obtained at four key growth stages (Zadoks growth stage: 31, 47, 65, 75 of winter wheat)  [31]  (Table  2 ). Canopy reflectances were measured by an ASD FieldSpec Handheld spectrometer (Analytical Spectral Devices Inc. , USA) with a spectral range of 350-2500 nm. Under clear sky conditions between 10:00 and 14:00 Beijing time, the spectrometer was held at a height of 1. 0 m above the canopy to ensure the same corresponding at different growth stages. A 40 cm by 40 cm BaSO4 calibration panel served as a black, baseline reflectance. To reduce the possible effects of sky and field conditions, spectral measurements were taken at four sites in each plot and then averaged to represent the canopy reflectance of each plot. Vegetation and panel radiance measurements were taken as the average of 20 scans at an optimized integration time, with a dark current correction for each spectrometry measurement.
During the inversion of the N-PROSAIL model to retrieve crop parameters, nine parameters needed to be determined (Table  3 ). These parameters were determined at different growth stages. LAI and LND were the main retrieval parameters and they were given intervals based on calibration dataset. C w , C m and Soil brightness parameter (R soil ) values were obtained by averaging measured values at different growth stages. The solar zenith angle (θ s ) was calculated for the time in the experiment when the hyperspectral data was measured. Leaf structure parameter (Ns), hot spot parameter (S L ) and leaf inclination distribution (LID) values were firstly optimized by the SCE-UA method using the 2012-2013, 2013-2014 and 2015-2016 wheat experiment data, where parameters of LAI, LND, C w , C m , R soil , θs were known inputs.
The determination coefficient (R 2 ) and root mean square error (RMSE) were used to test the general performance of different models in this study. All calculations were made using the MATLAB (v2007, MathWorks Inc. , Natick, MA, USA), and all graphs were made using the R statistical software RStudio (v1. 0. 44, RStudio Inc. , Boston, MA, USA). (R 700 -R 670 -0. 2(R 700 -R 550 ))(R 700 /R 670 ); MTVI2: 0. 5(1. 2(R 800 -R 500 )-2. 5(R 670 -R 550 ))/ sqrt (2(R 800 + 1) 2 -(6R 800 -5sqrt(R 670 )) -0. 5) Eitel et al. [40] mND705 (R 750 -R 705 )/(R.
75 and 1. 32 g•m -2 , respectively. Then, the validation dataset was used to validate the model stability, and the model inversion with the 3rd-par setting (R 2 = 0. 82 and RMSE = 0. 95 g•m -2 ) also performed better than the inversions with the 1st-par setting (R 2 = 0. 76 and RMSE = 1. 47 g•m -2 ) and the 2nd-par setting (R 2 = 0. 82 and RMSE = 1. 03 g•m -2 ). The results of this study suggest that the 3rd-par setting in the N-PROSAIL model inversion is the best choice.
Funding This study was funded through a  McGill University Health Centre Research Award. EV receives a salary support from a  Fonds de Recherche Québec Santé Clinical Research Scholar-Junior 1 Award. SCB is supported by the  Bio & Medical Technology Development Program of the National Research Foundation  funded by the  Ministry of Science and ICT  ( NRF-2017M3A9B4050335 ). SJ is supported by The  Danish Rheumatism Association  ( A-3865 ). AEC is supported by an  Arthritis Society Chair in Rheumatic Diseases. The Hopkins Lupus Cohort is supported by a  National Institutes of Health  grant ( R01 AR069572 ) awarded to MP. The Birmingham SLICC cohort was funded by a  Lupus UK  grant awarded to CG. Competing interests None declared. Patient consent for publication Not required. Ethics approval  McGill University Health Centre.