dataesr/scientific-paragraphs-categorization huggingface dataset:
For intermediate taxonomic levels, it is up to the user to decide which one s/he prefers. It is of note that, except for the centrality metric of dRep, the five other metrics used by dRep are available amongst the 30 metrics offered by TQMD and can be used through its customisable ranking formula (see Materials and Methods). A-D is a program that is more recent but, as of April 2021 Dataset Starting TQMD -JELLYFISH k12 dRep Intersection # gen. # spec. # repr. # spec. h. CPU # repr. # spec. h. CPU # repr. # spec. Bacteroidetes 1,127 528 789 517 60 835 516 50 707 511 Streptomycetales 648 220 486 207 40 430 189 250 392.
(2018)  for the sites of Versailles, Grignon, Rothamsted, and Ultuna. We estimated the content of the centennially stable SOC fraction for the Askov and La Cabaña sites using the same Bayesian curve-fitting method described by  Cécillon et al. (2018). The Bayesian inference method was performed using Python 2. 7 and the PyMC library  (Patil et al. , 2010). For the second version of PARTY SOC , we aimed at reducing the potential bias towards an overestimation of the centennially stable SOC fraction content at reference sites using Eq. (1) (Supplement Table  S1 ). This overestimation is possible at reference sites with an LTBF treatment, as SOC inputs to bare fallow topsoils are low but not null (e. g. Jenkinson and Coleman, 1994; Petersen et al. , 2005). Similarly, C 4 -plant-derived SOC inputs are possible after conversion to C 3 plants at the site of La Cabaña.
A further aim was to test the model sensitivity for global change evaluations, by simulating 6-year replicates of the cropping season used for calibration, giving a view of the organic evolution of the soils and crops generally used in the Mediterranean region. The MOMOS model (microorganisms and organic matters of soils): coupling the decomposition and vegetal production in connection with soil climate, biological data (TAO, transformation of added organic matters), and soil texture. MB is microbial biomass, VL j,k and VS j,k are labile and stable fractions of each organ k of plant j or organic fertilizer OF. HL and HS are labile and stable humus, respectively, inorgN is the inorganic N continuously exchanged with the MB and roots of plants. SWM is the soil water module associated with organic transformation.
Most of the C and N flows were predicted by the same decomposition parameters (violet thick solid lines in Figure  1 ) in the equation system showing the C:N ratio of each dead plant organ B j,k e entering decomposition (which can be stored in a database of plants). N specific flows (violet thick dashed lines in the pink rectangle of Figure  1 ) express exchanges of inorganic N with MB decomposers by microbial immobilization and microbial mineralization. Two other N specific flows (violet thick dashed lines in Figure  1 ) concern (i) the adsorption of inorganic N by the roots of plant j, and (ii) N fixation by root microbial symbionts, then its transfer into plant organs. Three flows are C specific (thin solid blue lines in Figure  1 ). Two of them express microbial respiration adjusted by the MOMOS quadratic law and the root respiration associated with each plant species.
C Sequestration in Mediterranean Limestone Soil An interesting aspect of the modeling approach was enabling simulations of a given situation for a given time. The MOMOS simulations of 6-year replicates of the experimental cereal/legume intercropping predicted the C evolutions of Figure  3. Meteorological data were measured on the site, while other data are replicates of the 2011 cropping season. The cumulated C flows for six cropping seasons were predicted up to 2300 g C m -2 (Figure  3a ), near the total-C of Figure  2b. The flow of the C inputs from plant debris was found to be always greater than the accumulated CO 2 -C losses by microbial respiration, but the difference tended to decrease at the end of the simulations. About 80% of the CO 2 -C outputs originated from microbial respiration, while 20% originated from cereal respiration.
Projection is monoscopic while OST-HMD is stereoscopic, both are colocated and no real object occlusion is managed. Rendering is shared by both displays. Monoscopic TVs are also associated with mixed reality devices by several mixed reality systems. Kawakita et al. (Kawakita and Nakagawa, 2014 ) complete a TV program with a mobile device running augmented reality techniques. Collocation results from the detection of a 2D tag displayed by the TV screen by the mobile device, which produces a pose estimation latency compared to inside-out reconstruction techniques embedded by ARCore or ARKit mobile devices, Hololens or Magic Leap. But real occlusions of the TV tags brake collocation. Still, interactions are distributed between the TV and the mobile device, so any user interaction with one of these devices impacts both.
Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e. g. , organizations, locations,. ) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context dependent. While the context can be represented by contextual features, the global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).
Thus, given a word, the contextual features are "dynamically" generated instead of being static as the global one. Some examples are ELMo  [31] , BERT  [6] , and XLNet  [40]. In terms of global features, there exist several tokens that are always parts of an entity. The most obvious cases, as an example in the CoNLL 2003 dataset, are the names of countries include U. S. (377 mentions), Germany (143 mentions), Australia (136 mentions), to mention a few. However, it is not true for all tokens in an entity. The token may or may not be part of an entity (e. g, "Jobs said" vs. "Jobs are hard to find") and may belong to different entity types depending on the context (e. g, "Washington" can be classified as a person or a location). Meanwhile, the contextual features are based on neighboring tokens, as well as the token itself.
They aim to represent word semantics in context to solve the problem of using global features, so as to improve the prediction performance (e. g, "Jobs" in "Jobs said" and "Jobs are hard to find" will have different representations). In this paper, we present a joint architecture to enhance the NER performance simultaneously with static and dynamic embeddings  5. Extensive experiments on CoNLL 2003 dataset suggest that our strategy surpasses the systems with standalone feature representation. The main contributions of this paper are: -We introduce a new architecture that combines the contextual features from XLNet and the global features from GCN to enhance NER performance. -We demonstrate that our model outperforms the systems using only contextual or global features alone and has a competitive result compared with SOTAs on CoNLL 2003 dataset.
Implementation details Global embeddings with GCN The sentences are annotated with universal dependencies from spaCy to create a graph of relations where words become nodes and dependencies become edges. The dataset is then converted into 124 nodes and 44 edges with the training corpus size of approximately 2 billion words, the vocabulary size of 222,496, and the dependency context vocabulary size of 1,253,524. Next, the graph embeddings are fed into 2 Graph Convolution layers with a Dropout of 0. 5 after each layer to avoid overfitting. The global features are captured before the last Linear layer. We perform batch gradient descent using the whole dataset for every training iteration, which is a feasible option as long as the dataset fits in memory. We take advantage of TensorFlow for efficient GPU-based implementation of Equation 2 using sparse-dense matrix multiplications.
Furthermore, the benefit of the joint architecture is illustrated in Fig. 2. While contextual features (XLNet), which is used in the majority of recent SOTA approaches, misclassifies the entity, the prediction from GCN and the combined model correctly tags "MACEDONIA" as the name of a location, confirming our hypothesis on the effect of global features. In Figure  3 , we compare our results with reported SOTA results on the same dataset from 2017 up to now. It can be observed that our results are competitive compared with SOTA  approaches as the difference is by a small margin (the current benchmark is 94. 3 % F 1 -score, compared to 93. 82 % achieved by our approach). Moreover, we notice that NER performance can be boosted with external knowledge (i. e. leveraging pretrained embeddings), as proven in our approach as well as in top benchmarks  [22, 23, 24].
Conclusion and future work We propose a novel hierarchical neural model for NER that uses both global features captured via graph representation and contextual features at the sentence level via XLNet pretrained model. The combination of global and contextual embeddings is proven to have a significant effect on the performance of NER tasks. Empirical studies on the CoNLL 2003 English dataset suggest that our approach outperforms systems using only global or contextual features, and is competitive with SOTA methods. Given the promising results in English, our future work will consist of adapting the method to other languages, as well to a cross-lingual experimental setting. In addition, we will consider further developing the method by also incorporating background knowledge from knowledge graphs and ontologies.
Acknowledgements This work has been supported by the  European Union 's  Horizon 2020 research and innovation program  under grants  770299  (NewsEye) and 825153 (EMBEDDIA). The work of S. P. has also received financial support from the  Slovenian Research Agency  for research core funding for the  Knowledge Technologies programme  (No. P2-0103 ) and the project  CANDAS  (No. J6-2581 ).
This is an ideal time to (re)explore concepts from signal and image analysis and adapt them to LSB astronomy. With this in mind we developed DAWIS, an algorithm optimised for the detection of LSB sources and highly parallelized to be run on large samples of images. Such a new algorithm needs to be tested on simulations and contrasted with previous detection methods. To run the tests, we create images of simulated galaxy clusters and ICL using the Galsim package  (Rowe et al. 2015). These images only reproduce the photometric aspect of galaxy clusters and cannot be used to draw conclusions on the properties of ICL. However their content is a known value, which allowed us to estimate the efficiency of DAWIS and of previous methods for detecting ICL. We were also able to constrain the different biases and contamination effects occurring when applying these methods.
This paper is organised as follows. In Section 2 we give some context on the various detection methods used to detect ICL in deep images and different effects limiting and contaminating this detection. In Section 3 we present the technicalities of DAWIS and the core of the algorithm. In Section 4 we describe how we simulated mock images of galaxy clusters with ICL with the modeling package Galsim. In Section 5 we apply the different detection methods to the simulations and present the results. In Section 6, we run DAWIS on real data, and compare the results with previous works. In Section 7 we discuss the results and the performances of the methods, both on simulations and on real data. We assume a standard ΛCDM cosmology with Ω m = 0. 3, Ω Λ = 0. 7 and H 0 = 70 km. s -. Mpc -1.
DAWIS: implementation and parallelization DAWIS is implemented with an emphasis on modularity (e. g. , a set of modules that can be moved or replaced by new versions), as the algorithm can still be upgraded to increase the quality of the method or modified according to new analysis goals. We chose to write the main layer of modules in Python, as it is a very commonly used, versatile and accessible open-source language. However this versatility has a cost in terms of numerical performance, which led us to support the Python modules by Fortran 90 codes where the main numerical computations are done. 9 Apply CLEAN factor z = δz. As explained in Section 2. 3. 4, one of the main limitations of wavelet based algorithms is computation time, which prevents the application of previous packages like OV_WAV on large samples of images or to very large images.
15 End loop on k. 16 Set k = k + 1. rithm 2 and when performing the multiscale analysis with Algorithm 3), the Fortran modules are parallelized in shared memory using OpenMP 3. Complementary, we use the new Python package Ray 4  to distribute processes when working on many small arrays (the restorations of numerous objects to compute the associated synthesis atom are independent from each other and can be distributed, such as Algorithm 4 and Algorithm 5 for example). We display here a CPU computing time scaling test on mock data for both types of parallelizations. For the shared memory parallelization, the test is set on an image of size 4096 × 4096 pixels (giving a wavelet data cube of 4096 × 4096 × 10 wavelet coefficients). Algorithm 2 and Algorithm 3 are run on the image first serially (one CPU), and then by increasing progressively the number of CPU.
Simulations A new detection algorithm such as DAWIS must be tested and compared to more traditional methods. For this purpose, we take an image analysis approach and create mono-chromatic mock images of galaxy clusters simulated with the Galsim package  (Rowe et al. 2015) , emulating the photometric aspect of galaxy clusters (galaxies + ICL) and the properties of the Canada France Hawaii Telescope (CFHT) wide field camera MegaCam. The choice of MegaCam has been made to prepare upcoming ground surveys such as the UNIONS survey  (Ibata et al. 2017). Note however that the same approach can (and should in the future) be applied to simulations of HST images for example. From an astrophysics point-of-view, this approach is not the most realistic since the simulated images contain only light profiles and an artificial background.
Example on real astronomical data In this section we demonstrate the capabilities of DAWIS on a galaxy cluster image at optical wavelengths. The targeted galaxy cluster is LCDCS 0541, which is part of both the Las Campanas Distant Cluster Survey (LCDCS;  Gonzalez et al. 2001 ) and the Dark energy American French Team / French American DArk energy Team (DAFT/FADA;  Guennou et al. 2010)  surveys. The redshift of this cluster is 0. 542, and a deep 2x2 arcmin 2 HST Advanced Camera for Survey (ACS) image in the F814W filter is available. The photometric depth of the image was estimated  (Guennou et al. 2012, hereafter G12)  to be around µ F814W ∼ 24 mag. arcsec -  for extended sources with SExtractor (∼ 1. 25 arcsec, 1. 8σ detection limit). Details about the data and the reduction procedure are available in G12.
We chose LCDCS 0541 and these data because this target was the case study of G12, where a multiscale analysis was performed to detect ICL in the images of ten galaxy clusters using OV_WAV, better suited for this task than SExtractor. By running DAWIS on the same exact cluster image, we can compare the results of DAWIS to those obtained by this earlier multiscale analysis method (for more details on the differences between both approaches, see Section 2 for traditional multiscale analysis methods, and Section 3 for an indepth description of DAWIS). In G12, OV_WAV was run two consecutive times on the LCDCS 0541 image. The first run allowed the modeling of sources with characteristic sizes up to j = 10 (1024 pixels), and the second run up to j = 7 (128 pixels). The detected and restored sources were removed from the original scene, and the ICL was detected by applying a 2.
To estimate the efficiency of the algorithm, we simulated in Section 4 mock images of galaxy clusters with noise using the modeling package Galsim. Galaxies are represented by Sérsic profiles, and the ICL by a large and faint exponential profile, the properties of which are controlled by few parameters, notably an input ICL fraction. The images are only emulating the MegaCam instrument properties, but the same study with simulations of other instruments should be led in the future. We take advantage of this work to test in Section 5 the efficiency of other detection methods that we roughly classified in two categories: surface brightness threshold and fitting algorithms. We apply these two methods in the same way as DAWIS to the simulations, and compare the resulting measured fluxes and ICL fractions.
We dismiss the afterglow-only and afterglow-plus-KN models because they predict i-band flux at the time of the GMOS data point that is too faint by 1. 6 +1. 8  -0. 3 mag -inconsistent with the observations at the ∼5σ level (see Extended Data Figure  6  and Extended Data Table  7 ). We repeated the analysis of all three scenarios including the GMOS data point in order to do Bayesian model comparison. The Bayes factor between the afterglow-plus-KN and afterglow-only models is ∼ 1, indicating that neither model is favored over the other. However, the Bayes factor between the afterglow-plus-SN and afterglow-only model is 10 5. 5 , indicating that the afterglow-plus-SN model is strongly favored. Based on the compilation of Ref. 23  of GRBs with associated SNe, GRB 200826A is the shortest LGRB found with an associated collapsar, with an observed T 90 of 1.
Optical. We ingested the GBM localization map into the GROWTH Target-of-Opportunity (ToO) marshal, an interactive tool design to plan and schedule ToO observations for ZTF  73. The observation plan generated by the ToO marshal relies on gwemopt  [104] [105] [106]  , a code that optimizes the telescope scheduling process for skymaps with a healpix format, like the Fermi-GBM maps. The gwemopt procedure involves slicing the skymap into predefined tiles of the size and shape of the ZTF field-of-view, determining which fields have the highest enclosed probability, and optimizing observations based on airmass and visibility windows. For this purpose, we used a modified version of the "greedy" algorithm described in Ref. 107  , implemented within gwemopt. The resulting optimized plan for the optical follow-up of GRB 200826A consisted of four primary ZTF fields and one secondary field b.
The fields were observed in the rand g-band for 300 s each, starting 4. 9 hours after the GBM detection. The observing plan for the first night covered 186 deg 2 , corresponding to 77% of the GBM region (see Fig. 2 ). Once the Konus-Wind data became available on ground at about 17:55 UT, the 288 arcmin 2 IPN error box was derived using Konus, GBM, and HEND data, which allowed the X-ray Telescope (XRT  108  ) on board Swift to initiate a ToO at about 20:45 UT. The IPN box was published later at about 21:30 UT  109. Reference images of the fields are then subtracted from the ZTF ToO observations and any high significance difference (>5σ) generates an alert  72, 110  that contains relevant information about the transient. We queried the stream of alerts using three different tools: the GROWTH marshal  111  , the Kowalski infrastructure 112c and AMPEL  113-116d.
Additionally, we searched the Minor Planet Center (MPC) to ensure that our potential counterparts were not consistent with known solar system objects. Forced photometry  124  was performed using data from the 10 nights previous to the trigger, to remove young SNe from the sample. A final quality check was done by querying for alerts around each candidate, and rejecting transients that have multiple alerts within a radius of 15" as these alerts could suggest artifacts, ghosts, or slow-moving objects near the stationary points  125. After the first night of observations 28195 alerts were generated in the region and 14 sources passed every stage of our filtering criteria  126. The SGRB afterglow, ZTF20abwysqy  127  , was not in our first selection as there was a ZTF alert 11" NE; however, it passed all the other filtering criteria.
Two of the ZTF ToO fields triggered during the first night covered > 99% of the triangulated IPN region. However, none of the candidates reported in Ref. 126  fell there. For our second night of observations, we scheduled 600 s observations in rand g-band and queried the database. Probability of Chance Coincidence. We roughly calculate the probability of chance coincidence (p) of this optical transient to be independent of GRB 200826A. For this we follow Ref. 128.
Follow-up The summary of the follow-up results can be found in Extended Data Table  1 ,Extended Data Table  3 , and Extended Data Table  4. Optical/NIR. Here we present our optical and near infrared (NIR) follow-up results. In addition to our observations, MASTER  129  and Kitab follow-up with a clear filter  130, 131  led to upper limits of 18. 3 mag and 20. 4 mag respectively. Las Cumbres Observatory. We performed follow-up of ZTF20abwysqy with the Spectral camera mounted at the 2-m Faulkes Telescope North (FTN), located at Haleakala Observatory. Starting on 2020-08-28 11:32:26 UT we acquired three sets of images with 300s exposures each in gand r-bands through the LCO observation portal e. Furthermore, we obtained a second e https://observe. lco. global/ (reference) epoch in gand r-bands on 2020-09-02 UT.
Our images were reduced by an automatic subtraction pipeline. Our pipeline retrieves images from LCO, stacks them, extracts sources from the image using Source Extractor 132 and performs photometric calibration of sources using the PS1 catalog. Then, the pipeline performs image subtraction using the High Order Transform of Psf ANd Template Subtraction code (HOTPANTS  133  ) to subtract a PSF-scaled reference image aligned using SCAMP  134. Our photometry measurements reported in gand r-band were determined after host subtraction using the LCO reference images in the same filters acquired on 2020-09-05. Hale Telescope. We obtained two epochs of dithered J-band imaging with the Wide Field Infrared Camera (WIRC  135  ) on the Palomar Hale 200-in (P200) telescope on 2020-08-28 UT and on 2020-09-04 UT, spending an hour integrating on target during each epoch.
We used standard procedures to perform bias and flat-field correction. The astrometry was calibrated against the SDSS catalog (release DR16  140  ) and frames were aligned using SCAMP and stacked with SWarp. After stacking the images, we extracted sources using Source Extractor and the magnitudes were calibrated against 45 PS1 stars in the field on average. We used HOTPANTS to perform image subtraction between the first and third epochs and found a source in the r-band at the location of ZTF20abwysky with a magnitude of 24. 46±0. 12 mag (see Extended Data Table  1 ). We determine the magnitudes of the host galaxy using the second epoch of observations. To verify this result, we used the 80 days i-band GMOS-North (see description in the paragraph below) as a reference and the HOTPANTS subtraction shows a source with r-band magnitude of 24.