dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples of embedding-based algorithms are Text Enhanced Network Embedding (TENE)  [16]  and Binarized Attributed Network Embedding (BANE)  [17]. Both of these algorithms aim at learning a low-dimensional vector representation for each node and its associated attributes in the attributed graph. BANE uses Weisfeiler-Lehman graph kernels  [41]  to encode dependencies between node edges and attributes into a binary code representation. This representation encodes firstorder proximities  [42]  between nodes. TENE aims to jointly learn vector representations based on both first-order and second-order proximities  [42] , as well as on the text cluster membership matrix. Although one can represent string labels as attribute vectors (e. g. , by representing a string as a vector of -grams and their frequencies  [10]  or their tf-idf scores  [16] ) and then apply an attributed graph clustering algorithm to our problem, such a representation inevitably loses information and thus severely degrades the quality of clustering, as we show in our experiments (Section 7).
Example 1. Let  1 = aba and  2 = bab be two sequences. The 2-grams of both of these sequences are ab and ba and each of these 2-grams appears only once in  1 or in  2. Thus,  1 and  2 have the same vector representation  (1, 1)  where the first 1 denotes the frequency of ab and the second 1 denotes the frequency of ba, assuming a lexicographic order of -grams. Since  1 and  2 have the same vector representation, they are treated as equal by attributed graph clustering algorithms, although they are not. Similarly, consider a dataset { 1 ,  2 ,  3 } with  3 = ccc. The vector representation of both  1 and  2 when using td-idf scores instead of frequencies is (log 2 (3∕2), log 2 (3∕2)), so again  1 and  2 are treated as equal in similarity computation. Thus, the similarity information of sequences is not captured well after they are represented based on -grams.
We report results for the best embedding method for each competitor and. The real-valued embedding constructed by TADW or TENE was fed into -means, following  [55] , while the binary embedding of BANE was fed into -medoids  [54]  (with Hamming distance). We also implemented variants of CA and MH that represent a string using a vector of -grams, as TADW and TENE do, reporting results for the best embedding method and. We refer to the variant of CA (respectively, MH) as CA vec (respectively, MH vec ). Methods for clustering strings (-medoids  [54] , -means  [1] ) or graphs (spectral clustering  [1] ) performed worse than  [12, 16, 17] ; thus we omit their results. We summarize all tested methods in Table  1 , for ease of reference. Datasets and Setup. We used the Ciao (CIAO) and Epinions (EPIN) datasets from https://www.
cse. msu. edu/~tangjili/datasetcode/ truststudy. htm. In these datasets, each user (node) is associated with a (potentially empty) sequence of reviewed products (string), and an edge connects users who trust each other. Table  2  summarizes the characteristics of the datasets we used. Clustering quality was quantified using: (I) the Average Sum of Pairwise Edit distances (ASPE), defined as 1 𝑘 ∑ 𝑐∈ ∑ 𝑢,𝑣∈𝑐 𝑑 𝐸 ( (𝑢),  (𝑣)) ; and (II) Modularity  [2] , a well-established measure of network (graph) clustering quality, expressed as the fraction of the edges that fall within the given clusters minus the expected fraction if edges were distributed at random. Small ASPE and large Modularity values are preferred. The speed of convergence of ESR (see Theorem 1) was measured using Average Relative Difference (ARD)  [45] , defined as 1 |𝑉 | 2 ∑ 𝑢,𝑣∈𝑉 𝑅 𝓁+1 (𝑢,𝑣)-𝑅 𝓁 (𝑢,𝑣) 𝑅 𝓁 (𝑢,𝑣) , where  and  + 1 are two consecutive iterations of the fixed-point iteration algorithm.
ARD was also used with Ê (defined as Ê(, ) = 1 - Ê (, )) and SimRank. By default, we used  = 15,  = 2,  = 10 -9 , and  = 5. We also used the default value  = 300  [54]  and default parameter values for competitors. The proxy measures were implemented following  [56]. All results are averaged over 10 runs. All algorithms were implemented in Python and executed on an Intel i9 at 3. 70 GHz with 64 GB RAM. Our source code is available at https://rebrand. ly/SGcode. String vs. Vector Representation. We show that representing strings as attribute vectors has a negative impact on clustering quality via comparing our algorithms to the variants CA vec and MH vec. As can be seen in Figs. 2  and 3 , CA vec resulted in higher (worse) ASPE than both CA and CA. For example, the average ASPE for CA vec was higher than that of CA (respectively, CA  ) by 104% (respectively, 56%) on average (over the two datasets).
Also, CA vec resulted in lower (worse) Modularity than both CA and CA. For example, the average Modularity for CA vec was lower than that of CA (respectively, CA  ) by 18% (respectively, 33%) (over the two datasets). These results show the benefit of measuring similarity by using strings directly as our algorithms do. Since CA vec and MH vec performed worse than CA and MH, we do not report results for them in the remaining of this section. Clustering Quality. We show that our algorithms created clusters containing nodes with similar strings, as ASPE is lower than that of most competitors (see Fig. 4(a) ), which are also structurally similar, as Modularity is higher than that of most competitors  (see Fig. 4(b) ). In addition, the use of proxy measures by our algorithms did not substantially affect clustering quality, as CA  and MH  performed very similarly to CA and MH, respectively (see Fig.
A. 8 in Appendix A). On the other hand, the competitors achieved a worse clustering than our algorithms, when considering both ASPE and Modularity together, and some were worse in both of these measures. For example, TENE performed poorly in terms of both ASPE and Modularity, while BANE and TADW performed the worst in terms of Modularity and worse than MH and MH  in terms of ASPE. AGC performed the worst in terms of ASPE but the best in terms of Modularity for  < 36. For  ≥ 36, AGC did not terminate because the eigenvalue decomposition it employs to find the convolution failed. Similar results were obtained for the EPIN dataset (see Figs. The reason that AGC performed poorly with respect to ASPE is that it favors Modularity by design, since it assumes that nodes that are close in the graph will likely be clustered together, as mentioned in Section 2.
7 (a) and 7(b), respectively. As expected, CA  and MH  were faster than CA and MH, since the last two algorithms need to compute edit distance instead of the more efficient to compute proxy measures. For example, MH  was two orders of magnitude faster than MH in the case of clustering CIAO and even faster in the case of clustering EPIN. In addition, CA  was approximately two times faster than CA. The impact of using the proxy measure in the algorithms for SGC was less significant compared to the algorithms for SGM, because in the former there are fewer edit distance computations, as in SGC there is no need to compute all pairwise distances between strings. CA was faster than MH, as expected by the complexity analysis (see  Section 6). For example, CA was more than 50 times faster than MH in the case of the CIAO dataset and more than two order of magnitudes faster in the case of the EPIN dataset.
Thus, we can construct  ′ by first clustering the sequence graph corresponding to  with  equal to the number of clusters in the ground truth clustering, and then creating, for each resultant cluster , a cluster  ′ in  ′ that is comprised of the non-empty strings corresponding to the nodes in. After that, we can compare  ′ with the ground truth clustering  using measures that compare clusterings (e. g. , the measures in  [57] [58] [59] ). In what follows, we first discuss the data and setup we used and then the results of our case study. Data and Setup. We used three referred to as Ebolavirus (EBOL), Influenza (INFL), and Coronavirus (COR). characteristics of these datasets are summarized in Table  3. In these datasets, each record is a genomic sequence of a different virus type (e. g. , in EBOL, there are 59 records and each record corresponds to a different type of Ebolavirus).
All genomic sequences were downloaded from the NCBI GenBank [60] based on their accession numbers provided in  [61]. For each dataset, we obtained the phylogenetic tree from  [62] , and the ground truth clustering from the NCBI GenBank [60] using the BioPython library  [63]. Specifically, each cluster in the ground truth clustering is comprised of all sequences with the same value in the Organism field, for EBOL and INFL, or all sequences with the same value in the last element of the Taxonomy field for COR (since all sequences in this dataset had the same value in Organism). It can be readily verified from Figs. B. 9, B. 10, and B. 11 in Appendix B that the phylogenetic trees we used are in accordance with the ground truth clustering. That is, each ground truth cluster contains leaves that are close together in the phylogenetic tree.
6%, 15. 2%, and 16% larger on average (over all datasets) than those of MH, respectively. In addition, the use of proxy measures in our methods did not substantially affect clustering quality. This is encouraging as our methods using proxy measures, namely CA  and MH  , are more efficient, as discussed in Section 7. On the other hand, the competitors did not perform well. For example, the ACC, NMI, and macro- 1 scores for CA were 91. 8%, 159. 5%, and 121. 2% larger on average (over all datasets) than the best competitor TADW. The main reason for the poor performance of competitors is that, in the application we consider, only the leaves of a phylogenetic tree have non-empty strings associated with them, while a large number of non-leaf nodes are associated with the empty string. This leads the competitors to construct clusters with leaf nodes associated with different strings, as their assumptions (close nodes in the tree should be clustered together for AGC, and low order proximities are sufficient to cluster nodes in the tree for BANE and TENE) are invalidated.
Acknowledgments H. Zhong is supported by a  CSC Scholarship, UK. G. Loukides is supported by the  Leverhulme Trust ,  UK RPG-2019-399  project. S. P. Pissis is partially supported by the  ALPACA  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 956229  and by the  PANGAIA  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 872539.
Dust Emissivity Past analyses based on spectra and imaging data indicate that grains are predominantly composed of silicates and organics (e. g. ,  Chen et al. 2007; Ballering et al. 2016). Since the IRS β Pic spectrum shows prominent emission features associated with silicates, we investigate lab-measured optical constants for silicate species. The grain composition primarily affects the central wavelength location of emission features. In addition, for every grain species, four additional grain properties (crystallinity, Fe/Mg ratio, shape, and temperature) can shift the central wavelength features around. Therefore, we explore the Jena database for the most suitable optical constant measurements. First, we select grain species based on the observed central peak wavelengths and rule out the species from visual examinations.
We thank  Alycia Weinberger ,  Benjamin Montesinos ,  Francisca Kemper ,  John Debes ,  Roc Cutri ,  Bin Ren , and  Jingwen Fan  for useful discussions. C. L. acknowledges support from the  STScI Director's Discretionary Fund  and  NASA  through the  FINESST program  (Award No. 80NSSC21K1844 ). This work is supported by the  National Aeronautics and Space Administration  under grant No. 80NSSC21K1844  issued through the  Mission Directorate. This work is based on archival data obtained with the  Spitzer Space Telescope , which was operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This research has made use of the  NASA/IPAC Infrared Science Archive , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology.
This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , and  NEOWISE , which is a project of the  Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE are funded by the  National Aeronautics and Space Administration. This research has made use of  NASA 's Astrophysics Data System. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France  (Wenger et al. 2000). This research has made use of the VizieR catalog access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published in 2000, A&AS 143, 23 (Ochsenbein et al.
Data According to long-term weekly measurements made by a hydrological station at Ruelle (within 1 km from Touvre resurgence) between 1895 and 1996, the minimum interannual weekly discharge is 1. 4 m 3 /s, the average interannual discharge is 11. 1 m 3 /s and the maximum interannual discharge is 40 m 3 /s. In order to properly address this issue, the daily time step is the most appropriate. In the context of this study, we relied on daily data measured a little upstream from the Touvre resurgence at Foulpougne. The Touvre station at Foulpougne resurgence has been monitored since 1980. In addition, to consider the infiltrations on the karstic impluvium, we used the daily discharge data measured at the stations of Montbron for the Tardoire, Feuillade for the Bandiat and Saint Ciers for the Bonnieure, also extracted from the HYDRO database (http://www.
Acknowledgments: The authors would like to thank the  French Karst National Observatory Service (SNO KARST)  initiative at the  INSU/CNRS  that aims to strengthen dissemination of knowledge and promotes cross-disciplinarily research on karst systems at the national scale, for their support on the use of the KarstMod model and for financial support of R. A internship. We are very grateful to the  Agence de l'Eau Adour Garonne  for providing water withdrawal data and to  Meteo-France  for providing hydro meteorological data.
EukProt is a database of published and publicly available predicted protein sets selected to represent the breadth of eukaryotic diversity, currently including 993 species from all major supergroups as well as orphan taxa. The goal of the database is to provide a single, convenient resource for gene-based research across the spectrum of eukaryotic life, such as phylogenomics and gene family evolution. Each species is placed within the UniEuk taxonomic framework in order to facilitate downstream analyses, and each data set is associated with a unique, persistent identifier to facilitate comparison and replication among analyses. The database is regularly updated, and all versions will be permanently stored and made available via FigShare. The current version has a number of updates, notably 'The Comparative Set' (TCS), a reduced taxonomic set with high estimated completeness while maintaining a substantial phylogenetic breadth, which comprises 196 predicted proteomes.
, 2014) , animals  (King & Rokas, 2017)  and fungi  (Kiss et al. , 2019; Li et al. , 2021; Strassert & Monaghan, 2022). Furthermore, analyses in diverse eukaryotes have the potential to reveal new genes and pathways for biological processes currently characterized only in these three well-studied lineages and their parasites  (del Campo et al. , 2014; Richter & Levin, 2019). In the ocean alone, planetary-scale metagenomics studies  (Bork et al. , 2015; Tara Oceans Coordinators et al. , 2020)  have already unveiled an extreme diversity of eukaryotic genes  (Carradec et al. , 2018)  whose phylogenetic origin and ecological function are mostly unknown. A critical prerequisite to all genomic studies is the underlying database of predicted proteins from which orthologs are extracted or other sequence analyses are performed.
Because no single source contains the protein predictions for the complete set of eukaryotes that have been sequenced at a genomic scale, each study needs to assemble their own database, reducing reproducibility among analyses (due to the inclusion of different taxa, or different data sets for the same taxon), and producing a significant barrier to new researchers entering the field. In addition, because the large majority of the protein data sets from diverse eukaryotes are not included in major public databases (see Figure  2 ), researchers cannot easily access them via standard tools such as NCBI BLAST  (Sayers et al. , 2020)  in order to find the homologs of a protein of interest, nor are they automatically integrated into major public databases containing annotations such as protein domains (e.
g. , Pfam  [El-Gebali et al. , 2019] , Interpro  [Mitchell et al. , 2019] ) or gene ontology (The Gene Ontology Consortium, 2019). To address this gap, we assembled a database of protein sequences -EukProt -either from newly performed protein prediction or by retrieving available sequences from a comprehensive set of species representing known eukaryotic diversity. We note that an existing database of genome-scale protein data sets, PhyloDB (https://github. com/allenlab/PhyloDB), contains 550 eukaryotic species with at least 500 proteins, as of version 1. 076. PhyloDB was most recently updated in 2015; EukProt includes data made available since then, allowing it to provide more species with generally higher completeness and representing a greater phylogenetic breadth. In addition, we placed each species within a universal eukaryotic taxonomic framework, UniEuk  (Berney et al.
, 2017) , in order to ensure that the evolutionary relationships among data sets are accurately and consistently described. We also provide estimated completeness statistics for each predicted proteome based on BUSCO scores and have selected a suggested subset of 196 proteomes for use in comparative genomics investigations. EukProt is designed to prioritize ease of use, with unique, persistent identifiers assigned to each data set and a standard system of nomenclature to facilitate repeatability of analyses. To this end, we have followed FAIR principles (Findable, Accessible, Interoperable, Reusable) in its construction  (Wilkinson et al. , 2016).
Species and strain identity We determined species and strain identities by reading the publications that described the data sets, consulting the literature for naming revisions, and comparing 18S ribosomal DNA sequences for each data set to reference sequence databases (see below for a description of how we retrieved 18S sequences). For species that were previously known by other names, we recorded these previous names in the metadata for the data set, except in cases where a species was originally assigned to a genus but not identified to the species level (e. g. , Goniomonas sp. , now identified as Goniomonas avonlea, is not listed as a previous name). This exception was not followed when the GenBank record description for the 18S sequence contained a different name than the one used in EukProt; in this case this previous name is always listed in the previous names field, in order to avoid confusion when retrieving 18S sequences from GenBank.
These levels are more arbitrarily defined but are intended to represent strictly monophyletic groupings that match one of the levels in the complete taxonomic pathways. As an illustrative example, diatoms are in the Diatomeae taxogroup2, which is within the Ochrophyta taxogroup1, which is within the Stramenopiles supergroup. Small, ecologically and morphologically homogeneous supergroups are not subdivided further; in such cases the "taxogroup1" and "taxogroup2" levels are the same as the "supergroup" level. The same approach will be used in EukRibo, a manually-curated database of reference ribosomal RNA gene sequences developed in parallel  (Berney 2022)  to help users link analyses of different types of genetic data.
Merging strains from the same species In general, we only included data from a single strain/isolate per species. However, when only a single transcriptome data set was available for a given strain of a species, and there were additional published transcriptome data sets for other strains of the same species, we combined them using CD-HIT  (Li & Godzik, 2006)  run with default parameter values, in order to guard against the possibility that a single transcriptome might lack genes expressed only in one condition or experiment. When multiple strains were merged to produce a species' data set (there were 41 such cases), this information is indicated in the metadata for the data set. When a data set is indicated as merged, but more than one strain name is not listed in the strain column, this indicates that the data set was distributed as merged and we were unable to determine the exact strains used.
Processing steps applied to publicly available data All sequences within each FASTA file are assigned a unique, standardized identifier based on the data set's EukProt ID and on the type of data (protein or transcriptome); this identifier is prepended to the existing FASTA header, separated by a space. Illegal characters are removed from sequences. EukProt metadata indicate the additional steps applied to each data set after downloading from the data source (if any). All software parameter values were default (unless otherwise specified below or in the metadata record for a given data set), as default parameter values are most likely to correspond to the options recommended for general use by the authors of the software. Due to the large volume of data sets we processed, and variability among them, we were not able to test parameter values beyond those specified below.
assemble mRNA De novo transcriptome assembly using Trinity v. 2. 8. 4, http://trinityrnaseq. github. io/  (Haas et al. , 2013). We trimmed Illumina input reads for adapters and sequence quality using the built-in '--trimmomatic' option (whose default trimming settings are based on optimal trimming parameters from  [MacManes, 2014] ). We trimmed 454 input reads prior to running Trinity with Trimmomatic v. 0. 3. 9, http://www. usadellab. org/cms/?page=trimmomatic  (Bolger et al. , 2014)  with the directives 'ILLUMINACLIP:[454 adapters FASTA file]:2:30:10 SLIDINGWINDOW:4:5 LEADING:5 TRAILING:5 MINLEN:25' (corresponding to the default Trinity trimming parameters, but for single-end reads). When the sequence library was described as stranded in the NCBI Sequence Read Archive, we used the corresponding 'SS_lib_type' option.
extractfeat, seqret, transeq, and trimseq From the EMBOSS package v. 6. 6. 0. 0, http://emboss. sourceforge. net/  (Rice et al. , 2000). We used extractfeat to produce coding sequences (CDS) from genomes with gene annotations in EMBL format but without publicly available protein sequences. We used seqret to convert FASTQ files to FASTA files. We used transeq to translate CDS directly into proteins. We used trimseq to trim EST sequences before translation with Transdecoder.
predict genes We used EukMetaSanity https://github. com/cjneely10/EukMetaSanity  (Neely et al. , 2021)  to perform automated annotation of genome sequences lacking publicly available protein predictions. We used the following parameters, as specified in  (Alexander et al. , 2021) : --min_contig 500 --min_contig_in_predict 500 --max_contig 100000000. These settings were designed for generalized gene prediction on genomes expected to be sampled from across eukaryotic diversity, which is also the case for the data sets in EukProt. We used the parameter --min_contig_in_predict 200 (as it matched the default minimum contig length in Trinity). By default, we selected the proteins at Tier 2 (predictions supported by at least 2 sources). If Tier 2 produced fewer than 15,000 predicted proteins, we instead selected Tier 1.