dataesr/scientific-paragraphs-categorization huggingface dataset:
Concerning the event-based video search, we presented methods for learning when ground-truth video data are available for a given event, as well as when solely a textual description of event is given without any positive video examples. Our experiments on a largescale video event dataset (MED16-EvalSub) showed that the exploitation of a larger number of video samples generally leads to better performance; but also in the more realistic scenario of zero-example event detection problem, we presented a state of the art method that achieves very promising results.
We have developed a novel versatile and scalable algorithm MolD to recover diagnostic combinations of nucleotides (DNCs) for pre-defined groups of DNA sequences, corresponding to taxa. We applied MolD to four published monolocus datasets to examine 1) which type of DNA characters compilation allows for more robust diagnosis, and 2) how the robustness of DNA based diagnosis changes depending on the sampled fraction of taxons diversity. We demonstrate that the redundant DNCs, termed herein sDNCs, allow for higher robustness. Furthermore, we show that a reliable DNA-based diagnosis may be obtained when a rather small fraction of the entire data set is available. Based on our results we propose improvements to the existing practices of handling DNA data in taxonomic descriptions, and discuss a workflow of contemporary systematic study, where the integrative taxonomy part precedes the proposition of a DNA based diagnosis and the diagnosis itself can be efficiently used as a DNA barcode.
e. in the cases where defining a restricted scope of analysis can be difficult. Currently only one computational tool, CAOS  (Sarkar et al. 2008 ) is capable of recovering composite DNA characters, but implemented through a tree based algorithm, it has some inherent limitations (see below), and is difficult to run. We developed a versatile and efficient algorithm MolD (for MOLecular Diagnosis) to identify diagnostic nucleotide characters for predefined groups of DNA sequences in large monolocus data sets. Predefined group of sequences in this context represents a species or a higher rank taxon, which we seek to diagnose. MolD is a tree independent algorithm (unlike CAOS), and therefore it i) allows to diagnose taxa that are not recovered as monophyletic in monolocus dataset (see below), and ii) does not require manual adjustments to run.
In turn, crucial advantage of MolD over nucDiag in the SPIDER package for R  (Brown et al. 2012 ) is that MolD identifies composite diagnostic characters (DNCs), which is crucial for larger data sets where no pure characters can be identified (see Discussion). We applied MolD to several published DNA data sets aiming to assess usefulness of the routinely sequenced barcode DNA markers to supply diagnostic DNA characters for taxa of different ranks and to evaluate two possible types of DNA based diagnoses in terms of operationability for taxonomic purposes and robustness. A diagnosis retrieved after the analysis of a finite data set is only assumed to be valid descriptors of the respective taxa in general. The validity of this extrapolation depends on how accurately the initial data set conveys diversity of the studied group.
Term definition The taxon being diagnosed from here onwards is termed focus taxon, and is opposed to all other taxa in the dataset that are referred to as non-focus taxa. Single nucleotide characters can be classified into pure diagnostic sites  (Sarkar et al. 2008) , i. e. positions in the nucleotide alignment in which all members of a focus taxon share a given nucleotide, and all members of non-focus taxa have different nucleotide(s). On the figure 1 position 256 is a pure diagnostic site for the focus taxon Conasprella. Unlike pure diagnostic sites, the private characters  (Sarkar et al. 2008 ) are the nucleotide positions at which some (but not all) members of the focus taxon, but none of the non-focus taxa members, share a given nucleotide (Fig 1, positions marked by asterisks). In addition, we introduce a new category of characters that we term nested characters, i.
Design of data analysis The program MolD (Molecular Diagnoses) written in Python 2. 7 is designed to retrieve pDNCs and sDNCs for the pre-defined assemblages of DNA sequences that correspond to taxa (which may range from species, subspecies, or even populations to any higher level). It constitutes the functional core and the main tool used in all analyses in the present study, combined with varying options of input and output. Complete published DNA sequence data sets, partial data sets derived from the complete ones after random resampling of DNA sequences, or data sets of modified DNA sequences from original data were used as inputs. The sets of independent pDNCs, and 25 shortest pDNCs recovered for taxa of different rank (i. e. pDNC-based diagnoses), or redundant sets of DNA characters (sDNC-based diagnoses) were the two main types of output.
Depending on what each particular analysis was aimed at, we also recorded proportion of variable sites in the DNA alignment for the focus taxa, validity of a diagnosis recovered for a partial data set against the respective complete data set. All analyses were implemented in Python 2. 7 scripts, and run in Spider environment for Python. All scripts used for the present study are available in the supplementary data. The command line based version of MolD can be used as is, and it is currently being developed into graphical interface enhanced software.
2014) (Table Table  1. Analysed data sets. While most analyses were performed on these main data sets, we retrieved additionally multiple COI sequences of the 4 species of Conus for which a population structure analysis was performed  (Duda et al. 2012) , as well as all ND2 sequences of Furnariidae, and most COI sequences of Odonata available in the GenBank. Thus complemented expanded data sets Furnariidae_788ND2, Odonata_4kCOI, and Conidae_COI_4spp were built (Table  1 ). The three expanded data sets were used to assess the reliability of the diagnoses recovered on the smaller main data sets. Up to 15 ambiguously called nucleotides were allowed in the Furnariidae_788ND2 and Odonata_4kCOI data sets, and up to 25 in the Conidae_COI_4spp. The criterion of the sequence quality was relaxed deliberately compared to the main data sets to test the robustness of the recovered DNA based diagnoses in the data sets including some low-quality DNA sequences.
The effect of taxonomic sampling on the reliability of the DNA based diagnosis: expectations We used random taxon resampling on published DNA datasets to assess the robustness of DNAbased diagnoses associated to different sampling fractions. Our rationale was that if we access a sufficiently large DNA sequence data set of some taxon, this data set may be used to model finite genetic diversity of this taxon. Then, we can select few focus taxa, representing both stem and crown group lineages of this taxon, and recover their DNA-based diagnoses from the entire data set. These diagnoses, although only partly capture identity of the respective focus taxa, in the framework of the model can be viewed as finite diagnoses. Then, by providing diagnoses to subsamples of this large data set, and comparing each of them against the finite diagnosis, the robustness of a diagnosis associated with each sub-sample can be estimated.
Using this approach we were sampling increasing fractions of four published data sets, each -for several focus taxa. The sub-samples of the entire dataset from here onwards are referred to as partial datasets. The robustness of the diagnosis compiled from a partial dataset is measured by the proportion of the entire-data-set-valid pDNCs, EDV-pDNCs, in this diagnosis -i. e. those pDNCs that although retrieved from a partial data set, remain valid pDNCs in the context of the entire data set. We expect that this proportion will be very low when few sequences or taxa are sampled, but as more sequences are added to the data set, the proportion of the EDV-pDNCs will increase until finally reaches 100% when all the diversity is sampled. The curve describing the proportion of the EDV-pDNCs as a function of the sampling fraction may reach a plateau earlier: in this case the sampling fraction at which the plateau is reached would mark the minimum taxonomic sampling sufficient to provide a robust DNA diagnosis.
pDNC-based diagnosis for species: haplotype resampling We carried out iterated resampling for the unique COI haplotypes of the C. chaldaeus, C. ebraeus, C. miliaris and C. sanguinolentus in the Conidae_COI4spp data set. At each iteration, n unique COI haplotypes were picked up randomly for a focus species, and thus created partial data set was completed by all records of Conidae_COI4spp for non-focus taxa. Thus obtained data set was passed to MolD to retrieve a set of pDNCs. The number of sampled unique haplotypes n was increasing from 2 and up to the total number of unique haplotypes available for each species, and was incremented by one until n equals 20, and by five subsequently. Ten iterations have been made for each tested number of unique haplotypes. The output parameters recorded after each iteration were 1) number of the COI sequences of the focus species sampled, 2) the proportion of variable sites among the sampled sequences of the focus taxon (hereafter PVS), as a measure of the molecular diversity of the sampled species, 3) the number of independent diagnostic nucleotide combinations sampled (NInd), and 4) the total number of pDNCs retrieved.
pDNC-based diagnosis for supraspecific taxa: species resampling We performed iterated taxonomic resampling for the subfamily Sclerurinae and the genus Synallaxis (Furnariidae) and for the genera Conus and Conasprella (Conidae). The expanded data set Furnariidae_788_ND2 was used for Sclerurinae and Synallaxis to enable inclusion of multiple sequences per species, and the main Conidae data set was used for Conasprella and Conus. An increasing number of species of the diagnosed taxon has been picked up randomly, starting from 2 and up to the total number of species in the data set, and a partial data set was thus built from the sequences of the picked species. Similar to the haplotype resampling, for each tested number of species, 10 iterations have been performed, and the increment was set to 1 for the Sclerurinae, Synallaxis and Conasprella (2, 3, 4 species resampled, and so on), or to 5 for Conus (2, 7, 12…).
The resampling was conducted under two different regimes in respect to the focus taxon -either all sequences of a picked species were added to the partial data set, or each picked species was represented by single sequence. By comparing the outputs of the resampling under these two regimes we were able to estimate how the number of sequences per species in the input data affects reliability of the resulting diagnosis. The number of species sampled from each of the nonfocus taxa was increasing with different increment, so that the proportion of species sampled from each taxon remained equal. In each iteration, a MolD run was performed on the sampled partial data set to retrieve a set of pDNCs for the focus taxon. The output parameters recorded for each iteration were 1) number of species of the focus taxon sampled, 2) total number of species sampled, 3) PVS in the focus taxon sequences, 4) total number of the pDNCs sampled, 5) Nind, 6) Proportion of the EDV-pDNCs.
A sDNC was output when it has scored 99 or 100 in three consecutive tests, which may be a very strict setting, but worked efficiently in the analysed data sets. Then, to evaluate robustness of an output sDNCs (retrieved from the main data sets), it was validated against respective expanded data set in the same manner, as the validity of the pDNCs was tested (see above). This analysis was performed for 15 species of Odonata, which were represented in the main Odonata COI data set by six or more sequences, and for four species of Conus, genetic diversity of which was notably better represented in the Conidae_COI4spp data set. Thus, we estimated whether the entire approach (data sets of artificially generated sequences), and the particular parameters that we employed to rate the sDNC-based diagnoses, can efficiently model the missing genetic diversity in the data set.
This analysis also enabled direct comparison with the resampling results obtained for the pDNCs. The same regime with all sequences of a picked species added to the partial data set has been used. The increment was set to 1 for Sclerurinae (1,2,3… until all the 17 species are sampled) and Profundiconus (Conidae), to 2 in Conasprella and Synallaxis (2,4,6…), and to 2 (in the range of 20) and subsequently changing to 5 in Conus  (2,4,6,8,10,12,14,16,18,20,25,30,. 185). Taxonomic resampling for cone snail taxa was performed on the main data set, and for Sclerurinae and Synallaxis -on the extended data set Furnariidae_788_ND2, to take into account multiple sequences per species. Ten iterations have been made for each sampled number of species. Validity of each of the 10 retrieved sDNC was checked against a complete data set, and it was assigned a value of 1 if it was an EDV-sDNC, or 0, if it was not.
On the contrary, in both Sclerurinae and Synallaxis, no clear pattern in relation to the sampled fraction can be noted, except for larger number of pDNCs being recovered with a reduced sampling. This inconsistency suggests that the total number of recovered pDNCs is altogether a poor proxy of the molecular identity of a taxon. V-(at reduced sampling regime resulted in even lower proportion of the EDV-pDNCs: even when all species are sampled the proportion of EDV-pDNCs ranges from 0. 14 (Sclerurinae) to 0. 7 (Conus). Our results thus convincingly demonstrate that the multiple pDNCs proposed for the fraction of a data set generally do not hold when tested against the whole data set. Diagnoses based on the sDNC for species-level taxa. The table 2 summarizes the sDNCs obtained for the 15 species of Odonata and 4 species of Conus based on the respective main data sets after validation on the artificially generated data sets.
The sDNC-based diagnoses of 12 species of Odonata from the main data set (48 species) remained valid diagnostic combinations of these species in the scope of the much larger Odonata_4kCOI data set. In two of the remaining cases (Crocothemis erythraea and Pseudagrion massaicum), the sequences attribution to species appears to be inconsistent in the GenBank. The COI sequences of Crocothemis erythraea and Crocothemis servilia do not form reciprocally monophyletic groups due to the highly divergent sequence Jalala7 (Supplementary Fig. 2A ), which was not included in the main Odonata data set. Similarly, the COI sequences of the Pseudagrion massaicum appeared intermixed, and in some cases identical to the sequences of Pseudagrion tanganyicum (Supplementary Fig. 2B ), the latter also not included in the main Odonata data set.
MolD and other tools for diagnostic DNA characters recovery The algorithm MolD is admittedly rough-and-ready -it does not take into account such parameters of a data set as the transition-transversion ratios or likelihoods of synonymous substitutions at the nucleotide positions included in the DNCs etc. These considerations are not within the scope of the present study, as it considers DNA-characters as is -i. e. not as evolving characters, but as fixed attributes of a set of specimens. We demonstrate that such model-free approach efficiently retrieves diagnostic combinations of nucleotides (DNCs) for pre-defined groups of DNA sequences in data sets of varying complexity. This algorithm is therefore scalable and versatile. Even for the taxa, where the shortest recovered pDNS comprised four nucleotide positions (Synallaxis), this pDNC was repeatedly resampled in the independent runs of MolD.
This suggests that the default 10-20,000 recursions allow for exhaustive sampling of the informative nucleotide positions across the DNA alignment leading to reproducible pDNCs output. Subsequently, the tested algorithm to transform pDNCs into a sDNC allows compilation of a robust DNA-based diagnosis. All analyses described herein, including those on the extensive DNA data sets downloaded from GenBank, or those that involved taxonomic resampling, were carried out on one CPU of a moderately powerful laptop. Therefore, the program can easily run on virtually any decent computer with Python installed. Most previous studies that report specific nucleotide substitutions have utilized one of the two softwares: function nucDiag in the SPIDER package for R  (Brown et al. 2012) , or CAOS (Characteristic Attribute Organization System -  Sarkar et al.
2008 -Jörger and Schrödl 2013; Churchill et al. 2014; Zielske and Haase 2015, etc. ). The former is only able to identify pure singlenucleotide diagnostic sites and can be efficient in the analysis of few species, but would generally fail when applied to data sets comprising hundreds of species, as pure single-nucleotide sites are less likely to exist as more and more sequences are included in the data set  (Fedosov et al. 2019). CAOS  (Sarkar et al. 2008 ) identifies private as well as pure characters of a focus taxon, and also allows compilation of composite DNA characters. This functionality makes CAOS a powerful and versatile program to identify DNA barcodes that can be directly used in DNA based descriptions  (Goldstein and DeSalle 2011; Jörger and Schrödl 2013; 2014). However, CAOS implements a tree based approach, which, on one hand, has an obvious merit -the identified characters, (termed Characteristic Attributes or CAs) are meaningful in evolutionary context, but, on the other hand, also has a drawback -only clades in the input tree can be diagnosed.
This requirement in practice may not be met  (Goldstein & DeSalle 2011) : the new taxa may not be monophyletic when the tree is reconstructed based on a single molecular marker (the one used to establish the diagnosis). It has been shown that up to 25% of the species are not monophyletic in the analyses of monolocus data sets  (Funk & Omland 2003) , and this proportion is expected to be even higher for more genetically heterogeneous higher rank taxa. MolD retrieves pure and composite nucleotide characters (similar to CAOS), but implements an alternative approach and does not postulate that the predefined groups are monophyletic for the DNA fragment used for the diagnosis. Therefore, the monophyly of these groups must have been previously established and our goal is not to verify the validity of these groups (whatever the criteria used to delimit them), but to propose a DNA-based diagnosis for each of them.
Our results show that reliable diagnoses can be recovered for species in data sets covering taxa as diversified and ancient as Odonata. Remarkably, out of 15 Odonata species diagnoses compiled with only 5% of the final species diversity covered (main data set VS expanded data set) 12 remained valid in the context of the expanded data set. Only in the case of Trithemis stictica, the COI-based diagnosis recovered for the main Odonata data set was not valid against the expanded data set, despite all sequences assignments were correct. In this sole case, a notably divergent haplotype of the focus taxon was missing in the main data set. Thus, it is not the number of species or sequences included in the data set, but rather proper representation of the extremes of the diversity range that can be seen as a main requirement for the taxonomic sampling leading to proposition of a reliable molecular diagnosis.
Essentially, same requirement defines sufficient dataset for the species delimitation based on monolocus data, or DNA barcoding. This requirement cannot be unequivocally translated into number of haplotypes and species, because some taxa are more difficult to diagnose than others. Nevertheless, when the effect of the haplotype sampling was tested, and the species coverage was the same (main data set VS expanded data set), we were able to repeatedly compile valid diagnoses for the four Conus species based only on <20% of their final haplotype diversity. As well, species coverage of ~20% was usually sufficient to repeatedly compile robust DNA based diagnoses for the analysed supraspecific taxa. However, as many as 50% of the final species number needed to be resampled to reliably diagnose Synallaxis as currently conceived.
What if no DNCs retrieved? The failure to recover a robust diagnosis may be due to a technical issue (incorrect attribution of a sequence to a taxon, inclusion of a contaminated sequence to a dataset, or a misalignment), or have a biological reason. It is essential that the DNA alignment used as an input for MolD is checked carefully to ensure that the sequences are genuine, naming is consistent throughout the data set, and the alignment is accurate. It is possible that no diagnosis can be returned for the highly diversified and thus heterogeneous taxa and/or for those that have diverged too little from the closest relative. The former case can be exemplified in our data by the subfamily Furnariinae, for which MolD was not able to recover sDNC, and no pDNCs were identified in the extended data set.
Additionally, the nucleotide alignment used to generate DNA-based diagnoses should be provided in supplementary data. Algorithm. We developed the algorithm MolD specifically to use DNA data in taxonomic descriptions of taxa. It has some advantages over CAOS, and only outputs DNA-based diagnosis if it meets pre-defined criteria of robustness. Nevertheless, the sets of characteristic attributes generated by the p-gnome module of CAOS  (Sarkar et al. 2008) , such as the barcodes presented by All the alignments, output files and scripts used in the present study are submitted to Github (https://github. com/SashaFedosov/MolD) and will be made publicly available upon acceptance of the paper.
Thus, in the four datasets this arbitrary reliability threshold is reached with less than 20% of species sampled, and almost all sDNCs retrieved for Sclerurinae, Profundiconus, Conasprella and Conus with a sampling fraction higher than 20% were EDV-sDNC. In the case of Synallaxis, however, the same threshold is reached only when 50% species are sampled. This genus also demonstrates the longest pDNCs among the analysed taxa (the shortest pDNC comprises 4 nucleotide positions -fig 2). The genus Synallaxis is the largest genus of the Furnariidae, nested within the crown group of the family, with a very short branch(Fig 5B). This topology of the tree suggests weak phylogenetic distinctiveness of Synallaxis, which explains the difficulties encountered in providing this genus a robust DNA-based diagnosis.
In particular, we show that such a representation learning approach is competitive with state-of-the-art chemogenomics with shallow methods, but not ultimately superior. We evaluate the most promising neuron network architectures and data augmentation techniques, such as multi-view and transfer learning, to improve the prediction performance of the chemogenomic network. Our results shed new insights on the design of chemogenomics approaches based on representation learning algorithms. Most importantly, we conclude from our observations that a promising research direction is to integrate heterogeneous sources of data such as various bioactivity datasets, or independently, multiple molecule and protein attribute views, instead of focusing on sophisticated, yet intuitively relevant, encoder's neuron network architecture.