dataesr/scientific-paragraphs-categorization huggingface dataset:
Enumerating hierarchical attributes has been also integrated in the Exceptional Model Mining problem  [3]. The common point between these works is the use of ontologies in the rule discovery to provide conjunctive clauses which cover a part of data that satisfy some constraints. In our work, the goal is to characterize the dataset based on an antichain that does not necessarily cover the same part of the data, but provides a good characterization of the overal dataset. Another specificity of our work is the explicit use of hierarchies to represent user background knowledge and integrate it in the interestingness measure. Several works have also used hierarchies to improve the prediction task. In  [11] , the authors propose an algorithms that, given a training set of objects described with ontology concepts, allows to select an antichain whose nodes will be used as features to learn a predictive model.
Other works proposed different ontology-aware classification approaches  [8, 20, 21]  based on well known models (decision trees, bayesian networks). While these works uses ontology structures to provide better prediction models, our approach aims to provide knowledge about a dataset to a specific user. Ontology structure has been used in other KDD tasks (ontology-based clustering, information extraction, recommender systems, etc. ). In the survey  [9]  a large spectrum of these works is reviewed. In this paper, we use an interestingness measure inspired by the FORSIED framework  [6, 7] , which defines the SI of a pattern as the ratio between the IC and the DL. The IC is the amount of information contained in the pattern. The quantification is based on the gain from a Maximum Entropy background model that depicts the current knowledge of a user, hence it is subjective, i.
EMPIRICAL RESULTS In this section, we report our experimental results. These experiments aim to evaluate the performance and the quality of results provided by MICA-Miner in real world datasets. The method was implemented in Java and the experiments were performed on a machine equipped with Intel(R) Xeon(R) CPU @ 4. 00GHz, and 128GB main memory, running Debian GNU/Linux 9. 6. The code and the data are available  2. Datasets and aims. We conduct experiments in three real world datasets whose main characteristics are given in  Table 1:  -FV: Foursquare venues  [10] , the nodes e i in this hierarchical dataset are categories of venues 3  (food, restaurant, event, etc. ). We aim to study the distribution of venue categories in Amsterdam and London, and discover which kind of categories are surprisingly frequent/rare in each of these cities.
We then consider two case studies: (1) FV-Ams for Amsterdam: the observed values xi are the counters of categories in Amsterdam, (2) FV-Lon for London: the observed values xi are the counters of categories in London. For both cases, the priors xi are the aggregation of categories counters throughout 20 well known cities from Europe and USA (London, Barcelona, New York, etc. ). -OFF: Open Food Facts 4  is a free food product database that gathers information and data on food products around the world. Each product is described by its food category (Plant-based food,. ) and its country. The categories represent the nodes e i of the hierarchy. We want to compare the distribution of food categories between France and USA, the two countries with the most contributions. Then, the values xi (resp.
xi ) are the number of products for each category in France (resp. USA). -EP_Abst: European Parliament 5  is a dataset that depicts ballots of the European Parliament between 2014 and 2019. Each ballot is described by the corresponding topics and the votes (for, against, abstain) of each deputy, whose political groups are given. Topics are organized following a hierarchy (e. g. , the topic "5. 10 Economic Union" is the strict predecessor of "5. 10. 02 Price policy, price stabilisation"). We build a hierarchical dataset for each political group  where the values xi correspond to the number of abstentions: the number of times a deputy from this group abstains in the topic e i. The priors xi are constructed from the total number of ballots by topic. In this experimental study, we aim to answer the following questions: How much time and memory does MICA-Miner consume in practice? How does the size of the hierarchy |E| impact the performance of MICA-Miner? What is the impact of model updating on the found antichains? Are the antichains found by MICA-Miner relevant and informative? Quantitative evaluation.
We study the runtime and memory consumption for (1) the computation of factor functions (see Section 3. 3), and (2) MICA-Miner. We vary the number of items in the real world datasets as follows: in order to decrease the number of items, we iteratively remove leaves that are the furthest from the root, and in order to increase the number of items, we duplicate the tree as many times as we need and then we add a new root that links all the duplications. Results are given in Figure  5 , column 1 for the factor functions, and column 2 for MICA-Miner. As it was theoretically expected, the computation of factors requires a time and memory that linearly increase with the number of items. Moreover, the time increases for MICA-Miner in a quadratic manner (a fitted model a • |E| 2 + b approximates the time with an average square error below 7(s 2 )), its memory consumption also follows the same quadratic trend.
Comparative evaluation. There is no approach in the literature that supports the discovery of subjectively interesting antichains in hierarchies. Nevertheless, we identify some baselines that we consider in our study to highlight the characteristics of the patterns found with MICA-Miner: (1) SI without update which returns the best results according to the SI measure without updating the background knowledge model; (2)WRAcc that uses the W RAcc measure  [14]. This measure has been largely used in Subgroup Discovery in order to evaluate the prevalence of a given class of objects in a specific subset of data (a subgroup). We adapted it to our problem in order to evaluate the prevalence/absence of nodes e ∈ P in the studied dataset. We consider that a subgroup is defined with the antichain P, the size of positive class is xi , and the size of negative class is xi.
We. -Redundancy: An important goal of the iterative updating of the model is to avoid communicating to the user similar information several times. For example, after informing the user that e i ="Restaurant" is prevalent, it is less informative to tell her that e π i ="Food" is also prevalent. We aim to measure this kind of redundancy between patterns returned by each approach based on the following measure defined for a set of patterns R:  6  reports the values of these properties in each of the three configurations (1) SI (our approach), (2) W RAcc measure, and (3) SI without update. These properties are computed based on the top 5 patterns in FV-Ams and EP-Abst, and on top 20 in OFF (since it is a much larger hierarchy). The average values of counters in W RAcc results are significantly large, its top patterns generally contains nodes with the highest counters in the dataset.
However, its average contrast is remarkably low. Figure  5  shows the first pattern found by W RAcc in FV-Ams. Most of its nodes are not significantly contrastive, i. e. , there is not a high difference between the observed value and the expected value. We can also notice that the values of redund are the lowest for the results of SI , and those of the W RAcc have the highest redund. The value of redund for SI is clearly lower than its value for "SI without update" in FV-Ams and EP-Abst. The difference is not remarkable for the OFF dataset, indeed, since it is a larger dataset, there is less chance to have redundancies in the results. To sum up, our method allows to discover more contrastive antichains than methods that do not take into account the prior beliefs. Furthermore, updating the background knowledge at each iteration makes it possible to provide less redundant results.
Illustrative results. We report in Figure  7  the top 3 contrastive antichains discovered by MICA-Miner in FV-Ams, FV-Lon, and OFF datasets. The blue color quantifies the expected value by the user (based on her beliefs over 20 cities), and the red color is the observed counter in Amsterdam (resp. London). The first antichain in Amsterdam informs that American Restaurants, Residential Buildings, and Airports are much less frequent than expected, while Marijuana Dispensary and Tram Station are significantly over-expressed. This last type of venue is indeed characteristic of Amsterdam, because it is authorized in this city while it is generally illegal in other cities. London is characterized with a high number of Fish & Chip Shops, Portuguese Restaurants, and Pubs, and an under-expression of Residences and Airports.
We point out that Airport venues can correspond to any place related to Airport (Airport Food Court, Airport Gate, Airport Tram, etc. ). After assimilating this first antichain, the user will change her expectations about the rest of the data. For example, she may assume that in general there is a high presence of Food venues and Bars in London, which is also incorporated in our model using the conditional expectation. Due to this hypothesis, the second antichain will notify the user that despite of the high observed values in the first antichain, there is some specific types of Food venues and Bars that are under expressed (American Restaurant, Donut Shop, etc. ). For OFF dataset, the Red color quantifies the observed counts of products from France, and blue color corresponds to the expected values derived from products distributions from USA.
CONCLUSION We have introduced the novel problem of mining contrastive antichains in hierarchically organized sets of counters. Prior beliefs are used to assess how contrastive an antichain is. Furthermore, the hierarchical relations are fully exploited to both propagate the beliefs and to update the background knowledge. We have proposed a greedy algorithm that efficiently and iteratively returns the most contrastive antichains. Extensive empirical results on several realworld datasets confirm that the contrastive antichains are intuitive and they capture insights that cannot be done with other interestingness measure or without updating the background knowledge. This paper opens up several avenues for research such as the integration of this model into the subgroup discovery / exceptional model mining framework.
Acknowledgements. This work was supported by the  ERC  under the  EU's Seventh Framework Programme  ( FP7/2007-2013) / ERC Grant  Agreement no. 615517 ,  FWO  (project no. G091017N ,  G0F9816N ), the  EU 's  Horizon 2020 research and innovation programme  and the  FWO  under the  Marie Skłodowska-Curie Grant  Agreement no. 665501 ,  Group Image Mining  (GIM) which joins researchers of  THALES Group and LIRIS Lab , and by the  ACA-DEMICS  grant of the  IDEXLYON , project of the  Université of Lyon ,  PIA  operated by  ANR-16-IDEX-0005.
MICA-MinerInput: H : the hierarchical dataset, pre-computed values of factor functions f 1 and f i,π i for each i ∈ 2, n. Output: R: the antichains sorted based on iteratively updated SI. 1 R ← ⟨⟩ 2 O ← ∅ 3 repeat 4 // Update the model with the sum-product algorithm 5 // and derive the probabilities Pr( ŷi | O) for each y i : 6 Sum-product(H, O) 7 // Compute an antichain with a greedy strategy: 8 P ← GreedySearch(H) 9 if P ∅ then 10 R. append(P ) 11 O ← O ∪ P 2• max(loд(A)2, |L(H )|) , see Appendix A. 2) and whose effectiveness is demonstrated experimentally. Before running this algorithm, we consider that the factor functions Algorithm 1:12 until P = ∅;.
, 2014). The prototype plasmid of the pT26-2 family was isolated from Thermococcus sp. 26-2 collected in the East Pacific Ocean  (Lepage et al. , 2004). Later, eight plasmids of the pT26-2 family were identified as integrated elements in the genomes of Thermococcales and Methanococcales  (Soler et al. , 2010)  as well as a free plasmid, pMEFER01, in the hyperthermophile Methanocaldococcus fervens  (Soler et al. , 2011). The pT26-2 plasmid family is composed of mid-sized plasmids ranging from 17 to 38 kb, which encode many transmembrane proteins and an AAA+ ATPase hypothesized to be involved in DNA transfer  (Soler et al. , 2010). Comparative analysis suggested that their sequences can be divided into two regions: a highly conserved region of 12 genes, which includes 7 core genes present in all 10 plasmids of the family known at that time  (t26-5p, 6p, 7p, 11p, 13p, 14p and 15p) , and a variable region that includes singleton ORFans (open reading frames without matches in current sequence databases) and genes of various origins  (Soler et al.
In order to identify strains which likely encountered plasmids of the pT26-2 family in the past but no longer encode them, we searched for CRISPR spacers against these plasmids in the CRISPRdb database  (Grissa et al. , 2007). We only found such spacers in the genomes of Thermococcales and Methanococcales, confirming that plasmids of the pT26-2 family have a restricted host range (Fig. 1A ). Notably, we did not detect any CRISPR spacers against these plasmids in the MAGs of Methanofastidiosa and Theionarchaea, which are sister groups of Thermococcales  (Adam et al. , 2017). Among the available genomes, 30% of the Thermococcales and 15% of the Methanococcales genomes contain a CRISPR spacer against plasmids of the pT26-2 family. When combined with the data on plasmid distribution, 56% of Thermococcales and 55% of Methanococcales have either a resident plasmid of the pT26-2 family or a CRISPR spacer against them (Fig.
The highly conserved region encodes a putative secretion system Plasmids of the pT26-2 family present a highly conserved region of 12 genes with conserved synteny (from t26-4p to t26-15p; Figs. 3  and 4 ), which includes the 7 core genes is present in all plasmids of the pT26-2 family  (Soler et al. , 2010). The 12 proteins of the highly conserved region do not share significant similarity with any other proteins in public databases outside of the pT26-2 family (as detectable by HMMER or BLASTP). Nine of these proteins contain at least one (and up to five) putative transmembrane helices. In some of these transmembrane proteins, additional domains were also detected, e. g. , a SH3 domain in the protein t26-13p, a carboxypeptidase regulatory domain in the protein t26-5p and a carbohydrate binding domain in the protein t26-10p.
Functional modules encoded by non-core genes The SiLiX analysis of all proteins encoded by plasmids of the pT26-2 family led to the classification of the 902 putative proteins into 356 families (Supporting Information Datafile S1 and Fig. S4 ). A majority of them (696 proteins) belong to the variable regions (Supporting Information Datafile S1). For instance, among the 356 identified proteins families, 309 correspond to proteins present in less than three plasmids of the pT26-2 family and 244 correspond to singletons. Most of these putative proteins, especially singletons, have small size, suggesting that their putative genes could be false ORFans, putative protogenes or new genes that recently originated de novo from proto-genes (Supporting Information Fig. S4 ). The few large genes present in the variable regions encode integrases (FamAll_0015, 24 and 70 in Supporting Information Datafile S1) and putative replication proteins (FamAll_0034 and 103 in Supporting Information Datafile S1).
This network show that some plasmids of the pT26-2 family shared genes with archaeal viruses or other unknown kind of archaeal MGEs. [Color figure can be viewed at wileyonlinelibrary. com] that includes both archaeal and eukaryotic replicative helicases. The first group is only present in four plasmids from Thermococcales and corresponds to the putative replication protein t26-22p previously detected in the prototype plasmid pT26-2  (Soler et al. , 2010). These proteins are composed of a large central P-loop NTPase domain framed by two short N-and C-terminal domains that have no detectable sequence similarities with other proteins in databases. A homologous central P-loop NTPase domain is present in primase/helicase Rep proteins encoded by pTIK4 and pORA1 plasmids of Sulfolobus neozealandicus  (Greve et al.
, 2010). In order to determine if this observation was still valid for our extended dataset, we carried out a clustering analysis based on pairwise protein similarity. Beside the integrases that we detected in plasmids of the pT26-2 family, we included in our dataset known archaeal integrases from different families and putative integrases that show sequence similarity to the pT26-2 integrase (Int pT26-2 ) but encoded by plasmids and/or viruses from different MGE families. Clustering analysis was performed using SiLiX with a minimum threshold of 25% identity over 40% of the protein  (Miele et al. , 2011). The result confirmed that the pT26-2-encoded integrases in Thermococcales and in Methanococcales correspond to two different types, as they are not connected to each other (Supporting Information Fig.
Methods Isolation, sequencing and detection of integrated copies of pGE2 in GE2 Plasmid pGE2 was isolated from a 50 ml of P. abyssi strain Pyrococcus GE2 culture in late exponential growth phase, using a modified alkaline-lysis method as previously described  (Erauso et al. , 1996). A shotgun plasmid library of clones of pGE2 was constructed in pUC18 vector and sequenced from both ends as described previously  (Gonnet et al. , 2011). The complete plasmid sequence was deposited to the GenBank under the following accession numbers: MN477947. Around 10 years ago, the pGE2 copy number was estimated using a real-time quantitative polymerase chain reaction (PCR)-based method  (Lee et al. , 2006; Providenti et al. , 2006). Two set of primers were tested for pGE2-specific primers targeting respectively the CDS7 (UVRD Helicase) and the CDS29 (putative Rep) and one pair, Arc344F-Uni516R, targeting the 16S rRNA gene of GE2 (sequences of the primers are given in the Supporting Information Table  S2 ).
pT26-2 family update In order to identify new members of the pT26-2 family  (Soler et al. , 2010) , each of the seven previously identified core genes (t26-5p, 6p, 7p, 11p, 13p, 14p and 15p) were used as query for homology search in complete archaeal genomes, using SynTax  (Oberto, 2013) , a web server linking protein conservation and synteny. In addition, we also screened by BLASTP search the NCBI non-redundant protein database to access to plasmids and non-complete genomes. Our strategy allows us to identify remnant element or would allow us to identify atypical related MGE (where some protein first though as the core could have been associated to other type of module). All DNA regions, even if they encode few putative core genes, were analysed. For each region, we determined their extremities by the identification of direct repeat sequences (att sites) resulting from the recombination reaction.
Host specificity determination The host specificity was determined by the presence of a CRISPRspacer against each pT26-2 element in the archaeal genomes present in the CRISPRdb database  (Grissa et al. , 2007;  http://crispr. i2bc. paris-saclay. fr/). At the time of the analysis, the database contained 232 complete archaeal genomes, including 27 and 15 genomes of Thermococcales and Methanococcales respectively at the time of the analysis. The nucleotide sequences of all identified plasmids of the pT26-2 family were compared by blastn approach against the spacer-sequences in the CRISPR database.
Orthologous protein identification For each plasmid of the pT26-2 family, the encoded proteins were extracted. In order to identify orthologous proteins, we used RBH a common strategy used in comparative genomics. Basically, a RBH is found when the proteins encoded by two genes, each in a different MGE, find each other as the best scoring match. NCBI's BLAST is the software most usually used for the sequence comparisons necessary to finding RBHs. The protein sequence comparisons were performed using the NCBI's BLAST version 2. 2. 28+, every BLAST score was normalized to the alignment of query and hit proteins to themselves. Proteins showing normalized bidirectional BLAST >30% were considered orthologous as recommended by  Lerat and colleagues (2003). Then, we tested the impact of the selection of each different plasmid of the pT26-2 family as a pivot MGE on the core protein number size (protein present in 80% of the tested plasmids).
SiLiX network SiLiX  (Miele et al. , 2011)  is a programme developed to cluster homologous proteins into families based on blastp results. All-against-all blastp analyses were performed on all encoded pT26-2 integrases with the addition of related integrases found in Thermococcales and Methanococcales and of an integrase and a part of the dataset recently used for the analysis of the SNJ2 integrase family  (Wang et al. , 2018). The all-against-all integrases blastp results were grouped using the SiLiX package v1. 2. 8 (http://lbbe. univ-lyon1. fr/SiLiX; Miele et al. , 2011). This approach for the clustering of homologous sequences is based on single transitive links with alignment coverage constraints. Several different criteria can be used separately or in combination to infer homology separately (percentage of identity, alignment score or E-value and alignment coverage).
Synteny conservation Synteny conservation among plasmids of the pT26-2 family was preliminary analysed using SynTax a web server linking protein conservation and synteny in complete archaeal genome. Then the synteny conservation among integrated and non-integrated plasmid of the pT26-2 family was confirmed using easyFig a Python application for the comparison of genomic loci based on side-by-side visualization of BLAST results  (Sullivan et al. , 2011).
Acknowledgements This work is supported by an  European Research Council (ERC)  grant from the  European Union 's  Seventh Framework Program  (FP/2007-2013)/Project  EVOMOBIL-ERC Grant  agreement no. 340440. C. B. is supported by  Ecole Normale Supérieure de Lyon. M. G. was supported by  Allocation de Recherche Doctorale de la Région Bretagne. G. E. was supported by grants from the  EU  Project  PYRED QLK3-CT-2001-01676. P. F. , J. O. and R. C. are also supported by an  ERC  grant from the  H2020 Program / Project HiPhore  agreement no. 772725. We are grateful to  Mart Krupovic  for his comments.
The unambiguous identification of cosmic neutrino sources however requires larger data samples, coupled with improved angular resolution and a full sky coverage of neutrino telescopes. In this context, neutrino telescopes installed in the Mediterranean Sea play a major role in complementing IceCube dataset and sky coverage, while also benefiting from the optical properties of seawater to achieve unprecedented pointing and reconstruction performances. This contribution presents the status of the currently operating detector ANTARES, along with its main scientific contributions, and describes the next-generation detector KM3NeT, now under construction off the French and Italian coasts. As of special interest for this conference, a short description of the acoustic instrumentation implemented in ANTARES and KM3NeT, and its possible use for neutrino detection, will be provided as well.
A tiltmeter and a compass enclosed in the local electronics module of each floor are used for the reconstruction of the exact orientation of the floor. Complementary oceanographic measurements are performed on a dedicated instrumented line, in particular to retrieve the sound velocity and to measure the sea current (O(few cm/s)). The good angular resolution achieved for reconstructed cascade events in seawater allowed for the inclusion of this topology in ANTARES latest searches for point-like sources. Assuming a flavoruniform flux of neutrinos at the source with an E -2 spectrum, a cascade-to-track ratio of 3:10 is expected at the detector. The most comprehensive analysis to date  [3]  uses a sample of 7629 tracks and 180 cascades collected between early 2007 and the end of 2015, corresponding to a livetime of 2424 days.