dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The results of the Bi-LSTM were: The LightGBM model was also tested by adding to the already mentioned properties additional information such as the user ID and information extracted from the tweet date such as day, month, the day of the week and time. The results obtained also indicate here that there is a correspondence between the use of emojis, the user, the time and the day. For example the Christmas tree in December or the heart emoji in the evening hours. The level of Precision obtained in this way was very high even if the F1 score is still lower than the BI-LSTM model. To avoid the unbalancing of the emojis present in the training dataset various undersampling and oversampling operations were performed without any appreciable results. Turning to the result of the ensemble of the two models we had a marked increase in the F1 score thanks to the substantial growth of the Recall in both cases.
In the tables 5 and 6 there are the results from the minimum and the maximum F1 score obtained during the process of the ensemble. The result of the validation was however very far from that obtained during the training phase. It will be necessary to evaluate if, as in the research Exploring Emoji Usage and Prediction Through a Temporal Variation Lens  (Barbieri et al. , 2018) , it was the time of the publication of the tweets is to be distant from the date of the tweets analyzed. If the tweets analyzed were too different from those of the training dataset, if the users in the test dataset have different behaviors, or if the system suffered from some kind of overfitting (visible in the third submission, gw2017_pe). Accuracy at 20 0. 982520 0. 929920 0. 662720 Table  7 : macro F1, micro F1, weighted F1, coverage error, accuracy at 5, 10, 15 and 20 for the three runs submitted.
l The second is that, especially in cases where a tweet begins by indicating a USERNAME, or in a mention or a direct response, the use of emoji takes on a sub-language value. That is, the use of a specific word or emoji has a meaning that only the tweet recipients know. Use of emoji and could be irony or just references to previous pasted experiences in common. l Thirdly, the strong imbalance of the training dataset is not the only reason for the unbalanced prediction of some emojis, as in the case of and.
Orabona, 4 -70125 Bari Serena Villata. 2018. Comparing Different Super-University of Bari Aldo Moro vised Approaches to Hate Speech Detection. In Pro-ceedings of Sixth Evaluation Campaign of Natural {firstname. lastname}@uniba. it Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2018). CEUR. org. Thomas Davidson, Elisabetta Fersini, Paolo Rosso, and Maria Anzovino. 2018b. Overview of the Task on Automatic Misogyny Identification at IberEval 2018. In IberEval@SEPLN. CEUR-WS. org. Paula Fortuna, Ilaria Bonavita, and Sérgio Nunes. 2018. Merging datasets for hate speech classifi- cation in Italian. In Proceedings of Sixth Evalua- tion Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2018). CEUR. org. Alessandra Teresa Cignarella, Simona Frenda, Vale- Björn Gambäck and Utpal Kumar Sikdar.
For what concerns the cross- domain datasets we observe poor performances over all the subtasks with all the employed mod- els, implying that the models have difficulties in cross-domain generalization. Model CH DI JO TW YT SVMa 0. 545 0. 514 0. 475 0. 539 0. 585 SVM 0. 550 0. 649 0. 555 0. 567 0. 555* STL 0. 545 0. 541 0. 500 0. 595* 0. 512 MTL 0. 640* 0. 676* 0. 470 0. 561 0. 546.
785 0. 806 0. 652 0. 583 Table 10: Classification results of the different learning models on the official HaSpeeDe test set in terms of F1-score. in the table) it is worth noting that linear SVM outperformed all the configurations based on Bi- LSTM. In addition, the MTL architecture results are slightly better than the STL ones (+1 f-score point with respect to the STL counterparts). Exter- nal sentiment resources were not particularly help- ful in this task, as shown by the result obtained by the 1L MTL NO SNT row. In the FB task, Bi- LSTMs sensibly outperformed linear SVMs (+5 f- score points in average); this is most probably due to longer text lengths that are found in this dataset with respect to the Twitter one. For what con- cerns the out-domain tasks, when testing models trained on Twitter and tested on Facebook (C TW column), we can notice an expected drop in per- formance with respect to the models trained on the FB dataset (15-20 points f-score points).
The proposed model is not claimed to be the best possible with this properties; rather, it is an extremely simple at- tempt to the task. Other models may be built in the future which do not use any information out- side of that provided in the learning dataset, but obtaining significantly better results than the pro- posed one. Still, the ranking position of the pro- posed model suggests that there is value in using knowledge outside of that available for a specific task, giving information about the language (and the meaning and sentiment of words and phrases): further research is needed to understand where the limits of strict character-level text analysis lay and the contexts in which it is a better or a worse solu- tion.
Task A: Misogyny Train Test Task B: Category Train Test Task B: Target Train Test Non-misogynous 2215 540 0 2215 540 0 2215 540 Misogynous 1785 460 Discredit 1014 141 Active 1058 401 Sexual harassment 352 44 Passive 727 59 Stereotype 179 140 Dominance 148 124 Derailing 92 11 Table 1: Distribution of tweets in the dataset ble of Classifiers (EoC) containing a Logistic Re- gression model, an SVM, a Random Forest, a Gra- dient Boosting model, and a Stochastic Gradient.
Different patterns of diversity reduction were observed at other important loci. The Wx 23 locus that affects amylose content and stickiness on cooking, the Badh2. 1  24  locus that affects aroma and their surrounding regions are highly diverse in the XI, cA and cB groups, which indicates complex histories for selection for different types of eating quality; by contrast, both loci and their surrounding regions show low diversity in GJ. The Rc 25 locus has very low diversity in all variety groups, with variable diversity in the surrounding regions in XI, cA and cB. We compared SNP variation among TE-related genes, NTE-related genes, 1,021 genes with validated functions curated in the OGRO/ QTARO database  26, 27  and a subset of 78 domestication and agronomically relevant genes (Supplementary Data 4).
5c, d ). Novel gene assemblies were validated by mapping raw reads of the 453 high-coverage genomes to the 12,465 novel genes; 11,792 genes (94. 6%) had >95% CDS and >85% gene-body coverages were present in at least two rice lines. By comparison, 99. 9% of Nipponbare RefSeq annotated genes were detected in the 453 high-coverage genomes (Extended Data Fig. 5e ). Approximately 30% of the full-length novel genes were expressed with >1 read per kilobase per million reads in one or more of the 226 publicly available RNA sequencing datasets  31  (Extended Data Fig. 5f, g ). Further, benchmarking universal single-copy orthologues  32  evaluation suggested little redundancy in predicted genes (Extended Data Fig. 5h ).
Evolution and domestication of rice To gain insights into the evolutionary history of the rice pan-genome, gene and gene family ages were estimated by aligning protein sequences to the NR protein database (ftp://ftp. ncbi. nlm. nih. gov/blast/db/) partitioned into 13 taxonomic levels (Extended Data Fig. 8a, b ). We observed that: (1) new genes and gene families evolved at alternating rates from phylostratum 1 (PS1) (approximately 3. 6 billion years ago) to the emergence of the terminal PS13 clade containing O. sativa (about 1. 5 million years ago); (2) there was an explosive emergence of new genes accompanying the appearance of Oryza at PS12; (3) core genes tended to be more ancient, and most novel genes or gene families were younger and shorter (Extended Data Fig. 8c, d ), consistent with recent reports for other species  33  ; (4) significantly (P value < 0.
7. 10)  47. The mapped reads were then sorted and duplicates were removed by Picard tools (release 1. 119) (http://broadinstitute. github. io/picard/). The reads around indels were realigned by GATK RealignerTargetCreator and IndelRealigner package (release 3. 2-2)  48. The variants were called for each accession by the GATK UnifiedGenotyper (release 3. 2-2)  48  with 'EMIT-ALL-SITES' option. A joint genotyping step for comprehensive SNP union and filtering step was performed on the 3,010 emit-all-sites VCF files. A variant position is reported if at least one sample supports it with QUAL no less than 30. A total of 29,399,875 SNPs (27,024,796 are bi-allelic) and 2,467,043 indels (small insertions and deletions <40 bp) were identified from the analyses of the genomes of 3,010 accessions. Three subsets of the 3K-RG Nipponbare SNPs were defined using the following filtering criteria: (1) a base SNP set of ~17 million SNPs created from the ~27 million high-quality bi-allelic SNPs by removing SNPs in which heterozygosity exceeds Hardy-Weinberg expectation for a partially inbred species, with inbreeding coefficient estimated as 1-H obs /H exp , in which H obs and H exp are the observed and expected heterozygosity, respectively (detailed in Supplementary Notes); (2) a filtered SNP set of ~4.
Population structure and SNP diversity. Multi-dimensional scaling analysis was performed using the 'cmdscale' function in R, using the IBS distance matrix of the 3K-RG genomes computed with PLINK  49, 50  on the filtered SNP set. The same distance matrix was used to construct a phylogenetic tree by the unweighted neighbour-joining method, implemented in the R package phangorn  52. The population structure of the 3K-RG dataset was analysed using ADMIXTURE software  46  on the core SNP set (version 0. 4, http://snp-seek. irri. org/download. zul). First, ADMIXTURE was run on 30 random 100,000-SNP subsets of the core SNP set with k (the number of groups) ranging from 5 to 18, and k = 9 was chosen because it was the minimal value of k to separate all previously known groups (cA, cB, XI, GJ-trp, GJ-tmp and part of GJ-sbtrp).
net/projects/novobreak/?source=navbar) against the Nipponbare RefSeq. SVs inferred by no less than 3 reads were further filtered with the following conditions: (1) more than four supporting split reads or (2) no fewer than three discordant read pairs. We detected deletions, inversions and duplications with sizes between 100 bp and 1 Mb, and translocations. Here, translocations were SVs with 'inter-chromosomal breakpoints'. All SVs that passed the filter criteria in the 3K-RG accessions were pooled together. Two adjacent SVs were identified as the same SV if their start and end positions varied no more than 1 kb, and the overlapping region was more than 50% of the total size. The presence-absence matrix of SVs in each accession was built based on this pooled SV dataset. To obtain reliable SV comparison analysis results, we focused only on the 453 high-depth accessions (Extended Data Fig.
On average, we needed to run SOAPdenovo ~3. 94 times for each rice accession. The quality of the genome assembly was evaluated for these contigs using QUAST version 2. 3  57. Sequencing and de novo assembly of IR 8 and N 22 reference genomes. High molecular weight DNA was extracted from young leaves adopting the protocol  58  with minor modifications. The PacBio library was prepared following the 20-kb protocol (see 'User-Bulletin-Guidelines-for-Preparing-20-kb-SMRTbell-Templates document. pdf ' , available from https://www. pacb. com/support/ documentation/?fwp_documentation_search="PN%20100-286-700-04") and was sequenced on an RSII sequencer with movie collection time of 6 h. The raw data of N 22 and IR 8 were assembled with FALCON  59  and Canu  60  , respectively. Contigs were polished twice with PacBio raw reads using Quiver (https://github.
Next, various contaminants including Archaea, bacteria, viruses, fungi and metazoans were removed. The non-redundant sequences were aligned to the NT database (downloaded from NCBI, 26 July 2014) with BLASTN with parameters '-evalue 1e-5 -best_hit_overhang 0. 25 -perc_identity 0. 5 -max_target_seqs 10'. Contigs of which the best alignments (considering E-values and identities) were not from Viridiplantae were considered as contaminants and were filtered out. The remaining contigs formed the non-redundant novel sequences. The rice species pan-genome was then generated by combining the Nipponbare RefSeq and non-redundant novel sequences. Annotation of the pan-genome. The gene-transcript annotation of the Nipponbare RefSeq was downloaded from the Rice Annotation Project  64  , and if a protein-coding gene contained multiple transcripts only the transcript with the longest open reading frame was selected as the representative for the gene.
Protein-coding genes on novel sequences were predicted using MAKER  65  , a gene prediction tool combining ab initio predictions, expression evidence and protein homologies. In detail, repeats were first masked (soft mask for low-complexity Article reSeArcH repeats) with RepeatMasker (www. repeatmasker. org) and RepeatRunner  66. Two ab initio predictors, SNAP  67  and AUGUSTUS  68  , were called by MAKER  65  to predict gene models with their default parameters for rice. All rice expressed sequence tags (ESTs) were downloaded from GenBank (15 December 2014) and were aligned to the novel sequences with BLASTN. All rice proteins were downloaded from NCBI (15 December 2014) and were aligned to the novel sequences with BLASTX. To obtain more informative alignments, Exonerate 69 was used to realign each sequence identified by BLAST around splice sites.
Augustus-3. 2. 3  68  and hmmer-3. 1b  71 ere used for gene prediction in BUSCO. BUSCO was run with genome mode with embryophyta_odb9 as a reference. Functional analysis. All protein sequences of pan-genome were extracted and aligned to the GO sequence database (http://geneontology. org/ on 4 April 2015) with BLASTP. Only alignments with E-values < 1 × 10 -5 and identity > 0. 3 were used. GO terms for each gene were estimated to be the same as those of its best-hit protein. In total, 20,842 (43. 3%) genes could be annotated. For a gene family, its GO terms are the non-redundant set of the GO terms of the genes within this gene family. Overall, 6,338 (26. 5%) gene families could be annotated. Enrichment of GO terms was carried out using the GOstats 72 package in R with all gene families as the background.
We defined genes (or gene families) that are neither major-group-unbalanced nor subpopulation-unbalanced to be 'random' genes. Gene and gene-family age. Gene ages were inferred with previously described methods  77. The NR protein database was downloaded from NCBI (28 March  2015) and all protein sequences were grouped according to 13 taxonomic levels (PS1: Cellular organisms; PS2: Eukaryota; PS3: Viridiplantae; PS4: Streptophyta, Streptophytina; PS5: Embryophyta; PS6: Tracheophyta, Euphyllophyta; PS7: Spermatophyta; PS8: Magnoliophyta, Mesangiospermae; PS9: Liliopsida, Petrosaviidae, Commelinids, Poales; PS10: Poaceae; PS11: BOP clade; PS12: Oryzoideae, Oryzeae, Oryza; and PS13: O. sativa) based on NCBI taxonomy. Thirteen BLASTP databases were built for protein sequences from PS1 to PS13.
All genes on pan-genome sequences were first translated into proteins, and were aligned to the 13 databases using BLASTP with E-values < 1 × 10 -5 and identity > 0. 3. The age of a gene was considered as the taxonomic level of the oldest aligned protein. Genes that failed to align to all databases were assigned gene ages of PS13 (O. sativa). Some PS13 genes were reassigned as PS12 genes if they could be covered by 446 wild rice genomes 2 with both gene-body coverage > 0. 95 and CDS coverage > 0. 95. The age of a gene family was considered as the age of the oldest gene within the gene family. Introgression test. To test whether an XI sample had a non-introgression haplotype at a locus, we defined a D-value for a sample x as D(x) = d(x,XI)d(x,GJ), in which d(x,XI) is the mean distance from sample x to a XI sample at the given locus.
GWAS was performed using filtered and linkage disequilibrium-pruned SNPs for historical trait data on source accessions for grain length and grain width (223,743 SNPs) and for newly acquired lesion length data for bacterial blight isolate C5 (148,999 SNPs). Manhattan plots for linkage disequilibrium-pruned datasets are shown to the left and quartile-quartile plots for expected versus observed -log(P) values to the right. Major peaks are annotated for known gene loci. Extended Data Fig. 11 | sd1 gene and its correlation with plant height. a, The plot shows the correlation of the gene presence or absence variation with plant height (n = 323). The P values were calculated with Spearman's correlation. b, Examples show that semi-dwarfism results from an approximately 385-bp deletion in the sd1 locus.
Code availability. Code for studying pan-genome and gene and gene family PAVs are now integrated and published as the EUPAN toolkit  28. Tailored novo-Break-germline is available at https://sourceforge. net/projects/novobreak/? source=navbar. Code for nucleotide diversity and SNP merging is available at https://github. com/dchebotarov/3k-SNP-paper. All other code is available from the corresponding authors upon request. Data availability. The BAM alignment file and variant calls in VCF format for each accession of the 3K-RG against Nipponbare RefSeq are freely downloadable from Amazon Public Data at https://aws. amazon. com/public-data-sets/3000-ricegenome/ and the Department of Science and Technology Advanced Science and Technology Institute of the Philippines (DOST-ASTI) IRODS site, as described on the 3K-RG project site (http://iric.
irri. org/resources/3000-genomes-project). The SV and PAV data of 3K-RG are available in the figshare database  78  (https:// doi. org/10. 6084/m9. figshare. c. 3876022. v1). The following web tools are available for the mining, analysis and visualization of the 3K-RG dataset: SNP-Seek, http://snp-seek. irri. org; RMBreeding databases, http://www. rmbreeding. cn/index. php; rice cloud of genetic data public projects, http://www. ricecloud. org/; IRRI Galaxy, http://galaxy. irri. org/; and the 3,000 rice pan-genome browser  79  , http://cgm. sjtu. edu. cn/3kricedb/. The 3K-RG sequencing data used for our analyses can be obtained via project accession PRJEB6180 from NCBI (https://www. ncbi. nlm. nih. gov/sra/?term= PRJEB6180), accession ERP005654 from DDBJ (https://www. ddbj. nig. ac. jp/index -e.
ISO Method Applied Without 3D City Models ISO 13790:2008 calculation method provides a deterministic model to calculate heating and cooling energy needs of buildings at three different temporal resolutions: hourly, monthly and seasonal  [8]. It is one of a series of calculation methods for the design and evaluation of energy performance of buildings. Many studies have applied the monthly and hourly approaches with different assumptions and contexts. Several national authorities have also adopted a performance-based method using a monthly energy balance, for example Romania  [9]  or South Korea  [10]. Vollaro et al. [11]  calculated the monthly cooling and heating energy performance of an old building in central Italy using the ISO 13790 based software named MC11300. The results were validated with dynamic simulation software (TRNSYS).
Kim et al. [12]  compared the simplified ISO 13790 method and dynamic (EnergyPlus software) simulation approach with regard to uncertainty. The different simulations were tested on a general five-story office building in the South Korea. They highlighted the importance of calibration for unknown parameters (numerical parameters and time constants) in a local application of the ISO 13790 monthly method. Kristensen et al. [13]  investigated the monthly and the hourly ISO 13790 method considering a building zone test case located in Denmark. Kokogiannakis et al. [14]  carried out a comparative analysis of the hourly and monthly ISO 13790 methods with simulation programs (e. g. , ESP-r and EnergyPlus). Several parameters of a typical office building were varied by considering different climate, internal heat gains, glazing areas, ventilation schedules, etc.
Corrado et al. [15]  proposed a useful implementation of the ISO 13790 monthly calculation method, in particular to supply a formulation of the dynamic parameters and adapt them to the local climate. The simulation was performed on some real buildings in Italy, assuming different climatic conditions. The method was then validated with EnergyPlus. Within the framework of a EU project ENTRANZE  [16] , cooling and heating energy demand of four different building types across 10 European cities (Seville, Madrid, Rome, Milan, Bucharest, Vienna, Paris, Prague, Berlin and Helsinki) were performed by EnergyPlus simulation software. The results were compared with the ISO 13790 hourly (implemented in spreadsheet) and ISO 13790 monthly (INVERT/EE-Lab program) methods. They concluded that due to different calculation approaches and different degrees of complexities of the building and usage description in the three simulation tools, the energy needs varied significantly.
Furthermore, the hourly and monthly methods were applied in an office building in Finland  [17]  and the results were validated with the IDA-ICE building energy software, considering it as a reference. Vartieres et al. [18]  applied three different modelling approaches (e. g. , ISO 13790 monthly, CODYBA and TRNSYS) to calculate the cooling energy demand of an office building in Bucharest. Then, a comparative assessment on the modelling input parameters and results were performed. Some studies have performed sensitivity analysis of the ISO 13790:2008 method to identify and rank the uncertain input parameters and to explore the behavior of related models  [19] [20] [21] [22] [23]. Recently, Kristensen and Petersen  [13]  gave a comprehensive overview of different local and global sensitivity approaches and compared three different but most commonly used sensitivity analysis methods, namely Local, Morris and Sobol.
 Definition of building boundaries for conditioned and unconditioned spaces,  Identification of the zones (single vs. multi zones),  Definition of the internal conditions for calculation of external climate, and other environmental data inputs (heat transfer losses, heat gain, etc. ),  Calculation of energy needs for heating and cooling, for each time step and building. The monthly method is used worldwide by different national energy standard organizations and the results are proven accurate and robust in a long-term urban energy scenario development for a district having hundreds of buildings  [2]. Advantages are highlighted in several studies, e. g. , References  [8, 12] , etc. (see Section 1). The monthly ISO method also produces good results compared to other software such as EnergyPlus  [13].
• Calculation of energy needs for heating and cooling, for each time step and building. The monthly method is used worldwide by different national energy standard organizations and the results are proven accurate and robust in a long-term urban energy scenario development for a district having hundreds of buildings  [2]. Advantages are highlighted in several studies, e. g. , References  [8, 12] , etc. (see Section 1). The monthly ISO method also produces good results compared to other software such as EnergyPlus  [13]. It is also proven that this simplified quasi-steady state monthly method is able to predict year-round energy needs, provided that the dynamic parameters are correctly determined  [15].