dataesr/scientific-paragraphs-categorization huggingface dataset:
Contrary to the MC predictor, the online version of the ML predictors is not considered as the training phase may have a high-processing cost, being likely unfeasible when the offloading process is handled in mobile devices. Therefore, new ORs can have more impact on the ML predictors performance as the predictors can only predict ORs that are present in the initial training sequence (75% of the sequence). Figure  20  shows the percentage of new ORs in the testing sequence when compared to the training sequence. Once more, similar results can be observed for both datasets where the lower number of new ORs in the test set is obtained for a ℎ ℎ = 40sec. Here, the median percentage of new ORs in the test set per user is ≈ 45% for ℎ ℎ = 40sec, showing that, for half of the users, the maximum accuracy that ML predictors would be able to achieve is ≈ 55%.
B. Mobility Regularity Effects The regularity of users' mobility is characterized by the ORs' visiting patterns, which might be used to improve mobility predictability. We further evaluate the regularity impact by answering the question: Can specific characteristics of regularity be used to improve the mobility predictability between ORs? 1) Relevance Filter: Offloading systems may try to improve the user's mobility regularity using relevance as a filter, e. g. considering only ORs that have a specific level of relevance to the users. ORs with a relevance lower than 7%, 10% and 15% were filtered from the sequence of visited ORs and the predictors re-trained. Figure  22  shows the accuracy of the Random Forest predictor (the best from Figure  19 ) for the different relevance filters, for both datasets.
The results show that the predictor's accuracy improves when ORs with low relevance are removed. In fact, the prediction accuracy improves approximately 20% in both datasets when a 15% relevance filter is used, reaching a median accuracy of approximately 55% for ℎ ℎ = {40, 20, 10} and 60% for ℎ ℎ = 5sec. Higher values for the relevance filter could be used to improve the mobility predictability in an offloading scenario, however, this affects the offloading opportunities. This trade-off will be further studied in Section VIII-C. 2) Multiple OR Prediction: Depending on how the offloading process is managed, offloading systems may consider more than one OR in advance to prepare the offloading process, e. g. , launching mechanisms to provide seamless network access to mobile devices. In this case, all the setup actions anticipating the users' arrival can be performed in two or more ORs, and the final OR choice can be made online using contextual information such as the current user's path.
As shown, for both datasets, the predictors' accuracy increases significantly when considering multiple options as the next OR; in these case, from ≈ 15% for = 2 to ≈ 27% for = 4. This reveals that the uncertainty of predicting the next visited OR is concentrated in a small subset of ORs which can be leverage by offloading systems. However, considering more than four possible next ORs does not bring significant added value as after > 4 only marginal are obtained.
However, most of the transition ORs are low-relevance ORs and removing them may affect the offloading opportunities provided to users in transit. Figure  24  and Figure  25  show the impact of removing low-relevance ORs on the number of transition ORs and the percentage of time inside ORs per trip (Porto's dataset), respectively. For clarity purposes, only results for ℎ ℎ = 10 are shown as similar results were found for other ℎ ℎ. Removing lowrelevance ORs decreases the offloading opportunities while the user is travelling. In fact, when considering only ORs with relevance higher than 15%, the number of trips where users cannot offload while travelling increases from 25% to 38% (Figure  24 ). Moreover, when offloading while travelling is possible, the median percentage of time spent by users inside ORs decreases from 37% to 28% (Figure  24 ).
D. Offloading Sites Considerations In a mobile data offloading scenario WiFi APs should provide Internet connectivity in the offloading sites. Larger ORs may require more APs and consequently more handovers to provide full WiFi connectivity during the offloading process. Figure  26  shows the maximum coverage of the offloading sites computed as the maximum euclidean distance between two geographical coordinates belonging to the same OR. Similar to the number of cells (see Section V-B), the maximum coverage of the ORs is very similar in both datasets. The horizontal lines in Figure  26  represent the typical coverage of an AP (44m) obtained in  [19]  through WiFi measurements in an urban scenario. As the vast majority of transition ORs belong to the low-relevance category, this shows that mobile devices will not need to perform multiple handovers during the offloading process as few APs, e.
Finally, the findings can be considered general since they are based on two datasets collected independently on two sides of the world. A high degree of similarity between the results from both Porto and Beijing datasets was found, indicating that offloading mobility is highly dependent on how regular people move in cities. Therefore, the findings and conclusion of this work are likely to be generalized to other urban scenarios. This work opens up new paths of research on the design of personalised offloading systems that adapt to the user's mobility. As next steps we see the assessment of different types of offloading mobility profiles, e. g. considering transportation mode, and the evaluation of the offloading opportunities and predictability for each. The design and evaluation of such systems considering individual mobility and specific applications is another line of future work.
This work is a result of the project  FLOYD  ( POCI-01-0247-FEDER-045912 ), funded by the  European Regional Development Fund (FEDER) , through the  Operational Competitiveness and Internationalization Programme  ( COMPETE 2020 ) and by  National Funds (OE) , through Fundacão para a Ciência e Tecnologia, I. P. ; and  UIDB/50008/2020  funded by the applicable financial framework (FCT/MCTES) (PIDDAC). It also has been supported by  FCT -Fundacão para a Ciência e Tecnologia  within the  R&D Units Project Scope :  UIDB/00319/2020  and is related to the  Hi!Paris  project entitled " AI for More Accessible Cities ". The authors  Emanuel Lima  and  Ana Aguiar  are with  Instituto de Telecomunicacões, Department of Electrical  and Computer Engineering from the  Faculty of Engineering of the University of Porto ,.
47; Supplementary Data 3b, Supplementary Fig. 1  and Supplementary Note 5). Although the high variability, these results are comparable with genomes previously obtained using similar sequencing and assembling strategies  35. Note that differences in sequencing coverage did not explain the observed differences in genome size or TE content across genomes (Supplementary Fig. 2 ). Similarly, differences in read length and N50 values do not correlate with differences in genome size, TE content, or BUSCO scores (Supplementary Fig. 2 ). After reference-guided scaffolding using the ISO1 reference genome, on average >90% of the contigs mapped to major chromosomal arms, which contained >98. 5% of the bases in the Genomes were sequenced using ONT except MUN-016 and TOM-007 that were sequenced using PacBio.
Additional information on the strains can be found in Supplementary Data 1 and on the sequencing in Supplementary Tables  S2  and S3. Besides contig-CUSCO scores (c. CUSCO) scaffold-CUSCO scores (sc. CUSCO) are also given (the later values are higher as expected if the piRNA flanking regions are present in the assembled genomes). a The 13 strains used in the construction of the de novo MCTE library. b The 11 strains used in the comparison of TE annotations using REPET, TIDAL and TEMP. c The 20 strains used in the cis-eQTL analysis. d Genome assembled using long-read sequencing data of the D. melanogaster reference genome provided in Solares et al. 18. de novo assembled genomes (Supplementary Data 3a). The scaffolded genomes also showed a high level of completeness, covering on average around 95% of ISO1 major chromosomal arms (Table  1 , Supplementary Fig.
3 ) and with an average of 99. 75% of the protein coding genes successfully transferred (Supplementary Data 3a). To quantify the accuracy of the TE sequences generated with long-read sequencing, we used our pipeline (from base calling to genome scaffolding) to process the ONT long-reads available for the reference genome  18. The newly assembled reference genome was 147. 8 Mb with a complete BUSCO score of 96% (Table  1 ). We identified 1842 orthologous TE insertions between our assembly and the FlyBase reference genome, with 99. 9% pairwise identity suggesting that our pipeline produces highly accurate TE sequences (Supplementary Data 4). We also used the pipeline applied in Berlin et al. 37  to annotate TEs in an ISO-1 assembly based on PacBio sequencing, to annotate TEs in our ISO-1 assembly based on the Solares et al.
Because the library required extensive manual curation, we focused on 13 genomes that represent the 12 geographical locations in our analysis (Table  1 ). Overall, the TEdenovo pipeline reconstructed 28,009 consensus sequences. After manual curation (Supplementary Note 6), the MCTE library ended up with 165 consensus sequences, which are 34 more sequences than the ones present in the Berkeley Drosophila Genome Project (BDGP) dataset for D. melanogaster 39 (Supplementary Data 5). The MCTE library sequences are representative of 146 TE families (13 of them represented by more than one consensus sequence), including three new families (see below). The second step of the annotation process used the TEannot pipeline of REPET to annotate all the TEs present in each one of the 32 genomes and the reference ISO1 genome using the MCTE library.
However, while the number of copies and the coordinates of TEs from families present in both annotations were very similar, our annotation strategy allowed us to annotate 468 copies from 28 TE families not present in the FlyBase annotation. While most of them correspond to known TE families, such as LARD, Kepler and THARE, 27 copies correspond to three new TE families (see below). Moreover, 15 copies belong to families such as gypsy10, BS4 and ZAM, which according to FlyBase were only present in the heterochromatic regions, but we found them in euchromatic regions as well (Supplementary Data 6a, Fig. 2a ). Although most of the new TE copies annotated only with REPET were small insertions, we also identified 50 insertions larger than 2 Kb (Fig. 2c , Supplementary Note 7). We further compared the number of TEs annotated in the 13 genomes with the previously available D.
melanogaster BDGP library and with the MCTE library (Supplementary Data 6d, Supplementary Note 7). We found that 42-44% of the copies annotated using the MCTE library were not annotated by the BDGP library. Overall, by creating a library that contains the TE diversity of 13 D. melanogaster strains from 12 geographical locations, we were able to identify TE copies from 25 known families not previously annotated in the reference euchromatic genome, and from three new families (see below). In total, 58% more insertions were annotated in the euchromatic reference genome using the MCTE library (1301 FlyBase vs 2059 REPET), and 42-44% more copies were identified using the MCTE library compared with the BDGP library when analyzing 13 other genomes. The new manually curated TE library allowed the identification of three new families in D.
melanogaster, two of them also present in other Drosophila species. Three consensus sequences in the MCTE library that failed to be assigned to any known family in the BDGP or the RepBase database were further analyzed using PASTEC  41. These new consensus sequences were classified as a Miniature Inverted Repeat Transposable Element (MITE), a Terminal Repeat Retrotransposon in Miniature (TRIM), and a Terminal Inverted Repeat (TIR) element (Fig. 3a ). Numerous Bari-like MITEs 42 and Mariner-like MITEs 43 have been previously described in D. melanogaster. However, the MITE consensus sequence identified in this work showed no significant alignments with any previously described MITEs (nucleotide identity percentage <50%), suggesting that it belongs to a new undescribed MITE family. On average, more than eight MITE copies were found in each D.
melanogaster strain. Identified copies were of variable length (Supplementary Fig. 4a ) and highly similar (average identity >89%, Supplementary Fig. 5 ). Moreover, the consensus sequence of the new MITE family showed no significant similarities with TEs identified in other five Drosophila genomes (Supplementary Data 7, Supplementary Note 8), suggesting that this element could have invaded the D. melanogaster genome recently. Regarding the new TRIM element, while the consensus sequence showed the typical TRIM structure (less than 1000 bp, with LTRs sequences between 100 bp and 250 bp, Fig. 3a ), no similarities with any known TE in public databases was found. Notably, this sequence was not the only TRIM element in the MCTE library since other TRIM consensus sequence showing similarities with a Kepler element was also found.
Short-read methods failed to detect up to 57% of the insertions detected by long-read based annotation. Besides comparing our TE annotations with those available in FlyBase, we also wanted to investigate how de novo annotations based on long-read sequencing assemblies compare with annotations based on short-read sequencing. Previous estimates suggested that shortreads failed to find 36-38% of the TE insertions annotated based on long-reads  8, 9. To estimate this percentage in our genomes, we compared the results obtained with the MCTE library in longreads using REPET, and in short-reads using two different tools: TEMP  45  and TIDAL 46 (Supplementary Data 8). For this comparison, we focused on 11 of the most complete genomes representative of the geographic variability of our samples and included in the previous subset of 13 genomes used to build the MCTE library (Table  1 , Supplementary Note 10).
The total number of TE insertions detected by each software was more similar for REPET and TEMP (6632 and 7430, respectively) than for TIDAL (9066) (Supplementary Data 8a). The number of TE insertions detected both by REPET and TIDAL (4041) is higher that the number of TE insertions detected by REPET and TEMP (3254). The overlap of the insertions detected both by TIDAL and TEMP is higher (4786), probably because the methodologies of these two software are more similar (Supplementary Data 8a). To estimate the false negative rate of TEMP and TIDAL and the false positive rate of REPET, we performed manual inspection for 300 TE insertions annotated by REPET. When comparing the TE annotations between REPET and TEMP, 120 TEs (40%) were correctly annotated by the two software, while 170 (57%) TEs annotated by REPET were missed by TEMP (Supplementary Data 8b).
When comparing REPET and TIDAL annotations, 212 TEs (71%) were correctly annotated by the two software, while 78 TEs (26%) were correctly annotated by REPET and missed by TIDAL (Supplementary Data 8b). Finally, 10 of the 300 TEs annotated by REPET, were false positives as we could not confirm their presence using Blast (see Methods). Additionally, we performed manual inspection of 50 TEs that were identified by TEMP/TIDAL but were not identified by REPET (Supplementary Data 8c). None of these insertions were present in the genome assemblies. For these TEs, we could not distinguish whether they were REPET false negatives or TEMP/TIDAL false positives. However, the majority of these insertions (39/50) have a frequency estimate <20% according to TEMP, suggesting that they could be false positives  45.
5b ). These results suggest that 20 genomes are enough to accurately identify most common TEs in populations, which is the subset of TEs expected to be enriched for candidate mutations  33. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • To determine whether the geographical origin of the strains affects the total number of TE copies identified and their frequency classification, we analyzed genomes according to the continental origin of the sequenced strain: North America, Europe and All populations (Supplementary Data 11a).
Overall, our results suggest that the analysis of 20 genomes accurately identifies most common and fixed TEs in a diverse set of populations. Still, because a proportion of the common TEs identified were continent specific, analyzing populations from other continents should lead to the identification of additional common TE insertions. Hundreds of de novo annotated TEs are associated with the expression of nearby genes. To determine whether TE insertions were associated with the level of expression of nearby genes, we looked for significant associations between cis-eQTLs and TE insertions using RNA-Seq data available for 20 of strains in our dataset  [55] [56] [57]  (Table  1 , Supplementary Data 2c). We focused on TE insertions located in high recombination regions as those insertions are more likely to be causal mutations.
We discarded the presence of other structural variants linked to our 18 candidate adaptive TEs that could also be driving positive selection (Table  3  and Supplementary Data 14). Moreover, our set of candidate adaptive TEs was enriched for signatures of selection compared with the whole dataset of TEs present at >5% population frequency (the minimum frequency required to calculate the selection statistics; X 2 test, p-value = 0. 0081). Given the small number of genomes analyzed, strong selection appears to be acting on these 18 insertions as exemplified by the Accord insertion  69, 70. However, further functional validation is needed before arriving at any conclusive evidence on the functional role of these TEs. Note that for one of these 18 insertions, we found significant association with the level of expression of the nearby gene in whole-body non-stress conditions (Fig.
Methods Sequenced strains. We sequenced the genomes of 32 D. melanogaster strains originally collected from natural populations. All the samples represent either isofemale or inbred stocks from such natural populations (Supplementary Data 1). 24 strains were obtained from 11 European natural populations and the remaining eight are RAL strains from the DGRP, obtained from North Carolina, US (Fig. 1 , Supplementary Data 1). All flies were reared on standard fly food medium in a 12:12 h light/dark cycle at 25 °C. DNA extraction and long-read sequencing. We sequenced two strains (MUN-016 and TOM-007) using Pacific Biosciences (PacBio) technology and the remaining 30 using Oxford Nanopore Technologies (ONT) and Illumina technologies. DNA for PacBio sequencing was extracted from 400 D. melanogaster 5-10 day-old female flies, using the Gentra Puregene Tissue Kit (Qiagen) following manufacturer's instructions.
Briefly, 400 flies from each strain were mechanically homogenized in 24 ml of lysis buffer (proteinase K added) and incubated overnight at 55 °C, and DNA was precipitated with isopropanol after RNAse treatment and protein precipitation. Finally, DNA was resuspended in 1,6 ml of Hydration Solution. DNA concentration was measured using a Nanodrop® spectrophotometer. Most DNA samples for ONT sequencing were extracted from 100 D. melanogaster 5-10 day-old female flies from each strain using the Blood and Cell Culture DNA Mini Kit (Qiagen) following manufacturer's instructions with small modifications (Supplementary Data 2; Supplementary Note 1). PacBio libraries were prepared using 20 Kb SMRTbell and were sequenced using the PacBio RSII System by Macrogen Inc. Korea. ONT libraries were constructed using the Ligation Sequencing Kit (SQK-LSK108 or SQK-LSK109) following manufacturer's instructions (Supplementary Data 2; Supplementary Note 1) and were sequenced in house using the MinION device.
We then aligned PacBio reads to the draft assembly using pbalign (SMRT Link v. 5. 0. 1) and used quiver (SMRT Link v. 5. 0. 1) to obtain the consensus sequences (polished assembly). PacBio-related programs were all run using default parameters (Supplementary Fig. 12a ). For ONT genomes, we also started with Canu (v. 1. 7)  76 with default options for building raw de novo assemblies. We then applied Racon (v. 1. 0)  78 , Nanopolish (v. 0. 10. 1) (https:// github. com/jts/nanopolish) and Pilon (v. 1. 22)  79 for obtaining final polished assemblies (Supplementary Fig. 12b , Supplementary Note 2). Genome deduplication, decontamination and scaffolding. Besides repetitive content, we found that raw de novo genome assembly sizes positively correlated with BUSCO Duplicates (Supplementary Note 3, Supplementary Figs.
18  genome and >89% of the TEs annotated in the ISO1 reference genome. For every TE pair, we performed global pairwise alignments using MAFFT v. 7. 4 aligner (parameters: mafft -globalpair -thread 4 -auto). For each pair we then calculated the pairwise identity in two ways: considering and not considering gaps in the alignment. Average gapignorant identity was 99. 9% and gap-aware identity was 98. 9%. Some TE families showed more variability than others but in most cases this variability was explained by individual TE insertions. Construction of the Manually Curated TE (MCTE) library. We used the REPET package (v. 2. 5)  38, 40, 41  for performing TE annotations using a manually curated TE (MCTE) library of consensus sequences. Briefly, REPET is composed of two main pipelines, TEdenovo dedicated to de novo detection of TE families and TEannot for the annotation and analysis of TEs in genomic sequences.
After running the TEannot pipeline over the euchromatic regions of each genome, we performed a post-annotation filtering step consisting in the removal of TE copies <100 bp, as REPET cannot accurately annotate these copies, and copies whose length overlapped >80% with satellite annotations. Multiple sequence alignments of TE insertions for manual curation were performed with MUSCLE (v. 3. 5) using Geneious (v. 10. 0. 2) for alignment and visualization (https://www. geneious. com). Identity values between TE copies and the consensus were obtained from REPET TEannot pipeline. Comparison with short-read-based TE annotations. We compared REPET TE annotations on the de novo assembled genomes using the MCTE library with the annotations performed by two short-read-based TE annotation software: TEMP (v.