dataesr/scientific-paragraphs-categorization huggingface dataset:
He et al. [5]  and Zhou et al. [6] , in addition to check-ins, also try to use more fine-grained information about the POIs. In the case of He et al. [5]  this includes general tags that may be related to categories but also to other information e. g. "godzilla". Zhou et al. [6]  use POI name and address tokens or more particularly token embeddings pre-computed on a domain-specific corpus. Recognising that collecting personal information may be difficult for a large number of POIs, other works are rather based on POI metadata only. Lagos et al. [7]  are the first to focus on increasing the POI classifier's coverage by using only the POI name, location, and time of opening attributes. They achieve state-of-art results for a large, multilingual POI database, illustrating that such an approach is feasible.
Background and motivation POI categorisation, is an important operation that can impact a lot of our location related services. Therefore, enhancing the quality of our category annotations is of high priority. As a first step to improving category annotations, we implemented and tested the flat POI classifier of Lagos et al. [7]. 3. The classifier encodes POI names and addresses using 1-gram character-based LSTMs and feeds the corresponding embeddings to a simple feedforward neural network. 4 In addition to the standard micro and macro prediction quality metrics  [15]  calculated on the development dataset (c. f. Section 6. 1), it was extremely important to qualify the errors made by the system before being able to deploy it. Most importantly, knowing that we have a very long tail in our data distribution, we had to understand the behaviour of the system on the corresponding POIs.
As illustrated in Figure  2 , while the system was doing well on category paths in the middle of the range, and comparatively well for paths with few POIs (the performance was deteriorating when only one POI was related to a specific category path), the performance was lower than expected on paths that were heavily populated. To qualify better the errors we extracted a set of 1000 misclassifications for further analysis. The sample was representative of the prediction probabilities one could find in the misclassifications of the development set i. e. if 15% of the misclassifications on the development set had a probability of over 0. 9, then we kept the same ratio in the set we extracted. Details of the analysis are presented in Table  1. As shown in Table  1 , misclassifications, i. e. disagreements between the labels predicted by the ML model and the ones found in the development dataset (which included  existing, manually entered, data), were largely due to incorrect labels in the development dataset.
Errors in the original label annotations were the cause of almost up to two thirds of the misclassifications when the probability given by the ML model was over 0. 9, and almost of one third when the value was between 0. 7 and 0. 9. Ontological and multilabeling issues (i. e. two different categories being correct for the same POI), were also discovered. For instance, semantically similar categories, such as ||Cafe, Dessert||Cafe 5  and ||Cafe, Dessert, accounted up to one third of the model's misclassifications when the output probability was between 0. 4 and 0. 7 (Acceptable column in Table  1 ). Based on the above analysis we found that: • The distribution of labels in the verified, corrected dataset is different from the one in our existing, manually entered, data. More specifically, several POIs initially attributed to over-represented classes were re-attributed to more specific category paths , and most importantly to long-tail classes  6.
In our experiments we use the verified, corrected dataset as our gold standard. • Minimising the number of under-specified labels is particularly important for improving data pertinence e. g. tagging a POI as ||Korean Food||Seafood||Sliced Raw Fish||Sashimi instead of ||Korean Food is much more informative. • Setting an appropriate threshold for the ML output probabilities is paramount. In our case, we set it at 0. 4. • The ML model can be useful not only for imputing labels for new POIs but also for curating existing annotations, in a principled manner.
Data We run our experiments on a large dataset including 828K POIs and 4093 unique category paths (we count only paths that appear as the label of at least one POI). Each POI is labelled with exactly one path. The maximum depth of the categorisation hierarchy is 5. The hierarchy is very fine-grained. For instance, there are 70 sub-categories of the Pizza category located at the third level in the hierarchy. To our knowledge this is the first work dealing with such a rich hierarchy in the POI classification domain. 9 he dataset is heavily imbalanced in terms of the number of POI instances attributed to each category  10. For instance, the top ten categories have more than 40% of the POIs attributed to them, while 461 categories have fewer than 5 POIs, resulting in a very long queue of sparsely represented categories.
The LSTM has recurrent droupout rate of 0. 3. The rest of the models have the same hyper-parameter values. Hcls: Hcls stands for the hybrid hierarchical model described in Section 5. 1. In Table  2 , we report only the model that performs the best (the one that takes into account only the second level of the hierarchy)  11. After each dense layer that corresponds to a hierarchical categorisation level (c. f. Section 5. 1), we insert a dropout layer (dropout rate of 0. 3). The rest of the implementation details are the same as for the baseline. Focal,cb: Given the fact that a lot of POIs are re-attributed in our gold dataset to long-tail categories, we have also implemented state-of-art approaches that counter the effect of a skewed data distribution by adjusting the weights of the samples from the small classes in the loss function.
Results Our models achieve the best results, as shown in Table  2. We achieve improvements up to 4. 89% in micro-F1 and 3. 98% in macro-F1 on the gold standard. Please note that the absolute scores may seem quite low, however, the gold standard consists exclusively of a subset of examples that the preliminary flat model fails to classify correctly as per the silver dataset, thus being very difficult to categorise (c. f. Section 3). The Hcls+CJL models performs better than the class-agnostic model, Hcls+CL , especially in terms of micro-F1. The difference is less important in macro-F1, as Hcls+CL applies costs to all misclassifications, implicitly pushing the model to predict long-tail categories in a stronger manner than in the case of the Hcls+CJL models. Because of that, more POIs from head categories  12  , are wrongly misclassified, resulting in the drop in micro-F1.
Deployment case study We developed the classification system and model for the dataset described in this paper in September 2020. We have released the model for the full POI database in November 2020. Among other things, corresponding predictions were used to semi-automatically correct existing annotations in the production database. For instance, on the dataset used in this paper alone, >11. 4K POIs were corrected in just a few days, as we focused on misclassifications with probability greater than 0. 9 (≈17K predictions). Corrected POIs are currently available online via Naver Maps. Note that data curation is resource intensive, so having a tool that allows to organise it in a principled manner is very.
e. over-represented categories, based on the misclassifications of an initial hierarchical classifier. Then we re-trained from scratch, introducing a weight to the standard cross-entropy loss function that specifically targeted incorrect predictions of these categories only. We find that on our gold standard we achieve improvements up to 4. 89% in micro-F1 and 3. 98% in macro-F1. Accordingly, our system has enabled the correction of thousands of POIs in our production database, showing that the resulting classifier may be used not only to impute categories to new POIs, but also to curate and correct manually added ones.
, 2019). The largest slip (> 200 mm) occurred at a depth of 4-7 km beneath the central portion of the HOBITSS network  (Wallace et al. , 2016). In subduction zones, increased pore fluid pressure due to mineral dehydration and compaction likely influences the mechanism of earthquakes and SSEs  (Saffer and Tobin, 2011; Chaves and Schwartz, 2016). Warren-Smith et al. (2019)  calculated earthquake focal mechanisms of local microseismicity using the data recorded by the HOBITSS deployment to determine the stress ratio variations within the subducting oceanic crust during the SSE cycle. The stress ratio = ( 1 -2 )/( 1 -3 ) is retrieved from focal mechanism inversion, where 1 , 2 and 3 are the maximum, intermediate and minimum principal stresses respectively. Because the Pacific plate is subducting at a shallow angle, 1 is nearly perpendicular to the interface and 3 is oriented down-dip and aligned with tensional slab pull.
Methods Velocity variations are determined using scattered waves retrieved from single station cross component correlations, which represent the waves traveling out from the station and back at later time. Seismic noise data are processed through to velocity changes using a Python package MSNoise 1. 5  (Lecocq et al. , 2014). Parameters in MSNoise are tested, evaluated, and determined, with the aim of maximizing the lag-time dependent signal-to-noise ratio (SNR;  Larose et al. , 2007; Clarke et al. , 2011)  of cross-correlation functions (e. g. ,  Yates, 2018). The coherence and correlation coefficient between current stacks and reference stacks are calculated to evaluate data quality (Supporting Information S1). The final parameters are listed in Table  S1. Each station is processed independently.
5-6 s, but we can still see similar velocity changes at longer periods (6-14 s; Figure  S11 ). Possibly there are signals at longer periods (6-14 s), but if so, they are weaker compared to short periods (2. 5-6 s). This does not affect our focus on depth, because there is little difference in depth sensitivity kernels between 2. 5-6 s and 2. 5-14 s (Figures  3  and 4 ). Therefore, below we present the results of 2. 5-14 s. LOBS8, LOBS9, and all EOBSs. The errors of the average dv/v are calculated by a bootstrap method. LOBS3 is excluded because it is located on the subducting plate where the SSE2 slip does not reach. The dv/v result from LOBS3 is also less reliable because LOBS3 recorded a dataset with more gaps compared to the other stations (Figure  S5 ).
Acknowledgments This research was supported by an  Endeavour Fund  grant (funding contract  C05X1605 ) from  Ministry of Business, Innovation and Employment. The ocean bottom seismic data were provided by instruments from the  Ocean Bottom Seismograph Instrument Pool  (https://www. iris. edu/hq /programs /obsip), funded by the  National Science Foundation (NSF) :  OCE-1333311 ,  OCE-1551683 ,  OCE-1333025 ,  OCE-1334654 ,  OCE-1551922 ; and the  Earthquake Research Institute (ERI) ,  University of Tokyo , funded by  JSPS KAKENHI :  JP16H06475. Additional funding support  came from strategic science investment funding (SSIF)  to  GNS Science  from  MBIE  ( C05X1909 Resilience  to  Nature's Challenges Kia Manawaroa -Nga Akina o Te Ao Turoa ) and the National Science Challenges (RNC2). Raw data from the.
Tissue specificity and specific responses to certain treatments may provide important clues about the causal gene or element, and these require well-processed and organized multi-omics data. In this study, we introduce Triti-Map, a tool for efficient gene mapping based on bulk-segregated DNA-or RNA-seq data and quantitative trait loci (QTL) data from Triticeae. Triti-Map contains a series of computational packages specifically optimized for Triticeae species, together with a web interface that integrates multi-omics data from Triticeae, to maximize the mining of public data and sequencing results to identify candidate genes. We illustrate how Triti-Map may be used to locate candidate genes that control disease resistance based on the bulked segregant ChIP-seq method, making it an easy-to-use resource for efficient gene mapping in Triticeae species.
Computational modules of Triti-Map for locating candidate intervals and detecting specific candidate sequences The major obstacles to Triticeae gene mapping include their large genomes and the high frequency of introgressed genes that may not be present in reference genomes. Here, we present Triti-Map, which contains a series of computational packages and a web-based interface specifically designed and optimized for Triticeae species to narrow down candidate intervals and detect specific candidate sequences (Figure  1 ). Figure  1  illustrates the workflow of the Triti-Map computational modules. Triti-Map uses a pair of bulk-sequencing datasets (another pair of parental The package uses the following analysis pipeline. In brief, the preprocessed reads are analyzed by two modules. The interval mapping module maps the reads to a reference genome, after which a traditional method is used for BSA-based interval detection.
Triti-Map, which accepts raw sequencing data (ChIP-seq, RNA-seq, or WGS data) from bulks with different traits, comprises the interval mapping module (blue) for locating genomic regions associated with a target trait, the de novo assembly module (orange) for assembling traitrelated sequences, and the web-based annotation module (green) for locating causal variants, candidate genes, or regulatory elements based on integrated multi-omics data and information on Triticeae species. The assembly module assembles the sequenced reads to identify sequences absent in the reference genome. The assembled sequences specific to the bulk that exhibits the target trait are kept for subsequent analyses. These two computational modules (i. e. , the interval mapping module and the de novo assembly module) integrate computational steps using Snakemake  (Koster and Rahmann, 2012).
Optimization to overcome specific challenges in Triticeae gene mapping The pipeline was optimized in the following ways to address specific challenges in Triticeae gene mapping (Figure  3 ). (1) The software and parameters were optimized to decrease the analysis time. Because of the large genomes and long chromosomes of Triticeae species, commonly used tools for analyzing genomic loci intervals (e. g. , bedtools; Quinlan and Hall, 2010) are very slow. We used GIGGLE  (Layer et al. , 2018) , which quickly compares a large number of wheat genomic intervals based on a temporal indexing scheme using a B+ tree to create a single index of the genome intervals, thereby significantly shortening the time required for analysis. To detect variants, we split the genome by chromosomes and used the Genome Analysis Toolkit (GATK) HaplotypeCaller (Van der Auwera et al.
, 2013) for parallel analysis. The default alignment program for DNA-seq-type sequence alignment is BWA-mem2  (Vasimuddin et al. , 2019) , which is faster than bwa  (Li and Durbin, 2009)  because of enhanced cache reuse, simplified algorithms, and the use of single instruction, multiple data (SIMD) wherever applicable. The alignment results produced by this program are identical to those of bwa. (2) Given the frequent introgressions and distant hybridizations between Triticeae species, a candidate gene may be absent in the mapped interval of the reference genome. Triti-Map can retrieve new bulk-specific sequences via de novo assembly and comparison. Furthermore, it collects Triticeae genomic regions colinear with candidate regions and provides detailed functional annotations to increase the chances of identifying a candidate gene for a particular trait.
(3) Regarding sequence comparisons and functional annotations, because of the rapid increase in available genome sequences and multi-omics data, rather than using a local database, the pipeline uses the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) RESTful APIs  (Madeira et al. , 2019)  to obtain up-to-date sequence information. Multiple strategies were used to simplify the use of Triti-Map. The software and environment required for analyses can be quickly deployed through Conda. All of the analysis modules were developed and integrated based on the Snakemake workflow management system. Moreover, the configuration is simple, flexible, and easy to use. Interval mapping (Figure  3 , left) and de novo sequence assembly (Figure  3 , right) can be conducted separately or together with other modules (Figure  3 ).
The Triti-Map usage document (https://github. com/fei0810/Triti-Map/ wiki) provides complete instructions and a case study. Triti-Map and bulk-segregated ChIP-seq used to identify a disease-resistance gene and its promoter region not present in the reference genome We next illustrate how Triti-Map and bulk-segregated ChIP-seq help to detect a disease-resistance gene that is not present in the reference genome as recently reported  (Wu et al. , 2021). Two bulked segregant pools were collected from the F 2 progeny of a cross between Xuezao (susceptible to powdery mildew) and 3D249 (resistant to powdery mildew). For each pooled sample, a ChIP-seq analysis was performed for three histone marks (H3K4me3, H3K27me3, and H3K36me3) that are closely associated with gene activities. The sequencing data were analyzed using the interval mapping module of the Triti-Map package, resulting in the identification of a 6-Mb region on chromosome  7A (chr7A: 724,111,912-730,119,678)  that is highly associated with powdery mildew resistance (Figure  4A , Supplemental Figures  1  and 2 ).
High-quality SNPs within this region were used as input data for the Triti-Map web-based annotation module. Nonsynonymous mutations were detected in two disease resistance-related genes (TraesCS7A02G551900 and TraesCS7A02G555200). In addition, the integrated epigenetic and motif information revealed the presence of the DNase I hypersensitive site (DHS), as well as H3K36me3, H3K4me3, and H3K9ac, in the TraesCS7A02G551900 promoter, suggesting that the promoter is highly accessible to transcription factors (e. g. , autonomously replicating sequence-binding factor 1 [ABF1]) (Figure  4B ; Supplemental Table  2 ). However, the results of an experimental validation indicated that these two genes did not fully segregate with the disease-resistance trait. We hypothesized that the candidate gene was present in other species or wheat populations but not in the reference genome.
We used the assembly module to detect disease resistancespecific sequences. Among the 10,429 resistant pool-specific scaffolds, 1,704 were partially mapped to Triticeae genomic regions colinear with the candidate region using the collinearity function of the web-based annotation module (Figure  4C ). A subsequent functional annotation using the EMBL-EBI hmmscan API detected nine sequences encoding protein domains common among R genes. In addition, four sequences encoding the NB-ARC (nucleotide-binding domain shared with apoptotic protease activating factor-1 [APAF-1], various R proteins, and CED-4) domain and five sequences encoding the leucine-rich repeat (LRR) domain were detected (Supplemental Table  3 ). The EMBL-EBI blast API of the assembly module detected sequences encoding one NB-ARC domain and one LRR domain that were highly similar (>99.
Plant Communications A candidate gene may be introduced from distant relatives or lost during evolution. To assess these two possibilities, we performed collinearity and evolutionary analyses. Colinear regions corresponding to the candidate region of chromosome 7A were detected in both chromosome 7B and chromosome 7D, both of which contain an R gene with high sequence homology to Pm60. No highly homologous gene was detected in the tetraploid The major steps that were optimized are marked by the following numbers: (1) steps that use specific tools or parameters that shorten the analysis time; (2) steps that split genomes for parallel analyses; (3) steps in which candidate sequences are filtered according to the colinear regions of candidate intervals across Triticeae species; (4) steps that use APIs from public databases to ensure timely updates and minimize local data storage.
DISCUSSION In this study, we developed Triti-Map, consisting of a suite of scripts and a web-based platform, specifically to address the challenges of Triticeae gene mapping (i. e. , large genomes and frequent introgressions), including cases in which the candidate gene is not present in the available reference genome sequences. First, this pipeline increased the likelihood of identifying agronomic genes absent from the reference genome by integrating information on de novo assembled sequences, colinear regions in Triticeae species corresponding to candidate loci, and homologous sequences in public databases. Second, because the ChIP-based strategy could help capture core genomic regions, including genes and regulatory elements  (Qi et al. , 2018) , bulk-segregated ChIP-seq facilitated the detection of non-reference genes and elements and decreased the time and labor required for the analysis and enrichment of the core genome, including both gene body and regulatory regions.
Third, in addition to ChIP-seq data, the Triti-Map pipeline accepts other types of DNA-and RNA-seq data and traditional QTL data as the input for locating genes and elucidating their functions. The workflow of this pipeline based on Snakemake is easy to maintain and expand. The ability to add more interval mapping and analysis modules later contributes to the broad utility and flexibility of Triti-Map. Fourth, a comprehensive collection of multi-omics data and a systematic curation of functional and evolutionary information facilitate the functional characterization of sequences. This is useful for precisely and reliably localizing candidate genes and for hypothesis-driven research into specific mechanisms. There are several publicly available web-based resources for wheat genomic data mining, including resources for visualizing and analyzing BSA results  (Zhang et al.
, 2021)  and for identifying collinearity and homology among Triticeae species  (Chen et al. , 2020). A database of wheat genomic variations and a wheat multi-omics database are also available  (Blake et al. , 2019; Wang et al. , 2020; Chen et al. , 2020; Zhang et al. , 2021; Ma et al. , 2021). Table  2  compares Triti-Map with five wheat genome variation databases and published wheat multiomics databases. Further mining of the data obtained from bulk-segregated ChIP-seq and Triti-Map using these resources will provide insights into downstream mechanisms. We anticipate that Triti-Map will improve gene isolation and cloning as well as breeding in Triticeae by decreasing the time and labor required to identify agronomically important genes.
Data pre-processing The following data processing pipeline is included in Triti-Map. Triti-Map accepts ChIP-seq and whole-genome sequencing (WGS) (DNA-seq data) or RNA-seq data as the input. The fastp (0. 20. 1) program  (Chen et al. , 2018)  is used to remove adapter sequences and low-quality sequencing bases from the raw data. The pre-processed DNA-seq reads are mapped to the reference genome using the default parameters of BWA-mem2  (Vasimuddin et al. , 2019). The RNA-seq reads are mapped using the two-pass mode of STAR  (Dobin et al. , 2013). In brief, the reads are mapped to the reference genome, after which the junction information obtained from the mapping results is used to reconstruct the genome index and perform a second round of mapping.