dataesr/scientific-paragraphs-categorization huggingface dataset:
Residential populations for FUA Our analysis relies on the 2015 residential population estimates mapped into the global Human Settlement Population (GHS-POP) project  31. This spatial raster dataset depicts the distribution of population expressed as the number of individuals per cell on a grid of cells 250 m long. Residential population estimates for the target year 2015 are provided by CIESIN GPWv4. 10 39 and were disaggregated from census or administrative units to grid cells. We downloaded population tiles that cover land on the globe in the Mollweide projection (EPSG:54009) and in raster format (. tif files). These raster data are made of pixels of width 250 m with associated value the number of people living in the cell. We processed the downloaded tiles with Python 3. 7. 6  37  and package gdal 3.
0. 2  38 to convert the raster files into vectorized shapefiles. The resulting shapefiles are comprised of polygons with field value the population in each polygon. Since the polygonization process merges adjacent pixels with common value into single polygons, populations for each polygon must be recomputed from polygon area and density through the simple following rule: Pop polygon = Pop pixel × Area polygon Area pixel (2) where Area pixel = 250 × 250 = 62 500 m 2. This leaves us with a list of 224 shapefiles of population that cover land area on earth. By intersecting the resulting shapefiles with OECD shapefiles delineating Functional Urban Areas (FUA) in OECD countries  29  (reprojected into Mollweide projection), we can build a population-grided dataset of cities in OECD countries. These resulting files are the population substrates used for measuring population living close to MRT stations.
Measure of People Near Transit (PNT) In order to measure PNT within urban areas, we must bind transit systems with their respective FUAs. We need to retrieve -and merge -all available GTFS files pertaining to a specific urban area and make sure that no rapid transit agency is excluded in the process. Most GTFS files for cities in the world are collected by the OpenMobility-Data platform 32. For each city in our dataset, we cross-checked the OpenMobil-ityData with Wikipedia local network information  8  to ensure that we considered all agencies of rapid transit within the urban area. For some European countries (Germany, France), GTFS files were not availaible on OpenMobilityData and have been retrieved from other sources  33, 34. We also note that GTFS format is not common in South Korea, Japan and in the United Kingdom where we only found GTFS data for Manchester area on OpenMobil-ityData 32 while we directly used station coordinates for London 40.
We were thus left with a list of 85 urban areas in the world for which we had complete, reliable and extensive data. From route-typed stop coordinates within that dataset, we can extract MRT stops (excluding buses and ferries) and buffer -still using gdal -catchment areas for several distance thresholds: 500 m, 1 000 m and 1 500 m. Intersecting the resulting buffers with the populationgrided shapefiles gives us the total population living within catchment areas, that can be expressed as a share of the total urban area population resulting in the value of the PNT metric. Our results are shown in Online-only Table  3.
Technical Validation The most thorough and exhaustive measure of PNT in urban areas in existing literature is a 2016 report from the Institute for Transportation and Development Policy 27. To validate our results and our methodology, we compared them with those results. Out of the 12 OECD cities considered in  27  , 11 are in our dataset: 5 in the United States, 2 in Spain, 1 in Canada, 1 in France, 1 in the United Kingdom and 1 in the Netherlands (see Table  4 ). Unfortunately, we found no data in the remaining city: Seoul. Out of these 11 cities, we had at first glance similar results for only two cities: Chicago (13% for both) and Vancouver (19% vs 23%). The discrepancies observed for the other cases stem from different definitions of cities and from the different transit systems that were taken into account.
Automatic harmonic analysis has been an enduring focus of the MIR community, and has enjoyed a particularly vigorous revival of interest in the machine-learning age. We focus here on the specific case of Roman numeral analysis which, by virtue of requiring key/functional information in addition to chords, may be viewed as an acutely challenging use case. We report on three main developments. First, we provide a new meta-corpus bringing together all existing Roman numeral analysis datasets; this offers greater scale and diversity, not only of the music represented, but also of human analytical viewpoints. Second, we examine best practices in the encoding of pitch, time, and harmony for machine learning tasks. The main contribution here is the introduction of full pitch spelling to such a system, an absolute must for the comprehensive study of musical harmony.
Analysis Datasets In the last decade, several corpora of human harmonic analyses have been published, spanning classical, jazz and pop/rock repertoires. Among these, the most relevant to the present study on functional harmonic analysis are those datasets expressed in RNs and focussed on Western Classical music: • 'TAVERN'  (Devaney et al. , 2015) , • 'ABC'  (Neuwirth et al. , 2018) , • 'BPS-FH'  (Chen and Su, 2018) , and • 'Roman-Text'  (Tymoczko et al. , 2019). Table  1  summarises the scale and repertoire focus of these corpora, and Section 3. 1 discusses the slight variations in the standards used. RN notation was initially designed for Western Classical music and while it can be (and is) profitably applied to wider repertoires such as pop/rock (see for instance  Duinker (2019) ), datasets on harmonic analysis of that wider repertoire do not generally include functional labels.
Aim and Contents While the type and degree of descriptive detail involved in RN analysis may be more or less appropriate depending on the musical circumstance, most other representations of harmonic analysis can be derived from the details held within it. As such, an automatic system that takes in a musical source and returns full RN analysis constitutes a defining benchmark for performance in any aspect of automatic harmonic analysis. This paper sets out our attempts to realise that goal. Section 2 completes the motivation for this study and approach by setting out some specific examples of the ambiguities involved in harmonic analysis, Section 3 proceeds to the method used, Section 4 turns to the results and some interesting edge-cases, and Section 5 provides an outlook. All software developed for this project is freely available under an open-source licence at https://gitlab.
Indeed, there are arguably no non-harmonic tones until measure 23 (see Figure  2 ). Here, in order to separate harmonic from non-harmonic, we have to select between two (or more) possible options: F minor (with F, A♭, and C in the chord, excluding B, D) or B diminished 7th (with B, D, F, and A♭ in the chord, and C eliminated). The preference for leaping to consonant notes would guide us towards the later view, though credible arguments can be (and have been) made on both sides on the basis of the wider progression. Table  1 : The contents of our meta-corpus, drawing together existing harmonic analysis datasets. The relative size of each corpus is given by the total, combined number of RNs in the analyses, the number of measures in the scores, and also the 'Quarter length': a metric for the total length in quarter notes.
Meta-corpus We prepared a meta-corpus of harmonic analyses, combining all previously published corpora of RN analyses as discussed above and itemised in Table  1. To bring these corpora together, we developed a set of new open source converter tools which we offer to the community. The RN annotations and these tools are available at https://gitlab. com/algomus. fr/functional-harmony, with links to the corresponding scores. Altogether, the corpus comprises 201 scores and over 70,000 RN annotations. We sought to convert each representation standard directly, without changing or interpreting those analyses except in case of clear errors. Details are available on request, and we are in the process of integrating the conversion code into music21's sub-library for parsing Roman text to support future efforts bringing corpora together  (Cuthbert and Ariza, 2010).
For the ABC corpus, we used the version reported by  Tymoczko et al. (2019). 12 In all cases, the. rntxt files set out the identity of analysts, proof-readers, and converters involved, and the original datasets are available online for comparison. 3 ). The analyses are written in. rntxt format  (Tymoczko et al. , 2019) , as explained in Section 3. 1. The 'rules' in the second and third column are set out at the beginning of Section 2. Different Annotators. Among these datasets, 'TAVERN' is the only one to include more than one alternative reading of the same piece by different annotators, a feature that is useful for communicating to the algorithm that there can be multiple valid readings of the same passage. For example, some annotators may prefer to define fewer chord changes in order to emphasise longer-range structure of the piece (excluding many notes as non-chord tones); others may include more of those notes, leading to a narrower focus on momentary changes.
RN Additionally, while the other datasets consistently offer one analysis per piece, by drawing them together, we have integrated a broad range of analytical perspectives. Again we consider that diversity an asset above and beyond the simple gain in scale, though we are not in a position to make any claims towards a 'balance' of approaches represented, and certainly not to 'representativeness'. For instance, each of the original datasets focuses on a different style, and so there is a non-separable correlation between annotators and musical genres. Future work could explore interannotator stylistic variance, in order to get a data-driven sense of the variety of approaches and how best to balance them as the provision of corpora continues to grow. Data Formats. Whatever the original formats of our data, we have elected here to use three formats which we find collectively offer the best balance between uniformity and suitability to the range of tasks involved.
Encoding Input and Outputs Identifying best practice in the encoding of musical information for machine learning is an open problem  (Huang et al. , 2018; Briot et al. , 2020). One consideration we know to be highly relevant in determining the best approach is the size of the dataset. While our dataset is larger than previous efforts, it is still small by the standards and requirements of machine learning. This redoubles the significance of the representation format: we want to include all relevant information, but the more we compress that information, the fewer parameters the system has to learn, and the more one can achieve with a smaller dataset. This section addresses the three primary aspects of data encoding for our purposes: time, pitch, and RN representation. Time. The literature proposes two main approaches to time encoding.
RN Output Labels. Continuing to follow Σ i c i = 123 ≪ ∏ i c i ≈ 22⋅10 6 , where c i is the number of output classes for each separate target label. It also improves the interpretability of the results, allowing one to focus on each aspect separately. This comes at the cost of a potential for self-contradictory outputs in which the six sub-labels have different ideas about the chord. In practice, we find that this is only rarely a problem, arising in the particular case of the 'no chord' label used by the ABC dataset (only) for passages with rests and/or single line melodies. Given the inconsistency in the source data, we do not include a provision for the 'no chord' case. Instead, we fill any such gap with a continuation of the foregoing chord, except in the case of beginnings, for which we start the first chord early.
Data Augmentation by Transposition In practice, keys are not used equally. It is common in both analysis and generation tasks to augment the dataset by transposing it to multiple keys  (Huang et al. , 2018; Chen and Su, 2019). While a single piece in two transpositions should not be considered equivalent to two distinct pieces (for reasons somewhat analogous to the status of multiple analyses of the same piece discussed above), transposition does stand to augment considerably the overall size of the dataset. While working within the 'CP' encoding space (as is the case for all work based on MIDI), there are only 12 distinct transpositions: one for each distinct pitch class. When including pitch spelling, transposition moves not through a circle, but a spiral, potentially infinitely. Clearly, some constraint is required to limit this pitch space.
Network Architecture We propose a neural network architecture that divides the process of RN analysis into two separate but interconnected parts (see Figure  5 ). The first part analyses the local context with a window size of 2 quarter notes. This corresponds to the human analyst distinguishing between harmonic and non-harmonic tones, producing a chordal reduction and deriving the Quality, Inversion, and Root labels. The second part, in turn, focuses on the more global matters of chord progressions and key selection. The RN analysis emerges from the structure and pattern of those progressions, expressed in the Key, Degree 1, and Degree 2 labels. The local part (Conv) is a 1-D implementation of the convolutional architecture 'DenseNet'  (Huang et al. 2016 ). We convolve along the time domain and encode pitches as different feature maps on independent channels (analogous to different colour channels in image analysis).
Network Training We randomly allocated 90% of the available scores to the training set, reserving the remaining 10% for validation. Importantly, we implemented this proportion not only for the corpus overall, but for each of the corpora individually. For the special case of TAVERN, those works assigned to the training set included the score and both of the corresponding analyses; pieces in the validation by contrast included only one of the analyses (randomly selected). In order to provide direct comparison with  Chen and Su (2018, 2019) , we also calculated results using only their dataset, divided in the same way. We trained in two ways. In the first (global) approach, all six labels are predicted at the end of the second part (Dil or GRU). In the second (local) method, the Quality, Inversion, and Root labels are determined at the end of the first, local part of the network and used to determine the key and degree.
As discussed, we did not enforce consistency between labels. Given the lack of local context, PoolGRU can only be trained in global mode. The network was encoded in Python v3. 7 using Tensorflow v1. 14. The code is available at https://gitlab. com/algomus. fr/functional-harmony and was initially forked from  Chen and Su (2018) , but all dataset conversions, encodings, and models are original work. Our best model has about 94,000 trainable weights in total: 33,000 for the local part, 43,000 for the global, and the remaining 18,000 for the fully connected layers. Depending on the model, the total training time ranges from 20 minutes to 3 hours, when run on a CPU-only high-performance-computing server.
Overall Metrics The results for our best model (ConvGRU, PSb, global learning) are summarized and compared with  Chen and Su (2018, 2019)  in Table  5. The first row sets out the results obtained by our best model using the new metacorpus, while the second row reports on results limited to Chen and Su's own dataset for direct comparison. At a glance, one can see that our proposal achieves a small but significant improvement over the previous state-of-the-art while also taking full pitch spelling into account. Our emphasis here is on comparing the different encodings and the architectures, and on attempting to identify edge cases. To that effect, we have trained on all possible combinations of the six pitch encodings, the two architectures (and the baseline), and the two training types (except for PoolGRU, which is only applicable for global training).
Regarding issue 1, the prediction for measures 34 and 35 is made of four different chord labels, while in the reference dataset there are only two. This is strictly connected with issue 2, as the 'mis'-labeled chord is a German sixth, unidentified by our system. Lastly, 'the different but acceptable' reading is pertinent in the case of measures 36 and following, which the dataset analyses in terms of G minor, the system views in G major, and is in fact an ambiguous mixture of the two (as discussed in Section 2). Finally, we found some cases we consider unacceptable readings, where the most compelling musical reading diverges from the statistically normative case. For example, in Beethoven's sixth sonata (op. 10 no. 2, Figure  6 ), the exposition includes a theme in C major which from measure 41 is repeated in the parallel key of C minor.
Improvements A simple right/wrong accuracy metric is not the best way to measure the performance of an RN analysis algorithm, as several different readings are often equally viable. Even taking this into account, the 43% total accuracy that we report is still far from ideal. In this concluding section, we propose some ideas for improving these results. As mentioned above, this field would benefit greatly from larger datasets, covering both a wider repertoire and multiple, alternative readings of the same works. It would also be useful to explore wider encoding options, and not just for pitch and time: for the repertoires discussed here, metrical position, dynamics, texture, and other score indications are also strongly attested to have a bearing on harmonic analysis. Including those parameters may improve performance, though informal testing of metrical strength did not yield significant gains, and the quality of machine-readable score encoding often prohibits a serious analysis of parameters like dynamics.
Applications We view the whole endeavour of (semi-)automated harmonic analysis as a means to the end of understanding harmony better. As such, one goal is to produce harmonic analyses at a sufficiently high quality level that they constitute a reliably usable dataset -and object of studyin themselves. This would enable us to scale up questions of how harmonies 'tend to' be used, enabling the field of corpus analysis to realise its potential. We consider it important that the models we proposed can be adapted to other kinds of harmonic analyses. As discussed above, harmonies in lead sheets have a different ontological status from RNs, and the repertoires represented are stylistically divergent, but the technical problem is comparable and often contained within the framework of the RN analysis.
; Laitz (2016). 2 http://jazzparser. granroth-wilding. co. uk/ParserPaper. html. 3 http://rockcorpus. midside. com. 4 https://zenodo. org/record/1476555#. XebL6C3 Myu4. 5 h t t p : / / j a z z o m a t. h f m -w e i m a r. d e / d b f o r m a t / dbcontent. html. 6 https://zenodo. org/record/1290737$\sharp$. W6vIKxNKixM. 7 http://ddmal. music. mcgill. ca/research/billboard. 8 https://csml. som. ohio-state. edu/home/index. php/ iRb_Jazz_Corpus. 9 http://www. isophonics. net/datasets. 10 'Mid-piece' is significant because of the relatively common practice of ending minor key pieces with a major triad (with the so-called 'Picardie' third). 11 The chord could be re-spelled enharmonically as B diminished, though even that would hardly help the wider reading. 12 See https://github. com/DCMLab/ABC/issues for ongoing discussion over issues with the original corpus.
Acknowledgements We gratefully acknowledge support from the  CPER MAuVE, ERDF, Hauts-de-France  ( Gianluca Micchi ), and  Cornell University's Active Learning Initiative  and the  Université de Lille's 'Invited Researcher Residency' scheme (Mark Gotham ). We also wish to thank the  Mésocentre de Lille  for their computing resources, and especially  Cyrille Toulet  for his assistance.
Tip 12: Demonstrate the value for active clinicians of the shift to online learning The COVID epidemic has required every clinician to learn about the virus and its implications at a rapid rate over a short period of time. Online learning has been an important component of this professional learning. The global pandemic is also a unique opportunity to observe the way clinicians rapidly acquire new knowledge in active practice. Covid-19 as a disease did not exist previously so clinicians have been addressing the challenge of sharing professional knowledge at what appears to be faster than the spread of disease itself and the predominant methods of sharing this knowledge is mediated through the internet. The number of publications in the World Health Organisation's online database is rapidly expanding and at the time of writing stands at almost 9000  (World Health Organisation, 2020).
Acknowledgements Figure 1 was published in Pusic,  M. V, Boutis ,  K, Santen , S. A. and Cuter, W. B. (2019) 'How does Master Adaptive Learning ensure optimal pathways to expertise?', in Cutrer, W. , Pusic, M. , Gruppen, L. D. , Hammoud, M. M. et al. (eds) in The Master Adaptive Learner. New York, New York: Elsevier Health Sciences, pp  174-192. Copyright 1979. Figure 2 reprinted by permission from  Springer Nature Customer Service Centre GmbH: Springer Nature. Merrill, D. M (2002) ' First principles of instruction' ,  Educational Technology Research and Development ,  50  (3), pp  43-59 ,  Copyright 2002. Sandars J, Correia R, Dankbaar M, de Jong P, Goh P, Hege I, Masters K, Oh S, Patel R, Premkumar K, Webb A , Pusic M MedEdPublish https://doi. org/10. 15694/mep. 2020. 000082. 1.
Advances in sequencing technologies have enabled exploration of epigenetic and transcription profiles at a genome-wide level. The epigenetic and transcriptional landscape is now available in hundreds of mammalian cell and tissue contexts. Many studies have performed multi-omics analyses using these datasets to enhance our understanding of relationships between epigenetic modifications and transcription regulation. Nevertheless, most studies so far have focused on the promoters/enhancers and transcription start sites, and other features of transcription control including exons, introns and transcription termination remain under explored. We investigated interplay between epigenetic modifications and diverse transcription features using the data generated by the Roadmap Epigenomics project. A comprehensive analysis of histone modifications, DNA methylation, and RNA-seq data of about thirty human cell lines and tissue types, allowed us to confirm the generality of previously described relations, as well as to generate new hypotheses about the interplay between epigenetic modifications and transcript features.
Background Epigenetic modifications of the DNA sequence and DNAassociated proteins along with transcriptional machinery is thought to be the main driver shaping mammalian genome during development and disease  1. Epigenetic modifications include DNA methylation, histone variants and histone post-translational modifications (such as acetylations and methylations), and facilitate tissue specific expression  2. The advent and maturation of sequencing technologies have facilitated large scale generation of epigenomic data across diverse organisms in multiple cell and tissue types. Accordingly, consortia were established to generate large epigenomic datasets, including ENCODE  3  , Roadmap Epigenomics 2 , Blueprint epigenome  4  for human, modENCODE  5  for model organisms, and FAANG  6, 7  for farm animal species.