dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with Other Work We compare the performance, energy efficiency, accuracy as well as UEE with the original SkyNet, where the energy efficiency is given by frames per Joule (FPJ). The original SkyNet is implemented on Ultra96, we transfer the design to ZCU104 without any modification. We choose the data achieved at the threshold frequency of 840mV/370MHz as the first candidate and that achieved at 840mV/379MHz as the second candidate. As shown in Table. 2, our work achieves 54% improvement in performance, 38% improvement in energy efficiency and 106% improvement in UEE without any degradation in accuracy compared to the original SkyNet. If we relax the requirement on accuracy, we can achieve 56% improvement in performance, 47% improvement in energy efficiency and 121% improvement in UEE at the cost of 0.
Theoretical Background and Research Model Mobile wallet (m-wallet) refers basically to a transaction modality in which the users can utilize their devices to perform the payments and other transactions  [5] -  [7]. Also, simply, a mobile wallet is a digital wallet, in that a preload of money can be used to perform transactions by using different channels  [10]. That is, not only smartphones can be used, but also other devices like wearables. According to  Türkmen and Değerli (2015) , "Mobile wallets are digital wallets which are accessed through mobile devices, in that sense m-wallet app has the ability to communicate with the bank and retailer system, based on hardware and software infrastructures enabling secure storage, processing and communication of the data provided by the wallet holder, the wallet issuer and the application/service providers"  [16, p.
, 2013) , but also depending on the printing techniques (e. g. relief embossing, tactile screen with audio descriptions, or 3D printing). Tactile maps can also be interactive, as well as current web maps, and it was shown that such interactivity increases the chances of understanding the geography behind the map for visual impaired people  (Brock et al. , 2015). Now, considering our four-step process to automatically generate tactile maps from images of maps for geography and history lessons, some of these steps have been approached in recent research projects. For step (2), data collection, the main task is to select the appropriate layers of vector data from the existing geographical databases. But some information necessary for visually impaired people might be lacking in geographic databases collected for topographic mapping  (Touya et al.
, 2019). For instance, pavements and zebra crossing are rarely collected in such databases but are necessary when maps of daily travel are created. In this case, it is possible to generate this missing information from high resolution aerial images with deep learning techniques  (Fillières-Riveau et al. , 2019). At step (3), the vector geographic data collected at the previous step needs to be simplified to meet the drastic specifications of tactile maps. Many tools already exist for this task, many of them based on OpenStreetMap data, a quite complete list of such applications can be found in  (Wabiński and Mościcka, 2019). But as illustrated by the Mapy. cz applications  ( Červenka et al. , 2016) , the maps remain often too complex to be fully usable for visually impaired people, or even as base maps for transcriptors.
Problem Formalisation One of the most important characteristics of maps, and maybe the most important regarding generalisation and simplification, is the scale of the map. The scale of the map first helps to define the size of the symbols in the map  (Ruas, 2004). For instance, if a line needs to be longer than 12. 7 mm to be understood as a line in the tactile map  ( Štampach and Mulíčková, 2016) , defining the scale of the map helps to define the minimum length of lines that should be kept from our geographic database. But map scale is also related to the use and the content of the map  (Ruas, 2004 , Mackaness, 2007). Topographic maps at the 1:25,000 scale are often used for hiking and contain information about terrain and all the individual buildings are represented. We propose three different ways to formalise our problem of inference of the map scale: • learn a regression function that gives the numerical value of the scale as output of the CNN; • classify the maps into scale categories; • classify the maps into specific geographic extents from which the scale can be derived: if the extent of the map is the whole France and the map is to be printed on a 20 * 20 cm device, the scale of the map can be deduced.
Category The third solution to undirectly learn map scale derives from the maps collected in our dataset (we have many maps of Europe, but not so many of other continents, many maps of France, but no so many of other countries), but also the global set of characteristics we plan to infer on maps. Indeed, in addition to scale, we need to know the geographical extent of the map, and we can derive scale from the extent and the size of the output. In this case, the categories correspond to the geographic extents that were redundant enough in our dataset: World, Europe, France, Paris. We tested several CNN architectures to test our different formalisms to infer scale, and we finally opted for rather simple CNN that is a mix between LeNet models  (Lecun et al. , 1998)  used for handwritten character recognition, and AlexNet  (Krizhevsky et al.
Data Preparation Our main source of maps is a French collaborative project where teachers create open source textbooks, including geography and history textbooks for all classes (https://www. lelivrescolaire. fr/). We extracted maps from the textbooks with screen capture tools, keeping only the map and not the text that describes the map. When a legend was included along the map, it was included in the image, as it was anticipated that the legend could be used by the CNN to capture the map characteristics better. As the number of maps collected this way was quite small, we added maps that are not extracted from geography or history textbooks: we extracted several types of maps from Google Maps, OpenStreetMap, and the geoportal of the French mapping agency 2. In most cases, the scale of the map was not available, so we used the available scale bars to measure the scale of the map; then, we stored for each map its scale (i.
Results and Evaluation The model was implemented with Keras Python library, and run in the Google Colaboratory platform. From the 450 maps in our dataset, we kept 93% for training, and only 7% (32 images) for the evaluation of the model. These 32 evaluation images were carefully chosen to be as diverse as possible in terms of scale and geographic extent. Results of the regression model are really poor and we decided not to push them further. In the best cases, the predicted normalized scale is half the reference value, but most of the time, the predicted value is three times smaller than the reference scale. These results can be explained by a training dataset that contains a very small number of unique scale values, from which the regression was clearly difficult. The results of scale classification are also disappointing.
We trained the model with 1,500 images, but the results are once again disappointing with a similar 60% accuracy of classification on the evaluation images. Table  2  shows the results obtained on the evaluation dataset when learning the geographical extent of the maps. Globally, the classification accuracy is around 71%, but this accuracy is heterogeneous between classes. The accuracy raises to 81% for the two classes with the most instances in the training set ("World" and "France".
The problem of the required square size of the images that occurs with scale inference also occurs with the CNN used for content inference. But as VGG-16 is a deeper model than the one used for scale inference, we need much more training examples to avoid over-fitting. To solve both issues, rather than adding a black strip   When the map images are augmented by cutting them in four, there might be some images with a large portion of the legend, and only a small portion of the image depicting the map (Figure  9 ). To avoid this problem the maps used for content inference are cropped to remove the legend as much as possible (in the map of Figure  9 , the bottom of the image is cropped). After the augmentation process, our dataset contains 1,600 map images, which remains quite small for a model as deep as VGG-16.
Results and Evaluation The VGG-16 model was loaded with the Keras Python library, and the last fully connected was changed to fit our multi-label Figure  9 : This map of Africa contains a large legend, but when cut in four for data augmentation, the bottom left part (red dashes) is mainly the legend and the sea, and we do not see the map so much. output. Only this new final layer was trained with our maps, allowing us to benefit from the effective training of the other layers with a much higher number of training images. Table  3  shows the best results we obtained for content inference. We did not test the label "vegetation" because there was not enough training examples. From the 1,600 images of our dataset, we used 88% of the dataset for training and 12% (192 images) for evaluation. Table  3  shows the best results we obtained to label the maps of our evaluation dataset.
To go further, several possible improvements have been discussed in the previous section. Beside the obvious necessity for a larger and more diverse training dataset, the most important topics to tackle seem to be the projections and the legend (or even the text accompanying the map). This paper presents the starting point of a large project 3 and there are many issues left to automatically derive a tactile map from the image of the map for people with normal vision. Besides the possible improvements of step (1) described above, step (2) on data collection, (3) on the simplification and generalisation of the collected data, and (4) on the derivation of a 3D model from the 2D simplified map, all need to be addressed in future years. Experiments with professional transcriptors and visually impaired people wil be carried out throughout the project to verify the usability of all our current and future propositions.
Minjinia reveals new data on 'placoderm' endoskeleton and tissue diversity from Mongolia-an otherwise extremely poorly known biogeographic realm for early gnathostomes. The phylogenetic placement of this 'acanthothoracid'-like taxon crownward of all non-maxillate 'placoderms', in conjunction with possession of extensive endochondral bone, highlights the importance of material from traditionally undersampled geographic areas. The presence of endochondral bone renews the hypothesis that this tissue is evolutionarily ancient and was lost secondarily in chondrichthyans 7,8. This view is overall consistent with evidence of generalised bone loss in chondrichthyans, potentially as a result of the suppression of bone-generating molecular genetic pathways  6, 38. Continued work in Mongolia and re-evaluation of phylogenetic datasets will be necessary to address this, with the results likely to lead to substantial re-evaluation of gnathostome phylogeny.
YAA provided background on the geology, palaeontology, and stratigraphy of the type location; EZ and YAA organized field logistics and permitting. MDB, SG, MC, RPD, and AJ undertook the anatomical interpretation and prepared the figures. MDB and SG conducted the phylogenetic analyses. RS conducted the parsimony branch support analyses. TG wrote the script for generating MrBayes partitions from TNT's character fits table and conducted the likelihood and model-fitting analyses. The manuscript was written by MDB, RPD, and SG.
2 (Volume Graphics, Germany), and prepared images in Blender. Phylogenetic analysis. We conducted a parsimony analysis using TNT 1. 5  39  and Bayesian analysis using MrBayes v 3. 2. 7  40. The dataset consisted of 95 taxa and 284 discrete characters based on a pre-existing dataset  41. We employed Osteostraci and Galeaspida as composite outgroups. We conducted parsimony analysis using both equal weights and implied weights methods. Global settings were 1000 search replicates and a hold of up to 1 million trees. Equal weights parsimony analyses were conducted using the ratchet with default settings. Implied weights parsimony used a concavity parameter of 3 and the search was without the ratchet. Command lists are included in Supplementary Information. We conducted Bayesian analysis using both a partitioned and unpartitioned dataset.
We used the Mkv model  42  and gamma rate distribution. We ran the analyses for 5 million generations with a relative burn-in fraction of 0. 25. Runs were checked for convergence using Tracer  43. We partitioned the dataset using a newly proposed method  24  that partitions the data according to homoplasy levels. Using the results of implied weights parsimony conducted in TNT, we created a text table of character fit values. We wrote an R  44  script to generate a list of partition commands for MrBayes. To reduce the number of partitions with small numbers of characters, we concatenated the partitions by rounding the fitness scores to 2 significant figures, yielding 10 individual partitions. We assessed parsimony ancestral states visually using Mesquite  45. Likelihood and Bayesian ancestral states were estimated in R using the castor package  46.
M. Bolortsetseg generously assisted MDB with contacts and field experience in Mongolia. Fieldwork was supported by National Geographic Society grants CRE 8769-10 and GEFNE35-12 to MDB. ALJ's field contributions were supported by funds from the Anna Maria Lundin's stipend from Smålands Nation, Uppsala University. RS's field contributions were supported by a Royal Society Research Grant and the University of Manchester. The majority of this work was supported by the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement number 311092 to MDB. RPD was also supported by the Île-de-France DIM (domaine d'intérêt majeur) matériaux anciens et patrimoniaux grant PHARE. Stig Walsh is thanked for access and loan of specimen at the National Museums of Scotland.
Data availability The holotype specimen of Minjinia turgenensis will be permanently deposited in the collections of the Institute of Paleontology, Mongolian Academy of Sciences. Original tomograms are available at (doi:10. 6084/m9. figshare. 12301229) and rendered models are available at (doi:10. 6084/m9. figshare. 12301223). The phylogenetic character list and dataset are available as Supplementary Information S1 and S2. The LifeScience Identifier for Minjinia turgenensis is urn:lsid:zoobank. org:act:82A1CEEC-B990-47FF-927A-D2F0B59AEA87 Code availability R code for generating partitions based on character fits and code for likelihood ancestral states reconstructions and plots are available in the Supplementary Information.
Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review.
In particular, the practice of "Evidence Mapping" is increasingly being used to characterize important areas of study relevant to a given topic along with important gaps in the literature  (6). Evidence maps result from a systematic search of a broad field and are undertaken to identify gaps in knowledge and guide future research needs. Typically, the results are presented in a user-friendly format, often as a visual figure or graph, or as a searchable database  (6). However, constructing detailed evidence maps can be a resource-intensive procedure, thereby limiting their utility for practical implementation, particularly for rapidly evolving topics when the time available to conduct research and generate findings may be especially limited. Given the growing number of COVID-19 studies being published on a daily basis, the ability to efficiently synthesize knowledge that could be used to improve public health is paramount.
METHODS We followed a framework similar to our previously published, proof-of-concept application of the rEM methodology to a case study of low-calorie sweeteners and adverse health outcomes  (1). An analogous seven-step process (Figure  1 ) was used to conduct the rEM, with modifications specific to this study question: (1) identify the scope of the evidence map; (2) develop a comprehensive search strategy; (3) establish study eligibility criteria and a systematic study selection process; (4) carry out title and abstract screening; (5) define risk and protective factors and develop search strategies to reflect tag definitions;  (6)  apply and verify risk/protective factor tags;  (7)  create the evidence map. We made a minor modification to the process in Step 2: instead of developing a comprehensive search strategy, we utilized an existing dataset, the CORD-19 dataset (https:// www.
Step 1: Identify the Scope of the Evidence Map To establish the scope of the evidence map, we developed a study goal, study questions, and PECO statement, which were designed to provide a framework for development of the primary review  Study Goal: To create an evidence map that classifies COVID-19 risk and protective factors, susceptible sub-groups, and the intersections of these categories. Study Questions:   (8). By searching these six data sources, the dataset attempts to provide a complete collection of the literature. However, due to the novelty of COVID-19, and a lack of standard terminology at the time, it cannot be ruled out that some relevant references were missed. Step  The inclusion and exclusion criteria used for the screening process of titles and abstracts in SWIFT-Active Screener.
Within these broader categories, we included various subcategories which are also based on validated, commonly noted topics  (11) (12) (13)    The four broad susceptibility categories and subcategories, as well as the number of documents tagged in each category. a Broad susceptibility categories and subcategories were based on validated, commonly noted risk factor definitions  (11) (12) (13). b n refers to the number of tagged references in each susceptibility category (i. e. , Behavioral, Physiological, Demographic, Environmental, and their subcategories). Each reference can be tagged with one or more categories. learning software application (https://www. sciome. com/swiftreview/). SWIFT-Review provides tools to assist with searching, categorization, and pattern visualization in literature search results, utilizing statistical modeling, and machine learning methods  (14).
First, we created an initial set of keywords for COVID-19 risk and protective factors for each subcategory using keywords developed by examining relevant Medical Subject Heading (MeSH) search terms used to categorize literature at the National Library of Medicine. Although the CORD-19 dataset is not indexed according to MeSH, we were nevertheless able to use the MeSH terminology as a basis for developing corresponding free text search terms. These searches were executed in SWIFT-Review, with iterative refinement as described in Step 6, to label the titles and abstracts of all included references and assign relevant risk/protective factor tags. Step 6: Apply and Verify Risk/Protective Factor Tags We imported the included titles and abstracts into SWIFT-Review to identify COVID-19 risk and protective factors, susceptibility categories and susceptible sub-groups.
For example, if an abstract was automatically labeled with the Demographic Category risk factor "age" because it mentioned age of study subjects, but a manual review of the reference identified that it did not mention age in the context of a risk factor associated with COVID-19, the tag in SWIFT-Review was subsequently removed. c) SWIFT-Review was used to compute term frequencyinverse document frequency (TF-IDF) scores for each set of correctly labeled documents and to identify and rank additional potential keywords for enrichment (i. e. , "fingerprint analysis") in studies labeled with each COVID-19 susceptibility category. TF-IDF scores are a statistical measure used to evaluate how important a term is within a document or literature corpus. Terms having high TF-IDF scores for a particular subset of documents are good candidates for sensitive and precise search terms as they are enriched in that subset relative to the remainder of the dataset.
RESULTS As shown in the PRISMA Diagram (15) (Figure  2 ), 45,781 records were identified via the CORD-19 dataset. Restricting the dataset to records published January 1-April 3, 2020 reduced the number of studies to 4,330 records. We screened 3,521 (81%) titles and abstracts to reach an estimated 99% predicted recall; 809 articles were not screened. Among the articles screened, 3,304 articles were excluded and 217 relevant studies were included. Because our literature search was conducted in early 2020 during the initial stages of the pandemic, we made the decision beforehand to include pre-prints (articles that have not yet been peer reviewed) in order to fully capture as much relevant scientific information as possible. The aim of our rEM was to provide an initial overview of where research has been conducted and which outcomes were investigated.
g. , as of May 2020, over 15,000 articles have been added to the CORD-19 dataset since we downloaded references on April 3, 2020). For example, racial disparities in COVID-19 outcomes have been noted in a body of recently published studies  (75) (76) (77) ; however, our literature set (focused on literature primarily coming out of China in early 2020) did not cite race as a risk factor. Furthermore, with the increased availability of literature, it is likely that the number of peer-reviewed manuscripts is higher than when we initiated this review. Ideally, this evidence map could be updated on an ongoing basis to incorporate newer scientific evidence as it becomes available, and used to continually monitor what we know about the potential risk and protective factors of COVID-19 as scientific discoveries evolve.
with literature included from PubMed, the World Health Organization (WHO) COVID-19 database of publications, the Chan Zuckerberg Initiative, Elsevier open access data, and preprints from medRxiv and bioRxiv given the open access availability of a comprehensive literature set on COVID-19 (Open Research Dataset: CORD-19, search conducted on April 3, 2020), we decided to utilize this existing dataset. This dataset is hosted by Semantic Scholar in collaboration with the Allen Institute for AI and is updated on a weekly basis, 1. What risk factors in human populations are reported in recently published (January-April 2020) COVID-19 literature? 2. What protective factors (i. e. , factors reported to lead to better outcomes for those who contract COVID-19) are reported in recently published (January-April 2020) COVID- 19 literature? PECO statement: Participants: Humans Exposure: COVID-19 virus Comparator: Compare humans who contracted COVID-19 vs.