dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and population platforms We used data from three participatory surveillance platforms in the USA, the UK, and Israel (two platforms per country), spanning a 4-month period of observation early in the pandemic (April 1 to July 31, 2020) to estimate odds ratios (ORs) for symptoms on self-reported SARS-CoV-2 test positivity among self-identified nonhealth-care workers (because health-care workers generally received different access to testing). Mapping of survey questions across platforms and survey language used is provided in the appendix (pp 10-15). The Carnegie Mellon University (CMU; Pittsburgh, PA, USA) and University of Maryland (UMD; College Park, MD, USA) Facebook COVID-19 Symptom Survey (CMU/UMD) is one of the three participatory surveillance platforms providing the data used in this study, with respondents from the three study countries (the USA, the UK, and Israel).
Added value of this study We identified symptoms that were or were not consistently associated with a positive SARS-CoV-2 test across various testing conditions by use of six datasets from three COVID-19 surveillance platforms in the USA, the UK, and Israel. These platforms are web-based and smartphone-based, as well as cross-sectional and longitudinal. The study period of 4 months covered varying COVID-19 prevalence during the fall of the first wave and, in some areas, rise of the second wave. Importantly, these collaborative analyses used large-scale surveillance data to track and highlight the value of individual symptoms, specifically anosmia, fever, and respiratory symptoms, to predict SARS-CoV-2 test positivity by region, platform, demographic factors, calendar time, timing of testing, illness duration, exposure and outcome ascertainment, and illness.
Implications of all the available evidence Despite differences in syndromic surveillance methods, access to and timing of SARS-CoV-2 testing, and disease prevalence, anosmia or ageusia were consistently the strongest predictors of COVID-19 infection across all platforms over time. The odds of a positive COVID-19 test was nearly 17 times higher among individuals with anosmia or ageusia than those without these symptoms. Fever and respiratory symptoms (shortness of breath and cough) also ranked highly in their association with test positivity. This large, collaborative analysis showed that anosmiaageusia, fever, shortness of breath, and cough are suitable empirical signals of ongoing COVID-19 transmission and could be particularly useful in regions where testing data are sparse or delayed. A prospective, iterative, surveillance data-based approach, using multiple datasets such as presented here, is likely to play an important role in other epidemiological contexts.
The survey was launched in the USA on April 6, 2020, and outside the USA on April 23, 2020. Anonymous surveys with non-missing self-reported age and sex and from respondents who did not work in a health-care setting were assessed for inclusion. Survey sampling strategies were used to increase representativeness of the source population for each nation by sampling from the Facebook active user base and ranking across census age, sex, and geo graphical region to develop survey weights. Data documentation for sampling methods have been published elsewhere. 20 Primary analyses across all cohorts represent weighted parameters. Unweighted sensitivity analyses are detailed in the appendix (pp 1-2). This study was approved by the Boston Children's Hospital Institutional Review Board (P00023700 ). The ZOE COVID Symptom Study App (ZOE) is another of the three participatory surveillance platforms providing the data used in this study, with respondents from the USA and the UK.
Research studies on data collected through the app are approved by King's College London Ethics Committee REMAS ID 18210 (review reference LRS-19/20-18210), and all participants provided consent. Through a partnership between the UK Department for Health and Social Care, tests were made available to UK users of the app upon invitation from the app maintainers (ZOE) from April 26, 2020. By design, invited app users who logged being healthy twice in 9 days followed by an unhealthy report were invited to take a COVID-19 test. All test results were analysed in this study's main analysis. Multiple tests per user were censored within the symptom window following the test or once a test resulted positive. The Corona Israel (Israel-Corona) study is the final participatory surveillance platform providing data for this study, with respondents from Israel.
Israel-Corona data were collected through a voluntary online survey that included a 1-min, anonymous online questionnaire. The survey was first published on March 14, 2020. 5 urvey responses were collected directly through the online platform. Responders were asked to report information on age, gender, geo graphical location, previous medical conditions, and whether they were a health-care worker, as well as symptoms occurring in the preceding 24 h for themselves and for each member of the family. Additionally, respondents were asked to report any SARS-CoV-2 testing and test results. This study was approved by the Weizmann Institute of Science Review Board. The Board waived informed consent as all identifying information was removed before the analysis.
To ensure privacy, Facebook users who responded to the cross-sectional survey did not contribute longitudinal data. We ran additional analyses in the US-ZOE and UK-ZOE surveys to assess the relevance of different symptoms when considering symp toms reported after a SARS-CoV-2 test, stratified by geographical region (USA vs UK), symptom onset-to-test duration (early [≤3 days] vs late [>3 days]), and periods of varying symptom criteria for testing access (narrow vs broad). We did sensitivity analyses of US-CMU/UMD data to assess the impact of illness duration on effect estimates. The primary outcome was self-reported result of a SARS-CoV-2 test (ie, positive vs negative). Tests reported as pending or result unknown were excluded. Testing counts and positive test proportions were tabulated as the number of users (ZOE) or surveys (CMU/UMD and Israel-Corona) and the ratio of test positives to total tests reported with results.
Trends in national testing data and proportion of positive tests were generally consistent with platformspecific tests reported (figure  1 ), with cross-correlations higher than 0•9 for testing (US-ZOE 0•97, UK-ZOE 0•96, US-CMU/UMD 0•99, UK-CMU/UMD 0•94, Israel-CMU/UMD 0•99) and higher than 0•8 for proportion of positive tests (US-ZOE 0•99, UK-ZOE >0•99, US-CMU/UMD 0•83, UK-CMU/UMD 0•94), except for testing (0•67) and proportion of positive tests (0•39) in Israel-Corona (the smallest study) and the proportion of positive tests in Israel-CMU/UMD (0•15). The median (range) proportion of positive tests across the six datasets was 7•05 (0•25-14•2). Although the CMU/UMD positivity proportion was higher than the national proportion (eg, US-CMU/UMD symptomatic test positivity is a sub sample of all positive tests), the trend was representative (unweighted, incident-prevalent, and outlier sensitivity analyses are shown in the appendix, pp 1-4).
For example, active invitation to test from a platform has the potential to capture individuals with symptomatic infec tion earlier than governmentinvited testing, even though symptoms of brief duration at the time of testing might be less predictive of test positivity. The importance of pharyngitis and gastrointestinal symptoms, for example, might be for individuals with multiple symptoms at presentation. We hypothesise that these findings might be due to clustering of symptoms or the phase of illness when testing was completed. The CMU/UMD Facebook active user base sampling scheme  20  might have contributed to the more stable precision, although the timing of the tests relative to onset of specific symptoms cannot be ascertained. Future directions for this type of collaboration could include discriminating COVID-19 from seasonal respiratory pathogens such as influenza,  27, 33  although few datasets  1, 2  exist from which to define discriminating symptoms a priori.
A prospective, iterative, surveillance data-based approach, using multiple datasets such as that done here, is likely to play an important role. Our study has some limitations. These findings should be interpreted with the caveat that, by its nature, realtime participatory syndromic surveillance inherently has potential biases related to, for example, generalisability and selection bias (eg, whether participants are representative of the source population, participation is differential regarding exposure or outcome, or the platforms have covariates for crucial effect modifiers), and measurement bias (eg, survey question misunderstanding, differential missing data or error in selfreporting due to incentive to record being healthy when being monitored, survey misuse, or one-time sur veys without longitudinal follow-up of future outcomes).
Acknowledgments ZOE Global provided in-kind support for all aspects of building, running, and supporting the ZOE app and service to all users worldwide. Support for this study was provided by the  National Institute for Health Research (NIHR)-funded Biomedical Research Centre based at Guy's and St Thomas' (GSTT) NHS Foundation Trust. This work was supported by the  UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare  ( 104691 ). Investigators also received support from the  Wellcome Trust  ( WT203148/Z/16/Z ,  WT213038/Z/18/Z , and  W212904/Z/18/Z ),  Medical Research Council  (MRC;  MR/V005030/1  and  MR/M004422/1 ),  British Heart Foundation ,  Alzheimer's Society ,  EU ,  NIHR ,  COVID-19 Driver Relief Fund, Innovate UK , the  NIHR-funded BioResource , and the  Clinical Research Facility and Biomedical Research Centre  based at GSTT NHS Foundation Trust, in partnership with Kings College London.
This work was also supported by the  National Core Studies , an initiative funded by  UK Research and Innovation ,  NIHR , and the  Health and Safety Executive. The  COVID-19 Longitudinal Health and Wellbeing National Core Study  was funded by the  MRC  ( MC_PC_20030 ). SO was supported by the  French Government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency  ( ANR-19-P3IA-0002 ). ATC was supported by a  Stuart and Suzanne Steele MGH   Research Scholar Award. LHN, DAD, ADJ, and ATC were supported by the  Massachusetts Consortium on Pathogen Readiness  and  M Schwartz  and  L Schwartz. CMA acknowledges funding from the  National Institutes of Health (NIH ;  K23 DK120899 ) and the  Boston Children's Hospital Office of Faculty Development Career Development Award.
In this paper, we extract spectral image features from a hyperspectral image database, and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, the LDMGI (local discriminant models and global integration) algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve the efficiency of hyperspectral image database queries. The experimental results show that the hyperspectral images of color hyperspectral images are highly consistent and indistinguishable, and need to be processed by the machine learning algorithm.
Different pretreatment methods have little influence on the identification accuracy of the LDMGI model, and the combined pretreatment has better identification accuracy. The average classification accuracy of the LDMGI model training set is 95. 62%, the average classification accuracy of cross-validation is 94. 36%, and the average classification accuracy of the test set is 89. 62%. Therefore, using big data analysis technology to process spectral features in hyperspectral image databases can improve query efficiency and more accurate query results.
Introduction In recent years, deep learning has made significant progress in the hyperspectral image classification task, but manual labeling of hyperspectral images is still time-consuming and laborious, and deep learning methods usually rely on a large number of manually labeled samples, when only a small number of labeled samples, the deep learning model is difficult to train. Therefore, using the deep learning method for hyperspectral image classification is still faced with the problem of lack of labeled training samples and deep neural network training. At present, many scholars have researched image data sets. For example, Xie t has collected a set of data sets called palette and text, which contains more than 10000 pairs of color themes and their annotation texts  [1]. Then, Lee s uses the data set to train a set of conditions to generate the countermeasure network and converts the database records input by users into several groups of color theme outputs containing five colors  [2].
In addition, Baloch g proposed a machine learning-based color theme aesthetic evaluation method  [7]. Dabov K uses 24 pre-defined color emotions to achieve image emotion transfer  [8]. By analyzing a data set containing 1000 color combinations, Othman h realizes image index based on color emotion  [9]. Luc implements image color editing based on the database record concept, but only supports 15 predefined database record concepts  [10]. However, the above research was done spontaneously by volunteers on the Internet, and the labeling task has certain requirements on the color matching level and aesthetics of the taggers. It is difficult to guarantee the quality of training data under the condition that the self standard of the tagger cannot be ensured. In this paper, we extract spectral image features from a hyperspectral image database and use big data technology to classify spectra hierarchically, so as to achieve the purpose of efficient database matching.
Hyperspectral Adaptive Database The adaptive representation of hyperspectral data is used to construct a graph to reveal the hyperspectral characteristics of data. Multi structure manifold embedding designs hyperspectral maps and hypermaps through hyperspectral representation and reveals the inherent hyperspectral structure in high-dimensional data by combining these maps. However, the hyperspectral representation model based on L1 normal form produces unstable hyperspectral representation due to the potential instability of hyperspectral decomposition. In order to alleviate this instability, some improved hyperspectral representation models are applied to hyperspectral image feature extraction. A constrained hyperspectral image is smoothed by adding a manifold regularization term to hyperspectral optimization.
A new hyperspectral optimization model is designed based on local manifold discriminant learning. The similarity weight based on euclidean distance is used to constrain the hyperspectral representation model to reveal the hyperspectral structure of data based on the local manifold. Although the above hyperspectral representation model makes the hyperspectral decomposition more stable by imposing manifold constraints, the manifold constraints depend on the quality of the predefined similarity measure matrix. The similarity measure matrix predefined by Gaussian kernel function is usually affected by noise and other factors, so it is difficult to design an appropriate predefined similarity measure matrix. To solve these problems, this paper proposes a local discriminant and global hyperspectral preserving projection algorithm based on Hyperspectral representation and learning graph regularization.
To solve the above problem, this paper adopts a set of flexible schemes, instead of the implementation of the clustering method, and fixed two of these colors as pure black and pure white, and removed them before the final output. After the above steps, this method can extract n color themes from n images, and make each color theme contain K colors. As the data comes from the Internet image database or online search platform, most of the collected images are real-life images rather than synthetic images. Therefore, among these color themes, the color theme mode with higher frequency may be expected by users. Because the colors in the color theme are unordered, two-color themes m and N need to be defined first        k j j h jh j h h j jh nb D s p s p G 2 1 1 ) ( M (4) u n y y k j k h n t n r hr ij j h 2 1 1 1 1 2 N           (5) It is used to quantitatively describe the degree to which they belong to the same color theme.
When defining this metric, we need to ensure that it satisfies not only the basic properties of the metric, but also the properties that the measurement results are independent of the order of colors in the color theme; It defines the metric of two color themes in the color theme space as     T i )) 0 , (max( color M/N   (6) Where color represents the measurement of two colors in the color space. In addition, since the output result is only a single color. The range and diversity of database records supported by this method are better than this method. To sum up, the results of the qualitative analysis show that this method is slightly better than this method in terms of the correctness and diversity of output results and the acceptable input range.
Methods In this paper, we extract spectral image features from a hyperspectral image database and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, LDMGI algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve the efficiency of hyperspectral image database queries.
Design This paper proposes a method to automatically generate color themes from database records, which supports the generation of multiple different color themes from the same database record. In this paper, we propose to use the real image as a new data source to solve the problem of color theme generation based on database records. Thanks to more diversified data, this method can support more abundant database record input than existing methods, and even for some rare input, this method can calculate reasonable results. This paper puts forward a set of quality indicators so that the final output results can be arranged in the order of good to bad, thus saving the user's search time. This method involves the training of neural networks, and the number of super parameters generated in the process reduces the workload, so it is easier to debug and analyze, and more robust.
As shown in Figure  2 , the two groups of results were analyzed qualitatively and quantitatively in terms of correctness and diversity. Among them, correctness measures whether the generated color themes are close enough to the image information contained in the input database records, while diversity measures whether the generated color themes are rich enough, that is, whether the range of color themes is wide enough. In addition, in order to improve the comprehensiveness of comparative analysis, qualitative comparative experiments are carried out between this method and Heer's method. However, due to the limited input of database records supported by Heer and other methods, the number of examples that can be processed normally is small, and the quantitative results are not statistically significant.
Due to the lack of consideration of the internal image information and structure of the image data, the performance of this method is still poor for some input database records, as shown in Figure  3. On the one hand, most of the area in the input image corresponds to the sky or land, so without saliency detection, the color theme extracted directly must be the sky and land, not the spectrum itself. On the other hand, because the spectrum is translucent, to extract the color theme of the spectrum accurately, further soft segmentation is needed to separate or remove the color of the background sky or land. Therefore, to further improve the quality of output results, the introduction of saliency detection and image soft segmentation is indispensable. This will be the future work of this paper.
Conclusions In this paper, LDMGI algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve the efficiency of hyperspectral image database queries. This method can get relatively acceptable results. Among the first five-color themes, three-color themes contain red color. In addition, because the images supported by other methods are very limited, the test input images involved in the experiment can not be processed, so this paper manually replaces the input image with the image input which is the closest in semantics and can be accepted by this method.
6 and 4. 5 µm IRAC channels were created by R. McLure and used by  Merlin et al. (2018 Merlin et al. ( , 2019)) , although no catalogue was released for the latter. 1 https://archive. stsci. edu/prepds/hlf/ This new generation of data gathered since 2013, along with the introduction of new techniques and software such as t-phot  (Merlin et al. 2015 (Merlin et al. , 2016b)) , sparked the necessity of reviewing the original catalogue. In this paper we present ASTRODEEP-GS43, a new photometric and photo-z catalogue for GS intended to yield a comprehensive set of optical/NIR photometric information on the field before the advent of the upcoming next generation datasets based on new instruments. This release builds on the previously published CANDELS catalogue, and complements similar recently published efforts such as the 3D-HST catalogue by  Skelton et al.
0 to extract new photometry from the images of ground-based medium bands and Spitzer bands, using three substantial algorith- mic improvements with respect to the standard methods of v1. 0: (i) the use of priors from the closest-wavelength highresolution band for all the medium bands, as opposed to just use priors from the H detection band; (ii) the background subtraction during the fitting process and (iii) individual locally variable PSFs for IRAC (these techniques are presented in  Merlin et al. 2016b, and are summarized in Sect. 4). Also, two of the IRAC images (CH1 and CH2) are the new mosaics, which are deeper than the ones used for the CANDELS release, so that we gain a substantial amount of detections previously catalogued as upper limits. The paper is structured as follows. In Section 2 we describe the full dataset.
Subaru SuprimeCAM and Magellan FourStar bands One of the most important additions to this release is the photometric measurements for 18 medium bands (typical width 20-30 nm) from the Subaru SuprimeCAM  (Cardamone et al. 2010)  dataset. We also included new measurements on the 5 Magellan Baade FourStar bands  (Straatman et al. 2016). We stress that we did not directly include in our catalogue any previously published photometric measurement; rather, we used t-phot to obtain consistent and homogeneous photometry across the whole spectrum (see Section 4).