dataesr/scientific-paragraphs-categorization huggingface dataset:
Participant characteristics Of the 19 couples invited, nine patients and their spouses took part in this study (participation rate = 47%). Data saturation was reached when no new information (codes) emerged during the analysis of the interviews conducted with the last two participants (the ninth couple). The participants' ages ranged from 29 to 66 years old (patients' mean age = 51 ± 11. 8 years; spouses' mean age = 50 ± 11. 4 years). Couples had been living together for 22 years on average and were all married. The most common diagnoses were acute myeloid leukemia (4 patients) and acute lymphoblastic leukemia (3 patients). Six of the nine patients were men, and HSCT duration was 2. 5 ± 1. 6 years on average (8. 5 months to 5. 5 years). Complete participant characteristics are presented in Tables  2  and 3.
Practice and research implications Research on the intimacy of couples with cancer or a severe illness is rare. Thus, it would be interesting to conduct a longitudinal mixed methods study to gain a more holistic understanding of couples' intimacy after cancer diagnosis. Questionnaires could assess relationship intimacy, sexuality, communication, and emotions at different times, as well as collect medical and demographic characteristics. For the qualitative part of the study, participants could be asked to keep a diary of their interactions with their partner. This method allows participants to share what they wish with more autonomy, and it would provide more consistent insight into the couples' lived experiences. Then, an analysis of the qualitative data with the quantitative results could help identify how several factors measured quantitatively may impact the facilitators and obstacles to intimacy.
They find that the lockdown experience increased support for current decision-makers, institutions and regimes. By contrast, they do not find any effect of the lockdown on ideology or political interest, but a small positive effect on declared turnout rates. Our study contrasts with previous ones, as we focus on objective real-world data covering millions of voters to study the impact of the COVID-19 pandemic on electoral participation. We study French municipal-level data for two different municipal elections, contrasting 2020 with 2014. We focus on municipalities with more than 1,000 inhabitants located in metropolitan France as the voting system differs significantly below this threshold. Paris, Marseille, and Lyon, as well as Corsica and overseas territories, are dropped from the study for similar reasons.
Determinants of turnout in times of COVID-19 Since we use aggregate data (although at the municipality level, which is the finest aggregation level in France), we measure the cost and likelihood of being infected as follows: First, to gauge the perceived risk of being infected, we focus on the seven COVID-19 clusters in metropolitan France mentioned by popular media as of March 15 (figures in parentheses indicate the zip code of the city département): Auray (56), Bieville-Beuville  (14) , Bruz (35), Crepy-en-Valois (60), La Balme de Sillingy (74), Mulhouse (68), and Mery-sur-Oise (95). Fig 2 shows the location of the seven COVID-19 clusters according to the media at the time of election as well as the number of COVID-19 cases in France per département. We then calculate the distance between each municipality and the nearest COVID-19 cluster and use it as a proxy of perceived exposure to the virus in the locality.
Considering the available multimodal features from microblogs, notably, we propose in this paper an end-to-end model called deepMONITOR that is based on deep neural networks, by utilizing all three characteristics: post textual and image contents, as well as sentiment. deepMONITOR concatenates image features with the joint text and sentiment features to produce a reliable, fused classification. We conduct extensive experiments on two large-scale, real-world datasets. The results show that deepMONITOR achieves a higher accuracy than state-of-the-art methods.
This architecture combines the advantages of Convolutional Neural Network (CNN) for extracting local features and the memory capacity of Long Short-Term Memory Networks (LSTM) to connect the extracted features well. Second, we employ the pretrained VGG19 model  [26]  to extract salient visual features from post images. Image features are then fused with the joint representations of text and sentiment to classify messages. Eventually, we experimentally show that deepMONITOR outperforms state-of-the-art rumor detection models on two large multimedia datasets collected from Twitter. The remainder of this paper is organized as follows. In Section 2, we survey and discuss related works. In Section 3, we thoroughly details the deepMONITOR framework. In Section 4, we experimentally validate deepMONITOR with respect to the state of the art.
Datasets To provide a fair evaluation deepMONITOR's performance, we conduct experiments on two real-world social media datasets collected from Twitter. Let us first detail both datasets. FakeNewsNet  [24]  is one of the most comprehensive fake news detection benchmark. Fake and real news articles are collected from the fact-checking websites PolitiFact and GossipCop. Ground truth labels (fake or true) of news articles in both datasets are provided by human experts, which guarantees the quality of labels. We consider that all the tweets that discuss a particular news article bear the truth value, i. e. , the label of the article, because it contributes to the diffusion of a rumor (true or false), even if the tweet denies or remains skeptical regarding the veracity of the rumor. Since we are particularly interested in images in this work, we extract and exploit the image information of all tweets.
We first remove duplicated and low-quality images. We also remove duplicated tweets and tweets without images, finally obtaining 207,768 tweets with 212,774 attached images. We carefully split the training and testing datasets so that tweets concerning the same events are not contained in both the training and testing sets. DAT@Z20 is a novel dataset we collected from Twitter. More concretely, we retrieve all statements and reports of various nature verified by human experts from a fact-checking website; specifically contents published on June 1 st , 2020. To guarantee a high quality ground truth, we retain only the data and metadata from 8,999 news articles explicitly labeled as fake or real. To extract tweets that discuss news articles, we create queries with the most representative keywords from the articles' abstracts and titles.
Then, we refine keywords by adding, deleting or replacing words manually with respect to each article's context. We use the Twitter API to obtain the searched tweets by sending, as arguments, the queries prepared previously. Moreover, we employ Twitter Get status API to retrieve the available surrounding social context (retweets, reposts, replies, etc. ) of each tweet. Since we aim to build a multimedia dataset with images, we collect both the tweets' textual contents and attached images. Thus, from the 2,496,982 collected tweets, we remove text-only tweets and duplicated images to obtain 249,076 tweets with attached images. Finally, we split the whole dataset into training and testing sets and ensure that they do not contain any common event. Tweets take the label of the news articles they refer to, for the same reason as above.
Experimental Settings To learn a textual representation of tweets, we use the pretrained GloVe word embedding model  [22]  after standard text preprocessing. We obtain a k = 50dimensional word embedding vector for each word in both datasets. One reason to choose the GloVe model is that the embedding is trained on tweets. We set the Text-CNN network's filters number to d = 32 and the window size of filters to {4, 6, 8}. We extract 14 sentiment features from both datasets (Table  2 ). The hidden size of the fully connected layer of sentiment features is 32. The joint representation of text and sentiment uses a first LSTM with hidden size 64 and a second LSTM with hidden size 32. Image features come from the output of the antepenultimate layer of the pretrained VGG-19 model, to generate a 4096-dimensional vector.
This vector is fed to a fully connected layer with hidden size 32. The final multimodal feature representation is fed into a fully connected layer with hidden size 10. deepMONITOR uses a batch size of 64 instances. In our experiments, each dataset was separated into 70% for training and 30% for testing. The number of iterations is 100 in the training stage with an early stopping strategy on both datasets. The learning rate is 10 -2.
Textual features are indeed more transferable and help capture the more shareable patterns contained in texts to assess the veracity of messages. The reason is probably that both dataset have sufficient data diversity. Thus, useful linguistic patterns can be extracted for rumor detection. 4. Visual information is more important than sentiment information. Although images are challenging in terms of semantics, the use of the powerful tool VGG19 allows extracting useful features representations. 5. The performance achieved with sentiment information is the worst among multimodal variants, because without textual and visual contents, the actual meaning of tweets is lost. However, its contribution is non-negligible since the use of sentiment features (deepMONITOR-vs. deepMONITOR) can improve accuracy by 6% and 4% on FakeNewsNet and DAT@Z20, respectively.
LRCN Analysis In this subsection, we analyze the importance of the LRCN component from the quantitative and qualitative perspectives. Quantitative Analysis From deepMONITOR, we design two new models, removing the text-CNN in the first (deepMONITOR-CNN), and the two LSTM networks in the second (deepMONITOR-LSTM). Then, we run the two models on the FakeNewsNet dataset. Figure  6  displays the results in terms of F 1 score and accuracy. Figure  6  shows that both accuracy and F 1 score of deepMONITOR are better than those of deepMONITOR-CNN and deepMONITOR-LSTM.
Qualitative Analysis To further analyze the importance of the LRCN component in deepMONITOR, we qualitatively visualize the feature representation C T S learned by deepMONITOR, deepMONITOR-CNN and deepMONITOR-LSTM on the testing data of FakeNewsNet with t-SNE  [19]  (Figure  7 ). The label of each post is fake (orange color) or real (blue color). We can observe that deepMONITOR-CNN and deepMONITOR-LSTM can learn discriminable features, but the learned features are intertwined. In contrast, the feature representations learned by deepMONITOR are more discriminable and there are bigger segregated areas among samples with different labels. This is because, in the training stage, the.
Conclusion In this paper, we propose deepMONITOR, a deep hybrid model for rumour classification in microblogs. The model extracts and concatenates textual, visual and sentiment information altogether. For a given message, we first fuse text and emotional signals with an LRCN network, which is an appropriate architecture for problems that have a 1-dimension structure of words in a sentence, such as microblog posts. This joint representation is then fused with image features extracted from a pretrained deep CNN. Extensive experiments on two large-scale dataset collected from Twitter show that deepMONITOR outperforms state-of-the-art methods. A future line of research is to further investigate the contribution of sentiment features in the detection of rumors. Dedicating a deep submodel for learning such features instead of using our current, lexicon-based approach could indeed further improve the performance of deepMONITOR.
Earthquake Without Co-seismic Slope Failure Based on the Global Historical Tsunami Database (NCEI-NOAA), there are actually no historical record describing tsunamis exceeding 8-10 m runup anywhere in Tonga produced by either the largest historically known regional earthquakes (up to MW 8. 4) or by far-sources teletsunamis. Computer simulations of earthquake-triggered tsunami along the Tonga-Kermadec Trench (TKT: Figure  1 ) were performed by  Frohlich et al. (2009). The authors selected a worst case earthquake scenario of Mw 9, with a slip of ∼30 m along faults and dimensions ∼120 km × 1,000 km. Simulations provided peak-to-trough amplitudes of ∼12 m on the west side of Tongatapu. This value is at the lower limit of the wave heights needed to move the largest Fahefa boulders, e. g. Maui Rock, making this source possible, but quite unlikely without a local coseismic submarine landslide.
Pyroclastic Density Currents (PDCs) Entering the Sea Located north of Tongatapu, Tofua volcano (Figure  1 ) displays a 5 km-wide caldera with steep inner walls rising up to 200 m above sea level. This island has been mantled by thick ignimbrite, which form high pumice cliffs in the south slope of the volcano. Radiocarbon dating of a carbonised tree trunk recovered from the base of the ignimbrite sequence gave an age of 970 ± 50 years BP  (Caulfield et al. , 2011)  corresponding to a calendar age range of 1,044-1158 CE (using CALIB 8. 2), which is inconsistent with a 15th century tsunamigenic eruption. In addition, numerical modelling performed by one of the coauthors (KK) using VOLCFLOW code show that voluminous PDCs or debris avalanche entering the sea from the south flank of Tofua do not provide tsunami waves higher than 15 m at Tongatapu and only 5 m at the SE shore where the highest boulders have been described (SI Appendix, Supplementary Figure  S3 ).
ACKNOWLEDGMENTS A special thanks to the  LabEx DynatiTe  which made this project possible. All samples analyzed in the study were collected under a Research Permit issued to DVB by the  Prime Minister's Office ,  Government of Tonga, Nuku'alofa, Tonga. We acknowledge the people of Tonga who provided useful information in the field. Dallas Abbott  for her information concerning the potential meteor crater in the Southwestern Pacific.
FUNDING The field trip of the French team was carried out in the frame of the  Cluster of Excellence (LabEx) "DynamiTe ", with reference  ANR11-LABX-0046  ( SarDyn Program TongaPag ), which is funded by the  French National Agency for Research. This field trip was co-funded by the  Laboratory of Physical Geography ,  UMR 8591 CNRS-Paris 1-UPEC. Field work of the Tongan team was funded by the  Tongan Ministry of Land and Natural Resources, Natural Resources Division. Laboratory analysis was funded by the  Institut Universitaire de France  in the frame of the  IUF program  of FL CV was funded by  VEILA  project  H2020-MSCA-IF-2017.
The lower an individual's socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection.
Low SES infection burden is spatially heterogeneous, and high in the southeastern US Our results thus far characterize the mechanistic role that social and healthcare-based factors play on influenza burden in low SES populations in data-driven controlled experiments. Here, we aim to characterize how macroscopic factors impact influenza dynamics in low-SES populations, integrating our theoretical findings with population-level data. For population-level influenza data, we used medical claims of ILI at the county level in 25 states in the US, based on sufficient data availability. This data stream has been demonstrated to provide enhanced surveillance opportunities for influenza-like illness  [31, 35]. However, when we compare county ILI burden with the proportion of the county's population of low SES, we find a negative relationship, indicating lower levels of influenza in counties with larger low SES populations (S35(A) Fig).
This pattern is counter to our previous mechanistic model findings and to past small scale studies, suggesting that there may be measurement biases in these surveillance data (S35(B) Fig). To better estimate influenza burden in low SES populations, we fit a Bayesian spatial hierarchical model that accounts for measurement biases and borrows information from spatial covariates pertaining to low SES individuals and the mechanistic modeling experiments. Here, we define low SES ILI as an incidence ratio, where low SES ILI cases are normalized by the number of 1000 visits, to account for spatio-temporal variation in database coverage and healthcare-seeking. Thus, the model outcome data is the rate of ILI healthcare visits per 1000 healthcare visits within each county. Our model estimates of this low SES ILI incidence ratio show a positive relationship with low SES population size (S35(C) Fig) , and allow us to consider spatial disparities in influenza burden.
The model also produced a set of estimates for the effect of each hypothesized ecological measure. We find that low sickness absenteeism and high susceptibility are significantly associated with influenza in low SES populations. This supports our previous finding that multiple mechanisms compound to result in disproportionate low SES influenza burden. Low SES absenteeism is here measured by student absenteeism, which may not be a perfect measure of sickness absenteeism or paid sick leave access. However, other fine-scale data was lacking, and a student's ability to be absent is related to a parent's ability to be home to care for the child, and differences in access to paid sick leave by SES have been related to student sickness absenteeism levels due to influenza  [38] [39] [40] [41]. To validate our findings, we compared our model estimates to previous estimates of influenza incidence ratios, stratified by poverty level.
This is not a direct comparison, as previous studies present the incidence ratios for the entire population, not just for low SES individuals within those populations. Our results show more consistently high incidence ratios compared to the larger increases between poverty rates in prior studies. We attribute this to the incorporation of the measurement process into our models, which accounts for undersurveillance of low SES infection, whereas healthcare access and healthcare seeking differences may have missed low SES cases in prior studies. Ideally, data on respiratory infection of low SES individuals would be available at a fine spatial scale to more directly assess the validity of our models, but the lack of such a dataset highlights the need for future surveillance and data collection that focuses attention on lower SES populations.
Our work has several limitations. The network structure of our epidemiological model is based on one social survey from 2007 in Europe, and may be less representative of the United States today. Additionally, survey data was not collected for the SES of the contacts of survey participants, which required us to make assumptions which could affect our results about SES assortativity. Additional social contact data collection across the United States that accounts for SES heterogeneity would be useful for future studies given the large socioeconomic inequality in the country  [42, 43]. In our spatial ecological model, we assume that disproportionate burden in low SES populations remains constant over influenza seasons. While there may be variation in the dynamics of ILI among low SES populations over time, time-varying data on our covariates is currently unavailable and we do expect socioeconomic factors and health behaviors to remain relatively consistent across seasons.
Future work could focus on temporal variation in low SES ILI dynamics. Additionally, our spatial ecological model is only able to provide estimates for half of the states in the US, and the states are mostly on the coasts. This highlights the need for more data collection pertaining to low SES individuals, not only for epidemiological data, but also for a wide variety of other topics to provide covariate data and to create a better understanding of at-risk populations. A main limitation of the spatial inferential model is the identifiability of separate effects. Here, we have identified possible associations in our model, but this is only the start to disentangling the factors that contribute to health inequities. The lack of significant association with the other incorporated covariates does not indicate that these are not important to inequities in influenza transmission in low SES populations.
These impacts may be obscured by several issues. The covariate data may be impacted by its own biases, insufficient sample sizes, and other limitations. When ubiquitous systemic inequities go unaccounted for in data collection and processing, the signal of low SES individuals may be obscured. We aimed to counteract this by only using covariate data specific to low SES populations, but this was parsed out from data collected for the whole population that included demographic data, identifying potentially lower SES individuals. Next, there may be other factors relating to increased influenza transmission that may not be identifiable when focused on mechanistic explanations, and the model may not be able to parse synergistic factors. Our network epidemic model demonstrates that multiple factors of inequity can compound one another non-linearly, and statistically identifying individual effects remains a challenge due to lack of data and statistical limitations.
Materials and methods In this study, we use (1) a mechanistic network epidemiological model to assess influenza transmission in the presence of individual-level socioeconomic status (SES)-based social and healthcare-based variation; and (2) an inferential spatial model to geographically localize influenza-like illness (ILI) burden among low SES populations in the presence of population-level variation in social and health indicators. Data and code for the implementation of these methods are available at  [45].
An egocentric ERGM allows for the construction of sociocentric networks based on egocentrically sampled data, in which participants (or egos) report the identity of their contacts (or alters), who may or may not be study participants. Our egocentric ERGM model was based on the POLYMOD dataset, a large, egocentric contact survey that identifies close interactions of over 7000 individuals across eight European countries  [34]. Nodes in the network had the following attributes: (a) age, grouped as infants-toddlers (age 0-4), school-aged children (age 5-18), adults (age 19-64), and elderly (age 65-100); (b) sex, classified as male or female; (c) contact location, in which a node can have known home contacts and known school or work contacts; (d) education level as a proxy for socioeconomic status  [33] , grouped as low education (less than a high school education), medium education (high school or vocational school education), or high education (any university education or beyond).