dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis was performed in IBM SPSS Statistics 25 using the nonlinear regression function and in MATLAB R2014a (MathWorks, Natick, Massachusetts, USA) for computing the error bounds on the modelled regressions. To compute the regression error bounds, we perturbed the parameter estimates 10,000 times from a set of parameter values randomly chosen from a normal probability distribution with mean and standard deviation equal to the estimated value and its standard error, respectively. Therefore, when the modelled curves do not fall within another curve's uncertainty bound, this indicates a significant difference between lines at P < 0. 05. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by  Leverhulme Trust  project grant  RPG-2013-260  to C. S. G. , T. A. Q. and T. B. L. that supported S. D. B. , K. M. S. , L. W. and B. H. , as well as a BBSRC SWDTP PhD studentship to T. D. G. (funded by  BBSRC  grant  BB/J014400/1 ) a  BBSRC  SWBio PhD studentship to B. M. E. (funded by  BBSRC  grant  BB/M009122/1 ), and an  EPSRC  BCCS PhD studentship to A. M. (funded by  EPSRC  grant  EP/E501214/1 ). A  University of Exeter  award to T. A. Q. co-funded S. D. B. We are indebted to  Don Grierson FRS  and  Enrico Coen  FRS who independently suggested the centrifugation assay. We thank the  Dolan and Schiefelbein laboratories and Nottingham Arabidopsis Stock Centre  for seed stocks. We also thank workshop staff at the  Universities of Bristol and Exeter  for constructing the flume and plant growth boxes.
) the leadership models certainly deserve even more attention. Another direction of development of the opinion dynamics models was brought by combining them with studies of sentiment (or emotion) in interpersonal communications, especially those conducted via electronic means. The development of effective sentiment analysis software, such as SentiStrength  [87, 88]  has allowed to classify medium-and large-scale datasets of Internet discussions and social media activities. It has been found that emotions not only "accompany" exchanges of opinions but also, in many cases, determine the outcomes of such exchanges and shape the communication processes. Starting from these observations, ABMs including emotions as parameters allowed to reproduce a wide range of characteristic features of communications on a wide range of topics  [89] [90] [91] [92] [93] [94].
Is there any threshold above which the impact of such driving event becomes (semi-)permanent? Is the decrease of importance mainly due to a gradual memory fading, or to competition with more recent, and equally "important" events? How do individual memory effects "translate" into collective memory? What processes govern habituation to events spanning long periods of time? As the above example shows, the "relaxation" model does not need to be very complicated. Moreover, because of the dominance of social networks in large-scale communications, we may expect that there would be ample empirical data to compare the models with reality. There are excellent examples of recent works exploring this direction  [115] [116] [117].
Ethical Dilemmas One of the lessons of the recent decade is that the availability of increasingly detailed data on our behaviors, and the tools to analyze it open the way not only to understand social behaviors but also to monitor (often in real time) and manipulate behaviors, both on an individual level and for social groups. The tools initially focused on the data stored in the records of our Internet activities (some of which we provide voluntarily, some of which are collected without our knowledge, thought with formal approval 1  ). Today, the social networks and Internet activity datasets are (may be) coupled with records from CCTV monitoring, face and speech recognition algorithms, mobile phone data, including geographic positioning, etc. The advances in AI technologies further improve the whole system capacity to record and analyze our patterns of behavior.
The same increased attention to the polarization origins and mechanisms has been present in the social sciences. For example, Ref. 114 discuss the challenges in understanding the datasets obtained from "big data" mining. As they rightly state, the access to incredibly rich data from social networks, while alleviating many of the problems present in the traditional social science methods, has not brought a deep understanding of the social and political phenomena. One of the indicated reasons, in our opinion the most important one, is the need to provide conceptual foundation in which the huge volumes of data could be understood. This actually is a huge change for models, which could provide a "test and understand" framework. Unfortunately, the problems present in the empirical description of political phenomena confirm the intuition that the process may be quite complex and depend on many factors, many of which have not been incorporated into the modeling frameworks.
CoRoT-30 CoRoT-30 is one of the 5 722 target stars observed by CoRoT for 81 days during the seventh long run in constellation Aquila from 2011 April 5 to 2011 June 30 (LRc07 for short). This star belongs to the subset of 3401 MON stars for which a single LC is available (MON stands for monochromatic). Figure  1  (top) shows the photometric mask used by CoRoT to produce the LC superimposed on a Palomar Observatory Sky Survey (POSS) image of the sky. The stars were identified by query of Exodat, the CoRoT entry database  (Deleuil et al. 2009). Because the fluxes of several neighboring stars leak into the photometric masks (Fig. A. 1), their contributions were computed based on the procedure described by  Bordé et al. (2010) , but improved in the following way: the models for the stellar point spread functions (PSFs) were chosen according to the stellar colors.
2. Normalized light curves of  and  corrected for the contamination by the neighbors. The sampling period changes from 512 to 32 s after 31. 8 days for CoRoT-30 and 48. 5 days for CoRoT-31. An LC that is evenly rebinned at 512 s is overplotted in black. sizes are scaled along the seeing variations during a time series, or uses fixed apertures throughout. Both methods were tried, and the better light curve, shown in Fig. 4 , is based on fixed aperture sizes and was derived from a comparison with eight reference stars. This software has been used extensively during the photometric follow-up of the CoRoT mission and is considered as robust. Because of the poor weather conditions, the curve is relatively noisy, with an estimated point-to-point scatter of about 3 mmag, and a red component on a timescale of about 1 h and an amplitude of about 5 mmag.
Spectral analysis We derived the spectroscopic parameters of the host stars from the coadded HARPS spectra, which have an S/N per pixel at 550 nm of about 30 (CoRoT-30) and about 25 (CoRoT-31). The coadded spectra were obtained by shifting the HARPS spectra of CoRoT-30 and CoRoT-31 to the laboratory rest frame using the measured velocities and the known barycentric Earth RV at the time of the observation, and combining only those that were not contaminated by the scattered moonlight. Following the procedures described in Fridlund et al. (  2010 ) and  Gandolfi et al. (2015) , we carried out two independent analyses. The first analysis relies on the use of a customised IDL software suite, which fits the observed echelle data to a grid of theoretical model spectra calculated with the program SPECTRUM  (Gray & Corbally 1994).
We used ATLAS9 planeparallel model atmospheres  (Kurucz 1979) , assuming local thermodynamic equilibrium (LTE) and solar atomic abundances as given in  Grevesse & Sauval (1998). The second method relies on the use of the semi-automatic package spectroscopy made easy (SME, version 2. 1;  Valenti & Piskunov 1996; Valenti & Fischer 2005). SME is especially designed to determine photospheric stellar parameters from the match of the observed data to the synthetic spectra generated from the parameterized atmospheres. It uses a very large set of 1D LTE plane-parallel model atmosphere grids  (Kurucz 1993 (Kurucz , 2013;; Hauschildt et al. 1999; Gustafsson et al. 2008). We adopted the calibration equations from  Bruntt et al. (2010)  to estimate the microturbulent (v micro ) and macroturbulent (v macro ) velocities.
Data modeling We used the PASTIS package  (Díaz et al. 2014)   For both datasets, the log-likelihood function was constructed assuming uncorrelated normally distributed additive errors of two independent types: the first error, with known variance σ 2 i , describes the well-characterized instrumental noise for measurement i, and the second error, with unknown variance 2 identical for all measurements, describes all remaining unexplained effects of astrophysical (e. g. , stellar variability) or instrumental origin: ln L = - 1 2 n p + n v ln(2π) - 1 2         n p i=1 ln σ 2 p,i + 2 p + n v j=1 ln σ 2 v, j + 2 v         - 1 2 χ 2 p + χ 2 v , where n is the number of measurements in each dataset, where p and v is the subscript for photometric and velocimetric data, and where χ 2 = n i=1 r 2 i σ 2 i + 2 , with r i the residuals.
The MAP models for the transits and RVs are shown in Figs. 3  and 7 , respectively. There is a clear outlier in the CoRoT-31 b data set. Removal of the outlier leads to a larger amplitude of 123 ± 20. This value is in agreement with our determination, constituting a difference of less than 1σ when normal distributions are assumed for both determinations. As expected, the precision on the semi-amplitude improves from about 40 to 16%. However, we did not find any reason to remove it from the analysis: its S/N, moonlight contamination level, and other proxies where all nominal, and we therefore decided to retain it.
1997a,b)  to the photometric magnitudes. The theoretical photometric data were calculated as follows. Filter transmission curves for the relevant filters were retrieved from the literature. We multiplied each of the filter transmission curves by the model spectrum that corresponded A122, page 7 of 17 A&A 635, A122 (2020) Notes. For the LC and RV combined analysis, the given values are the MAP and the standard deviation of the marginal posterior distribution of the parameters. In brackets we list the 95% highest density intervals (HDI) for the same distributions. HDI is defined as the interval containing 95% of the distribution mass, such that no point outside the interval has a higher density than any point within it. to the solar spectrum, scaled by 4 3 π R 2 , to obtain the total flux f λ , where λ refers to the filter band.
Acknowledgements. R. F. D. acknowledges the  Swiss National Centre for Competence in Research "PlanetS"  supported by the  Swiss National Science Foundation (SNSF). The  German CoRoT team (TLS  and  University of Cologne ) acknowledges  DLR  grants  50OW0204 ,  50OW0603 ,  50OW1403 , and  50QM1004. S. C. C. B. acknowledges support from  Fundação para a Ciência e a Tecnologia (FCT)  through national funds and by  FEDER  through  COM-PETE2020  by these grants  UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672  and  PTDC/FIS-AST/1526/2014 & POCI-01-0145-FEDER-016886 ; and also acknowledges support from  FCT  through  Investigador FCT  contracts  IF/01312/2014/CP1215/CT0004. This research has made use of the ExoDat Database, operated at  LAM-OAMP, Marseille, France , on behalf of the  CoRoT-Exoplanet program , of  NASA 's Astrophysics Data System, of the Python language with the Matplotlib plotting library and the Scipy scientific computing tools, as well as of Jupyter notebooks for interactive data science and scientific computing.
The activity degree provides information about the "social addiction" of a vehicle (i. e. , how social a vehicle is in a VSN), mainly depending on how many messages it uses to send when connected to a vehicular social network and its willingness to communicate. As expected, we observe a Pareto distribution of social vehicles, meaning that most nodes present a low social degree, while only few vehicles have a high social degree; 3) Data from each vehicle (i. e. , the activity degree) are collected through an edge computing-based architecture, where a central node (i. e. , a Road Side Unit) gathers such information to be processed and shared within the network. This paper is organised as follows. Section II presents an overview of the main techniques for message forwarding based on vehicle social behaviour.
In  [12] , da Cunha et al. study social metrics in order to characterise vehicle mobility in a realistic dataset. Mobility in VSNs can be modeled as a social network following the laws of the degree distribution and the short distance among nodes. It was also possible to verify that the use of social metrics can help in improving the performance of protocols, network infrastructure, and to propose new services to assist the drivers. How to define which social metric efficiently models the activities of members in social networks remains a challenge. Traditional social metrics (i. e. , closeness, betweenness, and bridging) are application-oriented, usually exploited in the design of routing protocols in VSNs while achieving higher delivery ratio, and shorter end-to-end delays. In  [13] , a node is selected as next hop not only according to traditional greedy approaches (i.
One characteristic common to all the previous works is the focus on selecting the best vehicles for rebroadcasting data messages according to social metrics, like the clustering coefficient and the node degree. More generally, it is possible to define a social degree that allows the most appropriate vehicle for data forwarding to be selected in order to maximise network performance. A tentative definition of social degree has been proposed in  [18]  as a score that evaluates the probability that within a given time period, a vehicle can establish a reliable social connection with a neighbouring vehicle. In  [19] , Neto et al. consider the Waze dataset and investigate the posting behaviour of users in vehicular social networks. They observe the influence of speed and delay on the posting activity of vehicles so that most contributions to the vehicular social network are triggered during rush hours, mainly on weekdays.
The major trend to make social networks available among vehicles is Mobile Social Software (MoSoSo)  [29]. MoSoSo is a class of mobile applications related to the concept of mobile Internet, with the aim of supporting social interactions among interconnected mobile users for data sharing. Commercial applications of VSNs have recently started emerging in the daily routine of vehicles. As an example, the Twittermobile car developed by Ford  [30]  allows Twitter messages to be sent containing information ranging from the driver's mood to real-time traffic warnings. Similarly, traffic related voice tweets can be shared through NaviTweet  [31] , such that the driver's preferences can be incorporated into the navigator's route calculation. RoadSpeak  [22]  is a voice chat system used by daily driving commuters or a group of people who are on a commuter bus or train.
Information about the social degree of each vehicle is spread within a cluster through a V2I link to a Road Side Unit (RSU). Each vehicle shares its information about activity degree, which is updated in a given time interval (e. g. , every 3 minutes in a 30-minutes journey). The RSU can then relay the received information on to other vehicles in the network. In our architecture, a message can be (i) a request for processing or data analysis, (ii) an action trigger, or (iii) a collection of data. Messages are interchanged among nodes that belong to either the same island or to other islands. A request for data analysis or processing indicates the need for data intensive processing over a data-set. In our scenario, the activity degree of the i-th node is computed. An action trigger can be a request to, if certain conditions hold, elect a node as a social node or hub.
g. the Nutritional Phenotype database (www. dbnp. org)] in order to allow joint data analyses at the individual level from multiple nutrition studies. Within the context of the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI), a metadata was built including the minimal information to connect existing and future studies and increases data sharing  [26]. All the efforts will facilitate the integration of data from different sources in order to identify lifestyle determinants at multiple levels in the context of SM.
Electronic health records EHRs generally describe an extremely secure healthcare database comprising patient personal information, healthservice encounter information, medical histories and diagnoses, management/treatment details, copies of clinical correspondence (such as referral and discharge letters), lab test results, imaging and other specialist investigation data. They are notoriously heterogeneous in their representations and include numerical and categorical values, datetime objects and natural language free-text. The wealth of information contained within free-text sections is especially unstructured and prone to individual clinician writing styles and abbreviations. Current progress towards integrating EHRs with the demands of data analysis is still at the developmental stage. OpenEHR  [31]  offers a series of data architectures and standards that have been developed to maximize interoperability and is under consideration for adoption (at least partially) in the future planning of a number of healthcare providers.
DL and SM Given the numerous applications of classical statistics and data mining in medicine over the past decade  [2, 4] , one may argue why a new paradigm, i. e. the DL one, is required. To tackle this question, we here consider a prototypical example, involving the creation of a model for personalized Parkinson's disease (PD) risk estimation. PD is the second most common age-related neurodegenerative disease after Alzheimer's disease (AD), with an average onset at 55 years, and with symptoms including tremor at rest, rigidity, slowness or absence of voluntary movement, postural instability, and freezing episodes  [35, 36]. A key question is still to be answered: can the risk of developing PD be assessed on a personal basis? In other words, given all data that can be collected from a person, can a personalized risk probability be synthesized? As we will here see, the complexity of the problem implies that no simple answer can be manually created, and the nature and structure of the data encoding such answer prevent a solution based on classical data mining algorithms.
• Classification which is performed based on the output from the convolutional and pooling layers using a fully connected network. While CNNs require a large amount of annotated data for its proper learning and interpretation of features constructed proves challenging due to its black box nature  [68] , they have revolutionized the field of computer vision and have been applied to a wide variety of tasks such as medical image segmentation and diagnosis each achieving remarkable performance  [69, 70]. Examples include a CNN-based computer-aided detection system developed for detection and classification of lesions in mammograms without any human intervention  [71]. The proposed method achieved the state-of-the-art performance on the public INbreast database with AUC = 0. 95 and the second place in the Digital Mammography DREAM Challenge with AUC = 0.
Based on a DL strategy, Xu et al. [79]  introduced a Stacked Sparse Autoencoder to identify distinguishing features of nuclei on high-resolution breast cancer histopathology images. An improved F-measure 84. 49% and an average area under Precision-Recall curve 78. 83% have been achieved. A three-layer of denoising autoencoder was implemented within a novel framework called 'deep patient' used to infer a set of generate features from a large-scale EHR database to facilitate clinical predictive modelling  [80].
Examples include python-based Keras  [88] , C++based Caffe  [89]  and TensorFlow  [90] , and Deeplearning4j in Java (https://deeplearning4j. org/). One of potentially serious problems when applying DL is overfitting especially when sufficient amount of adequate training is not available. Common techniques to reduce overfitting include the use of regularization  [91]. Examples include weight regularization and the dropout approach introduced in 2014  [92]. Parameter sharing in which a set of parameters are shared across layers is another approach for controlling the complexity of a DL model  [93].
A DNN-MDA approach has also been shown of interest in determining important variables in complex datasets, in the context of biomarker discovery  [98]. Then, DL has shown its powerfulness to explore structural relations between annotated metabolites or proteins, using structural-similarity scoring  [99] [100] [101]. Finally, Hierarchical multi-label DL was applied to predict enzyme function that can be of great interest for new enzyme design or enzyme-related disease diagnosis  [101]. Multidimensional and multiscale data integration is of major interest to model complex biological systems. Using either statistical methods (e. g. correlations), functional analyses or meta-analyses from different studies, they are generally performed to investigate multiscale relations within systems or validity of links between multi datasets across various health status conditions  [102].
Biomarker identification DL has been widely applied in medical image analysis  [105]  in particular to replace known classifiers and identify new biomarkers  [106]. Also, DL algorithms have been used to develop Downloaded from https://academic. oup. com/bib/article/22/2/1543/5983437 by KIM Hohenheim user on 21 April 2022 an accurate biomarker of chronological age using eye cornea images  [107]  and also applied in neuroimaging to identify biomarkers of brain aging using CNNs  [108]. In addition, DL methodologies can help tease out correct combinations of proteins/genetic signatures that can differentiate between different patient groups from large datasets (Figure  6 ). Although the field is still in its infancy, some studies have started to apply the framework. For example, a recent study on atrial fibrillation (AF) integrated genomic, epigenomic and transcriptomic datasets to identify AF-related genes  [109].
Disease classification DL has been extensively applied for disease classification, particularly in cancer research. Tran et al. applied DL to identify subtypes from breast cancer gene expression data but also the activity of key transcription factors  [101]. Interestingly, this study showed that the deep architecture trained on one dataset could extract the same biological features in other datasets acquired with different technology. DL models also allowed multi-omics integration for identifying survival subgroups of hepatocellular carcinoma  [113]. More recently, the DL approach was applied with the same objective to metabolomics data, as an alternative to ML methods. Alakwaa et al. [114]  showed the higher accuracy of the DL model to predict oestrogen receptor status in breast cancer using a public dataset than when using SVM and RF methods.
Moreover, the interpretation of hidden layers allowed identifying eight underlying pathways. In all these publications, DL was undoubtedly of major interest both for an integrative classification of disease subtypes from omics data, but also in terms of interpretation. The increasing availability of large clinical datasets and medical insurance data with diagnosis and treatment details opened the opportunity to map diseases comorbidities  [115]. One of the most interesting papers, which are a motivation behind this work, is the human disease network  [116]  in which a scalable DL approach was adopted to forecasting disease trajectories over time. The human disease network consists of disorders and diseases linked by the known disorder-gene associations, which offers a platform to explore in a single graph-theoretic framework all known phenotype and disease-gene associations.