dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors gratefully acknowledge two anonymous referees for their helpful comments which resulted in a substantially improved version of this article, as well as  Antoine Usseglio-Carleve  for pointing us to the cubature package in R for numerical integration tasks. T. Mao  gratefully acknowledges the financial support from  Natural Science Foundation of Anhui Province  (No. 2208085MA07 ) and the  National Natural Science Foundation of China  (No. 71921001 ). G. Stupfler gratefully acknowledges support from the  French ANR  (grant  ANR-19-CE40-0013 , the  EUR CHESS  under grant  ANR-17-EURE-0010 , and the  Centre Henri Lebesgue  under grant  ANR-11-LABX-0020-01 ), from an  AXA Research Fund   Award  on " Mitigating risk in the wake of the COVID-19 pandemic ", and from the  TSE-HEC ACPR  Chair.
Empirical Results In a comprehensive empirical analysis, we compare our proposed modeling approach with classical methods from the literature. We consider two datasets, the 49 daily industry portfolios (FF49) from the Kenneth R. French homepage and the 42 hourly cryptocurrencies from the Bitfinex homepage such that at least half a year of observations are available; see Table  3  in Appendix C. We excluded the cryptocurrencies that exhibit almost no variation over time, like DAI. In the case of the FF49 dataset, we consider the Fama-French three factors (FF-3), the Fama-French five factors (FF-5) and an additional Momentum factor (FF-3 + MOM and FF-5 + MOM). So the common risk factors include the excess return on the market factor, which includes all NYSE, AMEX, and NASDAQ firms (Mkt-RF), the high minus low factor (HML), the conservative minus aggressive factor (CMA), the robust minus weak factor (RMW), the small minus big factor (SMB) and the momentum factor (MOM).
We also would like to emphasize that pooling the data as we do here tends to drive up kurtosis values, whereas they appear closer to Gaussian for many of the much shorter moving windows for the FF49 data. On the other hand, the cryptocurrencies appear to exhibit heavy tails also in rolling windows. However these remain heuristic arguments, as assessing the maximally existing moment of the underlying distribution is nearly futile (see, e. g. ,  McNeil et al. , 2015; Paolella, 2016 , and the numerous references therein). Interestingly the market factor of the FF49 (Mkt-RF) and the market factor of the cryptocurrencies (CMkt) exhibit very similar behavior in these summary statistics. Overall, the summary statistics provide a heuristically based argument that modeling the factors themselves with a heavy-tailed distribution might be beneficial.
We propose this method as the optimal combination between good performance and estimation complexity. Further, in the case of the FF49 dataset, the five Fama-French factors are used as predictors. For an extensive performance overview of all combinations of model structures and factors, see Appendix D. An issue that arises for the practical use of the HGH model (and thus also in the Factor-HGH) is the choice of the ordering of marginals. For this rolling window exercise, the reordering of the components in 𝐙 𝑡 is done beforehand with 1'000 datapoints. On those datasets, the same approach as in Näf et al. (  2019 ) is used, but separately for factors and returns. That is, while we change the ordering within factors and Table  2 : Comparison of the gross performance for mean-expected shortfall porfolios for the FF49 dataset and the crypto dataset.
* in the top row denotes annualized quantities. The FF49 dataset contains daily returns of 49 industry portfolios from 03-01-2000 until 28-05-2021. The crypto dataset contains hourly returns of 42 cryptocurrencies from 01-01-2021 until 16-06-2021. Rebalancing is done every 5 days for the FF49 data and every 5 hours for the crypto data, respectively. The Factor-HGH model is estimated with 𝐿 (𝑟) equal to the identity matrix. Within a dataset, the best performance statistics are marked in bold (not including the market portfolio). returns by a permutation matrix 𝐏, we still have that for 𝑡 = 1, … , 𝑇 ,.
Return 𝐏𝐙 𝑡 = [ 𝐏 1 𝐟 𝑡 𝐏 2 𝐫 𝑡 ] for two permutation matrices 𝐏 1 , 𝐏 2. In the case of the FF49 dataset, using the FF5 factors, we barely see a performance difference between the model approaches. Even more surprisingly, the intercept-only model (using no factor information at all) is performing similarly compared to the more sophisticated model structures. Although better than the base HGH model, which uses no factor information, our Factor-HGH model falls behind all the classical Gaussian models. It seems that heavy tail adjusted residuals bring no benefit in this rather Gaussian universe. Though, with a Sharpe-ratio difference of only 0. 14 to the Fama-French model, the damage appears limited. Further, the classical Fama-French model is slightly better than the Gaussian-Cholesky model structure.
Conclusion This paper details a method for incorporating exogenous common factor information into a tractable model that allows for non-elliptic behavior in the factors and asset returns, notably semi-heavy tails, margin asymmetry, and, notably, heterogeneous tail behavior. Parameter estimation is straightforward and fast, via an ECME algorithm; as is conducting mean-ES portfolio optimization. The proposed model structure is shown to generalize several existing factor models, including the classical Gaussian factor models of  Fama and French (1993) ,  Carhart (1997), and Fama and French (2015). The empirical analysis indicates that classical Gaussian models and the new Factor-HGH models perform similarly for the standard FF49 data set. Irrespective of the model used, incorporation of the common factor information does not lead to markedly improved performance in the FF49 universe: The interceptonly model is almost as good as the more sophisticated factor models.
We aim to include about 1800 adult patients with cancer treated with immunotherapy in a specifically recruited prospective cohort, and to additionally obtain data from historical real-world databases (ie, databiobanks) and medical administrative registries (ie, national cancer registries) in which relevant data regarding other adult patients with cancer treated with immunotherapy has already been stored. In the prospective cohort, clinical health status, HRQoL and psychosocial well-being will be monitored until 18 months after treatment initiation through questionnaires (at baseline and 3, 6, 12 and 18 months thereafter), and by data extraction from electronic patient files. Using advanced statistical methods, including causal inference methods, artificial intelligence algorithms and simulation modelling, we will use data from the QUALITOP cohort to improve the understanding of the complex relationships among treatment regimens, patient characteristics, irAEs and HRQoL.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ The 'monitoring multidimensional aspects of QUAlity of Life after cancer ImmunoTherapy, an Open smart digital Platform for personalised prevention and patient management' (QUALITOP) project will create an international, multicentre, real-world cohort that aggregates data of multiple types and from multiple sources. ⇒ The collected data will contribute to a medical data lake underlying a smart digital platform, which may be used by various stakeholders. ⇒ Despite its potential benefits, the QUALITOP project relies on data from heterogeneous patient groups and from partly validated patient questionnaires. ⇒ As this project started during the COVID-19 pandemic, we expect to limit recruitment shortage by study extension and enrichment of historical databases with retrospective data.
METHODS AND ANALYSIS Study design The 'Monitoring multidimensional aspects of QUAlity of Life after cancer ImmunoTherapy, an Open smart digital Platform for personalised prevention and patient management' (QUALITOP) project is an international, multicentre, real-world, observational cohort study. We will provide insights into the medical and psychosocial determinants of quality of life after cancer immunotherapy, making use of big data analyses, artificial intelligence (AI) and simulation modelling, before integrating the results in an information technology platform developed for the project. Additional information can be found on the project's website. 22  This study is registered at ClinicalTrials. gov under identifier NCT05626764. We will study adverse events and quality of life among patients with cancer during and after immunotherapy.
The QUALITOP cohort will combine a historical cohort of existing patients and a prospective cohort enrolled specifically for this project (figure  1 ). The historical cohort will comprise patient data routinely collected in existing databases and medical registries in Spain, France, Portugal and the Netherlands, for which existing informed consent allows the reuse of data within the context of this European collaboration. For the prospective cohort, patients will be recruited in the same countries under the coordination of Hospital Clinic de Barcelona (IDIBAPS), Hospices Civils de Lyon, Instituto Português de Oncologia Lisboa, and Amsterdam University Medical Centers and University Medical Center Groningen, respectively. Figure  2  shows the study timeline. Note that patients will not be included in both the historic and prospective cohorts.
Study outcomes The primary outcome of the QUALITOP study is HRQoL, combining the patient's perspective of their physical, psychological and social functioning. 23  We will measure this outcome repeatedly in the prospective cohort and obtain data for a selection of patients and time points in the historic cohort. The secondary outcome of the QUAL-ITOP study is the incidence and severity of irAEs, which we will extract from the electronic records for patients in both cohorts.
Data collection Overview of data sources and timeline Patient data for both the historic and prospective cohorts will come from existing and new databases at sites in France, the Netherlands, Portugal and Spain, as summarised in table 1 and detailed in online supplemental file 1. Each study site has different specialisations and will cover different oncological diagnoses and therapies. Figure  2  shows the proposed timeline of patient monitoring in the historic and prospective cohorts. Data for eligible patients from the historic cohorts were collected between 2016 and 2021, while patient inclusion for the prospective cohorts was initiated in April 2021 and will continue until January 2023. Afterwards, inclusion is intended to be continued in a sustainability programme. We will monitor patients closely for the first 6 months of treatment or until cessation, after which patients will enter a phase of less intensive monitoring until 18 months after treatment initiation or the QUALITOP project ends (figure  2 ).
Data collection in the prospective cohort Except in France, data from the prospective arm of the cohort are being collected and managed in Research Electronic Data Capture (REDCap), hosted by the participating institutions. 24 25  REDCap is a secure, web-based platform designed to support data capture for research studies. It provides the following: (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for data integration and interoperability with external sources. In France, data collection is being managed in Easily, a web-based electronic health record platform developed locally and hosted at Hospices Civils de Lyon.
Clinical data Clinical data will be manually extracted from electronic patient files for each routine visit in the first 6 months Open access of treatment and at fixed time points in the following year (9, 12 and 18 months). The timing of routine visits will differ by treatment type (ICI or CAR T-cell). We will assess medical history, medication use, prior anticancer treatments and cancer characteristics at the initiation of immunotherapy. Both at baseline and during follow-up, we will collect data from physical examinations (ie, weight, performance status, blood pressure), laboratory assessments (ie, C reactive protein, neutrophils, leucocytes) and related to irAEs according to the Common Terminology Criteria for Adverse Events, V. 5, of the National Cancer Institute. 26  Data about treatment for irAEs will be collected according to BioPortal's Drug Ontology,  27  available in REDCap.
Psychosocial questionnaires We developed psychosocial questionnaires to assess the multiple dimensions of quality of life and its potential psychosocial determinants in patients, necessary for the minimal data set of each patient included in the prospective cohort. A more in-depth questionnaire is issued at baseline and a shorter version is issued during follow-up at 3, 6, 12 and 18 months. We also modified the questionnaire slightly for patients receiving CAR T-cell therapy. Table  2  summarises the domains included in each version of the questionnaire. The questionnaire as a whole was not pretested (because it was constructed during the COVID-19 pandemic, and it was not possible to meet with patients). However, it was reviewed by oncologists in all the countries involved in the data collection.
Data analysis plan Data harmonisation and handling of missing data To enable analyses with the data from the historical and/or prospective QUALITOP cohorts, we must first harmonise the generated data. Separate analyses may be required for the historical datasets given their heterogeneous structures. Although the structure of data to be collected for the prospective cohort has been harmonised beforehand, differences in patient populations, treatments and legislations between the five participating centres mean that differences will exist. Where these differences result in missing data, we will handle missingness separately for each analysis after careful Open access of the mechanism, paying close attention to associations between missingness, outcomes and exposures. 47  The method used will also depend on the nature of the statistical analysis, such as multiple imputation for regression-based methods  48  and the missing indicator approach for machine learning algorithms.
Directed acyclic graphs,  55  informed by clinical frameworks like that depicted in figure  3 , will be developed in collaboration with partners to inform variable selection. These methods will help us to determine the causal effect of irAEs on HRQoL components. Intermediate analyses will be performed to identify the prognostic factors associated with irAEs, and boosting Open access methods  56  will be used to determine those factors and their appropriate functional forms. The historical datasets will inform this step. To further address the relationships between irAEs and HRQoL, we will use mediation analysis to disentangle the direct effect of individual characteristics and treatment on HRQoL, considering the effect mediated by irAEs. 57  This should uncover the factors driving HRQoL and could subsequently inform personalised care to maximise HRQoL.
This stage will use machine learning algorithms, such as random forests,  58  to develop a prediction model for future HRQoL based on current demographic, psychosocial and clinical information. The data collected in the QUALITOP project will benefit from repeated assessments of HRQoL over 18 months, facilitating the study of both individual trajectories over time and the causes and timing of changes in HRQoL. We will use mixed effect models and item response models to analyse the repeated measurements,  59  while simultaneously considering joint modelling to account for death as a competing event. 60  We will then combine the outputs of the disparate analyses to develop a causal loop diagram to illustrate the complex web of medical and psychosocial factors affecting quality of life. 61  This diagram will inform the development and validation of a quantitative simulation model, using a system dynamics method to understand HRQoL after cancer immunotherapy under different hypothetical public health scenarios.
Medical data lake and smart digital platform The QUALITOP project also aims to develop data management principles in a smart digital platform and associated medical data lake (figure  4 ) that will enable networked medical agencies to share and exchange trusted and secure medical data with automated and robust controls based on Findable, Accessible, Interoperable, Reusable principles. 62  The digital platform will use the medical, psychological and psychosocial data collected in the historic and prospective QUALITOP cohorts. By employing monitoring technologies and advanced data analytics, the data lake and smart digital platform will allow for the determination of predictive markers in subpopulations associated with irAE development and HRQoL impairment. We will use data-driven automation, prediction and decision support analytics with technologies such as AI to make predictions and recommendations for a given set of operator-defined objectives.
By leveraging modern analytics and data management capabilities and working with AI methods such as machine learning to improve the HRQoL of patients undergoing immunotherapy and to minimise the risks of relapse, healthcare organisations can transform existing networks into smart digital healthcare ecosystems. Patient monitoring using the smart digital platform Finally, the smart digital platform aims to allow not only collaborative, integrated and personalised case monitoring but also actionable treatment adjustments or recommendations. These benefits will help reinforce treatment planning and improve the effectiveness of actions designed to reduce treatment effects, making room for the necessary corrective actions at different stages. Data from the historic Immucare database will be used to develop and test the clustering algorithms that will be integrated in the smart digital platform and used to simplify the data, look for patterns and similarities, and ultimately contribute to personalised patient monitoring.
Ethical considerations The QUALITOP project will be conducted according to the Declaration of Helsinki. The local ethics committees of all participating centres have granted ethical approval (Personal protection committee Hospices Civils de Lyon, Medical Ethics Committee University Medical Center Groningen, Medical Ethics Committee Amsterdam University Medical Centers, Ethics Committee for Health Instituto Português de Oncologia Lisboa, Ethics Committee Hospital Clinic of Barcelona). Patients will be invited to participate by their treating physician and will be required to provide signed informed consent. For the historic cohort, data from existing study databases and medical administrative registries will only be used if patients had provided signed informed consent that allowed the reuse of data for (international) scientific purposes.
For example, the COVID-19 pandemic has already affected patient inclusion in the QUALITOP cohorts. We hope to resolve this with the received 6-month extension from the European Union, as well as efforts to retrospectively enrich the historical databases that are part of QUALITOP. Potential effects on treatment regimens and HRQoL may need to be considered in the statistical analyses. We also anticipate regulatory challenges for the smart digital platform, but by respecting the strict European regulations that exist to ensure patient privacy, we expect to deliver this with little difficulty. The QUALITOP project will expand knowledge about the health statuses and quality of life of patients after treatment with either ICI or CAR T cells in real-world settings, delivering a smart digital platform that can empower patients with cancer and inform healthcare providers.
Data Our empirical context is based on the population of SMEs in Norway that have made either domestic or international M&As in the period from 2000 to 2013. The context is well suited for investigating the role of CEO wealth in understanding firms' foreign acquisition activity as Norway is an open economy with a small domestic market due to the country's limited population. Thus, expanding beyond the Norwegian market plays on important role for firms' growth. However, the extent to which firms engage in CBAs varies significantly. We use the SDC Platinum M&A database to track CBA activity by Norwegian firms for the period 2000-2013. The database contains information about parent companies, the location of subsidiaries, industry classification, and status of the acquisitions transaction. We include only completed acquisitions in which the acquiring firm has a majority stake in the target company as this requires higher resource commitment and increases firms' risk exposure  (Anderson & Gatignon, 1986).
We supplement these data with financial, accounting and ownership structure data from the Center for Corporate Governance Research (CCGR). The database contains extensive firm-level information submitted to the state agency (The Brønnøysund Register) for all registered firms in Norway. Data on CEO age, gender, education, work experience and other socio-demographic characteristics is obtained through Statistics Norway (SSB). In addition, we collect detailed information about individuals' wealth reported in their tax returns from SSB. The information about wealth and income is very detailed and precise since it is reported directly to the tax authorities from employers, banks and financial institutions, and other relevant administrative entities. As a result, these data are much more reliable than survey data recording income and wealth based on individuals' self-reported figures.
The three most important sources of wealth in the data are deposits in banks, financial assets (bonds, stocks, mutual funds, etc. ), and real estate property. It is important to note that each part of these data comes from a different agency or data provider. After getting the permission to obtain the data from each entity, the entity (e. g. , CCGR for firm level variables including personal identifier of its CEO) sends the data to Statistics Norway. Statistics Norway matches different datasets together and supplement it with other personal data on CEOs (e. g. , wealth, marital status, age) and then anonymizes the data and return it to us. Therefore, we cannot identify companies or individuals within our sample. We adopt the European Commission definition of SMEs according to which SMEs are firms that employ 250 or fewer employees and do not exceed the equivalent of EUR 50 million in annual turnover  (European Commission, 2000; OECD, 2005).
Independent variables Our main independent variable is Log wealth, for which the data is obtained from the tax records. It is the sum of financial and non-financial assets such as real estate. We use the average wealth during our sample period in the cross-sectional regressions, but we get very similar results when using 3-year or 5-year moving averages. We also use a timevarying wealth variable as our main independent variable in our panel regressions. Financial assets include bank deposits, bonds, stocks, mutual funds, and money market funds. Prior studies have used similar data in Norway (which are typically rare outside Nordic countries) to address research questions related to individuals' and entrepreneurs' wealth  (Fagereng, Mogstad, & Rønning, 2021; Hvide & Møen, 2010).