dataesr/scientific-paragraphs-categorization huggingface dataset:
Granger-causality tests We proceed to test for the direction of causality among debt and growth variables. Table  8  displays the results of the Granger-causality tests obtained from PVAR estimations based on the original data (panel a) and on their de-factored counterparts (panel b). First, we find that, under the influence of global shocks, household and corporate debt significantly Granger-cause output growth as shown in panel (a). As anticipated by the IRFs previously set out, we find that output growth Granger-causes household, corporate and public debt using the de-factored data. However, such a relationship is not bi-directional, so that not all types of debt Granger-cause growth when global shocks are removed, as shown in Panel (b). In both cases, we do not find evidence that public debt Granger-causes output growth, which signals that such a link, is possibly, in the form of contemporaneous association rather than causality.
Forecast Error Variance decomposition Table  9  reports the results of the forecast error variance decomposition (FEVD) analysis following the panel VAR estimation, highlighting the importance of output in explaining the variability of the debt aggregates, and vice versa. Panel (a) presents the results without de-factoring. In line with the findings of the IRFs and Granger-causality for the same set of variables, we find that 7. 3% and 3. 4% of the FEVD of output growth, over a time of twelve quarters, can be explained by household and corporate debt. Public debt has a low forecast power of 0. 5% over twelve quarters for output growth. However, 9. 6% of the error variance forecast of public debt can be explained by output growth over twelve months. Furthermore, and in line with previous results, all debt variables have relatively low forecast power for output growth using the de-factored data as shown panel (b).
Robustness Analysis We carry out a battery of robustness checks with respect to the estimated common factors, the sample, alternative variables definitions, and specification. First, we tweak the way we compute the common factors. Specifically, we do not apply any standardisation to the estimated factors in the PC analysis and use the non-standardized factors to produce non-standardised version of the de-factored series for the panel ARDL and panel VAR analyses. Second, we check for the robustness of our results to changes in the composition of the sample by excluding countries where the common factor dominates the series (i. e. , countries that feature an R 2 in Table  3  greater than 70%), and countries where the common factor is less relevant (i. e. , R 2 ˂ 25%). Third, we re-estimate the common factors by using a different dataset consisting of annual series from  Jordà et al.
Prior to the factor extraction process, the raw data are de-meaned to account for the observed time trend across i. The data are also standardized to remove excessive heteroskedasticity in the panel. Such pre-whitening procedure is required for consistent estimation of the factors' number, see, e. g. , Greenaway-McGrevy et al. (2012). Further, the PC estimator is consistent with certain types of unknown breaks or time variation in the factor loading (Stock and Watson 2009).
The farmer's choice of engagement in collaborative water management (y 2 ) is assumed to reflect a latent, unobserved variable y 2 * that represents the benefits (net of costs and transaction costs) drawn from different levels of participation. We observe the different modalities of y 2 if the underlying latent variable exceeds given thresholds (θ). q q q q q q ì ï £ ï ï ï ï < £ ï ï í ï < £ ï ï ï ï > ï ï î 2 1 1 2 2 2 2 2 3 2 3 0 1 2 3 * * * * if y if y y if y if y The latent variable y 2 * depends on a vector of observed explanatory variables X: m = +  2 2 2 * y X where B 2 is a vector of the parameters to be estimated and μ 2 is a random error term, assumed to be normally distributed. The parameters in both models were obtained through maximum likelihood estimation using Stata statistical software.
ACKNOWLEDGEMENTS This work was supported by the  Office Français de la Biodiversité (OFB) , formerly the  Agence Française pour la Biodiversité (AFB) , within the framework of the Irstea-AFB 2016-2018 research agreement. We are grateful to  Delphine Loupsans  ( OFB ) for her help and to  Kevin Martin  for his contribution to data collection as a research assistant at Irstea. We also thank all the farmers and other stakeholders who were interviewed for this research. Finally, the paper benefited greatly from the comments and suggestions of three anonymous reviewers.
IV CHN,mkt ckt = shEXP ORT toCHN k(U S),1999 * ln(GDP c) ct * Inst Robots CHN,t (6) In the third channel we consider the role of China as a competitor in European markets. In Eq. 7, we use as industry variation the share of imports from China by US in 1999 of goods produced by a given industry k, shIM P ORT f romCHN,k U S,1999. It is important to take note of the difference with the first channel: in this case we consider the output market for industry k whereas Eq. 5 considers the input reliance for industry k. The instrument captures robot adoption as escape competition  (Aghion, Bloom, Blundell, Griffith & Howitt 2005). IV CHN,comp ckt = shIM P ORT f romCHN,k U S,1999 * ln(GDP c) ct * Inst Robots CHN,t (7) 3 Data and descriptive statistics We construct our estimation sample combining different sources.
Labor data come from Timmer et al. (2019), who report the labor share, and the share accrued to different business functions for countries and industries in WIOD 2013 industry classification. They define 4 broad groups of occupations: Fabrication (FAB), R&D, Management (MGT), and Marketing (MAR). 4 GVC position is measured using international input-output tables from the WIOD 2013 release. We follow the methodologies and definitions proposed by  Antràs et al. (2012)  and  Miller & Temurshoev (2017)  to calculate upstreamness, Ups, as distance from final good production. To construct the instrument we source gravity variables from the CEPII gravity dataset. in our study. Looking first at labor shares, top 5 countries have more than the 70% of value added that is absorbed by labor, with UK reaching the 80%.
This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101004703  (PILLARS). Lionel Fontagné  thanks the support of the  EUR  grant  ANR-17-EURE-0001. We wish to thank the participants to the  PILLARS Econpol , ETSG and the  UNTANGLED conferences , and to  RIE Workshop "China  and the  Global Economy"  for their comments and suggestions.
To gather information for technology stocks, we combine two different sources. EU KLEMS provides data on capital stock at the country-industry level disaggregated for ten different categories. First, we match the EU-KLEMS to the WIOD industry nomenclature. Then, for each country-industry we define the stock in ICT and non-ICT capital. ICT capital comprises computers and hardware (IT), Tobacco (15-16), Textiles & Apparel (17-18), Leather & Footwear (19), Wood & Cork (20), Pulp, Paper, Printing & Publishing (21-22), Coke, Refined petroleum and Nuclear (23), Chemicals (24), Rubber & Plastics (25), Other non-metallic mineral (26), Basic metals and Fabricated metals (27-28), Machinery (29), Electrical & Optical equipment (30-33), Transport equipment (34-35), Other Manufacturing (36- 37). communication technologies (CT) and Software and Databases.
Acknowledgments DL acknowledges support from ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ). LD acknowledges support from  Spanish Ministerio de Ciencia e Innovación  through Grant  PID2020-113455GB-I00  and  Universidad Complutense de Madrid  through " Convocatoria plurianual para la recualificación del Sistema Universitario Español  para 2021-2023 ( MV24/21 )" funded by  NextGenerationEU. RP is supported by the  Israeli Science Foundation  (Grant  776/19 ). We acknowledge fruitful discussions with  Luca Peliti  and  Haim Permuter.
The aim of this paper is to illustrate all the clinical epidemiology searches made within the French network REIN to improve CKD stage 4-5 care in older adults. We summarize various studies describing clinical practice, care organization, prognosis and health economics evaluation in order to develop personalized care plans and decision-making tools. In France, for 20 years now, various databases have been mobilized including the national REIN registry which includes all patients receiving dialysis or transplantation. REIN data are indirectly linked to the French administrative healthcare database. They are also pooled with data from the PSPA cohort, a multicenter prospective cohort study of patients aged 75 or over with advanced CKD, monitored for 5 years, and the CKD-REIN clinical-based prospective cohort which included 3033 patients with CKD stage 3-4 from 2013 to 2016.
By providing data and indicators, registries are invaluable tools for following the evolution of care and may highlight variability in medical practices for advanced CKD like dialysis initiation, kidney transplant evaluation, modalities and location of treatment  (25, 26). Registry data may also improve health system planning and policy-making by indicating efficient, costeffective care strategies  (27, 28). The aim of this paper is to illustrate all the clinical epidemiology searches made within the French Renal Epidemiology and Information Network (REIN) over the last 20 years, in order to improve advanced CKD care in the older adults. We summarized various studies describing the clinical practice, care organization, prognosis and health economics evaluations in order to develop and improve personalized care plans and decision-making tools.
Data sources In France, the national REIN registry includes all patients receiving renal replacement therapy (RRT) -dialysis or transplantation -for End-Stage Kidney Disease (ESKD)  (25, 29). Recording began in 2002 and the registry grew progressively to include the entire country (including overseas territories) in 2012. It aims to be a tool for public health decision support, evaluation and research. Anonymized data are available for researchers on request after validation by the REIN scientific committee. The data collection is ruled with an implicit consent with a drop-out option. Patients are informed on the purpose of the registry and the possible data linkage and data use via publicly available information (poster and welcome booklet in nephrology centers). Since 2019, the intention has been to expand it to patients with Stage 4 and 5 CKD not treated with RRT  (30).
REIN data are indirectly linked to the French administrative healthcare database which consists of two main databases: the hospital discharge summaries database and the national health insurance claims database and it covers 98. 8% of the French population, over 66 million people, from birth (or immigration) to death  (31, 32). Analyzing this database provides insights into the economic burden of CKD and is a means to evaluate care consumption. REIN was also linked to the PSPA cohort, a multicenter prospective cohort study of patients aged 75 years and older with advanced CKD monitored for 5 years. Details of the study design have been previously published and are summarized hereafter  (33). Twenty-four French hospital nephrology centers were involved with an inclusion period of 4 months at each center.
The study was approved by the Ethical Review Board of the research institution (Number 16. 04. 06), conducted in accordance with international clinical practice guidelines and registered on ClinicalTrials. gov under the number NCT02910908. The CKD-REIN clinical-based prospective cohort enrolled 3033 patients with CKD stage 3-4 conducted in 40 nephrology outpatient facilities between 2013 and 2016  (34). Follow-up lasted 5 years, including after initiation of renal replacement therapy. The primary objective of the CKD-REIN cohort study was to develop a research platform to address key questions regarding various patient-level factors and biomarkers associated with CKD outcomes, and to assess clinical practices and healthcare systemlevel determinants of CKD outcomes. The study is registered on ClinicalTrials.
High hospitalization rates and care costs In 2019, based on hospital database-REIN linkage, we were able to show that hospitalization rates increase with age (Table  1 ). They represent 1. 5 hospitalizations per 1 personyear in patients age 75 years and over and 20% of these are related to cardiovascular disease and cardiovascular events (Table  2 )  (72). A health economic evaluation was performed based on the REIN and the National Health Insurance Database, to compare Relative risk of death after renal transplantation in older patients compared to wait-listed patients (source REIN registry). Adapted from  Legeai et al. , 2018 (69). the efficiency of different care strategies. The overall mean monthly cost spent for a stable patient increases with age  (27, 73). These higher costs are mainly explained by the higher costs of the dialysis treatment modality: hospital-based HD or nurseassisted home APD  (73).
Thus, in the coming years, with the help of registries and cohorts of CKD patients, it is important to develop approaches based on quality of life data or relevant proxies such as time spent in hospital. New prognostic scores could thus be developed for an even more patientcentered therapeutic education  (91). From a public health view point, the needs in health care are moving fast and the care offer must adapt to the most vulnerable frail patients. Therefore, registry data, in providing epidemiological knowledge, by describing specific health consumption in detail, are appropriate for better predicting the needs for suitable care arrangements i. e. facility-based, Monthly incidence rates over time of hospitalizations associated with palliative care and death from all causes during the first four years after starting dialysis, according to age (source REIN registry linked to hospital database).
(93). Embedded within a registry, randomized trials capitalize on established and secure data collection system. It is a new way to decrease research cost by avoiding extra collection of information that already exist and are available. In older patients, avoiding extra visits is alos an asset. The linkage between registry data and health databases is an opportunity to assess expenditures and health care use. In this older population, the medication burden and inappropriate prescription risk are high  (35). The linkage between registry data and health databases is an opportunity for in-depth assessment of drug use at "low cost" without the need to go back to the patient or the medical files. Moreover health economics evaluations are necessary for health authorities and health providers to be able to offer sustainable arrangements.
As CKD is most often incurable, the most important area of work remains prevention. In order to better identify the target population for this prevention, linking the registry data to health insurance data could eventually allow us to better identify potential CKD patients at an earlier stage. Work on the French health insurance database is currently underway to develop an algorithm to recognize these patients, the first version of which was recently published  (94). Finally, the REIN registry, backed by its expertise and national network, was able to set up epidemiological monitoring of chronic dialysis and/or transplant patients affected by SARS-CoV-2 very quickly  (95). A weekly newsletter has been set up (still on-going), sent to all nephrologists, using information transmitted by all the REIN research assistant working with the nephrologists in the regions.
Acknowledgments The authors wish to thank all REIN registry participants, especially the nephrologists and professionals in charge of data collection and quality control. Dialysis and Transplantation units participating in the registry are listed in the REIN annual report (https://www. agence-biomedecine. fr/Les-chiffres-du-R-E-I-N). We also wish to thank  Teresa Sawyers , Medical Writer at the  BESPIM, Montpellier University Hospital , for her invaluable help in revising and editing the text.
Huge amount of data are nowadays produced by a large and disparate family of sensors, which typically measure multiple variables over time. Such rich information can be profitably organized as multivariate time-series. Collect enough labelled samples to set up supervised analysis for such kind of data is challenging while a reasonable assumption is to dispose of a limited background knowledge that can be injected in the analysis process. In this context, semi-supervised clustering methods represent a well suited tool to get the most out of such reduced amount of knowledge. With the aim to deal with multivariate time-series analysis under a limited background knowledge setting, we propose a semi-supervised (constrained) deep embedding time-series clustering framework that exploits knowledge supervision modeled as Must-and Cannot-link constraints.
More in detail, our proposal, named conDetSEC (constrained Deep embedding time SEries Clustering), is based on Gated Recurrent Units (GRUs) with the aim to explicitly manage the temporal dimension associated to multi-variate time series data. conDetSEC implements a procedure in which an embedding generation step is combined with a clustering refinement step. Both steps exploit the small amount of available knowledge provided by Must-and Cannot-link constraints. More specifically, during the data embedding generation the constraints are used by jointly optimizing the network parameters via both unsupervised and semi-supervised tasks, while at the refinement step they are used in conjunction with the goal to stretch the embedding manifold towards the clustering centroids to recover a more clear cluster structure.
To this end, we propose a neural network architecture based on Gated Recurrent Units (GRUs), in which the clustering process is based on a two steps procedure involving an embedding generation and a clustering refinement step, both exploiting the small amount of available knowledge provided by ML and CL constraints. More specifically, during the data embedding generation the constraints are used by jointly optimizing the network parameters via both unsupervised and semi-supervised tasks, while at the refinement step they are used in conjunction with the objective to stretch the embedding manifold towards the clustering centroids to recover a more clear cluster structure. With the aim to assess the behavior of our framework, we provide an experimental analysis on six real-world time-series benchmarks coming from different domains, that shows the effectiveness and flexibility of our approach.
Recently, Fontes et al. [12]  proposed a framework involving fuzzy c-means based constrained clustering of time series in a pattern reconciliation context. The technique of data reconciliation is oriented to the minimization of measurement errors in the data by imposing physical constraints associated with the production system (e. g. , mass and energy balances). That is, in this case the method deals with soft constrains related to the physical properties of the cluster members in a specific domain, and not with the classic Must-link and Cannot-link constraints generally adopted in data science methods. Summarizing, constrained clustering of multi-variate time series is still an open problem, as also recently observed by the authors in  [12]. This represents a major motivation behind the proposal of the conDetSEC framework, which could fill this gap in literature, thus contributing in addressing this task in a plethora of practical problems in different domains.
Methodology In this section we introduce a new constrained clustering framework especially tailored to manage multi-variate time series data referred as conDetSEC (constrained DEep Time Series Embedding Clustering). Let X ¼ X i f g n i¼1 be a multi-variate time-series dataset where X i is a time-series and X ij 2 R d is the multidimensional vector of the time-series X i at timestamp j. The maximum length of a time-series is referred as T. Given X, a set of Mustlink constraints ML and Cannot-link constraints CL, the goal of con-DetSEC is to partition X into a predefined number of clusters, by exploiting the supervision supplied by the set of ML and CL constraints. We remind that Must-link (resp. Cannot-link) constraints are defined over pair of time-series and indicate that two multivariate time-series should (resp.
g. , K-means) on the new data representation. The final clustering assignment is derived by applying the K-means clustering algorithm on the embeddings produced by conDetSEC. Algorithm 1 summarizes the conDetSEC framework. The first stage (lines 1-7) is devoted to learn multi-variate time series representation exploiting the supervision supplied by the available constraints. To this purpose, we set up a reconstruct task in which GRU autoencoders are employed to encode/decode the sequential information. A visual summary of the autoencoder structure we have conceived is depicted in Fig. 2. Here, we use two GRU autoencoders AE f and AE b. Each autoencoder has two components, an encoder (enc f and enc b ) and a decoder (dec f and dec b ). More precisely, AE f encodes (enc f ) and decodes (dec f ) the original time series considering the natural order (forward) while AE b encodes (enc b ) and decodes (dec b ) the time series in reverse order (backward) w.
Data and Experimental Settings The evaluation has been carried out on six benchmarks  [11]  coming from disparate application domains and characterized by contrasted features in terms of number of samples, number of attributes (dimensions) and time series lengths. More in detail: The ArabDigit dataset contains timeseries of mel-frequency cepstrum coefficients (MFCCs) corresponding to spoken Arabic digits. It includes data from 44 male and 44 female native Arabic speakers. The JapVowel benchmark still involves a speech classification task. Nine male speakers uttered two Japanese vowels/ae/ successively. For each utterance discrete-time series of LPC cepstrum coefficients are extracted. The Dordogne benchmark involves a classification task where time series are extracted from multi-temporal satellite image acquisitions.
The underlying task consists in the classification of each (pixel) multi-variate time series to one of the several land covers available in the nomenclature associated to the study site (i. e. Crop, Water, Urban settlement, Forest, etc. ). The ECG5000 benchmark contains time series coming from the medical domain and describing people with severe congestive heart failure. The class values were obtained by automated annotation. The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. The HAR (Human Activity Recognition) dataset has been collected from 30 subjects performing six different activities (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). It consists of inertial sensor data that was collected using a smartphone carried by the subjects.
In addition, we have empirically observed that considering a smaller number of constraints does not introduce any kind of supervision in the competing approaches. For this reason, we have chosen a constraint range that can provide room for investigation according with the considered competing methods. Due to the random sample selection process and the non deterministic nature of the clustering algorithms, we repeat the sample selection step 5 times for each number of constraints. Finally, for each level of supervision, we report the average values of Normalized Mutual Information and Adjusted Rand Index. For all the methods, the number of clusters is equal to the number of classes. conDetSEC is implemented via the Tensorflow python library and the implementation is available online  1. Model parameters are learnt using the Adam optimizer  [18]  with a learning rate equal to 5 Â 10 À4 for both stages of our framework (embedding generation and clustering refinement).