dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, the few studies that did study geriatric syndromes collected data either by comprehensive geriatric assessment  [10, 11]  or patient self-reporting  [4, 12]. The present study explored multimorbidity patterns in geriatric patients attending an acute hospital, examining both chronic diseases and geriatric syndromes. Better knowledge of how these conditions cluster in older individuals could help clinicians and researchers better understand poor health outcomes in certain types of patients. Furthermore, our results may help guide the implementation of prevention strategies and the design of clinical practice guidelines adapted to the specific healthcare needs of this population group.
Study design, population and variables This retrospective observational study included patients of 65 years or more who attended the Unit of Social and Clinical Assessment (UVSS) of the Miguel Servet University Hospital (HUMS) in Zaragoza (Spain) during 2011. This UVSS is located in a public tertiary hospital of the Aragon Health Service (SALUD) that serves a population of approximately 400,000 inhabitants. It consists of an interdisciplinary team (geriatrician, nurse, and social and administrative worker), one of whose main objective is to detect and evaluate recently hospitalized geriatric patients at risk of disability and dependency, and to minimize these risks. The dataset included information on the clinical, functional, cognitive and social statuses of all attended patients. Functional assessment was conducted using the Barthel Index  [13] , and pre-and post-admission cognitive assessment was carried out using the nationally validated Red Cross Mental Scale (RCMS)  [14]  and Pfeiffer's test  [15] , respectively.
The diagnoses of each patient before and during hospitalization were grouped in Expanded Diagnosis Clusters (EDCs) using the ACG System. Only chronic EDCs included on a validated list of 115 EDCs published by Salisbury et al. [16]  were considered. Information on the following geriatric syndromes was included in the database: immobility, urinary incontinence, constipation, pressure ulcers, cognitive decline, dementia, delirium, depression, falls, insomnia, visual impairment, hearing loss, malnutrition, dysphagia, and pain. Immobility was defined as a decreased ability to perform activities of daily living due to impairment of motor functions. Urinary incontinence was defined as an objectively demonstrable involuntary loss of urine. Constipation was defined as less than two bowel movements per week.
Pressure ulcers were defined according to the four grades established by the European Pressure Ulcer Advisory Panel (EPUAP)  [17]. Cognitive impairment was assessed using Pfeiffer's test, which was performed at the time of hospitalization, once a previous diagnosis of dementia and/or delirium had been ruled out. A previous diagnosis of dementia was based on the patient's medical record data and was established using the RCMS. The ACG System included dementia and delirium in a single EDC category (NUR 11 "Dementia and delirium"). The criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR)  [18]  were used for the diagnosis of depression and insomnia. A fall was defined as the result of any event that caused the patient to end up on the ground against their will, according to the WHO definition  [19].
Acknowledgements The perspectives put forward in this paper are based on discussions among scientists and practitioners who attended the workshop -Evaluating Forest Conservation Initiatives: New Tools and Policy Needs‖ on 10-12 December 2013, in Barcelona, Spain. We are grateful for support from the  European Association of Environmental and Resource Economists , the  Robert Bosch Foundation , the  Autonomous University of Barcelona , the  Center for International Forestry Research , and the  ERA-Net BiodivERsA  funded  INVALUABLE  project with the  French national funder Agence Nationale de la Recherche. The  INVALUABLE  project generously covered publication fees. We also thank the workshop participants:  A. Angelsen ,  R. Arriagada ,  N. Asquith ,  M. Bottrill ,  J. Bush ,  E. Cisneros ,  S.
2. This paper provides novel evidence on the main factors behind consumer choices using the OECD Survey on Household Environmental Behaviour and Attitudes. Previous studies investigated technology adoption controlling mainly for households' socio-economic characteristics, dwelling or spatial characteristics (e. g. climate zone, urban/rural area), along with policy and economic variables, such as available subsidies and energy prices  (Michelsen et al. 2012, Sardianou and Genoudi 2013). Thanks to the richness of the underlying dataset, this study draws on a larger set of variables, including also households' beliefs, attitudes and behaviours regarding the environment and their knowledge about their energy use and spending. Moreover, previous studies usually focus on one or two technologies, such as residential solar thermals  (Mills et al.
5. The Survey data were collected through an online questionnaire. Households were asked to answer a similar questionnaire in five thematic areas: energy use, water consumption, waste generation and recycling, food consumption and personal transport choices. This survey is the second of its kind and was carried out in early 2011, while the first was launched in 2008. The more recent survey, which is the basis for the analysis in this paper, collects data from a sample of more than 12 000 respondents, approximately 1 000 households for each country: Australia (shorthand: AUS), Canada (shorthand: CAN), Chile (shorthand: CHL), France (shorthand: FRA), Israel (shorthand: ISR), Japan (shorthand: JPN), Korea (shorthand: KOR), the Netherlands (shorthand: NLD), Spain (shorthand: ESP), Sweden (shorthand: SWE) and Switzerland (shorthand: CHE).
Schmoller-Menger dispute started soon after the beginning of the institutionalisation of experimentally oriented economics which happened with the creation in 1873 of the VereinfürSozialpolitik. Boyle-Hobbes dispute started in 1660, when the Royal Society of London had been founded, the cradle of the institution of science. The activities of both societies were similar in several respects: they represented efforts to collect data in the framework of experimental situations, working out of detailed reports and collective evaluation of obtained results. The reports of the Royal Society served to enlarge the number of witnesses of experiments and in this way "to make virtual witnessing a practical option for the validation of experimental performances" [22a, p. 69]. Boyle insisted on his "lack of preconceived expectations, and, especially, of theoretical investments in the outcome of experiments (Ibid.
A large number of non linear conditional heteroskedastic models have been proposed in the literature and practitioners do not have always the tools to choose the correct specification. In this article, our main interest is to know if usual choice criteria lead them to choose the good specification in regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH model and the Markov-Switching GARCH models. Thanks to simulation experiments, we highlight that information criteria and loss functions can lead practitioners to do a misspecification. Indeed, depending on the Data Generating Process used in the experiment, the choice of a criteria to select a model is a difficult issue. We argue that if selection criteria lead to choose the wrong model, it's rather due to the difficulty to estimate such models with Quasi Maximum Likelihood Estimation method (QMLE).
1 Introduction  Engle (1982)  developed the Autoregressive Conditional Heteroskedasticity (ARCH) models which provide a fruitfulness framework to analyze volatility and financial time series. From this finding, it is one of the most active research topics in financial econometrics. In 1986, Bollerslev proposed Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. In GARCH modeling, the volatility is a linear function of past volatility and squared residual past shocks. These models are of the form ε t = η t √ h t where h t is a positive process (the volatility) and η t an identically and independently distributed random variable with zero mean and a unit variance. However, some empirical works have shown that these models does not always adequately fit the data over a long period of time.
However, when practitioners study empirical data, they have to choose a specific model to estimate and to forecast conditional volatility. A misspecification will lead to bad forecast and a not consistent estimation. In this article, we are interested in knowing if usual choice criteria lead the practitioner to choose the good specification in regime switching framework. To provide an answer to this question, we perform Monte Carlo experiments: we simulate data following some Data Generating Processes (DGP) as LST-GARCH or MS-GARCH and then, we estimate it with different specifications. We apply selection criteria to these estimations and look at which model is chosen by the criteria. Some of our results are very surprising in the sense that, depending on the way we simulate data are simulated, selection criteria can lead to misspecification.
Design of the experiments The main idea of the paper is to provide some Monte-Carlo experiments to see if the classical information criteria and loss functions lead practitioners to choose the good specification 3. When we have to select a conditional volatility model, it can be done in two ways. First, we can estimate model with different specifications and look at information criteria. Secondly, we can decide to choose the model using in-sample forecasts evaluation. In this part, we focus only on estimation. The Monte-Carlo experiments are the following: first data are generated following processes described in section 2. Then, the three models are estimated by QMLE method. Finally, selection criteria and loss functions presented in section 3 are computed. The model for which selection criteria are minimum is selected.
The percentage of choice of a specific model done by the different criteria is reported for each experiences. The study is limited to LST-GARCH(1,1) and MS-GARCH(1,1) with two regimes. In each experience, T = 2000 observations are simulated. The use of ergodic probabilities for the Markov Chain and sample data variance for the conditional variance is recommended to initialize the likelihood function. The starting values of parameters are the true generating values of these parameters when they are known. However when a misspecified model is estimated, starting values choice is more complicated: for MS-GARCH simulated processes, starting values of the LST-GARCH estimation are the following: ω = ω MS,1 + ω MS,2 2 α 1 = α MS,1 + α MS,1 2 α 2 = |α MS,1 -α MS,1 | 2 and β = β MS,1 + β MS,1 2 where ω, α 1 , α 2 and β are the parameters in the LST-GARCH(1,1) model given by equation (3).
Coefficients indexed by MS, j are the coefficients used to simulate the data with MS-GARCH processes  (Haas and Klaassen) , with j = 1, 2 the state of the world. This choice is made on the presumption that the estimated parameters of LST-GARCH processes will be the mean of the true values of the simulated MS-GARCH. The starting value of the parameter θ is set in a region where the transition function do not take on the extreme values ; however it is well known the estimation of this parameter is really difficult. When LST-GARCH processes are simulated, we use ω MS, j = ω α MS, j = α 1 β MS, j = β for j = 1, 2 the two different regimes. In contrast with previous starting values, we have the presumption that the estimated parameters of MS-GARCH on data simulated by a LST-GARCH will be very close in both regimes.
Experiment 1: simulations of MS-GARCH processes The data generating processes are the MS-GARCH as follows: r t = ε t , (18) and ε t = η t h t,∆ t (19) where η t is a random variable following a normal distribution nid(0, 1) and h jt = ω j + α j ε 2 t-1 + β j h t-1 (20) j = 1, 2 or in matrix form, Three transition matrices are tested: P 1 = 0. 1 0. 9 0. 9 0. 1 , P 2 = 0. 5 0. 5 0. 5 0. 5 and P 3 = 0. 9 0. 1 0. 1 0. 9. Matrix P 1 represents the case where regime switches occur often, the second one, P 2 , when probabilities to stay in the same regime are equal to the probabilities to switch of regime. The last case, matrix P 3 , represents persistent simulated regimes. In a fourth experiment, we use different regime specifications, one regime where the conditional variance is large but which occurs very few times and another with a lower variance but it is the most common regime: h t = ω + αε 2 t-1 + β h t-1 ( ω = 0.
1 0. 05 , α 1 = 0. 4 0. 1 , β = 0. 9 0 0 0. 4 or β = 0. 9 0. 4 with P 4 = 0. 1 0. 1 0. 9 0. 9 The first regime occurs very few times since p 11 = p(∆ t = 1|∆ t-1 = 1) = 0. 1 and p 21 = p(∆ t = 1|∆ t-1 = 2) = 0. 1. In Tables  1, 2 , 3 and 4, we present the percentages of choices given by different selection criteria for these four experiments. Our results show that when regimes are difficult to identify (i. e. , many regimes switches or one regime occurs few times) and data are generated in the sense of Klaassen, information criteria and loss functions do not find always the true DGP and can lead practitioners to make wrong choices. In the first experiment, the matrix P 1 is used to simulate the data. Results in Table  1  highlight two facts. When data are simulated in the sense of Klaassen, BIC is minimum 58.
45% of time for LST-GARCH, that means it is the best model to estimate such data. If we decide to select a model with respect to the AIC, the MS-GARCH of Haas will be chosen 49. 35% of time. Loss functions compute with the true volatility seem to work better but Haas specification is still chosen about once over five. Loss functions computed with ε 2 t select often the wrong model whatever the process used to simulate the data. With the matrix P 2 (Table  2 ), there are less regime switches. Again, there are no selection problems if data are generated with MS-GARCH of Haas but if the DGP is the Klaassen MS-GARCH, information criteria still lead to do wrong choices nearly half the time. However, the frequency of wrong choices is lower than in the case with many regime switches. BIC selects now 54.
65% of time (21. 20% in the first case) the true specification. As in the previous case, in-sample forecasts selection method improves the frequency of good choices. In this experiment, loss functions computed with ε 2 t return good results when data are simulated with MS-GARCH of Klaassen. It contrast with bad results obtain where the DGP is the MS-GARCH of Haas. This improvement of good selection is accentuated when data are generated with persistent regimes: at least, all the decision criteria lead to do good choices (Table  3 ). The error of specification falls down at about less than 5%. In Table  4 , the results are similar to the results in Table  1. The both variances are totally different: in the first regime, the variance is very high and very low in the second. The world is not often in the high volatility regime (10%) and, as in Table  1 , choice criteria lead to choose a misspecification when MS-GARCH of Klaassen is the DGP.
This leads to a higher AIC and BIC. The loss functions computed with the true volatility are not affected by the likelihood value and are more efficient than the information criteria. Finally, depending on the loss function and the DGP, the use of ε 2 t have a different impact. For example, the frequency of good selections done by the QLIKE loss function decreases when regimes become more persistent and the DGP is the MS-GARCH of Haas whereas it increases when the DGP is the MS-GARCH of Klaassen. However, this proxy works better than information criteria when data are simulated by Klaassen MS-GARCH. A better proxy should give better results, so we encourage practitioners to use in-sample forecasts to select RS-GARCH models in this case. Similar remarks are made in  Hansen and Lunde (2006) ,  Patton (2011)  and  Laurent et al (2009)  in selection models using out-sample forecasts approach.
The authors cited above recommend the realized volatility for example. This experiment highlights three main things: first, when data are simulated in the sense of Haas, information criteria are powerful to select the good model among these three RS-GARCH. Secondly, loss functions seem to work well when the DGP is MS-GARCH of Klaassen. Finally, although ε 2 t is not a good proxy, it gives good results when data are generated in the sense of Klaassen. We explain these three facts by the difficulty to estimate such models. In a similar way, data have been simulated following LST-GARCH processes and estimated with MS-GARCH. Data generating processes are described in section 2 by equations (  1 ), (  2 ) and (3). More specially, h t = ω + α 1 ε 2 t-1 + α 2 F(ε t-1 )ε 2 t-1 + β h t-1 (22) with ω = 0.
This paper examines the impact of a misspecification on selection of regime switching models in the conditional volatility. Having a good estimation of these models is an important issue. RS-GARCH models can take account structural changes and provide good indicators to explain financial crisis  7. By Monte-Carlo experiments, we can highlight if the widely use Information Criteria are great to choose a regime switching conditional volatility model. In the first experiment, data are simulated following MS-GARCH models of Haas and Klaassen with many transition matrices. When data are simulated in the sense of  Klaassen, results  show that BIC could lead to select most of time LST-GARCH model when the regimes are not persistent or one of them occurs too often. In the same way, AIC selects MS-GARCH of Haas.
Concentrations of PM 2. 5 were calculated for the EnerGEO emission scenarios by the GAINS model. Both primary PM emission sources (dust) as well as secondary aerosols were taken into account. GAINS uses the socalled pollution transfer matrices, which determine to what extend emissions of PM precursors from country x contribute to the concentration of the pollutant in grid y. These "country_to_grid" matrices were obtained by the "atmospheric chemistry and transport" model from EMEP  (Simpson et al. , 2012). Grid resolution used in our study was 50 × 50 km. Our method of calculating health impacts required data on annual concentrations in each grid cell. Since the GAINS scenarios were only available for 2005, 2020, 2030, 2040 and 2050, the PM 2. 5 values have been linearly interpolated for the years in-between.
(2002). Pope et al. (2002)  studied the effect of PM 2. 5 over approximately 1. 2 million of people (or adults) living in the US from 1982 to 1998. Volunteers were older than 30 and data were collected by the American Cancer Society. A link between concentrations of fine particles and loss of life expectancy has been confirmed by recent studies performed under the auspices of the World Health Organization (compare  Lim et al. , 2012; WHO, 2013). These studies suggest even higher relative risk factors than Pope. Thus estimates using  Pope (2002)  are on a conservative side. He used dose-response analyses from  Cox (1972)  and he found that the relation between the mortality rate without exposure μ b (t, a), and the mortality with exposure μ e (t, a) is the following: μ b t; a ð Þ ¼ μ e t; a ð Þe β pm b t ð Þ-pm e t ð Þ ð Þ ð12Þ where: • pm e (t) is the actual particle concentration at time t.
NUMERICAL WORK For each process defined in the introduction, we compare the performance of the two crossing-tree partition functions. Estimation based on wavelet coefficients is also presented. The partition functions are estimated from an average of 1000 realizations of 2 15 sample points each. The wavelet partitions functions are estimated using Daubechies' wavelets with 3 vanishing moments, from scale 2 3 to 2 12 using Matlab routines from  [10, 11]. The crossing tree partition function is estimated from scale 2 2 to 2 5. The range of scales differs for wavelet methods and the crossing tree, since they are calculated differently: the scales chosen to analyse the crossing tree are computed from the spatial fluctuations of the signal. We focus our attention to the estimation of the negative moments, which is the main objective of the present contribution.
However, it would be unrealistic to expect physicians to carry out such a task, which would be both difficult and timeconsuming. Various attempts have been made to increase the understanding and use of safety information about drugs. Commercial editors of drug databases have developed systems for comparing the safety aspects of the SPCs of two drugs, by contrasting the contraindications or side effects of the drugs compared, for example. However, the information provided is still time-consuming to read and would benefit from synthetic, and possibly graphical approaches to decrease the cognitive burden of the comparison of textual elements of this kind. An iconic language was developed in a previous study  [7] , for the presentation of medical concepts. This language has been applied to the presentation of drug contraindications and interactions and has been shown to make the reading of drug monographs more rapid  [8].
Identification of the corpus of information concerning the drug in the used sources For data input into our system, we used the SPCs and evaluation reports of the French National Authority for Health (HAS). SPC information was extracted from the Claude Bernard database  [15] , which uses reference terminologies, such as MedDRA, ATC, IDC10, to index information contained in free text. The characteristics of novelty of the drug were determined by considering all the drugs with the same indication described in the report of the French National Authority for Health (HAS)  [13]. These drugs are presented by pharmacotherapeutic group in the report. For each of these drugs, we used the SPC information available from the Claude Bernard database, including, in particular, the paragraphs concerning composition, pharmacodynamics, therapeutic indications, dosage, route of administration, technical and regulatory data (approval date, price, etc.
Methods used for system implementation We developed the prototype in PHP and SQL. We selected five new pharmaceutical products with very different innovative characteristics, to illustrate the functioning of the prototype. For each drug, we manually collected information from the SPC and evaluation reports, for the previously established elements. We created a database for the storage of all data on these products. The conclusions of the experts were extracted from the evaluation reports. The prototype uses the information provided by the French Claude Bernard drug database  [15]  and the evaluation report. We applied several rules when generating the interface, to ensure homogeneity in terms of the representation of information. The routes of administration are represented by pictograms. The information about the new drug is always presented before the information about the drug with which it is compared.
Materials and methods A self-administered survey (e-questionnaire) was sent by e-mail to all members of the French-speaking Nephrology Society between 19 October 2008 and 12 January 2009. The questionnaire included questions on demographic data and about the RRT modality they would choose if they had ESRD. We then used the French Renal Epidemiology and Information Network (REIN) registry  [9]  to select a reference population of 20-to 64-year-old patients with ESRD who began RRT [HD, PD or pre-emptive transplantation (PT)] between 1 January 2008 and 31 December 2008 and who came from the same regions as the respondent nephrologists. We included 3094 patients. Sex, age, region of residence and first RRT were recorded. To compare the nephrologist's RRT choice and the nephrologist's prescription, we first estimated the nephrologist's response (expected response) based on the hypothesis that the nephrologist's choice would match the patient's treatment.