dataesr/scientific-paragraphs-categorization huggingface dataset:
Data This study uses US monthly data for the period 1942:M1 to 2016:M12 (900 observations). 10 Data relative to the returns on the market portfolio have been downloaded from CRSP database. The stock market return ("mkt") is computed as the value-weighted return on all NYSE, AMEX, and NASDAQ stocks obtained from the CRSP data files. We have selected the constant 10-year maturity Treasury bond as our long term fixed income instrument. 11 Its monthly returns ("bond") were downloaded from the CRSP files. The riskless rate is the one-month Treasury bill rate ("r") downloaded from the CRSP "Fama Risk Free Rates" data file. The choice of predictors is more arbitrary, and many candidates have been proposed in the literature. As an illustration, we adopt the dividend yield and the default spread that are variously but routinely used since the early start of  Fama and French (1989).
12 The dividend yield ("dy") is measured as the total dividends paid off during the last 12 months divided by the actual value of the value-weighted market portfolio. We select this measure as monthly or quarterly dividend yields cannot be used because seasonality is predominant. We construct the dividend yield series from the two series of stock market returns with and without dividends. The default spread, a measure of financial duress, is computed as the spread between the yield of a 10-year Baa-rated bond and that of a 10-year Aaa-rated bond ("def "). This predictor is constructed from the relevant series in FRED R ⃝ database. Summary statistics are reported in Table  1. Panel A of Table  1  reports statistics for the one-month T-bill rate, the returns and excess returns (using the T-bill rate) on the risky assets and the predictors.
The second contribution of the paper consists in focusing on the relationship between productivity and senior's employment from the supply side (instead of the demand side, as most of the literature). We exploit a unique cross-sectional French database drawn from the survey Passage à la retraite ("Transitions from work to retirement") conducted in 2006 that contains information about the intended retirement age of respondents aged between 50 and 69. To investigate the effect of technical change and onthe-job training on retirement intentions, we use the Changements Organisationnels et Informatisation survey (COI, "Organizational Changes and Computerization" survey) conducted in the same year and construct aggregate variables within a local labor market, made up of a specific industry and occupation.
Using such recent databases constitutes a great advantage with respect to the existing literature on the subject, since by 2006 there was no incertitude concerning the diffusion process of new technologies among occupations. The main findings of our paper can be summarized as follows. The theoretical framework and numerical simulations predict that workers whose productivity is improved together with the state of technology (by means of on-the-job training) tend to retire later than workers who do not receive training and bear a relative skill obsolescence in case of technological change. These findings are confirmed by our empirical results. In case of technological change, workers employed in jobs displaying a high average training rate plan to retire later than those occupying jobs with a low average training rate.
Data and descriptive statistics The purpose of our empirical analysis is to assess whether productivity influences worker's intended retirement age. Our intuition is close to  Bartel and Sichermann (1993)  and  Friedberg (2003). In case of a technological change, workers are expected to advance their retirement date but only if they suffer a drop in their productivity. If their skills are updated, we expect that technical change encourages workers to delay their exit age. A difficulty here is that we do not have a direct measure of the worker's productivity in our datasets and especially we do not know how it may vary after a shift in the technological frontier. To overcome this problem, we proceed in the following way. We argue that workers benefiting from on-the-job training after a technological change will see their productivity indexed to the state of technology, whereas workers not concerned with a training program will suffer skill obsolescence.
The "Transitions from work to retirement" survey We exploit a unique cross-sectional survey entitled "Transitions from work to retirement" (TWR hereafter) conducted by the National Institute of Statistics and Economic Studies in France in 2006. This data set corresponds to an ad hoc module of the annual French Labour Force survey conducted during that specific year and concerns all people aged from 50 to 69 years old (so born between 1937 and 1956). Detailed demographic and economic characteristics of respondents are obtained by matching the TWR survey with the 2006 Labour Force survey. Our main variable of interest is the intended retirement age. While some authors have focused on transitions from work to retirement (see  Behaghel et al. (2010) ) in France, we choose to focus on retirement intentions of older workers.
Several studies have shown the relevance of such self-reported indicators. Using the first two waves of the Health and Retirement Study,  Dwyer and Hu (2000)  have shown that among workers who planned at wave 1 to retire by wave 2, more than one half was effectively fully or partially retired the next wave. Using the same dataset, Benitez-Silva and  Dwyer (2005)  conclude that retirement intentions are consistent with rational behavior. To provide some evidence on the reliability of retirement intentions for the French case, we have estimated robust correlation between the proportion of workers aged 50-55 years old in 2006 and expecting to leave the labor market at 60 or after, computed at the industry-occupation level from the 2006 TWR survey, and the proportion of workers aged 55-59 years old still employed five years later, also computed at the industry-occupation level using the 2011 French Labour Force survey.
A simple strategy to avoid this selection bias consists in keeping the youngest workers only. Thus, we restrict our sample to working male respondents aged 55 years old or less and exclude self-employed workers. Also, we decide to focus on the retirement intentions of men only as the retirement behavior of women may be more affected by family considerations. This leaves us with a sample of 1,430 male respondents. Our dependent variable is the age at which workers intend to leave the labor market. Respondents have to choose one of the three following categories in the TWR questionnaire: i) before reaching 60 years old, ii) between 60 and 64 years old, or iii) after 64 years old. 10 The TRW dataset includes a large set of control variables that may explain retirement intentions. For each respondent, we have information on age, gender, marital status, health status, educational level, working in private or public sector, job tenure, full-time or part-time job, and monthly wage.
Aggregate data on technological change and productivity Next, we need some information on the way workers are affected by technological changes in their work environment and also on the probability that their skills are updated after such a shock. As far as we know, there is no individual survey that gathers information on retirement intentions, technical changes in the work environment and training participation. So, we choose to merge the TWR individual data on retirement intentions with aggregate data on ICT adoption and participation to firm-sponsored training session coming from a matched employer-employee survey on organizational change and computerization (COI) conducted in 2006 by the Ministry of Labour and the Centre d'Etudes de l'Emploi. As a few sectors are missing in this database, (e.
g. associative sector or some public administrations), we end up with a sample of 1,159 male respondents after merging TWR data with COI. The employee-level COI survey allows to measure the incidence of technical change and on-the-job training accounting simultaneously for the industrial and occupational specificity of the respondent's job. This is in line with our theoretical model in which the effect of a technological change on retirement intentions may vary between different types of jobs. An important issue here consists in the choice of the aggregation level. This implies a trade-off between having the finest decomposition and having a sufficient number of individuals in each industry-occupation cell. In what follows, we define industries using the NAF-36 classification and consider four occupations: managers, technicians, clerical workers and blue-collar workers.
Our data was collected in 2006, several years after the ICT boom. So simple indicators of ICT use do not correspond to a technological change. Therefore, we consider the probability for workers of having experienced a technical change in their work environment over the last three years. 11 We compute this indicator for each industry-occupation cell. Respondents were also asked whether they have participated over the last three years to a firm-sponsored training session on the use of new softwares or new computer devices. We use this information to define for each industry-occupation cell the proportion of workers having been trained. This type of training relates to some specific skills that may become quickly obsolete after a technological change. We choose to consider the proportion of trained workers rather than the duration of training, given that 75% of the training sessions on the use of computer tools last for less than one week.
13 This does not have any effect on our results. As our variable of technical change is self-reported by the worker, it may be subject to classical measurement errors. To ensure the validity of this indicator as a good proxy for measuring technical change, we exploit the information contained in the COI data at the employer-level. More precisely, employers are asked about the introduction of some modern management tools and ICT equipment in their firm, at the time of the survey (in 2006) and also three years before  (in 2003). Regarding ICT, we take 15 items into account and provide a description of these items in Table  3. We rely on indicators built by  Bigi et al. (2013)  synthesizing the intensity of ICT use in 2003 and 2006 by Multiple Correspondence Analysis (MCA) and then compute an indicator of intensity of technical change for each firm between these two years.
Descriptive statistics We build our theoretical model on the main assumption that, in case of technological change, older workers occupying complex jobs receive training even though their working horizon is short. Using the 2000 French Complementary Survey on Training,  Langot and Moreno-Galbis (2013)  show that 34% of managers and 21% of technicians between 56 and 60 years old still receive firm-sponsored training in case of technological change. However, these figures may be subject to some selection bias. Indeed, we have already shown in Figure  1  that after 55, the probability to remain employed falls dramatically. Using our training variable from the COI employee-level data and restricting our sample to workers aged 50-55, we investigate whether access to training may vary across jobs of different skill levels, holding the working horizon constant.
12 Using data from the Continuing Vocational Training Survey conducted on a representative sample of French firms,  Lambert et al. (2009)  show that the average duration of a training spell is 28 hours. 13 These additional results are available upon request. 14 The MCA generates quantitative scores or "dimensions" that maximize the average correlation among the qualitative variables. The indicator of ICT-intensity used for each firm in 2006 is computed by regressing the first dimension, reflecting the intensity of use of ICT tools, on the 15 items reported in Table  3. The estimated coefficients represent the weight of each item and can be interpreted as a metric, determined by the set of situations specific to 2006. The synthetic indicator is obtained from a weighted sum of these items. Note that this metric estimated in 2006 has been used to compute the synthetic indicator of intensity of ICT use in 2003 for temporal comparison, as recommended by Greenan 2014, 3:8 http://www.
Indeed ECG signal analysis has a long story after the implementation of the ambulatory monitoring by Holter at the beginning of the fifties. Recent measurement methods, see  [8, 10] , allow us to record ECG data for healthy people over a long period of time: long distance (marathon) runners, individuals daily (24 hours) records, etc. We then obtain large datasets that allow us to characterise the variations of heartbeat durations in the two components of the nervous autonomous system: the parasympathetic and the orthosympathetic ones. Until recently, the analysis of these signals was usually made thanks to the experienced eye of cardiologists. Software solutions have recently been introduced and allow some data summary statistics or possibly indications about the physiological state of a user.
This is measured by the modulus of the complex wavelet coefficients |W i (b)| 2 for i = 1, 2, with W i (b) = R ψ i (t -b) X(t) dt, where ψ 1 and ψ 2 are suitable wavelets chosen with disjoint frequency supports corresponding to the LF and HF bands. These wavelet coefficients are then computed at each second, i. e. , the difference between two consecutive values for b is equal to 1 second. Then we consider the change point problem of the mean of the multivariate time series Z 1 (b), Z 2 (b) where Z 1 (b) = log |W 1 (b)| 2 , and Z 2 (b) = log |W 2 (b)| 2 , (2) and we use a simple method well suited for big datasets, that is the Filtered Derivative with p-value (FDpV) method  [6, 15]. How to choose the wavelets ψ 1 and ψ 2 ? In the idealistic case, we would use two filters ψ 1 and ψ 2 with compact support, the Fourier transforms of which have support inside the ortho-sympathetic and para-sympathetic bands.
Real case analysis In this section we apply classification techniques to a real case by investigating heart rate variability of an emergency doctor during 24 hours at the Clermont-Ferrand hospital. Among workers, emergency physicians represent a population at risk because their work consists in management of life threatening emergencies, lack of sleep and fatigue. It is a job demanding long working hours, sustained vigilance and unpredicted stressful situations. In this paper we want to classify different states of HR activity during a work day. For this purpose we make a blind separation of groups that highlights nycthemeral cardiac behaviour. Though we dispose of larger data set with ECG measurements, we will only focus on one subject to show the efficiency of our approach. A further discriminant analysis will be investigated for the whole data collected from the 19 emergency doctor measurements.
Data description This paper present a methodological approach and can be seen as an introductory work for further study that will concern large number of subjects. Indeed, we dispose of a huge dataset collected from the emergency service of the University Hospital of Clermont-Ferrand, France. The data that we have concerns 19 physicians where + + + + + + + + + + + + + + + + + + Figure 2. Illustration on how we construct the variables ∆τ, ∆θ exclusion criteria were endocrine disease, pregnancy, deleterious life event, any current illness, anti-inflammatory or chronotropic drugs. etc. The ECG signal analysis was investigated on 24 hours electrocardiogram recorded from 8h30 until 8h30 the next morning; more details about the protocol and data collection can be found in  [10]. For technical testing of our approach, we will only use one subject for which the ECG signal has been cleaned by removing the Holter artifact measures and converted to RR signal.
Data Collection Data collection was through individual encounters (about 30 minutes in length). After completing the informed consent process, participants' provided demographic information (sex, age, marital status), self-reported health conditions and diagnoses, socio-economic status (educational level and housing information), and whether the participant had ever shared medicines with anyone. Participants completed three pile sort exercises. Afterwards, brief conversations occurred with participants (for instance, concerning the rationale behind their pile sort groupings). In the first free pile sort (sort 1), participants were given a set of 44 randomized cards with names and photos of medicines and other household items that could be shared. The 34 non-medicine items were chosen from among items identified during a previous free listing exercise related to things that people could share.
The scores were used in the non-parametric analyses of ordinal data to compare the group of medicines to the other items. This activity also generated groups for non-metric multidimensional scaling analysis as in the other pile sorts. For the free pile sort of 33 items from the local materia medica (sort 3), participants were given a set of 33 randomized cards with names and photos of pharmaceuticals, over-the-counter medicines, and home remedies related to five health conditions (headache, hypertension, intestinal parasites, anemia, and a chest cold or ''tight breathing'') as well as several ''unclassified'' items (Table  S1  in File S1). The home remedies were chosen from among those listed during a past free listing exercise. Participants sorted the cards into piles as previously described, and the results were recorded using 33 by 33 matrices.
Statistics Statistical analysis of the data was conducted using both descriptive and analytical methods  [30, 31]. Analysis of continuous data was by t-test for two groups and one-way analysis of variance for multiple groups. Ordinal data was compared for two groups by Wilcoxon rank sum test and for multiple groups by the Kruskal-Wallis test. Categorical data was analyzed using contingency table methods. For pile sort 1, the individuals' pile sort data were combined as described by Bernard  [32]  using a spread sheet program (Excel 2010, Microsoft, Redmond, Washington, USA) to generate a summary matrix showing the percentage of all sorts in which any two items were in the same pile. Summary matrices were also generated for sort 2 and sort 3. Non-metric multidimensional scaling analysis was applied to the three summary matrices using a public domain anthropology analytical program (Anthropac 4.
983, Analytic Technologies, Natick, Massachusetts, USA). Non-metric multidimensional scaling analysis uses the summary matrix to construct an association map in which the distances between items offer a visual representation of the collective opinion of the participants regarding the relationships among the items. The numerical values associated with the four categories of shareability on the analogue scale in the constrained pile sort (sort 2) were used to generate a mean shareability score and its SD for each of the 44 items. A commercial statistics program (Stata/IC 10, StataCorp, College Station, Texas, USA) was used for the standard statistical analyses. Statistical results with probability (P) values less than 0. 05 were considered significant. Overall Type I error was controlled to a level of 0.
In general, medication adherence is sub-optimal with estimated non-adherence rates of 25%  [34]. Attempts to improve adherence and the rational use of medicines have produced only modest results. Reviews of published studies and of 121 evaluable interventions from the World Health Organization's database for developing and transitional countries indicate that improvements are often small and may not correlate with improved clinical outcomes  [35, 36]. Discussions of the rational use of medicines generally do not even consider medicine sharing and its implications  [6]. Strategies might most productively focus on consumers because those that depend heavily on professional education maybe less likely to produce large and durable changes  [37]. The study findings suggest that specific groups of things in the first pile sort tended to have a similar candidacy for sharing as illustrated in Figure  3.
Related works Though most practitioners are perfectly aware both of the crucial impact of scaling range selection on estimation performance and of the difficulties an objective and automated selection raise, this issue remains barely addressed. Essentially, scaling range can be selected either from fundamental arguments related to the physics (the physiology, etc. ) underlying the data at hand (e. g. , Kolmogorov dissipation scale in hydrodynamic turbulence  [5] , sympathetic-parasympathetic frequency band split in heart rate variability  [14] ), or from empirical data analysis. In this latter perspective, visual inspection and empirical experience remain the dominant practice amongst practitioners. This is however obviously a tedious and error prone procedure, notably for large databases, where each signal needs to be inspected and where noise level corruption is likely to vary individually from one signal to another, a very common observation in biomedical data notably.
1. Performance is reported and discussed with respect to optimal mean square error, data length and trade-off between estimation and analysis, for perfect multifractal processes in Section 4. 2. Performance and robustness are further evaluated against noise corrupted multifractal processes (cf. Section 4. 3), or multifractal processes with upper cutoff (cf. Section 4. 4), or multifractal processes suffering from both corruptions at fine and coarse scales, with in addition random amplitude effects (cf. Section 4. 5). To finish with, the procedure is illustrated at work for adult heart rate variability analysis, and applied to a large database (cf. Section 5). A MATLAB procedure, designed by ourselves, will be made publicly available from our webpages at the time of publication.
Random lower and upper departures from scaling Let us now turn to a case study that very much resembles the real-world situation where practitioners analyze a large dataset, consisting of a large number of time series: Departures from exact scale invariance may occur at both fine and coarse scales simultaneously and may involve scales that vary from one time series to another. This is notably the case in biomedical applications, such as the one described in Section 5 below, where the level of noise varies from one subject to another, thus changing the lower cutoff, and where either the mechanisms producing scaling or seasonal trends may vary from subject to subject, thus modifying the upper cutoff. For such situations, practitioners must either perform a systematic check for each subject or adopt a fixed scaling range that globally matches the dataset.
Scale invariance in heart rate variability Heart rate variability (HRV) data is now commonly used to asses the cardiologic health status, with the leading theme that a strong variability indicates good health  [14]. It is now well documented that the variability of heart rate can be interestingly quantified by scale invariance and multifractal properties and attributes, cf. e. g. ,  [29, 13]  and references therein. Recently, it has been shown that the  wavelet leader multifractal formalism yields fruitful analysis of HRV (cf. [30, 31] ). In the present contribution, 54 (2-hour long) records are studied, taken from the Normal Sinus Rhythm RR Interval Database, made available by Physionet (http:// www. physionet. org/physiobank/database/nsr2db/, cf. [32] ). All records correspond to subjects with normal sinus rhythm, with a balanced male-female ratio (30/24), and with mean age (7 standard deviation) of 61:36 ð 7 11:63Þ years.