dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, AROME exhibits the strongest negative relative bias (-6%) and the highest relative RMSE (12%). DSLF and DSLFnew have equivalent scores with a relative bias of -3% and a relative RMSE of 11%. Finally, SAFRAN has a relative bias of + 1% and a relative RMSE of 11%. These global scores are close to the sensor uncertainties in mountains, which does not enable to choose the "best product". However, some trends are identified such as an underestimation of LW↓ by DSLF, 285 DSLFnew and AROME. The performance of LSA SAF products and models is also clearly better in terms of LW↓ than SW↓, because of lower biases and RMSE. The yearly cycles of SW↓ irradiances are illustrated at Carpentras for reference (Fig. 4a ) and at Péone mountain station (Fig. 4b ). They show higher RMSE in Spring and Summer for each dataset, lowest RMSE for DSSF and highest RMSE for AROME during the whole year, except in December.
(2016). The dependence of the different irradiance products with the altitude was further explored with the study of altitudinal gradients. Figure  7  represents the vertical evolution of the LW↓ and SW↓ averaged over the SAFRAN massifs of the French Alps and the Pyrenees by steps of 100 m of 330 elevation over the whole study period, together with the associated standard deviations. The strong dependency of LW↓ irradiance with altitude is confirmed in Fig. 7a  for the French Alps and Fig. 7b  for the Pyrenees. As a reference, the altitudinal gradient for annual LW↓ means of -29 W m -2 km -1 found by  Marty et al. (2002)  in the Swiss Alps is plotted in dashed line, while Table 3 lists the mean altitudinal gradient for each dataset in both domains. All datasets present a 335 steady decrease of LW↓ with altitude, and are close to each other below 1200 m approximately.
7a  and Fig. 7b ), whereas they can reach similar values for SW↓ (Fig. 7c  and Fig. 7d ). This spatial variability at a given altitude is particularly marked at low-  higher than the other datasets. In winter, SW↓ radiations are low and the snow albedo is high: their contribution to the surface energy budget is much lower than in spring. Thus, LW↓ radiations have a higher relative contribution during the accumulation period. However, during the melting period (from mid-March to mid-May here), the contribution of SW↓ radiations is the highest, due to higher extra-terrestrial solar fluxes, longer days and lower snow albedo: because of their higher SW↓, A-Cro simulations melt faster than AS-Cro, which reduces their bias. These trends can also be observed when looking at maps of spatially distributed snowpack simulations.
Quality of irradiance datasets in mountainous terrain We presented an overview of the quality of several irradiance datasets through an in-depth assessment of the irradiance fields in mountainous terrain. In terms of SW↓ irradiances, DSSF exhibits best scores in mountains, particularly below 2000 m. Above 2000 m, its RMSE is similar to SAFRAN and AROME, due to a strong negative bias. AROME presents systematic and large overestimations 430 of SW↓ irradiances, contrarily to SAFRAN tendency to underestimate them. The spatial variations of SW↓ irradiances are better represented in DSSF and AROME than in SAFRAN. In terms of LW↓ irradiances, the obtained errors are comparable and it is difficult to identify the best product. The use of forecasts at higher spatial resolution to compute DSLFnew enhances the topographic dependence, which limits the underestimation of LW↓ irradiance at low and mid-altitudes found with DSLF, but 435 strengthens the negative bias at high altitude.
Sensitivity of snowpack simulations to the radiative forcing DSSF and DSLFnew irradiance datasets were used to replace AROME irradiance forecasts as radiative forcing of Crocus simulations. The rest of the atmospheric forcing was taken from AROME 465 forecasts. A similar experiment was done with SAFRAN irradiances. The performance of the snowpack simulations was degraded when using DSSF and DSLFnew products, with an increased positive snow depth bias. On the contrary, the use of SAFRAN irradiances was found to decrease the positive bias obtained with AROME-Crocus. Vionnet et al. (2016)  and  Quéno et al. (2016)  already showed an overestimation of snow depth by AROME-Crocus in the French Alps and the Pyrenees respec-470 tively. In addition,  Quéno et al. (2016)  partly attributed this overestimation to an underestimation of strong melting.
However, the prevailing effect of LW↓ compared to SW↓ is specific to high latitudes, because of the 500 lack of solar insolation in winter. In our study, we showed that the new LW↓ forcing from DSLFnew (with a positive bias compared to AROME) had a significant impact on the mass budget during the whole winter at low altitudes (Fig. 10 ), while the impact was more limited at high altitudes (Fig. 11 ). It can be explained by decreasing LW↓ irradiances with altitude together with increasing SW↓ irradiances, leading to a more significant impact of SW↓ at high altitudes. It is also due to the 505 earlier snowmelt at low altitudes, which limits the crucial role played by SW↓ in spring. Furthermore, the differences between the different radiative forcing datasets mainly consist of biases rather than random errors: a typical example is the difference of SW↓ at high altitudes between AROME and DSSF shown in Fig.
11. Their effect is then cumulated during the whole season, rather than counterbalanced, which increases their impact. It is consistent with the outcomes of Raleigh to the albedo effect. Our example at high altitude (Fig. 11 ) does not follow this rule because of marked SW↓ differences in spring, when the albedo decreases. Finally, although the SWE is not 515 impacted by the differences in incoming radiations at high altitude during the accumulation period (Fig. 11 ), impacts are to be expected in terms of snow surface temperatures, with possible consequences on the snow metamorphism processes. Lapo et al. (2015b)  indeed showed more sensitivity of the snowpack simulations to radiation errors at the coldest sites when evaluated in terms of snow surface temperature rather than SWE. Future works could thus focus on the impact of the different 520 incoming radiation datasets on the surface energy budget and the resulting effects on the snowpack stratigraphy.
Conclusions In this paper, we assessed the quality of satellite-derived incoming radiation products (DSSF for solar irradiance and DSLF for longwave irradiance) in mountainous terrain, by conducting a thorough 525 inter-comparison study involving kilometric resolution forecasts from the NWP system AROME and fields from the SAFRAN analysis system. A new satellite-derived product for LW↓ iradiance (DSLFnew) was developed using the DSLF algorithm fed by AROME forecasts. An evaluation of all available products was performed against in situ measurements using four years of data in the French Alps and the Pyrenees. The result analysis showed that DSSF products are best for solar radiations, 530 despite an underestimation at the highest altitudes, while AROME is associated with a strong positive bias and SAFRAN with a negative bias.
In terms of longwave radiations, contrasted results were obtained at the mountain stations, all falling within the range of uncertainty of sensors. A systematic underestimation by AROME, DSLF and DSLFnew was highlighted. The negative bias of DSLF was reduced by DSLFnew up to mid-altitudes but enhanced at high altitudes due to a too strong 535 altitudinal gradient. A spatial comparison of the datasets showed that AROME and DSSF better represent the spatial variability of SW↓ fluxes in mountains by comparison with SAFRAN. These results are encouraging and highlight the potential benefits of using DSSF, DSLF and DSLFnew as radiative forcing for snowpack modelling in mountainous terrain. Their relatively good quality in mountains as compared to lower altitudes also supports the use of these data as climatological 540 inputs and/or validation datasets for NWP models over complex domains such as mountains, where incoming radiation measurements are scarce.
An evaluation of distributed snowpack simulations by Crocus driven by AROME and the different radiation datasets was then conducted in the French Alps and the Pyrenees. We showed that replacing AROME radiations by DSSF and DSLFnew increased the positive bias of snow depth, despite 545 an overall better performance of these datasets in terms of incoming radiations. Therefore, an improved meteorological forcing does not ensure more accurate snowpack simulations. This is mostly due to error compensations within the atmospheric forcing and the snowpack model. Complementary studies are sorely needed to identify the cause of the underestimated melting, which cannot be attributed to radiative fluxes. They should tackle factors such as the turbulent fluxes simulated by 550 AROME-Crocus and the albedo parametrisation in Crocus.
230 different irradiance datasets: a) AROME radiation forecasts (simulations named A-Cro hereafter), b) SAFRAN radiation reanalyses (simulations named AS-Cro hereafter), c) DSSF and DSLFnew (simulations named AL-Cro hereafter). In order to include DSSF and DSLFnew products in AROME forcing, the interpolation on AROME grid was made to minimize the effect of elevation difference on the incoming radiations. Among the four nearest LSA SAF grid points, the grid point with the 235 minimum altitude difference with AROME grid point was chosen. Similarly to Hinkelman et al.
and mid-altitudes (< 1800 m) in the Pyrenees for AROME and DSSF, reflecting a good representation of the strong climate heterogeneity between French and Spanish foothills. SAFRAN, which gives homogeneous analyses per massif, does not account for the spatial variability within the massif as is the case for AROME and DSSF. 4 Impact of the radiation products on snowpack simulations 360 Snowpack simulations were performed over four winters from 2010 to 2014 to assess the impact of the different irradiance datasets as radiative forcing. Table4 summarizesthe bias and RMSE for the three simulations (A-Cro, AL-Cro and AS-Cro) compared at 172 stations of the French Alps and the Pyrenees over the period. The scores are aggregated by domain and elevation range. As shown by Vionnet et al. (2016) and Quéno et al.
355 and mid-altitudes (< 1800 m) in the Pyrenees for AROME and DSSF, reflecting a good representation of the strong climate heterogeneity between French and Spanish foothills. SAFRAN, which gives homogeneous analyses per massif, does not account for the spatial variability within the massif as is the case for AROME and DSSF. 4 Impact of the radiation products on snowpack simulations 360 Snowpack simulations were performed over four winters from 2010 to 2014 to assess the impact of the different irradiance datasets as radiative forcing. Table4 summarizesthe bias and RMSE for the three simulations (A-Cro, AL-Cro and AS-Cro) compared at 172 stations of the French Alps and the Pyrenees over the period. The scores are aggregated by domain and elevation range. As shown by Vionnet et al. (2016) and Quéno et al.
The definition of health generated in 1948 by the World Health Organization remains fit for current purposes: "not merely the absence of disease or infirmity but a state of complete physical, mental and social well-being"  [2]. Particularly, the role of well-being at work plays a major role in social well-being  [3] , and research remains limited by few large-scale investigations into physical and mental well-being in this setting. Therefore, we built a software tool based on three major health-related categories derived from the World Health Organization definition of health: physical, mental, and work-related well-being. Taken separately, there is strong evidence between those factors and health-related outcomes but data are not typically gathered and synthesized in workplace settings. Within physical well-being, we investigate nutrition, physical activity, sleep, and musculoskeletal function.
Despite the strong evidence of the benefits of stress management, diet counseling, and exercise training in patients, intervention studies on broad and representative samples of employees remain scarce. Preventive programs specific to work organizations can be implemented at the worksite  [50] [51] [52] [53] [54] [55]. Whereas other interventional software programs focus on specific problems such as musculoskeletal disorders  [56] , WittyFit aims to promote health with a holistic understanding of worker health, dynamically aligned to updated scientific knowledge (evidence-based medicine). The development of the health-promoting WittyFit software will be the first interventional study with an epidemiological cohort design that is inclusive of specific details on both leisure time and working conditions, providing a global understanding of individuals.
WittyFit software will support recommendations to promote more personalized health prevention initiatives focusing on physical, mental, and work-related well-being. The shorter term hypothesis is that effective use of the WittyFit software will increase well-being and improve health-related behaviors. The ultimate hypothesis of the development of the WittyFit software is that it will increase life expectancy. Therefore, the overall aim was to build an epidemiological database generated using Wittyfit and combining major lifestyle parameters. We aim to use these data to generate initiatives for decreasing premature mortality and morbidity in conjunction with improved well-being. WittyFit will function to generate a powerful database for strengthening the evidence and advancing knowledge on the relationships between work, behavior, and health derived from a large amount of epidemiological data.
Participants All workers agreeing to participate in the WittyFit study will be included from any companies willing to permit their employees to be invited to participate. The WittyFit concept follows an epidemiological design without sample size limitation. Like the Nutrinet-Santé study  [57] [58] [59] [60] [61] [62] [63] , the WittyFit study is never expected to end. Recruitment will commence in January 2016, and data collection will be ongoing due to the prolonged longevity of the project. Duration of participation per individual is unlimited. A staggered strategy will be to target a sample size of 200 workers from Voyages-SNCF. com within the first 6 months in order to improve and evolve the WittyFit software. WittyFit will then extend to other companies in the following year.
Economic Outcomes and Outcomes Provided by Employers (WittyFit) Economic data will be provided by volunteering employers from participating companies: professional roles (unskilled, skilled, mid-level workers, and senior executives), occupational sector, type of contract (full-time, part-time), absenteeism, turnover, and sales revenue and benefits. These data will be continuously updated and monitored. When a participant disappears from the database, the company will provide the relevant reason: retirement, relocation to another company, or death (premature mortality) (Textbox 1).
Medical Outcomes (WittyFit Research) WittyFit is based on two databases: WittyFit, which deals with behavioral data, and WittyFit Research, which deals with medical data. To guarantee the highest level of security, the two databases are separate and do not interact; physicians from the University Hospital of Clermont-Ferrand will be the only researchers with access to medical data. Behavioral data (WittyFit) are those within the three major health-related categories: physical, mental, and work-related well-being. Medical data (WittyFit Research) are medical history and chronic diseases classified using the International Classification of Diseases, medications classified using the Anatomical Therapeutic Chemical classification, any clinical data known by participants such as heart rate or blood pressure, and the most common biological data such as blood glucose levels, hemoglobin A 1c , or blood cholesterol levels (Textbox 1).
Confidentiality All medical data (medical history, treatments, etc) will be only accessible from a carefully restricted WittyFit researcher access platform. All data are anonymous, and the name of the employee is never entered into the database. The database is implemented from a human resource-generated number, which is then automatically converted into another number in the WittyFit database. Data provided by employers (such as professional roles, occupational sector, type of contract, and absenteeism) are automatically associated with the human resource-generated number.
Manager Feedback The second feature of WittyFit is that managers will receive anonymous feedback on the general state of health and problems encountered by their employees (see Figure  5 ) for whole or sectional divisions of the workplace if the sample size is sufficiently high (ratios of 1/10). Middle managers will be able to target specific actions such as promoting physical activity at work or workplace initiatives assisting employees to quit smoking. They will have access to the Best Practice database. Middle managers will also have access to the Forum Manager anonymous community to share their experience. They will.
Connected Objects Eventually, WittyFit will further evolve with the waves of connected objective measures of health and lifestyle, further increasing its data relevance, with the use of devices such as pedometers, heart rate monitors, accelerometers, and thermometers. Only volunteering workers will connect these objects to WittyFit. We will soon propose devices able to analyze these objective measures. For example, we have the expertise for objective measure of stress from heart rate variability or skin conductance  [73] [74] [75].
Analyses of Shorter Term Objectives To study WittyFit's effect on morbidity and well-being, analyses will include comparisons with historical cohorts and literature and within the paired data from individual participants as they generate their own basis for comparison. To study longitudinal evolution of parameters associated to morbidity and well-being, models for repeated measures will be performed using a random-effects model (linear or generalized linear) along with fixed effects for descriptive data (eg, gender, age, body mass index, type of contract [full-time, part-time], professional roles [unskilled, skilled, mid-level workers, and senior executives] and occupational sector [commercial activities and service, health and social services, manufacturing/blue collar industries, finance officers]) taking into account within and between participant variability and company variability.
Summary The main aim of WittyFit is to provide early comprehensive and targeted behavioral interventions to improve well-being, morbidity, and ultimately life expectancy. We aim to build an epidemiological database combining major lifestyle parameters including those at work. Morbidity before retirement has a huge cost, burdening both public health and workplace finances. Therefore, there is an urgent need for a tool such as WittyFit. WittyFit is the synthesis of several novelties. Because all parameters (physical, mental, and work-related well-being) are linked, providing a multifaceted understanding of individuals is needed. Whereas other interventional software focuses on a specific aim (relaxation, identification of musculoskeletal disorders and stress)  [56] , WittyFit aims to promote health with a holistic understanding of worker health, dynamically aligned to updated scientific knowledge (evidence-based medicine).
The findings from WittyFit should be significant for practical use in public health, especially as software and mobile apps are being increasingly used in health interventions  [96]. The cost up until now to design WittyFit has been €1 million. Investments are still ongoing (and will continue) for a continuous improvement of WittyFit. This amount of money may seem huge, but dissemination of WittyFit within companies and contracts permits us to have an equilibrate business model (WittyFit was created in 2014). The challenging target will be to build a personal health system for workers with the capacity to interact with different physical activity and stress levels, sleep, or other health-related objectives using a computing system such as the one we are developing  [73]. We will have the possibility to register a license and develop industrial applications.
Conclusion In conclusion, the name WittyFit came from Witty and Fitness. The concept of WittyFit reflects the concept of health from the World Health Organization: being spiritually and physically healthy. WittyFit is a health-monitoring, health-promoting initiative that may improve the health of workers and health of companies. WittyFit will evolve with the waves of connected objects further increasing its data accuracy with objective measures. WittyFit may constitute a powerful epidemiological database. Finally, the WittyFit concept may be spread in the general population and be a generalizable useful tool for prevention.
In this paper, we propose a novel modeling and a new methodology for estimating the location of block boundaries in a random matrix consisting of a block-wise constant matrix corrupted with white noise. Our method consists in rewriting this problem as a variable selection issue. A penalized least-squares criterion with an 1 -type penalty is used for dealing with this problem. Firstly, some theoretical results ensuring the consistency of our block boundaries estimators are provided. Secondly, we explain how to implement our approach in a very efficient way. This implementation is available in the R package blockseg which can be found in the Comprehensive R Archive Network. Thirdly, we provide some numerical experiments to illustrate the statistical and numerical performance of our package, as well as a thorough comparison with existing methods.
Introduction Detecting automatically the block boundaries in large block wise constant matrices corrupted with noise is a very important issue which may have several applications. One of the main situations in which this problem occurs is in the study of HiC data. It corresponds to one of the most recent chromosome conformation capture technologies that have been developed to better understand the influence of the chromosomal conformation on the cells functioning. This technology is based on a deep sequencing approach and provides read pairs corresponding to pairs of genomic loci that physically interacts in the nucleus, see  [12]  for more details. The raw measurements provided by HiC data are often summarized as a square matrix where each entry at row i and column j stands for the total number of read pairs matching in position i and position j, respectively, see  [4]  for further details.
Competitors and implementation details In our experiments, we compare our methodology with popular methods for segmentation and variable selection that we adapted to the specific problem of two-dimensional change-points detection:  as change-points for the two-dimensional data. We use the implementation provided by the publicly available R package rpart. 2. Second, we adapt Harchaoui and Lévy-Leduc's method  [8]  (hereafter HL), which is the exact one-dimensional counterpart of our approach. To analyse two-dimensional data, we apply this procedure to each row of Y in order to recover the change-points of each row. The change-points appearing in the different rows are claimed to be change-points for the twodimensional data either if they appear at least in one row (variant HL1) or if they appear in ([n/2] + 1) rows (variant HL2).