dataesr/scientific-paragraphs-categorization huggingface dataset:
AM3  [3] , EMFStore  [12] , or MORSE  [6]  just store information about the model structure through metamodel references. Nevertheless, the global metadata that  [3]  could associate to model transformation artifacts is interesting and it has been improved in this paper. Other approaches such as ModelCVS  [10]  and AMOR  [2]  extract automatic and predefined data from the metamodels to use it as a knowledge base for querying and merging operations. As for the description of model transformations, in  [19]  the authors present an extension of the QVT-r language which is able to express alternatives (and their impact on non-functional properties) in the design of transformations. The concept of quality-driven model transformations is also addressed in  [7]  where design guides are proposed to define model transformations with "alternatives" based on non-functional properties.
Electronics, data acquisition, and analysis The CUORE electronics will read the thermistor voltage signal to computer via the front end electronics, high-precision 18-bit digitizers, and the data acquisition software Apollo. Each front-end channel consists of load resistors, a detector biasing system, a two-stage amplifier system with programmable gain up to 10000, and a 6-pole anti-aliasing Bessel filter with cutoff frequencies in the range 15-120 Hz. We have prepared a single chassis system with a full chain CUORE electronics for testing on a mini bolometer array during upcoming commissioning tests of the CUORE cryostat. The DAQ software Apollo reads in the digitized waveforms at 125 S/s, runs trigger algorithms, and records both the continuous and triggered data on the disk for offline analysis.
Apollo has been used successfully for CUORE-0 data taking  [25]. Current development focuses on scaling from one DAQ computer use case (as in CUORE-0) to a small DAQ cluster to acquire the ∼1000 channels in CUORE. Our custombuilt data analysis software framework Diana has been used extensively for analysis of Cuoricino, R&D runs, and more recently CUORE-0. New features such as noise decorrelation  [38]  and browser-based data quality monitoring are being implemented.
Conclusion and outlook We reviewed the physics reach and current construction status of the CUORE experiment. Data taking is expected to start in 2015. With excellent energy resolution and large isotope mass, CUORE is one of the most competitive 0νββ experiments under construction. Future bolometer program such as the IHE can explore the inverted mass hierarchy region to |m ββ | ∼ 10 meV by utilizing additional light readout to further reduce background. We described the conceptual design of IHE and its physics impact on 130 Te, 82 Se, 116 Cd or 100 Mo 0νββ searches.
Acknowledgements The CUORE Collaboration thanks the directors and staff of the  Laboratori Nazionali del Gran Sasso  and the technical staff of our laboratories. This work was supported by the  Istituto Nazionale di Fisica Nucleare (INFN) ; the Director,  Office of Science, of the U. S. Department of Energy  under Contract Nos. DE-AC02-05CH11231  and  DE-AC52-07NA27344 ; the  DOE Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551  and  DEFG03-00ER41138 ; the  National Science Foundation  under Grant Nos. NSF-PHY-0605119 ,  NSF-PHY-0500337 ,  NSF-PHY-0855314 ,  NSF-PHY-0902171 , and  NSF-PHY-0969852 ; the  Alfred P. Sloan Foundation ; the  University of Wisconsin Foundation ; and  Yale University. This research used resources of the  National Energy Research Scientific Computing Center (NERSC).
This article presents a planning problem in a distribution network incorporating two levels inventory management of perishable products, lot-sizing, multi-sourcing and transport capacity with a homogeneous fleet of vehicles. A mixed integer linear programming (MILP) and a greedy heuristic were developed to solve this real planning problem. There are some instances for which the solver cannot give a good lower bound within the limited time and for other instances it takes a lot of time to solve MILP. The greedy heuristic is an alternative to the mixed integer linear program to quickly solve some large instances taking into account original and difficult constraints. For some instances the gap between the solutions of the solver (MILP) and the heuristic becomes quite significant. A multi-start iterated local search (MS-ILS), using the variable neighborhood descent (VND) and a greedy randomized heuristic, has been implemented.
The lost quantities in the warehouse (the batches that are not consumed before expiry date) are calculated using constraint (23). Constraints (  24 )-(  26 ) check that the transport capacity is not exceeded by the number of pallets and the total weight of shipped products. The linearisation of the variables of gap are made with constraints (  27 )-(  31 ) for the stock at the distribution centers and the stock at the warehouses. The variables are defined in the lines (32)-(  39 ). qi ipt = FPO ipt if IsFPO ipt = 1 ∀i ∀p ∀t (8) qi ipt ≥ isqi ipt • (1 -IsFPO ipt ) • QT min ipt ∀i ∀p ∀t (9) qi ipt ≤ isqi ipt • M ∀i ∀p ∀t ( There are some instances for which the solver cannot give a good lower bound within the limited time and for other instances it takes a lot of time to solve MILP. A greedy heuristic has been implemented as an alternative to the mixed integer linear program to quickly solve some large instances.
Instances The 20 first instances have been extracted from actual databases representing real distribution networks for customers of FuturMaster, a french software publisher. The 40 other instances have been randomly generated to have wider diversity for computational evaluation. First database has provided 10 small instances with the number of products varies from 2 to 10, 2 warehouses, 2 distribution centers and horizon times is from 10 to 20 periods. The 10 instances from second database have the number of products that varies from 5 to 20, 11 warehouses, 145 distribution centers and horizon times varying from 10 to 20 periods. We have tried to evaluate greater instances with this database but when the number of products exceeds 20 the solver is out of memory. The random generator of instances has provided 40 instances with the number of products varying from 5 to 50, warehouses from 5 to 10, distribution centers from 10 to 20 and horizon times from 10 to 30 periods.
Results The results of MILP solver and greedy heuristic tial solution) for all instances are in Table  1. The default parameter of relative gap between lower bound (LB) and upper bound (UB), 0. 01, is used as stop criterion. The computational time is in seconds. The gap between greedy heuristic solution cost (H) and lower bound of MILP (H/LB) and its computational times given in We note that for the instances from the database 1 the MILP resolution finds a good solution with 0. 001 as average gap between lower and upper bound for 29. 51 seconds. For the instances extracted from the database 2 and from the random generator, the computational time of MILP resolution increases: in average 7427. 13 seconds for database 2 and 1418. 28 seconds for the instances generated randomly. The gap UB/LB decreases too, and for some instances of the random generator, the solver does not provide a good lower bound after 1 hour and half of calculation: 11 are not represented in the results and heuristic results are not compared with MILP solver.
Greedy heuristic is very fast (in average less than 1 second) but for the instances of the database 2 (16. 626%) and the random generator (31. 829% ) the average gap, between the LB and the cost of heuristic solution, becomes quite significant. The worst cases are due to the structure of the greedy heuristic. MS-ILS is compared with an exact resolution of the MILP and we show the improvement of the solution quality of the heuristic and the VND in Table  2. The average gap between the lower bound of MILP resolution and the MS-ILS is 3. 445%. The average gap between the solver solution and greedy heuristic solution (initial solution) is 22. 233% then this gap decreases by 18. 674% when the MS-ILS is tested with three runs; the ILS is applied with 10 calls of VND. The average computational time of calculation of MS-ILS is 1 890.
2013 ) and a 'total-denial' OSE. The NRT OSEs were performed in parallel with an operational ocean forecast system, where data were systematically withheld from the NRT OSE, to provide an up-to-date demonstration of the impact of each component of the current GOOS on operational ocean forecasts. The 'total-denial' OSE, while not produced in NRT, demonstrates the impact of a total loss of observations to a forecast system, quantifying the degradation of the forecast system over time. This paper also presents some results related to the investigation of alternative methods that allow a direct assessment of the relative impact of ocean observations in a given system. This paper provides an update on this community's contribution to this field, under the auspices of GODAE OceanView. Many studies have previously quantified the impact of each GOOS component.
To evaluate each OSE, the Observation minus Background (OmB) values for temperature and salinity are analysed using all Argo data. The OmB statistics provide an indication of the forecast error, quantifying the misfit to observations immediately before assimilation. Figure  1  shows the global root-mean-squared difference (RMSD) between the observations and the background temperature and salinity fields immediately before assimilation for 2012 for all OSEs. When no Argo data are assimilated the OmB values increase by 40-45% compared to the OSE that assimilates all Argo data. The relative degradation is approximately uniform down to 2000 m depth. When data from only half the Argo floats are assimilated, the OmB values increase by about 15% for temperature and salinity. The regional impact of Argo floats is sometimes very large (not shown).
The neglect of Argo data degrades the properties in the mixed layer and at depth in key regions, such as the Mediterranean outflow and in the Labrador Sea during periods of convection (not shown), which are known model weaknesses. This shows the importance of Argo data for initialising crucial aspects of the ocean circulation, such as the depth of the Mediterranean outflow. In the Labrador Sea, when no Argo data are assimilated, the warmer water between 200 and 400 m depth beneath the thermocline under the winter convection events  (Yashayaev & Loder 2009) , are not generated or maintained (Figure  2 ). The impact of Argo data on the upper ocean indicates that Argo is critical for constraining the broad scale properties of the ocean, upon which the mesoscale circulation depends.
FOAM NRT OSEs A number of NRT OSE experiments were performed in 2011 with the UK Met Office's operational ocean forecasting system, FOAM  (Storkey et al. 2010; Lea 2012 , Lea et al. 2013; Blockley et al. 2013). These experiments adopt the same approach as that described above for the Mercator system, except that they were performed by running a parallel, identical version of the UK operational system, with different data types systematically withheld. Each month a single additional OSE is performed alongside the operational system. The operational system assimilated all available data. On average, each month, the operational system assimilates 120K XBT, 900K TAO, 1. 1M altimetric, 30M SST, and 1. 3M Argo observations (where K denotes thousands, and M denotes millions). For each OSE, a different set of observations was excluded from the assimilation.
For example, if a particular ftp server used to obtain observations was shut down temporarily, for how long could a given operational system continue to provide useful forecasts before it should be stopped because it is too inaccurate? Clearly, this depends on the particular observation dataset, forecasting system and application. Regardless, such knowledge is required to ensure product quality for users. To provide a rough estimate of the decrease in analysis and forecast skill for such an event, a simple OSE is performed using the experimental Canadian Global Ice Ocean Prediction System (GIOPS)  (Smith et al. 2014). All observations that would normally be assimilated are simply withheld from the forecast system and the misfit between the model fields and observations is assessed over time.
2008 ) and recently adopted for ocean applications  (Oke et al. 2009; Dibarboure et al. 2011a; Sakov et al. 2012). It provides a measure of the information content of each assimilated observation, based on the assumed error estimates used in the assimilation or analysis system. The implementation used here is similar to that described by  Dibarboure et al. (2011a). DFS is calculated as the trace of the HK matrix, where H is the observation operator that interpolates the background field to the observation location, and K is the Kalman gain matrix. The optimal interpolation method used in the ARMOR3D system uses a Gauss-Markov estimator that computed HK explicitly, along with the error covariance matrix or formal mapping error  (Bretherton et al. 1976 ). The DFS is computed on each HK matrix, so that the local mapping 'gain' in information from each dataset (here, in situ and satellite data) can be computed.
The partial DFS is associated with a particular dataset and is computed from the partial trace of the HK matrix, taking only elements associated with the dataset to be analysed. Partial DFS associated with the dataset i is written as DFS(i). Here, the fraction of the percentage of the overall information content (%IC) is computed for in situ data and satellite data (%IC = DFS(i) / Σ i DFS(i) x 100); and the fraction of information of each data type actually exploited by the optimal interpolation system (i. e. the amount of information not lost to duplicate data and measurement error) (%IC exploited = DFS(i) / N(i), where N(i) is the actual number of observations from each dataset i). Time series of the global average of two DFS metrics are presented in Figure  5  for the temperature at 100 m depth, and the information content of each satellite altimeter is shown in Figure  6.
This number is really determined by the decorrelation lengthscales used in the optimal interpolation Figure  5. Time series of the spatial mean (thin lines) plus/minus the standard deviation (dashed lines) of the information content (pre sented as a percentage), computed from the DFS, for the ARMOR3D temperature at 100 m depth. The in situ data refers to Argo profiles, and the synthetic data refers to the profiles generated by projecting satellite data over depth. method and by the space/time distribution of observations. In some areas, redundant in situ observations show lower values for the information content, and isolated observations (like in the Southern Ocean) have values close to 100%. Similarly, the fraction of the information from the synthetic field dataset actually exploited by the optimal interpolation method is also quite constant over time with mean values around 20% and associated mean standard deviation of the order of 6%.
Discussion and conclusions In this study, results are presented from a series of OSEs using eddy-permitting global ocean forecast and analysis systems, and seasonal prediction systems. Each study takes a different approach to quantifying the impact of observations on each forecast/analysis system. This includes traditional OSEs, where data are systematically withheld for a 1-10 year data assimilating model run; a series of 1-month long NRT OSEs  (Lea et al. 2013) ; a total data-denial OSE, where all data are presumed to become unavailable; and an analysis of assimilation/ mapping diagnostics (using DFS metrics). These different approaches provide clear demonstrations of the value of different observation types. In all but one study above, the impact of Argo data is explicitly assessed. All the studies draw the unanimous conclusion that Argo data are a critical data type for ocean forecasting; providing unique and valuable information about the ocean properties and circulation down to depths of 2000 m.
Specifically, it is shown that Argo data are important for constraining the mixed layer depths and water mass properties of the ocean interior. Several studies also demonstrate the value of altimeter data and re-confirmed conclusions from previous studies; indicating that it is the most critical data type for initialising the mesoscale ocean circulation (in eddy-permitting systems) where at least a constellation of three altimeters is needed; and also the basin-scale circulation in the extra-tropics and boundary currents (using seasonal prediction systems). Although TAO/Pirata data are limited in their extent to the tropics; the studies presented in this paper demonstrate that within about 10 degrees of the equator, the TAO/Pirata data are as important for ocean/seasonal forecasting as Argo and altimeter data.
These results indicate the possibility that the current degrading of the TAO array system, taking place after the summer of 2012 [see www. iocgoos. org/tpos2020] might induce serious deterioration in the forecast/analysis systems, and imply that the prompt recovery of the array to the original state is highly desirable. Similarly, although XBT data represent only a small component of the GOOS now that the Argo array is complete, the impact of XBT data in the vicinity of the XBT transects is very significant, with the lack of XBT data degrading forecast systems for some time after their neglect. The experiments conducted by the Japanese group, using the MOVE-G system, is perhaps the most comprehensive assessment of the impact of Argo in the published literature; hinting that future enhancements to Argo, with more floats, may continue to yield benefits to seasonal predictions.
Acknowledgements The authors acknowledge the constructive comments of four anonymous reviewers that led to significant improvements in this paper. P. R. Oke  and  T. Rykova  acknowledge the contri butions of the Bluelink science team. The work presented using the Mercator system and the ARMOR3D system has been carried out as part of the European E  AIMS  project (Grant Agree ment number  312642 ). The  MOVE G OSEs  are supported by  JSPS KAKENHI  Grant Number  24740324. Satellite altimetry is provided by  NASA ,  NOAA  and  CNES. Drifter data are provided by  NOAA AOML  and SST observations are provided by  NOAA  and  Remote Sensing Systems. Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it [www. argo. ucsd.
Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.
We also evaluate the agreement of sleep duration parameter derived from this method with self-reported sleep duration in a large sample of community dwelling older adults. Finally, we investigate the impact of method configuration and individual factors such as socioeconomic position, reports of sleep disturbances and depression on agreement between selfreported sleep duration and two accelerometer-assessed sleep parameters, time in bed and total sleep duration. In order to validate our sleep detection algorithm we compared it with data from a polysomnography study in a sample of 28 sleep clinic patients.
Study Population Data are drawn from the Whitehall II Study, established in 1985/88, as previously described  [20]. Accelerometer measurement was added to the study at the 2012/13 wave of data collection for participants seen at the central London clinic and for those living in the South-Eastern regions of England who underwent a clinical evaluation at home  [6]. We used a second study on sleep clinic patients in order to validate our sleep detection algorithm against polysomnography. These data come from 28 adult patients who were scheduled for a one night polysomnography (PSG) assessment at the Freeman Hospital, Newcastle upon Tyne, UK, as part of their routine clinical assessment and were subsequently invited to participate in the study.
Instrumentation Participants in the Whitehall II Study had no contraindications (allergies to plastic or metal, travelling abroad the following week) and were asked to wear a tri-axial accelerometer (GEN-EActiv, Activinsights Ltd, Kimbolton, UK) on their non-dominant wrist for nine (24-h) consecutive days. They were asked to complete a simple sleep log every morning which consisted of two questions: 'what time did you first fall asleep last night?' and 'what time did you wake up today (eyes open, ready to get up)?' The accelerometer was configured to collect data at 85. 70 Hz with a ±8g dynamic range. A more elaborate description of the accelerometer protocol can be found in a recent publication  [6]. In the second study, polysomnography (Embletta 1 , Denver) was performed using a standard procedure, including video recording, a sleep electroencephalogram (leads C4-A1 and C3-A2), bilateral eye movements, submental EMG, and bilateral anterior tibialis EMG to record leg movements during sleep.
Data Processing Sleep detection algorithm. Wrist-worn accelerometer data allow estimation of the arm angle relative to the horizontal plane. By visualizing the arm angle, sleep is characterised as a period marked by a low frequency of changes in arm angle, see thin black line in Fig 1. The accelerometer also had a light sensor (Fig  1 ) but we decided not to use it for sleep detection as despite its appeal for visual interpretation it can be misleading when the light sensor is covered by a sleeve or bedding. Previously published methods were used to minimize sensor calibration error  [21]  and to detect and impute accelerometer non-wear periods  [12]. Arm angle was estimated as follows: angle ¼ tan À1 a z ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi a x 2 þ a y 2 p ! Á 180=p , where a x , a y and a z are the median values of the three orthogonally positioned raw acceleration sensors in g-units (1g = 1000 mg) derived based on a rolling five second time window.
However, if the sleep log indicated that the participant was a day sleeper (potential night worker) with sleep onset before noon and a wake up time after noon then a time window from 6pm to 6pm was used. The first and ninth night (24h) were excluded from the analysis, leading to a maximum of seven nights per person in the analysis. Additionally, nights with less than 16 hours of detected wear time were excluded from the analysis. The sleep detection algorithm resulted in one or more sustained inactivity bouts detected per day and for each bout, the duration, the time of sleep onset, and the time of waking were recorded. Upon publication of this paper our method will be made freely available as an expansion of open-source R-package GGIR on CRAN (https://cran. r-project. org) [12, 21]. Sleep detection aided by sleep log.