dataesr/scientific-paragraphs-categorization huggingface dataset:
e. simply by providing a MetaboLights accession number), leading to the capability to reanalyze data and compare the results with the original publication findings. Furthermore, it demonstrates the value of standardised dataset descriptions such as nmrML  (Schober et al. , 2017)  and ISA format  (Rocca-Serra et al. , 2016; Sansone et al. , 2012)  for representing NMR based studies, as well as the potential of the VRE to foster reproducibility. Furthermore, the data processing steps are trackable and replicable as each container/tool is versioned for a specific release and data processing steps and the corresponding parameters are taken care of by the workflow engine. In addition, the cli KubeNow is using speciffic pinned versions of all dependant software and all versions of software is stored in the user config dir created by the init-command.
This level of complexity is not resolvable today through server less approaches and requires more isolated approaches based on containers, such as the one presented here. In summary, we showed that microservices allow for efficient horizontal scaling of analyses on multiple computational nodes, enabling the processing of large datasets. By applying a number of data [mzML  (Martens et al. , 2011) , nmrML] and metadata standards [ISA serializations for study descriptions  (Rocca-Serra et al. , 2016; Sansone et al. , 2012) ], we also demonstrated a high level of interoperability in the context of metabolomics, by providing completely automated start-to-end analysis workflows for mass spectrometry and NMR data. In addition, many of the state-of-the-art tools such as components of XCMS ONLINE  (Warth et al.
, 2017)  and MetaboAnalyst  (Xia et al. , 2012)  can be incorporated in the workflows, providing more refined workflows. The ability to instantiate VREs close to large datasets, such as on local servers within a hospital for Demonstrator 2, makes it possible to use the VRE on sensitive data that is not allowed to leave the current environment for ELSI reasons. While the current PhenoMeNal VRE implementation uses Docker for software containers and Kubernetes for container orchestration, the microservice methodology is general and not restricted to these frameworks. Likewise, the choice of Luigi and Galaxy was here used to demonstrate the capabilities of workflow management microservices in cloud environments. In fact, our microservice architecture supports other major workflow engines such as Nextflow  (Di Tommaso et al.
, 2017)  or Snakemake  (Ko ¨ster and Rahmann, 2012). Hence it is possible to use any of such workflow engines in our VRE and still produce reproducible results. In addition, despite some of our workflows were novel in the context of metabolomics (e. g. Demonstrator 2) and can be readily applied on other datasets, their main contribution in this work is to showcase scalability and interoperability of the microservices methodology. Finally, we emphasise that the presented methodology goes beyond metabolomics and can be applied to virtually any field, lowering the barriers for taking advantage of cloud infrastructures and opening up for large-scale integrative science.
Funding This research was supported by The  European Commission 's  Horizon 2020 programme funded  under grant agreement number  654241  (PhenoMeNal), The  Swedish Research Council FORMAS, Uppsala Berzelii Technology Centre for Neurodiagnostics, A ˚ke Wiberg Foundation  and the  Nordic e-Infrastructure Collaboration (NeIC)  via the Glenna2 and Tryggve2 projects. We kindly acknowledge contributions by  Daniel Jacob  (INRA) and to cloud resources by  SNIC Science Cloud ,  Embassy Cloud , c-Pouta and CityCloud. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
First, we propose an online prediction framework, based on Support Vector Regression (SVR)  [4]  and trained with real network traces, able to predict these instantaneous density. SVR is known to be more efficient for large dataset processing since it uses only few samples (support vectors) from the training dataset, which reduces notably the processing time. In addition, it only requires minimal computational resources, unlike other regression tools, such as artificial neural networks (ANN) which consumes more processing time, needs more complex configuration, and suffers from lack of generality causing probably under/over fitting issues. So, we look at base station load profiles extracted from a real dataset of call detail records (CDRs), analyze and then classify into three groups based on the users' density fluctuation pattern throughout the day.
The normalized PSD Welch's estimation is represented in Fig. 1. From this figure, we observe some frequencies with high amplitude values, such as a peak at 10 0 (1 cycle/day) which describes a daily periodicity, another peak at 10 0. 3 (2 cycle/day) corresponding to a 12h-periodicity and the peak near to 10 -1 (0. 14 cycles/day) which corresponds to a weekly cycle. Then, for the training step, each vector X i,t must be trained with its corresponding label y i,t which stands for the base station UEs' density at the time interval x 1 i,t. We use here a training dataset of eleven weeks, as history time-series. To find the optimal value of the model parameters C, and γ (the latter parameters belongs to the RBF kernel function), we used the cross-validation technique and we fix the optimal parameter which minimize the error as depicted in Fig.
2. SVR prediction efficiency is validating by using testing data extracted also from Dakar dataset, which corresponds to the data of the week just after the data window used for the training step. Fig. 3  shows a comparison between a predicted and real base station data and we can see that the prediction is very close to the real load. The accuracy of the load SVR-based prediction is evaluated by two major indicators: The Mean Squared Error (MSE) measurement and the squared correlation coefficient. MSE gives an overall idea of the errors which occurred during forecasting and measures the average squared deviation of predicted values from the real data. Its mathematical expression is as follows: MS E = 1/n 1≤k≤n (y k -f k ) 2 (5) where y k corresponds to the real load value at time k, f k represents the predicted value and n is the total number of points constituting the load (real and predicted) time series.
The model MSE average reaches a minimal value of 4. 3 * 10 -4 with large testing history data and this value is very acceptable for long time-series forecasting. On the other hand, the squared correlation coefficient (or the coefficient of determination R 2 ) is a statistical measurement of how close the real data are to the prediction. For our model, this performance measurement reaches a value equal to 0. 91. Thus, these metrics prove that our prediction model has a good accuracy. In this contribution, we integrate a set of users' density timeseries profiles, extracted from a realistic dataset of mobile traces provided by Orange Senegal,in ns-3 to implement a realistic traffic generator. The trained dataset of the prediction model is issued from this traffic generator. The code source of the ns-3 traffic generator and the realistic traces are published 1 to be used as an open source mobile traces.
The users' density profiles inferred and classified into three profile classes of base stations UEs' density in reference  [32]. Each base station profile belongs to a specific pattern of network usage and load fluctuation, which depend on the nature of its location. Fig. 4  depicts a simple illustration of these load profile shapes:  The red curve belongs to the "Night peak profile" and represents residential areas, green curve corresponds to areas with "Day peak profile" where most of business areas and activities 1 https://github. com/SHM91/NS3-RealisticTrafficGen1 are located, and the blue curve with a "constant load profile" corresponds to mixed areas. [32]  presented a general approach and demonstrated the ability and the potential of using machine learning tools to optimize networks.
It checks its saved real database of similar traffic, calculates the average and gets the number of predicted users in the area for that hour and for that specific traffic. The user density patterns utilized during this process are extracted from real network traces. Therefore, it gives a close idea of the actual number of UEs present in the network. Based on this information, the optimal transmission probability θ is calculated in order to get the best discovery performance. This value will be used throughout the whole hour, until the next period of time where the predicted number of UEs may change. Data: Total number of discovery resources (N r ) and number of subframes dedicated to discovery (N t ) Result: Probability of transmission to be used (θ) for each hour of the day do get the total number of predicted users in the area for that hour based on the predicted user density pattern (N u ); for any given UE performing D2D discovery do compute θ (using Eq.
General Assumptions Table  2  summarizes the list of network parameters and their values used for our simulations. We don't include a measure of interference as a parameter because in  [34]  it was already shown that the results obtained with simpler air propagation models still hold, and this simplifies the analysis, allowing us to focus on the elements that actually have a significant impact on the discovery performance. Sets of UEs density patterns are extracted from Dakar data to be integrated in ns-3 to implement a realistic traffic generator tool. The tool is then used to generate realistic pattern to train SVR model in order to make a prediction of the UEs density evolution within each area and then use the predicted values for the D2D Direct discovery algorithm. In our scenarios, we are interested in comparing the performance (in terms of time required to complete discovery, and messages sent) of our discovery algorithm with that of the discovery mechanism defined in the 3GPP standard (with different operational parameters).
Urban floods The current context of climate change, leading to an increase in natural disasters of about 2% / year over the past fifteen years (Catastrophes Naturelles-Observatoire permanent des catastrophes naturelles et des risques naturels, 2016), associated with the increase in people and goods in urban areas, has considerably weakened cities. Among these disasters, flood risk seems to be the most problematic  (Vinet, 2010) , constituting 47% of climate-related disasters over the period 1995-2015 (UNISDR and Center for Research on the Epidemiology of Disasters (CRED), 2015). In total, the EM-DAT (Emergency Events Database) recorded an average of 318 natural disasters in 2017, affecting 122 countries. While the number of deaths in developed countries has decreased in response to this risk, 2017 is the second most costly year (Center for Research on the Epidemiology of Disasters (CRED), 2018).
In order to calculate resilience, we chose to study the impact of each variable on overall resilience, as each variable can have a negative or positive effect. What determines this impact is the ratio of the variable to the overall data. As an example, if there is a high rate of houses built between 1970 and 1994 compared to the total of built houses at the IRIS scale, the impact will be negative because such houses are, for instance, more vulnerable to damages and likely economic losses. (Mileti, 1999). For each variable established, we therefore estimated the impact it would have on overall resilience  (Cutter et al. , 2010). All raw data acquired via INSEE were therefore transformed into comparable ratios using percentages. This standardization was essential to avoid the problems inherent in pooling different units of measurement, since our variables were delineated in a number of statistical units, ranges and scales.
Moreover, this collaborative work guarantees acceptance and appropriation of a concept that is still abstract on many points. The fact that each indicator and variable is measurable independently of each other makes it possible to point out the limits and fragilities of the territory. As a result, it is easier to advise planners on the measures to be taken to better prepare the territory for a crisis. These results are already used within the community, the city of Avignon thus having a new database of social resilience at local scale. With these new data, the city is creating a decision support tool to influence urban renewal projects to analyze the quality of social and urban life. The combination of these results should be of interest in understanding global resilience and building a strategy to increase urban capacity to cope with floods.
With regard to international genericity, it is necessary to study data in free circulation in each state. The quality and the ease of obtaining them can then vary. Another limitation concerns the tool used by managers, the Feature Manipulation Engine (FME). Although used on a large scale in French communities, this tool still pays off. Our next step is to study the feasibility of creating a QGIS plugin that would directly integrate our computer script and resilience processing. The analysis, from the data transformation to the mapping of the resilience would thus be only on a tool in free circulation, free and downloading-free. Finally, the last limit of a work that aims to clarify and operationalize resilience, as mentioned by  (Bakkensen et al. , 2017) , is the bias in the definition and construction of measurement indicators.
Apparatus We implemented the software using C++ and openFrameworks. It showed a user interface that displays the questions for the user study and gives feedback of the device interpretation in a form of a gradient visualization on a distant screen (Fig. 6 ). The slider was taped on the table to prevent it from moving. We placed a keyboard on the left side to use its spacebar to finalize the input on the prototype. A 1440×900px (~287×180mm) laptop was positioned approximately 50 cm behind the slider, displaying the questions of the transportation survey and a graphical representation of the participant's answer below the questions. The slider on the screen had 1000px width (~199 mm), and 10mm movement of a SplitSlider thumb moved a cursor on the screen around 114px (~23mm). One question was displayed at once.
Monitoring stopover timing, duration, and population size of migratory birds at such staging sites is therefore important to detect changes in these parameters and guide conservation and management actions in order to better protect these sites and the migratory species that rely on them  (Piersma & Baker, 2000). While timing and duration of stopover can be directly measured for individuals with radio or satellite transmitters, in most cases, stopover duration is estimated from mark-recapture data of individually marked animals. To derive accurate estimates of timing and duration of stopover from marked individuals, it is important to account for imperfect detection, as individuals may have been present before their first observation and may have stayed for some time after their last observation  (Kaiser, 1999).
, 2011). During this fieldwork, 2-4 observers visited the intertidal feeding areas and high tide roosts daily to collect the observations (see field reports available at www. globalflywaynetwork. com. au). Observers always carefully noted whether scans of the number of marked and unmarked birds performed on the same day involved different parts of the population (sampling without replacement) or whether scans were random samples of the population (sampling with replacement). Samples without replacement were merged into one large sample. As years differed in the first and last date of fieldwork, we used data from 10 to 30 May to allow comparison between years and to have sufficient data to estimate daily arrival and encounter probabilities. Moreover, this is the core period of stopover of the subspecies piersmai, whereas the rogersi subspecies usually stops over earlier and longer  (Rogers et al.
, 2010)  and contains much fewer color-banded individuals as this subspecies mainly winters in New Zealand (Figure  1 ). The number of different individuals of red knots observed per year during the selected period (10-30 May) ranged from 70 in 2009 to 312 in 2015. For a summary of the collected data, see   (Pollock, Hines, & Nichols, 1985). We used the tests for CJS models to each year's data using R (R Core Team, 2015) and package R2ucare  (Gimenez, Lebreton, Choquet, & Pradel, 2018). We separately show the results of Test3. SR and Test2. CT (as well as the overall test) as these tests tend to become significant when there is heterogeneity in, respectively, ϕ and p. The transience test (Test3. SR) was significant in 2014 only (χ 2 = 30. 36, df = 16, p = 0. 016), whereas the trap dependence test (Test2.
CT) was significant in 2012 (χ 2 = 32. 37, df = 15, p = 0. 006) and 2015 (χ 2 = 55. 84, df = 18, p < 0. 001). An overview of all test results is listed in Supporting Information Table  S1. In addition to these tests, we investigated support for individual heterogeneity by comparing models with and without hidden classes of individuals (finite mixtures) with contrasting ϕ and p. To do so, we first explored support for daily variation in ϕ and p by comparing constant versus time-dependent parameterizations using the computationally efficient frequentist formulation of the JSSA model as implemented in program MARK (the POPAN model). Arrival probabilities were modeled time-dependent in all models, as we considered it biologically unrealistic that arrival probabilities would be the same at all days.
| Bayesian formulation of finite-mixture Jolly-Seber model combined with ring density model To account for the individual heterogeneity in ϕ and p that we detected in our dataset, we refined and extended the Bayesian model developed by  Lyons et al. (2016)  to include finite mixtures. This model uses the hierarchical state-space formulation with data augmentation developed by  Royle and Dorazio (2008)  and implemented by  Kéry and Schaub (2012). The state process is defined using latent state variable z i,t for individual i in the augmented dataset, where z i,t = 0 means that the individual has not yet arrived or has departed, whereas z i,t = 1 means that the individual is present at the site. Index i ranges from 1 to A (number of individuals in the augmented dataset) and index t from 1 to K (the number of sampling occasions, i.
e. , days). The inclusion of each individual in the augmented dataset is estimated by the latent variable w i , modeled as: where Ψ is the inclusion probability (being a function of the length of the augmented dataset, A). The total number of marked individuals in the population is then estimated as We set A at three times the observed number of marked individuals. To verify that A was chosen sufficiently large (A >> M*), we visually verified that the posterior distribution of M* was not truncated  (Kéry & Schaub, 2012). The mixture class h that individual i belongs to is modeled as where Ω is a vector describing the discrete distribution of the mixture classes. The length of the vector reflects the number of mixture classes, which in our (particular) study case is set to two. (1) The arrival probabilities (β t ), reflecting the proportion of the overall population using the site that arrives at each day t, are modeled as in  Lyons et al.
| Binomial model for ring density data The proportion of marked individuals in the population ( π) is estimated from the ring density data (i. e. , counts of marked and unmarked individuals) as where m s refers to the number of marked animals in the scan sample and N s to the total number of animals (marked and unmarked) scanned. It is assumed that the proportion of marked individuals is constant over the season. The estimated proportion of marked individuals is then used to estimate the total population size ( N * ) at the staging site: A key assumption of the Jolly-Seber model to produce unbiased parameter estimates, in particular of population size, is that all individuals (marked and unmarked) have the same entry, staying and encounter probabilities. By including mixtures in the model, we now account for heterogeneity in the form of two hidden groups that have distinct encounter and/or staying probabilities.
| Simulation study We used simulations to assess the bias in model parameter estimates when heterogeneity in staying and encounter probabilities is ignored. To reflect the situation of the red knots in Bohai Bay, we assumed 15 occasions, 500 marked individuals, and two groups of individuals that differed in either or both encounter probability (p = 0. 2 vs. p = 0. 8) and staying probability (ϕ = 0. 5 vs. ϕ = 0. 9), with the proportion of individuals in the two groups being either low (0. 2) or high (0. 8). For the cases without heterogeneity, we assumed p = 0. 5 and ϕ = 0. 7. Arrival, staying, and encounter probabilities were assumed to be constant over time. This resulted in eight different scenarios, as summarized in Table  3. For each scenario, 1,000 mark-recapture datasets were simulated (n sim = 1,000) and analyzed with the Jolly-Seber model in program MARK  (White & Burnham, 1999)  using R (R Core Team, 2015) and RMark  (Laake, 2013).
9%, and 9. 4%, respectively. Heterogeneity in staying probabilities alone (scenarios 3 and 4 in Table  3 ) caused only small bias of population size estimates (-3. 2% and -2. 7%), but the scenario where the minority of individuals had high staying probabilities (scenario 3) caused considerable overestimation (by 17. 1%) of stopover duration. Heterogeneity in both p and ϕ (scenarios 5 to 8, Table  3 ) caused most severe bias when the minority of birds had high encounter and staying probabilities (scenario 5, Table  3 ), with population size being underestimated by 50. 5% and stopover duration being overestimated by 41. 3%. In all four scenarios, stopover duration was considerably biased (in either direction), with absolute bias ranging from 16. 8% to 41. 3% (Table  3 ). Analyzing the simulated datasets of scenarios 1 and 5 (i.
| General conclusions In this study, we extended the Bayesian superpopulation model developed by  Lyons et al. (2016)  by accounting for heterogeneity in encounter and staying probabilities through finite mixtures in order to provide unbiased estimates of stopover timing, duration, and population size of migratory birds. Using a combination of analyses of simulated and real data, we showed that heterogeneity in encounter and staying probabilities is present in real datasets and can cause severely biased parameter estimates, especially of stopover duration and population size. Accounting for heterogeneity where needed, the model estimated that between 10 and 30 May, a total of 50,000-100,000 red knots used the Luannan Coast of Bohai Bay (China) for about a week (6 to 9 days), to refuel for their (presumably) final migratory flight to the New Siberian Islands.
11. 004), and by the Spinoza Premium 2014 from the Netherlands Organisation for Scientific Research (NWO). The present analysis was financially supported by NWO-Rubicon grant no. 82514022 awarded to T. L. conceived the ideas and T. L. , R. P. , and O. G. developed the model. T. P. planned and raised the financial support for the color-marking of red knots in Northwest Australia and the fieldwork in Bohai Bay, all carried out by field teams led by C. H. T. L. analyzed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.