dataesr/scientific-paragraphs-categorization huggingface dataset:
LCZ maps of the city are referred to as the 'level 0' product as they represent the first level of information about urban areas. Levels 1 and 2 represent more detailed and higher resolution information; the reader is referred to  Ching et al. (2018)  for further details. The WUDAPT community provides a procedure for generating the LCZ level 0 product, which uses freely available Landsat imagery, Google Earth for creating the training areas  (TA) , and the open source SAGA software  (Conrad et al. , 2015) , embedded in a workflow that is intended to be easy to follow so that community members around the world can produce LCZ maps of their own cities. However, there are other approaches for generating LCZ level 0 products, which can also lead to the more detailed level 1 and 2 data. One approach is to sample the urban landscape using fieldwork; this requires considerable expertise as shown by  Houet and Pigeon (2011)  and  Leconte et al.
The submitted data is made available via the WUDAPT website but it is further processed in the WUDAPT production chain (Fig. 2 ) before inclusion in the database and portal. More specifically, whenever a training data or Landsat image file is created or modified, a hash value (a number of fixed size to identify unique datasets) is calculated and compared with the previously processed versions. If the file is new, the classification, postprocessing, and documentation process is started. This involves an automated quantitative quality assessment (see below), conversion into multiple file formats such as geoTIFF, and the creation of metadata factsheets. Finally, a manual review is done by experts (see section 3. 2). The LCZ maps are then included in the Database (DB). The LCZ maps and metadata are made accessible via the the WUDAPT portal (https://wudapt.
(2018). Since the aggregated classes between the schemes do not match perfectly, they remain difficult to interpret visually, despite the use of similar colours in Fig. 6. However, the scatterplots and the fraction maps reveal substantial agreement. In particular, set pairs full, built, and open shown in Fig. 7  agree very well, while the correlation is high for set pair compact but the LCZ shows higher fractions. There is a weak accordance between the two products for set pair commercial. Generally, the study showed good agreement between the all built-up aggregated classes of the GHSL-LABEL and WUDAPT-LCZ (in particular open and compact classes between both data sets), but no universal relation could be found. However, specific problems in LCZ maps typically result in lower agreement between the datasets.
Paris, Nantes and Toulouse, France A 'bottom-up' approach based on administrative data and GIS was recently proposed to automatically generate LCZ maps for French cities. A vector administrative data set was developed in the nationallyfunded MApUCE project and is currently available for 82 French cities. LCZ maps based on this database were obtained through a semi-automatic cluster classification method that takes regional variations into account and allows for more precise definitions of the UCPs for each LCZ type (for more details see  Hidalgo et al. 2018 ). The classification method was tested on three cities, namely Paris, Nantes and Toulouse. These cities have a typical European morphological structure with a denser city centre (mostly LCZ 2) surrounded by large urban suburbs of residential housing (LCZ 6).
Szeged, Hungary Lelovics et al. (  2014 ) presented a bottom-up approach to LCZ mapping using various databases and urban surface parameters. As a first step, all parameters that define the LCZ classes (Stewart and Oke, 2012) were collected on a spatial unit called lot area polygons  (Gál and Unger, 2009). In the next step, the method decides on the first and second most likely LCZ category for each polygon. Finally, the polygons are aggregated to achieve areas of adequate size, comparable to the source area of the temperature observations at 2 m height  (Oke 2004 , Unger 2009 ). The methodology requires many input data sets (e. g. 3D building database, Corine Land Cover, RapidEye satellite imagery, topographic maps, road database) to calculate the urban surface parameters, which is one of its main limitations; application of this method in other cities is, therefore, not a straightforward task.
Finally, considerable improvement in the LCZ maps can be achieved by combining multiple training datasets from different operators, which is evidence for the 'wisdom of the crowd'. Using the mode of all available classifications or using multiple training datasets for one classification can produce higher accuracies than the mean accuracy of individual classifications of a city, especially for the urban LCZ types. The results showed, however, that after ten to fifteen TA sets, the increase in accuracy saturates and the LCZ map does not improve anymore. From these results, the HUMINEX concludes that at least ten individual TA sets from untrained operators should be used for one city to produce an LCZ map of good quality, although this aspect needs further investigation  (Bechtel et al. , 2017b).
Improvements in LCZ mapping The quality approaches described in this paper are generally being applied to LCZ maps developed using the WUDAPT production workflow as described in section 2. However, there are many ongoing developments with respect to improving the actual method used to derive the LCZ maps  (Bechtel et al. , 2017a)  and hence improve their overall quality. These include the transferability of TAs, the use of new data sets and features and new approaches to LCZ classification. A global LCZ map database that has been derived from a uniform classification model is needed to perform standardized urban climate studies globally. However, TAs are not yet available for every city around the world. Hence it would be useful to determine whether TAs from one city can be used to create LCZ maps in another city where no training data exists.
First, multi-source satellite data are well integrated into a multi-view model to achieve improved LCZ classification results. Second, the classifiers based on source cities were iteratively adapted into the target cities by adding more valid samples. The experimental results showed that the proposed approach could derive LCZ maps for the four testing cities with an OA of about 70%, which is over 10% more accurate than conventional approaches with the same settings. Compared with conventional approaches, the novel approach does not need prior knowledge about the target cities, and it can automatically generate worldwide LCZ maps to support urban-climate studies for cities in the world. Incorporating more data sources in online processing platforms can also help to address the issue of transferability.
This includes the use of different Earth observation data sets such as Sentinel 1, Sentinel 2 and the Defense Meteorological Program (DMSP) Operational Linescan System (OLS) night-time lights product. Such online processing platforms can also facilitate implementation of the online crossvalidation bootstrapping procedure (see section 3. 1), and provide the ability to scale-up from single cities over countries to continents. The latter is illustrated in Fig. 15 , which provides an example LCZ map for Belgium processed using Google Earth Engine  (Gorelick et al. , 2017) , which used seasonal Landsat 8 composites and TAs from Antwerp, Brussels, Ghent and Leuven  (Verdonck et al. , 2017; Bechtel et al. , 2017b). Considering the limitations of moderate resolution satellite images, other types of data sets should be included in LCZ mapping, such as ASTER, Sentinel 2, SAR data (e.
Conclusions The WUDAPT initiative is producing LCZ maps for many cities around the world, contributed by a community of researchers interested in creating a global database of the form and function of cities for urban climate modelling and other applications. Before the LCZ maps are released to the wider community via the WUDAPT portal, they must undergo quality assessment and are then published with metadata related to the quality achieved. Different approaches used in the quality assessment process were highlighted in this paper. In particular the bootstrapping cross-validation and the review were described, which are currently part of the standard LCZ quality assessment procedure. In addition, comparisons with independent data sets of urban structure and urban canopy parameters at country and city level were presented, where some of these additional approaches may become included in the standard quality assessment protocol in future.
To date, the results from the different quality processes show that the LCZ maps are generally of moderate quality, i. e. 50-60% OA, but this is much higher when considering all built-up classes together or using weights that take the morphological and climatic similarity of certain classes into account. Currently the training data for the LCZs are contributed by researchers from around the world, which can be very different and can affect the final quality of the LCZ maps. One reason is that some LCZ classes are difficult to distinguish from satellite imagery and it requires expert knowledge or training to create a suitable TA set. Another reason is that people may have different opinions when interpreting LCZ types. The impact of the operators on the classification accuracy was found to be considerable based on experiences from the HUMINEX, which stresses the necessity of strict quality assessment and review.
Moreover, WUDAPT level 0 maps have been found to agree with observed temperature differences, which is also encouraging for their further use. In parallel, there are ongoing developments to improve the methods used to produce LCZ maps and hence the accuracy, e. g. through the addition of other types of data sources such as SAR images, very high resolution images, and ground level images such as Google Street View, as well as experimentation with other classification algorithms and deep learning techniques. Additional efforts to generate spatially precise UCPs, the so-called Level 1 and 2 data, are described elsewhere in this special issue. Acknowledgements: Most of all we thank all WUDAPT contributors. This work was partly supported by the Cluster of Excellence 'CliSAP' (EXC177), University of Hamburg, funded through the German Science Foundation (DFG), the DAAD (Project ID 57320776), the research project '.
Cities in Change-Development of a multi-sectoral urban-development-impact model (UrbMod)' (LFF-FV17), funded by the State of Hamburg, the EU FP7 funded ERC grant Crowdland (No. 617754), the Hungarian Scientific Research Fund (NKFI K 120346) and by the ÚNKP-17-4 New National Excellence Program of the Ministry of Human Capacities. Work on French cities was funded by the French National Research Agency (Agence Nationale de la Recherche) under grant #ANR-13-VBDU-004. Matthias Demuzere is funded by the Belgian Science Policy Office (BELSPO) in the framework of the STEREO III programme, project SAT-EX (SR/00/306). This study is partially supported by the Vice-Chancellor's One-off Discretionary Fund of The Chinese University of Hong Kong titled "World-wide Urban Data Bank on Physical Forms of Cities & Impact Studies of Urban Forms on Human Comfort, Health and Environment Well-being" and the General Research Fund Project Grants (Project No.
OOMAO end-to-end simulations The Object-Oriented Matlab Adaptive Optics (OOMAO) toolbox, presented by  Conan & Correia (2014) , provides end-to-end simulations. For each time step, OOMAO generates a turbulent wavefront with a Von-Kármán spectrum defined as W φ,VK (f) = 0. 023r -5/3 0        1 L 0 2 + f 2        -11/6 , (18) where we have chosen the outer scale L 0 = 30 m. Since 1/L 0 f AO , the Von-Kármán spectrum is consistent with our PSF model using the Kolmogorov spectrum above the AO cutoff frequency. OOMAO then propagates the wavefront through the telescope, simulates the wavefront sensor measurement, performs the wavefront reconstruction, and simulates the mirror wavefront deformation. For each time step, we get a short exposure PSF. Integration over time allows us to retrieve the long-exposure PSF.
Parameter 10 -2 -10 -1 eps 5 × 10 -2 m -1 α y 10 -2 -10 -1 eps 5 × 10 -2 m -1 β 1. 1 -3 1 + eps 1. 6 - θ 0 -π - 0 rad C 10 -3 -10 -2 0 10 -2 rad 2 m 2 A 10 -1 -10 0 2 rad 2 Table  2. Typical range of PSF parameters for OOMAO simulations and SPHERE/ZIMPOL instrument, lower bounds and values used as initial guess for the minimizer. Typical ranges are indicative and may vary according to the considered instrument. The value 'eps' denotes the machine precision. Parameters do not have any upper bound. Each PSF is then fitted using the L (S, δ x , δ y ) criterion given to an optimizer (e. g. Levenberg-Marquardt, trust region, or Markov chain Monte Carlo). We used the Trust Region Reflective algorithm, called 'trf', from the Python/SciPy  (Jones et al. 2001)  library. This algorithm is gradient based, said to be robust, and allows bounds on the parameters.
The robustness of this algorithm was verified for our applications of it to PSF fitting, even though the convexity of the problem is not demonstrated. So far, we have not encountered any local minimum and residuals are always small. For all PSFs, the same initial conditions are provided to the fitting algorithm (see Table  2 ), in particular we used the same value of r 0 = 18 cm. Using the same initial parameters {S, δ x , δ y } init for all fits ensures that our model is suited for minimization procedures and that convergence is ensured even if starting far from the true values. Fitting results are presented on Fig. 3. Our model fits well the OOMAO-generated PSF on both the corrected and the uncorrected area, residuals being on average one to two decades below the PSF. Let us define the relative error between fitted PSF and data PSF as h = i, j γ • h i, j (S, δ x , δ y ) + ζ -d i, j 2 i, j d i, j.
Regarding the flux, we consider the relative error between the flux γ analytically estimated by our model fitting method, and the OOMAO flux that is directly the sum of the data on all the pixels: γ = γ -i, j d i, j i, j d i, j. (20) On all our OOMAO simulations, we find an average relative error of -1. 96%, indicating a small underestimation of the flux with our fitting method. The standard deviation of this relative error is 1. 11%, and the range of variation is [-3. 43%, 2. 77%].
One of its applications in non-coronagraphic mode is the observation of asteroids  (Vernazza et al. 2018; Viikinkoski et al. 2018; Fétick et al. 2019 ) as part of an ESO Large Program (ID 199. C-0074, PI P. Vernazza). PSFs from stars were observed with the ZIMPOL N_R filter (central wavelength 645. 9 nm, width 56. 7 nm) during the Large Program. When PSFs are saved together with the SPARTA telemetry, we are able to correlate r 0 given by our fitting and r 0 given by SPARTA. In our sample, 28 PSFs were saved along with the SPARTA telemetry. These PSFs were obtained during different nights, on stars of different magnitudes, with various seeing conditions. We fitted these 28 PSFs with our PSF model. Figure  7  shows three of the 28 fittings, for the smallest r 0 of the sample, the median r 0 , and the largest r 0 , respectively.
For the 40 datacubes PSF, the relative error is h = 3. 3 × 10 -3. This result is similar to the previous ones on OOMAO and SPHERE/ZIMPOL. Secondary stars in the field also count in the residual error computation. The evolution of r 0 with the wavelength is shown on Fig. 10  for one datacube PSF. A least square fitting between our data points and the theoretical λ 6/5 evolution of the r 0 gives a spectral averaged estimation of r 0 = 13. 3 cm at 500 nm. Our fitted r 0 matches well the theory, with a standard deviation of 0. 3 cm. (orange line). The best match between data and theory is achieved for r 0 = 13. 3 cm. The grey area corresponds to missing wavelengths due to the sodium notch filter. So far each spectral bin is fitted independently, however the spectral deterministic trend we recover is an asset for PSF determination.
Since these parameters are physical, their values in our PSF model can be correlated to external measurements. Tests on both OOMAO simulations and on-sky data (from the SPHERE/ZIMPOL and MUSE instruments) confirmed the physical meaning of the r 0 parameter used in our PSF. The ultimate goal would be to only use physical parameters in the PSF description. Our model has already shown usability for different seeings and different instruments, with different AO-correction quality. This shows the robustness and versatility of the model. We also plan to use it to parameterize the PSF for the future instruments on bigger telescopes such as the Extremely Large Telescope (ELT). Finally, the small number of parameters makes this model suited for image post-processing techniques such as deconvolution of long-exposure images.
Acknowledgements. This work was supported by the  French Direction Générale de l'Armement (DGA)  and  Aix-Marseille Université (AMU). This work was supported by the  Action Spécifique Haute Résolution Angulaire (ASHRA)  of  CNRS/INSU  co-funded by  CNES. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  730890. This material reflects only the authors views and the.
We connected the possible extensions from shorter to longer patterns, such that the data are represented as a 'tree'. Each of these trees corresponds to an experimental condition. At each node of a tree, the circles should be read from left to right and denote the corresponding patterns; for instance, the pattern AAAB shows the activity level elicited by the item B when it was preceded by three As. X and Y denote the pooling of both stimuli in conditions in which both stimuli are equiprobable. In the frequency-biased condition, we report the activity evoked by item B, the most frequent stimulus. Activity levels across sensors were averaged using a topographical filter within a late time window (from 500 to 730 ms) poststimulus onset. The topographical filters were obtained by contrasting rare and frequent patterns (e.
Stimuli Auditory and visual stimulation was delivered via Psychtoolbox  (Brainard, 1997)  running on MATLAB (Mathworks). The experiment was split into four different conditions. In each of these conditions, two different sounds were presented. These sounds were French syllables and were reported as being perceived without ambiguity by the subjects. The pair of syllables was changed at the beginning of each block: /ka/ and /pi/ in the first run, /ky/ and /te/ in the second one, /ku/ and /te~/ in the third one, and /pø/ and /to/ in the fourth one. Each syllable was pseudo-randomly labelled as A or B and lasted about 200 milliseconds, with a SOA between 2 syllables of 1. 4 s (see Figure  1A ). A fixation dot was displayed at the center of the screen while syllables were presented. To ensure subjects' attentional focus on the auditory stimulation, they were occasionally asked (every 12 to 18 sounds) to report an explicit discrete prediction (i.
Data acquisition Subjects' brain activity was recorded using a 306 channels (102 triplets of sensors each composed of 1 magnetometer and two orthogonal planar gradiometers) whole-head Elekta Neuromag MEG system with an acquisition sampling rate of 1 kHz and hardware bandpass filtering between 0. 1 and 330 Hz. Prior to installing the subjects in the MEG room, we digitized three head landmarks (nasion and pre-auricular points), four head position indicator (HPI) coils placed over frontal and mastoı ¨dian skull areas, as well as about 60 additional locations over subjects' skull using a three-dimensional Fastrak system (Polhemus). Thanks to HPI landmarks, head position within the MEG helmet was measured at the beginning of each run. Electrocardiogram, vertical and horizontal oculogram were recorded in order to monitor heart rate and eye movements during the experiment.
Pre-processing Raw MEG signals were first corrected for between-session head movements and bad channels using MaxFilter (Elekta). Following pre-processing steps were performed using Fieldtrip in MATLAB (Mathworks). First, MEG data was epoched between À250 ms to 1 s with respect to sound onset. Power line artefacts were then removed by filtering out the 50 Hz frequency and its 100 and 150 Hz harmonics. Trials exhibiting muscle and/or other movements' artefacts and/or SQUIDs' jumps that were detected based on semi-automatic methods (using the variance of MEG signals over sensors and the first order derivative of MEG signals over time) were then completely removed from the dataset. MEG signals were then low-pass filtered below 30 Hz and downsampled to 250 Hz. An ICA was then performed and components exhibiting topographical and time courses signatures of eye blinks or cardiac artefacts were subsequently removed from the data.
More details are provided in  Meyniel et al. (2016)  and the MATLAB code for all ideal observers is available  (Meyniel and Maheu, 2018 ; copy archived at https://github. com/elifesciences-publications/MinimalTransitionProbsModel). It is important to notice that IF and AF can be analytically derived from TP: A ¼ AjB AjB þ BjA and alt: ¼ 2 Á AjB Á BjA AjB þ BjA Thus, IF and AF are subsumed in the space of TP (see Figure  1A ).
Figure3. Brain responses are modulated by local statistics. The brain response to a given observation is plotted as a function of the recent history of events. We connected the possible extensions from shorter to longer patterns, such that the data are represented as a 'tree'. Each of these trees corresponds to an experimental condition. At each node of a tree, the circles should be read from left to right and denote the corresponding patterns; for instance, the pattern AAAB shows the activity level elicited by the item B when it was preceded by three As. X and Y denote the pooling of both stimuli in conditions in which both stimuli are equiprobable. In the frequency-biased condition, we report the activity evoked by item B, the most frequent stimulus. Activity levels across sensors were averaged using a topographical filter within a late time window (from 500 to 730 ms) poststimulus onset.
In-vitro kinetic constants, available from the Brenda database  [7, 8] , are widely used and unknown k cat values have been estimated by machine learning  [9]. Directly inserting measured or sampled kinetic constants into models can lead to inconsistencies because thermodynamic dependencies between kinetic constants will be ignored. To address this problem, methods to construct consistent parameter sets have been devised  [10, 11, 12, 13, 14]  and applied in modelling  [15]. In parallel, there have been attempts to estimate kinetic constants in vivo from flux, metabolite, and enzyme data  [16, 17]. Methods for parameter fitting have been developed and benchmarked  [18, 19] , and the question of parameter identifiability has been addressed  [20]. Large models have been parameterised  [21, 22] , and pipelines for model parameterization have been developed  [23, 24].
D Example model The E. coli central carbon metabolism model, taken from  [31] , comprises 40 metabolites and 30 reactions and contains 107 K M values and 167 kinetic constants in total (K M values as well as forward and reverse k cat values). The model structure is shown in Figure  16  and described at https://github. com/liebermeister/cmb (in the file resources/data/data-organisms/escherichia coli/network/ecoli noor 2016. tsv). To model aerobic growth on glucose, I used a data set from  [31] , which gathered measured flux data from  [40] , proteomics data from  [41] , and metabolomics data from  [42]. To model several metabolic states, I used a data set from  [16] , where a larger network model had been considered, proteomics data from different sources were used, and flux data had been computed by FBA.