dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition All subjects were scanned at the Brain Research Imaging Centre, Edinburgh (UK) in a 1. 5 T scanner (General Electric, Milwaukee, WI, USA). A coronal high resolution 3D T1-weighted (FSGE, 1*1. 3*1 mm voxel size, TE 4. 01 ms TR 9. 8 ms flip angle 8°), an axial T2-weighted (SE, 1*1*2 mm voxel size, TE 104. 9 ms TR 1320 ms flip angle 8°), and a T2 FLAIR volume were acquired for each subject, and reviewed by a consultant radiologist ensuring their good health. Additional details can be found in  [21].
Materials We chose 3 existing software packages to analyse the raw T1w data of each of the 10 subjects: FreeSurfer  [24] [25] [26] , BrainSuite  [3] , and BrainGyrusMapping  [2]. A Linux version of FreeSurfer version 6. 0 (freesurfer-Linux-centos6_x86_64-stable-pub-v6. 0. 0-2beb96c) was downloaded onto the department's server and run using the default recon-all command, which allowed us to compare their older Desikan-Killiany protocol  [27]  to its updated version, the Desikan-Killiany-Tourville protocol  [19]. BrainSuite version 13a (build#1744, built with Qt 4. 8. 4 on Sept 112,013) was installed and run on a Windows 7, 64-bit operating system with 16G RAM, using the BrainSuite GUI. We used the default Cortical Surface Extraction Sequence, while refining the sulcal curves for accuracy. A BrainGyrusMapping (BGM, v 11.
Parcels, metrics and statistical analysis Package parcels The cortical parcellation protocols, and in turn the derived parcels, differed across the 3 packages. We assessed parcels generated by FreeSurfer's 2 latest and most suitable protocols for cortical analysis: the Desikan-Killiany (DK,  [27] ) and the Desikan-Killiany-Tourville (DKT,  [19] ) protocols. The DKT protocol was introduced in version 5. 3 as an improvement on the DK protocol, offering better parcellation accuracy, clarity and consistency. BrainSuite parcellations are based on an adaptation of the LONI curve protocol  [31] , whereas the BrainGyrusMapping parcellations are done according to Neuromorphometrics' brainCOLOR whole-brain protocol  [28]. We focused our package analysis on 3 regions per subject hemisphere: the superior frontal gyrus (SFG) of the frontal lobe, the supramarginal gyrus (SMG) of the parietal lobe, and the cingulate gyrus (CG) of the cingulate cortex.
Metrics and statistical analysis Various metrics are automatically calculated by each of the tools. We chose the 3 most popular and relevant ones for our ageing population: grey matter thickness (GM th , e. g. ,  [32-34, 40, 41] ), grey matter volume (GM vol , e. g. ,  [41, 42] ), and white matter surface area (WM sa , e. g. ,  [41, 42] ). Both FreeSurfer and BrainSuite calculate these 3 metrics whilst BrainGyrusMapping provides GM vol only. Several parcels were combined to form a region of interest depending on the region and package considered (Table  2 ). Metrics for such regions were derived by combining the original parcels' metrics. For the case of GM th , this meant averaging individual parcel metrics,   and for the case of GM vol and WM sa , this meant adding individual metrics. Statistical analyses consisted of (i) descriptive statistics (medians and 95% Bayesian highest density intervals (HDIs) for each metric, region of interest (ROI), and hemisphere and (ii) a percentile bootstrap between packages on relative median differences.
Here the ground truth values are subtracted from each measure, and those measures are then compared across packages. This enables us to compare packages relative to a common reference. The percentile bootstrap was adjusted for multiple comparisons per metric (i. e. all measurements for each hemisphere/ROI included in a single procedure to maintain the type 1 error at 5%  [43] ). The raw data (tsv files) and the Matlab script we wrote to perform the data analysis are available in the Edinburgh DataShare repository  [44].
Results Repeatability was observed for all packages, with identical results generated for each of the 3 runs (see tsv files of the Edinburgh DataShare repository  [44] ). Parcellation influences were also evident visually. We highlighted them using screenshots taken from various angles (see Additional file 1). We identified 6 double CG occurrences in this dataset: 4 in the left hemisphere (subjects 1, 5, 6 and 8) and 2 in the right hemisphere (subjects 6 and 10).
Looking at the subject's plots (Fig. 1 ) reveals where differences are coming from. For the SMG volumes, larger differences were produced by BrainSuite. Its protocol vaguely defines the SMG, with only mention of it containing Brodmann area 40 and bordering the superior temporal gyrus  [20, 31] , hence the discrepancies within this package and across packages. For the CG volumes, when double gyri were present, they were not captured properly leading to underestimations, except for BGM especially in the right hemisphere. In addition, volume missing in the CG are sometimes misattributed to the SFG, in particular for BrainSuite. For instance, in subject 5, there is an omission of the upper CG fold caused by a double cingulate sulcus, making its SFG larger (see Additional file 1: Figure  S1q-t ).
This is explained (i) by the fact that thickness is not expected to change at the borders of parcels, and therefore Fig. 2  Violin plots show ROI cortical thickness in mm computed by Masks2Metrics (M2M), FreeSurfer (FS-DK, FS-DKT), and BrainSuite (BS) (the middle lines represent the medians, boxes the 95% Bayesian confidence intervals, and the density of the random average shifted histograms). Line plots show the relative difference from each package (FS, BS) to the ground truth estimates (M2M) for each subject (each line is a subject). Double CG occurrences were observed for subjects 1, 5, 6, and 8 in the left hemisphere, and subjects 6 and 10 in the right hemisphere. BrainSuite failed for subjects 4 and 6 differences in volume between DK and DKT do not translate into differences in thickness and (ii) BrainSuite combines grey and white matter thicknesses rather than just grey matter (see Discussion).
The SMG is also highly variable across the cohort, mainly due to its posterior border, as is its segmentation across the packages. The size of our dataset and the use of 1. 5 T MRI images are of course a limitation. There are variations which depends on age (in adults) that would be better captured with a larger sample capturing a wider range of age and higher resolution images. This is particularly true for gyrification (the process and the extent of folding) which varies with age  [5]  and can thus impact on the identification of anatomical branches and borders. The current dataset was nevertheless variable enough to highlight issues in automated packages. For what is reported here, i. e. that the differences observed mainly stem from how anatomical variability in additional gyri and branching is handled, aging or higher resolution imaging has no impact.
One step prior to parcellation in automated and semi-automated tools is the pre-processing phase. In FreeSurfer, for example, amongst other things, that phase is used to derive white and grey matter masks  [1]. These are consequently split in the processing stage, as per a parcellation protocol, to form parcels. Such mask effects were not investigated in this manuscript although it could be contributing to differences, especially for thickness. Package inconsistency across sites (e. g. ,  [47] ) and operating systems (e. g. ,  [16] ) is another aspect to consider, although was not a contributing factor to our study as each package was run on only one computer and one operating system. Finally, and most relevant here, differences in algorithms can also account for observed differences. Volume is simply derived by counting the number of voxels in each parcel and thus directly reflects differences in parcellation protocols.
Conclusions We previously investigated package differences in terms of their parcellation protocol definitions, raising awareness of the associated uncertainties stemming from the well-reported anatomical variability that they are likely to encounter  [20]. In our present work, we quantify the effects of these uncertainties through a healthy middle-aged dataset and manually-derived ground truth data with associated morphometrics. We show that multi-atlas parcellation (BGM) is the most accurate method and therefore encourage more research and usage of such tools. Explicit definition of the method used to compute thickness and surface area is another major factor, and since multi-atlas methods are currently limited to volume, we recommend using FeeeSurfer's DKT approach with manual editing to derive grey matter thickness and white matter surface area.
Availability of data and materials The datasets analysed during the current study are available in the Edinburgh Datashare repository, at https://doi. org/10. 7488/ds/2239 [22]. The Matlab (https://uk. mathworks. com/products/matlab. html, R2016a) code (. m file) we wrote for the statistical analysis as well as the datasets (tsv files) generated during the current study are available in the Edinburgh Datashare repository at https://doi. org/10. 7488/ds/2376 [44]. To derive the ground truth metrics for each of the subjects' ground truth gyri of interest, we ran our software, Masks2Metrics (M2M), version 1. 0  [29, 48]  freely available to all users under the GNU General Public License. The latest version of the software is available on GitHub  [30]. All data generated or analysed during this study are included in this published article and its additional files.
g. , placement of VMs, VNFs or SFCs), or online/offline placement or VMs placement considering resiliency. We are the first to propose an approach that considers the online placement of SFCs requests in DC topologies while taking resiliency requirements into account. Moreover, unlike previous works, we evaluate our proposed solution on very large topologies, considering real data center topology sizes. In our previous work  [23] , we proposed a stochastic approach for the case where SFCs are requested by tenants unaware of the infrastructure of the data center network and that only provide the SFC they want to deploy along with the required availability level. This work was tailored for Fat-Tree data-center topologies. In this paper, we propose a deterministic solution for deploying SFCs in arbitrary multi-tier data center topologies, where the requested SFCs are directly deployed by the DC owners that know in advance the minimum number of replicas needed as they have a perfect knowledge of the infrastructure and of the SLA they provide to their tenants.
g. , through LBaaS in OpenStack. To preserve performance while sharing the same physical hosts between many tenants, the total amount of the physical host resources is always larger than the sum of the used resources by various VMs deployed on that host. As we do not consider the deployment phase of SFCs and given that we consider Fat-Tree and Spine-and-Leaf topologies in this paper, we can safely assume that the network provides infinite bandwidth w. r. t. SFCs demands. 3) Online Placement: In some specific private cloud deployments, one can control the workload and thus apply offline optimization techniques to decide on the placement of virtual service chain functions in the data center. However, in the general case of a public cloud, the workload and the management of the infrastructure are handled by different independent entities (i.
The three topologies are representative of today's data centers and are directly comparable (they have either the same number of fault domains, or the same number of hosts and cores). Resources are homogeneous in the topologies: all hosts have the same number of cores (4 cores per host); all links between aggregation and core switches in the Fat Tree and between leaf and spine switches are 10 Gbps links; and hosts are connected to their ToR/leaf switch through a 1 Gbps link. To ensure that we are not studying transient results with the workload, we verified that the whole system is in steady state before running a workload of 1,000 service requests. We fixed T IA to the value 0. 01 such that in the ideal case, the Fat-Tree topology would be loaded at about 90%. Because of space limitations, we fixed R to be equal for each chain in a run, however the algorithm allows using a different value of R for each chain.
ACKNOWLEDGMENT This work is funded by the  French ANR  through the  Investments for the Future Program  under grant  ANR-11-LABX-0031-01. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www. grid5000. fr).
Such knowledge is needed to guide the representation of relevant physical processes and forcing mechanisms in dynamical forecast models or the choice of predictors in statistical methods. In addition, information from all types of observations (not just SL) is essential for defining the initial states of forecast systems. In this paper we examine the status of observing and modeling systems relevant for both monitoring and predicting coastal SL. (By coastal SL we mean SL at the coast, e. g. , as seen by tide gauges, or over contiguous shelf and continental slope regions, in contrast with SL over the deep/open ocean; other terminology used here is consistent with the definitions proposed by  Gregory et al. , 2019. ) Emphasis is on variability at monthly and longer timescales. The main thrusts of the paper have to do with the need to: treat data and model issues in tandem; highlight the importance for coastal SL of many different datasets (besides SL per se), physical processes, and timescales; and examine the differences and connections between SL variability in the coastal and open oceans.
Currently, most of the world coastlines are monitored by tide gauges (Figure  2 ), generally operated by national and sub-national agencies. Many of these tide gauge records are compiled and freely distributed by international databases. Among these, the PSMSL 1  , hosted by the National Oceanography Centre in Liverpool, is the largest data bank of long-term monthly mean SL records for more than 2000 tide gauge stations  (Holgate et al. , 2013;  see Figure  2 ). Other data portals provide higher frequency (hourly and higher) SL observations required for the study of tides and extreme SL and/or real time measurements needed for operational services or tsunami monitoring and warning systems; this is the case of the UHSLC 2  , the European Copernicus Marine Environment Monitoring Service 3  or the Flanders Marine Institute 4  that hosts the GLOSS monitoring facility for real time data.
One major underlying assumption of the GPS-derived VLM at tide gauges is that the trend estimated from the shorter length of the GPS series is representative of the longer period covered by the tide gauge. When this is the case, GPS VLM reaches an accuracy one order of magnitude better than SL trends  (Wöppelmann and Marcos, 2016). Another limitation is the accuracy of the reference frame on which the GPS velocities rely  (Santamaría-Gómez et al. , 2017). Global GPS velocity fields are routinely computed and distributed by different research institutions (International GNSS Service, Jet Propulsion Laboratory, University of Nevada, University of La Rochelle). Among these, only the French SONEL 6  data center, hosted at the University of La Rochelle, provides GPS observations and velocity estimates focused on tide gauge stations, where possible providing links to PSMSL, to form an integrated observing system within the GLOSS program.
These measurements are generally obtained from dedicated field experiments or local/regional observing systems (e. g. ,  Heslop et al. , 2012; Rudnick et al. , 2017)  and are focused in areas of particular oceanographic interest (e. g. , strong ocean currents, intense atmosphere-ocean interactions, fisheries). The hydrographic data scarcity in the shallow regions is a major hurdle to understand the small scale coastal dynamics and their impact on SL. On the other hand, sea surface temperature has shown covariability with SL along some coastal zones at interannual to decadal time scales, which is related to the fact that both are partly driven by air-sea heat fluxes  (Meyssignac et al. , 2017a). High-resolution, remote-sensed sea surface temperature can thus provide useful spatially detailed information for interpretation of SL features over the coastal zone  (Marcos et al.
, 2019). Ocean bottom pressure is another factor related to SL variability, especially over the continental shelves (e. g. ,  Marcos and Tsimplis, 2007; Calafat et al. , 2013; Piecuch and Ponte, 2015). Currently, satellite gravimetry, starting in 2002 with the launch of the GRACE mission, is the main source of observations of OBP changes over the deep ocean that allows separating the mass component from observed SL  (Chambers et al. , 2004). Available GRACE observations have relatively coarse resolution (∼300 km) and can be contaminated by leakage from larger land water fluctuations, but recent work by  Piecuch et al. (2018b)  highlights their usefulness in understanding the tide gauge records. Alternatively, OBP observations are also provided by in situ moored buoys. The largest network of OBP recorders is maintained and its data distributed by NOAA through the National Data Buoy Center website 7.
EXISTING MODELING SYSTEMS Modeling systems are essential for SL forecasts and projections. This section reviews the status of both regional model/data assimilation systems producing mostly short-term forecasts (order of days to weeks) and global coupled models used in long term climate projections. The discussion of the short-term forecast systems serves to highlight many issues of potential relevance (e. g. , resolution, timescale interactions, data assimilation) for coastal SL prediction at the longer timescales as well.
Coastal Models and Sea Level Forecasts In a very broad sense, a SL forecast can rely on three different approaches: (i) the use of realistic numerical models to resolve the processes that govern the ocean dynamics; (ii) the use of observations, which combined with statistical techniques are used to identify space and time patterns and extrapolate them into the future (e. g. , linear regressions, ANN), and (iii) the hybrid approach, which combines the first two in a wide variety of ways. For instance, a given numerical model forecast can incorporate data assimilation to reduce the forecast errors and/or use an ensemble of forecasts to present the predictions with confidence intervals. Kourafalou et al. (2015a,b)  and De Mey-Frémaux et al. (  2019 ) define a COFS as a combination of a comprehensive observational network and an appropriate modeling system that ensures the ongoing monitoring of changes in the coastal ocean and supports forecasting activities that can deliver useful and reliable ocean services.
The Coastal Ocean and Shelf Seas Task Team within the Global Ocean Data Assimilation Experiment OceanView 8  is an example of an effort that fosters the international coordination of these activities. An adequate COFS should be able to monitor, predict and disseminate information about the coastal ocean state covering a wide range of coastal processes. These include: tides, storm surges, coastal-trapped waves, surface and internal waves, river plumes and estuarine processes, shelf dynamics, slope currents and shelf break exchanges, fronts, upwelling/downwelling and mesoscale and sub-mesoscale eddies. These variations occur over a wide range of time and space scales and have magnitudes of order 10 -1 -10 1 m (Figure  1 ). The numerical models that integrate the primitive equations for solving the physical processes in a given COFS can vary in terms of complexity, from the more simplistic 2D shallow water equation models to the state-of-the-art 3D community models, such as the Regional Ocean Modeling System (ROMS 9  ,  Shchepetkin and McWilliams, 2005)  and the Hybrid Coordinate Ocean Model (HYCOM 10  ,  Chassignet et al.
g. ,  Simpson and Sharples, 2012) , a common adopted strategy is the use of a downscaling approach where remote forcing (e. g. , large-scale currents and associated thermohaline gradients, tidal currents, swell) are incorporated in the COFS via initial and boundary conditions derived from coarser Ocean Forecasting Systems (see  Tonani et al. , 2015  for a worldwide list of such systems). The COFS forcing functions should represent all important shelf and coastal processes that influence SL, such as air-sea interaction, which close to coastal regions is affected by various time and space scales, and land-sea interaction, via coastal runoff, which governs buoyancy-driven currents that are further modified by the wind-driven circulation and shelf topography. An ideal COFS should include a robust data assimilation scheme capable of handling intrinsic anisotropy of the coastal region  (Barth et al.
, 2007; Li et al. , 2008; Tandeo et al. , 2014; Stanev et al. , 2016). Several factors account for COFS uncertainties: imperfect atmospheric forcing fields; errors in boundary conditions propagating into the finer scale model domain; bathymetric errors; lack of horizontal and vertical resolution and numerical noise and bias; errors in parameterizations of atmosphereocean interactions and sub-grid turbulence; intrinsic limited model predictability (strong non-linearity), among many others. To improve prediction skill, data assimilation is used as a way of combining the results of numerical simulations with observations, so that an optimized representation of reality can be achieved. For this purpose, a range of algorithms is used in COFS such as the Optimal Interpolation (OI), the three-dimensional variational (3DVAR), the Ensemble Kalman Filter (EnKF), and the four-dimensional variational (4DVAR) data assimilation methods  (Martin et al.
Also, we should remember that the original idea behind the GLOSS initiative to use GNSS was to provide vertical positions and rates for the tide gauge benchmarks that are used to vertically reference the tide gauges  (Carter, 1994). As the system evolved, however, the GNSS stations and the resulting VLM estimates were not always tied to the benchmarks and are therefore not directly related to the motion of the tide gauge zero point  (Woodworth et al. , 2017b). This prevents the absolute positioning of the tide gauges, and leaves questions as to the relevance of the GNSS VLM rates to the tide gauge zero point rates. To be more specific, GNSS/tide gauge co-location data are provided in the SONEL databank (see text footnote 6), which is recognized as the GLOSS data center for GNSS. About 80% of the GLOSS tide gauges have a permanent GNSS station closer than 15 km (Figure  8 ), but many of these stations were not installed specifically for the monitoring of the tide gauge zero point, which explains why only 28% are closer than 500 m.
To frame present status and future development of SL services that can address the challenges facing coastal communities, it is useful to consider the example of PSMSL (introduced in section "Existing Observing Systems"). Established in 1933, PSMSL is the global data bank for long term SL change information from tide gauges (Figure  2 , section "Sea Level Observations"). Over the past few decades PSMSL has been providing the SL community with additional services relating to the acquisition, analysis, interpretation of SL data and a wide range of advice to tide gauge operators and data analysts. With new challenges due to climate change and SL rise there is an urgent need worldwide to support decisions on managing exposure to climate variability and change. The PSMSL will address these needs by offering a range of services including expert advice, bespoke climate information, value added services and solutions to help build capacity in developing countries.