dataesr/scientific-paragraphs-categorization huggingface dataset:
The working point selected for this analysis leads to an overall 75% b jet selection efficiency and 45% purity (i. e. true b jet fraction of the tagged sample) as determined from an inclusive multijet sample. The selection is motivated by the balance between statistical and systematic uncertainties. The b jet shapes ρ b ( r) are extracted following the jet-track correlation method  [16]  for six bins of p trk T with boundaries 1, 2, 3, 4, 8, 12, and 300 GeV, which are the same as the binning scheme used in Ref  [16]. Corrections for detector and jet-track pair acceptance effects are derived by using the event-mixing technique. The background is then subtracted from measured correlations in a data-driven manner using the measured signal in a data sideband far from the jet axis in a large-η region  [6].
Comparisons of jet shapes from PbPb and pp data show that the presence of the quark-gluon plasma modifies the energy flow around b jets. These modifications include a depletion of the transverse momenta in the range of about r 0. 4 and an enhancement at larger radial distances from the jet axis. Furthermore, the large r enhancement is found to be greater than that previously reported for inclusive jets in PbPb collisions. This observation is consistent with an increased medium response to the propagation of a heavier quark. The enhancement is centrality dependent and is largest in the most central collisions. This measurement provides new constraints for theoretical calculations of parton flavor dependence of energy loss and jet-medium interactions in the quark-gluon plasma.
Acknowledgements We congratulate our colleagues in the  CERN accelerator departments  for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the  Worldwide LHC Computing Grid  and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the  LHC , the CMS detector, and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS ,  MOST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES and CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT and ERDF (Estonia) ;  Academy of Finland, MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3 (France) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIH (Hungary) ;  DAE and DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP and NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE and UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand) ;  PAEC (Pakistan) ;  MES and NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI and PCTI (Spain) ;  MoSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI and NSTDA (Thailand) ;  TUBITAK and TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC (United Kingdom) ;  DOE  and  NSF  ( USA ).
Patient Recruitment Ten patients with primary stage I-II EC who underwent laparoscopic surgery at the departments of gynecological surgery of the Montpellier and Nimes University Hospitals between July 2019 and September 2021 (ClinicalTrials. gov accessed on 12 December 2020 Identifier: NCT04021459) were recruited. All patients underwent pelvic MRI and only patients with stage FIGO I and II EC were included. Patients with non-endometrioid carcinoma, laparotomy, other concomitant cancer, and preoperative hemoglobin levels <9 g/L were excluded. The present study was approved by the local Ethics Committee (IDRCB 2019-A01105-52) and a signed informed consent was obtained from all patients with EC.
Statistical Analysis A number of observations and percentages were reported for qualitative variables; median, minimum, and maximum values were presented for quantitative variables. Continuous clinicopathologic variables were dichotomized at the median value. CTC count (mean number per ml) was compared with the clinicopathologic characteristics using the Wilcoxon Mann-Whitney test or the Kruskal-Wallis test. All statistical tests were twosided and p < 0. 05 was considered significant. Statistical analyses were performed with SAS version 9 (SAS Institute, Cary, NC, USA).
Statistical Analysis A number of observations and percentages were reported for qualitative variables; median, minimum, and maximum values were presented for quantitative variables. Continuous clinicopathologic variables were dichotomized at the median value. CTC count (mean number per ml) was compared with the clinicopathologic characteristics using the Wilcoxon Mann-Whitney test or the Kruskal-Wallis test. All statistical tests were two-sided and p < 0. 05 was considered significant. Statistical analyses were performed with SAS version 9 (SAS Institute, Cary, NC, USA).
IV. EXPERIMENTAL RESULTS To assess the proposed method, a number of models are trained using the bottleneck-modulated architecture from Fig. 1  or the equivalent fixed-rate multimodel baseline (same backbone transform without modulation). Either mean squared error (mse) or structural similarity index measure (SSIM) are used as distortion metrics in the loss function of the models, optimized using Adam  [31]. The reduced-complexity models use N = 64 and M = 192 filters per layer, while the Ballé2018 architecture is as in  [9]  (N = 128, M = 192), including range-adaptive normalization instead of uniform normalization. The proposed model is compared to said multimodel baseline and to JPEG 2000  [5] , and code and visual examples are available at a GitHub repository at the time of submission.
1 Three different remote sensing datasets are used in our experiments to show the general validity of the proposal as follows. 1) 12-bit simulated panchromatic Pléiades images of 50 cm resolution. A total of 96 820 × 820 images are used in training and 32 820 × 820 images are used in testing. These datasets come from panchromatic, multispectral, and hyperspectral images, and have different spatial resolutions, thus, the robustness of the proposed method in spatial decorrelation can be assessed in a variety of conditions. The models are evaluated under the mse and SSIM metrics. Results for mse are converted to peak signal-to-noise ratio (PSNR) as PSNR = 20 log 10 ((2 b -1)/(mse) 1/2 ) dB, with b = 12 for Pléiades and b = 16 for Landsat 8 and AVIRIS. SSIM results are converted to a decibel scale as SSIM(dB) = -10 log 10 (1 -SSIM).
Fig. 2  reports the rate-distortion performance of the different models we tested, trained for either mse or SSIM. As is clear from those diagrams, our reduced-complexity bottleneck-modulated models performed on par with the equivalent reduced-complexity multimodel baseline, on par with the Ballé2018 multimodel baseline, and decisively surpassed JPEG 2000 in all datasets, both under PSNR and SSIM as target or evaluation distortion metrics. As expected, our models  performed more competitively under the metric they were optimized for. These results show that the architecture simplification from  [15]  and bottleneck modulation as we propose do not significantly compromise compression performance on a variety of datasets with different characteristics when optimized for the mse and SSIM metrics.
Beyond mse and SSIM quantitative metrics, Figs. 3  and 4  provide some qualitative assessment. Fig. 3  shows a visual comparison of a Pléiades image compressed at low bitrate using mse or SSIM-optimized models of the proposed architecture. It can be observed that the SSIM-optimized model recovered certain features more accurately, such as the grooves in the earth near the plane, and generally producing more clearly defined edges. See our Github for more visual examples for Landsat 8 and AVIRIS datasets. Fig. 4  reports the average spectral angle (SA) loss between the original spectral pixel (x ∈ R n ) and the distorted spectral pixel ( x ∈ R n ), computed as SA = arccos (x T x/∥x∥∥ x∥) for multispectral Landsat 8 and hyperspectral AVIRIS images. As shown, our reduced-complexity single model once more surpasses JPEG 2000 and performs on par with the other learned models.
V. CONCLUSION A reduced-complexity neural multirate compression architecture for remote sensing data is proposed, which can compress images at multiple and varying bitrates in a single execution, introducing, for the first time, a novel scheme that allows compression at a user-defined bitrate. Experimental results show the proposed method performs on par with the multimodel baseline and is superior to current JPEG 2000 standard in compression of remote sensing images of varying sources and resolutions. Finally, as was the case for the reduced-complexity baseline  [15] , the proposed encoder could be feasibly run on board using currently available hardware.
As the Reynolds number increases, the large-eddy simulation (LES) of complex flows becomes increasingly intractable because near-wall turbulent structures become increasingly small. Wall modeling reduces the computational requirements of LES by enabling the use of coarser cells at the walls. This paper presents a machine-learning methodology to develop data-driven wall-shear-stress models that can directly operate, a posteriori, on the unstructured grid of the simulation. The model architecture is based on graph neural networks. The model is trained on a database which includes fully developed boundary layers, adverse pressure gradients, separated boundary layers, and laminar-turbulent transition. The relevance of the trained model is verified a posteriori for the simulation of a channel flow, a backward-facing step and a linear blade cascade.
, 2020). This has been motivated by the recent emergence of data-driven approaches with capability beyond those of handcrafted models in various complex tasks, such as image classification, speech recognition, natural language processing, short-term weather prediction, or protein structure prediction  (LeCun et al. , 2015; Jumper et al. , 2021; Ravuri et al. , 2021). Supervised machine-learning wall-shear-stress models have proven capable of discriminating and operating in non-equilibrium flow regions, provided that similar flow phenomena were encountered in the training dataset  (Dupuy et al. , 2022). The studies on machine-learning wall-shear-stress modeling have demonstrated the advantages of going beyond algebraic formulations and capturing topological flow features to improve the wall friction prediction.
A database of seven direct numerical simulations or high-fidelity wall-resolved LESs is used to train the model and validate the model: two channel-flow simulations, the simulation of the flow in a diffuser, two simulations of flows over backward-facing steps (BFSs) and two simulations of flows in a linear blade cascade. This provides a variety of flow phenomena, including fully developed boundary layers, adverse pressure gradients, separated boundary layers, and laminar-turbulent transition. Instantaneous snapshots from each simulation are filtered onto coarse meshes representative of a LES with a wall-shear-stress model, of which only near-wall nodes are used by the model for its prediction. The analysis is restricted to coarse tetrahedral meshes, for their ubiquity in simulations of complex industrial flows, although the method could in principle be applied to any type of mesh.
An Encode-Process-Decode architecture  (Battaglia et al. , 2018)  is used for the GNN, with no global features to ensure the spatial locality of the prediction. The inputs of the model are the displacement vector of the mesh edges and the three components of velocity at each node, scaled to ensure the Mach number invariance of the model at the incompressible limit, and augmented by arbitrary rotations and reflections of the coordinate system. The model is implemented a posteriori by coupling the compressible massively parallel flow solver AVBP  (Schönfeld and Rudgyard, 1999  to a code dedicated to inference, using a message passing interface (MPI;  Serhani et al. , 2022). A channel flow, a BFS and a linear blade cascade configuration are simulated for these a posteriori tests. In each case, the simulations are compared to the corresponding simulation using an algebraic model based on the law of the wall to assess the relevance of the model.
Dataset The machine-learning wall-shear-stress model developed in this paper relies on a database of direct numerical simulations or high-fidelity wall-resolved LESs which have been produced by various research groups (Figure  1 ). Namely, the database includes the simulations of: a fully developed channel flow at friction Reynolds number Re τ ¼ 180 (CF1), performed at Imperial College London  (Agostini and Vincent, 2020)  using the high-order flux reconstruction method of  Huynh (2007)  in the CFD code PyFR  (Witherden et al. , 2014) ; a fully developed channel flow at friction Reynolds number Re τ ¼ 950 (CF2), performed at the Polytechnic University of Madrid (Del  Álamo and Jiménez, 2003; Lozano-Durán and Jiménez, 2014; 2015)  using a hybrid Fourier-Chebyshev spectral method; a three-dimensional diffuser (3DD) corresponding to the geometry "Diffuser 1" of  Cherry et al.
(2011)  and  Zambonini et al. (2017) , performed using a cell-vertex finite-element method  (Schönfeld and Rudgyard, 1999 and a two-step Taylor-Galerkin scheme (Colin and Rudgyard, 2000). The N65 case is subdivided into two sub-configurations that differ only by the inlet boundary condition: a simulation with an incidence angle of 4°(N65a) and a simulation with an incidence angle of 7°(N65b). The two simulations are described in Appendix C. The database is heterogeneous in terms of flow solvers and numerical methods since the 3DD, BFS, N65a, and N65b simulations were performed using finite-element methods, the CF1 and APG simulations using high-order methods and the CF2 simulation using a spectral method. We believe that data-driven modeling approaches should demonstrate their ability to learn from such heterogeneous databases because heterogeneous databases can more easily scale and cover a large diversity of flow configurations.
The numerical parameters of each simulation are summarized in Table  1. A wide variety of flow behavior is represented by the dataset. The CF1 and CF2 simulations are focused on providing data on fully developed boundary layers. The 3DD and BFS simulations include fully separated flow regions. The APG simulation includes regions of incipient separation, that is with a backflow probability between 1 and 20%  (Simpson, 1989). The N65a and N65b simulations include transitional boundary layers, through small-scale flow separation, on the blade surface, a light adverse pressure gradient as well as a large region of unsteady three-dimensional corner separation on the junction between the endwall and the suction side of the blade. For a given model to produce accurate predictions in all simulations, an ability to discriminate between these various flow regimes will thus be required.
Data preparation The instantaneous fields of the a priori database need to be preprocessed for use by the machine-learning procedure. Namely, the fields are filtered onto coarse meshes representative of a LES with a wall-shearstress model, as is customary in a priori tests for LES  (Leonard, 1974; Sagaut, 2006). We focus on tetrahedral meshes, for their simplicity, their ability to represent arbitrarily complex domains and widespread use in simulations of complex industrial flows. The preparation of the data for the machine-learning algorithm is represented in Figure  1  and is as follows: 1. For each simulation, a series of coarse tetrahedral meshes are constructed using the mesh adaptation library MMG3D  (Dobrzynski and Frey, 2008; Dapogny et al. , 2014). All coarse tetrahedral meshes have a uniform target refinement, although there are local variations of cell sizes resulting from the meshing algorithm, since regular tetrahedra do not fill space (as may be seen in Figure  2 ).
The resulting tetrahedral meshes are conformal and referred to as coarse near-wall meshes hereafter. Unless otherwise specified, N H ¼ 3 is used. 3. A fine tetrahedral mesh is produced, associated with a nominal edge length e þ ¼ 4. The instantaneous fields of the a priori database are interpolated linearly onto this fine mesh. The resulting fields are used as source data for the filtering operation. 4. The instantaneous data are filtered onto and at the resolution of the coarse near-wall meshes. This low-pass filtering operation attenuates large frequencies, associated with turbulent scales that cannot be resolved using the target computational grid. A surface filter is used to filter the WSS and a volume filter is used to filter the velocity components in the bulk of the domain. We denote (Á) the volume filter associated with the local filter width Δp 0 ¼ Ṽ1=3 p 0 and (Á S ) the surface filter associated with the local filter width ΔS p 0 ¼ S1=2 p 0 , with Ṽp 0 and Sp 0 , respectively, the nodal volume of node p 0 and the nodal area of p 0 on the surface W.
In addition to the physical assumptions related to the model architecture, the present learning process ensures the equivariance of the model to Galilean transformations, orthogonal transformations such as rotations or reflections, and "changes of scales," hereafter referred to as Mach number equivariance. We furthermore assume that the flow is isothermal incompressible and obeys the incompressible Navier-Stokes equations without external body force. This is consistent with the selection of simulations in the database of Section 2. 1, which involves neither significant temperature variations nor compressibility effects.
Indeed, the scaled variables tend to a constant as the Mach number of the flow tends to zero, at constant Reynolds number and mesh resolution  (Paolucci, 1982). In other words, for an isothermal incompressible flow without external body force, the scaled variables depend on the Reynolds number of the flow, which governs the flow physics, but are unaffected by a change of Mach number, which is not a relevant physical parameter. This scaling is a natural extension of the scaling used by  Dupuy et al. (2022)  for a Cartesian grid. It is purely local and only involves quantities that can be computed in a LES. Enforcing the equivariance of the model under these transformations increases the sample efficiency of each graph in the numerical dataset and improves the generalization of the model to different configurations.
A priori tests This section presents a priori tests of the machine-learning wall-shear-stress model, based solely on the numerical database described in Section 2. 1. The model is trained using the simulations CF1, CF2, 3DD, and N65a. In each training simulation, a subset of the data is isolated for validation purpose. The split between training and validation datasets is performed independently for each simulation. In the CF1, CF2, and 3DD simulations, the snapshots associated with the later time steps are reserved for validation purpose. The BFS and APG simulations are only used as test simulations, to test the performance of the (a) For the N65 configuration, training is exclusively performed on the sub-configuration N65a, that is with an incidence angle I ¼ 4°, while testing is exclusively performed on the sub-configuration N65b, that is with an incidence angle I ¼ 7°.
In the two channel-flow simulations (CF1 and CF2), the reference data points are scattered around the law of the wall, with a large variance associated with velocity and WSS fluctuations. This behavior is reproduced by the model, but the variance of the data points around Reichardt's law is clearly underestimated. In the 3DD simulation, most data points are scattered below Reichardt's law in the u þ y þ ð Þ graph, which is consistent with the presence of separated regions. The GNN WSS model reproduces this behavior, although the predictions are more biased toward the law of the wall and extreme points well above Reichardt's law are missing at low y þ. The presence of data points that deviates from the law of the wall is also correctly predicted by the model in the test BFS and APG simulations, suggesting reliable generalization to other flow configurations with separation.
Finally, the NACA 65-009 blade simulation (N65b) is most saliently associated with a transitional boundary layer on the blade surface, and most data points are found above Reichardt's law on the u þ y þ ð Þ graph. This is qualitatively reproduced by the model but with a clearly lower variance. Overall, the model thus leads to markedly different distributions of u þ y þ ð Þin each simulation, indicating an ability to locally discriminate, to some extent, between the various type of boundary layers, fully developed, separated, or transitional, present in the numerical database. Several quantitative measures of the agreement between the predicted WSS and corresponding local instantaneous reference WSS are given in Table  4. First, we use the concordance coefficient of  Lin (1989)  between the predicted WSS Y and the reference WSS τ ref , defined as   In addition, the explained variance R 2 is used to compare the variance of the residual of the model to the underlying WSS variance: C τ ¼ 〈τ ref Y〉 A À 〈τ ref 〉 A 〈Y〉 A 〈τ 2 ref 〉 A À 〈τ ref 〉 2 A þ 〈Y 2 〉 A À 〈Y〉 2 A þ 〈τ ref 〉 A À 〈Y〉 A ð Þ 2 : (13) R 2 ¼ 1 À 〈 τ ref À Y ð Þ 2 〉 A 〈 τ ref À 〈τ ref 〉 ð Þ 2 〉 A : (14) The Reichardt-residual explained variance similarly compares the model residual to the residual that would be obtained using Reichardt's law to compute the WSS, R 2,Reichardt ¼ 1 À 〈 τ ref À Y ð Þ 2 〉 A 〈 τ ref À τ Reichardt ð Þ 2 〉 A : (15) The concordance coefficient C τ is equal to zero if there is no linear correlation between the model and the reference and equal to 1 if and only if the predicted WSS is equal to the reference WSS for each time step.
Note that the values obtained for different datasets can only be compared with great care, since these three indicators depend not only on the intrinsic performance of the model but also on the underlying distribution of the data. For instance, the value of C τ associated with Reichardt's law is larger in the 3DD and BFS simulations than in the TCF1 and TCF2 simulations, although the model performs worse in these two simulations. In all simulations, the GNN WSS model agrees well with the reference WSS and improves by all measures the results compared to Reichardt's law baseline. Even in the case of a channel flow (case CF1 or CF2), for which the law of the wall is most well suited, the GNN WSS model reduces the residual variance of a model based on Reichardt's law by around a third. Figure  6  shows that this variance reduction is consistently obtained for a wide range of distances to the wall.
In the 3DD, BFS, and N65b simulations on the other hand, variations of model performance with y þ may be observed. In particular, very low value of y þ are associated with a very large value of R 2,Reichardt because those corresponds to separated flow regions that are not accurately described by Reichardt's law. Finally, the instantaneous agreement between the predicted and reference WSS is reflected in a improvement of the time-and space-average predicted WSS, as may be seen Figure  7. Note that the Table  4. A priori validation: Integral measures of the disagreement between the reference wall shear stress and the prediction of the graph neural network WSS model in the CF1, CF2, 3DD, BFS, APG, and N65b datasets.