dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Data analysis was divided into two parts: validation of the Loadsol ® insoles compared to the force plates only during trials at SSS and quantification of asymmetry based on insole data during trials at SSS, LSS, and FSS. Loadsol ® validation. ASCII insole data and force plate data were post-processed using MATLAB ®. To synchronize the insoles and force plates, the first NGrF value above the threshold of 20 N for both measuring devices was taken as the first instance of ground contact, and force plate data were resampled at 100 Hz to correspond to the frequency of the insoles. The steps simultaneously recorded by both insoles and force platforms were extracted based on the SSS trial data. Changes in NGrF over the cycle and four specific parameters (Figure  1 ), considered to be clinically relevant, were compared for each lower limb (intact and prosthetic) with both the insoles and force plates: 1.
Acknowledgements The authors would like to express their gratitude to the IRR center and the  INI center  for prosthesis fitting. They also acknowledge the essential contributions of  Marie-Agnès Haldric  for the technical support,  Florian Guillaume  for the technical discussions,  Julia Facione  for the recruitment of transfemoral amputees, and  Jonathan Pierret  for his statistical support. The study was performed in  Institut de Biomécanique Humaine Georges Charpak, Arts et Metiers ParisTech, Paris, France , and  Centre de médecine physique et de réadaptation Louis Pierquin ,  IRR-UGECAM, Nancy Cedex, France.
The collision velocity, as well as related phenomena, were monitored using a high-speed camera HPV-1 (Shimazu Co. , Ltd), which provides shooting at 1 million frames per second. A 2-kW metal halide lamp (Iwasaki Electric Co. , Ltd. ) was used as a light source. Three experiments with different impact velocities (542 m/s, 735 m/s and 1000 m/s) were implemented. The welded samples shown in Fig. 1f  were cut along the welding direction, and then ground using SiC sanding papers and polished using fine diamond suspensions. The structure of the samples was characterized by optical microscopy (Carl Zeiss AxioObserver Z1m microscope), scanning electron microscopy (Carl Zeiss EVO 50 XVP microscope) and transmission electron microscopy (Tecnai G2 20 microscope). The received data were later compared with the results of numerical simulation.
Description of SPH simulation of the plates collision To analyze the peculiarities of the plastic flow at the conditions of high-velocity oblique collision an Ansys Autodyn 17 software was used. A mathematical model was based on the equations of conservation of masses, momentum, and energy written inthe differential form as it is shown in  [25]. This approach is widely used to describe the impact processes in hydrodynamics of fluids and solids, and its derivation may be found in  [5, 25]. Aiming analyzing conditions corresponding to the shock jump, the modified Mie-Gruneisen equation of state was used. The coefficients of the equation of state for steel 1006 are shown in Table  1. The variation of materials' strength depending on loading conditions was estimated using the Johnson-Cook model.
Here, z ∈ [0, L] is the coordinate along which the flow is directed, υ 0 (z) = 2R g T(z) is the probable molecular speed, R g is the specific gas constant, T(z) is the imposed linear temperature distribution along the channel wall, and P(z) is the unknown pressure distribution and it is part of the solution. Additionally, G P (δ) and G T (δ) are the dimensionless flow rates, also known as kinetic coefficients  [24, 25, 27, 33]  for the pressure-and temperature-driven flows, respectively. They depend on the local gas rarefaction parameter, defined as δ(z) = P(z)R µ(z)υ 0 (z) , (2) where µ(z) is the local dynamic viscosity. In the present work, the kinetic coefficients G P (δ) and G T (δ) are retrieved from a kinetic database, which has been developed based on the linearized Shakhov model subject to pure diffuse boundary conditions in the range δ ∈ [0, 50].
[22]  and numerically modelled by Armfield et al. [23]  and others  [30, 31]. An extensive investigation on recirculation and separation by Clausen et al. [24]  has become a popular test case for the validation of numerical codes, integrated in the 'ERCOFTAC conical diffuser' database. Clausen's conical diffuser is considered an extreme case which is sensitive to Swirl Number (Sn) affecting pressure recovery Cp  [23] , but where both separation and recirculation are absent, and thus is chosen here to run the UQ investigation. Computational Fluid Dynamics (CFD) has the capability to compute complex engineering applications employing real gas as working fluid  [25]. However, the investigations mentioned above consider ideal gas, which fail to comprehensively represent the dense gas in lowtemperature ORCs.
Governing equations and turbulence Model Reynolds-Averaged Navier Stokes (RANS) equations for viscous compressible flows were applied in a finite volume solver adapted to accommodate for dense gas simulations. The   turbulence model is implemented, extending previous work by From et al  [26]  and its  full validation is presented in  [26]. The NIST REFPROP database is coupled with the CFD solver to model the dense gas properties  [27].
Conclusion In this study, uncertainty quantification analysis of dense gas conical diffuser is conducted with uncertain operating conditions for varied geometric conditions. A robust framework is established to couple a gPC approach with a Computational Fluid Dynamics solver using the NIST REFPROP database. The deterministic and stochastic results for the dense gas conical diffuser are investigated comprehensively by density-induced swirl flow. The difference between deterministic results and the stochastic mean values of the velocity u (under diffuser length = 410mm -diffuser half cone angle = 12°; diffuser length = 510mmdiffuser half cone angle = 10°; diffuser length = 610 mm -diffuser half cone angle = 8°; diffuser length = 610mm -diffuser half cone angle = 12°) is due to local non-linear effects in the recirculation regions.
Acknowledgments The authors would like to acknowledge the  High Performance Computing (HPC)  facilities at  Queensland University of Technology (QUT)  for its support to conduct the numerical simulations. Dr. E. Sauret would like to thank the  Australian Research Council  for its financial support ( DE130101183 ). The first author would like to thank  R. Persky ,  C. S. From , and  S. T. W Kuruneru  in  CFD subgroup in Laboratory  for Advanced Modelling and Simulation in Engineering and  Science (LAMSES)  in  QUT.
The weight losses at the highest temperature ranges are 3. 5 and 3. 1% for the WO3/Csuc and WO3/Ccell composites, respectively. To precisely characterize the oxide/carbon multi-materials, the chemical composition of the carbon phases were determined by elemental analysis. Table  1  gathers the data relating to the saccharide molecules after pyrolysis at 500 °C under argon for 2 h. As reported, the resulting products The amounts of carbon in the WO 3 /carbon composites were determined by means of thermogravimetric analyses, as reported in Figure  2. Two weight losses for the WO 3 /C suc and WO 3 /C cell composites were recorded: below 300 • C and between 300 to 700 • C. While the first weight loss can be attributed to the release of the adsorbed water molecules and embedded solvents on or within the oxide/carbon composites, the second one was interpreted as the oxidation of the carbon-rich phases.
The weight losses at the highest temperature ranges are 3. 5% and 3. 1% for the WO 3 /C suc and WO 3 /C cell composites, respectively. To precisely characterize the oxide/carbon multi-materials, the chemical composition of the carbon phases were determined by elemental analysis. Table  1  gathers the data relating to the saccharide molecules after pyrolysis at 500 • C under argon for 2 h. As reported, the resulting products are mainly made of carbon with amounts that ranged from 86 to 82 wt. % with the use of sucrose and cellulose as reactants, respectively. The presence of the hydrogen and oxygen heteroatoms could be explained by the low temperature that was used for the pyrolysis, which caused the incomplete carbonization of carbohydrates. In the literature, temperatures close to 900 • C are classically used  [28].
The weight losses at the highest temperature ranges are 3. 5 and 3. 1% for the WO3/Csuc and WO3/Ccell composites, respectively. To precisely characterize the oxide/carbon multi-materials, the chemical composition of the carbon phases were determined by elemental analysis. Table  1  gathers the data relating to the saccharide molecules after pyrolysis at 500 °C under argon for 2 h. As reported, the resulting products are mainly made of carbon with amounts that ranged from 86 to 82 wt. % with the use of sucrose and  The SEM images of the tungsten (VI) oxide/carbon composites are presented in Figure  3. Both composites, WO 3 /C suc (Figure  3A ) and WO 3 /C cell (Figure  3B ), are made of micron-sized particles with surfaces that appear to be very rough. After careful observation, this roughness corresponds to the highly agglomerated very small particles with very similar sizes to the pristine tungsten (VI) oxide particles that were used for the WO 3 /carbon composites' preparation (Figure  S2 ).
Supplementary Materials: The following are available online at http://www. mdpi. com/2311-5629/5/3/37/s1, Figure  S1 : FTIR spectra of the different WO 3 /C composites. The pristine WO 3 material is also reported. Figure  S2 : SEM view of the pristine WO 3 material. Author Contributions: P. G. suggested and guided this research, and wrote the paper. Q. M. , A. B. and P. G. carried out the experimental work including materials syntheses, the diverse characterisations (TGA, XRD, sensitivities and combustion tests) and the corresponding data interpretation. F. S. performed the electron microscopy coupled to the energy-dispersive X-ray spectroscopy. R. G. and D. S. supervised closely the study, the interpretation of data and helped in the proofreading of the manuscript.
Magnetocaloric feedstock elaboration Elaboration of the composite by extrusion is commonly used in compounding during plastic manufacturing, and was previously employed by  Cheng et al. (1997). This process continuously supplies homogeneous, highly loaded mixtures through a system of a corotating twin screws. The equipment used a modular screw profile (length-to-diameter ratio L/D = 40, diameter D = 16 mm). The maximal torque and pressure were 120 Nm and 10 MPa, respectively. The configuration of the element screw produced conveying, melting, kneading, and pumping zones, as shown in Fig. 2. The thermoregulated barrel contained two feeding areas and could be regulated in the temperature range 20 to 400 °C. During the processing, the data were recorded to determine the influence of the processing parameters on the die pressure and the screw torque curves.
02 Nm, which suggested the formation of some highly viscous flow mixtures corresponding to extrusion processing behaviour (compounding and shaping). In conclusion, the feasibility of using feedstock during the extrusion processes was demonstrated by mixing and torque behaviour analyses. The purpose of this investigation is to characterise the rheological behaviour in support of the shaping process performed on the custom-made magnetocaloric composite. The analyses were carried out by using a twin-bore capillary rheometer 250 mm in length and a barrel 15 mm in diameter. The composite was extruded through several tungsten carbide dies with lengths of 16 and 32 mm and diameters of 1 and 2 mm. Other zero-length dies were used so that the Bagley correction could be applied to the data for a physical length of 0.
2 mm. Under conditions of steady flow and from measurements of the volumetric flow rate and the pressure drop through the rheometer, the shear viscosity can be determined as the ratio of the shear stress to the shear rate. To determine the true viscosity, procedural corrections, namely, the Bagley and Weissenberg-Rabinowitsch corrections, and rheological methodologies were employed, which are the same as those described by  Claudel et al. (2017). Rheological analysis of the data was performed with the Rosand Flowmaster software. Rheometer tests for all the elaborated composites were conducted to obtain the steadystate shear flows at different shear rates ranging from 10 2 to 10 3 s -1 , which correspond to the extrusion process parameters under different applied temperatures, to investigate the influence of shear rate on shear viscosity.
7 mL. min -1 with 50 mM sodium nitrate. A Viscotek tri-SEC model 270 was used for light scattering and differential pressure detection, and a Viscotek VE 3580 RI detector was used for the determination of polymer concentration while UV was monitored at 280 nm with a LDC/Milton Roy Spectromonitor 3000. A refractive index increment per unit concentration increment (dn/dc) value of 0. 146 mL/g was used for concentration determination. Data were collected with Omnisec 4. 7 software (Viscotek).
The organic phase was fully evaporated at RT and then hydrolyzed with 2 M TFA at 120 °C for 2 h. The sample were then converted into alditol acetates as described in § 2. 4. 2. and were injected into a Trace GC Ultra gas chromatograph (Thermo Scientific) mounted with an OV-1 capillary column (length: 30 m, internal diameter: 0. 32 mm, column oven temperature 60 °C-315 °C (3 °C/min), carrier gas H 2 ) coupled to a ISQ EC single quadrupole mass spectrometer (Thermo Scientific). The data were recorded with Xcalibur software (Thermo Scientific). The samples were tested in duplicate.
Acknowledgment We thank  Charlène Launay  and the PVPP technical staff, and in particular  Sylviane Daniel , for their assistance in the laboratory and  Amel Allag  for grain dissection. We also thank  Mathieu Fanuel  at  BIBS  for kindly performing MALDI-TOF MS experiment. YV has received funding from the  European Union  through the  7 th Framework Program  for research and the  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie  grant agreement No  FP7-267196-MSCA-COFUND-AgreenSkills  and  H2020-708329-MSCA-IF-2015. Part of this research was funded by the  Research National Agency  in the framework of the  BREEDWHEAT  project [ ANR -10 -BTBR -03 ]. The structural analyses were performed using the equipment of the  BIBS facility in Nantes  ( UR1268   BIA ,  IBiSA ,  Phenome-Emphasis-FR  (grant number  ANR-11-INBS-0012 )).
Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.
Introduction Over the last years, and in particular with the emergence of deep convolutional networks (ConvNets), computer vision systems have become accurate and reliable enough to perform tasks of practical relevance autonomously and over long periods of time. This has opened opportunities for the Communicated by Thomas Brox. A preliminary version of this manuscript was published at GCPR 2019  (Sun and Lampert 2018). This work was in parts funded by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 308036. B Christoph H. Lampert chl@ist. ac. at Rémy Sun remy. sun@ens-rennes. fr 1 ENS Rennes, Bruz, France 2 IST Austria, Klosterneuburg, Austria deployment of automated image recognition systems in many commercial settings, such as video surveillance, self-driving vehicles, and social media.
It also requires neither access to internal layers of the network nor a manipulation of the input images. By adjusting the batch size, the true positive rate can be improved without increasing the false positive rate at the same time. This is in contrast to single sample tests that only allow for a trade-off between both quantities. Given the importance of the problem, in this work we put particular emphasis on a thorough experimental evaluation. We demonstrate the power of KS(conf) using five state-of-the-art ConvNets architectures (ResNet50, VGG19, SqueezeNet, MobileNet25, NASNetAlarge), chal-lenging real-world image datasets (ImageNet ILSVRC 2012, Animals with Attributes 2, DAVIS) and a variety of possible out-of-specs scenarios (new classes, change of low-level image properties, loss of variability, problems in the image acquisition setup).
The granularity column specifies the type of input data (single samples, batches of samples, complete datasets, time series) that the methods take as input. For a definition of the criteria, see Sect. 2. As entries, ✓ indicates that a method has a certain property, ✗ indicates that it does not, ✓/✗ indicates that methods might or might not have this property, -indicates that this property does not apply in the situation.
Classical outlier detection techniques work either probabilistically, e. g. by nonparametric density estimation  (Knorr and Ng 1997) , or geometrically, e. g. by one-class classification  (Tax 2001). Neither approach is directly suitable for natural image data, though, because of the high data dimensionality and diversity of image data. Recent work on robust learning (Konstantinov and Lampert 2019) aims at identifying outliers on the level of datasets instead of individual samples. This allows the use of unreliable data sources, but it happens at training time and does not address the problem of out-of-specs detection at prediction time. Failure prediction Similar approaches as for outlier detection have been used to identify failures, i. e. predict when the label predicted by a classifier is incorrect  (Bansal et al.
2006; Saenko et al. 2010). Both approaches, however, require some information about how the data distribution will change, at least in the form of unlabeled samples. If even a labeled dataset for the new distribution is available, transfer learning methods, such as model fine-tuning can be employed  (Kuzborskij et al. 2013; Pan and Yang 2009). In all cases, the criterion of being agnostic to the out-of-specs conditions is violated, and often also the black-box ready and pretrained-ready criteria. An exception is  (Royer and Lampert 2015) , which post-processes the output scores at prediction time to match a new distribution. This, however, is not a passive technique, and it only works for a specific class of out-of-specs situations, namely changes in class priors.
Experimental Setup With our objective of practical usefulness in mind, we aim in our experiments for results that can be expected to generalize also to future image classification systems. We therefore emphasize three aspects in our experiments: (1) tackling a challenging task of high-resolution natural image classification; (2) obtaining results for a diverse set of ConvNet classifiers that constitute the state-of-the-art in different application scenarios; (3) working under as realistic conditions as possible, in particular not making use of information that is not available for real-world systems. This focus constitutes a major difference to many existing works that provide results on simpler datasets, such as MNIST or CIFAR, benchmark only few and relatively small network architectures, or adjust hyper-parameters on out-of-specs data.
Specifically, we reports results for five popular ConvNet architectures: ResNet50  (He et al. 2016 ) and VGG19  (Simonyan and Zisserman 2014)  are standards in the computer vision community; SqueezeNet  (Iandola et al. 2016)  and MobileNet25  (Howard et al. 2017 ) have smaller computational and memory requirements, making them suitable, e. g. , for mobile and embedded applications; NASNetAlarge  (Zoph et al. 2018 ) achieves state-of-the-art performance in the ImageNet challenges, but is quite large and has high computational requirements. Technical details of the networks are given in Table  2. All classifiers are pretrained on the training part of the ImageNet ILSVRC 2012 dataset  (Russakovsky et al. 2015)  (1. 2 million training images of 1000 classes), which is the dataset most commonly used for this purpose.
2 We use the 50. 000 validation images of the same dataset as reference set for within-specs behavior. We do not make use of ground truth labels of the validation set (or the test set) at any time, as As additional data sources, we use the 10 test classes (proposed split) of the Animals with Attributes 2 (AwA2) dataset  (Xian et al. 2018 ). These are 7913 natural images of similar appearance as ILSVRC, but showing classes that are not present in the larger dataset. Additionally, we also use the 3456 images from the DAVIS  (Perazzi et al. 2016 ) dataset (480p part). Those images are in fact video frames and therefore also exhibit different characteristics than the still images of ILSVRC, such as motion blur. To simulate repeating images, for each test image we form virtual data sets that consist of as many copies of that images as currently required.
Results: True Positive Rate The ultimate quality measure for any test is whether, at a fixed FPR, it can reliably detect if the input distribution changes in any way. For this, we run the different batch-based tests on different out-of-specs situations: as a diverse scenario, we use the classifiers to make predictions on a mixture of all AwA2 classes or on the DAVIS data. As a specific scenario, we use each of the AwA2 classes individually as out-of-specs data source. Finally, as static scenario, we use all ILSVRCtest data, AwA2 data or DAVIS data, but the classifiers run The first two cases correspond to a situation in which the classifier operates unperturbed, but on unexpected data. The third case reflects the situation where the input to the classifier is perturbed, e. g. because of network problems or by explicit manipulation.
Note that it can still be interpreted as change in data distribution, only that the distribution at prediction time is a delta-peak on a single image. For the diverse and specific situations, we compute the fraction of positive tests out of randomly created 10,000 batches. For the static situation, we create one batch for each image of the respective dataset and report the averages. In all cases, the system runs completely out-of-specs. Therefore, all positive tests are correct and reported averages directly correspond to the TPR. Because of the large number of scenarios and results, we only report on some characteristic cases and unexpected findings. The full set can be found in the accompanying website. Specifically, we first provide a quantitative summary (Table  4 ) and we then discuss the qualitative dependence between TPR and FPR (Fig.
Quantitative Summary Table It shows that in the diverse setting with all AwA2 classes or the DAVIS dataset as out-of-specs data, KS(conf), the mean test and, to a lesser degree, the symmetric mean test do a good job detecting the change of distribution. The logarithmic variants overall achieve lower TPRs. In the specific scenario, only KS(conf) reliably detects all of the 10 out-of-specs cases. The other tests show a more diverse picture. Out of the 10 object classes used, only 5 are reliably detected by all tests. Of the remaining ones, the mean test misses four completely. The symmetric mean test does better, but still often fails to identify the out-of-specs situation for 2 of the classes. The log-mean test and symmetric log-mean do worse than their non-logarithmic counterparts across the board and have problems with all five classes.