dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, in our point of view, there is a sixth limitation: 6. Inability to reconstruct the deleted variables: some reduction methods act by eliminating the less significant variables, thus making their usage impossible in the Reduced Order Model (ROM). Auto-encoders  [11, 12]  have been proposed to both reduce the dimension of data and decode the reduced data to recover the high dimensional data. In  [2] , a neural decoder is proposed to recover high dimensional state variables by using reduced variables. In this paper, a simpler decoder is proposed, but it enables to reduce the original Bond Graph. In bond graphs proper modeling literature, we find some model deduction techniques  [13, 14] , and other model reduction ones. One of the model reduction techniques is the modal analysis approach  [15]  which expresses the reduced model in terms of modal coordinates instead of physical ones.
In order to cover the variation of the model inputs, we generate the simulation data using a Design of Experiment (DOE). We will distinguish the training inputs sets to build the RBG, and the testing inputs sets to validate the already built RBG. Besides addressing the limitations 1 to 5, the obtained RBG surpass the sixth limitation by recovering the eliminated variables thanks to a reconstruction ANN as explained in next section. In the outline of this paper, we will first present the methodology through a reduction process for nonlinear DAE systems (section 2). Then, we will reinterpret the reduced DAE system in bond graph form through an illustrative example (section 3). Finally, the methodology is applied to an industrial cabin thermal model in order to assess the speedup and the accuracy of the RBG (section 4).
Dimensionality reduction prior the selection of primary variables The primary variables are selected by using a dimensionality reduction method. In this work, the dimensionality reduction is performed via the non-centered Principal Component Analysis (PCA), also known as Singular Value Decomposition (SVD). We consider the training set of simulation data $ ( Y , F ), … , -< , Z. , … , ( [ , \ )%, where Y ,. , [ # are sampling time instants and F , … , \ # ⊂ are obtained by a design of experiment. The vectors -< , Z. are solutions of the original DAE system. The simulation data are rearranged in a matrix form prior applying the truncated singular value decomposition that extracts a reduced basis denoted by ^ according to a tolerance _ `ab : & ( Y , F ), … , ( [ , \ )' = ^cd e + f, ‖f‖ < _ `ab , ^ef = (31) where ‖.
Principal variables By using the reduced matrix ^, a reduced approximation of can be introduced, when considering the simulation data of the training set. The reduced coordinates vector of this approximation is denoted q ∈ ℝ r. It can be computed by using the orthogonal projection of the data on the reduced basis: q-< , Z. = ^e -< , Z. (33) By construction the following property holds: (36) Thanks to the Discrete Empirical Interpolation Method (DEIM)  [30] , the reduced coordinates q related to the training set can also be computed by using i interpolation points, or interpolation indexes, related to the column vectors of ^. Here, is the set of these interpolation indexes, which can be obtained through the algorithm  [30]  presented in the Appendix, such that: q-< , Z. ≈ -^ , :. vF -< , Z.
Theoretical results and stabilization of the hybrid model In order to show theoretical results, in this section, we restrict our attention to linear ANNs by choosing linear activation functions E (F) (/) = / and E (8) (/) = /. Then, if ^ is an exact reduced basis, the best linear ANNs are such that: w (F) = ^ , : -^ , :. vF , x (F) = (38) w (8) = ^ , : -^ , :. vF , x (8) = (39) Here, an exact reduced basis means that for all simulation data * in the validation set the following properties hold: * = ^ -^ , :. Since the reduced approximation is computed via the singular value decomposition of the available training set simulation data, it is the best low rank approximation of these data according to the Young Eckart theorem. Then, w (F) , x (F) , w (8) and x (8) , are the best meta-parameters for the linear activation function in the proposed hybrid modeling.
For practical reasons of stability, we found that we should restrict the number of modes involved in the ANNs. We restrict them to the i z first modes, where i z is a hyper-parameter of the method that must be calibrated during the training phase. Then the projection of the simulation data on the reduced basis reads: q {-< , Z. = argmin q * s^ , 1: i z q * --< , Z. s ∀ €, • Then the reduced coordinates read: q {-< , Z. = W^ , 1: i z l ^ , 1: i z X vF ^ , 1: i z l -< , Z. (44) So, the weights of the stabilized ANNs are: w (F) = ^ , 1: i z W^ , 1: i z l ^ , 1: i z X vF ^ , 1: i z l , x (F) = (45) w (8) = ^ , 1: i z W^ , 1: i z l ^ , 1: i z X vF ^ , 1: i z l , x (8) = (46) 3 Illustrative example.
In this approach, the product of the effort variable and the flow variable is no longer a power, but the global characteristics of a BG in terms of structure, causality and equations are conserved in PBGs. The PBG modeling approach applied to thermal domain is shown in the right image of Figure  1. As part of this work, we will build the cabin thermal models using the AMESim software (LMS Imagine. Lab) which is based on the PBG approach. We note that this software does not represent neither the causality stroke nor the half-headed arrow. Then, in order to make the bond graphs more understandable, we indicate on the sketches the positive direction of heat fluxes. In the sequel, we refer to pseudo bond graph models in abbreviate manner as bond graph models. As an illustrative example to the hybrid modeling methodology proposed in this paper, we consider a simplified cabin system composed of two cabin walls (a roof and a windshield) and one air zone.
3D printing of pieces by FDM All processing steps are summarized in Fig. 2  from compounding to 3D printing. Objects were then printed with filaments prepared with both formulations (PBAT-10 %-wt FS and PBAT-10 %-wt FG-FS) on a Ultimaker 3 extended fused deposition modelling printer (Ultimaker, The Netherlands) fitted with a 0. 8 mm die nozzle. The printer was controlled by the Ultimaker cura 4. 6. 1 software. The platform temperature was set at 55 • C and the nozzle temperature at 190 • C based on 0. 2 mm layers thickness and no strand overlap. The filling rate was set to 100 % with an octahedral pattern. Two simple-geometry samples were printed: a right parallelepiped (20 mm × 10 mm × 5 mm) and a hollow right parallelepiped (15 mm × 5 mm × 2 mm) (Fig. 2a  & b ). Complex objects (articulated wings of a dragon and dragonflies) were also printed for testing more challenging shapes in order to highlight the potential of these filaments (Fig.
Fluorescence properties FG-FS were characterized as disks (circular tablets) and prepared with 150 mg of material pressed for 5 min under a pressure of 10 tonnes. Fluorescence emission was measured for a range of fluorescent excitations using spectrofluorometer (Jasco FP-8300, Lisses, France). 3D spectra acquisition parameters were as follows: range (precision) for excitation and emission were 350-550 nm (2 nm) and 450-610 nm (0. 2 nm), respectively; excitation and emission bandwidth were 2. 5 nm; scan speed was 1000 nm/min; response was 0. 2 s; voltage was 380 V. Spectra acquisition was performed using Jasco Spectra Manager software. For ageing measurements, disks were kept in a dark room under control temperature (22 • C) and analysed when necessary. Impact of temperature on fluorescence intensity was performed by placing the disk between heating plates at the desired temperature and time, then by measuring directly after its fluorescence emission intensity.
Particle size distribution The particle size distribution (PSD) of the FS and FG-FS powder was measured by a laser diffraction granulometer Mastersizer 2000 (Malvern, UK) equipped with the Hydro 2000S system. First direct measurement was carried out on the powder dispersed in ethanol (96 % v/v) to evaluate the apparent size of the powder. Then, 3 min of ultrasonic treatment was applied using the ultrasonic deagglomeration system of the Mastersizer Hydro 2000S. The probe power was set to 100 % (75 W). The suspension was then stirred at 3000 rpm during 5 min to remove remaining bubbles prior to the measurement of the desagglomerated size of the powder. The refractive index of wood sawdust (1. 53) was used to proceed with the data and results are expressed in volume. The mean particle size (d m ), the 10th (d 10 ), 50th (d 50 ) and 90th (d 90 ) percentile, and the Specific Surface Area (SSA) were extracted from the PSD.
Image acquisition. The samples were scanned in the SkyScan 1272 X-rays microtomograph (Bruker μCT, Kontich, Belgium) without filter at a pixel size of 1. 5 μm with an applied X-ray tube voltage of 34 kV or 38 kV and a source current of 227 μA or 242 μA for PBAT-10 %-wt FG-FS objects and PBAT-10 %-wt FS objects, respectively. Camera pixel binning was set to 1 × 1. The scan orbit was 180 • with a rotation step of 0. 2 •. 2. 4. 6. 3. Image reconstruction. Reconstruction was carried out with a modified Feldkamp algorithm  (Feldkamp et al. , 1984) , using the Sky-ScanTM NRecon software accelerated by GPU  (Yan et al. , 2008). Gaussian smoothing, ring artefact reduction and beam hardening correction were applied. 2. 4. 6. 4. Image analysis. Volume of interest selections (sample volume), segmentations to binary and morphometric analysis of the samples were all performed using SkyScan CT-Analyser ("CTAn") software.
Image segmentation to binary was carried out thanks to a global segmentation algorithm. Morphometric parameters in 3D (porosity, pores and particles size) were based on analysis of a Marching Cubes  (Lorensen and Cline, 1987)  type model with a rendered surface. Particle and pore size in 3D were calculated using the local thickness or "sphere-fitting" (double distance transform) method  (Borgefors, 1996; Remy and Thiel, 2002). Note that for the porosity, this analysis makes it possible to distinguish between closed porosity and open porosity. Contrary to open porosity, closed porosity is the porosity embedded in the polymer with no contact with the environment. 2. 4. 6. 5. 3D model construction. Surface-rendered 3D models were constructed for 3D viewing analysed regions, using SkyScan CTVolume ("CTVox") software.
Maximum pooling is a popular choice for this downsampling operation. The pooling operations have been little revised beyond the main current maximum, average, and stochastic pooling options despite indications that choosing multiple pooling functions can improve performance  [12]. Sharma et al. analyzed and discussed qualitatively the performances of pooling strategies on different datasets  [13]. Lee et al. [6]  experimentally demonstrate that their pooling operations combining maximum and average pooling provide an increase in invariance properties over the conventional pooling. Lee et al. proposed to combine pooling filters that are themselves learned. In  [14] , Gulcehre et al. investigate a novel nonlinear unit, called L p unit that generalizes a number of conventional pooling operators such as mean, root mean square, and maximum pooling.
Pooling and Statistics In Statistics, "pooling" describes the practice of bringing together small datasets that are assumed to have the same value of a characteristic, e. g. , a mean, and using the larger combined set (the "pool") to get a more precise estimate of this feature. Poolability can be formulated on the basis of the concept of statistical equivalence. Sheskin compiled in  [17]  a bibliography dealing with pooling procedures, for example to combine several independent tests of the same hypothesis. The goal of pooling is to transform the convolutional characteristics into a new representation that preserves important information while ignoring irrelevant details. For instance, if a t-test between the two within-group slopes is not "passed", these characteristics cannot be grouped  [18].
Implementation The experiments using the aforementioned algorithms were implemented in Python© 3. 7 using Tensorflow and Keras frameworks except for the cascaded network for which the authors provide an implementation based on Niftynet  [33]. Computation were completed on a Tesla VT100 CPU @ 3. 60 Ghz with 64 GB of RAM. This study focuses on the use of a magnetic resonance imaging (MRI) dataset of acquired brain tumors from the challenge of multimodal brain tumor segmentation Brain Tumor Segmentation (BraTS) challenge  [34]. The BraTS publicly available dataset contains preoperative MRI scans for 285 patients. The database is divided into two categories: High-Grade Gliomas (HGG) (210 patients) and Low-Grade Gliomas (LGG) (75 patients). Four MRI modalities of each scan are presented: native (T1), postcontrast T1-weighted (T1Gd), T2-weighted (T2) and T2 Fluid Attenuated Inversion Recovery (FLAIR).
Miccai BraTS Dataset Segmentation of brain tumors from multiple modalities can produce a prediction that facilitates surgical planning, postoperative analysis and radiotherapy  [35]. Brain tumors require early detection and sometimes prolonged treatment. They can be benign or malignant when they have a faster growth rate, although benign tumors are slower in growth and include low-grade variants  (1) (2) (3) (4). Lower grade glioma (LGG) have a higher life expectancy and do not require immediate treatment. Both cases still require neuroimaging prior, during and after treatment. Medical imaging helps to assess tumor progression, surgical planning, and overall treatment  [34]. Glioblastoma (GBM) is a very aggressive grade-4 brain tumor, the deadliest among cancers with a five-year survival rates of only 7%.
Experiment Details In the first experiment we consider 2D U-Net, 3D U-Net, and Cascaded Network for which the training details are presented in Table  3. The second experiment combines the best method in terms of the highest Dice (i. e. , 2D U-Net) with the proposed enhancement methods. Hence, the results of the retrained model are presented following a curricular learning (CL) and data augmentation (DA). The third experiment considers the equally weighted majority voting performed using the 3D U-Net, Cascaded Network and the best performing model (i. e. , 2D U-Net + CL) from the second experiment. When used, all the DA and CL transformation are applied on 25% of the initial training dataset. Curricular learning was first proposed by Bengio  [37]  to deal with nonconvex optimization to avoid the local optimum issue.
The intuition behind Curricular Learning is to mimic the learning of human with a gradual training process with examples sorted in an increasing level of difficulty. Following this idea, we propose to pretrain the considered models from artificially downsampled MRIs by a progressive increasing level of resolutions. This enhancement was carried out by downsampling then upsampling by successive factors equal to eight, four and two. Hence, the first model is trained with the data that is downsampled/upsampled by a factor eight. Once saved, it is retrained with the data that is downsampled/upsampled by a factor four. This process is then repeated with the data downsampled/upsampled by a factor two. Finally, the resulting model is trained with the data in its original resolution. Data augmentation is used to improve the robustness of the model by artificially increasing the size of the training dataset.
This allows the design of compact magnetic 'funnels', concentrating magnetic field lines and thus increasing the magnetic field strength, which, in turn, allows for a more compact design and shorter undulator periods. Moreover, this means that more periods (N ) can be fit into a given undulator length L. The reduction of the horizontal electron emittance in DLSRs thus lends many potential scientific opportunities and will drive exciting innovations along the full technology chain, from the sources (in particular, undulators), through improved Xray optics (regarding minimization of optical imperfections and aberrations), detector technology, to handling of large data volumes. The first DLSR, MAX-IV in Lund, Sweden, came online in Summer 2016, while a second greenfield facility. Sirius in Campinas, Brazil and the ESRF, the first facility to undergo an upgrade from third generation to DLSR status, both began pilot experiments in early 2020.
Codes for Ligand-Field Multiplet Calculations Regarding the practical aspects of calculating core level spectra, one faces yet another level of complexity as the plethora of theoretical methods mentioned above are implemented in a comparably large number of computational packages. Instead of trying to provide an overview of all the available computational tools, we will focus on introducing Quanty, one of the currently available software packages that can be used to perform ligand-field multiplet calculations, and Crispy a graphical user interface that uses Quanty as a computational engine. Quanty  [9] [10] [11] [12]  is developed by Maurits Haverkort and his collaborators at the Institute of Theoretical Physics at Heidelberg University. It is a computational library that can be used to write quantum mechanical programs in the second quantization formalism.
After the diagonalization of the Hamiltonian and the selection of the lowest eigenstates, using, for example, Boltzmann statistics, Quanty can be used to calculate the spectrum. As mentioned previously, this is done using a Green's function approach, thereby avoiding the sum-over-states calculation, which can lead to an important reduction in computational time. The core of the library is written using the C/C++ programming language for maximum efficiency. The users do not interact directly with this part of the code, but rather with the Lua-based layer that wraps it. To run calculations the users are required to write small programs using the functions defined in Quanty. Doing this in a scripting language such as Lua has the advantage of providing an ideal environment for experimentation, circumventing the limitations of compiled programming languages such as C or C++.
While this is indeed very helpful, it is not uncommon for such programs to reach more than a few tens of lines of code, which in itself can be intimidating for the majority of new users. Also, as it is the case for many scientific libraries, because of the flexibility given to the users when writing these programs, it is impossible to check for all the things that might be incorrect. This leads to errors that are difficult to trace even for experienced users. To help users to more easily perform Quanty calculations, one of us (M. Retegan) has developed a friendly user interface that exposes the library's capabilities for a large part of core level spectroscopies. Crispy  [13]  was developed using the Python programming language and relies on additional packages from the Python ecosystem. The main window of the application is shown in Fig.
4. 2. Using Crispy, the users can quickly adjust the parameters of the calculation, run it, and plot the resulting spectrum without the need of writing any programs. The approach has many advantages for novice users, but even experienced ones can use Crispy to generate a starting program that will become the basis of their calculation. Crispy is a free and open-source program that can be installed on any operating system that has an up-to-date Python distribution. For Windows ® and macOS ® the program comes in easy to install packages that can be downloaded from the official website, http:// www. esrf. eu/computing/scientific/crispy.
Acknowledgements We thank our scientific committee for their choice of programme and their selection of lecturers. We are also grateful to the lecturers for their commitment to preparing interesting and pedagogical talks as well as the manuscripts presented in this book. They are also warmly thanked for their lively contribution throughout the duration of the school. Acknowledgements Financial support from the  Agence Nationale de la Recherche, France , under grant agreement No. ANR-17-CE24-0025  (TOPSKY), the  DARPA   TEE program , through grant MIPR No. HR0011831554  and the  Horizon2020 Framework Programme  of the  European Commission , under  FET-Proactive Grant  agreement No. 824123  ( SKYTOP ), is acknowledged.
Bioprinting procedure The formulated bioink was transferred into a 3cc cartridge and connected to a 300 µm diameter inkjet needle printhead (Needle CF-300 inner diameter (ID) = 0. Prior to printing, a pattern was generated through regenHU's BIOCAD software. The pattern consists of two layers of a disk formed from a circle of 3. 9 mm in radius filled with lines separated by 0. 7 µm each, and two layers of an upper circle round the edge of the disk with 3. 75 mm in radius. The design file was exported into a BIOCAD file format (. bcd) which was then imported into a file containing the G-code (. iso). This latter is readable by the HMI software which was able to list the instructions that control the movements and the tools of the regenHU bioprinter. The bioink was printed into a well-plate with a pressure set up at 0.
Results showed that a cross-linked hydrogel with sufficient stiffness and resistance through manipulation, can be obtained starting from 0. 025 wt% of LAP and 15 seconds of 365 nm UV-exposure, with a minimum amount of 7. 8 wt% ELPA (data not shown). Moreover, the potential cytotoxicity and phototoxicity of the LAP were evaluated on adult NHF. To do so, concentrations from 0 to 0. 3 wt% of LAP, free in the culture media, were tested with or without UV light, and NHF morphology and survival were analysed after 24 hours (Figure  S8 ). Results showed that without UV light exposure, LAP did not have any cytotoxicity for all concentrations tested on NHF culture. However, UV-exposed LAP was cytotoxic above 0. 025 wt%. To conclude, based on the first gelation trials and these cytotoxicity experiments, our ELPA bioink composition was fixed at 2 wt% CNF, 7.
In comparison, ELPA-COLL and ELPA-GRGDS enabled fibroblasts adhesion after 24 h as NHF presented a characteristic dendritic morphology, associated with high cell survival at day 7 (Figures  2A(c, d, e, f, i, j, k, l )). To confirm these results, quantitative measurements of ATP using CellTiterGlo® 3D kit were also achieved (Figure  2B ). The data were interpreted and compared to live/dead tests results. Obtained luminescence data (RLU) are related to the ATP quantity and correlated to alive cells. Measurements were performed just after plating (time 0), 24 hours and 7 days after plating to follow cell viability. In ELPA bioink, RLU data were non-significantly different between time 0, 24 hours and day 7 proving that cells remained alive and were metabolically active but did not proliferate.
In ELPA-COLL and the ELPA-GRGDS bioinks, RLU were similar at time 0 and 24 hours. However, we observed a significant increase at day 7 for ELPA-GRGDS while it was difficult to observe the same phenomenon for ELPA-COLL due to larger data variability. To summarize, ELPA was biocompatible but did not allow NHF adhesion and proliferation. Adding an adhesion peptide to ELPA, either collagen I protein or GRGDS allowed NHF attachment and proliferation. Finally, biocompatibility tests were performed after printing to validate that the printing process did not affect cell viability. To do so, ELPA, ELPA-COLL and ELPA-GRGDS were mixed with NHF, inkjet-printed, and then analyzed through live/dead test to evaluate the viability of the cells after 3 days (Figure  2C ). Under stress, NHF within ELPA showed mainly dead cells (red fluorescence) with a small amount of viable round shaped cells (Figures  2C(a, b )).