dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper we reviewed three methods for estimating local affine maps between images. They provide first-order approximations of local geometry. This information is proved to be beneficial for homography estimation, for which we presented several RANSAC versions that systematically improved results in four well known datasets  [26, 39, 41, 82]. The proposed RANSACs regularly improved the number of successes in retrieving the ground truth homographies with respect to a baseline RANSAC, and, in a minor degree, with respect to well-established homography estimation methods like USAC  [55]  and ORSA  [43]. The Number of False Alarms (NFA) from the a-contrario procedure  [11]  helps us measure the soundness of estimated homographies and allows for adaptive  thresholds of inlier/outlier discrimination.
In this follow-up article, we investigate the use of convolutional neural network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously, we have constructed a Neural Network architecture suitable for the derivation of T eff , g log , M H [ ] / , and v i sin e. The network was constrained by applying it to databases of AFGK synthetic spectra at different resolutions. Then, parameters of A stars from Polarbase, SOPHIE, and ELODIE databases are derived, as well as those of FGK stars from the spectroscopic survey of stars in the solar neighbourhood. The network model's average accuracy on the stellar parameters is found to be as low as 80 K for T eff , 0. 06 dex for g log , 0. 08 dex for M H [ ] / , and 3 km/s for v i sin e for AFGK stars.
Of course, other sources of uncertainty can affect the predicted results when applied to real observations, as will be discussed in this article. Once the architecture and parameters are set, we applied our technique to AFGK observed spectra  (Gebran et al. 2016 , Kassounian et al. 2019 , Paletou et al. 2015). The construction of the training databases is explained in Section 2. The preprocessing steps are detailed in Section 3. The construction of the NN model with all the details is discussed in Section 4. The application of the method to AFGK stars is found in Section 5. The discussion and conclusion can be found in Section 6.
Training databases A grid of 12 training databases was constructed for the purpose of this study. Other than modifying the stellar labels (T eff , g log , and M H [ ] / ², v i sin e ), we have constructed a series of grids with the same range of stellar labels but at different resolving powers. The purpose is to analyze the effect of resolution on the accuracy of the derived stellar label and to check the capability of our technique when applied to low-, medium-, and highresolution spectrometers. We have followed the same strategy as in Paper I. We have first calculated a series of ATLAS9 (Kurucz 1992) model atmospheres using the opacity distribution function of  Castelli and Kurucz (2003)  and with a mixing length parameter of 0. 5 for T 7,000 K 8,500 K eff ≤ ≤ , and 1. 25 for T 7,000 K eff ≤  (Smalley 2004).
Principal component analysis (PCA) for preprocessing Before applying the NN to the training database, a dimension reduction technique is applied. This step consists in reducing the size of the spectra from a sampling size of N λ to n N k λ <. Depending on the resolving power, N λ ranges between 4,750 and 19,000 data points. The n k reduced data points are found by projecting the flux of each spectrum onto the first 50 principal components (PCs). Technically, we apply this PCA on the training database and find the eigenvectors λ e k ( ) of the variance-covariance matrix C: The third column displays the steps in the parameter range. Note that the steps in T eff and g log are the steps in ATLAS9 model atmo- spheres. Many databases were constructed for different resolving powers ranging from 1,000 to 115,000.
Random step means that there is no restriction on the number selection. C M M M M ¯¯, T ( ) ( ) = - ⋅ - (1)  2 M H [ ] / refers to an overall metallicity and not to the iron abundance. All elemental abundances are scaled according to M H [ ] /. where the training database M is an N N λ spectra × matrix containing the fluxes of the synthetic spectra. The value of n k is found by analyzing the reconstructed error  (Gebran et al. 2022 ). Having n 50 k = reduces the mean reconstructed error to a value less than 0. 5%. As a similar technique to PCA, one could also use the encoder part of an autoencoder in order to reduce the dimensionality of the database  (Kjaersgaard et al. 2021). We have chosen PCA to be consistent with our previous findings in  Gebran et al. (2016 Gebran et al. ( , 2022)).
DL We start by applying data augmentation as a regularization technique to all the training databases (see Section 4. 1. 1 of Paper I for technical explanations). This is done in order to take into account the noise in the real observed spectra and some modifications that could occur in the shape of the observed spectra due to bad normalization or inappropriate data reduction. Every spectrum (including the augmented ones) in each database is represented by 50 data points, and they correspond to a specific T eff , g log , M H [ ] / , and v i sin e. This is true of all resolving powers. An NN is then used to link these data points to their corresponding labels. Four different NNs were built, one for each stellar label (T eff , g log , M H [ ] / , v i sin e ). The initializers, optimizers, learning rates, dropout fraction, pooling layers, activation functions, loss functions, epochs, and batches are constrained according to the methodology of Paper I.
Architecture An infinite number of architectures could be applied to our purpose. The main goal is to find the most accurate transformation between the matrix of spectral coefficients (the 50 projected ones) and the labels. The best architecture will be selected according to its simplicity (size and calculation time) and the accuracy of the results. Fully dense NNs, Convolutional Neural Networks (CNN), and a combination of both were tested for each stellar label. In each case, we have iterated on the number of layers, and number of neurons in each layer, and the size of the filters in the case of CNNs. As explained previously, network parameters were derived for each NN. For every network and every resolving power, each augmented database was divided into 70% for training, 20% for validation, and 10% for testing.
Gaussian signalto-noise ratio was selected randomly between 5 and 300 and applied to each spectrum of the 10% test spectra in order to check the accuracy of the technique on noisy data. All our calculations are performed using the opensource programming language, Python, specifically with the Keras³ interface on the TensorFlow⁴. We have used the KerasTuner⁵ package (O'Malley et al. 2019), a scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. It was used to derive the optimized number of layers as well as the filter sizes in the case of CNNs. Linking the number of layers and dimensions of the filters with the size of the database as well as the size of each spectrum in the database is not an easy task. In order to avoid over-and under-fitting, these two parameters should be optimized.
Resolution effect Spectroscopic surveys are based on instruments that have different resolving powers. For that reason, we have applied our technique to different databases that are similar in parameter ranges (Table  1 ), except for the resolving power. Our tests contain spectra with a low resolution down to 1,000 and a high resolution of up to 115,000. Once the networks are trained using 70% of the data, we have derived the accuracy of the parameters for the validation, test, and noisy test data. The best accuracy reached as a function of the resolution is displayed in Figure  3. For each stellar label, the derived accuracy for the noisy test data is representative of the error bar that should be assigned to the observed spectra. For example, when analyzing spectra at a resolving power of 50,000, the equatorial projected rotational velocity should be assigned an error σ ~2.
For the A stars, we used the list of Gebran et al. (2016) and selected the ones that have the most values published in the literature. We ended up with 89 observed A stars with more than nine values for T eff in the Vizier catalog. These A stars were observed with NARVAL and ESPaDOnS high-resolution spectropolarimeters, which have a spectral resolving power of 65,000 in polarimetric mode and 76,000 when used for spectroscopy. These spectra were retrieved from PolarBase  (Petit et al. 2014 ), a high-resolution spectropolarimetric stellar observations database. Another part of the A stars spectra was observed with the ELODIE  (Baranne et al. 1996 , Moultaka et al. 2004 ) and SOPHIE  (Perruchot et al. 2011 , Ilovaisky et al. 2008 ) spectrographs with a resolving power of 42,000 and 75,000, respectively.
1: ), but one should consider the accuracy of the model as a minimum limit for the stellar parameters and then calculate the total accuracy depending on the radiative transfer code specificity. σ σ σ. We have used a large range of spectral types and found acceptable values for accuracy. One could use a combination of a stellar library with synthetic data adapted for each spectral type and luminosity range or a large database of observed stars with accurate stellar parameters. However, NN proves to be a fast (refer to Paper I for computational time) and accurate way to derive stellar parameters and can handle a large amount of data. These results are very promising, as they are less accurate than those usually found with photometric techniques  (Smalley 2005 , Jin-Meng et al. 2021 , Green et al.
2021) , spectroscopic ones  (Gill et al. 2018 , Gebran et al. 2016 , Ting et al. 2019 , Kassounian et al. 2019 , Tabernero et al. 2022) , or a combination of both  (Adelman et al. 2002 , Heiter et al. 2015). The last two rows deal with a limited cataloged sample, the one used to plot Figures  4 5 6 7. In future work, we will be testing the effect of specific spectral regions on the stellar parameters. This will be done through autoencoders, a type of unsupervised learning technique, leading to a more "intelligent" and compact database construction. One straightforward application is the use of such a network in order to derive the stellar parameters of Gaia spectra  (Collaboration et al. 2016). The radial velocity spectrometer  (Cropper et al. 2018) HIP HD T eff (K) T c eff (K) T m eff (K) log g (dex) log g g c (dex) log g m (dex) [ / ] M H (dex) [ ∕ ] X H c (dex) [ ∕ ] M H m (dex) v sini e (km/.
The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3. 2 mm × 15. 97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm 3 ) was reduced by 2fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.
SRμCT The experiment was performed at the ID17 station of the ESRF. The sample was mounted on a 5-axis tomography stage. The detection system was composed of a FReLoN CCD camera coupled with an indirect detection optic system  [15]  leading to a final pixel size of the acquired images of 7. 8 × 7. 8 μm 2. During the acquisitions, the intact pancreas samples were inserted in a plastic tube filled with 4% PFA. Images were taken using the following settings: 1) continuous rotation mode at a speed of 0. 3 • /second; the total rotation range was 180 • ; the integration time of a single projection of 0. 1 s; The X-ray beam was monochromatic with an energy of 30 keV. Image pre-processing, phase-retrieval and tomographic reconstructions have been performed using the PyHST2 software  [16]. The reconstructed and analysed volumes present a voxel size of 7.
Data pre-processing and augmentation The resulting dataset consists of 20 volumes for two types of mice (diabetic and healthy), with each tomographic image slice size of 2048 × 2048 pixels. The image contrast between islets and the surrounding tissues is comparably low. Therefore, pre-processing is applied to obtain high-quality inputs. In particular, contrast limited adaptive histogram equalization (CLAHE)  [18]  is used to enhance the image contrast. The ground truths of 49 images were manually labelled by experts in islet biology and then randomly divided into training (30 images) and testing (the rest images) sets. Each training image is cropped into overlapped 128 × 128 patches as inputs, while each testing one is cropped into 16 × 16 non-overlapped 128 × 128 patches for the final prediction after piecing their results.
Quantitative analysis of all islets in the whole pancreas We then applied the AA-Net to the reconstructed SRμCT images dataset. The dataset consists of 16 rodent pancreases and each pancreas was made of 3000 reconstructed images (Fig. 4A , Figs. S6A-B ). This application allows us to segment all islets from the pancreas and compute their 3D structures. We then calculated the total number of islets in the pancreases. In the wild-type Wistar rat, each pancreas contained 7305 ± 748 islets, while in the diabetic GK rat, the number of islets in the pancreas significantly decreased to 5945 ± 763. In the C57BL/6 mouse, the pancreas contained 1726 ± 201 islets. After destroying the islets with a 5-day streptozotocin (STZ) treatment, the number of islets per pancreas was decreased to 1416 ± 201 (Figs.
How can radio resources be sliced to support traffic flows with different characteristics and QoS/SLA requirements? We address the first two questions by investigating the behavior of a vRPA using a test-bed implementation and conducting a thorough measurement campaign. In particular, we leverage a srsRAN  [13]  implementation of an eNB and investigate its CPU consumption under different experimental settings. It is worth noting that CPU utilization is a key metric used to track the system performance behavior, however modern processor technology is much more complex, as a single processor package may encompass multiple cores with dynamically changing frequencies. These technological advances can thus change the behavior of CPU utilization reporting mechanisms. Nevertheless, our analysis is carried out in the same environment, which will not only provide qualitative insights but quantitative predictions as well.
Test-bed architecture Fig. 1 (a) provides a snapshot of the test-bed we developed, while Fig. 1 (b) represents its architecture. We leverage software defined radio (SDR) interfaces enabling point-to-point communications between vRPA and UE. An vRPA implements the necessary processing stack to transfer data to/from UEs. In our case, the vRPA acts as a virtual eNB implemented at the edge of the network. The connectivity between the vRPA and UEs are supported by means of an LTE radio link implemented using the srsRAN  [13] , an open-source SDR LTE stack implementation offering Evolved Packet Core (EPC), eNB, and UE applications. It is compliant with LTE Release 9 and supports up to 20 MHz bandwidth channels as well as transmission modes from 1 to 4, all using the frequency division duplexing (FDD) configuration.
As RF frontend, Ettus Universal Software Radio Peripheral (USRP) B210 devices are used to perform up/down-conversion, filtering, amplification and AD/DA conversion of the UE and eNB LTE signals. All the RF front-ends are connected to the vRPA and the UEs via USB 3. 0. Then, physical layer is implemented through a set of OFDMA-modulated channels, using RB filling across ten 1-ms subframes forming a frame. The edge host and the mobile terminals are each installed in Ubuntu 18. 04 systems. The edge host is equipped with an Intel i7-7700HQ 4cores CPU and 8 GB of DDR4 RAM, while the UEs feature an Intel i7-8550U 4-cores CPU and 16 GB of DDR4 RAM. Each Ubuntu system is connected to USRP B210 boards using USRP Hardware Driver v3. 15. In order to faciltate the experiments, all performance management features in the BIOS (e.
CPU utilization The CPU usage is analyzed using an Intel Core i7-7700HQ 2. 80 GHz CPU. It is calculated using the top process in Linux, which provides a dynamic real-time view of a running system managed by the kernel. Specifically, the percentage of consumed CPU is collected by sampling the top reports every second, over a period of 200 s. Fig. 2 (a) shows the average CPU utilization (left-hand side y-axis) and the throughput (right-hand side y-axis) obtained over 10 iterations of the virtual eNB, as the MCS varies and for a single connected UE. For this experiment, UDP DL traffic is generated at the eNB at 30 Mbps, setting the number of allocated RBs to 50. Also, we set the transmission gain to its maximum value, thus ensuring that the SNR does not drop below 32 dB. From the plot, we can observe that, as also shown in  [3] , the CPU utilization of the virtual eNB increases as the MCS index grows from 0 to 27.
In conclusion, in all of the considered scenarios, the radio resource consumption is lower under CES than under SRS, which confirms the validity of our approach. In summary, CES performs a dual role of reducing the CPU cost and at the same time fulfilling the SLA requirements (i. e. , increasing the utility of eMBB users to meet the target rate and maintaining uRLLC target delay). This strategy drives CES to efficiently allocate radio resources at the edge devices where computing resources are constrained. Slice isolation. Isolation performance in network slicing can be evaluated by measuring the impact that changes (e. g. , in traffic demand) occurring in certain slices have on another slice. To correctly measure and evaluate the isolation performance of the CES scheme, we consider two scenarios: (i) Given the eMBB traffic demand, the traffic demand of the uRLLC slice is varied and the subsequent effect on the data rate of eMBB users is evaluated; (ii) The delay experienced by uRLLC traffic is assessed, as the traffic demand of the eMBB slice varies while keeping that of the uRLLC slice fixed.
Tables  4  and 5  illustrate the observed data rate of eMBB UEs (𝑥 𝑜 𝑒,𝑡 ) and experienced delay of uRLLC UEs (𝐷 𝑡 ), for different values of uRLLC and eMBB traffic demand. Looking at the difference between the target and achieved data rate, it can be noted that the performance of the eMBB slice under CES is not affected much by the variation of uRLLC traffic and this holds also for different values of eMBB demand. Tables 4 and 5 also present the observed uRLLC delay for different values of eMBB traffic. Importantly, the variation of the eMBB demand does not affect the delay of the uRLLC slice, which remains always below the max tolerable delay value (set to 5 ms) regardless of the value of uRLLC traffic, thus highlighting again a very good level of isolation between the two slices. We evaluated the performance of CES only in terms of the number of allocated radio resources, since, as it can be noted by comparing (2) and (3), the dominant impact on the CPU consumption is represented by the number of connected UEs, rather than by the number of allocated RBs.
Acknowledgments The work has been partially supported by  POLITO/EURECOM joint Ph. D. program under ' Zero-touch 5G networks management project ', by the  European Union  under the  Italian National Recovery and   Resilience Plan (NRRP) of NextGenerationEU , partnership on '' Telecommunications of the Future '' ( PE00000001   -program  '' RESTART '') and by the  NPRP-S 13th Cycle  grant No. NPRP13S-0205-200265  from the  Qatar National Research Fund  (a member of  Qatar Foundation ). The findings herein reflect the work, and are solely the responsibility, of the authors.
[2014] ). In addition, the chosen measure P X is the Lebesgue measure restricted to X, which is nite because X is compact. In (17), the integration is performed using a Sobol' sequence with n. points integration points (package randtoolbox  Dutang and Savicky [2013] ). The criterion is optimized with the genetic algorithm Genoud (with pop. size 50d) (package rgenoud,  Mebane Jr and Sekhon [2011] ). Unless explicitly stated, κ in (  19 ) is set to 1, initial DoEs are obtained by LHS optimized by maximizing minimal distances between the points (Latin Hypercube Sampling,  Dupuy et al. [2015] ) with size to be specied in the following, and n. points is set to 10 4. The volume of Γ and Γi pχ j,n q∆Γ are approached using a quasi-Monte Carlo methods  (Lemieux [2009] ) with a Sobol' sequence of size 10 4.
Acknowledgements This research was conducted with the support of the consortium in  Applied Mathematics CIRO-QUO , gathering partners in technological and academia in the  development of advanced methods for Computer Experiments  (https://doi. org/10. 5281/zenodo. 6581216 ). This work was also carried out as part of the  HPC/ AI/HPDA Convergence  for the  Energy Transition Inria/IFPEN joint research laboratory (High-performance computation / Articial Intelligence / High-Performance Data Analysis ). The authors would like to express their gratitude to  Morgane Menz  from  IFP Énergies Nouvelles  for her valuable help on numerical aspects and in particular for the study of the robustness of estimators with respect to the GP stationarity assumption. In addition, all the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.
Conclusions We presented a complete tableau calculus for Regular MaxSAT, proved its soundness and completeness and defined its extension to Weighted Regular MaxSAT. As future work, we plan to extend the calculus to other formalisms that may be better suited to encode specific optimization problems. For instance, in Weighted Partial Regular MaxSAT, we have hard and weighted soft clauses; in Regular MinSAT, the goal is to maximize the number of unsatisfied clauses; and in first-order logic, we can make use of quantifiers and predicates.
Acknowledgements: This work has been supported by the  French Agence Nationale de la Recherche , reference  ANR-19-CHIA-0013-01 , and grants  PID2019-111544GB-C21 ,  PID2022-139835NB-C21  and  TED2021-129319B-I00  funded by  MCIN/AEI/10. 13039/501100011033.
DATA AND DESCRIPTIVE STATISTICS 4. 1 Data The public availability of Ethereum's transaction history allows us to reconstruct the pools' balance sheets at any desired time point. By collecting the daily snapshots of users' positions from Compound V2's subgraph,  7    denoted as U, along with their respective lending (𝑑 𝑢 𝑖 ) and borrowing (𝑏 𝑢 𝑖 ) balances for each asset, including accrued interests from both lending and borrowing. In order to convert all balances to US Dollars, we rely on the market prices (𝑝) of the tokens provided by Compound's oracle. This results in a sizable dataset containing the daily positions of 422,459 users across 19 pools, spanning from January 1, 2020, to June 30, 2023. Using this information, we construct the liability matrices and balance sheets for each daily snapshot, following the procedure outlined in Section 3.