dataesr/scientific-paragraphs-categorization huggingface dataset:
7 ), the advantage of this approach is twofold: (1) it allows to explore different hypotheses on the companion orbital properties; and (2) it allows to assess the companion mass detection probabilities in the combined separation range covered by RV and imaging (i. e. from the star's close proximity out to the imager's field of view). MESS2 was successfully applied to famous systems such as AU Mic  (Lannier et al. , 2017) , HD95086  (Chauvin et al. , 2018) , β Pictoris  (Lagrange et al. , 2018) , and GJ504  (Bonnefoy et al. , 2018). We applied MESS2 to the HD113337 SOPHIE RV data set detailed in BO19+ (Sect. 2). We mainly used the RV corrected from the 2-planet Keplerian fit performed by BO19+, assuming This simulation was made in the case of the young age solution and for an inclination within 10 to 30 •.
This can be expected as we computed the former 1-D detection limits as if assuming the HD113337 system was seen face-on (i. e. the most favourable case for imaging companions). We emphasize that we used only one epoch of observation regarding the imaging data within our MESS2 analysis. A way to increase the sensitivity to companions within the imaging separation domain is to combine multiple high-contrast images acquired at different epochs (see e. g. Lannier et al. , 2017 , Lagrange et al. , 2018). We additionally tested the impact of assuming the presence of only one RV-detected GP in the HD113337 system (HD113337 b, confirmed planet) on our MESS2 detection probabilities. To do so, we used the SOPHIE RV data corrected from the 1-planet Keplerian model corresponding to planet b only, while keeping the longer-term RV variability (see more details in BO19+).
We also found hints of possible inner disk components, which would be in agreement with previous SED studies. Next, we took new high-contrast images of the system's outer environment with the LBTI imager. We used both the deduced contrast limits and previous RV data to explore the complete GP-type companion (mass, separation) range up to 80-100 au from HD113337. At the same time, we took advantage of the age solutions and disk inclination value that we found to characterize the corresponding sensitivity to companions. Interestingly, this allowed us to deduce hints of the possible true masses of the HD113337 b confirmed GP and of the candidate HD113337 c. Furthermore, we were able to bring the first constraints on the presence of additional undetected companions at larger separations using the MESS2 tool.
While it was not the main topic of this study, an important issue with the HD113337 system is to determine once and for all if the candidate companion HD113337 c is a real one (see BO19+). At this time, we are carrying out an additional long-term RV monitoring of HD113337 with the SOPHIE spectrograph to increase our RV time span. This could allow us to remove the ambiguity of the RV and spectral line profile long-term signals. Another compelling possibility is to combine our RV data with astrometric data from HIPPARCOS and/or Gaia. The tentative second GP that might orbit around HD113337 is an ideal target within this context. Furthermore, the RV + astrometric data combination would bring more constraints on the system's inclination and might even allow to confirm if the GP(s) orbit within the same plane as the outer debris disk.
Acknowledgements. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is supported by the  National Science Foundation  under Grants No. AST-1211929  and  AST-1411654. This work was supported by the  Programme National de Physique Stellaire (PNPS)  of  CNRS/INSU  co-funded by  CEA  and  CNES. We acknowledge financial support from  LabEx OSUG@2020  (Investissements d'avenir - ANR10LABX56 ). K. Y. L. S. acknowledges the partial support from the  NASA  grant  NNX15AI86G. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.
cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the  Gaia Multilateral Agreement. This research has made use of the SIMBAD and VIZIER databases 5 at the  CDS, Strasbourg (France) , and of electronic bibliography maintained by the  NASA  Astrophysics Data System (ADS) system. It has made use of the  Jean-Marie Mariotti Center Aspro service 6. We would like to thank our anonymous referee for the appreciative comments.
Assuming a proper spectral dataset of complexed and pure API, machine learning is a relevant technique that is able to learn dependencies directly from spectral data. As model APIs, the following prodrug β-lactam analogs were proposed: cefuroxime axetil (CA), cefetamet pivoxil (CT), and pivampicillin (PA). The prodrugs compared to acidic forms of selected β-lactam analogs are characterized by better bioavailability, more acceptable taste, and greater chemical stability. As a consequence of changing the acidic nature of hydrophilic β-lactam analogs to lipophilic prodrugs, the solubility decreased significantly. Considering the solubility issue of prodrugs, the use of solubilizers as drug carriers in their formulation such as cyclodextrins is fully justified. In this paper, α-, β-, and γ-cyclodextrins (αCD, βCD, γCD, respectively) were used along with their synthetic analogs, including hydroxypropyl derivatives of all used natural CDs (HPαCD, HPβCD, HPγCD) as well as methyl-βCD (mβCD) as carriers.
The second part of the experimental studies was the spectral analysis of the prodrug β-lactam analogs-CDs complexes obtained in relation to the changes of the positions of changes of the bands and vibration intensity observed for the APIs and cyclodextrins themselves. Using the theoretical approach, the theoretical spectra of API and cyclodextrin were determined, and, on the basis of the high consistency, the characteristic band was assigned to all analyzed compounds. Spectra of all samples in the dataset are presented in the supplementary material (Figures  S22-S42 ). The spectra were cropped to range 400-2000 cm -1 and subjected to analysis with the machine learning approach.
Machine Learning DSC is the method of choice for investigating cyclodextrin complexes as it provides reliable information about the complex formation in the sample by a simple comparison of several major thermal peaks on thermograms of the complexed system in a physical mixture. On the other hand, an application of a more specific spectral method such as tFTIR/ATR is limited due to complex, interpretively difficult dependencies between changes in spectra of isolated constituents, physical mixtures, and complexed samples. The motivation for the use of a machine learning technique lies in the application of tFTIR/ATR spectra as a specific fingerprint of the sample, carrying information about bands involved in complex formation. The presented algorithm utilized a dataset of known data constituted by spectra of samples divided to one of two classes-complexed and noncomplexed-depending on whether the DSC confirmed complex formation in the sample or not.
The binding enthalpies in relation to enthalpies of energetically favored binding modes in a water environment were reported in Table  2. Structures of binding modes derived from machine learning results are collected in the supplementary material (Figures  S53-S55 ). Analysis showed that CA binds mostly with the axetil group, where interactions of carbonyl groups in the (acetyloxy)ethyl group and ((aminocarbonyl)oxy)methyl group are observed. The different mode of binding is possibly observed for the CA-HPβCD system. Molecular modeling studies showed that among all CA-CDs confirmed complexes, only CA-HPβCD showed positive enthalpy of binding when docked with the axetil domain included into the HPβCD lipophilic cavity. The machine learning model predicted a false negative for this example, which may suggest that the considered system has a unique binding mode in the whole dataset and cannot be predicted based on other examples.
Molecular modeling studies showed that among all CA-CDs confirmed complexes, only CA-HPβCD showed positive enthalpy of binding when docked with the axetil domain included into the HPβCD lipophilic cavity. The machine learning model predicted a false negative for this example, which may suggest that the considered system has a unique binding mode in the whole dataset and cannot be predicted based on other examples. The most probable binding mode for CT according to machine learning analysis involves interactions of the pivoxil group and some interaction among the thiazole ring and cephem group with the cyclodextrins. The interactions of thiazole and cephem groups may be observed in Figure  S54. The considered groups interact with polar, hydroxypropyl-rich moieties placed on the outer side of cyclodextrin.
Computation The theoretical investigation consisted of several stages which allowed the acquisition of the optimal geometry of isolated molecules and the ingredients of the complexes. Initial conformation of the α-, β-, and γ-cyclodextrins were acquired from their crystal structure  [32] [33] [34]. Initial conformations of the remaining cyclodextrins, as well as the APIs, were obtained with the application of a conformational search using a genetic algorithm implemented in Open Babel library  [35]. Conformation search was followed by DFT B3LYP 6-311G(3df,3dp) geometry optimization of the most promising conformers of prodrug β-lactam analogs and DFT B3LYP 6-31G(d,p) for CDs using the Gaussian09 package  [36]. Molecular docking and the relaxation part of the docked system through molecular dynamics were applied in order to establish stable conformations of complexes.
The most energetically favored conformations of systems using MMFF94 were optimized with parametrized method 7 (PM7) implemented in MOPAC  [37]. As a result, thermodynamically stable conformations were acquired and set as the geometry for calculation of FTIR spectra using PM7 as well. An automated machine learning approach was applied for the selection of a classification model, allowing the optimizations of hyperparameters in regard to an accuracy metric. The goal of this approach was to maximize the accuracy of a classification of tFTIR/ATR spectra of given samples to one of two classes indicating that the sample contains cyclodextrin complex or is a physical mixture. The code in Python for data manipulation and processing was developed and is available upon request. The machine learning code was based on TPOT  [38]  and scikit-learn  [39]  libraries.
The TPOT allowed us to select the best-suited algorithm from the ones available in scikit-learn and to optimize parameters of the model implementing the selected algorithm. The algorithm selected by TPOT was an ensemble of randomized decision trees, as implemented in the ExtraTreesClassifier class of scikit-learn. The default parameters of the model were optimal. Input data were multispectral tFTIR/ATR images in the range 400-2000 cm -1 of all samples, including complexed systems as well as physical mixtures for a total of 42 samples. The model was tested with a leave-one-out, cross-validation (LOOCV) approach. The LOOCV involved iterative testing of the model, where during each iteration, one of the samples was used for testing and the rest for training. The protocol involves an iteration through all of the 42 samples in a dataset dividing them into 41 training samples in each iteration and a test sample indicated by an iterator.
Conclusions Cyclodextrin complexes were successfully prepared using the coprecipitation method in 20 of 21 systems of prodrug β-lactam analogs. The DSC as the method of choice for the confirmation of complex identification was used. Both melting point temperature shifts, as well as changes in the intensity of endothermic peaks on the DSC curves, were considered as indicators for complex formation. The only system which did not expose any significant changes in the DSC curve of the complexed sample in relation to the physical mixture was CA-HPαCD; thus, it was considered as noncomplexed. tFTIR and ATR spectra were subjected to automatic analysis for the detection of an inclusion complex using a machine learning approach. The algorithm built according to the automatic machine learning paradigm was able to successfully predict cyclodextrin complex formation using only spectral data as inputs.
A distributed nonintrusive framework The framework consists in a set of python classes. The parallel processing is performed by MPI for Python (mpi4py, see  [22, 23, 21] ) and the framework can be used indifferently in sequential or in parallel. In the latter case, we suppose that the high-fidelity commercial code provides the mesh, loading and solutions independently for a collection of subdomains Ω l , 1 ≤ l ≤ n d , which constitute a partition of the structure Ω: Ω l ∩ Ω l = ∅ for all l = l , and  3. In our application, Ω l are the subdomains used in a domain decomposition method, but they can come from any suddivision of the high-fidelity solutions. In this section, we explain how the steps of reduced order modeling described in the previous section are carried-out in a nonintrusive and distributed fashion in our framework.
Data generation In our nonintrusive context, we recall that we restrict ourselves to the use of commercial software. Depending on the software, some data may need to be reconstructed. For instance, with Zset  [39] , we dispose of σ, and all the internal variables on all the integration points of the mesh. With Ansys Mechanical, these dual quantities are interpolated at the vertices of the mesh. To be as general as possible, we suppose that we only dispose of the displacement field, solution to (1a)-(1f). We can reconstruct the complete history of all dual quantities at all the integration points using a constitutive law solver, based on the knowledge of displacement field. To do so, we interfaced our python library to the constitutive law API Umat  [46] , making possible the use of an external reusable plugin.
4. The complete procedure takes 6h31min to compute an approximation for the 100 cycles, whereas the high-fidelity model would have taken 9 days and 14 hours, which corresponds to a speedup of approximately 35. While this speedup may appear low, we recall that all the offline stage is taken into account, and that we used a state-of-the-art AMPFETI solver for the high-fidelity reference. Using a standard commercial software for the high-fidelity reference would have lead to a more impressive speedup. With this test-case, we illustrate that a computation that takes many days even with stateof-the-art high-fidelity solvers can be done overnight, which is of great practical interest in design phases of a mechanical part in an industrial context. The set of integration points selected for the operator compression step is represented in Figure  8.
The heuristic used in the operator compression step was chosen to favor scalability; it is desirable to also guarantee some accuracy properties. Besides, the current implementation requires to load in memory, locally in each subdomain, the high-fidelity data for all time steps. Even if we can always increase the number of computer nodes to increase the available memory, it is desirable to devise memory-free strategies, where the data is not simultaneously loaded in memory for all the time steps. This would enable to reduce computations with a very large number of snapshots. [29] L. Giraldi, A. Litvinenko, D. Liu, H. Matthies, and A. Nouy. To be or not to be intrusive? the solution of parametric and stochastic equations-the "plain vanilla" galerkin case. SIAM Journal on Scientific Computing, 36(6):A2720-A2744, 2014.
In this paper we propose a method for accurate ego-lane localization using camera images, on-board sensors and lanes number information from OpenStreetMap (OSM). The novelty relies in the probabilistic framework developed, as we introduce a modular Bayesian Network (BN) to infer the ego-lane position from multiple inaccurate information sources. The flexibility of the BN is proven, by first, using only information from surrounding lane-marking detections and second, by adding adjacent vehicles detection information. Afterward, we design a Hidden Markov Model (HMM) to temporary filter the outcome of the BN using the lane change information. The effectiveness of the algorithm is first verified on recorded images of national highway in the region of Clermont-Ferrand. Then, the performances are validated on more challenging scenarios and compared to an existing method, whose authors made their datasets public.
I. INTRODUCTION Vehicle localization is one of the critical features of Advanced driver assistance Systems (ADAS)  [1]. These systems will recommand lane change  [2]  to drivers in order to maintain the driver's safety  [3]. Therefore, an accurate but also a reliable ego-vehicle localization is needed. Ego-vehicle localization can be interpreted as the knowledge of two key parts: first, the road on which the vehicle travels, which is performed by Map-Matching methods, and second the position of the host vehicle with respect to the corresponding road. In  [4]  we have tackled the Road-level localization issue, where we proposed a probabilistic framework for Map-Matching and lane number estimation using the Open-StreetMap (OSM) database. Thus, in this paper, we suggest to separate the localization of the host vehicle on a road in two parts: first, the Ego lane-level localization, where we estimate the ego-position within a lane, and second the Lanelevel localization by determining the ego-lane on the road.
Hence, we added a vehicle detector to the network. Furthermore, we introduce a Hidden Markov Model to filter the outcome of the Bayesian Network. Compared to  [15] , no heuristics are made to design the HMM. Thus, we propose a probabilistic formalization for the HMM modeling. At last, we tested our algorithm on two different datasets. First on a collected datasets with two lanes road and which will be available online for other researchers, Second, from some highways images with four lanes road. These datasets are refered in  [15]. The remainder of the paper is organized as follows: Section II introduces the problem formulation. Section III describes the ego-lane level localization process. In Section IV we detail the lane-level localization algorithm. Following, Section V presents the real-world experimental results.
In order to evaluate our proposed method, we tested our algorithm on real driving data-sets. The first one, was collected in the region of Clermont-Ferrand in France, where we drove our vehicle on two lanes road in national highway for a total of 838 frames. Given the number of frames, these datasets were only used to show the effectiveness of the algorithm. In addition, we wanted to compare the performances of our algorithm to the literature on more challenging scenarios. Naturally, we turned our attetion to the KITTI datasets  [19]. However, these datasets contain few lanes and few lane-changing scenarios. Thus, we tested our algorithm on some datasets refered in  [15]   1. Unfortunately, all the data were not ready yet. But, we were able to test on some of them, noted as the A4-Highway Italy, for a total of 9528 frames.
The collected datasets were manually annotated in per-frame basis in order to determine the correct ego-lane classification. In the following, we will refers to our datasets as "2-lanes" and the A4-Highway Italy as "4-lanes". To show the increment of each component added. We first, determined the ego-lane using only the BN with adjacent lanes. In second instance, we introduced the adjacent vehicles detection in the BN. In all instances, we filtered the outcome of the BN with the HMM. All the results obtained are summarized in Table  I. Considering the results, the increment provided by each module is clearly illustrated. in all cases the BN+ALD+AVD provides more accurate classification then the BN+ALD, which suggests that the addition of an another information source will also improve the accuracy obtained.
Beside that, in all instances, the HMM improves highly the classification accuracy compared to the outcome of the BN. Which is attributable to the temporal cohesion added in the model, for example, the vehicle can not travel from leftmost lane to rightmost lane in two consecutive frames. Another interesting point is the classification accuracy achieved for the 2-lanes datasets. We notice that the accuracy achieved with the BN+ALD+HMM is the same as the one achieved with the BN+ALD+AVD+HMM, which shows that the HMM was sufficiently robust in the first place and the addition of the adjacent vehicles detector did not affect the results. After investigation on the incorrect results, it appears that the false classifications obtained using the BN+ALD are due to two main reasons: the lane marking are not detected and the lane marking are wrongly detected.
For the first case, this can be explained if the lane marking are missing or hidden by an object. For the second case, it shows the limitation of the lane marking detector used. Furthermore, even if the introduction of the adjacent vehicle detection shows excellent results, there are some cases where the vehicle detection are not relevant, for example if the vehicle detected is in the nearby road. To overcome this issue, we will have to determine the localization of the detected vehicle relative to the ego-vehicle, which is not the case since we use only images as input. Finally, despite that the author in  [15]  did not take into account the lane change scenarios and designed empirically the HMM. We manage to outperform his results on the same datasets. Indeed, the author achieved 77% correct classifications, where we were able to reach 85.
VI. CONCLUSION In this paper, we tackled the problem of ego-lane determination. Unlike other works, we presented a modular Bayesian Network that takes as input different detection results. Moreover, we designed a Hidden Markov Model to filter the outcome of the proposed network. Furthermore, we showed that the proposed algorithm improve lane level localization. In this way, we tested our algorithm on realworld datasets, and we showed the increment provided by each detector. For our future works, we are currently working on adding detectors from different sensors, i. e. , lidar, radar to enhance the proposed framework. In the other hand, we are working on questioning the lanes number information coming from OSM, as we intend to compare it with detection results. Furthermore, we intend to develop a more flexible probabilistic framework, which will be able to adapt to lane number change.
ENA  (1) , EGA  (2)  and Ensembl (3) are the most common sources of 1D genomic data in Europe, whose contents are visualized thanks to tools such as "Genome browsers"  (4) (5) (6) (7) (8)  that provide an integrated view of both sequence and annotations (2D genomics). Many research infrastructures deal with this level of data, Galaxy  (9) , being by far the most popular. A second level of data includes nucleic acid and protein structures determined at the atomistic level by X-Ray crystallography or NMR spectroscopy and deposited in the Protein Data Bank  (10). Analysis and visualization of data at this level have, in practice, little overlap with the 2D genomics level. This represents a major caveat for people interested in specific DNA-protein complexes. An additional type of data includes simulation trajectories, i.