dataesr/scientific-paragraphs-categorization huggingface dataset:
2014. Béarnais (Gascon). Journal of the International Phonetic Association 44(3), 343-350. https://doi. org/10. 1017/S002510031400005X. Moran,  Steven ,  Nicholas A. Lester  &  Eitan  Grossman. 2021. Inferring recent evolutionary changes in speech sounds. Philosophical Transactions of the Royal Society B, 376(1824). 20200198. https://doi. org/10. 1098/rstb. 2020. 0198. Moran, Steven & Daniel McCloy (eds. ). 2019. PHOIBLE 2. 0. Max Planck Institute for the Science of Human History. https://phoible. org/. Mortensen, David R. , Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W. Black, Florian Metze & Graham Neubig. 2020. AlloVera: A multilingual allophone database. ArXiv:2004. 08031 [Cs]. http://arxiv. org/abs/2004. 08031. Namboodiripad, Savithry & Marc Garellek.
Data structures In our Matlab implementation  [28] , meshes over  ⊂ R 3 are manipulated via the toolbox openMsh from  [29]  The points of  are stored in the array 𝚟𝚝𝚡. Each line of vtx corresponds to a vertex 𝑉 ∈ , the three column giving the 𝑥, 𝑦 and 𝑧 coordinates. The lines of the array 𝚎𝚕𝚝 are mutually distinct and encode the simplices of , referring to the vertices by their index in 𝚟𝚝𝚡. This is essentially the same data structure as in the widely used meshing tool Gmsh  [30]. Many alternative choices of data structure exist to represent more general simplicial complexes, see  [31]  for a review.
Example of implementation We represent generalized meshes (in Matlab) using the following data structure:  𝑖 𝐹 ⟷ 𝑗 ⟺ { (𝚗𝚎𝚒_𝚎𝚕𝚝[𝑖, 𝛼], 𝚗𝚎𝚒_𝚏𝚌𝚝[𝑖, 𝛼]) = (𝑗, 𝛽) , 𝐹 𝛼 (𝑖) = 𝐹 𝛽 (𝑗) = 𝐹 , 𝑖 𝐹 ⟷⟂ ⟺ { (𝚗𝚎𝚒_𝚎𝚕𝚝[𝑖, 𝛼], 𝚗𝚎𝚒_𝚏𝚌𝚝[𝑖, 𝛼]) = (0, 0) , 𝐹 𝛼 (𝑖) = 𝐹. for all 𝑖, 𝑗 ∈ {1 , … , 𝑁 elt }. Here, for 𝛼 ∈ {1 , … , 𝑛 + 1}, 𝐹 𝛼 (𝑖) denotes the facet of the simplex attached to the element 𝑖, obtained by removing the vertex in position 𝛼 in the 𝑖th line of 𝚎𝚕𝚝. Example 2. 9. For the mesh  * 1 of Example 2. 3, this gives the following arrays:   𝚟𝚝𝚡 = ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ A B C D E F G H I J ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ , 𝚎𝚕𝚝 = ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ 1 2 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ , 𝚗𝚎𝚒_𝚎𝚕𝚝 = ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ 2 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ , 𝚗𝚎𝚒_𝚏𝚌𝚝 = ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ 3 2 0 0 1 1 0 3 2 0 3 2 0 3 2 3 0 2 0 3 1 0 3 2 0 3 2 0 2 2 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ with the conventions above.
For more details, we refer to our Matlab implementation  [28]. Remark 2. 10. One can see that a GeneralizedMesh instance representing a non-branching mesh uses up about three times as much memory as the corresponding msh instance. For meshes that do not differ much from a regular mesh, this cost can easily be reduced by only storing those pairs of elements which are not adjacent although they share a facet. Furthermore, the field nei_fct need not be stored, as it can easily be deduced from nei_elt. With those simple changes, storing a normal mesh as a GeneralizedMesh incurs no extra memory cost compared to an msh.
rubra flowers are still unknown  [11]. Specialized fruit metabolites belong to various molecular families  [8, 12]. These are of high interest from economic and health points of view due to the antioxidant or antimicrobial activities of fruit essential oils and hydrolates  [13, 14]. The characterization of the volatile organic compounds (VOCs) is routinely performed by gas chromatography (GC) coupled to a mass spectrometer (MS) or a flame ionization detector (FID). The data generated can be compared to those present in databases such as the National Institute of Standards and Technology (NIST) or Wiley's SpectraBase  [15]. More recently, we introduced a workflow for GC-EI-MS data analysis that includes the visualization of the spectra collection as a molecular network  [16, 17]. This method allows the fast and reliable classification of the EI-MS (electron ionization) data leading to accurate molecule class identification together with home-made databases.
We converted the NIST and Wiley spectral libraries in order to make them interpretable by the MetGem software. Headspacesolid-phase microextraction-GC-MS (HS-SPME-GC-MS)  [18]  and molecular networking analysis were combined to identify the VOCs diversity of S. rubra dried fruit and its hydrolate. The biological activity of the hydrolate against 3 human pathogens, namely Candida albicans, methicillin-resistant Staphylococcus aureus and Tricophyton rubrum, was tested. Finally, its cytotoxic activity was studied with human MRC-5 cell line.
HS-SPME-GC-MS analyses A volume of 5 mL of hydrolate or 5 g of dried fruit was introduced into a headspace vial (HS), the liquid/gaseous phase equilibration took place for more than 24 h, and then exposed to a conditioned (260 °C for 20 min) solid-phase micro-extraction (SPME) fiber Divinylbenzene Carbon Wide Range Polydimethylsiloxane (DVB/C-WR/PDMS), 80 µm, Agilent Technologies, Waldbronn, Germany) for 5 min without heating. The split ratio was 100:1 for the hydrolate sample and splitless for the dried fruit sample. Following this step, the compounds retained by the fiber were desorbed by introducing the fiber into the GC injection port at 250 °C for 3 min and data acquisition was simultaneous with desorption. The analyses were performed on an Agilent 7890B-GC (Agilent Technologies, Waldbronn, Germany) coupled to an Agilent 7000D triple-Quadrupole-GC (Agilent Technologies, Waldbronn, Germany).
Data processing Chemical diversity analysis of fruit VOCs was performed on MassHunter Unknown Analysis (Agilent, B. 09. 00), using the coupled spectral libraries from Wiley (2011) and NIST (2017). Default parameters were retained for the analyses with the exception of annotation assignment where only those with a score above 80% were considered. The retention indexes were determined by querying databases. In parallel, molecular networks of the HS-SPME-GC-MS data were created  [16]. The raw data in. d were converted to. mzML using the Peak Picking function of MSConvert (v. 3. 0. 20344), a module of the Proteowizard package  [19] , the CWT algorithm with "SNR = 0", and "Peak Spacing = 0". The. mzML files were then imported into MZmine (v. 2. 53)  [20]. Only the ions detected above 500 arbitrary unit (a.
Molecular network by MetGem Molecular networks were created using MetGem software (v. 1. 3. 6 -20220401 -Nightly Cl builds: https://github. com/metgem/metgem/releases/tag/ nightly). The parameters for the cosine score calculation were: "m/z = 0. 3", "minimum matched peaks = 3" (MMP) and the option "treat as MS1 data" was used. t-SNE representation must be performed with MMP ≥ 3 for electroionization analyses. The parameters to create the t-SNE representation were: "cosine score(s) above: 0. 6", "number of iterations = 10,000", "learning rate = 10", "perplexity = 6" and "early exaggeration = 12". Annotation was performed by querying the coupled spectral libraries from Wiley (2011) and NIST (2017). The MMP used to match the experimental spectra to those in the databases was 5, although many peaks are present in the spectra obtained by electroionization.
This parameter can be modified after an initial analysis according to the molecular families studied. The data in native format were converted by the software Lib2NIST Library Conversion Tool v. 1. 0. 6. 3 into. msp, a format interpretable by MetGem. Then the. msp was modified by replacing the "Names" in "NAMES" and the "Exact Mass: x" in "Exact Mass: 0" so that no correspondence of m/z of parent ions is established between the databases and the data resulting from the analyses in HS-SPME-GC-MS.
Experiments were performed in triplicate. After 72 h of incubation, 100 µL of CellTiter Glo reagent was added for 15 min before recording the luminescence with the spectrophotometric plate reader PolarStar Omega (BMG LabTech). The dose-response curves were plotted with Graph Prism software, and the IC 50 values were calculated from the polynomial curves using the Graph Prism software.
Characterization of the chemical diversity of S. rubra hydrolate and dried fruit by molecular networks The resulting data can also be used to create molecular networks by pre-processing the data with MZmine v. 2. 53 and MetGem v. 1. 4. 1  [17, 20]. Molecular networks allow comparison of chemical diversity between different complex samples. Their principle is based on the assumption that structurally close molecules share similar fragmentation patterns. Thus, it is possible to visualize clusters grouping structurally similar molecules and to annotate them by correspondence with spectral banks  [26]. Two tools, among others, are currently available for generating molecular networks from mass spectrometry data: GNPS and MetGem  [17, 27]. In contrast to GNPS, MetGem is a software package offering the user a certain modularity, i.
e. real-time parameter modification, generation of several molecular networks, and the ability to generate a t-SNE representation. This algorithm represents the chemical diversity of one or more samples in a 2-dimensional space, where the distances between nodes are the corollary of the spectral similarity between the studied molecules. This representation can be used to determine whether there are structural similarities between different clusters of the same molecular network  [17, 28]. MetGem is able to reprocess MS1 data from both MALDI-ToF and GC-MS analyses  [16, 29]. However, GC-MS databases are not open-source, e. g. , NIST, Wiley. It is however possible to query them through MetGem after their conversion into a dedicated format. ). t-SNE representation of some of the chemical diversity of the VOCs in dried fruit and hydrolate.
Cluster A contains monoterpene hydrocarbons, cluster B contains oxygenated monoterpenes and cluster C contains sesquiterpenoids, grouping sesquiterpene hydrocarbons and oxygenated sesquiterpenes. The color code shows the main molecular families. The green nodes are the ions detected in the hydrolate and the red nodes correspond to the ions detected in the dried fruit. The total molecular network contains 1088 nodes, 482 of which are shown in this figure, i. e. , those corresponding to VOC. 199 nodes are singletons and 407 correspond to polysiloxane derivatives (shown in Figure  S1 ). Pie-charts correspond to the peak areas of each detected ion using MZmine. Node size is defined by the maximum area for a peak corresponding to an ion detected in both samples. fragments m/z 93. 1, 81. 1, 79. 1, 69.
e. monoterpene hydrocarbons, oxygenated monoterpenes and sesquiterpenoids are distinguished (Figure  2 ). On the other hand, compounds belonging to highly volatile families as well as oxygenated molecules with n C < 10 are dispersed in the network (Figure  2 ). The MetGem annotations are consistent with those performed by the MassHunter Unknown Analysis (Agilent, B. 09. 00) algorithm. This observation is only valid for ions with a high relative intensity (relative area > 0. 2%). Indeed, when the intensity is below this threshold, the cosine scores decrease (see  Figure S3  in Supplementary Data). This results in uncertain matches with the spectral banks, so it remains and will remain necessary to check the data manually. Moreover, this methodology was not able to annotate the ion detected at Retention Time (RT) = 13.
73 min (Table  1 ). The use of MetGem therefore complements the MassHunter software, as it facilitates the visualization of data obtained from GC-MS analyses. Indeed, it is possible to apply additional filters to this visualization (retention time, common fragments. ). In addition, a large number of samples can be analyzed simultaneously to determine whether they share a similar chemical diversity. However, many nodes are annotated as contaminants, in particular polysiloxanes, but belong to clusters not shown in Figure  2  (Figure  S1 ), thereby not interfering with data analysis since the respective surface area of each of these compounds was not taken into account in the analysis. The exploration and annotation of the chemical diversity of VOC is now possible through the use of numerous libraries.
Conclusions In this work we identified 98. 5% of the number of peaks corresponding to VOCs emanating from the S. rubra dried fruit and hydrolate by HS-SPME-GC-MS using the NIST and Wiley databases. The analysis performed on MassHunter Unknown Analysis served as a guide to verify the annotation results obtained by MetGem. The use of MetGem proves to be a complementary methodology to the use of MassHunter, as numerous filters can be used when processing data from a large number of samples. In addition, MetGem can now be used to query databases specific to GC-MS analyses. Finally, the hydrolate of this fruit has a relative antifungal activity of 5% against T. rubrum. In general, this study presents a protocol to exploit data from GC-MS analyses on MetGem and query spectral banks such as NIST and Wiley participating in the characterization of the chemical composition of the Amazonian fruit diversity.
8 5. 8 SH α-Calamenene 1527 2. 7 7. 4 SH Isoledene 1373 - 0. 8 SH α-Dehydro-ar-himachalene 1523 - 0. 2 SH α-Calacorene 1543 0. 3 2 SH α-Corocalene 1629 - 0. 2 OS Guaijol-acetate 1727 - 0. 2 OS γ-Amorphene 1496 1 - OS β-Caryophyllene epoxide 1578 - 0. 8 OS Caryophylenol 1568 - 0. 3 OS Isoshyobunone ND - 0. 1 OS (S)-Cubenol 1636 - 0. 2 Unidentified Unidentified ND - 0. 1 Theoretical retention indices are determined by NIST, Wiley and The Pherobase databases. Relative peak areas were calculated using MassHunter. MH: Monoterpene hydrocarbons, OM: Oxygenated monoterpenes, SH: Sesquiterpene hydrocarbons, OS: Oxygenated sesquiterpenes.
39 • ± 2. 91 • with the virtual prisms compared to 4. 30 • ± 3. 49 • with the real prisms), but that these effects were not sustained beyond 2 h regardless of exposure modality. The virtual measurements obtained with our software showed excellent metrological qualities (ICC = 0. 95, error = 0. 52 • ± 1. 18 • ), demonstrating its validity and reliability for quantifying deviation during pointing movements. Overall, our results suggest that our virtual reality software (Virtualis, Montpellier, France) could provide an easy and reliable means of quantifying and rehabilitating spatial neglect. Further validation of these results is required in individuals with unilateral spatial neglect.
The position of the different devices over time was captured using two infrared base stations included in the HTC Vive ® system. The Protopointage software (Virtualis, Montpellier, France), developed in 2019 and later modified in 2022, was used to create a virtual environment for the study  [27]. The participant was positioned in front of a table with a neutral background scene, and during the virtual prism exposure phase, the entire scene was shifted 10 degrees to the right (Figure  4 ). Targets randomly appeared on the table, both to the left and right, at 17 degrees In the case of the mSSA task performed under virtual conditions, the setup was the same as for virtual prism exposure. Participants were asked to place their dominant hand against their sternum while holding the controller. A black screen was projected in the headset, and they were instructed to make a total of 10 pointing movements straight ahead.
The position of the different devices over time was captured using two infrared base stations included in the HTC Vive ® system. The Protopointage software (Virtualis, Montpellier, France), developed in 2019 and later modified in 2022, was used to create a virtual environment for the study  [27]. The participant was positioned in front of a table with a neutral background scene, and during the virtual prism exposure phase, the entire scene was shifted 10 degrees to the right (Figure  4 ). Targets randomly appeared on the table, both to the left and right, at 17 degrees from the participant's median axis (corresponding to the targets on the physical support used in the study). During pointing, the participant's virtual hand was concealed for 90% of the movement and only appeared in the final 10%, reducing real-time visual feedback.
Measuring Tool To compare the validity and reliability of the HTC Vive ® system, we recorded pointing movements using both a validated motion capture tool and the HTC Vive ® system. The CMS20s tool (Zebris Medical GmbH, Isny, Germany), which is widely validated and accepted as a gold standard in motion capture, allowed for a high spatial accuracy of better than 1. 5 mm. Specifically, three markers were positioned on the controller to determine the position of its tip, and the sampling frequency was set to 50 Hz. Prior to each participant's session, the virtual plane of the table was calibrated according to the real plane of the table. The deviation angle provided by the Zebris mocap system, which served as the gold standard, was extracted from the motion data and compared with the deviation angle provided by the ProtoPointage software, which utilized the motion data from the HTC Vive ® system.
Data Processing The data processing for this study was carried out using SciLab 6. 1. 0. For the Zebris motion capture system, the end of each pointing movement was defined as the moment when the position of the controller was closest to a virtual point located at the end of the table in front of the participant. The deviation angle was then computed as the difference between the final position of the controller and the median axis (Figure  3 ). The angles were directly exported from the ProtoPointage software (v0. 2. 1) for the HTC Vive ® system. The median angle of the 10 pointing movements was recorded for further analysis with both systems. To account for the participants' initial deviation when evaluating the aftereffect, the post-exposure deviation, which was used for hypothesis H1c, was corrected by subtracting the pre-exposure deviation from it (Equation (  1 )).
By having participants view the non-deviated scene for a few seconds between exposure and the mSSA test, the expected perceptual-motor conflict was created, allowing for the measurement of the deviation. remembered the deviated environment and had become accustomed to the deviation, resulting in no perceptual-motor conflict during the mSSA test. By having participants view the non-deviated scene for a few seconds between exposure and the mSSA test, the expected perceptual-motor conflict was created, allowing for the measurement of the deviation. In 2022, during our use of the updated Virtualis software, we encountered a similar issue as in our 2021 pretests. Specifically, we found that the visualization of the undeviated environment between exposure and the mSSA measurement, no aftereffect was observed during the virtual reality protocol.
Perspectives In addition to being at least as effective as the conventional prism exposure method, the immersive virtual reality rehabilitation system offers numerous advantages. Firstly, the proximal path of the upper limb, which is crucial in perceptual-motor recalibration, can be easily masked by computer adjustment, unlike the classical method which employs a head rest. Secondly, the virtual reality system allows for multiple computer adjustments, facilitating personalized rehabilitation programs that can adapt to the patients' disorders and progress. Finally, since the sensory-motor parameter measurement and exposure can be carried out under virtual conditions, the measurement procedure is significantly In 2022, during our use of the updated Virtualis software, we encountered a similar issue as in our 2021 pretests.
FRAMEWORK DEVELOPMENT In our framework Figure  5 , the marine engineer uses the machine space of the DT vessel and the DT telexistence robot to operate the latter. He takes advantage of the CAD models of the asset to be inspected with combining the RGB camera sensors and the available data. We are simulating a marine diesel engine and generating synthetic data to emulate a maintenance scenario. The data can be made available to the chief engineer and captain, the vessel manufacturer and the vessel owner through a cloud-based database and web interface. The telexistence interface displays the data in an immersive and interactive virtual environment for the maintainer. Finally, the maintainer can report and store maintenance reports, including inspection photos, in the database so that they can be accessed by the relevant stakeholders.
MAINTENANCE SCENARIO CREATION To assist the evaluation of our proposed framework we have designed a task that the marine engineer could do in the machinery space. The task will be done with a real robot but under simplified conditions, as we do not have easy access to a real machinery space. The scenario is described in Figure  6. (1) The condition-based maintenance system is detecting a leak and requested an operator to inspect three areas of the engine to locate the leak. (2) The remote marine engineer is using the telexistence framework to immerse himself in the DT and take control of the inspection robot. (3) A guided inspection procedure is displayed to the user for him to move the robot equipped with the camera. (4) The image data are recorded and transmitted to the marine engineering department for quick and safe decision-making.
Introduction: Deep learning-based approaches are increasingly being used for the reconstruction of accelerated MRI scans. However, presented analyses are frequently lacking in-detail evaluation of basal measures like resolution or signal-to-noise ratio. To help closing this gap, spatially resolved maps of image resolution and noise enhancement (g-factor) are determined and assessed for typical model-and data-driven MR reconstruction methods in this paper. Methods: MR data from a routine brain scan of a patient were undersampled in retrospect at R = 4 and reconstructed using two data-driven (variational network (VN), U-Net) and two model based reconstructions methods (GRAPPA, TVconstrained compressed sensing). Local resolution was estimated by the width of the main-lobe of a local point-spread function, which was determined for every single pixel by reconstructing images with an additional small perturbation.
G-factor maps were determined using a multiple replica method. Results: GRAPPA showed good spatial resolution, but increased g-factors (1. 43-1. 84, 75% quartile) over all other methods. The images delivered from compressed sensing suffered most from low local resolution, in particular in homogeneous areas of the image. VN and U-Net show similar resolution with mostly moderate local blurring, slightly better for U-Net. For all methods except GRAPPA the resolution as well as the g-factors depend on the anatomy and the direction of undersampling. Conclusion: Objective image quality parameters, local resolution and g-factors have been determined. The examined data driven methods show less local blurring than compressed sensing. The noise enhancement for reconstructions using CS, VN and U-Net is elevated at anatomical contours but is drastically reduced with respect to GRAPPA.
Introduction Together with recent advances in machine learning and neural network-based computer vision, data-driven methods have seen increasing interest in the MRI academic literature. Several distinct tasks are suited for the application of machine learning, including reconstruction of undersampled data  [1, 2]. The data-driven modelling capabilities of neural networks have shown enormous potential for improving and accelerating reconstruction tasks, and are capable of coping with undersampled acquisitions. However, unlike in classical MRI, the transformation properties and quality of advanced reconstruction methods, data-driven as well as model-driven, depend on local structure and are not uniform across the imaged field of view. Therefore signal abnormalities, e. g. pathological structures, should be judged in view of the local quality and reliability of the imaging method.