dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS AND DISCUSSION Properties of the resulting dataset The resulting version of the dataset (see Table  6 ) is built on 18 + 3 manuscripts. All alignments are original alignments, but some draw their original transcription from online projects (cf. Acknowledgements). The current version of the dataset shows a wide variety of genres, and thus a wide vocabulary. From medical and grammatical content to literary and scholastic, a certain level of arbitrariness is introduced in the sequence of characters as they are not as repetitive and predictable from the machine as in a homogeneous genre or topic-driven dataset. The collection was built not to be representative of one specific use of the Latin language and is not thematically unified, while the CREMMA Medieval dataset focuses more on literary texts, specifically hagiographic and chanson de geste texts.
18 Characterisation of scripts was made by the transcriber where the information was not available on the notice of the manuscript. The criteria followed for the Gothic scripts are those of  Derolez (2003). Character frequencies in the CREMMA Medieval and the Medii Aevi datasets We set up this corpus to both complement the CREMMA Medieval dataset and grow the available set of data for Latin through the Middle Ages, noting that at least two datasets for Medieval Latin existed already (Caroline Minuscule and Eutyches) in abbreviated form for pre-10 th century documents.
PAGES Unlike CREMMA Medieval, our approach has been feature-driven to compensate for rare characters in the dataset network. In this regard, we succeeded, as we have a higher frequency of special characters in our dataset than in Pinche's dataset, despite being smaller overall (see Table  7  and Figure  5 ). Only three characters are more represented in CREMMA Medieval: the Tironian Et, the superscript combining R (common on words such as "grand"), and "&". The character ꝯ is equally present in both datasets: resolved as con-or com-, it is often used in words such as ꝯmence (commence). Some very frequent diacritics, such as the horizontal lines and vertical lines transcribed as tildes, are more frequent in our dataset, by a factor of 2. 51 for horizontal ones and of 3. 93 for vertical ones.
IMPLICATIONS/APPLICATIONS With this addition to the overall amount of datasets available, we now have 1. 149 million characters for medieval manuscripts with book scripts, ranging from the 9 th to the 15 th century. These data offer more than characters: we can imagine using them in the context of linguistic studies (evolution of dialects, abbreviation usage, etc. ) thanks to the shared transcription norm, or in codicology studies (evolution of layouts, relation between layouts) using the common segmentation vocabulary, both using the original data or automatically annotated one. HTR data and models have a fairly high level of reuse potential. First and foremost, while it is still relatively a rare reuse, these data, visualised correctly, can easily serve as teaching materials: e-teaching of paleography has been gaining some traction, 20 but simply moving away from printed to digital and interactive hand-outs using open data and transcription is a first step that undoubtedly some have already taken.
(2018)  proposed also a stylometrical analysis of data obtained through automatic transcription. As a direct output, we trained a model which would allow for transcribing or starting the transcription of Latin medieval manuscripts. In order to evaluate the gain from our data, we trained three models: 22    The results show a massive improvement for the in-domain Latin dataset (see Table  10 ) and an insignificant one for Old French. The addition of Medii Aevi provides overall better results on out-of-domain datasets: UBL Mss. 758 and Berlin, Hdschr. 25 transcriptions improved by 4. 2 points at least while Berlin, Germ. Oct. 511 (BGO), the smallest transcription set of the dataset, only improved by 2. 4. This improvement derives equally from the simple addition of Latin into the model, as shown by the clear gap between the mixed model with Carolingian data: not only the model might benefit from Latin in general (as potentially shown by the simple addition of the Carolingian data), but it also gains in performance out of the amount of data from the same period as CREMMA Medieval.
We actually see in Table  11  that there are much fewer errors on characters that saw their frequencies reach new highs. The All model does only a fourth of the error of the Only Old French model on tildes or two-thirds on vertical tildes for the UBL manuscript. The -rum abbreviation (ꝵ) or the -et/-ed/-ibus one (;) are quite new to Medieval datasets in general, which explains the clear difference in results. Overall, this dataset helped create a model allowing for readable outputs (see Appendix Table  12  for a sideby-side comparison) on Medieval manuscripts, or at least transcriptions that can help produce new data.
Table9). Laur. Plut. 39. 34 notably so, as it only contains 3 abbreviated words which is a single character abbreviation(⁊, et). A little less than half of our manuscripts are less abbreviated than the most abbreviated text in the CREMMA Medieval dataset, while the other half can exceed it by up to ten points. However, both languages show similar maximum frequencies in terms of non-single letter abbreviations (abbreviations made up of a single Unicode codepoint such as ⁊, &, ꝑ). 19.
to help transcribe Latin and Medieval French manuscripts, which is the end goal of this paper; 2. a model containing every dataset but our own, to evaluate the impact regarding the quantity of data we add for Latin (i. e. , to find out if the original Carolingian datasets were enough to break the language model of the Old French datasets);3. a model containing only Old French data, from incunabula of the 15 th century to the main dataset CREMMA Medieval. From Medii Aevi, as stated earlier, all aligned data from the Faithful Transcription Data Set are kept for testing, as an out-of-domain set. Each model uses at least 10% of the pages of each dataset for the development set. CREMMA Medieval and Medii Aevi are split furthermore with another 10% subset for evaluation, proposing "in Domain" evaluation.
General accuracy results of the models. Model All contains all data presented in Table 1, model No CREMMA Medii Aevi contains everything but the present dataset, model Only Old French contains all datasets but Latin one (Eutyches, Caroline, CREMMA Medii Aevi). Two types of test sets are present: the "In Domain" dataset are pages from the same manuscripts as the models, all others (UBL 758, BGO 511, and B. H. 25) are manuscripts from the Faithful Transcriptions Data Set aligned in CREMMA Medii Aevi but not used for training purposes.
Table9). Laur. Plut. 39. 34 notably so, as it only contains 3 abbreviated words which is a single character abbreviation(⁊, et). A little less than half of our manuscripts are less abbreviated than the most abbreviated text in the CREMMA Medieval dataset, while the other half can exceed it by up to ten points. However, both languages show similar maximum frequencies in terms of non-single letter abbreviations (abbreviations made up of a single Unicode codepoint such as ⁊, &, ꝑ). 19 LANG TYPE WORDS WORDS % UNIQUE UNIQUE FREQ. OF UNIQUE WORDS > 1 WORDS WORDS % Latin abbr. 6,855 11. 94% 1,460 6. 24% 279 Latin others 50,557 88. 06% 21,935 93. 76% 5,025 Old French abbr. 5,755 4. 15% 1,457 4. 89% 286 Old French others 132,828 95. 85% 28,315 95. 11% 8,726.
2023, in preparation). This paper reports on the interesting object 2MASS J04124068+2438157 in our M-dwarf disk sample, which hosts a very extended dust disk composed of two dust rings. We arrange the paper as follows. Section 2 provides a detailed summary of the source properties. Section 3 describes the ALMA and Keck observations, as well as their corresponding data reduction. Section 4 presents our characterization of the disk morphology, and Section 5 discusses this target in the context of other disks and the origin of the dust rings. A summary is then given in Section 6.
ALMA Observations The object 2M041240 was observed with ALMA Band 6 receivers on UT 2021 August 27, during the Return to Operations phase after the Covid19 shutdown, as part of program 2019. 1. 00566. S (PI: G. Herczeg). The observations were performed with 39 antennas spanning baselines from 92 m to 10. 8 km, with a total on-source time of 16. 8 minutes. The receivers were configured into four spectral windows (SPWs), including two continuum SPWs centered at 217 and 234. 4 GHz, each with a bandwidth of 1. 875 GHz. The two remaining SPWs targeted 12 CO, 13 CO, and C 18 O J = 2 -1 lines at a channel spacing of 0. 244 MHz (∼0. 3 km s -1 ). The raw visibilities were first pipeline calibrated using the specified CASA version 6. 1. 1  (CASA Team et al. 2022)  to flag problematic data segments, correct for bandpass responses, set absolute flux scales, and solve for complex gain variations.
The single integration time is 0. 5 s and each exposure frame comprises 60 integrations. During the observation, we obtained 105 frames that cover a parallactic angle change of 97°. 5 to perform angular differential imaging (ADI). The total on-target integration time is 3150 s. The raw exposures of 2M041240 from Keck/NIRC2 require pre-and post-processing to reveal the surroundings that have been overwhelmed by starlight. We followed  Xuan et al. (2018) , which customized the VIP package  (Gomez Gonzalez et al. 2017) , to perform pre-processing of the data, including flat-fielding, bad-pixel removal, background removal, and image centering. We then performed ADI post-processing using the principal-component-analysis-based Karhunen-Loève image projection algorithm (KLIP;  Amara & Quanz 2012; Soummer et al.
Four additional disk geometry parameters (inclination i, position angle PA, and offsets from the phase center Δ R. A. and Δ decl. ) were also included in the fitting. The model visibilities are then calculated through Hankel transformation  (Pearson 1999 ) and sampled at the same observed spatial frequencies. The comparison of model and data visibilities uses a Gaussian likelihood c µ -  ( ) exp 2 2 , where c = å - | ( ) ( )| V u v V u v w , , k k k k k 2 obs mod 2 , with w k as the observed visibility weights. We assumed uniform priors for the fitted parameters and explored the parameter space with 50 walkers and 5000 steps using a Markov Chain Monte Carlo (MCMC) method (emcee, Foreman-Mackey et al. 2013). As the number of steps largely exceeds the autocorrelation time, which is on the order of 100 steps, the MCMC chains are believed to have reached a stationary state.
As 2M041240 only contains two rings, it is unclear if these simulations with such an equation of state are applicable to 2M041240. The brightness difference between the two dust rings may also reflect their different dust evolution. If there is only a weak pressure bump at B70 (in contrast to a strong one at B116), then millimeter-sized particles may not be effectively trapped   (2018b)  are shown for comparison. The lupus sample is adopted for its better coverage at the faint end. The fainter and more extended disk in Lupus than our target is J16090141-3925119, which also surrounds an M4 star. Based on the ALMA image in  Ansdell et al. (2016 Ansdell et al. ( , 2018)) , it remains unclear if it is a disk with a large cavity or a close binary system. (The data used to create this figure are available.
Following the criterion proposed in  Kanagawa et al. (2021) , the B116 ring in the disk of 2M041240 might indicate the initial location of the migrating planet, while its present location is very uncertain, as it highly depends on many other disk properties, in particular disk viscosity. Though current ground-based AO facilities are only sensitive to planets with a few Jupiter masses, JWST opens a new discovery space. To evaluate the prospects for detecting a putative planet in the disk of 2M041240 with JWST/NIRCam using a coronagraph, we simulate the observations using python package pynrc  (Leisenring et al. 2022) , consistent with the observational setup and results from  Carter et al. (2022)  and  Girard et al. (2022). Considering the expected contrast between the planet and the star (plus the inner disk), F444W is the optimal band to search for a gas giant planet.
Aeronautics and  Space Administration  under grant No. 17-XRP17 _ 2-0012  issued through the  Exoplanets Research Program. D. H. is supported by  Center for Informatics and Computation in Astronomy (CICA)  grant and grant No. 110J0353I9  from the  Ministry of Education of Taiwan. D. H. also acknowledges support from the  National Science and Technology Council of Taiwan  through grant No. 111B3005191. G. J. H and Y. S. are supported by grant  12173003  from the  National Natural Science Foundation of China. G. D. M. acknowledges support from  FONDECYT  project  11221206 , from  ANID-Millennium Science Initiative -ICN12_009 , and the  ANID BASAL  project  FB210003. D. J. is supported by  NRC Canada  and by an  NSERC   Discovery Grant. Y. L. acknowledges the financial support by the  Natural Science Foundation of China  (grant No.
11973090 ). L. C. was supported by  ANID  through the  Milennium Science Initiative Program  ( NCN2021_080 ) and the  FONDECYT  Grant  1211656. E. R. acknowledges financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 864965 , PODCAST) C. F. M is funded by the  European Union  (ERC, WANDA,  101039452 ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( PROTOPLANETS , grant agreement No.
101002188 ). This research was funded in part by the  Gordon and Betty Moore Foundation  through grant  GBMF8550  to M. Liu. K. W. is supported by  NASA  through the  NASA   Hubble Fellowship  grant  HST-HF2-51472. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. J. L. acknowledges funding from  JWST/NIRCam  contract to the  University of Arizona ,  NAS5-02105  This research is partially supported by  NASA ROSES XRP , award  80NSSC19K0294. Some of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California  and the  National Aeronautics and Space Administration.
medizin. uni-muenster. de/duneuro  (Schrader et al. , 2021) , where the FEM calculations were performed. Analytical EEG solutions were calculated using the fieldtrip toolbox  (Oostenveld et al. , 2011). An example data set including somatosensory data was uploaded to Zenodo https://zenodo. org/record/3888381#. Yf0tT_ so9H4. For a comparison of runtime and memory usage, the forward calculation is split into five steps. The time necessary to create a driver, i. e. , the time DUNEuro needs to setup the volume conductor, the times needed to assemble the stiffness matrix and AMG solver, the transfer matrix solving process using Dune-ISTL  (Bastian et al. , 2021) , and the calculation of the final lead field matrix. All computations are performed on a bluechip workstation with an AMD Ryzen Threadripper 3960X and 128 GB RAM.
Somatosensory data and dipole scan To investigate CutFEM's influence on source reconstruction, an electric stimulation of the median nerve was performed on the same subject the anatomical data was acquired from. The subject gave written informed consent before the experiment and had no history of neurological or psychiatric disorders. The institution's ethical review board (Ethik Kommission der Ärztekammer Westfalen-Lippe und der WWU) approved all experimental procedures on 2 February 2018  (Ref. No. 2014-156f-S). The stimuli were monophasic square-wave pulses of 0. 5ms width in random intervals between 350 and 450ms. The stimulus strength was adjusted such that the right thumb moved clearly. EEG data were measured using an 80 channel cap (EASYCAP GmbH, Herrsching, Germany, 74 channel EEG plus additional 6 channels EOG to detect eye artifacts).
EEG positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U. S. A. ). In total, 2,200 stimuli were digitally filtered between 20 and 250 Hz (50 Hz notch) and averaged to improve signal-to-noise ratio. A single dipole scan was conducted over the whole source space using the data at the peak and the CutFEM lead field. The P20/N20 component typically exerts a high signal-tonoise ratio and a strongly dipolar topography, making it an ideal candidate for a dipole scan approach as motivated for example by  Buchner et al. (1994). Results.
Data availability statement The data analyzed in this study is subject to the following licenses/restrictions: the software in which the new methodology was implemented can be found under https://www. medizin. uni-muenster. de/duneuro, an example data set can be found under https://zenodo. org/record/3888381#. Yf0tT_so9H4. The dataset used in the realistic head model section is from a different subject than the one uploaded to Zenodo but otherwise identical. Requests to access these dataset should be directed to tim. erdbruegger@uni-muenster. de.
The structure elucidation of small organic molecules (<1500 Dalton) through 1D and 2D nuclear magnetic resonance (NMR) data analysis is a potentially challenging, combinatorial problem. This publication presents Sherlock, a free and open-source Computer-Assisted Structure Elucidation (CASE) software where the user controls the chain of elementary operations through a versatile graphical user interface, including spectral peak picking, addition of automatically or user-defined structure constraints, structure generation, ranking and display of the solutions. A set of forty-five compounds was selected in order to illustrate the new possibilities offered to organic chemists by Sherlock for improving the reliability and traceability of structure elucidation results.
Introduction NMR spectroscopy is the most widely used analytical method for the thorough identification of organic chemical compounds. Preliminarily to de novo structure elucidation by NMR, database lookup for an already known compound (dereplication) or for structure fragments has been included in several existing CASE systems using the spectral fingerprint of one-dimensional (1D) NMR experiments as a search key. Through-bond proximity relationships between atoms became available with the possibility of routinely recording two-dimensional (2D) NMR spectra. The typical collection of NMR data used for computer-assisted structure elucidation (CASE) might contain 1D spectra such as 1 H, 13 C and DEPT as well as the 2D HSQC, HMBC and COSY spectra. The HSQC experiment indicates direct bonds between heavy atoms (non-hydrogen) and protons whereas HMBC and COSY spectra bear hints about long-range chemical shift correlations between protons and heavy atoms (HMBC) or between protons (COSY).
The Test Dataset The performance of Sherlock was evaluated with 45 test data sets which are available as freely accessible archives (see  Section 5. 2 ). An overview of the structure search settings and results is given in Table  1. The system presented here was able to handle and solve problems with a number of heavy atoms up to 40 (test case  19). More is likely possible but was not evaluated. With the inclusion of the default settings, the generated lists of solutions contained the expected molecule in 37 of 45 cases. The last five problems (41 to 45) could not be solved within an acceptable time. There, the structure search was manually interrupted after three hours of computing time. This does not necessarily mean that the CASE system is not able to solve them. Further analysis by NMR experts might lead to results based on additional user-defined constraints.
Finally, to enable the generation of the desired molecule, the modification of the hybridization and set neighbours threshold to 0. 1% and 100% was necessary. Table  1. Overview of the test set used for the validation of Sherlock with planar chemical structures, the number of results regarding the applied constraints and execution time (structure generation, filtering, ranking), the average deviation as well as the number of matching and total signals. Compound names without an asterisk indicate that the dereplication was successful by using the default settings. One asterisk means that parameter adjustments were needed to achieve dereplication, while entries with two asterisks were not found in the knowledge base. Until now and to the best of our knowledge, only commercial and closed-source software exist which include a frontend offering spectrum processing routines to obtain structural information from NMR data and a backend which provides a suite of CASE-related routines.
As a result, 38 of 40 candidate lists contained the desired structure in the first place whereas two of them were ranked #6 and #8. This indicates that the prediction and ranking modules of Sherlock are reliable and the expected compound is often situated in the top ten of the ranked candidate list. A full spectrum-to-spectrum assignment (Table  1 ) was produced in 31 cases among 40. A 0. 83 ppm overall mean average chemical shift deviation value was achieved.
# The number of unassigned signals ranges from one to four in the 9 remaining cases. One reason why the prediction of signals might fail is that there is no entry in the HOSE code library and thus no prediction value is available. This problem will be overcome when larger open-access collections of assigned NMR spectra will become available. Another cause for a missing signal could be a larger difference in the given maximum shift tolerance or average deviation. Furthermore, diastereotopic carbons might not be distinguished by their 3D HOSE code since the stereochemical information in the output of pyLSD is not provided and no method in Sherlock is implemented to provide it. In addition, the required stereochemical properties, as suggested  [18, 19] , are not available for all compounds in Sherlock's structure-to-spectrum database which leads to the same problem (mentioned above) during the HOSE code knowledge base creation and thus also affects the prediction capability.
Sherlock provides the search for fragments for 13 C query spectra. This was tested for the first 40 case studies as well, by incorporating the first entry of the ranked fragment list in the input file of the structure generator. In the vast majority of the cases  (32)   After the three necessary parameter adjustments and the elucidation for each of the first 40 test examples, the rank of the expected compound was determined. As a result, 38 of 40 candidate lists contained the desired structure in the first place whereas two of them were ranked #6 and #8. This indicates that the prediction and ranking modules of Sherlock are reliable and the expected compound is often situated in the top ten of the ranked candidate list. A full spectrum-to-spectrum assignment (Table  1 ) was produced in 31 cases among 40.