dataesr/scientific-paragraphs-categorization huggingface dataset:
During the AIMD calculations, the systems were heated from 100 to 2000 K over 500 time steps. The complete AIMD trajectories were next subsampled, and around 500 configurations were used to train preliminary MTPs. The accuracies of preliminary fitted MTPs were then examined over the complete dataset, and configurations with worst extrapolation grades  [28]  were defined and incorporated into the original dataset. The final MTPs with enhanced accuracy and stability were then passively fitted using the improved training dataset. Phonon dispersion relations were also obtained using the fitted MTPs by employing the PHONOPY  [29]  code using 6 × 6 × 1 supercells. Classical molecular dynamics (CMD) simulations on the basis of MTPs were conducted using the LAMMPS  [30]  package with the quasi-static loading approach proposed in our recent studies  [31, 32] , with a fixed time step of 0.
Funding: This research was funded by  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy  within the Cluster of  Excellence PhoenixD  ( EXC 2122 , Project  ID 390833453 ). A. V. S. is supported by the  Russian Science Foundation  (Grant No. 18-13-00479 , https://rscf. ru/project/18-13-00479/,accessed on  24 August 2022 ).
71). For comparison, mention of 'commonspatial patterns' associated with BCI increased from 71 to 119 articles per year (mean citations: 20. 75) and 'deep learning' from 15 to 179 articles per year (mean citations: 11. 67) in the same period. We argue that the gap between the observed performance of RG applied to BCI and the proposal number of contributions in this field may be attributed to some combination of a perceived lack of easily accessible resources to make RG widely available to BCI research (e. g. 65. 7% of respondents to the workshop questionnaire had never used RG before) and the lack of reproducible tools for benchmarking different methods while taking into consideration datasets heterogeneity (discussed at the previous BCI meeting workshop  [81] ). This workshop was created to address these issues by increasing awareness of available resources for RG and encourage benchmarking with tools such as MOABB on a larger scale of datasets  [79].
We encourage everyone to report benchmarking results. Further, we invite everyone to join us by using the open-source RG tools, and by contributing to the improvement of these tools either by providing feedback, or contributing to the open-source project pyriemann. All the workshop resources are accessible, including slides, code tutorial, online demo, exhaustive workshop Q&A, and linked data: https://github. com/ lkorczowski/BCI-2021-Riemannian-Geometry-workshop.
PyRiemann  [85]  is an actively maintained Python package for manipulating covariance matrices. It implements multiple data transformation techniques and classification methods. Workshop participants were guided through a Python notebook and instructed on using this library with concrete examples. The RG framework includes multiple signal classification strategies and BCI researchers use many other algorithms, such as Logistic Regression, Regulated LDA, Support Vector Machines, and Neural Networks  [86]. Valid comparisons between methods are essential. The Mother Of All BCI Benchmarks (MOABB)  [79, 87]  project offers comprehensive comparison tools that enable ranking new and existing algorithms with publicly available datasets, paving the way for reproducible research. We reviewed a practical example and explained the underlying code.
The consensus on the current performance of AI-BCI methods was that they perform essentially on par with the best non-deep decoding algorithms. However, a rigorous comparison of state-of-the-art Riemannian decoding methods  [76, 101]  with AI algorithms has yet to be done. The workshop participants concluded that a large-scale brain decoding challenge, e. g. hosted by a major AI or machine learning conference, would be well suited for realizing a fair comparison of competing decoding architectures (e. g. https://beetl. ai/). The workshop participants then considered which issues prevent, at least so far, AI methods from revolutionizing BCI systems in the same way they have already transformed other data-driven applications. The primary bottleneck identified in the discussion was the absence of large-scale datasets in the field of BCI.
These datasets would ideally comprise thousands or even millions of BCI users from heterogeneous settings, i. e. including numerous experimental paradigms, recording setups, and user groups. While the workshop participants acknowledged the efforts of the BCI community to record large-scale datasets  [102] , they also noted that collecting datasets on a similar scale as those available in other scientific disciplines  [103]  is probably beyond the capabilities of the academic community. Consequently, the discussion shifted to the role of commercial BCI applications in recording and providing access to large-scale datasets. Several consumer EEG headsets have reached market readiness with the expectation of prompt deployment in passive BCI applications  [104]. Comprehensive access to data recorded by these applications could provide the large-scale datasets required to realize the full potential of AI-BCI systems.
In particular, the heterogeneous nature of such data, which stands in contrast to the homogeneous data typically recorded in academic settings, could be considered an advantage. The diversity of data might be leveraged to create feature representations that are user-as well as hardware-independent. Such feature representations would be essential to realize zero-training BCIs for commercial applications  [105] [106] [107]. However, leveraging commercially recorded EEG datasets poses significant practical, legal, and ethical challenges. It is unclear what incentives companies would have to share their data publicly. Also, procedures would have to be developed that realize informed consent and honor data privacy regulations. The workshop participants considered an active engagement of the BCI community with industrial partners essential to make large-scale datasets a reality and realize the full potential of AI-BCI systems.
Workshop participants agreed that these modern machine learning approaches should benefit from additional data in future studies, and noted that all of the work presented used less than 20 minutes of neural recordings. Further, their performance did not saturate with training data quantity subsampled within these limited datasets. A second challenge is how to obtain highly informative neural correlates about speech intent. Previous research almost exclusively relied on ECoG signals, which are not regularly used for long-term measurement. However, high-quality speech decoding and synthesis can also be achieved using penetrating microarrays implanted in the dorsal motor cortex  [194] , even though that area is not typically associated with speech production  [195]. These Utah arrays have been used for multiple-year recordings in a number of participants and achieved high performance in, e.
Fifth, all presenters agreed that data sharing is key to accelerating progress. One recently shared large dataset of speech perception in fMRI, ECoG, and sEEG, along with the associated impressive reconstruction quality provides the public research community with a fully annotated dataset  [208]. Bedside evaluation to assess conscious awareness after coma requires inferences based on patients' motor responsiveness  [209]  with limited diagnostic precision and prognostic information, increasing the ethical difficulty of decisions on life-prolonging therapies. Technologies such as functional neuroimaging and BCIs provide objective tools for diagnostic, prognostic, and therapeutic purposes  [210]. About two-thirds of patients clinically diagnosed with 'unresponsive wakefulness syndrome (UWS)' (or 'persistent vegetative state') may show residual brain activity in PET studies  [211]  and are hence actually in a minimally conscious state (MCS) with a better chance of recovery.
Information on providing such input can be found through the working group website. Another standardization project, IEEE P2731: Standard for a Unified Terminology for Brain-Computer Interfaces (BCI) (https://sagroups. ieee. org/2731/) aims at developing a comprehensive BCI lexicography and a functional model of BCI systems  [312] [313] [314]. It is also working on identifying the required information to be stored in BCI files to enable efficient sharing of data and tools among stakeholders  [315]. These activities can contribute to the development of standard experimental and usage protocols, benchmarking procedures, and increased interoperability of neurotechnology systems. Overall, this workshop highlighted the need to continuously evaluate the state-of-the-art and the implications of neurotechnologies.
Next steps for practically useful BCI ethics Organizer: Brendan Allison (UC San Diego) Additional Presenters: Pim Haselager (Radboud University Nijmegen), Dr. Sonja Kleih-Dahms, (University of Würzburg), Donatella Mattia (Fondazione Santa Lucia, IRCCS) This workshop was designed not for review or abstract academic discourse, but to develop practical next steps for BCI-related ethical issues. The organizers briefly presented examples of these issues  [325] [326] [327] [328] [329]  to promote discussion. A public database of ethical use cases was proposed to raise awareness with an associated forum where people could share their perspectives on each case. The ethical use cases could also help professors and others who want to teach BCI ethics. Further discussion and development of ethical use cases would benefit from an ongoing collaborative effort, perhaps via online seminars, to develop a framework, assign people to develop different use cases, and create an online database.
Indeed, some forprofit and non-profit entities do provide good BCI content. The ongoing increase in online BCI-related classes, conferences, workshops, competitions, and other activities has led to ample recorded material from reputable organizers and speakers that is usually available for free. Many participants had seen online postings from, and/or been directly contacted by, people who believe that they are being involuntarily mind-controlled by a BCI or a similar device. A few participants reported trying to direct such persons to appropriate mental health professionals, but without apparent success. Next steps at this time are not obvious aside from a possible paper or position statement with suggested guidelines, developed with mental health experts. The workshop focused on specific, actionable next steps to raise awareness of ethical issues in BCI and further engage relevant groups through workshops, papers, online discussions and a database of use cases and surveys  [330] [331] [332].
Stimuli, Apparatus & Design. Participants viewed tasks on a 24-in BenQ G2420HD monitor sitting approximately 60 cm away, where stimuli presentation was controlled via MATLAB R2015b (Mathworks Inc. , Natick, MA, USA) using the third version of the Psychophysics toolbox  (Brainard & Vision, 1997; Kleiner et al. , 2007; Pelli, 1997). The screen was set to 75hz. All stimuli were black (i. e. , RGB values of 0, 0, 0; 1. 11 cd/m 2 ) and white  (i. e. , RGB values of 255, 255, 255; 218. 8 cd/m 2 ) drawings on a grey background  (i. e. , RGB values of 128, 128, 128; 70. 88 cd/m 2 ). The fixation marker was an empty circle made from a black line drawing subtending a radius of 1. 2° located in the center of the screen. Two target placeholders were located at 8. 7° away from the fixation marker on the left and right side of the screen.
Methods Procedure. Participants completed four tasks: A non-cueing task, a single exogenous cueing task, a single endogenous cueing task, and a double cueing task where both exogenous and endogenous orienting occured. Participants performed 10 practice trials prior to each task which consisted of 384 trials each. Task order was randomized across participants. In the current manuscript, we analyzed data for the non-cueing, the exogenous cueing, and the double cueing tasks in this manuscript. Participants were asked to stare at the center of the screen throughout the experiment, while we assessed their eye movements using electro-oculogram. We jittered the latencies between the fixation point and the cues, between the endogenous and the exogenous cues, and between the cues and the target event based on a uniform distribution to minimize the effects of temporal prediction following spatial cueing.
Electrodes were referenced online to C4, and rereferenced offline to an average reference. Preprocessing and analysis were conducted in BrainVision Analyzer (ActiChamp System; Brain Products GmbH Inc. ; Gilching, Germany) and MATLAB (R2020a; Mathworks Inc. , Natick, MA) using Brainstorm  (Tadel et al. , 2011)  and custom scripts. We downsampled the data to 250 Hz and applied two IIR Butterworth filters: a first High-pass 0. 1 Hz filter of 8th order and a 60Hz notch filter. We then topographically interpolated bad channels (1. 12% of channels). Next, we performed an independent component analysis to identify artifacts relating to eye movements and blinks using the BrainVision Analyzer Ocular correction ICA tool. We corroborated these analyses with the electro-oculogram. We corrected the EEG signal following these artifacts.
We visually inspected EEG signals to manually remove atypical noise patterns, as well as activity exceeding ± 200 μV across all electrodes. All triggers were realigned according to our photodiode stimulations linked to the onset of the exogenous cue, the endogenous cue, and the target event. For target-related events, in lieu of the adjacent response filter method  (Woldorff, 1993) , we removed event-related responses from exogenous and endogenous cueing by taking the residuals of the linear regression model using the non-cueing condition as baseline. A hierarchical linear regression was run with condition as a dummy coded fixed factor, and subjects as a random factor. 'Raw' Residuals were obtained from the MATLAB function residuals() for all channels and subjects. Event-related potentials.
Separate trials were used for the training and testing of our classifiers following a three-fold cross-validation plan. Classifiers were trained separately for each participant and time-points across the cuerelated and target-related time series. We performed multivariate decoding analyses using linear support vector machine (SVM) implemented in MATLAB's (i. e. , fitcsvm). Following our three-fold cross-validation procedure, 2/3 of trials were randomly selected to train the classifier, and the remaining 1/3 of the trials were used to test the performance of the SVM. Specifically, we organized the data of each participant into three equally sized bins of trials and averaged these trials together to produce three separate time-series. Two of the three averaged time-series served as the training dataset, and the remaining average served as the testing set.
Decoding accuracy was determined on the correct identification of the data we left-out. We iterated through all possible combinations of training such that each averaged timeseries served as the testing set once. For each participant, we performed 50 iterations of the above binning, averaging, and decoding procedure. As we mentioned previously, we applied our decoding strategy across two decoding challenges. Challenge I: Generalization of decoding going from single to double cueing. We wanted to assess how classifiers trained in the single cueing condition generalize to the double cueing condition. Here, we trained and tested the SVM classifiers following the approach we described previously on the data from the exogenous single cueing condition. Next, we tested the ability of these SVM classifiers to decode exogenous attention in the context of double cueing.
We applied cluster-corrected permutation tests to control for family-wise type-II error. We performed 1000 permutations where we randomly varied the data and then contrasted observed cluster sizes against surrogate distributions for the null hypothesis. The cluster forming threshold was set to p <. 05. We applied this statistical approach for all decoding analyses. Challenge II: Decoding cueing conditions. Our secondary and complementary analysis focused on decoding the cueing conditions (i. e. , exogenous single cueing versus double cueing). We followed a similar three-fold procedure as described previously, except for the following changes described below. For exogenous orienting cue-related EEG, we examined the cue-related lateralized ERP, LAI and the lateralized alpha phase clustering.
M. L. acknowledges support from the  Fonds de Recherche du Québec -Nature et Technologies. J. D. C. acknowledges a doctoral fellowship from the  Natural Science and Engineering Research Council of Canada. S. B. is supported by the  NIH  ( R01 EB026299 ), a  Discovery grant  from the  Natural Science and Engineering Research Council of Canada  ( 436355-13 ), the  CIHR Canada research Chair in Neural Dynamics of Brain Systems , the  Brain Canada Foundation  with support from  Health Canada , and the  Innovative Ideas program  from the  Canada First Research Excellence Fund , awarded to  McGill University for the Healthy Brains for Healthy Lives initiative  ( 1c-II -14 ). This work was supported by a grant from  CIHR  and  Canada Research Chair  to A. R. J. S. acknowledges grants from the  ANR (France) :  ANR-16-ASTR-0014  and  ANR-17-EURE-0017.
We present the discovery and characterization of a transiting sub-Neptune that orbits the nearby (28 pc) and bright (V = 8. 37) K0V star HD 207897 (TOI-1611) with a 16. 20-day period. This discovery is based on photometric measurements from the Transiting Exoplanet Survey Satellite mission and radial velocity (RV) observations from the SOPHIE, Automated Planet Finder, and HIRES high-precision spectrographs. We used EXOFASTv2 to model the parameters of the planet and its host star simultaneously, combining photometric and RV data to determine the planetary system parameters. We show that the planet has a radius of 2. 50 ± 0. 08 R E and a mass of either 14. 4 ± 1. 6 M E or 15. 9 ± 1. 6 M E with nearly equal probability. The two solutions correspond to two possibilities for the stellar activity period.
3 d. However, our preliminary analysis based on an erroneous estimate of the rotational period of the star caused us to attribute the 16. 3 d signal to stellar spot modulation at P rot /2, and observations of the star were stopped. After the release of the TESS data, we resumed SOPHIE observations to gather more data to better separate the activity signal and planetary RV variation, and also to search for other possible planets. The star was observed again in 2020 with a simultaneous Fabry-Perot (FP) reference spectrum. Twenty-four additional high-resolution spectra with an RV RMS of 4. 43 m s -1 were gathered. Our final SOPHIE dataset includes 68 spectra. With an exposure time ranging from 1000 to 1500 s, we achieved a median S/N of 97. 7 per pixel at 550 nm. The mean RV uncertainty, computed using the quadratic sum of photon noise and wavelength calibration error, is 1.
HIRES From the summit of Maunakea, we first observed HD 207897 using the Keck I telescope and HIRES spectrometer (Vogt 1994) from 2003 July 7 as part of the California Planet Search, and additional RVs were collected for 11 months beginning on 2020 January 21. Thirty-seven RVs were collected using the B5 decker (0. 87 × 5. 0 ), resulting in a resolution of 50 000. The median exposure time was 231 s, the average S/N per pixel was 220, and the internal uncertainty was 1. 04 m s -1. The Doppler pipeline and observing setup follows the California Planet Search procedure outlined in  Howard et al. (2010). The RV data set are presented in Table  A. 2. We note that data before 2004 July 9 (three data points) used a different CCD detector. For the sake of simplicity, we only used them in our RV periodogram by applying an offset term (Fig.
APF From the summit of Mt. Hamilton at the Lick Observatory, we collected 23 RVs of HD 207897 using the Levy Spectrograph  (Burt et al. 2014 ) on the APF from 2020 June 2 until 2021 February 28. Twenty-three RVs were collected. Their median exposure time was 1200s, the S/N per pixel was 86, and the internal uncertainty was 3. 0 m s -1. With a resolution of 100 000, this slit-fed spectrograph uses the iodine-cell technique to calculate RVs according to  Butler et al. (1996). For additional details on the instrumental setup and data reduction, see  Fulton et al. (2015). The full list of corrected RVs can be found in.
FIES From the Roque de los Muchachos Observatory in La Palma, Spain, we observed with the FIbre-fed Échelle Spectrograph (FIES;  Telting et al. 2014 ) at the 2. 56 m Nordic Optical Telescope and obtained four spectra between 2019 December 27 and 2020 January 15. We used the high-resolution fibre (R ∼ 67 000) and extracted the spectra following  Buchhave et al. (2010). The S/N per resolution element at 550 nm ranges between 62 and 127. We used the FIES data for stellar classification (see Sect. 3. 1) and refrained from including them in the RV analysis because all the observations were acquired near phases 0. 25 and 0. 75.
We also used a field of view windowed to 6 × 6 arcsec. All frames were reduced by the instrument pipeline  (Hormuth et al. 2008) , which performs the basic reduction (bias subtraction and flat field correction), aligns all frames, and measures the Strehl ratio  (Strehl 1902)  of the individual images. This metric was then used to select the best images to perform the final stacking. We used the best 10% of the frames to produce the final high-resolution image. The final image does not show signs of close companions. We performed a dedicated search by removing the instrumental point spread function scaled to the target peak flux. No additional sources were present in the image. We then followed the procedures described in  Lillo-Box et al. (2012 , 2014 ) by using our own developed astrasens package 5  to obtain the sensitivity of our image by performing an injection or retrieval of artificial sources.
041%. We consider this probability low and the odds that such an undetected source might be an appropriate binary capable of mimicking the transit signal to be negligible. In addition, by using tpfplotter code 8  (Aller et al. 2020) , we plotted the target pixel file from TESS for HD 207897 b (see Fig. 3 ). We searched for possible light contamination by considering the nearby stars TIC 264678535 (T mag = 13. 3), TIC 264678538 (T mag = 15. 69) and TIC 264678529 (T mag = 14. 39), 8 https://github. com/jlillo/tpfplotter which all lie inside the selected apertures analyzed by PDC-SAP. Because the Gaia G bandpass is quite similar to the TESS bandpass, we used the Gaia fluxes of these stars and estimated the level of contamination. The total flux due to nearby stars was only 0. 8% of the HD 207897 flux, which is automatically corrected by SPOC.
The abundances of C and O are very difficult to determine for stars cooler than about 5200 K  (Delgado Mena et al. 2010; Bertran de Lis et al. 2015). We estimated the abundances of these elements empirically by using a machine-learning algorithm (we used the estimator RandomForestRegressor) from the Python Scikit-learn package  (Pedregosa et al. 2011). The estimation of C and O was based on the abundance of Mg and Fe  (Delgado Mena et al. 2017). Our initial sample was based on the HARPS sample, for which abundances of Mg and Fe are available  (Adibekyan et al. 2012 ). Then we derived the O abundance for 535 stars and the C abundance for 758 stars following the method described in our previous works  (Delgado Mena et al. 2010; Bertran de Lis et al. 2015). These samples were used as training and test datasets.