dataesr/scientific-paragraphs-categorization huggingface dataset:
Meta-ACG preprocessor ACGtk  (Pogodalla, 2016)  is a toolkit offering an environment to develop and test ACG grammars. Feature structures have not been implemented yet in this program. We implement the preprocessor presented in part 2. 1 as a python program called macg. Given an input file written in a specially designed language, called meta-ACG language, this program generates an ACG grammar. This output consists in tree files: deep syntax signature, surface syntax signature and surface lexicon (see definition 11). The syntax of the meta-ACG language is greatly inspired by NLTK  (Bird et al. , 2009) , except that variables are declared with @. See Fig. 5    The meta-ACG preprocessor has two main goals: 1. Making it possible to develop and test ACG grammars with feature structures.
Limitations and future prospects The macg program is still under development. We intend to add morphological rules and macros to facilitate even more the lexicon organization. Inequalities, default values and constraint equations could also be added in the future. Although Tab. 1 gives an implementation of lists in our setting, the current meta-ACG language lacks primitives, like concatenation, to work with lists more easily. Technically, list concatenation can be written down by enumerating all elementwise operations as different rules. But this is not convenient. This also holds for sets, which are commonly used on LOCAL features in HPSG (e. g.
SLASH). Because of FS enumeration, there is an inevitable combinatorial explosion. This affects parsing time complexity exponentially in the number of attributes and the highest rank. In practice, we observe that our program actually runs slowly if complex type structures (e. g. lists as presented here) are involved. For instance, it took 1 hour to run macg on the very short hpsg. macg included example grammar, creating an intermediary grammar of several gigabytes. Therefore, this preprocessor approach might not be well suited for large-scale grammars. However, it offers a valuable tool for a quick development of experimental fragment grammars and prototypes. Finally, we are planning to add the possibility to define a lexicon to type-theoretic semantics.
A large observed relative inclination means a system is misaligned, while a system with small observed relative inclination could be aligned or misaligned depending on the relative (unknown) longitude of the ascending nodes of the exoplanet and binary orbit. However, if many systems are observed, an overabundance of small relative inclinations has the physical interpretation that an overabundance of systems tends to be aligned since the longitude of the ascending nodes of misaligned systems is expected to be distributed randomly and independently of relative inclination. A diagram of the relevant parameters described in this paper is presented in Figure  1. This paper is organized as follows. In Section 2 we present the Gaia Early Data Release 3 (EDR3), TESS, and groundbased spectroscopic and photometric observations used in our study.
Observations/Data To investigate whether there is a tendency toward alignment between the orbits of visual binary stars and their planetary systems, we need both a sample of likely transiting planet candidates and a constraint on the orbital inclinations of any visual binary companions to these planet host stars. For the former, we make use of planet candidates discovered by the TESS mission and vetted with ground-based follow-up photometric and spectroscopic observations. For the latter, we use astrometric observations from Gaia, archival broadband photometry, and metallicity measurements from both new and archival spectra to determine the masses of the binary components using isochrone fitting. We also perform a variety of cuts on our sample of visual binaries with exoplanets and a control sample.
Identification with TESS We start with the list of planet candidates reported by the TESS mission (also known as TESS Objects of Interest, or TOIs). TESS uses four 10 cm cameras to repeatedly image 96°b y 24°regions of the sky for 28 days at a time. After the completion of each 28 day observation (called a sector), TESS moves to a new field of view and repeats the process. Over the course of its 2 year primary mission, TESS observed approximately 70% of the sky, and is continuing to observe in an extended mission. The TESS CCDs read out images of the sky every 2 s, but the data volume required to download each 2 s image from orbit would be prohibitively large. Instead, TESS coadds the 2 s images into longer observations before beaming the data back to Earth. Most of the sky is coadded to long-cadence Full Frame Images (FFIs) with exposure times of 30 minutes (in the primary mission) or 10 minutes (in the extended mission), while the pixels surrounding 20,000 preselected stars are coadded to 2 minutes; for the extended mission, 1000 of these targets are coadded to 20 s.
The diagram is centered on the primary star. Apparent relative alignment is calculated as |90°-i|, where i is the inclination of the binary system (the transiting exoplanets will always have approximately 90°inclination). In the diagram, the green wedge is the inclination of the binary system. The primary star is the star that hosts the exoplanet, while the binary companion is the companion star without detected exoplanets. The angular momentum of the star is the axis that the star rotates on. The exoplanet (orange) orbits at 90°to the line of sight. Once the data have been received on Earth, they are analyzed as described by  Guerrero et al. (2021)  to process the observations and identify planet candidates. We base our sample on the list of all TESS planet candidates that had been reported online as of 2020 December 15.
Sample of Visual Binaries with Planet Candidates We identify planet-candidate-hosting stars that also happen to reside in a visual binary system matching the underlying Gaia DR2 ID of items in the TESS Input Catalog (TIC) to a catalog of visual binary stars identified in Gaia data by  El-Badry & Rix (2018). This work reports approximately 53,000 visual binary systems within 200 pc of the Sun and with projected separations between 50 and 50,000 au derived from Gaia DR2 astrometric observations. Although the catalog has binaries with separations as small as 50 au, the vast majority of binaries in the catalog have much wider separations. At wider separations, it is more likely that the Gaia spacecraft will resolve the individual stars in the binary system. In total, after all cuts were performed, we identified a sample of 67 visual binary systems including a TESS planet-candidate host star with projected semimajor axes ranging from 61 to 34,700 au and parallaxes ranging from 5 to 48 mas.
Stellar Mass Determination We derive masses for the stars in our samples using the Isochrones Python package  (Morton 2015). Given observable parameters like a star's apparent magnitudes in different bandpasses, its trigonometric parallax, and spectroscopic parameters, Isochrones determines the star's most likely physical parameters and their uncertainties by comparing measured parameters to those predicted by stellar evolution and atmosphere models. Isochrones supports several different suites of isochrone models; we use the MIST isochrones  (Choi et al. 2016; Dotter 2016 ). We input each star's parallax, metallicity (when available from archival spectroscopy or NRES, TRES, FIES, or CHIRON), and apparent magnitudes in the G, BP, and RP bandpasses from Gaia and the J, H, and K bandpasses from the 2MASS survey  (Skrutskie et al.
LOFTI Modeling Linear Orbits for The Impatient (LOFTI;  Pearce et al. 2020 ) uses measurements of the relative positions of two stars (in both R. A. , Δα, and decl. , Δδ), relative proper motions, relative radial velocity (if available), and stellar masses to constrain orbits of visual binaries. In our analysis, we take all of these 93 https://github. com/timothydmorton/isochrones/blob/ c134d271950fe63bd5e84ede4530585eba3f48a4/isochrones/priors. py#L364 94 Available at https://github. com/awmann/M_-M_K-. parameters from Gaia. LOFTI calculates relative proper motion and position as primary minus companion, while relative radial velocity is calculated as companion minus primary. LOFTI uses the Orbits for the Impatient (OFTI) sampling and rejection technique  (Blunt et al. 2017)   ) is greater than a randomly chosen number in the range [0, 1] to form a posterior distribution of orbital parameters.
Future Work We have presented evidence showing that the orbits of widebinary stars are preferentially aligned with the orbits of close-in planets in those systems. Although the alignment appears to be statistically significant, it is based on a relatively small sample of planetary systems. Confirming the conclusions of this work with a larger sample of binary stars hosting transiting exoplanets would be highly valuable. Fortunately, it should be straightforward to increase the size of our sample. Recently, El-Badry et al. (2021) released a sample of over a million wide-binary stars from Gaia EDR3, including 274 visual binaries found to have exoplanets by TESS as of 2021 February 1. With this new sample of binaries, it will be possible to triple the size of our current sample. In addition, with improved Gaia astrometry from future data releases, such as more systems with radial velocity and an astrometric acceleration term, the inclinations of systems can be better constrained.
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 work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by  NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by the  NSF. This article is based on observations made with the MuSCAT2 instrument, developed by  ABC , at  Telescopio Carlos Sánchez  operated on the island of Tenerife by the  IAC in the Spanish Observatorio del Teide. This paper is based on observations made with the MuSCAT3 instrument, developed by the  Astrobiology Center  and under financial supports by  JSPS KAKENHI  ( JP18H05439 ) and  JST PRESTO  ( JPMJPR1775 ), at Faulkes Telescope North on Maui, HI, operated by the  Las Cumbres Observatory.
JP17H04574. This work is partly supported by  JSPS KAKENHI  grant No. JP20K14518 , and by  Astrobiology Center SATELLITE Research  project  AB022006. M. T. is supported by  MEXT/JSPS KAKENHI  grant Nos. 18H05442 ,  15H02063 , and  22000005. This work is partly supported by  JSPS KAKENHI  grant No. JP21K13955. This work is partly supported by  JSPS KAKENHI  grant No. 20K14521. C. R. -L. acknowledges financial support from the  State Agency for Research of the Spanish MCIU  through the  Center of Excellence Severo Ochoa  award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ). M. R. acknowledges support from the  Universidad Católica de lo Santísima Concepción  grant  DI-FIAI 03/2021. P. J. A. acknowledges support from grant  AYA2016-79425-C3-3-P  of the  Spanish Ministry of Economy and Competitiveness (MINECO)  and the  Centre of Excellence "Severo Ochoa " award  to the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ).
M. G. and E. J. are F. R. S. - FNRS Senior Research Associates. K. K. M. acknowledges support from the  New York Community Trustʼs Fund for Astrophysical Research. This work has been carried out within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation. This work makes use of observations from the ASTEP telescope. ASTEP benefited from the support of the  French and Italian polar agencies IPEV  and PNRA in the framework of the  Concordia station program  and from  Idex UCAJEDI  ( ANR-15-IDEX-01 ). Facilities: Gaia, TESS, FLWO:1. 5 m (TRES), CTIO:1. 5 m (CHIRON),  UKST (RAVE) ,  LCOGT (NRES) , FIES, LAMOST, APOGEE, GALAH, ASTEP (Guillot et al. 2015),  MuSCAT  (Narita et al. 2015), MuSCAT2 (Narita et al. 2019), MuSCAT3 (Narita et al. 2020), SIRIUS (Nagayama et al.
Plain Language Summary ExoMars Trace Gas Orbiter has been in low orbit around Mars since early 2018. During 2018 a large amount of dust was lifted from Mars' surface into the atmosphere. This eventually obscured most of the planet, forming a global dust storm. These storms have a large effect on the temperature and weather in Mars' atmosphere. We used observations from the spacecraft's thermal infrared instrument. This measures how much light comes from the atmosphere and surface at wavelengths where heat is emitted, and can be used to work out the atmospheric temperature and dust content at different times of day. We combined these observations with a numerical model of Mars' atmosphere, considering uncertainties in the measured atmospheric properties and the modeled atmosphere. We compared our results with temperature and dust observations made by Mars Climate Sounder on board Mars Reconnaissance Orbiter.
Assimilating observations over a range of local times gives us confidence in our results related to the diurnal cycle. The diurnal tide strengthened considerably at midlatitudes during the GDS, at the expense of the diurnal tide near the equator (Figure  19 ), and the semi-diurnal tide increased in strength everywhere, particularly in the lower atmosphere  (Figures 17c and 20). The diurnal cycle was also verified against independent Curiosity-REMS surface pressure measurements. Despite the large difference in surface elevation between the GCM topography and the rover, the analysis surface pressures at the peak of the storm accurately reproduced the diurnal cycle measured by Curiosity (Figure  21 ). Assimilation during the onset of a Mars GDS is a particularly hard stress test of our assimilation scheme, so it is encouraging that we are able to reproduce many features of independent observational data sets.
Data Availability Statement Data sets related to this article can be found on the Figshare online open access repository  (Young et al. , 2022). The processed TIRVIM data and retrievals used in this paper are available in NetCDF format on the Institut Pierre Simon Laplace (IPSL) data server  (Guerlet, 2021). The observations use version seven of the retrieval algorithm, and version four of the IKI calibration algorithm. Mars Climate Sounder observations are available on the NASA Planetary Data System Atmospheres node at https://pds-atmospheres. nmsu. edu/data_and_services/atmospheres_ data/MARS/mars_reconnaissance_orbiter. html. Mars Science Laboratory Curiosity rover Rover Environmental Monitoring Station observations are available on the NASA Planetary Data System Atmospheres node at https:// atmos.
Population Data were collected from a group of CLBP patients and from a matched group of healthy NLBP subjects. The study protocol was approved by the Intercommunale de Santé Publique du Pays de Charleroi Ethics Committee (ISPPC/OM008) under the number B325-2020-43666 and complied with the Helsinki Declaration on the Ethical Principles for Medical Research Involving Human Beings. All of the patients and subjects received an information sheet explaining the purpose of the study and gave informed consent before participation. Twenty CLBP patients were recruited on a voluntary basis between the 3 November and the 1 December 2020 from the pool of patients treated for CLBP at the University Hospital of Charleroi (CHUC) in the "Sport Santé" department in Monceau-sur-Sambre, Belgium. The inclusion criteria were: the presence of LBP diagnosed by a physician and lasting longer than three months, ability to perform three trunk flexions with the lower limbs extended, aged between 18 and 65 years, body mass index (BMI) between 18 and 35 kg•m -2 , and a pain score of less than 8/10 on a verbal Numerical Rating Scale (NRS) the day of the test.
The computer screen was visible to neither examiner #1 nor the participant. The data were stored on the computer's hard disk drive for later analysis. The IMUs used are part of a homemade system called DYSKIMOT (for Motor Dyskinesia), which has been presented in detail previously in  [27]. The DYSKIMOT system software (v. 3. 1) recorded the data from the three IMUs with a sampling frequency of 100 Hz. The system is based on commercial IMUs (LSM9DS1, SparkFun Electronics, Niwot, CO, USA) equipped with a triaxial accelerometer, gyroscope, magnetometer, and thermometer. Time series of accelerations (Acc X, Y, Z) along each of the 3 axes of each sensor and angular velocities (Gyr X, Y, Z) were recorded. The DYSKIMOT software also computed the angular accelerations (AccA X, Y, Z) in real-time by numerical differentiation (method) and the corrected angles (AngC X, Y, Z) by numerical integration and linear drift subtraction of the angular velocities.
The examiner was present near the participant during the test but gave no verbal instructions. A second examiner (examiner #2) recorded the data from the IMUs on a computer located less than 3 m from the patient or subject, with the IMUs connected to the computer via a cable  [27]. Examiner #2 informed the participant of the time remaining every 10 s. The computer screen was visible to neither examiner #1 nor the participant. The data were stored on the computer's hard disk drive for later analysis. The IMUs used are part of a homemade system called DYSKIMOT (for Motor Dyskinesia), which has been presented in detail previously in  [27]. The DYSKIMOT system software (v. 3. 1) recorded the data from the three IMUs with a sampling frequency of 100 Hz. The system is based on commercial IMUs (LSM9DS1, SparkFun Electronics, Niwot, CO, USA) equipped with a triaxial accelerometer, gyroscope, magnetometer, and thermometer.
Time series of accelerations (Acc X, Y, Z) along each of the 3 axes of each sensor and angular velocities (Gyr X, Y, Z) were recorded. The DYSKIMOT software also computed the angular accelerations (AccA X, Y, Z) in real-time by numerical differentiation (method) and the corrected angles (AngC X, Y, Z) by numerical integration and linear drift subtraction of the angular velocities. Typical traces are shown in Figure  2. The DYSKIMOT IMU casing was 3D printed using PLA (Polylactic Acid), the most widely used polymer in 3D printing. Other polymers may have been used, such as ABS (Acrylonitrile butadiene styrene) or TPU (Thermoplastic polyurethane). The latter choice made it possible to produce flexible parts that would presumably be more comfortable for the user. casing was 3D printed using PLA (Polylactic Acid), the most widely used polymer in 3D printing.
SampEn and Complexity Factors An R (v 4. 1. 0, R Core Team, Auckland, New Zealand) routine was developed to calculate SampEn for all recorded time series. In accordance with Yentes  [19] , we used the following parameters in the algorithm (R-Studio) for calculating the SampEn: vector length m = 2, tolerance r = 0. 2 SD and the number of points in the data series N = 6000. The details can be found in  [20]. The values obtained from Gyr and Acc, respectively, were referred to as SampEn Gyr X, Y, Z and SampEn Acc X, Y, Z. In the case of the b&r test, the Gyr Y time series should have considerable practical implications since it follows the direction of the performed movement. Therefore, specific computations were performed for this time series. The Gyr Y of SENS2 was subtracted from the same time series of SENS1 (Gyr Y) and SENS3 (-Gyr Z; SENS3 has a different orientation, see Figure  1 ).
SampEn was computed for the resulting time series. The calculation of SampEn using these data should provide a relevant estimation of the complexity of angular velocity variations in lumbar and hip -flexion during the b&r test. The SampEn values obtained in this way were termed the lumbar flexion complexity factor (LCF) and the hip flexion complexity factor (HCF), and together formed what we called complexity factors (CF). Statistical comparisons between all SampEn and CF in CLBP patients and NLBP subjects were performed with a t-test or with a Mann-Whitney rank sum when the Shapiro-Wilk normality test failed. The mean, SD, standard error of measurement (  √ , where N is the sample size), 95% confidence interval (CI denotes half-width), and minimum detectable change (. √ ) were calculated for the differences in parameter values between the two groups.
Statistical tests were performed with SigmaPlot software (v. 14. 0, Systat Software, San Jose, CA, United States of America) with a significance level of 5%. As in  [2] , we considered the first 10 s of the time series as the warm-up period, and any data collected after 70 s were omitted from analyses. Therefore, only data between 10 and 70 s were used for the analysis; all of the time series lasted 1 min.
SampEn was computed for the resulting time series. The calculation of SampEn using these data should provide a relevant estimation of the complexity of angular velocity variations in lumbar and hip -flexion during the b&r test. The SampEn values obtained in this way were termed the lumbar flexion complexity factor (LCF) and the hip flexion complexity factor (HCF), and together formed what we called complexity factors (CF). Statistical comparisons between all SampEn and CF in CLBP patients and NLBP subjects were performed with a t-test or with a Mann-Whitney rank sum when the Shapiro-Wilk normality test failed. The mean, SD, standard error of measurement (SEM = SD √ N , where N is the sample size), 95% confidence interval (CI denotes half-width), and minimum detectable change (MDC = 1. 96 √ 2 SEM) were calculated for the differences in parameter values between the two groups.
Data Segmentation The first way to apply ML is to use and analyze a given time series as a whole. A second way is to divide the time series into cycles and use them as independent data related to a particular participant. Since we have explored both options, further details on segmentation into cycles are provided below. The different b&r cycles can be clearly seen in Figure  2. Each cycle corresponds to the repetition of b&r trunk movements carried out by a participant. The start and end of each cycle were determined by identifying the successive minima of the Acc Z time series for SENS1, i. e. , the time series with the highest signal-to-noise ratio. The segmentation algorithm, which divides the time series into distinct cycles, consists of five steps: (1) averaging the original Acc Z time series using a rolling centered window (width: 25 points or 0.
1 Scikit-learn library. Statistical describers were maximum, minimum, mean, median, 1st quartile, 3rd quartile, and SD for each available time series (each component X, Y, and Z was treated as an independent time series). These parameters, referred to as raw data features, were used as discriminators for ML algorithms. The performance evaluation of each ML algorithm was measured after an n-fold cross-validation process, scaling of the time series, and 100 training repetitions with a random ordering of NLBP and CLBP. The computed SampEn and CF were also included as features in the ML algorithms in a second step. The accuracy and AUC of the ML algorithms were computed using only raw data features and only SampEn or CF. The added value of SampEn and CF in identifying CLBP was assessed.
Cycle Segmentation The result of the segmentation process was a data set of 1678 cycles labeled with their respective index (CLBP patient or healthy NLBP subject). Figure  3  show the principle of segmentation. Figure  4  shows all the computed cycles for Gyr Y of SENS2 as a function of time in the form of a mean cycle and a shaded area indicating the cycle's SD. gyroscope from the sensor 2 (Gyr Z SENS2), Hip Complexity Factor (HCF), the Y-axis of the gyroscope from the sensor 2 (Gyr Y SENS2), the X-axis of the accelerometer from the sensor 2 (Acc X SENS2).