dataesr/scientific-paragraphs-categorization huggingface dataset:
As discussed in Section II-A, our problem is a superset of the trajectory compression one. Therefore, our findings can also be leveraged to help the task of trajectory compression and make it more viable in practice at the data collection stage, by sampling the user movements with a loss of information that is controllable. To conclude our discussion, we would like to clarify the limitations of our work. First, our analysis in Section III is based on a dataset of trajectories of 119 individuals, whereas our experimental evaluation of DuctiLoc in Section V relies six testers; while the acknowledged regularity of human mobility yields promises for the wider applicability of our results, at this time we cannot generalize them beyond such data subjects and additional tests would be needed to that end.
In a previous work,  Boualit et al. (2022)  developed an 8-day protocol that the authors tested using two organophosphate insecticides (OPIs), diazinon (DZN) and chlorpyrifos (CPF), for AChE inhibition. Organophosphate insecticides are a major group of chemicals used as neurotoxic insecticides and target AChE. In the present study, using the same protocol, we evaluated the effects of these compounds on tadpole swimming behavior of Xenopus laevis, a common model organism for amphibians. For this purpose, we developed a tracking application using OpenCV, an open-source Python library aimed at video vision  (Bradski, 2000). From those videos, we measured the mean instantaneous speed and speed change, the total period of activity, the number of activity periods, and two direction-related components, the direction change and the standard deviation of the direction change (SDDC).
Behavioral traits Behavior recording. On days 6, 7, and 8, tadpole swimming behavior was recorded. The protocol was based on a "homemade" video box. This box was conceived for standardizing the video recording sessions. The three-dimensional (3D) plan of this item is available in the data repository, and a picture is included in the Supporting Information. Briefly, at the bottom of the box were six enlightened wells, each containing a 12-cm glass Petri dish filled with 50 mL of FETAX solution, a standard medium for raising X. laevis larvae. At the top of the box was a camera (Nikon ® D750). Larvae were individually transferred to a Petri dish using a plastic Pasteur pipette whose end had been cut to enlarge the diameter, and their swimming behavior was recorded for 10 min (six individuals per video).
No indications of injury during transport were observed. The order of recording was chosen for homogeneously distributed concentrations within the Petri dish positions in the setting. Video processing. Trajectories were extracted from videos using OpenCV, an open source and cross-platform library aimed at real-time computer vision. In the present study, we used Python as the programming language for OpenCV. This new approach has the advantage of allowing the development of a tailored solution for tracking swimming tadpoles while using an easy programming language. Conceptually, our computing solution is simple (Figure  1 ) and relies on blob detection and blob tracking (a blob is a group of connected pixels in an image that share some common property, e. g. , the same gray-scale value): (i) The larvae are sometimes reflected by the Petri dish wall, which can depress the tracking efficiency.
Behavioral component analyses. In the present study, after removing the first minute of each video as a period of acclimation, the R package TrajR  (McLean & Skowron Volponi, 2018)  was used for extracting behavioral components. TrajR is a toolkit for animal trajectory description and analysis. The trajectory study was divided into two parts: (i) Analysis of speed-related components: mean speed, mean speed change, total period of activity (the total period faster than 0 cm/s), and number of periods of activity; (ii) Analysis of direction-related components: mean direction change and SDDC as defined in  Kitamura and Imafuku (2015). Directional change is an index of nonlinearity, while SDDC describes irregularity.
Statistical analyses All statistical analyses were performed with R: A language and environment for statistical computing (2022). Extreme outliers were removed from the data set using the boxplot method. An extreme outlier was defined as data point lying outside three times the interquartile range. Normality and homogeneity of variance of residuals were graphically checked. When the residuals were not normally distributed, square root or log transformation was applied. After transformation, all residuals were distributed normally. The α significance level of all tests was set at 0. 05. Impact between control conditions. The impacts of developmental stage and of clutch identity (DZN or CPF test) were tested by computing mixed-effects models for testing the impact of stage on the behavioral components.
Acknowledgments-We thank  B. Guillet  from the  University of Rennes (France)  for her Xenopus laevis expertise. We thank  M. Faria  and  L. Monbaron  for their assistance in the laboratory. We thank  F. Breider  and  D. Grandjean  from the  Ecole Polytechnique de Lausanne (EPFL)  for measuring the pesticide concentrations. We thank  R. Rohr  for his statistical advice. We also thank two anonymous reviewers for their interesting comments. The present study was supported by the  Swiss National Science Foundation  ( FNS 205321_182182 ). Open access funding provided by  Universite de Lausanne.
Grid Setup The four SE grids used in this study were generated using the Community Mesh Generation Toolkit (https:// github. com/ESMCI/Community_Mesh_Generation_Toolkit) which provides programs to create rotated and regionally refined grids for a desired resolution. To avoid edges and corners near the region of interest, the cubed sphere was rotated to approximately center the KORUS-AQ domain on a cube face. As shown in Figure  1  (panels a and b), the spectral elements for the uniform (ne30) and (ne60) grids are created by subdividing each cube face into (30x30) and (60x60) elements, respectively. The "ne" resolution label stands for the number of elements in each coordinate direction on a cube face  (Lauritzen et al. , 2018). For the two refined grids (panels c and d), the elements of the ne30 grid over the Korean peninsula are further subdivided into ne30x8 and ne30x16 resolutions, with the refined regions for these corresponding to elements with ne240 (∼1/8th degree) and ne480 (∼1/16th degree) resolutions, respectively.
Emissions Two anthropogenic emission inventories are used in this work. The Copernicus Atmospheric Monitoring System version 5. 1 (CAMS-GLOB-ANTv5. 1) is used  (Elguindi et al. , 2020)  for global emissions outside of Asia. Within the Asia region, either the CAMS-GLOB-ANTv5. 1 or the KORUSv5 inventory is used to investigate the effects of emissions on air quality simulation with different grid resolutions. The CAMS-GLOB-ANTv5. 1 inventory is based on two global emission inventories. The Emissions Database for Global Atmospheric Research (EDGAR) v5  (Crippa et al. , 2019)  is used up to 2015, while the recent Community Emissions Data System (CEDS)  (McDuffie et al. , 2020)  is used to extrapolate the emissions from 2016 to 2021. The speciation of Non-Methane Volatile Organic Compounds (NMVOC) is based on  Huang et al.
The total emissions of key anthropogenic species in the model are shown in Table  1. There are notable differences between the KORUSv5 and CAMS-GLOB-ANTv5. 1 inventories even for major pollutants: the CO emissions in KORUSv5 are approximately half of those in CAMS-GLOB-ANTv5. 1, and CAMS-GLOB-ANTv5. 1 inventory has three times as much SO 2 as KORUSv5. The largest difference is seen for toluene, which is seven times higher in KORUSv5 for South Korea. The VOC speciation in the KORUSv5 inventory was specially developed for South Korea in 2016, whereas the VOC speciation in the CAMS-GLOB-ANTv5. 1 inventory is the same for all Asian countries based on 2012 data  (Huang et al. , 2017). The SO 2 emissions were also significantly updated in KORUSv5 especially for large point sources. The KORUSv5 inventory speciates NO x into NO and NO 2 , while CAMS-GLOB-ANTv5.
12 database  (Mills et al. , 2016; Neely & Schmidt, 2016). The biomass burning emissions are calculated using two estimates, the Quick Fire Emissions Dataset (QFED) v2. 5_r1  (Darmenov and da Silva, 2015)  and the Fire INventory from NCAR (FINN) v1. 5  (Wiedinmyer et al. , 2011). Global CO 2 emissions from QFED v2. 5_r1 are multiplied by emission factors collated for FINN v1. 5 to construct the emission of each chemical species required in MUSICAv0 simulations. The biogenic emissions are calculated online using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2. 1  (Guenther et al. , 2012). Unlike the offline emissions, the emission totals vary depending on the land model (CLM5) configurations and spatial resolutions used. Several factors, such as temperature, solar radiative flux, and leaf area index (LAI) affect the online biogenic emissions.
Acknowledgments This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement No. 1852977. This research was supported by  NASA ACCDAM  (award  80NSSC21K1439 ) and  NCAR Advanced Study Program  postdoctoral fellowship. PTR-MS measurements aboard the  NASA  DC-8 during KORUS-AQ were supported by the  Austrian Federal Ministry for Transport, Innovation and Technology (bmvit-FFG-ASAP ). P. Eichler ,  L. Kaser ,  T. Mikoviny , and  M. Müller  contributed to the PTR-MS data acquisition and analysis. We thank  William H. Brune  for use of the HO x measurements,  Glenn Diskin  for use of the CO measurements,  John D. Crounse  and  Paul O. Wennberg  for use of the HNO 3 measurements,  Andrew Weinheimer  for use of the O 3 and NO y measurements, and  Greg Huey  for use of the PAN and SO 2 measurements during KORUS-AQ.
Data Availability Statement Aircraft and surface measurements during the KORUS-AQ field campaign are available at the NASA data archive: https://www-air. larc. nasa. gov/missions/korus-aq/. MUSICAv0 is a configuration of CESM2. 2, which is an open-source community model available from: https://github. com/ESCOMP/CESM. The code modifications for including diurnal cycle of anthropogenic emissions, grid information files, and simulation results are available at [https://doi. org/10. 5281/zenodo. 8044736]. CAMS-GLOB-ANTv5. 1 emission inventory is available at ECCAD database (https://eccad. aeris-data. fr). The KORUSv5 emission inventory is available at http://aisl. konkuk. ac. kr/#/emission_data/korus-aq_emissions.
Numerical setup In this work, we perform radiation hydrodynamics calculations with the code HERACLES  (González et al. 2007; Vaytet et al. 2011; Dessart et al. 2015; Sect. 2. 2 ) and nonlocal thermodynamic equilibrium (NLTE) radiative transfer calculations with the code CMFGEN  (Hillier & Dessart 2012; Dessart et al. 2015; Sect. 2. 3)  for a variety of ejecta-CSM interactions in order to compute the evolution of the gas and radiation over time. We first start in the next section by describing the initial conditions for the ejecta and CSM that are used for the HERACLES calculations. The CMFGEN calculations are done as a post-processing step on selected snapshots from the HERACLES calculations. The overall approach is similar to that presented in  Dessart et al. (2017)  and more recently in  Jacobson-Galán et al.
, and Jacobson-Galán, W. V. : A&A, 677, A105 (2023)  0. 1 The dot marks the location of the photosphere assuming the material is ionized (κ = 0. 34 cm 2  g -1 ). For details, see Sect. 2. 1. Dessart et al. (2017)  apart from the density break set at about 5 × 10 14 cm, which was used in that former study to match the observed properties of SN 2013fs. Case (2) corresponds to a CSM structure similar to that used by  Morozova et al. (2017) , that is the CSM is very dense and confined to the immediate vicinity of the progenitor surface. Here, we achieved this by adopting an atmosphere with H ρ of 0. 5 R ⋆ until a radius of about 1000 R ⊙ where the density has dropped to 2 × 10 -10 g cm -3. Beyond that external radius, we added a wind corresponding to various mass-loss rates, as in Case (1). This was required practically because HERACLES is a Eulerian code and thus this outer region corresponds to the space into which the explosion will expand.
Radiation hydrodynamics simulations The interaction configurations described above are used as initial conditions for 1D multigroup radiation-hydrodynamics simulations with the code HERACLES. We use eight groups that cover from the ultraviolet to the far infrared: one group for the entire Lyman continuum, two groups for the Balmer continuum, two for the Paschen continuum, and three groups for the Brackett continuum and beyond. We adopt a simple equation of state that treats the gas as ideal with an adiabatic index of γ = 5/3. We account for electron-scattering as well as bound-free opacity, which dominate over line opacity in ionized type II SN ejecta. The effect of line opacity is treated accurately in the post-processing step with CMFGEN. In HERACLES, the opacities are stored in a table with entries for density and temperature, with a corresponding ionization computed with a Saha solver  (Dessart et al.
2022; Kozyreva et al. 2022; Vasylyev et al. 2022). The adoption of a large outer density, whether it is that of the CSM or the surface layers of a stellar evolution model, is problematic both numerically and physically. Numerically, a large density is often adopted in Lagrangian codes because there is no need to accommodate for the outer space into which the ejecta will expand. That is the outer grid point will expand as dictated by the motion of the outermost shell, thereby stretching the radial grid, but the outermost layer will always be at the same Lagrangian mass. In a Eulerian code like HERACLES, this choice of outer density is invalid since the outer grid point must be placed very far above the initial extent of the star and CSM. However, from a physics standpoint, adopting a large outer density is inadequate because the entire grid becomes opticallythick at the time of shock breakout.
Acknowledgements. This research was supported by the  Munich Institute for Astro-, Particle and BioPhysics (MIAPbP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany 's  Excellence Strategy  - EXC-2094 -390783311. This work was supported by the " Programme National de Physique Stellaire" of CNRS/INSU  co-funded by  CEA  and  CNES. This work was granted access to the HPC resources of CINES under the allocation  2020 -A0090410554  and of TGCC under the allocation  2021 -A0110410554  made by  GENCI, France. This research has made use of  NASA 's Astrophysics Data  System Bibliographic Services.
Therefore, three input typologies are possible for the discriminator  (Li et al. 2019a ): true labelled data x L from the real dataset, unla- belled data x U still coming from the real dataset, and synthetically generated data G(z). Thus, p M (y = K + 1|x) indicates the probability that the discriminator recognizes fake generated data, which is related to the term 1 -D(x) in the original GAN loss statement. Therefore, adding the generated data to the initial dataset which contains both labelled and unlabelled data, the loss function to train the discriminator classifier becomes: In this way, the discriminator loss has been decomposed into two contributions. The model can train and learn directly from unlabeled data, assuming that these data correspond to one of the K possible classes. The unsupervised loss may be rewritten considering p M (y = K + 1|x) = 1 -D(x) , eventually obtaining a formally identical min-max problem loss function Another typical problem is the image-to-image translation which attempts to find a mapping function G ∶ X → Y from a domain X to an output domain Y, exploiting available images in pairs for the training process.
The GAN learned the statistical characteristics of real P-waves examples and offered, in this case, an interesting compact representation of the seismic waves, crucial for the subsequent classification task. Indeed, they combined the trained discriminator with a random forest (RF) classifier to categorize actual seismic P-waves events from noise sources. Because of the statistical learning of the input earthquake P-waves, the GAN generator represented, in this case, a relevant tool for seismic signal synthesis for AI-based data augmentation in seismology applications. Despite the advantage of avoiding complex physical modelling of the P-wave source and wave propagation, the main drawback is the problematic interpretation of the features automatically extracted by the GAN discriminator. In  Kim and Torbol (2019) , synthetic seismic time histories were produced to perform a dataset augmentation for subsequent practical training of EEW systems.
2017 ). The considered dataset was composed of signals cropped around their peak, and sixteen statistical features were extracted for diagnostic purposes, comparing the uniformity of the information in evaluating the cumulative density functions (CDF) for the magnitude of the seismic events and the hypocentral distance. A comprehensive discussion on precision-recall graphs focused especially on teleseismic signals and how they minimized the false positive alerts, while almost zeroing the false negative rates. The authors demonstrated that the more complex models directly trained on raw seismic signals which also automatically learn the feature extraction, i. e. the CNN and the GAN+RF, remarkably outperform the simpler classifiers in terms of reliability of the real-time EEW system even with high-noise data.
In  Li et al. (2021a) , the authors adopted a DCGAN combined with an LSTM network to deliver a discriminator model able to distinguish seismic signals from noise ones within complex ambient noises, e. g. in nuclear explosion monitoring which blast events are discriminated from seismic ones. Firstly, the DCGAN model was trained based on seismic data from the international monitoring system array MKAR  (Mikhailova and Sokolova 2019). Then, the dataset was preprocessed to extract six real seismic P-wave, and S-wave phases  (Storchak et al. 2003). Thereafter, exploiting the feature extraction of the trained discriminator, this network was combined with an LSTM classifier to address the actual discriminative classification task.
Synthetic generation and augmentation For decades, researchers in the earthquake engineering field have aimed to efficiently detect seismic events to mitigate potential hazards and find earthquake-resistant design solutions. Many algorithms have been proposed to tackle this task, often requiring an extensive highquality dataset, time-consuming procedures, and seismic data covering extreme events characterized by high return periods. However, the number of seismic time-series data is limited and does not cover more than a half-century. Nowadays, current trends attempt to provide innovative solutions such as the synthetic generation and augmentation of seismic signals. For the very first time, in  Wang et al. (2019a) , the authors adopted the GAN to generate artificial seismic signals. Despite real seismic signals being affected by many natural factors, they may be considered the summation of low-frequency seismic waveform and high-frequency noise.
Moreover, they usually involve three spatial component signals (two planar and one vertical component). Therefore, the authors proposed EarthquakeGen, i. e. a combined model of cGAN and DCGAN whose generator works with three parallel pipelines to create a three-dimensional multi-frequency superimposed time-series resembling the real seismic signals. The author demonstrated the effectiveness of the trained generator in synthetically generating new data which are different from the actual samples but with the same probability distribution. The synthetic data have been combined with the real ones to augment the existing dataset, demonstrating an accuracy improvement of the classifier. In  Wang et al. (2021) , the same authors extended the previous contribution by providing a new model named SeismoGen to generate synthetic and realistic seismic signals for data augmentation purposed for the detection of earthquake events.
The authors pointed out the importance of generalization and robustness of the data augmentation procedure, warning about the risk of jeopardizing the semantic content of the original data if unrealistic synthetic signals are added to the original dataset. Their cGAN adopted three pipelines to generate three-component seismic signals separately, including P-waves and S-waves parts. Given the implemented GAN's conditional structure, the model can synthesize labelled data with two different labels, i. e. background noise and earthquake signal. In conclusion, the authors warned about some limitations to be aware of. Since they adopted three stations in the Oklahoma region, the synthesized signals would probably reflect only seismic events, which are likely to occur in this specific region. Moreover, since an inner imbalance lives in existing databases due to a limited number of records with low epicentral distance, the GAN model output may also reflect this bias.
Moreover, the authors showed the frequency content through the spectrograms of the generated and real signals. This comparison was missing in previous works  (Wang et al. 2019a ). The same authors in Li et al. (2020a) adopted a cGAN for synthetic earthquake generation to provide a data augmentation tool. The model consists of 3 parts: a generator with an encoder-decoder U-net structure, a discriminator with a CNN structure, and a pre-trained CNN feature extractor not present in the optimization training process. In addition to the adversarial loss, the authors also considered the content loss, i. e. the MSE between the actual and synthetic feature maps. The goal was to retain the consistency of high-level features, considering a weighted content loss with the adversarial one. The generated artificial signals were evaluated by visual inspection criterion and a timefrequency domain analysis through the Short-Time Fourier Transform (STFT), proving a high degree of similarity with the real data.
The authors in Gatti and Clouteau (2020) conceived an innovative and comprehensive study for a more reliable generation of synthetic ground motions for digital twins of the earth-structure systems oriented to seismic risk assessment by leveraging the adversarial framework offered by WGAN. Specifically, the main idea was to integrate and combine physics-based simulations with actual recorded seismic data provided by existing databases. The authors detailed the mathematical framework for this hybrid method, exploiting information obtained from the feature extraction of variational auto-encoders (VAE) and GAN to give a more compact latent space representation with hidden variables. These meaning features have been encoded into a Gaussian manifold known distribution connected with the low-dimensional latent space through a nonlinear stochastic process.