dataesr/scientific-paragraphs-categorization huggingface dataset:
Coronal observations with Hinode EIS The EIS Chief Observer for 2 April was D. H. Brooks, and HOP 436 was supported by EIS with four runs of the study HPW021VEL260x512v2 beginning at 03:39, 07:39, 11:53 and 18:21 UT (see Fig. 3  and Table  1  for the timing of these observations relative to the flare evolution). The study resulted in one raster of size 260 × 512 with a duration of 61 min, discussed in Sect. 3. 1. The level-0 data were obtained from the Hinode/EIS archive 5. The data were calibrated with the eis_prep standard calibration routine from the Solar Software IDL library. This routine removes the CCD dark current, cosmic ray pattern, and hot and dusty pixels from detector exposures and provide the 5 http://solarb. mssl. ucl. ac. uk/SolarB/SearchArchive. jsp A130, page 6 of 22 radiometric calibration to physical units (erg cm -2 s -1 sr -2 Å -1 ).
The 2 slit is used and the spatial coverage along the slit is 152 in the Y-direction. A comprehensive study of these observations is given in Sect. 4. 2. 2, for which the software and data for generating the figures are available in the GitHub repository pryoung/papers/2023_orbiter_flare  (Young 2023b). During the first Flare_SNS_v2 observing period, the Hinode spacecraft was tracking solar rotation and so the EIS slit drifted westwards by around 10. The x location at the beginning of the sequence was +917 , and the centre of the slit was at y = +279.
We note that the non-thermal counts after the third HXR peak are 6 Solar Software; eis_auto_fit. pro Fig. 6. X-ray lightcurves of the impulsive phase of the M3. 9 flare. Top: GOES-16 soft X-ray fluxes. The GOES fluxes have been shifted in time to be consistent with the observations at Solar Orbiter. Bottom: STIX HXR count rates in five energy bands: three broad energy bands that are dominated by thermal emission, and two energy bands that show non-thermal emission. The vertical dotted lines indicate the three major non-thermal peaks. A pre-event background has been subtracted from all STIX count rates. The high-energy count rates after the third peak are enhanced due to a detector saturation issue. affected by an instrumental issue that will be solved by a new release of the STIX analysis software currently under development.
A movie is available online and at https://owncloud. ias. u-psud. fr/index. php/s/nedpLpAzsmRTdYz. EUI/FSI 174 Å. These maps are overlaid with the closest in time magnetograms obtained by PHI/HRT (LOS magnetic flux density contours from 13:00, 13:40, 15:30 and 23:00 UT), as well as HXR from STIX showing the non-thermal HXR sources (intensity contours between 25 and 50 keV) at 13:20 and 13:30 UT (see also Sect. 4. 3 for a description of this dataset). Flare ribbons are seen clearly as high intensity emissions in three of the lines (Lyβ, C iii, and O vi), while the Ne viii line shows some of the most intense flare kernels. The system of ribbons is more complex than the typical two J-shape ribbons often reported in the literature in the context of eruptive flares (see, e. g. ,  Moore et al. 1995; Démoulin et al.
3. 1. The EUI/FSI 174 Å + PHI/HRT overlay shows that flare loops are both anchored between the positive polarity of AR 12976 and the negative polarity of the emerging bipole, as well as that of the positive polarity of the emerging bipole and the negative polarity of AR 12975. The different orientations of the flare loops are also visible in the hotter Ne viii line. We also observed the Fe xviii 974. 86 Å emission line in the SPICE dataset. This line has been observed previously (e. g. ,  Teriaca et al. 2012)   Further hot flare loops are seen to appear subsequently, while the C iii intensity map shows the flare ribbons at the footpoints of the flare loops (Fig. 12 ). We notice that the second set of flare loops to appear at 13:45 UT is connecting the positive polarity of AR 12976 to the negative polarity of the emerging bipole, as discussed above for Fig.
Both datasets were spatially aligned, with details of the method given in Appendix A. 1. For the EIS frame (panel a), the x-axis is wavelength converted to line-of-sight (LOS) velocity. Panel b shows the AIA image nearest in time to the EIS exposures, as determined from the mid-points of the exposures. The y-ranges for the two sets of images are the same, and the location of the EIS slit is shown on the AIA image. Inspection of the EIS spectral images shows many transient, compact brightenings on the short-wavelength side of the 256. 32 Å line with LOS velocities between -50 and -400 km s -1. These can be directly associated with bright filament plasma in the AIA images, and demonstrate that the filament plasma is moving towards the Earth. For the simplistic model of the filament leg being a curved cylinder (as shown in panels c and d) that is approximately moving radially away from the Sun's centre, the Doppler shifts imply plasma moving away from the Sun.
There may also be a tilting of this cylinder towards the Earth during the early phase of the eruption that contributes to the blue-shifted emission. The standard flare model predicts that the flux rope will unwind as the eruption progresses. This was shown, for example, in panels (g, h, i) of Fig. 1  in  Barczynski et al. (2020)  where the numerical modelling made with the OHM code  (Aulanier et al. 2012 ) allowed to quantify the flux rope expansion. This MHD simulation confirmed the predictions that the twist per unit length decreases due to this expansion. Here, evidence for this unwinding comes from both AIA image sequences and the EIS spectral data. Between 13:10 UT and 13:15 UT, faint, thin emission threads can be seen in front of the dark absorption cloud of the filament at about position (+870, +315) in the AIA 304 Å images (movie attached to Fig.
The heating leads to plasma with a wide range of temperature, as demonstrated by Fig. 17. This is similar to what is found in both the intensity and Doppler maps obtained from the SPICE dynamics studies with a relatively wide range of temperatures. The part of the filament evolution that corresponds to the transition from feature EIS-2 to EIS-3 (Fig. 14 ) is shown in four different emission lines. The temperatures of maximum ionisation (obtained from the CHIANTI database) are displayed. We conclude that the filament evolution is remarkably similar in all four lines. This demonstrates that each bright clump or thread of the filament is a multi-thermal structure. The filament plasma is weakly visible in Fe xvi 262. 99 Å (2. 8 MK), but not in hotter lines. In particular, the filament is not heated to flare temperatures (10 MK).
Energetic electrons as seen in X-rays with STIX We employed the pixelated (information on individual detectors and pixels contained in the L1 'pixel' data) nature of the STIX science data to reconstruct the HXR sources. Here we focus on the non-thermal HXR emission (i. e. , energies above 25 keV) that emanates from the chromospheric footpoints and is generated by non-thermal electrons. STIX images were generated using the maximum entropy method (MEM_GE,  Massa et al. 2020) , where visibilities were computed after integrating the counts over the energy range 25-50 keV, and over time intervals with durations between two and three minutes that cover the three major HXR peaks. The time bins are indicated in Fig. 6  with the vertical dashed lines. The location of the sources was determined using the pointing information provided by the STIX aspect system  (Warmuth et al.
Acknowledgements. Solar Orbiter is a mission of international cooperation between  ESA  and  NASA , operated by  ESA. We thank the  Solar Orbiter  instrument teams for making the data accessible, and for Hinode,  IRIS  for coordinating the observations during the 'long-term AR' SOOP. The development of the SPICE instrument was funded by  ESA  and  ESA  member states ( France ,  Germany ,  Norway ,  Switzerland ,  United Kingdom). The SPICE hardware consortium was led by  Science and Technology Facilities Council (STFC) RAL Space  and included  Institut d'Astrophysique Spatiale (IAS) ,  Max-Planck-Institut für Sonnensystemforschung (MPS) ,  Physikalisch-Meteorologisches Observatorium Davos  and  World Radiation Center (PMOD/WRC) ,  Institute of Theoretical Astrophysics (University of Oslo) ,  NASA Goddard Space Flight Center (GSFC)  and  Southwest Research Institute (SwRI).
The French contribution is funded by  CNES. L. B. R. acknowledges financial support from the  Spanish MCIN/AEI   10. 13039/501100011033  through projects  RTI2018-096886-B-C5 ,  PID2021-125325OB-C5 , and the  "Center of Excellence Severo Ochoa " award  CEX2021-001131-S  to  Instituto de Astrofísica de Andalucía , all co-funded by  European Regional Development Funds. Hinode is a Japanese mission developed and launched by  ISAS/JAXA , with  NAOJ  as domestic partner and  NASA  and  STFC (UK)  as international partners. It is operated by these agencies in cooperation with  ESA  and  NSC (Norway). IRIS  is a  NASA  small explorer mission developed and operated by  LMSAL  with mission operations executed at  NASA Ames Research Center  and major contributions to downlink communications funded by  ESA  and the  Norwegian Space Centre.
50OT1904. D. B. is funded under  STFC  consolidated grant number  ST/S000240/. The work of D. H. B. was performed under contract to the  Naval Research Laboratory  and was funded by the  NASA   Hinode programme. A. N. Z. thanks the  Belgian Federal Science Policy Office (BELSPO)  for the provision of financial support in the framework of the  PRODEX Programme  of the  European Space Agency (ESA)  under contract number  4000136424. We recognise the collaborative and open nature of knowledge creation and dissemination, under the control of the academic community as expressed by  Camille Noûs  at http: //www. cogitamus. fr/manifesteen. html. This research made use of several open-source software packages including SunPy (The SunPy Community 2020), sunraster, AstroPy (Astropy Collaboration 2022), and matplotlib (Hunter 2007).
Classification encoder + backpropagation phase 1 backpropagation phase 2 backpropagation phase 1 Figure 1. General Reinforced Imitation (GRI) is applied to visual-based autonomous driving in an end-to-end pipeline composed of a perception module encoding RGB images from the three cameras on the driving agent and a decision-making module inferring an action from the encoded features. This pipeline is trained in two phases: (1) Visual encoders are pretrained on a perception dataset on several auxiliary tasks, which are semantic segmentation, road type classification, relevant traffic light presence, and if there is such a traffic light, its state and the distance to it. (2) Visual encoders are frozen and a GRI-based DRL network is trained with both pre-generated expert data with an offline demonstration agent and an online exploration agent gathering data from a simulator.
Indeed, image inputs are often of high dimensions thus requiring larger DRL networks which are usually difficult to train to convergence. Therefore, for vision-based DRL, one can encode the sensors' signal in a more compact and semantically rich representation to train the DRL network on this predefined latent space as in D. Gordon et al. [10]. This latent space can be obtained by pretraining a visual encoder on some visual tasks, such as segmentation or classification. Based on this principle,  Toromanoff et al. [26]  introduced the Implicit Affordances (IAs) method. They design and train an efficient DRL agent on CARLA, winning the CARLA challenge two years in a row (ranked #4 at the time of writing). To do so, they propose an end-to-end pipeline composed of two subsystems trained successively.
This architecture allows to keep the same accuracy on classification and segmentation metrics as if we were using a single bigger encoder for all the auxiliary tasks like in Toromanoff et al. [26] , while reducing the encoder latent space dimension by a factor of ∼ 5. The visual part for the CARLA Leaderboard has been trained on a dataset of 400,000 samples which corresponds to 44 hours of driving. This dataset has been generated with the CARLA autopilot on every town with random trajectories. Each sample of the dataset is composed of three images from the three cameras and the corresponding ground truth information, which are segmentation maps from CARLA, booleans indicating the presence of an intersection and the presence of a traffic light in front of the car. And if there is a traffic light, a class corresponding to its color, and the distance to it in meters.
Design of the Decision Subsystem The decision subsystem takes as input four consecutive encodings of the three camera images and outputs an action. An action is defined by the combination of the desired steering of the wheel, and the throttle or brake to apply. Generating data on the CARLA simulator is very computationally expensive. We used a Rainbow-IQN Ape-X  [25] , which is a distributed DRL backbone, to mitigate this issue. Rainbow-IQN Ape-X being based on DQN  [17] , the action state has to be discrete. Therefore, we discretized the action state in 27 steering values, and 4 throttle or brake values. The discretized action space contains 27 × 4 = 108 actions. We called this setup GRI for Autonomous Driving (GRIAD). We diagram it in Fig. 3. The demonstration dataset contains 200,000 samples, which correspond to 22 hours of driving, generated using the autopilot from CARLA on predefined tracks published by the CARLA team  1.
Each sample from the demonstration dataset consists in three images from the three cameras and a discrete action obtained by mapping continuous actions of the expert to our discrete set of RL actions. We did not use any data augmentation. We note that the autopilot makes driving errors such as collisions, red light infractions, or the car getting stuck for hundreds of frames. As a result ∼ 10% of our demonstrations corresponds to poor action choices. However, we decided to use this demonstration dataset as it is in order to assess the robustness of our method to noisy demonstrations. In our experiments on CARLA, GRIAD had a total of 12 agents, including 3 demonstration agents, running in a distributed setup and sending data to the memory buffer. As demonstration agents have been constrained to send data at the same frequency as exploration agents, this is equivalent to having p demo = 25%.
GRIAD on CARLA On the CARLA leaderboard. We trained GRIAD for 60M steps (∼45M exploration steps + 200,000 expert data sampled ∼15M times). Both visual and decision-making parts were trained on all available maps with all available weather. We compare to LBC  [3] , IAs [26], Transfuser+  [19]  and World on Rails [2]. Our method outperforms World on Rails, the previous leading method on the CARLA leaderboard, by ∼ 17% on the main metric, the driving score, while using fewer sensors. Our method also gives a higher driving score than Transfuser+  [19] , submitted a month after GRIAD, while using more cameras and a Li-DAR. CARLA Leaderboard results are presented in Tab. Ablation study on the NoCrash benchmark. We compared GRIAD to regular RL by using the same global architecture with and without demonstration agents on the NoCrash benchmark  [4].
In the NoCrash benchmark, agents need to be trained on a single environment (Town01) under a specific set of training weather. Then, agents are evaluated on several scenarios with different traffic density on the training (Town01) and test (Town02) town with training and test sets of weather. Table  2. Ablation study of GRIAD using the NoCrash benchmark. Score is the percentage of road completed without any crash. GRIAD experimentally shows to generalize more on test weather than RL with 12M and 16M steps and globally gives the best agent. We note that, for computational reason, neither the RL nor GRIAD was trained until convergence. Hence, comparison to the state-of-the-art should rather be done on the CARLA Leaderboard with the fully trained GRIAD model, cf Tab. 1. For these experiments, GRIAD was trained on 16M sam-ples corresponding to 12M exploration steps + 25,000 expert data which have been sampled 4M times in total.
We present an ablation study to show how GRIAD compares to RL without GRI i. e. without demonstration agents, using two vanilla RL models: one trained on 12M exploration steps and the other on 16M exploration steps. Each agent was trained using the exact same visual encoder trained on another demonstration dataset of 100,000 samples coming exclusively from Town01 under training weather. Results are presented in Tab. 2. We first observe that GRIAD systematically gives better results than RL with 12M steps, while taking approximately the same time to train (+∼ 4%). Indeed, as demonstration agents do not require any interaction with the simulator, we can add them at a negligible cost and still improve results. We also observe that while RL with 16M steps does better than GRIAD on train weather, GRIAD gives better results on the test weather while being ∼ 25% faster to train.
GRI on the Mujoco benchmark To further validate the GRI method we conducted experiments on selected Mujoco [24] environments, shown in Fig. 4. Expert data were generated using chainerrl [8] pretrained RL agent weights. For each environment, the value of r demo was chosen as the highest value chainerrl expert agent reached during the generation of the dataset. As we did not find real expert data on Mujoco environments, expert data is not always significantly better than our trained vanilla RL network. Hence, this study assesses the efficiency of GRI even with suboptimal expert data. Study on the proportion of demonstration agents. Experiments being faster on Mujoco environments than on CARLA we were able to investigate the impact of the proportion of demonstration agents. For these experiments we used a GRI-SAC i.
e. a GRI algorithm using SAC  [11]  as DRL backbone, and we vary the proportion of demonstration agents between 0% and 40%. Each experiment has been repeated three times, with different seeds. Fig. 5  presents the results with the variances and the evaluation re- ward of the expert. Experiments were conducted with public code from GitHub 2  which has been adapted with GRI. We observe three different dynamics. • For HalfCheetah-v2, a difficult task on which the expert is significantly stronger than the trained SAC, we observe that the beginning of the training is slower using GRI-SAC; we call this a warm up phase which we will explain further in Sec. 4. 3. However the rewards turns out to become significantly higher after some time. On this game, GRI-SAC is better than SAC with every proportion of demonstration agents.
Limitations The main limitations of this method are consequences of our initial hypothesis that demonstration data can always be associated with a constant maximal reward r demo. A first limitation occurs if the demonstration data is not constantly optimal, e. g. due to low expert performance on some aspect of a given task, as this introduces noise in the reward function. This is the case in our demonstration dataset on the CARLA simulator, as expert data have been generated using an imperfect autopilot containing ∼ 10% noisy demonstrations. Still, GRIAD showed to improve our model by a significant margin over vanilla RL. Therefore, we can consider the GRI setup to present some robustness to noisy demonstrations. A second limitation of our approach is the warm-up phase on some difficult environments, as observed in Fig.
Conclusion We present GRI, a method that efficiently leverages both expert demonstrations and environment exploration. GRI is straightforward to implement over any off-policy deep reinforcement learning algorithm. Thus, GRI-based algorithms improve data efficiency compared to vanilla reinforcement algorithms and do not suffer from distribution shift as much as behavior cloning methods. This method also proved to be robust to noisy demonstrations in the expert dataset. We applied GRI to visual-based autonomous driving with the distributed GRIAD algorithm and outperformed the previous state-of-the-art on the CARLA Leaderboard. Finally, we showed its generalizability using different DRL backbones on several Mujoco continuous control environments and highlighted its robustness.
Medical diagnostic methods that utilise modalities of patient symptoms such as speech are increasingly being used for initial diagnostic purposes and monitoring disease state progression. Speech disorders are particularly prevalent in neurological degenerative diseases such as Parkinson's disease, the focus of the study undertaken in this work. We will demonstrate state-of-the-art statistical time-series methods that combine elements of statistical time series modelling and signal processing with modern machine learning methods based on Gaussian process models to develop methods to accurately detect a core symptom of speech disorder in individuals who have Parkinson's disease. We will show that the proposed methods out-perform standard best practices of speech diagnostics in detecting ataxic speech disorders, and we will focus the study, particularly on a detailed analysis of a well regarded Parkinson's data speech study publicly available making all our results reproducible.
e. , mixed dysarthria) independently of the final diagnosis, and no speech feature (or a set) has yet to be found discriminative of the different types  [1, [12] [13] [14]. Furthermore, this clinical system relies entirely on subjective auditory-perceptual observations requiring advanced expert clinical training  [10, 13]. Automatic Speaker Recognition (ASR) represent the ideal tool for automatically detecting and monitoring the range of diversity in dysarthria symptoms. Different types of ASR systems could be used  [15, 16]. For example, there are ASR speakerindependent (SI) systems, trained on large multispeaker datasets, or ASR speaker-dependent (SD) systems, trained by an existing SI model to a target speaker or by a unique target speaker's speech data  [10, 17]. Commercially developed SI have low error rates for healthy speakers but appear to perform considerably worse with speech impairments tasks  [10, 18].
Secondly, it makes statistical contributions related to developing non-linear and non-stationary time-series methods based on Empirical Mode Decomposition (EMD)  [37] , where a novel stochastic model representation is established for the EMD which then allows a statistical treatment of EMD to be considered. This is important to undertake statistical analysis tasks such as estimation and inference and to accurately incorporate statistical uncertainty quantification in out-of-sample predictions and forecasts, distinct from model from naive extrapolation, often used in the absence of a stochastic model for EMD. We will show that the implemented methodology outperforms traditional speech methodologies with accuracy scores greater than 80% on the data set collected and provided by King's College given at  [38] , available at https://zenodo.
The considered dataset, described in subsection 7. 1, leads to a text-dependent environment where controls (healthy subjects) and sick patients read a given text. Reasons to employ such a specific set of sentences using the reading text task are clarified when discussing the experimental set-up in Section 7. The other relevant feature used in  [29]  correspond to the MGDCCs, exploiting the modified group delay function. As studied in  [42] [43] [44] , the instantaneous frequency (IF) is a function assigning a frequency to a given time, whereas the group delay (GD) is a function assigning a time to a given frequency and, therefore, the question of interest here is whether the two functions are inverses of each other. In practice, this is not always the case because the IF function may not be invertible.