dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The M w 5. 9 Genaveh earthquake occurred on 2021 April 18 in the SDE of ZFFB. The coseismic slip distribution of the Genaveh earthquake has been investigated by  Golshadi et al. (2022)  and  Jafari et al. (2023)  based on satellite data (see Table  2 ). Golshadi et al. (2022)  suggested 5. 0 × 9. 5km 2 for the fault plane and the fault top-edge depth at 4 km. Our obtained source geometry based on InSAR data conforms to their finding although we use a different downsampling methodology. There are differences between the results of  Golshadi et al. (2022)  and our study concerning slip depth, and the amount of maximum slip. Golshadi et al. (2022)  obtained a maximum slip around 4. 5 km depth, whereas we obtained a maximum slip at depths of 5-6 km. The localization of the  Golshadi et al.
Conclusion We present a detailed analysis of the M w 5. 9 Genaveh earthquake on 2021 April 18 as a well-recorded example of seismic activity in the South Dezful Embayment of Zagros foreland folded belt. We analyzed the Genaveh earthquake sequence using local and regional seismic data and constrained the co-seismic slip of the mainshock with InSAR modeling. Coseismic uplift in the Gulkhari anticline shows the surface configuration of this fold, reflecting subsurface structural conditions. This earthquake involved a gently (~20°) NE-dipping fault plane (named Gulkhari fault). The main slip of the mainshock was restricted to depths of between 4-7 with a maximum slip of 1 m within the sedimentary cover which highlights the influence of lithology in the rupture propagation. Aftershocks of the Genaveh earthquake are widely distributed and dominated by reverse faulting at centroid depths of 4-10 km.
Acknowledgements We thank all data research centers for providing the data used in this study. We thank  Isapron Sethanant  for sharing the plotting codes. We appreciate the Seismica community that supports open-access publishing in seismology and earthquake science. We would like to thank  E. Nissen  and an anonymous reviewer for their careful reviews, the associate editor  G. Funning , and editor  Y. J. Tan  for their comments and for handling the manuscript. Zahradnik ,  J. ,  Jansky ,  J. , and  Plicka ,  V. Detailed Waveform  Inversion for Moment Tensors of M~4 Events: Examples from the Corinth Gulf, Greece. Bulletin of the Seismological Society of America, 98:2756-2771" 2008. doi: 10. 1785/0120080124. The article Co-seismic slip of the 18 April 2021 M w 5. 9 Genaveh earthquake in the  South Dezful Embayment of Zagros   (Iran)  and its aftershock sequence © 2023 by  M.
Red stars denote events with a magnitude larger than 5. Black vectors for each event show the change in location from the epicenter given in the arrival time data file, which varies from event to event. (c) Fit between observed phase arrivals (Pg: red crosses, Sg: red circles, Pn: green crosses, Sn: green circles) and theoretical travel times (red and green lines) calculated from the simple flat-layered velocity model determined for this dataset, for epicentral distances of up to 4°. The vertical dashed line at 1. 2°i ndicates the cut-off distance used in the calibrated location of the hypocentroid (228 readings).
Moreover, completeness of partition trees ensures that for all concrete traces w ∈ W, there exists a node in the partition tree such that if ({ {P} }, ∅, []) w = ⇒ (P ′ , ϕ ′ , H ′ ) or ({ {Q} }, ∅, []) w = ⇒ (P ′ , ϕ ′ , H ′ ) then Γ(η) contains a symbolic process representing (P ′ , ϕ ′ , H ′ ). Formally, we show that:   (η))) ⩽ compute(H(Γ Q (η))) which allows us to conclude the proof. □ As a direct corollary we obtain that we can decide determinate may testing for purely probabilistic processes as it coincides with trace equivalence. Corollary 2. Let P, Q ∈ MP pp and. = be defined by a subterm convergent destructor rewrite system. Determinate may testing preorder ({ {P} }, ∅) ⩽ d-may ({ {Q} }, ∅) is decidable. We have implemented the procedure described above in the DEEPSEC tool. The input language has been extended with a probabilistic choice operator + p where p is a real number in ]0, 1[.
Analysis with DEEPSEC We have analyzed the Dining Cryptographers protocol using our probabilistic extension of the DEEPSEC tool  [27]. The encoding of processes DC B for B ∈ W 1 is straightforward and follows directly the modeling above. Although DEEPSEC does not support xor in general, we can easily encode the ternary version using Restricting to a ternary xor is not a loss of generality as the protocol does not take any input from the adversary and only honest users construct xor-terms. As a first result we can show that ∀B, B ′ ∈ W 1. DC B ≈ tr DC B ′ This result is actually not satisfactory, as trace equivalence is strictly weaker than may testing (which also motivated the pen and paper proof of the previous section). More interesting is the fact that we can show that anonymity is broken when we use a biased coin, i.
CogPo aims at describing paradigms in cognitive psychology  [10]  and contains most of the elements mentioned in section 2. 1. Like EXPO, it characterizes a paradigm in a scientific domain rather than modeling an experiment in action, which generates data. The Ontology for Biomedical Investigations (OBI) is a very detailed ontology for clinical and biomedical studies  [1]. Its objective is to provide an ontological model that a biomedical study will instantiate. It already addresses many use cases and will likely be extended to others. Our approach is different: we propose a minimal structure, leaving the domain modules to model the domain in more detail. Furthermore, levels of granularity of study events, the data they generate, and the corresponding protocols are only marginally considered in OBI.
The case studies presented in this section assume no winds and ISA conditions. Then, for altitudes below the tropopause altitude, 𝜏 ℎ = -𝜆 𝜏 in (7) (𝜆 𝜏 the temperature lapse rate). In hydrostatic equilibrium and regardless of the pressure altitude, 𝑝 ℎ = -𝜌𝑔, where the density of the air is 𝜌 = 𝑝/𝑅𝜏. Noting that ℎ and 𝑣 ℎ depend on temperature and its deviation Δ𝜏, the latter in ISA conditions is 0𝐾, then ℎ 𝑝 = ℎ and 𝑣 ℎ = ℎ. Within the EKF-IMM, a time-invariant transition probability matrix is considered, with 𝑟 𝑗𝑖 = 0. 02 for 𝑗 ≠ 𝑖 and 𝑟 𝑖𝑖 = 0. 98. The initial system state x 0 can be obtained from measurements except for the aircraft mass. The initial aircraft mass is considered to be shared by the airline (airlines can deliver it depending on their policy and data confidentiality).
V. Results with Real Data A set of FDR data of a narrow-body jet aircraft from a European airline is taken to further validate the proposed method. Measurements can be obtained from secondary surveillance radars to feed the proposed filtering technique. Some companies (e. g. , OpenSky  [44] , flightradar24, and FlightAware) provide secondary surveillance radar data, mostly ADS-B, but with some limitations. For instance, secondary surveillance radars do not have a worldwide coverage, or some of this data was not available in the past; for OpenSky, only data after 2013 is available. Airlines policy toward not sharing FDR data adds to the challenge of real data accessibility. For this study, some challenges had to be addressed: i) the data set did not contain the (true) GM, which is the object of estimation by our application; ii) the measurements (i.
For this work, it was not possible to obtain the ADS-B data that was broadcast by the aircraft available in the FDR data set. Thus, the ADS-B information used in this validation has been synthesized from the FDR dataset. We assume that the potential differences between the broadcast parameters through ADS-B and the recorded counterpart in the FDR is not noticeable. • The FDR data available was also analyzed to obtain the corresponding parameters p, to compute the control u. • To address the last challenge, historical re-analysis weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF) was used to provide the weather and atmospheric conditions for the particular FDR flight under analysis. ECMWF website provides reanalysis and weather forecast data. Reanalyzed data is obtained from the reassessment of the forecast model according to its counterparts' archived observations (i.
e. , historical model). The true state vector (i. e. , ground truth) is generated by extracting pressure altitude, along-path distance, and aircraft mass directly from the FDR data set. Notice that FDR contains ground speed, while true airspeed-a state variable-is computed through the effect of the wind-available from ECMWF-on the ground speed. Moreover, atmospheric conditions-temperature and pressure variables-of the true state vector are also acquired from ECMWF.
In the cruise phase, altitude, speed, and FPA are constant, which leads to identify several guidance modes with a close probability. Considering the summation of these probabilities leads to the correct ALT-SPD mode identification. Notice that the overall performance is reasonably good, but extracting the data from the FDR to build the scenario reduces the accuracy of the estimated active guidance modes. The results for RVT2 show the same impact of the noisy FDR data. Again, the different guidance modes are correctly identified almost all the time, with some short misidentifications induced by such noisy FDR data. Finally, Table  11  shows the mean RMSE for each state variable, showing again a good performance of the EKF-IMM filter. It is worth noting that the aircraft motion model in (  1 ) is generic, nevertheless, the wind has been neglected for our case studies.
As shown in (  1 ), the weather model affects the accuracy of the along-path distance variable (𝑠) in the EKF-IMM filtering method. Table  11  shows a relatively larger estimation error for this state variable in all validation trajectories. Overall, we can still say that the IMM-based GM identification approach is a promising solution, given that even with real (and noisy) FDR data the filter is able to identify the correct modes almost all the time. This real data analysis completes the statistical characterization conducted with synthetic data (i. e. , ground truth-like, ADS-B and EHS-like simulated trajectories) described in Sec. IV, and shows that in real-life applications the uncertainty of both the weather and parameters vector must be taken into account.
VI. Conclusion Although aircraft trajectories are planned through aircraft intents, some guidance commands may change during the flight to cope with execution uncertainty. Hence, aircraft intent uncertainty may impact the trajectory prediction (TP) performance, a problem that can be mitigated with the identification of these guidance modes. It is worth noting that the proposed estimation method relies only on surveillance data broadcast by the target aircraft. Then, this could be part of a ground-based air traffic management (ATM) tool, but also on-board the aircraft to allow self-separation or conformance monitoring capabilities. An IMM-based filter is proposed to identify the active guidance mode among all possible command pairs. Several validation trajectories are considered to perform a statistically meaningful optimal EKF-IMM analysis in representative scenarios.
The aim of such analysis is to obtain the nominal baseline performance of the proposed approach, providing a benchmark for the design of methods for real-life conditions. The performance of the proposed approach is clear from the results obtained, where in clean configurations, the active guidance mode can be unambiguously identified in climb/descent flight phases. The limitations of the method were also shown through a low-altitude descent, with different non-clean configurations (i. e. , flaps/slats and landing gear). In such cases, the filter is not always able to identify the correct mode but still can provide some meaningful information. The statistical characterization of the filter under nominal conditions is further completed with two real scenarios. Again, even with the FDR data limitations to construct the test setup, it is shown that the filter can still provide a good performance.
Defining profiles of rhythmic abilities in musicians and non-musicians To define a signature of rhythmic abilities based on the results of BAASTA tasks that should allow teasing apart musicians from non-musicians, we processed data from 55 measures using Sparse Learning and Filtering algorithms (SLF;  Combettes & Pesquet, 2007; Jiu et al. , 2021). SLF aims at selecting a minimal set of variables obtained from perceptual and motor tasks of BAASTA (Perc: perceptual; M: motor) that capture the most relevant differences between musicians and non-musicians. This set of measures was then entered in a model, aimed at classifying individuals as musicians or non-musicians based on their rhythmic performance. The outcome model, including a limited set of measures, is parsimonious and affords a significant gain in statistical power relative to a model that includes all the variables.
5, p <. 0001), relative to the prediction of a model using the entire set of variables. Accuracy for the three models is shown in Figure  1. Model PMI included a) two perceptual measures, reflecting the ability to detect whether a metronome is aligned to the beat of music or not (P1), or whether there is an irregularity in an isochronous sequence (P2), b) two motor measures, indicating the alignment of participants' taps with the beat of a metronome (M1) and music (M2), and c) the four interactions between these measures. We tested the capacity of the three models to discriminate between musicians and nonmusicians by taking the outcome of the models (value from 0 to 1, with 0 indicating nonmusicians, and 1, musicians) using the entire dataset (i. e. , including the three phases of the classifier validation).
Here we filled this gap by using machine learning techniques to extract a parsimonious model of rhythmic abilities from a large set of perceptual and motor tasks  (BAASTA, Dalla Bella et al. , 2017). Machine learning is perfectly suited for this task  (Hastie et al. , 2009; Jones, 2014) , by allowing the reduction of complexity in a large multivariate dataset, while maintaining high classification accuracy, limiting overfitting, and increasing generalization to new data. As a result, a reduced set of explaining variables (i. e. , a handful of perceptual and motor measures) forming a profile of rhythmic abilities is clearly easier to understand and interpret than a larger dataset. The model of rhythmic abilities we obtained (PMI), summarized by an undirected graph, was not only very successful in separating musicians and non-musicians but also its components could capture the variability of nonmusicians' performance.
, 2019; Stober et al. , 2014) , and more recently in music cognition (e. g. , for emotion detection,  Vempala & Russo, 2017). A similar approach based on machine learning and graph theory as used to model individual differences in rhythmic abilities could be purposefully extended to other music abilities such as pitch perception and production, or improvisation (e. g. ,  Farrugia et al. , 2021,  for a single-case study). By exploiting machine learning starting from a dataset issued from behavioral tests we demonstrated that the complexity of rhythmic abilities (e. g. ,  Fiveash et al. , 2022) , or rhythm intelligences,  (Kraus, 2021) , at least when considering BPS skills, can be captured by a minimal set of behavioral measures and their interaction. Detecting profiles of rhythmic abilities is valuable to shed light on individual variability, its underlying mechanisms (e.
e. , triangle sound) was superimposed onto the music, either aligned or non-aligned to the beat. When non-aligned, the metronome was either phase shifted (with the sounds presented before or after the musical beats by 33% of the music IBI, while keeping the tempo), or period shifted (with the tempo of the metronome changed by ± 10% of the IBI). Participants judged whether the metronome was aligned or not with the musical beat. These perceptual tasks were implemented using Matlab software (version 7. 6. 0). In the first 3 tasks, there were three blocks of trials and a maximumlikelihood adaptive procedure (MLP)  (Green, 1993; MATLAB MLP toolbox, Grassi & Soranzo, 2009)  was used to obtain perceptual thresholds (for details, see Dalla  Bella et al. , 2017). All tasks were preceded by 4 examples and 4 practice trials with feedback.
Trials were divided into 10 experimental blocks (6 trials x 10 block overall) and presented in random order. A training block preceded the first experimental trial. In all the motor tasks, the performance was recorded via a Roland SPD-6 MIDI percussion pad. Stimulus presentation and response recording was controlled by MAX-MSP software (version 6. 0). A MIDI response latency of 133 ms (Leipzig testing) and 100 ms (Montpellier testing) was subtracted from the tapping data before further analysis.
BAASTA For Duration discrimination, Anisochrony detection with tones and with music, we calculated mean thresholds (percentage of the standard duration in the three task) across the three blocks or trials. We rejected the blocks including more than 1/3 of False Alarms, when a difference in duration, or that the sequence beat was irregular, was reported while there was no difference/no deviation from isochrony in the stimulus. In the BAT, we calculated the sensitivity index (d') for the entire set of 72 stimuli, and separately for each or the 3 tempos (medium, fast, and slow). d' was calculated based on the number of Hits (when a misaligned metronome was correctly detected) and False alarms (when a misalignment was erroneously reported). Motor data obtained from tapping tasks were pre-processed as follows  (as in Dalla Bella et al.
Finally, we analyzed the data from the Adaptive tapping task by first calculating an adaptation index (see  Schwartze et al. , 2011). To do so, we fitted a regression line obtain the slope of ITIs functions relative to the final sequence tempo; the value of the slope corresponds to the adaptation index. When the value is 1, the adaptation is perfect; lower and higher values than 1 indicate undercorrection and overcorrection, respectively. We calculated this index separately for tempo acceleration (i. e. , faster tempi with final sequence IOIs < 600 ms) and tempo deceleration (slower tempi with final sequence IOIs > 600 ms). In addition, error correction was portioned into the two contributors, phase correction and period correction  (Repp & Keller, 2004). Phase and period correction were estimated from the two parameters alpha and beta of the fitted two-process model of error correction (as in  Schwartze et al.
, 2011). Finally, in the same task we calculated the sensitivity index (d') for detecting tempo changes based on the number of Hits (when a tempo acceleration or deceleration was correctly detected) and False Alarms (when a tempo acceleration or deceleration was reported while there was no change or the opposite change). The full list of the 55 variables (7 perceptual, 48 motor) obtained from BAASTA and used as input database for clustering and data modelling is provided in Supplementary materials.
We maximized the well-known Newman's modularity that measures the clustering quality versus a null model. The degree of node v is defined by and is the sum of degrees. For a given partition of the node's set , the Modularity ( ) is defined as indicated in equation 5. (5) Here, if (that is, and are in the same cluster) and otherwise. The null model is given by the value that is the probability for a random edge to appear between and. Then, the clustering algorithm searches for a partition that maximizes (equation 5). Modularity maximization led to partition the group of non-musicians into two clusters, each including 20 participants (for a representation of heat matrix showing that intra-cluster similarities are larger than inter-cluster similarities in the dataset, see Supplementary materials).
This research was funded by the European Community's Seventh Framework Programme (EBRAMUS project, FP7 Initial Training Network, grant agreement no. 238157) and by FEDER funds (Languedoc-Roussillon Region, BAASTA-FEDER project) to SDB and SAK; by Canada Research Chair program funding to SDB (CRC in music auditory-motor skill learning and new technologies), and by international mobility funds from IMT Mines Alès to SJ.
The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020-March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more notmisinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions.
BERT was found to provide superior results for different NLP tasks, including word embedding and sequence classification  50, 51. Schütz et al. 52  experimented with multiple deep learning models, mostly variants of BERT, to detect fake news, and concluded that BERT transformers are a promising approach, as they achieve notable results even when using smaller datasets. Several research papers have used NLP approaches, including BERT, to investigate tweets related to COVID-19 misinformation and conspiracy theories  20, 53, 54. Batzdorfer et al. 53  investigated the motifs and dynamics of COVID-19 conspiracy theory tweets by comparing tweets from a group of users that discussed a conspiracy and a group that did not. Micallef et al. 54  used BERT embeddings to investigate and counter misinformation in tweets related to COVID-19 over a period of 5 months.
Elroy and Yosipof 20 used combinations of a Covid-Twitter-BERT  55  , a BERT model that was pre-trained on COVID-19-related data, with transformation to sentence embedding using Sentence BERT  56  , in order to classify 5G conspiracy tweets as opposing or supporting the COVID-19 5G conspiracy. The Robustly Optimized BERT Pretraining Approach (RoBERTa) pre-trained BERT using different design decisions, leading to improved performance and state-of-the-art results  [57] [58] [59]. Our study is the first to use RoBERTa to classify tweets regarding earthquake misinformation or not-misinformation. Workflow. The data processing and analyses consist of four steps, as depicted in Fig. 1  and described in the following sections. Step 1: Dataset. The data was collected using the full search endpoint of Twitter API's v2, which is limited to academic research and provides access to the full Twitter archive through the use of a search query.
The search query retrieves tweets matching the specified criteria throughout the full archive. In our study, we used the search query [[predict OR forecast OR warn OR updates OR alert] AND [earthquake OR quake OR [seismic AND event] OR seismicity OR shaking OR EQ]]. With this query, we collected 82,129 tweets related to the subject of earthquake prediction, forecasts, and notifications, and the metadata of the users that posted the tweets, over a period of 2 years from 1 March 2020 to 31 March 2022. This study focused on both misinformation and not-misinformation to evaluate the scale of misinformation and avoid a biased impression that social media is rife with misinformation  43. Figure  1. Overview of the data collection, fine-tuning, processing, and analysis. Step 2: Manual classification.