dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  Stephen Volante CT , an  American Translators Association  member, for his editing support and  María Fernanda Merino  for providing strategic guidance. IQuartil SAS has hosted the AMORE Platform, and we are incredibly grateful to  Pablo Zapata  and  Lizardo Vanegas  for improvements to the AMORE Platform and the AMORE Project website. The development of the AMORE Platform used for this study was led by  Luis Gabriel Cuervo ,  Daniel Cuervo ,  Luis Fernando Pinilla , and  Pablo Zapata. An early prototype of the AMORE Platform was developed under the guidance of the principal investigator (LGC) by Team 33 of the 2020 cohort of the Data  Science for All -Correlation Once  certificate training program. Members of Team 33 included  Daniel Cuervo ,  Catherine Cabrera ,  Dario Mogollón ,  Juan G.
Method The objective of this section is to present the simplified program FLAGS/MCOL, i. e. its global functioning and the different modules, while its graphical user interface (GUI) is presented in Appendix A. Both FLAGS solver and its graphical interface were developed in Java language. Ship collision or grounding analysis is commonly split into two distinct processes: • External dynamics 1 FLooding Accident REsponse: https://www. flare-project. eu/. • Internal mechanics On one hand, external dynamics target the global rigid body movements of the ship, which are governed not only by the impact force but also by the hydrodynamic loads acting on the immersed part of the hull. Internal mechanics, on the other hand, focuses on the modes of deformation and failure of the crushed components: bottom/double bottom, floors and girders in bottom grounding; hull plating, decks and bulkheads in side grounding.
External dynamics Initially developed at the end of the 90's by Mitsubishi Heavy Industry, the external dynamics program MCOL was completely rewritten by Le Sourne et al. (  2001 ) who included large rotational movements as well as Coriolis and drag damping forces for ship-submarine collision analysis. Resulting solver was then implemented in Ls-Dyna F. E. solver and later coupled with the ship collision S. E. solver SHARP  (Le Sourne et al. , 2012). Using the 2nd Newton's law, the equation of a grounded ship movement may be written as  Ferry et al. (2002) : 𝑀[ ẏ] + 𝐺[𝑦] = [𝐹 𝐶 (𝑥)] + [𝐹 𝑊 (𝑦, 𝑥)] + [𝐹 𝐻 (𝑦, 𝑥)] + [𝐹 𝑉 (𝑦, 𝑥)] (14) Here, vector  denotes the position of the ship centre of gravity (CoG) with respect to the earth-fixed coordinate system while vector  denotes its absolute body-fixed velocity.
Note that the ship bow was represented by an inclined bottom plating located at the fore part of the model and making an upward angle of 35°with a horizontal plane. Two impact locations were considered, the first at the centre line, i. e. ,  = 0 (Centred impact) and the second at  = ∕4 = 7. 125 m from   the centre line (Off-centred impact). The ship was given an horizontal surge velocity of 5 knots (≈ 2. 572 m∕s) and a friction coefficient  = 0. 3 was set between the structure and the rock. Regarding the latter, coefficients  and  were set to 3. 7 and 6 respectively in order to represent a sharp rock -see Fig. 5. The impact height was set to 2 m in order to trigger the rupture of both the bottom and double bottom. Aalto and MSRC performed numerical analyses using Ls-Dyna/MCOL finite element software.
Abstract In this paper, we investigate brain activity associated with complex visual tasks, showing that electroencephalography (EEG) data can help computer vision in reliably recognizing actions from video footage that is used to stimulate human observers. Notably, we consider not only typical "explicit" video action benchmarks, but also more complex data sequences in which action concepts are only referred to, implicitly. To this end, we consider a challenging action recognition benchmark dataset -Moments in Time -whose video sequences do not explicitly visualize actions, but only implicitly refer to them (e. g. , fireworks in the sky as an extreme example of "flying"). We employ such videos as stimuli and involve a large sample of subjects to collect a high-definition, multi-modal EEG and video data, designed for understanding action concepts.
In particular, we attempt to classify EEG signals related to higher levels of reasoning associated to dynamic stimuli, and specifically devoted to the problem of understanding the concept behind an action. To this end, we built a dataset of EEG recordings acquired from 50 different subjects visually stimulated by videos from the recently designed Moments in Time (MiT) dataset  [17] , referring to the following 10 categories of actions: Cooking, Fighting, Flying, Hugging, Kissing, Running, Shooting, Surfing, Throwing and Walking. The peculiarity of MiT dataset is the huge intra-class variability, which makes it one of the most challenging datasets that are currently available in computer vision for action recognition  [17]. For instance, prototypical videos explicitly visualizing a "flying" action might represent a bird or an airplane in the sky, but we are also considering videos representing such class in an indirect and implicit manner (e.
The proposed Action Concepts dataset We first review publicly available EEG benchmark datasets. Second, we detail the main features of the dataset we acquired, presenting the phase of stimuli selection and subsequent acquisition of EEG recordings from a pool of selected participants. Existing EEG public benchmarks. Electroencephalography (EEG) is a powerful brain activity recording device for a variety of applications. For instance, the problem of recognizing the affective and emotional content of a stimulus was tackled by several works. Event-related potentials can show the connection between a selective processing of emotional stimuli and the activation of motivational systems in the brain  [24]. Using gathered data under psychological emotion stimulation experiments, one can successfully train a support vector machine to disambiguate between emotions  [25].
This makes our work pretty unique in the panorama of the multimodal learning framework. Original characteristics of the dataset. In this work, we exploit EEG to handle dynamical stimuli which consist of 3-seconds video footages extracted from Moments in Time (MiT) dataset  [17]. By design, such dataset was created to guarantee remarkable inter-class and intra-class variations among actions, representing dynamical events at different levels of abstraction (i. e. , , "opening" doors, drawers, curtains, presents, eyes, mouths, and even flower petals). While using MiT videos as stimuli for EEG, we attempt to investigate to which extent EEG can convey patterns capable of distinguishing video sequences that, despite their visual diversity, subsume the same category. In fact, very few datasets combine EEG with dynamical stimuli, and fMRI is usually preferred  [39, 40, 41, 42, 43] ).
Specifically, in Figure  1 (b), we compare our Action Concepts dataset with those already present in the literature, categorizing the application task (sleep monitoring, motor imagery decoding for BCI, seizure prediction for epilepsy, sound/object classification and emotion recognition). As first peculiar aspect, our dataset ensures high resolution considering a large number of electrodes  (64). Second, in terms of key statistical features, existing datasets are extremely unbalanced in terms of number of stimuli vs. number of classes vs. number of subjects considered in the acquisition. In our case, we have 1) a large number of stimuli, 2) a large number of participants (both ensuring high variability within the data), and also 3) more classes, making the classification problem inherently harder.
In fact, accounting for many subjects is beneficial when using EEG data since this lends more diversity of the data recorded, and hence more capacity to detect the effect of interest, eventually increasing the reliability of the findings of the study. Overall, unlike the most of existing studies, our proposed dataset is extremely balanced with respect to the above discussed crucial indicators. Besides, the distinctive feature of our dataset is the targeted application. We do not just recognize what is explicitly visualized in the stimuli adopted in the EEG data acquisition, but rather, we attempt to recognize what is implicitly visualized in the stimuli themselves. For instance, consider the case of the cooking action in Figure  1 (a): there are heterogeneous scenarios (involving a smoky pan, a dough, a chocolate cream or even a chef) referring to the same action of interest.
In other words, each subject is likely to have her/his own biases in understanding activities and, by design, such biases will be captured in EEG signals captured when showing MiT videos to the participants. The fact that, in those videos, the action is not explicitly displayed, but simply implied, will cause EEG signals not just to reflect the perception task of visually parsing the video that has been given as stimulus. Differently, in those EEG signals, we are going to capture the mental processes leading to understand to which extent the video matches the subjects' personal concept related to a given action. Of course, this depends upon the subject who is watching the video and reasoning on it, but we should also note another important aspect that guided us in the data analysis. Let us make an example: despite a video of fireworks may not be strictly related to "flying" for some individuals, still, the same video will be definitely not categorized as an instance of a "cooking" activity from any subject.
Conclusions In this paper, we investigate the problem of understanding action concepts with the support of EEG data for which, with respect to prior work  [44, 45, 46, 47, 12, 48, 49, 50, 51, 35] , a higher-level cognitive task is investigated. In particular, we attempted to go beyond the analysis of brain activity which pertains to decoding what individuals merely watch in a video stimulus. Rather, we aimed at investigating what the video stimuli actually mean for human subjects considering own interpretation bias for each action concept. We did it empirically by stimulating a pool of 50 subjects through a selection of videos from the Moments in Time dataset  [17]  while recording their brain activity by EEG. The peculiarity of the selected videos is that they are designed for action recognition in scenarios where an action is not always explicitly visualized, being only implicit (as illustrated in Figure  1 (a) as an example).
We claim that EEG can go beyond the bare appearance of the stimuli, conveying useful discriminative patterns for the classification of high-level concepts (in this case, related to actions), even if the aforementioned concept is not explicitly visualized, but only (vaguely) implied. Therefore, we have shown to be able to recognize actions' concepts taking benefit of EEG data captured from visually stimulated subjects. We also performed an extensive experimental analysis of video and EEG data segregately, adopting fusion methods, and exploiting EEG as privileged information for video-based classification models. To conclude, we speculate that, in these complex scenarios, video classification can be strongly supported by the mental processes that are elicited in a subject who is reasoning about how much the observed footage is prototypical for the ground-truth class.
Dataset collection Collecting stimuli: automatic video selection. We exploited an automatic machine learning algorithm to decide which video to include within our analysis. We adopted a ResNet-50 convolutional neural network  [52]  pre-trained on ImageNet  [52]  to process videos instead of images as done in  [17]. From each video of the Moments in Time training set, 5 random frames are subsampled to fine-tune the ResNet-50's weights. During inference, a video is classified into one of the 10 classes considering 5 randomly sampled frames followed by a majority voting over the predicted labels. We selected 24 videos per class (240 videos overall) from the MiT validation set by considering only the videos which were correctly classified by the model. Among them, we selected the 12 videos classified with maximal confidence (i.
The exclusion criteria included the presence of a history of any neurological or psychiatric disease, use of active drugs, abuse of any drugs (including nicotine and alcohol) as well as any skin condition that could be worsened by the use of the EEG cap. The study was approved by a governative Ethics Committee (Comitato Etico Regione Liguria) and was conducted in accordance with the ethical standards laid out in the 1964 Declaration of Helsinki. All participants had normal or corrected-to-normal vision and were right-handed. EEG acquisition procedure. The participants were asked to sit in a dimly illuminated room, maintaining one meter distance from the screen. There, the EEG cap and EOGs (electrooculography) sensors were put on the head and connected to the EEG amplifier. All the sections of the experiment were run using PsychoPy software  [53].
EEG data recording and pre-processing. EEG data were recorded using 64 Ag-AgCl electrodes of an active electrode system (ActiCap, Brain Products, GmbH, Munich, Germany) referenced to FCz. Horizontal and vertical EOG were recorded from the outer canthi of the eyes and from above and below the observers right eye, respectively. The EEG signal was amplified with a BrainAmp amplifier (Brain Products, GmbH), digitised at a 5000 Hz sampling rate for recording. No filters were applied during signal recording. Electrode impedances were kept below 10 kΩ throughout the experimental procedure. EEG data were analysed using MATLAB TM version R2018a and FieldTrip toolboxes  [57]. Data were downsampled to 250 Hz, and a band-pass filter (0. 5100 Hz) and a notch filter (50 Hz) were applied to extract the signal of interest and remove power line noise.
Subsequently, data was segmented into epochs (i. e. , , trials) from 0 to 5000 ms after the start of each trial. With this segmentation, data from one second before (ISI) and one second after each video were taken into account. Each trial was baseline corrected by removing the values averaged over a period of 1000 ms (from 0 to 1000 ms after the trial started, i. e. , , the ISI). After visual inspection, trials affected by prominent artifacts (such as major muscle movement and electric artifacts) were removed, and bad channels were deleted. However, their values are spherically interpolated using independent component analysis (ICA) so that, effectively, the number of electrodes is always the same across all the different participants. The signal was referenced to the common average of all electrodes  [58] , and ICA was applied to remove the remaining artifacts related to eye-blinks, eye movements, and heartbeat.
After removing the remaining artifacts, noisy channels were spatially interpolated: at the end of this stage, a total number of 5339 EEG recordings was obtained. Dataset split and train-test protocols. In order to train and validate the computational models described in the next section, the 240 videos were randomly divided in three disjoint splits   2 ). Let us define x t as a vector concatenating the registered electrical activity of the brain, at a given timestamp t in correspondence of all the electrodes (x (n) ) mounted on the scalp. Over a total of 64 channels, vertical EOG (VEOG) and horizontal EOG (HEOG) are removed as it is usually performed in related studies to get rid of ocular artifacts. Therefore, x t = [x (1) t ,. , x (n) t ,. x (62) t ] ∈ R 62 for each t = 1,. , 750 since 750 is the number of timestamps (250 timestamps per second are acquired and each MiT video is lasting 3 seconds).
e. , MLP fed with DE features). In each model, the input vector to a softmax operator is extracted, averaged together and the final classification performance is computed by arg-maxing over it. Subject Consensus: technical details (Figure  2 ). We implemented subjects' consensus by considering the baseline EEG model consisting in DE features fed into a multi-layer perceptron MLP. In particular, we took advantage of the logits of that model (that is, the vectorial representation which is normalized into a probability density by applying a softmax operator). When a specific video footage needs to be classified, we considered all the subjects to which that footage was presented as stimulus during the database acquisition. In order to classify such footage, we compute the logits of the DE+MLP that processed all the available EEG recordings (belonging to different subjects) corresponding to that footage.
Data Availability The dataset collected analysed during the current study is available from the corresponding author on reasonable request and will be made public upon paper acceptance. (a) Exemplar footages for Action Concepts Understanding (images are selected from the public dataset  [17] ). (b) Statistics of publicly available EEG datasets. [17] ) related to some of the selected actions for the task of concept understanding, i. e. , , recognizing actions that are implied as opposed to be explicitly visualized. The "Cooking" action is represented by the smoke coming out from a pan, the creation of a dough, the mixing of a chocolate cream, or the garnishing of a dessert (top-left). The "Flying" action is represented from footages in which pigs fly, fireworks are shot in the sky; alternatively, it is also depicted as a scene in which the panorama is filmed from an airplane window, and as Santa Claus delivering presents (top-right).
Parameter tuning The parameters to be tuned of the proposed GA are listed in Table  2. These parameters has been tuned by employing an automatic algorithm configuration tool, the IRACE package (López-Ibánez, Dubois-Lacoste, Cáceres, Birattari, & Stützle, 2016). This automatic approach has been widely used for tuning configurable algorithms with a number of parameters that can be preset and affect the computational efficiency  (Dell, Iori, Novellani, & Stützle, 2016; Franzin & Stützle, 2019; Pinto, Ribeiro, Rosseti, & Plastino, 2018). The IRACE package tunes parameters in parallel mode based on a method named iterated racing that consists of three main steps: (1) sampling parameter combinations according to a particular distribution; (2) selecting the best combinations from newly sampled ones via racing method; (3) updating the sampling distribution to bias the procedure towards better combinations.
The IRACE package needs the target algorithm, the ranges of the parameters to be tuned, a set of training instances and a set of initial parameters combinations (optional) as the input. A training budget is preset by users to control the maximum number of experiments to be executed, after which a set of elite parameters combinations will be returned. The training instances are generated based on benchmark instances with production capacity 40 and 80 from  Bajwa, Sox, et al. (2016)  and  Couzon et al. (2020). As backlogging is not allowed in  Bajwa, Sox, et al. (2016)  and  Couzon et al. (2020) , in our training instances, backlogging cost b jt is set to be 2h jt in line with  Cheng et al. (2001) ,  Toledo et al. (2013) , and  Goren and Tunali (2016). The training budget of IRACE is set to be 3000 in our experiments, and a set of initial parameters combinations, obtained by preliminary experiments, is fed to enhance the tuning procedure (see Table  3 ).
In addition, each large-sized instance set is solved under demand scenario without seasonality and 7 different production capacities varying from to 50 to 200. In total, there are 105 large-sized instances. The data of all these large-sized instances is included in the supplementary material and available on the internet 3. Numerical results on instances with 10 products and 6 time periods are reported in Table  10. The first two columns represent instance set index and capacity, and the objective values and the corresponding computational times are reported from columns 3 to 6. The last column represents the improvement of the objective value obtained by the GA compared to that of the model based heuristic, which is calculated as Imp = Obj G-Obj M Obj M × 100%, where Obj G and Obj M are the objective    values obtained by the proposed GA and the model based heuristic respectively.
Context. The detection of small exoplanets with the radial velocity (RV) technique is limited by various poorly known noise sources of instrumental and stellar origin. As a consequence, current detection techniques often fail to provide reliable estimates of the significance levels of detection tests in terms of false-alarm rates or p-values. Aims. We designed an RV detection procedure that provides reliable p-value estimates while accounting for the various noise sources typically affecting RV data. The method is able to incorporate ancillary information about the noise (e. g. , stellar activity indicators) and specific data-or context-driven data (e. g. instrumental measurements, magnetohydrodynamical simulations of stellar convection, and simulations of meridional flows or magnetic flux emergence).
The overall stellar noise affects the frequency range in which planets can be found and may dominate the ∼10 cm s -1 amplitude level that is typical of an Earth-like exoplanet orbiting a Sun-like star at 1 AU. The development of techniques to mitigate stellar activity for the study of exoplanets is an active field of research. Several methods and tools have been developed to account for these different sources in the RV time series (see Meunier 2021 for a recent review). First, some specific observation strategies are commonly used to reduce the contribution of short-timescale activity components  (Hatzes et al. 2011; Dumusque et al. 2011). Second, to correct for variability that evolves at the stellar rotation period (spots and plages), the most basic technique consists of fitting a model to the RV dataset (e.
g. , sinusoidal functions at the stellar rotation period  (Boisse et al. 2011) , or quasi-periodic Gaussian processes (see, e. g. ,  Aigrain et al. 2012; Baluev 2011 Baluev , 2013b;; Rajpaul et al. 2015 Rajpaul et al. , 2021;; Jones et al. 2022 )). To constrain the parameters of these models, activity indicators (e. g. , the S-index,  Wilson 1968 ; log R HK ,  Noyes et al. 1984 ; or the bisector span,  Queloz et al. 2001 ) are commonly used if correlations with the RV dataset are found. Some relations with photometry  (Aigrain et al. 2012; Yu et al. 2018)  or the study of the chromatic dependence of the stellar activity  (Meunier et al. 2017; Tal-Or et al. 2018; Dumusque 2018)  can also be used. Recent advances in RV extraction processes from stellar spectra in particular are very promising  (Davis et al.
In practice, specific noise models are applied to correct RV data for the activity, and the p-values of the test statistics are evaluated on the RV residuals assuming they follow the statistics of a WGN, or that they have a known covariance matrix. When the noise is considered WGN, some methods are based on approximate analytical expressions  (Baluev 2008). Other methods compute the FAP using the formula that would be obtained for independent components, where an ad hoc number of independent frequencies is plugged in (see  Horne & Baliunas 1986; Schwarzenberg-Czerny 1998; Cumming 2004 , as well as  Süveges 2014 and Süveges et al. 2015  for a critical analysis of this approach). Finally, another family of approaches is based on Monte Carlo or bootstrap techniques  (Paltani 2004; Schwarzenberg-Czerny 2012) , sometimes aided by results from extreme-value distributions  (Süveges 2014; Süveges et al.