dataesr/scientific-paragraphs-categorization huggingface dataset:
Probabilistic Modelling of Breach Distributions Based on the developed accident database, a detailed statistical analysis was undertaken to derive breach distributions for pertinent ship types and hazards. Most of the breach information mentioned in the database contains qualitative descriptions, for example, relating to the breach as the hole, gash, tear, crack, above/below the waterline, etc. , with no real quantitative measures of the damage opening. Table  1  shows the number of samples provided for the different damage locations in collision and grounding accidents, whilst Table  2  indicates the total number of breach data (quantitative measures) available in the database for collision, side, and bottom grounding. The figures clearly indicate that the recorded number of cases is scarce, especially for damage penetration, where such information has been registered in only one case (bottom grounding).
Direct Approach to Modelling Breach Distributions The first step of the methodology is to run a very large number of scenarios for each hazard in question, namely collision, side grounding and bottom grounding, considering a reference ship. The aim is to simulate a large range of representative breaches, adopting, for example, a design of experiment strategies and using suitable crash analysis software. In the example presented here, the Super-Element software SHARP is utilised and the hazard considered is a collision. In the particular example considered, a collision scenario is defined by the following parameters: (a) striking ship type, (b) striking ship initial surge velocity, (c) struck ship initial surge velocity, (d) impact longitudinal position, (e) collision angle, (f) striking ship draft and (g) struck ship draft.
The super-element structural description has been modelled for a section that is 100 m long along the ship parallel body and centred on the mid-ship section. All materials have been modelled as rigid-perfectly plastic with S235 mild steel properties (see Table  7 ). The failure strain-which in SHARP is compared to the averaged tension stress within the super-elements-has been considered equal to 10%. Similar values have been observed by other authors to provide a good fit between super-element predictions and experimental results, such as Zhang  [54] , Lützen  [9] , and Buldgen et al. [55]. The SHARP super-element model of the struck ship is shown in Figure  19. Its hydrodynamic properties, as required by MCOL, have been obtained using the BV Hydrostar software  [56]. properties, as required by MCOL, have been obtained using the BV Hydrostar software  [56].
• Figure  23  shows that simulated damages with large penetration have a lower vertical limit close to the ship bottom. This was expected given the bow shapes of the striking ships. • Figure  21  shows that the domain is well-populated due to the large striking s database. No damages have been simulated, with the damage upper limit slig above the waterline and the damage lower limit slightly below. The simulatio such damages would typically require that the damage is due to the bulb of the s ing ship only and that the combination of striking ship draft and bulb height is a quate.
• Figure  23  shows that simulated damages with large penetration have a lower ver limit close to the ship bottom. This was expected given the bow shapes of the strik ships. In Figure  24 , the results from Figure  20  are shown after clustering the data into ei the striking ship's initial velocity or collision angle. It is observed, as expected, that striking ship's initial velocity has a significant influence on the damage extent and the collision angle has a strong impact on the damage length. • Figure  21  shows that the domain is well-populated due to the large striking ships database. No damages have been simulated, with the damage upper limit slightly above the waterline and the damage lower limit slightly below. The simulation of such damages would typically require that the damage is due to the bulb of the striking ship only and that the combination of striking ship draft and bulb height is adequate.
Data Filters Focusing on the scope of the database development, the following filters are em ployed to extract the casualty and fleet at risk data: • Accident period: 1 January 1999 to 31 October 2020 • Accident type: collision and grounding • Ship size: GT ≥ 3500 • Ship length (overall): ≥80 m • Ship type: Cruise, RoPax, Pure passenger, and RoPax (Rail) • Location: worldwide • Class type: IACS and non-IACS (for the fleet at risk) Keeping in mind the 1995 SOLAS Conference and scope of the FLARE project, worl wide accidents during the last 20 years have been investigated. The identification of d ferent accidents into collision and grounding is in line with the definition of accident typ.
1. Data Filters Focusing on the scope of the database development, the following filters are employed to extract the casualty and fleet at risk data: • Accident period: 1 January 1999 to 31 October 2020 • Accident type: collision and grounding • Ship size: GT ≥ 3500 • Ship length (overall): ≥80 m • Ship type: Cruise, RoPax, Pure passenger, and RoPax (Rail) • Location: worldwide • Class type: IACS and non-IACS (for the fleet at risk).
3. 1. 1. 11 Data Filters Focusing on the scope of the database development, the following filters are em ployed to extract the casualty and fleet at risk data: • Accident period: 1 January 1999 to 31 October 2020 • Accident type: collision and grounding • Ship size: GT ≥ 3500 • Ship length (overall): ≥80 m • Ship type: Cruise, RoPax, Pure passenger, and RoPax (Rail) • Location: worldwide • Class type: IACS and non-IACS (for the fleet at risk) Keeping in mind the 1995 SOLAS Conference and scope of the FLARE project, worl wide accidents during the last 20 years have been investigated. The identification of d ferent accidents into collision and grounding is in line with the definition of accident typ.
3. 1 1. 1. Data Filters Focusing on the scope of the database development, the following filters are employed to extract the casualty and fleet at risk data: • Accident period: 1 January 1999 to 31 October 2020 • Accident type: collision and grounding • Ship size: GT ≥ 3500 • Ship length (overall): ≥80 m • Ship type: Cruise, RoPax, Pure passenger, and RoPax (Rail) • Location: worldwide • Class type: IACS and non-IACS (for the fleet at risk).
Acknowledgments This research is supported by  Program for New Century Excellent Talents in University in China  ( NCET-13-0037 ),  Humanities and Social Sciences Foundation of Ministry of Education of China  ( 14YJA630019 ),  National Natural Science Foundation of China  ( 71873015 ),  China Scholarship Council  ( 202106030155 ) and by the  Spanish Government project  (Grant no. PGC2018-099402-B-I00 ).
In this context, one of the ONERA activities was focused on the aeroelastic adjoint capability applied to flexible wing design in collaboration with Airbus Commercial Aircraft and DLR. The Airbus XRF-1 research test case, representative of a long-range aircraft configuration, was shared among partners. In  [4]  'frozen-flexibility' approaches have been illustrated in order to consider structural deformation in the shape optimisation loop while avoiding the increase of both computational cost and code complexity associated with a fully-differentiated aero-elastic solver. The jig-to-flight shape displacements are pre-computed for each flight condition of interest by performing fully-coupled aeroelastic computations on the baseline geometry. Then, a classical multipoint "rigid" adjoint-based aerodynamic optimisation is performed on the flight shape resulting from the jig one augmented by the baseline structural displacements.
Optimisation problem formulation and approaches The optimisation of the wing shape parameters introduced in §2. 1 seeks the constraint minimisation of the sum of the aerodynamic drag over three flight conditions for the same cruise altitude and speed. The values of CL=0. 45, CL=0. 5 and CL=0. 55 are selected, where the lower and higher lift conditions are essentially introduced to ensure a smooth performance in the neighbourhood of the target cruise point CL=0. 5. The expression of the objective function J is simply given by: J = CDff|CL=0. 45 + CDff|CL=0. 5 + CDff|CL=0. 55,  (1)  with CDff denoting the far-field drag coefficient  [12] , extracted from the post-processing of the whole flow field through the in-house ONERA code FFD72. Moreover, thanks to such analysis the drag coefficient CDff is decomposed into its different physical contributions: friction drag (CDf), viscous-pressure drag (CDvp), wave drag (CDw) and induced drag (CDi).
ACKNOWLEDGEMENTS We would like to thank  Airbus Operations SAS  for providing us with the XRF-1 test case and the PADGE modeller, and the  DLR Institutes of Aerodynamics and Flow Technology and of Aeroelasticity  to provide us with the wing structure model. We would also thank them for fruitful discussions on the analysis of the results. The MADELEINE project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  769025.
Last Fall, the ACS Research Data Policy was launched on the ACS Publishing Center to provide best practice recommendations for data citation, Data Availability Statements, and the use of appropriate data repositories. In addition, the ACS Publishing Center also features a section dedicated to ACS Research Data Guidelines. These guidelines are a living reference source to support researchers to include data as a prioritized publication component. Current guidelines include Biological data, Simulations, Machine Learning, Computational Data, and Organic chemistry data, and coming soon in 2022, Nanoscience, Materials science, and Energy. In March 2022, ACS Organic & Inorganic Au rolled out our new data check. This process is analogous to the process at our sister journals, The Journal of Organic Chemistry and Organic Letters.
The central question of this paper is whether using hypothetical choices in surveys on subjective well-being and health is problematic and biases responses. To that end, we compare hypothetical answers and BTS-incentivized answers in tasks that are widely used to measure subjective well-being and health. We ran a large-scale, online experiment with 864 participants. Because the effect of incentives may depend on the quality of the data, we included treatments with and without defaults to exogenously influence data quality. Defaults strongly affect insurance and pension plan choice, and organ donation  (Johnson, et al. 1993 , Madrian and Shea 2001 , Johnson and Goldstein 2003). They are used to 'nudge' people in desired directions and they are the standard tool of behavioral insights teams.
1  In our study, defaults introduced a quantifiable bias, and we measured the extent to which incentives could reduce this bias. We found that without defaults, incentives had no effect: data quality was good for both hypothetical and incentivized questions. This is consistent with  Abeler, Nosenzo, and Raymond (2019) , who show that people have a strong preference for truth-telling even when it is easy to lie. With defaults, however, incentives had an effect: they reduced the default bias, mainly because participants applied more cognitive effort. Incentives were only effective in the subjective well-being questions where responses were quick, and hence, more automatic. For the more complex health questions, which required more time to respond, incentives did not reduce the default bias. Our take-away message is that if researchers are careful about data quality and avoid defaults, and, probably, other biases, then hypothetical answers cause no problems in survey data.
Experimental treatments We randomly allocated the 864 participants to four different treatments, making sure that the number of participants per treatment The question wordings were taken from: https://media. gallup. com/dataviz/www/WP_Questions_WHITE. pdf (last access 04/05/2018). The emotions we used were a subset of those reported in the world data-base of happiness: https://worlddatabaseofhappiness. eur. nl/hap_cor/desc_hind. php?ind=1439 (last access 04/05/2018). We also elicited global life satisfaction via Cantril's Self-Anchoring Scale, which asks participants to evaluate their current life situation on a ladder ranging from 0, the worst possible life, to 10, the best possible life. Cantril's Self-Anchoring Scale was the only non-binary question in the survey and we, therefore, do not report it in this paper.
The SG is commonly seen as the most difficult task in health state valuation and the response times in our study confirmed this. In the SG, we found no evidence of default bias. In  Prelec's (2004)  BTS, participants have to perform an extra task, to predict the answers of other participants. Consequently, the BTS requires more cognitive effort. To test whether this extra cognitive effort affected data quality, we collected additional data for the Control and Defaults treatments without the prediction task. 12 Detailed results are in Table  B. 1 in Online Appendix B. Excluding predictions had no effect in the Control treatment, but the effect of defaults was slightly weaker when participants were not asked to make predictions. This suggests that the extra prediction in the Control treatment was close to the maximum effort our participants were willing to make for the flat fee.
The interval between each t i and t i+1 is not monotonous, which is consistent with the event data asynchronicity. The event cameras' particularities described above grant them advantages in many application cases compared to more traditional visual sensors  (Gallego et al. , 2020). A significant field of applications is robotics: autonomous vehicles, drones and other embedded systems requiring low latency and low power data handling. Furthermore, they evolve in uncontrolled lightning conditions thus require a high dynamic range and high contrast range. Because of their operating mode, event based cameras are useful in tasks featuring movements, such as object tracking  (Glover and Bartolozzi, 2016) , gesture recognition  (Amir et al. , 2017) , optical flow estimation  (Orchard et al. , 2013; Paredes-Valles et al.
Motivations While desktop event vision solutions can afford large amount of computing resources, embedded application typically face limitations in CPU, memory, and energy usage. Event data is sparse, and yet in some highly dynamic and textured scenes, the event flow can be very high, up to 25M event per second, which requires significant processing resources to handle. Most existing event vision approaches preprocess event data by simply downscaling the pixel coordinates, which is actually a max-pooling operation in the event domain. One obvious issue of this simple event funnelling is that the resulting event density drastically increases in the reduced spatial dimension, causing an important information loss. In embedded systems, only relevant information should be stored and processed under the form of downscaled event data.
For example, event-based odometry applied to robotics requires only movement information in order to process the information in real time: this need is typically answered by pooling the events in frames  (Rebecq et al. , 2017) , resulting in the loss of the data's asynchronicity. In neuromorphic machine learning, spiking neural networks usually can't handle the flow of information the event data represents and downscale this input by a first preprocessing layer, before actually learning anything. We can also mention the residual graph convolutionnal techniques which handles each input event as a node in the graph  (Bi et al. , 2019) : this leads to a low computational efficiency and a substantial simulation run, which can be improved solely by spatially reducing the input neuromorphic data.
The aforementioned examples are just a few reasons why event downscaling is a real challenge in neuromorphic computer vision and needs to be formalized. The contributions of this paper are twofold: we design and implement a number of methods to reduce the size of event data and we assess the proposed reduction methods by analysing reduced data and also based on the classification results. The remainder of the paper is organized as follows: firstly an introduction to event-based cameras and the motivations behind this work, then a review of previous use of event data, followed by a complete description of the 6 spatial downscaling methods implemented in this work, and finally a a comparison summarising the advantages and disadvantages of each method.
RELATED WORK Reducing the size of visual data is straightforward for grayscale and RGB images with interpolation. When it comes to event data, size reduction is much less trivial. And yet, many papers discussing neuromorphic computer vision are faced with a substantial event flow, sometimes too great to perform the task at hand. In those case, the authors address this issue with various techniques presented bellow. A first solution simply consists in creating event frames, then processing this data as standard visual images, as seen in  (Fang et al. , 2021)  and  (Huang, 2021). This enables the same model to be applied to any king of visual data, neuromorphic or not. However its main drawback is that all the data's asynchronicity, which is a real advantage of event data, is disregarded; all events are gathered and their timestamps discretised.
Spiking neural networks handling event data can consider each pixel as an input neuron, and each event produced as an input spike to the neural model. However this first layer is often too important for efficient computation, thus most authors applying this type of neuromorphic computing on event data resolve to convolve the input. For example,  (Cordone et al. , 2021)  apply multiple convolution layers to the dataset DVS128 Gesture before processing it. The non uniform sampling employed by  (Bi et al. , 2019)  can also be mentioned, although it only produces satisfying results on a sparse dataset with well-defined empty areas like the one they use. This simply amounts to implement a non-uniform grid sampling inspired from traditional data processing to event data. To the best of our knowledge, the only existing tool for event data downscaling is Tonic's "Downsample" method  (Lenz et al.
, 2021). Tonic is a Python library for neuromorphic data handling, which has been created on an original idea dating back from the 2019 Telluride neuromorphic workshop. It facilitates dataset downloads and its conversion to different event representations (frames, time surfaces, voxel grids, etc). It also proposes event transformation, such as denoising, event dropping, polarities merging, spatial and temporal jittering and downsampling. The "Downsample" method simply multiplies the event coordinates by a spatial factor without dropping any event. It can also be applied to temporal downsampling following the same mechanism. Although it is extremely fast, it has the major flaw of keeping all the original events thus leading to an increasingly high density in the produced visual data. The following subsections present the 7 methods considered in this work to downscale spatially the data produced by an event camera.
Simple Event Funnelling A first solution to reduce event data simply is to divide each event's coordinates in x and y by the downscaling factor, then remove all the reduced events duplicates (i. e. , keep only one occurrence of each event for a specific set of coordinates (x, y,t, p) -see Fig. 4 ). This method follows the same logic as Tonic's downscaling approach and can also be extended to temporal reduction. Thanks to its quickness and simplicity, this solution could answer the need for downscaling events in real time on embedded systems. However, the main drawback is that this process does not drop any event. The reduced area undergoes a high density of events, yielding an information loss due to the accumulation of pixel activation (see Fig. 4 ). Event funnelling tends to drastically increase pixel activity (see Fig.
Log-luminance Reconstruction From an optical point of view, downscaling the spatial size of an event recording boils down to averaging the luminance captured by a subset of pixels in the original sensor. In a physical implementation of an event camera formed by a 2D array of pixels as the one shown in Fig. 2 , this method could be implemented by averaging the photocurrents I ph generated by neighbor pixels and performing the transimpedance logarithmic conversion and posterior voltage amplification and differentiations as explained in Section. Introduction. In order to implement this behavior, the log-luminance is reconstructed for each pixel in the fullscale dataset: each event occurring on the same pixel increases (if positive) or decreases (if negative) the log-luminance level by one unit.
From a set of known log-luminance levels x ′ , the cubic interpolation outputs the timestamps y ′ where the events' logluminance crosses those levels. This approach reconstructs a curve close to an actual log-luminance curve recorded by an event camera, granting a high score of optical consistency to this approach. However, the existing tools allowing the cubic interpolation of y values according to a set of x parameters requires the x set to be strictly monotonous, without any duplicates. The first hinder can be removed by splitting the (x, y) dataset presented in Eq. 3 at the different log-luminance's extrema: S i (x, y) = ∑ n ext i=0 ∑ t y ext i+1 t=t y ext i (x t , y t ) (4) with S i the ith subset of the events comprised between two extrema, n ext the total number of extrema in the dataset i.
Events to video to events Since it is possible to reconstruct a grayscale video from an event stream, a last simple technique would be to use existing downscaling methods on a grayscale reconstruction of the event data, then translate this reduced image back into events. This is equivalent to reconstructing a downscaled luminance and computing downscaled events from it, but it has some important drawbacks. The main one lies in the total loss of event data's asynchronicity since the grayscale video produced from the neuromorphic data is built from the pooling of information into frames. Moreover, its implementation in practice is computationally heavy, which is not suited for embedded applications. Lastly, this method needs two sets of grayscale data to be created during the process (fullscale from events, then downscale) leading to high memory consumption, while embedded systems are limited in data storage.
A pipeline implementing such a downscaling process is the following: 1. translate the event data into grayscale frames thanks to Events-to-Video library introduced in  (Rebecq et al. , 2019). This module actually consists in a machine learning model trained on a set of datasets comprising neuromorphic and grayscale recordings of the same scenes. 2. rescale the grayscale frames thus obtained with one of the many existing image processing tools. 3. finally translate the downscaled grayscale frames into events by using the library Video-to-Events from  (Gehrig et al. , 2020). This library reconstructs the log-luminance variations between each grayscale frame, then outputs the corresponding events according to user-defined negative and positive contrast thresholds. This pipeline requires the event dataset to be downscaled either to be one of the datasets the Eventsto-Video model has been trained on, or to contain a set of grayscale frames recorded simultaneously as the neuromorphic data.