dataesr/scientific-paragraphs-categorization huggingface dataset:
Sparse Proper Generalized Decomposition (s-PGD) Sparse Proper Generalized Decomposition is an alternative procedure for constructing a sparse approximation in a high-dimensional space from a limited dataset. It is developed to circumvent the drawbacks of the previous method by dealing with the curse of dimensionality as well as the nonstructured datasets. It is also based on the concept of separate variables as depicted in Eq. (  9 ). As the problem under study involves ten parameters, it is assumed that the objective function w b (p) (corresponding to a macroscopic property) lives in a 10-dimensional space Ω p = Ω p 1 × • • • × Ω p 10 ⊂ R 10. Furthermore, it is assumed that the value of this mechanical property is known only at few locations where it is calculated numerically. In order to predict the mechanical properties of any new woven composite arrangement, without resorting to numerical computations, s-PGD provides thus an approximate solution w p to the objective function using the PGD framework.
Results and discussion In this section, a comparative analysis of these three non-intrusive PGD methods is discussed on three levels. Fist, a test dataset of 160 samples a 2 = 2 mm, h = 0. 3 mm, c = 0. 2 mm, c f 2 = 50%, v f = 0. 3, E m = 2074 MPa and v m = 0. 3) as depicted in Figure  9  and (a 1 = 0. 8 mm, a 2 = 0. 8 mm, h = 0. 2 mm, c = 0. 1 mm, c f 2 = 30%, v f = 0. 2, E m = 500 MPa and v m = 0. 2 ) as shown in Figure  10. Finally, a quantitative comparison is presented in the Table  2 , indicating the error obtained and the volume of data requested by each technique. The error produced by each method in the test dataset is expressed as follows: E rr = T k=1 w(p k ) -w app (p k ) 2 T k=1 w(p k ) 2 (26) where T = 160 refers to the total number of test dataset. w and w app represent the reference and the approximate solution of each mechanical property, respectively, at the test point p k.
Macroscopic properties predicted by s-PGD This model has been trained with 200 samples given by the Latin Hypercube Sampling (LHS) strategy. This strategy consists of generating points in a quasi-random way under the condition that only one sample is placed on the same row and column. That is why, generating a training dataset of 200 it is proposed to approximate this residual through non-intrusive PGD such as s-PGD. Here, the SSL level 0 has been chosen to construct this approximation trying to obtain excellent predictions for the points involved along the boundary of the parametric domain as presented in the previous subsection. Summarizing, the following outcomes and key points can be derived: • ANOVA-PGD model is appropriate for the presented problem since it requires a few dozen of unit cells and a few hundred of FE computations, to achieve a good approximation.
• s-PGD fails to well approximate the solution along the domain's boundary. Also, it uses a huge number of unit cells to build the database in contrast to other presented methods. • SSL brings about significant errors inside the parametric domain for the first two hierarchical levels. It requires a huge number of precomputed solutions in order to reach an accuracy comparable to that of ANOVA-PGD. However, improving the approximation level leads to an exponential increase in the number of samples as well as the unit cell generations. (a) E 1 (s-PGD) (b) E 1 (SSL level 0) (c) E 1 (SSL level 1) (d) E 1 (ANOVA-PGD). Journal Pre-proof (a) E 1 (s-PGD) (b) E 1 (SSL level 0) (c) E 1 (SSL level 1) (d) E 1 (ANOVA-PGD).
• This technique well approximates the multi-parametric solution using a reduced number of high-fidelity solutions and related microstructures in the offline stage, compared to the other presented non-intrusive PGD techniques. • The same formulation has been successfully applied to approximate the multiparametric solution for other balanced weave reinforcement architectures including 2/2 Twill weave and 5H Satin weave pattern, as well as for unbalanced weave architecture such as 2/3 Twill weave pattern. • A Graphical User Interface application (GUI) has been created using Matlab App Designer as shown in Figure  11. This application can be easily employed by designers and engineers to deduce, instantaneously, the macroscopic properties of woven composite for any given set of microstructural parameters by setting cursors and without any microstructure generation and FE computation using periodic homogenization.
Such a tool allows hence assessing in real-time the influence of the input parameters on the overall composite behavior. It can be also employed as a decision-making tool towards optimal material selection. The results presented in this work make non-intrusive PGD-based formulations a promising technique for solving high-dimensional parametric solutions of many engineering problems of composites using a limited dataset, especially, where a huge database is impossible or difficult to build. This research has focused on studying the linear elastic properties of woven fabric composites taking into account geometrical and material parameters. There are many opportunities for future works, for instance, by incorporating more parameters, including yarn angle and total fibre volume fraction, or by investigating other types of composites such as the 3D woven composites (interlock woven fabric composite).
AI tools implemented on big data from electronic health records used heterogeneous, massive, and complex datasets that require integration of different types of information identifying clusters and correlations, and extracting value to transform large volumes data into information and knowledge in the form of predictive models to improve decisionmaking quality  [2]. This became technically possible by building big data platforms to store and integrate high-capacity and high-diversity biological, clinical, environmental, and lifestyle information related to health status collected from individuals and population at one or more time points for real-time demand  [3]. Advances in AI have helped big data technology to progress beyond the simple analysis of the traditional hypothesis and query paradigm.
Emergency and trauma surgeons are aware of the importance of recording, collecting high-quality surgical and clinical data (health data, images, videos) and of disposing of large, international, secured databases (registers) to train algorithms with high reliability and accuracy. Emergency and trauma surgeons strongly believe that the implementation of AI in emergency and trauma settings is a major opportunity to improve outcomes, and are ready to be involved in future research projects about AI application in supporting decision-making and surgical procedures. The ARIES survey  [19]  showed that emergency surgeons' ability to adopt new technologies is significantly correlated with interest and expectations, consequently, increasing interest and grounding the implementation process in surgeons' expectations with usability testing and user-centered design has the potential to overcome disillusionment and barriers.
This would require some investment into the software development of the system, e. g. to adapt the treatment planning workflow and tailor the optimisation algorithms to the available hardware. GammaPlan Lightning is the most recent example of a proposed novel inverse planning approach (see Sjölund et al 2019) being integrated into the clinical treatment planning system. The computationally expensive convex underestimator approach could be used as a benchmark to assess other viable optimisation methods. One could create a number of artificial test cases which can be optimised to a very high degree with the MILP approach. Publishing these test cases with the appropriate results could then allow others to benchmark their solutions without having to run the costly optimisation themselves.
e. iso-centre), a varying dose-rate level between individual fractions, a single repair rate process, and first-order polynomials for the induction of lethal damage, sublethal damage and translation from sublethal to lethal damage. This simplification of the model can be formulated as the general LQ model with an additional correction function describing the dose delivery. Pop et al (2000)  derived a bi-exponential version of this BED model from animal studies and this BED model has since been well established and used by numerous studies over the past decades. As such we refer the reader to the previous work cited in the manuscript for a thorough analysis of the underlying principles. This section covers the steps required to create this convex relaxation (MILP) of the general MINLP treatment planning problem.
Acknowledgements This work is supported by the  EPSRC -funded  UCL Centre for Doctoral Training in Medical Imaging  [ EP/L016478/ 1 ], the  Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)  [ NS/A000050/1 ], the  Wellcome/ EPSRC Centre for Medical Engineering  [ WT 203 148/Z/16/Z ], the  NIHR BRC  based at Guys and  St Thomas Trust  and the  CRUK ARTNET Network Accelerator Award  [ A21993 ]. Ethical statement This research study was conducted retrospectively using non-identifiable human subject data. Applicable law and standards of ethics have been respected in accordance with the principles embodied in the  Declaration of Helsinki  and in accordance with local statutory requirements. Conflict of interest  Jamie R McClelland  reports grants from  Elekta Instrument AB , but outside the scope of the submitted work.
For memory optimisation and better comparison between the model and the observations, the assimilation window is divided into time slots of 1 h. During each time slot, the observations are read, the observation operator is executed, its output field is interpolated to the locations and times of the observations for comparison with the observations and the innovation vector is calculated and stored. In this study, the length of the assimilation window is of the order of 3 h: The period over which the cost function is minimised. The assimilation chain is driven by the MODIS AOD from the C61 collection at the 550 nm wavelength of the two instruments Terra and Aqua. The MODIS data that feed the assimilation chain concern both the deep blue and dark target products (e. g. ,  [55] ). The MODIS instruments on board the Terra and Aqua satellites, both on the EOS (Earth Observation System) platform, are designed to measure atmospheric aerosols on a global scale.
AERONET data are available at three levels: Level 1 (unfiltered), Level 1. 5 (cloud filtered) and Level 2 (cloud filtered and quality assured). For more information on the AERONET project, the reader can refer to "https://aeronet. gsfc. nasa. gov/" (accessed on 30 March 2022). Figure  2  shows the statistics in terms of correlation, bias and RMSE between the MODIS analyses versus available AERONET observations with respect to each measurement's site over the two years of comparison 2018 and 2019. Generally, the correlation coefficient is greater than 0. 7 for the majority of AERONET stations. Similarly, the bias and root mean square error between the assimilated product and the AERONET measurements are relatively low for the two years of comparison. Overall, we note that there is no systematic bias and that it can be positive or negative.
g. ,  [59, 60] ). In conclusion, the results presented in this figure illustrate the very good behaviour of the MOCAGE model using MODIS AOD data assimilation to better represent the AOD distribution at the global scale compared to the AERONET in situ observations over the two years of comparison. In order to assess the overall behaviour of MODIS assimilated fields with respect to AERONET observations over the whole period of comparison, we represent in Figure  3  a scatter plot of AERONET AOD versus assimilated MODIS AOD for the two years of study, 2018 and 2019. The correlation between AERONET observations and the assimilated MODIS AOD output for the year of 2018 (2019) is 0. 781 (0. 812), whereas the bias is -0. 01 (-0. 007) and the RMSE is 0. 128 (0. 136). We note also that the AODs from the MODIS AOD assimilated field are overestimated for low AOD values (lower than 0.
The AOD from the assimilation chain highlights very clearly the desert dust event with relatively high AOD values around 22 March 2018. The scores in terms of correlation, bias and RMSE between the MODIS assimilation run and the AERONET in situ measurements for all the stations of comparison are given in Table  2. Generally, the agreement between MODIS analyses issued from the assimilation chain and AERONET data is very good over the three months of comparison. In order to have an idea about the spatial extension as well as the evolution of this event, we show in Figure  8  maps in terms of the longitude-latitude of different parameters issued from the pre-operational chain during the desert dust event on 22 March 2018 at 12:00. These parameters concern the surface desert dust concentration (Figure  8a ), the desert dust AOD (Figure  8b ) and surface PM10 concentration (Figure  8c ).
First, we will evaluate the assimilated run compared to the MOCAGE free model run in terms of vertical profiles during the Australian fire event. Figure  9a  represents a set of trajectories from the CALIOP instrument collected during 6 January 2020 over the Australian region. The colour code refers to the number of all the ascending and descending profiles collected during that day. Figure  9b  shows all vertical profiles corresponding to the trajectories of Figure  9a  in terms of backscatter coefficients (in m -1 •sr -1 ), and Figure  9c  shows the same vertical profiles in terms of the backscatter coefficient from the free model run. The comparison between the three products shows that the free model field produces the same structures as for the CALIOP measurements in terms of backscatter coefficient but the values are systematically underestimated.
Conclusions In this study we presented an assimilation system that operates in a pre-operational mode on a daily basis. It uses the assimilation of AOD observations from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument onboard both Terra and Aqua satellites at the global scale with a resolution of 1 • × 1 •. We validated this assimilation chain over a two-year period, 2018 and 2019, using the assimilation diagnostics in terms of OMF and OMA as well as the independent AOD data from the AERONET network. The added value of the fields resulting from this assimilation chain compared to those resulting from the free model run without assimilation was demonstrated and quantified. We also highlighted the capacity of this chain to predict and document extreme events such as desert dust transport or biomass wildfire propagation.
This chain has been improved since 2020 with the addition of other observations in the assimilation system in terms of AOD observations from the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument as well as lidar profiles from the MPL (mini pulse lidars) network located over France and part of the E-PROFILE network of the European Environment Agency. The inclusion of these two sets of data in the chain has been validated. Improvements even in the scores compared to the AERONET data have been noticed. The work presented in this paper constitutes the basis of the operational chain of Météo-France for aerosol assimilation. This chain has been in operational mode since January 2022.
Acknowledgments: This work is funded in France by the  Centre National de Recherches Météorologiques (CNRM) of Météo-France  and the  Centre National de Recherches Scientifiques (CNRS). We would also like to thank  ECMWF  for providing ERA5 analyses,  NASA  for providing MODIS and AERONET data and the  ICARE , the  French atmospheric composition database (CNES  and  CNRS-INSU) , for providing CALIOP data. We also would like to thank all the reviewers as well as the Editor for their valuable comments which were very helpful in improving the paper.
Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of text-driven image transformation. SIMAT contains 6k images and 18k "transformation queries" that aims at either replacing scene elements or changing their pairwise relationships. The goal is to retrieve an image consistent with the (source image, transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful.
Introduction Many works aim at learning a multimodal space in which images and text can be embedded and compared  (Wang et al. , 2016; Faghri et al. , 2018; Engilberge et al. , 2018; Zheng et al. , 2020b). This is especially useful for image and text retrieval tasks, i. e. given an input from one modality, find a corresponding sample of the other modality in a database. Such embeddings have other interesting applications. It has been shown in  (Vo et al. , 2019a; Jia et al. , 2021)  that textual input can be used to replace scene elements in images or change their properties with simple arithmetic operations in the latent space, combined with nearest neighbour search. In this work, we aim at evaluating multimodal embeddings on the task of semantic image transformation: given an image and a transformation query formulated in the text domain, the task is to find an image that satisfies the transformation query while being semantically as similar as possible to the input image.
For example, with the cat→dog transformation, an image showing a cat sitting in the grass should be transformed into an image with a dog sitting in the grass (Figure  1 ). A dataset to evaluate text-driven image transformation must contain feasible (image, transforma-tion) queries: the transformation man→dog can be applied to an image with "A man is running on the beach", but not to "A man is speaking on the phone". We create a corpus from Visual Genome images and annotations  (Krishna et al. , 2017)  and ensure that this requirement is met. Evaluating semantic text-to-image transfer is challenging: First, we need to ensure that the requested transfer was performed (the cat was replaced by a dog). Then, we need to verify that the modification is minimal: the dog should be sitting on grass and ideally, all other visual elements should not be changed.
We use OSCAR  (Li et al. , 2020b)  as an external oracle to assess whether these two conditions are met. We choose to transform images by encoding the transformation query as a delta vector in the multimodal space, before adding it to an image embedding and retrieving the closest image in a database (see Figure  1 ). This procedure is applied at inference and solely relies on the image/text alignment without needing any transformation example. Are multimodal spaces like CLIP  (Wang et al. , 2016)  structured well enough for this kind of operations? We know that word and sentence embeddings trained on vast amounts of data have been shown to possess geometric properties that can be useful for text transformation  (Mikolov et al. , 2013; Logeswaran and Lee, 2018). Therefore, we wanted to study whether it is necessary to consider independent text embeddings such as LASER  (Artetxe and Schwenk, 2019)  and LaBSE  (Feng et al.
, 2020)  as building blocks to multimodal embeddings better suited to text-driven image transformation. We evaluate our methods on the SIMAT database, as well as on the smaller SIC112 dataset  (Vo et al. , 2019b). The contribution of this work are: • SIMAT, a dataset of 6 000 images and 18 000 transformation queries, to evaluate algorithms on the task of text-driven image transformation. • A novel principled evaluation metric to use in conjunction with SIMAT. • detailed experiments to measure which multimodal embeddings work best within the delta vectors framework to transform images.
The instruction is a simple word pair in our case, but it can also be a more complex instruction in natural language. Several datasets exist to tackle this problem: The CSS dataset  (Vo et al. , 2019b)  which is a synthetic dataset with simple colored geometrical objects based on CLEVR  (Johnson et al. , 2017). The Fashion200k dataset  (Han et al. , 2017)  provides around 200k images of fashion products, each annotated with a compact attribute description. Similarly, the Fashion-IQ dataset  (Guo et al. , 2019)  was built to advance research on interactive fashion image retrieval. The MIT-States dataset  (Isola et al. , 2015) , also commonly used, is a dataset of ≈60k images, each annotated with an object/noun label and a state/adjective label such as new car or broken window. Those datasets are designed to evaluate text-driven image retrieval on narrow domains, which gives more control over what attributes can be changed and ensures that the transformation is always feasible.
Another common characteristic is the focus on changing object properties rather than objects relationships. We focus on more realistic images, and study object transformations where an object should be replaced by another without changing the high-level subject-object interaction. Methods for solving the text-driven image retrieval task  (Vo et al. , 2019b; Anwaar et al. , 2021; Song and Soleymani, 2019)  all focus on supervised learning: a fraction of the dataset is used for training and the remaining for testing. Instead, we want to measure if multimodal embeddings trained with an image/text matching objective can be used to solve this task without any transformation example. Text-driven Image Editing. Some work focus on directly modifying the pixels of query images instead of performing the retrieval step  (Shi et al.
, 2020). Zheng et al. (2020a)  encode images as a graph of interacting objects which lets the user modify an image by editing its scene graph. GANs are also frequently used to modify images based on some natural language input  (Nam et al. , 2018; Li et al. , 2020a; Xia et al. , 2020). Lastly, CLIP  (Radford et al. , 2021)  can be used in combination with a StyleGAN generator to make semantic edits in images, as exemplified in  (Patashnik et al. , 2021). 3 The SIMAT database.
Requirements To evaluate text-driven image transformation, we first need a list of images with some transformation queries (e. g. a man sleeping on the beach, with the query man→woman). We want simple images (so that the query is unambiguous) and relevant transformation queries. 1  Second, a database of images that we will use for the retrieval step. And finally, a criterion to decide, based on the retrieved image, if the transformation is successful or not. It is the case if only the element designated by the transformation query has changed, while keeping the rest of visual elements as similar as possible. Vo et al. (2019a)  try to solve these requirements with a dataset of ≈1,500 images from Google Image Search queries, dubbed SIC112, with each image annotated with a actor-action-environment triplet, such as (woman, walking, street), among a set of 112 possible triplets.
Transformation queries then consist in changing either the subject, action or environment. This set of images is also used as a database for retrieval, which has two advantages: (i) transformation queries are always possible by design of the dataset, and (ii) the quality of the retrieved image is measured by checking if its annotation triplet was indeed the one expected by the transformation query. We scale this approach to a larger number of annotation triplets, that take the more general form of (subject, relationship, object). However, we observed that due to the larger triplet vocabulary, images can be accurately described by multiple such triplets, which skews the evaluation metric: an image would often be rejected for not being annotated with the expected triplet while still being visually correct.