dataesr/scientific-paragraphs-categorization huggingface dataset:
The techniques to achieve exploitation behavior can be subdivided into cross-validation-based, geometry-based and queryby-committee-based methods. For each of the given subclasses multiple references have been collected. In a next step 14 different techniques adaptive sampling techniques have been thoroughly reviewed in order to propose a clear overview of the current state-of-the-art. Due to a lack of thorough comparisons as well as distinctions between the methods found in the literature a comparative review of these 14 methods has been conducted. Here the methods have been studied on 27 different benchmark tests of various dimensions and complexities in order to highlight the respective strengths and weaknesses. In order to provide transparency the MATLAB code which has been used to obtain the presented results has been made publicly available.
34b. Results corresponding to Problem are summed up in Fig. 35. From initial an metamodel, which is rather poorly fitted (see Fig. 35a ), improvements of the performance are detailed in Fig. 35b. From 5 initial samples to 10 samples, several adaptive techniques such as AME, EI, MEPE or WAE fail to majorly improve the predictions. However with 20 samples in the dataset, most methods perform well, CVVor, Lipschitz and WAE being the only ones not able to reach 80% improvement. It can be observed through the error evolution given in Fig. 35c  and d , that Lipschitz bad performances are corrected once 21 samples are reached and the method is even a high performer after 25 samples in the dataset. Looking at the other convergence behaviors indicate that after 25 samples most methods perform well, but the error evolution depends significantly on the considered approach.
They outperform ACE, LOLA and CVVor. Problem P5, displayed in Fig. 37  leads to similar difficulties for EI, WAE and in a restricted manner for ACE also. Other methods perform well. It can be noticed that Lipschitz, EIGF and MIPT show error fluctuation when the number of samples are less than 10 due to the restricted size of the dataset. Afterwards the convergence behavior is smooth. For Problem P6 (Fig. 38 ), not only ACE, EI and WAE fail, but also Lipschitz. Besides, CVVor and LOLA have a slow converge rate. AME, EIGF, MASA, SFCVT, MEPE, SSA, MIPT and TEAD yield similarly promising results. Only ACE and WAE fail to provide good metamodels for Problem P7 illustrated in Fig. 39. WAE diverges with the worst prediction results after finding 60 samples from an initial dataset with 10 samples.
These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage these two modalities.
MOTIVATION AND SIGNIFICANCE OF THE TOPIC Recently, new datasets dealing with natural disaster detection were proposed. Most of them are constituted of RGB or multispectral images. These datasets are built from acquisitions performed by sensors with a high resolution, either temporal (e. g. Landsat, Sentinel 2) as in the MediaEval 2019 Multimedia Satellite task  (Bischke et al. , 2019)  or spatial (e. g. Quickbird, WorldView) as in XView2  (Gupta et al. , 2019). AI and, in particular, deep learning techniques proved to be efficient in retrieving semantic land cover information and specific behaviors from such datasets  (Zhu et al. , 2017 , Audebert et al. , 2019). However, the analysis of optical images time series may be impossible when looking at areas where the cloud cover is important. Even when the Earth's surface is visible, wetlands and floods are very difficult to characterize visually.
, 2000). As recent work  (Gómez-Chova et al. , 2015)  has shown, multimodal machine learning can leverage the complementary information from multiple sensors to improve the accuracy of the models. For example, in MediaEval 2017,  (Bischke et al. , 2017)  learned deep models to perform flood detection in natural images using ancillary data from social networks. Our work digs in the same direction: we aim to provide strategies to perform multimodal flood detection to leverage as many remote sensing data as possible. We present in this paper the new SEN12-FLOOD dataset composed of both Sentinel 1 and Sentinel 2 images to foster the development of new flood detection techniques (Fig. 1 ). Then, we propose a first baseline based on off-the-shelf deep networks for multimodal time series analysis to classify the images in the dataset.
PRESENTATION OF THE SEN12-FLOOD DATASET The city-centered satellite sequences provided by the Media-Eval 2019 Multimedia Satellite task  (Bischke et al. , 2019)  give access to series of multispectral Sentinel 2 images. The observed areas correspond to African, Iranian, and Australian cities and their surroundings with or without a flood event occurring during the time series. These images are composed of 12 bands, 10m ground-sampling distance and are provided with Level 2A atmospheric correction. Here, we propose a new dataset corresponding to the Sentinel 1 sequences for the same areas and periods. However, since SAR is independent of cloud cover, more SAR images are retrieved for the same time period, leading to a higher sampling rate. This SAR dataset is composed of roughly two times more images than the optical one.
To leverage both SAR and optical modalities, we merge the MediaEval dataset and our own in the new SEN12-FLOOD dataset. Each image has a binary label specifying whether a flood event is visible or not in the observed area. The labels have been provided by the original MediaEval 2019 dataset and were obtained from the Copernicus Emergency Management Service 1. The Sentinel 1 images were downloaded from the Scientific ESA hub website 2. The data were acquired in Interferometric Wide Swath (IW) mode at polarization VV and VH. The SAR images are delivered in Ground Range Detected High Resolution (GRDH) products with a resolution of 10 × 10 m. Preprocessing -including radiometric calibration  (Miranda, 2015)  as well as Range Doppler Terrain Correction using the shuttle radar topographic mission digital elevation model -was applied to the SAR images thanks to the SNAP ESA software (Brockmann Consult, C-S, 2019).
The dataset is composed of 412 time series with 4 to 20 optical images and 10 to 58 SAR images in each sequence. On average, there are 9 optical and 14 SAR images per sequence. The period of acquisition goes from December 2018 to May 2019. A flood event is occuring in 40% of the optical Sentinel 2 images and in 47% of the SAR Sentinel 1 images. As in the MediaEval dataset, once a flood occurred in a sequence, all the subsequent images are labeled as flooded which corresponds to the hypothesis that the surface still presents characteristic modifications after the event. 1 https://emergency. copernicus. eu/ 2 https://scihub. copernicus. eu/.
BENEFIT OF MULTISPECTRAL AND SAR DATASET This dataset has been constituted to train a new architecture of neural networks for dual-mode and multi-temporal flood classification. We provide an in-depth study of the various components of the model. Indeed, our goal is to assess the relevance of each modality and the contribution of temporal analysis. First, SAR images are expected to help the ground classification generally conducted on multispectral data. For example, the normalized water difference index  (Gao, 1996)  is widely used to detect the presence of water bodies. However, depending on the sensor, this index may suffer from one drawback: bands associated with the near-infrared and short-waved infrared can present a loss of resolution compared to the RGB ones. On the other hand, SAR images are more sensitive to the geometrical distribution of the backscattering elements.
FLOOD DETECTION In order to show the interest of this dataset, we used the stateof-the-art ResNet-50  (He et al. , 2015)  network for the detection  of flooding events in each image. This network was designed for RGB images classification and the first convolutional layer was modified to take into account the correct number of bands for multispectral (12 bands) and SAR (2 bands) data. This implies that the network had to be retrained from scratch for these two configurations while on the RGB images the pretrained network could be used explaining part of the accuracy gap in the baseline. For the SAR and multispectral configuration, the models were trained for 400 epochs using Stochastic Gradient Descent with a learning rate of 7. 10 -6. The ResNet-50 network may not be optimal when considering a large number of channels, which may also explain the lower accuracy on multispectral data.
DISCUSSION The accuracy scores obtained with the ResNets on each image as well as the proposed network on each sequence are presented in Table  1  where the metric was computed for each image in the dataset. The experiments performed on the SEN12-FLOOD dataset give several insights on the interest to use optical and SAR images for flood detection. On the first hand, state of the art techniques perform well on both optical and SAR images and manage to retrieve most of the flood phenomena. On the second hand, it appears clearly that the temporal dimension is a key to characterize these events. Indeed, considering the temporal dimension leads to a significant error reduction using optical as well as SAR images. Moreover, SAR and optical modalities appear to be complementary for the detection of flood phenomena as the best accuracy on the dataset is achieved by using both kinds of data.
CONCLUSION In this paper we presented a new dataset composed of optical and SAR images for the detection of flood events in time series. We also proposed a baseline for multitemporal classification of floods based on spatial-temporal porcessing by residual and GRU networks. Our experiments show the interest to consider both of these modalities for this task. Future work may include the search for better fusion strategies as well as the efficient processing of multispectral data. The SEN12-FLOOD dataset as well as the code of the proposed approach can be downloaded at https://clmrmb. github. io/SEN12-FLOOD.
2 , Extended Data Fig. 5 ). The model reliably reproduces non-exhaust vehicular emissions, SIA, POA and SOA concentrations (Methods section 'Air-quality model')  24. Modelled OP v shows a good agreement with measured OP v also for sites and   and 30 ), PM 10 vehicular exhaust emissions (HOA), PM 10 vehicular non-exhaust emissions (which includes vehicular wear and SCOA), residential PM 10 , mainly from heating emissions (similar to gas phase emissions, Supplementary Fig. 30 ), total NH 3 , SO 2 and total NO x emissions. periods that were not included in the training dataset (Extended Data Fig. 6 , Supplementary Fig. 26 ). This signifies that the model accurately represents the PM mass concentrations from various sources and supports the OP m parameters derived from ambient observations. In Europe, OP v and PM 10 concentrations have hotspots in urban environments (for example, Paris and the Po valley), although PM 10 concentrations are more regional than OP v (Fig.
32. Data corrections were applied as outlined in Allan et al. 33  and Daellenbach et al. regarding the offline application of the aerosol mass spectrometer  13, 14. Field blanks were handled in the same way as exposed samples and showed a negligible signal. We analysed 15 trace elements (Al, Fe, Ti, As, Cd, Cu, Mn, Mo, Ni, Pb, Rb, Sb, Se, V, Zn) by inductively coupled plasma mass spectrometry after acid digestion. Briefly, each sub-sample was acid-digested using HNO 3 , and H 2 O 2 in a microwave oven. Analysis took place with an inductively coupled plasma mass spectrometer (ELAN 6100 DRC II, Perkin Elmer)  34. We measured 19 polyaromatic hydrocarbons: acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, 2-methylfluoranthene, pyrene, benzo[a]anthracene, chrysene, retene, benzo[e]pyrene, benzo[ j]fluoranthene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, dibenzo[a,h]anthracene, benzo[g,h,i]perylene, indeno[1,2,3-cd]pyrene and coronene.
Source apportionment of OA and trace elements Positive matrix factorization. We performed source apportionment of the OA and of trace metals using positive matrix factorization, which is a statistical unmixing model  54. Positive matrix factorization explains the variability in a chosen dataset (x i,j ) with linear combinations of constant factor profiles (f k,j ) representing the chemical composition of the sources/components) and their contribution varying in time and/or space (g i,k ). Since positive matrix factorization allows only for positive results (g i,k , f k,j ), it is ideal for environmental purposes. The residual is termed e i,j. The index i represents a measurement (time and space), j an observed variable (trace metal, ion in aerosol mass spectrometer), and k is a factor. ∑ x g f e = + ( 1 ) i j k p i k k j i j , =1 , , , This equation is solved by Multilinear Engine 2 (ref.
55 ) and controlled by the software Source Finder (SoFi 56 ). Sources of OA. Multilinear Engine 2 allows using a priori information by constraining known factor profiles g i,k within a certain range defined by the scalar a value ( a 0 ≤ ≤1). f f af = ′ ± ′ (2) k j k j k j , , , For the source apportionment of the OA in PM 10 at the nine sites in Switzerland and Liechtenstein during 2013 (see Daellenbach et al. 14  ), we used reference spectra representing HOA and COA (Crippa et al. 57  ). The sources' contributions to water-soluble OA were subsequently corrected to contributions to OA using source-specific recoveries derived in Daellenbach et al. 13  and confirmed in other studies  16, 58. The evaluation, sensitivity tests (a-value, extreme bootstrap scenarios, source-specific recoveries) and uncertainty estimates of the organic source apportionment results were presented in Daellenbach et al.
We used PM 10 and PM 2. 5 samples including samples from an urban roadside site (Bern, Bollwerk, 24 samples), an urban background site (Zurich, Kaserne, 36 samples), Magadino (rural site affected by wood burning, 58 samples), S. Vittore (wintertime wood-burning pollution episode at rural site, 1 sample) a rural background site (Payerne, MeteoSuisse, 24 samples) and a road tunnel (4 samples). The data matrix x i,j consists of the concentrations determined for the mentioned trace metals (if the concentration was smaller than the half the quantification limit, QL/2). The error matrix s i,j was computed using the quantification limit and a term depending on the concentration. s x x = (QL ) + ( ) = (QL ) + (3) i j j i j j i j , 2 , 2 2 , As an overall measure of the quality of the data representation, we used Q/Q exp (where Q = ∑ i j e s , ( ) ( ) i j i j , 2 , 2 and Q nm p n m = -( + ), where n and m are the number of samples and the number of variables).
5-dimensional volatility basis set OA chemistry/partitioning module  85. The volatility basis set module of the model was modified to separate the contributions of specific OA sources including biomass burning, gasoline and diesel vehicles, other anthropogenic activities and biogenic sources  86, 87. The parametrization of biomass burning and vehicles was updated based on Ciarelli et al. 86  and Platt et al. 88  , respectively. The source-specific anthropogenic emissions were obtained from the high-resolution European emission inventory TNO-MACC-III (The Netherlands Organization for Applied Scientific Research-Monitoring Atmospheric Composition and Climate)  89. Biogenic emissions were prepared using the PSI model  87. The initial and boundary conditions were obtained from the MOZART (Model of Ozone and Related Chemical Tracers) global model datasets MOZART-4/ GEOS-5, which are widely used in chemical transport models.
The MOZART-4 data divided dust into four size bins ranging from 0. 05 µm to 5. 0 µm, and with the largest contribution found in dust bin 2 (0. 5-1. 25 µm)  [90] [91] [92]. The modelled gas and aerosol concentrations (OA separated into different POA and SOA categories, NH 4 + , NO 3 -, SO 4 2-and elemental carbon) were in good agreement with data from the European air-quality database AirBase v7 and measurements from Aerodyne aerosol chemical speciation monitor or Aerodyne aerosol mass spectrometer stations  24, 86, 87. The modelled OA components were reclassified into four categories to match the source apportionment of OP: (1) HOA: the sum of POA from vehicles and from other fossil fuel combustion emissions; (2) BBOA: the POA from biomass burning (and other residential heating sources such as coal); (3) bioSOA: the SOA from biogenic emissions; and (4) aSOA: the sum of SOA from all anthropogenic sources (Supplementary Fig.
08 nmol min -1 per µg of source mass versus OP m (BBOA(France)) = 0. 22 nmol min -1 per µg of source mass; OP m (vehicular wear(MLR)) = 3. 51 nmol min -1 per µg of source mass versus OP m (vehicular wear(France)) = 1. 40 nmol min -1 per µg of source mass). We also compare the modelled OP v to measurements from 2011 that were not part of the training dataset. We use samples from Zurich (Switzerland, 19 samples) and from an additional site in Lens  34  , France (95 samples, Extended Data Fig. 6 ). All the predicted OP assays show remarkable agreement with the measurements (Supplementary Fig. 26 ), with R values of 0. 83, 0. 71, 0. 67 for DTT v , AA v and DCFH v , respectively. The model accurately captures DTT v (-13%) and AA v (5%), whereas DCFH v is underestimated by 40%, as assessed by the median relative difference between model and measured OP v.
Acknowledgements We thank the  Swiss Federal Office of Environment ; Liechtenstein; Ostluft; and the  Swiss cantons Basel, Graubünden and Thurgau. We also thank  AWEL Zurich  for providing us with samples collected in Islisbergtunnel. For the air-quality modelling, we thank the  TNO  for providing anthropogenic emissions, the  European Centre for Medium-Range Weather Forecasts  ( ECMWF ) for access to the meteorological data,  National Center for Atmospheric Research (NCAR)  for the initial and boundary conditions, the  European Environmental Agency (EEA)  for the air-quality data and the  Swiss National Supercomputing Centre (CSCS). The support by Ramboll for the CAMx model is gratefully acknowledged. We acknowledge the  Swiss Federal Laboratories for Materials Science and Technology (Empa)  and the  National Air Pollution Monitoring Network (NABEL)  for providing air-quality data.
We acknowledge the use of country borders from https://thematicmapping. org/downloads/ world_borders. phpshared under a Creative Commons Attribution-Share Alike license. We thank  N. Marchand  for scientific discussions. K. R. D. acknowledges support by  VULCAIN  and  Swiss National Science Foundation  mobility grant  P2EZP2_181599. M. G. and J. D. acknowledge financial support by the  Swiss National Science Foundation  grant  CR32I3_166325. A. A. and O. F. acknowledge financial support by the  French Ministry of Environment. G. U. and J. L. J. thank the programmes  LEFE CHAT  (grant  863353 ), LABEX  OSUG@2020  ( ANR-10-LABX-56 ),  ANR-19-CE34-0002-01 , and the "  Investissements d'avenir" programme  ( ANR-15-IDEX-02 ) for supporting this work and funding analytical instruments.
A clear disadvantage of this approach is that GWASs may prioritize SNPs for which the biological function is yet unknown or unclear. 7 Hence, GWAS usually identifies SNPs that need to be subjected to further analyses to understand the pathways between the SNPs and the outcome. Close collaboration with geneticists and biologists in consortia, such as the Gentrepreneur Consortium (Van der Loos et al. 2010) and the Social Science Genetic Association Consortium, 8 is therefore a prerequisite for the success of GWAS analysis. The combination of a very stringent significance level and the small effect sizes of individual SNPs implies that large samples are needed to be adequately powered for gene discovery. The typical dataset has only several thousands of observations, and therefore, datasets need to be combined into mega-analyses or meta-analyses.
(2013)  was underpowered. Although the availability of genetic data is rapidly increasing, genetic data are collected primarily for medical purposes, and measures for 7 Relatedly, GWAS models usually use a very small number of control variables to capture the full relationship between the SNP and the outcome. For example,  Van der Loos et al. (2013)  control for only sex, age, and genetic relatedness in their GWAS on self-employment. The use of a small number of control variables causes the interpretation of the estimated effects to be not as straightforward because there may be many pathways through which a SNP influences a behavioral outcome. 8 https://www. thessgac. org/. entrepreneurship are not always available in medical datasets. There is progress in the collection of genetic data in surveys with an economic focus (such as the US Health and Retirement Study and the English Longitudinal Study of Ageing), but at this moment, a sufficiently large analysis sample for a GWAS on entrepreneurship is not available.
14 For illustration purposes, we analyzed all available mental health related polygenic risk scores in the Health and Retirement Study in the present study. The set of polygenic risk scores includes traits for which the link with entrepreneurship in not always evident. Therefore, future studies may use theoretical or other insights for selecting the most promising candidates from the set of available polygenic risk scores rather than using them all. However, the fact that ADHD is found to be the strongest association in our analyses builds confidence in our approach since there are several nongenetic studies showing a similar link  (Verheul et al. 2015 (Verheul et al. , 2016;; Antshel 2017; Wiklund et al. 2017; Lerner et al. 2019). Nevertheless, future studies need to replicate the current findings in independent datasets to investigate their robustness and generalizability.
Datasets that contain both genetic data and entrepreneurship information are relatively scarce  (Van der Loos et al. 2013 ), but the advent of large genotyped biobanks such as the UK Biobank  (Bycroft et al. 2018 ) and the Estonian Biobank  (Leitsalu et al. 2015)  is currently changing the landscape. Hence, a sufficiently powered GWAS on entrepreneurship may soon become feasible. Because of data constraints, the latest and largest GWAS on entrepreneurship used self-employment as a proxy for entrepreneurship  (Van der Loos et al. 2013). With more data becoming available, future GWASs of entrepreneurship may benefit from the analysis of an entrepreneurship measure less episodic in nature, such as serial or high-performance entrepreneurship. With more precise classification of individuals into occupational groups, the GWAS becomes more powerful and hence the chance to detect associations between individual genetic variants and entrepreneurship becomes larger.