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Do communities with the same level of inequality but a different level of income polarisation perform differently in terms of public schooling? To answer this question, we extend the theoretical model of schooling choice and voting developed by de la Croix and Doepke (2009), introducing a more general income distribution characterised by a three-member mixture instead of a single uniform distribution. We show that not only income inequality, but also income polarisation, matters in explaining disparities in public education quality across communities. Public schooling is an important issue for the middle class, which is more inclined to pay higher taxes in return for better public schools. Contrastingly, poorer households may be less concerned about public education, while rich parents are more willing to opt-out of the public system, sending their children to private schools. Using micro-data covering 724 school districts of California and introducing a new measure of income polarisation, we find that school quality in low-income districts depends mainly on income polarisation, while in richer districts it depends mainly on income inequality.

International audience; Matrices with the displacement structures of circulant, Toeplitz, and Hankel types as well as matrices with structures generalizing these types are omnipresent in computations of sciences and engineering. In this paper we present efficient and memory-reduced quantum algorithms for solving linear systems with such structures by devising an approach to implement the block-encodings of these structured matrices. More specifically, by decomposing n×n dense matrices into linear combinations of displacement matrices, we first deduce the parametrized representations of the matrices with displacement structures so that they can be treated similarly. With such representations, we then construct ε-approximate block-encodings of these structured matrices in two different data access models, i.e., the black-box model and the quantum random access memory (QRAM) data structure model. It is shown the quantum linear system solvers based on the proposed block-encodings provide a quadratic speedup with respect to the dimension over classical algorithms in the black-box model and an exponential speedup in the QRAM data structure model. In particular, these linear system solvers subsume known results with significant improvements and also can motivate new instances where there was no specialized quantum algorithm before. As an application, one of the quantum linear system solvers is applied to the linear prediction of time series, which justifies the claimed quantum speedup is achievable for problems of practical interest.

Visible-near infrared (vis–NIR) spectroscopy has been widely used to characterize soil information from field to global scales. Before applying a calibrated spectral predictive model to acquire soil information, either independent validation ork-fold cross validation is used to evaluate model performance. However, there is no consensus on which validation strategy is more suitable and robust when evaluating model performance for the studies in different scales. The objective of this study is to evaluate and compare the model performance of two validation strategies coupling different calibration sizes (a ratio of calibration to validation of 2:1, 4:1 and 9:1) and calibration sampling strategies (random sampling (RS), rank, Kennard-Stone (KS), rank-Kennard-Stone (RKS) and conditioned Latin hypercube sampling (cLHS)) across scales. A total of 17,272 vis–NIR spectra of mineral soils from LUCAS data (continental scale) and theirsoil organic carbon(SOC) and clay contents were used in this study, and the dataset was further split into national (2761 samples in France) and five regional datasets (110 to 248 samples from five French administrative regions). To eliminate the effect of changing validation set on the model performance, a consistent test set (20% of total samples at each scale) was split to evaluate all the combinations involved in two validation strategies. The Lin’s concordancecorrelation coefficient(CCC) of the cubist model were stable for both SOC and clay for different calibration sizes, calibration sampling and validation strategies for a large calibration size (>1400) at the national and continental scales. A larger calibration size can potentially improve model performance for a small dataset (k-fold cross validation would be appropriate; (2) for a small data set (dozens to hundreds),k-fold cross validation and/or repeated random sampling for independent validation would be more robust for spectral predictive model evaluation.

16 Pags.- 12 Figs.- 8 Tabls., Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper., Nejc Bezak and Matjaž Mikoš would like to acknowledge the support of the Slovenian Research Agency through grant P2-0180. Diana Vieira is funded by national funds (OE), through FCT – Fundação para a Ciência e a Tecnologia, I.P., in the scope of the framework contract foreseen - DL57/2016 (CDL-CTTRI-97-ARH/2018 - REF.191-97-ARH/2018), and acknowledges CESAM financial support of through (UIDP/50017/2020+UIDB/50017/2020). Jae E. Yang and Pasquale Borrelli are funded by the EcoSSSoil Project, Korea Environmental Industry & Technology Institute (KEITI), Korea (Grant No. 2019002820004). Walter Chen is funded by the Ministry of Science and Technology (Taiwan) Research Project (Grant Number MOST 109-2121-M-027-001).

International audience; This study investigates the differences between zombie firms and non-zombie firms in corporate social responsibility activities such as reporting, disclosure and fulfillment. Using Chinese listing company data collected from 2009 to 2016, we apply a three stage model with a double Heckman correction to deal with potential self-selection/endogeneity bias and to measure the differences consistently. We found that zombie firms are less willing to release standalone corporate social responsibility reports than non-zombie firms. Among companies that release standalone corporate social responsibility reports, the corporate social responsibility disclosure of zombie firms is at least not worse than non-zombie firms, but the corporate social responsibility fulfillment is significantly lower. We conclude from this gap between disclosure and fulfillment to the hypocritical behavior of zombie firms, due to the absence of control in corporate social responsibility. We suggest that government should enhance supervision over zombie firms’ corporate social responsibility activities and subsidies towards them in order to lower their economic damage. Supplementary analyses provide some clues concerning the heterogeneity of inconsistence in term of external support characteristics, ownership and censorship which require further studies.

In this paper, we propose a new saliency based stereoscopic image retargeting method based on the characteristics of the Human Visual System (HVS). A new stereoscopic saliency detection method is designed by adopting low-level features of intensity, color, texture and depth. Besides, the viewing bias factors including center bias and depth bias existing in the HVS are adopted for stereoscopic visual attention modeling. Since the HVS is sensitive to edges in images, we fuse the saliency and edge maps to predict the visual significance of image pixels for image resizing. With the visual significance measure, we propose an image resizing method by minimizing the structure and depth distortion for stereoscopic image retargeting. Experimental results have shown that both our stereoscopic saliency detection and image retargeting methods can obtain better performance than the existing related methods on the public databases.

In this paper, we propose a novel universal framework for salient object detection, which aims to enhance the performance of any existing saliency detection method. First, rough salient regions are extracted from any existing saliency detection model with distance weighting, adaptive binarization, and morphological closing. With the superpixel segmentation, a Bayesian decision model is adopted to refine the rough saliency map to obtain a more accurate saliency map. An iterative optimization method is designed to obtain better saliency results by exploiting the characteristics of the output saliency map each time. Through the iterative optimization process, the rough saliency map is updated step by step with better and better performance until an optimal saliency map is obtained. Experimental results on the public salient object detection datasets with ground truth demonstrate the promising performance of the proposed universal framework subjectively and objectively.

In the previous years, a lot of saliency detection algorithms have been designed for saliency computation of visual content. Recently, stereoscopic display techniques have developed rapidly, which results in much requirement of stereoscopic saliency detection for emerging stereoscopic applications. Different from 2D saliency prediction, stereoscopic saliency detection methods have to consider depth factor. We design a novel stereoscopic saliency detection algorithm by machine learning technique. First, the features of luminance, color and texture are extracted to calculate the feature contract for predicting feature maps of stereoscopic images. Furthermore, the depth features are extracted for depth feature map computation. Sematic features including the center-bias factor and other top-down cues are also applied as the features in the proposed stereoscopic saliency detection method. Support Vector Regression (SVR) is applied to learn the saliency detection model of stereoscopic images. Experimental results obtained on a public large-scale eye tracking database demonstrate that the proposed method can predict better saliency results for stereoscopic images than other existing ones.

In this paper, we investigate the visual attention modeling for stereoscopic video from the following two aspects. First, we build one large-scale eye tracking database as the benchmark of visual attention modeling for stereoscopic video. The database includes 47 video sequences and their corresponding eye fixation data. Second, we propose a novel computational model of visual attention for stereoscopic video based on Gestalt theory. In the proposed model, we extract the low-level features, including luminance, color, texture, and depth, from discrete cosine transform coefficients, which are used to calculate feature contrast for the spatial saliency computation. The temporal saliency is calculated by the motion contrast from the planar and depth motion features in the stereoscopic video sequences. The final saliency is estimated by fusing the spatial and temporal saliency with uncertainty weighting, which is estimated by the laws of proximity, continuity, and common fate in Gestalt theory. Experimental results show that the proposed method outperforms the state-of-the-art stereoscopic video saliency detection models on our built large-scale eye tracking database and one other database (DML-ITRACK-3D).

International audience; In this paper, we propose a computational model of visual attention for stereoscopic video. Low-level visual features including color, luminance, texture and depth are used to calculate feature contrast for spatial saliency of stereoscopic video frames. Besides, the proposed model adopts motion features to compute the temporal saliency. Here, we extract the relative planar and depth motion for temporal saliency calculation. The final saliency map is computed by fusing the spatial and temporal saliency together. Experimental results show the promising performance of the proposed method in saliency prediction for stereoscopic video.