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Previous studies have shown that focal accent is an important means to transfer information. As a type of focus, contrastive focus appears as the correction to reject the presupposed information. The phonetic variations between two kinds of contrastive focus, which differ in the order of two constituents (rejection and correction), has not be investigated. Based on the BLCU-SAIT speech corpus, the current study aims to fill the gap by analyzing F0, intensity and duration of 315 Mandarin utterances produced by 21 native Chinese speakers. The acoustic data of both two kinds of contrastive focus show on-focus expansion of duration, F0 and intensity and post-focus compression of F0 and intensity. In-depth statistical analyses reveal contrastive focus at the latter part of the sentence demonstrates more salient prosodic variations than that in the front part.

With rapid environmental degeneration and socio-economic development, the human settlement environment (HSE) has experienced dramatic changes and attracted attention from different communities. Consequently, the spatial-temporal evaluation of natural suitability of the human settlement environment (NSHSE) has become essential for understanding the patterns and dynamics of HSE, and for coordinating sustainable development among regional populations, resources, and environments. This study aims to explore the spatial-temporal evolution of NSHSE patterns in 1997, 2005, and 2009 in Fengjie County near the Three Gorges Reservoir Area (TGRA). A spatially weighted NSHSE model was established by integrating multi-source data (e.g., census data, meteorological data, remote sensing images, DEM data, and GIS data) into one framework, where the Ordinary Least Squares (OLS) linear regression model was applied to calculate the weights of indices in the NSHSE model. Results show that the trend of natural suitability has been first downward and then upward, which is evidenced by the disparity of NSHSE existing in the south, north, and central areas of Fengjie County. Results also reveal clustered NSHSE patterns for all 30 townships. Meanwhile, NSHSE has significant influence on population distribution, and 71.49% of the total population is living in moderate and high suitable districts.

Due to the limitation of labeled training samples, computational complexity, and other difficulties, active learning (AL) algorithms aiming at finding the most informative training samples have been an active topic of research in remote sensing image classification in the last few years. Usually, AL follows an iterative scheme, and the search of new samples relies on the whole image, resulting in that an approach may turn out to be prohibitive when the data sets are huge, e.g., hyperspectral data. Large amounts of unlabeled samples are easy to collect indeed, with respect to the cost of labeled sample collection. However, algorithm complexity, data storage capacity and processing times are also limited. Therefore, a sample set smaller in size, and consisting of the most valuable information, is preferable. In this work, we propose a design protocol to generate a more significant candidate sample set for active learning, aiming at reducing the unlabeled sample search complexity, and eventually improving the classification performance. The basic idea is providing the initial labeled and unlabeled samples that are composed of mixed or pure samples for AL heuristics, to find out which one is better for AL from the low-cost sample design point of view. For comparison and validation purposes, six state-of-the-art AL methods (including breaking ties, margin sampling, margin sampling by closest support vectors, normalized entropy query-by-committee, multi-class level uncertainty and multi view adaptive maximum disagreement based active learning) were tested on real hyperspectral images with different resolution both with and without the proposed sample design protocol. Experimental results confirmed the advantages of the proposed technique. New Sample design protocol for active learning was investigated.Three hyperspectral images were used to evaluate the performance of the method.The values of mixed pixels, both for labeled and unlabeled samples, were confirmed.

Compared to the urban sprawl in western countries, China’s version varies, although it retains a dispersed development pattern, which is an important feature. A new framework is provided in this paper to allow for a deeper understanding of China’s spatio-temporal sprawl patterns along its urban fringes. In this framework, Shannon’s entropy analysis and discontinuity index have been employed to quantify the spatial patterns of urban sprawl. Furthermore, a new approach, Spatial Change Frequency Analysis (SCFA), has been proposed in this paper to analyze the temporal patterns of urban sprawl related to individual pixel based on multi-temporal Landsat TM images. With two indicators, namely Spatial Change Frequency (SCF) and Spatial Change Intensity (SCI), this approach appears to effectively reveal the abnormal land use changes in the urban fringes. Jiangning District of Nanjing City, Jiangsu Province, a fast growing urban area, was selected as the study area due to its unique characteristics of urban fringes. The results indicate that the effectiveness of urban land-use planning and implementation of land-use planning policies play important roles in mitigating the negative effects of urban sprawl. Finally, some suggestions are proffered to support sustainable development at the urban fringes of Nanjing City.

Supervised classification of quad-polarimetric SAR images is often constrained by the availability of reliable training samples. Active learning (AL) provides a unique capability at selecting samples with high representation quality and low redundancy. The most important part of AL is the criterion for selecting the most informative candidates (pixels) by ranking. In this paper, class supports based on the posterior probability function are approximated by ensemble learning and majority voting. This approximation is statistically meaningful when a large enough classifier ensemble is exploited. In this work, we propose to use extreme learning machines and apply AL to quad-polarimetric SAR image classification. Extreme learning machines are ideal because of their fast operation, straightforward solution and strong generalization. As inputs to the so-called active extreme learning machines, both polarimetric and spatial features (morphological profiles) are considered. In order to validate the proposed method, results and performance are compared with random sampling and state-of-the-art AL methods, such as margin sampling, normalized entropy query-by-bagging and multiclass level uncertainty. Experimental results for four quad-polarimetric SAR images collected by RADARSAT-2, AirSAR and EMISAR indicate that the proposed method achieves promising results in different scenarios. Moreover, the proposed method is faster than existing techniques in both the learning and the classification phases.

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998–2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran’s I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998–2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran’s I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.

Remote sensing has been used in karst studies. The aim of this work is to investigate the use of MODIS time series to monitor and estimate karst rocky desertification (KRD). We propose a new phenology-tuned KRD model based BFAST (Breaks For Additive Seasonal and Trend) approach, Our strategy for KRD monitoring involving several steps: (a) phenology decomposition with BFAST; (b) seasonal parameters extraction; (c) phenology-tuned KRD modeling and ecological responses assessment. The strategy is applied in KRD monitoring and ecological responses assessment with the 4-year MODIS NDVI products that cover Qiubei and Yanshan counties, Yunnan province, and the results indicate that our approach is valid in ecological responses assessment of KRD.