Mapping Land Subsidence Related to Underground Coal Fires in the Wuda Coalfield (Northern China) Using a Small Stack of ALOS PALSAR Differential Interferograms

Coal fires have been found to be a serious problem worldwide in coal mining reserves. Coal fires burn valuable coal reserves and lead to severe environmental degradation of the region. Moreover, coal fires can result in massive surface displacements due to the reduction in volume of the burning coal and can cause thermal effects in the adjacent rock mass particularly cracks and fissures. The Wuda coalfield in Northern China is known for being an exclusive storehouse of prime coking coal as well as for being the site of occurrence of the maximum number of known coal fires among all the coalfields in China and worldwide, and is chosen as our study area. In this study, we have investigated the capabilities and limitations of ALOS PALSAR data for monitoring the land subsidence that accompanies coal fires by means of satellite differential interferometric synthetic aperture radar (DInSAR) observations. An approach to map the large and highly non-linear subsidence based on a small number of SAR images was applied to the Wuda coalfield to reveal the spatial and temporal signals of land subsidence in areas affected by coal fires. The DInSAR results agree well with coal fire data obtained from field investigations and thermal anomaly information, which demonstrates that the capability of ALOS PALSAR data and the proposed approach have remarkable potential to detect this land subsidence of interest. In addition, our results also provide a spatial extent and temporal evolution of the land subsidence behavior accompanying the coal fires, which indicated that several coal fire zones suffer accelerated ongoing land subsidence, whilst other coal fire zones are newly subsiding areas arising from coal fires in the period of development

Callback2Vec: Callback-aware hierarchical embedding for mobile application

Although numerous embedding approaches have been proposed for code representation of mobile applications, insufficient attention has been paid to its essential running nature: event-driven. As a result, the contextual semantics of event-driven callbacks re hardly captured. Existing solutions either discard the information of event callbacks such as their sequences, or simply treat event callbacks as ordinary APIs. Both of the solutions deviate from the actual running behavior of the applications and thus suffer from critical information loss of the callback contexts. To address the problem, in this paper, a callback based hierarchical embedding approach Callback2Vec is proposed, in which ordinary APIs and callbacks are distinguished and tackled at different levels in a top-down fashion. As such, the contextual semantics of callbacks can be reasonably represented by the embedding vectors. In particular, a fine-grained callback-sequence-generation algorithm is devised to capture the running behavior of callbacks. To evaluate the representation capability of Callback2Vec, a systematic analysis targeting at the embedding results is conducted, whereby the conventional embedding characteristics are rigorously investigated and new implications are identified. Of significance, the proposed embedding approach has been validated to be capable of providing novel solutions for typical downstream applications, through comprehensive experiments with large scale public datasets

Numerical Simulation of the Diurnal Cycle of a Precipitation System during KWAJEX by 2D and 3D Cloud-Resolving Models

Two-dimensional (2D) and three-dimensional (3D) cloud-resolving model (CRM) results from the Tropical Rainfall Measuring Mission Kwajalein Experiment (KWAJEX) were applied to analyze the diurnal cycle of cloud development in the tropics. Cloud development is intimately associated with the growth of secondary circulation, which can be analyzed in the budget of perturbation kinetic energy (PKE). The ice and liquid water path (IWP+LWP) is a fundamental parameter for estimating clouds, with the analyzed results suggesting that (1) the ice and liquid water path (IWP+LWP) and PKE values attained in convective regions were higher during the nighttime than during the daytime and that the maxima of IWP+LWP and PKE occurred at midnight in the lower troposphere in the 3D model run, and that (2) the IWP+LWP and PKE values in stratiform regions were much higher in the afternoon than in the morning, while the maxima of IWP+LWP and PKE occurred in the afternoon in the middle troposphere in the 2D model run. Further analysis demonstrated that both the high IWP+LWP and PKE values in the lower troposphere at midnight were mainly associated with the warm–humid lower troposphere in convective regions. However, those in the middle troposphere in the afternoon were primarily linked to the dry–cold upper troposphere and moist–warm lower troposphere in stratiform regions. The results further revealed that (1) both IWP+LWP and PKE exhibited shorter time scales in the 2D model runs than in the 3D model runs and that (2) the maximum IWP+LWP values occurred in the afternoon in the 2D model runs and at midnight in the 3D model runs

Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products

Early prediction for mode anomaly in generative adversarial network training: an empirical study

Mode anomaly (MA for short) significantly blocks the application of generative adversarial networks (GANs). Although diverse metrics have been proposed to measure the MA, and a lot of efforts have been made to resolve the MA, none of them gives a quantitative definition for MA detection. Moreover, very few studies concentrate on the early-stage prediction of MA. In this paper, we make the first effort to this field with a systematic empirical study. To this end, we first give a fine-grained definition where the MA is categorized into three typical sub-patterns. Afterwards, traditional MA metrics are studied with extensive experiments on numbers of representative combinations of subjects (including 13 GANs and 3 datasets) to explore their sensitivity for the MA across different training steps. We find that in most of cases, the MA can be reasonably predicted in very early training stage through our sensitivity studies. Under the insight, we propose a novel prediction strategy using conception of “anomaly sign”. The evaluation results on diverse experimental subjects demonstrate the feasibility and high accuracy for the early prediction of MA. We also discuss the prediction efficiency, as well as analyze the prediction effectiveness from human perception

Review sharing via deep semi-supervised code clone detection

Code review as a typical type of user feedback has recently drawn increasing attentions for improving code quality. To carry out research on code review, sufficient review data is normally required. As a result, recent efforts commonly focus on analysis for projects with sufficient reviews (called “s-projects”), rather than projects with extremely few ones (called “f-projects”). Actually, through statistics on public platforms, the latter ones dominate open source software, in which novel approaches should be explored to improve their review-based code improvement. In this paper, we try to address the problem via building a review sharing channel where the informative review can be reasonably delivered from s-projects to the f-projects. To ensure the accuracy of shared reviews, we introduce a novel code clone detection model based on Convolutional Neural Network (CNN), and build suitable “s-projects, f-projects” pairs through the clone detection. Especially, to alleviate the dataset heterogeneity between the training and testing, an autoencoder-based semi-supervised learning strategy is employed. Furthermore, to improve the sharing experience, heuristic filtering tactics are applied to reduce the time cost. Meanwhile, the LDA (Latent Dirichlet Allocation)-based ranking algorithm is used for presenting diverse review themes. We have implemented the sharing channel as a prototype system RSharer+, which contains three representative modules: data preprocessing, code clone detection and review presentation. The collected datasets are first transformed into context-sensitive numerical vectors in the data proprecessing. Then in the clone detection, data vectors are trained and tested on the BigCloneBench and real code-review pairs. At last, the presentation module provides review classification and theme extraction for better sharing experience. Extensive comparative experiments on hundreds of real labelled code fragments demonstrate the precision of clone detection and the effectiveness of review sharing

The main evolution of remote sensing for land resources of China in recent years

The development of earth observation techniques has expanded and deepened the application area of remote sensing further. Meanwhile, the application on remote sensing for land resources has entered a new era marked by high spatial resolution, high spectral resolution and high temporal resolution. Nowadays, remote sensing techniques serve as an important support for the great discovery of geosciences and the modernization of geological work. The remote sensing satellites of our country develop to groups, networks and seriations; its application area develops to fine and quantitative direction; the recent researches both in basic field and application area develop to integration. In the following years, the need for remote sensing application will be more and more imminent. It is an inevitable tide for land remote sensing to use all-weather and multi-angle observation system by which we can observe the earth real-time to survey, suspect and research the resources, energy, and environment What is more, impractical hyper-spectral remote sensing system is an urgent technical platform problem to be solved for further application in remote sensing for resources and energy. ? 2008 SPIE.EI