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

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

Deep Review Sharing

Review-Based Software Improvement (RBSI for short) has drawn increasing research attentions in recent years. Relevant efforts focus on how to leverage the underlying information within reviews to obtain a better guidance for further updating. However, few efforts consider the Projects Without sufficient Reviews (PWR for short). Actually, PWR dominates the software projects, and the lack of PWR-based RBSI research severely blocks the improvement of certain software. In this paper, we make the first attempt to pave the road. Our goal is to establish a generic framework for sharing suitable and informative reviews to arbitrary PWR. To achieve this goal, we exploit techniques of code clone detection and review ranking. In order to improve the sharing precision, we introduce Convolutional Neural Network (CNN) into our clone detection, and design a novel CNN based clone searching module for our sharing system. Meanwhile, we adopt a heuristic filtering strategy to reduce the sharing time cost. We implement a prototype review sharing system RSharer and collect 72,440 code-review pairs as our ground knowledge. Empirical experiments on hundreds of real code fragments verify the effectiveness of RSharer. RSharer also achieves positive response and evaluation by expert developers

Autonomous Permission Recommendation

Modern smartphone operating systems (e.g., Android 6.0 and later versions) employ an ask-on-first-use policy to regulate app permissions. To assist users in policy decisions, relevant efforts have been focusing on leveraging contexts to capture users' privacy preferences. However, these techniques have various limitations, such as heavily relying on users' historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, and hard to determine whether a permission shall be granted. To address this problem, we propose an autonomous permission recommendation system, AutoPer+, to automatically recommend users the permission decisions at runtime. The main insight of our proposed system is that the natural language description of an app reflects its functionality and its similarity to other apps, and thus can be used to analyze whether a permission is needed indeed by it, and the apps similar to it. First, we introduce a multi-topic model into app functionality mining, and design a topic-permission mapper for the proposed recommendation system. Then we propose a deep semi-supervised machine using Long Short-Term Memory (LSTM) neural networks to identify similar apps, by which we can explore privacy permission usage in a cluster of apps. Finally, we capture a trade-off between privacy and utility to present a systematic recommendation. In addition to the permission decision ('Allow' or 'Deny'), the permission explanations are also provided for users to make decisions (called 'Ask'). We evaluate the proposed system via extensive comparison experiments on 31,023 Android apps. The results show that our approach achieves an accuracy of 84.1%. Moreover, we conduct user studies via installing AutoPer+ in the participants' own Android devices. We receive positive responses from the participants, which implies AutoPer+ is potentially for real-world deployment for enhancing current permission recommendation

Fe-Based MOFs for Photocatalytic CO₂ Reduction: Role of Coordination Unsaturated Sites and Dual Excitation Pathways

The utilization of solar energy for the conversion of CO₂ into valuable organic products is one of the best solutions to solve the problems of global warming and energy shortage. The development of photocatalysts capable of reducing CO₂ under visible light, especially those containing earth-abundant metals, is significant. Herein we report that a series of earth-abundant Fe-containing MOFs (MIL-101­(Fe), MIL-53­(Fe), MIL-88B­(Fe)) show photocatalytic activity for CO₂ reduction to give formate under visible light irradiation. The direct excitation of the Fe–O clusters in these MOFs induces the electron transfer from O^2– to Fe³⁺ to form Fe²⁺, which is responsible for the photocatalytic CO₂ reduction. Among the three investigated Fe-based MOFs, MIL-101­(Fe) showed the best activity due to the existence of the coordination unsaturated Fe sites in its structure. All three amine-functionalized Fe-containing MOFs (NH₂-MIL-101­(Fe), NH₂-MIL-53­(Fe) and NH₂-MIL-88B­(Fe)) showed enhanced photocatalytic activity in comparison to the unfunctionalized MOF, due to the existence of dual excitation pathways: i.e., excitation of an NH₂ functionality followed by an electron transfer to the Fe center in addition to the direct excitation of Fe–O clusters

Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrive time (BAT) of the human brain using resting-state CO2 fluctuations as a natural 'contrast media'. The deep-learning network was trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which included data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibited excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging