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Abstract High-latitude and altitude cold regions are affected by climate warming and permafrost degradation. One of the major concerns associated with degrading permafrost is thaw subsidence (TS) due to melting of excess ground ice and associated thaw consolidation. Field observations, remote sensing, and numerical modeling are used to measure and estimate the extent and rates of TS across broad spatial and temporal scales. Our new data synthesis effort from diverse permafrost regions of North America and Eurasia, confirms widespread TS across the panarctic permafrost domain with rates of up to 2 cm yr⁻¹in the areas with low ice content and more than 3 cm yr⁻¹in regions with ice-rich permafrost. Areas with human activities or areas affected by wildfires exhibited higher subsidence rates. Our findings suggest that permafrost landscapes are undergoing geomorphic change that is impacting hydrology, ecosystems, and human infrastructure. The development of a systematic TS monitoring is urgently needed to deliver consistent and continuous exchange of data across different permafrost regions. Integration of coordinated field observations, remote sensing, and modeling of TS across a range of scales would contribute to better understanding of rapidly changing permafrost environments and resulting climate feedbacks. 

Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the fingerprint to software noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise. 

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time. 

Abstract Global agricultural trade creates multiple telecoupled flows of nitrogen (N) and phosphorus (P). The flows of physical and virtual nutrients along with trade have discrepant effects on natural resources in different countries. However, existing literature has not quantified or analyzed such effects yet. Here we quantified the physical and virtual N and P flows embedded in the global agricultural trade networks from 1997 to 2016 and elaborated components of the telecoupling framework. The N and P flows both increased continuously and more than 25% of global consumption of nutrients in agricultural products were related to physical nutrient flows, while virtual nutrient flows were equivalent to one-third of the nutrients inputs into global agricultural system. These flows have positive telecoupling effects on saving N and P resources at the global scale. Reducing inefficient trade flows will enhance resource conservation, environmental sustainability in the hyper-globalized world.