Nutrient Cycling and Plant Nutrition in Forest Ecosystems

Nutrient cycling is essential for maintaining nutrient supply to forest plants and for enhancing forest productivity. Nutrient cycling is also strongly linked to greenhouse gas emissions and thus to global climate change. Nutrient cycling and plant nutrition can be severely affected by anthropogenic and natural disturbance regimes. This Special Issue will provide an avenue to publish recent progress on research on nutrient cycling and plant nutrition in forest ecosystems and how nutrient cycling and plant nutrition are affected by disturbance regimes such as harvesting, atmospheric deposition and climate change

BBR-induced Stark shifts and level broadening in helium atom

The precise calculations of blackbody radiation (BBR)-induced Stark shifts and depopulation rates for low-lying states of helium atom with the use of variational approach are presented. An effect of the BBR-induced induced Stark-mixing of energy levels is considered. It is shown that this effect leads to a significant reduction of lifetimes of helium excited states. As a consequence the influence of Stark-mixing effect on the decay rates of metastable states in helium is discussed in context of formation processes of the cosmic microwave background

Efficient Simulation for Fixed-Receiver Bistatic SAR with Time and Frequency Synchronization Errors

Time and frequency synchronization is the key technique of bistatic synthetic aperture radar (BiSAR) system, and raw data simulation is an effective tool for verifying the time and frequency synchronization techniques. According to the two-dimensional (2-D) frequency spectrum of fixed-receiver BiSAR with time and frequency synchronization errors, a rapid raw data simulation method is proposed in this paper. Through 2-D inverse Stolt transform in 2-D frequency domain and phase compensation in Range-Doppler frequency domain, this method can realize two-dimensional spatial variation simulation for fixed-receiver BiSAR with time and frequency synchronization errors in a reasonable time consumption. Then the simulation efficiency of scene raw data can be significantly improved. Simulation results of point targets and extended scene are presented to validate the feasibility and efficiency of the proposed simulation method

Remote sensing and optimized neural networks for landslide risk assessment: Paving the way for mitigating Afghanistan landslide damage

Landslides caused by mega earthquakes and other extreme climate change pose a major threat to lives and infrastructure. However, the lack of a detailed and timely landslide inventory and relevant risk assessment attributable to ongoing conflicts limits the effective prevention measures in Afghanistan. This study presents the first landslide inventory covering the whole nation of Afghanistan from 2015 to the present utilizing Google Earth Pro imagery and manual interpretation. Based on this inventory of 3,260 mapped landslides, we analyzed the distributional characteristics of landslides in Afghanistan and conducted a risk assessment that included landslide susceptibility and hazard, and vulnerability of the bearing areas. The existing regional studies attest to the accuracy and reliability of the inventory, and the results of the risk assessment using the optimized neural network method in this study are well validated. This study can provide a good database for the Afghan government to carry out relevant pre-disaster warnings and post-disaster reconstruction, which can help to delineate hotspots where landslides may occur, and reduce potential economic losses and human casualties from future landslides

Local Manifold Augmentation for Multiview Semantic Consistency

Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation. Such variation is not directly accessible and therefore simulated by data augmentations. However, commonly adopted augmentations are handcrafted and limited to simple geometrical and color changes, which are unable to cover the abundant intra-class variation. In this paper, we propose to extract the underlying data variation from datasets and construct a novel augmentation operator, named local manifold augmentation (LMA). LMA is achieved by training an instance-conditioned generator to fit the distribution on the local manifold of data and sampling multiview data using it. LMA shows the ability to create an infinite number of data views, preserve semantics, and simulate complicated variations in object pose, viewpoint, lighting condition, background etc. Experiments show that with LMA integrated, self-supervised learning methods such as MoCov2 and SimSiam gain consistent improvement on prevalent benchmarks including CIFAR10, CIFAR100, STL10, ImageNet100, and ImageNet. Furthermore, LMA leads to representations that obtain more significant invariance to the viewpoint, object pose, and illumination changes and stronger robustness to various real distribution shifts reflected by ImageNet-V2, ImageNet-R, ImageNet Sketch etc

Distilling Representations from GAN Generator via Squeeze and Span

In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The controllable synthesis ability of GAN generators suggests that they maintain informative, disentangled, and explainable image representations, but leveraging and transferring their representations to downstream tasks is largely unexplored. In this paper, we propose to distill knowledge from GAN generators by squeezing and spanning their representations. We squeeze the generator features into representations that are invariant to semantic-preserving transformations through a network before they are distilled into the student network. We span the distilled representation of the synthetic domain to the real domain by also using real training data to remedy the mode collapse of GANs and boost the student network performance in a real domain. Experiments justify the efficacy of our method and reveal its great significance in self-supervised representation learning. Code is available at https://github.com/yangyu12/squeeze-and-span.Comment: 16 pages, NeurIPS 202

The biodiversity and stability of alpine meadow plant communities in relation to altitude gradient in three headwater resource regions

Kobresia pygmaea meadow community diversities in relation to altitude gradients (4200, 4300, 4400, 4450) on free grazing grassland was studied in the range of Chenduo county, Yushu prefecture, Qinghai province. Species richness and diversity index of vegetations in the four altitudes were comparatively analyzed. The results showed that the shape of species richness responsive curves to altitude gradient is “Bell-shape”. There were the same 11 common species in the four communities. The relative abundance of K. pygmaea decreased along increasing altitude. Moreover, the fuzzy membership functions were used to calculate the degree of stability, showing medium altitude > high altitude > low altitude, which suggested that grass land vegetation in low altitude of the sampling site had lower diversity, and the grade of species vulnerability risks may be decided with the help of the degree of stability.Key words: Alpine meadow, Yangtze, Yellow and Yalu Tsangpo river source region, altitude gradient, species diversity, membership functions

ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. Specifically, it targets minimizing the mutual information between visible and invisible regions of the foreground and background to spur interlayer independence. Through in-depth theoretical and experimental analyses, we justify that explicit layer independence modeling is critical to suppressing information leakage and contributes to impressive segmentation performance gains. Also, our ILSGAN achieves strong state-of-the-art generation quality and segmentation performance on complex real-world data.Comment: Accepted by AAAI 202