Impact of forest fire on radial growth of tree rings and their element concentrations of Pinus sylvestris and Larix gmelinii in northern China

AimsThrough analyzing the responses of the radial growth and element concentrations (B, Mg, Al, K, Ca, Mn, Fe, Zn, Na, P, Ni, and Cu) of tree rings of two dominant tree species to forest fires, we aimed to investigate the relationship between tree rings and the fires. MethodsWe sampled wood cores of Pinus sylvestris and Larix gmelinii in the northern forest region of China, where forest fires happened in 1990 and 2008. The ring-width growth of P. sylvestris and L. gmelinii from 1986 to 1995 and 2004 to 2013 in two sites of Tahe County were measured. Element concentrations in tree rings were determined using inductively coupled plasma mass spectrometry (ICP-MS). ResultsOur results showed that tree-ring radial growth was largely reduced after the fire, together with the increase in concentrations of B, Al, Mn, and Fe but the decrease in some samples in K. Strong correlations were observed between tree-ring growth and concentrations of Mg and Mn of P. sylvestris and Znof L. gmelinii. DiscussionThe results provide evidence that variations in tree-ring growth and element concentrations, particularly concentrations of B, Al, Mn, and Fe, are potentially useful to monitor forest fires, which add new insights into the study of forest fire history

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm

Estimation and analysis of the multi-frequency and multi-channel DCB for BDS-3

Differential code bias (DCB) is one of the major errors in ionospheric modeling, satellite navigation, positioning, and timing. A new series of DCBs is derived from BDS multi-frequency and multi-channel signals. Firstly, this paper analyzes the code observation combination and estimable DCB type for BDS-3, establishes the mathematical model of multi-frequency and multi-channel DCB estimation, and estimates more than 20 types of DCB by using IGS data. On this basis, the precision, accuracy, and monthly stability of various DCBs are compared and analyzed comprehensively. The results indicate that the closure errors of BDS-3 DCBs are basically within 0.2 ns, which shows good precision. The estimated results have a good agreement with the DCB products provided by CAS and DLR. Six types of DCB differences with CAS are basically within 0.1 ns. Four types of DCB differences with DLR are basically within 0.2 ns. Due to the influence of error propagation, the accuracy and reliability of DCB obtained by linear transformation are not as good as DCB estimated directly. The monthly mean STD of BDS-3 DCBs is 0.083 ns, showing good medium- and long- term stability. Compared with that of BDS-2, the DCB stability of BDS-3 is relatively better

Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China

Forest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more complex than in pure forested areas since more socio-economic factors and human activity are involved in the ignition and spread of fire. The large and increasing number of lives being exposed to wildfire hazard highlights the need to understand the characteristics of these fires so that forest fire prediction and prevention can be efficient. In this study, Ripley's K(d) function and Random Forests (RF) were applied to analyze the drivers, spatial distribution and risk patterns of fires in Yichun, a typical FC in China. The results revealed a clustered distribution of forest fire ignitions in Yichun, as well as identified the driving factors and their dynamic influence on fire occurrence. Fire risk zones were identified based on RF modelling. Improved preventive measures can be implemented in the fire prone areas to reduce the risk of fire in Yichun by considering the factors identified in this study

HCSC: Hierarchical Contrastive Selective Coding

Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image representations can greatly benefit the semantic understanding on various downstream tasks. Existing contrastive representation learning methods lack such an important model capability. In addition, the negative pairs used in these methods are not guaranteed to be semantically distinct, which could further hamper the structural correctness of learned image representations. To tackle these limitations, we propose a novel contrastive learning framework called Hierarchical Contrastive Selective Coding (HCSC). In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space. To make image representations better fit such semantic structures, we employ and further improve conventional instance-wise and prototypical contrastive learning via an elaborate pair selection scheme. This scheme seeks to select more diverse positive pairs with similar semantics and more precise negative pairs with truly distinct semantics. On extensive downstream tasks, we verify the superior performance of HCSC over state-of-the-art contrastive methods, and the effectiveness of major model components is proved by plentiful analytical studies. We build a comprehensive model zoo in Sec. D. Our source code and model weights are available at https://github.com/gyfastas/HCSCComment: Accepted by CVPR 2022. arXiv v3: 800 epoch multi-crop model released; arXiv v2: more model weights released; arXiv v1: code & model weights release