Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin By Multi-Temporal SAR Data

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multi-temporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multi-temporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (pre-flood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and an SHPS-based multi-temporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.This work was financially supported by the National Natural Science Foundation of China (grant numbers 41904001 and 41774006), the China Postdoctoral Science Foundation (grant number 2018M640733), the National Key Research and Development Program of China (grant number 2019YFC1509201), and the National Postdoctoral Program for Innovative Talents (grant number BX20180220)

GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians

Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry, and sophisticated interaction with light. This paper presents GaussianHair, a novel explicit hair representation. It enables comprehensive modeling of hair geometry and appearance from images, fostering innovative illumination effects and dynamic animation capabilities. At the heart of GaussianHair is the novel concept of representing each hair strand as a sequence of connected cylindrical 3D Gaussian primitives. This approach not only retains the hair's geometric structure and appearance but also allows for efficient rasterization onto a 2D image plane, facilitating differentiable volumetric rendering. We further enhance this model with the "GaussianHair Scattering Model", adept at recreating the slender structure of hair strands and accurately capturing their local diffuse color in uniform lighting. Through extensive experiments, we substantiate that GaussianHair achieves breakthroughs in both geometric and appearance fidelity, transcending the limitations encountered in state-of-the-art methods for hair reconstruction. Beyond representation, GaussianHair extends to support editing, relighting, and dynamic rendering of hair, offering seamless integration with conventional CG pipeline workflows. Complementing these advancements, we have compiled an extensive dataset of real human hair, each with meticulously detailed strand geometry, to propel further research in this field

An Agent-Based Model of a Pricing Process with Power Law, Volatility Clustering, and Jumps

In this paper, we propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes. We assume that there are two types of agents in the financial market: speculators and fundamental investors. Speculators use past prices to predict future prices and only buy assets whose prices are expected to rise. Fundamental investors attach a certain value to each asset and buy when the asset is undervalued by the market. When the expectations of agents are exogenously driven, that is, entirely shaped by exogenous news, then they can be modeled as following a random walk. We assume that the information related to the two types of agents in the model will arrive randomly with a certain probability distribution and change the viewpoint of the agents according to a certain percentage. Our simulated results show that this model can simulate well the random walk of asset prices and explain the power-law tail distribution of returns, volatility clustering, jumps, and structural changes of asset prices

Feasible Cluster Model Method for Simulating the Redox Potentials of Laccase CueO and Its Variant

Laccases are regarded as versatile green biocatalysts, and recent scientific research has focused on improving their redox potential for broader industrial and environmental applications. The density functional theory (DFT) quantum mechanics approach, sufficiently rigorous and efficient for the calculation of electronic structures, is conducted to better comprehend the connection between the redox potential and the atomic structural feature of laccases. According to the crystal structure of wild type laccase CueO and its variant, a truncated miniature cluster model method was established in this research. On the basic of thermodynamic cycle, the overall Gibbs free energy variations before and after the one-electron reduction were calculated. It turned out that the trends of redox potentials to increase after variant predicted by the theoretical calculations correlated well with those obtained by experiments, thereby validating the feasibility of this cluster model method for simulating the redox potentials of laccases

The Effect of Exogenous Oxytetracycline on High-Temperature Anaerobic Digestion of Elements in Swine Wastewater

Tetracycline antibiotics (TCs) are a common type of antibiotic found in swine wastewater. Oxytetracycline (OTC) is a significant type of TC. This study mainly examined the influence of OTC on high-temperature anaerobic digestion by adding OTC to collections of swine wastewater at different times during the digestion process. The results showed that high-temperature anaerobic digestion was suitable for the removal of TCs, with an 87% OTC removal efficiency achieved by day 20. Additionally, OTC added from external sources was found to inhibit the chlortetracycline degradation process and affect the first-order degradation kinetic model of TCs. Complexation reactions were the main ways in which OTC affected the heavy metal content of the water. The exogenous addition of OTC was found to inhibit the activity of some digester microbial strains, reduce the proportion of dominant strains, such as MBA03, and kill certain specific strains. This performance alteration was most obvious when OTC was added in the middle of the reaction

The Effect of Exogenous Oxytetracycline on High-Temperature Anaerobic Digestion of Elements in Swine Wastewater

Tetracycline antibiotics (TCs) are a common type of antibiotic found in swine wastewater. Oxytetracycline (OTC) is a significant type of TC. This study mainly examined the influence of OTC on high-temperature anaerobic digestion by adding OTC to collections of swine wastewater at different times during the digestion process. The results showed that high-temperature anaerobic digestion was suitable for the removal of TCs, with an 87% OTC removal efficiency achieved by day 20. Additionally, OTC added from external sources was found to inhibit the chlortetracycline degradation process and affect the first-order degradation kinetic model of TCs. Complexation reactions were the main ways in which OTC affected the heavy metal content of the water. The exogenous addition of OTC was found to inhibit the activity of some digester microbial strains, reduce the proportion of dominant strains, such as MBA03, and kill certain specific strains. This performance alteration was most obvious when OTC was added in the middle of the reaction

Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width

Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021), we make a step towards drawing a phase diagram for three-layer ReLU NNs with infinite width. First, we derive a normalized gradient flow for three-layer ReLU NNs and obtain two key independent quantities to distinguish different dynamical regimes for common initialization methods. With carefully designed experiments and a large computation cost, for both synthetic datasets and real datasets, we find that the dynamics of each layer also could be divided into a linear regime and a condensed regime, separated by a critical regime. The criteria is the relative change of input weights (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) as the width approaches infinity during the training, which tends to $0$, $+\infty$ and $O(1)$, respectively. In addition, we also demonstrate that different layers can lie in different dynamical regimes in a training process within a deep NN. In the condensed regime, we also observe the condensation of weights in isolated orientations with low complexity. Through experiments under three-layer condition, our phase diagram suggests a complicated dynamical regimes consisting of three possible regimes, together with their mixture, for deep NNs and provides a guidance for studying deep NNs in different initialization regimes, which reveals the possibility of completely different dynamics emerging within a deep NN for its different layers.Comment: arXiv admin note: text overlap with arXiv:2007.0749

Quantum Mechanical Investigation of the Oxidative Cleavage of the C−C Backbone Bonds in Polyethylene Model Molecules

Recalcitrant plastic waste has caused serious global ecological problems. There is an urgent need to develop environmentally friendly and efficient methods for degrading the highly stable carbon skeleton structure of plastics. To that end, we used a quantum mechanical calculation to thoroughly investigate the oxidative scission of the carbon-carbon (C–C) backbone in polyethylene (PE). Here, we studied the reaction path of C–C bond oxidation via hydroxyl radical in PE. The flexible force constants and fuzzy bond orders of the C–C bonds were calculated in the presence of one or more carbocations in the same PE carbon chain. By comparison, the strength of the C–C bond decreased when carbocation density increased. However, the higher the density of carbocations, the higher the total energy of the molecule and the more difficult it was to be generated. The results revealed that PE oxidized to alcohol and other products, such as carboxylic acid, aldehyde and ketone, etc. Moreover, the presence of carbocations was seen to promote the cleavage of C–C backbones in the absence of oxygen