The Effect of Urbanization and Economic Performance on Metropolitan Water Consumption: Theoretic Model and Evidence from Guangzhou of China

This paper examines the effect of urbanization and economic performance on metropolitan water consumption in Guangzhou of China. We develop social and individual optimal models to reveal the impact of urbanization and economic performance on metropolitan water consumption. Based on aggregated annual data from 1949 to 2014, the empirical results from OLS and ARDL suggest that previous water consumption per capita, urbanization and GDP per capita each play vital roles impacting metropolitan water consumption per capita in Guangzhou

Gas Consumption and Metropolitan Economic Performance: Models and Empirical Studies from Guangzhou, China

This study builds a theoretic model to estimates the relationship between gas consumption and metropolitan economic performance with annual data from 1978 to 2013 for Guangzhou in China. Based on Granger Causality Test with VECM, empirical results show that there is Granger causality from GDP to gas consumption for long run in Guangzhou. Keywords Gas Consumption, Metropolitan Performance, Guangzhou JEL Classifications: C5, E1, Q4, R

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Implicit neural representation has opened up new avenues for dynamic scene reconstruction and rendering. Nonetheless, state-of-the-art methods of dynamic neural rendering rely heavily on these implicit representations, which frequently struggle with accurately capturing the intricate details of objects in the scene. Furthermore, implicit methods struggle to achieve real-time rendering in general dynamic scenes, limiting their use in a wide range of tasks. To address the issues, we propose a deformable 3D Gaussians Splatting method that reconstructs scenes using explicit 3D Gaussians and learns Gaussians in canonical space with a deformation field to model monocular dynamic scenes. We also introduced a smoothing training mechanism with no extra overhead to mitigate the impact of inaccurate poses in real datasets on the smoothness of time interpolation tasks. Through differential gaussian rasterization, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed, making it well-suited for tasks such as novel-view synthesis, time synthesis, and real-time rendering

DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

While self-supervised representation learning (SSL) has received widespread attention from the community, recent research argue that its performance will suffer a cliff fall when the model size decreases. The current method mainly relies on contrastive learning to train the network and in this work, we propose a simple yet effective Distilled Contrastive Learning (DisCo) to ease the issue by a large margin. Specifically, we find the final embedding obtained by the mainstream SSL methods contains the most fruitful information, and propose to distill the final embedding to maximally transmit a teacher's knowledge to a lightweight model by constraining the last embedding of the student to be consistent with that of the teacher. In addition, in the experiment, we find that there exists a phenomenon termed Distilling BottleNeck and present to enlarge the embedding dimension to alleviate this problem. Our method does not introduce any extra parameter to lightweight models during deployment. Experimental results demonstrate that our method achieves the state-of-the-art on all lightweight models. Particularly, when ResNet-101/ResNet-50 is used as teacher to teach EfficientNet-B0, the linear result of EfficientNet-B0 on ImageNet is very close to ResNet-101/ResNet-50, but the number of parameters of EfficientNet-B0 is only 9.4\%/16.3\% of ResNet-101/ResNet-50. Code is available at https://github. com/Yuting-Gao/DisCo-pytorch.Comment: ECCV 202

Sharp Bounds for the General Sum-Connectivity Indices of Transformation Graphs

Given a graph G, the general sum-connectivity index is defined as χα(G)=∑uv∈E(G)dGu+dGvα, where dG(u) (or dG(v)) denotes the degree of vertex u (or v) in the graph G and α is a real number. In this paper, we obtain the sharp bounds for general sum-connectivity indices of several graph transformations, including the semitotal-point graph, semitotal-line graph, total graph, and eight distinct transformation graphs Guvw, where u,v,w∈+,-

Prepreg and Core Dielectric Permittivity (ϵr) Extraction for Fabricated Striplines\u27 Far-End Crosstalk Modeling

As the data rate and density of digital high-speed systems are getting higher, far-end crosstalk (FEXT) noise becomes one of the major issues that limit signal integrity performance. It was commonly believed that FEXT would be eliminated for strip lines routed in a homogeneous dielectric, but in reality, FEXT can always be measured in strip lines on the fabricated printed circuit boards. A slightly different dielectric permittivity (ϵr) of prepreg and core may be one of the major contributors to the FEXT. This article is focusing on providing a practical FEXT modeling methodology for strip lines by introducing an approach to extract ϵr of prepreg and core. Using the known cross-sectional geometry and measured S-parameters of the coupled strip line, the capacitance components in prepreg and core are separated using a two-dimensional solver, and the ϵr of prepreg and core is determined. A more comprehensive FEXT modeling approach is proposed by applying extracted inhomogeneous dielectric material information

miR-136-5p Regulates the Inflammatory Response by Targeting the IKKβ/NF-κB/A20 Pathway After Spinal Cord Injury

Background/Aims: miR-136-5p participates in recovery after spinal cord injury (SCI) via an unknown mechanism. We investigated the mechanism underlying the involvement of miR-136-5p in the inflammatory response in a rat model of SCI. Methods: Sprague-Dawley rat astrocytes were cultured in vitro to construct a reporter plasmid. Luciferase assays were used to detect the ability of miR-136-5p to target the IKKβ and A20 genes. Next, recombinant lentiviral vectors were constructed, which either overexpressed miR-136-5p or inhibited its expression. The influence of miR-136-5p overexpression and miR-136-5p silencing on inflammation was observed in vivo in an SCI rat model. The expression of IL-1β, IL-6, TNF-α, IFN-α, and related proteins (A20, IKKβ, and NF-κB) was detected. Results: In vitro studies showed that luciferase activity was significantly activated in the presence of the 3’ untranslated region (UTR) region of the IKKβ gene after stimulation of cells with miR-136-5p. However, luciferase activity was significantly inhibited in the presence of the 3’UTR region of the A20 gene. Thus, miR-136-5p may act directly on the 3’UTR regions of the IKKβ and A20 genes to regulate their expression. miR-136-5p overexpression promoted the production of related cytokines and NF-κB in SCI rats and inhibited the expression of A20 protein. Conclusion: Overexpression of miR-136-5p promotes the generation of IL-1β, IL-6, TNF-α, IFN-α, IKKβ, and NF-κB in SCI rats but inhibits the expression of A20. Under these conditions, inflammatory cell infiltration into the rat spinal cord increases and injury is significantly aggravated. Silencing of miR-136-5p significantly reduces the protein expression results described after miR-136-5p overexpression and ameliorates the inflammatory cell infiltration and damage to the spinal cord. Therefore, miR-136-5p might be a new target for the treatment of SCI

Hesperidin Protects against Acute Alcoholic Injury through Improving Lipid Metabolism and Cell Damage in Zebrafish Larvae

Alcoholic liver disease (ALD) is a series of abnormalities of liver function, including alcoholic steatosis, steatohepatitis, and cirrhosis. Hesperidin, the major constituent of flavanone in grapefruit, is proved to play a role in antioxidation, anti-inflammation, and reducing multiple organs damage in various animal experiments. However, the underlying mechanism of resistance to alcoholic liver injury is still unclear. Thus, we aimed to investigate the protective effects of hesperidin against ALD and its molecular mechanism in this study. We established an ALD zebrafish larvae model induced by 350 mM ethanol for 32 hours, using wild-type and transgenic line with liver-specific eGFP expression Tg (lfabp10α:eGFP) zebrafish larvae (4 dpf). The results revealed that hesperidin dramatically reduced the hepatic morphological damage and the expressions of alcohol and lipid metabolism related genes, including cyp2y3, cyp3a65, hmgcra, hmgcrb, fasn, and fads2 compared with ALD model. Moreover, the findings demonstrated that hesperidin alleviated hepatic damage as well, which is reflected by the expressions of endoplasmic reticulum stress and DNA damage related genes (chop, gadd45αa, and edem1). In conclusion, this study revealed that hesperidin can inhibit alcoholic damage to liver of zebrafish larvae by reducing endoplasmic reticulum stress and DNA damage, regulating alcohol and lipid metabolism

Online Prototype Alignment for Few-shot Policy Transfer

Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment. Many traditional approaches of domain adaptation in RL manage to learn a mapping function between the source and target domain in explicit or implicit ways. However, they typically require access to abundant data from the target domain. Besides, they often rely on visual clues to learn the mapping function and may fail when the source domain looks quite different from the target domain. To address these problems, we propose a novel framework Online Prototype Alignment (OPA) to learn the mapping function based on the functional similarity of elements and is able to achieve the few-shot policy transfer within only several episodes. The key insight of OPA is to introduce an exploration mechanism that can interact with the unseen elements of the target domain in an efficient and purposeful manner, and then connect them with the seen elements in the source domain according to their functionalities (instead of visual clues). Experimental results show that when the target domain looks visually different from the source domain, OPA can achieve better transfer performance even with much fewer samples from the target domain, outperforming prior methods.Comment: This paper has been accepted at ICML202