Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

The impact of monetary policy shocks on income inequality: a tale of two countries

The easing monetary policy after the global financial crisis triggered wide concerns on the responses of income inequality. In this paper, we investigate impact of monetary policy shocks on income inequality. We propose a general equilibrium model and show that monetary policies could affect income inequality by affecting the earnings of high-income households in financial markets and business operations. Using a TVP-FAVAR model, we find contradictory distributional effects of monetary policy shocks in China and the US. Specifically, expansionary monetary policy shocks persistently increase income inequality in China but decrease income inequality in the US. Moreover, the impacts are volatile in the short-term, but stabilise after 10 periods. The investigation on the responses of top 1% and bottom 50% income share confirms the finding of contradictory distributional effects of monetary policy shocks

FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table

Low-Light Video Enhancement (LLVE) has received considerable attention in recent years. One of the critical requirements of LLVE is inter-frame brightness consistency, which is essential for maintaining the temporal coherence of the enhanced video. However, most existing single-image-based methods fail to address this issue, resulting in flickering effect that degrades the overall quality after enhancement. Moreover, 3D Convolution Neural Network (CNN)-based methods, which are designed for video to maintain inter-frame consistency, are computationally expensive, making them impractical for real-time applications. To address these issues, we propose an efficient pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to maintain inter-frame brightness consistency effectively. Specifically, we design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive enhancement, which addresses the low-dynamic problem in low-light scenarios. This enables FastLLVE to perform low-latency and low-complexity enhancement operations while maintaining high-quality results. Experimental results on benchmark datasets demonstrate that our method achieves the State-Of-The-Art (SOTA) performance in terms of both image quality and inter-frame brightness consistency. More importantly, our FastLLVE can process 1,080p videos at $\mathit{50+}$ Frames Per Second (FPS), which is $\mathit{2 \times}$ faster than SOTA CNN-based methods in inference time, making it a promising solution for real-time applications. The code is available at https://github.com/Wenhao-Li-777/FastLLVE.Comment: 11pages, 9 Figures, and 6 Tables. Accepted by ACMMM 202

Ferroptosis Holds Novel Promise in Treatment of Cancer Mediated by Non-coding RNAs

Ferroptosis is a newly identified form of regulated cell death that is associated with iron metabolism and oxidative stress. As a physiological mechanism, ferroptosis selectively removes cancer cells by regulating the expression of vital chemical molecules. Current findings on regulation of ferroptosis have largely focused on the function of non-coding RNAs (ncRNAs), especially microRNAs (miRNAs), in mediating ferroptotic cell death, while the sponging effect of circular RNAs (circRNAs) has not been widely studied. In this review, we discuss the molecular regulation of ferroptosis and highlight the value of circRNAs in controlling ferroptosis and carcinogenesis. Herein, we deliberate future role of this emerging form of regulated cell death in cancer therapeutics and predict the progression and prognosis of oncogenesis in future clinical therapy.publishedVersio

Linking ethylene to nitrogen-dependent leaf longevity of grass species in a temperate steppe

Author's manuscript made available in accordance with the publisher's policy.Background and Aims Leaf longevity is an important plant functional trait that often varies with soil nitrogen supply. Ethylene is a classical plant hormone involved in the control of senescence and abscission, but its role in nitrogen-dependent leaf longevity is largely unknown. Methods Pot and field experiments were performed to examine the effects of nitrogen addition on leaf longevity and ethylene production in two dominant plant species, Agropyron cristatum and Stipa krylovii, in a temperate steppe in northern China. Key Results Nitrogen addition increased leaf ethylene production and nitrogen concentration but shortened leaf longevity; the addition of cobalt chloride, an ethylene biosynthesis inhibitor, reduced leaf nitrogen concentration and increased leaf longevity. Path analysis indicated that nitrogen addition reduced leaf longevity mainly through altering leaf ethylene production. Conclusions These findings provide the first experimental evidence in support of the involvement of ethylene in nitrogen-induced decrease in leaf longevity

Machine Learning for Mie-Tronics

Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization methods, relying on the iterative exploration of various geometries and materials, are employed to obtain the desired multipolar moments, these approaches are computationally demanding and less effective for intricate designs. In this study, we unveil the potential of machine learning for designing dielectric meta-atoms with desired multipolar moments up to the octupole terms. Specifically, we develop forward prediction models to unravel the intricate relationship between the scattering response and the topological attributes of individual meta-atoms, and an inverse design model to reconstruct scatterers with the targeted multipolar moments. Utilizing a tandem network trained to tailor dielectric meta-atoms for generating intended multipolar moments across a broad spectral range, we further demonstrate the generation of uniquely shaped meta-atoms for exciting exclusive higher order magnetic response and establishing super-scattering regime of light-matter interaction. We also illustrate the accurate prediction of electric field distributions within the given scatterer. Our versatile methodology can be readily applied to existing datasets and seamlessly integrated with various network architectures and problem domains, making it a valuable tool for the design of different platforms at nanoscale.Comment: 19 pages, 5 figures, 1 tabl

APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning

Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions. Prior works on improving the logical reasoning ability of language models require complex processing of training data (e.g., aligning symbolic knowledge to text), yielding task-specific data augmentation solutions that restrict the learning of general logical reasoning skills. In this work, we propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. We select a subset of Wikipedia, based on a set of logical inference keywords, for continued pretraining of a language model. We use two self-supervised loss functions: a modified masked language modeling loss where only specific parts-of-speech words, that would likely require more reasoning than basic language understanding, are masked, and a sentence-level classification loss that teaches the model to distinguish between entailment and contradiction types of sentences. The proposed training paradigm is both simple and independent of task formats. We demonstrate the effectiveness of APOLLO by comparing it with prior baselines on two logical reasoning datasets. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA. The code base has been made publicly available.Comment: Accepted at ACL 2023, code available at https://github.com/INK-USC/APOLL

Spatiotemporal Variation Characteristics of Groundwater Storage and Its Driving Factors and Ecological Effects in Tibetan Plateau

Known as the “Asian Water Tower”, the Tibetan Plateau (TP) is a rich water resource and serves an important ecological function. Climate change may cause changes to the water cycle, and these changes may affect the alpine vegetation growth. However, the variation characteristics of groundwater storage (GWS) and its driving factors and associated ecological effects in the TP are poorly understood. In this study, terrestrial water storage changes retrieved by GRACE (Gravity Recovery and Climate Experiment) were combined with GLDAS (Global Land Data Assimilation System) to estimate the GWS changes in the TP. The temporal and spatial variation characteristics of GWS were identified using linear regression and the modified Mann–Kendall (MMK) test, respectively. The analyses showed that the GWS of the TP decreased at an average rate of −0.89 mm/a from January 2003 to December 2021, but since January 2016, it gradually recovered at a rate of 1.47 mm/a. This shows that the GWS in the eastern and northern parts of the TP is decreasing, while the GWS in the western and southern parts is increasing. The influence of climate change on GWS in time and space was determined using the correlation analysis method. Decreased precipitation and permafrost degradation caused by increasing temperatures will lead to a decrease in GWS. On the other hand, rising temperatures may result in an increase in GWS in regions where glaciers are distributed. In this study, the ecological effects were represented by the relationship between GWS and vegetation change. A decline in GWS means that the vegetation will not receive enough water, leading to a decrease in the NDVI and the eventual degradation of grassland to sand, desert, or other kinds of unused land on the TP. On the other hand, an increase in GWS would promote vegetation restoration. The results of this study offer a new opportunity to reveal the groundwater changes in a cryosphere region and to assess the impact of changes in hydrological conditions on ecology.</p