On phase at a resonance in slow-fast Hamiltonian systems

We consider a slow-fast Hamiltonian system with one fast angular variable (a fast phase) whose frequency vanishes on some surface in the space of slow variables (a resonant surface). Systems of such form appear in the study of dynamics of charged particles in inhomogeneous magnetic field under influence of a high-frequency electrostatic waves. Trajectories of the averaged over the fast phase system cross the resonant surface. The fast phase makes $\sim \frac {1}{\varepsilon}$ turns before arrival to the resonant surface ($\varepsilon$ is a small parameter of the problem). An asymptotic formula for the value of the phase at the arrival to the resonance was derived earlier in the context of study of charged particle dynamics on the basis of heuristic considerations without any estimates of its accuracy. We provide a rigorous derivation of this formula and prove that its accuracy is $O(\sqrt \varepsilon)$ (up to a logarithmic correction). Numerics indicate that this estimate for the accuracy is optimal

The Impact of Green Finance Policy on Corporate ESG Performance：Evidence from China’s Listed Companies

After decades of exploration and development, China's green financial market has initially established a green financial system framework and formed the concept of green development. China has now entered an accelerated period for the formation of a green financial policy system. The Chinese government integrates the sustainable development into the development strategy of enterprises through policy guidance. However, the current empirical research is relatively lacking, mainly focusing on qualitative analysis. At the same time, as China has set out the aims of "3060" and "carbon peaking and carbon neutrality" in recent years, ESG has gradually become an important factor that cannot be ignored in the development of enterprises. The development goals of enterprises have also shifted from pursuing a single economic benefit to the mutual development of economic benefits and social benefits. With the gradual improvement of the current systematic ESG rating system, a large amount of data shows no contradiction between obtaining economic benefits and practising social responsibility. In light of this, the paper aims to investigate whether green finance policy impacts Chinese listed companies' ESG performance. This paper selects Chinese listed companies registered in the pilot zones announced in 2017 as research object. Eighty-one listed companies from 2014 to 2020 were taken as the samples of this study. A DID model was built to analyze the impact of China’s green finance policies. According to the empirical evidence of this study, after the policy is implemented in the pilot area, the ESG performance of listed companies has positive correlation with the policy implementation. It shows that green finance policy can effectively promote enterprises to improve ESG performance. However, when the heterogeneity analysis is carried out, the implementation effect of green finance policy shows different results for enterprises of different scales. Implementing green finance policies can significantly improve the ESG scores of large-scale enterprises, while it negatively affects small and medium-sized enterprises. Based on theoretical and empirical research results, this paper gives suggestions and countermeasures for policy optimization in the pilot zones. The research content and conclusions of this paper have a certain role in evaluating the policy effects and have policy guidance implications. At the same time, it also have a certain guiding role in discussing the ESG performance of enterprises under the conditions of green financial policies

Boosting Method in Approximately Solving Linear Programming with Fast Online Algorithm

In this paper, we develop a new algorithm combining the idea of ``boosting'' with the first-order algorithm to approximately solve a class of (Integer) Linear programs(LPs) arisen in general resource allocation problems. Not only can this algorithm solve LPs directly, but also can be applied to accelerate the Column Generation method. As a direct solver, our algorithm achieves a provable $O(\sqrt{n/K})$ optimality gap, where $n$ is the number of variables and $K$ is the number of data duplication bearing the same intuition as the boosting algorithm. We use numerical experiments to demonstrate the effectiveness of our algorithm and several variants

Saliency-Augmented Memory Completion for Continual Learning

Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.Comment: Published at SIAM SDM 2023. 15 pages, 6 figures. Code: https://github.com/BaiTheBest/SAM

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that the transferability of adversarial examples can be improved by attacking diverse models simultaneously, model augmentation methods which simulate different models by using transformed images are proposed. However, existing transformations for spatial domain do not translate to significantly diverse augmented models. To tackle this issue, we propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models. Specifically, we apply a spectrum transformation to the input and thus perform the model augmentation in the frequency domain. We theoretically prove that the transformation derived from frequency domain leads to a diverse spectrum saliency map, an indicator we proposed to reflect the diversity of substitute models. Notably, our method can be generally combined with existing attacks. Extensive experiments on the ImageNet dataset demonstrate the effectiveness of our method, \textit{e.g.}, attacking nine state-of-the-art defense models with an average success rate of \textbf{95.4\%}. Our code is available in \url{https://github.com/yuyang-long/SSA}.Comment: Accepted by ECCV 202

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%