Popular newspapers in post-Mao Guangzhou: toward a social history of Southern Weekend, 1984-2010

This project sets out to understand the Chinese press within a regional context during the post-Mao reform era. As an extension of the Party press, post-Mao popular newspapers grew from within the Party-state bureaucracies in response to the economic and social reform since the late 1970s and early 1980s. Foregrounded by the history of Southern Weekend [Nanfang Zhoumo], a news weekly based in Guangzhou yet with national influence especially since the late 1990s, the study aims to examine how popular newspapers have explored the forms and politics of their journalism under new historical conditions. For each period of development, the project worked to locate the key transformations of the Guangzhou press, and then characterized the journalistic paradigm of Southern Weekend in reference to the sources of change. It presents a journalism history of what I call the "Party-popular" expanding from cultural to social and political realms in the post-Mao Chinese society

A Partially Feasible Distributed SQO Method for Two-block General Linearly Constrained Smooth Optimization

This paper discusses a class of two-block smooth large-scale optimization problems with both linear equality and linear inequality constraints, which have a wide range of applications, such as economic power dispatch, data mining, signal processing, etc.Our goal is to develop a novel partially feasible distributed (PFD) sequential quadratic optimization (SQO) method (PFD-SQO method) for this kind of problems. The design of the method is based on the ideas of SQO method and augmented Lagrangian Jacobian splitting scheme as well as feasible direction method,which decomposes the quadratic optimization (QO) subproblem into two small-scale QOs that can be solved independently and parallelly. A novel disturbance contraction term that can be suitably adjusted is introduced into the inequality constraints so that the feasible step size along the search direction can be increased to 1. The new iteration points are generated by the Armijo line search and the partially augmented Lagrangian function that only contains equality constraints as the merit function. The iteration points always satisfy all the inequality constraints of the problem. The theoretical properties, such as global convergence, iterative complexity, superlinear and quadratic rates of convergence of the proposed PFD-SQO method are analyzed under appropriate assumptions, respectively. Finally, the numerical effectiveness of the method is tested on a class of academic examples and an economic power dispatch problem, which shows that the proposed method is quite promising

Accelerated degradation modeling considering long-range dependence and unit-to-unit variability

Accelerated degradation testing (ADT) is an effective way to evaluate the reliability and lifetime of highly reliable products. Existing studies have shown that the degradation processes of some products are non-Markovian with long-range dependence due to the interaction with environments. Besides, the degradation processes of products from the same population generally vary from each other due to various uncertainties. These two aspects bring great difficulty for ADT modeling. In this paper, we propose an improved ADT model considering both long-range dependence and unit-to-unit variability. To be specific, fractional Brownian motion (FBM) is utilized to capture the long-range dependence in the degradation process. The unit-to-unit variability among multiple products is captured by a random variable in the degradation rate function. To ensure the accuracy of the parameter estimations, a novel statistical inference method based on expectation maximization (EM) algorithm is proposed, in which the maximization of the overall likelihood function is achieved. The effectiveness of the proposed method is fully verified by a simulation case and a microwave case. The results show that the proposed model is more suitable for ADT modeling and analysis than existing ADT models

FP-PET: Large Model, Multiple Loss And Focused Practice

This study presents FP-PET, a comprehensive approach to medical image segmentation with a focus on CT and PET images. Utilizing a dataset from the AutoPet2023 Challenge, the research employs a variety of machine learning models, including STUNet-large, SwinUNETR, and VNet, to achieve state-of-the-art segmentation performance. The paper introduces an aggregated score that combines multiple evaluation metrics such as Dice score, false positive volume (FPV), and false negative volume (FNV) to provide a holistic measure of model effectiveness. The study also discusses the computational challenges and solutions related to model training, which was conducted on high-performance GPUs. Preprocessing and postprocessing techniques, including gaussian weighting schemes and morphological operations, are explored to further refine the segmentation output. The research offers valuable insights into the challenges and solutions for advanced medical image segmentation

Environmental regulations and corporate cash holdings

The impact of environmental regulations on corporate performance and decisions has attracted significant attention from academics, practitioners and policymakers. We extend this line of research to examine the impact of regional environmental regulations on firms’ cash holdings. We find that environmental regulations motivate firms to increase cash holdings. Further analyses reveal that firms increase cash holdings due to having less debt financing, decreased sales and more green innovation, all caused by environmental regulations. Under regulatory pressure, firms operating in more competitive industries, facing more financial constraints, having more environmental expenditure and belonging to the secondary sector tend to hold more cash than other firms, while firms with better CSR performance do not maintain as high cash holdings as their counterparts. We further demonstrate that increased cash holdings caused by the imposition of environmental regulations increase firm value

Siamese Labels Auxiliary Network(SiLaNet)

Auxiliary information attracts more and more attention in the area of machine learning. Attempts so far to include such auxiliary information in state-of-the-art learning process have often been based on simply appending these auxiliary features to the data level or feature level. In this paper, we intend to propose a novel training method with new options and architectures. Siamese labels, which were used in the training phase as auxiliary modules. While in the testing phase, the auxiliary module should be removed. Siamese label module makes it easier to train and improves the performance in testing process. In general, the main contributions can be summarized as, 1) Siamese Labels are firstly proposed as auxiliary information to improve the learning efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary Network (SilaNet), which is to assist the training of the model; 3) Siamese Labels Auxiliary Network is applied to compress the model parameters by 50% and ensure the high accuracy at the same time. For the purpose of comparison, we tested the network on CIFAR-10 and CIFAR100 using some common models. The proposed SilaNet performs excellent efficiency both on the accuracy and robustness