Social Capital, Endogenous Labor Supply, and Economic Development

Social capital has been increasingly recognized as an important determinant of economic growth in the literature of economic growth. Nevertheless, there are only a few rigorous dynamic growth models which explicitly deal with dynamic interdependence between social capital, physical capital, and economic structure. The purpose of this study is to incorporate social capital in a neoclassical economic growth theory. We propose a dynamic model with interdependence between economic structural change, wealth accumulation, and social capital accumulation. Social capital positively affects total factor productivities and is accumulated through investment, leisure activities, and production. We simulate the model. The study focuses on effects of changes in some parameters on the equilibrium and transitional processes of the economic dynamics. We get some insights through including social capital in economic growth modelling. For instance, if society has lower trust (possibly if we interpret social capital as guanxi in Chinese societies) which results in a rise in depreciation rate of social capital, the economy suffers from falling social capital, productivities, national capital, and national output; consumers have lower income, wealth, and consumption; they also have to spend more time on investing in social capital

Economic Growth with Tourism and Environmental Change

The main purpose of this study is to examine dynamic interactions between economic growth, environmental change, and tourism. Although tourism is playing an increasingly important role in different economies, there are only a few theoretical models to dynamic economic and environmental issues with endogenous tourism. On the basis of the Solow-Uzawa growth model, the neoclassical growth model with environmental change, and ideas from tourism economics, we develop a three-sector growth model. The industrial and service sectors are perfectly competitive. The environment sector is financially supported by the government. We introduce taxes not only on producers, but also on consumers’ incomes from wage, land, and interest of wealth, consumption of goods and services, and housing. We simulate the motion of the national economy and examine effects of changes in some parameters. The comparative dynamic analysis with regard to the rate of interest, the price elasticity of tourism, the global economic condition, the total productivity of the service sectors, and the propensity to save provides some important insights into the complexity of open economies with endogenous wealth and environment

Exact results for the extreme Thouless effect in a model of network dynamics

If a system undergoing phase transitions exhibits some characteristics of both first and second order, it is said to be of 'mixed order' or to display the Thouless effect. Such a transition is present in a simple model of a dynamic social network, in which $N_{I/E}$ extreme introverts/extroverts always cut/add random links. In particular, simulations showed that $\left\langle f\right\rangle$, the average fraction of cross-links between the two groups (which serves as an 'order parameter' here), jumps dramatically when $\Delta \equiv N_{I}-N_{E}$ crosses the 'critical point' $\Delta _{c}=0$, as in typical first order transitions. Yet, at criticality, there is no phase co-existence, but the fluctuations of $f$ are much larger than in typical second order transitions. Indeed, it was conjectured that, in the thermodynamic limit, both the jump and the fluctuations become maximal, so that the system is said to display an 'extreme Thouless effect.' While earlier theories are partially successful, we provide a mean-field like approach that accounts for all known simulation data and validates the conjecture. Moreover, for the critical system $N_{I}=N_{E}=L$, an analytic expression for the mesa-like stationary distribution, $P\left( f\right)$, shows that it is essentially flat in a range $\left[ f_{0},1-f_{0}\right]$, with $f_0 \ll 1$. Numerical evaluations of $f_{0}$ provides excellent agreement with simulation data for $L\lesssim 2000$. For large $L$, we find $f_{0}\rightarrow \sqrt{\left( \ln L^2 \right) /L}$ , though this behavior begins to set in only for $L>10^{100}$. For accessible values of $L$, we provide a transcendental equation for an approximate $f_{0}$ which is better than $\sim$1% down to $L=100$. We conjecture how this approach might be used to attack other systems displaying an extreme Thouless effect.Comment: 6 pages, 4 figure

Presbycusis-Related Tinnitus and Cognitive Impairment: Gender Differences and Common Mechanisms

Presbycusis-related tinnitus and cognitive impairment are common in the elderly and generate a massive burden on family and society. Except for age, the study explored the gender differences in the prevalence of the three diseases. We found that women have an advantage in maintaining better cognitive and auditory functions. Recent studies suggest the complex links among the three diseases. Peripheral hearing loss can affect sound coding and neural plasticity, which will lead to cognitive impairment and tinnitus. The deficits of the central nervous system, especially central auditory structures, can, in turn, cause the presbycusis. The interaction among three diseases indicated that comprehensive assessment, intervention and treatment in consideration of hearing loss, tinnitus and cognitive impairment are important to decay aging

An inspection technology of inner surface of the fine hole based on machine vision

Fine holes are an important structural component of industrial components, and their inner surface quality is closely related to their function.In order to detect the quality of the inner surface of the fine hole,a special optical measurement system was investigated in this paper. A sight pipe is employed to guide the external illumination light into the fine hole and output the relevant images simultaneously. A flexible light array is introduced to suit the narrow space, and the effective field of view is analyzed. Besides, the arc surface projection error and manufacturing assembly error of the device are analyzed, then compensated or ignored if small enough. In the test of prefabricated circular defects with the diameter {\phi}0.1mm, {\phi}0.2mm, 0.4mm distance distribution and the fissure defects with the width 0.3mm, the maximum measurement error standard deviation are all about 10{\mu}m. The minimum diameter of the measured fine hole is 4mm and the depth can reach 47mm

Fast Iterative Reconstruction for Multi-spectral CT by a Schmidt Orthogonal Modification Algorithm (SOMA)

Multi-spectral CT (MSCT) is increasingly used in industrial non-destructive testing and medical diagnosis because of its outstanding performance like material distinguishability. The process of obtaining MSCT data can be modeled as nonlinear equations and the basis material decomposition comes down to the inverse problem of the nonlinear equations. For different spectra data, geometric inconsistent parameters cause geometrical inconsistent rays, which will lead to mismatched nonlinear equations. How to solve the mismatched nonlinear equations accurately and quickly is a hot issue. This paper proposes a general iterative method to invert the mismatched nonlinear equations and develops Schmidt orthogonalization to accelerate convergence. The validity of the proposed method is verified by MSCT basis material decomposition experiments. The results show that the proposed method can decompose the basis material images accurately and improve the convergence speed greatly

Federated Unlearning for Human Activity Recognition

The rapid evolution of Internet of Things (IoT) technology has spurred the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) is frequently utilized to build a global HAR model by aggregating user contributions without transmitting raw individual data. Despite substantial progress in user privacy protection with FL, challenges persist. Regulations like the General Data Protection Regulation (GDPR) empower users to request data removal, raising a new query in FL: How can a HAR client request data removal without compromising other clients' privacy? In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data. Our method employs a third-party dataset unrelated to model training. Using KL divergence as a loss function for fine-tuning, we aim to align the predicted probability distribution on forgotten data with the third-party dataset. Additionally, we introduce a membership inference evaluation method to assess unlearning effectiveness. Experimental results across diverse datasets show our method achieves unlearning accuracy comparable to \textit{retraining} methods, resulting in speedups ranging from hundreds to thousands

Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization

Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8% on average.Comment: CVPR 202