Improving Fast Adversarial Training With Prior-Guided Knowledge.

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box attack scenarios. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high training time.

Effects of tai chi, ba duan jin, and walking on the mental health status of urban older people living alone: the mediating role of social participation and the moderating role of the exercise environment.

With the global trend of aging, lacking of interpersonal communication and spiritual support and companionship have had a great impact on the mental health of older people living alone. This study examines the multifaceted impacts of engaging in tai chi, ba duan jin, and walking on the mental wellbeing of older people residing alone in urban areas. Additionally, this research aims to explore the association between tai chi, ba duan jin, and walking, and the mental health status of urban older people living alone, by considering the mediating influence of social participation and the moderating influence of the exercise environment.

The influence of tai chi on the death anxiety of elderly people living alone: the chain mediating effect of social support and psychological capital.

Background: Population aging is a global trend, and the number of older adults living alone is increasing. Tai chi, a traditional Chinese exercise, has been shown to improve the physical and mental health of older adults.

Aim: To investigate the effects of tai chi on death anxiety in older adults living alone and the role of social support and psychological capital in this relationship.

The Study of COVID-19's Perceptions as a Motivation to Exercise and Keep Fit Among Older Adults.

Since the end of 2019, a novel coronavirus pandemic (COVID-19), characterized by solid infectivity, rapid communication and diverse communication routes, has become widespread worldwide. This study investigates the motivations of older adults to exercise and keep fit due to the COVID-19 pandemic. The research is based on a survey of older adults in Chengdu, Sichuan Province, China.

Versatile Weight Attack via Flipping Limited Bits.

Many attack paradigms against deep neural networks have been well studied, such as the backdoor attack in the training stage and the adversarial attack in the inference stage. In this article, we study a novel attack paradigm, the bit-flip based weight attack, which directly modifies weight bits of the attacked model in the deployment stage. To meet various attack scenarios, we propose a general formulation including terms to achieve effectiveness and stealthiness goals and a constraint on the number of bit-flips.

Generalizable Black-Box Adversarial Attack With Meta Learning.

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability.

Boosting Fast Adversarial Training With Learnable Adversarial Initialization.

Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training. However, most AT methods are in face of expensive time and computational cost for calculating gradients at multiple steps in generating adversarial examples. To boost training efficiency, fast gradient sign method (FGSM) is adopted in fast AT methods by calculating gradient only once.

l-Box ADMM: A Versatile Framework for Integer Programming.

This paper revisits the integer programming (IP) problem, which plays a fundamental role in many computer vision and machine learning applications. The literature abounds with many seminal works that address this problem, some focusing on continuous approaches (e.g.

A Coupled Hidden Conditional Random Field Model for Simultaneous Face Clustering and Naming in Videos.

For face naming in TV series or movies, a typical way is using subtitles/script alignment to get the time stamps of the names, and tagging them to the faces. We study the problem of face naming in videos when subtitles are not available. To this end, we divide the problem into two tasks: face clustering which groups the faces depicting a certain person into a cluster, and name assignment which associates a name to each face.