Search

The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots. Deep neural networks are most widely used in the field of images and most easily cheated by image adversarial examples. The research on the defense techniques for image adversarial examples is an important tool to improve the security of AI applications. There is no standard explanation for the existence of image adversarial examples, but it can be observed and understood from different dimensions, which can provide insights for proposing targeted defense approaches. This paper sorts out and analyzes current mainstream hypotheses of the reason for the existence of adversarial examples, such as the blind spot hypothesis, linear hypothesis, decision boundary hypothesis, and feature hypothesis, and the correlations between various hypotheses and typical adversarial example generation methods. Based on this, this paper summarizes the image adversarial example defense techniques in two dimensions, model-based and data-based, and compares and analyzes the adaptation scenarios, advantages and disadvantages of different technical methods. Most of the existing image adversarial example defense techniques are aimed at defending against specific adversarial example generation methods, and there is no universal defense theory and method yet. In the real application, it needs to consider the specific application scenarios, potential security risks and other factors, optimize and combine the configuration in the existing defense methods. Future researchers can deepen their technical research in terms of generalized defense theory, evaluation of defense effectiveness, and systematic protection strategies.

Purpose: Anxiety and depression are often underdiagnosed in patients with chronic obstructive pulmonary disease (COPD). This study aimed to develop and validate a screening tool for anxiety and depression in COPD patients. Methods: Stable COPD patients were consecutively recruited from November 2021 to October 2023 and underwent a psychiatric interview to diagnose generalized anxiety disorder (GAD) and/or major depressive disorder (MDD) based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Patients were split into training and validation sets according to their recruitment time. We assessed known risk factors and used core items from the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) to develop a prediction nomogram. Multivariable logistic regression with least absolute shrinkage and selection operator (LASSO) were used to construct the nomogram. Results: Among the enrolled COPD patients (n = 329), 58 (25.6%) in the training cohort and 33 (32.4%) in the validation cohort were diagnosed with GAD and/or MDD. Three variables were identified in the prediction nomogram: COPD Assessment Test score and two core items from PHQ-ADS. The under the curve (AUC) value for the nomogram was 0.826 (95% CI: 0.755–0.897) and 0.855 (95% CI: 0.767–0.942) in the training and validation cohorts, respectively. The calibration curve was close to the diagonal. The discriminatory power of the screening nomogram was comparable to that of PHQ-ADS (AUC: 0.826 vs. 0.831, P = 0.832). Conclusion: The new screening tool for GAD and MDD in COPD patients is concise and valid, with discriminatory power comparable to existing anxiety/depression screening questionnaires. [ABSTRACT FROM AUTHOR]

Background: Although previous studies have reported the variables that influence opioid use or dropout among participants receiving methadone treatment, limited attention has been given to the variables related to transitions among no opioid use, opioid use, and dropout. Methods: This retrospective study utilized data collected from June 2010 to June 2022 at 11 methadone treatment clinics in Guangdong Province, China. Two transient states (no opioid use and opioid use) and 1 absorbing state (dropout) were defined based on monthly urine morphine test results and daily methadone intake records. We used a multistate model to explore the variables associated with transitions among no opioid use, opioid use, and dropout among participants. Results: Among 3136 participants, with an average treatment duration of 497 days, 1646 (52.49%) underwent at least 1 period of opioid use, resulting in 3283 transitions from no opioid use to opioid use. The transitions between no opioid use and opioid use were significantly associated with variables such as age, gender, employment status, marital status, living situation, travel time to the clinic, human immunodeficiency virus and hepatitis C virus infection statuses, average methadone dosage, and attendance rates. The variables influencing participants' dropout varied depending on their opioid use behaviors. Additionally, the probability of a specified opioid use state remaining unchanged or transitioning to a different state at a defined time point would change over time. Conclusions: The opioid use behaviors of participants are dynamic. Methadone providers should offer targeted interventions based on participants' opioid use behaviors to effectively decrease rates of opioid use and improve retention. [ABSTRACT FROM AUTHOR]