Confidence bands for survival functions under semiparametric random censorship models

In medical reports point estimates and pointwise confidence intervals of parameters are usually displayed. When the parameter is a survival function, however, the approach of joining the upper end points of individual interval estimates obtained at several points and likewise for the lower end points would not produce bands that include the entire survival curve with a given confidence. Simultaneous confidence bands, which allow confidence statements to be valid for the entire survival curve,would be more meaningful This dissertation focuses on a novel method of developing one-sample confidence bands for survival functions from right censored data. The approach is model- based, relying on a parametric model for the conditional expectation of the censoring indicator given the observed minimum, and derives its chief strength from easy access to a good-fitting model among a plethora of choices currently available for binary response data. The substantive methodological contribution is in exploiting an available semiparametric estimator of the survival function for the one-sample case to produce improved simultaneous confidence bands. Since the relevant limiting distribution cannot be transformed to a Brownian Bridge unlike for the normalized Kaplan{Meier process, a two-stage bootstrap approach that combines the classical bootstrap with the more recent model-based regeneration of censoring indicators is proposed and a justification of its asymptotic validity is also provided. Several different confidence bands are studied using the proposed approach. Numerical studies, including robustness of the proposed bands to misspecification, are carriedout to check efficacy. The method is illustrated using two lung cancer data sets

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.Comment: Accepted by ICSE 201

Towards Certified Probabilistic Robustness with High Accuracy

Adversarial examples pose a security threat to many critical systems built on neural networks (such as face recognition systems, and self-driving cars). While many methods have been proposed to build robust models, how to build certifiably robust yet accurate neural network models remains an open problem. For example, adversarial training improves empirical robustness, but they do not provide certification of the model's robustness. On the other hand, certified training provides certified robustness but at the cost of a significant accuracy drop. In this work, we propose a novel approach that aims to achieve both high accuracy and certified probabilistic robustness. Our method has two parts, i.e., a probabilistic robust training method with an additional goal of minimizing variance in terms of divergence and a runtime inference method for certified probabilistic robustness of the prediction. The latter enables efficient certification of the model's probabilistic robustness at runtime with statistical guarantees. This is supported by our training objective, which minimizes the variance of the model's predictions in a given vicinity, derived from a general definition of model robustness. Our approach works for a variety of perturbations and is reasonably efficient. Our experiments on multiple models trained on different datasets demonstrate that our approach significantly outperforms existing approaches in terms of both certification rate and accuracy

Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System

In recent years, the security issues of artificial intelligence have become increasingly prominent due to the rapid development of deep learning research and applications. Backdoor attack is an attack targeting the vulnerability of deep learning models, where hidden backdoors are activated by triggers embedded by the attacker, thereby outputting malicious predictions that may not align with the intended output for a given input. In this work, we propose a novel black-box backdoor attack based on machine unlearning. The attacker first augments the training set with carefully designed samples, including poison and mitigation data, to train a `benign' model. Then, the attacker posts unlearning requests for the mitigation samples to remove the impact of relevant data on the model, gradually activating the hidden backdoor. Since backdoors are implanted during the iterative unlearning process, it significantly increases the computational overhead of existing defense methods for backdoor detection or mitigation. To address this new security threat, we proposes two methods for detecting or mitigating such malicious unlearning requests. We conduct the experiment in both exact unlearning and approximate unlearning (i.e., SISA) settings. Experimental results indicate that: 1) our attack approach can successfully implant backdoor into the model, and sharding increases the difficult of attack; 2) our detection algorithms are effective in identifying the mitigation samples, while sharding reduces the effectiveness of our detection algorithms

Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems

The rapid advance of deep reinforcement learning techniques enables the oversight of safety-critical systems through the utilization of Deep Neural Networks (DNNs). This underscores the pressing need to promptly establish certified safety guarantees for such DNN-controlled systems. Most of the existing verification approaches rely on qualitative approaches, predominantly employing reachability analysis. However, qualitative verification proves inadequate for DNN-controlled systems as their behaviors exhibit stochastic tendencies when operating in open and adversarial environments. In this paper, we propose a novel framework for unifying both qualitative and quantitative safety verification problems of DNN-controlled systems. This is achieved by formulating the verification tasks as the synthesis of valid neural barrier certificates (NBCs). Initially, the framework seeks to establish almost-sure safety guarantees through qualitative verification. In cases where qualitative verification fails, our quantitative verification method is invoked, yielding precise lower and upper bounds on probabilistic safety across both infinite and finite time horizons. To facilitate the synthesis of NBCs, we introduce their $k$-inductive variants. We also devise a simulation-guided approach for training NBCs, aiming to achieve tightness in computing precise certified lower and upper bounds. We prototype our approach into a tool called $\textsf{UniQQ}$ and showcase its efficacy on four classic DNN-controlled systems.Comment: This work is a technical report for the paper with the same name to appear in the 36th International Conference on Computer Aided Verification (CAV 2024

EFFECTS OF RUNNING BIOMECHANICS ON THE OCCURRENCE OF ILIOTIBIAL SYNDROME IN MALE RUNNERS — A PROSPECTIVE STUDY

This study aimed to determine the gait characteristics that easily induce ITBS and explore the gait changes after the occurrence of ITBS. 30 healthy male runners participated in our study, 15 in ITBS and control group respectively. All participants underwent two gait trials, namely, before the first day of their routine running and after 8 weeks. After 8 weeks of running, the ITBS group exhibited greater peak anterior pelvic tilt and hip flexion angle than the control group. The ITBS group showed increased peak trunk inclination angle, whereas the control group demonstrated lower peak hip flexion and peak hip adduction than those at the beginning of running. Decreased peak hip flexion and peak hip adduction angle was a gait adjustment strategy that could be used to avoid ITBS occurrence. Excessive trunk posture and pelvic activity during running are also ITBS risk factors

EFFECTS OF PNF INTERVENTION ON PAIN, JOINT PROPRIOCEPTION AND KNEE MOMENTS IN THE ELDERLY WITH KNEE OSTEOARTHRITIS DURING STAIR ASCENDING

In this study, we aimed to explore the effects of a 6-week proprioceptive neuromuscular facilitation (PNF) intervention on stair pain, joint proprioception, and external knee moment in the elderly patients with knee osteoarthritis (KOA) during stair ascending. A total of 27 elderly patients with KOA participated in our study. Fourteen of the patients were included in the PNF group, and 13 were included in the control group. The WOMAC measures for specific pain and joint motion sense measures were used, and gait test were performed at weeks 0 and 6. After a 6-week PNF intervention, the PNF group showed a decreased “using stairs” pain score, decreased difficulty with “climbing stairs” score, decreased joint kinesthesia threshold, increased knee flexion moment (KFM), and decreased knee adduction moment (KAM) during climbing stairs. We suggest the use of PNF intervention, which relieves joint pain, enhances muscles strength and proprioception recovery, increases KFM, and decreases KAM, in the treatment of KOA in elderly patients