SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning

Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, impressive performance can be achieved on various tasks with very little labeled data. The growing adoption of SSL has led to an increase in security research on SSL encoders and associated Trojan attacks. Trojan attacks embedded in SSL encoders can operate covertly, spreading across multiple users and devices. The presence of backdoor behavior in Trojaned encoders can inadvertently be inherited by downstream classifiers, making it even more difficult to detect and mitigate the threat. Although current Trojan detection methods in supervised learning can potentially safeguard SSL downstream classifiers, identifying and addressing triggers in the SSL encoder before its widespread dissemination is a challenging task. This challenge arises because downstream tasks might be unknown, dataset labels may be unavailable, and the original unlbeled training dataset might be inaccessible during Trojan detection in SSL encoders. We introduce SSL-Cleanse as a solution to identify and mitigate backdoor threats in SSL encoders. We evaluated SSL-Cleanse on various datasets using 1200 encoders, achieving an average detection success rate of 82.2% on ImageNet-100. After mitigating backdoors, on average, backdoored encoders achieve 0.3% attack success rate without great accuracy loss, proving the effectiveness of SSL-Cleanse.Comment: 9 pages, 6 figures, 4 table

Instrumental activities of daily living trajectories and risk of mild cognitive impairment among Chinese older adults: results of the Chinese longitudinal healthy longevity survey, 2002–2018

BackgroundThe association between the instrumental activities of daily living (IADL) score and the risk of initial cognitive function impairment is inconclusive. We aimed to identify distinctive IADL trajectories and examine their relationship with the onset of mild cognitive impairment (MCI) among Chinese older people.MethodsThe study used six-wave longitudinal data from the Chinese Longitudinal Healthy Longevity Survey conducted between 2002 and 2018. It included a total of 11,044 Chinese people aged 65 years or older. A group-based trajectory model was used to identify distinctive trajectories of the IADL score, and the Cox proportional hazards model was used to explore the hazard ratio of various trajectories at the onset of MCI. Interaction analysis was used to analyze individual modification between the IADL trajectories and the onset of MCI. Finally, we adopted four types of sensitivity analysis to verify the robustness of the results.ResultsDuring a median follow-up of 16 years, the incidence of MCI was 6.29 cases per 1,000 person-years (95% confidence interval [CI] 5.92–6.68). Three distinct IADL trajectory groups were identified: a low-risk IADL group (41.4%), an IADL group with increasing risk (28.5%), and a high-risk IADL group (30.4%). Using the Cox proportional hazards model after adjusting for covariates, we found that compared with the low risk IADL group, the hazard ratio of the IADL group with increasing risk was 4.49 (95% CI = 3.82–5.28), whereas that of the high-risk IADL group was 2.52 (95% CI 2.08–3.05). Treating the IADL group with increasing risk as the reference, the hazard ratio for the high-risk IADL group was 0.56 (95% CI 0.48–0.66). Interaction analyses showed that age and residence were significant moderators (P for interaction <0.05).ConclusionA group-based trajectory model was developed to classify older people into three distinct trajectory groups of the IADL score. The IADL group with increasing risk had a greater risk of MCI than the high-risk IADL group. In the IADL group with increasing risk, city residents of ≥80 years were the most likely to develop MCI

Virus efficacy of recombined Autographa californica M nucleopolyhedrovirus (AcMNPV) on tea pest Ectropis obliqua

Ectropis obliqua is a major tea pest and chitin synthase (CHS) plays a key role in the pest growth and development. A 192 bp conserved domain from E. obliqua CHS gene was cloned and it was used to construct recombined Autographa californica M nucleopolyhedrovirus (AcMNPV) with double-stranded RNA interference (dsRNAi) method. The recombined AcMNPV virus could propagate in host cells sf9. Injection test showed that the virus efficacy of the recombined AcMNPV on E. obliqua larvae was significantly enhanced. It is considered that the CHS dsRNAi mediated by the nuclear polyhedrosis virus will be interesting for development of alternative bio-pesticide to control the tea pest E. obliqua.Keywords: Chitin synthase, baculovirus, double-stranded RNA interference, Ectropis obliquaAfrican Journal of Biotechnology Vol. 9(33), pp. 5412-5418, 16 August, 201

Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space

This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. Recent advanced methods proposed several lightweight transferability metrics to predict the fine-tuning results. However, these approaches only capture static representations but neglect the fine-tuning dynamics. To this end, this paper proposes a new transferability metric, called \textbf{S}elf-challenging \textbf{F}isher \textbf{D}iscriminant \textbf{A}nalysis (\textbf{SFDA}), which has many appealing benefits that existing works do not have. First, SFDA can embed the static features into a Fisher space and refine them for better separability between classes. Second, SFDA uses a self-challenging mechanism to encourage different pre-trained models to differentiate on hard examples. Third, SFDA can easily select multiple pre-trained models for the model ensemble. Extensive experiments on $33$ pre-trained models of $11$ downstream tasks show that SFDA is efficient, effective, and robust when measuring the transferability of pre-trained models. For instance, compared with the state-of-the-art method NLEEP, SFDA demonstrates an average of $59.1$\% gain while bringing $22.5$x speedup in wall-clock time. The code will be available at \url{https://github.com/TencentARC/SFDA}.Comment: ECCV 2022 camera ready. 24 pages, 11 tables, 5 figure

The Application and Analysis of Mechatronics System in Mechanical Engineering

In recent years, China's economic level has been steadily improved, and the development of various industries has greatly improved the quality of life of people, and the use of machinery has contributed greatly to this process. In view of the application of mechatronics in mechanical engineering, this article makes a simple discussion on three aspects of its application field, development trend and coping strategy. And to help the development of mechanical engineering, to make the contribution of our country's economic development