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

TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models

Large Language Models (LLMs) are progressively being utilized as machine learning services and interface tools for various applications. However, the security implications of LLMs, particularly in relation to adversarial and Trojan attacks, remain insufficiently examined. In this paper, we propose TrojLLM, an automatic and black-box framework to effectively generate universal and stealthy triggers. When these triggers are incorporated into the input data, the LLMs' outputs can be maliciously manipulated. Moreover, the framework also supports embedding Trojans within discrete prompts, enhancing the overall effectiveness and precision of the triggers' attacks. Specifically, we propose a trigger discovery algorithm for generating universal triggers for various inputs by querying victim LLM-based APIs using few-shot data samples. Furthermore, we introduce a novel progressive Trojan poisoning algorithm designed to generate poisoned prompts that retain efficacy and transferability across a diverse range of models. Our experiments and results demonstrate TrojLLM's capacity to effectively insert Trojans into text prompts in real-world black-box LLM APIs including GPT-3.5 and GPT-4, while maintaining exceptional performance on clean test sets. Our work sheds light on the potential security risks in current models and offers a potential defensive approach. The source code of TrojLLM is available at https://github.com/UCF-ML-Research/TrojLLM.Comment: Accepted by NeurIPS'2

Dietary patterns and breast cancer risk, prognosis, and quality of life: A systematic review

BackgroundStatistics indicate that the morbidity of breast cancer is increasing globally, and its (overall figures) incidence has now surpassed that of lung cancer for the first time. The relation between a whole dietary pattern, rather than of a single food or nutrient, and breast cancer (BC) should be examined for findings to capture the complexities of diet and the potential for synergism between dietary components. Hence, the effects of dietary patterns on breast cancer have recently attracted increasing attention.ObjectiveTo systematically review the effects of dietary patterns on breast cancer risk, prognosis, and quality of life in survivors.MethodsThis systematic review was conducted following PRISMA guidelines and was registered in PROSPERO. Data from Ovid, China Biomedical Literature Database, Wanfang Data Knowledge Service Platform, CNKI, PubMed, Weipu, The Cochrane Library, Duxiu Data, ProQuest, Embase, Web of Science, and Scopus Database were retrieved and evaluated.ResultsA total of 47 studies that investigated the association between eating patterns and breast cancer were identified. Ten studies evaluated the effect of the model on treatment outcome and prognosis of breast cancer and two cross-sectional studies examined the influence of dietary patterns on quality of life. The resulting favorable dietary patterns were shown to regulate metabolic biomarkers, antioxidants, anti-inflammatory agents, and protective genes, and inhibit cell proliferation and invasion.ConclusionNumerous studies have examined the effects of healthy eating, plant-based, anti-inflammation, low-fat, and other favorable dietary patterns in relation to breast cancer. However, few studies reported significant associations and the studies had limitations, suggesting that the current findings should be interpreted with caution.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, CRD4202 2350171

Decomposition analysis of carbon emissions: Considering China’s energy efficiency

Carbon emissions reduction has become a public concern since Paris Climate Conference in 2015, while energy efficiency improvements and the development of clean energy are crucial to carbon reduction. To investigate carbon dioxide emissions in China at different phases in 2000–2016, this research adopts the logarithmic mean Divisia index (LMDI) technique to study four drivers: population, economic development level, energy intensity, and carbon emission intensity. The results show that: (1) The rate of increase in CO2 emissions has decreased from an average of 10.6% in 2000–2011 to 0.79% in 2011–2016; (2) Declining energy intensity (-39.89%) is the largest contributor to China’s carbon reduction among the four drivers in 2012–2016, followed by declining carbon emission intensity (-9%); (3) With rising energy efficiency and increased use of clean energy, China is transitioning to a sustainable economy. More innovative green technologies should be employed to enhance the efficiency of energy use and optimize energy structure to combat climate change

Narrative medicine as a teaching strategy for nursing students to developing professionalism, empathy and humanistic caring ability: a randomized controlled trial

Abstract Background Narrative medicine has become a solution to cultivate medical students’ ability of empathy and humanistic care. However, the role of narrative medicine is lacking in the study of professionalism. The aim of this study was to analyze the effects of narrative medical theory learning and narrative writing on professionalism, empathy and humanistic care ability of nursing students. Methods This cluster randomized controlled trial was conducted between June 2021 and June 2022 in two universities in Jiangsu, China. The participants of this study were 85 nursing students who were randomly divided into the intervention group (n = 43) or the control group (n = 42). Participants in the intervention group were trained in narrative medical theory learning and narrative writing based on a Web-based platform, while those in the control group were not. Self-report questionnaires of professionalism, empathy and humanistic care ability were used before and after intervention. Results The results showed that the professionalism score of the intervention group was (68.7 ± 6.8 vs. 64.5 ± 7.5; P = 0.005), empathy (99.4 ± 15.7 vs. 92.2 ± 14.6; P = 0.014) and humanistic care ability (127.6 ± 20.0 vs. 113.3 ± 18.8; P = 0.004) were better than the control group. Conclusion The results of this quantitative study suggest that narrative medical theory education and narrative writing based on the network platform can promote the development of professionalism, empathy and humanistic care ability of nursing undergraduates

Emotion Recognition Based on Multichannel Physiological Signals with Comprehensive Nonlinear Processing

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%

Unexpected rDNA divergence between two morphologically minimalistic nematodes with description of a new species (Tylenchomorpha: Tylenchidae)

Species of the family Tylenchidae are encountered in large numbers in soils. The genus Labrys was recently described characterised by a remarkable lip pattern that differs from all other known Tylenchidae genera. Here we describe a curious new species, Labrys fujianensis sp. n., that morphologically fits the genus Labrys but which is genetically divergent. The phylogeny was inferred based on 18S and 28S rDNA and light and scanning electron microscopy were used to extract detailed morphologies. The phylogenetic position of this species and its phenotypic convergence are discussed. The possibility of a long-branch attraction artefact was inspected both by removal of variable nucleotide sites and monophyletic testing of topologies. The results confirmed the divergent positioning of the presented species and it is demonstrated that the genetic diversity in Tylenchidae may be much higher than expected due to morphological homoplasy