Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can also raise security and privacy concerns, such as copyright infringement, and sensitive data leakage. To tackle these issues, we propose a method, Unlearnable Diffusion Perturbation, to safeguard images from unauthorized exploitation. Our approach involves designing an algorithm to generate sample-wise perturbation noise for each image to be protected. This imperceptible protective noise makes the data almost unlearnable for diffusion models, i.e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data. Theoretically, we frame this as a max-min optimization problem and introduce EUDP, a noise scheduler-based method to enhance the effectiveness of the protective noise. We evaluate our methods on both Denoising Diffusion Probabilistic Model and Latent Diffusion Models, demonstrating that training diffusion models on the protected data lead to a significant reduction in the quality of the generated images. Especially, the experimental results on Stable Diffusion demonstrate that our method effectively safeguards images from being used to train Diffusion Models in various tasks, such as training specific objects and styles. This achievement holds significant importance in real-world scenarios, as it contributes to the protection of privacy and copyright against AI-generated content

Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk

Testosterone acts at the cell surface to induce granulosa/theca cell death via an apoptotic pathway in Atlantic croaker (Micropogonias undulatus)

textThe teleost ovarian follicle undergoes extensive remodeling and regression during the reproductive cycle—a process involving apoptosis and cell death. However, the hormonal regulation of these processes remains unclear. In the current study the role of testosterone in regulating regression of Atlantic croaker (Micropogonias undulatus) ovarian follicles was investigated in co-cultured granulosa/theca (G/T) cells. Testosterone (T) treatment enhanced serum starvation-induced cell death and apoptosis of G/T cells during the mature stage of oocyte maturation. This effect was mimicked by a cell-impermeable T conjugate, T-bovine serum albumin, indicating that this androgen action is initiated at the cell surface. Mibolerone, a nuclear androgen receptor agonist, was ineffective in promoting apoptosis and cell death, which suggests that T actions are independent from the nuclear receptor. Together, the data suggests that T-induction of apoptosis and cell death are through a novel membrane androgen receptor in the croaker ovary. T treatment also increased expression of a pro-apoptotic member of the Bcl-2 gene family, Bax, and two Bax upstream regulators, JNK and p53. These results suggest that T induces cell death of G/T cells in croaker through the apoptotic pathway involving JNK, p53 and Bax. An opposite response of cell death protection by T was also observed in G/T cells cultured from late-stage ovaries. This response was accompanied by a rapid increased ERK-1/-2 phosphorylation not seen in Mibolerone treatment. By examining the role of T in croaker follicle cell death and elucidating the corresponding basic mechanisms of androgen action, we are learning more about the regulatory components involved in the breakdown and remodeling stages of the teleost reproductive cycle.Marine Scienc

Fault Diagnosis Analysis of Angle Grinder Based on ACD-DE and SVM Hybrid Algorithm

Due to the complex structure of the angle grinder and the existence of multiple rotating parts, the coupling phenomenon of the data results in the complexity and chaos of the data. The market scale of angle grinder is huge. Manual diagnosis and traditional diagnosis are difficult to meet the requirements, so a fault diagnosis method of angle grinder that is based on adaptive parameters and chaos theory of dual-strategy differential evolution algorithm (ACD-DE) and SVM model hybrid algorithm is proposed by combining a chaos-mapping algorithm, dynamic and adaptive scale factor, and crossover factor. The effectiveness and robustness of the algorithm are proven by solving eight test functions. The acceleration signal is decomposed by wavelet packet decomposition and reconstruction, and a variety of sensor signals are processed and constructed as feature vectors. The training set and the test set of the fault diagnosis model are divided. SVM model is used as the fault diagnosis model and optimized by ACD-DE. Based on the fault data of the angle grinder, the hybrid algorithm is compared with other optimization algorithms and other machine learning models; the comparison results show that the performance of the improved differential evolution algorithm is improved, in which the precision rate is 98.81%, the recall rate is 98.74%, and the F1 score is 0.9877. Experiments show that the hybrid algorithm has strong diagnosis accuracy and robustness