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

Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval

3D object retrieval has a compelling demand in the field of computer vision with the rapid development of 3D vision technology and increasing applications of 3D objects. 3D objects can be described in different ways such as voxel, point cloud, and multi-view. Among them, multi-view based approaches proposed in recent years show promising results. Most of them require a fixed predefined camera position setting which provides a complete and uniform sampling of views for objects in the training stage. However, this causes heavy over-fitting problems which make the models failed to generalize well in free camera setting applications, particularly when insufficient views are provided. Experiments show the performance drastically drops when the number of views reduces, hindering these methods from practical applications. In this paper, we investigate the over-fitting issue and remove the constraint of the camera setting. First, two basic feature augmentation strategies Dropout and Dropview are introduced to solve the over-fitting issue, and a more precise and more efficient method named DropMax is proposed after analyzing the drawback of the basic ones. Then, by reducing the over-fitting issue, a camera constraint-free multi-view convolutional neural network named DeepCCFV is constructed. Extensive experiments on both single-modal and cross-modal cases demonstrate the effectiveness of the proposed method in free camera settings comparing with existing state-of-theart 3D object retrieval methods

Experimental methodology for bubble content measurement of thin films

The inclusion of micro bubbles often exists in polyimide films (PI) during the liquid molding process, which can largely affect the mechanical performance. However, there lacks an effective method to quantify the bubble content. Herein, an obvious tension variation of a pre-stressed polyimide film is observed firstly in a vacuum test. This phenomenon is assumed that, when the vacuum level of the testing environment increases, the inner bubbles would expand mainly out of plane, resulting in an in-plane contraction, and the tension varies accordingly. This tension variation ability is found quite stable despite of the stochastic micro-bubble distribution, which could be utilized to quantify the bubble content. Moreover, a film with meso-size bubbles is tested to have the similar tension variation ability. According to the experimental results, the tension variation ability can be basically stable when the bubbles in the length and width directions reach a certain number (e.g. 10)

Radix Puerariae and Fructus Crataegi mixture inhibits renal injury in type 2 diabetes via decreasing of AKT/PI3K

Abstract Background Radix puerariae (RP) is a herbal medicines for diabetes, mainly because of anti-oxidative, insulin resistance and hypoglycemic effect. Fructus crataegi (FC) also possesses strong antioxidant activity in vitro. This study focused on the effects of herbal mixture of RP and FC (RPFC) on renal protection through a diabetic rat model. Methods Type 2 Diabetic model was established with high fat diet followed by injecting rats a low dose of STZ (25 mg/kg body weight). Rats were randomly divided into five groups: normal, high fat diet, diabetes mellitus, high fat diet plus RPFC prevention, and RPFC prevention before diabetes mellitus. RPFC was given to rats daily by intragastric gavage. The blood bio-chemical index and renal pathological changes were examined. The later includes hematoxylin and eosin staining, periodic acid schiff staining, and Masson trichrome staining. Protein levels of were determined by Western blot and immunohistochemical staining. mRNA levels were detected by RT-PCR. Results Rats prevented with RPFC resulted in decreasing blood glucose with corresponding vehicle treated rats. Glomerulus mesangial matrix expansion, renal capsule constriction, and renal tubular epithelial cell edema were less severe following RPFC prevention. Moreover, RPFC prevention reduced protein levels of PI3K, AKT, α-SMA and collagen IV in the kidney of diabetic rats. Conclusion Combined prevention with RPFC may inhibit the PI3K/AKT pathway in the kidney, thereby prevent renal injury in diabetic rats