Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness

Adversarial attacks can mislead neural network classifiers. The defense against adversarial attacks is important for AI safety. Adversarial purification is a family of approaches that defend adversarial attacks with suitable pre-processing. Diffusion models have been shown to be effective for adversarial purification. Despite their success, many aspects of diffusion purification still remain unexplored. In this paper, we investigate and improve upon three limiting designs of diffusion purification: the use of an improved diffusion model, advanced numerical simulation techniques, and optimal control of randomness. Based on our findings, we propose Purify++, a new diffusion purification algorithm that is now the state-of-the-art purification method against several adversarial attacks. Our work presents a systematic exploration of the limits of diffusion purification methods

Enhancing Adversarial Robustness via Score-Based Optimization

Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial intelligence. Recent studies have suggested that score-based diffusion models are effective in adversarial defenses. However, existing diffusion-based defenses rely on the sequential simulation of the reversed stochastic differential equations of diffusion models, which are computationally inefficient and yield suboptimal results. In this paper, we introduce a novel adversarial defense scheme named ScoreOpt, which optimizes adversarial samples at test-time, towards original clean data in the direction guided by score-based priors. We conduct comprehensive experiments on multiple datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results demonstrate that our approach outperforms existing adversarial defenses in terms of both robustness performance and inference speed

Unified Near-field and Far-field Localization with Holographic MIMO

Localization which uses holographic multiple input multiple output surface such as reconfigurable intelligent surface (RIS) has gained increasing attention due to its ability to accurately localize users in non-line-of-sight conditions. However, existing RIS-enabled localization methods assume the users at either the near-field (NF) or the far-field (FF) region, which results in high complexity or low localization accuracy, respectively, when they are applied in the whole area. In this paper, a unified NF and FF localization method is proposed for the RIS-enabled localization system to overcome the above issue. Specifically, the NF and FF regions are both divided into grids. The RIS reflects the signals from the user to the base station~(BS), and then the BS uses the received signals to determine the grid where the user is located. Compared with existing NF- or FF-only schemes, the design of the location estimation method and the RIS phase shift optimization algorithm is more challenging because they are based on a hybrid NF and FF model. To tackle these challenges, we formulate the optimization problems for location estimation and RIS phase shifts, and design two algorithms to effectively solve the formulated problems, respectively. The effectiveness of the proposed method is verified through simulations

Effect of Copper Vapor on Radiation Properties of C4F7N Gas Mixtures

C4F7N and its mixture with buffer gases are regarded as the most promising SF6-alternative gases in gas circuit breakers. The switching arc can severely ablate the electrodes, producing copper metal vapor that combine with the C4F7N gas mixture to chang radiation characteristics. This paper compares the net emission coefficient of C4F7N mixtures at various mixing ratios and assesses the effect of 20% copper vapor. It is found that adding copper vapor can greatly enhance radiation

Mutual effects between Pinus armandii and broadleaf litter during mixed decomposition

Mixed-decomposition effects are commonly observed in natural and planted forests and affect nutrient cycling in a forest ecosystem. However, how one litter type affects the decomposition of another is still poorly understood. In this study, Pinus armandii litter was mixed with Betula albosinensis, Catalpa fargesii, Populus purdomii, Eucommia ulmoides, and Acer tsinglingense litter. The mixtures were placed in litterbags and buried in soil with consistent moisture for a 180-day indoor simulated decomposition experiment. The litterbags were periodically harvested during decomposition; the litter residues of different species were separated, and the biomass dynamics of each litter type were simulated. In addition, the soil sucrase, cellulase and polyphenol oxidase activities were also detected three times. The mutual effects of needle and broadleaf litter during mixed decomposition and the possible underlying mechanisms were investigated. The results indicated that (i) during the decomposition experiment, P. armandii needles significantly inhibited the decomposition of broadleaf litter in the first 3 months, while the broadleaf litter accelerated the decomposition of P. armandii needles in only approximately 40% of the cases. However, the inhibitory effects of needles on broadleaf litter decomposition subsequently exhibited significant weakening, while the accelerating effects of broadleaf litter were significantly enhanced. The effects of mixed decomposition on the activities of three enzymes can only partially explain the interactions between different litter types; (ii) the prediction by the decomposition model showed that most of the broadleaf litter types could continuously accelerate the decomposition of P. armandii needles throughout the mixed decomposition process, while the decomposition of broadleaf litter would be significantly inhibited at least in the short term. In general, four of the five broadleaf litter types (excluding E. ulmoides) could accelerate the early decomposition of P. armandii needles and consequently accelerate nutrient cycling in P. armandii pure forests. These species could be used for the transformation of pure P. armandii pure forests to mixed forests

Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation

Integrated sensing and communication (ISAC) has attracted much attention as a promising approach to alleviate spectrum congestion. However, traditional ISAC systems rely on phased arrays to provide high spatial diversity, where enormous power-consuming components such as phase shifters are used, leading to the high power consumption of the system. In this article, we introduce holographic ISAC, a new paradigm to enable high spatial diversity with low power consumption by using reconfigurable holographic surfaces (RHSs), which is an innovative type of planar antenna with densely deployed metamaterial elements. We first introduce the hardware structure and working principle of the RHS and then propose a novel holographic beamforming scheme for ISAC. Moreover, we build an RHS-enabled hardware prototype for ISAC and evaluate the system performance in the built prototype. Simulation and experimental results verify the feasibility of holographic ISAC and reveal the great potential of the RHS for reducing power consumption. Furthermore, future research directions and key challenges related to holographic ISAC are discussed