Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks

Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has not been sufficiently explored. One fundamental question is whether existing T2I DMs are robust against variations over input texts. To answer it, this work provides the first robustness evaluation of T2I DMs against real-world attacks. Unlike prior studies that focus on malicious attacks involving apocryphal alterations to the input texts, we consider an attack space spanned by realistic errors (e.g., typo, glyph, phonetic) that humans can make, to ensure semantic consistency. Given the inherent randomness of the generation process, we develop novel distribution-based attack objectives to mislead T2I DMs. We perform attacks in a black-box manner without any knowledge of the model. Extensive experiments demonstrate the effectiveness of our method for attacking popular T2I DMs and simultaneously reveal their non-trivial robustness issues. Moreover, we provide an in-depth analysis of our method to show that it is not designed to attack the text encoder in T2I DMs solely

Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks

We study building a multi-task agent in Minecraft. Without human demonstrations, solving long-horizon tasks in this open-ended environment with reinforcement learning (RL) is extremely sample inefficient. To tackle the challenge, we decompose solving Minecraft tasks into learning basic skills and planning over the skills. We propose three types of fine-grained basic skills in Minecraft, and use RL with intrinsic rewards to accomplish basic skills with high success rates. For skill planning, we use Large Language Models to find the relationships between skills and build a skill graph in advance. When the agent is solving a task, our skill search algorithm walks on the skill graph and generates the proper skill plans for the agent. In experiments, our method accomplishes 24 diverse Minecraft tasks, where many tasks require sequentially executing for more than 10 skills. Our method outperforms baselines in most tasks by a large margin. The project's website and code can be found at https://sites.google.com/view/plan4mc.Comment: 19 page

Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation

Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, \emph{e.g.}, household robots and rescue robots. In this task, an embodied agent must search for and navigate to the sound source with egocentric visual and audio observations. However, the existing methods are limited in two aspects: 1) poor generalization to unheard sound categories; 2) sample inefficient in training. Focusing on these two problems, we propose a brain-inspired plug-and-play method to learn a semantic-agnostic and spatial-aware representation for generalizable visual-audio navigation. We meticulously design two auxiliary tasks for respectively accelerating learning representations with the above-desired characteristics. With these two auxiliary tasks, the agent learns a spatially-correlated representation of visual and audio inputs that can be applied to work on environments with novel sounds and maps. Experiment results on realistic 3D scenes (Replica and Matterport3D) demonstrate that our method achieves better generalization performance when zero-shot transferred to scenes with unseen maps and unheard sound categories

EM Scattering from Complex Targets above a Slightly Rough Surface

Abstract-A hybrid approach which combines the "four-path" model with a quasi-image method is developed in this paper to deal with the high frequency EM scattering problems of complex targets located above a slightly rough surface. The computation process of the multipath scatterings in the "four-path" model is greatly simplified by using the image targets to treat the shadowing effects and a damped reflection coefficient to correct the scattered field of the rough surface. The effectiveness and efficiency of this method are verified by numerical results, which show that the proposed approach can be used to quickly and accurately evaluate the high frequency scattering from complex targets above a slightly rough surface

Rapeseed bee pollen alleviates chronic non-bacterial prostatitis via regulating gut microbiota.

peer reviewed[en] BACKGROUND: Rapeseed bee pollen has been recognized as a critical treatment for chronic non-bacterial prostatitis (CNP) and it also can modulate gut microbiota and improve gut health. This study aimed to explore the anti-prostatitis effects of rapeseed bee pollen with or without wall-disruption, and to investigate the connection between this treatment and gut microbiota. RESULTS: The results reveal that rapeseed bee pollen can effectively alleviate chronic non-bacteria prostatitis by selectively regulating gut microbiota, with higher doses and wall-disrupted pollen showing greater efficacy. Treatment with a high dose of wall-disrupted rapeseed bee pollen (WDH, 1.26 g kg-1 body weight) reduced prostate wet weight and prostate index by approximately 32% and 36%, respectively, nearly the levels observed in the control group. Wall-disrupted rapeseed bee pollen treatment also reduced significantly (p < 0.05) the expression of proinflammatory cytokines (IL-6, IL-8, IL-1β, and TNF-α), as confirmed by immunofluorescence with laser scanning confocal microscope. Our results show that rapeseed bee pollen can inhibit pathogenic bacteria and enhance probiotics, particularly in the Firmicutes-to-Bacteroidetes (F/B) ratio and the abundance of Prevotella (genus). CONCLUSION: This is the first study to investigate the alleviation of CNP with rapeseed bee pollen through gut microbiota. These results seem to provide better understanding for the development of rapeseed bee pollen as a complementary medicine

Phenolamide and flavonoid glycoside profiles of 20 types of monofloral bee pollen.

peer reviewedThis study aimed at investigating phenolamides and flavonoid glycosides in 20 types of monofloral bee pollen. The plant origins of pollen samples were determined by DNA barcoding, with the purities to over 70 %. The 31 phenolamides and their 33 cis/trans isomers, and 25 flavonoid glycosides were identified; moreover, 19 phenolamides and 14 flavonoid glycosides as new-found compounds in bee pollen. All phenolics and flavonoids are present in the amidation or glycosylation form. The MS/MS cleavage modes of phenolamides and flavonoid glycosides were summarized. Isorhamnetin-3-O-gentiobioside presented the highest levels 23.61 mg/g in apricot pollen. Phenolamides in 11 types of pollen constituted over 1 % of the total weight, especially 3.9 % in rose and 2.8 % in pear pollen. Tri-p-coumaroyl spermidine and di-p-coumaroyl-caffeoyl spermidine respectively accounted for over 2.6 % of the total weight in pear and rose pollen. The richness in phenolamides and flavonoid glycosides can offer bee pollen more bioactivities as functional foods