MeshAdv: Adversarial Meshes for Visual Recognition

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201

Disruption of the Gene Encoding Endo-β-1, 4-Xylanase Affects the Growth and Virulence of Sclerotinia sclerotiorum

Sclerotinia sclerotiorum (Lib.) de Bary is a devastating fungal pathogen with worldwide distribution. S. sclerotiorum is a necrotrophic fungus that secretes many cell wall-degrading enzymes (CWDEs) that destroy plant’s cell-wall components. Functional analyses of the genes that encode CWEDs will help explain the mechanisms of growth and pathogenicity of S. sclerotiorum. Here, we isolated and characterized a gene SsXyl1 that encoded an endo-β-1, 4-xylanase in S. sclerotiorum. The SsXyl1 expression showed a slight increase during the development and germination stages of sclerotia and a dramatic increase during infection. The expression of SsXyl1 was induced by xylan. The SsXyl1 deletion strains produce aberrant sclerotia that could not germinate to form apothecia. The SsXyl1 deletion strains also lost virulence to the hosts. This study demonstrates the important roles of endo-β-1, 4-xylanase in the growth and virulence of S. sclerotiorum

Three-dimensional Magnetic Restructuring in Two Homologous Solar Flares in the Seismically Active NOAA AR 11283

We carry out a comprehensive investigation comparing the three-dimensional magnetic field restructuring, flare energy release, and the helioseismic response, of two homologous flares, the 2011 September 6 X2.1 (FL1) and September 7 X1.8 (FL2) flares in NOAA AR 11283. In our analysis, (1) a twisted flux rope (FR) collapses onto the surface at a speed of 1.5 km/s after a partial eruption in FL1. The FR then gradually grows to reach a higher altitude and collapses again at 3 km/s after a fuller eruption in FL2. Also, FL2 shows a larger decrease of the flux-weighted centroid separation of opposite magnetic polarities and a greater change of the horizontal field on the surface. These imply a more violent coronal implosion with corresponding more intense surface signatures in FL2. (2) The FR is inclined northward, and together with the ambient fields, it undergoes a southward turning after both events. This agrees with the asymmetric decay of the penumbra observed in the peripheral regions. (3) The amounts of free magnetic energy and nonthermal electron energy released during FL1 are comparable to those of FL2 within the uncertainties of the measurements. (4) No sunquake was detected in FL1; in contrast, FL2 produced two seismic emission sources S1 and S2 both lying in the penumbral regions. Interestingly, S1 and S2 are connected by magnetic loops, and the stronger source S2 has weaker vertical magnetic field. We discuss these results in relation to the implosion process in the low corona and the sunquake generation.Comment: 12 pages, 9 figures, accepted to the Astrophysical Journa

ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback

Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversational LLMs on chemical reaction and retrosynthesis. While drug editing, a critical task in the drug discovery pipeline, remains largely unexplored. To bridge this gap, we propose ChatDrug, a framework to facilitate the systematic investigation of drug editing using LLMs. ChatDrug jointly leverages a prompt module, a retrieval and domain feedback (ReDF) module, and a conversation module to streamline effective drug editing. We empirically show that ChatDrug reaches the best performance on 33 out of 39 drug editing tasks, encompassing small molecules, peptides, and proteins. We further demonstrate, through 10 case studies, that ChatDrug can successfully identify the key substructures (e.g., the molecule functional groups, peptide motifs, and protein structures) for manipulation, generating diverse and valid suggestions for drug editing. Promisingly, we also show that ChatDrug can offer insightful explanations from a domain-specific perspective, enhancing interpretability and enabling informed decision-making. This research sheds light on the potential of ChatGPT and conversational LLMs for drug editing. It paves the way for a more efficient and collaborative drug discovery pipeline, contributing to the advancement of pharmaceutical research and development