293 research outputs found
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
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