Dynamics-aware Adversarial Attack of Adaptive Neural Networks

In this paper, we investigate the dynamics-aware adversarial attack problem of adaptive neural networks. Most existing adversarial attack algorithms are designed under a basic assumption -- the network architecture is fixed throughout the attack process. However, this assumption does not hold for many recently proposed adaptive neural networks, which adaptively deactivate unnecessary execution units based on inputs to improve computational efficiency. It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture change afterward. To address this issue, we propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. More specifically, we reformulate the gradients to be aware of the potential dynamic changes of network architectures, so that the learned attack better "leads" the next step than the dynamics-unaware methods when network architecture changes dynamically. Extensive experiments on representative types of adaptive neural networks for both 2D images and 3D point clouds show that our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods

OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving

Understanding how the 3D scene evolves is vital for making decisions in autonomous driving. Most existing methods achieve this by predicting the movements of object boxes, which cannot capture more fine-grained scene information. In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes. We propose to learn a world model based on 3D occupancy rather than 3D bounding boxes and segmentation maps for three reasons: 1) expressiveness. 3D occupancy can describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3) versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the modeling of the world evolution, we learn a reconstruction-based scene tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the surrounding scenes. We then adopt a GPT-like spatial-temporal generative transformer to generate subsequent scene and ego tokens to decode the future occupancy and ego trajectory. Extensive experiments on the widely used nuScenes benchmark demonstrate the ability of OccWorld to effectively model the evolution of the driving scenes. OccWorld also produces competitive planning results without using instance and map supervision. Code: https://github.com/wzzheng/OccWorld.Comment: Code is available at: https://github.com/wzzheng/OccWorl

Effect of Aurantii Fructus Immaturus Flavonoid on the Contraction of Isolated Gastric Smooth Muscle Strips in Rats

This study was designed to investigate the effect of Aurantii fructus immaturus flavonoid (AFIF) on the contraction of isolated gastric smooth muscle in rats and explore its underlying mechanisms. Isolated antral longitudinal smooth muscle strip (ALSMS) and pyloric circular smooth muscle strip (PCSMS) of rats were suspended in tissue chambers. The responses of ALSMS and PCSMS to administration of AFIF were observed. Cyclic guanosine monophosphate (cGMP) and protein kinase G (PKG) levels of PCSMS were measured by ELISA kits. In this study, AFIF showed no significant effect on ALSMS contraction, but it dose-dependently reduced the mean contraction amplitude of PCSMS. When the concentration of AFIF reached 3000 g/mL, 6000 g/mL, and 10000 g/mL, its inhibitory effect on PCSMS contraction was significant. This effect of AFIF was weakened in Ca 2+ -rich environment. And N -nitro-L-arginine methyl (L-NAME), the inhibitor of nitric oxide synthase (NOS), significantly inhibited AFIF's action in comparison with control ( < 0.05). After incubation with AFIF for 30 min, levels of cGMP and PKG in PCSMS were significantly increased compared with control ( < 0.05). Our results suggest that AFIF has a dose-dependent diastolic effect on PCSMS in rats, which may be related to the regulatory pathway of NO/cGMP/PKG/Ca 2+

Evolution and Comprehensive Analysis of DNaseI Hypersensitive Sites in Regulatory Regions of Primate Brain-Related Genes

How the human brain differs from those of non-human primates is largely unknown and the complex drivers underlying such differences at the genomic level remain unclear. In this study, we selected 243 brain-related genes, based on Gene Ontology, and identified 184,113 DNaseI hypersensitive sites (DHSs) within their regulatory regions. To performed comprehensive evolutionary analyses, we set strict filtering criteria for alignment quality and filtered 39,132 DHSs for inclusion in the investigation and found that 2,397 (~6%) exhibited evidence of accelerated evolution (aceDHSs), which was a much higher proportion that DHSs genome-wide. Target genes predicted to be regulated by brain-aceDHSs were functionally enriched for brain development and exhibited differential expression between human and chimpanzee. Alignments indicated 61 potential human-specific transcription factor binding sites in brain-aceDHSs, including for CTCF, FOXH1, and FOXQ1. Furthermore, based on GWAS, Hi-C, and eQTL data, 16 GWAS SNPs, and 82 eQTL SNPs were in brain-aceDHSs that regulate genes related to brain development or disease. Among these brain-aceDHSs, we confirmed that one enhanced the expression of GPR133, using CRISPR-Cas9 and western blotting. The GPR133 gene is associated with glioblastoma, indicating that SNPs within DHSs could be related to brain disorders. These findings suggest that brain-related gene regulatory regions are under adaptive evolution and contribute to the differential expression profiles among primates, providing new insights into the genetic basis of brain phenotypes or disorders between humans and other primates

Single-molecule level control of host-guest interactions in metallocycle-C60 complexes

Host−guest interactions are of central importance in many biological and chemical processes. However, the investigation of the formation and decomplexation of host−guest systems at the single-molecule level has been a challenging task. Here we show that the single-molecule conductance of organoplatinum(II) metallocycle hosts can be enhanced by an order of magnitude by the incorporation of a C60 guest molecule. Mechanically stretching the metallocycle-C60 junction with a scanning tunneling microscopy break junction technique causes the release of the C60 guest from the metallocycle, and consequently the conductance switches back to the free-host level. Metallocycle hosts with different shapes and cavity sizes show different degrees of flexibility to accommodate the C60 guest in response to mechanical stretching. DFT calculations provide further insights into the electronic structures and charge transport properties of the molecular junctions based on metallocycles and the metallocycle-C60 complexes

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Diffusion-SDF: Text-to-Shape via Voxelized Diffusion

With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3D modeling framework called Diffusion-SDF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3D data representation and shape generation, thereby failing to generate highly diversified 3D shapes conforming to the given text descriptions. To address this, we propose a SDF autoencoder together with the Voxelized Diffusion model to learn and generate representations for voxelized signed distance fields (SDFs) of 3D shapes. Specifically, we design a novel UinU-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SDF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that Diffusion-SDF generates both higher quality and more diversified 3D shapes that conform well to given text descriptions when compared to previous approaches. Code is available at: https://github.com/ttlmh/Diffusion-SDFComment: Accepted to CVPR 2023, project page: https://ttlmh.github.io/DiffusionSDF