Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion

Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. Robust training and defend-by-denoising are typical strategies for defending adversarial perturbations. However, they either induce massive computational overhead or rely heavily upon specified priors, limiting generalized robustness against attacks of all kinds. To remedy it, this paper introduces a novel distortion-aware defense framework that can rebuild the pristine data distribution with a tailored intensity estimator and a diffusion model. To perform distortion-aware forward diffusion, we design a distortion estimation algorithm that is obtained by summing the distance of each point to the best-fitting plane of its local neighboring points, which is based on the observation of the local spatial properties of the adversarial point cloud. By iterative diffusion and reverse denoising, the perturbed point cloud under various distortions can be restored back to a clean distribution. This approach enables effective defense against adaptive attacks with varying noise budgets, enhancing the robustness of existing 3D deep recognition models.Comment: Accepted by ACM MM 202

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition

Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition models and propose Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds. Specifically, we leverage a supervised contrastive loss to facilitate the alignment and uniformity of the hypersphere features extracted by the recognition model, and design a pair of centralizing losses with the dynamic prototype guidance to avoid these features deviating from their belonging category clusters. To provide the more challenging corrupted point clouds, we adversarially train a noise generator along with the recognition model from the scratch, instead of using gradient-based attack as the inner loop like previous adversarial training methods. Comprehensive experiments show that the proposed PointCAT outperforms the baseline methods and dramatically boosts the robustness of different point cloud recognition models, under a variety of corruptions including isotropic point noises, the LiDAR simulated noises, random point dropping and adversarial perturbations

Wogonin induces cell cycle arrest and erythroid differentiation in imatinib-resistant K562 cells and primary CML cells

Wogonin, a flavonoid derived from Scutellaria baicalensis Georgi, has been demonstrated to be highly effective in treating hematologic malignancies. In this study, we investigated the anticancer effects of wogonin on K562 cells, K562 imatinib-resistant cells, and primary patient-derived CML cells. Wogonin up-regulated transcription factor GATA-1 and enhanced binding between GATA-1 and FOG-1, thereby increasing expression of erythroid-differentiation genes. Wogonin also up-regulated the expression of p21 and induced cell cycle arrest. Studies employing benzidine staining and analyses of cell surface markers glycophorin A (GPA) and CD71 indicated that wogonin promoted differentiation of K562, imatinib-resistant K562, and primary patient-derived CML cells. Wogonin also enhanced binding between GATA-1 and MEK, resulting in inhibition of the growth of CML cells. Additionally, in vivo studies showed that wogonin decreased the number of CML cells and prolonged survival of NOD/SCID mice injected with K562 and imatinib-resistant K562 cells. These data suggested that wogonin induces cycle arrest and erythroid differentiation in vitro and inhibits proliferation in vivo

The water lily genome and the early evolution of flowering plants

Water lilies belong to the angiosperm order Nymphaeales. Amborellales, Nymphaeales and Austrobaileyales together form the so-called ANA-grade of angiosperms, which are extant representatives of lineages that diverged the earliest from the lineage leading to the extant mesangiosperms1–3. Here we report the 409-megabase genome sequence of the blue-petal water lily (Nymphaea colorata). Our phylogenomic analyses support Amborellales and Nymphaeales as successive sister lineages to all other extant angiosperms. The N. colorata genome and 19 other water lily transcriptomes reveal a Nymphaealean whole-genome duplication event, which is shared by Nymphaeaceae and possibly Cabombaceae. Among the genes retained from this whole-genome duplication are homologues of genes that regulate flowering transition and flower development. The broad expression of homologues of floral ABCE genes in N. colorata might support a similarly broadly active ancestral ABCE model of floral organ determination in early angiosperms. Water lilies have evolved attractive floral scents and colours, which are features shared with mesangiosperms, and we identified their putative biosynthetic genes in N. colorata. The chemical compounds and biosynthetic genes behind floral scents suggest that they have evolved in parallel to those in mesangiosperms. Because of its unique phylogenetic position, the N. colorata genome sheds light on the early evolution of angiosperms.Supplementary Tables: This file contains Supplementary Tables 1-21.National Natural Science Foundation of China, the open funds of the State Key Laboratory of Crop Genetics and Germplasm Enhancement (ZW201909) and State Key Laboratory of Tree Genetics and Breeding, the Fujian provincial government in China, the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement and the Special Research Fund of Ghent University.http://www.nature.com/naturecommunicationsam2021BiochemistryGeneticsMicrobiology and Plant Patholog

Electrochemical Energy Storage Properties of High-Porosity Foamed Cement

Foamed porous cement materials were fabricated with H2O2 as foaming agent. The effect of H2O2 dosage on the multifunctional performance is analyzed. The result shows that the obtained specimen with 0.6% H2O2 of the ordinary Portland cement mass (PC0.6) has appropriate porosity, leading to outstanding multifunctional property. The ionic conductivity is 29.07 mS cm−1 and the compressive strength is 19.6 MPa. Furthermore, the electrochemical energy storage performance is studied in novel ways. The PC0.6 also shows the highest areal capacitance of 178.28 mF cm−2 and remarkable cycle stability with 90.67% of initial capacitance after 2000 cycles at a current density of 0.1 mA cm−2. The superior electrochemical energy storage property may be attributed to the high porosity of foamed cement, which enlarges the contact area with the electrode and provides a rich ion transport channel. This report on cement–matrix materials is of great significance for large scale civil engineering application

Effect of graphene oxide on strength and interfacial transition zone of recycled aggregate concrete

This paper studies the effect of graphene oxide (GO) on the strength and interface transition zone of recycled aggregate concrete (RAC). The results show that the addition of GO enhances the RAC strength, and the compressive strength of the sample containing GO is improved by 7% ∼20.6% at 28 days, compared with the reference group. Meanwhile, with the addition of GO, the total porosity and the number of harmful pores (> 100 nm) of RAC samples decreased by 8.1% ∼35.7% and 3% ∼39.1%, respectively. It is observed from the nano scale characteristics that the addition of GO can significantly reduce pore phase and unhydrated phase content in the matrix, and increase the volume fraction of C–S–H phase, especially the high-density C–S–H phase. In addition, the width of the interface transition zone between old mortar and new mortar containing GO sample is relatively reduced by 25%, but there is no obvious change in the interface transition zone of old aggregate mortar. The strengthening effect of GO on RAC strength is due to the nucleation of GO and the filling effect of micro-aggregate, improving the pore structure and interface transition zone of RAC

Initiative Defense against Facial Manipulation

Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number of entertainment applications but also incurs severe threats to individual privacy and even political security meanwhile. To mitigate such risks, many countermeasures have been proposed. However, the great majority methods are designed in a passive manner, which is to detect whether the facial images or videos are tampered after their wide propagation. These detection-based methods have a fatal limitation, that is, they only work for ex-post forensics but can not prevent the engendering of malicious behavior. To address the limitation, in this paper, we propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. The basic idea is to actively inject imperceptible venom into target facial data before manipulation. To this end, we first imitate the target manipulation model with a surrogate model, and then devise a poison perturbation generator to obtain the desired venom. An alternating training strategy are further leveraged to train both the surrogate model and the perturbation generator. Two typical facial manipulation tasks: face attribute editing and face reenactment, are considered in our initiative defense framework. Extensive experiments demonstrate the effectiveness and robustness of our framework in different settings. Finally, we hope this work can shed some light on initiative countermeasures against more adversarial scenarios

High cycle fatigue behaviour of Invar 36 alloy fabricated by laser powder bed fusion

The Invar 36 alloy was additively manufactured by laser powder bed fusion (PBF-LB), and systematical observations and experiments for microstructure, defects, metallography, especially high cycle fatigue behaviour and fractography were conducted. Inadequate laser energy density results in hardly overlapping melting traces, generating numerous defects. Accordingly, the fabricated Invar 36 alloy presents an inferior high cycle fatigue life, as it failures from the rapid aggregation of the defects. In contrast, an adequate laser energy density remarkably enlarges the overlapping between adjacent melting traces. The large molten pools with steady boundaries are beneficially to generate favourable microstructures and low porosity. Consequently, the Invar 36 alloy shows superior high cycle fatigue life, completely generated from small crack propagation, long crack propagation and final fracture stages. Above experimental results and analysis primarily link up the PBF-LB process, microstructures (defects) and high cycle fatigue performance for PBF-LB Invar 36 alloy