Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks

Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack success rates in white-box setting, more focus is shifted to black-box attacks, of which the transferability of adversarial examples has gained significant attention. In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success. But little work pays attention to its possible limitation. In this work, we observe that the deviation between the original gradient and the generated noise may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability. To this end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM). Specifically, we use data rescaling to substitute the sign function without extra computational cost. We further propose a Depth First Sampling method to eliminate the fluctuation of rescaling and stabilize the gradient update. Our method could be used in any gradient-based attacks and is extensible to be integrated with various input transformation or ensemble methods to further improve the adversarial transferability. Extensive experiments on the standard ImageNet dataset show that our method could significantly boost the transferability of gradient-based attacks and outperform the state-of-the-art baselines.Comment: 10 pages, 6 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2204.0288

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.Comment: 9 pages, 6 figures, AAAI 2024 accepte

Metasurface-based Spectral Convolutional Neural Network for Matter Meta-imaging

Convolutional neural networks (CNNs) are representative models of artificial neural networks (ANNs), that form the backbone of modern computer vision. However, the considerable power consumption and limited computing speed of electrical computing platforms restrict further development of CNNs. Optical neural networks are considered the next-generation physical implementations of ANNs to break the bottleneck. This study proposes a spectral convolutional neural network (SCNN) with the function of matter meta-imaging, namely identifying the composition of matter and mapping its distribution in space. This SCNN includes an optical convolutional layer (OCL) and a reconfigurable electrical backend. The OCL is implemented by integrating very large-scale, pixel-aligned metasurfaces on a CMOS image sensor, which accepts 3D raw datacubes of natural images, containing two-spatial and one-spectral dimensions, at megapixels directly as input to realize the matter meta-imaging. This unique optoelectronic framework empowers in-sensor optical analog computing at extremely high energy efficiency eliminating the need for coherent light sources and greatly reducing the computing load of the electrical backend. We employed the SCNN framework on several real-world complex tasks. It achieved accuracies of 96.4% and 100% for pathological diagnosis and real-time face anti-spoofing at video rate, respectively. The SCNN framework, with an unprecedented new function of substance identification, provides a feasible optoelectronic and integrated optical CNN implementation for edge devices or cellphones with limited computing capabilities, facilitating diverse applications, such as intelligent robotics, industrial automation, medical diagnosis, and astronomy

An exploration of the correlations between seven psychiatric disorders and the risks of breast cancer, breast benign tumors and breast inflammatory diseases: Mendelian randomization analyses

BackgroundPrevious observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.MethodsDuring our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs’ effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.ResultsWhen the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10−6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10−6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10−5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.ConclusionsThe correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases

Nanostructure and Oxidation Reactivity of Nascent Soot Particles in Ethylene/Pentanol Flames

As byproducts of the combustion process of hydrocarbon fuels, soot particles are difficult to remove, and they can greatly harm human health and pollute the environment. Therefore, the formation and growth processes of the soot particles has become a study focus of researchers. In this paper, the nanostructure and oxidation reactivity of carbonaceous particles collected from ethylene inverse diffusion flames with or without the additions of three pentanol isomers (1-pentanol, 3-methyl-1-butanol, and 2-methyl-1-butanol) were investigated in detail. The nanostructure and oxidation characteristics of nascent soot particles were characterized using high resolution transmission electron microscopy (HRTEM), X-ray diffractometry (XRD) and thermogravimetric analysis (TGA). It was found that the nascent soot cluster of pure ethylene flame had a loose structure, while the additions of pentanol isomers made the soot agglomerates more compact and delayed the growth of graphitic structures. The pentanol isomer additions also contributed to a higher disorder of the crystallite arrangement in the soot nanostructure. According to the TGA experiments, the results showed that the addition of pentanol isomers enhanced the oxidation reactivity of soot particles, which could help to reduce soot particle emissions

Hepatoprotective Potential of Partially Hydrolyzed Guar Gum against Acute Alcohol-Induced Liver Injury in Vitro and Vivo

Natural polysaccharides, particularly galactomannans, are potential candidates for treatment of alcoholic liver diseases (ALD). However, applications are restricted due to the physicochemical properties associated with the high molecular weight. In this work, guar gum galactomannans were partially hydrolyzed by β-mannanase, and the molecular mechanisms of hepatoprotective effects were elucidated both in vitro and in vivo. Release of lactate dehydrogenase and cytochrome C were attenuated by partially hydrolyzed guar gum (PHGG) in HepG2 cells, due to protected cell and mitochondrial membrane integrity. PHGG co-administration decreased serum amino transaminases and cholinesterase levels of acute alcohol intoxicated mice, while hepatic pathologic morphology was depleted. Activity of superoxide dismutase, catalase, and glutathione peroxidase was recovered to 198.2, 34.5, 236.0 U/mg protein, respectively, while malondialdehyde level was decreased by 76.3% (PHGG, 1000 mg/kg∙day). Co-administration of PHGG induced a 4.4-fold increment of p-AMPK expression, and lipid metabolism was mediated. PHGG alleviated toll-like-receptor-4-mediated inflammation via the signaling cascade of MyD88 and IκBα, decreasing cytokine production. Moreover, mediated expression of Bcl-2 and Bax was responsible for inhibited acute alcohol-induced apoptosis with suppressed cleavage of caspase 3 and PARP. Findings gained suggest that PHGG can be used as functional food supplement for the treatment of acute alcohol-induced liver injury

Polyethylene Glycol Priming Enhances the Seed Germination and Seedling Growth of <i>Scutellaria baicalensis</i> Georgi under Salt Stress

Seed priming has become a practical pre-sowing strategy to deal with abiotic stresses. This study aims to explore the effects of polyethylene glycol (PEG) priming on seed germination and seedling growth of Scutellaria baicalensis Georgi under salt stress. Regardless of seed priming, salt stress significantly inhibited the seed germination and seedling growth of S. baicalensis. PEG priming significantly alleviates the inhibitory effects of salt stress on seed germination and seedling growth when compared to non-priming and water priming. Among all treatments, PEG priming exhibited the highest germination rate, germination potential, seed vigor index, fresh weight, dry weight, and plant length; the highest contents of proline, soluble sugar, and soluble protein; the highest K+/Na+ ratio and relative water content; the highest antioxidant activities and contents; but the lowest H2O2, malondialdehyde (MDA) content, and relative electrical conductivity in response to salt stress. In addition, PEG priming had the highest transcript levels of antioxidant-related genes among all treatments under NaCl stress. Taken together, the results demonstrated that seed priming with PEG could be recommended as an effective practice to enhance the germination and early seedling growth of S. baicalensis under saline conditions

Mechanical performance of recycled aggregate concrete in green civil engineering: Review

The use of recycled aggregate concrete can save natural resources and reduce environmental pollution, which has been widely concerned in recent years. The physical and mechanical properties of recycled aggregate concrete (RAC) are studied based on the literature on waste concrete coarse aggregate (RCCA) and waste brick coarse aggregate (RBCA) published around the world in the last 40 years, in order to understand and study the performance of common RAC. The common characteristics (water absorption, density, and crushing value) of two recycled aggregates with the same particle size were compared. The effects of effective water–cement ratio (e–W/C), aggregate cement ratio (A/C), and recycled aggregate content on the compressive properties of recycled concrete are explained, and the microstructure of recycled concrete is explored. The influence of recycled aggregate content on its elastic modulus (Ec) and the correlation between Ec and the compressive strength of recycled concrete are also investigated. Finally, prospects were made for two types of RAC for further research by scholars