Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense

Improved Dimensional Stability and Mold Resistance of Bamboo via In Situ Growth of Poly(Hydroxyethyl Methacrylate-N-Isopropyl Acrylamide)

Bamboo is a natural and renewable building material but its application has been limited due to the low dimensional stability and poor durability against mold. In this study, monomers of hydroxyethyl methacrylate (HEMA) and N-isopropyl acrylamide (NIPAM) were impregnated in bamboo to facilitate the in situ growth of poly-HEMA and NIPAM (PHN) copolymer. Prior to that, the effects of different reaction conditions, including the molar ratio of HEMA to NIPAM and their concentrations, the amount of initiator (ammonium persulfate, APS) and crosslinking agents (N,N′-Methylenebisacrylamide (MBA), and glutaric dialdehyde (GA)) on the swelling capacity of PHN were optimized. The formation of PHN was confirmed by using Fourier transform infrared spectroscopy and thermogravimetric analysis, which shows the characteristics peaks of both HEMA and NIPAM, and increased pyrolysis and glass transition temperatures, respectively. After impregnation of PHN pre-polymerization formulation to bamboo, it was observed that PHN filled most of the pits in the bamboo cell wall and formed a tight network. Moreover, the dimensional stability of PHN treated bamboo was significantly improved with an anti-swelling efficiency of 49.4% and 41.7%, respectively, after wetting–drying and soaking–drying cycles. A mold infection rate of 13.5% was observed in PHN-treated bamboo as compared to a 100% infected control group after a 30-day mold resistance test. Combined results indicate that in situ polymerization of HEMA and NIPAM in bamboo is a promising method to develop exterior used bamboo products with enhanced dimensional stability and mold resistance

GPLEXUS: Enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species. Here, we present a new approach, GPLEXUS (http://plantgrn.noble.org/GPLEXUS/), which integrates a series of novel algorithms in a parallel-computing environment to construct and analyze genome-wide GANs. GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method and runs ∼1000 times faster. GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional subnetworks. Furthermore, GPLEXUS includes a novel \u27condition-removing\u27 method to identify the major experimental conditions in which each subnetwork operates from very large-scale gene expression datasets across several experimental conditions, which allows users to annotate the various subnetworks with experiment-specific conditions. We demonstrate GPLEXUS\u27s capabilities by construing global GANs and analyzing subnetworks related to defense against biotic and abiotic stress, cell cycle growth and division in Arabidopsis thaliana. © The Author(s) 2013

A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The ‘network layer performance evaluation metrics’ are obtained from the number of pixel activations in the heat map. The network layer with the lowest ‘network layer performance evaluation metrics’ is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet

Liner ship fleet deployment with uncertain demand

This paper points out that the deployment problem of the liner ship fleet with uncertain demand is different from other logistics problems with uncertain demand (e.g., truck transport and airlines) because container ships operate 24 h a day and 7 days a week. This difference is largely ignored in the literature. To address this problem, a multi-level optimization model is developed. In addition to liner ship fleet deployment, the model is applicable to other liner shipping decision problems, such as network design with uncertain demand, and to port operations planning problems, such as berth planning with uncertain ship arrival times

Research on Aerodynamic Design of an End Wall Based on a Quasi-3D Optimization Method

To investigate the effects of different passage structures on the aerodynamic performance of the transonic fans, this paper develops a reliable and practical quasi-3D optimization method for the hub based on the experimental data of Stage 67. In the method, the hub profile of Stage 67 can be optimized without changing the geometrical data of the blades. The optimization results show that stream tube diffusion characteristics depend on the hub profile’s curvature in the boundary layer near the hub. In the front part of the hub, the end wall with a concave construction can enhance the expansion of the stream tubes near the root of the rotor blade, which helps control the diffusion flow of viscous fluid effectively to decrease the low-energy fluid’s energy degradation and radial secondary flow in the boundary layer. In the latter part of the hub, the end wall with a convex construction facilitates the shrinkage of stream tubes to decrease the secondary flow loss and separated flow loss by controlling the separation of the boundary layer efficiently. This construction of the hub profile is beneficial to promote the aerodynamic performance of a transonic fan

LegumeIP: An Integrative Platform for Comparative Genomics and Transcriptomics of Model Legumes

In this chapter, we introduce the latest development of LegumeIP: a platform of comparative genomics and transcriptomics, and then describe some practical usages of the LegumeIP for studying gene functions, molecular mechanisms underpinning the plant-rhizobia interactions, and genome evolution with respect to nitrogen fixing in several agriculturally important model legume species. LegumeIP currently hosts large-scale genomics and transcriptomics data that include (i) genomic sequences of three model legumes, Medicago truncatula, Glycine max (soybean), Lotus japonicus, and two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt, InterProScan, Gene Ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases, comprising a total of 222,217 protein-coding gene sequences; (ii) large-scale compendium gene expression data sets compiled from various tissues of multiple species. These include 104 microarray data sets from L. japonicus, 156 microarray data sets from M. truncatula gene atlas database, and 14 RNA-seq data sets from G. max. These data are further compiled centering on four tissues: nodules, flowers, roots, and leaves being shared by all species; (iii) systematic synteny analysis among M. truncatula, G. max, L. japonicus, and A. thaliana; (iv) reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species; and (v) genome-wide reconstruction of gene coexpression networks. The usefulness of this platform in facilitating molecular research of legume species is demonstrated by two case studies, in which SymRK (symbiosis receptor-like kinase) genes for symbiosis analysis and nitrogen-fixation-related genes in M. truncatula were identified through integrative analysis of gene expression and constructed coexpression networks provided by the LegumeIP platform. The LegumeIP is freely available at http://plantgrn.noble.org/LegumeIP/

Transcription factor c-fos induces the development of premature ovarian insufficiency by regulating MALAT1/miR-22-3p/STAT1 network

Abstract Background The current study attempted to investigate the role of transcription factor c-fos in the development of premature ovarian insufficiency (POI) as well as the underlying mechanism involving the MALAT1/miR-22-3p/STAT1 ceRNA network. Methods Bioinformatics analysis was performed to extract POI-related microarray dataset for identifying the target genes. Interaction among c-fos, MALAT1, miR-22-3p, and STAT1 was analyzed. An in vivo POI mouse model was prepared followed by injection of sh-c-fos and sh-STAT1 lentiviruses. Besides, an in vitro POI cell model was constructed to study the regulatory roles of c-fos, MALAT1, miR-22-3p, and STAT1. Results c-fos, MALAT1, and STAT1 were highly expressed in ovarian tissues from POI mice and CTX-induced KGN cells, while miR-22-3p was poorly expressed. c-fos targeted MALAT1 and promoted MALAT1 transcription. MALAT1 competitively bound to miR-22-3p and miR-22-3p could suppress STAT1 expression. Mechanically, c-fos aggravated ovarian function impairment in POI mice and inhibited KGN cell proliferation through regulation of the MALAT1/miR-22-3p/STAT1 regulatory network. Conclusion Our findings highlighted inducing role of the transcription factor c-fos in POI through modulation of the MALAT1/miR-22-3p/STAT1 ceRNA network

A Nanoscale Sensor Based on a Toroidal Cavity with a Built-In Elliptical Ring Structure for Temperature Sensing Application

In this article, a refractive index sensor based on Fano resonance, which is generated by the coupling of a metal–insulator–metal (MIM) waveguide structure and a toroidal cavity with a built-in elliptical ring (TCER) structure, is presented. The finite element method (FEM) was employed to analyze the propagation characteristics of the integral structure. The effects of refractive index and different geometric parameters of the structure on the sensing characteristics were evaluated. The maximum sensitivity was 2220 nm/RIU with a figure of merit (FOM) of 58.7, which is the best performance level that the designed structure could achieve. Moreover, due to its high sensitivity and simple structure, the refractive index sensor can be applied in the field of temperature detection, and its sensitivity is calculated to be 1.187 nm/℃