Fast Adversarial Training with Smooth Convergence

Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as catastrophic overfitting when dealing with large perturbation budgets, \ie the adversarial robustness of models declines to near zero during training. To address this, we analyze the training process of prior FAT work and observe that catastrophic overfitting is accompanied by the appearance of loss convergence outliers. Therefore, we argue a moderately smooth loss convergence process will be a stable FAT process that solves catastrophic overfitting. To obtain a smooth loss convergence process, we propose a novel oscillatory constraint (dubbed ConvergeSmooth) to limit the loss difference between adjacent epochs. The convergence stride of ConvergeSmooth is introduced to balance convergence and smoothing. Likewise, we design weight centralization without introducing additional hyperparameters other than the loss balance coefficient. Our proposed methods are attack-agnostic and thus can improve the training stability of various FAT techniques. Extensive experiments on popular datasets show that the proposed methods efficiently avoid catastrophic overfitting and outperform all previous FAT methods. Code is available at \url{https://github.com/FAT-CS/ConvergeSmooth}

Physicochemical Characteristics and Spoilage Bacteria of Modified Atmosphere Packaged Braised Chicken during Refrigeration

In order to explore dynamic changes in the physicochemical characteristics and microbial diversity of modified atmosphere packaged braised chicken during refrigeration, high-throughput sequencing technology and traditional isolation and identification methods were combined to study the physical and microbial changes of modified atmosphere packaged (25% CO2/75% N2) braised chicken during storage at 4 ℃ for 10 days. The results showed that total volatile basic nitrogen (TVB-N) content and total bacterial count increased significantly during storage (P 0.05). These changes were related to the gas composition in modified atmosphere packaging. The microbial species richness of braised chicken was the highest at the early stage of storage, and then decreased gradually. At the initial stage of storage, the relative abundance of Enterobacteriaceae, Acinetobacter and Aeromonas were high. At the middle and late stages of storage, the relative abundance of Weissella and Enterobacter were high. The relative abundance of Weissella, Enterobacter, Lactococcus and Leuconostoc increased gradually during the whole storage period and their growth was dominant. Six species of spoilage organisms were isolated and identified, namely, Lactobacillus curvatus, Bacillus cereus, Bacillus safensis, Serratia liquefaciens, Hafnia sp. and Enterobacter sp., which could be the major causes of the rot of braised chicken under modified atmosphere packaging conditions

Distractor-aware Event-based Tracking

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency

Comparative analysis of 17 complete chloroplast genomes reveals intraspecific variation and relationships among Pseudostellaria heterophylla (Miq.) Pax populations

Pseudostellaria heterophylla (Miq.) Pax is a well-known medicinal and ecologically important plant. Effectively distinguishing its different genetic resources is essential for its breeding. Plant chloroplast genomes can provide much more information than traditional molecular markers and provide higher-resolution genetic analyses to distinguish closely related planting materials. Here, seventeen P. heterophylla samples from Anhui, Fujian, Guizhou, Hebei, Hunan, Jiangsu, and Shandong provinces were collected, and a genome skimming strategy was employed to obtain their chloroplast genomes. The P. heterophylla chloroplast genomes ranged from 149,356 bp to 149,592 bp in length, and a total of 111 unique genes were annotated, including 77 protein-coding genes, 30 tRNA genes, and four rRNA genes. Codon usage analysis showed that leucine had the highest frequency, while UUU (encoding phenylalanine) and UGC (encoding cysteine) were identified as the most and least frequently used codons, respectively. A total of 75–84 SSRs, 16–21 short tandem repeats, and 27–32 long repeat structures were identified in these chloroplast genomes. Then, four primer pairs were revealed for identifying SSR polymorphisms. Palindromes are the dominant type, accounting for an average of 47.86% of all long repeat sequences. Gene orders were highly collinear, and IR regions were highly conserved. Genome alignment indicated that there were four intergenic regions (psaI-ycf4, ycf3-trnS, ndhC-trnV, and ndhI-ndhG) and three coding genes (ndhJ, ycf1, and rpl20) that were highly variable among different P. heterophylla samples. Moreover, 10 SNP/MNP sites with high polymorphism were selected for further study. Phylogenetic analysis showed that populations of Chinese were clustered into a monophyletic group, in which the non-flowering variety formed a separate subclade with high statistical support. In this study, the comparative analysis of complete chloroplast genomes revealed intraspecific variations in P. heterophylla and further supported the idea that chloroplast genomes could elucidate relatedness among closely related cultivation materials

Tree species and genetic diversity increase productivity via functional diversity and trophic feedbacks

Addressing global biodiversity loss requires an expanded focus on multiple dimensions of biodiversity. While most studies have focused on the consequences of plant interspecific diversity, our mechanistic understanding of how genetic diversity within plant species affects plant productivity remains limited. Here, we use a tree species × genetic diversity experiment to disentangle the effects of species diversity and genetic diversity on tree productivity, and how they are related to tree functional diversity and trophic feedbacks. We found that tree species diversity increased tree productivity via increased tree functional diversity, reduced soil fungal diversity, and marginally reduced herbivory. The effects of tree genetic diversity on productivity via functional diversity and soil fungal diversity were negative in monocultures but positive in the mixture of the four tree species tested. Given the complexity of interactions between species and genetic diversity, tree functional diversity and trophic feedbacks on productivity, we suggest that both tree species and genetic diversity should be considered in afforestation