Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks

Adversarial training has shown promise in building robust models against adversarial examples. A major drawback of adversarial training is the computational overhead introduced by the generation of adversarial examples. To overcome this limitation, adversarial training based on single-step attacks has been explored. Previous work improves the single-step adversarial training from different perspectives, e.g., sample initialization, loss regularization, and training strategy. Almost all of them treat the underlying model as a black box. In this work, we propose to exploit the interior building blocks of the model to improve efficiency. Specifically, we propose to dynamically sample lightweight subnetworks as a surrogate model during training. By doing this, both the forward and backward passes can be accelerated for efficient adversarial training. Besides, we provide theoretical analysis to show the model robustness can be improved by the single-step adversarial training with sampled subnetworks. Furthermore, we propose a novel sampling strategy where the sampling varies from layer to layer and from iteration to iteration. Compared with previous methods, our method not only reduces the training cost but also achieves better model robustness. Evaluations on a series of popular datasets demonstrate the effectiveness of the proposed FB-Better. Our code has been released at https://github.com/jiaxiaojunQAQ/FP-Better

Simultaneous Distillation Extraction of Some Volatile Flavor Components from Pu-erh Tea Samples—Comparison with Steam Distillation-Liquid/Liquid Extraction and Soxhlet Extraction

A simutaneous distillation extraction (SDE) combined GC method was constructed for determination of volatile flavor components in Pu-erh tea samples. Dichloromethane and ethyl decylate was employed as organic phase in SDE and internal standard in determination, respectively. Weakly polar DB-5 column was used to separate the volatile flavor components in GC, 10 of the components were quantitatively analyzed, and further confirmed by GC-MS. The recovery covered from 66.4%–109%, and repeatability expressed as RSD was in range of 1.44%–12.6%. SDE was most suitable for the extraction of the anlytes by comparing with steam distillation-liquid/liquid extraction and Soxhlet extraction. Commercially available Pu-erh tea samples, including Pu-erh raw tea and ripe tea, were analyzed by the constructed method. the high-volatile components, such as benzyl alcohol, linalool oxide, and linalool, were greatly rich in Pu-erh raw teas, while the contents of 1,2,3-Trimethoxylbenzene and 1,2,4-Trimethoxylbenzene were much high in Pu-erh ripe teas

OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

Vision-language pre-training (VLP) models demonstrate impressive abilities in processing both images and text. However, they are vulnerable to multi-modal adversarial examples (AEs). Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios. Recent works have indicated that leveraging data augmentation and image-text modal interactions can enhance the transferability of adversarial examples for VLP models significantly. However, they do not consider the optimal alignment problem between dataaugmented image-text pairs. This oversight leads to adversarial examples that are overly tailored to the source model, thus limiting improvements in transferability. In our research, we first explore the interplay between image sets produced through data augmentation and their corresponding text sets. We find that augmented image samples can align optimally with certain texts while exhibiting less relevance to others. Motivated by this, we propose an Optimal Transport-based Adversarial Attack, dubbed OT-Attack. The proposed method formulates the features of image and text sets as two distinct distributions and employs optimal transport theory to determine the most efficient mapping between them. This optimal mapping informs our generation of adversarial examples to effectively counteract the overfitting issues. Extensive experiments across various network architectures and datasets in image-text matching tasks reveal that our OT-Attack outperforms existing state-of-the-art methods in terms of adversarial transferability

Undrained shear strength of soft clay reinforce with single 16mm diameter encapsulated bottom ash column

Soft clay soil can be categorized as problematic soil. It consists of low shear strength, low permeability and high compressibility characteristics affect the stability and settlement of the structures constructed on this type of soil. A careful design analysis could be taken for any structure built on it. However, those characteristics could be improved through many methods and the easiest method that is being used in the construction field was stone column. On the other hand, coal is one of the world’s most important sources of energy. Disposal of bottom ash become environmental issues if it is not effectively reused or recycled for other application. This study is to present suitability in term of shear strength by using bottom ash to replace sand or granular material in column for ground improvement technique using laboratory scale model. Since sand is one of non-renewable material so by using by-product or waste material such bottom ash we can reduce the cost of construction as well as keep the non-renewable natural material in balance. Several experimental procedures are carried out to know the physical and mechanical properties of bottom ash and kaolin clay sample. Kaolin is being used as soil sample and bottom ash as the reinforced columns. The shear strength of the encapsulated bottom ash column measured by Unconfined Compression Test. A total 4 batches of kaolin sample had been tested and each batch consist of 5 specimens represent sample without bottom ash, partially penetration and fully penetration for singular bottom ash column. The specimen used were 50mm in diameter and 100mm in height. The diameter of bottom ash is 16mm and the height of the column are 60mm, 80mm and 100mm. The encapsulated bottom ash was installed at the centre of the specimen. The encapsulated bottom ash column with 10.24% area replacement ratio are 58.21%, 58.66% and 42.58% at sample penetration ratio, Hc/Hs of 0.6, 0.8 and 1.0 respectively. It can be concluded that the shear strength of soft clay could be improved by installation of encapsulated bottom ash column. However the value of shear strength of soft clay inserted with partially penetration column increased more significant compared to the fully penetration column

Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging

Fast Adversarial Training (FAT) not only improves the model robustness but also reduces the training cost of standard adversarial training. However, fast adversarial training often suffers from Catastrophic Overfitting (CO), which results in poor robustness performance. Catastrophic Overfitting describes the phenomenon of a sudden and significant decrease in robust accuracy during the training of fast adversarial training. Many effective techniques have been developed to prevent Catastrophic Overfitting and improve the model robustness from different perspectives. However, these techniques adopt inconsistent training settings and require different training costs, i.e, training time and memory costs, leading to unfair comparisons. In this paper, we conduct a comprehensive study of over 10 fast adversarial training methods in terms of adversarial robustness and training costs. We revisit the effectiveness and efficiency of fast adversarial training techniques in preventing Catastrophic Overfitting from the perspective of model local nonlinearity and propose an effective Lipschitz regularization method for fast adversarial training. Furthermore, we explore the effect of data augmentation and weight averaging in fast adversarial training and propose a simple yet effective auto weight averaging method to improve robustness further. By assembling these techniques, we propose a FGSM-based fast adversarial training method equipped with Lipschitz regularization and Auto Weight averaging, abbreviated as FGSM-LAW. Experimental evaluations on four benchmark databases demonstrate the superiority of the proposed method over state-of-the-art fast adversarial training methods and the advanced standard adversarial training methods

Poly(ADP-ribose) polymerases regulate cell division and development in Arabidopsis roots

Root organogenesis involves cell division, differentiation and expansion. The molecular mechanisms regulating root development are not fully understood. In this study, we identified poly (ADP-ribose) polymerases (PARPs) as new players in root development. PARP catalyzes poly (ADP-ribosyl)ation of proteins by repeatedly adding ADP-ribose units onto proteins using nicotinamide adenine dinucleotide (NAD+) as the donor. We found that inhibition of PARP activities by 3-aminobenzomide (3-AB) increased the growth rates of both primary and lateral roots, leading to a more developed root system. The double mutant of Arabidopsis PARPs, parp1parp2, showed more rapid primary and lateral root growth. Cyclin genes regulating G1-to-S and G2-to-M transition were up-regulated upon treatment by 3-AB. The proportion of 2C cells increased while cells with higher DNA ploidy cells declined in the roots of treated plants, resulting in an enlarged rootmeristematic zone. The expression level of PARP2 was very low in the meristematic zone but high in the maturation zones, consistent with a role of PARP in inhibiting mitosis and promoting cell differentiation. Our results suggest that PARPs play an important rolein root development by negatively regulating root cell division

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

The emergence of Deep Neural Networks (DNNs) has revolutionized various domains, enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving. However, this progress has also exposed a concerning vulnerability: adversarial examples. These crafted inputs, imperceptible to humans, can manipulate machine learning models into making erroneous predictions, raising concerns for safety-critical applications. An intriguing property of this phenomenon is the transferability of adversarial examples, where perturbations crafted for one model can deceive another, often with a different architecture. This intriguing property enables "black-box" attacks, circumventing the need for detailed knowledge of the target model. This survey explores the landscape of the adversarial transferability of adversarial examples. We categorize existing methodologies to enhance adversarial transferability and discuss the fundamental principles guiding each approach. While the predominant body of research primarily concentrates on image classification, we also extend our discussion to encompass other vision tasks and beyond. Challenges and future prospects are discussed, highlighting the importance of fortifying DNNs against adversarial vulnerabilities in an evolving landscape

FoxG1 Directly Represses Dentate Granule Cell Fate During Forebrain Development

The cortex consists of 100s of neuronal subtypes that are organized into distinct functional regions; however, the mechanisms underlying cell fate determination remain unclear. Foxg1 is involved in several developmental processes, including telencephalic patterning, cell proliferation and cell fate determination. Constitutive disruption of Foxg1 leads to the transformation of cortical neurons into Cajal-Retzius (CR) cells, accompanied by a substantial expansion of the cortical hem through the consumption of the cortex. However, rather than the induction of a cell fate switch, another group has reported a large lateral to medial repatterning of the developing telencephalon as the explanation for this change in cell type output. Here, we conditionally disrupted Foxg1 in telencephalic progenitor cells by crossing Foxg1fl/fl mice with Nestin-CreERTM mice combined with tamoxifen (TM) induction at distinct developmental stages beginning at E10.5 to further elucidate the role of FoxG1 in cell fate determination after telencephalon pattern formation. The number of dentate gyrus (DG) granule-like cells was significantly increased in the cortex. The increase was even detected after deletion at E14.5. In vivo mosaic deletion and in vitro cell culture further revealed a cell-autonomous role for FoxG1 in repressing granule cell fate. However, the cortical hem, which is required for the patterning and the development of the hippocampus, was only slightly enlarged and thus may not contribute to the cell fate switch. Lef1 expression was significantly upregulated in the lateral, cortical VZ and FoxG1 may function upstream of Wnt signaling. Our results provide new insights into the functions of FoxG1 and the mechanisms of cell fate determination during telencephalic development

Research progress of chilled meat freshness detection based on nanozyme sensing systems

peer reviewedIt is important to develop rapid, accurate, and portable technologies for detecting the freshness of chilled meat to meet the current demands of meat industry. This report introduces freshness indicators for monitoring the freshness changes of chilled meat, and systematically analyzes the current status of existing detection technologies which focus on the feasibility of using nanozyme for meat freshness sensing detection. Furthermore, it examines the limitations and foresees the future development trends of utilizing current nanozyme sensing systems in evaluating chilled meat freshness. Harmful chemicals are produced by food spoilage degradation, including biogenic amines, volatile amines, hydrogen sulfide, and xanthine, which have become new freshness indicators to evaluate the freshness of chilled meat. The recognition mechanisms are clarified based on the special chemical reaction with nanozyme or directly inducting the enzyme-like catalytic activity of nanozyme