Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform

Analyzing the ocean acoustic environment is a tricky task. Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition. Existing recognition systems are weak in addressing the variable underwater environment, thus leading to disappointing performance in practical application. In order to keep the recognition system robust in various underwater environments, this work proposes an adaptive generalized recognition system - AGNet (Adaptive Generalized Network). By converting fixed wavelet parameters into fine-grained learnable parameters, AGNet learns the characteristics of underwater sound at different frequencies. Its flexible and fine-grained design is conducive to capturing more background acoustic information (e.g., background noise, underwater transmission channel). To utilize the implicit information in wavelet spectrograms, AGNet adopts the convolutional neural network with parallel convolution attention modules as the classifier. Experiments reveal that our AGNet outperforms all baseline methods on several underwater acoustic datasets, and AGNet could benefit more from transfer learning. Moreover, AGNet shows robust performance against various interference factors

Analysis of interspecies adherence of oral bacteria using a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis profiling.

Information on co-adherence of different oral bacterial species is important for understanding interspecies interactions within oral microbial community. Current knowledge on this topic is heavily based on pariwise coaggregation of known, cultivable species. In this study, we employed a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) to systematically analyze the co-adherence profiles of oral bacterial species, and achieved a more profound knowledge beyond pairwise coaggregation. Two oral bacterial species were selected to serve as "bait": Fusobacterium nucleatum (F. nucleatum) whose ability to adhere to a multitude of oral bacterial species has been extensively studied for pairwise interactions and Streptococcus mutans (S. mutans) whose interacting partners are largely unknown. To enable screening of interacting partner species within bacterial mixtures, cells of the "bait" oral bacterium were immobilized on nitrocellulose membranes which were washed and blocked to prevent unspecific binding. The "prey" bacterial mixtures (including known species or natural saliva samples) were added, unbound cells were washed off after the incubation period and the remaining cells were eluted using 0.2 mol x L(-1) glycine. Genomic DNA was extracted, subjected to 16S rRNA PCR amplification and separation of the resulting PCR products by DGGE. Selected bands were recovered from the gel, sequenced and identified via Nucleotide BLAST searches against different databases. While few bacterial species bound to S. mutans, consistent with previous findings F. nucleatum adhered to a variety of bacterial species including uncultivable and uncharacterized ones. This new approach can more effectively analyze the co-adherence profiles of oral bacteria, and could facilitate the systematic study of interbacterial binding of oral microbial species

ent-Kaurane diterpenoids from the plant Wedelia trilobata

Four new ent-kaurane diterpenoids, namely, 3α-tigloyloxypterokaurene L(3) (1), ent-17-hydroxy-kaura-9(11),15-dien-19-oic acid (2), and wedelobatins A (3) and B (4), together with 11 known ent-kaurane diterpenoids (5-15), were isolated from the ethanol extract of Wedelia trilobata. All the structures of 1–15 were elucidated on the basis of spectroscopic studies. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s13659-013-0029-4 and is accessible for authorized users

Nitrogen-doped graphene-ionic liquid-glassy carbon microsphere paste electrode for ultra-sensitive determination of quercetin

The analysis of quercetin (Qu) is of great significance owing to its multiple biomedical effects. In this work, a nitrogendoped graphene-ionic liquid-glassy carbon microsphere paste electrode (N-GE/GCILE) was constructed for the determination of Qu. Cyclic voltammetry (CV) and square wave voltammetry (SWV) were employed to investigate the electrochemical behavior of Qu. In comparison with unmodified glassy carbon microsphere paste electrode, the modified electrode exhibited better electrocatalytic activity towards Qu. The influencing conditions on sensitivity such as the amount of modifier, accumulation potential and time, and electrolyte pH value were respectively discussed. Under the optimized conditions, two linear ranges of 0.002- 0.1 μM and 0.1-10 μM were obtained, with a detection limit of 1 nM (S/N=3). The method was applied in Qu determination in blueberry juice with the recoveries of 102.5-105.0 %

Frequency stability in modern power network from complex network viewpoint

Acknowledgement The work is supported in part by Key Program of Nature Science Fund of Shaanxi Province (2016ZDJC-01), IRT of Shaanxi Province (2013KCT-04).Peer reviewedPostprin

An Embarrassingly Simple Backdoor Attack on Self-supervised Learning

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to eliminating the need for labeled data, research has found that SSL improves the adversarial robustness over supervised learning since lacking labels makes it more challenging for adversaries to manipulate model predictions. However, the extent to which this robustness superiority generalizes to other types of attacks remains an open question. We explore this question in the context of backdoor attacks. Specifically, we design and evaluate CTRL, an embarrassingly simple yet highly effective self-supervised backdoor attack. By only polluting a tiny fraction of training data (<= 1%) with indistinguishable poisoning samples, CTRL causes any trigger-embedded input to be misclassified to the adversary's designated class with a high probability (>= 99%) at inference time. Our findings suggest that SSL and supervised learning are comparably vulnerable to backdoor attacks. More importantly, through the lens of CTRL, we study the inherent vulnerability of SSL to backdoor attacks. With both empirical and analytical evidence, we reveal that the representation invariance property of SSL, which benefits adversarial robustness, may also be the very reason making \ssl highly susceptible to backdoor attacks. Our findings also imply that the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL.Comment: The 2023 International Conference on Computer Vision (ICCV '23