Observation of non-Hermitian corner states in non-reciprocal topolectrical circuits

Exploring topological phases in non-Hermitian systems has attracted significant recent attention. One intriguing question is how topological edge states compete with the non-Hermitian skin effect. Here, we report the experimental observation of corner states in a two-dimensional non-reciprocal rhombus honeycomb electric circuit. The system is non-reciprocal and non-Hermitian because the introduced capacitance between two nodes depends on the current direction. The current-inversion negative impedance converters (INIC) is employed to realize the non-reciprocal coupling in circuit. Skin effect thus emerges due to the non-reciprocity and prevails in dragging the corner state into the bulk. The non-Bloch winding number defined in generalized Brillouin zone is adopted to characterize the topological phase transition. Interestingly, we find that the non-Bloch Z2 Berry phase can serve as an invariant to describe the non-Hermitian topology. By tuning the non-reciprocal parameter, we observe unbalanced distribution of corner states emerging on two acute angles of the rhombus lattice, with the localization length of the left corner state increasing exponentially with the degree of non-reciprocity.Comment: 8 pages, 7 figure

Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China

Background China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose. Methods The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey. Results Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]). Conclusions This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China

Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does \textit{not} necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor.Comment: 32 page

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

Magnetic skyrmion generation by reflective spin-wave focusing

We propose a method to generate magnetic skyrmions by focusing spin waves totally reflected by a curved film edge. Based on the principle of identical magnonic path length, we derive the edge contour that is parabolic and frequency-independent. Micromagnetic simulations are performed to verify our theoretical design. It is found that under proper conditions, magnetic droplet first emerges near the focal point where the spin-wave intensity has been significantly enhanced, and then converts to magnetic skyrmion accompanied by a change of the topological charge. The phase diagram about the amplitude and frequency of the driving field for skyrmion generation is obtained. Our finding would be helpful for the designment of spintronic devices combing the advantage of skyrmionics and magnonics.Comment: 5 pages, 5 figure

Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem. Previously proposed Fair Robust Learning (FRL) adaptively reweights different classes to improve fairness. However, the performance of the better-performed classes decreases, leading to a strong performance drop. In this paper, we observed two unfair phenomena during adversarial training: different difficulties in generating adversarial examples from each class (source-class fairness) and disparate target class tendencies when generating adversarial examples (target-class fairness). From the observations, we propose Balance Adversarial Training (BAT) to address the robust fairness problem. Regarding source-class fairness, we adjust the attack strength and difficulties of each class to generate samples near the decision boundary for easier and fairer model learning; considering target-class fairness, by introducing a uniform distribution constraint, we encourage the adversarial example generation process for each class with a fair tendency. Extensive experiments conducted on multiple datasets (CIFAR-10, CIFAR-100, and ImageNette) demonstrate that our method can significantly outperform other baselines in mitigating the robust fairness problem (+5-10\% on the worst class accuracy

30 GHz surface acoustic wave transducers with extremely high mass sensitivity

A nano-patterning process is reported in this work, which can achieve surface acoustic wave (SAW) devices with an extremely high frequency and a super-high mass sensitivity. An integrated lift-off process with ion beam milling is used to minimize the short-circuiting problem and improve the quality of nanoscale interdigital transducers (IDTs). A specifically designed proximity-effect-correction algorithm is applied to mitigate the proximity effect occurring in the electron-beam lithography process. The IDTs with a period of 160 nm and a finger width of 35 nm are achieved, enabling a frequency of ∼30 GHz on lithium niobate based SAW devices. Both centrosymmetric type and axisymmetric type IDT structures are fabricated, and the results show that the centrosymmetric type tends to excite lower-order Rayleigh waves and the axisymmetric type tends to excite higher-order wave modes. A mass sensitivity of ∼388.2 MHz × mm2/μg is demonstrated, which is ∼109 times larger than that of a conventional quartz crystal balance and ∼50 times higher than a conventional SAW device with a wavelength of 4 μm

Theories for triboelectric nanogenerators: A comprehensive review

Triboelectric nanogenerators (TENGs) have attracted much attention as energy harvesting and sensor devices. Compared with experimental means, theoretical analysis is of low cost and time-saving for behavior prediction and structural optimization and is more powerful for understanding the working mechanism of TENGs. In this article, the theoretical system for performance simulation of TENGs has been reviewed systematically. The parallel-plate capacitor model, the distance-dependent electric field (DDEF) model, figures of merit (FOMs), and multi-parameter analysis are introduced. The parallel-plate capacitor model is the most fundamental model of TENGs, which is used to simulate the output of TENGs with planar configurations. For non-planar TENGs, the DDEF model is proposed, according to which the electric field is assumed to be distance-dependent rather than being uniform throughout the space. Further, to realize the standardization of TENGs, a series of FOMs are proposed as the standardized evaluation tools for TENGs’ output performance, which are used to reflect the influence of device parameters on the output from different aspects. Lastly, the multi-parameter analysis is introduced to consider the impact of multiple parameters on the output of TENGs simultaneously. These theories constitute the theoretical simulation system of TENGs, which could be used to guide the experimental work on TENGs and boost device optimization in commercial manufacturing

Recent Progress in Sensing Technology Based on Triboelectric Nanogenerators in Dynamic Behaviors

Under the trend of the rapid development of the internet of things (IoT), sensing for dynamic behaviors is widely needed in many fields such as traffic management, industrial production, medical treatment, building health monitoring, etc. Due to the feature of power supply independence and excellent working performance under a low-frequency environment, triboelectric nanogenerators (TENGs) as sensors are attracting more and more attention. In this paper, a comprehensive review focusing on the recent advance of TENGs as sensors for dynamic behaviors is conducted. The structure and material are two major factors affecting the performance of sensors. Different structure designs are proposed to make the sensor suitable for different sensing occasions and improve the working performance of the sensors. As for materials, new materials with stronger abilities to gain or lose electrons are fabricated to obtain higher surface charge density. Improving the surface roughness of material by surface engineering techniques is another strategy to improve the output performance of TENG. Based on the advancement of TENG structures and materials, plenty of applications of TENG-based sensors have been developed such as city traffic management, human–computer interaction, health monitoring of infrastructure, etc. It is believed that TENG-based sensors will be gradually commercialized and become the mainstream sensors for dynamic sensing