In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.

Transparent conductive electrodes (TCEs) are of enormous significance to the emergence of flexible and wearable electronics and continued growth of modern devices. Versatile and tunable TCEs, featuring with not only high optical transmittance but also intriguing features of electrochemical energy-storage capability, remain a significant challenge. Here we develop capacitive active films comprised of graphene-conjugated V2O5@poly (3,4-ethylene dioxythiophene) ternary composite (V2O5@PEDOT/rGO) on silver nanowire coated substrates as solid-state super/pseudocapacitors. The constructed electrodes exhibit improved electrolyte ions interaction with effective graphene layer, achieving high areal capacitance 0.6-1.2 mF.cm−2 with 0.5M LiCl electrolytes at optical transparency >60% with record durability. As demonstrated, the kinetic blocking of PEDOT layer and anchoring capability of graphene upon amphoteric soluble vanadium ions from layered V2O5 nanoribbons/nanobelts contribute synergistically to the unusual electrochemical stability, also shown using scanning electrochemical microscopy (SECM) providing electroactivity sites and ion transportation rates. As-fabricated symmetric solid-state supercapacitors delivered broad potential window >1.4 V under two different electrolyte environments (aqueous LiCl and LiCl/PVA gel) and demonstrated higher power and energy density (0.27 μWh.cm−2) outperforming previously reported devices at <0.1 μWh.cm−2. The electrochemical properties are also discussed in terms of solvation in polymer gel electrolyte ions.

Study Region -This study is conducted for a cluster of watersheds within the Matagorda Basin along the south Texas coast in the United States. Study Focus - Retrospective streamflow simulations of two hydrologic models of contrasting formulations and complexity, namely the TxRR, a simple analog model, and the National Water Model (NWM), a land surface-hydrologic model that explicitly accounts for surface energy balance in calculating water budget, and whose output. The comparison was motivated by a) the need for improving the modeling of runoff dynamics for watersheds along central Texas coast through the introduction of NWM to inform freshwater inflow monitoring and flood planning, and b) the need to better understand the potential ability of the NWM to address shortcomings of TxRR related to the latter’s mechanistic deficiencies in a region known for hydroclimatic extremes. The study focuses on the temporal scale and climate dependence in the relative performance of the two models, the behaviors of models during extreme floods and droughts, and the relative efficacy of parameter transfer schemes. It further seeks to relate the performance differentials to model physics and calibration approaches. New Hydrologic Insights – NWM, with sophisticated representations of energe closure, is advantageous to TxRR in capturing low-frequency (interannual) variations in streamflow modulated by the surface energy balance, as evidenced by the superior correlation of its streamflow simulations at the interannual range. Yet, its overall performance trails behind TxRR at daily scale, and it tends to underpredict runoff volumes during two major floods. These features underscore the tendency of the model to suppress soil moisture at the onset of these events. On the other hand, TxRR outperforms in reproducing the volumes for the flooding events, but it overpredicts runoff during the extreme drought episode of 2011, likely an outcome of inadequate representation of the impacts of root zone soil moisture in regulating runoff. TxRR’s parameteralization scheme proves more effective for adaptation across watersheds due to the presence of steep gradation in soil properties .

In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.