QueryNet: Attack by Multi-Identity Surrogates

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable Adversarial Examples (AEs) because of their Gradient Similarity (GS), i.e., surrogates' attack gradients are similar to the victim's ones. However, it is generally neglected to exploit their similarity on outputs, namely the Prediction Similarity (PS), to filter out inefficient queries by surrogates without querying the victim. To jointly utilize and also optimize surrogates' GS and PS, we develop QueryNet, a unified attack framework that can significantly reduce queries. QueryNet creatively attacks by multi-identity surrogates, i.e., crafts several AEs for one sample by different surrogates, and also uses surrogates to decide on the most promising AE for the query. After that, the victim's query feedback is accumulated to optimize not only surrogates' parameters but also their architectures, enhancing both the GS and the PS. Although QueryNet has no access to pre-trained surrogates' prior, it reduces queries by averagely about an order of magnitude compared to alternatives within an acceptable time, according to our comprehensive experiments: 11 victims (including two commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image queries, and no access to the victim's training data. The code is available at https://github.com/Sizhe-Chen/QueryNet.Comment: QueryNet reduces queries by about an order of magnitude against SOTA black-box attack

Does ownership concentration affect corporate environmental responsibility engagement? The mediating role of corporate leverage

This paper examines the effect of ownership concentration on engagement in corporate environmental responsibility (CER) in time and spatial dimensions. The time dimension focuses on the macroeconomic environment, in particular, periods of rapid and moderate-speed economic growth. The spatial dimension focuses on industry characteristics and different types of ownership (state or private). Further, it explores the mediating role of corporate leverage using panel regression models and stepwise regression with a sample of Chinese A-share listed companies over the period 2008–2016. The results show that ownership concentration has a significantly negative effect on CER. In addition, when we consider the macroeconomic growth rate, ownership type, and industry characteristics, the effect is heterogeneous. In periods with rapid economic growth, ownership concentration has a significantly negative effect on CER whereas it is not significant in a period with moderate economic growth. Further, the negative effect exists at state-owned and non-state-owned companies and at non-heavy-polluting industries. Corporate leverage has a partial mediating effect between ownership concentration and engagement in CER

Protein-Protein Affinity Determination by Quantitative FRET Quenching.

The molecular dissociation constant, Kd, is a well-established parameter to quantitate the affinity of protein-protein or other molecular interactions. Recently, we reported the theoretical basis and experimental procedure for Kd determination using a quantitative FRET method. Here we report a new development of Kd determination by measuring the reduction in donor fluorescence due to acceptor quenching in FRET. A new method of Kd determination was developed from the quantitative measurement of donor fluorescence quenching. The estimated Kd values of SUMO1-Ubc9 interaction based on this method are in good agreement with those determined by other technologies, including FRET acceptor emission. Thus, the acceptor-quenched approach can be used as a complement to the previously developed acceptor excitation method. The new methodology has more general applications regardless whether the acceptor is an excitable fluorophore or a quencher. Thus, these developments provide a complete methodology for protein or other molecule interaction affinity determinations in solution

Art and history go hand in hand: the evolution of Chinese national vocal music from the Yan'an period to reform and opening up

The Yan'an area was an important base for China's revolution. It was also in Yan'an that the Chinese Communist Party brought a lot of political and theoretical knowledge to the local people and created a lot of artistic forms, one of which was vocal music. Driven by the "Red Gene," the work of the Red Army in the Yan'an area became smoother, and they communicated with the local people on a spiritual level through vocal works. In the past 40 years of reform and opening up, the development of the times and social changes, the public aesthetic concept has also quietly developed and changed, which has had a great impact on the development of national vocal music: the creation of a large number of excellent national vocal works to glorify the new era; the performance of both the traditional singing bright and sweet and Western Bel canto singing transparent and round. In terms of education, a large number of national vocal educators such as Jin Tielin, Zou Wenqin, Ma Qiuhua, etc. They have established a perfect education and training system and complete singing skills. This Paper discusses the development of national vocal music and analyzes the important breakthroughs and achievements in the field of national vocal music in three stages, and composes the characteristics of national vocal music development itself, and analyzes and researches the various factors affecting it, so that we can think of danger in peace and create glory again

Art and history go hand in hand: the evolution of Chinese national vocal music from the Yan'an period to reform and opening up

The Yan'an area was an important base for China's revolution. It was also in Yan'an that the Chinese Communist Party brought a lot of political and theoretical knowledge to the local people and created a lot of artistic forms, one of which was vocal music. Driven by the "Red Gene," the work of the Red Army in the Yan'an area became smoother, and they communicated with the local people on a spiritual level through vocal works. In the past 40 years of reform and opening up, the development of the times and social changes, the public aesthetic concept has also quietly developed and changed, which has had a great impact on the development of national vocal music: the creation of a large number of excellent national vocal works to glorify the new era; the performance of both the traditional singing bright and sweet and Western Bel canto singing transparent and round. In terms of education, a large number of national vocal educators such as Jin Tielin, Zou Wenqin, Ma Qiuhua, etc. They have established a perfect education and training system and complete singing skills. This Paper discusses the development of national vocal music and analyzes the important breakthroughs and achievements in the field of national vocal music in three stages, and composes the characteristics of national vocal music development itself, and analyzes and researches the various factors affecting it, so that we can think of danger in peace and create glory again

Spatial relevancy of digital finance in the urban agglomeration of Pearl River Delta and the influence factors

At present, the rapid development of digital finance is closely related to the economic development of urban agglomerations. An urban agglomeration provides conditions for digital finance to form a spatial relevancy network. Exploring the development of digital finance in the urban agglomeration of the Pearl River Delta (PRD), which is the bellwether of China's economy, can provide important practical experience for the economic construction of coastal areas and even the whole country. In this study, using the urban digital finance index issued by the Guangzhou Institute of International Finance, we measured the intensity and direction of the spatial relevancy of digital finance in the PRD urban agglomeration by applying the gravity model, modified in the calculation of distance between cities. Then, we examined the influencing factors of the spatial network of digital finance through the quadratic assignment procedure (QAP) approach. The achieved results are as follows. First, although the overall density is low, the network is tightly connected and stable. Second, in terms of individual characteristics of the network, Guangzhou, Shenzhen, Foshan still play the leading roles in the spatial network of digital finance. Third, the digital finance network does not have bidirectional spillover block. The links between segments are relatively loose. Fourth, economic level, degree of opening up, Internet level and geographical location are important factors in driving the formation of spatial relevancy of digital finance in the PRD urban agglomeration

Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that allows users to specify local domain knowledge to enable fine-grained search. In Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint local searching strategies to different parts of the target program. To further optimize local searching strategies, we design an interval-based algorithm that automatically isolates the context of variables for different local searching strategies, avoiding conflicts between local searching strategies for the same variable. We implement Eunomia as a symbolic execution platform targeting WebAssembly, which enables us to analyze applications written in various languages (like C and Go) but can be compiled into WebAssembly. To the best of our knowledge, Eunomia is the first symbolic execution engine that supports the full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated microbenchmark suite for symbolic execution and six real-world applications. Our evaluation shows that Eunomia accelerates bug detection in real-world applications by up to three orders of magnitude. According to the results of a comprehensive user study, users can significantly improve the efficiency and effectiveness of symbolic execution by writing a simple and intuitive Aes script. Besides verifying six known real-world bugs, Eunomia also detected two new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 202

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process