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

Online Continual Learning via Logit Adjusted Softmax

Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting. Inter-class imbalance during training has been identified as a major cause of forgetting, leading to model prediction bias towards recently learned classes. In this paper, we theoretically analyze that inter-class imbalance is entirely attributed to imbalanced class-priors, and the function learned from intra-class intrinsic distributions is the Bayes-optimal classifier. To that end, we present that a simple adjustment of model logits during training can effectively resist prior class bias and pursue the corresponding Bayes-optimum. Our proposed method, Logit Adjusted Softmax, can mitigate the impact of inter-class imbalance not only in class-incremental but also in realistic general setups, with little additional computational cost. We evaluate our approach on various benchmarks and demonstrate significant performance improvements compared to prior arts. For example, our approach improves the best baseline by 4.6% on CIFAR10

Religious atmosphere, seismic impact, and corporate charitable donations in China

This study examines the external socio-cultural and natural environment factors that driving corporate philanthropy in China. We focus on two predominant influences: religiosity, specifically the traditional Three-Teachings (Confucianism, Buddhism, and Taoism), and seismic activities. Using a large sample of 31,673 firm-year observations from Chinese listed firms from 2009 to 2020, our findings reveal that (a) firms immersed in more pronounced religious-cultural presence have higher donation incentives, and (b) firms experiencing higher seismic impacts or are located in high seismic risk areas show heightened corporate philanthropic tendencies. Our multidisciplinary approach bridges various academic disciplines, presenting an innovative framework for understanding the intersection of corporate philanthropy, socio-cultural environments, and natural disasters in China. Overall, we highlight the importance of external environmental factors in shaping corporate charitable behaviours

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

High‑Throughput Electron Diffraction Reveals a Hidden Novel Metal–Organic Framework for Electrocatalysis

AbstractMetal‐organic frameworks (MOFs) are known for their versatile combination of inorganic building units and organic linkers, which offers immense opportunities in a wide range of applications. However, many MOFs are typically synthesized as multiphasic polycrystalline powders, which are challenging for studies by X‐ray diffraction. Therefore, developing new structural characterization techniques is highly desired in order to accelerate discoveries of new materials. Here, we report a high‐throughput approach for structural analysis of MOF nano‐ and sub‐microcrystals by three‐dimensional electron diffraction (3DED). A new zeolitic‐imidazolate framework (ZIF), denoted ZIF‐EC1, was first discovered in a trace amount during the study of a known ZIF‐CO3‐1 material by 3DED. The structures of both ZIFs were solved and refined using 3DED data. ZIF‐EC1 has a dense 3D framework structure, which is built by linking mono‐ and bi‐nuclear Zn clusters and 2‐methylimidazolates (mIm−). With a composition of Zn3(mIm)5(OH), ZIF‐EC1 exhibits high N and Zn densities. We show that the N‐doped carbon material derived from ZIF‐EC1 is a promising electrocatalyst for oxygen reduction reaction (ORR). The discovery of this new MOF and its conversion to an efficient electrocatalyst highlights the power of 3DED in developing new materials and their applications