Synthesis and optical property of one-dimensional spinel ZnMn2O4 nanorods

Spinel zinc manganese oxide (ZnMn2O4) nanorods were successfully prepared using the previously synthesized α-MnO2 nanorods by a hydrothermal method as template. The nanorods were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, UV-Vis absorption, X-ray photoelectron spectroscopy, surface photovoltage spectroscopy, and Fourier transform infrared spectroscopy. The ZnMn2O4 nanorods in well-formed crystallinity and phase purity appeared with the width in 50-100 nm and the length in 1.5-2 μm. They exhibited strong absorption below 500 nm with the threshold edges around 700 nm. A significant photovoltage response in the region below 400 nm could be observed for the nanorods calcined at 650 and 800°C

An analysis of cost efficiency and scale economies in Mainland China and Hong Kong commercial banks

This study estimates the cost efficiency and scale economies of the Mainland China and Hong Kong banking sector by employing the stochastic frontier approach. The main objective is to assess the level of cost efficiency and whether the two banking sector enjoy the economies of scale in the post-reform period from 2001 to 2014. Distinct from previous studies, a one-stage approach is used to simultaneously control for the impact of heteroscedasticity on the estimation of economies of scale as well as estimating the impact of potential factors on bank cost efficiency. On average, the estimated score of cost efficiency in Mainland China and Hong Kong is 92.57 percent compared with “best practice” banks in the selected sample, and that profitability (ROA) and capitalization (equity ratio) are significant factors determining the inefficiency. Banks with higher equity and lower capitalization tend to have higher levels of cost inefficiency. Moreover, the evidence suggests that Mainland China and Hong Kong banks on average close to constant return to scale, suggesting that the two banking sectors have operated at the optimal scale and they do not enjoy scale economies

Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion

Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. Robust training and defend-by-denoising are typical strategies for defending adversarial perturbations. However, they either induce massive computational overhead or rely heavily upon specified priors, limiting generalized robustness against attacks of all kinds. To remedy it, this paper introduces a novel distortion-aware defense framework that can rebuild the pristine data distribution with a tailored intensity estimator and a diffusion model. To perform distortion-aware forward diffusion, we design a distortion estimation algorithm that is obtained by summing the distance of each point to the best-fitting plane of its local neighboring points, which is based on the observation of the local spatial properties of the adversarial point cloud. By iterative diffusion and reverse denoising, the perturbed point cloud under various distortions can be restored back to a clean distribution. This approach enables effective defense against adaptive attacks with varying noise budgets, enhancing the robustness of existing 3D deep recognition models.Comment: Accepted by ACM MM 202

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition

Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition models and propose Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds. Specifically, we leverage a supervised contrastive loss to facilitate the alignment and uniformity of the hypersphere features extracted by the recognition model, and design a pair of centralizing losses with the dynamic prototype guidance to avoid these features deviating from their belonging category clusters. To provide the more challenging corrupted point clouds, we adversarially train a noise generator along with the recognition model from the scratch, instead of using gradient-based attack as the inner loop like previous adversarial training methods. Comprehensive experiments show that the proposed PointCAT outperforms the baseline methods and dramatically boosts the robustness of different point cloud recognition models, under a variety of corruptions including isotropic point noises, the LiDAR simulated noises, random point dropping and adversarial perturbations

Diversity-Aware Meta Visual Prompting

We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone. A challenging issue in visual prompting is that image datasets sometimes have a large data diversity whereas a per-dataset generic prompt can hardly handle the complex distribution shift toward the original pretraining data distribution properly. To address this issue, we propose a dataset Diversity-Aware prompting strategy whose initialization is realized by a Meta-prompt. Specifically, we cluster the downstream dataset into small homogeneity subsets in a diversity-adaptive way, with each subset has its own prompt optimized separately. Such a divide-and-conquer design reduces the optimization difficulty greatly and significantly boosts the prompting performance. Furthermore, all the prompts are initialized with a meta-prompt, which is learned across several datasets. It is a bootstrapped paradigm, with the key observation that the prompting knowledge learned from previous datasets could help the prompt to converge faster and perform better on a new dataset. During inference, we dynamically select a proper prompt for each input, based on the feature distance between the input and each subset. Through extensive experiments, our DAM-VP demonstrates superior efficiency and effectiveness, clearly surpassing previous prompting methods in a series of downstream datasets for different pretraining models. Our code is available at: \url{https://github.com/shikiw/DAM-VP}.Comment: CVPR2023, code is available at https://github.com/shikiw/DAM-V

1,2-Bis[5-(4-cyano­phen­yl)-2-methyl-3-thien­yl]-3,3,4,4,5,5-hexa­fluoro­cyclo­pent-1-ene: a photochromic diaryl­ethene compound

The mol­ecules of the title compound, C29H16F6N2S2, a photochromic dithienylethene with 4-cyano­phenyl substituents, adopt an anti­parallel arrangement that is reponsible for photoactivity. The mol­ecule lies on a twofold rotation axis. The dihedral angle between the nearly planar cyclo­pentenyl and heteroaryl rings is 142.5 (3)°, and that between the heteroaryl and benzene rings is 22.4 (3)°. The distance between the heteroaryl rings of adjacent mol­ecules is 3.601 (2) Å, indicating a π–π interaction

Efficient Routing Protection Algorithm Based on Optimized Network Topology

Network failures are unavoidable and occur frequently. When the network fails, intra-domain routing protocols deploying on the Internet need to undergo a long convergence process. During this period, a large number of messages are discarded, which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers (ISP). Therefore, improving the availability of intra-domain routing is a trending research question to be solved. Industry usually employs routing protection algorithms to improve intra-domain routing availability. However, existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures, which increases the cost of the network and impedes the methods deployed in practice. To address the issues, this study proposes an efficient routing protection algorithm based on optimized network topology (ERPBONT). ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source purposes. Firstly, the backup path with the minimum path coincidence with the shortest path is described as an integer programming problem. Then the simulated annealing algorithm ERPBONT is used to find the optimal solution. Finally, the algorithm is tested on the simulated topology and the real topology. The experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path, and significantly improves the routing availability