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

A study of the driving factors of the intention and behavioral deviations of rural residents in waste classification

Introduction: The deviation between the stated intentions and actual actions of rural residents regarding waste classification constitutes a significant impediment to the effective implementation of environmental management strategies in rural areas. It is therefore recommended that steps be taken to reduce the deviation between the stated intentions and actual behaviors of rural residents. Doing so will help to reinforce environmental governance in rural communities and provide the necessary support for rural revitalization.Methods: This study establishes an analytical framework for examining the deviation between the internal perceived efficacy and external environmental policies among rural residents. The relationship between intention and behavior can be classified into three distinct scenarios: “intention with behavior,” “intention without behavior,” and “no intention with behavior.” Furthermore, an empirical analysis is conducted using survey data collected by Nanjing Agricultural University in the China Land Economic Survey in June and July 2021.Result: The results show that 1) the perceived efficacy has a significant positive influence on the deviation between the intention and behavior of rural residents in domestic waste classification, while the environmental policy has a significant negative effect on it; 2) the guiding policy has a significant negative moderating effect on the influence of perceived efficacy on the deviation between the intention and behavior of rural residents and the situation of “with intention and without behavior,” while the reward–punishment policy has a significant positive moderating effect on the influence of perceived efficacy on “without intention and behavior;” 3) the perceived efficacy has a masking effect on the impact of environmental policies on the deviation between the intention and behavior or “with intention and without behavior” of rural residents and a partial mediating effect on the impact of the environmental policy on “with intention and behavior” or “without intention and behavior.”Discussion: In consideration of these findings, the study proposes policy recommendations that emphasize the interconnectivity of the government, village collective organizations, and rural residents. The recommendations include the implementation of environmental policies and initiatives designed to enhance rural residents’ awareness of waste classification

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

OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources, incurring inevitable additional costs. In this paper, we present OPERA, a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix, i.e., MLLMs tend to generate new tokens by focusing on a few summary tokens, but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previously generated tokens, and re-allocate the token selection if necessary. With extensive experiments, OPERA shows significant hallucination-mitigating performance on different MLLMs and metrics, proving its effectiveness and generality. Our code is available at: https://github.com/shikiw/OPERA.Comment: CVPR 2024, code is available at https://github.com/shikiw/OPER