Can BRICS De-dollarize the Global Financial System?

This study develops a 'Pathways to De-dollarization' framework and applies it to analyze the institutional and market mechanisms that BRICS (Brazil-Russia-India-China-South Africa) countries have created at the BRICS, sub-BRICS, and BRICS Plus levels. This title is also available as Open Access on Cambridge Core

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

The goal of scene text image super-resolution is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for scene text image super-resolution. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets and can improve the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training. Available Codes:https://github.com/Lenubolim/TextDiff

Interference mechanism in coalbed methane wells and impacts on infill adjustment for existing well patterns

This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities, China (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPublisher PD

ZSTAD: Zero-Shot Temporal Activity Detection

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities

On the decay of strength in Guilin red clay with cracks

In order to research the effect of cracks in red clay on shear strength through dry-wet cycle test, the experimenters used imaging software and a mathematical model to determine fractal dimension and crack ratio of surface cracks in red clay in Guilin, China. After each dry-wet cycle, direct shear tests were carried out on the sample, and such variables as matrix suction on the crack propagation process of red clay were analyzed. The mechanics model was established and obtained the critical condition of soil cracks. The results show that with the increase in the number of dry-wet cycles the shear strength of the samples would decrease. But the rule of shear strength of sample 3 is slightly different from samples 1 and 2. The shear strength of red clay has a good correlation with fractal dimension and crack ratio, which could be an identification index of the strength of red clay

EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e. dark corners, dense hairs), leading to poor generalization in unseen environments. To address this issue, we propose a novel domain generalization method called EPVT, which involves embedding prompts into the vision transformer to collaboratively learn knowledge from diverse domains. Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset. To facilitate knowledge sharing and the interaction of different prompts, we introduce a domain prompt generator that enables low-rank multiplicative updates between domain prompts and the shared prompt. A domain mixup strategy is additionally devised to reduce the co-occurring artifacts in each domain, which allows for more flexible decision margins and mitigates the issue of incorrectly assigned domain labels. Experiments on four out-of-distribution datasets and six different biased ISIC datasets demonstrate the superior generalization ability of EPVT in skin lesion recognition across various environments. Our code and dataset will be released at https://github.com/SiyuanYan1/EPVT.Comment: 12 pages, 5 figure

The stability evaluation of clay tunnels via the non-linear deterioration of physical and mechanical properties of surrounding rocks

Simple, fast, and reliable methods for the stability evaluation of tunnels can facilitate the construction and development of tunneling projects. The problems related to tunnel stability at this stage can be well analyzed via theoretical analysis method, model test method, or numerical analysis method. On the other hand, those methods are hard to be effectively analyzed these projects with higher importance, shorter decision and design period, and more urgent construction period. This paper proposed research works on the stability evaluation of clay tunnels. Firstly, a state function with the variables of stress and strain state is presented to predict the stress and strain states of surrounding rocks caused by tunnel excavation, which characterize the physical-mechanical state of surrounding rocks (also called stability state). Secondly, the non-linear deterioration of the physical and mechanical properties of surrounding rocks will be simulated, and the expressions and calculation methods of the tunnel stability reserve factor will be yielded. Finally, the results of the proposed method were compared with the strength reduction method and the limit equilibrium method with a clay tunnel example. The comparison between the three feature points of the arch crown, sidewall, and arch bottom showed that the stability reserve factor of the clay tunnel was smaller than those of the strength reduction method and the limit equilibrium method. The values of limit displacement obtained by the proposed method were closer to the field monitoring data than that of the strength reduction method. Therefore, this study could be better applied to the stability evaluation of clay tunnels