Perceived macroeconomic uncertainty and export: evidence from cross-country data

The Perceived Macroeconomic Uncertainty (PMU) is seen as unpredictable volatility about the future economic development at aggregate level. While prior research explains how uncertainty (in general) influences international trade flows, research on the role of PMU in international trade flows is scarce. This article attempts to address this lack of understanding. Utilizing the gravity model and multicountry level data, our results show that: (1) the level of PMU in both importing countries and exporting countries has a significant negative impact on exports, but the effect of PMU of importing countries is larger than that of PMU of exporting countries; (2) PMU in importing countries has a trade diversion effect, suggesting that exporters are more willing to export to countries with relatively lower level of PMU; (3) the negative effects of PMU on trade have declined after the 2008 Great Financial Crisis, which may be related to the relative stability of the PMU index since the Great Financial Crisis and the increased concern of traders about other factors, such as trade policy uncertainty and Sino-US economic conflicts. Our research enriches prior findings that examine the effects of uncertainty on trade flows and carries important policy implication

Tourism Flows Prediction based on an Improved Grey GM(1,1) Model

AbstractThis study analyzes the factors affecting the tourist flow. These factors include tourism resources, traffic conditions and so on. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting model still have some potential problems that need to be improved, such as applicate range and prediction accuracy. It is found that original data and background value are main factors affecting the accuracy of the proposed model's application. To solve these problems, this study develops a optimization model for the GM(1,1) model problem which includes optimization of initial and background values. In order to reduce errors caused by back-ground values, the “new information prior using” principle is followed, and a liner function is dopted in the construe of background. Numerical examples verified that the simulation and prediction accuracy of the short-term forcasts is significantly increased. As a result, the newly improved model yields a high prediction capability

DPPMask: Masked Image Modeling with Determinantal Point Processes

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks

Uncovering the essential genes of the human malaria parasite Plasmodium falciparum by saturation mutagenesis

Malaria is caused by eukaryotic Plasmodium spp. parasites that classically infect red blood cells. These are difficult organisms to investigate genetically because of their AT-rich genomes. Zhang et al. have exploited this peculiarity by using piggyBac transposon insertion sites to achieve saturation-level mutagenesis for identifying and ranking essential genes and drug targets (see the Perspective by White and Rathod). Genes that are current candidates for drug targets were identified as essential, in contrast to many vaccine target genes. Notably, the proteasome degradation pathway was confirmed as a target for developing therapeutic interventions because of the several essential genes involved and the link to the mechanism of action of the current frontline drug, artemisinin

Why Is the Correlation between Crude Oil Prices and the US Dollar Exchange Rate Time-Varying?—Explanations Based on the Role of Key Mediators

Using DCC-GARCH model, this paper finds that, since 1990, the relationship between crude oil prices and the US dollar index is time-varying, demonstrating a process of ‘very weak correlation—negative correlation—enhanced negative correlation—weakening negative correlation’, but the existing research does not provide enough reasonable explanation. Therefore, this paper proposed a ‘key mediating factors’ hypothesis which points out that whether there is a common ‘key mediating factor’ is important source of the time-varying relationship between two assets. We argue that market trend and financial market sentiment undertook the role of ‘key mediating factor’ during the period of the 2002 to the financial crisis and financial crisis to 2013, while other periods lack the ‘key mediating factors’

Single-Image Super-Resolution Neural Network via Hybrid Multi-Scale Features

In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images. Different from most existing convolutional neural network (CNN) based solutions, our proposed network depends on the observation that image features extracted by CNN contain hybrid multi-scale features: both multi-scale local texture features and global structural features. By effectively exploiting these multi-scale and local-global features, our network involves far fewer parameters, leading to a large decrease in memory usage and computation during inference. Our network benefits from three key modules: (1) an efficient and lightweight feature extraction module (EFblock); (2) a hybrid multi-scale feature enhancement module (HMblock); and (3) a reconstruction–restoration module (DRblock). Experiments on five popular benchmarks demonstrate that our super-resolution approach achieves better performance with fewer parameters and less memory consumption, compared to more than 20 SOTAs. In summary, we propose a novel multi-scale super-resolution neural network (HMSF), which is more lightweight, has fewer parameters, and requires less execution time, but has better performance than the state-of-the-art methods. Compared to SOTAs, this method is more practical and better suited to run on constrained devices, such as PCs and mobile devices, without the need for a high-performance server

A Novel Nonlinear Parameter Estimation Method of Soft Tissues

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise

3D Multi-Organ and Tumor Segmentation Based on Re-Parameterize Diverse Experts

Automated segmentation of abdominal organs and tumors in medical images is a challenging yet essential task in medical image analysis. Deep learning has shown excellent performance in many medical image segmentation tasks, but most prior efforts were fragmented, addressing individual organ and tumor segmentation tasks with specialized networks. To tackle the challenges of abdominal organ and tumor segmentation using partially labeled datasets, we introduce Re-parameterizing Mixture-of-Diverse-Experts (RepMode) to abdominal organ and tumor segmentation. Within the RepMode framework, the Mixture-of-Diverse-Experts (MoDE) block forms the foundation, learning generalized parameters applicable across all tasks. We seamlessly integrate the MoDE block into a U-shaped network with dynamic heads, addressing multi-scale challenges by dynamically combining experts with varying receptive fields for each organ and tumor. Our framework incorporates task encoding in both the encoder–decoder section and the segmentation head, enabling the network to adapt throughout the entire system based on task-related information. We evaluate our approach on the multi-organ and tumor segmentation (MOTS) dataset. Experiments show that DoDRepNet outperforms previous methods, including multi-head networks and single-network approaches, giving a highly competitive performance compared with the original single network with dynamic heads. DoDRepNet offers a promising approach to address the complexities of abdominal organ and tumor segmentation using partially labeled datasets, enhancing segmentation accuracy and robustness