Magnitude-image based data-consistent deep learning method for MRI super resolution

Magnetic Resonance Imaging (MRI) is important in clinic to produce high resolution images for diagnosis, but its acquisition time is long for high resolution images. Deep learning based MRI super resolution methods can reduce scan time without complicated sequence programming, but may create additional artifacts due to the discrepancy between training data and testing data. Data consistency layer can improve the deep learning results but needs raw k-space data. In this work, we propose a magnitude-image based data consistency deep learning MRI super resolution method to improve super resolution images' quality without raw k-space data. Our experiments show that the proposed method can improve NRMSE and SSIM of super resolution images compared to the same Convolutional Neural Network (CNN) block without data consistency module.Comment: Accepted by IEEE CBMS 202

Mean reflected BSDE driven by a marked point process and application in insurance risk management

This paper aims to solve a super-hedging problem along with insurance re-payment under running risk management constraints. The initial endowment for the super-heding problem is characterized by a class of mean reflected backward stochastic differential equation driven by a marked point process (MPP) and a Brownian motion. By Lipschitz assumptions on the generators and proper integrability on the terminal value, we give the well-posedness of this kind of BSDEs by combining a representation theorem with the fixed point argument.Comment: arXiv admin note: text overlap with arXiv:2310.1472

Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

Reflected BSDE driven by a marked point process with a convex/concave generator

In this paper, a class of reflected backward stochastic differential equations (RBSDE) driven by a marked point process (MPP) with a convex/concave generator is studied. Based on fixed point argument, $\theta$-method and truncation technique, the well-posedness of this kind of RBSDE with unbounded terminal condition and obstacle is investigated. Besides, we present an application on the pricing of American options via utility maximization, which is solved by constructing an RBSDE with a convex generator.Comment: arXiv admin note: substantial text overlap with arXiv:2310.1472

Quadratic exponential BSDEs driven by a marked point process

In this paper, the well-posedness of quadratic exponential backward stochastic differential equations driven by marked point process (MPP) under unbounded terminal condition is studied based on a fixed point argument, $\theta$-method and an approximation procedure. We also prove the solvability of the mean reflected quadratic exponential backward stochastic differential equations driven by marked point process via $\theta$-method

Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph

Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases

Stability of the Interface Between Two Immiscible Liquids During Injection Into a Tapered Hele-Shaw Cell

In the early twentieth century, petroleum and mining engineers noticed that water does not displace oil uniformly. This phenomenon, when water penetrates through oil, is now known as viscous fingering. This discovery and the following extensive research have contributed to enhancing oil recovery. In this paper, we describe a numerical study conducted on the stability of the interface between two immiscible liquids in converging and diverging Hele-Shaw cells with varying gradients. Hele-Shaw cells are narrow flow geometries that mimic the properties of a porous medium with fixed permeability. By using computational tools built on the OpenFOAM platform, the multiphase flow dynamics can be accurately resolved and observed at small scales. The flow is computed in several designed tapered cell, which emulate natural heterogeneity in an actual porous medium. By analyzing the finger length under the same time period in both parallel cells and tapered cells, we found that the diverging cell relatively decreases the growth compared with the converging cell. Our primary conclusion, confirming previous theoretical predictions, is that the gradient of the tapered geometry variation has an effect on the sign of interfacial growth rate, which means the interface could be destabilized or stabilized depending on the absolute value of the gradient

Prompt Tuning based Adapter for Vision-Language Model Adaption

Large pre-trained vision-language (VL) models have shown significant promise in adapting to various downstream tasks. However, fine-tuning the entire network is challenging due to the massive number of model parameters. To address this issue, efficient adaptation methods such as prompt tuning have been proposed. We explore the idea of prompt tuning with multi-task pre-trained initialization and find it can significantly improve model performance. Based on our findings, we introduce a new model, termed Prompt-Adapter, that combines pre-trained prompt tunning with an efficient adaptation network. Our approach beat the state-of-the-art methods in few-shot image classification on the public 11 datasets, especially in settings with limited data instances such as 1 shot, 2 shots, 4 shots, and 8 shots images. Our proposed method demonstrates the promise of combining prompt tuning and parameter-efficient networks for efficient vision-language model adaptation. The code is publicly available at: https://github.com/Jingchensun/prompt_adapter