Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement

This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using the quantitative $T_1$ mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, with great potential to enhance the clinical translation of qMRI

Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction

This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy and efficiency for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains

Another reason for the counterintuitive effects of thank-you gifts on charitable giving

Current studies on the effect of thank-you gifts on charitable giving are primarily based on the conclusion of a milestone paper, “The counterintuitive effects of thank-you gifts on charitable giving” which argued that thank-you gifts are mainly driven by lower feelings of altruism. This article argues that the question design in “The counterintuitive effects of thank-you gifts on charitable giving” may lead to a biased conclusion. This article added an extra treatment group to the original study and found that the authors neglected the critical impact of participants’ inference about the usage of the money

The trilateral game of privacy perception, financial regulation and central bank digital currency issuance

The goal of this study is to clarify how privacy protection affects the insurance of central bank digital currency (CBDC). By constructing a tripartite game model involving consumers, commercial banks, and regulators, this paper explores the impact of privacy protection on the issuance of CBDC. The findings show that privacy protection is critical to ensuring successful adoption of CBDCs. The central bank must strike a balance between protecting user privacy while also regulating usage and ensuring convenience for users. However, due to opportunistic behavior by both commercial banks and consumers during this process, negative reactions are possible. Based on the findings of this research, it is suggested that central banks should encourage commercial banks to participate in CBDC issuance by promoting appropriate data sharing and offering guidance. Additionally, they should focus on consumer education and expectation management to promote CBDC adoption. Commercial banks must also embrace digital transformation and adapt to the changing financial landscape to remain competitive while providing innovative financial services to customers

CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning

The motivations of users to make interactions can be divided into static preference and dynamic interest. To accurately model user representations over time, recent studies in sequential recommendation utilize information propagation and evolution to mine from batches of arriving interactions. However, they ignore the fact that people are easily influenced by the recent actions of other users in the contextual scenario, and applying evolution across all historical interactions dilutes the importance of recent ones, thus failing to model the evolution of dynamic interest accurately. To address this issue, we propose a Context-Aware Pseudo-Multi-Task Recommender System (CPMR) to model the evolution in both historical and contextual scenarios by creating three representations for each user and item under different dynamics: static embedding, historical temporal states, and contextual temporal states. To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization. Within the PMTL paradigm, CPMR employs a shared-bottom network to conduct the evolution of temporal states across historical and contextual scenarios, as well as the fusion of them at the user-item level. In addition, CPMR incorporates one real tower for incremental predictions, and two pseudo towers dedicated to updating the respective temporal states based on new batches of interactions. Experimental results on four benchmark recommendation datasets show that CPMR consistently outperforms state-of-the-art baselines and achieves significant gains on three of them. The code is available at: https://github.com/DiMarzioBian/CPMR.Comment: Accepted by CIKM 2023. Alias: "Modeling Context-Aware Temporal Dynamics via Pseudo-Multi-Task Learning

The ride comfort and energy-regenerative characteristics analysis of hydraulic-electricity energy regenerative suspension

For optimization of performances of a hydraulic-electricity energy regenerative suspension (HERS) unit, the tradeoff point was determined based on study of ride comfort and energy-regenerative characteristics of a HERS unit in this study. A HERS unit as a new energy reclaiming suspension device is equipped with an energy-harvesting hydraulic electromagnetic shock absorber (HESA). The HESA together with a quarter car was modeled based on theoretical analysis and experiments, in which the root mean square (RMS) values of the sprung mass vibration acceleration and the recovered power are regarded as the optimization objectives under different road excitation conditions such as the constraints (natural frequency, dynamic displacement, and dynamic load of wheels). The HERS unit was optimized after the relationship between the ride comfort and the energy regeneration was obtained. In comparison with the traditional suspension, the HERS unit may be utilized to improve the ride comfort and meet the vehicle-driving requirements. Moreover, the total input power may be saved by 34-100 W on average while the vibration acceleration is among 0.65-1.06 m/s2. Furthermore, it is verified that the HERS damping force control is the feasible under various load currents

Ferritin level prospectively predicts hepatocarcinogenesis in patients with chronic hepatitis B virus infection

Previous studies have detected a higher level of ferritin in patients with hepatocellular carcinoma (HCC), but a potential causal association between serum ferritin level and hepatocarcinogenesis remains to be clarified. Using a well-established prospective cohort and longitudinally collected serial blood samples, the association between baseline ferritin levels and HCC risk were evaluated in 1,152 patients infected with hepatitis B virus (HBV), a major risk factor for HCC. The association was assessed by Cox proportional hazards regression model using univariate and multivariate analyses and longitudinal analysis. It was demonstrated that HBV patients who developed HCC had a significantly higher baseline ferritin level than those who remained cancer-free (188.00 vs. 108.00 ng/ml, P\u3c0.0001). The patients with a high ferritin level (≥200 ng/ml) had 2.43-fold increased risk of HCC compared to those with lower ferritin levels [hazard ratio (HR), 2.43; 95% confidence interval, 1.63-3.63]. A significant trend of increasing HRs along with elevated ferritin levels was observed (P for trend \u3c0.0001). The association was still significant after multivariate adjustment. Incorporating ferritin into the α-fetoprotein (AFP) model significantly improved the performance of HCC prediction (the area under the curve from 0.74 to 0.77, P=0.003). Longitudinal analysis showed that the average ferritin level in HBV patients who developed HCC was persistently higher than in those who were cancer-free during follow-up. HCC risk reached a peak at approximately the fifth year after baseline ferritin detection. Moreover, stratified analyses showed that the association was noted in both males and females, and was prominent in patients with a low AFP value. In short, serum ferritin level could independently predict the risk of HBV-related HCC and may have a complementary role in AFP-based HCC diagnosis. Future studies are warranted to validate these findings and test its clinical applicability in HCC prevention and management. © 2018, Spandidos Publication