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This paper investigates the cointegration between possible determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of WTI and newly-launched Shanghai crude oil futures (SC) via the Autoregressive Distributed Lag (ARDL) model and Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results confirm that economic policy uncertainty, stock markets, interest rates and coronavirus panic are important drivers of WTI futures prices. Our findings also suggest that the US and China's stock markets play vital roles in movements of SC futures prices. Meanwhile, CSI300 stock index has a significant positive short-run impact on SC futures prices while S\&P500 prices possess a positive nexus with SC futures prices both in long-run and short-run. Overall, these empirical evidences provide practical implications for investors and policymakers.

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell's equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell's equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses., Comment: 13 pages, 9 figures, submitted to IEEE transactions on Image Processing

A permanent magnet array (PMA) is a preferred source of magnetic field for body-part-dedicated low-field (<0.5 T) portable magnetic resonance imaging (MRI) because it has a small footprint, no power consumption, and no need for a cooling system. The current popular PMA is limited by the transversal field below 100 mT, where advanced technologies developed for the long-bore MRI systems (e.g., multi-channel techniques) cannot be applied. In this paper, a sparse high-performance PMA is proposed based on inward-outward ring pairs and using magnet blocks that can be bought off the shelf, targeting on portable head imaging. Through a fast genetic algorithm (GA)-based optimization, the proposed PMA has a longitudinal magnetic field with an average field strength of 111.40 mT and a monotonic field pattern with inhomogeneity of 10.57 mT (an RF bandwidth of <10%) within a Field of View (FoV) of 20 cm in diameter and 4.5 cm in length. The resulting field was validated using analytic calculations and numerical simulations. The encoding capability of the designed PMA was examined by checking the quality of the simulated reconstructed images. The proposed field outperforms a linear pattern for encoding. The PMA is 57.91 cm wide, 38 cm long, has a 5-gauss range of 87x87x104 cm^3, allowing an operation in a small space. It weights 126.08kg, comprising of a stationary main array that supplies a strong homogeneous field, and a light rotatable sub-gradient-array (5.12kg) that supplies a monotonic field for signal encoding. It has a magnetic field generation efficiency of 0.88mT/kg, the highest among sparse PMAs that offer a monotonic field pattern. The force experienced by each magnet was calculated to validate the feasibility of physical implementation. The proposed PMA can be a promising alternative to supply the main and gradient fields combined for dedicated portable MRI.

We revisit the dynamic relationship between domestic economic policy uncertainty and stock markets using the symmetric thermal optimal path (TOPS) method. We observe different interaction patterns in emerging and developed markets. Economic policy uncertainty drives the stock market in China, while stock markets play a leading role in the UK and the US. Meanwhile, the lead-lag relationship of the three countries reacts significantly to extreme events. Our findings have important implications for investors and policy makers.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

We utilize the symmetric thermal optimal path (TOPS) method to examine the dynamic interaction patterns between the VIX and VIX futures markets. We document that the VIX dominates the VIX futures more in the first few years, especially before the introduction of VIX options. We further observe that the TOPS paths show an alternate lead-lag relationship instead of a dominance between the VIX and VIX futures in most of the time periods. Meanwhile, we find that the VIX futures have been increasingly more important in the price discovery since the launch of several VIX ETPs., Comment: 8 pages including 4 figures