Symbolic bisimulation for quantum processes

With the previous notions of bisimulation presented in literature, to check if two quantum processes are bisimilar, we have to instantiate the free quantum variables of them with arbitrary quantum states, and verify the bisimilarity of resultant configurations. This makes checking bisimilarity infeasible from an algorithmic point of view because quantum states constitute a continuum. In this paper, we introduce a symbolic operational semantics for quantum processes directly at the quantum operation level, which allows us to describe the bisimulation between quantum processes without resorting to quantum states. We show that the symbolic bisimulation defined here is equivalent to the open bisimulation for quantum processes in the previous work, when strong bisimulations are considered. An algorithm for checking symbolic ground bisimilarity is presented. We also give a modal logical characterisation for quantum bisimilarity based on an extension of Hennessy-Milner logic to quantum processes.Comment: 30 pages, 7 figures, comments are welcom

Numerical investigation on aggregate settlement and its effect on the durability of hardened concrete

Vibrating consolidation process is widely applied to field construction of cement concrete. However, high-frequency vibration can easily lead to the settlement of coarse aggregates (CAs) and then affects the durability of hardened concrete. This study has developed a 3-D concrete model to investigate the CA settlement caused by vibration and its effect on long-term chloride transport in concrete. Based on the proposed model, the influence mechanism of CA settlement on both chloride concentration distribution and initiation time of reinforcement corrosion is discussed in detail. The results indicate that due to the settlement, a more obvious fluctuation of chloride concentration along the height direction of concrete specimen can be observed with the increase of vibration time. According to the model prediction, the corrosion of the top steel bar initiates 1.03–1.80 years earlier than that of the bottom steel bar under different vibration time. The proposed model provides a new method to probe into the influence of vibration-induced settlement on chloride ingress in hardened concrete

Normalized solutions for Sobolev critical Schr\"odinger-Bopp-Podolsky systems

We study the Sobolev critical Schr\"odinger-Bopp-Podolsky system \begin{gather*} -\Delta u+\phi u=\lambda u+\mu|u|^{p-2}u+|u|^4u\quad \text{in }\mathbb{R}^3, -\Delta\phi+\Delta^2\phi=4\pi u^2\quad \text{in } \mathbb{R}^3, \end{gather*} under the mass constraint $$\int_{\mathbb{R}^3}u^2\,dx=c$$ for some prescribed $c>0$, where $2<p<8/3$, $\mu>0$ is a parameter, and $\lambda\in\mathbb{R}$ is a Lagrange multiplier. By developing a constraint minimizing approach, we show that the above system admits a local minimizer. Furthermore, we establish the existence of normalized ground state solutions.Comment: 19 page

GRO J1655-40: from ASCA and XMM-Newton Observations

We have analysed four ASCA observations (1994--1995, 1996--1997) and three XMM-Newton observations (2005) of this source, in all of which the source is in high/soft state. We modeled the continuum spectra with relativistic disk model kerrbb, estimated the spin of the central black hole, and constrained the spectral hardening factor f_col and the distance. If kerrbb model applies, for normally used value of f_col, the distance cannot be very small, and f_col changes with observations.Comment: 2 pages, 1 figure, Conference proceedings to appear in "The Central Engine of Active Galactic Nuclei", ed. L. C. Ho and J.-M. Wang (San Francisco: ASP

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency

Full-resolution MLPs Empower Medical Dense Prediction

Dense prediction is a fundamental requirement for many medical vision tasks such as medical image restoration, registration, and segmentation. The most popular vision model, Convolutional Neural Networks (CNNs), has reached bottlenecks due to the intrinsic locality of convolution operations. Recently, transformers have been widely adopted for dense prediction for their capability to capture long-range visual dependence. However, due to the high computational complexity and large memory consumption of self-attention operations, transformers are usually used at downsampled feature resolutions. Such usage cannot effectively leverage the tissue-level textural information available only at the full image resolution. This textural information is crucial for medical dense prediction as it can differentiate the subtle human anatomy in medical images. In this study, we hypothesize that Multi-layer Perceptrons (MLPs) are superior alternatives to transformers in medical dense prediction where tissue-level details dominate the performance, as MLPs enable long-range dependence at the full image resolution. To validate our hypothesis, we develop a full-resolution hierarchical MLP framework that uses MLPs beginning from the full image resolution. We evaluate this framework with various MLP blocks on a wide range of medical dense prediction tasks including restoration, registration, and segmentation. Extensive experiments on six public well-benchmarked datasets show that, by simply using MLPs at full resolution, our framework outperforms its CNN and transformer counterparts and achieves state-of-the-art performance on various medical dense prediction tasks.Comment: Under Revie