Ultrastrong coupling phenomena beyond the Dicke model

We study effective light-matter interactions in a circuit QED system consisting of a single $LC$ resonator, which is coupled symmetrically to multiple superconducting qubits. Starting from a minimal circuit model, we demonstrate that in addition to the usual collective qubit-photon coupling the resulting Hamiltonian contains direct qubit-qubit interactions, which have a drastic effect on the ground and excited state properties of such circuits in the ultrastrong coupling regime. In contrast to a superradiant phase transition expected from the standard Dicke model, we find an opposite mechanism, which at very strong interactions completely decouples the photon mode and projects the qubits into a highly entangled ground state. These findings resolve previous controversies over the existence of superradiant phases in circuit QED, but they more generally show that the physics of two- or multi-atom cavity QED settings can differ significantly from what is commonly assumed.Comment: 11 pages, 8 figure

PeMNet for Pectoral Muscle Segmentation

X.Y. holds a CSC scholarship with the University of Leicester. The authors declare that there is no conflict of interest. This paper is partially supported by Royal Society International Exchanges Cost Share Award, UK (RP202G0230); Medical Research Council Confidence in Concept Award, UK (MC_PC_17171); Hope Foundation for Cancer Research, UK (RM60G0680); Sino-UK Industrial Fund, UK (RP202G0289); Global Challenges Research Fund (GCRF), UK (P202PF11); British Heart Foundation Accelerator Award, UK (AA/18/3/34220); Guangxi Key Laboratory of Trusted Software (kx201901); MCIN/AEI/10.13039/501100011033/ and FEDER Una manera de hacer Europa under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects.As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.CSCRoyal Society International Exchanges Cost Share Award, UK RP202G0230Medical Research Council Confidence in Concept Award, UK MC_PC_17171Hope Foundation for Cancer Research, UK RM60G0680Sino-UK Industrial Fund, UK RP202G0289Global Challenges Research Fund (GCRF), UK P202PF11British Heart Foundation Accelerator Award, UK AA/18/3/34220Guangxi Key Laboratory of Trusted Software kx201901FEDER Una manera de hacer Europa RTI2018-098913-B100Junta de AndaluciaEuropean Commission CV20-45250 A-TIC-080-UGR18 B-TIC-586-UGR20 P20-00525MCIN/AEI/10.13039/501100011033

Application of RetCamⅡ in the screening of neonatal fundus disease

AIM: To investigate the safe and reliable examination method for neonatal fundus screening.<p>METHODS: Fundus information of 2 836 neonates performed by RetCamⅡ in our hospital from January 1, 2012 to December 31, 2012 were retrospectively analyzed, including 1 625 cases(57.30%)of premature infants which were first examined 1-4 weeks after birth and 1 211 cases(42.70%)of term infants which were first examined within 4 weeks after birth.<p>RESULTS: Totally 454 cases of abnormalfundus were found, including 207 cases(12.74%)of retinopathy of prematurity(ROP), ROPⅠ in 118 cases(57%), ROPⅡ in 58 cases(28.02%), ROPⅢ in 23 cases(11.11%), ROPⅣ in 8 cases(3.86%), no case of ROPV. A total of 247(20.40%)term infants had abnormal fundus, of which 68 cases(27.53%)were developmental or hereditary disease, retinoblastoma in 1 case(0.40%), retinal hemorrhage in 102 cases(41.30%), retinal exudative changes in 68 cases(27.53%), optic atrophy in 5 cases(2.02%)and optic disc edema in 3 cases(1.21%).<p>CONCLUSION: Neonatal fundus diseases were so various and harmful that early screening should be attended to. Premature infants and term infants with high risk are treated as focus group of fundus screening and RetCamII examination is safe and effective

Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems