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Baoyu Wei,1 Zhaoqi Wang,1 Shihao Wu,1 John Orgah,1 Jinqiang Zhu,1 Wanshan Song2 1State Key Laboratory of Component-Based Chinese Medicine, Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People’s Republic of China; 2Department of Acupuncture and Cerebropathy, Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300250, People’s Republic of ChinaCorrespondence: Wanshan Song; Jinqiang Zhu Email sws19870604@163.com; zhujinqiang1860@163.comAbstract: Vascular dementia (VaD), a cognitive disorder caused by cerebrovascular pathologies, is the most common cause of dementia in the elderly, being second only to Alzheimer’s disease. Researches have shown that adequate cerebral blood flow (CBF) is the first condition for maintaining the structural integrity and normal function of the brain, and VaD is generally considered to be resulted from neuronal loss due to reduced CBF. Collateral circulation, a compensation mechanism for CBF, provides an alternative vascular pathway for blood to reach ischemic tissues, which has been confirmed to be associated with better clinical outcomes of ischemic diseases. At present, considerable effort has been devoted to enhancing the functional prognosis of acute ischemic stroke by improving collateral circulation. Since ischemic stroke is the primary contributor to VaD, it is necessary to explore whether improving collateral circulation is beneficial to prevent or slow the progression of VaD. This article reviews the compensatory characteristics of different levels of cerebral collateral circulation, addresses the relationship between collateral circulation and VaD, and highlights that improving collateral circulation may be a potential adjunctive strategy in preventing and slowing the progression of VaD.Keywords: vascular dementia, cerebral blood flow, collateral circulation, pathology, precaution, palliation

The ratio of dilepton production cross sections on a proton, using the γp→l+l−p process, above and below the dimuon production threshold allows one to extract the effective lepton-proton interaction, which is required to be identical for electrons and muons if lepton universality is exact. To test for a scenario of broken universality at the percent level, of the size that could explain the different proton charge radii extracted from electron scattering and from muonic hydrogen spectroscopy, we evaluate all one-loop QED corrections to this process, including the full lepton mass dependencies. We furthermore show that two photon–exchange processes with both photons attached to the proton line vanish after averaging over dilepton angles and estimate the relatively small radiation off the proton. We compare the full one-loop calculation with a soft-photon approximation of the same order and present estimates for a planned experiment.

The ratio of di-lepton production cross sections on a proton, using the $��p\rightarrow l^+ l^- p$ process, above and below di-muon production threshold allows to extract the effective lepton-proton interaction, which is required to be identical for electrons and muons if lepton universality is exact. To test for a scenario of broken universality at the percent level, of the size which could explain the different proton charge radii extracted from electron scattering and from muonic hydrogen spectroscopy, we evaluate all one-loop QED corrections to this process, including the full lepton mass dependencies. We furthermore show that two-photon exchange processes with both photons attached to the proton line vanish after averaging over di-lepton angles, and estimate the relatively small radiation off the proton. We compare the full one-loop calculation with a soft-photon approximation of the same order, and present estimates for a planned experiment., arXiv admin note: text overlap with arXiv:1802.07174

Geometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results.