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您的检索式：作者名="Wu Linyang"
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| 1 | Sleep and Cognitive Abnormalities in Acute Minor Thalamic Infarction显示文摘In order to characterize sleep and the cognitive patterns in patients with acute minor thalamic infarction(AMTI), we enrolled 27 patients with AMTI and 12 matched healthy individuals. Questionnaires about sleep and cognition as well as polysomnography(PSG) were performed on days 14 and 90 post-stroke. Compared to healthy controls, in patients with AMTI, hyposomnia was more prevalent; sleep architecture was disrupted as indicated by decreased sleep efficiency, increased sleep latency, and decreased non-rapid eye movement sleep stages 2 and 3;more sleep-related breathing disorders occurred; and cognitive functions were worse, especially memory. While sleep apnea and long-delay memory recovered to a large extent in the patients, other sleep and cognitive function deficit often persisted. Patients with AMTI are at an increased risk for hyposomnia, sleep structure disturbance,sleep apnea, and memory deficits. Although these abnormalities improved over time, the slow and incomplete improvement suggest that early management should be considered in these patients. | Wei Wu Linyang Cui Ying Fu Qianqian Tian Lei Liu Xuan Zhang Ning Du Ying Chen Zhijun Qiu Yijun Song Fu-Dong Shi Rong Xue | 2016 | Neuroscience Bulletin2016，32，4： | 28 |
| 2 | Two- echelon supply chain models: Considering duopollstic retailers' different competitive behaviors 显示文摘 | Shan- LinYang Yong- Wu Zhou | 2006 | International Journal of Production Economics2006，9，103： | 1 |
| 3 | Assembly language and assembler for deep learning accelerators显示文摘Deep learning accelerators(DLAs)have been proved to be efficient computational devices for processing deep learning algorithms.Various DLA architectures are proposed and applied to different applications and tasks.However,for most DLAs,their programming interfaces are either difficult to use or not efficient enough.Most DLAs require programmers to directly write instructions,which is time-consuming and error-prone.Another prevailing programming interface for DLAs is high-performance libraries and deep learning frameworks,which are easy to be used and very friendly to users,but their high abstraction level limits their control capacity over the hardware resources thus compromises the efficiency of the accelerator.A design of the programming interface is for DLAs.First various existing DLAs and their programming methods are analyzed and a methodology for designing programming interface for DLAs is proposed,which is a high-level assembly language(called DLA-AL),assembler and runtime for DLAs.DLA-AL is composed of a low-level assembly language and a set of high-level blocks.It allows experienced experts to fully exploit the potential of DLAs and achieve near-optimal performance.Meanwhile,by using DLA-AL,end-users who have little knowledge of the hardware are able to develop deep learning algorithms on DLAs spending minimal programming efforts. | 兰慧盈 Wu Linyang Han Dong Du Zidong | 2019 | High Technology Letters2019，25，4： | 1 |
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