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Increased Risk of Ischemic Stroke during Sleep in Apneic Patients.
BACKGROUND AND PURPOSE:The literature indicates that obstructive sleep apnea (OSA) increases the risk of ischemic stroke. However, the causal relationship between OSA and ischemic stroke is not well established. This study examined whether preexisting OSA symptoms affect the onset of acute ischemic stroke. METHODS:We investigated consecutive patients who were admitted with acute ischemic stroke, using a standardized protocol including the Berlin Questionnaire on symptoms of OSA prior to stroke. The collected stroke data included the time of the stroke onset, risk factors, and etiologic subtypes. The association between preceding OSA symptoms and wake-up stroke (WUS) was assessed using multivariate logistic regression analysis. RESULTS:We identified 260 subjects with acute ischemic strokes with a definite onset time, of which 25.8% were WUS. The presence of preexisting witnessed or self-recognized sleep apnea was the only risk factor for WUS (adjusted odds ratio=2.055, 95% confidence interval=1.035-4.083, p=0.040). CONCLUSIONS:Preexisting symptoms suggestive of OSA were associated with the occurrence of WUS. This suggests that OSA contributes to ischemic stroke not only as a predisposing risk factor but also as a triggering factor. Treating OSA might therefore be beneficial in preventing stroke, particularly that occurring during sleep
Linguicism? English as the Gatekeeper in South Korea: A Qualitative Study about Mother\u27s Perspectives and Involvement in Their Child\u27s English Education
The rising demand to learn English has become a common phenomenon in many parts of the world. South Korea is no exception. English has become the most important foreign language in a largely monolingual society that rarely uses English in its daily context (J.S.-Y. Park, 2009). English has gained high status since the Korean War (1950-1953) in the Korean context (Grant & Lee, 2010). Education policies in the 1990s further strengthened its stance boosting the âEnglish feverâ. Because English stands as the gatekeeper to college admission, employment and promotion, Koreans invest heavily into English learning. But the financial expenditure of English education differs along the socioeconomic spectrum, creating inequality. Such inequalities have been referred as the English Divide âwhere English speakers have more power and access to resources while the non-English speaker are disadvantaged in many waysâ (Tsuda, 2008). Learning English begins at a young age in Korea. For young students, mothers are the managers who decide where and how to get more English education. Korean mothers have been known for their fervor in their pursuit of their childâs academic success contributing to the competitive environment that aims for high academic achievement. In this study, I take a look into how mothers are contributing to reinforcing the stature of English. This study investigates 10 Korean mothers on their perspectives of English and how their perspectives influence their involvement in their childâs English education. Findings reveal that social demands, the effect of globalization and competition among students and mothers prompts them to closely monitor their childâs English education. Conversing with other mothers provide insightful feedback about hagwon and where their child is at compared to others but also creates anxiety and further competition
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Many recent works on knowledge distillation have provided ways to transfer
the knowledge of a trained network for improving the learning process of a new
one, but finding a good technique for knowledge distillation is still an open
problem. In this paper, we provide a new perspective based on a decision
boundary, which is one of the most important component of a classifier. The
generalization performance of a classifier is closely related to the adequacy
of its decision boundary, so a good classifier bears a good decision boundary.
Therefore, transferring information closely related to the decision boundary
can be a good attempt for knowledge distillation. To realize this goal, we
utilize an adversarial attack to discover samples supporting a decision
boundary. Based on this idea, to transfer more accurate information about the
decision boundary, the proposed algorithm trains a student classifier based on
the adversarial samples supporting the decision boundary. Experiments show that
the proposed method indeed improves knowledge distillation and achieves the
state-of-the-arts performance.Comment: Accepted to AAAI 201
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