nicle wrote:
新聞出來了
被兒子踢...(恕刪)
窩裡反
上次說要告媒體
這次連上次的一起告好了
我看恐怕還是心虛不敢
我只想知道
當初城邦何社長說要幫忙捐出來的版稅
捐了多少啊
是不是可以秀個捐款收據給大家看啊
一場家庭倫理親情大悲劇爸爸答應給兒子版稅, 兒子嫌少不爽拿, 老爸一氣之下一毛不給, 兒子抓狂以攻擊老爸為職志.
找到週刊所寫的部落格 http://wallee1.pixnet.net/blog , 果然沖著吳永志來的,
可是吳經國從小在美國長大耶, 中文程度有這麼好喔?? 吳經國說他太太是學文學的...我想是吧!!引人想像啊~哈
"爸爸答應了沒兌現" , 要是我講這句話, 不被我老頭打爆才怪. 原來就是要老子給錢喔, 養兒子念博士幹麻啦~
快點告你老爸吧~說到要做到, 不要讓人看衰, 我想知道誰說的是真的.
耳邊隱約聽到音樂響起~
張震嶽 - 我要錢
作詞：張震嶽 / 作曲：張震嶽
就是因為認識了一個新的女生
所以我買了一頂新的帽子
她說她禮拜三願意跟我出去逛逛街
可惜口袋裡剩下兩張五十元
喔 媽媽 我要錢 喔 爸爸 我要錢
喔 媽媽 我要錢 喔 爸爸 我需要你的錢
為了那個女生我想了很多方法
說說笑話吃吃水果看電影
你也知道交朋友常常需要金錢幫忙
可能還需要買一套洋裝給她
喔 媽媽 我要錢 喔 爸爸 我要錢
喔 媽媽 我要錢 喔 爸爸 我需要你的錢
我承認有時候常偷開車子出去玩
但至少沒有發生什麼意外
也承認在外面偷抽煙但我已經戒掉
希望可以再多給幾張啊
喔 媽媽 我要錢 喔 爸爸 我要錢
喔 媽媽 我要錢 喔 爸爸 我需要你的錢
媽媽 我要錢 爸爸 我要錢
媽媽 我要錢 爸爸 我需要你的錢
好像沒有提到他是「醫學博士」也.
搜尋"Michael Wu berkeley",
就找到了http://biophysics.berkeley.edu/students.php?ID=35
這是柏克萊Berkeley實驗室的網頁(這很難造假吧!不過,小心為上,可以寫信跟柏克萊求證)
簡單翻譯一下
2001入學 2008年畢業於Jack Gallant的實驗室
主修(或專長):數學.物理.生物學
這應該是為什麼他堅持要校稿吧!因為他是「生物物理學」的Ph.D.
不讓他買了四個野雞大學博士學位的老爸亂寫吧!!!
(真的有人相信亞當夏娃的血型是A型???)
Michael Wu
Class of 2001
Graduated in 2008
E-mail:
Undergraduate Institution: University of California, Berkeley
Major: Applied Math, Physics and Molecular & Cell Biology
Origin: Redwood City, CA
Lab: Jack Gallant
Location:
Research
Nonlinear Spatiotemporal Receptive Field (STRF) Estimation by Neural Computational and Machine Learning Methods
My research will focus on the computational analysis and modeling of neurophysiological data collected from experiments that are designed to unravel the visual processing in the cortex. If one treats a neuron as a computing unit that transforms input (stimuli) into action potentials, then its transfer function, or spatiotemporal receptive field (STRF), can be estimated using mathematical techniques. The kernel might reveal the computations mediating extraction of visual features to which the cell is tuned. More sophisticated nonlinear kernel estimation methods could enable us to understand processing in extra-striate visual areas, such as V2 and V4.
I will use techniques in statistics (principal component analysis, independent component analysis and data manifold modeling), engineering (linear systems analysis and neural networks), and machine learning (support vector machine and boosting) to analyze the physiological data in order to model the complex processing of the visual system. By using natural scene stimuli and simulated saccades in the stimulus set, I should drive visual neurons efficiently so that sufficient data can be collected during an experiment. I will first attempt to characterize the linear properties of cortical cells using Wiener kernel analysis. To capture the nonlinear properties of neuron, I also plan to use neural network, support vector machines and other nonlinear techniques to study the visual pathways. With knowledge extraction techniques and unsupervised learning, I hope to visualize the kernel of extra-striate cells.
Ever since Hubel and Wiesel (1959), we have made much progress in understanding the function of the visual system. However, progress in fully characterizing neurons in primary visual cortex (V1) and extra-striate visual areas has been limited by the complex nonlinearities within the system. Further progress in this area will depend critically on finding an effective method to estimate the input-output relationship of nonlinear visual neurons. Applying the more sophisticated nonlinear kernel estimation methods to extra-striate cortex could lead to the understanding of higher visual functions, such as object recognition, form vision and visual attention.
Although there are no efficient algorithms for object recognition and form vision, our brain does it reliably in almost real time. Successful models of the visual cortex could lead to novel algorithms that are superior to conventional computer vision. The human brain is by no means faster than a computer, but it is definitely superior in certain tasks. Understanding the working of our brain could lead to novel computers that could perform tasks that currently can only be performed by humans.
Publications
Michael CK Wu, Stephen V David, and Jack L Gallant, "Complete Functional Characterization of Sensory Neurons by System Identification," Annual Review of Neuroscience 29, pp 477-505. (2006)
Ryan Prenger, Michael CK Wu, Stephen V David, and Jack L Gallant, "Nonlinear V1 Responses to Natural Scenes Revealed by Neural Network Analysis," Neural Networks 17, pp663-679, 2004.
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