有陰影的地方，必定有光
孤單時，仍要守護你心中的思念
那時候，未來遙遠而沒有形狀，
夢想還不知道該叫什麼名字。
我常常一個人，走很長的路，
在起風的時候覺得自己像一片落葉。
仰望星空，我想知道：
有人正從世界的某個地方朝我走來嗎？
像光那樣，從 一顆星到達另外一顆星。
後來，你出現了。又離開了。
我們等候著青春，卻錯過了彼此。
--幾米
http://www.jimmyspa.com/jimmy/jimmybookdetail.aspx?id=61
8/09 。灰色的彩虹
灰色的彩虹/范瑋琪
作詞：王雅君
作曲：范瑋琪
我從秋天等到 安靜的落葉
還不夠時間 倒帶想念
就像電影情節 最後完結篇
褪色的畫面 沒有笑臉
我的記憶搖晃著昨天 我還有感覺
一抬頭什麼都看不見 雨後的無言
紅橙黃綠我都找不到的晴天
從此 我們兩個世界
在灰色季節漸漸忘記你的一切
過幾年 我在原點
彩虹出現 而我再也找不到
-美麗的蝴蝶 偏偏飛不上天
對你的想念 再也寄不到
-你世界 地址是再見
---
8/08 。《 For Today’s Graduate, Just One Word: Statistics》
From The New York Times/ August 5, 2009
http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=3
By STEVE LOHR
“People think of field archaeology as Indiana Jones, but much of what you really do is data analysis,” she said.
Now Ms. Grimes does a different kind of digging. She works at Google, where she uses statistical analysis of mounds of data to come up with ways to improve its search engine.
Ms. Grimes is an Internet-age statistician, one of many who are changing the image of the profession as a place for dronish number nerds. They are finding themselves increasingly in demand — and even cool.
“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”
The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field,computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.
Yet data is merely the raw material of knowledge. “We’re rapidly entering a world where everything can be monitored and measured,” said Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business. “But the big problem is going to be the ability of humans to use, analyze and make sense of the data.”
The new breed of statisticians tackle that problem. They use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food shipments.
Even the recently ended Netflix contest, which offered $1 million to anyone who could significantly improve the company’s movie recommendation system, was a battle waged with the weapons of modern statistics.
Though at the fore, statisticians are only a small part of an army of experts using modern statistical techniques for data analysis. Computing and numerical skills, experts say, matter far more than degrees. So the new data sleuths come from backgrounds like economics, computer science and mathematics.
They are certainly welcomed in the White House these days. “Robust, unbiased data are the first step toward addressing our long-term economic needs and key policy priorities,” Peter R. Orszag, director of the Office of Management and Budget, declared in a speech in May. Later that day, Mr. Orszag confessed in a blog entry that his talk on the importance of statistics was a subject “near to my (admittedly wonkish) heart.”
I.B.M., seeing an opportunity in data-hunting services, created a Business Analytics and Optimization Services group in April. The unit will tap the expertise of the more than 200 mathematicians, statisticians and other data analysts in its research labs — but that number is not enough. I.B.M. plans to retrain or hire 4,000 more analysts across the company.
In another sign of the growing interest in the field, an estimated 6,400 people are attending the statistics profession’s annual conference in Washington this week, up from around 5,400 in recent years, according to the American Statistical Association. The attendees, men and women, young and graying, looked much like any other crowd of tourists in the nation’s capital. But their rapt exchanges were filled with talk of randomization, parameters, regressions and data clusters. The data surge is elevating a profession that traditionally tackled less visible and less lucrative work, like figuring out life expectancy rates for insurance companies.
Ms. Grimes, 32, got her doctorate in statistics from Stanford in 2003 and joined Google later that year. She is now one of many statisticians in a group of 250 data analysts. She uses statistical modeling to help improve the company’s search technology.
For example, Ms. Grimes worked on an algorithm to fine-tune Google’s crawler software, which roams the Web to constantly update its search index. The model increased the chances that the crawler would scan frequently updated Web pages and make fewer trips to more static ones.
The goal, Ms. Grimes explained, is to make tiny gains in the efficiency of computer and network use. “Even an improvement of a percent or two can be huge, when you do things over the millions and billions of times we do things at Google,” she said.
It is the size of the data sets on the Web that opens new worlds of discovery. Traditionally, social sciences tracked people’s behavior by interviewing or surveying them. “But the Web provides this amazing resource for observing how millions of people interact,” said Jon Kleinberg, a computer scientist and social networking researcher at Cornell.
For example, in research just published, Mr. Kleinberg and two colleagues followed the flow of ideas across cyberspace. They tracked 1.6 million news sites and blogs during the 2008 presidential campaign, using algorithms that scanned for phrases associated with news topics like “lipstick on a pig.”
The Cornell researchers found that, generally, the traditional media leads and the blogs follow, typically by 2.5 hours. But a handful of blogs were quickest to quotes that later gained wide attention.
The rich lode of Web data, experts warn, has its perils. Its sheer volume can easily overwhelm statistical models. Statisticians also caution that strong correlations of data do not necessarily prove a cause-and-effect link.
For example, in the late 1940s, before there was a polio vaccine, public health experts in America noted that polio cases increased in step with the consumption of ice cream and soft drinks, according to David Alan Grier, a historian and statistician at George Washington University. Eliminating such treats was even recommended as part of an anti-polio diet. It turned out that polio outbreaks were most common in the hot months of summer, when people naturally ate more ice cream, showing only an association, Mr. Grier said.
If the data explosion magnifies longstanding issues in statistics, it also opens up new frontiers.
“The key is to let computers do what they are good at, which is trawling these massive data sets for something that is mathematically odd,” said Daniel Gruhl, an I.B.M. researcher whose recent work includes mining medical data to improve treatment. “And that makes it easier for humans to do what they are good at — explain those anomalies.”
------
PHDPHDPHDPHDPHDPHDPHDPHDPHDPHDPHD......
http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=3
By STEVE LOHR
MOUNTAIN VIEW, Calif. — At Harvard, Carrie Grimes majored in anthropology and archaeology and ventured to places like Honduras, where she studied Mayan settlement patterns by mapping where artifacts were found. But she was drawn to what she calls “all the computer and math stuff” that was part of the job.
“People think of field archaeology as Indiana Jones, but much of what you really do is data analysis,” she said.
Now Ms. Grimes does a different kind of digging. She works at Google, where she uses statistical analysis of mounds of data to come up with ways to improve its search engine.
Ms. Grimes is an Internet-age statistician, one of many who are changing the image of the profession as a place for dronish number nerds. They are finding themselves increasingly in demand — and even cool.
“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”
The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field,computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.
Yet data is merely the raw material of knowledge. “We’re rapidly entering a world where everything can be monitored and measured,” said Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business. “But the big problem is going to be the ability of humans to use, analyze and make sense of the data.”
The new breed of statisticians tackle that problem. They use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food shipments.
Even the recently ended Netflix contest, which offered $1 million to anyone who could significantly improve the company’s movie recommendation system, was a battle waged with the weapons of modern statistics.
Though at the fore, statisticians are only a small part of an army of experts using modern statistical techniques for data analysis. Computing and numerical skills, experts say, matter far more than degrees. So the new data sleuths come from backgrounds like economics, computer science and mathematics.
They are certainly welcomed in the White House these days. “Robust, unbiased data are the first step toward addressing our long-term economic needs and key policy priorities,” Peter R. Orszag, director of the Office of Management and Budget, declared in a speech in May. Later that day, Mr. Orszag confessed in a blog entry that his talk on the importance of statistics was a subject “near to my (admittedly wonkish) heart.”
I.B.M., seeing an opportunity in data-hunting services, created a Business Analytics and Optimization Services group in April. The unit will tap the expertise of the more than 200 mathematicians, statisticians and other data analysts in its research labs — but that number is not enough. I.B.M. plans to retrain or hire 4,000 more analysts across the company.
In another sign of the growing interest in the field, an estimated 6,400 people are attending the statistics profession’s annual conference in Washington this week, up from around 5,400 in recent years, according to the American Statistical Association. The attendees, men and women, young and graying, looked much like any other crowd of tourists in the nation’s capital. But their rapt exchanges were filled with talk of randomization, parameters, regressions and data clusters. The data surge is elevating a profession that traditionally tackled less visible and less lucrative work, like figuring out life expectancy rates for insurance companies.
Ms. Grimes, 32, got her doctorate in statistics from Stanford in 2003 and joined Google later that year. She is now one of many statisticians in a group of 250 data analysts. She uses statistical modeling to help improve the company’s search technology.
For example, Ms. Grimes worked on an algorithm to fine-tune Google’s crawler software, which roams the Web to constantly update its search index. The model increased the chances that the crawler would scan frequently updated Web pages and make fewer trips to more static ones.
The goal, Ms. Grimes explained, is to make tiny gains in the efficiency of computer and network use. “Even an improvement of a percent or two can be huge, when you do things over the millions and billions of times we do things at Google,” she said.
It is the size of the data sets on the Web that opens new worlds of discovery. Traditionally, social sciences tracked people’s behavior by interviewing or surveying them. “But the Web provides this amazing resource for observing how millions of people interact,” said Jon Kleinberg, a computer scientist and social networking researcher at Cornell.
For example, in research just published, Mr. Kleinberg and two colleagues followed the flow of ideas across cyberspace. They tracked 1.6 million news sites and blogs during the 2008 presidential campaign, using algorithms that scanned for phrases associated with news topics like “lipstick on a pig.”
The Cornell researchers found that, generally, the traditional media leads and the blogs follow, typically by 2.5 hours. But a handful of blogs were quickest to quotes that later gained wide attention.
The rich lode of Web data, experts warn, has its perils. Its sheer volume can easily overwhelm statistical models. Statisticians also caution that strong correlations of data do not necessarily prove a cause-and-effect link.
For example, in the late 1940s, before there was a polio vaccine, public health experts in America noted that polio cases increased in step with the consumption of ice cream and soft drinks, according to David Alan Grier, a historian and statistician at George Washington University. Eliminating such treats was even recommended as part of an anti-polio diet. It turned out that polio outbreaks were most common in the hot months of summer, when people naturally ate more ice cream, showing only an association, Mr. Grier said.
If the data explosion magnifies longstanding issues in statistics, it also opens up new frontiers.
“The key is to let computers do what they are good at, which is trawling these massive data sets for something that is mathematically odd,” said Daniel Gruhl, an I.B.M. researcher whose recent work includes mining medical data to improve treatment. “And that makes it easier for humans to do what they are good at — explain those anomalies.”
------
PHDPHDPHDPHDPHDPHDPHDPHDPHDPHDPHD......
7/07 。Fever103°
── read by Sylvia Plath
【Fever103°】
高熱103°
Pure? What does it mean?
純潔？這是什麼意思？
The tongues of hell
地獄之舌
Are dull, dull as the triple
遲鈍，鈍如三重之
Tongues of dull, fat Cerebus
舌附於遲鈍肥胖的塞伯斯身上
Who wheezes at the gate. Incapable
它在冥府的大門口喘息。無能
Of licking clean
舔淨
The aguey tendon, the sin, the sin.
寒戰的牛腱，罪惡，罪惡。
The tinder cries.
火種在泣訴。
The indelible smell
驅不散的氣味是
Of a snuffed candle!
撲鼻的蠟燭！
Love, love, the low smokes roll
親愛的，這低低的煙霧自我身上
From me like Isadora's scarves, I'm in a fright
飄出如伊莎朵拉的圍巾。我恐怕
One scarf will catch and anchor in the wheel.
有條圍巾會緊緊纏住輪子。
Such yellow sullen smokes
如此黃且陰鬱的煙霧
Make their own element. They will not rise,
自己衍生出元素。它們不會上昇，
But trundle round the globe
只是繞著地球滾動
Choking the aged and the meek,
使年老和溫馴的人窒息，
The weak
羸弱的
Hothouse baby in its crib,
溫室中的嬰兒在加欄的小床內，
The ghastly orchid
慘白的果園
Hanging its hanging garden in the air,
把空中花園懸掛於半空，
Devilish leopard!
兇殘的花豹！
Radiation turned it white
輻射使它變白
And killed it in an hour.
不到一個小時就斃命。
Greasing the bodies of adulterers
在通姦者的身上塗抹油脂
Like Hiroshima ash and eating in.
像廣島的灰燼，並且吞噬著。
The sin. The sin.
罪惡。罪惡。
Darling, all night
親愛的，整個晚上
I have been flickering, off, on, off, on.
我都閃爍不定，暗，明，暗，明。
The sheets grow heavy as a lecher's kiss.
被褥變得和色鬼的親吻同樣地沈重。
Three days. Three nights.
三天。三夜。
Lemon water, chicken
檸檬水，雞肉
Water, water make me retch.
汁，水汁使我嘔吐。
I am too pure for you or anyone.
我太純潔了不適合你或任何人。
Your body
你的身體
Hurts me as the world hurts God. I am a lantern─
刺傷了我就像世人刺傷了上帝。我是一盞燈籠──
My head a moon
我的頭是日本紙做的
Of Japanese paper, my gold beaten skin
月亮，黃金槌薄的皮膚
Infinitely delicate and infinitely expensive.
極其精美極其昂貴。
Does not my heat astound you. And my light.
我的熱度沒有嚇壞你嗎？還有我的光。
All by myself I am a huge camellia
獨處時我是株巨大的山茶
Glowing and coming and going, flush on flush.
閃爍且來回走動著，枝葉茂密。
I think I am going up,
我想我在上升，
I think I may rise─
我想我可以升起──
The beads of hot metal fly, and I, love, I
灼熱的金屬珠子飛著，而我，親愛的，我
Am a pure acetylene
是純潔的乙炔
Virgin
處女
Attended by roses,
由玫瑰守護著，
By kisses, by cherubim,
由吻，由帶翼的天使，
By whatever these pink things mean.
由粉紅色事物所代表的一切涵義。
Not you, nor him.
不是你，不是他
Not him, nor him
不是他，也不是他
(My selves dissolving, old whore petticoats)─
（我的自我逐漸瓦解，老妓女的襯裙）──
To Paradise.
飛向天堂。
-- Sylvia Plath(希薇亞‧普拉斯)
譯註：
伊莎朵拉即舞蹈家鄧肯。她在參加宴會出來後，踏上汽車，當汽車發動時，她頸上的長圍巾被捲進輪中，將她活活絞死。
6/14 。獨白
「我是俗麗且難居留的現前的一名囚徒，這時，一切形式的人類社會，都已經到達其循環的極致，再也無法想像社會可以有什麼新鮮的形式。而我從你的聲音裡，聽到了使城市存活的看不見的理由，也許，透過這些理由，已經死去的城市，將會復活」~卡爾維諾《看不見的城市》
這一個月以來幾乎每天完全重複一樣的事，起床，讀書，家教，晚餐，補習，看書，洗澡，上網，焦慮，睡覺，焦慮。間或穿插圖書館，誠品，咖啡店。除了買食物點餐之外，完全不需要開口說話。大多數的日子只吃一餐，而且固定吃一樣的餐廳的一樣的便當，間或穿插星巴克的抹茶拿鐵或鹹花生的蘋果派。單調循環的生活日復一日。
(考生的世界真是狹小。一樣的生活極致循環，沒有事就不用說話，幾乎像是停止了步伐似的。但我所包含的世界越變得那樣小，越變成靜止的東西，我越覺得我的世界好像充滿了奇妙的事物和奇妙的人。然後就自然地發展出幼稚的興趣，像是把剩下的飯菜排成笑臉; 或把競爭變成遊戲，像是為了跟隔壁裝用功的男生競爭下課也假裝用功查單字; 或從極低的笑點中得到樂趣，像是發現《明天過後》跟《Finding Nemo(海底總動員)》根本是同一部片 - -都是爸爸找兒子+環保議題。)
準備RE其實蠻快樂的，(雖然常常形單影隻的，)由於RE是考邏輯推理能力，而不是考僵化的修辭、外在的文法規則等。完全必須依據個人理性能力推理，所以準備RE是一種不斷的成長與進化的過程，而不是知識碎片的堆積。而且老方跟謝老師真的都是很有智慧的人，聽他們講課並不會讓我想依賴他們去達成些什麼，而是更明確地看到自己的不足，再感到一些焦急。「被寵壞是不行的，」驕傲也僅是一種奢侈的多餘。
我很快樂喔，只是有點淡淡的小桑心。張懸的那句歌詞一點都沒錯，「孤獨中的快樂不能用來解決失落。」有時候，仍然會感到荒蕪。
但仍得繼續下去。唯一能夠放鬆的時刻是騎腳踏車在都市裡穿梭的空檔，像是前幾天去補習的途中經過公園路，逆光看著日光下的行道樹時，突然有一種春日早晨陽光落在小葉欖仁上的清新感。有短暫的時間我放的很空，也許你們或多或少都懂那種感覺，但肩膀確實是更鬆下來了一點。畫面映在視網膜上，然後跑到身體的深處待了下來。一瞬間我想起南方的樣子及今年春天所發生的許多事，像是那個陽光灑滿一地的早晨，及雨夜中所看到的最美麗的光影。
若說記憶是在不同的平台上相互牽引著的單位。那麼為什麼記憶會變得模糊，或是失去存在感呢?
出門前又下起細細的雨，我把腳踏車移進屋簷下，再將淺黑色的坐墊擦乾。其實我也感到很困惑。再怎樣透明的回憶，也會漸漸失去原有的模樣，染上其他的顏色。厚重的記憶、期待些什麼改變的記憶、寬容及忌妒，它們在你沒注意到的時候沉默地離開了最初的場所。就像是一種旅行，我想。為你保留的溫柔與記憶在星期天決定集體出走到遙遠的城市，直到我再也記不得它們的樣子，剩下獨白的聲音。
---
【關於我愛你】-- 張懸
作詞：焦安溥 作曲：焦安溥 編曲：Algae
你眷戀的 都已離去
你問過自己無數次
想放棄的 眼前全在這裡
超脫和追求時常是混在一起
你擁抱的 並不總是也擁抱你
而我想說的 誰也不可惜
去揮霍和珍惜是同一件事情
我所有的何妨 何必
何其榮幸
在必須發現我們終將一無所有前
至少你可以說 我懂 活著的最寂寞
我擁有的都是僥倖啊 我失去的都是人生
當你不遺忘也不想曾經
我愛你
在必須感覺我們終將一無所有前
你做的 讓你可以說
是的 我有見過我的夢
我擁有的都是僥倖啊 我失去的都是人生
因為你擔心的是你自已
我愛你
這一個月以來幾乎每天完全重複一樣的事，起床，讀書，家教，晚餐，補習，看書，洗澡，上網，焦慮，睡覺，焦慮。間或穿插圖書館，誠品，咖啡店。除了買食物點餐之外，完全不需要開口說話。大多數的日子只吃一餐，而且固定吃一樣的餐廳的一樣的便當，間或穿插星巴克的抹茶拿鐵或鹹花生的蘋果派。單調循環的生活日復一日。
(考生的世界真是狹小。一樣的生活極致循環，沒有事就不用說話，幾乎像是停止了步伐似的。但我所包含的世界越變得那樣小，越變成靜止的東西，我越覺得我的世界好像充滿了奇妙的事物和奇妙的人。然後就自然地發展出幼稚的興趣，像是把剩下的飯菜排成笑臉; 或把競爭變成遊戲，像是為了跟隔壁裝用功的男生競爭下課也假裝用功查單字; 或從極低的笑點中得到樂趣，像是發現《明天過後》跟《Finding Nemo(海底總動員)》根本是同一部片 - -都是爸爸找兒子+環保議題。)
準備RE其實蠻快樂的，(雖然常常形單影隻的，)由於RE是考邏輯推理能力，而不是考僵化的修辭、外在的文法規則等。完全必須依據個人理性能力推理，所以準備RE是一種不斷的成長與進化的過程，而不是知識碎片的堆積。而且老方跟謝老師真的都是很有智慧的人，聽他們講課並不會讓我想依賴他們去達成些什麼，而是更明確地看到自己的不足，再感到一些焦急。「被寵壞是不行的，」驕傲也僅是一種奢侈的多餘。
我很快樂喔，只是有點淡淡的小桑心。張懸的那句歌詞一點都沒錯，「孤獨中的快樂不能用來解決失落。」有時候，仍然會感到荒蕪。
但仍得繼續下去。唯一能夠放鬆的時刻是騎腳踏車在都市裡穿梭的空檔，像是前幾天去補習的途中經過公園路，逆光看著日光下的行道樹時，突然有一種春日早晨陽光落在小葉欖仁上的清新感。有短暫的時間我放的很空，也許你們或多或少都懂那種感覺，但肩膀確實是更鬆下來了一點。畫面映在視網膜上，然後跑到身體的深處待了下來。一瞬間我想起南方的樣子及今年春天所發生的許多事，像是那個陽光灑滿一地的早晨，及雨夜中所看到的最美麗的光影。
若說記憶是在不同的平台上相互牽引著的單位。那麼為什麼記憶會變得模糊，或是失去存在感呢?
出門前又下起細細的雨，我把腳踏車移進屋簷下，再將淺黑色的坐墊擦乾。其實我也感到很困惑。再怎樣透明的回憶，也會漸漸失去原有的模樣，染上其他的顏色。厚重的記憶、期待些什麼改變的記憶、寬容及忌妒，它們在你沒注意到的時候沉默地離開了最初的場所。就像是一種旅行，我想。為你保留的溫柔與記憶在星期天決定集體出走到遙遠的城市，直到我再也記不得它們的樣子，剩下獨白的聲音。
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【關於我愛你】-- 張懸
作詞：焦安溥 作曲：焦安溥 編曲：Algae
你眷戀的 都已離去
你問過自己無數次
想放棄的 眼前全在這裡
超脫和追求時常是混在一起
你擁抱的 並不總是也擁抱你
而我想說的 誰也不可惜
去揮霍和珍惜是同一件事情
我所有的何妨 何必
何其榮幸
在必須發現我們終將一無所有前
至少你可以說 我懂 活著的最寂寞
我擁有的都是僥倖啊 我失去的都是人生
當你不遺忘也不想曾經
我愛你
在必須感覺我們終將一無所有前
你做的 讓你可以說
是的 我有見過我的夢
我擁有的都是僥倖啊 我失去的都是人生
因為你擔心的是你自已
我愛你
6/06 。Have this I wish tonight
“Be careful what you wish for because you may actually get it.”
今天在念RE時看到了這句話，覺得自己已經能夠透徹的暸解這句諺語的意涵：許願一定要小心許喔。因為我們的世界不是圓滿的，老天爺給你一個東西，一定會從你身上搶走一個東西。
「一種駭人的現實光在閃耀，就是這麼閃耀著。那來自一片春心—因為青春—」~吳爾芙《自己的房間》
2X歲了(不要問我到底幾歲了，我不會回答你喔，耶)。由於計劃趕不上變化:p，這一年對我來說，是個意外的long vacation，我努力在長大前補上一些曾經錯身而過的閱讀及青春;學了許多以前一直想學習的知識，做了許多過去從沒做過的事;認識了很多好朋友，但也常一個人靜靜地困坐在混亂、孤獨之中。即使我想熱愛這個世界，但還是經常難過失望淚眼茫茫。是這樣吧，在自溺與自省之間，在呼吸與沉澱之間，一切才會變得真實。
「生命是一場不得不如此的揮霍，」她說。
但也是因為孤獨跟痛苦喔，讓一個人可以在各個面向探索新的領域、在書寫與喃喃中看見自己，然後反覆地參悟愛與命運的蒼茫。
所以後來的我終於確認，
所謂的永遠是如此短暫而無憾，同時
明白永遠 永遠不是 關鍵字。
這個寬闊的世界，美好而殘缺。我想我們一直都很努力的活著。And Life is a journey, 這是一段段看見自己最脆弱及最堅強地方的過程。的確就是這樣噢。就像是在筆記本的一側寫下過太多失去的東西，所以絲毫不願放棄去期待溫暖的理想及理想中的溫度。所謂的許願能力。
Then it comes to my birthday! :)
生日前一天，忙到忘了要訂餐廳，十點多補完習才突然想起來跟A4約好要happy! 還好後來總算有訂到位。而且生日剛開始，凌晨一點，居然是在修改別人的經濟作業中展開。然後修改當中又該死地想到另一個有趣的問題，於是小腦袋不知不覺就運轉到凌晨四點。緊接著四點多看了一部短片後沮喪的大哭(原因就不說了…)，最後在溫溼的枕上與天光初現中睡去。
不過起床時看見美好的天氣，突然鬆了口氣。再怎麼迷惘都會過去的吧，我這樣想。
後來過了好開心的生日。媽嘛還傳簡訊給我(居然記得真是感人)，爸拔很典型地打了一通電話來給他的寶貝女兒(內容超級肉麻但好笑的是我居然偷偷哭了)。
謝謝各方的生日注糊及幫我祝壽的大家。
禮物我真的好喜翻：精緻的蛋糕們、穿起來像網拍GIRL的衣服、花俏的手環跟小花、閃亮亮的唇蜜、可以邊騎車邊聽音樂的大鼻子、洗完會甜到像糖果的沐浴乳、上萬言書的卡片…還有家教學生的榜單!!(喔耶)
在最黑暗的時候，最微小的光才是最明亮的。
謝謝你們，是因為你們，我的生命才如此豐盛。
muamua~我愛泥們
最後是給A4的其他三位成員：
畢業快樂，我想要跟你們說。大學生涯終於結束了（是呀，我的已經在去年冬天就結束了，但當你們之中的最後一人也離開了學校，大學對我而言就真的要成為一個漸漸遠去的風景），但接下來的精彩才正要展開。我還太年輕，沒資格說些期許的話（那聽起來會很老，我已經夠老了 :p），但無論如何請相信，生活是掌握在自己的手中。
Good luck, and best wishes to you all.
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《克痢屍釘之酒國名花》
中間那杯是20%左右的雞尾酒(我在吞火耶)
喝完過後五分鐘我就開始手舞足蹈了…
再過半個小時…
就變這樣了--
醉很大。
(所以以後請不要問我酒量如何 以上就是標準示範)
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