## 2010年11月17日 星期三

### 19.優格Yogurt 20.醋Vinegar

1.地瓜Sweet Potato

2.綠豆Mung Beens

3.燕麥Oats

4.薏仁Job’s Tears

5.小米Millet

6.糙米Brown Rice

7.紅豆Small Red Beans

8.胡蘿蔔Carrot

9.山藥Yam

10.牛蒡Lapp

11.蘆筍Asparagus

12.洋蔥Onion

13.蓮藕Lotus Root

14.白蘿蔔Radish

15.山茼蒿

16.地瓜葉Sweet Potato’s Left

17.蘿蔔葉Radish’s Left

18.川七

19.優格Yogurt

20.醋Vinegar

### 人到達一個年齡，清楚甚麼該要，甚麼不該要，是一種智慧。

A guy is 70 years old and loves to fish.

He was sitting in his boat the other day when he heard a voice say, 'Pick me up.'

He looked around and couldn't see anyone.

He thought he was dreaming when he heard the voice say again, 'Pick me up.'

He looked in the water and there, floating on the top, was a frog.

The man said, 'Are you talking to me?'

The frog said, 'Yes, I'm talking to you.'

Pick me up then, kiss me and I'll turn into the most beautiful woman you have ever seen.

I'll make sure that all your friends are envious and jealous because I will be your bride!'

The man looked at the frog for a short time, reached over, picked it up carefully, and placed it in his front pocket.

The frog said, 'What, are you nuts? Didn't you hear what I said? I said kiss me and I will be your beautiful bride.'

He opened his pocket, looked at the frog and said,
'Nah, at my age I'd rather have a talking frog.'

"人到達一個年齡，清楚甚麼該要，甚麼不該要，是一種智慧。 "

## 2010年10月5日 星期二

### 二次無理數的連分數

$\frac{\sqrt{N}+u}{d}=a+\frac{1}{a_1+\frac{\sqrt{N}+u_1}{d_1}}$

x^2

## 2010年9月24日 星期五

### matrix

$\mathbf{A} = \begin{pmatrix} a + b + c & uv\\ a + b & u + v \end{pmatrix} \begin{vmatrix} 30 & 7\\ 3 & 17 \end{vmatrix}$

## 2010年9月23日 星期四

### Result, #, 計算

$n=4,x_1=3,x_2=2,x_3=3,x_4=4,$ 求 \\
$\sum_{i=1}^{n} x_{i}^{2} =38, \qquad \prod_{i=1}^{n} x_{i}^{2} =5184.$
\\

## 2010年9月18日 星期六

### cnn.com Cardinal Newman

Cardinal Newman: Who was he?On Sunday Pope Benedict XVI will conduct an open-air beatification Mass for the English cardinal John Henry Newman. Yet the majority of British people know little about the cardinal or how significant he was in Catholicism. FULL STORY

### 家庭消費力 全台第一

《住展》雜誌研發長倪子仁表示，如今大台北地區土地價格屢創新高，在土地成本過高的壓力下，許多重量級開發商，紛紛拓展土地開發區域，特別是擁有竹科高所得收入群的新竹市與竹北市，最受建商青睞。

## 2010年9月17日 星期五

### SAD HAPPY ANGRY LED LOGHT

SAD HAPPY ANGRY LED LOGHT

## 2010年9月15日 星期三

### CNN news

Tokyo intervened in the currency markets for the first time in more than six years to weaken the yen, after the currency broke through Y83 against the U.S. dollar and threatened exporter profits and business sentiment.

## 2010年9月7日 星期二

### Blog Test and list

Blog Test and list

## 2010年8月29日 星期日

### 各公民營行庫 房貸利率一覽表 (更新時間：2010/7/22)

「歡喜理財家房貸」不循環額度：前六個月1.50%(IR+0.47%)，第七至十二個月1.70%(IR+0.67%)，第2 年起1.99%(IR+0.96%)。

「美利貸」專案依指標利率加碼1.1%~1.5%，現為2.135%~2.535%

「輕鬆還」房貸專案2.29%~6.08%，機動計息

## 2010年7月23日 星期五

### 一台1124元！ 印度推最便宜平板電腦

http://www.libertytimes.com.tw/2010/new/jul/24/today-int4.htm

Hello Kitty

## 2010年3月7日 星期日

### Psychology of advanced mathematical thinking

Psychology of advanced mathematical thinking
Title: Psychology of advanced mathematical thinking
Author: Tall D.
Read "Psychology of advanced mathematical thinking"
"Psychology of advanced mathematical thinking" pages map

See

## 2010年1月30日 星期六

### NComputing Brings Inverse Cloud Computing To Joe The Plumber

My apologies, but with the election over, I just couldn’t resist the urge to use ‘Joe the Plumber’. What a joke, but I’ll tell you what isn’t a joke, Ncomputing’s networked computer system. The tiny, fanless box contains no CPU or extreme hardware, but allows its user to perform their day-to-day PC tasks, such as Web surfring, document editing and more. It works by connecting to one central computer that does all the heavy lifting that is shared with other users. As a result, NComputing’s machines are ultra green using 95% less energy than the laptop I write this post on – about 1-4watts. Also, with no moving parts in the NComputers there’s less to breakdown and less heat dissipation, which means no cooling fans or costly, nonecofriendly A/C.

The NComputers already in use by over a million people in India, Bangladesh and Macedonia and are largely utilized by schools, business and public access areas.

Official product page here

### Mathematical Proof of the Inevitability of Cloud Computing

http://cloudonomics.wordpress.com/2009/11/30/mathematical-proof-of-the-inevitability-of-cloud-computing/

http://cloudonomics.wordpress.com/2009/11/30/mathematical-proof-of-the-inevitability-of-cloud-computing/

In the emerging business model and technology known as cloud computing, there has been discussion regarding whether a private solution, a cloud-based utility service, or a mix of the two is optimal. My analysis examines the conditions under which dedicated capacity, on-demand capacity, or a hybrid of the two are lowest cost. The analysis applies not just to cloud computing, but also to similar decisions, e.g.: buy a house or rent it; rent a house or stay in a hotel; buy a car or rent it; rent a car or take a taxi; and so forth.

To jump right to the punchline(s), a pay-per-use solution obviously makes sense if the unit cost of cloud services is lower than dedicated, owned capacity. And, in many cases, clouds provide this cost advantage.

Counterintuitively, though, a pure cloud solution also makes sense even if its unit cost is higher, as long as the peak-to-average ratio of the demand curve is higher than the cost differential between on-demand and dedicated capacity. In other words, even if cloud services cost, say, twice as much, a pure cloud solution makes sense for those demand curves where the peak-to-average ratio is two-to-one or higher. This is very often the case across a variety of industries. The reason for this is that the fixed capacity dedicated solution must be built to peak, whereas the cost of the on-demand pay-per-use solution is proportional to the average.

Also important and not obvious, leveraging pay-per-use pricing, either in a wholly on-demand solution or a hybrid with dedicated capacity turns out to make sense any time there is a peak of “short enough” duration. Specifically, if the percentage of time spent at peak is less than the inverse of the utility premium, using a cloud or other pay-per-use utility for at least part of the solution makes sense. For example, even if the cost of cloud services were, say, four times as much as owned capacity, they still make sense as part of the solution if peak demand only occurs one-quarter of the time or less.

In practice, this means that cloud services should be widely adopted, since absolute peaks rarely last that long. For example, today, Cyber Monday, represents peak demand for many etailers. It is a peak who’s duration is only one-three hundred sixty-fifth of the time. Online flower services who reach peaks around Valentine’s Day and Mother’s day have a peak duration of only one one-hundred eightieth of the time. While retailers experience most of their business during one month of the year, there are busy days and slow days even during those peaks. “Peak” is actually a fractal concept, so if cloud resources can be provisioned, deprovisioned, and billed on an hourly basis or by the minute, then instead of peak month or peak day we need to look at peak hours or peak minutes, in which case the conclusions are even more compelling.

I look at the optimal cost solutions between dedicated capacity, which is paid for whether it is used or not, and pay-per-use utilities. My assumptions for this analysis are that pay-per-use capacity is 1) paid for when used and not paid for when not used; 2) the cost for such capacity does not depend on the time of request or use; 3) the unit cost for on-demand or dedicated capacity does not depend on the quantity of resources requested; 4) there are no additional relevant costs needed for the analysis; 5) all demand must be served without delay.

These are assumptions which may or may not correspond to reality. For example, with respect to assumption (1), most pay-per-use pricing mechanisms offered today are pure. However, in many domains there are membership fees, non-refundable deposits, option fees, or reservation fees where one may end up paying even if the capacity is not used. Assumption (2) may not hold due to the time value of money, or to the extent that dynamic pricing exists in the industry under consideration. A (pay-per-use) hotel room may cost $79 on Tuesday but$799 the subsequent Saturday night. Assumption (3) may not hold due to quantity discounts or, conversely, due to the service provider using yield management techniques to charge less when provider capacity is underutilized or more as provider capacity nears 100% utilization Assumption (4) may or may not apply based on the nature of the application and marginal costs to link the dedicated resources to on-demand resources vs. if they were all dedicated or all on-demand. As an example, there may be wide-area network bandwidth costs to link an enterprise data center to a cloud service provider’s location. Finally, assumption (5) actually says two things. One, that we must serve all demand, not just a limited portion, and two, that we don’t have the ability to defer demand until there is sufficient capacity available. Serving all demand makes sense, because presumably the cost to serve the demand is greatly exceeded by the revenue or value of serving it. Otherwise, the lowest cost solution is zero dedicated and zero utility resources; in other words, just shut down the business. In some cases we can defer demand, e.g., scheduling elective surgery or waiting for a restaurant table to open up. However, most tasks today seem to require nearly real-time response, whether it’s web search, streaming a video, buying or selling stocks, communicating, collaborating, or microblogging.

It is tempting to view this analysis as relating to “private enterprise data centers” vs. “cloud service providers,” but strictly speaking this is not true. For example, the dedicated capacity may be viewed as owned resources in a co-location facility, managed servers or storage with fixed capacity under a long term lease or managed services contract, or even “reserved instances.” By “dedicated” we really mean “fixed for the time period under consideration.” For this reason, I will use the terms “pay-per-use” or “utility” rather than “cloud” except when providing colloquial interpretations.

Let the demand D for resources during the interval 0 to T be a function of time D(t), 0 <= t <= T.