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Re: Safir's blog

GERALD. Still, there are many different kinds of women, aren't there?

LORD ILLINGWORTH. Only two kinds in society: the plain and the coloured.

GERALD. But there are good women in society, aren't there?

LORD ILLINGWORTH. Far too many.

GERALD. But do you think women shouldn't be good?

LORD ILLINGWORTH. One should never tell them so, they'd all become good at once. Women are a fascinatingly wilful sex. Every woman is a rebel, and usually in wild revolt against herself.

GERALD. You have never been married, Lord Illingworth, have you?

LORD ILLINGWORTH. Men marry because they are tired; women because they are curious. Both are disappointed.

GERALD. But don't you think one can be happy when one is married?

LORD ILLINGWORTH. Perfectly happy. But the happiness of a married man, my dear Gerald, depends on the people he has not married.

...............

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Re: Safir's blog

1. Why was Asia so much ahead, but then fell behind? Why did Europe rocket past Asia?

Hypothesis: Europe competed internally and was technologically so much ahead in due time so as to dominate (with this internal competition being responsible for Europe being ahead). Once Asia fell behind, it was suddenly in the middle of a competition and would not be satisfied until it was first again. Why? Due to inferiority and insecurity complex. And the government teaching people history of our past grandeur, making people angry about the present. When Asia was ahead, the rulers thought it better to “not rock the boat”, for personal benefit.

Evidence: I've seen none, aside from x telling me these things.

2. Explain the dynamics of Latin America (South America). What are the main problems in S. America? Why those problems (colonial history?)? How does one fix them?

Furthermore, explain Latin Americans. I don't understand Latin Americans.

3. Explain why Africa is such a problem. Was it ever ahead? Why is it so disastrous?

Hypothesis: Today: Culture emphasizing victimhood and anger, so energy is directed to defiance (I won't do anything, you make me do it!”) rather than obedience and productivity. In the past, disaster due to slavery cutting the continent's growth curve by 200+ years.

Evidence: I've seen African-Americans be very lazy and angry, as well as defiant and wary of being taken advantage of.

Bonus question: What was Africa's culture like pre-colonialists? Is it still present?

4. Explain Eastern Europeans. I don't understand Eastern Europeans.

5. Explain Asians. Why so much emphasis on school, working hard, and obedience? A culture of hierarchy and obedience (as opposed to Africans being wary of the history of hierarchy and opposing it, emphasizing equality)?

6. Macro-level hypothesis: Obedience is good for productivity. Equality is bad for productivity.

Just to be clear, these generalization are only accurate in specific regions (e.g. African-American descriptions in America, and elsewhere probably they're far happier and thus my description would not hold true...probably better to compare African-Americans to others who've been subjected to the tyranny of hierarchical oppression).

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Re: Safir's blog

Going through a bit of a saccharine phase so thought I might as well revive this.

A few things in mind:

1. Girls are emotional, guys logical. That means you shouldn't try to explain anything to the girls. Also, you have to be positive all the time, negativity kills it for girls who are tuned to things emotionally.

2. Girls scare easily.

3. If you like something, don't try to understand it. That's called the sausage rule.

4. For god sakes don't try to explain your faults, or their faults or talk about anything negative. It does not help.

5. Girls like to meet lots of new guys because the old ones tend to disappoint them quickly.

6. Be pleasant and respectful. Don't be too intense. And for god sakes don't try to outdo her, or convince her you know more about something. And don't try to correct her.

7. On that note, leave your ego at the door. Or better yet, get rid of it entirely. It does you no good anyways.

8. Don't try to change her. Jesus Christ. Also don't hold your breath on her changing to become more like you. It might happen over time but it'll be subtle and for god sakes it's not because of anything you did. You're not that great.

9. You can find a logical girl but they're not gonna be as pretty as the emotional ones. Bank on it.

10. Just because the girl seems to hold more guy-like values or other qualities that don't hold with this list, don't hold your breath on its continued existence. All girls eventually trend towards this behavior, simply because it's the most beneficial existence for them (well all girls who can afford to do so, namely the pretty ones). They either find their path here directly, or through the influence of their girlfriends.

11. Girls tend to be on-the-moment, go-with-the-flow type of people. Don't try to excessively make plans, or hold them accountable on plans. It just doesn't fit their way of being of living emotionally.

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Re: Safir's blog

A little R datadump

-----------------

R: basic operations in R,

Syntax to define functions

myfct <- function(arg1, arg2, ...) {

function_body

}

Syntax to call functions

myfct(arg1=..., arg2=...)

logical statements,

object indexing,

seats <- read.table("seats.csv"

,sep=","

,header=TRUE

)

region <- (seats$Region.Name)

europe <- seats[region=="Europe", ]

europe.female <- seats[region=="Europe" & sex=="Female", ]

par(mfrow=c(2,3))

boxplot (europe.female$PercentSeats~europe.female$Year

,main="Europe"

,xlab=""

,ylab=""

,col="steelblue3")

data import and subsetting.

mydata <- read.table("data.txt",header=TRUE)

mydata <- read.table("data.csv", header = TRUE, sep=",") # import from a CSV

mydata <- read.csv("data.csv", header=T)

mydata <- read.table("data.csv", header = TRUE, sep=";")

mydata <- read.csv2("data.csv", header=T)

Variable graphing (i.e histograms, density plots, barplots, dotplots and boxplots).

##Scatterplot

plot(Obama~Predict.Obama

,data=poll.data)

##Stacked dotplot

stripchart(poll.data$Predict.Obama

,axes=FALSE)

#Histogram

hist(poll.data$Predict.Obama

)

#Density plot

plot(density(poll.data$Predict.Obama)

,axes=FALSE)

#Frequency table

table(poll.data$Mode)

##Bar plot

barplot(table(poll.data$Mode)

)

boxplot (oceania.female$PercentSeats~oceania.female$Year

)

Frequency distribution tables,

real.kobe <- streak.len(kobe$basket)

table(real.kobe)

#from kobe.txt data

fake.kobe <- streak.len(fake.kobe.simple)

table(fake.kobe)

#from out simulation

random sampling

toss = c("H", "M")

fake.kobe.simple = sample(toss, prob=c(0.45,0.55),

size=133, replace=TRUE)

fake.kobe.simple

table(fake.kobe.simple)

#works

probability functions (i.e. d*, r*, q* and p* functions).

## CUMULATIVE DISTRIBUTION FUNCTIONS

X Normally distributed with mean=0.4 and sd=1

P(X <= 0))

pnorm(x=0,mean=0.4,sd=1)

Y Normally distributed,

P(0 < Y <= 2)

pnorm(0)-pnorm(2)

P(Y > 1)

1-pnorm(1)

upper-tail probability

pnorm(1, lower.tail=FALSE)

## QUANTILE FUNCTIONS

inverse of the CDF is q*

the z-score

left-tail probability of 0.03,

qnorm(0.03)

right tail probability

qnorm(0.03,lower.tail=FALSE)

Monte Carlo integration.

Analyzes a large chunk of random numbers and looks at how they interact with each other

Used when problem is too big to solve via traditional close analysis

--

supp. links

http://en.wikibooks.org/wiki/R_Programming

http://manuals.bioinformatics.ucr.edu/home/programming-in-r#TOC-Functions

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Re: Safir's blog

You shouldn't think about the future all the time or even most times. But if you want real behavior changes, you need constant emotional reminders of things that trigger positive images for future goals and what you want to reach. Just make sure they're discreet and most of the time not there, and only there to nudge you during complacency.

That's why I changed my avatar to what it is. IS Quarterly reminds me of LGM. I can work on my issues that matter to me, doesn't matter what the public talks about. From there, I'll make my issue as connected to field standards as I can.

 

EDIT: Boeuf, ignore the ramblings of a moron here :D

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Progress so far on Ruby

 

Have a list of sources in mind and listed basically

The main source in codeacademy is fired up and going

Unilateral, 100% focus is necessary, essentially blind focus on a single visual spot, so text density is gold.

Fatigue, caffeine, music boredom, music non-boring

Visual insta updates on progress

Poly bell schedule 

 

 

Soccer stuff

 

Cover wide of the ball possession player

Press double 

Cover the space for a few seconds but after that back double on the ball possessor

3-4-2-1 to 4-3-2-1 to 5-2-2-2-1 essentially the same front and wide formation

Only ruin the setup to destroy edges if forced 

 

 

 

 

 

 

 

 

 

 

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Absolutely destroyed someone with a half time switch :D

 

Defensively switched from double press to occasionally 1v1 containing and completely destroyed his offense

Offensively combated his slow low block by speed passing through him. Once he began covering for speed passes tons of space behind appeared for my guys to run into, finally space appeared for those through passes I had tried unsuccessfully do for the first 45 min.

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100% focus feels good because of the a-ha moment at the end when you go from not understanding something to understanding something. The 0-1 switch is so much more powerful than moving a figure from arbitrary to arbitrary + 1.

 

Tunnel vision and obsessive focus on that one spot one the page is important.

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100% focus feels good because of the a-ha moment at the end when you go from not understanding something to understanding something. The 0-1 switch is so much more powerful than moving a figure from arbitrary to arbitrary + 1

 

This is the central aspect of the moving from external to internal motivation

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Switched my camera angle and instantly had the other guy pinned in his own half

 

Usually my default camera angle gives me an advantage but not against this guy, he either had the same setup or the one that perfectly countered mine. A switch to the standard setup completely destroyed both his offense and defense! :D

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He started with the backlog, saying it is merely a set of hypotheses and beliefs rather than a form of requirements. The hypotheses need to be turned into experiments, which provide the data that can either substantiate or refute the hypotheses. “Learning leads to the next set of hypotheses. It’s not a random walk; it’s not a set guesswork; it’s a set of rigorous tests of beliefs using data to match against the beliefs,” he said.
 

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