Replication in distributed systems

http://en.wikipedia.org/wiki/Replication_(computing)

Note the advantages and disadvantages of synchronous and asynchronous replication methods

Payoffs for Mixed Strategies

https://www2.bc.edu/~sonmezt/E308SL7.pdf

Further, one could apply the ideas presented in the above pdf, to a one-shot simultaneous game. (i.e. a simultaneous game played only once)
Assume a 2-person game where the goal of the game is to win.

1. Lets assume a one-shot game. In this situation, what if the Payoff Matrix has no obvious Nash Equilibria i.e. at least 1 player finds that its best to change the decision. What is the best strategy in this situation. One way to think is as follows
1. Assume that the game is a repeated game. Now if one does not want to be exploited, the strategy is to mix up one's decisions. How should this mixing be done ?  In order to minimize the payoffs of the other person, it is best to seek the minimum of the maximum payoffs the other player -- why so ? (have a look at the article), and in doing so one finds the mixed strategy.
2. Once one knows the mixed strategy, choose the decision with the highest yeild in the long-run as the decision for the one-shot game. 

Thus, one can think through the decisions for games that have no Nash Equilibria by thinking through a repeated-game and then using that as a decision in a one-shot game. Of course, if the game does have Nash Equilibria then those are the rational choices.

Also, of course that real life interactions are very rarely one-shot i.e. most are repeat-games, and that is why many other factors start coming into the picture. (including ethics and what not). As an example have a look at this which suggests how and why social-diversity may in fact lead to cooperation and altruism.

Stack Machines vs. Register Machines & more

http://www.codeproject.com/Articles/461052/Stack-based-vs-Register-based-Virtual-Machine-Arch

Java VMs are stack-machines, here is one link
and the Android Davlik VM is based on a register-based-architecture.

"The stack machine style is in contrast to register file machines which hold temporary values in a small fast visible array of similar registers, or accumulator machines which have only one visible general-purpose temp register, or memory-to-memory machines which have no visible temp registers."

Here is a comparison (USENIX) of stack vs register machines. To quote from the paper:

"For example, the local variable assignment a = b + c might be translated to stack JVM code as ILOAD c, ILOAD b, IADD, ISTORE a. In a virtual register machine, the same code would be a single instruction IADD a, b, c. Thus, virtual register machines have the potential to significantly reduce the number of instruction dispatches."

and also

"In the stack-based JVM, a local variable is accessed using an index, and the operand stack is accessed via the stack pointer. In the register-based JVM both the local variables and operand stack can be considered as virtual registers for the method. There is a simple mapping from stack locations to register numbers, because the height and contents of the JVM operand stack are known at any point in a program"

Here is a comparison (Wikipedia) of different Application Virtual Machines

This is of course not a big deal, i.e. stack machines are just a way of compact code, but under the hood it of course uses registers to execute the code. 

ARPs and VLANs

Quick note: https://learningnetwork.cisco.com/message/88223
Also, note how inter-VLAN routing is set up : http://www.cisco.com/en/US/docs/switches/lan/catalyst5000/hybrid/routing.html#wp29604


VLAN Hopping:
Making this as a security note.

Also, a quick recap of LAN vs. MAN vs. WAN protocols. Note specifically the different protocols that are used by each kind of network.

Image Processing in Matlab

Found a couple of links on the Mathworks page related to a few (perhaps basic) aspects of image processing.

1. http://www.mathworks.com/help/toolbox/images/f20-9579.html Link to ideas (with examples) such as image registration, filtering, ROI enhancement, morphological enhancements. etc.
2. http://www.mathworks.com/help/toolbox/images/f8-20792.html Link to some examples about Color space. I will soon upload a pictorial form of how colors are represented by different types of color spaces. One common type of color space is RGB color space, which one can image as a sphere with 3 "corners" jutting out. The color space of a device is a subset of this color space. There are different ways of looking at it e.g. RGB, Hue/Sat/Value, YUV (e.g. using the Jab notation e.g. 4:2:0, :4:1:1 etc), etc.

Will update as i find out more info/links.

Linux Networking Stack Performance

Here is a link about the TCP/IP stack on Linux 2.4 and 2.6. The key insights are as follows

1. Kernel Locking improvements: For SMP, Linux 2.6 still does use the "Big Kernel Lock" to prevent different CPUs from accessing the kernel at the same time , but it has finer granularity. 
2. Efficient copy routines in 2.6 
3. 2.6 Kernel uses the O(1) scheduler, and post 2.6.23 uses a O(log n) scheduler . See this and this  and this (Info about how sessions are made up of processes and how tty is associated with the session, processes/tasks are made up of threads that share the thread group id, the O(1) algo using 2 runqueues per CPU, task priority, nice values etc.)

St. Petersburg Paradox

http://en.wikipedia.org/wiki/St._Petersburg_paradox is a 'paradox' where a player pays some fixed amount (say $20) to enter the game, and it appears that the expected gain from the game is infinite dollars. Therefore, the debate is that the player should pay infinite dollars for the entrance (not $20). The counter argument is that the player argues that i've paid $20, but the heads could show up at the first coin flip itself and so i will be left with a loss. Further, even though i may get a lot of money later the probability of that happening is extremely low -- this diminishing probabilities of riches seems to put off most people - yet - mathematically, the "expected" returns are infinite. This is thus argued to be a case for the rejection of mathematical expectation i.e. our psychology goes against the mathematical (long term and statistical certainty) because in real-life we cannot play infinitely long and we do not have infinite (or large) amounts to bet.

Btw, as a tangential (not at all related to betting and expectations but a side-point about the usage of the word 'average') and a somewhat funny point (though unrelated to the expectation issue), is that one can have a population where almost EVERYONE is above average. e.g. if there are just a few people with 1 leg, 1.99 could be the average number of legs in the population which is <2 for almost everyone. Of course this is kind of obvious that we aren't talking about the median, but ive often seen these two things being confused.  

Book on Algorithms in Computational Geometry

Book on Algorithms in Computational Geometry e.g. convex hulls, line intersections, triangulations, point location/trapezoidal maps, Voronoi Diagrams etc.

To be honest, this was amongst the more beautiful things i have seen of late

One example of the utility of this is GPSes, which as i understand will use methods to find out "intersections" of layers of roads etc, followed by some kind of routing algorithms (Dijkstra?).

If a region/node has a weight, instead of an edge having a weight and if we want to minimize the weight, one way to do that is to from one node, construct edges to "neighboring" nodes with a weight of the destination node and then use Dijkstra?(confirm)

Some machine Learning info

I am going to list things i came across while my feeble attempts at learning machine-learning.


Classification:
E.g. To do document classification we perform at least some of the following tasks.

1. Get some training data, clean and stem the text.
2. Represent your documents as vectors 
3. Perform feature selection
4. Select your classifier and train it, 
5. Run the classification on some test data to see how well it performs

References : http://pauldix.blogs.com/pauldix/goruco/goruco_talk.pdf (have a look at this for a quick overview of classification methods and things like Naive Bayes, SVM, etc)

Some DHCP/IPTables/Squid Fun

This is one example of using IP tables (Wikipedia, Tutorial) on the router to redirect packets to a gateway/proxy (such as Squid), when connected to the wireless on the airport/home etc. The interesting thing (for me) about the post, was the use of DHCP ranges so as to give a different experience of the internet to a certain section of people. Useful to note for future i thought.

A note on IpTables from here:

iptables is built on top of netfilter, the packet alteration framework for Linux 2.4.x and 2.6.x. It is a major rewrite of its predecessor ipchains, and is used to control packet filtering, Network Address Translation (masquerading, portforwarding, transparent proxying), and special effects such as packet mangling.

Advances in Video Coding

A list of recent advances in video coding 

1. Distributed Coding : Wyner-Ziv coding where source statistics are exploited at the decoder allowing relatively lighter encoding phases. H.264 is used as an intraframe encoder and Wyner-Ziv frames are inserted in between intra-frames, where after integer transform of the frame, followed by LDPC coding of the transformed-data after which only parity bits are sent
2. Texture based methods 
3. Scalable coding: Temporal, Spatial, SNR: e.g. code "enhancement" layers as B frames with a hierarchical structure
4. Multi-view coding
5. ROI based coding: Region of interest. See this
6. Conditional Replenishment : See this

Ref[1]

Further, there are advances known for video streaming such as 

1. Adaptive bitrate coding: Encode the video with multiple bitrates and chunk it. 
2. Byte streams (instead of bitstreams)
3. Reduce Pre-roll time
4. Multi-source video streaming over WLANS. ref[2]

High Performance Web Sites

Some links about this:

http://stevesouders.com/hpws/rules.php

Book on Cognitive Science

http://www.goertzel.org/books/complex/contents.html

Liked what ive read so far in the book about Cognitive Science, AI, Dynamical Systems overview etc.