One of the most challenging tasks for search engines like Google, Yahoo, Bing, etc is to rank-order websites in terms of relevance in search lists with the most relevant appearing on the top in what is termed as SERP (Search Engine Results Page). On the other side website owners would always like to have their websites appear on top of search lists. Here comes the understanding of the mechanism that enables search engines to rank-order websites in terms of relevance from millions and millions of websites. In general any search engine would do the following in the background:

1) Crawl the web for all web pages with public access.
2) Index relevant keywords or phrases.
3) Rate importance of each page and display in search lists according to the rating (like PageRank of Google)

The last step is fuelled by algorithms that use the concept of `Eigen vectors’ (a mathematical concept) in quantitatively determining the rating / relevance.

A web is a composition of web pages and each page is scored for relevance. Assigning a score to any given web page is that the page’s score is derived from the links made to that page from other web pages. The links to a given page are called the backlinks for that page. The web thus becomes a democracy where pages vote for the importance of other pages by linking to them. Let us assume a web with four pages with the following characteristic back links.

a) Page 1 links to page 3, 4 and 2
b) Page 2 links to 3 and 4
c) Page 3 links to 1
d) Page 4 links to 3 and 1

Hence the number of backlinks would be as follows:

I) page 1 would be 2 (from c and d ie from pages 3 and 4);
II) page 2 would be 1 (from a ie from page 1);
III)page 3 would be 3 (from a, b and d ie, from pages 1,2 and 4);
IV) page 4 would be 2 (from a and b ie, from pages 1 and 2).

Thus, page 3 would be the one with the top rank; pages 1 and 4 would tie for the next rank; and page 2 would be ranked last. But the drawback here is that it does not discriminate between a page with a backlink from an unimportant page and a page with a backlink from an important page. ie, a link to your homepage directly from Yahoo would increase your rank than a link from a site like . This leads to a concept where score of a page is determined by sum of scores of pages that link to the page. In our example, score of page 3 would be the sum of scores of pages that link to page 3 which is sum of scores of page 1, page 2 and page 4. But this results in a fact that a web page does get extra influence simply by linking to lots of other pages. To avoid this, the following is applied. In our example, page 1 has links to three other pages namely 3,4 and 2. Hence the score of page 3 would be boosted by one-third of score of page 1, rather than the score of page 1. In this manner each web page gets a total of one vote, weighted by that web page’s score, that is evenly divided up among all of its outgoing links.

Let us list out the scores of the four web pages in our example.
i) Score of page 1 would be (score of page 3 divided by 1) + (score of page 4 divided by two)
Why is that so ? Page 1 has two back links one from page 3 and one from page 4 (Ref I); Page 3 has one link which is to page 1 and page 4 has two links which is to page 3 and page 1 (Ref c and d) and thus divide score of page 3 by 1 and divide score of page 4 by 2 and sum these two components to get the score of page 1.
Similarly, the scores of other pages would be as follows:
ii) Score of page 2 would be score of page 1 divided by 3
iii) Score of page 3 would be (score of page 1 divided by 3) + (score of page 2 divided by 2) + (score of page 4 divided by 2)
iv) Score of page 4 would be (score of page 1 divided by 3) + (score of page 2 divided by 2)

Now comes the application of core mathematics of matrices and determinants. The above (i to iv) can be visualised in terms of a matrix named `A’. ie, scores of page 1, 2, 3 and 4 can be written in a matrix format with the coeffecients forming the elements of the matrix, as under:

0—- 0—-1—-1/2
1/3– 0—-0—-0
1/3– 1/2–0—-1/2
1/3– 1/2–0—-0

This is a square matrix meaning number of rows and columns are same, which is a condition for determining the eigen vector. For every matrix with an eigen vector there is an eigen value, and the product of eigen value and the eigen vector would give the matrix. I have not explained the methodology of determination of eigen value and eigen vector as it can be found in any basic mathematics book. The eigen vector of the above matrix would give a matrix with four columns and one row ( 4 X 1 matrix) representing score of page 1, page 2, page 3 and page 4, which is:


From the above, page 1 has a higher score followed by page 3, then by page 2 and finally page 4.
The point to note is that the page containing maximum backlinks (page 3) is not the one with maximum score. It might seem surprising that page 3, linked to by all other pages, is not the most important. To understand this, note that page 3 links only to page 1 and so casts its entire vote for page 1. This, with the vote of page 2, results in page 1 getting the highest importance score.




Gogobot (, the place where millions of passionate travelers provide insider tips on the world’s best destinations, recently announced the long-awaited launch of its mobile application for Android devices. The Android app joins the iPhone in the Gogobot app lineup, extending the range of mobile offerings for its social network of travelers. Like the iOS version, the Android app allows Gogobot users to search and book hotels, restaurants and things to do in more than 60,000 destinations worldwide. The app is available in English, German, Italian, Hebrew, Portuguese and Japanese.

The Android app integrates features new to Gogobot’s mobile users, including the ability to search and book a hotel on the fly with filters for real-time hotel pricing and availability; user ratings; hotel class; and social recommendations, which Gogobot is known for. The app also includes enhanced restaurant filters that allows for searching and selecting restaurants by cuisine as well as the ability to book a table on the go through a seamless integration with OpenTable.

The app features a smart integration of Google Street View for Android, which uses the phone’s compass to turn an Android device into a virtual window, with 360-degree panoramic views of millions of Gogobot locations. Anyone can remotely scope out neighborhoods, restaurants or hotels found on Gogobot to make more informed decisions about their travels.

The app is optimised for Android tablets and phones, with a newly designed user interface, so users can easily and seamlessly browse, discover, and book their travel plans.

Like the rest of the service, recommendations are curated for each user based on the input of friends in their network.

Gogobot’s new Android app allows users to:

Easily and quickly explore relevant places:
* Search for attractions, restaurants, and hotels, either near your current location or within any city in the world
* Receive recommendations and tips sorted according to your friends’ advice
* Browse detailed information on each location, including photos, maps, addresses, phone numbers, operating hours, and user reviews, as well as nearby hotels, restaurants, and attractions
* See a 360-degree panoramic view of a place via Google Street View to get a better sense of an unfamiliar location

Get the best deal on a hotel and book in a snap:
* Filter hotel results by availability, price, minimum user rating, and hotel class
* Book hotels directly on mobile via
* Filter restaurants by cuisine and friend ratings
* Make restaurant reservations through OpenTable

Socialise around travel and share experiences with friends:
* Turn great travel moments into digital postcards of places visited and creatively modify photos with filters and custom titles before sharing them on Facebook and Gogobot
* Check-ins at various locations, which is reflected on your passport and shared with friends
* View and share personalised guides of every location visited



Researchers led by Nick Goldman at the European Bioinformatics Institute, UK synthesised DNA to encode an eclectic mix of information in its adenine, thymine, cytosine and guanine components. They used these “letters” to record an audio file of 26 seconds of King’s speech, all 154 of Shakespeare’s sonnets, a digital photo of their laboratory and the famous paper in which James Watson and Francis Crick first described the double-helical structure of DNA.

The team built on previous DNA-encoding techniques by adding error correction, allowing content to be retrieved with 100 per cent accuracy. The process involved the conversion of ones and zeroes of digital information into the four letter alphabet of DNA code (A,T,C and G) which was used to create synthetic DNA strands. Machines read the DNA molecules and recovered the encoded information.

DNA-based memory is sought after because DNA can last for thousands of years without special storage, other than being somewhere cold, dark and dry. In theory, DNA can encode roughly the capacity of 100 billion DVDs per gram of single-stranded DNA.


Ever wondered what makes financial markets move!!! Let me introduce two broad categories of investors, namely, risk-averse and risk-appetite. Risk-averse category of investors are, as the name suggests, averse to risk and they are always ready to sacrifice return inorder to have a safe investment. Risk-appetite category of investors, on the other hand are ready to take risks with an expectation of getting higher returns. The movement of investors between these two categories is all that makes the financial markets move up or down and this movement is based on the continuous flow of facts, figures, events, etc..

Before proceeding further, let me give an example where the Government of India is issuing a 5 year bond (government security) with a yield of 5%. This is a sovereign bond which usually is considered risk-free, meaning investors assume that there will not be any default by the government in servicing coupon and redemption. When the investor gets a risk free return of 5%, would he not expect a higher return in a risky environment, for eg, stock markets? This is one of the reasons why generally stock market moves up when interest rates come down and the vice-versa. When interest rates are brought down, there is a shift from the risk averse category of investors to the risk-appetite category of investors which leads to flow of money in stock markets. This illustrates that interest rates is the fundamental factor for a financial market.

Why does any economy bring down the interest rates? Interest rates are usually determined by Central Banks (like RBI in India). In an economy there are broadly two categories of people, namely, savings oriented and spending oriented. The more one spends the more the prices of goods and services rise and leads to an inflationary environment. This is where Central Banks step in wherein they usually rise interest rates which leads to a shift from spending-oriented category to savings-oriented category on account of higher interest rates, which leads to lesser spending and thus low inflation and the same concept can be used to explain why Central Banks reduce interest rates.

More often than not the above concepts culminate to a probable theme that a falling interest rate environment usually drives the stock markets up and that is what is happening currently in indian stock markets.


Ursula Burns grew up in a housing project on Manhattan’s Lower East Side and now  runs Xerox

 Burns was raised  by her single mother in a housing project in Manhattan, an area known for hub of gangs. Her mother ran a daycare center  out of her home and ironed shirts so that she could afford to send Ursula to  Catholic school. She went to NYU, and from there became an intern at Xerox.

In 1980, Burns first worked for Xerox as a summer intern, permanently joining a year later, in 1981, after completing her master’s degree. She worked in various roles in product development and planning in the remainder of the 1980s throughout her 20s.

In January 1990, she was offered a role of an executive assistant to a senior executive and then on quickly rose through the ranks. In June 1991, she became executive assistant to then chairman and chief executive Paul Allaire. In 1999, she was named vice president for global manufacturing.

In 2000, Burns was named a senior vice president and began working closely with soon to be CEO Anne Mulcahy, in what both women have described as a true partnership. Nine years later, in July 2009, she was named CEO, succeeding Mulcahy, who remained as chairwoman until May 2010.

She’s now Xerox’s CEO and Chairwoman. She’s the first African-American woman to be the head of a  Fortune 500 Company.