Movies that shaped me.

These are the list of movies, all of which I had watched in single screen theatres, and those which etched their marks on me. I only included those ones whose initial release was after my birth.

Some notable omissions are Ten Commandments (1956), Ben-hur (1959) and Where Eagles Dare (1968) and the Bengali movie Sonar Kella (1974).

• Honey, I Shrunk the Kids - 1989
• Terminator 2 - 1991
• Jurassic Park - 1993
• Forrest Gump - 1994
• Lion King - 1994
• Hum Aapke Hai Kaun - 1994 (The last movie I watched without spectacles)
• Dilwale Dulhaniya Le Jayenge - 1995
• Independence Day - 1996
• Titanic - 1997
• Men in Black - 1997
• Saving Private Ryan - 1998
• Satya - 1998
• Sarfarosh - 1999
• Sangharsh - 1999
• Mission Kashmir - 2000 (During college, I had a crush on Preity Zinta)
• A Beautiful Mind - 2001
• Bourne Identity - 2002
• Bruce Almighty - 2003
• The Terminal - 2004
• Dhoom - 2004
• Bunty aur Babli - 2005
• Laage Raho Munnabhai - 2006
• Chak De India - 2007
• Dark Knight - 2008

Since 2009, all movies I watched were in smaller screens of modern multiplexes. Unfortunately it does not have the same impact as the panoramic large and wide screen of the old theatres. Also, gone are the days of whistles and catcalls. Nowadays, the patrons of multiplexes are rather sophisticated. In fact after 2015, I have rarely been to any cinema hall. 

Its not that good movies aren't made. 

Only that it is hard to etch anything, anymore on my toughened soul.  

How promoters having significant holdings can manipulate their company's stock prices ?

Recently Adani Group has been in the news after Hindenburg Research accused it of manipulating their stock prices. Whether the Adani Group has really done so, is a matter of regulatory probe. This article attempts to explain to the common investor, how this can be easily done by any promoter who holds  significant shares of his own company. And why do some promoters do it ?

Imagine you are a promoter of a publicly listed company whose current share price is Rs.100 and you happen to own 90% equity, say, 10 crore shares. So, the current market cap of your company is approximately Rs.1111 crores and you own Rs.900 Cr of it. 

Now, you want to expand your business and need a loan. So, you approach a bank. The banks will ask for collateral where you have the option to pledge your shares. Now, the quantum of loan you can secure will be more if your market cap is higher, since, it means a higher collateral for the bank. This is where the limited public shareholding, in this case 10%, comes to your help. 

All the common shares are not traded in the open public market. It is only that 10% that gets traded which amounts to roughly 1.1 crore shares. Now, you reach out to a friendly broker who agrees to buy your shares from you at a higher price. 

Lets, do some calculation here. 

Say, you put in Rs.1 Cr and you end up buying your own shares from the secondary market at the current market price of Rs.100. You immediately sell it to the friendly broker at a small premium, say Rs.101. You need to compensate the broker for taking this risk, hence, you need to pay an additional, say, Rs.25 lakhs to the broker. All this buying activity will trigger an interest in the company. Hence, the broker will in turn try to sell these shares to the common gullible investor. This means that your market price will further increase, may be to Rs.104 - 105, even higher. So, your holding i.e. 10 crore shares now is  Rs.1050 Cr which means a gain of Rs.250 Cr. You will incur some expenses, but in the bull trend thus created, you might even make an extra small profit. 

So, by spending Rs.15 lakhs you gain around Rs.250 Cr. 

Hence, companies with a limited concentration in holding can play this dirty game of propping up their own share prices significantly by spending much smaller amounts of money.

Of course this is a gain in paper which will translate to a higher amount of loan from the bank thereby funding your business expansion. 

Whether the Adani Group really resorted to such practices will be known in future but there is no doubt that their share prices had an abnormal growth in the last few years. It is too good to be true, especially in a jugaad economy as ours. 

Healthy and emerging countries

To keep myself engaged in this lock-down period, I wanted to find out the countries where life expectancy at birth is above 60 (typical retirement age in emerging economies) and also where the Gross National Income growth (CAGR) at Purchasing Power Parity (period 2010 - 2019) is modest.

Source: World Bank

Tool: Python/Pandas

Country	GNI (PPP) – rounded	Life Expectancy at birth
China	10.209	76.704
India	6.388	69.416
Indonesia	5.367	71.509
Korea, Rep.	4.707	82.6268292682927
United States	3.325	78.5390243902439
Antigua and Barbuda	3.071	76.885
Armenia	3.38	74.945
Bangladesh	10.343	72.32
Bolivia	7.904	71.239
Botswana	3.521	69.275
Bulgaria	3.441	74.9634146341464
Cambodia	7.845	69.57
Chile	3.779	80.042
Congo, Dem. Rep.	5.543	60.368
Costa Rica	5.575	80.095
Dominican Republic	4.746	73.892
Ecuador	3.685	76.8
El Salvador	3.717	73.096
Estonia	5.211	78.2439024390244
Ethiopia	9.357	66.24
Fiji	5.401	67.341
Georgia	4.426	73.6
Ghana	6.781	63.78
Grenada	4.119	72.384
Guatemala	5.781	74.063
Guinea	3.617	61.185
Guyana	6.658	69.774
Honduras	3.723	75.088
Hong Kong SAR, China	4.702	84.9341463414634
Iceland	8.064	82.8609756097561
Ireland	3.695	82.2560975609756
Israel	4.291	82.8024390243903
Kenya	6.899	66.342
Kiribati	5.609	68.116
Kosovo	3.448	72.1951219512195
Kyrgyz Republic	4.285	71.4
Lao PDR	11.058	67.61
Latvia	3.886	74.7829268292683
Lithuania	4.7	75.6804878048781
Malaysia	3.441	75.997
Maldives	5.5	78.627
Malta	3.208	82.4487804878049
Mauritius	5.19	74.4163414634146
Mongolia	7.329	69.689
Myanmar	5.617	66.867
Nepal	8.117	70.478
New Zealand	4.112	81.8585365853659
Nicaragua	3.03	74.275
Pakistan	4.495	67.114
Panama	8.357	78.329
Papua New Guinea	5.344	64.263
Paraguay	3.423	74.131
Peru	4.826	76.516
Philippines	5.539	71.095
Romania	4.309	75.3585365853659
Rwanda	3.342	68.7
Sao Tome and Principe	6.31	70.17
Seychelles	5.727	72.8414634146341
Singapore	3.187	83.1463414634146
Solomon Islands	9.578	72.835
Sri Lanka	5.85	76.812
St. Lucia	3.381	76.057
Tanzania	4.608	65.015
Thailand	5.252	76.931
Uruguay	5.025	77.77
Uzbekistan	3.08	71.573
Vietnam	8.197	75.317
Zimbabwe	9.191	61.195

The NAME search problem

There are countably finite number of people and organizations with similar names. Any computerized system attempting to find the exact person (or entity) is extremely difficult to build. 

There is an other angle to the problem, as well. The number of (identity) cards a person carries these days is pretty high.

1. Aadhar Card

2. PAN Card

3. Driver's License

4. Voter ID Card

5. Credit Card

6. Debit Card

7. Ration Card

8. Passport

9. Mobile Phone (registered with the telco)

There might be more that I may have missed. What happens is that in rural India, the same person has different names in these different cards which opens up the system for abuse. My building caretaker is called 'Shambhu'; phonetically this is the exact spelling but he happens to have different names such as 'Sambhu' or 'Shamvu' in different cards. Recently money came to his Jan Dhan account and got automatically deducted for a gas cylinder that he never received. 

Prime Minister Modi's ambitious plan of linking everything with Aadhar could have solved it but Aadhar has its own problems. 

In search engines precision and recall are inversely proportional. This problem cannot be solved with search engines like Solr or ElasticSearch.

The only name search that works perfectly are Domain Name Systems. Although domain name servers are examples of the best distributed databases, the effort is centrally co-ordinated by ICAAN. A politically fragmented world can probably never be able to solve this pressing issue of true identity. 

Pitfalls of genetic algorithms

The reader may study about the basics of genetic algorithm here.

There algorithms are generally used for optimization i.e. finding the global maxima instead of regional ones.

Although such algorithms borrow from evolutionary biology they violate one fundamental principle. No one thought in 1000 A.D that we could send a mission to Mars. This is exactly where genetic algorithms fail. The endgame has to be decided at the start i.e. you need to set your expected maxima even before you are running the algorithm.

What if the management makes a mistake in their initial estimate ?

I think linear programming based optimizations are still better. 

BSNL revival

Value added services. Have to be pre-paid plans.

1. Service for parents allowing access to movie and adult websites.

2. Free educational videos for minors.

What needs to be done ?

Parental control embedded via a default application download as soon as the SIM registers on the network. The app will block access to service #1 unless a numeric password is registered. If the app is uninstalled, then internet services will stop working.

Challenges -

a. Legal roadblock for service #1, especially adult content.

b. Nationwide 4G license.

(in my estimate, animation based adult websites can work even with 3G provided bandwidth)

Drug discovery

During this times of the global Covid-19 pandemic when pharmaceutical companies are scrambling to get the vaccine out, I think I should share my two cents on computer aided drug discovery.

There is no substitute to clinical trials and the classical case of developing a vaccine takes around 10 - 12 years but the current situation is different. This warrants open clinical trials.

What is a medical drug ?

It is a chemical substance which blocks or activates a certain protein. Virus, simply put is a speck of protein. 

In my startup days, we developed something, we called contextual search, which simply put, is suppose you search for Sachin Tendulkar the search engine automatically popped up with a suggestion, say, Rahul Dravid.

How we achieved it ?

We had downloaded the entire Wikipedia data and linked the associated names using category. If you scroll down to the bottom of any page, you will find associated categories with the entity on whom the page is written; for e.g. "Cricketers at the 1992 World Cup" for Sachin Tendulkar and so on. These pieces of information was used to relate other entities falling in the same category ordered by the maximum matches.

How can it be applied to drug discovery ?

If one looks at the economics behind it, the more accurate the computer prediction, the lesser the time to bring it to market because of the likelihood of success in clinical trials.

Generally pharma companies start with a pretty huge list of chemicals whose physical, electromagnetic and other properties are already known. The next stage is identifying the similar properties of the harmful bacteria or in the present case the virus. Contextual search can kick in at this precise point. In the parlance of pharma companies it is known as virtual screening.

The three main methods are as follows.

1. Molecular Docking

2. Quantitative Structure Activity Screening

3. Pharmacopeia Mapping

The first method does not need any explanation whereas the second is matching the physical properties of the foreign body to the possible drug and the third is probably about the electromagnetic properties.

Thereafter one has to run a slightly modified form of contextual search (or one can call it an advanced search) where certain categories are also matched along with the entity. Basically if one is using a open source search engine like Solr one has to match on multiple fields (in this case such properties) from the pre-existing database of chemicals. 

There is no doubt that the above is an over-simplified explanation of a very complex process but my fellow computer engineers can get a basic idea from a fellow novice.