I Love Rabbit

https://qr.ae/TWhrfp

Last year, we Sichuanese consumed 300 million rabbits, (70% of the total in China)

I still prefer this hot and spicy rabbit heads I had in Zigong, chopped in halves

Those in Chengdu are cold

The meat on the cheeks, the brain in the skull, the chewy tongues are my favorite

eyeballs, hmmm…okay…

The price is crazy

It used to be 2.5 yuan per head in the 90s, now soars to 12 yuan, same speed of the housing price in Chengdu, reflecting our inflation.

This one is also from Zigong. Still rabbit

Zigong dishes rank the first in Chengdu because they make you uncomfortable after dinner, but more addicted to it after you take the pills.

When picking meat to lose weight, beef is superb, but much more expensive than rabbit.

So, rabbit wins.

Frank Lichtenberg and the cost of saving lives through pharmaceuticals

https://marginalrevolution.com/marginalrevolution/2019/04/frank-lichtenberg-and-the-cost-of-saving-lives-through-pharmaceuticals.html

Humans are living longer, better lives thanks to innovations in prescription drugs over the past three decades, according to several new studies by Frank Lichtenberg, the Courtney C. Brown Professor of Business.

Every year, according to Lichtenberg’s research, drugs launched since 1982 are adding 150 million life-years to the lifespans of people in 22 countries that he analyzed. He calculated the average pharmaceutical expenditure per life-year saved at $2,837 — a bargain, he says.

“According to most health economists and policymakers, if you could extend someone’s life by a year for less than $3,000, that is highly cost effective,” says Lichtenberg, who gathered new data for these studies to cast a never-before seen view of the econometrics of prescription drugs. “People might be surprised by how cost-effective drugs appear to be in general.”

…To tease out the answer, the professor gathered data on drug launches and the age-standardized premature mortality rate by country, disease, and year. Drawing on data from the World Health Organization, the United Nations, consulting company IQVIA, and French database Theriaque, Lichtenberg was able to identify the role that pharmaceutical innovation played in reducing the number of years of life lost due to 66 diseases in 27 countries. (“Years of life lost” is an estimate of the average years a person would have lived if he or she had not died prematurely.)

Between 1982 and 2015, for example, the US saw the launch of 719 new drugs, the most of any country in the sample; Israel had about half as many launches. By looking at the resultant change in each country between mortality and disease, Lichtenberg calculated that the years of life lost before the age of 85 in 2013 would have been 2.16 times as high if no new drugs had been launched after 1981. For a subset of 22 countries with more full data, the number of life-years gained in 2013 from drugs launched after 1981 was 148.7 million.

Here is more from Stephen Kurczy, and here is previous MR coverage of Lichtenberg and his work.  Given these estimates, do you really think we should be spending less on pharmaceuticals?

Podcast #474: The Surprises of Romantic Attraction

According to the popular, evolutionary theory of human attraction, people select romantic partners based on objective assessments of what’s called their “mate value” — the extent to which an individual possesses traits like good looks and status. But is that really all that’s behind the way people pair up?

My guest today has done a series of studies which add greater nuance to the mysteries of romantic attraction. His name is Paul Eastwick and he’s a professor of psychology at USC Davis. We begin our conversation unpacking the fact that there’s sometimes a gap between the sexual and romantic partners people say they prefer in the abstract, and the partners they actually choose in real life. We then turn to whether or not the popular idea that men value physical attractiveness more than women, and that women value status and resources more than men, is really true. We also talk about how people’s consensus over who is and isn’t attractive changes over time, and whether it’s true that people of equal attractiveness generally end up together. We end our conversation discussing how these research-based insights can be applied to the real world of dating, and why less attractive people may have better luck meeting people offline than on.

Some interesting insights in this show that lend credence to the old adage that there’s someone for everyone.

Show Highlights

  • What’s the accepted theory of how men and women are attracted to each other?
  • How “mate value” is calculated 
  • Is it true that men value physical appearance more than women?
  • The self-insight gap that plagues daters 
  • Why trait-based compatibility doesn’t give the full picture of a relationship’s potential
  • The importance of “fit” when it comes to compatibility
  • How physical attractiveness changes over time as we get to know people
  • Do equally attractive people always end up with each other?
  • What does modern science say about pick-up artist techniques?
  • Tips for how to think about modern dating apps 

Resources/People/Articles Mentioned in Podcast

What are the most important algorithms needed to solve graph problems?

Source: https://www.quora.com/What-are-the-most-important-algorithms-needed-to-solve-graph-problems

Graphs is an interesting topic as such. Even more, the concepts and algorithms used to tackle graph problems are elegant. Compiling a list of graph theory concepts would  be a lot tedious but if your focus is on sport programming then I might make sense.

My pick would be:

0. The basics – graph notations, graph representations

1. graph traversal (BFS / DFS ) – perhaps the most versatile topic in graph theory. Just look at the applications of these methods, both have their own unique properties.

2. Shortest Path (Dijkstra / Bellman Ford / Floyd-Warshall)

3. Minimum spanning trees (Prim’s / Kruskal’s)

4. Euler tour trees

5. Lowest Common Ancestor (LCA algo : I, II, III, IV)

6. Min cut / Max Flow / Matching : topcoder

7. Strongly connected components : SCC

8. Articulation points and edges : Explanation of Algorithm for finding articulation points or cut vertices of a graph

Also there are some optimization techniques like Heavy light decomposition

These are the basics of graph to get on with graph questions.

Dynamic programming and memorization: bottom-up vs top-down approaches

Source: https://stackoverflow.com/questions/6164629/dynamic-programming-and-memoization-bottom-up-vs-top-down-approaches

rev4: A very eloquent comment by user Sammaron has noted that, perhaps, this answer previously confused top-down and bottom-up. While originally this answer (rev3) and other answers said that “bottom-up is memoization” (“assume the subproblems”), it may be the inverse (that is, “top-down” may be “assume the subproblems” and “bottom-up” may be “compose the subproblems”). Previously, I have read on memoization being a different kind of dynamic programming as opposed to a subtype of dynamic programming. I was quoting that viewpoint despite not subscribing to it. I have rewritten this answer to be agnostic of the terminology until proper references can be found in the literature. I have also converted this answer to a community wiki. Please prefer academic sources. List of references: {Web: 1,2} {Literature: 5}

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