specifically: better at Marathon/optimization problems

This article addresses the long-standing question of why students and faculty remain resistant to active learning. Comparing passive lectures with active learning using a randomized experimental approach and identical course materials, we find that students in the active classroom learn more, but they feel like they learn less. We show that this negative correlation is caused in part by the increased cognitive effort required during active learning.

https://news.ycombinator.com/item?id=21164005

It might sound a bit stupid, but I just realized that a better reading strategy could help me learn faster, almost three times as fast as before.

To enter a research field, we sometimes have to read tens of research papers. We could alternatively read summaries like textbooks and survey papers, which are generally more comprehensive and more friendly for non-experts. But some fields don’t have good summaries out there, for reasons like the fields being too new, too narrow, or too broad.

...

Part 1. Taking good notes and keeping them organized.

Where we store information greatly affects how we access it. If we can always easily find some information — from Google or our own notes — then we can pick it up quickly, even after forgetting it. This observation can make us smarter.

Let’s do the same when reading papers. Now I keep searchable notes as follows:
- For every topic, create a document that contains the notes for all papers on this topic.[1]
- For each paper, take these notes: summaries, quotes, and sufficient bibliographic information for future lookup.[2, pages 95-99]
- When reading a new paper, if it cites a paper that I have already read, review the notes for the cited paper. Update the notes as needed.
This way, we won’t lose what we have read and learned.

Part 2. Skipping technical sections for 93% of the time.

Only 7% of readers of a paper will read its technical sections.[1] Thus, if we want to read like average, it might make sense to skip technical sections for roughly 93% of papers that we read. For example, consider reading each paper like this:
- Read only the big-picture sections — abstract, introduction, and conclusion;
- Scan the technical sections — figures, tables, and the first and the last paragraphs for each section[2, pages 76-77] — to check surprises;
- Take notes;
- Done!
In theory, the only 7% of the papers that we need to read carefully would be those that we really have to know well.

https://news.ycombinator.com/item?id=20715136
https://jakeseliger.com/2010/10/02/how-to-get-your-professors’-attention-or-how-to-get-the-coaching-and-mentorship-you-need/

this guy really didn't take that long to reach red..., as of today he's done 20 contests in 2y to my 44 contests in 7y (w/ a long break)...>_>

tho he has 3 times as many submissions as me. maybe he does a lot of virtual rounds?

some snippets from the PDF guide linked:
1400-1900:
To be rating 1900, skills as follows are needed:
- You know and can use major algorithms like these:
Brute force DP DFS BFS Dijkstra
Binary Indexed Tree nCr, nPr Mod inverse Bitmasks Binary Search
- You can code faster (For example, 5 minutes for R1100 problems, 10 minutes for
R1400 problems)

If you are not good at fast-coding and fast-debugging, you should solve AtCoder problems. Actually, and statistically, many Japanese are good at fast-coding relatively while not so good at solving difficult problems. I think that’s because of AtCoder.

I recommend to solve problem C and D in AtCoder Beginner Contest. On average, if you can solve problem C of AtCoder Beginner Contest within 10 minutes and problem D within 20 minutes, you are Div1 in FastCodingForces :)

...

Interestingly, typical problems are concentrated in Div2-only round problems. If you are not good at Div2-only round, it is likely that you are not good at using typical algorithms, especially 10 algorithms that are written above.

If you can use some typical problem but not good at solving more than R1500 in Codeforces, you should begin TopCoder. This type of practice is effective for people who are good at Div.2 only round but not good at Div.1+Div.2 combined or Div.1+Div.2 separated round.

Sometimes, especially in Div1+Div2 round, some problems need mathematical concepts or thinking. Since there are a lot of problems which uses them (and also light-implementation!) in TopCoder, you should solve TopCoder problems.

I recommend to solve Div1Easy of recent 100 SRMs. But some problems are really difficult, (e.g. even red-ranked coder could not solve) so before you solve, you should check how many percent of people did solve this problem. You can use https://competitiveprogramming.info/ to know some informations.

1900-2200:
To be rating 2200, skills as follows are needed:
- You know and can use 10 algorithms which I stated in pp.11 and segment trees
(including lazy propagations)
- You can solve problems very fast: For example, 5 mins for R1100, 10 mins for
R1500, 15 mins for R1800, 40 mins for R2000.
- You have decent skills for mathematical-thinking or considering problems
- Strong mental which can think about the solution more than 1 hours, and don’t give up even if you are below average in Div1 in the middle of the contest

This is only my way to practice, but I did many virtual contests when I was rating 2000. In this page, virtual contest does not mean “Virtual Participation” in Codeforces. It means choosing 4 or 5 problems which the difficulty is near your rating (For example, if you are rating 2000, choose R2000 problems in Codeforces) and solve them within 2 hours. You can use https://vjudge.net/. In this website, you can make virtual contests from problems on many online judges. (e.g. AtCoder, Codeforces, Hackerrank, Codechef, POJ, ...)

If you cannot solve problem within the virtual contests and could not be able to find the solution during the contest, you should read editorial. Google it. (e.g. If you want to know editorial of Codeforces Round #556 (Div. 1), search “Codeforces Round #556 editorial” in google) There is one more important thing to gain rating in Codeforces. To solve problem fast, you should equip some coding library (or template code). For example, I think that equipping segment tree libraries, lazy segment tree libraries, modint library, FFT library, geometry library, etc. is very effective.

2200 to 2400:
Rating 2200 and 2400 is actually very different ...

To be rating 2400, skills as follows are needed:
- You should have skills that stated in previous section (rating 2200)
- You should solve difficult problems which are only solved by less than 100 people in Div1 contests

...

At first, there are a lot of educational problems in AtCoder. I recommend you should solve problem E and F (especially 700-900 points problem in AtCoder) of AtCoder Regular Contest, especially ARC058-ARC090. Though old AtCoder Regular Contests are balanced for “considering” and “typical”, but sadly, AtCoder Grand Contest and recent AtCoder Regular Contest problems are actually too biased for considering I think, so I don’t recommend if your goal is gain rating in Codeforces. (Though if you want to gain rating more than 2600, you should solve problems from AtCoder Grand Contest)

For me, actually, after solving AtCoder Regular Contests, my average performance in CF virtual contest increased from 2100 to 2300 (I could not reach 2400 because start was early)

If you cannot solve problems, I recommend to give up and read editorial as follows:
Point value 600 700 800 900 1000-
CF rating R2000 R2200 R2400 R2600 R2800
Time to editorial 40 min 50 min 60 min 70 min 80 min

If you solve AtCoder educational problems, your skills of competitive programming will be increased. But there is one more problem. Without practical skills, you rating won’t increase. So, you should do 50+ virtual participations (especially Div.1) in Codeforces. In virtual participation, you can learn how to compete as a purple/orange-ranked coder (e.g. strategy) and how to use skills in Codeforces contests that you learned in AtCoder. I strongly recommend to read editorial of all problems except too difficult one (e.g. Less than 30 people solved in contest) after the virtual contest. I also recommend to write reflections about strategy, learns and improvements after reading editorial on notebooks after the contests/virtual.

In addition, about once a week, I recommend you to make time to think about much difficult problem (e.g. R2800 in Codeforces) for couple of hours. If you could not reach the solution after thinking couple of hours, I recommend you to read editorial because you can learn a lot. Solving high-level problems may give you chance to gain over 100 rating in a single contest, but also can give you chance to solve easier problems faster.

"Hard problems" is subjective. A good rule of thumb for learning problem solving (at least according to me) is that your problem selection is good if you fail to solve roughly 50% of problems you attempt. Anything in [20%,80%] should still be fine, although many people have problems staying motivated if they fail too often. Read solutions for problems you fail to solve.

(There is some actual math behind this. Hopefully one day I'll have the time to write it down.)
- misof in a comment
--
I don't believe in any of things like "either you solve it in 30mins — few hours, or you never solve it at all". There are some magic at first glance algorithms like polynomial hashing, interval tree or FFT (which is magic even at tenth glance :P), but there are not many of them and vast majority of algorithms are possible to be invented on our own, for example dp. In high school I used to solve many problems from IMO and PMO and when I didn't solve a problem I tried it once again for some time. And I have solved some problems after third or sth like that attempt. Though, if we are restricting ourselves to beginners, I think that it still holds true, but it would be better to read solutions after some time, because there are so many other things which we can learn, so better not get stuck at one particular problem, when there are hundreds of other important concepts to be learnt.

Vox tries its hand at an explainer about the Sam Harris / Charles Murray interview. Some criticism from Gene Expression, The Misrepresentation Of Genetic Science In The Vox Piece On Race And IQ. From Elan, The Cherry-Picked Science In Vox’s Charles Murray Article. From Sam Harris, an accusation that the article just blatantly lies about the contents of the publicly available podcast (one of the authors later apologizes for this, but Vox hasn’t changed the article). From Professor Emeritus Richard Haier, who called it a “junk science piece” and tried to write a counterpiece for Vox (they refused to publish it, but it’s now up on Quillette). And even from other Vox reporters who thought it was journalistically shoddy. As for me, I think the article was as good as it could be under the circumstances – while it does get some things wrong and is personally unfair to Murray, from a scientific point of view I’m just really glad that the piece admits that IQ is real, meaningful, and mostly hereditary. This was the main flashpoint of the original debate twenty-five years ago, it’s more important than the stuff on the achievement gap, and the piece gets it entirely right. I think this sort of shift from debating the very existence of intelligence to debating the details is important, very productive, and worth praising even when the details are kind of dubious. This should be read in the context of similar recent articles like NYMag’s Yes, There Is A Genetic Component To Intelligence and Nature’s Intelligence Research Should Not Be Held Back By Its Past.

interesting comment thread on media treatment of HBD and effect on alt-right: http://slatestarcodex.com/2017/06/14/links-617-silinks-is-golden/#comment-510641

AskHistorians: Did Roman legionnaires get PTSD? “For the Romans, people experiencing intrusive memories were said to be haunted by ghosts…those haunted by ghosts are constantly depicted showing many symptoms which would be familiar to the modern PTSD sufferer.”

The best new blog I’ve come across recently is Sam[]zdat, which among other things has been reviewing various great books. Their Seeing Like A State review is admittedly better than mine, but I most appreciated The Meridian Of Her Greatness, based on a review of Karl Polanyi’s The Great Transformation. Go for the really incisive look at important ideas and social trends, stay for the writing style.

What lesson should we draw about Democrats’ prospects from the Republicans’ 7 point win in the Montana special election? (point, counterpoint).

An analysis showing Donald Trump’s speech patterns getting less fluent and more bizarre over the past few years – might he be suffering from mild age-related cognitive impairment? Also, given that this can be pretty subtle (cue joke about Trump) and affect emotional stability in complicated ways, should we be more worried about electing seventy-plus year old people to the presidency?

PNAS has a good (albeit kind of silly) article on claims that scientific progress has slowed.

New study finds that growth mindset is not associated with scholastic aptitude in a large sample of university applicants. Particularly excited about this one because an author said that my blog posts about growth mindset inspired the study. I’m honored to have been able to help the progress of science!

Why do some schools produce a disproportionate share of math competition winners? May not just be student characteristics.

My post The Control Group Is Out Of Control, as well as some of the Less Wrong posts that inspired it, has gotten cited in a recent preprint article, A Skeptical Eye On Psi, on what psi can teach us about the replication crisis. One of the authors is someone I previously yelled at, so I like to think all of that yelling is having a positive effect.

A study from Sweden (it’s always Sweden) does really good work examining the effect of education on IQ. It takes an increase in mandatory Swedish schooling length which was rolled out randomly at different times in different districts, and finds that the districts where people got more schooling have higher IQ; in particular, an extra year of education increases permanent IQ by 0.75 points. I was previously ambivalent about this, but this is a really strong study and I guess I have to endorse it now (though it’s hard to say how g-loaded it is or how linear it is). Also of note; the extra schooling permanently harmed emotional control ability by 0.5 points on a scale identical to IQ (mean 100, SD 15). This is of course the opposite of past studies suggest that education does not improve IQ but does help non-cognitive factors. But this study was an extra year tacked on to the end of education, whereas earlier ones have been measuring extra education tacked on to the beginning, or just making the whole educational process more efficient. Still weird, but again, this is a good experiment (EDIT: This might not be on g)

- steady rise till a slight dip starting early 2000s, hmm
- dip in early 60s lol

People with high self-control had social networks with higher friend self-control.

“Fanatics got into the Capitol building and committed a mass shooting on Congress while it was in session, and you’ve never heard of them…people have completely forgotten that in 1972 we had over nineteen hundred domestic bombings in the United States” A review of Days Of Rage and history lesson on the 1970s underground. Highly recommended.

New Yorker: The Mosul Dam in Iraq could fail soon, potentially causing a flash flood and hundreds of thousands of deaths.

An ecologist denounces calls to “drain the swamp” as an insult to swamps: “Given the sea of misinformation we currently find ourselves swimming in, I feel this is as good a time as any to clarify what swamps actually are and why they should be regarded as wonderful and valuable parts of nature rather than objects of derision and hatred.” If any of you are oceanographers, can you troll the Washington Post for me by denouncing their use of the term “sea of misinformation”?

The Seasteading Institute announces a deal with French Polynesia to build the first seastead in a lagoon there. I’m still confused on whether they’ve got funding or anything else besides the location. Still a big step.

80,000 Hours’ guide to what charities to give to this season. A good supplement to GiveWell’s Top Charities list

Vox: Why the war on poverty failed, and what to do now. In the form of a long and detailed history of Brooklyn’s Bedford-Stuyveysant neighborhood. Not clear that anyone actually knows what to do now beyond a few good common-sense suggestions.

RIP utilitarian philosopher Derek Parfit: “When I believed the non-reductionist view [of personal identity], I also cared more about my inevitable death. After my death, there will [be] no one living who will be me. I can now redescribe this fact. Though there will later be many experiences, none of these experiences will be connected to my present experiences by chains of such direct connections as those involved in experience-memory, or in the carrying out of an earlier intention. Some of these future experiences may be related to my present experiences in less direct ways. There will later be some memories about my life. And there may later be thoughts that are influenced by mine, or things done as the result of my advice. My death will break the more direct relations between my present experiences and future experiences, but it will not break various other relations. This is all there is to the fact that there will be no one living who will be me. Now that I have seen this, my death seems to me less bad.”

Alex K. Chen on Quora on the speculation that Ritalin may be long-term safer than Adderall. See also this review article. This is definitely not yet psychiatric common knowledge or consensus.

Meta-analysis in the American Journal Of Nutrition: Red meat does not increase risk of cardiovascular disease.

Acemoglu and Restropo: Economic stagnation is not due to aging populations.

User dogtasteslikechicken at the r/slatestarcodex subreddit gives a good summary of the Flynn Effect. But it looks like he is confused about some of the same things I am. For example, rich people and the nobility probably had good nutrition and education in the past. So we might expect a Flynn effect based on nutrition and education not to affect them as much. But if this were true, we would expect a skewed or bimodal distribution in the past (un-Flynned poor people with bad nutrition + Flynned rich people with good nutrition), which I don’t think ever clearly showed up.

Some fallout from the Buzzfeed story on growth mindset I linked last week. The Spectator published what I think is a really nasty and evidence-free denunciation of the phenomenon. Mindset researcher David Yeager has tried to set the record straight and argues that growth mindset actually replicates just fine, eg in this paper, and that several other large and rigorous replications are being attempted. Dweck herself has a reply up here. And Timothy Bates put his failed replications online here. Looks like it will be an interesting year in this field.

Does pupil size correlate with intelligence? (blog post, paper)

keep in mind rationalists have no taste
http://lesswrong.com/r/discussion/lw/p9u/book_review_mathematics_for_computer_science/

We could each gain great insight into ourselves if only we could consistently take the features we believe apply to many folks around us, and honestly ask ourselves if they apply to us as well. Folks around us are often boring, failures, irritating, misguided, vain, and, yes, dying. Are we?

Building on my intro to innovation, which summarized previous work, let me now offer a new insight: the max net-impact innovations, by far, have been meta-innovations, i.e., innovations that changed how fast other innovations accumulated.

very interesting discussion (I should start reading these guys more)

edit:
actually, should have read more thorougly, errata at https://www.reddit.com/r/slatestarcodex/comments/56yuac/scott_alexander_doesnt_like_growth_mindset_yet/

distillation of CFAR

I’ll organize my thoughts around the following ideas.

- Prioritize reading readable sources
- Build narratives
- Study other mathematician’s taste
- Do one early side project
- Find a clump of other graduate students
- Cast a wide net when looking for an advisor
- Don’t just work on one thing
- Don’t graduate until you have to

better than the title suggests, eg, Ben Franklins personal routine looks a lot like what I arrived at independently

Scott Alexander on being bad at math (some interesting convos in the comments)

growth mindset commentary
some funny comments on Scott Alexander

edit: lol audience envy seems to be a running theme for Andrew Gelman http://www.overcomingbias.com/2009/07/academias-function.html#comment-518308298

? http://www.habitlab.org/success-factoring/

http://lesswrong.com/tag/willpower/
http://lesswrong.com/tag/akrasia/
scoring by orthonormalist: http://lesswrong.com/lw/1sm/akrasia_tactics_review/
http://lesswrong.com/lw/9wr/my_algorithm_for_beating_procrastination/
Scott willpower highlights: http://slatestarcodex.com/2015/03/16/list-of-passages-i-highlighted-in-my-copy-of-willpower/
Scott willpower review: http://slatestarcodex.com/2015/03/12/book-review-willpower/
https://slatestarcodex.com/2014/10/26/alcoholics-anonymous-much-more-than-you-wanted-to-know/
https://slatestarcodex.com/2015/03/11/too-good-to-be-true/
beeminder Scott discussion: http://forum.beeminder.com/t/slate-star-codex-on-willpower/593/
https://www.reddit.com/r/slatestarcodex/comments/3t14j3/beeminder_for_adhd/
https://www.reddit.com/r/slatestarcodex/comments/4qe7i6/whats_a_good_starting_point_to_learn_more_about/
http://lesswrong.com/lw/iuf/how_to_beat_procrastination_to_some_degree_if/

The highest-level hack I've found useful is to make a habit of noticing and recording the details of any part of my life that gives me trouble. It's amazing how quickly patterns start to jump out when you've assembled actual data about something that's vaguely frustrated you for a while.

http://well.blogs.nytimes.com/2015/01/19/writing-your-way-to-happiness/

http://www.overcomingbias.com/2011/12/easy-job-fix.html
I’ve been slowly working my way through Triver’s book Folly of Fools. Chapter six reviews the many amazing benefits that appear to arise from having people write about their troubles. For example:

>Writing about job loss improves one’s chance of reemployment. This sort of writing appears to be cathartic – people immediately feel better. More striking, at least in one study, is a sharply increased chance of getting a job. After six months, 53 percent of writers had found a new job, compared with only 18 percent of non writers. One effect of writing is that it helps you work through your anger so it is not displaced onto a new, prospective employer or, indeed, revealed to the employer in any form.

...

This suggests an easy way to increase employment, at least if the problem is employee attitudes. Digging more, I found this ’01 review, which seems to confirm the benefits of writing therapy. It all does seem a bit hard to believe, but stranger things have been true.

https://www.lesswrong.com/posts/o7nRiBP9W8xR5E4v5/meta-analysis-of-writing-therapy

Not Learning From Failure—the Greatest Failure of All: https://journals.sagepub.com/doi/abs/10.1177/0956797619881133
Our society celebrates failure as a teachable moment. Yet in five studies (total N = 1,674), failure did the opposite: It undermined learning.
...
Why does failure undermine learning? Failure is ego threatening, which causes people to tune out. Participants learned less from personal failure than from personal success, yet they learned just as much from other people’s failure as from others’ success. Thus, when ego concerns are muted, people tune in and learn from failure.

Terry Tao:
I find that blogging about material that I would otherwise forget eventually is extremely valuable in this regard. (I end up consulting my own blog posts on a regular basis.) EDIT: and now I remember I already wrote on this topic: terrytao.wordpress.com/career-advice/write-down-what-youve-d‌​one

fedja:
The only way to cope with this loss of memory I know is to do some reading on systematic basis. Of course, if you read one paper in algebraic geometry (or whatever else) a month (or even two months), you may not remember the exact content of all of them by the end of the year but, since all mathematicians in one field use pretty much the same tricks and draw from pretty much the same general knowledge, you'll keep the core things in your memory no matter what you read (provided it is not patented junk, of course) and this is about as much as you can hope for.

Relating abstract things to "real life stuff" (and vice versa) is automatic when you work as a mathematician. For me, the proof of the Chacon-Ornstein ergodic theorem is just a sandpile moving over a pit with the sand falling down after every shift. I often tell my students that every individual term in the sequence doesn't matter at all for the limit but somehow together they determine it like no individual human is of any real importance while together they keep this civilization running, etc. No special effort is needed here and, moreover, if the analogy is not natural but contrived, it'll not be helpful or memorable. The standard mnemonic techniques are pretty useless in math. IMHO (the famous "foil" rule for the multiplication of sums of two terms is inferior to the natural "pair each term in the first sum with each term in the second sum" and to the picture of a rectangle tiled with smaller rectangles, though, of course, the foil rule sounds way more sexy).

One thing that I don't think the other respondents have emphasized enough is that you should work on prioritizing what you choose to study and remember.

Timothy Chow:
As others have said, forgetting lots of stuff is inevitable. But there are ways you can mitigate the damage of this information loss. I find that a useful technique is to try to organize your knowledge hierarchically. Start by coming up with a big picture, and make sure you understand and remember that picture thoroughly. Then drill down to the next level of detail, and work on remembering that. For example, if I were trying to remember everything in a particular book, I might start by memorizing the table of contents, and then I'd work on remembering the theorem statements, and then finally the proofs. (Don't take this illustration too literally; it's better to come up with your own conceptual hierarchy than to slavishly follow the formal hierarchy of a published text. But I do think that a hierarchical approach is valuable.)

Organizing your knowledge like this helps you prioritize. You can then consciously decide that certain large swaths of knowledge are not worth your time at the moment, and just keep a "stub" in memory to remind you that that body of knowledge exists, should you ever need to dive into it. In areas of higher priority, you can plunge more deeply. By making sure you thoroughly internalize the top levels of the hierarchy, you reduce the risk of losing sight of entire areas of important knowledge. Generally it's less catastrophic to forget the details than to forget about a whole region of the big picture, because you can often revisit the details as long as you know what details you need to dig up. (This is fortunate since the details are the most memory-intensive.)

Having a hierarchy also helps you accrue new knowledge. Often when you encounter something new, you can relate it to something you already know, and file it in the same branch of your mental tree.

Ravi Vakil's advice for PhD students

General advice:
Think actively about the creative process. A subtle leap is required from undergraduate thinking to active research (even if you have done undergraduate research). Think explicitly about the process, and talk about it (with me, and with others). For example, in an undergraduate class any Ph.D. student at Stanford will have tried to learn absolutely all the material flawlessly. But in order to know everything needed to tackle an important problem on the frontier of human knowledge, one would have to spend years reading many books and articles. So you'll have to learn differently. But how?

Don't be narrow and concentrate only on your particular problem. Learn things from all over the field, and beyond. The facts, methods, and insights from elsewhere will be much more useful than you might realize, possibly in your thesis, and most definitely afterwards. Being broad is a good way of learning to develop interesting questions.

When you learn the theory, you should try to calculate some toy cases, and think of some explicit basic examples.

Talk to other graduate students. A lot. Organize reading groups. Also talk to post-docs, faculty, visitors, and people you run into on the street. I learn the most from talking with other people. Maybe that's true for you too.

Specific topics:
- seminars
- giving talks
- writing
- links to other advice