Well, I think I am taking a break from politics, at least until I have a stronger financial backing. I have a bigger impact on society with my research.
]]>Even if you don’t live in Newton, MA, it may interest you to know how the marijuana industry is doing everything it can to win this ballot, including rigging the election twice (one and two), and even hiring a national, professional political consulting company. To know more see the opt out website.
]]>What does this mean? Again, the only things that matter are those that you can prove. Still, here are some options:
What do you think?
]]>The two questions are:
Yes, the motion to put Question 2 on the ballot passed by 1 vote. Each of those 11 councilors can go home feeling satisfied that they bear full responsibility for ignoring the clear preference of their constituents. It doesn’t matter what the chief of the Newton police says, or what the former head of the NewtonWellsely hospital says, or what any of the other dozens of highprofile people say, or that you collected thousands of signatures. Those 11 councilors know what’s best for Newton. (Oh, and by the way, the upper bound is meaningless and can be easily increased. )
Before they convened to deliberate I sent them this message:
I doubt they could have even collected 70 for Question 2.
But the real problem is the rule I mentioned before, that if both questions have a majority of yes votes, the one with the highest number of yes votes will prevail. To illustrate, consider the following realistic scenario. Suppose that a resident of Newton loathes recreational marijuana establishments. When they go to the ballot, they obviously vote yes on Question 1. What should they do about Question 2? If Question 1 loses, they are better off if Question 2 wins. Suppose they also vote yes on 2, and that 99% of Newton residents behaves this way. Then it’s enough that a merry 1% band of business(wo)men vote no on Question 1 and yes on Question 2, and they harness all the votes that people cast to their own advantage.
There do exist fair ways of having both questions on the ballot, but this isn’t one. The current setup forces people who really want to ban recreational marijuana to strategize by voting no on question 2, and risk that if Question 1 loses, they end up with unlimited recreational stores.
Maybe it’s a little hard to understand this in terms of marijuana. Consider the following scenario:
It is not going to be easy, but it seems that in the upcoming campaign we will have to convince people to answer ‘NO’ to question 2.
]]>
There are many classes of functions on bits that we know are fooled by bounded independence, including smalldepth circuits, halfspaces, etc. (See this previous post.)
On the other hand the simple parity function is not fooled. It’s easy to see that you require independence at least . However, if you just perturb the bits with a little noise , then parity will be fooled. You can find other examples of functions that are not fooled by bounded independence alone, but are if you just perturb the bits a little.
In [3] we proved that any distribution with independence about fools spacebounded algorithms, if you perturb it with noise. We asked, both in the paper and many people, if the independence could be lowered. Forbes and Kelley have recently proved [2] that the independence can be lowered all the way to , which is tight [1]. Shockingly, their proof is nearly identical to [3]!
This exciting result has several interesting consequences. First, we now have almost the same generators for spacebounded computation in a fixed order as we do for any order. Moreover, the proof greatly simplifies a number of works in the literature. And finally, an approach in [4] to prove limitations for the sum of smallbias generators won’t work for space (possibly justifying some optimism in the power of the sum of smallbias generators).
My understanding of all this area is inseparable from the collaboration I have had with Chin Ho Lee, with whom I coauthored all the papers I have on this topic.
Let be a function. We want to show that it is fooled by , where has independence , is the noise vector of i.i.d. bits coming up with probability say , and is bitwise XOR.
The approach in [3] is to decompose as the sum of a function with Fourier degree , and a sum of functions where has no Fourier coefficient of degree less than , and and are bounded. The function is immediately fooled by , and it is shown in [3] that each is fooled as well.
To explain the decomposition it is best to think of as the product of functions on bits, on disjoint inputs. The decomposition in [3] is as follows: repeatedly decompose each in lowdegree and highdegree . To illustrate:
This works, but the problem is that even if each time has degree , the function increases the degree by at least per decomposition; and so we can afford at most decompositions.
The decomposition in [2] is instead: pick to be the degree part of , and are all the Fourier coefficients which are nonzero in the inputs to and whose degree in the inputs of is . The functions can be written as , where is the highdegree part of and is .
Once you have this decomposition you can apply the same lemmas in [3] to get improved bounds. To handle spacebounded computation they extend this argument to matrixvalued functions.
In [3] we asked for tight “bounded independence plus noise” results for any model, and the question remains. In particular, what about highdegree polynomials modulo ?
[1] Ravi Boppana, Johan Håstad, Chin Ho Lee, and Emanuele Viola. Bounded independence vs. moduli. In Workshop on Randomization and Computation (RANDOM), 2016.
[2] Michael A. Forbes and Zander Kelley. Pseudorandom generators for readonce branching programs, in any order. In IEEE Symp. on Foundations of Computer Science (FOCS), 2018.
[3] Elad Haramaty, Chin Ho Lee, and Emanuele Viola. Bounded independence plus noise fools products. SIAM J. on Computing, 47(2):295–615, 2018.
[4] Chin Ho Lee and Emanuele Viola. Some limitations of the sum of smallbias distributions. Theory of Computing, 13, 2017.
]]>
Mr. President,
It has been an honor to serve you in the Cabinet as Administrator of the EPA. Truly your confidence in me has blessed me personally and enabled me to advance your agenda beyond what anyone anticipated at the beginning of your administration. Your current steadfastness and resolute commitment to get results for the American people both with regard to improved environmental obstacles and historical regulatory reform is a fact occurring at an unprecedented pace and I thank you for the opportunity to serve you and the American people in helping to achieve those ends. That is why it is hard for me to advise you I am stepping down as administrator of the EPA as of July 6. It is extremely difficult for me to cease serving you in this role, first because I count it as a blessing to be serving you in any capacity, but also because of the transformative work that is occurring; however, the unrelenting attacks on me personally, my family are unprecedented and have taken a sizable toll on all of us. My desire in service to you has always been to bless you as you make important decisions for the American people. I believe you are serving as president today because of God’s providence. I believe that same providence brought me in to your service. I pray as I have served you that I have blessed you and enabled you to effectively lead the American people. Thank you again Mr. President for the honor of serving you and I wish you Godspeed in all that you put your hand to.
Mr. President,
It has been an honor to serve you in the Cabinet as Administrator of the EPA. Truly, your confidence in me has blessed me personally and enabled me to advance your agenda beyond what anyone anticipated at the beginning of your Administration. Your courage, steadfastness and resolute commitment to get results for the American people, both with regard to improved environmental outcomes as well as historical regulatory reform, is in fact occurring at an unprecedented pace and I thank you for the opportunity to serve you and the American people in helping achieve those ends.
That is why it is hard for me to advise you I am stepping down as Administrator of the EPA effective as of July 6. It is extremely difficult for me to cease serving you in this role first because I count it a blessing to be serving you in any capacity, but also, because of the transformative work that is occurring. However, the unrelenting attacks on me personally, my family, are unprecedented and have taken a sizable toll on all of us.
My desire in service to you has always been to bless you as you make important decisions for the American people. I believe you are serving as President today because of God’s providence. I believe that same providence brought me into your service. I pray as I have served you that I have blessed you and enabled you to effectively lead the American people. Thank you again Mr. President for the honor of serving you and I wish you Godspeed in all that you put your hand to.
The letter also makes me think that I should have added “to worship God” to this list.
The EPA chief is approved by Congress. So if you care about your health get ready for November. If you’ll be traveling start looking into absentee voting for your state.
]]>In 2016 Massachusetts voters voted to legalize Marijuana. Except they didn’t know what they were voting for! In Colorado and Washington, the question of legalization and commercialization were completely separate. The marijuana industry apparently learned from that and rigged the Massachusetts ballot question so that a voter legalizing marijuana would also be mandating communities to open marijuana stores. For Newton, MA, this means at least 8 stores. When voters were recently polled, it became clear that the vast majority did not know that this was at stake, and that the majority of them in fact does not want to open marijuana stores in their communities. For example, when I voted I didn’t know that this was at stake. Read the official Massachusetts document to inform voters, see especially the summary on pages 1213. There is no hint that a community would be mandated by state law to open marijuana stores unless it goes through an additional legislative crusade. Instead it says that communities can choose. I think I even read the summary back then.
Now to avoid opening stores in Newton, MA, we need a new ballot question. The City Council could have put this question on the ballot easily, but a few days ago decided that it won’t by a vote of 13 to 8. You can find the list of names of councilors and how they voted here.
Note that the council was not deciding whether or not to open stores, it was just deciding whether or not we should have a question about this on the ballot.
Instead now we are stuck doing things the hard way. To put this question on the ballot, we need to collect 6000 signatures, or 9000 if the city is completely uncooperative, a possibility which now unfortunately cannot be dismissed.
However we must do it, for the alternative is too awful. Most of the surrounding towns (Wellesley, Weston, Needham, Dedham, etc.) have already opted out. So if Newton opens stores, it basically becomes the hub for west suburban marijuana users, at least some of whom would drive under the influence of marijuana (conveniently undetectable). Proposed store locations include sites on the way to elementary schools, and there is an amusing proposal to open a marijuana store in a prime Newton Center Location, after Peet’s Coffee moves out (they lost the bid for renewal of the lease). The owners of the space admit that people have asked them for a small grocery store instead, but they think that a marijuana store would bring more traffic and business to Newton Center. I told them to open a gym instead. That too would bring traffic and business, but in addition it would have other benefits that cannabis does not have.
]]>This is the post about l2w version 1.0, a Latex to WordPress converter painstakingly put together by me with big help from the LaTeX community. Click here to download it. Below is an example of what you can do, taken at random from my class notes which were compiled with this script. I also used this in conjunction with Lyx for several posts such as I believe P=NP, so you can also call this a Lyx to WordPress converter. I just export to latex and then run l2w.
This might work out of the box. More in detail, it needs tex4ht (which is included e.g. in MiKTeX distributions) and Perl (the script only uses minimalistic, shell perl commands). Simply unzip l2w.zip, which contains four files. The file post.tex is this document, which you can edit. To compile, run l2w.bat (which calls myConfig5.cfg). This will create the output post.html which you can copy and past in the wordpress HTML editor. I have tested it on an old Windows XP machine, and a more recent Windows 7 with MixTeX 2.9. I haven’t tested it on linux, which might require some simple changes to l2w.bat. For LyX I add certain commands in the preamble, and as an example the .lyx source of the post I believe P=NP is included in the zip archive.
The nonmath source is compiled using fullfledged LaTeX, which means you can use your own macros and bibliography. The math source is not compiled, but more or less left as is for wordpress, which has its own LaTeX interpreter. This means that you can’t use your own macros in math mode. For the same reason, label and ref of equations are a problem. To make them work, the script fetches their values from the .aux file and then crudely applies them. This is a hack with a rather unreadable script; however, it works for me. One catch: your labels should start with eq:.
I hope this will spare you the enormous amount of time it took me to arrive to this solution. Let me know if you use it!
First, some of the problematic math references:
Equation (1).
Next, some weird font stuff: , , .
Lemma 1. Suppose that distributions over are wise indistinguishable distributions; and distributions over are wise indistinguishable distributions. Define over as follows:
: draw a sample from , and replace each bit by a sample of (independently).
Then and are wise indistinguishable.
To finish the proof of the lower bound on the approximate degree of the ANDOR function, it remains to see that ANDOR can distinguish well the distributions and . For this, we begin with observing that we can assume without loss of generality that the distributions have disjoint supports.
Claim 2. For any function , and for any wise indistinguishable distributions and , if can distinguish and with probability then there are distributions and with the same properties (wise indistinguishability yet distinguishable by ) and also with disjoint supports. (By disjoint support we mean for any either or .)
Proof. Let distribution be the “common part” of and . That is to say, we define such that multiplied by some constant that normalize into a distribution.
Then we can write and as
where , and are two distributions. Clearly and have disjoint supports.
Then we have
Therefore if can distinguish and with probability then it can also distinguish and with such probability.
Similarly, for all such that , we have
Hence, and are wise indistinguishable.
Equipped with the above lemma and claim, we can finally prove the following lower bound on the approximate degree of ANDOR.
Theorem 3. ANDOR.
Proof. Let be wise indistinguishable distributions for AND with advantage , i.e. . Let be wise indistinguishable distributions for OR with advantage . By the above claim, we can assume that have disjoint supports, and the same for . Compose them by the lemma, getting wise indistinguishable distributions . We now show that ANDOR can distinguish :
Therefore we have ANDOR.
In this subsection we discuss the approximate degree of the surjectivity function. This function is defined as follows.
Definition 4. The surjectivity function SURJ, which takes input where for all , has value if and only if .
First, some history. Aaronson first proved that the approximate degree of SURJ and other functions on bits including “the collision problem” is . This was motivated by an application in quantum computing. Before this result, even a lower bound of had not been known. Later Shi improved the lower bound to , see [AS04]. The instructor believes that the quantum framework may have blocked some people from studying this problem, though it may have very well attracted others. Recently Bun and Thaler [BT17] reproved the lower bound, but in a quantumfree paper, and introducing some different intuition. Soon after, together with Kothari, they proved [BKT17] that the approximate degree of SURJ is .
We shall now prove the lower bound, though one piece is only sketched. Again we present some things in a different way from the papers.
For the proof, we consider the ANDOR function under the promise that the Hamming weight of the input bits is at most . Call the approximate degree of ANDOR under this promise ANDOR. Then we can prove the following theorems.
Theorem 6. ANDOR for some suitable .
In our settings, we consider . Theorem 5 shows surprisingly that we can somehow “shrink” bits of input into bits while maintaining the approximate degree of the function, under some promise. Without this promise, we just showed in the last subsection that the approximate degree of ANDOR is instead of as in Theorem 6.
Proof of Theorem 5. Define an matrix s.t. the 0/1 variable is the entry in the th row th column, and iff . We can prove this theorem in following steps:
Now we prove this theorem step by step.
Then the polynomial for ANDOR is the polynomial with replaced as above, thus the degree won’t increase. Correctness follows by the promise.
Clearly it is a good approximation of ANDOR. It remains to show that it’s a polynomial of degree in ’s if is a polynomial of degree in ’s.
Let’s look at one monomial of degree in : . Observe that all ’s are distinct by the promise, and by over . By chain rule we have
By symmetry we have , which is linear in ’s. To get , we know that every other entry in row is , so we give away row , average over ’s such that under the promise and consistent with ’s. Therefore
In general we have
which has degree in ’s. Therefore the degree of is not larger than that of .
Proof idea for Theorem 6. First, by the duality argument we can verify that if and only if there exists wise indistinguishable distributions such that:
Claim 7. OR.
The proof needs a little more information about the weight distribution of the indistinguishable distributions corresponding to this claim. Basically, their expected weight is very small.
Now we combine these distributions with the usual ones for And using the lemma mentioned at the beginning.
What remains to show is that the final distribution is supported on Hamming weight . Because by construction the copies of the distributions for Or are sampled independently, we can use concentration of measure to prove a tail bound. This gives that all but an exponentially small measure of the distribution is supported on strings of weight . The final step of the proof consists of slightly tweaking the distributions to make that measure .
Groups have many applications in theoretical computer science. Barrington [Bar89] used the permutation group to prove a very surprising result, which states that the majority function can be computed efficiently using only constant bits of memory (something which was conjectured to be false). More recently, catalytic computation [BCK14] shows that if we have a lot of memory, but it’s full with junk that cannot be erased, we can still compute more than if we had little memory. We will see some interesting properties of groups in the following.
Some famous groups used in computer science are:
An example is . Generally we have
The group was invented by Galois. (If you haven’t, read his biography on wikipedia.)
Quiz. Among these groups, which is the “least abelian”? The latter can be defined in several ways. We focus on this: If we have two highentropy distributions over , does has more entropy? For example, if and are uniform over some elements, is close to uniform over ? By “close to” we mean that the statistical distance is less that a small constant from the uniform distribution. For , if uniform over , then is the same, so there is not entropy increase even though and are uniform on half the elements.
Definition 8.[Measure of Entropy] For , we think of for “high entropy”.
Note that is exactly the “collision probability”, i.e. . We will consider the entropy of the uniform distribution as very small, i.e. . Then we have
Theorem 9.[[Gow08], [BNP08]] If are independent over , then
where is the minimum dimension of irreducible representation of .
By this theorem, for high entropy distributions and , we get , thus we have
If is large, then is very close to uniform. The following table shows the ’s for the groups we’ve introduced.












should be very small  






Here is the alternating group of even permutations. We can see that for the first groups, Equation ((2)) doesn’t give nontrivial bounds.
But for we get a nontrivial bound, and for we get a strong bound: we have .
[Amb05] Andris Ambainis. Polynomial degree and lower bounds in quantum complexity: Collision and element distinctness with small range. Theory of Computing, 1(1):37–46, 2005.
[AS04] Scott Aaronson and Yaoyun Shi. Quantum lower bounds for the collision and the element distinctness problems. J. of the ACM, 51(4):595–605, 2004.
[Bar89] David A. Mix Barrington. Boundedwidth polynomialsize branching programs recognize exactly those languages in NC. J. of Computer and System Sciences, 38(1):150–164, 1989.
[BCK14] Harry Buhrman, Richard Cleve, Michal Koucký, Bruno Loff, and Florian Speelman. Computing with a full memory: catalytic space. In ACM Symp. on the Theory of Computing (STOC), pages 857–866, 2014.
[BKT17] Mark Bun, Robin Kothari, and Justin Thaler. The polynomial method strikes back: Tight quantum query bounds via dual polynomials. CoRR, arXiv:1710.09079, 2017.
[BNP08] László Babai, Nikolay Nikolov, and László Pyber. Product growth and mixing in finite groups. In ACMSIAM Symp. on Discrete Algorithms (SODA), pages 248–257, 2008.
[BT17] Mark Bun and Justin Thaler. A nearly optimal lower bound on the approximate degree of AC0. CoRR, abs/1703.05784, 2017.
[Gow08] W. T. Gowers. Quasirandom groups. Combinatorics, Probability & Computing, 17(3):363–387, 2008.