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  1. Lee, James R. (Ed.)
    Computational pseudorandomness studies the extent to which a random variable Z looks like the uniform distribution according to a class of tests ℱ. Computational entropy generalizes computational pseudorandomness by studying the extent which a random variable looks like a high entropy distribution. There are different formal definitions of computational entropy with different advantages for different applications. Because of this, it is of interest to understand when these definitions are equivalent. We consider three notions of computational entropy which are known to be equivalent when the test class ℱ is closed under taking majorities. This equivalence constitutes (essentially) the so-called dense model theorem of Green and Tao (and later made explicit by Tao-Zeigler, Reingold et al., and Gowers). The dense model theorem plays a key role in Green and Tao’s proof that the primes contain arbitrarily long arithmetic progressions and has since been connected to a surprisingly wide range of topics in mathematics and computer science, including cryptography, computational complexity, combinatorics and machine learning. We show that, in different situations where ℱ is not closed under majority, this equivalence fails. This in turn provides examples where the dense model theorem is false. 
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  2. A major open problem in proof complexity is to prove superpolynomial lower bounds for AC0[p]-Frege proofs. This system is the analog of AC0 [p], the class of bounded depth circuits with prime modular counting gates. Despite strong lower bounds for this class dating back thirty years ([28, 30]), there are no significant lower bounds for AC0 [p]-Frege. Significant and extensive degree lower bounds have been obtained for a variety of subsystems of AC0[p]-Frege, including Nullstellensatz ([3]), Polynomial Calculus ([9]), and SOS ([14]). However to date there has been no progress on AC0 [p]-Frege lower bounds. In this paper we study constant-depth extensions of the Polynomial Calculus [13]. We show that these extensions are much more powerful than was previously known. Our main result is that small depth (≤ 43) Polynomial Calculus (over a sufficiently large field) can polynomially effectively simulate all of the well-studied semialgebraic proof systems: Cutting Planes, Sherali-Adams, Sum-of-Squares (SOS), and Positivstellensatz Calculus (Dynamic SOS). Additionally, they can also quasi-polynomially effectively simulate AC0[q]-Frege for any prime q independent of the characteristic of the underlying field. They can also effectively simulate TC0-Frege if the depth is allowed to grow proportionally. Thus, proving strong lower bounds for constant-depth extensions of Polynomial Calculus would not only give lower bounds for AC0 [p]-Frege, but also for systems as strong as TC0-Frege. 
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  3. One powerful theme in complexity theory and pseudorandomness in the past few decades has been the use of lower bounds to give pseudorandom generators (PRGs). However, the general results using this hardness vs. randomness paradigm suffer from a quantitative loss in parameters, and hence do not give nontrivial implications for models where we don’t know super-polynomial lower bounds but do know lower bounds of a fixed polynomial. We show that when such lower bounds are proved using random restrictions, we can construct PRGs which are essentially best possible without in turn improving the lower bounds. More specifically, say that a circuit family has shrinkage exponent Γ if a random restriction leaving a p fraction of variables unset shrinks the size of any circuit in the family by a factor of p Γ + o (1) . Our PRG uses a seed of length s 1/(Γ + 1) + o (1) to fool circuits in the family of size s . By using this generic construction, we get PRGs with polynomially small error for the following classes of circuits of size s and with the following seed lengths: (1) For de Morgan formulas, seed length s 1/3+ o (1) ; (2) For formulas over an arbitrary basis, seed length s 1/2+ o (1) ; (3) For read-once de Morgan formulas, seed length s .234... ; (4) For branching programs of size s , seed length s 1/2+ o (1) . The previous best PRGs known for these classes used seeds of length bigger than n /2 to output n bits, and worked only for size s = O ( n ) [8]. 
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