We address the problem of organizing a set $T$ of shared data into
the memory modules of a Distributed Memory Machine (DMM) in order
to minimize memory access conflicts (i.e. memory contention)
during read operations.
Previous solutions for this problem can be found as fundamental ... more >>>

We show the following Reduction Lemma: any
$\epsilon$-biased sample space with respect to (Boolean) linear
tests is also $2\epsilon$-biased with respect to
any system of independent linear tests. Combining this result with
the previous constructions of $\epsilon$-biased sample space with
respect to linear tests, we obtain the first efficient
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We show how to simulate any BPP algorithm in polynomial time
using a weak random source of min-entropy $r^{\gamma}$
for any $\gamma >0$.
This follows from a more general result about {\em sampling\/}
with weak random sources.
Our result matches an information-theoretic lower bound ... more >>>

We present the first worst-case hardness conditions
on the circuit complexity of EXP functions which are
sufficient to obtain P=BPP. In particular, we show that
from such hardness conditions it is possible to construct
quick Hitting Sets Generators with logarithmic prize.
... more >>>

The efficient construction of Hitting Sets for non trivial classes
of boolean functions is a fundamental problem in the theory
of derandomization. Our paper presents a new method to efficiently
construct Hitting Sets for the class of systems of boolean linear
functions. Systems of boolean linear functions ... more >>>

We show that hitting sets can derandomize any BPP-algorithm.
This gives a positive answer to a fundamental open question in
probabilistic algorithms. More precisely, we present a polynomial
time deterministic algorithm which uses any given hitting set
to approximate the fractions of 1's in the ... more >>>

We prove an optimal bound on the Shannon function
$L(n,m,\epsilon)$ which describes the trade-off between the
circuit-size complexity and the degree of approximation; that is
$$L(n,m,\epsilon)\ =\
\Theta\left(\frac{m\epsilon^2}{\log(2 + m\epsilon^2)}\right)+O(n).$$
Our bound applies to any partial boolean function
and any ... more >>>