In the theory of pseudorandomness, potential (uniform) observers
are modeled as probabilistic polynomial-time machines.
In fact many of the central results in
that theory are proven via probabilistic polynomial-time reductions.
In this paper we show that analogous deterministic reductions
are unlikely to hold. We conclude that randomness ... more >>>

We present algorithms for learning depth two neural networks where the
hidden nodes are threshold gates with constant fan-in. The transfer
function of the output node might be more general: we have results for
the cases when the threshold function, the logistic function or the
identity function is ... more >>>

Given a finite set $S$ of points (i.e. the stations of a radio
network) on a $d$-dimensional Euclidean space and a positive integer
$1\le h \le |S|-1$, the \minrangeh{d} problem
consists of assigning transmission ranges to the stations so as
to minimize the total power consumption, provided ... more >>>