8,670 research outputs found
Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover
Stochastic Boolean Function Evaluation is the problem of determining the
value of a given Boolean function f on an unknown input x, when each bit of x_i
of x can only be determined by paying an associated cost c_i. The assumption is
that x is drawn from a given product distribution, and the goal is to minimize
the expected cost. This problem has been studied in Operations Research, where
it is known as "sequential testing" of Boolean functions. It has also been
studied in learning theory in the context of learning with attribute costs. We
consider the general problem of developing approximation algorithms for
Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for
evaluating Boolean linear threshold formulas. We also present an approximation
algorithm for evaluating CDNF formulas (and decision trees) achieving a factor
of O(log kd), where k is the number of terms in the DNF formula, and d is the
number of clauses in the CNF formula. In addition, we present approximation
algorithms for simultaneous evaluation of linear threshold functions, and for
ranking of linear functions.
Our function evaluation algorithms are based on reductions to the Stochastic
Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and
Krause. They presented an approximation algorithm for the problem, called
Adaptive Greedy. Our main technical contribution is a new approximation
algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an
extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito,
which is a generalization of Hochbaum's algorithm for the classical Set Cover
Problem. We also give a new bound on the approximation achieved by the Adaptive
Greedy algorithm of Golovin and Krause
Notes on stochastic (bio)-logic gates: the role of allosteric cooperativity
Recent experimental breakthroughs have finally allowed to implement in-vitro
reaction kinetics (the so called {\em enzyme based logic}) which code for
two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the
stochastic OR (and NOR). This accomplishment, together with the already-known
single-input gates (performing as YES and NOT), provides a logic base and paves
the way to the development of powerful biotechnological devices. The
investigation of this field would enormously benefit from a self-consistent,
predictive, theoretical framework. Here we formulate a complete statistical
mechanical description of the Monod-Wyman-Changeaux allosteric model for both
single and double ligand systems, with the purpose of exploring their practical
capabilities to express logical operators and/or perform logical operations.
Mixing statistical mechanics with logics, and quantitatively our findings with
the available biochemical data, we successfully revise the concept of
cooperativity (and anti-cooperativity) for allosteric systems, with particular
emphasis on its computational capabilities, the related ranges and scaling of
the involved parameters and its differences with classical cooperativity (and
anti-cooperativity)
On a representation of time space-harmonic polynomials via symbolic L\'evy processes
In this paper, we review the theory of time space-harmonic polynomials
developed by using a symbolic device known in the literature as the classical
umbral calculus. The advantage of this symbolic tool is twofold. First a moment
representation is allowed for a wide class of polynomial stochastic involving
the L\'evy processes in respect to which they are martingales. This
representation includes some well-known examples such as Hermite polynomials in
connection with Brownian motion. As a consequence, characterizations of many
other families of polynomials having the time space-harmonic property can be
recovered via the symbolic moment representation. New relations with
Kailath-Segall polynomials are stated. Secondly the generalization to the
multivariable framework is straightforward. Connections with cumulants and Bell
polynomials are highlighted both in the univariate case and in the multivariate
one. Open problems are addressed at the end of the paper
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
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