4,689 research outputs found
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network
BACKGROUND
It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems.
RESULTS
Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system.
CONCLUSION
We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data
On Multiphase-Linear Ranking Functions
Multiphase ranking functions () were proposed as a means
to prove the termination of a loop in which the computation progresses through
a number of "phases", and the progress of each phase is described by a
different linear ranking function. Our work provides new insights regarding
such functions for loops described by a conjunction of linear constraints
(single-path loops). We provide a complete polynomial-time solution to the
problem of existence and of synthesis of of bounded depth
(number of phases), when variables range over rational or real numbers; a
complete solution for the (harder) case that variables are integer, with a
matching lower-bound proof, showing that the problem is coNP-complete; and a
new theorem which bounds the number of iterations for loops with
. Surprisingly, the bound is linear, even when the
variables involved change in non-linear way. We also consider a type of
lexicographic ranking functions, , more expressive than types
of lexicographic functions for which complete solutions have been given so far.
We prove that for the above type of loops, lexicographic functions can be
reduced to , and thus the questions of complexity of
detection and synthesis, and of resulting iteration bounds, are also answered
for this class.Comment: typos correcte
Fixed-point elimination in the intuitionistic propositional calculus
It is a consequence of existing literature that least and greatest
fixed-points of monotone polynomials on Heyting algebras-that is, the algebraic
models of the Intuitionistic Propositional Calculus-always exist, even when
these algebras are not complete as lattices. The reason is that these extremal
fixed-points are definable by formulas of the IPC. Consequently, the
-calculus based on intuitionistic logic is trivial, every -formula
being equivalent to a fixed-point free formula. We give in this paper an
axiomatization of least and greatest fixed-points of formulas, and an algorithm
to compute a fixed-point free formula equivalent to a given -formula. The
axiomatization of the greatest fixed-point is simple. The axiomatization of the
least fixed-point is more complex, in particular every monotone formula
converges to its least fixed-point by Kleene's iteration in a finite number of
steps, but there is no uniform upper bound on the number of iterations. We
extract, out of the algorithm, upper bounds for such n, depending on the size
of the formula. For some formulas, we show that these upper bounds are
polynomial and optimal
Unified force law for granular impact cratering
Experiments on the low-speed impact of solid objects into granular media have
been used both to mimic geophysical events and to probe the unusual nature of
the granular state of matter. Observations have been interpreted in terms of
conflicting stopping forces: product of powers of projectile depth and speed;
linear in speed; constant, proportional to the initial impact speed; and
proportional to depth. This is reminiscent of high-speed ballistics impact in
the 19th and 20th centuries, when a plethora of empirical rules were proposed.
To make progress, we developed a means to measure projectile dynamics with 100
nm and 20 us precision. For a 1-inch diameter steel sphere dropped from a wide
range of heights into non-cohesive glass beads, we reproduce prior observations
either as reasonable approximations or as limiting behaviours. Furthermore, we
demonstrate that the interaction between projectile and medium can be
decomposed into the sum of velocity-dependent inertial drag plus
depth-dependent friction. Thus we achieve a unified description of low-speed
impact phenomena and show that the complex response of granular materials to
impact, while fundamentally different from that of liquids and solids, can be
simply understood
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