3,197 research outputs found
Easy identification of generalized common and conserved nested intervals
In this paper we explain how to easily compute gene clusters, formalized by
classical or generalized nested common or conserved intervals, between a set of
K genomes represented as K permutations. A b-nested common (resp. conserved)
interval I of size |I| is either an interval of size 1 or a common (resp.
conserved) interval that contains another b-nested common (resp. conserved)
interval of size at least |I|-b. When b=1, this corresponds to the classical
notion of nested interval. We exhibit two simple algorithms to output all
b-nested common or conserved intervals between K permutations in O(Kn+nocc)
time, where nocc is the total number of such intervals. We also explain how to
count all b-nested intervals in O(Kn) time. New properties of the family of
conserved intervals are proposed to do so
Algorithmic Aspects of Switch Cographs
This paper introduces the notion of involution module, the first
generalization of the modular decomposition of 2-structure which has a unique
linear-sized decomposition tree. We derive an O(n^2) decomposition algorithm
and we take advantage of the involution modular decomposition tree to state
several algorithmic results. Cographs are the graphs that are totally
decomposable w.r.t modular decomposition. In a similar way, we introduce the
class of switch cographs, the class of graphs that are totally decomposable
w.r.t involution modular decomposition. This class generalizes the class of
cographs and is exactly the class of (Bull, Gem, Co-Gem, C_5)-free graphs. We
use our new decomposition tool to design three practical algorithms for the
maximum cut, vertex cover and vertex separator problems. The complexity of
these problems was still unknown for this class of graphs. This paper also
improves the complexity of the maximum clique, the maximum independant set, the
chromatic number and the maximum clique cover problems by giving efficient
algorithms, thanks to the decomposition tree. Eventually, we show that this
class of graphs has Clique-Width at most 4 and that a Clique-Width expression
can be computed in linear time
The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning
In this paper, we address the problem of creating believable agents (virtual
characters) in video games. We consider only one meaning of believability,
``giving the feeling of being controlled by a player'', and outline the problem
of its evaluation. We present several models for agents in games which can
produce believable behaviours, both from industry and research. For high level
of believability, learning and especially imitation learning seems to be the
way to go. We make a quick overview of different approaches to make video
games' agents learn from players. To conclude we propose a two-step method to
develop new models for believable agents. First we must find the criteria for
believability for our application and define an evaluation method. Then the
model and the learning algorithm can be designed
High-frequency issues using rotating voltage injections intended for position self-sensing
The rotor position is required in many control schemes in electrical drives. Replacing position sensors by machine self-sensing estimators increases reliability and reduces cost. Solutions based on tracking magnetic anisotropies through the monitoring of the incremental inductance variations are efficient at low-speed and standstill operations. This inductance can be estimated by measuring the response to the injection of high-frequency signals. In general however, the selection of the optimal frequency is not addressed thoroughly. In this paper, we propose discrete-time operations based on a rotating voltage injection at frequencies up to one third of the sampling frequency used by the digital controller. The impact on the rotation-drive, the computational requirement, the robustness and the effect of the resistance on the position estimation are analyzed regarding the signal frequency
Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors
We introduce and experimentally validate a new macro-level model of the CPU
temperature/power relationship within nanometer-scale application processors or
system-on-chips. By adopting a holistic view, this model is able to take into
account many of the physical effects that occur within such systems. Together
with two algorithms described in the paper, our results can be used, for
instance by engineers designing power or thermal management units, to cancel
the temperature-induced bias on power measurements. This will help them gather
temperature-neutral power data while running multiple instance of their
benchmarks. Also power requirements and system failure rates can be decreased
by controlling the CPU's thermal behavior.
Even though it is usually assumed that the temperature/power relationship is
exponentially related, there is however a lack of publicly available physical
temperature/power measurements to back up this assumption, something our paper
corrects. Via measurements on two pertinent platforms sporting nanometer-scale
application processors, we show that the power/temperature relationship is
indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range.
Our data suggest that, for application processors operating between 20{\deg}C
and 50{\deg}C, a quadratic model is still accurate and a linear approximation
is acceptable.Comment: Submitted to SAMOS 2014; International Conference on Embedded
Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV
Learning a Representation of a Believable Virtual Character's Environment with an Imitation Algorithm
In video games, virtual characters' decision systems often use a simplified
representation of the world. To increase both their autonomy and believability
we want those characters to be able to learn this representation from human
players. We propose to use a model called growing neural gas to learn by
imitation the topology of the environment. The implementation of the model, the
modifications and the parameters we used are detailed. Then, the quality of the
learned representations and their evolution during the learning are studied
using different measures. Improvements for the growing neural gas to give more
information to the character's model are given in the conclusion
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