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0708.3699
|
Convolutional Entanglement Distillation
|
quant-ph cs.IT math.IT
|
We develop a theory of entanglement distillation that exploits a
convolutional coding structure. We provide a method for converting an arbitrary
classical binary or quaternary convolutional code into a convolutional
entanglement distillation protocol. The imported classical convolutional code
does not have to be dual-containing or self-orthogonal. The yield and
error-correcting properties of such a protocol depend respectively on the rate
and error-correcting properties of the imported classical convolutional code. A
convolutional entanglement distillation protocol has several other benefits.
Two parties sharing noisy ebits can distill noiseless ebits ``online'' as they
acquire more noisy ebits. Distillation yield is high and decoding complexity is
simple for a convolutional entanglement distillation protocol. Our theory of
convolutional entanglement distillation reduces the problem of finding a good
convolutional entanglement distillation protocol to the well-established
problem of finding a good classical convolutional code.
|
0708.3723
|
Parametric Stiffness Analysis of the Orthoglide
|
cs.RO
|
This paper presents a parametric stiffness analysis of the Orthoglide, a
3-DOF translational Parallel Kinematic Machine. First, a compliant modeling of
the Orthoglide is conducted based on an existing method. Then stiffness matrix
is symbolically computed. This allows one to easily study the influence of the
geometric design parameters on the matrix elements. Critical links are
displayed. Cutting forces are then modeled so that static displacements of the
Orthoglide tool during slot milling are symbolically computed. Influence of the
geometric design parameters on the static displacements is checked as well.
Other machining operations can be modeled. This parametric stiffness analysis
can be applied to any parallel manipulator for which stiffness is a critical
issue.
|
0708.3761
|
Multi-agent systems, Equiprobability, Gamma distributions and other
Geometrical questions
|
nlin.CD cond-mat.stat-mech cs.MA physics.soc-ph
|
A set of many identical interacting agents obeying a global additive
constraint is considered. Under the hypothesis of equiprobability in the
high-dimensional volume delimited in phase space by the constraint, the
statistical behavior of a generic agent over the ensemble is worked out. The
asymptotic distribution of that statistical behavior is derived from
geometrical arguments. This distribution is related with the Gamma
distributions found in several multi-agent economy models. The parallelism with
all these systems is established. Also, as a collateral result, a formula for
the volume of high-dimensional symmetrical bodies is proposed.
|
0708.3764
|
A Radio Resource Management strategy for downlink cooperation in
distributed networks
|
cs.IT math.IT
|
This paper has been withdrawn by the author
|
0708.3809
|
Design Strategies for the Geometric Synthesis of Orthoglide-type
Mechanisms
|
cs.RO
|
The paper addresses the geometric synthesis of Orthoglide-type mechanism, a
family of 3-DOF parallel manipulators for rapid machining applications, which
combine advantages of both serial mechanisms and parallel kinematic
architectures. These manipulators possess quasi-isotropic kinematic
performances and are made up of three actuated fixed prismatic joints, which
are mutually orthogonal and connected to a mobile platform via three
parallelogram chains. The platform moves in the Cartesian space with fixed
orientation, similar to conventional XYZ-machine. Three strategies have been
proposed to define the Orthoglide geometric parameters (manipulator link
lengths and actuated joint limits) as functions of a cubic workspace size and
dextrous properties expressed by bounds on the velocity transmission factors,
manipulability or the Jacobian condition number. Low inertia and intrinsic
stiffness have been set as additional design goals expressed by the minimal
link length requirement. For each design strategy, analytical expressions for
computing the Orthoglide parameters are proposed. It is showed that the
proposed strategies yield Pareto-optimal solutions, which differ by the
kinematic performances outside the prescribed Cartesian cube (but within the
workspace bounded by the actuated joint limits). The proposed technique is
illustrated with numerical examples for the Orthoglide prototype design.
|
0708.3811
|
An Exhaustive Study of the Workspaces Tolopogies of all 3R Orthogonal
Manipulators with Geometric Simplifications
|
cs.RO
|
This paper proposes a classification of three-revolute orthogonal
manipulators that have at least one of their DH parameters equal to zero. This
classification is based on the topology of their workspace. The workspace is
characterized in a half-cross section by the singular curves. The workspace
topology is defined by the number of cusps and nodes that appear on these
singular curves. The manipulators are classified into different types with
similar kinematic properties. Each type is evaluated according to interesting
kinematic properties such as, whether the workspace is fully reachable with
four inverse kinematic solutions or not, the existence of voids, and the
feasibility of continuous trajectories in the workspace. It is found that
several orthogonal manipulators have a "well-connected" workspace, that is,
their workspace is fully accessible with four inverse kinematic solutions and
any continuous trajectory is feasible. This result is of interest for the
design of alternative manipulator geometries.
|
0708.3829
|
A Non Parametric Model for the Forecasting of the Venezuelan Oil Prices
|
cs.CE cs.NE
|
A neural net model for forecasting the prices of Venezuelan crude oil is
proposed. The inputs of the neural net are selected by reference to a dynamic
system model of oil prices by Mashayekhi (1995, 2001) and its performance is
evaluated using two criteria: the Excess Profitability test by Anatoliev and
Gerko (2005) and the characteristics of the equity curve generated by a trading
strategy based on the neural net predictions.
-----
Se introduce aqui un modelo no parametrico para pronosticar los precios del
petroleo Venezolano cuyos insumos son seleccionados en base a un sistema
dinamico que explica los precios en terminos de dichos insumos. Se describe el
proceso de recoleccion y pre-procesamiento de datos y la corrida de la red y se
evaluan sus pronosticos a traves de un test estadistico de predictibilidad y de
las caracteristicas del Equity Curve inducido por la estrategia de compraventa
bursatil generada por dichos pronosticos.
|
0708.3896
|
The Isoconditioning Loci of Planar Three-DOF Parallel Manipulators
|
cs.RO
|
The subject of this paper is a special class of three-degree-of-freedom
parallel manipulators. The singular configurations of the two Jacobian matrices
are first studied. The isotropic configurations are then found based on the
characteristic length of this manipulator. The isoconditioning loci for the
Jacobian matrices are plotted to define a global performance index allowing the
comparison of the different working modes. The index thus resulting is compared
with the Cartesian workspace surface and the average of the condition number.
|
0708.3900
|
Inference from correlated patterns: a unified theory for perceptron
learning and linear vector channels
|
cs.IT cond-mat.dis-nn math.IT
|
A framework to analyze inference performance in densely connected
single-layer feed-forward networks is developed for situations where a given
data set is composed of correlated patterns. The framework is based on the
assumption that the left and right singular value bases of the given pattern
matrix are generated independently and uniformly from Haar measures. This
assumption makes it possible to characterize the objective system by a single
function of two variables which is determined by the eigenvalue spectrum of the
cross-correlation matrix of the pattern matrix. Links to existing methods for
analysis of perceptron learning and Gaussian linear vector channels and an
application to a simple but nontrivial problem are also shown.
|
0708.3920
|
Kinematic analysis of the 3-RPR parallel manipulator
|
cs.RO
|
The aim of this paper is the kinematic study of a 3-RPR planar parallel
manipulator where the three fixed revolute joints are actuated. The direct and
inverse kinematic problem as well as the singular configuration is
characterized. On parallel singular configurations, the motion produce by the
mobile platform can be compared to the Reuleaux straight-line mechanism.
|
0708.3936
|
Working and Assembly Modes of the Agile Eye
|
cs.RO
|
This paper deals with the in-depth kinematic analysis of a special spherical
parallel wrist, called the Agile Eye. The Agile Eye is a three-legged spherical
parallel robot with revolute joints in which all pairs of adjacent joint axes
are orthogonal. Its most peculiar feature, demonstrated in this paper for the
first time, is that its (orientation) workspace is unlimited and flawed only by
six singularity curves (rather than surfaces). Furthermore, these curves
correspond to self-motions of the mobile platform. This paper also demonstrates
that, unlike for any other such complex spatial robots, the four solutions to
the direct kinematics of the Agile Eye (assembly modes) have a simple geometric
relationship with the eight solutions to the inverse kinematics (working
modes).
|
0708.4149
|
On the complexity of nonnegative matrix factorization
|
cs.NA cs.IR
|
Nonnegative matrix factorization (NMF) has become a prominent technique for
the analysis of image databases, text databases and other information retrieval
and clustering applications. In this report, we define an exact version of NMF.
Then we establish several results about exact NMF: (1) that it is equivalent to
a problem in polyhedral combinatorics; (2) that it is NP-hard; and (3) that a
polynomial-time local search heuristic exists.
|
0708.4155
|
Coexistence of Social Norms based on In- and Out-group Interactions
|
nlin.AO cs.MA physics.soc-ph
|
The question how social norms can emerge from microscopic interactions
between individuals is a key problem in social sciences to explain collective
behavior. In this paper we propose an agent-based model to show that randomly
distributed social behavior by way of local interaction converges to a state
with a multimodal distribution of behavior. This can be interpreted as a
coexistence of different social norms, a result that goes beyond previous
investigations. The model is discrete in time and space, behavior is
characterized in a continuous state space. The adaptation of social behavior by
each agent is based on attractive and repulsive forces caused by friendly and
adversary relations among agents. The model is analyzed both analytically and
by means of spatio-temporal computer simulations. It provides conditions under
which we find convergence towards a single norm, coexistence of two opposing
norms, and coexistence of a multitude of norms. For the latter case, we also
show the evolution of the spatio-temporal distribution of behavior.
|
0708.4164
|
Asymptotic improvement of the Gilbert-Varshamov bound for linear codes
|
cs.IT math.IT
|
The Gilbert-Varshamov bound states that the maximum size A_2(n,d) of a binary
code of length n and minimum distance d satisfies A_2(n,d) >= 2^n/V(n,d-1)
where V(n,d) stands for the volume of a Hamming ball of radius d. Recently
Jiang and Vardy showed that for binary non-linear codes this bound can be
improved to A_2(n,d) >= cn2^n/V(n,d-1) for c a constant and d/n <= 0.499. In
this paper we show that certain asymptotic families of linear binary [n,n/2]
random double circulant codes satisfy the same improved Gilbert-Varshamov
bound.
|
0708.4170
|
Raising a Hardness Result
|
cs.AI cs.CC cs.LO
|
This article presents a technique for proving problems hard for classes of
the polynomial hierarchy or for PSPACE. The rationale of this technique is that
some problem restrictions are able to simulate existential or universal
quantifiers. If this is the case, reductions from Quantified Boolean Formulae
(QBF) to these restrictions can be transformed into reductions from QBFs having
one more quantifier in the front. This means that a proof of hardness of a
problem at level n in the polynomial hierarchy can be split into n separate
proofs, which may be simpler than a proof directly showing a reduction from a
class of QBFs to the considered problem.
|
0708.4214
|
High Rate Single-Symbol Decodable Precoded DSTBCs for Cooperative
Networks
|
cs.IT math.IT
|
Distributed Orthogonal Space-Time Block Codes (DOSTBCs) achieving full
diversity order and single-symbol ML decodability have been introduced recently
for cooperative networks and an upper-bound on the maximal rate of such codes
along with code constructions has been presented. In this report, we introduce
a new class of Distributed STBCs called Semi-orthogonal Precoded Distributed
Single-Symbol Decodable STBCs (S-PDSSDC) wherein, the source performs
co-ordinate interleaving of information symbols appropriately before
transmitting it to all the relays. It is shown that DOSTBCs are a special case
of S-PDSSDCs. A special class of S-PDSSDCs having diagonal covariance matrix at
the destination is studied and an upper bound on the maximal rate of such codes
is derived. The bounds obtained are approximately twice larger than that of the
DOSTBCs. A systematic construction of S-PDSSDCs is presented when the number of
relays $K \geq 4$. The constructed codes are shown to achieve the upper-bound
on the rate when $K$ is of the form 0 modulo 4 or 3 modulo 4. For the rest of
the values of $K$, the constructed codes are shown to have rates higher than
that of DOSTBCs. It is also shown that S-PDSSDCs cannot be constructed with any
form of linear processing at the relays when the source doesn't perform
co-ordinate interleaving of the information symbols.
|
0708.4219
|
Secure Transmission with Multiple Antennas: The MISOME Wiretap Channel
|
cs.IT math.IT
|
The role of multiple antennas for secure communication is investigated within
the framework of Wyner's wiretap channel. We characterize the secrecy capacity
in terms of generalized eigenvalues when the sender and eavesdropper have
multiple antennas, the intended receiver has a single antenna, and the channel
matrices are fixed and known to all the terminals, and show that a beamforming
strategy is capacity-achieving. In addition, we show that in the high
signal-to-noise (SNR) ratio regime the penalty for not knowing eavesdropper's
channel is small--a simple ``secure space-time code'' that can be thought of as
masked beamforming and radiates power isotropically attains near-optimal
performance. In the limit of large number of antennas, we obtain a
realization-independent characterization of the secrecy capacity as a function
of the number $\beta$: the number of eavesdropper antennas per sender antenna.
We show that the eavesdropper is comparatively ineffective when $\beta<1$, but
that for $\beta\ge2$ the eavesdropper can drive the secrecy capacity to zero,
thereby blocking secure communication to the intended receiver. Extensions to
ergodic fading channels are also provided.
|
0708.4311
|
2006: Celebrating 75 years of AI - History and Outlook: the Next 25
Years
|
cs.AI
|
When Kurt Goedel layed the foundations of theoretical computer science in
1931, he also introduced essential concepts of the theory of Artificial
Intelligence (AI). Although much of subsequent AI research has focused on
heuristics, which still play a major role in many practical AI applications, in
the new millennium AI theory has finally become a full-fledged formal science,
with important optimality results for embodied agents living in unknown
environments, obtained through a combination of theory a la Goedel and
probability theory. Here we look back at important milestones of AI history,
mention essential recent results, and speculate about what we may expect from
the next 25 years, emphasizing the significance of the ongoing dramatic
hardware speedups, and discussing Goedel-inspired, self-referential,
self-improving universal problem solvers.
|
0708.4324
|
Sensitivity Analysis of the Orthoglide, a 3-DOF Translational Parallel
Kinematic Machine
|
cs.RO
|
This paper presents a sensitivity analysis of the Orthoglide, a 3-DOF
translational Parallel Kinematic Machine. Two complementary methods are
developed to analyze its sensitivity to its dimensional and angular variations.
First, a linkage kinematic analysis method is used to have a rough idea of the
influence of the dimensional variations on the location of the end-effector.
Besides, this method shows that variations in the design parameters of the same
type from one leg to the other have the same influence on the end-effector.
However, this method does not take into account the variations in the
parallelograms. Thus, a differential vector method is used to study the
influence of the dimensional and angular variations in the parts of the
manipulator on the position and orientation of the end-effector, and
particularly the influence of the variations in the parallelograms. It turns
out that the kinematic isotropic configuration of the manipulator is the least
sensitive one to its dimensional and angular variations. On the contrary, the
closest configurations to its kinematic singular configurations are the most
sensitive ones to geometrical variations.
|
0708.4328
|
Dualities Between Entropy Functions and Network Codes
|
cs.IT math.IT
|
This paper provides a new duality between entropy functions and network
codes. Given a function $g\geq 0$ defined on all proper subsets of $N$ random
variables, we provide a construction for a network multicast problem which is
solvable if and only if $g$ is entropic. The underlying network topology is
fixed and the multicast problem depends on $g$ only through edge capacities and
source rates. Relaxing the requirement that the domain of $g$ be subsets of
random variables, we obtain a similar duality between polymatroids and the
linear programming bound. These duality results provide an alternative proof of
the insufficiency of linear (and abelian) network codes, and demonstrate the
utility of non-Shannon inequalities to tighten outer bounds on network coding
capacity regions.
|
0708.4407
|
Algebraic Distributed Differential Space-Time Codes with Low Decoding
Complexity
|
cs.IT cs.DM math.IT math.RA
|
The differential encoding/decoding setup introduced by Kiran et al,
Oggier-Hassibi and Jing-Jafarkhani for wireless relay networks that use
codebooks consisting of unitary matrices is extended to allow codebooks
consisting of scaled unitary matrices. For such codebooks to be usable in the
Jing-Hassibi protocol for cooperative diversity, the conditions involving the
relay matrices and the codebook that need to be satisfied are identified. Using
the algebraic framework of extended Clifford algebras, a new class of
Distributed Differential Space-Time Codes satisfying these conditions for power
of two number of relays and also achieving full cooperative diversity with a
low complexity sub-optimal receiver is proposed. Simulation results indicate
that the proposed codes outperform both the cyclic codes as well as the
circulant codes. Furthermore, these codes can also be applied as Differential
Space-Time codes for non-coherent communication in classical point to point
multiple antenna systems.
|
0709.0035
|
On The Limitations of The Naive Lattice Decoding
|
cs.IT math.IT
|
In this paper, the inherent drawbacks of the naive lattice decoding for MIMO
fading systems is investigated. We show that using the naive lattice decoding
for MIMO systems has considerable deficiencies in terms of the rate-diversity
trade-off. Unlike the case of maximum-likelihood decoding, in this case, even
the perfect lattice space-time codes which have the non-vanishing determinant
property can not achieve the optimal rate-diversity trade-off. Indeed, we show
that in the case of naive lattice decoding, when we fix the underlying lattice,
all the codes based on full-rate lattices have the same rate-diversity
trade-off as V-BLAST. Also, we drive a lower bound on the symbol error
probability of the naive lattice decoding for the fixed-rate MIMO systems (with
equal numbers of receive and transmit antennas). This bound shows that
asymptotically, the naive lattice decoding has an unbounded loss in terms of
the required SNR, compared to the maximum likelihood decoding.
|
0709.0116
|
On Ultrametric Algorithmic Information
|
cs.AI cs.CL
|
How best to quantify the information of an object, whether natural or
artifact, is a problem of wide interest. A related problem is the computability
of an object. We present practical examples of a new way to address this
problem. By giving an appropriate representation to our objects, based on a
hierarchical coding of information, we exemplify how it is remarkably easy to
compute complex objects. Our algorithmic complexity is related to the length of
the class of objects, rather than to the length of the object.
|
0709.0124
|
High Rate Single-Symbol ML Decodable Precoded DSTBCs for Cooperative
Networks
|
cs.IT math.IT
|
Distributed Orthogonal Space-Time Block Codes (DOSTBCs) achieving full
diversity order and single-symbol ML decodability have been introduced recently
by Yi and Kim for cooperative networks and an upperbound on the maximal rate of
such codes along with code constructions has been presented. In this paper, we
introduce a new class of Distributed STBCs called Semi-orthogonal Precoded
Distributed Single-Symbol Decodable STBCs (S-PDSSDC) wherein, the source
performs co-ordinate interleaving of information symbols appropriately before
transmitting it to all the relays. It is shown that DOSTBCs are a special case
of S-PDSSDCs. A special class of S-PDSSDCs having diagonal covariance matrix at
the destination is studied and an upperbound on the maximal rate of such codes
is derived. The bounds obtained are approximately twice larger than that of the
DOSTBCs. A systematic construction of S-PDSSDCs is presented when the number of
relays $K \geq 4$. The constructed codes are shown to achieve the upperbound on
the rate when $K$ is of the form 0 or 3 modulo 4. For the rest of the values of
$K$, the constructed codes are shown to have rates higher than that of DOSTBCs.
It is shown that S-PDSSDCs cannot be constructed with any form of linear
processing at the relays when the source doesn't perform co-ordinate
interleaving of the information symbols. Simulation result shows that S-PDSSDCs
have better probability of error performance than that of DOSTBCs.
|
0709.0145
|
Estimating Random Variables from Random Sparse Observations
|
cs.IT math.IT math.PR
|
Let X_1,...., X_n be a collection of iid discrete random variables, and
Y_1,..., Y_m a set of noisy observations of such variables. Assume each
observation Y_a to be a random function of some a random subset of the X_i's,
and consider the conditional distribution of X_i given the observations, namely
\mu_i(x_i)\equiv\prob\{X_i=x_i|Y\} (a posteriori probability).
We establish a general relation between the distribution of \mu_i, and the
fixed points of the associated density evolution operator. Such relation holds
asymptotically in the large system limit, provided the average number of
variables an observation depends on is bounded. We discuss the relevance of our
result to a number of applications, ranging from sparse graph codes, to
multi-user detection, to group testing.
|
0709.0178
|
Effective Generation of Subjectively Random Binary Sequences
|
cs.HC cs.AI
|
We present an algorithm for effectively generating binary sequences which
would be rated by people as highly likely to have been generated by a random
process, such as flipping a fair coin.
|
0709.0218
|
Inferring Neuronal Network Connectivity using Time-constrained Episodes
|
cs.DB q-bio.NC
|
Discovering frequent episodes in event sequences is an interesting data
mining task. In this paper, we argue that this framework is very effective for
analyzing multi-neuronal spike train data. Analyzing spike train data is an
important problem in neuroscience though there are no data mining approaches
reported for this. Motivated by this application, we introduce different
temporal constraints on the occurrences of episodes. We present algorithms for
discovering frequent episodes under temporal constraints. Through simulations,
we show that our method is very effective for analyzing spike train data for
unearthing underlying connectivity patterns.
|
0709.0259
|
Spectrum Sensing in Wideband OFDM Cognitive Radios
|
cs.IT math.IT
|
In this paper, detection of the primary user (PU) signal in an orthogonal
frequency division multiplexing (OFDM) based cognitive radio (CR) system is
addressed. According to the prior knowledge of the PU signal known to the
detector, three detection algorithms based on the Neyman-Pearson philosophy are
proposed. In the first case, a Gaussian PU signal with completely known
probability density function (PDF) except for its received power is considered.
The frequency band that the PU signal resides is also assumed known. Detection
is performed individually at each OFDM sub-carrier possibly interfered by the
PU signal, and the results are then combined to form a final decision. In the
second case, the sub-carriers that the PU signal resides are known.
Observations from all possibly interfered sub-carriers are considered jointly
to exploit the fact that the presence of a PU signal interferers all of them
simultaneously. In the last case, it is assumed no PU signal prior knowledge is
available. The detection is involved with a search of the interfered band. The
proposed detector is able to detect an abrupt power change when tracing along
the frequency axis.
|
0709.0355
|
Solution of moving-boundary problems by the spectral element method
|
cs.CE cs.NA
|
This paper describes a novel numerical model aiming at solving
moving-boundary problems such as free-surface flows or fluid-structure
interaction. This model uses a moving-grid technique to solve the
Navier--Stokes equations expressed in the arbitrary Lagrangian--Eulerian
kinematics. The discretization in space is based on the spectral element
method. The coupling of the fluid equations and the moving-grid equations is
essentially done through the conditions on the moving boundaries. Two- and
three-dimensional simulations are presented: translation and rotation of a
cylinder in a fluid, and large-amplitude sloshing in a rectangular tank. The
accuracy and robustness of the present numerical model is studied and
discussed.
|
0709.0409
|
A DH-parameter based condition for 3R orthogonal manipulators to have 4
distinct inverse kinematic solutions
|
cs.RO
|
Positioning 3R manipulators may have two or four inverse kinematic solutions
(IKS). This paper derives a necessary and sufficient condition for 3R
positioning manipulators with orthogonal joint axes to have four distinct IKS.
We show that the transition between manipulators with 2 and 4 IKS is defined by
the set of manipulators with a quadruple root of their inverse kinematics. The
resulting condition is explicit and states that the last link length of the
manipulator must be greater than a quantity that depends on three of its
remaining DH-parameters. This result is of interest for the design of new
manipulators.
|
0709.0509
|
Filtering Additive Measurement Noise with Maximum Entropy in the Mean
|
cs.LG
|
The purpose of this note is to show how the method of maximum entropy in the
mean (MEM) may be used to improve parametric estimation when the measurements
are corrupted by large level of noise. The method is developed in the context
on a concrete example: that of estimation of the parameter in an exponential
distribution. We compare the performance of our method with the bayesian and
maximum likelihood approaches.
|
0709.0518
|
Rate Regions for the Partially-Cooperative Relay Broadcast Channel with
Non-causal Side Information
|
cs.IT math.IT
|
In this work, we consider a partially cooperative relay broadcast channel
(PC-RBC) controlled by random parameters. We provide rate regions for two
different situations: 1) when side information (SI) S^n on the random
parameters is non-causally known at both the source and the relay and, 2) when
side information S^n is non-causally known at the source only. These achievable
regions are derived for the general discrete memoryless case first and then
extended to the case when the channel is degraded Gaussian and the SI is
additive i.i.d. Gaussian. In this case, the source uses generalized dirty paper
coding (GDPC), i.e., DPC combined with partial state cancellation, when only
the source is informed, and DPC alone when both the source and the relay are
informed. It appears that, even though it can not completely eliminate the
effect of the SI (in contrast to the case of source and relay being informed),
GDPC is particularly useful when only the source is informed.
|
0709.0522
|
Qualitative Belief Conditioning Rules (QBCR)
|
cs.AI
|
In this paper we extend the new family of (quantitative) Belief Conditioning
Rules (BCR) recently developed in the Dezert-Smarandache Theory (DSmT) to their
qualitative counterpart for belief revision. Since the revision of quantitative
as well as qualitative belief assignment given the occurrence of a new event
(the conditioning constraint) can be done in many possible ways, we present
here only what we consider as the most appealing Qualitative Belief
Conditioning Rules (QBCR) which allow to revise the belief directly with words
and linguistic labels and thus avoids the introduction of ad-hoc translations
of quantitative beliefs into quantitative ones for solving the problem.
|
0709.0566
|
Discovering Patterns in Multi-neuronal Spike Trains using the Frequent
Episode Method
|
cs.DB q-bio.NC
|
Discovering the 'Neural Code' from multi-neuronal spike trains is an
important task in neuroscience. For such an analysis, it is important to
unearth interesting regularities in the spiking patterns. In this report, we
present an efficient method for automatically discovering synchrony, synfire
chains, and more general sequences of neuronal firings. We use the Frequent
Episode Discovery framework of Laxman, Sastry, and Unnikrishnan (2005), in
which the episodes are represented and recognized using finite-state automata.
Many aspects of functional connectivity between neuronal populations can be
inferred from the episodes. We demonstrate these using simulated multi-neuronal
data from a Poisson model. We also present a method to assess the statistical
significance of the discovered episodes. Since the Temporal Data Mining (TDM)
methods used in this report can analyze data from hundreds and potentially
thousands of neurons, we argue that this framework is appropriate for
discovering the `Neural Code'.
|
0709.0599
|
On Universal Properties of Capacity-Approaching LDPC Ensembles
|
cs.IT math.IT
|
This paper is focused on the derivation of some universal properties of
capacity-approaching low-density parity-check (LDPC) code ensembles whose
transmission takes place over memoryless binary-input output-symmetric (MBIOS)
channels. Properties of the degree distributions, graphical complexity and the
number of fundamental cycles in the bipartite graphs are considered via the
derivation of information-theoretic bounds. These bounds are expressed in terms
of the target block/ bit error probability and the gap (in rate) to capacity.
Most of the bounds are general for any decoding algorithm, and some others are
proved under belief propagation (BP) decoding. Proving these bounds under a
certain decoding algorithm, validates them automatically also under any
sub-optimal decoding algorithm. A proper modification of these bounds makes
them universal for the set of all MBIOS channels which exhibit a given
capacity. Bounds on the degree distributions and graphical complexity apply to
finite-length LDPC codes and to the asymptotic case of an infinite block
length. The bounds are compared with capacity-approaching LDPC code ensembles
under BP decoding, and they are shown to be informative and are easy to
calculate. Finally, some interesting open problems are considered.
|
0709.0670
|
Using Data Compressors to Construct Rank Tests
|
cs.DS cs.IT math.IT
|
Nonparametric rank tests for homogeneity and component independence are
proposed, which are based on data compressors. For homogeneity testing the idea
is to compress the binary string obtained by ordering the two joint samples and
writing 0 if the element is from the first sample and 1 if it is from the
second sample and breaking ties by randomization (extension to the case of
multiple samples is straightforward). $H_0$ should be rejected if the string is
compressed (to a certain degree) and accepted otherwise. We show that such a
test obtained from an ideal data compressor is valid against all alternatives.
Component independence is reduced to homogeneity testing by constructing two
samples, one of which is the first half of the original and the other is the
second half with one of the components randomly permuted.
|
0709.0674
|
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective
Attention, Curiosity & Creativity
|
cs.AI cs.GR
|
I postulate that human or other intelligent agents function or should
function as follows. They store all sensory observations as they come - the
data is holy. At any time, given some agent's current coding capabilities, part
of the data is compressible by a short and hopefully fast program / description
/ explanation / world model. In the agent's subjective eyes, such data is more
regular and more "beautiful" than other data. It is well-known that knowledge
of regularity and repeatability may improve the agent's ability to plan actions
leading to external rewards. In absence of such rewards, however, known beauty
is boring. Then "interestingness" becomes the first derivative of subjective
beauty: as the learning agent improves its compression algorithm, formerly
apparently random data parts become subjectively more regular and beautiful.
Such progress in compressibility is measured and maximized by the curiosity
drive: create action sequences that extend the observation history and yield
previously unknown / unpredictable but quickly learnable algorithmic
regularity. We discuss how all of the above can be naturally implemented on
computers, through an extension of passive unsupervised learning to the case of
active data selection: we reward a general reinforcement learner (with access
to the adaptive compressor) for actions that improve the subjective
compressibility of the growing data. An unusually large breakthrough in
compressibility deserves the name "discovery". The "creativity" of artists,
dancers, musicians, pure mathematicians can be viewed as a by-product of this
principle. Several qualitative examples support this hypothesis.
|
0709.0680
|
Designing a Virtual Manikin Animation Framework Aimed at Virtual
Prototyping
|
cs.RO
|
In the industry, numerous commercial packages provide tools to introduce, and
analyse human behaviour in the product's environment (for maintenance,
ergonomics...), thanks to Virtual Humans. We will focus on control. Thanks to
algorithms newly introduced in recent research papers, we think we can provide
an implementation, which even widens, and simplifies the animation capacities
of virtual manikins. In order to do so, we are going to express the industrial
expectations as for Virtual Humans, without considering feasibility (not to
bias the issue). The second part will show that no commercial application
provides the tools that perfectly meet the needs. Thus we propose a new
animation framework that better answers the problem. Our contribution is the
integration - driven by need ~ of available new scientific techniques to
animate Virtual Humans, in a new control scheme that better answers industrial
expectations.
|
0709.0787
|
Sound Generation by a Turbulent Flow in Musical Instruments -
Multiphysics Simulation Approach -
|
physics.comp-ph cs.CE physics.flu-dyn
|
Total computational costs of scientific simulations are analyzed between
direct numerical simulations (DNS) and multiphysics simulations (MPS) for sound
generation in musical instruments. In order to produce acoustic sound by a
turbulent flow in a simple recorder-like instrument, compressible fluid dynamic
calculations with a low Mach number are required around the edges and the
resonator of the instrument in DNS, while incompressible fluid dynamic
calculations coupled with dynamics of sound propagation based on the
Lighthill's acoustic analogy are used in MPS. These strategies are evaluated
not only from the viewpoint of computational performances but also from the
theoretical points of view as tools for scientific simulations of complicated
systems.
|
0709.0883
|
Liquid State Machines in Adbiatic Quantum Computers for General
Computation
|
cs.CC cs.NE
|
Major mistakes do not read
|
0709.0907
|
Computability of probability measures and Martin-Lof randomness over
metric spaces
|
cs.IT math.IT
|
In this paper we investigate algorithmic randomness on more general spaces
than the Cantor space, namely computable metric spaces. To do this, we first
develop a unified framework allowing computations with probability measures. We
show that any computable metric space with a computable probability measure is
isomorphic to the Cantor space in a computable and measure-theoretic sense. We
show that any computable metric space admits a universal uniform randomness
test (without further assumption).
|
0709.0993
|
The Description of Information in 4-Dimensional Pseudo-Euclidean
Information Space
|
cs.IT math.IT nlin.AO physics.soc-ph
|
This article is presented new method of description information systems in
abstract 4-dimensional pseudo-Euclidean information space (4-DPIES) with using
special relativity (SR) methods. This purpose core postulates of existence
4-DPIES are formulated. The theorem setting existence criteria of the invariant
velocity of the information transference is formulated and proved. One more
theorem allowed relating discrete parameters of information and continuous
space-time treating and also row of supplementary theorems is formulated and
proved. For description of dynamics and interaction of information, in article
is introduced general parameter of information - generalized information
emotion (GIE), reminding simultaneously on properties the mass and the charge.
At performing calculation of information observable parameters in the
information space is introduced continual integration methods of Feynman. The
applying idea about existence of GIE as measures of the information inertness
and the interaction carrier, and using continual integration methods of Feynman
can be calculated probability of information process in 4-DPIES. In this frame
presented approach has allowed considering information systems when interest is
presented with information processes, their related with concrete definition
without necessity. The relation between 4-DPIES and real systems parameters is
set at modelling of matching between observable processes and real phenomena
from information interpretation.
|
0709.1074
|
Johnson Type Bounds on Constant Dimension Codes
|
cs.IT math.IT
|
Very recently, an operator channel was defined by Koetter and Kschischang
when they studied random network coding. They also introduced constant
dimension codes and demonstrated that these codes can be employed to correct
errors and/or erasures over the operator channel. Constant dimension codes are
equivalent to the so-called linear authentication codes introduced by Wang,
Xing and Safavi-Naini when constructing distributed authentication systems in
2003. In this paper, we study constant dimension codes. It is shown that
Steiner structures are optimal constant dimension codes achieving the
Wang-Xing-Safavi-Naini bound. Furthermore, we show that constant dimension
codes achieve the Wang-Xing-Safavi-Naini bound if and only if they are certain
Steiner structures. Then, we derive two Johnson type upper bounds, say I and
II, on constant dimension codes. The Johnson type bound II slightly improves on
the Wang-Xing-Safavi-Naini bound. Finally, we point out that a family of known
Steiner structures is actually a family of optimal constant dimension codes
achieving both the Johnson type bounds I and II.
|
0709.1099
|
Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise
Vehicle Localization and Road Matching
|
cs.AI cs.RO
|
This paper presents a multi-sensor fusion strategy for a novel road-matching
method designed to support real-time navigational features within advanced
driving-assistance systems. Managing multihypotheses is a useful strategy for
the road-matching problem. The multi-sensor fusion and multi-modal estimation
are realized using Dynamical Bayesian Network. Experimental results, using data
from Antilock Braking System (ABS) sensors, a differential Global Positioning
System (GPS) receiver and an accurate digital roadmap, illustrate the
performances of this approach, especially in ambiguous situations.
|
0709.1166
|
An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation
|
cs.DB
|
Monotonicity is a simple yet significant qualitative characteristic. We
consider the problem of segmenting a sequence in up to K segments. We want
segments to be as monotonic as possible and to alternate signs. We propose a
quality metric for this problem using the l_inf norm, and we present an optimal
linear time algorithm based on novel formalism. Moreover, given a
precomputation in time O(n log n) consisting of a labeling of all extrema, we
compute any optimal segmentation in constant time. We compare experimentally
its performance to two piecewise linear segmentation heuristics (top-down and
bottom-up). We show that our algorithm is faster and more accurate.
Applications include pattern recognition and qualitative modeling.
|
0709.1167
|
Using RDF to Model the Structure and Process of Systems
|
cs.AI
|
Many systems can be described in terms of networks of discrete elements and
their various relationships to one another. A semantic network, or
multi-relational network, is a directed labeled graph consisting of a
heterogeneous set of entities connected by a heterogeneous set of
relationships. Semantic networks serve as a promising general-purpose modeling
substrate for complex systems. Various standardized formats and tools are now
available to support practical, large-scale semantic network models. First, the
Resource Description Framework (RDF) offers a standardized semantic network
data model that can be further formalized by ontology modeling languages such
as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent
introduction of highly performant triple-stores (i.e. semantic network
databases) allows semantic network models on the order of $10^9$ edges to be
efficiently stored and manipulated. RDF and its related technologies are
currently used extensively in the domains of computer science, digital library
science, and the biological sciences. This article will provide an introduction
to RDF/RDFS/OWL and an examination of its suitability to model discrete element
complex systems.
|
0709.1190
|
Belief-Propagation for Weighted b-Matchings on Arbitrary Graphs and its
Relation to Linear Programs with Integer Solutions
|
cs.IT cs.AI math.IT
|
We consider the general problem of finding the minimum weight $\bm$-matching
on arbitrary graphs. We prove that, whenever the linear programming (LP)
relaxation of the problem has no fractional solutions, then the belief
propagation (BP) algorithm converges to the correct solution. We also show that
when the LP relaxation has a fractional solution then the BP algorithm can be
used to solve the LP relaxation. Our proof is based on the notion of graph
covers and extends the analysis of (Bayati-Shah-Sharma 2005 and Huang-Jebara
2007}.
These results are notable in the following regards: (1) It is one of a very
small number of proofs showing correctness of BP without any constraint on the
graph structure. (2) Variants of the proof work for both synchronous and
asynchronous BP; it is the first proof of convergence and correctness of an
asynchronous BP algorithm for a combinatorial optimization problem.
|
0709.1211
|
Likelihood ratios and Bayesian inference for Poisson channels
|
cs.IT math.IT math.ST stat.TH
|
In recent years, infinite-dimensional methods have been introduced for the
Gaussian channels estimation. The aim of this paper is to study the application
of similar methods to Poisson channels. In particular we compute the Bayesian
estimator of a Poisson channel using the likelihood ratio and the discrete
Malliavin gradient. This algorithm is suitable for numerical implementation via
the Monte-Carlo scheme. As an application we provide an new proof of the
formula obtained recently by Guo, Shamai and Verdu\'u relating some derivatives
of the input-output mutual information of a time-continuous Poisson channel and
the conditional mean estimator of the input. These results are then extended to
mixed Gaussian-Poisson channels.
|
0709.1227
|
Efficient Algorithms for Node Disjoint Subgraph Homeomorphism
Determination
|
cs.DS cs.DB
|
Recently, great efforts have been dedicated to researches on the management
of large scale graph based data such as WWW, social networks, biological
networks. In the study of graph based data management, node disjoint subgraph
homeomorphism relation between graphs is more suitable than (sub)graph
isomorphism in many cases, especially in those cases that node skipping and
node mismatching are allowed. However, no efficient node disjoint subgraph
homeomorphism determination (ndSHD) algorithms have been available. In this
paper, we propose two computationally efficient ndSHD algorithms based on state
spaces searching with backtracking, which employ many heuristics to prune the
search spaces. Experimental results on synthetic data sets show that the
proposed algorithms are efficient, require relative little time in most of the
testing cases, can scale to large or dense graphs, and can accommodate to more
complex fuzzy matching cases.
|
0709.1441
|
Information-theoretic analysis of MIMO channel sounding
|
cs.IT math.IT
|
The large majority of commercially available multiple-input multiple-output
(MIMO) radio channel measurement devices (sounders) is based on time-division
multiplexed switching (TDMS) of a single transmit/receive radio-frequency chain
into the elements of a transmit/receive antenna array. While being
cost-effective, such a solution can cause significant measurement errors due to
phase noise and frequency offset in the local oscillators. In this paper, we
systematically analyze the resulting errors and show that, in practice,
overestimation of channel capacity by several hundred percent can occur.
Overestimation is caused by phase noise (and to a lesser extent frequency
offset) leading to an increase of the MIMO channel rank. Our analysis
furthermore reveals that the impact of phase errors is, in general, most
pronounced if the physical channel has low rank (typical for line-of-sight or
poor scattering scenarios). The extreme case of a rank-1 physical channel is
analyzed in detail. Finally, we present measurement results obtained from a
commercially employed TDMS-based MIMO channel sounder. In the light of the
findings of this paper, the results obtained through MIMO channel measurement
campaigns using TDMS-based channel sounders should be interpreted with great
care.
|
0709.1516
|
On Universal Prediction and Bayesian Confirmation
|
math.ST cs.IT cs.LG math.IT stat.ML stat.TH
|
The Bayesian framework is a well-studied and successful framework for
inductive reasoning, which includes hypothesis testing and confirmation,
parameter estimation, sequence prediction, classification, and regression. But
standard statistical guidelines for choosing the model class and prior are not
always available or fail, in particular in complex situations. Solomonoff
completed the Bayesian framework by providing a rigorous, unique, formal, and
universal choice for the model class and the prior. We discuss in breadth how
and in which sense universal (non-i.i.d.) sequence prediction solves various
(philosophical) problems of traditional Bayesian sequence prediction. We show
that Solomonoff's model possesses many desirable properties: Strong total and
weak instantaneous bounds, and in contrast to most classical continuous prior
densities has no zero p(oste)rior problem, i.e. can confirm universal
hypotheses, is reparametrization and regrouping invariant, and avoids the
old-evidence and updating problem. It even performs well (actually better) in
non-computable environments.
|
0709.1667
|
Solving Constraint Satisfaction Problems through Belief
Propagation-guided decimation
|
cs.AI cond-mat.dis-nn cond-mat.stat-mech cs.CC
|
Message passing algorithms have proved surprisingly successful in solving
hard constraint satisfaction problems on sparse random graphs. In such
applications, variables are fixed sequentially to satisfy the constraints.
Message passing is run after each step. Its outcome provides an heuristic to
make choices at next step. This approach has been referred to as `decimation,'
with reference to analogous procedures in statistical physics.
The behavior of decimation procedures is poorly understood. Here we consider
a simple randomized decimation algorithm based on belief propagation (BP), and
analyze its behavior on random k-satisfiability formulae. In particular, we
propose a tree model for its analysis and we conjecture that it provides
asymptotically exact predictions in the limit of large instances. This
conjecture is confirmed by numerical simulations.
|
0709.1674
|
Matrix-Lifting Semi-Definite Programming for Decoding in Multiple
Antenna Systems
|
cs.IT math.IT
|
This paper presents a computationally efficient decoder for multiple antenna
systems. The proposed algorithm can be used for any constellation (QAM or PSK)
and any labeling method. The decoder is based on matrix-lifting Semi-Definite
Programming (SDP). The strength of the proposed method lies in a new relaxation
algorithm applied to the method of Mobasher et al. This results in a reduction
of the number of variables from $(NK+1)^2$ to $(2N+K)^2$, where $N$ is the
number of antennas and $K$ is the number of constellation points in each real
dimension. Since the computational complexity of solving SDP is a polynomial
function of the number of variables, we have a significant complexity
reduction. Moreover, the proposed method offers a better performance as
compared to the best quasi-maximum likelihood decoding methods reported in the
literature.
|
0709.1699
|
Efficient Tabling Mechanisms for Transaction Logic Programs
|
cs.LO cs.AI
|
In this paper we present efficient evaluation algorithms for the Horn
Transaction Logic (a generalization of the regular Horn logic programs with
state updates). We present two complementary methods for optimizing the
implementation of Transaction Logic. The first method is based on tabling and
we modified the proof theory to table calls and answers on states (practically,
equivalent to dynamic programming). The call-answer table is indexed on the
call and a signature of the state in which the call was made. The answer
columns contain the answer unification and a signature of the state after the
call was executed. The states are signed efficiently using a technique based on
tries and counting. The second method is based on incremental evaluation and it
applies when the data oracle contains derived relations. The deletions and
insertions (executed in the transaction oracle) change the state of the
database. Using the heuristic of inertia (only a part of the state changes in
response to elementary updates), most of the time it is cheaper to compute only
the changes in the state than to recompute the entire state from scratch. The
two methods are complementary by the fact that the first method optimizes the
evaluation when a call is repeated in the same state, and the second method
optimizes the evaluation of a new state when a call-state pair is not found by
the tabling mechanism (i.e. the first method). The proof theory of Transaction
Logic with the application of tabling and incremental evaluation is sound and
complete with respect to its model theory.
|
0709.1701
|
Enrichment of Qualitative Beliefs for Reasoning under Uncertainty
|
cs.AI
|
This paper deals with enriched qualitative belief functions for reasoning
under uncertainty and for combining information expressed in natural language
through linguistic labels. In this work, two possible enrichments (quantitative
and/or qualitative) of linguistic labels are considered and operators
(addition, multiplication, division, etc) for dealing with them are proposed
and explained. We denote them $qe$-operators, $qe$ standing for
"qualitative-enriched" operators. These operators can be seen as a direct
extension of the classical qualitative operators ($q$-operators) proposed
recently in the Dezert-Smarandache Theory of plausible and paradoxist reasoning
(DSmT). $q$-operators are also justified in details in this paper. The
quantitative enrichment of linguistic label is a numerical supporting degree in
$[0,\infty)$, while the qualitative enrichment takes its values in a finite
ordered set of linguistic values. Quantitative enrichment is less precise than
qualitative enrichment, but it is expected more close with what human experts
can easily provide when expressing linguistic labels with supporting degrees.
Two simple examples are given to show how the fusion of qualitative-enriched
belief assignments can be done.
|
0709.1744
|
Experiments with small helicopter automated landings at unusual
attitudes
|
cs.RO
|
This paper describes a set of experiments involving small helicopters landing
automated landing at unusual attitudes. By leveraging the increased agility of
small air vehicles, we show that it is possible to automatically land a small
helicopter on surfaces pitched at angles up to 60 degrees. Such maneuvers
require considerable agility from the vehicle and its avionics system, and they
pose significant technical and safety challenges. Our work builds upon previous
activities in human-inspired, high-agility flight for small rotorcraft.
However, it was not possible to leverage manual flight test data to extract
landing maneuvers due to stringent attitude and position control requirements.
Availability of low-cost, local navigation systems requiring no on-board
instrumentation has proven particularly important for these experiments to be
successful.
|
0709.1771
|
Variational local structure estimation for image super-resolution
|
cs.CV
|
Super-resolution is an important but difficult problem in image/video
processing. If a video sequence or some training set other than the given
low-resolution image is available, this kind of extra information can greatly
aid in the reconstruction of the high-resolution image. The problem is
substantially more difficult with only a single low-resolution image on hand.
The image reconstruction methods designed primarily for denoising is
insufficient for super-resolution problem in the sense that it tends to
oversmooth images with essentially no noise. We propose a new adaptive linear
interpolation method based on variational method and inspired by local linear
embedding (LLE). The experimental result shows that our method avoids the
problem of oversmoothing and preserves image structures well.
|
0709.1920
|
Bandwidth selection for kernel estimation in mixed multi-dimensional
spaces
|
cs.CV
|
Kernel estimation techniques, such as mean shift, suffer from one major
drawback: the kernel bandwidth selection. The bandwidth can be fixed for all
the data set or can vary at each points. Automatic bandwidth selection becomes
a real challenge in case of multidimensional heterogeneous features. This paper
presents a solution to this problem. It is an extension of \cite{Comaniciu03a}
which was based on the fundamental property of normal distributions regarding
the bias of the normalized density gradient. The selection is done iteratively
for each type of features, by looking for the stability of local bandwidth
estimates across a predefined range of bandwidths. A pseudo balloon mean shift
filtering and partitioning are introduced. The validity of the method is
demonstrated in the context of color image segmentation based on a
5-dimensional space.
|
0709.2065
|
Toward Psycho-robots
|
cs.AI
|
We try to perform geometrization of psychology by representing mental states,
<<ideas>>, by points of a metric space, <<mental space>>. Evolution of ideas is
described by dynamical systems in metric mental space. We apply the mental
space approach for modeling of flows of unconscious and conscious information
in the human brain. In a series of models, Models 1-4, we consider cognitive
systems with increasing complexity of psychological behavior determined by
structure of flows of ideas. Since our models are in fact models of the
AI-type, one immediately recognizes that they can be used for creation of
AI-systems, which we call psycho-robots, exhibiting important elements of human
psyche. Creation of such psycho-robots may be useful improvement of domestic
robots. At the moment domestic robots are merely simple working devices (e.g.
vacuum cleaners or lawn mowers) . However, in future one can expect demand in
systems which be able not only perform simple work tasks, but would have
elements of human self-developing psyche. Such AI-psyche could play an
important role both in relations between psycho-robots and their owners as well
as between psycho-robots. Since the presence of a huge numbers of
psycho-complexes is an essential characteristic of human psychology, it would
be interesting to model them in the AI-framework.
|
0709.2225
|
Improved Linear Parallel Interference Cancellers
|
cs.IT cs.SC cs.SD cs.SE math.IT
|
In this paper, taking the view that a linear parallel interference canceller
(LPIC) can be seen as a linear matrix filter, we propose new linear matrix
filters that can result in improved bit error performance compared to other
LPICs in the literature. The motivation for the proposed filters arises from
the possibility of avoiding the generation of certain interference and noise
terms in a given stage that would have been present in a conventional LPIC
(CLPIC). In the proposed filters, we achieve such avoidance of the generation
of interference and noise terms in a given stage by simply making the diagonal
elements of a certain matrix in that stage equal to zero. Hence, the proposed
filters do not require additional complexity compared to the CLPIC, and they
can allow achieving a certain error performance using fewer LPIC stages. We
also extend the proposed matrix filter solutions to a multicarrier DS-CDMA
system, where we consider two types of receivers. In one receiver (referred to
as Type-I receiver), LPIC is performed on each subcarrier first, followed by
multicarrier combining (MCC). In the other receiver (called Type-II receiver),
MCC is performed first, followed by LPIC. We show that in both Type-I and
Type-II receivers, the proposed matrix filters outperform other matrix filters.
Also, Type-II receiver performs better than Type-I receiver because of enhanced
accuracy of the interference estimates achieved due to frequency diversity
offered by MCC.
|
0709.2330
|
Queueing for ergodic arrivals and services
|
math.PR cs.IT math.IT
|
In this paper we revisit the results of Loynes (1962) on stability of queues
for ergodic arrivals and services, and show examples when the arrivals are
bounded and ergodic, the service rate is constant, and under stability the
limit distribution has larger than exponential tail.
|
0709.2346
|
Pushdown Compression
|
cs.IT cs.CC math.IT
|
The pressing need for eficient compression schemes for XML documents has
recently been focused on stack computation [6, 9], and in particular calls for
a formulation of information-lossless stack or pushdown compressors that allows
a formal analysis of their performance and a more ambitious use of the stack in
XML compression, where so far it is mainly connected to parsing mechanisms. In
this paper we introduce the model of pushdown compressor, based on pushdown
transducers that compute a single injective function while keeping the widest
generality regarding stack computation. The celebrated Lempel-Ziv algorithm
LZ78 [10] was introduced as a general purpose compression algorithm that
outperforms finite-state compressors on all sequences. We compare the
performance of the Lempel-Ziv algorithm with that of the pushdown compressors,
or compression algorithms that can be implemented with a pushdown transducer.
This comparison is made without any a priori assumption on the data's source
and considering the asymptotic compression ratio for infinite sequences. We
prove that Lempel-Ziv is incomparable with pushdown compressors.
|
0709.2401
|
Bootstrapping Deep Lexical Resources: Resources for Courses
|
cs.CL
|
We propose a range of deep lexical acquisition methods which make use of
morphological, syntactic and ontological language resources to model word
similarity and bootstrap from a seed lexicon. The different methods are
deployed in learning lexical items for a precision grammar, and shown to each
have strengths and weaknesses over different word classes. A particular focus
of this paper is the relative accessibility of different language resource
types, and predicted ``bang for the buck'' associated with each in deep lexical
acquisition applications.
|
0709.2410
|
Distributed Decision Through Self-Synchronizing Sensor Networks in the
Presence of Propagation Delays and Asymmetric Channels
|
cs.MA cs.DC
|
In this paper we propose and analyze a distributed algorithm for achieving
globally optimal decisions, either estimation or detection, through a
self-synchronization mechanism among linearly coupled integrators initialized
with local measurements. We model the interaction among the nodes as a directed
graph with weights (possibly) dependent on the radio channels and we pose
special attention to the effect of the propagation delay occurring in the
exchange of data among sensors, as a function of the network geometry. We
derive necessary and sufficient conditions for the proposed system to reach a
consensus on globally optimal decision statistics. One of the major results
proved in this work is that a consensus is reached with exponential convergence
speed for any bounded delay condition if and only if the directed graph is
quasi-strongly connected. We provide a closed form expression for the global
consensus, showing that the effect of delays is, in general, the introduction
of a bias in the final decision. Finally, we exploit our closed form expression
to devise a double-step consensus mechanism able to provide an unbiased
estimate with minimum extra complexity, without the need to know or estimate
the channel parameters.
|
0709.2445
|
A Simple Characterization of Strategic Behaviors in Broadcast Channels
|
cs.IT cs.GT math.IT
|
In this paper, we consider the problem of resource allocation among two
competing users sharing a binary symmetric broadcast channel. We model the
interaction between autonomous selfish users in the resource allocation and
analyze their strategic behavior in manipulating the allocation outcome. We
analytically show that users will improve their performance (i.e. gain higher
allocated rates) if they have more information about the strategy of the
competing user.
|
0709.2446
|
Learning for Dynamic Bidding in Cognitive Radio Resources
|
cs.LG cs.GT
|
In this paper, we model the various wireless users in a cognitive radio
network as a collection of selfish, autonomous agents that strategically
interact in order to acquire the dynamically available spectrum opportunities.
Our main focus is on developing solutions for wireless users to successfully
compete with each other for the limited and time-varying spectrum
opportunities, given the experienced dynamics in the wireless network. We
categorize these dynamics into two types: one is the disturbance due to the
environment (e.g. wireless channel conditions, source traffic characteristics,
etc.) and the other is the impact caused by competing users. To analyze the
interactions among users given the environment disturbance, we propose a
general stochastic framework for modeling how the competition among users for
spectrum opportunities evolves over time. At each stage of the dynamic resource
allocation, a central spectrum moderator auctions the available resources and
the users strategically bid for the required resources. The joint bid actions
affect the resource allocation and hence, the rewards and future strategies of
all users. Based on the observed resource allocation and corresponding rewards
from previous allocations, we propose a best response learning algorithm that
can be deployed by wireless users to improve their bidding policy at each
stage. The simulation results show that by deploying the proposed best response
learning algorithm, the wireless users can significantly improve their own
performance in terms of both the packet loss rate and the incurred cost for the
used resources.
|
0709.2506
|
Autoencoder, Principal Component Analysis and Support Vector Regression
for Data Imputation
|
cs.AI cs.DB
|
Data collection often results in records that have missing values or
variables. This investigation compares 3 different data imputation models and
identifies their merits by using accuracy measures. Autoencoder Neural
Networks, Principal components and Support Vector regression are used for
prediction and combined with a genetic algorithm to then impute missing
variables. The use of PCA improves the overall performance of the autoencoder
network while the use of support vector regression shows promising potential
for future investigation. Accuracies of up to 97.4 % on imputation of some of
the variables were achieved.
|
0709.2562
|
When are recommender systems useful?
|
cs.IR cs.CY cs.DL cs.DS physics.data-an physics.soc-ph
|
Recommender systems are crucial tools to overcome the information overload
brought about by the Internet. Rigorous tests are needed to establish to what
extent sophisticated methods can improve the quality of the predictions. Here
we analyse a refined correlation-based collaborative filtering algorithm and
compare it with a novel spectral method for recommending. We test them on two
databases that bear different statistical properties (MovieLens and Jester)
without filtering out the less active users and ordering the opinions in time,
whenever possible. We find that, when the distribution of user-user
correlations is narrow, simple averages work nearly as well as advanced
methods. Recommender systems can, on the other hand, exploit a great deal of
additional information in systems where external influence is negligible and
peoples' tastes emerge entirely. These findings are validated by simulations
with artificially generated data.
|
0709.2598
|
On the 3/4-Conjecture for Fix-Free Codes -- A Survey
|
cs.IT math.CO math.IT
|
In this survey we concern ourself with the question, wether there exists a
fix-free code for a given sequence of codeword lengths. For a given alphabet,
we obtain the {\em Kraftsum} of a code, if we divide for every length the
number of codewords of this length in the code by the total number of all
possible words of this length and then take summation over all codeword lengths
which appears in the code. The same way the Kraftsum of a lengths sequence
$(l_1,..., l_n) $ is given by $\sum_{i=1}^n q^{-l_i} $, where $q$ is the
numbers of letters in the alphabet. Kraft and McMillan have shown in
\cite{kraft} (1956), that there exists a prefix-free code with codeword lengths
of a certain lengths sequence, if the Kraftsum of the lengths sequence is
smaller than or equal to one. Furthermore they have shown, that the converse
also holds for all (uniquely decipherable) codes.\footnote{In this survey a
code means a set of words, such that any message which is encoded with these
words can be uniquely decoded. Therefore we omit in future the "uniquely
decipherable" and write only "code".} The question rises, if Kraft's and
McMillan's result can be generalized to other types of codes? Throughout, we
try to give an answer on this question for the class of fix-free codes. Since
any code has Kraftsum smaller than or equal to one, this answers the question
for the second implication of Kraft-McMillan's theorem. Therefore we pay
attention mainly to the first implication.
|
0709.2813
|
LDPC codes from Singer cycles
|
cs.IT math.IT
|
The main goal of coding theory is to devise efficient systems to exploit the
full capacity of a communication channel, thus achieving an arbitrarily small
error probability. Low Density Parity Check (LDPC) codes are a family of block
codes--characterised by admitting a sparse parity check matrix--with good
correction capabilities. In the present paper the orbits of subspaces of a
finite projective space under the action of a Singer cycle are investigated.
|
0709.2833
|
Distributed Space Time Codes with Low Decoding Complexity for
Asynchronous Relay Networks
|
cs.IT math.IT
|
Recently Li and Xia have proposed a transmission scheme for wireless relay
networks based on the Alamouti space time code and orthogonal frequency
division multiplexing to combat the effect of timing errors at the relay nodes.
This transmission scheme is amazingly simple and achieves a diversity order of
two for any number of relays. Motivated by its simplicity, this scheme is
extended to a more general transmission scheme that can achieve full
cooperative diversity for any number of relays. The conditions on the
distributed space time code (DSTC) structure that admit its application in the
proposed transmission scheme are identified and it is pointed out that the
recently proposed full diversity four group decodable DSTCs from precoded
co-ordinate interleaved orthogonal designs and extended Clifford algebras
satisfy these conditions. It is then shown how differential encoding at the
source can be combined with the proposed transmission scheme to arrive at a new
transmission scheme that can achieve full cooperative diversity in asynchronous
wireless relay networks with no channel information and also no timing error
knowledge at the destination node. Finally, four group decodable distributed
differential space time codes applicable in this new transmission scheme for
power of two number of relays are also provided.
|
0709.2851
|
Joint power control and user scheduling in multicell wireless networks:
Capacity scaling laws
|
cs.IT math.IT
|
We address the optimization of the sum rate performance in multicell
interference-limited singlehop networks where access points are allowed to
cooperate in terms of joint resource allocation. The resource allocation
policies considered here combine power control and user scheduling. Although
very promising from a conceptual point of view, the optimization of the sum of
per-link rates hinges, in principle, on tough issues such as computational
complexity and the requirement for heavy receiver-to-transmitter channel
information feedback across all network cells. In this paper, we show that, in
fact, distributed algorithms are actually obtainable in the asymptotic regime
where the numbers of users per cell is allowed to grow large. Additionally,
using extreme value theory, we provide scaling laws for upper and lower bounds
for the network capacity (sum of single user rates over all cells),
corresponding to zero-interference and worst-case interference scenarios. We
show that the scaling is either dominated by path loss statistics or by
small-scale fading, depending on the regime and user location scenario. We show
that upper and lower rate bounds behave in fact identically, asymptotically.
This remarkable result suggests not only that distributed resource allocation
is practically possible but also that the impact of multicell interference on
the capacity (in terms of scaling) actually vanishes asymptotically.
|
0709.3013
|
Supervised learning on graphs of spatio-temporal similarity in satellite
image sequences
|
cs.CV
|
High resolution satellite image sequences are multidimensional signals
composed of spatio-temporal patterns associated to numerous and various
phenomena. Bayesian methods have been previously proposed in (Heas and Datcu,
2005) to code the information contained in satellite image sequences in a graph
representation using Bayesian methods. Based on such a representation, this
paper further presents a supervised learning methodology of semantics
associated to spatio-temporal patterns occurring in satellite image sequences.
It enables the recognition and the probabilistic retrieval of similar events.
Indeed, graphs are attached to statistical models for spatio-temporal
processes, which at their turn describe physical changes in the observed scene.
Therefore, we adjust a parametric model evaluating similarity types between
graph patterns in order to represent user-specific semantics attached to
spatio-temporal phenomena. The learning step is performed by the incremental
definition of similarity types via user-provided spatio-temporal pattern
examples attached to positive or/and negative semantics. From these examples,
probabilities are inferred using a Bayesian network and a Dirichlet model. This
enables to links user interest to a specific similarity model between graph
patterns. According to the current state of learning, semantic posterior
probabilities are updated for all possible graph patterns so that similar
spatio-temporal phenomena can be recognized and retrieved from the image
sequence. Few experiments performed on a multi-spectral SPOT image sequence
illustrate the proposed spatio-temporal recognition method.
|
0709.3034
|
Query Evaluation in P2P Systems of Taxonomy-based Sources: Algorithms,
Complexity, and Optimizations
|
cs.DB cs.DC cs.DS cs.LO
|
In this study, we address the problem of answering queries over a
peer-to-peer system of taxonomy-based sources. A taxonomy states subsumption
relationships between negation-free DNF formulas on terms and negation-free
conjunctions of terms. To the end of laying the foundations of our study, we
first consider the centralized case, deriving the complexity of the decision
problem and of query evaluation. We conclude by presenting an algorithm that is
efficient in data complexity and is based on hypergraphs. More expressive forms
of taxonomies are also investigated, which however lead to intractability. We
then move to the distributed case, and introduce a logical model of a network
of taxonomy-based sources. On such network, a distributed version of the
centralized algorithm is then presented, based on a message passing paradigm,
and its correctness is proved. We finally discuss optimization issues, and
relate our work to the literature.
|
0709.3262
|
A software for learning Information Theory basics with emphasis on
Entropy of Spanish
|
cs.IT math.IT
|
In this paper, a tutorial software to learn Information Theory basics in a
practical way is reported. The software, called IT-tutor-UV, makes use of a
modern existing Spanish corpus for the modeling of the source. Both the source
and the channel coding are also included in this educational tool as part of
the learning experience. Entropy values of the Spanish language obtained with
the IT-tutor-UV are discussed and compared to others that were previously
calculated under limited conditions.
|
0709.3427
|
Mutual information for the selection of relevant variables in
spectrometric nonlinear modelling
|
cs.LG cs.NE stat.AP
|
Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, leading to overfitting and poor generalization abilities. In this
paper, we suggest the use of the mutual information measure to select variables
from the initial set. The mutual information measures the information content
in input variables with respect to the model output, without making any
assumption on the model that will be used; it is thus suitable for nonlinear
modelling. In addition, it leads to the selection of variables among the
initial set, and not to linear or nonlinear combinations of them. Without
decreasing the model performances compared to other variable projection
methods, it allows therefore a greater interpretability of the results.
|
0709.3461
|
Fast Algorithm and Implementation of Dissimilarity Self-Organizing Maps
|
cs.NE cs.LG
|
In many real world applications, data cannot be accurately represented by
vectors. In those situations, one possible solution is to rely on dissimilarity
measures that enable sensible comparison between observations. Kohonen's
Self-Organizing Map (SOM) has been adapted to data described only through their
dissimilarity matrix. This algorithm provides both non linear projection and
clustering of non vector data. Unfortunately, the algorithm suffers from a high
cost that makes it quite difficult to use with voluminous data sets. In this
paper, we propose a new algorithm that provides an important reduction of the
theoretical cost of the dissimilarity SOM without changing its outcome (the
results are exactly the same as the ones obtained with the original algorithm).
Moreover, we introduce implementation methods that result in very short running
times. Improvements deduced from the theoretical cost model are validated on
simulated and real world data (a word list clustering problem). We also
demonstrate that the proposed implementation methods reduce by a factor up to 3
the running time of the fast algorithm over a standard implementation.
|
0709.3541
|
Towards the Secrecy Capacity of the Gaussian MIMO Wire-tap Channel: The
2-2-1 Channel
|
cs.IT math.IT
|
We find the secrecy capacity of the 2-2-1 Gaussian MIMO wire-tap channel,
which consists of a transmitter and a receiver with two antennas each, and an
eavesdropper with a single antenna. We determine the secrecy capacity of this
channel by proposing an achievable scheme and then developing a tight upper
bound that meets the proposed achievable secrecy rate. We show that, for this
channel, Gaussian signalling in the form of beam-forming is optimal, and no
pre-processing of information is necessary.
|
0709.3586
|
Une adaptation des cartes auto-organisatrices pour des donn\'ees
d\'ecrites par un tableau de dissimilarit\'es
|
cs.NE cs.LG
|
Many data analysis methods cannot be applied to data that are not represented
by a fixed number of real values, whereas most of real world observations are
not readily available in such a format. Vector based data analysis methods have
therefore to be adapted in order to be used with non standard complex data. A
flexible and general solution for this adaptation is to use a (dis)similarity
measure. Indeed, thanks to expert knowledge on the studied data, it is
generally possible to define a measure that can be used to make pairwise
comparison between observations. General data analysis methods are then
obtained by adapting existing methods to (dis)similarity matrices. In this
article, we propose an adaptation of Kohonen's Self Organizing Map (SOM) to
(dis)similarity data. The proposed algorithm is an adapted version of the
vector based batch SOM. The method is validated on real world data: we provide
an analysis of the usage patterns of the web site of the Institut National de
Recherche en Informatique et Automatique, constructed thanks to web log mining
method.
|
0709.3587
|
Self-organizing maps and symbolic data
|
cs.NE cs.LG
|
In data analysis new forms of complex data have to be considered like for
example (symbolic data, functional data, web data, trees, SQL query and
multimedia data, ...). In this context classical data analysis for knowledge
discovery based on calculating the center of gravity can not be used because
input are not $\mathbb{R}^p$ vectors. In this paper, we present an application
on real world symbolic data using the self-organizing map. To this end, we
propose an extension of the self-organizing map that can handle symbolic data.
|
0709.3600
|
Cooperative Multiplexing in a Half Duplex Relay Network: Performance and
Constraints
|
cs.IT math.IT
|
Previous work on relay networks has concentrated primarily on the diversity
benefits of such techniques. This paper explores the possibility of also
obtaining multiplexing gain in a relay network, while retaining diversity gain.
Specifically, consider a network in which a single source node is equipped with
one antenna and a destination is equipped with two antennas. It is shown that,
in certain scenarios, by adding a relay with two antennas and using a
successive relaying protocol, the diversity multiplexing tradeoff performance
of the network can be lower bounded by that of a 2 by 2 MIMO channel, when the
decode-and-forward protocol is applied at the relay. A distributed D-BLAST
architecture is developed, in which parallel channel coding is applied to
achieve this tradeoff. A space-time coding strategy, which can bring a maximal
multiplexing gain of more than one, is also derived for this scenario. As will
be shown, while this space-time coding strategy exploits maximal diversity for
a small multiplexing gain, the proposed successive relaying scheme offers a
significant performance advantage for higher data rate transmission. In
addition to the specific results shown here, these ideas open a new direction
for exploiting the benefits of wireless relay networks.
|
0709.3639
|
Fast Selection of Spectral Variables with B-Spline Compression
|
cs.LG stat.AP
|
The large number of spectral variables in most data sets encountered in
spectral chemometrics often renders the prediction of a dependent variable
uneasy. The number of variables hopefully can be reduced, by using either
projection techniques or selection methods; the latter allow for the
interpretation of the selected variables. Since the optimal approach of testing
all possible subsets of variables with the prediction model is intractable, an
incremental selection approach using a nonparametric statistics is a good
option, as it avoids the computationally intensive use of the model itself. It
has two drawbacks however: the number of groups of variables to test is still
huge, and colinearities can make the results unstable. To overcome these
limitations, this paper presents a method to select groups of spectral
variables. It consists in a forward-backward procedure applied to the
coefficients of a B-Spline representation of the spectra. The criterion used in
the forward-backward procedure is the mutual information, allowing to find
nonlinear dependencies between variables, on the contrary of the generally used
correlation. The spline representation is used to get interpretability of the
results, as groups of consecutive spectral variables will be selected. The
experiments conducted on NIR spectra from fescue grass and diesel fuels show
that the method provides clearly identified groups of selected variables,
making interpretation easy, while keeping a low computational load. The
prediction performances obtained using the selected coefficients are higher
than those obtained by the same method applied directly to the original
variables and similar to those obtained using traditional models, although
using significantly less spectral variables.
|
0709.3640
|
Resampling methods for parameter-free and robust feature selection with
mutual information
|
cs.LG stat.AP
|
Combining the mutual information criterion with a forward feature selection
strategy offers a good trade-off between optimality of the selected feature
subset and computation time. However, it requires to set the parameter(s) of
the mutual information estimator and to determine when to halt the forward
procedure. These two choices are difficult to make because, as the
dimensionality of the subset increases, the estimation of the mutual
information becomes less and less reliable. This paper proposes to use
resampling methods, a K-fold cross-validation and the permutation test, to
address both issues. The resampling methods bring information about the
variance of the estimator, information which can then be used to automatically
set the parameter and to calculate a threshold to stop the forward procedure.
The procedure is illustrated on a synthetic dataset as well as on real-world
examples.
|
0709.3641
|
Representation of Functional Data in Neural Networks
|
cs.NE
|
Functional Data Analysis (FDA) is an extension of traditional data analysis
to functional data, for example spectra, temporal series, spatio-temporal
images, gesture recognition data, etc. Functional data are rarely known in
practice; usually a regular or irregular sampling is known. For this reason,
some processing is needed in order to benefit from the smooth character of
functional data in the analysis methods. This paper shows how to extend the
Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models
to functional data inputs, in particular when the latter are known through
lists of input-output pairs. Various possibilities for functional processing
are discussed, including the projection on smooth bases, Functional Principal
Component Analysis, functional centering and reduction, and the use of
differential operators. It is shown how to incorporate these functional
processing into the RBFN and MLP models. The functional approach is illustrated
on a benchmark of spectrometric data analysis.
|
0709.3642
|
Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data
Analysis
|
cs.NE
|
In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP)
to functional inputs. We show that fundamental results for classical MLP can be
extended to functional MLP. We obtain universal approximation results that show
the expressive power of functional MLP is comparable to that of numerical MLP.
We obtain consistency results which imply that the estimation of optimal
parameters for functional MLP is statistically well defined. We finally show on
simulated and real world data that the proposed model performs in a very
satisfactory way.
|
0709.3753
|
On Real-Time Communication Systems with Noisy Feedback
|
cs.IT cs.MA math.IT math.OC math.PR
|
We consider a real-time communication system with noisy feedback consisting
of a Markov source, a forward and a backward discrete memoryless channels, and
a receiver with finite memory. The objective is to design an optimal
communication strategy (that is, encoding, decoding, and memory update
strategies) to minimize the total expected distortion over a finite horizon. We
present a sequential decomposition for the problem, which results in a set of
nested optimality equations to determine optimal communication strategies. This
provides a systematic methodology to determine globally optimal joint
source-channel encoding and decoding strategies for real-time communication
systems with noisy feedback.
|
0709.3827
|
On Successive Refinement of Diversity for Fading ISI Channels
|
cs.IT math.IT
|
Rate and diversity impose a fundamental tradeoff in communications. This
tradeoff was investigated for Intersymbol Interference (ISI) channels in [4]. A
different point of view was explored in [1] where high-rate codes were designed
so that they have a high-diversity code embedded within them. Such diversity
embedded codes were investigated for flat fading channels and in this paper we
explore its application to ISI channels. In particular, we investigate the rate
tuples achievable for diversity embedded codes for scalar ISI channels through
particular coding strategies. The main result of this paper is that the
diversity multiplexing tradeoff for fading ISI channels is indeed successively
refinable. This implies that for fading single input single output (SISO) ISI
channels one can embed a high diversity code within a high rate code without
any performance loss (asymptotically). This is related to a deterministic
structural observation about the asymptotic behavior of frequency response of
channel with respect to fading strength of time domain taps.
|
0709.3915
|
Guessing Facets: Polytope Structure and Improved LP Decoding
|
cs.IT math.IT
|
In this paper we investigate the structure of the fundamental polytope used
in the Linear Programming decoding introduced by Feldman, Karger and
Wainwright. We begin by showing that for expander codes, every fractional
pseudocodeword always has at least a constant fraction of non-integral bits. We
then prove that for expander codes, the active set of any fractional
pseudocodeword is smaller by a constant fraction than the active set of any
codeword. We further exploit these geometrical properties to devise an improved
decoding algorithm with the same complexity order as LP decoding that provably
performs better, for any blocklength. It proceeds by guessing facets of the
polytope, and then resolving the linear program on these facets. While the LP
decoder succeeds only if the ML codeword has the highest likelihood over all
pseudocodewords, we prove that the proposed algorithm, when applied to suitable
expander codes, succeeds unless there exist a certain number of
pseudocodewords, all adjacent to the ML codeword on the LP decoding polytope,
and with higher likelihood than the ML codeword. We then describe an extended
algorithm, still with polynomial complexity, that succeeds as long as there are
at most polynomially many pseudocodewords above the ML codeword.
|
0709.3921
|
Geographic Gossip: Efficient Averaging for Sensor Networks
|
cs.IT cs.NI math.IT math.PR
|
Gossip algorithms for distributed computation are attractive due to their
simplicity, distributed nature, and robustness in noisy and uncertain
environments. However, using standard gossip algorithms can lead to a
significant waste in energy by repeatedly recirculating redundant information.
For realistic sensor network model topologies like grids and random geometric
graphs, the inefficiency of gossip schemes is related to the slow mixing times
of random walks on the communication graph. We propose and analyze an
alternative gossiping scheme that exploits geographic information. By utilizing
geographic routing combined with a simple resampling method, we demonstrate
substantial gains over previously proposed gossip protocols. For regular graphs
such as the ring or grid, our algorithm improves standard gossip by factors of
$n$ and $\sqrt{n}$ respectively. For the more challenging case of random
geometric graphs, our algorithm computes the true average to accuracy
$\epsilon$ using $O(\frac{n^{1.5}}{\sqrt{\log n}} \log \epsilon^{-1})$ radio
transmissions, which yields a $\sqrt{\frac{n}{\log n}}$ factor improvement over
standard gossip algorithms. We illustrate these theoretical results with
experimental comparisons between our algorithm and standard methods as applied
to various classes of random fields.
|
0709.3965
|
Evolving Classifiers: Methods for Incremental Learning
|
cs.LG cs.AI cs.NE
|
The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In this paper there is a comparison between Learn++, which
is one of the most recent incremental learning algorithms, and the new proposed
method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has
shown good incremental learning capabilities on benchmark datasets on which the
new ILUGA method has been tested. ILUGA has also shown good incremental
learning ability using only a few classifiers and does not suffer from
catastrophic forgetting. The results obtained for ILUGA on the Optical
Character Recognition (OCR) and Wine datasets are good, with an overall
accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT
for the difficult multi-class OCR dataset.
|
0709.3967
|
Classification of Images Using Support Vector Machines
|
cs.LG cs.AI
|
Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs are essentially binary classifiers, however, they can be adopted to
handle the multiple classification tasks common in remote sensing studies. The
two approaches commonly used are the One-Against-One (1A1) and One-Against-All
(1AA) techniques. In this paper, these approaches are evaluated in as far as
their impact and implication for land cover mapping. The main finding from this
research is that whereas the 1AA technique is more predisposed to yielding
unclassified and mixed pixels, the resulting classification accuracy is not
significantly different from 1A1 approach. It is the authors conclusions that
ultimately the choice of technique adopted boils down to personal preference
and the uniqueness of the dataset at hand.
|
0709.3974
|
Fitness landscape of the cellular automata majority problem: View from
the Olympus
|
cs.AI
|
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost.
|
0709.4010
|
Local search heuristics: Fitness Cloud versus Fitness Landscape
|
cs.AI
|
This paper introduces the concept of fitness cloud as an alternative way to
visualize and analyze search spaces than given by the geographic notion of
fitness landscape. It is argued that the fitness cloud concept overcomes
several deficiencies of the landscape representation. Our analysis is based on
the correlation between fitness of solutions and fitnesses of nearest solutions
according to some neighboring. We focus on the behavior of local search
heuristics, such as hill climber, on the well-known NK fitness landscape. In
both cases the fitness vs. fitness correlation is shown to be related to the
epistatic parameter K.
|
0709.4011
|
Measuring the Evolvability Landscape to study Neutrality
|
cs.AI
|
This theoretical work defines the measure of autocorrelation of evolvability
in the context of neutral fitness landscape. This measure has been studied on
the classical MAX-SAT problem. This work highlight a new characteristic of
neutral fitness landscapes which allows to design new adapted metaheuristic.
|
0709.4015
|
From Texts to Structured Documents: The Case of Health Practice
Guidelines
|
cs.AI
|
This paper describes a system capable of semi-automatically filling an XML
template from free texts in the clinical domain (practice guidelines). The XML
template includes semantic information not explicitly encoded in the text
(pairs of conditions and actions/recommendations). Therefore, there is a need
to compute the exact scope of conditions over text sequences expressing the
required actions. We present in this paper the rules developed for this task.
We show that the system yields good performance when applied to the analysis of
French practice guidelines.
|
0709.4035
|
Energy Efficient Estimation of Gaussian Sources Over Inhomogeneous
Gaussian MAC Channels
|
cs.IT math.IT
|
It has been shown lately the optimality of uncoded transmission in estimating
Gaussian sources over homogeneous/symmetric Gaussian multiple access channels
(MAC) using multiple sensors. It remains, however, unclear whether it still
holds for any arbitrary networks and/or with high channel signal-to-noise ratio
(SNR) and high signal-to-measurement-noise ratio (SMNR). In this paper, we
first provide a joint source and channel coding approach in estimating Gaussian
sources over Gaussian MAC channels, as well as its sufficient and necessary
condition in restoring Gaussian sources with a prescribed distortion value. An
interesting relationship between our proposed joint approach with a more
straightforward separate source and channel coding scheme is then established.
We then formulate constrained power minimization problems and transform them to
relaxed convex geometric programming problems, whose numerical results exhibit
that either separate or uncoded scheme becomes dominant over a linear topology
network. In addition, we prove that the optimal decoding order to minimize the
total transmission powers for both source and channel coding parts is solely
subject to the ranking of MAC channel qualities, and has nothing to do with the
ranking of measurement qualities. Finally, asymptotic results for homogeneous
networks are obtained which not only confirm the existing optimality of the
uncoded approach, but also show that the asymptotic SNR exponents of these
three approaches are all the same. Moreover, the proposed joint approach share
the same asymptotic ratio with respect to high SNR and high SMNR as the uncoded
scheme.
|
0709.4280
|
A converse to Moore's theorem on cellular automata
|
math.CO cs.IT math.DS math.GR math.IT
|
We prove a converse to Moore's ``Garden-of-Eden'' theorem: a group G is
amenable if and only if all cellular automata living on G that admit mutually
erasable patterns also admit gardens of Eden.
It had already been conjectured in that amenability could be characterized by
cellular automata. We prove the first part of that conjecture.
|
0709.4397
|
The Parallel-Sequential Duality : Matrices and Graphs
|
math.CO cs.IT math.IT
|
Usually, mathematical objects have highly parallel interpretations. In this
paper, we consider them as sequential constructors of other objects. In
particular, we prove that every reflexive directed graph can be interpreted as
a program that builds another and is itself builded by another. That leads to
some optimal memory computations, codings similar to modular decompositions and
other strange dynamical phenomenons.
|
0709.4464
|
Adaptive Investment Strategies For Periodic Environments
|
cs.CE cs.NE
|
In this paper, we present an adaptive investment strategy for environments
with periodic returns on investment. In our approach, we consider an investment
model where the agent decides at every time step the proportion of wealth to
invest in a risky asset, keeping the rest of the budget in a risk-free asset.
Every investment is evaluated in the market via a stylized return on investment
function (RoI), which is modeled by a stochastic process with unknown
periodicities and levels of noise. For comparison reasons, we present two
reference strategies which represent the case of agents with zero-knowledge and
complete-knowledge of the dynamics of the returns. We consider also an
investment strategy based on technical analysis to forecast the next return by
fitting a trend line to previous received returns. To account for the
performance of the different strategies, we perform some computer experiments
to calculate the average budget that can be obtained with them over a certain
number of time steps. To assure for fair comparisons, we first tune the
parameters of each strategy. Afterwards, we compare the performance of these
strategies for RoIs with different periodicities and levels of noise.
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0709.4466
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Serially Concatenated IRA Codes
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cs.IT math.IT
|
We address the error floor problem of low-density parity check (LDPC) codes
on the binary-input additive white Gaussian noise (AWGN) channel, by
constructing a serially concatenated code consisting of two systematic
irregular repeat accumulate (IRA) component codes connected by an interleaver.
The interleaver is designed to prevent stopping-set error events in one of the
IRA codes from propagating into stopping set events of the other code.
Simulations with two 128-bit rate 0.707 IRA component codes show that the
proposed architecture achieves a much lower error floor at higher SNRs,
compared to a 16384-bit rate 1/2 IRA code, but incurs an SNR penalty of about 2
dB at low to medium SNRs. Experiments indicate that the SNR penalty can be
reduced at larger blocklengths.
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