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1201.4139
|
Image decomposition with anisotropic diffusion applied to leaf-texture
analysis
|
cs.CV
|
Texture analysis is an important field of investigation that has received a
great deal of interest from computer vision community. In this paper, we
propose a novel approach for texture modeling based on partial differential
equation (PDE). Each image $f$ is decomposed into a family of derived
sub-images. $f$ is split into the $u$ component, obtained with anisotropic
diffusion, and the $v$ component which is calculated by the difference between
the original image and the $u$ component. After enhancing the texture attribute
$v$ of the image, Gabor features are computed as descriptors. We validate the
proposed approach on two texture datasets with high variability. We also
evaluate our approach on an important real-world application: leaf-texture
analysis. Experimental results indicate that our approach can be used to
produce higher classification rates and can be successfully employed for
different texture applications.
|
1201.4145
|
The Role of Social Networks in Information Diffusion
|
cs.SI physics.soc-ph
|
Online social networking technologies enable individuals to simultaneously
share information with any number of peers. Quantifying the causal effect of
these technologies on the dissemination of information requires not only
identification of who influences whom, but also of whether individuals would
still propagate information in the absence of social signals about that
information. We examine the role of social networks in online information
diffusion with a large-scale field experiment that randomizes exposure to
signals about friends' information sharing among 253 million subjects in situ.
Those who are exposed are significantly more likely to spread information, and
do so sooner than those who are not exposed. We further examine the relative
role of strong and weak ties in information propagation. We show that, although
stronger ties are individually more influential, it is the more abundant weak
ties who are responsible for the propagation of novel information. This
suggests that weak ties may play a more dominant role in the dissemination of
information online than currently believed.
|
1201.4210
|
Collaborative Personalized Web Recommender System using Entropy based
Similarity Measure
|
cs.IR cs.AI
|
On the internet, web surfers, in the search of information, always strive for
recommendations. The solutions for generating recommendations become more
difficult because of exponential increase in information domain day by day. In
this paper, we have calculated entropy based similarity between users to
achieve solution for scalability problem. Using this concept, we have
implemented an online user based collaborative web recommender system. In this
model based collaborative system, the user session is divided into two levels.
Entropy is calculated at both the levels. It is shown that from the set of
valuable recommenders obtained at level I; only those recommenders having lower
entropy at level II than entropy at level I, served as trustworthy
recommenders. Finally, top N recommendations are generated from such
trustworthy recommenders for an online user.
|
1201.4214
|
Channel Exploration and Exploitation with Imperfect Spectrum Sensing in
Cognitive Radio Networks
|
cs.IT math.IT
|
In this paper, the problem of opportunistic channel sensing and access in
cognitive radio networks when the sensing is imperfect and a secondary user has
limited traffic to send at a time is investigated. Primary users' statistical
information is assumed to be unknown, and therefore, a secondary user needs to
learn the information online during channel sensing and access process, which
means learning loss, also referred to as regret, is inevitable. In this
research, the case when all potential channels can be sensed simultaneously is
investigated first. The channel access process is modeled as a multi-armed
bandit problem with side observation. And channel access rules are derived and
theoretically proved to have asymptotically finite regret. Then the case when
the secondary user can sense only a limited number of channels at a time is
investigated. The channel sensing and access process is modeled as a bi-level
multi-armed bandit problem. It is shown that any adaptive rule has at least
logarithmic regret. Then we derive channel sensing and access rules and
theoretically prove they have logarithmic regret asymptotically and with finite
time. The effectiveness of the derived rules is validated by computer
simulation.
|
1201.4239
|
Dynamic Decision Making for Graphical Models Applied to Oil Exploration
|
stat.AP cs.AI stat.CO
|
This paper has been withdrawn by the authors. We present a framework for
sequential decision making in problems described by graphical models. The
setting is given by dependent discrete random variables with associated costs
or revenues. In our examples, the dependent variables are the potential
outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to
develop an optimal selection strategy that incorporates a chosen utility
function within an approximated dynamic programming scheme. We propose and
compare different approximations, from simple heuristics to more complex
iterative schemes, and we discuss their computational properties. We apply our
strategies to oil exploration over multiple prospects modeled by a directed
acyclic graph, and to a reservoir drilling decision problem modeled by a Markov
random field. The results show that the suggested strategies clearly improve
the simpler intuitive constructions, and this is useful when selecting
exploration policies.
|
1201.4243
|
Analysis of a Key Distribution Scheme in Secure Multicasting
|
cs.CR cs.DM cs.IT math.IT
|
This article presents an analysis of the secure key broadcasting scheme
proposed by Wu, Ruan, Lai and Tseng. The study of the parameters of the system
is based on a connection with a special type of symmetric equations over finite
fields. We present two different attacks against the system, whose efficiency
depends on the choice of the parameters. In particular, a time-memory tradeoff
attack is described, effective when a parameter of the scheme is chosen without
care. In such a situation, more than one third of the cases can be broken with
a time and space complexity in the range of the square root of the complexity
of the best attack suggested by Wu et al. against their system. This leads to a
feasible attack in a realistic scenario.
|
1201.4285
|
On Shore and Johnson properties for a Special Case of Csisz\'ar
f-divergences
|
cs.IT math.IT
|
The importance of power-law distributions is attributed to the fact that most
of the naturally occurring phenomenon exhibit this distribution. While
exponential distributions can be derived by minimizing KL-divergence w.r.t some
moment constraints, some power law distributions can be derived by minimizing
some generalizations of KL-divergence (more specifically some special cases of
Csisz\'ar f-divergences). Divergence minimization is very well studied in
information theoretical approaches to statistics. In this work we study
properties of minimization of Tsallis divergence, which is a special case of
Csisz\'ar f-divergence. In line with the work by Shore and Johnson (IEEE Trans.
IT, 1981), we examine the properties exhibited by these minimization methods
including the Pythagorean property.
|
1201.4291
|
Scaling of Congestion in Small World Networks
|
math.MG cond-mat.stat-mech cs.SI physics.soc-ph
|
In this report we show that in a planar exponentially growing network
consisting of $N$ nodes, congestion scales as $O(N^2/\log(N))$ independently of
how flows may be routed. This is in contrast to the $O(N^{3/2})$ scaling of
congestion in a flat polynomially growing network. We also show that without
the planarity condition, congestion in a small world network could scale as low
as $O(N^{1+\epsilon})$, for arbitrarily small $\epsilon$. These extreme results
demonstrate that the small world property by itself cannot provide guidance on
the level of congestion in a network and other characteristics are needed for
better resolution. Finally, we investigate scaling of congestion under the
geodesic flow, that is, when flows are routed on shortest paths based on a link
metric. Here we prove that if the link weights are scaled by arbitrarily small
or large multipliers then considerable changes in congestion may occur.
However, if we constrain the link-weight multipliers to be bounded away from
both zero and infinity, then variations in congestion due to such remetrization
are negligible.
|
1201.4334
|
Classification of Binary Self-Dual [48,24,10] Codes with an Automorphism
of Odd Prime Order
|
cs.IT math.CO math.IT
|
The purpose of this paper is to complete the classification of binary
self-dual [48,24,10] codes with an automorphism of odd prime order. We prove
that if there is a self-dual [48, 24, 10] code with an automorphism of type
p-(c,f) with p being an odd prime, then p=3, c=16, f=0. By considering only an
automorphism of type 3-(16,0), we prove that there are exactly 264 inequivalent
self-dual [48, 24, 10] codes with an automorphism of odd prime order,
equivalently, there are exactly 264 inequivalent cubic self-dual [48, 24, 10]
codes.
|
1201.4342
|
A Pareto-metaheuristic for a bi-objective winner determination problem
in a combinatorial reverse auction
|
cs.GT cs.AI math.OC
|
The bi-objective winner determination problem (2WDP-SC) of a combinatorial
procurement auction for transport contracts is characterized by a set B of
bundle bids, with each bundle bid b in B consisting of a bidding carrier c_b, a
bid price p_b, and a set tau_b transport contracts which is a subset of the set
T of tendered transport contracts. Additionally, the transport quality
q_{t,c_b} is given which is expected to be realized when a transport contract t
is executed by a carrier c_b. The task of the auctioneer is to find a set X of
winning bids (X subset B), such that each transport contract is part of at
least one winning bid, the total procurement costs are minimized, and the total
transport quality is maximized. This article presents a metaheuristic approach
for the 2WDP-SC which integrates the greedy randomized adaptive search
procedure with a two-stage candidate component selection procedure, large
neighborhood search, and self-adaptive parameter setting in order to find a
competitive set of non-dominated solutions. The heuristic outperforms all
existing approaches. For seven small benchmark instances, the heuristic is the
sole approach that finds all Pareto-optimal solutions. For 28 out of 30 large
instances, none of the existing approaches is able to compute a solution that
dominates a solution found by the proposed heuristic.
|
1201.4369
|
Exact solution of bond percolation on small arbitrary graphs
|
cond-mat.stat-mech cs.SI physics.soc-ph
|
We introduce a set of iterative equations that exactly solves the size
distribution of components on small arbitrary graphs after the random removal
of edges. We also demonstrate how these equations can be used to predict the
distribution of the node partitions (i.e., the constrained distribution of the
size of each component) in undirected graphs. Besides opening the way to the
theoretical prediction of percolation on arbitrary graphs of large but finite
size, we show how our results find application in graph theory, epidemiology,
percolation and fragmentation theory.
|
1201.4428
|
Error-Trellis Construction for Tailbiting Convolutional Codes
|
cs.IT math.IT
|
In this paper, we present an error-trellis construction for tailbiting
convolutional codes. A tailbiting error-trellis is characterized by the
condition that the syndrome former starts and ends in the same state. We
clarify the correspondence between code subtrellises in the tailbiting
code-trellis and error subtrellises in the tailbiting error-trellis. Also, we
present a construction of tailbiting backward error-trellises. Moreover, we
obtain the scalar parity-check matrix for a tailbiting convolutional code. The
proposed construction is based on the adjoint-obvious realization of a syndrome
former and its behavior is fully used in the discussion.
|
1201.4443
|
Dynamic behavior analysis for a six axis industrial machining robot
|
cs.RO
|
The six axis robots are widely used in automotive industry for their good
repeatability (as defined in the ISO92983) (painting, welding, mastic
deposition, handling etc.). In the aerospace industry, robot starts to be used
for complex applications such as drilling, riveting, fiber placement, NDT, etc.
Given the positioning performance of serial robots, precision applications
require usually external measurement device with complexes calibration
procedure in order to reach the precision needed. New applications in the
machining field of composite material (aerospace, naval, or wind turbine for
example) intend to use off line programming of serial robot without the use of
calibration or external measurement device. For those applications, the
position, orientation and path trajectory precision of the tool center point of
the robot are needed to generate the machining operation. This article presents
the different conditions that currently limit the development of robots in
robotic machining applications. We analyze the dynamical behavior of a robot
KUKA KR240-2 (located at the University of Bordeaux 1) equipped with a HSM
Spindle (42000 rpm, 18kW). This analysis is done in three stages. The first
step is determining the self-excited frequencies of the robot structure for
three different configurations of work. The second phase aims to analyze the
dynamical vibration of the structure as the spindle is activated without
cutting. The third stage consists of vibration analysis during a milling
operation.
|
1201.4445
|
Experimental Characterization of Robot Arm Rigidity in Order to Be Used
in Machining Operation
|
cs.RO
|
Attempts to install a rotating tool at the end of a robot arm
poly-articulated date back twenty years, but these robots were not designed for
that. Indeed, two essential features are necessary for machining: high rigidity
and precision in a given workspace. The experimental results presented are the
dynamic identification of a poly-articulated robot equipped with an integrated
spindle. This study aims to highlight the influence of the geometric
configuration of the robot arm on the overall stiffness of the system. The
spindle is taken into account as an additional weight on board but also as a
dynamical excitation for the robot KUKA KR_240_2. Study of the robotic
machining vibrations shows the suitable directions of movement in milling
process
|
1201.4469
|
Uncertainty Bounds for Spectral Estimation
|
cs.SY math.OC math.ST stat.TH
|
The purpose of this paper is to study metrics suitable for assessing
uncertainty of power spectra when these are based on finite second-order
statistics. The family of power spectra which is consistent with a given range
of values for the estimated statistics represents the uncertainty set about the
"true" power spectrum. Our aim is to quantify the size of this uncertainty set
using suitable notions of distance, and in particular, to compute the diameter
of the set since this represents an upper bound on the distance between any
choice of a nominal element in the set and the "true" power spectrum. Since the
uncertainty set may contain power spectra with lines and discontinuities, it is
natural to quantify distances in the weak topology---the topology defined by
continuity of moments. We provide examples of such weakly-continuous metrics
and focus on particular metrics for which we can explicitly quantify spectral
uncertainty. We then consider certain high resolution techniques which utilize
filter-banks for pre-processing, and compute worst-case a priori uncertainty
bounds solely on the basis of the filter dynamics. This allows the a priori
tuning of the filter-banks for improved resolution over selected frequency
bands.
|
1201.4477
|
Wireless Network Coding for MIMO Two-way Relaying using Latin Rectangles
|
cs.IT math.IT
|
The design of modulation schemes for the physical layer network-coded two-way
MIMO relaying scenario is considered, with $n_R$ antennas at the relay R, $n_A$
and $n_B$ antennas respectively at the end nodes A and B. We consider the
denoise-and-forward (DNF) protocol which employs two phases: Multiple access
(MA) phase and Broadcast (BC) phase. It is known for the network-coded SISO
two-way relaying that adaptively changing the networking coding map used at the
relay, also known as the denoising map, according to the channel conditions
greatly reduces the impact of multiple access interference which occurs at the
relay during the MA phase and all these network coding maps should satisfy a
requirement called the {\it exclusive law}. The network coding maps which
satisfy exclusive law can be viewed equivalently as Latin Rectangles. In this
paper, it is shown that for MIMO two-way relaying, deep fade occurs at the
relay when the row space of the channel fade coefficient matrix is a subspace
of a finite number of vector subspaces of $\mathbb{C}^{n_A+n_B}$ which are
referred to as the singular fade subspaces. It is shown that proper choice of
network coding map can remove most of the singular fade subspaces, referred to
as the removable singular fade subspaces. For $2^{\lambda}$-PSK signal set, it
is shown that the number of non-removable singular fade subspaces is a small
fraction of the total number of singular fade subspaces. The Latin Rectangles
for the case when the end nodes use different number of antennas are shown to
be obtainable from the Latin Squares for the case when they use the same number
of antennas. Also, the network coding maps which remove all the removable
singular singular fade subspaces are shown to be obtainable from a small set of
Latin Squares.
|
1201.4479
|
Distributed Data Storage in Large-Scale Sensor Networks Based on LT
Codes
|
cs.IT cs.DB math.IT
|
This paper proposes an algorithm for increasing data persistency in
large-scale sensor networks. In the scenario considered here, k out of n nodes
sense the phenomenon and produced ? information packets. Due to usually
hazardous environment and limited resources, e.g. energy, sensors in the
network are vulnerable. Also due to the large size of the network, gathering
information from a few central hopes is not feasible. Flooding is not a desired
option either due to limited memory of each node. Therefore the best approach
to increase data persistency is propagating data throughout the network by
random walks. The algorithm proposed here is based on distributed LT (Luby
Transform) codes and it benefits from the low complexity of encoding and
decoding of LT codes. In previous algorithms the essential global information
(e.g., n and k) are estimated based on graph statistics, which requires
excessive transmissions. In our proposed algorithm, these values are obtained
without additional transmissions. Also the mixing time of random walk is
enhanced by proposing a new scheme for generating the probabilistic forwarding
table of random walk. The proposed method uses only local information and it is
scalable to any network topology. By simulations the improved performance of
developed algorithm compared to previous ones has been verified.
|
1201.4480
|
A Solution to Fastest Distributed Consensus Problem for Generic Star &
K-cored Star Networks
|
cs.IT cs.DC math.IT
|
Distributed average consensus is the main mechanism in algorithms for
decentralized computation. In distributed average consensus algorithm each node
has an initial state, and the goal is to compute the average of these initial
states in every node. To accomplish this task, each node updates its state by a
weighted average of its own and neighbors' states, by using local communication
between neighboring nodes. In the networks with fixed topology, convergence
rate of distributed average consensus algorithm depends on the choice of
weights. This paper studies the weight optimization problem in distributed
average consensus algorithm. The network topology considered here is a star
network where the branches have different lengths. Closed-form formulas of
optimal weights and convergence rate of algorithm are determined in terms of
the network's topological parameters. Furthermore generic K-cored star topology
has been introduced as an alternative to star topology. The introduced topology
benefits from faster convergence rate compared to star topology. By simulation
better performance of optimal weights compared to other common weighting
methods has been proved.
|
1201.4499
|
Mathematical and computational modeling for describing the basic
behavior of free radicals and antioxidants within epithelial cells
|
cs.CE q-bio.QM
|
The traditional methods of the biology, based on illustrative descriptions
and linear logic explanations, are discussed. This work aims to improve this
approach by introducing alternative tools to describe and represent complex
biological systems. Two models were developed, one mathematical and another
computational, both were made in order to study the biological process between
free radicals and antioxidants. Each model was used to study the same process
but in different scenarios. The mathematical model was used to study the
biological process in an epithelial cells culture; this model was validated
with the experimental data of Anne Hanneken's research group from the
Department of Molecular and Experimental Medicine, published by the journal
Investigative Ophthalmology and Visual Science in July 2006. The computational
model was used to study the same process in an individual. The model was made
using C++ programming language, supported by the network theory of aging.
|
1201.4564
|
Homophily and Long-Run Integration in Social Networks
|
physics.soc-ph cs.SI
|
We model network formation when heterogeneous nodes enter sequentially and
form connections through both random meetings and network-based search, but
with type-dependent biases. We show that there is "long-run integration,"
whereby the composition of types in sufficiently old nodes' neighborhoods
approaches the global type distribution, provided that the network-based search
is unbiased. However, younger nodes' connections still reflect the biased
meetings process. We derive the type-based degree distributions and group-level
homophily patterns when there are two types and location-based biases. Finally,
we illustrate aspects of the model with an empirical application to data on
citations in physics journals.
|
1201.4565
|
Discrete Opinion models as a limit case of the CODA model
|
physics.soc-ph cs.MA cs.SI nlin.AO
|
Opinion Dynamics models can be, for most of them, divided between discrete
and continuous. They are used in different circumstances and the relationship
between them is not clear. Here we will explore the relationship between a
model where choices are discrete but opinions are a continuous function (the
Continuous Opinions and Discrete Actions, CODA, model) and traditional discrete
models. I will show that, when CODA is altered to include reasoning about the
influence one agent can have on its own neighbors, agreement and disagreement
no longer have the same importance. The limit when an agent considers itself to
be more and more influential will be studied and we will see that one recovers
discrete dynamics, like those of the Voter model in that limit
|
1201.4597
|
Fractal Descriptors Based on Fourier Spectrum Applied to Texture
Analysis
|
physics.data-an cs.CV
|
This work proposes the development and study of a novel technique for the
generation of fractal descriptors used in texture analysis. The novel
descriptors are obtained from a multiscale transform applied to the Fourier
technique of fractal dimension calculus. The power spectrum of the Fourier
transform of the image is plotted against the frequency in a log- log scale and
a multiscale transform is applied to this curve. The obtained values are taken
as the fractal descriptors of the image. The validation of the propose is
performed by the use of the descriptors for the classification of a dataset of
texture images whose real classes are previously known. The classification
precision is compared to other fractal descriptors known in the literature. The
results confirm the efficiency of the proposed method.
|
1201.4602
|
Bond percolation on a class of correlated and clustered random graphs
|
cond-mat.stat-mech cs.SI physics.soc-ph q-bio.PE
|
We introduce a formalism for computing bond percolation properties of a class
of correlated and clustered random graphs. This class of graphs is a
generalization of the Configuration Model where nodes of different types are
connected via different types of hyperedges, edges that can link more than 2
nodes. We argue that the multitype approach coupled with the use of clustered
hyperedges can reproduce a wide spectrum of complex patterns, and thus enhances
our capability to model real complex networks. As an illustration of this
claim, we use our formalism to highlight unusual behaviors of the size and
composition of the components (small and giant) in a synthetic, albeit
realistic, social network.
|
1201.4615
|
Augmented L1 and Nuclear-Norm Models with a Globally Linearly Convergent
Algorithm
|
cs.IT math.IT math.OC
|
This paper studies the long-existing idea of adding a nice smooth function to
"smooth" a non-differentiable objective function in the context of sparse
optimization, in particular, the minimization of
$||x||_1+1/(2\alpha)||x||_2^2$, where $x$ is a vector, as well as the
minimization of $||X||_*+1/(2\alpha)||X||_F^2$, where $X$ is a matrix and
$||X||_*$ and $||X||_F$ are the nuclear and Frobenius norms of $X$,
respectively. We show that they can efficiently recover sparse vectors and
low-rank matrices. In particular, they enjoy exact and stable recovery
guarantees similar to those known for minimizing $||x||_1$ and $||X||_*$ under
the conditions on the sensing operator such as its null-space property,
restricted isometry property, spherical section property, or RIPless property.
To recover a (nearly) sparse vector $x^0$, minimizing
$||x||_1+1/(2\alpha)||x||_2^2$ returns (nearly) the same solution as minimizing
$||x||_1$ almost whenever $\alpha\ge 10||x^0||_\infty$. The same relation also
holds between minimizing $||X||_*+1/(2\alpha)||X||_F^2$ and minimizing
$||X||_*$ for recovering a (nearly) low-rank matrix $X^0$, if $\alpha\ge
10||X^0||_2$. Furthermore, we show that the linearized Bregman algorithm for
minimizing $||x||_1+1/(2\alpha)||x||_2^2$ subject to $Ax=b$ enjoys global
linear convergence as long as a nonzero solution exists, and we give an
explicit rate of convergence. The convergence property does not require a
solution solution or any properties on $A$. To our knowledge, this is the best
known global convergence result for first-order sparse optimization algorithms.
|
1201.4672
|
Estimation of the Covariance Matrix of Large Dimensional Data
|
cs.IT math.IT
|
This paper deals with the problem of estimating the covariance matrix of a
series of independent multivariate observations, in the case where the
dimension of each observation is of the same order as the number of
observations. Although such a regime is of interest for many current
statistical signal processing and wireless communication issues, traditional
methods fail to produce consistent estimators and only recently results relying
on large random matrix theory have been unveiled. In this paper, we develop the
parametric framework proposed by Mestre, and consider a model where the
covariance matrix to be estimated has a (known) finite number of eigenvalues,
each of it with an unknown multiplicity. The main contributions of this work
are essentially threefold with respect to existing results, and in particular
to Mestre's work: To relax the (restrictive) separability assumption, to
provide joint consistent estimates for the eigenvalues and their
multiplicities, and to study the variance error by means of a Central Limit
theorem.
|
1201.4714
|
A metric learning perspective of SVM: on the relation of SVM and LMNN
|
cs.LG stat.ML
|
Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor
algorithm, LMNN, are two very popular learning algorithms with quite different
learning biases. In this paper we bring them into a unified view and show that
they have a much stronger relation than what is commonly thought. We analyze
SVMs from a metric learning perspective and cast them as a metric learning
problem, a view which helps us uncover the relations of the two algorithms. We
show that LMNN can be seen as learning a set of local SVM-like models in a
quadratic space. Along the way and inspired by the metric-based interpretation
of SVM s we derive a novel variant of SVMs, epsilon-SVM, to which LMNN is even
more similar. We give a unified view of LMNN and the different SVM variants.
Finally we provide some preliminary experiments on a number of benchmark
datasets in which show that epsilon-SVM compares favorably both with respect to
LMNN and SVM.
|
1201.4733
|
Du TAL au TIL
|
cs.CL cs.HC
|
Historically two types of NLP have been investigated: fully automated
processing of language by machines (NLP) and autonomous processing of natural
language by people, i.e. the human brain (psycholinguistics). We believe that
there is room and need for another kind, INLP: interactive natural language
processing. This intermediate approach starts from peoples' needs, trying to
bridge the gap between their actual knowledge and a given goal. Given the fact
that peoples' knowledge is variable and often incomplete, the aim is to build
bridges linking a given knowledge state to a given goal. We present some
examples, trying to show that this goal is worth pursuing, achievable and at a
reasonable cost.
|
1201.4737
|
Production System Rules as Protein Complexes from Genetic Regulatory
Networks
|
cs.NE
|
This short paper introduces a new way by which to design production system
rules. An indirect encoding scheme is presented which views such rules as
protein complexes produced by the temporal behaviour of an artificial genetic
regulatory network. This initial study begins by using a simple Boolean
regulatory network to produce traditional ternary-encoded rules before moving
to a fuzzy variant to produce real-valued rules. Competitive performance is
shown with related genetic regulatory networks and rule-based systems on
benchmark problems.
|
1201.4743
|
Voting Power : A Generalised Framework
|
math.ST cs.CY cs.GT cs.MA stat.OT stat.TH
|
This paper examines an area of Game Theory called Voting Power Theory. With
the adoption of a measure theoretic framework it argues that the many different
indices and tools currently used for measuring voting power can be replaced by
just three simple probabilities. The framework is sufficiently general to be
applicable to every conceivable type of voting game, and every possible
decision rule.
|
1201.4768
|
Completion Delay Minimization for Instantly Decodable Network Codes
|
cs.NI cs.IT math.IT
|
In this paper, we consider the problem of minimizing the completion delay for
instantly decodable network coding (IDNC), in wireless multicast and broadcast
scenarios. We are interested in this class of network coding due to its
numerous benefits, such as low decoding delay, low coding and decoding
complexities and simple receiver requirements. We first extend the IDNC graph,
which represents all feasible IDNC coding opportunities, to efficiently operate
in both multicast and broadcast scenarios. We then formulate the minimum
completion delay problem for IDNC as a stochastic shortest path (SSP) problem.
Although finding the optimal policy using SSP is intractable, we use this
formulation to draw the theoretical guidelines for the policies that can
efficiently reduce the completion delay in IDNC. Based on these guidelines, we
design a maximum weight clique selection algorithm, which can efficiently
reduce the IDNC completion delay in polynomial time. We also design a quadratic
time heuristic clique selection algorithm, which can operate in real-time
applications. Simulation results show that our proposed algorithms efficiently
reduce the IDNC completion delay compared to the random and maximum-rate
algorithms, and almost achieve the global optimal completion delay performance
over all network codes in broadcast scenarios.
|
1201.4777
|
A probabilistic methodology for multilabel classification
|
cs.AI cs.LG
|
Multilabel classification is a relatively recent subfield of machine
learning. Unlike to the classical approach, where instances are labeled with
only one category, in multilabel classification, an arbitrary number of
categories is chosen to label an instance. Due to the problem complexity (the
solution is one among an exponential number of alternatives), a very common
solution (the binary method) is frequently used, learning a binary classifier
for every category, and combining them all afterwards. The assumption taken in
this solution is not realistic, and in this work we give examples where the
decisions for all the labels are not taken independently, and thus, a
supervised approach should learn those existing relationships among categories
to make a better classification. Therefore, we show here a generic methodology
that can improve the results obtained by a set of independent probabilistic
binary classifiers, by using a combination procedure with a classifier trained
on the co-occurrences of the labels. We show an exhaustive experimentation in
three different standard corpora of labeled documents (Reuters-21578,
Ohsumed-23 and RCV1), which present noticeable improvements in all of them,
when using our methodology, in three probabilistic base classifiers.
|
1201.4779
|
On the ADI method for the Sylvester Equation and the
optimal-$\mathcal{H}_2$ points
|
math.NA cs.SY
|
The ADI iteration is closely related to the rational Krylov projection
methods for constructing low rank approximations to the solution of Sylvester
equation. In this paper we show that the ADI and rational Krylov approximations
are in fact equivalent when a special choice of shifts are employed in both
methods. We will call these shifts pseudo H2-optimal shifts. These shifts are
also optimal in the sense that for the Lyapunov equation, they yield a residual
which is orthogonal to the rational Krylov projection subspace. Via several
examples, we show that the pseudo H2-optimal shifts consistently yield nearly
optimal low rank approximations to the solutions of the Lyapunov equations.
|
1201.4787
|
PageRank and rank-reversal dependence on the damping factor
|
physics.soc-ph cs.SI physics.data-an
|
PageRank (PR) is an algorithm originally developed by Google to evaluate the
importance of web pages. Considering how deeply rooted Google's PR algorithm is
to gathering relevant information or to the success of modern businesses, the
question of rank-stability and choice of the damping factor (a parameter in the
algorithm) is clearly important. We investigate PR as a function of the damping
factor d on a network obtained from a domain of the World Wide Web, finding
that rank-reversal happens frequently over a broad range of PR (and of d). We
use three different correlation measures, Pearson, Spearman, and Kendall, to
study rank-reversal as d changes, and show that the correlation of PR vectors
drops rapidly as d changes from its frequently cited value, $d_0=0.85$.
Rank-reversal is also observed by measuring the Spearman and Kendall rank
correlation, which evaluate relative ranks rather than absolute PR.
Rank-reversal happens not only in directed networks containing rank-sinks but
also in a single strongly connected component, which by definition does not
contain any sinks. We relate rank-reversals to rank-pockets and bottlenecks in
the directed network structure. For the network studied, the relative rank is
more stable by our measures around $d=0.65$ than at $d=d_0$.
|
1201.4793
|
Space Shift Keying (SSK-) MIMO with Practical Channel Estimates
|
cs.IT math.IT
|
In this paper, we study the performance of space modulation for
Multiple-Input-Multiple-Output (MIMO) wireless systems with imperfect channel
knowledge at the receiver. We focus our attention on two transmission
technologies, which are the building blocks of space modulation: i) Space Shift
Keying (SSK) modulation; and ii) Time-Orthogonal-Signal-Design (TOSD-) SSK
modulation, which is an improved version of SSK modulation providing
transmit-diversity. We develop a single-integral closed-form analytical
framework to compute the Average Bit Error Probability (ABEP) of a mismatched
detector for both SSK and TOSD-SSK modulations. The framework exploits the
theory of quadratic-forms in conditional complex Gaussian Random Variables
(RVs) along with the Gil-Pelaez inversion theorem. The analytical model is very
general and can be used for arbitrary transmit- and receive-antennas, fading
distributions, fading spatial correlations, and training pilots. The analytical
derivation is substantiated through Monte Carlo simulations, and it is shown,
over independent and identically distributed (i.i.d.) Rayleigh fading channels,
that SSK modulation is as robust as single-antenna systems to imperfect channel
knowledge, and that TOSD-SSK modulation is more robust to channel estimation
errors than the Alamouti scheme. Furthermore, it is pointed out that only few
training pilots are needed to get reliable enough channel estimates for data
detection, and that transmit- and receive-diversity of SSK and TOSD-SSK
modulations are preserved even with imperfect channel knowledge.
|
1201.4895
|
Compressive Acquisition of Dynamic Scenes
|
cs.CV
|
Compressive sensing (CS) is a new approach for the acquisition and recovery
of sparse signals and images that enables sampling rates significantly below
the classical Nyquist rate. Despite significant progress in the theory and
methods of CS, little headway has been made in compressive video acquisition
and recovery. Video CS is complicated by the ephemeral nature of dynamic
events, which makes direct extensions of standard CS imaging architectures and
signal models difficult. In this paper, we develop a new framework for video CS
for dynamic textured scenes that models the evolution of the scene as a linear
dynamical system (LDS). This reduces the video recovery problem to first
estimating the model parameters of the LDS from compressive measurements, and
then reconstructing the image frames. We exploit the low-dimensional dynamic
parameters (the state sequence) and high-dimensional static parameters (the
observation matrix) of the LDS to devise a novel compressive measurement
strategy that measures only the dynamic part of the scene at each instant and
accumulates measurements over time to estimate the static parameters. This
enables us to lower the compressive measurement rate considerably. We validate
our approach with a range of experiments involving both video recovery, sensing
hyper-spectral data, and classification of dynamic scenes from compressive
data. Together, these applications demonstrate the effectiveness of the
approach.
|
1201.4897
|
Adaptive Systems with Closed-loop Reference Models: Stability,
Robustness and Transient Performance
|
math.OC cs.SY nlin.AO
|
This paper explores the properties of adaptive systems with closed-loop
reference models. Using additional design freedom available in closed-loop
reference models, we design new adaptive controllers that are (a) stable, and
(b) have improved transient properties. Numerical studies that complement
theoretical derivations are also reported.
|
1201.4906
|
Adaptive Shortest-Path Routing under Unknown and Stochastically Varying
Link States
|
cs.NI cs.LG
|
We consider the adaptive shortest-path routing problem in wireless networks
under unknown and stochastically varying link states. In this problem, we aim
to optimize the quality of communication between a source and a destination
through adaptive path selection. Due to the randomness and uncertainties in the
network dynamics, the quality of each link varies over time according to a
stochastic process with unknown distributions. After a path is selected for
communication, the aggregated quality of all links on this path (e.g., total
path delay) is observed. The quality of each individual link is not observable.
We formulate this problem as a multi-armed bandit with dependent arms. We show
that by exploiting arm dependencies, a regret polynomial with network size can
be achieved while maintaining the optimal logarithmic order with time. This is
in sharp contrast with the exponential regret order with network size offered
by a direct application of the classic MAB policies that ignore arm
dependencies. Furthermore, our results are obtained under a general model of
link-quality distributions (including heavy-tailed distributions) and find
applications in cognitive radio and ad hoc networks with unknown and dynamic
communication environments.
|
1201.4908
|
Self-Organisation of Evolving Agent Populations in Digital Ecosystems
|
cs.NE
|
We investigate the self-organising behaviour of Digital Ecosystems, because a
primary motivation for our research is to exploit the self-organising
properties of biological ecosystems. We extended a definition for the
complexity, grounded in the biological sciences, providing a measure of the
information in an organism's genome. Next, we extended a definition for the
stability, originating from the computer sciences, based upon convergence to an
equilibrium distribution. Finally, we investigated a definition for the
diversity, relative to the selection pressures provided by the user requests.
We conclude with a summary and discussion of the achievements, including the
experimental results.
|
1201.4914
|
Effective Clustering Algorithms for Gene Expression Data
|
cs.CE q-bio.GN q-bio.QM
|
Microarrays are made it possible to simultaneously monitor the expression
profiles of thousands of genes under various experimental conditions.
Identification of co-expressed genes and coherent patterns is the central goal
in microarray or gene expression data analysis and is an important task in
Bioinformatics research. In this paper, K-Means algorithm hybridised with
Cluster Centre Initialization Algorithm (CCIA) is proposed Gene Expression
Data. The proposed algorithm overcomes the drawbacks of specifying the number
of clusters in the K-Means methods. Experimental analysis shows that the
proposed method performs well on gene Expression Data when compare with the
traditional K- Means clustering and Silhouette Coefficients cluster measure.
|
1201.4949
|
Approximate Message Passing under Finite Alphabet Constraints
|
cs.IT math.IT
|
In this paper we consider Basis Pursuit De-Noising (BPDN) problems in which
the sparse original signal is drawn from a finite alphabet. To solve this
problem we propose an iterative message passing algorithm, which capitalises
not only on the sparsity but by means of a prior distribution also on the
discrete nature of the original signal. In our numerical experiments we test
this algorithm in combination with a Rademacher measurement matrix and a
measurement matrix derived from the random demodulator, which enables
compressive sampling of analogue signals. Our results show in both cases
significant performance gains over a linear programming based approach to the
considered BPDN problem. We also compare the proposed algorithm to a similar
message passing based algorithm without prior knowledge and observe an even
larger performance improvement.
|
1201.4955
|
Coordination, Differentiation and Fairness in a population of
cooperating agents
|
physics.soc-ph cs.SI
|
In a recent paper, we analyzed the self-assembly of a complex cooperation
network. The network was shown to approach a state where every agent invests
the same amount of resources. Nevertheless, highly-connected agents arise that
extract extraordinarily high payoffs while contributing comparably little to
any of their cooperations. Here, we investigate a variant of the model, in
which highly-connected agents have access to additional resources. We study
analytically and numerically whether these resources are invested in existing
collaborations, leading to a fairer load distribution, or in establishing new
collaborations, leading to an even less fair distribution of loads and payoffs.
|
1201.4999
|
A study of distributed QoS adapter in large-scale wireless networks
|
cs.NI cs.MA
|
Considering the comfortably establishing ad hoc networks, the use of this
type of network is increasing day to day. On the other side, it is predicted
that using multimedia applications will be more public in these network. As it
is known, in contrary to best-effort flows, the transmission of multimedia
flows in any network need support from QoS. However, the wireless ad hoc
networks are severely affected by bandwidth, and establishing a QoS in these
networks face problems. In this paper, we have proposed a thoroughly
distributed algorithm to support the QoS in ad hoc networks. This algorithm
guarantees the QoS of the real-time applications vis-a-vis each other and
best-effort flows as well. The algorithm suggested in this paper dynamically
regulates the Contention Window of the flows and serves the flows in terms of
their requests QoS choosing the smallest CW in every node. This algorithm also
uses the fixed and/or less stationary nodes for the transmission of real-time
flows by increasing the QoS of the multimedia flows. This algorithm is
preferred because it prioritizes the flows that are of the same class but have
not obtained favorite QoS compared to other flows of the same class in addition
to classifying the flows in the network and offering better services to the
classes of higher priority. All this occur without the controlled packets
forwarding and resource reserving and freeing method. We have proved the
correctness of this algorithm using Markov's mathematical model.
|
1201.5019
|
On the Exact Solution to a Smart Grid Cyber-Security Analysis Problem
|
math.OC cs.CE cs.SY
|
This paper considers a smart grid cyber-security problem analyzing the
vulnerabilities of electric power networks to false data attacks. The analysis
problem is related to a constrained cardinality minimization problem. The main
result shows that an $l_1$ relaxation technique provides an exact optimal
solution to this cardinality minimization problem. The proposed result is based
on a polyhedral combinatorics argument. It is different from well-known results
based on mutual coherence and restricted isometry property. The results are
illustrated on benchmarks including the IEEE 118-bus and 300-bus systems.
|
1201.5102
|
Conception and Use of Ontologies for Indexing and Searching by Semantic
Contents of Video Courses
|
cs.DL cs.IR
|
Nowadays, the video documents like educational courses available on the web
increases significantly. However, the information retrieval systems today can
not return to the users (students or teachers) of parts of those videos that
meet their exact needs expressed by a query consisting of semantic information.
In this paper, we present a model of pedagogical knowledge of current videos.
This knowledge is used throughout the process of indexing and semantic search
segments instructional videos. Our experimental results show that the proposed
approach is promising.
|
1201.5154
|
Finding short vectors in a lattice of Voronoi's first kind
|
cs.IT cs.DS math.IT
|
We show that for those lattices of Voronoi's first kind, a vector of shortest
nonzero Euclidean length can computed in polynomial time by computing a minimum
cut in a graph.
|
1201.5167
|
Interactive Encoding and Decoding Based on Binary LDPC Codes with
Syndrome Accumulation
|
cs.IT math.IT
|
Interactive encoding and decoding based on binary low-density parity-check
codes with syndrome accumulation (SA-LDPC-IED) is proposed and investigated.
Assume that the source alphabet is $\mathbf{GF}(2)$, and the side information
alphabet is finite. It is first demonstrated how to convert any classical
universal lossless code $\mathcal{C}_n$ (with block length $n$ and side
information available to both the encoder and decoder) into a universal
SA-LDPC-IED scheme. It is then shown that with the word error probability
approaching 0 sub-exponentially with $n$, the compression rate (including both
the forward and backward rates) of the resulting SA-LDPC-IED scheme is upper
bounded by a functional of that of $\mathcal{C}_n$, which in turn approaches
the compression rate of $\mathcal{C}_n$ for each and every individual sequence
pair $(x^n,y^n)$ and the conditional entropy rate $\mathrm{H}(X |Y)$ for any
stationary, ergodic source and side information $(X, Y)$ as the average
variable node degree $\bar{l}$ of the underlying LDPC code increases without
bound. When applied to the class of binary source and side information $(X, Y)$
correlated through a binary symmetrical channel with cross-over probability
unknown to both the encoder and decoder, the resulting SA-LDPC-IED scheme can
be further simplified, yielding even improved rate performance versus the bit
error probability when $\bar{l}$ is not large. Simulation results (coupled with
linear time belief propagation decoding) on binary source-side information
pairs confirm the theoretic analysis, and further show that the SA-LDPC-IED
scheme consistently outperforms the Slepian-Wolf coding scheme based on the
same underlying LDPC code. As a by-product, probability bounds involving LDPC
established in the course are also interesting on their own and expected to
have implications on the performance of LDPC for channel coding as well.
|
1201.5173
|
Timely Throughput of Heterogeneous Wireless Networks: Fundamental Limits
and Algorithms
|
cs.IT cs.NI math.IT
|
The proliferation of different wireless access technologies, together with
the growing number of multi-radio wireless devices suggest that the
opportunistic utilization of multiple connections at the users can be an
effective solution to the phenomenal growth of traffic demand in wireless
networks. In this paper we consider the downlink of a wireless network with $N$
Access Points (AP's) and $M$ clients, where each client is connected to several
out-of-band AP's, and requests delay-sensitive traffic (e.g., real-time video).
We adopt the framework of Hou, Borkar, and Kumar, and study the maximum total
timely throughput of the network, denoted by $C_{T^3}$, which is the maximum
average number of packets delivered successfully before their deadline. Solving
this problem is challenging since even the number of different ways of
assigning packets to the AP's is $N^M$. We overcome the challenge by proposing
a deterministic relaxation of the problem, which converts the problem to a
network with deterministic delays in each link. We show that the additive gap
between the capacity of the relaxed problem, denoted by $C_{det}$, and
$C_{T^3}$ is bounded by $2\sqrt{N(C_{det}+N/4)}$, which is asymptotically
negligible compared to $C_{det}$, when the network is operating at
high-throughput regime.
In addition, our numerical results show that the actual gap between $C_{T^3}$
and $C_{det}$ is in most cases much less than the worst-case gap proven
analytically. Moreover, using LP rounding methods we prove that the relaxed
problem can be approximated within additive gap of $N$. We extend the
analytical results to the case of time-varying channel states, real-time
traffic, prioritized traffic, and optimal online policies. Finally, we
generalize the model for deterministic relaxation to consider fading, rate
adaptation, and multiple simultaneous transmissions.
|
1201.5182
|
Data Mining as a Torch Bearer in Education Sector
|
cs.IR
|
Every data has a lot of hidden information. The processing method of data
decides what type of information data produce. In India education sector has a
lot of data that can produce valuable information. This information can be used
to increase the quality of education. But educational institution does not use
any knowledge discovery process approach on these data. Information and
communication technology puts its leg into the education sector to capture and
compile low cost information. Now a day a new research community, educational
data mining (EDM), is growing which is intersection of data mining and
pedagogy. In this paper we present roadmap of research done in EDM in various
segment of education sector.
|
1201.5198
|
Fragmentation transitions in multi-state voter models
|
physics.soc-ph cs.SI nlin.AO
|
Adaptive models of opinion formation among humans can display a fragmentation
transition, where a social network breaks into disconnected components. Here,
we investigate this transition in a class of models with arbitrary number of
opinions. In contrast to previous work we do not assume that opinions are
equidistant or arranged on a one-dimensional conceptual axis. Our investigation
reveals detailed analytical results on fragmentations in a three-opinion model,
which are confirmed by agent-based simulations. Furthermore, we show that in
certain models the number of opinions can be reduced without affecting the
fragmentation points.
|
1201.5217
|
Unsupervised Classification Using Immune Algorithm
|
cs.LG cs.AI
|
Unsupervised classification algorithm based on clonal selection principle
named Unsupervised Clonal Selection Classification (UCSC) is proposed in this
paper. The new proposed algorithm is data driven and self-adaptive, it adjusts
its parameters to the data to make the classification operation as fast as
possible. The performance of UCSC is evaluated by comparing it with the well
known K-means algorithm using several artificial and real-life data sets. The
experiments show that the proposed UCSC algorithm is more reliable and has high
classification precision comparing to traditional classification methods such
as K-means.
|
1201.5227
|
A New Local Adaptive Thresholding Technique in Binarization
|
cs.CV
|
Image binarization is the process of separation of pixel values into two
groups, white as background and black as foreground. Thresholding plays a major
in binarization of images. Thresholding can be categorized into global
thresholding and local thresholding. In images with uniform contrast
distribution of background and foreground like document images, global
thresholding is more appropriate. In degraded document images, where
considerable background noise or variation in contrast and illumination exists,
there exists many pixels that cannot be easily classified as foreground or
background. In such cases, binarization with local thresholding is more
appropriate. This paper describes a locally adaptive thresholding technique
that removes background by using local mean and mean deviation. Normally the
local mean computational time depends on the window size. Our technique uses
integral sum image as a prior processing to calculate local mean. It does not
involve calculations of standard deviations as in other local adaptive
techniques. This along with the fact that calculations of mean is independent
of window size speed up the process as compared to other local thresholding
techniques.
|
1201.5229
|
Cross-entropy optimisation of importance sampling parameters for
statistical model checking
|
cs.PF cs.CE cs.SY stat.CO
|
Statistical model checking avoids the exponential growth of states associated
with probabilistic model checking by estimating properties from multiple
executions of a system and by giving results within confidence bounds. Rare
properties are often very important but pose a particular challenge for
simulation-based approaches, hence a key objective under these circumstances is
to reduce the number and length of simulations necessary to produce a given
level of confidence. Importance sampling is a well-established technique that
achieves this, however to maintain the advantages of statistical model checking
it is necessary to find good importance sampling distributions without
considering the entire state space.
Motivated by the above, we present a simple algorithm that uses the notion of
cross-entropy to find the optimal parameters for an importance sampling
distribution. In contrast to previous work, our algorithm uses a low
dimensional vector of parameters to define this distribution and thus avoids
the often intractable explicit representation of a transition matrix. We show
that our parametrisation leads to a unique optimum and can produce many orders
of magnitude improvement in simulation efficiency. We demonstrate the efficacy
of our methodology by applying it to models from reliability engineering and
biochemistry.
|
1201.5241
|
Entropy functions and determinant inequalities
|
cs.IT math.IT
|
In this paper, we show that the characterisation of all determinant
inequalities for $n \times n$ positive definite matrices is equivalent to
determining the smallest closed and convex cone containing all entropy
functions induced by $n$ scalar Gaussian random variables. We have obtained
inner and outer bounds on the cone by using representable functions and
entropic functions. In particular, these bounds are tight and explicit for $n
\le 3$, implying that determinant inequalities for $3 \times 3$ positive
definite matrices are completely characterized by Shannon-type information
inequalities.
|
1201.5283
|
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
|
cs.LG
|
We study the non-smooth optimization problems in machine learning, where both
the loss function and the regularizer are non-smooth functions. Previous
studies on efficient empirical loss minimization assume either a smooth loss
function or a strongly convex regularizer, making them unsuitable for
non-smooth optimization. We develop a simple yet efficient method for a family
of non-smooth optimization problems where the dual form of the loss function is
bilinear in primal and dual variables. We cast a non-smooth optimization
problem into a minimax optimization problem, and develop a primal dual prox
method that solves the minimax optimization problem at a rate of $O(1/T)$
{assuming that the proximal step can be efficiently solved}, significantly
faster than a standard subgradient descent method that has an $O(1/\sqrt{T})$
convergence rate. Our empirical study verifies the efficiency of the proposed
method for various non-smooth optimization problems that arise ubiquitously in
machine learning by comparing it to the state-of-the-art first order methods.
|
1201.5338
|
On Constrained Spectral Clustering and Its Applications
|
cs.LG stat.ML
|
Constrained clustering has been well-studied for algorithms such as $K$-means
and hierarchical clustering. However, how to satisfy many constraints in these
algorithmic settings has been shown to be intractable. One alternative to
encode many constraints is to use spectral clustering, which remains a
developing area. In this paper, we propose a flexible framework for constrained
spectral clustering. In contrast to some previous efforts that implicitly
encode Must-Link and Cannot-Link constraints by modifying the graph Laplacian
or constraining the underlying eigenspace, we present a more natural and
principled formulation, which explicitly encodes the constraints as part of a
constrained optimization problem. Our method offers several practical
advantages: it can encode the degree of belief in Must-Link and Cannot-Link
constraints; it guarantees to lower-bound how well the given constraints are
satisfied using a user-specified threshold; it can be solved deterministically
in polynomial time through generalized eigendecomposition. Furthermore, by
inheriting the objective function from spectral clustering and encoding the
constraints explicitly, much of the existing analysis of unconstrained spectral
clustering techniques remains valid for our formulation. We validate the
effectiveness of our approach by empirical results on both artificial and real
datasets. We also demonstrate an innovative use of encoding large number of
constraints: transfer learning via constraints.
|
1201.5346
|
Tableau-based decision procedure for the multi-agent epistemic logic
with all coalitional operators for common and distributed knowledge
|
cs.LO cs.AI math.LO
|
We develop a conceptually clear, intuitive, and feasible decision procedure
for testing satisfiability in the full multi-agent epistemic logic CMAEL(CD)
with operators for common and distributed knowledge for all coalitions of
agents mentioned in the language. To that end, we introduce Hintikka structures
for CMAEL(CD) and prove that satisfiability in such structures is equivalent to
satisfiability in standard models. Using that result, we design an incremental
tableau-building procedure that eventually constructs a satisfying Hintikka
structure for every satisfiable input set of formulae of CMAEL(CD) and closes
for every unsatisfiable input set of formulae.
|
1201.5360
|
Characterization of Information Channels for Asymptotic Mean
Stationarity and Stochastic Stability of Non-stationary/Unstable Linear
Systems
|
cs.IT cs.SY math.IT math.OC
|
Stabilization of non-stationary linear systems over noisy communication
channels is considered. Stochastically stable sources, and unstable but
noise-free or bounded-noise systems have been extensively studied in
information theory and control theory literature since 1970s, with a renewed
interest in the past decade. There have also been studies on non-causal and
causal coding of unstable/non-stationary linear Gaussian sources. In this
paper, tight necessary and sufficient conditions for stochastic stabilizability
of unstable (non-stationary) possibly multi-dimensional linear systems driven
by Gaussian noise over discrete channels (possibly with memory and feedback)
are presented. Stochastic stability notions include recurrence, asymptotic mean
stationarity and sample path ergodicity, and the existence of finite second
moments. Our constructive proof uses random-time state-dependent stochastic
drift criteria for stabilization of Markov chains. For asymptotic mean
stationarity (and thus sample path ergodicity), it is sufficient that the
capacity of a channel is (strictly) greater than the sum of the logarithms of
the unstable pole magnitudes for memoryless channels and a class of channels
with memory. This condition is also necessary under a mild technical condition.
Sufficient conditions for the existence of finite average second moments for
such systems driven by unbounded noise are provided.
|
1201.5404
|
Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture
Models
|
cs.CV
|
A framework for adaptive and non-adaptive statistical compressive sensing is
developed, where a statistical model replaces the standard sparsity model of
classical compressive sensing. We propose within this framework optimal
task-specific sensing protocols specifically and jointly designed for
classification and reconstruction. A two-step adaptive sensing paradigm is
developed, where online sensing is applied to detect the signal class in the
first step, followed by a reconstruction step adapted to the detected class and
the observed samples. The approach is based on information theory, here
tailored for Gaussian mixture models (GMMs), where an information-theoretic
objective relationship between the sensed signals and a representation of the
specific task of interest is maximized. Experimental results using synthetic
signals, Landsat satellite attributes, and natural images of different sizes
and with different noise levels show the improvements achieved using the
proposed framework when compared to more standard sensing protocols. The
underlying formulation can be applied beyond GMMs, at the price of higher
mathematical and computational complexity.
|
1201.5411
|
Lower bounds on the Probability of Error for Classical and
Classical-Quantum Channels
|
cs.IT math.IT quant-ph
|
In this paper, lower bounds on error probability in coding for discrete
classical and classical-quantum channels are studied. The contribution of the
paper goes in two main directions: i) extending classical bounds of Shannon,
Gallager and Berlekamp to classical-quantum channels, and ii) proposing a new
framework for lower bounding the probability of error of channels with a
zero-error capacity in the low rate region. The relation between these two
problems is revealed by showing that Lov\'asz' bound on zero-error capacity
emerges as a natural consequence of the sphere packing bound once we move to
the more general context of classical-quantum channels. A variation of
Lov\'asz' bound is then derived to lower bound the probability of error in the
low rate region by means of auxiliary channels. As a result of this study,
connections between the Lov\'asz theta function, the expurgated bound of
Gallager, the cutoff rate of a classical channel and the sphere packing bound
for classical-quantum channels are established.
|
1201.5422
|
A Factor-Graph Representation of Probabilities in Quantum Mechanics
|
cs.IT math-ph math.IT math.MP quant-ph
|
A factor-graph representation of quantum-mechanical probabilities is
proposed. Unlike standard statistical models, the proposed representation uses
auxiliary variables (state variables) that are not random variables.
|
1201.5426
|
Constraint Propagation as Information Maximization
|
cs.AI
|
This paper draws on diverse areas of computer science to develop a unified
view of computation:
(1) Optimization in operations research, where a numerical objective function
is maximized under constraints, is generalized from the numerical total order
to a non-numerical partial order that can be interpreted in terms of
information. (2) Relations are generalized so that there are relations of which
the constituent tuples have numerical indexes, whereas in other relations these
indexes are variables. The distinction is essential in our definition of
constraint satisfaction problems. (3) Constraint satisfaction problems are
formulated in terms of semantics of conjunctions of atomic formulas of
predicate logic. (4) Approximation structures, which are available for several
important domains, are applied to solutions of constraint satisfaction
problems.
As application we treat constraint satisfaction problems over reals. These
cover a large part of numerical analysis, most significantly nonlinear
equations and inequalities. The chaotic algorithm analyzed in the paper
combines the efficiency of floating-point computation with the correctness
guarantees of arising from our logico-mathematical model of
constraint-satisfaction problems.
|
1201.5450
|
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
|
cs.RO
|
This article presents a new open-source C++ implementation to solve the SLAM
problem, which is focused on genericity, versatility and high execution speed.
It is based on an original object oriented architecture, that allows the
combination of numerous sensors and landmark types, and the integration of
various approaches proposed in the literature. The system capacities are
illustrated by the presentation of an inertial/vision SLAM approach, for which
several improvements over existing methods have been introduced, and that copes
with very high dynamic motions. Results with a hand-held camera are presented.
|
1201.5472
|
A multiagent urban traffic simulation
|
cs.AI
|
We built a multiagent simulation of urban traffic to model both ordinary
traffic and emergency or crisis mode traffic. This simulation first builds a
modeled road network based on detailed geographical information. On this
network, the simulation creates two populations of agents: the Transporters and
the Mobiles. Transporters embody the roads themselves; they are utilitarian and
meant to handle the low level realism of the simulation. Mobile agents embody
the vehicles that circulate on the network. They have one or several
destinations they try to reach using initially their beliefs of the structure
of the network (length of the edges, speed limits, number of lanes etc.).
Nonetheless, when confronted to a dynamic, emergent prone environment (other
vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather
reactive agent will activate more cognitive modules to adapt its beliefs,
desires and intentions. It may change its destination(s), change the tactics
used to reach the destination (favoring less used roads, following other
agents, using general headings), etc. We describe our current validation of our
model and the next planned improvements, both in validation and in
functionalities.
|
1201.5477
|
Entropy-growth-based model of emotionally charged online dialogues
|
physics.soc-ph cs.CL cs.SI physics.data-an
|
We analyze emotionally annotated massive data from IRC (Internet Relay Chat)
and model the dialogues between its participants by assuming that the driving
force for the discussion is the entropy growth of emotional probability
distribution. This process is claimed to be correlated to the emergence of the
power-law distribution of the discussion lengths observed in the dialogues. We
perform numerical simulations based on the noticed phenomenon obtaining a good
agreement with the real data. Finally, we propose a method to artificially
prolong the duration of the discussion that relies on the entropy of emotional
probability distribution.
|
1201.5484
|
Statistical analysis of emotions and opinions at Digg website
|
physics.soc-ph cs.CL cs.SI physics.data-an
|
We performed statistical analysis on data from the Digg.com website, which
enables its users to express their opinion on news stories by taking part in
forum-like discussions as well as directly evaluate previous posts and stories
by assigning so called "diggs". Owing to fact that the content of each post has
been annotated with its emotional value, apart from the strictly structural
properties, the study also includes an analysis of the average emotional
response of the posts commenting the main story. While analysing correlations
at the story level, an interesting relationship between the number of diggs and
the number of comments received by a story was found. The correlation between
the two quantities is high for data where small threads dominate and
consistently decreases for longer threads. However, while the correlation of
the number of diggs and the average emotional response tends to grow for longer
threads, correlations between numbers of comments and the average emotional
response are almost zero. We also show that the initial set of comments given
to a story has a substantial impact on the further "life" of the discussion:
high negative average emotions in the first 10 comments lead to longer threads
while the opposite situation results in shorter discussions. We also suggest
presence of two different mechanisms governing the evolution of the discussion
and, consequently, its length.
|
1201.5603
|
BIN@ERN: Binary-Ternary Compressing Data Coding
|
cs.IT cs.DS math.IT
|
This paper describes a new method of data encoding which may be used in
various modern digital, computer and telecommunication systems and devices. The
method permits the compression of data for storage or transmission, allowing
the exact original data to be reconstructed without any loss of content. The
method is characterized by the simplicity of implementation, as well as high
speed and compression ratio. The method is based on a unique scheme of
binary-ternary prefix-free encoding of characters of the original data. This
scheme does not require the transmission of the code tables from encoder to
decoder; allows for the linear presentation of the code lists; permits the
usage of computable indexes of the prefix codes in a linear list for decoding;
makes it possible to estimate the compression ratio prior to encoding; makes
the usage of multiplication and division operations, as well as operations with
the floating point unnecessary; proves to be effective for static as well as
adaptive coding; applicable to character sets of any size; allows for repeated
compression to improve the ratio.
|
1201.5604
|
Discrete and fuzzy dynamical genetic programming in the XCSF learning
classifier system
|
cs.AI cs.LG cs.NE cs.SY math.OC
|
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems.
|
1201.5608
|
Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks
|
cs.IT cs.NI math.IT
|
In this paper we introduce a novel modulation and multiplexing method which
facilitates highly efficient and simultaneous communication between multiple
terminals in wireless ad-hoc networks. We term this method Combinatorial
Channel Signature Modulation (CCSM). The CCSM method is particularly efficient
in situations where communicating nodes operate in highly time dispersive
environments. This is all achieved with a minimal MAC layer overhead, since all
users are allowed to transmit and receive at the same time/frequency (full
simultaneous duplex). The CCSM method has its roots in sparse modelling and the
receiver is based on compressive sampling techniques. Towards this end, we
develop a new low complexity algorithm termed Group Subspace Pursuit. Our
analysis suggests that CCSM at least doubles the throughput when compared to
the state-of-the art.
|
1201.5626
|
Conditional strategies and the evolution of cooperation in spatial
public goods games
|
physics.soc-ph cs.SI nlin.AO q-bio.PE
|
The fact that individuals will most likely behave differently in different
situations begets the introduction of conditional strategies. Inspired by this,
we study the evolution of cooperation in the spatial public goods game, where
besides unconditional cooperators and defectors, also different types of
conditional cooperators compete for space. Conditional cooperators will
contribute to the public good only if other players within the group are likely
to cooperate as well, but will withhold their contribution otherwise. Depending
on the number of other cooperators that are required to elicit cooperation of a
conditional cooperator, the latter can be classified in as many types as there
are players within each group. We find that the most cautious cooperators, such
that require all other players within a group to be conditional cooperators,
are the undisputed victors of the evolutionary process, even at very low
synergy factors. We show that the remarkable promotion of cooperation is due
primarily to the spontaneous emergence of quarantining of defectors, which
become surrounded by conditional cooperators and are forced into isolated
convex "bubbles" from where they are unable to exploit the public good. This
phenomenon can be observed only in structured populations, thus adding to the
relevance of pattern formation for the successful evolution of cooperation.
|
1201.5689
|
An Efficient Construction of Self-Dual Codes
|
cs.IT math.CO math.IT math.NT
|
We complete the building-up construction for self-dual codes by resolving the
open cases over $GF(q)$ with $q \equiv 3 \pmod 4$, and over $\Z_{p^m}$ and
Galois rings $\GR(p^m,r)$ with an odd prime $p$ satisfying $p \equiv 3 \pmod 4$
with $r$ odd. We also extend the building-up construction for self-dual codes
to finite chain rings. Our building-up construction produces many new
interesting self-dual codes. In particular, we construct 945 new extremal
self-dual ternary $[32,16,9]$ codes, each of which has a trivial automorphism
group. We also obtain many new self-dual codes over $\mathbb Z_9$ of lengths
$12, 16, 20$ all with minimum Hamming weight 6, which is the best possible
minimum Hamming weight that free self-dual codes over $\Z_9$ of these lengths
can attain. From the constructed codes over $\mathbb Z_9$, we reconstruct
optimal Type I lattices of dimensions $12, 16, 20,$ and 24 using Construction
$A$; this shows that our building-up construction can make a good contribution
for finding optimal Type I lattices as well as self-dual codes. We also find
new optimal self-dual $[16,8,7]$ codes over GF(7) and new self-dual codes over
GF(7) with the best known parameters $[24,12,9]$.
|
1201.5722
|
Sex differences in intimate relationships
|
physics.soc-ph cs.SI
|
Social networks have turned out to be of fundamental importance both for our
understanding human sociality and for the design of digital communication
technology. However, social networks are themselves based on dyadic
relationships and we have little understanding of the dynamics of close
relationships and how these change over time. Evolutionary theory suggests
that, even in monogamous mating systems, the pattern of investment in close
relationships should vary across the lifespan when post-weaning investment
plays an important role in maximising fitness. Mobile phone data sets provide
us with a unique window into the structure of relationships and the way these
change across the lifespan. We here use data from a large national mobile phone
dataset to demonstrate striking sex differences in the pattern in the
gender-bias of preferred relationships that reflect the way the reproductive
investment strategies of the two sexes change across the lifespan: these
differences mainly reflect women's shifting patterns of investment in
reproduction and parental care. These results suggest that human social
strategies may have more complex dynamics than we have tended to assume and a
life-history perspective may be crucial for understanding them.
|
1201.5767
|
Nodal domain partition and the number of communities in networks
|
physics.soc-ph cs.SI
|
It is difficult to detect and evaluate the number of communities in complex
networks, especially when the situation involves with an ambiguous boundary
between the inner- and inter-community densities. In this paper, Discrete Nodal
Domain Theory could be used to provide a criterion to determine how many
communities a network would have and how to partition these communities by
means of the topological structure and geometric characterization. By capturing
the signs of certain Laplacian eigenvectors we can separate the network into
several reasonable clusters. The method leads to a fast and effective algorithm
with application to a variety of real networks data sets.
|
1201.5805
|
Interference and X Networks with Noisy Cooperation and Feedback
|
cs.IT math.IT
|
The Gaussian $K$-user interference and $M\times K$ X channels are
investigated with no instantaneous channel state information (CSI) at
transmitters. First, it is assumed that the CSI is fed back to all nodes after
a finite delay (delayed CSIT), and furthermore, the transmitters operate in
full-duplex mode, i.e., they can transmit and receive simultaneously.
Achievable results are obtained on the degrees of freedom (DoF) of these
channels under the above assumption. It is observed that, in contrast with no
CSIT and full CSIT models, when CSIT is delayed, the achievable DoFs for both
channels with full-duplex transmitter cooperation are greater than the best
available achievable results on their DoF without transmitter cooperation. Our
results are the first to show that the full-duplex transmitter cooperation can
potentially improve the channel DoF with delayed CSIT. Then, $K$-user
interference and $K\times K$ X channels are considered with output feedback,
wherein the channel output of each receiver is causally fed back to its
corresponding transmitter. Our achievable results with output feedback
demonstrate strict DoF improvements over those with the full-duplex delayed
CSIT when $K>5$ in the $K$-user interference channel and $K>2$ in the $K\times
K$ X channel. Next, the combination of delayed CSIT and output feedback, known
as Shannon feedback, is studied and strictly higher DoFs compared to the output
feedback model are achieved in the $K$-user interference channel when K=5 or
$K>6$, and in the $K\times K$ X channel when $K>2$. Although being strictly
greater than 1 and increasing with size of the networks, the achievable DoFs in
all the models studied in this paper approach limiting values not greater than
2.
|
1201.5838
|
Rateless Codes for Finite Message Set
|
cs.IT math.IT
|
In this study we consider rateless coding over discrete memoryless channels
(DMC) with feedback. Unlike traditional fixed-rate codes, in rateless codes
each codeword is infinitely long, and the decoding time depends on the
confidence level of the decoder. Using rateless codes along with sequential
decoding, and allowing a fixed probability of error at the decoder, we obtain
results for several communication scenarios. The results shown here are
non-asymptotic, in the sense that the size of the message set is finite. First
we consider the transmission of equiprobable messages using rateless codes over
a DMC, where the decoder knows the channel law. We obtain an achievable rate
for a fixed error probability and a finite message set. We show that as the
message set size grows, the achievable rate approaches the optimum rate for
this setting. We then consider the universal case, in which the channel law is
unknown to the decoder. We introduce a novel decoder that uses a mixture
probability assignment instead of the unknown channel law, and obtain an
achievable rate for this case. Finally, we extend the scope for more advanced
settings. We use different flavors of the rateless coding scheme for joint
source-channel coding, coding with side-information and a combination of the
two with universal coding, which yields a communication scheme that does not
require any information on the source, the channel, or the amount the side
information at the receiver.
|
1201.5841
|
The thermodynamic cost of fast thought
|
cs.AI
|
After more than sixty years, Shannon's research [1-3] continues to raise
fundamental questions, such as the one formulated by Luce [4,5], which is still
unanswered: "Why is information theory not very applicable to psychological
problems, despite apparent similarities of concepts?" On this topic, Pinker
[6], one of the foremost defenders of the computational theory of mind [6], has
argued that thought is simply a type of computation, and that the gap between
human cognition and computational models may be illusory. In this context, in
his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides
further theoretical interpretation by differentiating the two assumed systems
of the cognitive functioning of the human mind. He calls them intuition (system
1) determined to be an associative (automatic, fast and perceptual) machine,
and reasoning (system 2) required to be voluntary and to operate logical-
deductively. In this paper, we propose an ansatz inspired by Ausubel's learning
theory for investigating, from the constructivist perspective [9-12],
information processing in the working memory of cognizers. Specifically, a
thought experiment is performed utilizing the mind of a dual-natured creature
known as Maxwell's demon: a tiny "man-machine" solely equipped with the
characteristics of system 1, which prevents it from reasoning. The calculation
presented here shows that [...]. This result indicates that when the system 2
is shut down, both an intelligent being, as well as a binary machine, incur the
same energy cost per unit of information processed, which mathematically proves
the computational attribute of the system 1, as Kahneman [7,8] theorized. This
finding links information theory to human psychological features and opens a
new path toward the conception of a multi-bit reasoning machine.
|
1201.5871
|
Null models for network data
|
math.ST cs.SI stat.ME stat.TH
|
The analysis of datasets taking the form of simple, undirected graphs
continues to gain in importance across a variety of disciplines. Two choices of
null model, the logistic-linear model and the implicit log-linear model, have
come into common use for analyzing such network data, in part because each
accounts for the heterogeneity of network node degrees typically observed in
practice. Here we show how these both may be viewed as instances of a broader
class of null models, with the property that all members of this class give
rise to essentially the same likelihood-based estimates of link probabilities
in sparse graph regimes. This facilitates likelihood-based computation and
inference, and enables practitioners to choose the most appropriate null model
from this family based on application context. Comparative model fits for a
variety of network datasets demonstrate the practical implications of our
results.
|
1201.5921
|
An iterative algorithm for parametrization of shortest length shift
registers over finite rings
|
cs.IT cs.SC math.IT
|
The construction of shortest feedback shift registers for a finite sequence
S_1,...,S_N is considered over the finite ring Z_{p^r}. A novel algorithm is
presented that yields a parametrization of all shortest feedback shift
registers for the sequence of numbers S_1,...,S_N, thus solving an open problem
in the literature. The algorithm iteratively processes each number, starting
with S_1, and constructs at each step a particular type of minimal Gr\"obner
basis. The construction involves a simple update rule at each step which leads
to computational efficiency. It is shown that the algorithm simultaneously
computes a similar parametrization for the reciprocal sequence S_N,...,S_1.
|
1201.5938
|
Comparing Methods for segmentation of Microcalcification Clusters in
Digitized Mammograms
|
cs.CV
|
The appearance of microcalcifications in mammograms is one of the early signs
of breast cancer. So, early detection of microcalcification clusters (MCCs) in
mammograms can be helpful for cancer diagnosis and better treatment of breast
cancer. In this paper a computer method has been proposed to support
radiologists in detection MCCs in digital mammography. First, in order to
facilitate and improve the detection step, mammogram images have been enhanced
with wavelet transformation and morphology operation. Then for segmentation of
suspicious MCCs, two methods have been investigated. The considered methods
are: adaptive threshold and watershed segmentation. Finally, the detected MCCs
areas in different algorithms will be compared to find out which segmentation
method is more appropriate for extracting MCCs in mammograms.
|
1201.5943
|
Cognitive Memory Network
|
cs.AI cs.CV cs.ET
|
A resistive memory network that has no crossover wiring is proposed to
overcome the hardware limitations to size and functional complexity that is
associated with conventional analogue neural networks. The proposed memory
network is based on simple network cells that are arranged in a hierarchical
modular architecture. Cognitive functionality of this network is demonstrated
by an example of character recognition. The network is trained by an
evolutionary process to completely recognise characters deformed by random
noise, rotation, scaling and shifting
|
1201.5944
|
A Neuron Based Switch: Application to Low Power Mixed Signal Circuits
|
cs.ET cs.NE q-bio.NC
|
Human brain is functionally and physically complex. This 'complexity' can be
seen as a result of biological design process involving extensive use of
concepts such as modularity and hierarchy. Over the past decade, deeper
insights into the functioning of cortical neurons have led to the development
of models that can be implemented in hardware. The implementation of
biologically inspired spiking neuron networks in silicon can provide solutions
to difficult cognitive tasks. The work reported in this paper is an application
of a VLSI cortical neuron model for low power design. The VLSI implementation
shown in this paper is based on the spike and burst firing pattern of cortex
and follows the Izhikevich neuron model. This model is applied to a DC
differential amplifier as practical application of power reduction
|
1201.5946
|
Feature selection using nearest attributes
|
cs.CV cs.AI
|
Feature selection is an important problem in high-dimensional data analysis
and classification. Conventional feature selection approaches focus on
detecting the features based on a redundancy criterion using learning and
feature searching schemes. In contrast, we present an approach that identifies
the need to select features based on their discriminatory ability among
classes. Area of overlap between inter-class and intra-class distances
resulting from feature to feature comparison of an attribute is used as a
measure of discriminatory ability of the feature. A set of nearest attributes
in a pattern having the lowest area of overlap within a degree of tolerance
defined by a selection threshold is selected to represent the best available
discriminable features. State of the art recognition results are reported for
pattern classification problems by using the proposed feature selection scheme
with the nearest neighbour classifier. These results are reported with
benchmark databases having high dimensional feature vectors in the problems
involving images and micro array data.
|
1201.5947
|
Examplers based image fusion features for face recognition
|
cs.CV cs.AI
|
Examplers of a face are formed from multiple gallery images of a person and
are used in the process of classification of a test image. We incorporate such
examplers in forming a biologically inspired local binary decisions on
similarity based face recognition method. As opposed to single model approaches
such as face averages the exampler based approach results in higher recognition
accu- racies and stability. Using multiple training samples per person, the
method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET,
99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face
databases. In addition to face recognition, the method also detects the natural
variability in the face images which can find application in automatic tagging
of face images.
|
1201.5959
|
Memory Based Machine Intelligence Techniques in VLSI hardware
|
cs.AI cs.RO
|
We briefly introduce the memory based approaches to emulate machine
intelligence in VLSI hardware, describing the challenges and advantages.
Implementation of artificial intelligence techniques in VLSI hardware is a
practical and difficult problem. Deep architectures, hierarchical temporal
memories and memory networks are some of the contemporary approaches in this
area of research. The techniques attempt to emulate low level intelligence
tasks and aim at providing scalable solutions to high level intelligence
problems such as sparse coding and contextual processing.
|
1201.6012
|
Construction of quasi-cyclic self-dual codes
|
cs.IT math.AC math.CO math.IT
|
There is a one-to-one correspondence between $\ell$-quasi-cyclic codes over a
finite field $\mathbb F_q$ and linear codes over a ring $R = \mathbb
F_q[Y]/(Y^m-1)$. Using this correspondence, we prove that every
$\ell$-quasi-cyclic self-dual code of length $m\ell$ over a finite field
$\mathbb F_q$ can be obtained by the {\it building-up} construction, provided
that char $(\mathbb F_q)=2$ or $q \equiv 1 \pmod 4$, $m$ is a prime $p$, and
$q$ is a primitive element of $\mathbb F_p$. We determine possible weight
enumerators of a binary $\ell$-quasi-cyclic self-dual code of length $p\ell$
(with $p$ a prime) in terms of divisibility by $p$. We improve the result of
[3] by constructing new binary cubic (i.e., $\ell$-quasi-cyclic codes of length
$3\ell$) optimal self-dual codes of lengths $30, 36, 42, 48$ (Type I), 54 and
66. We also find quasi-cyclic optimal self-dual codes of lengths 40, 50, and
60. When $m=5$, we obtain a new 8-quasi-cyclic self-dual $[40, 20, 12]$ code
over $\mathbb F_3$ and a new 6-quasi-cyclic self-dual $[30, 15, 10]$ code over
$\mathbb F_4$. When $m=7$, we find a new 4-quasi-cyclic self-dual $[28, 14, 9]$
code over $\mathbb F_4$ and a new 6-quasi-cyclic self-dual $[42,21,12]$ code
over $\mathbb F_4$.
|
1201.6022
|
Non-Random Coding Error Exponent for Lattices
|
cs.IT math.IT
|
An upper bound on the error probability of specific lattices, based on their
distance-spectrum, is constructed. The derivation is accomplished using a
simple alternative to the Minkowski-Hlawka mean-value theorem of the geometry
of numbers. In many ways, the new bound greatly resembles the Shulman-Feder
bound for linear codes. Based on the new bound, an error-exponent is derived
for specific lattice sequences (of increasing dimension) over the AWGN channel.
Measuring the sequence's gap to capacity, using the new exponent, is
demonstrated.
|
1201.6034
|
A Novel MCMC Based Receiver for Large-Scale Uplink Multiuser MIMO
Systems
|
cs.IT math.IT
|
In this paper, we propose low complexity algorithms based on Markov chain
Monte Carlo (MCMC) technique for signal detection and channel estimation on the
uplink in large scale multiuser multiple input multiple output (MIMO) systems
with tens to hundreds of antennas at the base station (BS) and similar number
of uplink users. A BS receiver that employs a randomized sampling method (which
makes a probabilistic choice between Gibbs sampling and random sampling in each
iteration) for detection and a Gibbs sampling based method for channel
estimation is proposed. The algorithm proposed for detection alleviates the
stalling problem encountered at high SNRs in conventional MCMC algorithm and
achieves near-optimal performance in large systems. A novel ingredient in the
detection algorithm that is responsible for achieving near-optimal performance
at low complexities is the joint use of a {\it randomized MCMC (R-MCMC)
strategy} coupled with a {\it multiple restart strategy} with an efficient
restart criterion. Near-optimal detection performance is demonstrated for large
number of BS antennas and users (e.g., 64, 128, 256 BS antennas/users). The
proposed MCMC based channel estimation algorithm refines an initial estimate of
the channel obtained during pilot phase through iterations with R-MCMC
detection during data phase. In time division duplex (TDD) systems where
channel reciprocity holds, these channel estimates can be used for multiuser
MIMO precoding on the downlink. Further, we employ this receiver architecture
in the frequency domain for receiving cyclic prefixed single carrier (CPSC)
signals on frequency selective fading between users and the BS. The proposed
receiver achieves performance that is near optimal and close to that achieved
with perfect channel knowledge.
|
1201.6043
|
The maximum number of minimal codewords in long codes
|
cs.IT math.CO math.IT
|
Upper bounds on the maximum number of minimal codewords in a binary code
follow from the theory of matroids. Random coding provide lower bounds. In this
paper we compare these bounds with analogous bounds for the cycle code of
graphs. This problem (in the graphic case) was considered in 1981 by Entringer
and Slater who asked if a connected graph with $p$ vertices and $q$ edges can
have only slightly more that $2^{q-p}$ cycles. The bounds in this note answer
this in the affirmative for all graphs except possibly some that have fewer
than $2p+3\log_2(3p)$ edges. We also conclude that an Eulerian (even) graph has
at most $2^{q-p}$ cycles unless the graph is a subdivision of a 4-regular graph
that is the edge-disjoint union of two Hamiltonian cycles, in which case it may
have as many as $2^{q-p}+p$ cycles.
|
1201.6046
|
Extended Extremes of Information Combining
|
cs.IT math.IT
|
Extremes of information combining inequalities play an important role in the
analysis of sparse-graph codes under message-passing decoding. We introduce new
tools for the derivation of such inequalities, and show by means of a concrete
examples how they can be applied to solve some optimization problems in the
analysis of low-density parity-check codes.
|
1201.6053
|
A Comparison Between Data Mining Prediction Algorithms for Fault
Detection(Case study: Ahanpishegan co.)
|
cs.LG
|
In the current competitive world, industrial companies seek to manufacture
products of higher quality which can be achieved by increasing reliability,
maintainability and thus the availability of products. On the other hand,
improvement in products lifecycle is necessary for achieving high reliability.
Typically, maintenance activities are aimed to reduce failures of industrial
machinery and minimize the consequences of such failures. So the industrial
companies try to improve their efficiency by using different fault detection
techniques. One strategy is to process and analyze previous generated data to
predict future failures. The purpose of this paper is to detect wasted parts
using different data mining algorithms and compare the accuracy of these
algorithms. A combination of thermal and physical characteristics has been used
and the algorithms were implemented on Ahanpishegan's current data to estimate
the availability of its produced parts.
Keywords: Data Mining, Fault Detection, Availability, Prediction Algorithms.
|
1201.6065
|
Throughput Optimal Switching in Multi-channel WLANs
|
cs.SY cs.NI
|
We observe that in a multi-channel wireless system, an opportunistic
channel/spectrum access scheme that solely focuses on channel quality sensing
measured by received SNR may induce users to use channels that, while providing
better signals, are more congested. Ultimately the notion of channel quality
should include both the signal quality and the level of congestion, and a good
multi-channel access scheme should take both into account in deciding which
channel to use and when. Motivated by this, we focus on the congestion aspect
and examine what type of dynamic channel switching schemes may result in the
best system throughput performance. Specifically we derive the stability region
of a multi-user multi-channel WLAN system and determine the throughput optimal
channel switching scheme within a certain class of schemes.
|
1201.6095
|
How Web 1.0 Fails: The Mismatch Between Hyperlinks and Clickstreams
|
cs.IR cs.SI physics.soc-ph
|
The core of the Web is a hyperlink navigation system collaboratively set up
by webmasters to help users find desired websites. But does this system really
work as expected? We show that the answer seems to be negative: there is a
substantial mismatch between hyperlinks and the pathways that users actually
take. A closer look at empirical surfing activities reveals the reason of the
mismatch: webmasters try to build a global virtual world without geographical
or cultural boundaries, but users in fact prefer to navigate within more
fragmented, language-based groups of websites. We call this type of behavior
"preferential navigation" and find that it is driven by "local" search engines.
|
1201.6112
|
An Efficient Method for Mining Event-Related Potential Patterns
|
cs.DB
|
In the present paper, we propose a Neuroelectromagnetic Ontology Framework
(NOF) for mining Event-related Potentials (ERP) patterns as well as the
process. The aim for this research is to develop an infrastructure for mining,
analysis and sharing the ERP domain ontologies. The outcome of this research is
a Neuroelectromagnetic knowledge-based system. The framework has 5 stages: 1)
Data pre-processing and preparation; 2) Data mining application; 3) Rule
Comparison and Evaluation; 4) Association rules Post-processing 5) Domain
Ontologies. In 5th stage a new set of hidden rules can be discovered base on
comparing association rules by domain ontologies and expert rules.
|
1201.6117
|
Continuous Time Channels with Interference
|
cs.IT math.IT
|
Khanna and Sudan \cite{KS11} studied a natural model of continuous time
channels where signals are corrupted by the effects of both noise and delay,
and showed that, surprisingly, in some cases both are not enough to prevent
such channels from achieving unbounded capacity. Inspired by their work, we
consider channels that model continuous time communication with adversarial
delay errors. The sender is allowed to subdivide time into an arbitrarily large
number $M$ of micro-units in which binary symbols may be sent, but the symbols
are subject to unpredictable delays and may interfere with each other. We model
interference by having symbols that land in the same micro-unit of time be
summed, and we study $k$-interference channels, which allow receivers to
distinguish sums up to the value $k$. We consider both a channel adversary that
has a limit on the maximum number of steps it can delay each symbol, and a more
powerful adversary that only has a bound on the average delay.
We give precise characterizations of the threshold between finite and
infinite capacity depending on the interference behavior and on the type of
channel adversary: for max-bounded delay, the threshold is at
$D_{\text{max}}=\ThetaM \log\min{k, M}))$, and for average bounded delay the
threshold is at $D_{\text{avg}} = \Theta(\sqrt{M \cdot \min\{k, M\}})$.
|
1201.6134
|
Synthetic sequence generator for recommender systems - memory biased
random walk on sequence multilayer network
|
cs.IR cs.CY
|
Personalized recommender systems rely on each user's personal usage data in
the system, in order to assist in decision making. However, privacy policies
protecting users' rights prevent these highly personal data from being publicly
available to a wider researcher audience. In this work, we propose a memory
biased random walk model on multilayer sequence network, as a generator of
synthetic sequential data for recommender systems. We demonstrate the
applicability of the synthetic data in training recommender system models for
cases when privacy policies restrict clickstream publishing.
|
1201.6224
|
Wikipedia Arborification and Stratified Explicit Semantic Analysis
|
cs.CL
|
[This is the translation of paper "Arborification de Wikip\'edia et analyse
s\'emantique explicite stratifi\'ee" submitted to TALN 2012.]
We present an extension of the Explicit Semantic Analysis method by
Gabrilovich and Markovitch. Using their semantic relatedness measure, we weight
the Wikipedia categories graph. Then, we extract a minimal spanning tree, using
Chu-Liu & Edmonds' algorithm. We define a notion of stratified tfidf where the
stratas, for a given Wikipedia page and a given term, are the classical tfidf
and categorical tfidfs of the term in the ancestor categories of the page
(ancestors in the sense of the minimal spanning tree). Our method is based on
this stratified tfidf, which adds extra weight to terms that "survive" when
climbing up the category tree. We evaluate our method by a text classification
on the WikiNews corpus: it increases precision by 18%. Finally, we provide
hints for future research
|
1201.6248
|
List Decoding Algorithms based on Groebner Bases for General One-Point
AG Codes
|
cs.IT cs.SC math.AC math.AG math.IT
|
We generalize the list decoding algorithm for Hermitian codes proposed by Lee
and O'Sullivan based on Gr\"obner bases to general one-point AG codes, under an
assumption weaker than one used by Beelen and Brander. By using the same
principle, we also generalize the unique decoding algorithm for one-point AG
codes over the Miura-Kamiya $C_{ab}$ curves proposed by Lee, Bras-Amor\'os and
O'Sullivan to general one-point AG codes, without any assumption. Finally we
extend the latter unique decoding algorithm to list decoding, modify it so that
it can be used with the Feng-Rao improved code construction, prove equality
between its error correcting capability and half the minimum distance lower
bound by Andersen and Geil that has not been done in the original proposal, and
remove the unnecessary computational steps so that it can run faster.
|
1201.6251
|
Real-time jam-session support system
|
cs.HC cs.LG cs.SD
|
We propose a method for the problem of real time chord accompaniment of
improvised music. Our implementation can learn an underlying structure of the
musical performance and predict next chord. The system uses Hidden Markov Model
to find the most probable chord sequence for the played melody and then a
Variable Order Markov Model is used to a) learn the structure (if any) and b)
predict next chord. We implemented our system in Java and MAX/Msp and compared
and evaluated using objective (prediction accuracy) and subjective
(questionnaire) evaluation methods.
|
1201.6257
|
Supercooperation in Evolutionary Games on Correlated Weighted Networks
|
physics.soc-ph cs.SI
|
In this work we study the behavior of classical two-person, two-strategies
evolutionary games on a class of weighted networks derived from
Barab\'asi-Albert and random scale-free unweighted graphs. Using customary
imitative dynamics, our numerical simulation results show that the presence of
link weights that are correlated in a particular manner with the degree of the
link endpoints, leads to unprecedented levels of cooperation in the whole
games' phase space, well above those found for the corresponding unweighted
complex networks. We provide intuitive explanations for this favorable behavior
by transforming the weighted networks into unweighted ones with particular
topological properties. The resulting structures help to understand why
cooperation can thrive and also give ideas as to how such supercooperative
networks might be built.
|
1201.6271
|
Quantized Network Coding for Sparse Messages
|
cs.IT math.IT
|
In this paper, we study the data gathering problem in the context of power
grids by using a network of sensors, where the sensed data have inter-node
redundancy. Specifically, we propose a new transmission method, calledquantized
network coding, which performs linear net-work coding in the field of real
numbers, and quantization to accommodate the finite capacity of edges. By using
the concepts in compressed sensing literature, we propose to use
l1-minimization to decode the quantized network coded packets, especially when
the number of received packets at the decoder is less than the size of sensed
data (i.e. number of nodes). We also propose an appropriate design for network
coding coefficients, based on restricted isometry property, which results in
robust l1-min decoding. Our numerical analysis show that the proposed quantized
network coding scheme with l1-min decoding can achieve significant
improvements, in terms of compression ratio and delivery delay, compared to
conventional packet forwarding.
|
1201.6278
|
Solving the accuracy-diversity dilemma via directed random walks
|
physics.data-an cs.IR
|
Random walks have been successfully used to measure user or object
similarities in collaborative filtering (CF) recommender systems, which is of
high accuracy but low diversity. A key challenge of CF system is that the
reliably accurate results are obtained with the help of peers' recommendation,
but the most useful individual recommendations are hard to be found among
diverse niche objects. In this paper we investigate the direction effect of the
random walk on user similarity measurements and find that the user similarity,
calculated by directed random walks, is reverse to the initial node's degree.
Since the ratio of small-degree users to large-degree users is very large in
real data sets, the large-degree users' selections are recommended extensively
by traditional CF algorithms. By tuning the user similarity direction from
neighbors to the target user, we introduce a new algorithm specifically to
address the challenge of diversity of CF and show how it can be used to solve
the accuracy-diversity dilemma. Without relying on any context-specific
information, we are able to obtain accurate and diverse recommendations, which
outperforms the state-of-the-art CF methods. This work suggests that the random
walk direction is an important factor to improve the personalized
recommendation performance.
|
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