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0910.1871
|
MIMO Wireless Communications under Statistical Queueing Constraints
|
cs.IT math.IT
|
The performance of multiple-input multiple-output wireless systems is
investigated in the presence of statistical queueing constraints. Queuing
constraints are imposed as limitations on buffer violation probabilities. The
performance under such constraints is captured through the effective capacity
formulation. A detailed analysis of the effective capacity is carried out in
the low-power, wideband, and high--signal-to-noise ratio (SNR) regimes. In the
low-power analysis, expressions for the first and second derivatives of the
effective capacity with respect to SNR at SNR= 0 are obtained under various
assumptions on the degree of channel state information at the transmitter.
Transmission strategies that are optimal in the sense of achieving the first
and second derivatives are identified. It is shown that while the first
derivative does not get affected by the presence of queueing constraints, the
second derivative gets smaller as the constraints become more stringent.
Through the energy efficiency analysis, this is shown to imply that the minimum
bit energy requirements do not change with more strict limitations but the
wideband slope diminishes. Similar results are obtained in the wideband regime
if rich multipath fading is being experienced. On the other hand, sparse
multipath fading with bounded number of degrees of freedom is shown to increase
the minimum bit energy requirements in the presence of queueing constraints.
Following the low-SNR study, the impact of buffer limitations on the high-SNR
performance is quantified by analyzing the high-SNR slope and the power offset
in Rayleigh fading channels. Finally, numerical results are provided to
illustrate the theoretical findings.
|
0910.1879
|
Recovering low-rank matrices from few coefficients in any basis
|
cs.IT cs.NA math.IT math.NA quant-ph
|
We present novel techniques for analyzing the problem of low-rank matrix
recovery. The methods are both considerably simpler and more general than
previous approaches. It is shown that an unknown (n x n) matrix of rank r can
be efficiently reconstructed from only O(n r nu log^2 n) randomly sampled
expansion coefficients with respect to any given matrix basis. The number nu
quantifies the "degree of incoherence" between the unknown matrix and the
basis. Existing work concentrated mostly on the problem of "matrix completion"
where one aims to recover a low-rank matrix from randomly selected matrix
elements. Our result covers this situation as a special case. The proof
consists of a series of relatively elementary steps, which stands in contrast
to the highly involved methods previously employed to obtain comparable
results. In cases where bounds had been known before, our estimates are
slightly tighter. We discuss operator bases which are incoherent to all
low-rank matrices simultaneously. For these bases, we show that O(n r nu log n)
randomly sampled expansion coefficients suffice to recover any low-rank matrix
with high probability. The latter bound is tight up to multiplicative
constants.
|
0910.1922
|
Binary Linear-Time Erasure Decoding for Non-Binary LDPC codes
|
cs.IT math.IT
|
In this paper, we first introduce the extended binary representation of
non-binary codes, which corresponds to a covering graph of the bipartite graph
associated with the non-binary code. Then we show that non-binary codewords
correspond to binary codewords of the extended representation that further
satisfy some simplex-constraint: that is, bits lying over the same symbol-node
of the non-binary graph must form a codeword of a simplex code. Applied to the
binary erasure channel, this description leads to a binary erasure decoding
algorithm of non-binary LDPC codes, whose complexity depends linearly on the
cardinality of the alphabet. We also give insights into the structure of
stopping sets for non-binary LDPC codes, and discuss several aspects related to
upper-layer FEC applications.
|
0910.1938
|
Information Retrieval via Truncated Hilbert-Space Expansions
|
cs.IR
|
In addition to the frequency of terms in a document collection, the
distribution of terms plays an important role in determining the relevance of
documents. In this paper, a new approach for representing term positions in
documents is presented. The approach allows an efficient evaluation of
term-positional information at query evaluation time. Three applications are
investigated: a function-based ranking optimization representing a user-defined
document region, a query expansion technique based on overlapping the term
distributions in the top-ranked documents, and cluster analysis of terms in
documents. Experimental results demonstrate the effectiveness of the proposed
approach.
|
0910.1943
|
Construction of a Large Class of Deterministic Sensing Matrices that
Satisfy a Statistical Isometry Property
|
cs.IT math.IT math.PR
|
Compressed Sensing aims to capture attributes of $k$-sparse signals using
very few measurements. In the standard Compressed Sensing paradigm, the
$\m\times \n$ measurement matrix $\A$ is required to act as a near isometry on
the set of all $k$-sparse signals (Restricted Isometry Property or RIP).
Although it is known that certain probabilistic processes generate $\m \times
\n$ matrices that satisfy RIP with high probability, there is no practical
algorithm for verifying whether a given sensing matrix $\A$ has this property,
crucial for the feasibility of the standard recovery algorithms. In contrast
this paper provides simple criteria that guarantee that a deterministic sensing
matrix satisfying these criteria acts as a near isometry on an overwhelming
majority of $k$-sparse signals; in particular, most such signals have a unique
representation in the measurement domain. Probability still plays a critical
role, but it enters the signal model rather than the construction of the
sensing matrix. We require the columns of the sensing matrix to form a group
under pointwise multiplication. The construction allows recovery methods for
which the expected performance is sub-linear in $\n$, and only quadratic in
$\m$; the focus on expected performance is more typical of mainstream signal
processing than the worst-case analysis that prevails in standard Compressed
Sensing. Our framework encompasses many families of deterministic sensing
matrices, including those formed from discrete chirps, Delsarte-Goethals codes,
and extended BCH codes.
|
0910.1954
|
Multi-channel Opportunistic Access: A Case of Restless Bandits with
Multiple Plays
|
cs.IT cs.DM math.IT math.OC
|
This paper considers the following stochastic control problem that arises in
opportunistic spectrum access: a system consists of n channels (Gilbert-Elliot
channels)where the state (good or bad) of each channel evolves as independent
and identically distributed Markov processes. A user can select exactly k
channels to sense and access (based on the sensing result) in each time slot. A
reward is obtained whenever the user senses and accesses a good channel. The
objective is to design a channel selection policy that maximizes the expected
discounted total reward accrued over a finite or infinite horizon. In our
previous work we established the optimality of a greedy policy for the special
case of k = 1 (i.e., single channel access) under the condition that the
channel state transitions are positively correlated over time. In this paper we
show under the same condition the greedy policy is optimal for the general case
of k >= 1; the methodology introduced here is thus more general. This problem
may be viewed as a special case of the restless bandit problem, with multiple
plays. We discuss connections between the current problem and existing
literature on this class of problems.
|
0910.1955
|
Microstructure reconstruction using entropic descriptors
|
cond-mat.stat-mech cs.CV
|
A multi-scale approach to the inverse reconstruction of a pattern's
microstructure is reported. Instead of a correlation function, a pair of
entropic descriptors (EDs) is proposed for stochastic optimization method. The
first of them measures a spatial inhomogeneity, for a binary pattern, or
compositional one, for a greyscale image. The second one quantifies a spatial
or compositional statistical complexity. The EDs reveal structural information
that is dissimilar, at least in part, to that given by correlation functions at
almost all of discrete length scales. The method is tested on a few digitized
binary and greyscale images. In each of the cases, the persuasive
reconstruction of the microstructure is found.
|
0910.2005
|
Modulation Codes for Flash Memory Based on Load-Balancing Theory
|
cs.IT math.IT
|
In this paper, we consider modulation codes for practical multilevel flash
memory storage systems with cell levels. Instead of maximizing the lifetime of
the device [Ajiang-isit07-01, Ajiang-isit07-02,
Yaakobi_verdy_siegel_wolf_allerton08, Finucane_Liu_Mitzenmacher_aller08], we
maximize the average amount of information stored per cell-level, which is
defined as storage efficiency. Using this framework, we show that the
worst-case criterion [Ajiang-isit07-01, Ajiang-isit07-02,
Yaakobi_verdy_siegel_wolf_allerton08] and the average-case criterion
[Finucane_Liu_Mitzenmacher_aller08] are two extreme cases of our objective
function. A self-randomized modulation code is proposed which is asymptotically
optimal, as, for an arbitrary input alphabet and i.i.d. input distribution.
In practical flash memory systems, the number of cell-levels is only
moderately large. So the asymptotic performance as may not tell the whole
story. Using the tools from load-balancing theory, we analyze the storage
efficiency of the self-randomized modulation code. The result shows that only a
fraction of the cells are utilized when the number of cell-levels is only
moderately large. We also propose a load-balancing modulation code, based on a
phenomenon known as "the power of two random choices" [Mitzenmacher96thepower],
to improve the storage efficiency of practical systems. Theoretical analysis
and simulation results show that our load-balancing modulation codes can
provide significant gain to practical flash memory storage systems. Though
pseudo-random, our approach achieves the same load-balancing performance, for
i.i.d. inputs, as a purely random approach based on the power of two random
choices.
|
0910.2029
|
A Framework For Intelligent Multi Agent System Based Neural Network
Classification Model
|
cs.NE cs.MA
|
TIntelligent multi agent systems have great potentials to use in different
purposes and research areas. One of the important issues to apply intelligent
multi agent systems in real world and virtual environment is to develop a
framework that support machine learning model to reflect the whole complexity
of the real world. In this paper, we proposed a framework of intelligent agent
based neural network classification model to solve the problem of gap between
two applicable flows of intelligent multi agent technology and learning model
from real environment. We consider the new Supervised Multilayers Feed Forward
Neural Network (SMFFNN) model as an intelligent classification for learning
model in the framework. The framework earns the information from the respective
environment and its behavior can be recognized by the weights. Therefore, the
SMFFNN model that lies in the framework will give more benefits in finding the
suitable information and the real weights from the environment which result for
better recognition. The framework is applicable to different domains
successfully and for the potential case study, the clinical organization and
its domain is considered for the proposed framework
|
0910.2034
|
Strategies for online inference of model-based clustering in large and
growing networks
|
stat.AP cs.LG
|
In this paper we adapt online estimation strategies to perform model-based
clustering on large networks. Our work focuses on two algorithms, the first
based on the SAEM algorithm, and the second on variational methods. These two
strategies are compared with existing approaches on simulated and real data. We
use the method to decipher the connexion structure of the political websphere
during the US political campaign in 2008. We show that our online EM-based
algorithms offer a good trade-off between precision and speed, when estimating
parameters for mixture distributions in the context of random graphs.
|
0910.2039
|
Higher coordination with less control - A result of information
maximization in the sensorimotor loop
|
cs.AI cs.IT cs.RO math.IT
|
This work presents a novel learning method in the context of embodied
artificial intelligence and self-organization, which has as few assumptions and
restrictions as possible about the world and the underlying model. The learning
rule is derived from the principle of maximizing the predictive information in
the sensorimotor loop. It is evaluated on robot chains of varying length with
individually controlled, non-communicating segments. The comparison of the
results shows that maximizing the predictive information per wheel leads to a
higher coordinated behavior of the physically connected robots compared to a
maximization per robot. Another focus of this paper is the analysis of the
effect of the robot chain length on the overall behavior of the robots. It will
be shown that longer chains with less capable controllers outperform those of
shorter length and more complex controllers. The reason is found and discussed
in the information-geometric interpretation of the learning process.
|
0910.2042
|
Minimax rates of estimation for high-dimensional linear regression over
$\ell_q$-balls
|
math.ST cs.IT math.IT stat.TH
|
Consider the standard linear regression model $\y = \Xmat \betastar + w$,
where $\y \in \real^\numobs$ is an observation vector, $\Xmat \in
\real^{\numobs \times \pdim}$ is a design matrix, $\betastar \in \real^\pdim$
is the unknown regression vector, and $w \sim \mathcal{N}(0, \sigma^2 I)$ is
additive Gaussian noise. This paper studies the minimax rates of convergence
for estimation of $\betastar$ for $\ell_\rpar$-losses and in the
$\ell_2$-prediction loss, assuming that $\betastar$ belongs to an
$\ell_{\qpar}$-ball $\Ballq(\myrad)$ for some $\qpar \in [0,1]$. We show that
under suitable regularity conditions on the design matrix $\Xmat$, the minimax
error in $\ell_2$-loss and $\ell_2$-prediction loss scales as $\Rq
\big(\frac{\log \pdim}{n}\big)^{1-\frac{\qpar}{2}}$. In addition, we provide
lower bounds on minimax risks in $\ell_{\rpar}$-norms, for all $\rpar \in [1,
+\infty], \rpar \neq \qpar$. Our proofs of the lower bounds are
information-theoretic in nature, based on Fano's inequality and results on the
metric entropy of the balls $\Ballq(\myrad)$, whereas our proofs of the upper
bounds are direct and constructive, involving direct analysis of least-squares
over $\ell_{\qpar}$-balls. For the special case $q = 0$, a comparison with
$\ell_2$-risks achieved by computationally efficient $\ell_1$-relaxations
reveals that although such methods can achieve the minimax rates up to constant
factors, they require slightly stronger assumptions on the design matrix
$\Xmat$ than algorithms involving least-squares over the $\ell_0$-ball.
|
0910.2065
|
Distributed Learning in Multi-Armed Bandit with Multiple Players
|
math.OC cs.LG math.PR
|
We formulate and study a decentralized multi-armed bandit (MAB) problem.
There are M distributed players competing for N independent arms. Each arm,
when played, offers i.i.d. reward according to a distribution with an unknown
parameter. At each time, each player chooses one arm to play without exchanging
observations or any information with other players. Players choosing the same
arm collide, and, depending on the collision model, either no one receives
reward or the colliding players share the reward in an arbitrary way. We show
that the minimum system regret of the decentralized MAB grows with time at the
same logarithmic order as in the centralized counterpart where players act
collectively as a single entity by exchanging observations and making decisions
jointly. A decentralized policy is constructed to achieve this optimal order
while ensuring fairness among players and without assuming any pre-agreement or
information exchange among players. Based on a Time Division Fair Sharing
(TDFS) of the M best arms, the proposed policy is constructed and its order
optimality is proven under a general reward model. Furthermore, the basic
structure of the TDFS policy can be used with any order-optimal single-player
policy to achieve order optimality in the decentralized setting. We also
establish a lower bound on the system regret growth rate for a general class of
decentralized polices, to which the proposed policy belongs. This problem finds
potential applications in cognitive radio networks, multi-channel communication
systems, multi-agent systems, web search and advertising, and social networks.
|
0910.2066
|
A Lossless Fuzzy Binary AND/OR Compressor
|
cs.IT math.IT
|
In this report, a new fuzzy 2bit-AND parallel-to-OR, or simply, a fuzzy
binary AND/OR (FBAR) text data compression model as an algorithm is suggested
for bettering spatial locality limits on nodes during database transactions.
The current model incorporates a four-layer application technique:
string-to-AND/OR pairwise binary bit + fuzzy quantum with noise conversions.
This technique promotes a lossless data compression ratio of 2:1 up to values
approximately = 3:1, generating a spatially-efficient compressed data file
compared to nowadays data compressors. Data decompression/specific data
reconstruction initiates an AND/OR pattern match technique in respect of fuzzy
quantum indicators in the binary function field. The reconstruction of data
occurs in the 4th layer using encryption methods. It is hypothesized that
significant data compression ratio of 2n:1 for n>3:1 ratios, e.g., 32~64:1 are
achievable via fuzzy qubit indexing over classical byte blocks for every bit
position fragmented into a (1/2 upper +1/2 lower)-bit noise frequency parallel
to its counterpart signal comprised of AND/ORed-bit polarity orientation, ready
for an identical data decompression.
|
0910.2099
|
Generalizations of Wei's Duality Theorem
|
cs.IT math.IT
|
Wei's celebrated Duality Theorem is generalized in several ways, expressed as
duality theorems for linear codes over division rings and, more generally,
duality theorems for matroids. These results are further generalized, resulting
in two Wei-type duality theorems for new combinatorial structures that are
introduced and named {\em demi-matroids}. These generalize matroids and are the
appropriate combinatorial objects for describing the duality in Wei's Duality
Theorem. A new proof of the Duality Theorem is thereby given that explains the
theorem in combinatorial terms. Special cases of the general duality theorems
are also given, including duality theorems for cycles and bonds in graphs and
for transversals.
|
0910.2173
|
Distributed Turbo-Like Codes for Multi-User Cooperative Relay Networks
|
cs.IT math.IT
|
In this paper, a distributed turbo-like coding scheme for wireless networks
with relays is proposed. We consider a scenario where multiple sources
communicate with a single destination with the help of a relay. The proposed
scheme can be regarded as of the decode-and-forward type. The relay decodes the
information from the sources and it properly combines and re-encodes them to
generate some extra redundancy, which is transmitted to the destination. The
amount of redundancy generated by the relay can simply be adjusted according to
requirements in terms of performance, throughput and/or power. At the
destination, decoding of the information of all sources is performed jointly
exploiting the redundancy provided by the relay in an iterative fashion. The
overall communication network can be viewed as a serially concatenated code.
The proposed distributed scheme achieves significant performance gains with
respect to the non-cooperation system, even for a very large number of users.
Furthermore, it presents a high flexibility in terms of code rate, block length
and number of users.
|
0910.2187
|
Computing abstractions of nonlinear systems
|
math.OC cs.SY math.DS
|
Sufficiently accurate finite state models, also called symbolic models or
discrete abstractions, allow one to apply fully automated methods, originally
developed for purely discrete systems, to formally reason about continuous and
hybrid systems, and to design finite state controllers that provably enforce
predefined specifications. We present a novel algorithm to compute such finite
state models for nonlinear discrete-time and sampled systems which depends on
quantizing the state space using polyhedral cells, embedding these cells into
suitable supersets whose attainable sets are convex, and over-approximating
attainable sets by intersections of supporting half-spaces. We prove a novel
recursive description of these half-spaces and propose an iterative procedure
to compute them efficiently. We also provide new sufficient conditions for the
convexity of attainable sets which imply the existence of the aforementioned
embeddings of quantizer cells. Our method yields highly accurate abstractions
and applies to nonlinear systems under mild assumptions, which reduce to
sufficient smoothness in the case of sampled systems. Its practicability in the
design of discrete controllers for nonlinear continuous plants under state and
control constraints is demonstrated by an example.
|
0910.2217
|
Finite element model selection using Particle Swarm Optimization
|
cs.AI
|
This paper proposes the application of particle swarm optimization (PSO) to
the problem of finite element model (FEM) selection. This problem arises when a
choice of the best model for a system has to be made from set of competing
models, each developed a priori from engineering judgment. PSO is a
population-based stochastic search algorithm inspired by the behaviour of
biological entities in nature when they are foraging for resources. Each
potentially correct model is represented as a particle that exhibits both
individualistic and group behaviour. Each particle moves within the model
search space looking for the best solution by updating the parameters values
that define it. The most important step in the particle swarm algorithm is the
method of representing models which should take into account the number,
location and variables of parameters to be updated. One example structural
system is used to show the applicability of PSO in finding an optimal FEM. An
optimal model is defined as the model that has the least number of updated
parameters and has the smallest parameter variable variation from the mean
material properties. Two different objective functions are used to compare
performance of the PSO algorithm.
|
0910.2240
|
Repeated Auctions with Learning for Spectrum Access in Cognitive Radio
Networks
|
cs.IT cs.LG math.IT math.OC
|
In this paper, spectrum access in cognitive radio networks is modeled as a
repeated auction game subject to monitoring and entry costs. For secondary
users, sensing costs are incurred as the result of primary users' activity.
Furthermore, each secondary user pays the cost of transmissions upon successful
bidding for a channel. Knowledge regarding other secondary users' activity is
limited due to the distributed nature of the network. The resulting formulation
is thus a dynamic game with incomplete information. In this paper, an efficient
bidding learning algorithm is proposed based on the outcome of past
transactions. As demonstrated through extensive simulations, the proposed
distributed scheme outperforms a myopic one-stage algorithm, and can achieve a
good balance between efficiency and fairness.
|
0910.2245
|
Searching for Minimum Storage Regenerating Codes
|
cs.IT math.IT
|
Regenerating codes allow distributed storage systems to recover from the loss
of a storage node while transmitting the minimum possible amount of data across
the network. We present a systematic computer search for optimal systematic
regenerating codes. To search the space of potential codes, we reduce the
potential search space in several ways. We impose an additional symmetry
condition on codes that we consider. We specify codes in a simple alternative
way, using additional recovered coefficients rather than transmission
coefficients and place codes into equivalence classes to avoid redundant
checking. Our main finding is a few optimal systematic minimum storage
regenerating codes for $n=5$ and $k=3$, over several finite fields. No such
codes were previously known and the matching of the information theoretic
cut-set bound was an open problem.
|
0910.2263
|
Minimum cost mirror sites using network coding: Replication vs. coding
at the source nodes
|
cs.IT math.IT
|
Content distribution over networks is often achieved by using mirror sites
that hold copies of files or portions thereof to avoid congestion and delay
issues arising from excessive demands to a single location. Accordingly, there
are distributed storage solutions that divide the file into pieces and place
copies of the pieces (replication) or coded versions of the pieces (coding) at
multiple source nodes. We consider a network which uses network coding for
multicasting the file. There is a set of source nodes that contains either
subsets or coded versions of the pieces of the file. The cost of a given
storage solution is defined as the sum of the storage cost and the cost of the
flows required to support the multicast. Our interest is in finding the storage
capacities and flows at minimum combined cost. We formulate the corresponding
optimization problems by using the theory of information measures. In
particular, we show that when there are two source nodes, there is no loss in
considering subset sources. For three source nodes, we derive a tight upper
bound on the cost gap between the coded and uncoded cases. We also present
algorithms for determining the content of the source nodes.
|
0910.2276
|
State of the Art Review for Applying Computational Intelligence and
Machine Learning Techniques to Portfolio Optimisation
|
cs.CE cs.AI
|
Computational techniques have shown much promise in the field of Finance,
owing to their ability to extract sense out of dauntingly complex systems. This
paper reviews the most promising of these techniques, from traditional
computational intelligence methods to their machine learning siblings, with
particular view to their application in optimising the management of a
portfolio of financial instruments. The current state of the art is assessed,
and prospective further work is assessed and recommended
|
0910.2279
|
Positive Semidefinite Metric Learning with Boosting
|
cs.CV cs.LG
|
The learning of appropriate distance metrics is a critical problem in image
classification and retrieval. In this work, we propose a boosting-based
technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One
of the primary difficulties in learning such a metric is to ensure that the
Mahalanobis matrix remains positive semidefinite. Semidefinite programming is
sometimes used to enforce this constraint, but does not scale well.
\BoostMetric is instead based on a key observation that any positive
semidefinite matrix can be decomposed into a linear positive combination of
trace-one rank-one matrices. \BoostMetric thus uses rank-one positive
semidefinite matrices as weak learners within an efficient and scalable
boosting-based learning process. The resulting method is easy to implement,
does not require tuning, and can accommodate various types of constraints.
Experiments on various datasets show that the proposed algorithm compares
favorably to those state-of-the-art methods in terms of classification accuracy
and running time.
|
0910.2304
|
Cooperative Multi-Cell Block Diagonalization with Per-Base-Station Power
Constraints
|
cs.IT math.IT
|
Block diagonalization (BD) is a practical linear precoding technique that
eliminates the inter-user interference in downlink multiuser multiple-input
multiple-output (MIMO) systems. In this paper, we apply BD to the downlink
transmission in a cooperative multi-cell MIMO system, where the signals from
different base stations (BSs) to all the mobile stations (MSs) are jointly
designed with the perfect knowledge of the downlink channels and transmit
messages. Specifically, we study the optimal BD precoder design to maximize the
weighted sum-rate of all the MSs subject to a set of per-BS power constraints.
This design problem is formulated in an auxiliary MIMO broadcast channel (BC)
with a set of transmit power constraints corresponding to those for individual
BSs in the multi-cell system. By applying convex optimization techniques, this
paper develops an efficient algorithm to solve this problem, and derives the
closed-form expression for the optimal BD precoding matrix. It is revealed that
the optimal BD precoding vectors for each MS in the per-BS power constraint
case are in general non-orthogonal, which differs from the conventional
orthogonal BD precoder design for the MIMO-BC under one single sum-power
constraint. Moreover, for the special case of single-antenna BSs and MSs, the
proposed solution reduces to the optimal zero-forcing beamforming (ZF-BF)
precoder design for the weighted sum-rate maximization in the multiple-input
single-output (MISO) BC with per-antenna power constraints. Suboptimal and
low-complexity BD/ZF-BF precoding schemes are also presented, and their
achievable rates are compared against those with the optimal schemes.
|
0910.2350
|
Quantum control theory and applications: A survey
|
quant-ph cs.SY math-ph math.MP
|
This paper presents a survey on quantum control theory and applications from
a control systems perspective. Some of the basic concepts and main developments
(including open-loop control and closed-loop control) in quantum control theory
are reviewed. In the area of open-loop quantum control, the paper surveys the
notion of controllability for quantum systems and presents several control
design strategies including optimal control, Lyapunov-based methodologies,
variable structure control and quantum incoherent control. In the area of
closed-loop quantum control, the paper reviews closed-loop learning control and
several important issues related to quantum feedback control including quantum
filtering, feedback stabilization, LQG control and robust quantum control.
|
0910.2381
|
Fractional differentiation based image processing
|
cs.CV
|
There are many resources useful for processing images, most of them freely
available and quite friendly to use. In spite of this abundance of tools, a
study of the processing methods is still worthy of efforts. Here, we want to
discuss the possibilities arising from the use of fractional differential
calculus. This calculus evolved in the research field of pure mathematics until
1920, when applied science started to use it. Only recently, fractional
calculus was involved in image processing methods. As we shall see, the
fractional calculation is able to enhance the quality of images, with
interesting possibilities in edge detection and image restoration. We suggest
also the fractional differentiation as a tool to reveal faint objects in
astronomical images.
|
0910.2405
|
Generating Concise and Readable Summaries of XML Documents
|
cs.IR cs.DB
|
XML has become the de-facto standard for data representation and exchange,
resulting in large scale repositories and warehouses of XML data. In order for
users to understand and explore these large collections, a summarized, bird's
eye view of the available data is a necessity. In this paper, we are interested
in semantic XML document summaries which present the "important" information
available in an XML document to the user. In the best case, such a summary is a
concise replacement for the original document itself. At the other extreme, it
should at least help the user make an informed choice as to the relevance of
the document to his needs. In this paper, we address the two main issues which
arise in producing such meaningful and concise summaries: i) which tags or text
units are important and should be included in the summary, ii) how to generate
summaries of different sizes.%for different memory budgets. We conduct user
studies with different real-life datasets and show that our methods are useful
and effective in practice.
|
0910.2486
|
A Construction of Systematic MDS Codes with Minimum Repair Bandwidth
|
cs.IT math.IT
|
In a distributed storage system based on erasure coding, an important problem
is the \emph{repair problem}: If a node storing a coded piece fails, in order
to maintain the same level of reliability, we need to create a new encoded
piece and store it at a new node. This paper presents a construction of
systematic $(n,k)$-MDS codes for $2k\le n$ that achieves the minimum repair
bandwidth when repairing from $k+1$ nodes.
|
0910.2502
|
Interference Channels with Strong Secrecy
|
cs.IT math.IT
|
It is known that given the real sum of two independent uniformly distributed
lattice points from the same nested lattice codebook, the eavesdropper can
obtain at most 1 bit of information per channel regarding the value of one of
the lattice points. In this work, we study the effect of this 1 bit information
on the equivocation expressed in three commonly used information theoretic
measures, i.e., the Shannon entropy, the Renyi entropy and the min entropy. We
then demonstrate its applications in an interference channel with a
confidential message. In our previous work, we showed that nested lattice codes
can outperform Gaussian codes for this channel when the achieved rate is
measured with the weak secrecy notion. Here, with the Renyi entropy and the min
entropy measure, we prove that the same secure degree of freedom is achievable
with the strong secrecy notion as well. A major benefit of the new coding
scheme is that the strong secrecy is generated from a single lattice point
instead of a sequence of lattice points. Hence the mutual information between
the confidential message and the observation of the eavesdropper decreases much
faster with the number of channel uses than previously known strong secrecy
coding methods for nested lattice codes.
|
0910.2525
|
Utility of Beamforming Strategies for Secrecy in Multiuser MIMO Wiretap
Channels
|
cs.IT math.IT
|
This paper examines linear beamforming methods for secure communications in a
multiuser wiretap channel with a single transmitter, multiple legitimate
receivers, and a single eavesdropper, where all nodes are equipped with
multiple antennas. No information regarding the eavesdropper is presumed at the
transmitter, and we examine both the broadcast MIMO downlink with independent
information, and the multicast MIMO downlink with common information for all
legitimate receivers. In both cases the information signal is transmitted with
just enough power to guarantee a certain SINR at the desired receivers, while
the remainder of the power is used to broadcast artificial noise. The
artificial interference selectively degrades the passive eavesdropper's signal
while remaining orthogonal to the desired receivers. We analyze the
confidentiality provided by zero-forcing and optimal minimum-power beamforming
designs for the broadcast channel, and optimal minimum-MSE beamformers for the
multicast channel. Numerical simulations for the relative SINR and BER
performance of the eavesdropper demonstrate the effectiveness of the proposed
physical-layer security schemes.
|
0910.2534
|
Optimal Multiplexing Gain of K-user Line-of-Sight Interference Channels
with Polarization
|
cs.IT math.IT
|
We consider the multiplexing gain (MUXG) of the fully connected K-user
line-of-sight (LOS) interference channels (ICs). A polarimetric antenna
composed of 3 orthogonal electric dipoles and 3 orthogonal magnetic dipoles is
considered where all 6 dipoles are co-located. In case of K-user IC with single
polarization, the maximum achievable MUXG is K regardless of the number of
transmit and receive antennas because of the key-hole effect. With
polarization, a trivial upper bound on the MUXG is 2K. We propose a zero
forcing (ZF) scheme for the K-user LOS IC, where each user uses one or more
polarimetric antennas. By using the proposed ZF scheme, we find minimal antenna
configurations that achieve this bound for K <= 5. For K > 5, we show that the
optimal MUXG of 2K is achieved with M = (K+1)/6 polarimetric antennas at each
user.
|
0910.2540
|
Effectiveness and Limitations of Statistical Spam Filters
|
cs.LG
|
In this paper we discuss the techniques involved in the design of the famous
statistical spam filters that include Naive Bayes, Term Frequency-Inverse
Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes
Additive Regression Tree. We compare these techniques with each other in terms
of accuracy, recall, precision, etc. Further, we discuss the effectiveness and
limitations of statistical filters in filtering out various types of spam from
legitimate e-mails.
|
0910.2586
|
Degeneracy: a link between evolvability, robustness and complexity in
biological systems
|
nlin.AO cs.NE
|
A full accounting of biological robustness remains elusive; both in terms of
the mechanisms by which robustness is achieved and the forces that have caused
robustness to grow over evolutionary time. Although its importance to topics
such as ecosystem services and resilience is well recognized, the broader
relationship between robustness and evolution is only starting to be fully
appreciated. A renewed interest in this relationship has been prompted by
evidence that mutational robustness can play a positive role in the discovery
of future adaptive innovations (evolvability) and evidence of an intimate
relationship between robustness and complexity in biology.
This paper offers a new perspective on the mechanics of evolution and the
origins of complexity, robustness, and evolvability. Here we explore the
hypothesis that degeneracy, a partial overlap in the functioning of
multi-functional components, plays a central role in the evolution and
robustness of complex forms. In support of this hypothesis, we present evidence
that degeneracy is a fundamental source of robustness, it is intimately tied to
multi-scaled complexity, and it establishes conditions that are necessary for
system evolvability.
|
0910.2593
|
A Component Based Heuristic Search Method with Evolutionary Eliminations
|
cs.AI cs.NE
|
Nurse rostering is a complex scheduling problem that affects hospital
personnel on a daily basis all over the world. This paper presents a new
component-based approach with evolutionary eliminations, for a nurse scheduling
problem arising at a major UK hospital. The main idea behind this technique is
to decompose a schedule into its components (i.e. the allocated shift pattern
of each nurse), and then to implement two evolutionary elimination strategies
mimicking natural selection and natural mutation process on these components
respectively to iteratively deliver better schedules. The worthiness of all
components in the schedule has to be continuously demonstrated in order for
them to remain there. This demonstration employs an evaluation function which
evaluates how well each component contributes towards the final objective. Two
elimination steps are then applied: the first elimination eliminates a number
of components that are deemed not worthy to stay in the current schedule; the
second elimination may also throw out, with a low level of probability, some
worthy components. The eliminated components are replenished with new ones
using a set of constructive heuristics using local optimality criteria.
Computational results using 52 data instances demonstrate the applicability of
the proposed approach in solving real-world problems.
|
0910.2718
|
Two-hop Secure Communication Using an Untrusted Relay
|
cs.IT math.IT
|
We consider a source-destination pair that can only communicate through an
untrusted intermediate relay node. The intermediate node is willing to employ a
designated relaying scheme to facilitate reliable communication between the
source and the destination. Yet, the information it relays needs to be kept
secret from it. In this two-hop communication scenario, where the use of the
untrusted relay node is essential, we find that a positive secrecy rate is
achievable. The center piece of the achievability scheme is the help provided
by either the destination node with transmission capability, or an external
"good samaritan" node. In either case, the helper performs cooperative jamming
that confuses the eavesdropping relay and disables it from being able to
decipher what it is relaying. We next derive an upper bound on the secrecy rate
for this system. We observe that the gap between the upper bound and the
achievable rate vanishes as the power of the relay node goes to infinity.
Overall, the paper presents a case for intentional interference, i.e.,
cooperative jamming, as an enabler for secure communication.
|
0910.2743
|
DILAND: An Algorithm for Distributed Sensor Localization with Noisy
Distance Measurements
|
cs.DC cs.IT math.IT
|
In this correspondence, we present an algorithm for distributed sensor
localization with noisy distance measurements (DILAND) that extends and makes
the DLRE more robust. DLRE is a distributed sensor localization algorithm in
$\mathbb{R}^m$ $(m\geq1)$ introduced in \cite{usman_loctsp:08}. DILAND operates
when (i) the communication among the sensors is noisy; (ii) the communication
links in the network may fail with a non-zero probability; and (iii) the
measurements performed to compute distances among the sensors are corrupted
with noise. The sensors (which do not know their locations) lie in the convex
hull of at least $m+1$ anchors (nodes that know their own locations.) Under
minimal assumptions on the connectivity and triangulation of each sensor in the
network, this correspondence shows that, under the broad random phenomena
described above, DILAND converges almost surely (a.s.) to the exact sensor
locations.
|
0910.2771
|
Cooperative Interference Management with MISO Beamforming
|
cs.IT math.IT
|
This correspondence studies the downlink transmission in a multi-cell system,
where multiple base stations (BSs) each with multiple antennas cooperatively
design their respective transmit beamforming vectors to optimize the overall
system performance. For simplicity, it is assumed that all mobile stations
(MSs) are equipped with a single antenna each, and there is one active MS in
each cell at one time. Accordingly, the system of interests can be modeled by a
multiple-input single-output (MISO) interference channel (IC), termed as
MISO-IC, with interference treated as noise. We propose a new method to
characterize different rate-tuples for active MSs on the Pareto boundary of the
achievable rate region for the MISO-IC, by exploring the relationship between
the MISO-IC and the cognitive radio (CR) MISO channel. We show that each
Pareto-boundary rate-tuple of the MISO-IC can be achieved in a decentralized
manner when each of the BSs attains its own channel capacity subject to a
certain set of interference-power constraints (also known as
interference-temperature constraints in the CR system) at the other MS
receivers. Furthermore, we show that this result leads to a new decentralized
algorithm for implementing the multi-cell cooperative downlink beamforming.
|
0910.2832
|
Expectation Maximization as Message Passing - Part I: Principles and
Gaussian Messages
|
cs.IT math.IT
|
It is shown how expectation maximization (EM) may be viewed as a message
passing algorithm in factor graphs. In particular, a general EM message
computation rule is identified. As a factor graph tool, EM may be used to break
cycles in a factor graph, and tractable messages may in some cases be obtained
where the sum-product messages are unwieldy.
As an exemplary application, the paper considers linear Gaussian state space
models. Unknown coefficients in such models give rise to multipliers in the
corresponding factor graph. A main attraction of EM in such cases is that it
results in purely Gaussian message passing algorithms. These Gaussian EM
messages are tabulated for several (scalar, vector, matrix) multipliers that
frequently appear in applications.
|
0910.2874
|
An Agent Based Classification Model
|
cs.AI cs.MA
|
The major function of this model is to access the UCI Wisconsin Breast Can-
cer data-set[1] and classify the data items into two categories, which are
normal and anomalous. This kind of classifi cation can be referred as anomaly
detection, which discriminates anomalous behaviour from normal behaviour in
computer systems. One popular solution for anomaly detection is Artifi cial
Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical
immunology and observed immune functions, principles and models which are
applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS
algorithm that is developed specifi cally for anomaly detection. It has been
successfully applied to intrusion detection in computer security. It is
believed that agent-based mod- elling is an ideal approach for implementing
AIS, as intelligent agents could be the perfect representations of immune
entities in AIS. This model evaluates the feasibility of re-implementing the
DCA in an agent-based simulation environ- ment called AnyLogic, where the
immune entities in the DCA are represented by intelligent agents. If this model
can be successfully implemented, it makes it possible to implement more
complicated and adaptive AIS models in the agent-based simulation environment.
|
0910.2917
|
Behavior Subtraction
|
cs.CV
|
Background subtraction has been a driving engine for many computer vision and
video analytics tasks. Although its many variants exist, they all share the
underlying assumption that photometric scene properties are either static or
exhibit temporal stationarity. While this works in some applications, the model
fails when one is interested in discovering {\it changes in scene dynamics}
rather than those in a static background; detection of unusual pedestrian and
motor traffic patterns is but one example. We propose a new model and
computational framework that address this failure by considering stationary
scene dynamics as a ``background'' with which observed scene dynamics are
compared. Central to our approach is the concept of an {\it event}, that we
define as short-term scene dynamics captured over a time window at a specific
spatial location in the camera field of view. We compute events by
time-aggregating motion labels, obtained by background subtraction, as well as
object descriptors (e.g., object size). Subsequently, we characterize events
probabilistically, but use a low-memory, low-complexity surrogates in practical
implementation. Using these surrogates amounts to {\it behavior subtraction}, a
new algorithm with some surprising properties. As demonstrated here, behavior
subtraction is an effective tool in anomaly detection and localization. It is
resilient to spurious background motion, such as one due to camera jitter, and
is content-blind, i.e., it works equally well on humans, cars, animals, and
other objects in both uncluttered and highly-cluttered scenes. Clearly,
treating video as a collection of events rather than colored pixels opens new
possibilities for video analytics.
|
0910.2961
|
Isotropic MIMO Interference Channels without CSIT: The Loss of Degrees
of Freedom
|
cs.IT math.IT
|
This paper studies two-user MIMO interference channel with isotropic fading.
We assume that users are equipped with arbitrary number of antennas and the
channel state information (CSI) is available at receivers only. An outer bound
is obtained for the degree of freedom region, which suggests the loss of
degrees of freedom due to the lack of CSI at transmitters under many
circumstances.
|
0910.3028
|
State of the cognitive interference channel: a new unified inner bound
|
cs.IT math.IT
|
The capacity region of the interference channel in which one transmitter
non-causally knows the message of the other, termed the cognitive interference
channel, has remained open since its inception in 2005. A number of subtly
differing achievable rate regions and outer bounds have been derived, some of
which are tight under specific conditions. In this work we present a new
unified inner bound for the discrete memoryless cognitive interference channel.
We show explicitly how it encompasses all known discrete memoryless achievable
rate regions as special cases. The presented achievable region was recently
used in deriving the capacity region of the general deterministic cognitive
interference channel, and thus also the linear high-SNR deterministic
approximation of the Gaussian cognitive interference channel. The high-SNR
deterministic approximation was then used to obtain the capacity of the
Gaussian cognitive interference channel to within 1.87 bits.
|
0910.3033
|
Degraded Compound Multi-receiver Wiretap Channels
|
cs.IT math.IT
|
In this paper, we study the degraded compound multi-receiver wiretap channel.
The degraded compound multi-receiver wiretap channel consists of two groups of
users and a group of eavesdroppers, where, if we pick an arbitrary user from
each group of users and an arbitrary eavesdropper, they satisfy a certain
Markov chain. We study two different communication scenarios for this channel.
In the first scenario, the transmitter wants to send a confidential message to
users in the first (stronger) group and a different confidential message to
users in the second (weaker) group, where both messages need to be kept
confidential from the eavesdroppers. For this scenario, we assume that there is
only one eavesdropper. We obtain the secrecy capacity region for the general
discrete memoryless channel model, the parallel channel model, and the Gaussian
parallel channel model. For the Gaussian multiple-input multiple-output (MIMO)
channel model, we obtain the secrecy capacity region when there is only one
user in the second group. In the second scenario we study, the transmitter
sends a confidential message to users in the first group which needs to be kept
confidential from the second group of users and the eavesdroppers. Furthermore,
the transmitter sends a different confidential message to users in the second
group which needs to be kept confidential only from the eavesdroppers. For this
scenario, we do not put any restriction on the number of eavesdroppers. As in
the first scenario, we obtain the secrecy capacity region for the general
discrete memoryless channel model, the parallel channel model, and the Gaussian
parallel channel model. For the Gaussian MIMO channel model, we establish the
secrecy capacity region when there is only one user in the second group.
|
0910.3068
|
An Evolutionary Squeaky Wheel Optimisation Approach to Personnel
Scheduling
|
cs.AI cs.CE cs.NE
|
The quest for robust heuristics that are able to solve more than one problem
is ongoing. In this paper, we present, discuss and analyse a technique called
Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel
scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the
original Squeaky Wheel Optimisation's effectiveness and execution speed by
incorporating two extra steps (Selection and Mutation) for added evolution. In
the Evolutionary Squeaky Wheel Optimisation, a cycle of
Analysis-Selection-Mutation-Prioritization-Construction continues until
stopping conditions are reached. The aim of the Analysis step is to identify
below average solution components by calculating a fitness value for all
components. The Selection step then chooses amongst these underperformers and
discards some probabilistically based on fitness. The Mutation step further
discards a few components at random. Solutions can become incomplete and thus
repairs may be required. The repairs are carried out by using the
Prioritization to first produce priorities that determine an order by which the
following Construction step then schedules the remaining components. Therefore,
improvement in the Evolutionary Squeaky Wheel Optimisation is achieved by
selective solution disruption mixed with interative improvement and
constructive repair. Strong experimental results are reported on two different
domains of personnel scheduling: bus and rail driver scheduling and hospital
nurse scheduling.
|
0910.3084
|
Self-Dual Codes over Z_2xZ_4
|
cs.IT math.IT
|
Self-dual codes over $\Z_2\times\Z_4$ are subgroups of $\Z_2^\alpha
\times\Z_4^\beta$ that are equal to their orthogonal under an inner-product
that relates to the binary Hamming scheme. Three types of self-dual codes are
defined. For each type, the possible values $\alpha,\beta$ such that there
exist a code $\C\subseteq \Z_2^\alpha \times\Z_4^\beta$ are established.
Moreover, the construction of a $\add$-linear code for each type and possible
pair $(\alpha,\beta)$ is given. Finally, the standard techniques of invariant
theory are applied to describe the weight enumerators for each type.
|
0910.3113
|
Which Digraphs with Ring Structure are Essentially Cyclic?
|
math.CO cs.DM cs.MA math.SP
|
We say that a digraph is essentially cyclic if its Laplacian spectrum is not
completely real. The essential cyclicity implies the presence of directed
cycles, but not vice versa. The problem of characterizing essential cyclicity
in terms of graph topology is difficult and yet unsolved. Its solution is
important for some applications of graph theory, including that in
decentralized control. In the present paper, this problem is solved with
respect to the class of digraphs with ring structure, which models some typical
communication networks. It is shown that the digraphs in this class are
essentially cyclic, except for certain specified digraphs. The main technical
tool we employ is the Chebyshev polynomials of the second kind. A by-product of
this study is a theorem on the zeros of polynomials that differ by one from the
products of Chebyshev polynomials of the second kind. We also consider the
problem of essential cyclicity for weighted digraphs and enumerate the spanning
trees in some digraphs with ring structure.
|
0910.3115
|
An Idiotypic Immune Network as a Short Term Learning Architecture for
Mobile Robots
|
cs.AI cs.NE cs.RO
|
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferability
|
0910.3117
|
An Immune Inspired Approach to Anomaly Detection
|
cs.AI cs.CR cs.NE
|
The immune system provides a rich metaphor for computer security: anomaly
detection that works in nature should work for machines. However, early
artificial immune system approaches for computer security had only limited
success. Arguably, this was due to these artificial systems being based on too
simplistic a view of the immune system. We present here a second generation
artificial immune system for process anomaly detection. It improves on earlier
systems by having different artificial cell types that process information.
Following detailed information about how to build such second generation
systems, we find that communication between cells types is key to performance.
Through realistic testing and validation we show that second generation
artificial immune systems are capable of anomaly detection beyond generic
system policies. The paper concludes with a discussion and outline of the next
steps in this exciting area of computer security.
|
0910.3119
|
FFT-based Network Coding For Peer-To-Peer Content Delivery
|
cs.IT math.IT
|
In this paper, we propose a structured peer-to-peer (P2P) distribution scheme
based on Fast Fourier Transform (FFT) graphs. We build a peer-to-peer network
that reproduces the FFT graph initially designed for hardware FFT codecs. This
topology allows content delivery with a maximum diversity level for a minimum
global complexity. The resulting FFTbased network is a structured architecture
with an adapted network coding that brings flexibility upon content
distribution and robustness upon the dynamic nature of the network. This
structure can achieve optimal capacity in terms of content recovery while
solving the problem of last remaining blocks, even for large networks.
|
0910.3124
|
An Immune Inspired Network Intrusion Detection System Utilising
Correlation Context
|
cs.AI cs.CR cs.NE
|
Network Intrusion Detection Systems (NIDS) are computer systems which monitor
a network with the aim of discerning malicious from benign activity on that
network. While a wide range of approaches have met varying levels of success,
most IDSs rely on having access to a database of known attack signatures which
are written by security experts. Nowadays, in order to solve problems with
false positive alerts, correlation algorithms are used to add additional
structure to sequences of IDS alerts. However, such techniques are of no help
in discovering novel attacks or variations of known attacks, something the
human immune system (HIS) is capable of doing in its own specialised domain.
This paper presents a novel immune algorithm for application to the IDS
problem. The goal is to discover packets containing novel variations of attacks
covered by an existing signature base.
|
0910.3148
|
Parameterized Complexity of the k-anonymity Problem
|
cs.DS cs.DB cs.DM
|
The problem of publishing personal data without giving up privacy is becoming
increasingly important. An interesting formalization that has been recently
proposed is the $k$-anonymity. This approach requires that the rows of a table
are partitioned in clusters of size at least $k$ and that all the rows in a
cluster become the same tuple, after the suppression of some entries. The
natural optimization problem, where the goal is to minimize the number of
suppressed entries, is known to be APX-hard even when the records values are
over a binary alphabet and $k=3$, and when the records have length at most 8
and $k=4$ . In this paper we study how the complexity of the problem is
influenced by different parameters. In this paper we follow this direction of
research, first showing that the problem is W[1]-hard when parameterized by the
size of the solution (and the value $k$). Then we exhibit a fixed parameter
algorithm, when the problem is parameterized by the size of the alphabet and
the number of columns. Finally, we investigate the computational (and
approximation) complexity of the $k$-anonymity problem, when restricting the
instance to records having length bounded by 3 and $k=3$. We show that such a
restriction is APX-hard.
|
0910.3275
|
Degrees of Freedom of Multi-Source Relay Networks
|
cs.IT math.IT
|
We study a multi-source Gaussian relay network consisting of $K$
source--destination pairs having $K$ unicast sessions. We assume $M$ layers of
relays between the sources and the destinations. We find achievable degrees of
freedom of the network. Our schemes are based on interference alignment at the
transmitters and symbol extension and opportunistic interference cancellation
at the relays. For $K$-$L$-$K$ networks, i.e., 2-hop network with $L$ relays,
we show $\min\{K,K/2+L/(2(K-1))\}$ degrees of freedom are achievable. For
$K$-hop networks with $K$ relays in each layer, we show the full $K$ degrees of
freedom are achievable provided that $K$ is even and the channel distribution
satisfies a certain symmetry.
|
0910.3301
|
Faster Algorithms for Max-Product Message-Passing
|
cs.AI cs.DS
|
Maximum A Posteriori inference in graphical models is often solved via
message-passing algorithms, such as the junction-tree algorithm, or loopy
belief-propagation. The exact solution to this problem is well known to be
exponential in the size of the model's maximal cliques after it is
triangulated, while approximate inference is typically exponential in the size
of the model's factors. In this paper, we take advantage of the fact that many
models have maximal cliques that are larger than their constituent factors, and
also of the fact that many factors consist entirely of latent variables (i.e.,
they do not depend on an observation). This is a common case in a wide variety
of applications, including grids, trees, and ring-structured models. In such
cases, we are able to decrease the exponent of complexity for message-passing
by 0.5 for both exact and approximate inference.
|
0910.3348
|
Algorithms for Image Analysis and Combination of Pattern Classifiers
with Application to Medical Diagnosis
|
cs.CV cs.AI cs.GT cs.NE
|
Medical Informatics and the application of modern signal processing in the
assistance of the diagnostic process in medical imaging is one of the more
recent and active research areas today. This thesis addresses a variety of
issues related to the general problem of medical image analysis, specifically
in mammography, and presents a series of algorithms and design approaches for
all the intermediate levels of a modern system for computer-aided diagnosis
(CAD). The diagnostic problem is analyzed with a systematic approach, first
defining the imaging characteristics and features that are relevant to probable
pathology in mammo-grams. Next, these features are quantified and fused into
new, integrated radio-logical systems that exhibit embedded digital signal
processing, in order to improve the final result and minimize the radiological
dose for the patient. In a higher level, special algorithms are designed for
detecting and encoding these clinically interest-ing imaging features, in order
to be used as input to advanced pattern classifiers and machine learning
models. Finally, these approaches are extended in multi-classifier models under
the scope of Game Theory and optimum collective deci-sion, in order to produce
efficient solutions for combining classifiers with minimum computational costs
for advanced diagnostic systems. The material covered in this thesis is related
to a total of 18 published papers, 6 in scientific journals and 12 in
international conferences.
|
0910.3349
|
b-Bit Minwise Hashing
|
cs.DS cs.DB cs.IR
|
This paper establishes the theoretical framework of b-bit minwise hashing.
The original minwise hashing method has become a standard technique for
estimating set similarity (e.g., resemblance) with applications in information
retrieval, data management, social networks and computational advertising.
By only storing the lowest $b$ bits of each (minwise) hashed value (e.g., b=1
or 2), one can gain substantial advantages in terms of computational efficiency
and storage space. We prove the basic theoretical results and provide an
unbiased estimator of the resemblance for any b. We demonstrate that, even in
the least favorable scenario, using b=1 may reduce the storage space at least
by a factor of 21.3 (or 10.7) compared to using b=64 (or b=32), if one is
interested in resemblance > 0.5.
|
0910.3372
|
Composition and Inversion of Schema Mappings
|
cs.DB
|
In the recent years, a lot of attention has been paid to the development of
solid foundations for the composition and inversion of schema mappings. In this
paper, we review the proposals for the semantics of these crucial operators.
For each of these proposals, we concentrate on the three following problems:
the definition of the semantics of the operator, the language needed to express
the operator, and the algorithmic issues associated to the problem of computing
the operator. It should be pointed out that we primarily consider the
formalization of schema mappings introduced in the work on data exchange. In
particular, when studying the problem of computing the composition and inverse
of a schema mapping, we will be mostly interested in computing these operators
for mappings specified by source-to-target tuple-generating dependencies.
|
0910.3485
|
A Fuzzy Petri Nets Model for Computing With Words
|
cs.AI
|
Motivated by Zadeh's paradigm of computing with words rather than numbers,
several formal models of computing with words have recently been proposed.
These models are based on automata and thus are not well-suited for concurrent
computing. In this paper, we incorporate the well-known model of concurrent
computing, Petri nets, together with fuzzy set theory and thereby establish a
concurrency model of computing with words--fuzzy Petri nets for computing with
words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of
transitions are some special words modeled by fuzzy sets. By employing the
methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which
makes it possible for computing with more words. The language expressiveness of
the two formal models of computing with words, fuzzy automata for computing
with words and FPNCWs, is compared as well. A few small examples are provided
to illustrate the theoretical development.
|
0910.3490
|
Adaptive model for recommendation of news
|
cs.IR cs.DL physics.soc-ph
|
Most news recommender systems try to identify users' interests and news'
attributes and use them to obtain recommendations. Here we propose an adaptive
model which combines similarities in users' rating patterns with epidemic-like
spreading of news on an evolving network. We study the model by computer
agent-based simulations, measure its performance and discuss its robustness
against bias and malicious behavior. Subject to the approval fraction of news
recommended, the proposed model outperforms the widely adopted recommendation
of news according to their absolute or relative popularity. This model provides
a general social mechanism for recommender systems and may find its
applications also in other types of recommendation.
|
0910.3494
|
Sum-capacity of Interference Channels with a Local View: Impact of
Distributed Decisions
|
cs.IT math.IT
|
Due to the large size of wireless networks, it is often impractical for nodes
to track changes in the complete network state. As a result, nodes have to make
distributed decisions about their transmission and reception parameters based
on their local view of the network. In this paper, we characterize the impact
of distributed decisions on the global network performance in terms of
achievable sum-rates. We first formalize the concept of local view by proposing
a protocol abstraction using the concept of local message passing. In the
proposed protocol, nodes forward information about the network state to other
neighboring nodes, thereby allowing network state information to trickle to all
the nodes. The protocol proceeds in rounds, where all transmitters send a
message followed by a message by all receivers. The number of rounds then
provides a natural metric to quantify the extent of local information at each
node.
We next study three network connectivities, Z-channel, a three-user double
Z-channel and a reduced-parametrization $K$-user stacked Z-channel. In each
case, we characterize achievable sum-rate with partial message passing leading
to three main results. First, in many cases, nodes can make distributed
decisions with only local information about the network and can still achieve
the same sum-capacity as can be attained with global information irrespective
of the actual channel gains. Second, for the case of three-user double
Z-channel, we show that universal optimality is not achievable if the per node
information is below a threshold. Third, using reduced parametrization $K$-user
channel, we show that very few protocol rounds are needed for the case of very
weak or very strong interference.
|
0910.3509
|
Slepian-Wolf Coding Over Cooperative Relay Networks
|
cs.IT math.IT
|
This paper deals with the problem of multicasting a set of discrete
memoryless correlated sources (DMCS) over a cooperative relay network.
Necessary conditions with cut-set interpretation are presented. A \emph{Joint
source-Wyner-Ziv encoding/sliding window decoding} scheme is proposed, in which
decoding at each receiver is done with respect to an ordered partition of other
nodes. For each ordered partition a set of feasibility constraints is derived.
Then, utilizing the sub-modular property of the entropy function and a novel
geometrical approach, the results of different ordered partitions are
consolidated, which lead to sufficient conditions for our problem. The proposed
scheme achieves operational separation between source coding and channel
coding. It is shown that sufficient conditions are indeed necessary conditions
in two special cooperative networks, namely, Aref network and finite-field
deterministic network. Also, in Gaussian cooperative networks, it is shown that
reliable transmission of all DMCS whose Slepian-Wolf region intersects the
cut-set bound region within a constant number of bits, is feasible. In
particular, all results of the paper are specialized to obtain an achievable
rate region for cooperative relay networks which includes relay networks and
two-way relay networks.
|
0910.3580
|
Set-Rationalizable Choice and Self-Stability
|
cs.MA
|
A common assumption in modern microeconomic theory is that choice should be
rationalizable via a binary preference relation, which \citeauthor{Sen71a}
showed to be equivalent to two consistency conditions, namely $\alpha$
(contraction) and $\gamma$ (expansion). Within the context of \emph{social}
choice, however, rationalizability and similar notions of consistency have
proved to be highly problematic, as witnessed by a range of impossibility
results, among which Arrow's is the most prominent. Since choice functions
select \emph{sets} of alternatives rather than single alternatives, we propose
to rationalize choice functions by preference relations over sets
(set-rationalizability). We also introduce two consistency conditions,
$\hat\alpha$ and $\hat\gamma$, which are defined in analogy to $\alpha$ and
$\gamma$, and find that a choice function is set-rationalizable if and only if
it satisfies $\hat\alpha$. Moreover, a choice function satisfies $\hat\alpha$
and $\hat\gamma$ if and only if it is \emph{self-stable}, a new concept based
on earlier work by \citeauthor{Dutt88a}. The class of self-stable social choice
functions contains a number of appealing Condorcet extensions such as the
minimal covering set and the essential set.
|
0910.3603
|
A complete solution to Blackwell's unique ergodicity problem for hidden
Markov chains
|
math.PR cs.IT math.IT
|
We develop necessary and sufficient conditions for uniqueness of the
invariant measure of the filtering process associated to an ergodic hidden
Markov model in a finite or countable state space. These results provide a
complete solution to a problem posed by Blackwell (1957), and subsume earlier
partial results due to Kaijser, Kochman and Reeds. The proofs of our main
results are based on the stability theory of nonlinear filters.
|
0910.3658
|
Secrecy Rate Region of the Broadcast Channel with an Eavesdropper
|
cs.IT math.IT
|
In this paper, we consider a scenario where a source node wishes to broadcast
two confidential messages to two receivers, while a wire-tapper also receives
the transmitted signal. This model is motivated by wireless communications,
where individual secure messages are broadcast over open media and can be
received by any illegitimate receiver. The secrecy level is measured by the
equivocation rate at the eavesdropper. We first study the general
(non-degraded) broadcast channel with an eavesdropper. We present an inner
bound on the secrecy capacity region for this model. This inner bound is based
on a combination of random binning, and the Gelfand-Pinsker binning. We further
study the situation in which the channels are degraded. For the degraded
broadcast channel with an eavesdropper, we present the secrecy capacity region.
Our achievable coding scheme is based on Covers superposition scheme and random
binning. We refer to this scheme as the Secret Superposition Scheme. Our
converse proof is based on a combination of the converse proof of the
conventional degraded broadcast channel and Csiszar Lemma. We then assume that
the channels are Additive White Gaussian Noise (AWGN) and show that the Secret
Superposition Scheme with Gaussian codebook is optimal. The converse proof is
based on Costas entropy power inequality. Finally, we use a broadcast strategy
for the slowly fading wire-tap channel when only the eavesdroppers channel is
fixed and known at the transmitter. We derive the optimum power allocation for
the coding layers, which maximizes the total average rate.
|
0910.3713
|
On Learning Finite-State Quantum Sources
|
quant-ph cs.LG
|
We examine the complexity of learning the distributions produced by
finite-state quantum sources. We show how prior techniques for learning hidden
Markov models can be adapted to the quantum generator model to find that the
analogous state of affairs holds: information-theoretically, a polynomial
number of samples suffice to approximately identify the distribution, but
computationally, the problem is as hard as learning parities with noise, a
notorious open question in computational learning theory.
|
0910.3768
|
On the transmit strategy for the interference MIMO relay channel in the
low power regime
|
cs.IT math.IT
|
This paper studies the interference channel with two transmitters and two
receivers in the presence of a MIMO relay in the low transmit power regime. A
communication scheme combining block Markov encoding, beamforming, and Willems'
backward decoding is used. With this scheme, we get an interference channel
with channel gains dependent on the signal power. A power allocation for this
scheme is proposed, and the achievable rate region with this power allocation
is given. We show that, at low transmit powers, with equal power constraints at
the relay and the transmitters, the interference channel with a MIMO relay
achieves a sum rate that is linear in the power. This sum rate is determined by
the channel setup. We also show that in the presence of abundant power at the
relay, the transmit strategy is significantly simplified, and the MAC from the
transmitters to the relay forms the bottle neck of the system from the sum rate
point of view.
|
0910.3811
|
Dynamics of the Orthoglide parallel robot
|
cs.RO
|
Recursive matrix relations for kinematics and dynamics of the Orthoglide
parallel robot having three concurrent prismatic actuators are established in
this paper. These are arranged according to the Cartesian coordinate system
with fixed orientation, which means that the actuating directions are normal to
each other. Three identical legs connecting to the moving platform are located
on three planes being perpendicular to each other too. Knowing the position and
the translation motion of the platform, we develop the inverse kinematics
problem and determine the position, velocity and acceleration of each element
of the robot. Further, the principle of virtual work is used in the inverse
dynamic problem. Some matrix equations offer iterative expressions and graphs
for the input forces and the powers of the three actuators.
|
0910.3840
|
Distributed Universally Optimal Strategies for Interference Channels
with Partial Message Passing
|
cs.IT math.IT
|
In distributed wireless networks, nodes often do not know the topology
(network size, connectivity and the channel gains) of the network. Thus, they
have to compute their transmission and reception parameters in a distributed
fashion. In this paper, we consider that each of the transmitter know the
channel gains of all the links that are at-most two-hop distant from it and the
receiver knows the channel gains of all the links that are three-hop distant
from it in a deterministic interference channel. With this limited information,
we find a condition on the network connectivity for which there exist a
distributed strategy that can be chosen by the users with partial information
about the network state, which achieves the same sum capacity as that
achievable by the centralized server that knows all the channel gains.
Specifically, distributed decisions are sum-rate optimal only if each connected
component is in a one-to-many configuration or a fully-connected configuration.
In all other cases of network connectivity, the loss can be arbitrarily large.
|
0910.3848
|
Equivalence Classes of Optimal Structures in HP Protein Models Including
Side Chains
|
cs.CE q-bio.BM
|
Lattice protein models, as the Hydrophobic-Polar (HP) model, are a common
abstraction to enable exhaustive studies on structure, function, or evolution
of proteins. A main issue is the high number of optimal structures, resulting
from the hydrophobicity-based energy function applied. We introduce an
equivalence relation on protein structures that correlates to the energy
function. We discuss the efficient enumeration of optimal representatives of
the corresponding equivalence classes and the application of the results.
|
0910.3880
|
Constraint-based Local Move Definitions for Lattice Protein Models
Including Side Chains
|
cs.CE q-bio.BM
|
The simulation of a protein's folding process is often done via stochastic
local search, which requires a procedure to apply structural changes onto a
given conformation. Here, we introduce a constraint-based approach to enumerate
lattice protein structures according to k-local moves in arbitrary lattices.
Our declarative description is much more flexible for extensions than standard
operational formulations. It enables a generic calculation of k-local neighbors
in backbone-only and side chain models. We exemplify the procedure using a
simple hierarchical folding scheme.
|
0910.3883
|
Variance Analysis of Randomized Consensus in Switching Directed Networks
|
cs.MA cs.DC cs.NI
|
In this paper, we study the asymptotic properties of distributed consensus
algorithms over switching directed random networks. More specifically, we focus
on consensus algorithms over independent and identically distributed, directed
Erdos-Renyi random graphs, where each agent can communicate with any other
agent with some exogenously specified probability $p$. While it is well-known
that consensus algorithms over Erdos-Renyi random networks result in an
asymptotic agreement over the network, an analytical characterization of the
distribution of the asymptotic consensus value is still an open question. In
this paper, we provide closed-form expressions for the mean and variance of the
asymptotic random consensus value, in terms of the size of the network and the
probability of communication $p$. We also provide numerical simulations that
illustrate our results.
|
0910.3913
|
How to Complete an Interactive Configuration Process?
|
cs.SE cs.AI cs.LO
|
When configuring customizable software, it is useful to provide interactive
tool-support that ensures that the configuration does not breach given
constraints.
But, when is a configuration complete and how can the tool help the user to
complete it?
We formalize this problem and relate it to concepts from non-monotonic
reasoning well researched in Artificial Intelligence. The results are
interesting for both practitioners and theoreticians. Practitioners will find a
technique facilitating an interactive configuration process and experiments
supporting feasibility of the approach. Theoreticians will find links between
well-known formal concepts and a concrete practical application.
|
0910.3928
|
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A
Comparative Study
|
cs.IT math.IT
|
In this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble
consisting of equipowered and equispaced pilots embedded in zeros, turns out to
perform at least as well as CP-OFDM. Simulations results are presented that
verify the analysis.
|
0910.3973
|
On the Delay-Throughput Tradeoff in Distributed Wireless Networks
|
cs.IT math.IT
|
This paper deals with the delay-throughput analysis of a single-hop wireless
network with $n$ transmitter/receiver pairs. All channels are assumed to be
block Rayleigh fading with shadowing, described by parameters
$(\alpha,\varpi)$, where $\alpha$ denotes the probability of shadowing and
$\varpi$ represents the average cross-link gains. The analysis relies on the
distributed on-off power allocation strategy (i.e., links with a direct channel
gain above a certain threshold transmit at full power and the rest remain
silent) for the deterministic and stochastic packet arrival processes. It is
also assumed that each transmitter has a buffer size of one packet and dropping
occurs once a packet arrives in the buffer while the previous packet has not
been served. In the first part of the paper, we define a new notion of
performance in the network, called effective throughput, which captures the
effect of arrival process in the network throughput, and maximize it for
different cases of packet arrival process. It is proved that the effective
throughput of the network asymptotically scales as $\frac{\log
n}{\hat{\alpha}}$, with $\hat{\alpha} \triangleq \alpha \varpi$, regardless of
the packet arrival process. In the second part of the paper, we present the
delay characteristics of the underlying network in terms of the packet dropping
probability. We derive the sufficient conditions in the asymptotic case of $n
\to \infty$ such that the packet dropping probability tend to zero, while
achieving the maximum effective throughput of the network. Finally, we study
the trade-off between the effective throughput, delay, and packet dropping
probability of the network for different packet arrival processes.
|
0910.3975
|
On the Delay of Network Coding over Line Networks
|
cs.IT math.IT
|
We analyze a simple network where a source and a receiver are connected by a
line of erasure channels of different reliabilities. Recent prior work has
shown that random linear network coding can achieve the min-cut capacity and
therefore the asymptotic rate is determined by the worst link of the line
network. In this paper we investigate the delay for transmitting a batch of
packets, which is a function of all the erasure probabilities and the number of
packets in the batch. We show a monotonicity result on the delay function and
derive simple expressions which characterize the expected delay behavior of
line networks. Further, we use a martingale bounded differences argument to
show that the actual delay is tightly concentrated around its expectation.
|
0910.4000
|
Path placement optimization of manipulators based on energy consumption:
application to the orthoglide 3-axis
|
cs.RO
|
This paper deals with the optimal path placement for a manipulator based on
energy consumption. It proposes a methodology to determine the optimal location
of a given test path within the workspace of a manipulator with minimal
electric energy used by the actuators while taking into account the geometric,
kinematic and dynamic constraints. The proposed methodology is applied to the
Orthoglide~3-axis, a three-degree-of-freedom translational parallel kinematic
machine (PKM), as an illustrative example.
|
0910.4012
|
Singularity Analysis of Lower-Mobility Parallel Manipulators Using
Grassmann-Cayley Algebra
|
cs.RO
|
This paper introduces a methodology to analyze geometrically the
singularities of manipulators, of which legs apply both actuation forces and
constraint moments to their moving platform. Lower-mobility parallel
manipulators and parallel manipulators, of which some legs do not have any
spherical joint, are such manipulators. The geometric conditions associated
with the dependency of six Pl\"ucker vectors of finite lines or lines at
infinity constituting the rows of the inverse Jacobian matrix are formulated
using Grassmann-Cayley Algebra. Accordingly, the singularity conditions are
obtained in vector form. This study is illustrated with the singularity
analysis of four manipulators.
|
0910.4033
|
Studying Maximum Information Leakage Using Karush-Kuhn-Tucker Conditions
|
cs.CR cs.IT cs.LO cs.PL math.IT
|
When studying the information leakage in programs or protocols, a natural
question arises: "what is the worst case scenario?". This problem of
identifying the maximal leakage can be seen as a channel capacity problem in
the information theoretical sense. In this paper, by combining two powerful
theories: Information Theory and Karush-Kuhn-Tucker conditions, we demonstrate
a very general solution to the channel capacity problem. Examples are given to
show how our solution can be applied to practical contexts of programs and
anonymity protocols, and how this solution generalizes previous approaches to
this problem.
|
0910.4116
|
Swarm Intelligence
|
cs.NE cs.AI
|
Biologically inspired computing is an area of computer science which uses the
advantageous properties of biological systems. It is the amalgamation of
computational intelligence and collective intelligence. Biologically inspired
mechanisms have already proved successful in achieving major advances in a wide
range of problems in computing and communication systems. The consortium of
bio-inspired computing are artificial neural networks, evolutionary algorithms,
swarm intelligence, artificial immune systems, fractal geometry, DNA computing
and quantum computing, etc. This article gives an introduction to swarm
intelligence.
|
0910.4128
|
Secure Communication in the Low-SNR Regime
|
cs.IT math.IT
|
Secrecy capacity of a multiple-antenna wiretap channel is studied in the low
signal-to-noise ratio (SNR) regime. Expressions for the first and second
derivatives of the secrecy capacity with respect to SNR at SNR = 0 are derived.
Transmission strategies required to achieve these derivatives are identified.
In particular, it is shown that it is optimal in the low-SNR regime to transmit
in the maximal-eigenvalue eigenspace of Phi = H_m* H_m - N_m/N_e H_e* H_e where
H_m and H_e denote the channel matrices associated with the legitimate receiver
and eavesdropper, respectively, and N_m and N_e are the noise variances at the
receiver and eavesdropper, respectively. Energy efficiency is analyzed by
finding the minimum bit energy required for secure and reliable communications,
and the wideband slope. Increased bit energy requirements under secrecy
constraints are quantified. Finally, the impact of fading is investigated, and
the benefits of fading in terms of energy efficiency are shown.
|
0910.4130
|
On the Achievable Throughput Region of Multiple-Access Fading Channels
with QoS Constraints
|
cs.IT math.IT
|
Effective capacity, which provides the maximum constant arrival rate that a
given service process can support while satisfying statistical delay
constraints, is analyzed in a multiuser scenario. In particular, we study the
achievable effective capacity region of the users in multiaccess fading
channels (MAC) in the presence of quality of service (QoS) constraints. We
assume that channel side information (CSI) is available at both the
transmitters and the receiver, and superposition coding technique with
successive decoding is used. When the power is fixed at the transmitters, we
show that varying the decoding order with respect to the channel state can
significantly increase the achievable throughput region. For a two-user case,
we obtain the optimal decoding strategy when the users have the same QoS
constraints. Meanwhile, it is shown that time-division multiple-access (TDMA)
can achieve better performance than superposition coding with fixed successive
decoding order at the receiver side for certain QoS constraints. For power and
rate adaptation, we determine the optimal power allocation policy with fixed
decoding order at the receiver side. Numerical results are provided to
demonstrate our results.
|
0910.4132
|
Collaborative Relay Beamforming for Secrecy
|
cs.IT math.IT
|
In this paper, collaborative use of relays to form a beamforming system with
the aid of perfect channel state information (CSI) and to provide
physical-layer security is investigated. In particular, a
decode-and-forward-based relay beamforming design subject to total and
individual relay power constraints is studied with the goal of maximizing the
secrecy rate. It is shown that the total power constraint leads to a
closed-form solution. The design under individual relay power constraints is
formulated as an optimization problem which is shown to be easily solved using
two different approaches, namely semidefinite programming and second-order cone
programming. Furthermore, a simplified and suboptimal technique which reduces
the computation complexity under individual power constraints is presented.
|
0910.4214
|
Congestion games with resource reuse and applications in spectrum
sharing
|
cs.GT cs.MA
|
In this paper we consider an extension to the classical definition of
congestion games (CG) in which multiple users share the same set of resources
and their payoff for using any resource is a function of the total number of
users sharing it. The classical congestion games enjoy some very appealing
properties, including the existence of a Nash equilibrium and that every
improvement path is finite and leads to such a NE (also called the finite
improvement property or FIP), which is also a local optimum to a potential
function. On the other hand, this class of games does not model well the
congestion or resource sharing in a wireless context, a prominent feature of
which is spatial reuse. What this translates to in the context of a congestion
game is that a users payoff for using a resource (interpreted as a channel) is
a function of the its number of its interfering users sharing that channel,
rather than the total number among all users. This makes the problem quite
different. We will call this the congestion game with resource reuse (CG-RR).
In this paper we study intrinsic properties of such a game; in particular, we
seek to address under what conditions on the underlying network this game
possesses the FIP or NE. We also discuss the implications of these results when
applied to wireless spectrum sharing
|
0910.4336
|
Minimal realizations of linear systems: The "shortest basis" approach
|
cs.IT cs.SY math.IT math.OC
|
Given a controllable discrete-time linear system C, a shortest basis for C is
a set of linearly independent generators for C with the least possible lengths.
A basis B is a shortest basis if and only if it has the predictable span
property (i.e., has the predictable delay and degree properties, and is
non-catastrophic), or alternatively if and only if it has the subsystem basis
property (for any interval J, the generators in B whose span is in J is a basis
for the subsystem C_J). The dimensions of the minimal state spaces and minimal
transition spaces of C are simply the numbers of generators in a shortest basis
B that are active at any given state or symbol time, respectively. A minimal
linear realization for C in controller canonical form follows directly from a
shortest basis for C, and a minimal linear realization for C in observer
canonical form follows directly from a shortest basis for the orthogonal system
C^\perp. This approach seems conceptually simpler than that of classical
minimal realization theory.
|
0910.4353
|
Nonapproximablity of the Normalized Information Distance
|
cs.CC cs.IT math.IT
|
Normalized information distance (NID) uses the theoretical notion of
Kolmogorov complexity, which for practical purposes is approximated by the
length of the compressed version of the file involved, using a real-world
compression program. This practical application is called `normalized
compression distance' and it is trivially computable. It is a parameter-free
similarity measure based on compression, and is used in pattern recognition,
data mining, phylogeny, clustering, and classification. The complexity
properties of its theoretical precursor, the NID, have been open. We show that
the NID is neither upper semicomputable nor lower semicomputable up to any
reasonable precision.
|
0910.4397
|
The Geometry of Generalized Binary Search
|
stat.ML cs.IT math.IT math.ST stat.TH
|
This paper investigates the problem of determining a binary-valued function
through a sequence of strategically selected queries. The focus is an algorithm
called Generalized Binary Search (GBS). GBS is a well-known greedy algorithm
for determining a binary-valued function through a sequence of strategically
selected queries. At each step, a query is selected that most evenly splits the
hypotheses under consideration into two disjoint subsets, a natural
generalization of the idea underlying classic binary search. This paper
develops novel incoherence and geometric conditions under which GBS achieves
the information-theoretically optimal query complexity; i.e., given a
collection of N hypotheses, GBS terminates with the correct function after no
more than a constant times log N queries. Furthermore, a noise-tolerant version
of GBS is developed that also achieves the optimal query complexity. These
results are applied to learning halfspaces, a problem arising routinely in
image processing and machine learning.
|
0910.4613
|
Fading Cognitive Multiple-Access Channels With Confidential Messages
|
cs.IT cs.CR math.IT
|
The fading cognitive multiple-access channel with confidential messages
(CMAC-CM) is investigated, in which two users attempt to transmit common
information to a destination and user 1 also has confidential information
intended for the destination. User 1 views user 2 as an eavesdropper and wishes
to keep its confidential information as secret as possible from user 2. The
multiple-access channel (both the user-to-user channel and the
user-to-destination channel) is corrupted by multiplicative fading gain
coefficients in addition to additive white Gaussian noise. The channel state
information (CSI) is assumed to be known at both the users and the destination.
A parallel CMAC-CM with independent subchannels is first studied. The secrecy
capacity region of the parallel CMAC-CM is established, which yields the
secrecy capacity region of the parallel CMAC-CM with degraded subchannels.
Next, the secrecy capacity region is established for the parallel Gaussian
CMAC-CM, which is used to study the fading CMAC-CM. When both users know the
CSI, they can dynamically change their transmission powers with the channel
realization to achieve the optimal performance. The closed-form power
allocation function that achieves every boundary point of the secrecy capacity
region is derived.
|
0910.4624
|
Convolution operations arising from Vandermonde matrices
|
cs.IT math.IT
|
Different types of convolution operations involving large Vandermonde
matrices are considered. The convolutions parallel those of large Gaussian
matrices and additive and multiplicative free convolution. First additive and
multiplicative convolution of Vandermonde matrices and deterministic diagonal
matrices are considered. After this, several cases of additive and
multiplicative convolution of two independent Vandermonde matrices are
considered. It is also shown that the convergence of any combination of
Vandermonde matrices is almost sure. We will divide the considered convolutions
into two types: those which depend on the phase distribution of the Vandermonde
matrices, and those which depend only on the spectra of the matrices. A general
criterion is presented to find which type applies for any given convolution. A
simulation is presented, verifying the results. Implementations of all
considered convolutions are provided and discussed, together with the
challenges in making these implementations efficient. The implementation is
based on the technique of Fourier-Motzkin elimination, and is quite general as
it can be applied to virtually any combination of Vandermonde matrices.
Generalizations to related random matrices, such as Toeplitz and Hankel
matrices, are also discussed.
|
0910.4627
|
Self-concordant analysis for logistic regression
|
cs.LG math.ST stat.TH
|
Most of the non-asymptotic theoretical work in regression is carried out for
the square loss, where estimators can be obtained through closed-form
expressions. In this paper, we use and extend tools from the convex
optimization literature, namely self-concordant functions, to provide simple
extensions of theoretical results for the square loss to the logistic loss. We
apply the extension techniques to logistic regression with regularization by
the $\ell_2$-norm and regularization by the $\ell_1$-norm, showing that new
results for binary classification through logistic regression can be easily
derived from corresponding results for least-squares regression.
|
0910.4632
|
Fast Algebraic Attacks and Decomposition of Symmetric Boolean Functions
|
cs.CR cs.IT math.IT
|
Algebraic and fast algebraic attacks are power tools to analyze stream
ciphers. A class of symmetric Boolean functions with maximum algebraic immunity
were found vulnerable to fast algebraic attacks at EUROCRYPT'06. Recently, the
notion of AAR (algebraic attack resistant) functions was introduced as a
unified measure of protection against both classical algebraic and fast
algebraic attacks. In this correspondence, we first give a decomposition of
symmetric Boolean functions, then we show that almost all symmetric Boolean
functions, including these functions with good algebraic immunity, behave badly
against fast algebraic attacks, and we also prove that no symmetric Boolean
functions are AAR functions. Besides, we improve the relations between
algebraic degree and algebraic immunity of symmetric Boolean functions.
|
0910.4667
|
Time Delay Estimation in Cognitive Radio Systems
|
cs.IT math.IT
|
In cognitive radio systems, secondary users can utilize multiple dispersed
bands that are not used by primary users. In this paper, time delay estimation
of signals that occupy multiple dispersed bands is studied. First, theoretical
limits on time delay estimation are reviewed. Then, two-step time delay
estimators that provide trade-offs between computational complexity and
performance are investigated. In addition, asymptotic optimality properties of
the two-step time delay estimators are discussed. Finally, simulation results
are presented to explain the theoretical results.
|
0910.4683
|
Competing with Gaussian linear experts
|
cs.LG
|
We study the problem of online regression. We prove a theoretical bound on
the square loss of Ridge Regression. We do not make any assumptions about input
vectors or outcomes. We also show that Bayesian Ridge Regression can be thought
of as an online algorithm competing with all the Gaussian linear experts.
|
0910.4686
|
Moderate Deviations of the Random Riccati Equation
|
math.PR cs.IT math.DS math.IT math.OC
|
We characterize the invariant filtering measures resulting from Kalman
filtering with intermittent observations (\cite{Bruno}), where the observation
arrival is modeled as a Bernoulli process. In \cite{Riccati-weakconv}, it was
shown that there exists a $\overline{\gamma}^{\{\scriptsize{sb}}}>0$ such that
for every observation packet arrival probability $\overline{\gamma}$,
$\overline{\gamma}>\overline{\gamma}^{\{\scriptsize{sb}}}>0$, the sequence of
random conditional error covariance matrices converges in distribution to a
unique invariant distribution $\mathbb{\mu}^{\overline{\gamma}}$ (independent
of the filter initialization.) In this paper, we prove that, for controllable
and observable systems, $\overline{\gamma}^{\{\scriptsize{sb}}}=0$ and that, as
$\overline{\gamma}\uparrow 1$, the family
$\{\mathbb{\mu}^{\overline{\gamma}}\}_{\overline{\gamma}>0}$ of invariant
distributions satisfies a moderate deviations principle (MDP) with a good rate
function $I$. The rate function $I$ is explicitly identified. In particular,
our results show:
|
0910.4688
|
Quickest detection in coupled systems
|
cs.IT math.IT
|
This work considers the problem of quickest detection of signals in a coupled
system of N sensors, which receive continuous sequential observations from the
environment. It is assumed that the signals, which are modeled a general Ito
processes, are coupled across sensors, but that their onset times may differ
from sensor to sensor. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended average Kullback-
Leibler divergence criterion is used as a measure of detection delay, with a
constraint on the mean time between false alarms. The case in which the sensors
employ cumulative sum (CUSUM) strategies is considered, and it is proved that
the minimum of N CUSUMs is asymptotically optimal as the mean time between
false alarms increases without bound.
|
0910.4699
|
Sum of Us: Strategyproof Selection from the Selectors
|
cs.GT cs.AI
|
We consider directed graphs over a set of n agents, where an edge (i,j) is
taken to mean that agent i supports or trusts agent j. Given such a graph and
an integer k\leq n, we wish to select a subset of k agents that maximizes the
sum of indegrees, i.e., a subset of k most popular or most trusted agents. At
the same time we assume that each individual agent is only interested in being
selected, and may misreport its outgoing edges to this end. This problem
formulation captures realistic scenarios where agents choose among themselves,
which can be found in the context of Internet search, social networks like
Twitter, or reputation systems like Epinions.
Our goal is to design mechanisms without payments that map each graph to a
k-subset of agents to be selected and satisfy the following two constraints:
strategyproofness, i.e., agents cannot benefit from misreporting their outgoing
edges, and approximate optimality, i.e., the sum of indegrees of the selected
subset of agents is always close to optimal. Our first main result is a
surprising impossibility: for k \in {1,...,n-1}, no deterministic strategyproof
mechanism can provide a finite approximation ratio. Our second main result is a
randomized strategyproof mechanism with an approximation ratio that is bounded
from above by four for any value of k, and approaches one as k grows.
|
0910.4711
|
Parallelization of the LBG Vector Quantization Algorithm for Shared
Memory Systems
|
cs.CV cs.DC
|
This paper proposes a parallel approach for the Vector Quantization (VQ)
problem in image processing. VQ deals with codebook generation from the input
training data set and replacement of any arbitrary data with the nearest
codevector. Most of the efforts in VQ have been directed towards designing
parallel search algorithms for the codebook, and little has hitherto been done
in evolving a parallelized procedure to obtain an optimum codebook. This
parallel algorithm addresses the problem of designing an optimum codebook using
the traditional LBG type of vector quantization algorithm for shared memory
systems and for the efficient usage of parallel processors. Using the codebook
formed from a training set, any arbitrary input data is replaced with the
nearest codevector from the codebook. The effectiveness of the proposed
algorithm is indicated.
|
0910.4738
|
On the connections between PCTL and Dynamic Programming
|
math.OC cs.SY
|
Probabilistic Computation Tree Logic (PCTL) is a well-known modal logic which
has become a standard for expressing temporal properties of finite-state Markov
chains in the context of automated model checking. In this paper, we give a
definition of PCTL for noncountable-space Markov chains, and we show that there
is a substantial affinity between certain of its operators and problems of
Dynamic Programming. After proving some uniqueness properties of the solutions
to the latter, we conclude the paper with two examples to show that some
recovery strategies in practical applications, which are naturally stated as
reach-avoid problems, can be actually viewed as particular cases of PCTL
formulas.
|
0910.4769
|
Enrichissement des contenus par la r\'eindexation des usagers : un
\'etat de l'art sur la probl\'ematique
|
cs.IR
|
Information retrieval (IR) is a user approach to obtain relevant information
which meets needs with the help of a IR system (IRS). However, the IRS shows
certain differences between user relevance and system relevance. These gaps are
essentially related to the imperfection of the indexing process (as approach
related to the IR), to problems related to the misunderstanding of the natural
language and the non correspondence between the real needs of the user and the
results of his query. As idea is to think about an ?intellectual? indexing that
takes into account the point of view of the user. By consulting the document,
user can build information as added-value on the existing content: new
information which grows contents and allows the semantic visibility or
facilitates the reading by the annotations, by links to other content, by new
descriptors, specific new abstracts of users: it is the reindexing of the
contents by the contribution or the vote of the uses
|
0910.4839
|
A $p$-adic RanSaC algorithm for stereo vision using Hensel lifting
|
cs.CV
|
A $p$-adic variation of the Ran(dom) Sa(mple) C(onsensus) method for solving
the relative pose problem in stereo vision is developped. From two 2-adically
encoded images a random sample of five pairs of corresponding points is taken,
and the equations for the essential matrix are solved by lifting solutions
modulo 2 to the 2-adic integers. A recently devised $p$-adic hierarchical
classification algorithm imitating the known LBG quantisation method classifies
the solutions for all the samples after having determined the number of
clusters using the known intra-inter validity of clusterings. In the successful
case, a cluster ranking will determine the cluster containing a 2-adic
approximation to the "true" solution of the problem.
|
0910.4874
|
K-User Fading Interference Channels: The Ergodic Very Strong Case
|
cs.IT math.IT
|
Sufficient conditions required to achieve the interference-free capacity
region of ergodic fading K-user interference channels (IFCs) are obtained. In
particular, this capacity region is shown to be achieved when every receiver
decodes all K transmitted messages such that the channel statistics and the
waterfilling power policies for all K (interference-free) links satisfy a set
of K(K-1) ergodic very strong conditions. The result is also of independent
interest in combinatorics.
|
0910.4899
|
Artificial Immune Systems
|
cs.AI cs.NE
|
The biological immune system is a robust, complex, adaptive system that
defends the body from foreign pathogens. It is able to categorize all cells (or
molecules) within the body as self-cells or non-self cells. It does this with
the help of a distributed task force that has the intelligence to take action
from a local and also a global perspective using its network of chemical
messengers for communication. There are two major branches of the immune
system. The innate immune system is an unchanging mechanism that detects and
destroys certain invading organisms, whilst the adaptive immune system responds
to previously unknown foreign cells and builds a response to them that can
remain in the body over a long period of time. This remarkable information
processing biological system has caught the attention of computer science in
recent years. A novel computational intelligence technique, inspired by
immunology, has emerged, called Artificial Immune Systems. Several concepts
from the immune have been extracted and applied for solution to real world
science and engineering problems. In this tutorial, we briefly describe the
immune system metaphors that are relevant to existing Artificial Immune Systems
methods. We will then show illustrative real-world problems suitable for
Artificial Immune Systems and give a step-by-step algorithm walkthrough for one
such problem. A comparison of the Artificial Immune Systems to other well-known
algorithms, areas for future work, tips & tricks and a list of resources will
round this tutorial off. It should be noted that as Artificial Immune Systems
is still a young and evolving field, there is not yet a fixed algorithm
template and hence actual implementations might differ somewhat from time to
time and from those examples given here.
|
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