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1009.5432
|
An approximative calculation of the fractal structure in self-similar
tilings
|
physics.soc-ph cond-mat.dis-nn cs.SI
|
Fractal structures emerge from statistical and hierarchical processes in
urban development or network evolution. In a class of efficient and robust
geographical networks, we derive the size distribution of layered areas, and
estimate the fractal dimension by using the distribution without huge
computations. This method can be applied to self-similar tilings based on a
stochastic process.
|
1009.5473
|
The thermodynamic temperature of a rhythmic spiking network
|
cs.NE cs.AI q-bio.NC
|
Artificial neural networks built from two-state neurons are powerful
computational substrates, whose computational ability is well understood by
analogy with statistical mechanics. In this work, we introduce similar
analogies in the context of spiking neurons in a fixed time window, where
excitatory and inhibitory inputs drawn from a Poisson distribution play the
role of temperature. For single neurons with a "bandgap" between their inputs
and the spike threshold, this temperature allows for stochastic spiking. By
imposing a global inhibitory rhythm over the fixed time windows, we connect
neurons into a network that exhibits synchronous, clock-like updating akin to
neural networks. We implement a single-layer Boltzmann machine without learning
to demonstrate our model.
|
1009.5520
|
Diversity and Polarization of Research Performance: Evidence from
Hungary
|
cs.SI stat.AP
|
Measuring the intellectual diversity encoded in publication records as a
proxy to the degree of interdisciplinarity has recently received considerable
attention in the science mapping community. The present paper draws upon the
use of the Stirling index as a diversity measure applied to a network model
(customized science map) of research profiles, proposed by several authors. A
modified version of the index is used and compared with the previous versions
on a sample data set in order to rank top Hungarian research organizations
(HROs) according to their research performance diversity. Results, unexpected
in several respects, show that the modified index is a candidate for measuring
the degree of polarization of a research profile. The study also points towards
a possible typology of publication portfolios that instantiate different types
of diversity.
|
1009.5614
|
Input Design for System Identification via Convex Relaxation
|
math.OC cs.SY math.ST stat.TH
|
This paper proposes a new framework for the optimization of excitation inputs
for system identification. The optimization problem considered is to maximize a
reduced Fisher information matrix in any of the classical D-, E-, or A-optimal
senses. In contrast to the majority of published work on this topic, we
consider the problem in the time domain and subject to constraints on the
amplitude of the input signal. This optimization problem is nonconvex. The main
result of the paper is a convex relaxation that gives an upper bound accurate
to within $2/\pi$ of the true maximum. A randomized algorithm is presented for
finding a feasible solution which, in a certain sense is expected to be at
least $2/\pi$ as informative as the globally optimal input signal. In the case
of a single constraint on input power, the proposed approach recovers the true
global optimum exactly. Extensions to situations with both power and amplitude
constraints on both inputs and outputs are given. A simple simulation example
illustrates the technique.
|
1009.5625
|
Decomposition of Unitary Matrices for Finding Quantum Circuits:
Application to Molecular Hamiltonians
|
quant-ph cs.IT math.IT
|
Constructing appropriate unitary matrix operators for new quantum algorithms
and finding the minimum cost gate sequences for the implementation of these
unitary operators is of fundamental importance in the field of quantum
information and quantum computation. Evolution of quantum circuits faces two
major challenges: complex and huge search space and the high costs of
simulating quantum circuits on classical computers. Here, we use the group
leaders optimization algorithm to decompose a given unitary matrix into a
proper-minimum cost quantum gate sequence. We test the method on the known
decompositions of Toffoli gate, the amplification step of the Grover search
algorithm, the quantum Fourier transform, and the sender part of the quantum
teleportation. Using this procedure, we present the circuit designs for the
simulation of the unitary propagators of the Hamiltonians for the hydrogen and
the water molecules. The approach is general and can be applied to generate the
sequence of quantum gates for larger molecular systems.
|
1009.5750
|
Use of multiple singular value decompositions to analyze complex
intracellular calcium ion signals
|
stat.AP cs.CV physics.bio-ph q-bio.QM
|
We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures;
the data are present as movies, or, more prosaically, time series of images.
This paper describes novel uses of singular value decompositions (SVD) and
weighted versions of them (WSVD) to extract the signals from such movies, in a
way that is semi-automatic and tuned closely to the actual data and their many
complexities. These complexities include the following. First, the images
themselves are of no interest: all interest focuses on the behavior of
individual cells across time, and thus, the cells need to be segmented in an
automated manner. Second, the cells themselves have 100$+$ pixels, so that they
form 100$+$ curves measured over time, so that data compression is required to
extract the features of these curves. Third, some of the pixels in some of the
cells are subject to image saturation due to bit depth limits, and this
saturation needs to be accounted for if one is to normalize the images in a
reasonably unbiased manner. Finally, the $\mathrm {Ca}^{2+}$ signals have
oscillations or waves that vary with time and these signals need to be
extracted. Thus, our aim is to show how to use multiple weighted and standard
singular value decompositions to detect, extract and clarify the $\mathrm
{Ca}^{2+}$ signals. Our signal extraction methods then lead to simple although
finely focused statistical methods to compare $\mathrm {Ca}^{2+}$ signals
across experimental conditions.
|
1009.5758
|
Face Detection with Effective Feature Extraction
|
cs.CV
|
There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector. Since, single feature is not discriminative enough to
separate faces from difficult non-faces, we further improve the generalization
performance of our simple features by introducing feature co-occurrences. We
demonstrate that our proposed features yield a performance improvement compared
to Haar-like features. In addition, our findings indicate that features play a
crucial role in the ability of the system to generalize.
|
1009.5760
|
Secret Key Agreement from Vector Gaussian Sources by Rate Limited Public
Communication
|
cs.IT math.IT
|
We investigate the secret key agreement from correlated vector Gaussian
sources in which the legitimate parties can use the public communication with
limited rate. For the class of protocols with the one-way public communication,
we show that the optimal trade-off between the rate of key generation and the
rate of the public communication is characterized as an optimization problem of
a Gaussian random variable. The characterization is derived by using the
enhancement technique introduced by Weingarten et.al. for MIMO Gaussian
broadcast channel.
|
1009.5761
|
Approximate Maximum A Posteriori Inference with Entropic Priors
|
cs.SD cs.LG
|
In certain applications it is useful to fit multinomial distributions to
observed data with a penalty term that encourages sparsity. For example, in
probabilistic latent audio source decomposition one may wish to encode the
assumption that only a few latent sources are active at any given time. The
standard heuristic of applying an L1 penalty is not an option when fitting the
parameters to a multinomial distribution, which are constrained to sum to 1. An
alternative is to use a penalty term that encourages low-entropy solutions,
which corresponds to maximum a posteriori (MAP) parameter estimation with an
entropic prior. The lack of conjugacy between the entropic prior and the
multinomial distribution complicates this approach. In this report I propose a
simple iterative algorithm for MAP estimation of multinomial distributions with
sparsity-inducing entropic priors.
|
1009.5762
|
Morphological dilation image coding with context weights prediction
|
cs.IT cs.MM math.IT
|
This paper proposes an adaptive morphological dilation image coding with
context weights prediction. The new dilation method is not to use fixed models,
but to decide whether a coefficient needs to be dilated or not according to the
coefficient's predicted significance degree. It includes two key dilation
technologies: 1) controlling dilation process with context weights to reduce
the output of insignificant coefficients, and 2) using variable-length group
test coding with context weights to adjust the coding order and cost as few
bits as possible to present the events with large probability. Moreover, we
also propose a novel context weight strategy to predict coefficient's
significance degree more accurately, which serves for two dilation
technologies. Experimental results show that our proposed method outperforms
the state of the art image coding algorithms available today.
|
1009.5764
|
The E8 Lattice and Error Correction in Multi-Level Flash Memory
|
cs.IT math.IT
|
A construction using the E8 lattice and Reed-Solomon codes for
error-correction in flash memory is given. Since E8 lattice decoding errors are
bursty, a Reed-Solomon code over GF($2^8$) is well suited. This is a type of
coded modulation, where the Euclidean distance of the lattice, which is an
eight-dimensional signal constellation, is combined with the Hamming distance
of the code. This system is compared with the conventional technique for flash
memories, BCH codes using Gray-coded PAM. The described construction has a
performance advantage of 1.6 to 1.8 dB at a probability of word error of
$10^{-6}$. Evaluation is at high data rates of 2.9 bits/cell for flash memory
cells that have an uncoded data density of 3 bits/cell.
|
1009.5773
|
Fast Reinforcement Learning for Energy-Efficient Wireless Communications
|
cs.LG
|
We consider the problem of energy-efficient point-to-point transmission of
delay-sensitive data (e.g. multimedia data) over a fading channel. Existing
research on this topic utilizes either physical-layer centric solutions, namely
power-control and adaptive modulation and coding (AMC), or system-level
solutions based on dynamic power management (DPM); however, there is currently
no rigorous and unified framework for simultaneously utilizing both
physical-layer centric and system-level techniques to achieve the minimum
possible energy consumption, under delay constraints, in the presence of
stochastic and a priori unknown traffic and channel conditions. In this report,
we propose such a framework. We formulate the stochastic optimization problem
as a Markov decision process (MDP) and solve it online using reinforcement
learning. The advantages of the proposed online method are that (i) it does not
require a priori knowledge of the traffic arrival and channel statistics to
determine the jointly optimal power-control, AMC, and DPM policies; (ii) it
exploits partial information about the system so that less information needs to
be learned than when using conventional reinforcement learning algorithms; and
(iii) it obviates the need for action exploration, which severely limits the
adaptation speed and run-time performance of conventional reinforcement
learning algorithms. Our results show that the proposed learning algorithms can
converge up to two orders of magnitude faster than a state-of-the-art learning
algorithm for physical layer power-control and up to three orders of magnitude
faster than conventional reinforcement learning algorithms.
|
1009.5829
|
Capacity Results for Relay Channels with Confidential Messages
|
cs.IT math.IT
|
We consider a communication system where a relay helps transmission of
messages from {a} sender to {a} receiver. The relay is considered not only as a
helper but as a wire-tapper who can obtain some knowledge about transmitted
messages. In this paper we study a relay channel with confidential
messages(RCC), where a sender attempts to transmit common information to both a
receiver and a relay and also has private information intended for the receiver
and confidential to the relay. The level of secrecy of private information
confidential to the relay is measured by the equivocation rate, i.e., the
entropy rate of private information conditioned on channel outputs at the
relay. The performance measure of interest for the RCC is the rate triple that
includes the common rate, the private rate, and the equivocation rate as
components. The rate-equivocation region is defined by the set that consists of
all these achievable rate triples. In this paper we give two definitions of the
rate-equivocation region. We first define the rate-equivocation region in the
case of deterministic encoder and call it the deterministic rate-equivocation
region. Next, we define the rate-equivocation region in the case of stochastic
encoder and call it the stochastic rate-equivocation region. We derive explicit
inner and outer bounds for the above two regions. On the
deterministic/stochastic rate-equivocation region we present two classes of
relay channels where inner and outer bounds match. We also evaluate the
deterministic and stochastic rate-equivocation regions of the Gaussian RCC.
|
1009.5894
|
Some Theorems on the Algorithmic Approach to Probability Theory and
Information Theory
|
cs.IT math.IT
|
This is a 1971 dissertation. Only its extended abstract was published at the
time. While some results appeared in other publications, a number of details
remained unpublished and may still have relevance.
|
1009.5900
|
On the Accuracy of the Wyner Model in Cellular Networks
|
cs.IT math.IT
|
The Wyner model has been widely used to model and analyze cellular networks
due to its simplicity and analytical tractability. Its key aspects include
fixed user locations and the deterministic and homogeneous interference
intensity. While clearly a significant simplification of a real cellular
system, which has random user locations and interference levels that vary by
several orders of magnitude over a cell, a common presumption by theorists is
that the Wyner model nevertheless captures the essential aspects of cellular
interactions. But is this true? To answer this question, we consider both
uplink and downlink transmissions, and both outage-based and average-based
metrics. For the uplink, for both metrics, we conclude that the Wyner model is
in fact quite accurate for systems with a sufficient number of simultaneous
users, e.g. CDMA. Conversely, it is broadly inaccurate otherwise. With
multicell processing, intracell TDMA is shown to be suboptimal in terms of
average throughput, in sharp contrast to predictions using the Wyner model.
Turning to the downlink, the Wyner model is highly inaccurate for outage since
it depends largely on the user locations. However, for average or sum
throughput, the Wyner model serves as an acceptable simplification in certain
special cases if the interference parameter is set appropriately.
|
1009.5944
|
Throughput-Optimal Random Access with Order-Optimal Delay
|
cs.IT math.IT
|
In this paper, we consider CSMA policies for scheduling of multihop wireless
networks with one-hop traffic. The main contribution of this paper is to
propose Unlocking CSMA (U-CSMA) policy that enables to obtain high throughput
with low (average) packet delay for large wireless networks. In particular, the
delay under U-CSMA policy becomes order-optimal. For one-hop traffic, delay is
defined to be order-optimal if it is O(1), i.e., it stays bounded, as the
network-size increases to infinity. Using mean field theory techniques, we
analytically show that for torus (grid-like) interference topologies with
one-hop traffic, to achieve a network load of $\rho$, the delay under U-CSMA
policy becomes $O(1/(1-\rho)^{3})$ as the network-size increases, and hence,
delay becomes order optimal. We conduct simulations for general random
geometric interference topologies under U-CSMA policy combined with congestion
control to maximize a network-wide utility. These simulations confirm that
order optimality holds, and that we can use U-CSMA policy jointly with
congestion control to operate close to the optimal utility with a low packet
delay in arbitrarily large random geometric topologies. To the best of our
knowledge, it is for the first time that a simple distributed scheduling policy
is proposed that in addition to throughput/utility-optimality exhibits delay
order-optimality.
|
1009.5949
|
Fast CRCs (Extended Version)
|
cs.IT math.IT
|
CRCs have desirable properties for effective error detection. But their
software implementation, which relies on many steps of the polynomial division,
is typically slower than other codes such as weaker checksums. A relevant
question is whether there are some particular CRCs that have fast
implementation. In this paper, we introduce such fast CRCs as well as an
effective technique to implement them. For these fast CRCs, even without using
table lookup, it is possible either to eliminate or to greatly reduce many
steps of the polynomial division during their computation.
|
1009.5959
|
On the Optimal Compressions in the Compress-and-Forward Relay Schemes
|
cs.IT math.IT
|
..... joint decoding provides more freedom in choosing the compression at the
relay.
However, the question remains whether this freedom of selecting the
compression necessarily improves the achievable rate of the original message.
It has been shown in (El Gamal and Kim, 2010) that the answer is negative in
the single-relay case. In this paper, it is further demonstrated that in the
case of multiple relays, there is no improvement on the achievable rate by
joint decoding either. More interestingly, it is discovered that any
compressions not supporting successive decoding will actually lead to strictly
lower achievable rates for the original message. Therefore, to maximize the
achievable rate for the original message, the compressions should always be
chosen to support successive decoding. Furthermore, it is shown that any
compressions not completely decodable even with joint decoding will not provide
any contribution to the decoding of the original message.
The above phenomenon is also shown to exist under the repetitive encoding
framework recently proposed by (Lim, Kim, El Gamal, and Chung, 2010), which
improved the achievable rate in the case of multiple relays. Here, another
interesting discovery is that the improvement is not a result of repetitive
encoding, but the benefit of delayed decoding after all the blocks have been
finished. The same rate is shown to be achievable with the simpler classical
encoding process of (Cover and El Gamal, 1979) with a block-by-block backward
decoding process.
|
1009.5972
|
The Attentive Perceptron
|
cs.LG
|
We propose a focus of attention mechanism to speed up the Perceptron
algorithm. Focus of attention speeds up the Perceptron algorithm by lowering
the number of features evaluated throughout training and prediction. Whereas
the traditional Perceptron evaluates all the features of each example, the
Attentive Perceptron evaluates less features for easy to classify examples,
thereby achieving significant speedups and small losses in prediction accuracy.
Focus of attention allows the Attentive Perceptron to stop the evaluation of
features at any interim point and filter the example. This creates an attentive
filter which concentrates computation at examples that are hard to classify,
and quickly filters examples that are easy to classify.
|
1009.5975
|
Information-Theoretic Analysis of an Energy Harvesting Communication
System
|
cs.IT math.IT
|
In energy harvesting communication systems, an exogenous recharge process
supplies energy for the data transmission and arriving energy can be buffered
in a battery before consumption. Transmission is interrupted if there is not
sufficient energy. We address communication with such random energy arrivals in
an information-theoretic setting. Based on the classical additive white
Gaussian noise (AWGN) channel model, we study the coding problem with random
energy arrivals at the transmitter. We show that the capacity of the AWGN
channel with stochastic energy arrivals is equal to the capacity with an
average power constraint equal to the average recharge rate. We provide two
different capacity achieving schemes: {\it save-and-transmit} and {\it
best-effort-transmit}. Next, we consider the case where energy arrivals have
time-varying average in a larger time scale. We derive the optimal offline
power allocation for maximum average throughput and provide an algorithm that
finds the optimal power allocation.
|
1009.5979
|
Performance Analysis of the Matrix Pair Beamformer with Matrix Mismatch
|
cs.IT math.IT
|
Matrix pair beamformer (MPB) is a blind beamformer. It exploits the temporal
structure of the signal of interest (SOI) and applies generalized
eigen-decomposition to a covariance matrix pair. Unlike other blind algorithms,
it only uses the second order statistics. A key assumption in the previous work
is that the two matrices have the same interference statistics. However, this
assumption may be invalid in the presence of multipath propagations or certain
"smart" jammers, and we call it as matrix mismatch. This paper analyzes the
performance of MPB with matrix mismatch. First, we propose a general framework
that covers the existing schemes. Then, we derive its normalized output SINR.
It reveals that the matrix mismatch leads to a threshold effect caused by
"steering vector competition". Second, using matrix perturbation theory, we
find that, if there are generalized eigenvalues that are infinite, the
threshold will increase unboundedly with the interference power. This is highly
probable when there are multiple periodical interferers. Finally, we present
simulation results to verify our analysis.
|
1009.5981
|
Empirical Bayes methods corrected for small numbers of tests
|
stat.ME cs.IT math.IT math.ST q-bio.QM stat.TH
|
Histogram-based empirical Bayes methods developed for analyzing data for
large numbers of genes, SNPs, or other biological features tend to have large
biases when applied to data with a smaller number of features such as genes
with expression measured conventionally, proteins, and metabolites. To analyze
such small-scale and medium-scale data in an empirical Bayes framework, we
introduce corrections of maximum likelihood estimators (MLE) of the local false
discovery rate (LFDR). In this context, the MLE estimates the LFDR, which is a
posterior probability of null hypothesis truth, by estimating the prior
distribution. The corrections lie in excluding each feature when estimating one
or more parameters on which the prior depends. An application of the new
estimators and previous estimators to protein abundance data illustrates how
different estimators lead to very different conclusions about which proteins
are affected by cancer.
The estimators are compared using simulated data of two different numbers of
features, two different detectability levels, and all possible numbers of
affected features. The simulations show that some of the corrected MLEs
substantially reduce a negative bias of the MLE. (The best-performing corrected
MLE was derived from the minimum description length principle.) However, even
the corrected MLEs have strong negative biases when the proportion of features
that are unaffected is greater than 90%. Therefore, since the number of
affected features is unknown in the case of real data, we recommend an
optimally weighted combination of the best of the corrected MLEs with a
conservative estimator that has weaker parametric assumptions.
|
1009.6008
|
Multiple Access Channels with Cooperative Encoders and Channel State
Information
|
cs.IT math.IT
|
The two-user Multiple Access Channel (MAC) with cooperative encoders and
Channel State Information (CSI) is considered where two different scenarios are
investigated: A two-user MAC with common message (MACCM) and a two-user MAC
with conferencing encoders (MACCE). For both situations, the two cases where
the CSI is known to the encoders either non-causally or causally are studied.
Achievable rate regions are established for both discrete memoryless channels
and Gaussian channels with additive interference. The achievable rate regions
derived for the Gaussian models with additive interference known non-causally
to the encoders are shown to coincide with the capacity region of the same
channel with no interference. Therefore, the capacity region for such channels
is established.
|
1009.6050
|
Comments on "Consensus and Cooperation in Networked Multi-Agent Systems"
|
cs.MA cs.NI math.OC
|
This note corrects a pretty serious mistake and some inaccuracies in
"Consensus and cooperation in networked multi-agent systems" by R.
Olfati-Saber, J.A. Fax, and R.M. Murray, published in Vol. 95 of the
Proceedings of the IEEE (2007, No. 1, P. 215-233). It also mentions several
stronger results applicable to the class of problems under consideration and
addresses the issue of priority whose interpretation in the above-mentioned
paper is not exact.
|
1009.6053
|
Efficient Sampling of Band-limited Signals from Sine Wave Crossings
|
cs.IT math.CV math.IT math.NA
|
This correspondence presents an efficient method for reconstructing a
band-limited signal in the discrete domain from its crossings with a sine wave.
The method makes it possible to design A/D converters that only deliver the
crossing timings, which are then used to interpolate the input signal at
arbitrary instants. Potentially, it may allow for reductions in power
consumption and complexity in these converters. The reconstruction in the
discrete domain is based on a recently-proposed modification of the Lagrange
interpolator, which is readily implementable with linear complexity and
efficiently, given that it re-uses known schemes for variable fractional-delay
(VFD) filters. As a spin-off, the method allows one to perform spectral
analysis from sine wave crossings with the complexity of the FFT. Finally, the
results in the correspondence are validated in several numerical examples.
|
1009.6057
|
Network Flows for Functions
|
cs.NI cs.DC cs.IT math.IT
|
We consider in-network computation of an arbitrary function over an arbitrary
communication network. A network with capacity constraints on the links is
given. Some nodes in the network generate data, e.g., like sensor nodes in a
sensor network. An arbitrary function of this distributed data is to be
obtained at a terminal node. The structure of the function is described by a
given computation schema, which in turn is represented by a directed tree. We
design computing and communicating schemes to obtain the function at the
terminal at the maximum rate. For this, we formulate linear programs to
determine network flows that maximize the computation rate. We then develop
fast combinatorial primal-dual algorithm to obtain $\epsilon$-approximate
solutions to these linear programs. We then briefly describe extensions of our
techniques to the cases of multiple terminals wanting different functions,
multiple computation schemas for a function, computation with a given desired
precision, and to networks with energy constraints at nodes.
|
1009.6079
|
A Multi-Interference-Channel Matrix Pair Beamformer for CDMA Systems
|
cs.CE cs.IT math.IT
|
Matrix pair beamformer (MPB) is a promising blind beamformer which exploits
the temporal signature of the signal of interest (SOI) to acquire its spatial
statistical information. It does not need any knowledge of directional
information or training sequences. However, the major problem of the existing
MPBs is that they have serious threshold effects and the thresholds will grow
as the interference power increases or even approach infinity. In particular,
this issue prevails in scenarios with structured interference, such as,
periodically repeated white noise, tones, or MAIs in multipath channels. In
this paper, we will first present the principles for designing the projection
space of the MPB which are closely correlated with the ability of suppressing
structured interference and system finite sample performance. Then a
multiple-interference-channel based matrix pair beamformer (MIC-MPB) for CDMA
systems is developed according to the principles. In order to adapt to dynamic
channels, an adaptive algorithm for the beamformer is also proposed.
Theoretical analysis and simulation results show that the proposed beamformer
has a small and bounded threshold when the interference power increases.
Performance comparisons of the MIC-MPB and the existing MPBs in various
scenarios via a number of numerical examples are also presented.
|
1009.6119
|
A Comprehensive Survey of Data Mining-based Fraud Detection Research
|
cs.AI cs.CE
|
This survey paper categorises, compares, and summarises from almost all
published technical and review articles in automated fraud detection within the
last 10 years. It defines the professional fraudster, formalises the main types
and subtypes of known fraud, and presents the nature of data evidence collected
within affected industries. Within the business context of mining the data to
achieve higher cost savings, this research presents methods and techniques
together with their problems. Compared to all related reviews on fraud
detection, this survey covers much more technical articles and is the only one,
to the best of our knowledge, which proposes alternative data and solutions
from related domains.
|
1009.6127
|
Efficient Knowledge Base Management in DCSP
|
cs.AI cs.DC
|
DCSP (Distributed Constraint Satisfaction Problem) has been a very important
research area in AI (Artificial Intelligence). There are many application
problems in distributed AI that can be formalized as DSCPs. With the increasing
complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too.
Thus, to use a limited storage place efficiently in solving DCSP becomes a very
important problem, and it can help to reduce searching time as well. This paper
provides an efficient knowledge base management approach based on general usage
of hyper-resolution-rule in consistence algorithm. The approach minimizes the
increasing of the knowledge base by eliminate sufficient constraint and false
nogood. These eliminations do not change the completeness of the original
knowledge base increased. The proofs are given as well. The example shows that
this approach decrease both the new nogoods generated and the knowledge base
greatly. Thus it decreases the required storage place and simplify the
searching process.
|
1009.6182
|
Goodput Maximization in Cooperative Networks with ARQ
|
cs.IT math.IT
|
In this paper, the average successful throughput, i.e., goodput, of a coded
3-node cooperative network is studied in a Rayleigh fading environment. It is
assumed that a simple automatic repeat request (ARQ) technique is employed in
the network so that erroneously received codeword is retransmitted until
successful delivery. The relay is assumed to operate in either
amplify-and-forward (AF) or decode-and-forward (DF) mode. Under these
assumptions, retransmission mechanisms and protocols are described, and the
average time required to send information successfully is determined.
Subsequently, the goodput for both AF and DF relaying is formulated. The
tradeoffs and interactions between the goodput, transmission rates, and relay
location are investigated and optimal strategies are identified.
|
1009.6197
|
Secure Relay Beamforming over Cognitive Radio Channels
|
cs.IT math.IT
|
In this paper, a cognitive relay channel is considered, and
amplify-and-forward (AF) relay beamforming designs in the presence of an
eavesdropper and a primary user are studied. Our objective is to optimize the
performance of the cognitive relay beamforming system while limiting the
interference in the direction of the primary receiver and keeping the
transmitted signal secret from the eavesdropper. We show that under both total
and individual power constraints, the problem becomes a quasiconvex
optimization problem which can be solved by interior point methods. We also
propose two sub-optimal null space beamforming schemes which are obtained in a
more computationally efficient way.
|
1009.6200
|
Optimal Power Allocation for Secrecy Fading Channels Under
Spectrum-Sharing Constraints
|
cs.IT math.IT
|
In the spectrum-sharing technology, a secondary user may utilize the primary
user's licensed band as long as its interference to the primary user is below a
tolerable value. In this paper, we consider a scenario in which a secondary
user is operating in the presence of both a primary user and an eavesdropper.
Hence, the secondary user has both interference limitations and security
considerations. In such a scenario, we study the secrecy capacity limits of
opportunistic spectrum-sharing channels in fading environments and investigate
the optimal power allocation for the secondary user under average and peak
received power constraints at the primary user with global channel side
information (CSI). Also, in the absence of the eavesdropper's CSI, we study
optimal power allocation under an average power constraint and propose a
suboptimal on/off power control method.
|
1009.6205
|
Channel Coding over Multiple Coherence Blocks with Queueing Constraints
|
cs.IT math.IT
|
This paper investigates the performance of wireless systems that employ
finite-blocklength channel codes for transmission and operate under queueing
constraints in the form of limitations on buffer overflow probabilities. A
block fading model, in which fading stays constant in each coherence block and
change independently between blocks, is considered. It is assumed that channel
coding is performed over multiple coherence blocks. An approximate lower bound
on the transmission rate is obtained from Feintein's Lemma. This lower bound is
considered as the service rate and is incorporated into the effective capacity
formulation, which characterizes the maximum constant arrival rate that can be
supported under statistical queuing constraints. Performances of variable-rate
and fixed-rate transmissions are studied. The optimum error probability for
variable rate transmission and the optimum coding rate for fixed rate
transmission are shown to be unique. Moreover, the tradeoff between the
throughput and the number of blocks over which channel coding is performed is
identified.
|
1009.6206
|
On the Effective Capacity of Two-Hop Communication Systems
|
cs.IT math.IT
|
In this paper, two-hop communication between a source and a destination with
the aid of an intermediate relay node is considered. Both the source and
intermediate relay node are assumed to operate under statistical quality of
service (QoS) constraints imposed as limitations on the buffer overflow
probabilities. It is further assumed that the nodes send the information at
fixed power levels and have perfect channel side information. In this scenario,
the maximum constant arrival rates that can be supported by this two-hop link
are characterized by finding the effective capacity. Through this analysis, the
impact upon the throughput of having buffer constraints at the source and
intermediate-hop nodes is identified.
|
1009.6215
|
How to Extract the Geometry and Topology from Very Large 3D
Segmentations
|
cs.CG cs.CV cs.DS
|
Segmentation is often an essential intermediate step in image analysis. A
volume segmentation characterizes the underlying volume image in terms of
geometric information--segments, faces between segments, curves in which
several faces meet--as well as a topology on these objects. Existing algorithms
encode this information in designated data structures, but require that these
data structures fit entirely in Random Access Memory (RAM). Today, 3D images
with several billion voxels are acquired, e.g. in structural neurobiology.
Since these large volumes can no longer be processed with existing methods, we
present a new algorithm which performs geometry and topology extraction with a
runtime linear in the number of voxels and log-linear in the number of faces
and curves. The parallelizable algorithm proceeds in a block-wise fashion and
constructs a consistent representation of the entire volume image on the hard
drive, making the structure of very large volume segmentations accessible to
image analysis. The parallelized C++ source code, free command line tools and
MATLAB mex files are avilable from
http://hci.iwr.uni-heidelberg.de/software.php
|
1010.0011
|
Deterministic Compressed Sensing Matrices from Additive Character
Sequences
|
cs.IT math.IT
|
Compressed sensing is a novel technique where one can recover sparse signals
from the undersampled measurements. In this correspondence, a $K \times N$
measurement matrix for compressed sensing is deterministically constructed via
additive character sequences. The Weil bound is then used to show that the
matrix has asymptotically optimal coherence for $N=K^2$, and to present a
sufficient condition on the sparsity level for unique sparse recovery. Also,
the restricted isometry property (RIP) is statistically studied for the
deterministic matrix. Using additive character sequences with small alphabets,
the compressed sensing matrix can be efficiently implemented by linear feedback
shift registers. Numerical results show that the deterministic compressed
sensing matrix guarantees reliable matching pursuit recovery performance for
both noiseless and noisy measurements.
|
1010.0012
|
An Embarrassingly Simple Speed-Up of Belief Propagation with Robust
Potentials
|
cs.CV cs.AI
|
We present an exact method of greatly speeding up belief propagation (BP) for
a wide variety of potential functions in pairwise MRFs and other graphical
models. Specifically, our technique applies whenever the pairwise potentials
have been {\em truncated} to a constant value for most pairs of states, as is
commonly done in MRF models with robust potentials (such as stereo) that impose
an upper bound on the penalty assigned to discontinuities; for each of the $M$
possible states in one node, only a smaller number $m$ of compatible states in
a neighboring node are assigned milder penalties. The computational complexity
of our method is $O(mM)$, compared with $O(M^2)$ for standard BP, and we
emphasize that the method is {\em exact}, in contrast with related techniques
such as pruning; moreover, the method is very simple and easy to implement.
Unlike some previous work on speeding up BP, our method applies both to
sum-product and max-product BP, which makes it useful in any applications where
marginal probabilities are required, such as maximum likelihood estimation. We
demonstrate the technique on a stereo MRF example, confirming that the
technique speeds up BP without altering the solution.
|
1010.0019
|
Mantis: Predicting System Performance through Program Analysis and
Modeling
|
cs.PF cs.AI cs.PL
|
We present Mantis, a new framework that automatically predicts program
performance with high accuracy. Mantis integrates techniques from programming
language and machine learning for performance modeling, and is a radical
departure from traditional approaches. Mantis extracts program features, which
are information about program execution runs, through program instrumentation.
It uses machine learning techniques to select features relevant to performance
and creates prediction models as a function of the selected features. Through
program analysis, it then generates compact code slices that compute these
feature values for prediction. Our evaluation shows that Mantis can achieve
more than 93% accuracy with less than 10% training data set, which is a
significant improvement over models that are oblivious to program features. The
system generates code slices that are cheap to compute feature values.
|
1010.0034
|
Spectral Control of Mobile Robot Networks
|
cs.MA cs.SY math.OC
|
The eigenvalue spectrum of the adjacency matrix of a network is closely
related to the behavior of many dynamical processes run over the network. In
the field of robotics, this spectrum has important implications in many
problems that require some form of distributed coordination within a team of
robots. In this paper, we propose a continuous-time control scheme that
modifies the structure of a position-dependent network of mobile robots so that
it achieves a desired set of adjacency eigenvalues. For this, we employ a novel
abstraction of the eigenvalue spectrum by means of the adjacency matrix
spectral moments. Since the eigenvalue spectrum is uniquely determined by its
spectral moments, this abstraction provides a way to indirectly control the
eigenvalues of the network. Our construction is based on artificial potentials
that capture the distance of the network's spectral moments to their desired
values. Minimization of these potentials is via a gradient descent closed-loop
system that, under certain convexity assumptions, ensures convergence of the
network topology to one with the desired set of moments and, therefore,
eigenvalues. We illustrate our approach in nontrivial computer simulations.
|
1010.0041
|
Throughput and Collision Analysis of Multi-Channel Multi-Stage Spectrum
Sensing Algorithms
|
cs.PF cs.IT cs.NI math.IT
|
Multi-stage sensing is a novel concept that refers to a general class of
spectrum sensing algorithms that divide the sensing process into a number of
sequential stages. The number of sensing stages and the sensing technique per
stage can be used to optimize performance with respect to secondary user
throughput and the collision probability between primary and secondary users.
So far, the impact of multi-stage sensing on network throughput and collision
probability for a realistic network model is relatively unexplored. Therefore,
we present the first analytical framework which enables performance evaluation
of different multi-channel multi-stage spectrum sensing algorithms for
Opportunistic Spectrum Access networks. The contribution of our work lies in
studying the effect of the following parameters on performance: number of
sensing stages, physical layer sensing techniques and durations per each stage,
single and parallel channel sensing and access, number of available channels,
primary and secondary user traffic, buffering of incoming secondary user
traffic, as well as MAC layer sensing algorithms. Analyzed performance metrics
include the average secondary user throughput and the average collision
probability between primary and secondary users. Our results show that when the
probability of primary user mis-detection is constrained, the performance of
multi-stage sensing is, in most cases, superior to the single stage sensing
counterpart. Besides, prolonged channel observation at the first stage of
sensing decreases the collision probability considerably, while keeping the
throughput at an acceptable level. Finally, in realistic primary user traffic
scenarios, using two stages of sensing provides a good balance between
secondary users throughput and collision probability while meeting successful
detection constraints subjected by Opportunistic Spectrum Access communication.
|
1010.0056
|
Online Learning in Opportunistic Spectrum Access: A Restless Bandit
Approach
|
math.OC cs.LG
|
We consider an opportunistic spectrum access (OSA) problem where the
time-varying condition of each channel (e.g., as a result of random fading or
certain primary users' activities) is modeled as an arbitrary finite-state
Markov chain. At each instance of time, a (secondary) user probes a channel and
collects a certain reward as a function of the state of the channel (e.g., good
channel condition results in higher data rate for the user). Each channel has
potentially different state space and statistics, both unknown to the user, who
tries to learn which one is the best as it goes and maximizes its usage of the
best channel. The objective is to construct a good online learning algorithm so
as to minimize the difference between the user's performance in total rewards
and that of using the best channel (on average) had it known which one is the
best from a priori knowledge of the channel statistics (also known as the
regret). This is a classic exploration and exploitation problem and results
abound when the reward processes are assumed to be iid. Compared to prior work,
the biggest difference is that in our case the reward process is assumed to be
Markovian, of which iid is a special case. In addition, the reward processes
are restless in that the channel conditions will continue to evolve independent
of the user's actions. This leads to a restless bandit problem, for which there
exists little result on either algorithms or performance bounds in this
learning context to the best of our knowledge. In this paper we introduce an
algorithm that utilizes regenerative cycles of a Markov chain and computes a
sample-mean based index policy, and show that under mild conditions on the
state transition probabilities of the Markov chains this algorithm achieves
logarithmic regret uniformly over time, and that this regret bound is also
optimal.
|
1010.0060
|
Design and Performance of Rate-compatible Non-Binary LDPC Convolutional
Codes
|
cs.IT math.IT
|
In this paper, we present a construction method of non-binary low-density
parity-check (LDPC) convolutional codes. Our construction method is an
extension of Felstroem and Zigangirov construction for non-binary LDPC
convolutional codes. The rate-compatibility of the non-binary convolutional
code is also discussed. The proposed rate-compatible code is designed from one
single mother (2,4)-regular non-binary LDPC convolutional code of rate 1/2.
Higher-rate codes are produced by puncturing the mother code and lower-rate
codes are produced by multiplicatively repeating the mother code. Simulation
results show that non-binary LDPC convolutional codes of rate 1/2 outperform
state-of-the-art binary LDPC convolutional codes with comparable constraint bit
length. Also the derived low-rate and high-rate non-binary LDPC convolutional
codes exhibit good decoding performance without loss of large gap to the
Shannon limits.
|
1010.0066
|
Continuous-time Discontinuous Equations in Bounded Confidence Opinion
Dynamics
|
math.OC cs.SI cs.SY math.DS
|
This report studies a continuous-time version of the well-known
Hegselmann-Krause model of opinion dynamics with bounded confidence. As the
equations of this model have discontinuous right-hand side, we study their
Krasovskii solutions. We present results about existence and completeness of
solutions, and asymptotical convergence to equilibria featuring a
"clusterization" of opinions. The robustness of such equilibria to small
perturbations is also studied.
|
1010.0122
|
Rule-based Generation of Diff Evolution Mappings between Ontology
Versions
|
cs.DB
|
Ontologies such as taxonomies, product catalogs or web directories are
heavily used and hence evolve frequently to meet new requirements or to better
reflect the current instance data of a domain. To effectively manage the
evolution of ontologies it is essential to identify the difference (Diff)
between two ontology versions. We propose a novel approach to determine an
expressive and invertible diff evolution mapping between given versions of an
ontology. Our approach utilizes the result of a match operation to determine an
evolution mapping consisting of a set of basic change operations
(insert/update/delete). To semantically enrich the evolution mapping we adopt a
rule-based approach to transform the basic change operations into a smaller set
of more complex change operations, such as merge, split, or changes of entire
subgraphs. The proposed algorithm is customizable in different ways to meet the
requirements of diverse ontologies and application scenarios. We evaluate the
proposed approach by determining and analyzing evolution mappings for
real-world life science ontologies and web directories.
|
1010.0145
|
Multi-Agent Programming Contest 2010 - The Jason-DTU Team
|
cs.MA
|
We provide a brief description of the Jason-DTU system, including the
methodology, the tools and the team strategy that we plan to use in the agent
contest.
|
1010.0150
|
Implementing Lego Agents Using Jason
|
cs.MA
|
Since many of the currently available multi-agent frameworks are generally
mostly intended for research, it can be difficult to built multi-agent systems
using physical robots. In this report I describe a way to combine the
multi-agent framework Jason, an extended version of the agent-oriented
programming language AgentSpeak, with Lego robots to address this problem. By
extending parts of the Jason reasoning cycle I show how Lego robots are able to
complete tasks such as following lines on a floor and communicating to be able
to avoid obstacles with minimal amount of coding. The final implementation is a
functional extension that is able to built multi-agent systems using Lego
agents, however there are some issues that have not been addressed. If the
agents are highly dependent on percepts from their sensors, they are required
to move quite slowly, because there currently is a high delay in the reasoning
cycle, when it is combined with a robot. Overall the system is quite robust and
can be used to make simple Lego robots perform tasks of an advanced agent in a
multi-agent environment.
|
1010.0155
|
An Investigation of the Advantages of Organization-Centered Multi-Agent
Systems
|
cs.MA
|
Whereas classical multi-agent systems have the agent in center, there have
recently been a development towards focusing more on the organization of the
system. This allows the designer to focus on what the system goals are, without
considering how the goals should be fulfilled. This paper investigates whether
taking this approach has any clear advantages to the classical way of
implementing multi-agent systems. The investigation is done by implementing
each type of system in the same environment in order to realize what advantages
and disadvantages each approach has.
|
1010.0177
|
Strongly Secure Communications Over the Two-Way Wiretap Channel
|
cs.IT math.IT
|
We consider the problem of secure communications over the two-way wiretap
channel under a strong secrecy criterion. We improve existing results by
developing an achievable region based on strategies that exploit both the
interference at the eavesdropper's terminal and cooperation between legitimate
users. We leverage the notion of channel resolvability for the multiple-access
channel to analyze cooperative jamming and we show that the artificial noise
created by cooperative jamming induces a source of common randomness that can
be used for secret-key agreement. We illustrate the gain provided by this
coding technique in the case of the Gaussian two-way wiretap channel, and we
show significant improvements for some channel configurations.
|
1010.0182
|
List decoding for nested lattices and applications to relay channels
|
cs.IT math.IT
|
We demonstrate a decoding scheme for nested lattice codes which is able to
decode a list of a particular size which contains the transmitted codeword with
high probability. This list decoder is analogous to that used in random coding
arguments in achievability schemes of relay channels, and allows for the
effective combination of information from the relay and source node. Using this
list decoding result, we demonstrate 1) that lattice codes may achieve the
capacity of the physically degraded AWGN relay channel, 2) an achievable rate
region for the two-way relay channel with direct links using lattice codes, and
3) that we may improve the constant gap to capacity for specific cases of the
two-way relay channel with direct links.
|
1010.0189
|
Reed-Muller Codes for Peak Power Control in Multicarrier CDMA
|
cs.IT math.IT
|
Reed-Muller codes are studied for peak power control in multicarrier
code-division multiple access (MC-CDMA) communication systems. In a coded
MC-CDMA system, the information data multiplexed from users is encoded by a
Reed-Muller subcode and the codeword is fully-loaded to Walsh-Hadamard
spreading sequences. The polynomial representation of a coded MC-CDMA signal is
established for theoretical analysis of the peak-to-average power ratio (PAPR).
The Reed-Muller subcodes are defined in a recursive way by the Boolean
functions providing the transmitted MC-CDMA signals with the bounded PAPR as
well as the error correction capability. A connection between the code rates
and the maximum PAPR is theoretically investigated in the coded MC-CDMA.
Simulation results present the statistical evidence that the PAPR of the coded
MC-CDMA signal is not only theoretically bounded, but also statistically
reduced. In particular, the coded MC-CDMA solves the major PAPR problem of
uncoded MC-CDMA by dramatically reducing its PAPR for the small number of
users. Finally, the theoretical and statistical studies show that the
Reed-Muller subcodes are effective coding schemes for peak power control in
MC-CDMA with small and moderate numbers of users, subcarriers, and spreading
factors.
|
1010.0200
|
Difference Antenna Selection and Power Allocation for Wireless Cognitive
Systems
|
cs.IT math.IT
|
In this paper, we propose an antenna selection method in a wireless cognitive
radio (CR) system, namely difference selection, whereby a single transmit
antenna is selected at the secondary transmitter out of $M$ possible antennas
such that the weighted difference between the channel gains of the data link
and the interference link is maximized. We analyze mutual information and
outage probability of the secondary transmission in a CR system with difference
antenna selection, and propose a method of optimizing these performance metrics
of the secondary data link subject to practical constraints on the peak
secondary transmit power and the average interference power as seen by the
primary receiver. The optimization is performed over two parameters: the peak
secondary transmit power and the difference selection weight $\delta\in [0,
1]$. We show that, difference selection using the optimized parameters
determined by the proposed method can be, in many cases of interest, superior
to a so called ratio selection method disclosed in the literature, although
ratio selection has been shown to be optimal, when impractically, the secondary
transmission power constraint is not applied. We address the effects that the
constraints have on mutual information and outage probability, and discuss the
practical implications of the results.
|
1010.0226
|
An Information-theoretic Approach to Privacy
|
cs.IT math.IT
|
Ensuring the usefulness of electronic data sources while providing necessary
privacy guarantees is an important unsolved problem. This problem drives the
need for an overarching analytical framework that can quantify the safety of
personally identifiable information (privacy) while still providing a
quantifable benefit (utility) to multiple legitimate information consumers.
State of the art approaches have predominantly focused on privacy. This paper
presents the first information-theoretic approach that promises an analytical
model guaranteeing tight bounds of how much utility is possible for a given
level of privacy and vice-versa.
|
1010.0237
|
Using Stochastic Models to Describe and Predict Social Dynamics of Web
Users
|
cs.CY cs.SI physics.soc-ph
|
Popularity of content in social media is unequally distributed, with some
items receiving a disproportionate share of attention from users. Predicting
which newly-submitted items will become popular is critically important for
both hosts of social media content and its consumers. Accurate and timely
prediction would enable hosts to maximize revenue through differential pricing
for access to content or ad placement. Prediction would also give consumers an
important tool for filtering the ever-growing amount of content. Predicting
popularity of content in social media, however, is challenging due to the
complex interactions between content quality and how the social media site
chooses to highlight content. Moreover, most social media sites also
selectively present content that has been highly rated by similar users, whose
similarity is indicated implicitly by their behavior or explicitly by links in
a social network. While these factors make it difficult to predict popularity
\emph{a priori}, we show that stochastic models of user behavior on these sites
allows predicting popularity based on early user reactions to new content. By
incorporating the various mechanisms through which web sites display content,
such models improve on predictions based on simply extrapolating from the early
votes. Using data from one such site, the news aggregator Digg, we show how a
stochastic model of user behavior distinguishes the effect of the increased
visibility due to the network from how interested users are in the content. We
find a wide range of interest, identifying stories primarily of interest to
users in the network (``niche interests'') from those of more general interest
to the user community. This distinction is useful for predicting a story's
eventual popularity from users' early reactions to the story.
|
1010.0280
|
Infinite Families of Optimal Splitting Authentication Codes Secure
Against Spoofing Attacks of Higher Order
|
cs.CR cs.DM cs.IT math.CO math.IT
|
We consider the problem of constructing optimal authentication codes with
splitting. New infinite families of such codes are obtained. In particular, we
establish the first known infinite family of optimal authentication codes with
splitting that are secure against spoofing attacks of order two.
|
1010.0287
|
Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop
MIMO Cooperative Systems
|
cs.LG
|
In this paper, we consider a queue-aware distributive resource control
algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay
buffering is an effective way to reduce the intrinsic half-duplex penalty in
cooperative systems. The complex interactions of the queues at the source node
and the relays are modeled as an average-cost infinite horizon Markov Decision
Process (MDP). The traditional approach solving this MDP problem involves
centralized control with huge complexity. To obtain a distributive and low
complexity solution, we introduce a linear structure which approximates the
value function of the associated Bellman equation by the sum of per-node value
functions. We derive a distributive two-stage two-winner auction-based control
policy which is a function of the local CSI and local QSI only. Furthermore, to
estimate the best fit approximation parameter, we propose a distributive online
stochastic learning algorithm using stochastic approximation theory. Finally,
we establish technical conditions for almost-sure convergence and show that
under heavy traffic, the proposed low complexity distributive control is global
optimal.
|
1010.0298
|
Steepest Ascent Hill Climbing For A Mathematical Problem
|
cs.AI
|
The paper proposes artificial intelligence technique called hill climbing to
find numerical solutions of Diophantine Equations. Such equations are important
as they have many applications in fields like public key cryptography, integer
factorization, algebraic curves, projective curves and data dependency in super
computers. Importantly, it has been proved that there is no general method to
find solutions of such equations. This paper is an attempt to find numerical
solutions of Diophantine equations using steepest ascent version of Hill
Climbing. The method, which uses tree representation to depict possible
solutions of Diophantine equations, adopts a novel methodology to generate
successors. The heuristic function used help to make the process of finding
solution as a minimization process. The work illustrates the effectiveness of
the proposed methodology using a class of Diophantine equations given by a1. x1
p1 + a2. x2 p2 + ...... + an . xn pn = N where ai and N are integers. The
experimental results validate that the procedure proposed is successful in
finding solutions of Diophantine Equations with sufficiently large powers and
large number of variables.
|
1010.0301
|
A Microwave Imaging and Enhancement Technique from Noisy Synthetic Data
|
cs.CV cs.NA
|
An inverse iterative algorithm for microwave imaging based on moment method
solution is presented here. The iterative scheme has been developed on
constrained optimization technique and is certain to converge. Different mesh
size for the model has been used here to overcome the Inverse Crime. The
synthetic data at the receivers is contaminated with different percentage of
noise. The ill-posedness of the problem is solved by Levenberg-Marquardt
method. The algorithm is applied to synthetic data and the reconstructed image
is then further enhanced through the Image enhancement technique
|
1010.0302
|
Spatial Networks
|
cond-mat.stat-mech cond-mat.dis-nn cs.SI physics.soc-ph q-bio.NC
|
Complex systems are very often organized under the form of networks where
nodes and edges are embedded in space. Transportation and mobility networks,
Internet, mobile phone networks, power grids, social and contact networks,
neural networks, are all examples where space is relevant and where topology
alone does not contain all the information. Characterizing and understanding
the structure and the evolution of spatial networks is thus crucial for many
different fields ranging from urbanism to epidemiology. An important
consequence of space on networks is that there is a cost associated to the
length of edges which in turn has dramatic effects on the topological structure
of these networks. We will expose thoroughly the current state of our
understanding of how the spatial constraints affect the structure and
properties of these networks. We will review the most recent empirical
observations and the most important models of spatial networks. We will also
discuss various processes which take place on these spatial networks, such as
phase transitions, random walks, synchronization, navigation, resilience, and
disease spread.
|
1010.0316
|
Two-User Gaussian Interference Channel with Finite Constellation Input
and FDMA
|
cs.IT math.IT
|
In the two-user Gaussian Strong Interference Channel (GSIC) with finite
constellation inputs, it is known that relative rotation between the
constellations of the two users enlarges the Constellation Constrained (CC)
capacity region. In this paper, a metric for finding the approximate angle of
rotation (with negligibly small error) to maximally enlarge the CC capacity for
the two-user GSIC is presented. In the case of Gaussian input alphabets with
equal powers for both the users and the modulus of both the cross-channel gains
being equal to unity, it is known that the FDMA rate curve touches the capacity
curve of the GSIC. It is shown that, with unequal powers for both the users
also, when the modulus of one of the cross-channel gains being equal to one and
the modulus of the other cross-channel gain being greater than or equal to one,
the FDMA rate curve touches the capacity curve of the GSIC. On the contrary, it
is shown that, under finite constellation inputs, with both the users using the
same constellation, the FDMA rate curve strictly lies within (never touches)
the enlarged CC capacity region throughout the strong-interference regime. This
means that using FDMA it is impossible to go close to the CC capacity. It is
well known that for the Gaussian input alphabets, the FDMA inner-bound, at the
optimum sum-rate point, is always better than the simultaneous-decoding
inner-bound throughout the weak-interference regime. For a portion of the weak
interference regime, it is shown that with identical finite constellation
inputs for both the users, the simultaneous-decoding inner-bound, enlarged by
relative rotation between the constellations, is strictly better than the FDMA
inner-bound.
|
1010.0333
|
Effects of Single-Cycle Structure on Iterative Decoding for Low-Density
Parity-Check Codes
|
cs.IT math.IT
|
We consider communication over the binary erasure channel (BEC) using
low-density parity-check (LDPC) codes and belief propagation (BP) decoding. For
fixed numbers of BP iterations, the bit error probability approaches a limit as
blocklength tends to infinity, and the limit is obtained via density evolution.
On the other hand, the difference between the bit error probability of codes
with blocklength $n$ and that in the large blocklength limit is asymptotically
$\alpha(\epsilon,t)/n + \Theta(n^{-2})$ where $\alpha(\epsilon,t)$ denotes a
specific constant determined by the code ensemble considered, the number $t$ of
iterations, and the erasure probability $\epsilon$ of the BEC. In this paper,
we derive a set of recursive formulas which allows evaluation of the constant
$\alpha(\epsilon,t)$ for standard irregular ensembles. The dominant difference
$\alpha(\epsilon,t)/n$ can be considered as effects of cycle-free and
single-cycle structures of local graphs. Furthermore, it is confirmed via
numerical simulations that estimation of the bit error probability using
$\alpha(\epsilon,t)$ is accurate even for small blocklengths.
|
1010.0344
|
Alternating-Offer Bargaining Games over the Gaussian Interference
Channel
|
cs.IT cs.GT math.IT
|
This paper tackles the problem of how two selfish users jointly determine the
operating point in the achievable rate region of a two-user Gaussian
interference channel through bargaining. In previous work, incentive conditions
for two users to cooperate using a simple version of Han-Kobayashi scheme was
studied and the Nash bargaining solution (NBS) was used to obtain a fair
operating point. Here a noncooperative bargaining game of alternating offers is
adopted to model the bargaining process and rates resulting from the
equilibrium outcome are analyzed. In particular, it is shown that the operating
point resulting from the formulated bargaining game depends on the cost of
delay in bargaining and how bargaining proceeds. If the associated bargaining
problem is regular, a unique perfect equilibrium exists and lies on the
individual rational efficient frontier of the achievable rate region. Besides,
the equilibrium outcome approaches the NBS if the bargaining costs of both
users are negligible.
|
1010.0410
|
Structure and Response in the World Trade Network
|
q-fin.GN cs.SI physics.soc-ph
|
We examine how the structure of the world trade network has been shaped by
globalization and recessions over the last 40 years. We show that by treating
the world trade network as an evolving system, theory predicts the trade
network is more sensitive to evolutionary shocks and recovers more slowly from
them now than it did 40 years ago, due to structural changes in the world trade
network induced by globalization. We also show that recession-induced change to
the world trade network leads to an \emph{increased} hierarchical structure of
the global trade network for a few years after the recession.
|
1010.0412
|
Sequences of Inequalities Among New Divergence Measures
|
cs.IT math.IT
|
There are three classical divergence measures exist in the literature on
information theory and statistics. These are namely, Jeffryes-Kullback-Leiber
J-divergence. Sibson-Burbea-Rao Jensen-Shannon divegernce and Taneja
arithemtic-geometric mean divergence. These three measures bear an interesting
relationship among each other and are based on logarithmic expressions. The
divergence measures like Hellinger discrimination, symmetric chi-square
divergence, and triangular discrimination are also known in the literature and
are not based on logarithmic expressions. Past years Dragomir et al., Kumar and
Johnson and Jain and Srivastava studied different kind of divergence measures.
In this paper, we have presented some more new divergence measures and obtained
inequalities relating these new measures and also made connections with
previous ones. The idea of exponential divergence is also introduced.
|
1010.0417
|
Visual-hint Boundary to Segment Algorithm for Image Segmentation
|
cs.CV
|
Image segmentation has been a very active research topic in image analysis
area. Currently, most of the image segmentation algorithms are designed based
on the idea that images are partitioned into a set of regions preserving
homogeneous intra-regions and inhomogeneous inter-regions. However, human
visual intuition does not always follow this pattern. A new image segmentation
method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is
more consistent with human perceptions. VHBS abides by two visual hint rules
based on human perceptions: (i) the global scale boundaries tend to be the real
boundaries of the objects; (ii) two adjacent regions with quite different
colors or textures tend to result in the real boundaries between them. It has
been demonstrated by experiments that, compared with traditional image
segmentation method, VHBS has better performance and also preserves higher
computational efficiency.
|
1010.0418
|
Quantum capacity under adversarial quantum noise: arbitrarily varying
quantum channels
|
quant-ph cs.IT math-ph math.IT math.MP
|
We investigate entanglement transmission over an unknown channel in the
presence of a third party (called the adversary), which is enabled to choose
the channel from a given set of memoryless but non-stationary channels without
informing the legitimate sender and receiver about the particular choice that
he made. This channel model is called arbitrarily varying quantum channel
(AVQC). We derive a quantum version of Ahlswede's dichotomy for classical
arbitrarily varying channels. This includes a regularized formula for the
common randomness-assisted capacity for entanglement transmission of an AVQC.
Quite surprisingly and in contrast to the classical analog of the problem
involving the maximal and average error probability, we find that the capacity
for entanglement transmission of an AVQC always equals its strong subspace
transmission capacity. These results are accompanied by different notions of
symmetrizability (zero-capacity conditions) as well as by conditions for an
AVQC to have a capacity described by a single-letter formula. In he final part
of the paper the capacity of the erasure-AVQC is computed and some light shed
on the connection between AVQCs and zero-error capacities. Additionally, we
show by entirely elementary and operational arguments motivated by the theory
of AVQCs that the quantum, classical, and entanglement-assisted zero-error
capacities of quantum channels are generically zero and are discontinuous at
every positivity point.
|
1010.0422
|
Convolutional Matching Pursuit and Dictionary Training
|
cs.CV
|
Matching pursuit and K-SVD is demonstrated in the translation invariant
setting
|
1010.0431
|
Multilevel compression of random walks on networks reveals hierarchical
organization in large integrated systems
|
physics.soc-ph cs.SI physics.comp-ph
|
To comprehend the hierarchical organization of large integrated systems, we
introduce the hierarchical map equation, which reveals multilevel structures in
networks. In this information-theoretic approach, we exploit the duality
between compression and pattern detection; by compressing a description of a
random walker as a proxy for real flow on a network, we find regularities in
the network that induce this system-wide flow. Finding the shortest multilevel
description of the random walker therefore gives us the best hierarchical
clustering of the network, the optimal number of levels and modular partition
at each level, with respect to the dynamics on the network. With a novel search
algorithm, we extract and illustrate the rich multilevel organization of
several large social and biological networks. For example, from the global air
traffic network we uncover countries and continents, and from the pattern of
scientific communication we reveal more than 100 scientific fields organized in
four major disciplines: life sciences, physical sciences, ecology and earth
sciences, and social sciences. In general, we find shallow hierarchical
structures in globally interconnected systems, such as neural networks, and
rich multilevel organizations in systems with highly separated regions, such as
road networks.
|
1010.0433
|
Derandomization and Group Testing
|
cs.IT math.IT
|
The rapid development of derandomization theory, which is a fundamental area
in theoretical computer science, has recently led to many surprising
applications outside its initial intention. We will review some recent such
developments related to combinatorial group testing. In its most basic setting,
the aim of group testing is to identify a set of "positive" individuals in a
population of items by taking groups of items and asking whether there is a
positive in each group.
In particular, we will discuss explicit constructions of optimal or
nearly-optimal group testing schemes using "randomness-conducting" functions.
Among such developments are constructions of error-correcting group testing
schemes using randomness extractors and condensers, as well as threshold group
testing schemes from lossless condensers.
|
1010.0476
|
Interference Alignment as a Rank Constrained Rank Minimization
|
cs.IT cs.DC cs.NI math.IT
|
We show that the maximization of the sum degrees-of-freedom for the static
flat-fading multiple-input multiple-output (MIMO) interference channel is
equivalent to a rank constrained rank minimization problem (RCRM), when the
signal spaces span all available dimensions. The rank minimization corresponds
to maximizing interference alignment (IA) so that interference spans the lowest
dimensional subspace possible. The rank constraints account for the useful
signal spaces spanning all available spatial dimensions. That way, we
reformulate all IA requirements to requirements involving ranks. Then, we
present a convex relaxation of the RCRM problem inspired by recent results in
compressed sensing and low-rank matrix completion theory that rely on
approximating rank with the nuclear norm. We show that the convex envelope of
the sum of ranks of the interference matrices is the normalized sum of their
corresponding nuclear norms and introduce tractable constraints that are
asymptotically equivalent to the rank constraints for the initial problem. We
also show that our heuristic relaxation can be tuned for the multi-cell
interference channel. Furthermore, we experimentally show that in many cases
the proposed algorithm attains perfect interference alignment and in some cases
outperforms previous approaches for finding precoding and zero-forcing matrices
for interference alignment.
|
1010.0485
|
Distributed Storage Codes Meet Multiple-Access Wiretap Channels
|
cs.IT cs.DC cs.NI math.IT
|
We consider {\it i)} the overhead minimization of maximum-distance separable
(MDS) storage codes for the repair of a single failed node and {\it ii)} the
total secure degrees-of-freedom (S-DoF) maximization in a multiple-access
compound wiretap channel. We show that the two problems are connected.
Specifically, the overhead minimization for a single node failure of an {\it
optimal} MDS code, i.e. one that can achieve the information theoretic overhead
minimum, is equivalent to maximizing the S-DoF in a multiple-access compound
wiretap channel. Additionally, we show that maximizing the S-DoF in a
multiple-access compound wiretap channel is equivalent to minimizing the
overhead of an MDS code for the repair of a departed node. An optimal MDS code
maps to a full S-DoF channel and a full S-DoF channel maps to an MDS code with
minimum repair overhead for one failed node. We also state a general framework
for code-to-channel and channel-to-code mappings and performance bounds between
the two settings. The underlying theme for all connections presented is
interference alignment (IA). The connections between the two problems become
apparent when we restate IA as an optimization problem. Specifically, we
formulate the overhead minimization and the S-DoF maximization as rank
constrained, sum-rank and max-rank minimization problems respectively. The
derived connections allow us to map repair strategies of recently discovered
repair codes to beamforming matrices and characterize the maximum S-DoF for the
single antenna multiple-access compound wiretap channel.
|
1010.0522
|
Strong direct product conjecture holds for all relations in public coin
randomized one-way communication complexity
|
cs.CC cs.IT math.IT
|
Let f subset of X x Y x Z be a relation. Let the public coin one-way
communication complexity of f, with worst case error 1/3, be denoted
R^{1,pub}_{1/3}(f). We show that if for computing f^k (k independent copies of
f), o(k R^{1,pub}_{1/3}(f)) communication is provided, then the success is
exponentially small in k. This settles the strong direct product conjecture for
all relations in public coin one-way communication complexity.
We show a new tight characterization of public coin one-way communication
complexity which strengthens on the tight characterization shown in [J.,
Klauck, Nayak 08]. We use the new characterization to show our direct product
result and this may also be of independent interest.
|
1010.0558
|
Analyzing Network Coding Gossip Made Easy
|
cs.DC cs.DS cs.IT math.IT
|
We give a new technique to analyze the stopping time of gossip protocols that
are based on random linear network coding (RLNC). Our analysis drastically
simplifies, extends and strengthens previous results. We analyze RLNC gossip in
a general framework for network and communication models that encompasses and
unifies the models used previously in this context. We show, in most settings
for the first time, that it converges with high probability in the
information-theoretically optimal time. Most stopping times are of the form O(k
+ T) where k is the number of messages to be distributed and T is the time it
takes to disseminate one message. This means RLNC gossip achieves "perfect
pipelining". Our analysis directly extends to highly dynamic networks in which
the topology can change completely at any time. This remains true even if the
network dynamics are controlled by a fully adaptive adversary that knows the
complete network state. Virtually nothing besides simple O(kT) sequential
flooding protocols was previously known for such a setting. While RLNC gossip
works in this wide variety of networks its analysis remains the same and
extremely simple. This contrasts with more complex proofs that were put forward
to give less strong results for various special cases.
|
1010.0601
|
A Random Matrix--Theoretic Approach to Handling Singular Covariance
Estimates
|
math.PR cs.IT math.IT math.ST physics.data-an stat.TH
|
In many practical situations we would like to estimate the covariance matrix
of a set of variables from an insufficient amount of data. More specifically,
if we have a set of $N$ independent, identically distributed measurements of an
$M$ dimensional random vector the maximum likelihood estimate is the sample
covariance matrix. Here we consider the case where $N<M$ such that this
estimate is singular and therefore fundamentally bad. We present a radically
new approach to deal with this situation. Let $X$ be the $M\times N$ data
matrix, where the columns are the $N$ independent realizations of the random
vector with covariance matrix $\Sigma$. Without loss of generality, we can
assume that the random variables have zero mean. We would like to estimate
$\Sigma$ from $X$. Let $K$ be the classical sample covariance matrix. Fix a
parameter $1\leq L\leq N$ and consider an ensemble of $L\times M$ random
unitary matrices, $\{\Phi\}$, having Haar probability measure. Pre and post
multiply $K$ by $\Phi$, and by the conjugate transpose of $\Phi$ respectively,
to produce a non--singular $L\times L$ reduced dimension covariance estimate. A
new estimate for $\Sigma$, denoted by $\mathrm{cov}_L(K)$, is obtained by a)
projecting the reduced covariance estimate out (to $M\times M$) through pre and
post multiplication by the conjugate transpose of $\Phi$, and by $\Phi$
respectively, and b) taking the expectation over the unitary ensemble. Another
new estimate (this time for $\Sigma^{-1}$), $\mathrm{invcov}_L(K)$, is obtained
by a) inverting the reduced covariance estimate, b) projecting the inverse out
(to $M\times M$) through pre and post multiplication by the conjugate transpose
of $\Phi$, and by $\Phi$ respectively, and c) taking the expectation over the
unitary ensemble. We have a closed analytical expression for
$\mathrm{invcov}_L(K)$ and $\mathrm{cov}_L(K)$ in terms of its eigenvalue
decomposition.
|
1010.0608
|
Real-time Robust Principal Components' Pursuit
|
cs.CV cs.IT math.IT
|
In the recent work of Candes et al, the problem of recovering low rank matrix
corrupted by i.i.d. sparse outliers is studied and a very elegant solution,
principal component pursuit, is proposed. It is motivated as a tool for video
surveillance applications with the background image sequence forming the low
rank part and the moving objects/persons/abnormalities forming the sparse part.
Each image frame is treated as a column vector of the data matrix made up of a
low rank matrix and a sparse corruption matrix. Principal component pursuit
solves the problem under the assumptions that the singular vectors of the low
rank matrix are spread out and the sparsity pattern of the sparse matrix is
uniformly random. However, in practice, usually the sparsity pattern and the
signal values of the sparse part (moving persons/objects) change in a
correlated fashion over time, for e.g., the object moves slowly and/or with
roughly constant velocity. This will often result in a low rank sparse matrix.
For video surveillance applications, it would be much more useful to have a
real-time solution. In this work, we study the online version of the above
problem and propose a solution that automatically handles correlated sparse
outliers. The key idea of this work is as follows. Given an initial estimate of
the principal directions of the low rank part, we causally keep estimating the
sparse part at each time by solving a noisy compressive sensing type problem.
The principal directions of the low rank part are updated every-so-often. In
between two update times, if new Principal Components' directions appear, the
"noise" seen by the Compressive Sensing step may increase. This problem is
solved, in part, by utilizing the time correlation model of the low rank part.
We call the proposed solution "Real-time Robust Principal Components' Pursuit".
|
1010.0609
|
Selfish Response to Epidemic Propagation
|
cs.SY cs.MA nlin.AO
|
An epidemic spreading in a network calls for a decision on the part of the
network members: They should decide whether to protect themselves or not. Their
decision depends on the trade-off between their perceived risk of being
infected and the cost of being protected. The network members can make
decisions repeatedly, based on information that they receive about the changing
infection level in the network.
We study the equilibrium states reached by a network whose members increase
(resp. decrease) their security deployment when learning that the network
infection is widespread (resp. limited). Our main finding is that the
equilibrium level of infection increases as the learning rate of the members
increases. We confirm this result in three scenarios for the behavior of the
members: strictly rational cost minimizers, not strictly rational, and strictly
rational but split into two response classes. In the first two cases, we
completely characterize the stability and the domains of attraction of the
equilibrium points, even though the first case leads to a differential
inclusion. We validate our conclusions with simulations on human mobility
traces.
|
1010.0621
|
Local Optimality of User Choices and Collaborative Competitive Filtering
|
stat.ML cs.IR cs.SI stat.AP
|
While a user's preference is directly reflected in the interactive choice
process between her and the recommender, this wealth of information was not
fully exploited for learning recommender models. In particular, existing
collaborative filtering (CF) approaches take into account only the binary
events of user actions but totally disregard the contexts in which users'
decisions are made. In this paper, we propose Collaborative Competitive
Filtering (CCF), a framework for learning user preferences by modeling the
choice process in recommender systems. CCF employs a multiplicative latent
factor model to characterize the dyadic utility function. But unlike CF, CCF
models the user behavior of choices by encoding a local competition effect. In
this way, CCF allows us to leverage dyadic data that was previously lumped
together with missing data in existing CF models. We present two formulations
and an efficient large scale optimization algorithm. Experiments on three
real-world recommendation data sets demonstrate that CCF significantly
outperforms standard CF approaches in both offline and online evaluations.
|
1010.0624
|
Eigenvalue Results for Large Scale Random Vandermonde Matrices with Unit
Complex Entries
|
math.PR cs.IT math.IT physics.data-an
|
This paper centers on the limit eigenvalue distribution for random
Vandermonde matrices with unit magnitude complex entries. The phases of the
entries are chosen independently and identically distributed from the interval
$[-\pi,\pi]$. Various types of distribution for the phase are considered and we
establish the existence of the empirical eigenvalue distribution in the large
matrix limit on a wide range of cases. The rate of growth of the maximum
eigenvalue is examined and shown to be no greater than $O(\log N)$ and no
slower than $O(\log N/\log\log N)$ where $N$ is the dimension of the matrix.
Additional results include the existence of the capacity of the Vandermonde
channel (limit integral for the expected log determinant).
|
1010.0642
|
Error Performance of Channel Coding in Random Access Communication
|
cs.IT math.IT
|
A new channel coding approach was proposed in [1] for random multiple access
communication over the discrete-time memoryless channel. The coding approach
allows users to choose their communication rates independently without sharing
the rate information among each other or with the receiver. The receiver will
either decode the message or report a collision depending on whether reliable
message recovery is possible. It was shown that, asymptotically as the codeword
length goes to infinity, the set of communication rates supporting reliable
message recovery can be characterized by an achievable region which equals
Shannon's information rate region possibly without a convex hull operation. In
this paper, we derive achievable bounds on error probabilities, including the
decoding error probability and the collision miss detection probability, of
random multiple access systems with a finite codeword length. Achievable error
exponents are obtained by taking the codeword length to infinity.
|
1010.0654
|
On Equivalence Between Network Topologies
|
cs.IT math.IT
|
One major open problem in network coding is to characterize the capacity
region of a general multi-source multi-demand network. There are some existing
computational tools for bounding the capacity of general networks, but their
computational complexity grows very quickly with the size of the network. This
motivates us to propose a new hierarchical approach which finds upper and lower
bounding networks of smaller size for a given network. This approach
sequentially replaces components of the network with simpler structures, i.e.,
with fewer links or nodes, so that the resulting network is more amenable to
computational analysis and its capacity provides an upper or lower bound on the
capacity of the original network. The accuracy of the resulting bounds can be
bounded as a function of the link capacities. Surprisingly, we are able to
simplify some families of network structures without any loss in accuracy.
|
1010.0670
|
Unconditionally Secure Computation on Large Distributed Databases with
Vanishing Cost
|
cs.CR cs.IT math.IT
|
Consider a network of k parties, each holding a long sequence of n entries (a
database), with minimum vertex-cut greater than t. We show that any empirical
statistic across the network of databases can be computed by each party with
perfect privacy, against any set of t < k/2 passively colluding parties, such
that the worst-case distortion and communication cost (in bits per database
entry) both go to zero as n, the number of entries in the databases, goes to
infinity. This is based on combining a striking dimensionality reduction result
for random sampling with unconditionally secure multi-party computation
protocols.
|
1010.0694
|
Statistical inference optimized with respect to the observed sample for
single or multiple comparisons
|
math.ST cs.IT math.IT q-bio.BM stat.ME stat.TH
|
The normalized maximum likelihood (NML) is a recent penalized likelihood that
has properties that justify defining the amount of discrimination information
(DI) in the data supporting an alternative hypothesis over a null hypothesis as
the logarithm of an NML ratio, namely, the alternative hypothesis NML divided
by the null hypothesis NML. The resulting DI, like the Bayes factor but unlike
the p-value, measures the strength of evidence for an alternative hypothesis
over a null hypothesis such that the probability of misleading evidence
vanishes asymptotically under weak regularity conditions and such that evidence
can support a simple null hypothesis. Unlike the Bayes factor, the DI does not
require a prior distribution and is minimax optimal in a sense that does not
involve averaging over outcomes that did not occur. Replacing a (possibly
pseudo-) likelihood function with its weighted counterpart extends the scope of
the DI to models for which the unweighted NML is undefined. The likelihood
weights leverage side information, either in data associated with comparisons
other than the comparison at hand or in the parameter value of a simple null
hypothesis. Two case studies, one involving multiple populations and the other
involving multiple biological features, indicate that the DI is robust to the
type of side information used when that information is assigned the weight of a
single observation. Such robustness suggests that very little adjustment for
multiple comparisons is warranted if the sample size is at least moderate.
|
1010.0696
|
Robust H_infinity Filter Design for Lipschitz Nonlinear Systems via
Multiobjective Optimization
|
cs.SY math.OC
|
In this paper, a new method of H_infinity observer design for Lipschitz
nonlinear systems is proposed in the form of an LMI optimization problem. The
proposed observer has guaranteed decay rate (exponential convergence) and is
robust against unknown exogenous disturbance. In addition, thanks to the
linearity of the proposed LMIs in the admissible Lipschitz constant, it can be
maximized via LMI optimization. This adds an extra important feature to the
observer, robustness against nonlinear uncertainty. Explicit bound on the
tolerable nonlinear uncertainty is derived. The new LMI formulation also allows
optimizations over the disturbance attenuation level (H_infinity cost). Then,
the admissible Lipschitz constant and the disturbance attenuation level of the
H_infinity filter are simultaneously optimized through LMI multiobjective
optimization.
|
1010.0725
|
Link Prediction in Complex Networks: A Survey
|
physics.soc-ph cs.SI physics.comp-ph
|
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.
|
1010.0743
|
Strong security and separated code constructions for the broadcast
channels with confidential messages
|
cs.IT cs.CR math.IT
|
We show that the capacity region of the broadcast channel with confidential
messages does not change when the strong security criterion is adopted instead
of the weak security criterion traditionally used. We also show a construction
method of coding for the broadcast channel with confidential messages by using
an arbitrary given coding for the broadcast channel with degraded message sets.
|
1010.0771
|
Genetic Algorithm for Mulicriteria Optimization of a Multi-Pickup and
Delivery Problem with Time Windows
|
cs.NE
|
In This paper we present a genetic algorithm for mulicriteria optimization of
a multipickup and delivery problem with time windows (m-PDPTW). The m-PDPTW is
an optimization vehicles routing problem which must meet requests for transport
between suppliers and customers satisfying precedence, capacity and time
constraints. This paper purposes a brief literature review of the PDPTW,
present an approach based on genetic algorithms and Pareto dominance method to
give a set of satisfying solutions to the m-PDPTW minimizing total travel cost,
total tardiness time and the vehicles number.
|
1010.0781
|
Transmission Capacity of Spectrum Sharing Ad-hoc Networks with Multiple
Antennas
|
cs.IT math.IT
|
Two coexisting ad-hoc networks, primary and secondary, are considered, where
each node of the primary network has a single antenna, while each node of the
secondary network is equipped with multiple antennas. Using multiple antennas,
each secondary transmitter uses some of its spatial transmit degrees of freedom
(STDOF) to null its interference towards the primary receivers, while each
secondary receiver employs interference cancelation using some of its spatial
receive degrees of freedom (SRDOF). This paper derives the optimal STDOF for
nulling and SRDOF for interference cancelation that maximize the scaling of the
transmission capacity of the secondary network with respect to the number of
antennas, when the secondary network operates under an outage constraint at the
primary receivers. With a single receive antenna, using a fraction of the total
STDOF for nulling at each secondary transmitter maximizes the transmission
capacity. With multiple transmit and receive antennas and fixing all but one
STDOF for nulling, using a fraction of the total SRDOF to cancel the nearest
interferers maximizes the transmission capacity of the secondary network.
|
1010.0803
|
Node similarity as a basic principle behind connectivity in complex
networks
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
How are people linked in a highly connected society? Since in many networks a
power-law (scale-free) node-degree distribution can be observed, power-law
might be seen as a universal characteristics of networks. But this study of
communication in the Flickr social online network reveals that power-law
node-degree distributions are restricted to only sparsely connected networks.
More densely connected networks, by contrast, show an increasing divergence
from power-law. This work shows that this observation is consistent with the
classic idea from social sciences that similarity is the driving factor behind
communication in social networks. The strong relation between communication
strength and node similarity could be confirmed by analyzing the Flickr
network. It also is shown that node similarity as a network formation model can
reproduce the characteristics of different network densities and hence can be
used as a model for describing the topological transition from weakly to
strongly connected societies.
|
1010.0846
|
A strong direct product theorem for two-way public coin communication
complexity
|
cs.CC cs.IT math.IT
|
We show a direct product result for two-way public coin communication
complexity of all relations in terms of a new complexity measure that we
define. Our new measure is a generalization to non-product distributions of the
two-way product subdistribution bound of [J, Klauck and Nayak 08], thereby our
result implying their direct product result in terms of the two-way product
subdistribution bound.
We show that our new complexity measure gives tight lower bound for the
set-disjointness problem, as a result we reproduce strong direct product result
for this problem, which was previously shown by [Klauck 00].
|
1010.0863
|
Coevolution of Glauber-like Ising dynamics on typical networks
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We consider coevolution of site status and link structures from two different
initial networks: a one dimensional Ising chain and a scale free network. The
dynamics is governed by a preassigned stability parameter $S$, and a rewiring
factor $\phi$, that determines whether the Ising spin at the chosen site flips
or whether the node gets rewired to another node in the system. This dynamics
has also been studied with Ising spins distributed randomly among nodes which
lie on a network with preferential attachment. We have observed the steady
state average stability and magnetisation for both kinds of systems to have an
idea about the effect of initial network topology. Although the average
stability shows almost similar behaviour, the magnetisation depends on the
initial condition we start from. Apart from the local dynamics, the global
effect on the dynamics has also been studied. These parameters show interesting
variations for different values of $S$ and $\phi$, which helps in determining
the steady-state condition for a given substrate.
|
1010.0886
|
A Platform-independent Programming Environment for Robot Control
|
cs.RO
|
The development of robot control programs is a complex task. Many robots are
different in their electrical and mechanical structure which is also reflected
in the software. Specific robot software environments support the program
development, but are mainly text-based and usually applied by experts in the
field with profound knowledge of the target robot. This paper presents a
graphical programming environment which aims to ease the development of robot
control programs. In contrast to existing graphical robot programming
environments, our approach focuses on the composition of parallel action
sequences. The developed environment allows to schedule independent robot
actions on parallel execution lines and provides mechanism to avoid
side-effects of parallel actions. The developed environment is
platform-independent and based on the model-driven paradigm. The feasibility of
our approach is shown by the application of the sequencer to a simulated
service robot and a robot for educational purpose.
|
1010.0924
|
Preserving Privacy in Sequential Data Release against Background
Knowledge Attacks
|
cs.DB
|
A large amount of transaction data containing associations between
individuals and sensitive information flows everyday into data stores. Examples
include web queries, credit card transactions, medical exam records, transit
database records. The serial release of these data to partner institutions or
data analysis centers is a common situation. In this paper we show that, in
most domains, correlations among sensitive values associated to the same
individuals in different releases can be easily mined, and used to violate
users' privacy by adversaries observing multiple data releases. We provide a
formal model for privacy attacks based on this sequential background knowledge,
as well as on background knowledge on the probability distribution of sensitive
values over different individuals. We show how sequential background knowledge
can be actually obtained by an adversary, and used to identify with high
confidence the sensitive values associated with an individual. A defense
algorithm based on Jensen-Shannon divergence is proposed, and extensive
experiments show the superiority of the proposed technique with respect to
other applicable solutions. To the best of our knowledge, this is the first
work that systematically investigates the role of sequential background
knowledge in serial release of transaction data.
|
1010.0933
|
Interference Alignment with Limited Feedback on Two-cell Interfering
Two-User MIMO-MAC
|
cs.IT math.IT
|
In this paper, we consider a two-cell interfering two-user multiple-input
multiple-output multiple access channel (MIMO-MAC) with limited feedback. We
first investigate the multiplexing gain of such channel when users have perfect
channel state information at transmitter (CSIT) by exploiting an interference
alignment scheme. In addition, we propose a feedback framework for the
interference alignment in the limited feedback system. On the basis of the
proposed feedback framework, we analyze the rate gap loss and it is shown that
in order to keep the same multiplexing gain with the case of perfect CSIT, the
number of feedback bits per receiver scales as $B \geq
(M\!-1\!)\!\log_{2}(\textsf{SNR})+C$, where $M$ and $C$ denote the number of
transmit antennas and a constant, respectively. Throughout the simulation
results, it is shown that the sum-rate performance coincides with the derived
results.
|
1010.0937
|
Signal Space Alignment for an Encryption Message and Successive Network
Code Decoding on the MIMO K-way Relay Channel
|
cs.IT math.IT
|
This paper investigates a network information flow problem for a
multiple-input multiple-output (MIMO) Gaussian wireless network with $K$-users
and a single intermediate relay having $M$ antennas. In this network, each user
intends to convey a multicast message to all other users while receiving $K-1$
independent messages from the other users via an intermediate relay. This
network information flow is termed a MIMO Gaussian $K$-way relay channel. For
this channel, we show that $\frac{K}{2}$ degrees of freedom is achievable if
$M=K-1$. To demonstrate this, we come up with an encoding and decoding strategy
inspired from cryptography theory. The proposed encoding and decoding strategy
involves a \textit{signal space alignment for an encryption message} for the
multiple access phase (MAC) and \textit{zero forcing with successive network
code decoding} for the broadcast (BC) phase. The idea of the \emph{signal space
alignment for an encryption message} is that all users cooperatively choose the
precoding vectors to transmit the message so that the relay can receive a
proper encryption message with a special structure, \textit{network code chain
structure}. During the BC phase, \emph{zero forcing combined with successive
network code decoding} enables all users to decipher the encryption message
from the relay despite the fact that they all have different self-information
which they use as a key.
|
1010.0979
|
Un Algorithme g\'en\'etique pour le probl\`eme de ramassage et de
livraison avec fen\^etres de temps \`a plusieurs v\'ehicules
|
cs.NE
|
The PDPTW is an optimization vehicles routing problem which must meet
requests for transport between suppliers and customers satisfying precedence,
capacity and time constraints. We present, in this paper, a genetic algorithm
for optimization of a multi pickup and delivery problem with time windows
(m-PDPTW). We purposes a brief literature review of the PDPTW, present an
approach based on genetic algorithms to give a satisfying solution to the
m-PDPTW minimizing the total travel cost.
|
1010.0980
|
Approche Multicrit\`ere pour le Probl\`eme de Ramassage et de Livraison
avec Fen\^etres de Temps \`a Plusieurs V\'ehicules
|
cs.NE
|
Nowadays, the transport goods problem occupies an important place in the
economic life of modern societies. The pickup and delivery problem with time
windows (PDPTW) is one of the problems which a large part of the research was
interested. In this paper, we present a a brief literature review of the VRP
and the PDPTW, propose our multicriteria approach based on genetic algorithms
which allows minimize the compromise between the vehicles number, the total
tardiness time and the total travel cost. And this, by treating the case where
a customer can have multiple suppliers and one supplier can have multiple
customers
|
1010.1016
|
Multilevel Coding Schemes for Compute-and-Forward
|
cs.IT math.IT
|
We investigate techniques for designing modulation/coding schemes for the
wireless two-way relaying channel. The relay is assumed to have perfect channel
state information, but the transmitters are assumed to have no channel state
information. We consider physical layer network coding based on multilevel
coding techniques. Our multilevel coding framework is inspired by the
compute-and-forward relaying protocol. Indeed, we show that the framework
developed here naturally facilitates decoding of linear combinations of
codewords for forwarding by the relay node. We develop our framework with
general modulation formats in mind, but numerical results are presented for the
case where each node transmits using the QPSK constellation with gray labeling.
We focus our discussion on the rates at which the relay may reliably decode
linear combinations of codewords transmitted from the end nodes.
|
1010.1024
|
Superselectors: Efficient Constructions and Applications
|
cs.DS cs.DM cs.IT math.IT
|
We introduce a new combinatorial structure: the superselector. We show that
superselectors subsume several important combinatorial structures used in the
past few years to solve problems in group testing, compressed sensing,
multi-channel conflict resolution and data security. We prove close upper and
lower bounds on the size of superselectors and we provide efficient algorithms
for their constructions. Albeit our bounds are very general, when they are
instantiated on the combinatorial structures that are particular cases of
superselectors (e.g., (p,k,n)-selectors, (d,\ell)-list-disjunct matrices,
MUT_k(r)-families, FUT(k, a)-families, etc.) they match the best known bounds
in terms of size of the structures (the relevant parameter in the
applications). For appropriate values of parameters, our results also provide
the first efficient deterministic algorithms for the construction of such
structures.
|
1010.1028
|
Stealing Reality
|
cs.SI physics.soc-ph
|
In this paper we discuss the threat of malware targeted at extracting
information about the relationships in a real-world social network as well as
characteristic information about the individuals in the network, which we dub
Stealing Reality. We present Stealing Reality, explain why it differs from
traditional types of network attacks, and discuss why its impact is
significantly more dangerous than that of other attacks. We also present our
initial analysis and results regarding the form that an SR attack might take,
with the goal of promoting the discussion of defending against such an attack,
or even just detecting the fact that one has already occurred.
|
1010.1037
|
Stratified economic exchange on networks
|
physics.soc-ph cs.SI nlin.CG
|
We investigate a model of stratified economic interactions between agents
when the notion of spatial location is introduced. The agents are placed on a
network with near-neighbor connections. Interactions between neighbors can
occur only if the difference in their wealth is less than a threshold value
that defines the width of the economic classes. By employing concepts from
spatiotemporal dynamical systems, three types of patterns can be identified in
the system as parameters are varied: laminar, intermittent and turbulent
states. The transition from the laminar state to the turbulent state is
characterized by the activity of the system, a quantity that measures the
average exchange of wealth over long times. The degree of inequality in the
wealth distribution for different parameter values is characterized by the Gini
Coefficient. High levels of activity are associated to low values of the Gini
coefficient. It is found that the topological properties of the network have
little effect on the activity of the system, but the Gini coefficient increases
when the clustering coefficient of the network is increased.
|
1010.1042
|
Hidden Markov Models with Multiple Observation Processes
|
math.PR cs.IT cs.LG math.IT
|
We consider a hidden Markov model with multiple observation processes, one of
which is chosen at each point in time by a policy---a deterministic function of
the information state---and attempt to determine which policy minimises the
limiting expected entropy of the information state. Focusing on a special case,
we prove analytically that the information state always converges in
distribution, and derive a formula for the limiting entropy which can be used
for calculations with high precision. Using this fomula, we find
computationally that the optimal policy is always a threshold policy, allowing
it to be easily found. We also find that the greedy policy is almost optimal.
|
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