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1201.2843
|
Nonparametric Sparse Representation
|
cs.CV
|
This paper suggests a nonparametric scheme to find the sparse solution of the
underdetermined system of linear equations in the presence of unknown impulsive
or non-Gaussian noise. This approach is robust against any variations of the
noise model and its parameters. It is based on minimization of rank pseudo norm
of the residual signal and l_1-norm of the signal of interest, simultaneously.
We use the steepest descent method to find the sparse solution via an iterative
algorithm. Simulation results show that our proposed method outperforms the
existence methods like OMP, BP, Lasso, and BCS whenever the observation vector
is contaminated with measurement or environmental non-Gaussian noise with
unknown parameters. Furthermore, for low SNR condition, the proposed method has
better performance in the presence of Gaussian noise.
|
1201.2845
|
Competition through selective inhibitory synchrony
|
q-bio.NC cs.NE
|
Models of cortical neuronal circuits commonly depend on inhibitory feedback
to control gain, provide signal normalization, and to selectively amplify
signals using winner-take-all (WTA) dynamics. Such models generally assume that
excitatory and inhibitory neurons are able to interact easily, because their
axons and dendrites are co-localized in the same small volume. However,
quantitative neuroanatomical studies of the dimensions of axonal and dendritic
trees of neurons in the neocortex show that this co-localization assumption is
not valid. In this paper we describe a simple modification to the WTA circuit
design that permits the effects of distributed inhibitory neurons to be coupled
through synchronization, and so allows a single WTA to be distributed widely in
cortical space, well beyond the arborization of any single inhibitory neuron,
and even across different cortical areas. We prove by non-linear contraction
analysis, and demonstrate by simulation that distributed WTA sub-systems
combined by such inhibitory synchrony are inherently stable. We show
analytically that synchronization is substantially faster than winner
selection. This circuit mechanism allows networks of independent WTAs to fully
or partially compete with each other.
|
1201.2859
|
Degraded Broadcast Channel with Side Information, Confidential Messages
and Noiseless Feedback
|
cs.IT math.IT
|
In this paper, first, we investigate the model of degraded broadcast channel
with side information and confidential messages. This work is from Steinberg's
work on the degraded broadcast channel with causal and noncausal side
information, and Csisz$\acute{a}$r-K\"{o}rner's work on broadcast channel with
confidential messages. Inner and outer bounds on the capacity-equivocation
regions are provided for the noncausal and causal cases. Superposition coding
and double-binning technique are used in the corresponding achievability
proofs.
Then, we investigate the degraded broadcast channel with side information,
confidential messages and noiseless feedback. The noiseless feedback is from
the non-degraded receiver to the channel encoder. Inner and outer bounds on the
capacity-equivocation region are provided for the noncausal case, and the
capacity-equivocation region is determined for the causal case. Compared with
the model without feedback, we find that the noiseless feedback helps to
enlarge the inner bounds for both causal and noncausal cases. In the
achievability proof of the feedback model, the noiseless feedback is used as a
secret key shared by the non-degraded receiver and the transmitter, and
therefore, the code construction for the feedback model is a combination of
superposition coding, Gel'fand-Pinsker's binning, block Markov coding and
Ahlswede-Cai's secret key on the feedback system.
|
1201.2868
|
On Ergodic Secrecy Capacity of Multiple Input Wiretap Channel with
Statistical CSIT
|
cs.IT math.IT
|
We consider the secure transmission in ergodic fast-Rayleigh fading
multiple-input single-output single-antennaeavesdropper (MISOSE) wiretap
channels. We assume that the statistics of both the legitimate and eavesdropper
channels is the only available channel state information at the transmitter
(CSIT). By introducing a new secrecy capacity upper bound, we prove that the
secrecy capacity is achieved by Gaussian input without prefixing. To attain
this, we form another MISOSE channel for upper-bounding, and tighten the bound
by finding the worst correlations between the legitimate and eavesdropper
channel coefficients. The resulting upper bound is tighter than the others in
the literature which are based on modifying the correlation between the noises
at the legitimate receiver and eavesdropper. Next, we fully characterize the
ergodic secrecy capacity by showing that the optimal channel input covariance
matrix is a scaled identity matrix, with the transmit power allocated uniformly
among the antennas. The key to solve such a complicated stochastic optimization
problem is by exploiting the completely monotone property of the ergodic
secrecy capacity to use the stochastic ordering theory. Finally, our simulation
results show that for the considered channel setting, the secrecy capacity is
bounded in both the high signal-to-noise ratio and large number of transmit
antenna regimes.
|
1201.2902
|
Acoustical Quality Assessment of the Classroom Environment
|
cs.LG
|
Teaching is one of the most important factors affecting any education system.
Many research efforts have been conducted to facilitate the presentation modes
used by instructors in classrooms as well as provide means for students to
review lectures through web browsers. Other studies have been made to provide
acoustical design recommendations for classrooms like room size and
reverberation times. However, using acoustical features of classrooms as a way
to provide education systems with feedback about the learning process was not
thoroughly investigated in any of these studies. We propose a system that
extracts different sound features of students and instructors, and then uses
machine learning techniques to evaluate the acoustical quality of any learning
environment. We infer conclusions about the students' satisfaction with the
quality of lectures. Using classifiers instead of surveys and other subjective
ways of measures can facilitate and speed such experiments which enables us to
perform them continuously. We believe our system enables education systems to
continuously review and improve their teaching strategies and acoustical
quality of classrooms.
|
1201.2905
|
NegCut: Automatic Image Segmentation based on MRF-MAP
|
cs.CV
|
Solving the Maximum a Posteriori on Markov Random Field, MRF-MAP, is a
prevailing method in recent interactive image segmentation tools. Although
mathematically explicit in its computational targets, and impressive for the
segmentation quality, MRF-MAP is hard to accomplish without the interactive
information from users. So it is rarely adopted in the automatic style up to
today. In this paper, we present an automatic image segmentation algorithm,
NegCut, based on the approximation to MRF-MAP. First we prove MRF-MAP is
NP-hard when the probabilistic models are unknown, and then present an
approximation function in the form of minimum cuts on graphs with negative
weights. Finally, the binary segmentation is taken from the largest eigenvector
of the target matrix, with a tuned version of the Lanczos eigensolver. It is
shown competitive at the segmentation quality in our experiments.
|
1201.2906
|
Quantum polar codes for arbitrary channels
|
quant-ph cs.IT math.IT
|
We construct a new entanglement-assisted quantum polar coding scheme which
achieves the symmetric coherent information rate by synthesizing "amplitude"
and "phase" channels from a given, arbitrary quantum channel. We first
demonstrate the coding scheme for arbitrary quantum channels with qubit inputs,
and we show that quantum data can be reliably decoded by O(N) rounds of
coherent quantum successive cancellation, followed by N controlled-NOT gates
(where N is the number of channel uses). We also find that the entanglement
consumption rate of the code vanishes for degradable quantum channels. Finally,
we extend the coding scheme to channels with multiple qubit inputs. This gives
a near-explicit method for realizing one of the most striking phenomena in
quantum information theory: the superactivation effect, whereby two quantum
channels which individually have zero quantum capacity can have a non-zero
quantum capacity when used together.
|
1201.2925
|
Combining Heterogeneous Classifiers for Relational Databases
|
cs.LG cs.DB
|
Most enterprise data is distributed in multiple relational databases with
expert-designed schema. Using traditional single-table machine learning
techniques over such data not only incur a computational penalty for converting
to a 'flat' form (mega-join), even the human-specified semantic information
present in the relations is lost. In this paper, we present a practical,
two-phase hierarchical meta-classification algorithm for relational databases
with a semantic divide and conquer approach. We propose a recursive, prediction
aggregation technique over heterogeneous classifiers applied on individual
database tables. The proposed algorithm was evaluated on three diverse
datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable
reduction in classification time without any loss of prediction accuracy.
|
1201.2931
|
The HIM glocal metric and kernel for network comparison and
classification
|
math.CO cs.SI physics.soc-ph
|
Due to the ever rising importance of the network paradigm across several
areas of science, comparing and classifying graphs represent essential steps in
the networks analysis of complex systems. Both tasks have been recently tackled
via quite different strategies, even tailored ad-hoc for the investigated
problem. Here we deal with both operations by introducing the
Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively
measure the difference between two graphs sharing the same vertices. The new
measure combines the local Hamming distance and the global spectral
Ipsen-Mikhailov distance so to overcome the drawbacks affecting the two
components separately. Building then the HIM kernel function derived from the
HIM distance it is possible to move from network comparison to network
classification via the Support Vector Machine (SVM) algorithm. Applications of
HIM distance and HIM kernel in computational biology and social networks
science demonstrate the effectiveness of the proposed functions as a general
purpose solution.
|
1201.2934
|
An Information-Theoretic Approach to PMU Placement in Electric Power
Systems
|
math.OC cs.DS cs.IT math.IT
|
This paper presents an information-theoretic approach to address the phasor
measurement unit (PMU) placement problem in electric power systems. Different
from the conventional 'topological observability' based approaches, this paper
advocates a much more refined, information-theoretic criterion, namely the
mutual information (MI) between the PMU measurements and the power system
states. The proposed MI criterion can not only include the full system
observability as a special case, but also can rigorously model the remaining
uncertainties in the power system states with PMU measurements, so as to
generate highly informative PMU configurations. Further, the MI criterion can
facilitate robust PMU placement by explicitly modeling probabilistic PMU
outages. We propose a greedy PMU placement algorithm, and show that it achieves
an approximation ratio of (1-1/e) for any PMU placement budget. We further show
that the performance is the best that one can achieve in practice, in the sense
that it is NP-hard to achieve any approximation ratio beyond (1-1/e). Such
performance guarantee makes the greedy algorithm very attractive in the
practical scenario of multi-stage installations for utilities with limited
budgets. Finally, simulation results demonstrate near-optimal performance of
the proposed PMU placement algorithm.
|
1201.2969
|
SparseDTW: A Novel Approach to Speed up Dynamic Time Warping
|
cs.DB cs.DS
|
We present a new space-efficient approach, (SparseDTW), to compute the
Dynamic Time Warping (DTW) distance between two time series that always yields
the optimal result. This is in contrast to other known approaches which
typically sacrifice optimality to attain space efficiency. The main idea behind
our approach is to dynamically exploit the existence of similarity and/or
correlation between the time series. The more the similarity between the time
series the less space required to compute the DTW between them. To the best of
our knowledge, all other techniques to speedup DTW, impose apriori constraints
and do not exploit similarity characteristics that may be present in the data.
We conduct experiments and demonstrate that SparseDTW outperforms previous
approaches.
|
1201.2980
|
Information algebra system of soft sets
|
cs.IT math.IT
|
Information algebra is algebraic structure for local computation and
inference. Given an initial universe set and a parameter set, we show that a
soft set system over them is an information algebra. Moreover, in a soft set
system, the family of all soft sets with a finite parameter subset can form a
compact information algebra.
|
1201.2984
|
Joint Robust Weighted LMMSE Transceiver Design for Dual-Hop AF
Multiple-Antenna Relay Systems
|
cs.IT math.IT
|
In this paper, joint transceiver design for dual-hop amplify-and-forward (AF)
MIMO relay systems with Gaussian distributed channel estimation errors in both
two hops is investigated. Due to the fact that various linear transceiver
designs can be transformed to a weighted linear minimum mean-square-error
(LMMSE) transceiver design with specific weighting matrices, weighted mean
square error (MSE) is chosen as the performance metric. Precoder matrix at
source, forwarding matrix at relay and equalizer matrix at destination are
jointly designed with channel estimation errors taken care of by Bayesian
philosophy. Several existing algorithms are found to be special cases of the
proposed solution. The performance advantage of the proposed robust design is
demonstrated by the simulation results.
|
1201.2985
|
Robust Transceiver Design for AF MIMO Relay Systems with Column
Correlations
|
cs.IT math.IT
|
In this paper, we investigate the robust transceiver design for dual-hop
amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel
estimation errors. Aiming at maximizing the mutual information under imperfect
channel state information (CSI), source precoder at source and forwarding
matrix at the relay are jointly optimized. Using some elegant attributes of
matrix-monotone functions, the structures of the optimal solutions are derived
first. Then based on the derived structure an iterative waterfilling solution
is proposed. Several existing algorithms are shown to be special cases of the
proposed solution. Finally, the effectiveness of the proposed robust design is
demonstrated by simulation results.
|
1201.2995
|
G-Lets: Signal Processing Using Transformation Groups
|
cs.CV
|
We present an algorithm using transformation groups and their irreducible
representations to generate an orthogonal basis for a signal in the vector
space of the signal. It is shown that multiresolution analysis can be done with
amplitudes using a transformation group. G-lets is thus not a single transform,
but a group of linear transformations related by group theory. The algorithm
also specifies that a multiresolution and multiscale analysis for each
resolution is possible in terms of frequencies. Separation of low and high
frequency components of each amplitude resolution is facilitated by G-lets.
Using conjugacy classes of the transformation group, more than one set of basis
may be generated, giving a different perspective of the signal through each
basis. Applications for this algorithm include edge detection, feature
extraction, denoising, face recognition, compression, and more. We analyze this
algorithm using dihedral groups as an example. We demonstrate the results with
an ECG signal and the standard `Lena' image.
|
1201.2999
|
Spatially Coupled Ensembles Universally Achieve Capacity under Belief
Propagation
|
cs.IT math.IT
|
We investigate spatially coupled code ensembles. For transmission over the
binary erasure channel, it was recently shown that spatial coupling increases
the belief propagation threshold of the ensemble to essentially the maximum
a-priori threshold of the underlying component ensemble. This explains why
convolutional LDPC ensembles, originally introduced by Felstrom and Zigangirov,
perform so well over this channel. We show that the equivalent result holds
true for transmission over general binary-input memoryless output-symmetric
channels. More precisely, given a desired error probability and a gap to
capacity, we can construct a spatially coupled ensemble which fulfills these
constraints universally on this class of channels under belief propagation
decoding. In fact, most codes in that ensemble have that property. The
quantifier universal refers to the single ensemble/code which is good for all
channels but we assume that the channel is known at the receiver. The key
technical result is a proof that under belief propagation decoding spatially
coupled ensembles achieve essentially the area threshold of the underlying
uncoupled ensemble. We conclude by discussing some interesting open problems.
|
1201.3016
|
Quasigroup based crypto-algorithms
|
math.GR cs.IT math.IT
|
Modifications of Markovski quasigroup based crypto-algorithm have been
proposed. Some of these modifications are based on the systems of orthogonal
n-ary groupoids. T-quasigroups based stream ciphers have been constructed.
|
1201.3019
|
Human behavior in Prisoner's Dilemma experiments suppresses network
reciprocity
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
During the last few years, much research has been devoted to strategic
interactions on complex networks. In this context, the Prisoner's Dilemma has
become a paradigmatic model, and it has been established that imitative
evolutionary dynamics lead to very different outcomes depending on the details
of the network. We here report that when one takes into account the real
behavior of people observed in the experiments, both at the mean-field level
and on utterly different networks the observed level of cooperation is the
same. We thus show that when human subjects interact in an heterogeneous mix
including cooperators, defectors and moody conditional cooperators, the
structure of the population does not promote or inhibit cooperation with
respect to a well mixed population.
|
1201.3052
|
Information spreading and development of cultural centers
|
physics.soc-ph cs.SI
|
The historical interplay between societies are governed by many factors,
including in particular spreading of languages, religion and other symbolic
traits. Cultural development, in turn, is coupled to emergence and maintenance
of information spreading. Strong centralized cultures exist thanks to attention
from their members, which faithfulness in turn relies on supply of information.
Here, we discuss a culture evolution model on a planar geometry that takes into
account aspects of the feedback between information spreading and its
maintenance. Features of model are highlighted by comparing it to cultural
spreading in ancient and medieval Europe, where it in particular suggests that
long lived centers should be located in geographically remote regions.
|
1201.3056
|
Power Allocation and Pricing in Multi-User Relay Networks Using
Stackelberg and Bargaining Games
|
cs.IT math.IT
|
This paper considers a multi-user single-relay wireless network, where the
relay gets paid for helping the users forward signals, and the users pay to
receive the relay service. We study the relay power allocation and pricing
problems, and model the interaction between the users and the relay as a
two-level Stackelberg game. In this game, the relay, modeled as the service
provider and the leader of the game, sets the relay price to maximize its
revenue; while the users are modeled as customers and the followers who buy
power from the relay for higher transmission rates. We use a bargaining game to
model the negotiation among users to achieve a fair allocation of the relay
power. Based on the proposed fair relay power allocation rule, the optimal
relay power price that maximizes the relay revenue is derived analytically.
Simulation shows that the proposed power allocation scheme achieves higher
network sum-rate and relay revenue than the even power allocation. Furthermore,
compared with the sum-rate-optimal solution, simulation shows that the proposed
scheme achieves better fairness with comparable network sum-rate for a wide
range of network scenarios. The proposed pricing and power allocation solutions
are also shown to be consistent with the laws of supply and demand.
|
1201.3059
|
Delay Sensitive Communications over Cognitive Radio Networks
|
cs.SY cs.NI
|
Supporting the quality of service of unlicensed users in cognitive radio
networks is very challenging, mainly due to dynamic resource availability
because of the licensed users' activities. In this paper, we study the optimal
admission control and channel allocation decisions in cognitive overlay
networks in order to support delay sensitive communications of unlicensed
users. We formulate it as a Markov decision process problem, and solve it by
transforming the original formulation into a stochastic shortest path problem.
We then propose a simple heuristic control policy, which includes a
threshold-based admission control scheme and and a largest-delay-first channel
allocation scheme, and prove the optimality of the largest-delay-first channel
allocation scheme. We further propose an improved policy using the rollout
algorithm. By comparing the performance of both proposed policies with the
upper-bound of the maximum revenue, we show that our policies achieve
close-to-optimal performance with low complexities.
|
1201.3060
|
On Order and Rank of Graphs
|
math.CO cs.IT math.IT
|
The rank of a graph is defined to be the rank of its adjacency matrix. A
graph is called reduced if it has no isolated vertices and no two vertices with
the same set of neighbors. Akbari, Cameron, and Khosrovshahi conjectured that
the number of vertices of every reduced graph of rank r is at most
$m(r)=2^{(r+2)/2}-2$ if r is even and $m(r) = 5\cdot2^{(r-3)/2}-2$ if r is odd.
In this article, we prove that if the conjecture is not true, then there would
be a counterexample of rank at most $46$. We also show that every reduced graph
of rank r has at most $8m(r)+14$ vertices.
|
1201.3088
|
An Adaptive Modulation Scheme for Two-user Fading MAC with Quantized
Fade State Feedback
|
cs.IT math.IT
|
With no CSI at the users, transmission over the two-user Gaussian Multiple
Access Channel with fading and finite constellation at the input, is not
efficient because error rates will be high when the channel conditions are
poor. However, perfect CSI at the users is an unrealistic assumption in the
wireless scenario, as it would involve massive feedback overheads. In this
paper we propose a scheme which uses only quantized knowledge of CSI at the
transmitters with the overhead being nominal. The users rotate their
constellation without varying their transmit power to adapt to the existing
channel conditions, in order to meet certain pre-determined minimum Euclidean
distance requirement in the equivalent constellation at the destination. The
optimal modulation scheme has been described for the case when both the users
use symmetric M-PSK constellations at the input, where $ M=2^\lambda $, $
\lambda $ being a positive integer. The strategy has been illustrated by
considering examples where both users use QPSK or 8-PSK signal sets at the
input. It is shown that the proposed scheme has better throughput and error
performance compared to the conventional non-adaptive scheme, at the cost of a
feedback overhead of just $\lceil \log_2(\frac{M^2}{8}-\frac{M}{4}+2)\rceil + 1
$ bits, for the M-PSK case.
|
1201.3107
|
Tacit knowledge mining algorithm based on linguistic truth-valued
concept lattice
|
cs.AI
|
This paper is the continuation of our research work about linguistic
truth-valued concept lattice. In order to provide a mathematical tool for
mining tacit knowledge, we establish a concrete model of 6-ary linguistic
truth-valued concept lattice and introduce a mining algorithm through the
structure consistency. Specifically, we utilize the attributes to depict
knowledge, propose the 6-ary linguistic truth-valued attribute extended context
and congener context to characterize tacit knowledge, and research the
necessary and sufficient conditions of forming tacit knowledge. We respectively
give the algorithms of generating the linguistic truth-valued congener context
and constructing the linguistic truth-valued concept lattice.
|
1201.3108
|
Groups, Graphs, Languages, Automata, Games and Second-order Monadic
Logic
|
math.GR cs.IT math.IT math.LO
|
In this paper we survey some surprising connections between group theory, the
theory of automata and formal languages, the theory of ends, infinite games of
perfect information, and monadic second-order logic.
|
1201.3109
|
Automatic system for counting cells with elliptical shape
|
cs.CV
|
This paper presents a new method for automatic quantification of ellipse-like
cells in images, an important and challenging problem that has been studied by
the computer vision community. The proposed method can be described by two main
steps. Initially, image segmentation based on the k-means algorithm is
performed to separate different types of cells from the background. Then, a
robust and efficient strategy is performed on the blob contour for touching
cells splitting. Due to the contour processing, the method achieves excellent
results of detection compared to manual detection performed by specialists.
|
1201.3116
|
Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using
Functional Data Analysis
|
cs.CV physics.data-an
|
This work proposes and study the concept of Functional Data Analysis
transform, applying it to the performance improving of volumetric
Bouligand-Minkowski fractal descriptors. The proposed transform consists
essentially in changing the descriptors originally defined in the space of the
calculus of fractal dimension into the space of coefficients used in the
functional data representation of these descriptors. The transformed decriptors
are used here in texture classification problems. The enhancement provided by
the FDA transform is measured by comparing the transformed to the original
descriptors in terms of the correctness rate in the classification of well
known datasets.
|
1201.3117
|
Design of Emergent and Adaptive Virtual Players in a War RTS Game
|
cs.NE cs.AI
|
Basically, in (one-player) war Real Time Strategy (wRTS) games a human player
controls, in real time, an army consisting of a number of soldiers and her aim
is to destroy the opponent's assets where the opponent is a virtual (i.e.,
non-human player controlled) player that usually consists of a pre-programmed
decision-making script. These scripts have usually associated some well-known
problems (e.g., predictability, non-rationality, repetitive behaviors, and
sensation of artificial stupidity among others). This paper describes a method
for the automatic generation of virtual players that adapt to the player
skills; this is done by building initially a model of the player behavior in
real time during the game, and further evolving the virtual player via this
model in-between two games. The paper also shows preliminary results obtained
on a one player wRTS game constructed specifically for experimentation.
|
1201.3118
|
Shape analysis using fractal dimension: a curvature based approach
|
physics.data-an cs.CV
|
The present work shows a novel fractal dimension method for shape analysis.
The proposed technique extracts descriptors from the shape by applying a
multiscale approach to the calculus of the fractal dimension of that shape. The
fractal dimension is obtained by the application of the curvature scale-space
technique to the original shape. Through the application of a multiscale
transform to the dimension calculus, it is obtained a set of numbers
(descriptors) capable of describing with a high precision the shape in
analysis. The obtained descriptors are validated in a classification process.
The results demonstrate that the novel technique provides descriptors highly
reliable, confirming the precision of the proposed method.
|
1201.3120
|
Ranking hubs and authorities using matrix functions
|
math.NA cs.NA cs.SI physics.soc-ph
|
The notions of subgraph centrality and communicability, based on the
exponential of the adjacency matrix of the underlying graph, have been
effectively used in the analysis of undirected networks. In this paper we
propose an extension of these measures to directed networks, and we apply them
to the problem of ranking hubs and authorities. The extension is achieved by
bipartization, i.e., the directed network is mapped onto a bipartite undirected
network with twice as many nodes in order to obtain a network with a symmetric
adjacency matrix. We explicitly determine the exponential of this adjacency
matrix in terms of the adjacency matrix of the original, directed network, and
we give an interpretation of centrality and communicability in this new
context, leading to a technique for ranking hubs and authorities. The matrix
exponential method for computing hubs and authorities is compared to the well
known HITS algorithm, both on small artificial examples and on more realistic
real-world networks. A few other ranking algorithms are also discussed and
compared with our technique. The use of Gaussian quadrature rules for
calculating hub and authority scores is discussed.
|
1201.3128
|
Maximum Throughput in Multiple-Antenna Systems
|
cs.IT math.IT
|
The point-to-point multiple-antenna channel is investigated in uncorrelated
block fading environment with Rayleigh distribution. The maximum throughput and
maximum expected-rate of this channel are derived under the assumption that the
transmitter is oblivious to the channel state information (CSI), however, the
receiver has perfect CSI. First, we prove that in multiple-input single-output
(MISO) channels, the optimum transmission strategy maximizing the throughput is
to use all available antennas and perform equal power allocation with
uncorrelated signals. Furthermore, to increase the expected-rate, multi-layer
coding is applied. Analogously, we establish that sending uncorrelated signals
and performing equal power allocation across all available antennas at each
layer is optimum. A closed form expression for the maximum continuous-layer
expected-rate of MISO channels is also obtained. Moreover, we investigate
multiple-input multiple-output (MIMO) channels, and formulate the maximum
throughput in the asymptotically low and high SNR regimes and also
asymptotically large number of transmit or receive antennas by obtaining the
optimum transmit covariance matrix. Finally, a distributed antenna system,
wherein two single-antenna transmitters want to transmit a common message to a
single-antenna receiver, is considered. It is shown that this system has the
same outage probability and hence, throughput and expected-rate, as a
point-to-point $2\times 1$ MISO channel.
|
1201.3133
|
Fractal Descriptors in the Fourier Domain Applied to Color Texture
Analysis
|
physics.data-an cs.CV math.DS
|
The present work proposes the development of a novel method to provide
descriptors for colored texture images. The method consists in two steps. In
the first, we apply a linear transform in the color space of the image aiming
at highlighting spatial structuring relations among the color of pixels. In a
second moment, we apply a multiscale approach to the calculus of fractal
dimension based on Fourier transform. From this multiscale operation, we
extract the descriptors used to discriminate the texture represented in digital
images. The accuracy of the method is verified in the classification of two
color texture datasets, by comparing the performance of the proposed technique
to other classical and state-of-the-art methods for color texture analysis. The
results showed an advantage of almost 3% of the proposed technique over the
second best approach.
|
1201.3140
|
Distributed Soft Coding with a Soft Input Soft Output (SISO) Relay
Encoder in Parallel Relay Channels
|
cs.IT math.IT
|
In this paper, we propose a new distributed coding structure with a soft
input soft output (SISO) relay encoder for error-prone parallel relay channels.
We refer to it as the distributed soft coding (DISC). In the proposed scheme,
each relay first uses the received noisy signals to calculate the soft bit
estimate (SBE) of the source symbols. A simple SISO encoder is developed to
encode the SBEs of source symbols based on a constituent code generator matrix.
The SISO encoder outputs at different relays are then forwarded to the
destination and form a distributed codeword. The performance of the proposed
scheme is analyzed. It is shown that its performance is determined by the
generator sequence weight (GSW) of the relay constituent codes, where the GSW
of a constituent code is defined as the number of ones in its generator
sequence. A new coding design criterion for optimally assigning the constituent
codes to all the relays is proposed based on the analysis. Results show that
the proposed DISC can effectively circumvent the error propagation due to the
decoding errors in the conventional detect and forward (DF) with relay
re-encoding and bring considerable coding gains, compared to the conventional
soft information relaying.
|
1201.3153
|
Fractal and Multi-Scale Fractal Dimension analysis: a comparative study
of Bouligand-Minkowski method
|
cs.CV
|
Shape is one of the most important visual attributes to characterize objects,
playing a important role in pattern recognition. There are various approaches
to extract relevant information of a shape. An approach widely used in shape
analysis is the complexity, and Fractal Dimension and Multi-Scale Fractal
Dimension are both well-known methodologies to estimate it. This papers
presents a comparative study between Fractal Dimension and Multi-Scale Fractal
Dimension in a shape analysis context. Through experimental comparison using a
shape database previously classified, both methods are compared. Different
parameters configuration of each method are considered and a discussion about
the results of each method is also presented.
|
1201.3160
|
Polynomial-Time, Semantically-Secure Encryption Achieving the Secrecy
Capacity
|
cs.IT cs.CR math.IT
|
In the wiretap channel setting, one aims to get information-theoretic privacy
of communicated data based only on the assumption that the channel from sender
to receiver is noisier than the one from sender to adversary. The secrecy
capacity is the optimal (highest possible) rate of a secure scheme, and the
existence of schemes achieving it has been shown. For thirty years the ultimate
and unreached goal has been to achieve this optimal rate with a scheme that is
polynomial-time. (This means both encryption and decryption are proven
polynomial time algorithms.) This paper finally delivers such a scheme. In fact
it does more. Our scheme not only meets the classical notion of security from
the wiretap literature, called MIS-R (mutual information security for random
messages) but achieves the strictly stronger notion of semantic security, thus
delivering more in terms of security without loss of rate.
|
1201.3172
|
Assessing the Value of 3D Reconstruction in Building Construction
|
cs.HC cs.CV
|
3-dimensional (3D) reconstruction is an emerging field in image processing
and computer vision that aims to create 3D visualizations/ models of objects/
scenes from image sets. However, its commercial applications and benefits are
yet to be fully explored. In this paper, we describe ongoing work towards
assessing the value of 3D reconstruction in the building construction domain.
We present preliminary results from a user study, where our objective is to
understand the use of visual information in building construction in order to
determine problems with the use of visual information and identify potential
benefits and scenarios for the use of 3D reconstruction.
|
1201.3204
|
Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy
|
cs.AI
|
Large-scale, parallel clusters composed of commodity processors are
increasingly available, enabling the use of vast processing capabilities and
distributed RAM to solve hard search problems. We investigate Hash-Distributed
A* (HDA*), a simple approach to parallel best-first search that asynchronously
distributes and schedules work among processors based on a hash function of the
search state. We use this approach to parallelize the A* algorithm in an
optimal sequential version of the Fast Downward planner, as well as a 24-puzzle
solver. The scaling behavior of HDA* is evaluated experimentally on a shared
memory, multicore machine with 8 cores, a cluster of commodity machines using
up to 64 cores, and large-scale high-performance clusters, using up to 2400
processors. We show that this approach scales well, allowing the effective
utilization of large amounts of distributed memory to optimally solve problems
which require terabytes of RAM. We also compare HDA* to Transposition-table
Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that,
in planning, HDA* significantly outperforms TDS. A simple hybrid which combines
HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated.
|
1201.3210
|
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
|
cs.IT math.IT
|
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.
|
1201.3227
|
When is a set of LMIs a sufficient condition for stability?
|
math.OC cs.SY
|
We study stability criteria for discrete time switching systems. We
investigate the structure of sets of LMIs that are a sufficient condition for
stability (i.e., such that any switching system which satisfies these LMIs is
stable). We provide an exact characterization of these sets. As a corollary, we
show that it is PSPACE-complete to recognize whether a particular set of LMIs
implies the stability of a switching system.
|
1201.3233
|
Variations of images to increase their visibility
|
cs.CV
|
The calculus of variations applied to the image processing requires some
numerical models able to perform the variations of images and the extremization
of appropriate actions. To produce the variations of images, there are several
possibilities based on the brightness maps. Before a numerical model, I propose
an experimental approach, based on a tool of Gimp, GNU Image Manipulation
Program, in order to visualize how the image variations can be. After the
discussion of this tool, which is able to strongly increase the visibility of
images, the variations and a possible functional for the visibility are
proposed in the framework of a numerical model. The visibility functional is
analogous to the fringe visibility of the optical interference.
|
1201.3249
|
A Spiking Neural Learning Classifier System
|
cs.NE cs.LG cs.RO
|
Learning Classifier Systems (LCS) are population-based reinforcement learners
used in a wide variety of applications. This paper presents a LCS where each
traditional rule is represented by a spiking neural network, a type of network
with dynamic internal state. We employ a constructivist model of growth of both
neurons and dendrites that realise flexible learning by evolving structures of
sufficient complexity to solve a well-known problem involving continuous,
real-valued inputs. Additionally, we extend the system to enable temporal state
decomposition. By allowing our LCS to chain together sequences of heterogeneous
actions into macro-actions, it is shown to perform optimally in a problem where
traditional methods can fail to find a solution in a reasonable amount of time.
Our final system is tested on a simulated robotics platform.
|
1201.3278
|
Capacity Region of Multiple Access Channel with States Known Noncausally
at One Encoder and Only Strictly Causally at the Other Encoder
|
cs.IT math.IT
|
We consider a two-user state-dependent multiaccess channel in which the
states of the channel are known non-causally to one of the encoders and only
strictly causally to the other encoder. Both encoders transmit a common message
and, in addition, the encoder that knows the states non-causally transmits an
individual message. We find explicit characterizations of the capacity region
of this communication model in both discrete memoryless (DM) and memoryless
Gaussian cases. In particular the capacity region analysis demonstrates the
utility of the knowledge of the states only strictly causally at the encoder
that sends only the common message in general. More specifically, in the DM
setting we show that such a knowledge is beneficial and increases the capacity
region in general. In the Gaussian setting, we show that such a knowledge does
not help, and the capacity is same as if the states were completely unknown at
the encoder that sends only the common message. The analysis also reveals
optimal ways of exploiting the knowledge of the state only strictly causally at
the encoder that sends only the common message when such a knowledge is
beneficial. The encoders collaborate to convey to the decoder a lossy version
of the state, in addition to transmitting the information messages through a
generalized Gel'fand-Pinsker binning. Particularly important in this problem
are the questions of 1) optimal ways of performing the state compression and 2)
whether or not the compression indices should be decoded uniquely. We show that
both compression \`a-la noisy network coding, i.e., with no binning and
non-unique decoding, and compression using Wyner-Ziv binning with backward
decoding and non-unique or unique decoding are optimal.
|
1201.3292
|
Entropy of dynamical social networks
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Human dynamical social networks encode information and are highly adaptive.
To characterize the information encoded in the fast dynamics of social
interactions, here we introduce the entropy of dynamical social networks. By
analysing a large dataset of phone-call interactions we show evidence that the
dynamical social network has an entropy that depends on the time of the day in
a typical week-day. Moreover we show evidence for adaptability of human social
behavior showing data on duration of phone-call interactions that significantly
deviates from the statistics of duration of face-to-face interactions. This
adaptability of behavior corresponds to a different information content of the
dynamics of social human interactions. We quantify this information by the use
of the entropy of dynamical networks on realistic models of social
interactions.
|
1201.3307
|
Multi-scale Community Detection using Stability Optimisation within
Greedy Algorithms
|
cs.DS cs.SI physics.soc-ph
|
Many real systems can be represented as networks whose analysis can be very
informative regarding the original system's organisation. In the past decade
community detection received a lot of attention and is now an active field of
research. Recently stability was introduced as a new measure for partition
quality. This work investigates stability as an optimisation criterion that
exploits a Markov process view of networks to enable multi-scale community
detection. Several heuristics and variations of an algorithm optimising
stability are presented as well as an application to overlapping communities.
Experiments show that the method enables accurate multi-scale network analysis.
|
1201.3315
|
Perfect Mannheim, Lipschitz and Hurwitz weight codes
|
cs.IT math.IT
|
In this paper, upper bounds on codes over Gaussian integers, Lipschitz
integers and Hurwitz integers with respect to Mannheim metric, Lipschitz and
Hurwitz metric are given.
|
1201.3316
|
Codes over Hurwitz integers
|
cs.IT math.IT
|
In this study, we obtain new classes of linear codes over Hurwitz integers
equipped with a new metric. We refer to the metric as Hurwitz metric. The codes
with respect to Hurwitz metric use in coded modu- lation schemes based on
quadrature amplitude modulation (QAM)-type constellations, for which neither
Hamming metric nor Lee metric. Also, we define decoding algorithms for these
codes when up to two coordinates of a transmitted code vector are effected by
error of arbitrary Hurwitz weight.
|
1201.3328
|
Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With
Imperfect Monitoring
|
cs.IT math.IT
|
We develop a novel design framework for dynamic distributed spectrum sharing
among secondary users (SUs) who adjust their power levels to compete for
spectrum opportunities while satisfying the interference temperature (IT)
constraints imposed by primary users. The considered interaction among the SUs
is characterized by the following three features. First, since the SUs are
decentralized, they are selfish and aim to maximize their own long-term payoffs
from utilizing the network rather than obeying the prescribed allocation of a
centralized controller. Second, the SUs interact with each other repeatedly and
they can coexist in the system for a long time. Third, the SUs have limited and
imperfect monitoring ability: they only observe whether the IT constraints are
violated, and their observation is imperfect due to the erroneous measurements.
To capture these features, we model the interaction of the SUs as a repeated
game with imperfect monitoring. We first characterize the set of Pareto optimal
payoffs that can be achieved by deviation-proof spectrum sharing policies,
which are policies that the selfish users find it in their interest to comply
with. Next, for any given payoff in this set, we show how to construct a
deviation-proof policy to achieve it. The constructed deviation-proof policy is
amenable to distributed implementation, and allows users to transmit in a
time-division multiple-access (TDMA) fashion. In the presence of strong
multi-user interference, our policy outperforms existing spectrum sharing
policies that dictate users to transmit at constant power levels
simultaneously. Moreover, our policy can achieve Pareto optimality even when
the SUs have limited and imperfect monitoring ability, as opposed to existing
solutions based on repeated games, which require perfect monitoring abilities.
|
1201.3337
|
A New Color Feature Extraction Method Based on Dynamic Color
Distribution Entropy of Neighborhoods
|
cs.CV cs.MM
|
One of the important requirements in image retrieval, indexing,
classification, clustering and etc. is extracting efficient features from
images. The color feature is one of the most widely used visual features. Use
of color histogram is the most common way for representing color feature. One
of disadvantage of the color histogram is that it does not take the color
spatial distribution into consideration. In this paper dynamic color
distribution entropy of neighborhoods method based on color distribution
entropy is presented, which effectively describes the spatial information of
colors. The image retrieval results in compare to improved color distribution
entropy show the acceptable efficiency of this approach.
|
1201.3382
|
Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery
|
stat.ML cs.LG
|
We consider the problem of using a factor model we call {\em spike-and-slab
sparse coding} (S3C) to learn features for a classification task. The S3C model
resembles both the spike-and-slab RBM and sparse coding. Since exact inference
in this model is intractable, we derive a structured variational inference
procedure and employ a variational EM training algorithm. Prior work on
approximate inference for this model has not prioritized the ability to exploit
parallel architectures and scale to enormous problem sizes. We present an
inference procedure appropriate for use with GPUs which allows us to
dramatically increase both the training set size and the amount of latent
factors.
We demonstrate that this approach improves upon the supervised learning
capabilities of both sparse coding and the ssRBM on the CIFAR-10 dataset. We
evaluate our approach's potential for semi-supervised learning on subsets of
CIFAR-10. We demonstrate state-of-the art self-taught learning performance on
the STL-10 dataset and use our method to win the NIPS 2011 Workshop on
Challenges In Learning Hierarchical Models' Transfer Learning Challenge.
|
1201.3408
|
The computation of first order moments on junction trees
|
cs.AI
|
We review some existing methods for the computation of first order moments on
junction trees using Shafer-Shenoy algorithm. First, we consider the problem of
first order moments computation as vertices problem in junction trees. In this
way, the problem is solved using the memory space of an order of the junction
tree edge-set cardinality. After that, we consider two algorithms,
Lauritzen-Nilsson algorithm, and Mau\'a et al. algorithm, which computes the
first order moments as the normalization problem in junction tree, using the
memory space of an order of the junction tree leaf-set cardinality.
|
1201.3410
|
Multiscale Fractal Descriptors Applied to Nanoscale Images
|
physics.data-an cond-mat.mes-hall cond-mat.mtrl-sci cs.CV
|
This work proposes the application of fractal descriptors to the analysis of
nanoscale materials under different experimental conditions. We obtain
descriptors for images from the sample applying a multiscale transform to the
calculation of fractal dimension of a surface map of such image. Particularly,
we have used the}Bouligand-Minkowski fractal dimension. We applied these
descriptors to discriminate between two titanium oxide films prepared under
different experimental conditions. Results demonstrate the discrimination power
of proposed descriptors in such kind of application.
|
1201.3417
|
Mining Educational Data to Analyze Students' Performance
|
cs.IR
|
The main objective of higher education institutions is to provide quality
education to its students. One way to achieve highest level of quality in
higher education system is by discovering knowledge for prediction regarding
enrolment of students in a particular course, alienation of traditional
classroom teaching model, detection of unfair means used in online examination,
detection of abnormal values in the result sheets of the students, prediction
about students' performance and so on. The knowledge is hidden among the
educational data set and it is extractable through data mining techniques.
Present paper is designed to justify the capabilities of data mining techniques
in context of higher education by offering a data mining model for higher
education system in the university. In this research, the classification task
is used to evaluate student's performance and as there are many approaches that
are used for data classification, the decision tree method is used here. By
this task we extract knowledge that describes students' performance in end
semester examination. It helps earlier in identifying the dropouts and students
who need special attention and allow the teacher to provide appropriate
advising/counseling. Keywords-Educational Data Mining (EDM); Classification;
Knowledge Discovery in Database (KDD); ID3 Algorithm.
|
1201.3418
|
Data Mining: A prediction for performance improvement using
classification
|
cs.IR
|
Now-a-days the amount of data stored in educational database increasing
rapidly. These databases contain hidden information for improvement of
students' performance. The performance in higher education in India is a
turning point in the academics for all students. This academic performance is
influenced by many factors, therefore it is essential to develop predictive
data mining model for students' performance so as to identify the difference
between high learners and slow learners student. In the present investigation,
an experimental methodology was adopted to generate a database. The raw data
was preprocessed in terms of filling up missing values, transforming values in
one form into another and relevant attribute/ variable selection. As a result,
we had 300 student records, which were used for by Byes classification
prediction model construction. Keywords- Data Mining, Educational Data Mining,
Predictive Model, Classification.
|
1201.3458
|
Detecting Priming News Events
|
cs.DB
|
We study a problem of detecting priming events based on a time series index
and an evolving document stream. We define a priming event as an event which
triggers abnormal movements of the time series index, i.e., the Iraq war with
respect to the president approval index of President Bush. Existing solutions
either focus on organizing coherent keywords from a document stream into events
or identifying correlated movements between keyword frequency trajectories and
the time series index. In this paper, we tackle the problem in two major steps.
(1) We identify the elements that form a priming event. The element identified
is called influential topic which consists of a set of coherent keywords. And
we extract them by looking at the correlation between keyword trajectories and
the interested time series index at a global level. (2) We extract priming
events by detecting and organizing the bursty influential topics at a micro
level. We evaluate our algorithms on a real-world dataset and the result
confirms that our method is able to discover the priming events effectively.
|
1201.3466
|
Detecting community structure in networks using edge prediction methods
|
physics.soc-ph cs.SI
|
Community detection and edge prediction are both forms of link mining: they
are concerned with discovering the relations between vertices in networks. Some
of the vertex similarity measures used in edge prediction are closely related
to the concept of community structure. We use this insight to propose a novel
method for improving existing community detection algorithms by using a simple
vertex similarity measure. We show that this new strategy can be more effective
in detecting communities than the basic community detection algorithms.
|
1201.3479
|
Simple Numerical Model of Laminated Glass Beams
|
cs.CE
|
This contribution presents a simple Finite Element model aimed at efficient
simulation of layered glass units. The adopted approach is based on considering
independent kinematics of each layer, tied together via Lagrange multipliers.
Validation and verification of the resulting model against independent data
demonstrate its accuracy, showing its potential for generalization towards more
complex problems.
|
1201.3519
|
Nanoscale ear drum: Graphene based nanoscale sensors
|
cond-mat.mtrl-sci cs.CE physics.chem-ph
|
The difficulty in determining the mass of a sample increases as its size
diminishes. At the nanoscale, there are no direct methods for resolving the
mass of single molecules or nanoparticles and so more sophisticated approaches
based on electromechanical phenomena are required. More importantly, one
demands that such nanoelectromechanical techniques could provide not only
information about the mass of the target molecules but also about their
geometrical properties. In this sense, we report a theoretical study that
illustrates in detail how graphene membranes can operate as
nanoelectromechanical mass-sensor devices. Wide graphene sheets were exposed to
different types and amounts of molecules and molecular dynamic simulations were
employed to treat these doping processes statistically. We demonstrate that the
mass variation effect and information about the graphene-molecule interactions
can be inferred through dynamical response functions. Our results confirm the
potential use of graphene as mass detector devices with remarkable precision in
estimating variations in mass at molecular scale and other physical properties
of the dopants.
|
1201.3545
|
On Natural Genetic Engineering: Structural Dynamism in Random Boolean
Networks
|
cs.CE q-bio.MN
|
This short paper presents an abstract, tunable model of genomic structural
change within the cell lifecycle and explores its use with simulated evolution.
A well-known Boolean model of genetic regulatory networks is extended to
include changes in node connectivity based upon the current cell state, e.g.,
via transposable elements. The underlying behaviour of the resulting dynamical
networks is investigated before their evolvability is explored using a version
of the NK model of fitness landscapes. Structural dynamism is found to be
selected for in non-stationary environments and subsequently shown capable of
providing a mechanism for evolutionary innovation when such reorganizations are
inherited.
|
1201.3584
|
Ecological analysis of world trade
|
q-fin.GN cond-mat.stat-mech cs.SI physics.soc-ph
|
Ecological systems have a high level of complexity combined with stability
and rich biodiversity. Recently, the analysis of their properties and evolution
has been pushed forward on a basis of concept of mutualistic networks that
provides a detailed understanding of their features being linked to a high
nestedness of these networks. It was shown that the nestedness architecture of
mutualistic networks of plants and their pollinators minimizes competition and
increases biodiversity. Here, using the United Nations COMTRADE database for
years 1962 - 2009, we show that a similar ecological analysis gives a valuable
description of the world trade. In fact the countries and trade products are
analogous to plants and pollinators, and the whole trade network is
characterized by a low nestedness temperature which is typical for the
ecological networks. This approach provides new mutualistic features of the
world trade highlighting new significance of countries and trade products for
the world trade.
|
1201.3592
|
Characterizing Interdisciplinarity of Researchers and Research Topics
Using Web Search Engines
|
cs.SI cs.DL physics.soc-ph
|
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.
|
1201.3599
|
Covariance Eigenvector Sparsity for Compression and Denoising
|
stat.AP cs.IT math.IT
|
Sparsity in the eigenvectors of signal covariance matrices is exploited in
this paper for compression and denoising. Dimensionality reduction (DR) and
quantization modules present in many practical compression schemes such as
transform codecs, are designed to capitalize on this form of sparsity and
achieve improved reconstruction performance compared to existing
sparsity-agnostic codecs. Using training data that may be noisy a novel
sparsity-aware linear DR scheme is developed to fully exploit sparsity in the
covariance eigenvectors and form noise-resilient estimates of the principal
covariance eigenbasis. Sparsity is effected via norm-one regularization, and
the associated minimization problems are solved using computationally efficient
coordinate descent iterations. The resulting eigenspace estimator is shown
capable of identifying a subset of the unknown support of the eigenspace basis
vectors even when the observation noise covariance matrix is unknown, as long
as the noise power is sufficiently low. It is proved that the sparsity-aware
estimator is asymptotically normal, and the probability to correctly identify
the signal subspace basis support approaches one, as the number of training
data grows large. Simulations using synthetic data and images, corroborate that
the proposed algorithms achieve improved reconstruction quality relative to
alternatives.
|
1201.3612
|
Spatiotemporal Gabor filters: a new method for dynamic texture
recognition
|
cs.CV
|
This paper presents a new method for dynamic texture recognition based on
spatiotemporal Gabor filters. Dynamic textures have emerged as a new field of
investigation that extends the concept of self-similarity of texture image to
the spatiotemporal domain. To model a dynamic texture, we convolve the sequence
of images to a bank of spatiotemporal Gabor filters. For each response, a
feature vector is built by calculating the energy statistic. As far as the
authors know, this paper is the first to report an effective method for dynamic
texture recognition using spatiotemporal Gabor filters. We evaluate the
proposed method on two challenging databases and the experimental results
indicate that the proposed method is a robust approach for dynamic texture
recognition.
|
1201.3667
|
A Logic of Interactive Proofs (Formal Theory of Knowledge Transfer)
|
cs.LO cs.CR cs.DC cs.MA math.LO
|
We propose a logic of interactive proofs as a framework for an intuitionistic
foundation for interactive computation, which we construct via an interactive
analog of the Goedel-McKinsey-Tarski-Artemov definition of Intuitionistic Logic
as embedded into a classical modal logic of proofs, and of the Curry-Howard
isomorphism between intuitionistic proofs and typed programs. Our interactive
proofs effectuate a persistent epistemic impact in their intended communities
of peer reviewers that consists in the induction of the (propositional)
knowledge of their proof goal by means of the (individual) knowledge of the
proof with the interpreting reviewer. That is, interactive proofs effectuate a
transfer of propositional knowledge (knowable facts) via the transmission of
certain individual knowledge (knowable proofs) in multi-agent distributed
systems. In other words, we as a community can have the formal common knowledge
that a proof is that which if known to one of our peer members would induce the
knowledge of its proof goal with that member. Last but not least, we prove
non-trivial interactive computation as definable within our simply typed
interactive Combinatory Logic to be nonetheless equipotent to non-interactive
computation as defined by simply typed Combinatory Logic.
|
1201.3674
|
On the Lagrangian Biduality of Sparsity Minimization Problems
|
cs.CV cs.LG stat.ML
|
Recent results in Compressive Sensing have shown that, under certain
conditions, the solution to an underdetermined system of linear equations with
sparsity-based regularization can be accurately recovered by solving convex
relaxations of the original problem. In this work, we present a novel
primal-dual analysis on a class of sparsity minimization problems. We show that
the Lagrangian bidual (i.e., the Lagrangian dual of the Lagrangian dual) of the
sparsity minimization problems can be used to derive interesting convex
relaxations: the bidual of the $\ell_0$-minimization problem is the
$\ell_1$-minimization problem; and the bidual of the $\ell_{0,1}$-minimization
problem for enforcing group sparsity on structured data is the
$\ell_{1,\infty}$-minimization problem. The analysis provides a means to
compute per-instance non-trivial lower bounds on the (group) sparsity of the
desired solutions. In a real-world application, the bidual relaxation improves
the performance of a sparsity-based classification framework applied to robust
face recognition.
|
1201.3688
|
A Classification of Unimodular Lattice Wiretap Codes in Small Dimensions
|
math.NT cs.IT math.IT
|
Lattice coding over a Gaussian wiretap channel, where an eavesdropper listens
to transmissions between a transmitter and a legitimate receiver, is
considered. A new lattice invariant called the secrecy gain is used as a code
design criterion for wiretap lattice codes since it was shown to characterize
the confusion that a chosen lattice can cause at the eavesdropper: the higher
the secrecy gain of the lattice, the more confusion. In this paper, a formula
for the secrecy gain of unimodular lattices is derived. Secrecy gains of
extremal odd unimodular lattices as well as unimodular lattices in dimension n,
16 \leq n \leq 23 are computed, covering the 4 extremal odd unimodular lattices
and all the 111 nonextremal unimodular lattices (both odd and even) providing
thus a classification of the best wiretap lattice codes coming from unimodular
lattices in dimension n, 8 < n \leq 23. Finally, to permit lattice encoding via
Construction A, the corresponding error correction codes are determined.
|
1201.3697
|
Energy Efficient Iterative Waterfilling for the MIMO Broadcasting
Channels
|
cs.IT math.IT
|
Optimizing energy efficiency (EE) for the MIMO broadcasting channels (BC) is
considered in this paper, where a practical power model is taken into account.
Although the EE of the MIMO BC is non-concave, we reformulate it as a
quasiconcave function based on the uplink-downlink duality. After that, an
energy efficient iterative waterfilling scheme is proposed based on the
block-coordinate ascent algorithm to obtain the optimal transmission policy
efficiently, and the solution is proved to be convergent. Through simulations,
we validate the efficiency of the proposed scheme and discuss the system
parameters' effect on the EE.
|
1201.3698
|
Energy Efficiency Scaling Law for MIMO Broadcasting Channels
|
cs.IT math.IT
|
This letter investigates the energy efficiency (EE) scaling law for the
broadcasting channels (BC) with many users, in which the non-ideal transmit
independent power consumption is taken into account. We first consider the
single antenna case with $K$ users, and derive that the EE scales as
$\frac{{\log_2 \ln K}}{\alpha}$ when $\alpha > 0$ and $\log_2 K$ when $\alpha =
0$, where $\alpha$ is the normalized transmit independent power. After that, we
extend it to the general MIMO BC case with a $M$-antenna transmitter and $K$
users each with $N$ antennas. The scaling law becomes $\frac{{M \log_2 \ln
NK}}{\alpha}$ when $\alpha > 0$ and $ \log_2 NK$ when $\alpha = 0$.
|
1201.3720
|
A Multimodal Biometric System Using Linear Discriminant Analysis For
Improved Performance
|
cs.CV
|
Essentially a biometric system is a pattern recognition system which
recognizes a user by determining the authenticity of a specific anatomical or
behavioral characteristic possessed by the user. With the ever increasing
integration of computers and Internet into daily life style, it has become
necessary to protect sensitive and personal data. This paper proposes a
multimodal biometric system which incorporates more than one biometric trait to
attain higher security and to handle failure to enroll situations for some
users. This paper is aimed at investigating a multimodal biometric identity
system using Linear Discriminant Analysis as backbone to both facial and speech
recognition and implementing such system in real-time using SignalWAVE.
|
1201.3723
|
Proportional Fair Coding for Wireless Mesh Networks
|
cs.NI cs.IT math.IT
|
We consider multi--hop wireless networks carrying unicast flows for multiple
users. Each flow has a specified delay deadline, and the lossy wireless links
are modelled as binary symmetric channels (BSCs). Since transmission time, also
called airtime, on the links is shared amongst flows, increasing the airtime
for one flow comes at the cost of reducing the airtime available to other flows
sharing the same link. We derive the joint allocation of flow airtimes and
coding rates that achieves the proportionally fair throughput allocation. This
utility optimisation problem is non--convex, and one of the technical
contributions of this paper is to show that the proportional fair utility
optimisation can nevertheless be decomposed into a sequence of convex
optimisation problems. The solution to this sequence of convex problems is the
unique solution to the original non--convex optimisation. Surprisingly, this
solution can be written in an explicit form that yields considerable insight
into the nature of the proportional fair joint airtime/coding rate allocation.
To our knowledge, this is the first time that the utility fair joint allocation
of airtime/coding rate has been analysed, and also, one of the first times that
utility fairness with delay deadlines has been considered.
|
1201.3740
|
Contractive Interference Functions and Rates of Convergence of
Distributed Power Control Laws
|
cs.IT cs.SY math.IT
|
The standard interference functions introduced by Yates have been very
influential on the analysis and design of distributed power control laws. While
powerful and versatile, the framework has some drawbacks: the existence of
fixed-points has to be established separately, and no guarantees are given on
the rate of convergence of the iterates. This paper introduces contractive
interference functions, a slight reformulation of the standard interference
functions that guarantees the existence and uniqueness of fixed-points along
with linear convergence of iterates. We show that many power control laws from
the literature are contractive and derive, sometimes for the first time,
analytical convergence rate estimates for these algorithms. We also prove that
contractive interference functions converge when executed totally
asynchronously and, under the assumption that the communication delay is
bounded, derive an explicit bound on the convergence time penalty due to
increased delay. Finally, we demonstrate that although standard interference
functions are, in general, not contractive, they are all para-contractions with
respect to a certain metric. Similar results for two-sided scalable
interference functions are also derived.
|
1201.3745
|
Social Networks Research Aspects: A Vast and Fast Survey Focused on the
Issue of Privacy in Social Network Sites
|
cs.SI
|
The increasing participation of people in online activities in recent years
like content publishing, and having different kinds of relationships and
interactions, along with the emergence of online social networks and people's
extensive tendency toward them, have resulted in generation and availability of
a huge amount of valuable information that has never been available before, and
have introduced some new, attractive, varied, and useful research areas to
researchers. In this paper we try to review some of the accomplished research
on information of SNSs (Social Network Sites), and introduce some of the
attractive applications that analyzing this information has. This will lead to
the introduction of some new research areas to researchers. By reviewing the
research in this area we will present a categorization of research topics about
online social networks. This categorization includes seventeen research
subtopics or subareas that will be introduced along with some of the
accomplished research in these subareas. According to the consequences (slight,
significant, and sometimes catastrophic) that revelation of personal and
private information has, a research area that researchers have vastly
investigated is privacy in online social networks. After an overview on
different research subareas of SNSs, we will get more focused on the subarea of
privacy protection in social networks, and introduce different aspects of it
along with a categorization of these aspects.
|
1201.3771
|
A Network Perspective on Software Modularity
|
cs.SE cs.SI nlin.AO physics.soc-ph
|
Modularity is a desirable characteristic for software systems. In this
article we propose to use a quantitative method from complex network sciences
to estimate the coherence between the modularity of the dependency network of
large open source Java projects and their decomposition in terms of Java
packages. The results presented in this article indicate that our methodology
offers a promising and reasonable quantitative approach with potential impact
on software engineering processes.
|
1201.3783
|
Network Analysis of Recurring YouTube Spam Campaigns
|
cs.SI physics.soc-ph
|
As the popularity of content sharing websites such as YouTube and Flickr has
increased, they have become targets for spam, phishing and the distribution of
malware. On YouTube, the facility for users to post comments can be used by
spam campaigns to direct unsuspecting users to bogus e-commerce websites. In
this paper, we demonstrate how such campaigns can be tracked over time using
network motif profiling, i.e. by tracking counts of indicative network motifs.
By considering all motifs of up to five nodes, we identify discriminating
motifs that reveal two distinctly different spam campaign strategies. One of
these strategies uses a small number of spam user accounts to comment on a
large number of videos, whereas a larger number of accounts is used with the
other. We present an evaluation that uses motif profiling to track two active
campaigns matching these strategies, and identify some of the associated user
accounts.
|
1201.3803
|
Image Labeling and Segmentation using Hierarchical Conditional Random
Field Model
|
cs.CV
|
The use of hierarchical Conditional Random Field model deal with the problem
of labeling images . At the time of labeling a new image, selection of the
nearest cluster and using the related CRF model to label this image. When one
give input image, one first use the CRF model to get initial pixel labels then
finding the cluster with most similar images. Then at last relabeling the input
image by the CRF model associated with this cluster. This paper presents a
approach to label and segment specific image having correct information.
|
1201.3821
|
A PCA-Based Super-Resolution Algorithm for Short Image Sequences
|
cs.CV
|
In this paper, we present a novel, learning-based, two-step super-resolution
(SR) algorithm well suited to solve the specially demanding problem of
obtaining SR estimates from short image sequences. The first step, devoted to
increase the sampling rate of the incoming images, is performed by fitting
linear combinations of functions generated from principal components (PC) to
reproduce locally the sparse projected image data, and using these models to
estimate image values at nodes of the high-resolution grid. PCs were obtained
from local image patches sampled at sub-pixel level, which were generated in
turn from a database of high-resolution images by application of a physically
realistic observation model. Continuity between local image models is enforced
by minimizing an adequate functional in the space of model coefficients. The
second step, dealing with restoration, is performed by a linear filter with
coefficients learned to restore residual interpolation artifacts in addition to
low-resolution blurring, providing an effective coupling between both steps of
the method. Results on a demanding five-image scanned sequence of graphics and
text are presented, showing the excellent performance of the proposed method
compared to several state-of-the-art two-step and Bayesian Maximum a Posteriori
SR algorithms.
|
1201.3825
|
A Complete Characterization of Irreducible Cyclic Orbit Codes and their
Pl\"ucker Embedding
|
cs.IT math.IT
|
Constant dimension codes are subsets of the finite Grassmann variety. The
study of these codes is a central topic in random linear network coding theory.
Orbit codes represent a subclass of constant dimension codes. They are defined
as orbits of a subgroup of the general linear group on the Grassmannian. This
paper gives a complete characterization of orbit codes that are generated by an
irreducible cyclic group, i.e. a group having one generator that has no
non-trivial invariant subspace. We show how some of the basic properties of
these codes, the cardinality and the minimum distance, can be derived using the
isomorphism of the vector space and the extension field. Furthermore, we
investigate the Pl\"ucker embedding of these codes and show how the orbit
structure is preserved in the embedding.
|
1201.3835
|
A Dynamic Model for Sharing Reputation of Sellers among Buyers for
Enhancing Trust in Agent Mediated e-market
|
cs.SI cs.HC cs.MA
|
Reputation systems aim to reduce the risk of loss due to untrustworthy
participants. This loss is aggravated by dishonest advisors trying to pollute
the e-market environment for their self-interest. A major task of a reputation
system is to promote and encourage advisors who repeatedly respond with fair
advice and to apply an opinion filtering or honesty checking mechanism to
detect and resist dishonest advisors. This paper provides a dynamic approach to
compute the aggregated shared reputation component by filtering out unfair
advice and then generating the aggregated shared reputation value. The proposed
approach is dynamic in nature as it is sensitive to the behaviour of advisors,
value of the current transaction and encourages the cooperation among buyers as
advisors. It provides incentive to honest advisors in lieu of repeated sharing
of honest opinion by increasing the weight of their opinion and by making the
increase in the reputation of honest advisors monotonically proportional to the
value of a transaction.
|
1201.3851
|
Combinatorial Modelling and Learning with Prediction Markets
|
cs.AI cs.GT q-fin.TR
|
Combining models in appropriate ways to achieve high performance is commonly
seen in machine learning fields today. Although a large amount of combinatorial
models have been created, little attention is drawn to the commons in different
models and their connections. A general modelling technique is thus worth
studying to understand model combination deeply and shed light on creating new
models. Prediction markets show a promise of becoming such a generic, flexible
combinatorial model. By reviewing on several popular combinatorial models and
prediction market models, this paper aims to show how the market models can
generalise different combinatorial stuctures and how they implement these
popular combinatorial models in specific conditions. Besides, we will see among
different market models, Storkey's \emph{Machine Learning Markets} provide more
fundamental, generic modelling mechanisms than the others, and it has a
significant appeal for both theoretical study and application.
|
1201.3868
|
A Dichotomy for 2-Constraint Forbidden CSP Patterns
|
cs.AI cs.CC
|
Although the CSP (constraint satisfaction problem) is NP-complete, even in
the case when all constraints are binary, certain classes of instances are
tractable. We study classes of instances defined by excluding subproblems. This
approach has recently led to the discovery of novel tractable classes. The
complete characterisation of all tractable classes defined by forbidding
patterns (where a pattern is simply a compact representation of a set of
subproblems) is a challenging problem. We demonstrate a dichotomy in the case
of forbidden patterns consisting of either one or two constraints. This has
allowed us to discover new tractable classes including, for example, a novel
generalisation of 2SAT.
|
1201.3880
|
Modelling and simulation of complex systems: an approach based on
multi-level agents
|
cs.MA cs.AI cs.HC
|
A complex system is made up of many components with many interactions. So the
design of systems such as simulation systems, cooperative systems or assistance
systems includes a very accurate modelling of interactional and communicational
levels. The agent-based approach provides an adapted abstraction level for this
problem. After having studied the organizational context and communicative
capacities of agentbased systems, to simulate the reorganization of a flexible
manufacturing, to regulate an urban transport system, and to simulate an
epidemic detection system, our thoughts on the interactional level were
inspired by human-machine interface models, especially those in "cognitive
engineering". To provide a general framework for agent-based complex systems
modelling, we then proposed a scale of four behaviours that agents may adopt in
their complex systems (reactive, routine, cognitive, and collective). To
complete the description of multi-level agent models, which is the focus of
this paper, we illustrate our modelling and discuss our ongoing work on each
level.
|
1201.3881
|
Agent-Based {\mu}-Tools Integrated into a Co-Design Platform
|
cs.HC cs.DC cs.MA
|
In this paper we present successively the proposition and the design of: 1)
{\mu}-tools adapted to collaborative activity of design, and 2) a multi-agent
platform adapted to innovative and distributed design of products or services.
This platform called PLACID (innovating and distributed design platform) must
support applications of assistance to actors implies in a design process that
we have called {\mu}-tools. {\mu}-tools are developed with an aim of bringing
assistance to Co-design. The use of the paradigm agent as well relates to the
modeling and the development of various layers of the platform, that those of
the human-computer interfaces. With these objectives, constraints are added to
facilitate the integration of new co-operative tools.
|
1201.3883
|
Dynamic Shared Context Processing in an E-Collaborative Learning
Environment
|
cs.HC cs.AI cs.CY
|
In this paper, we propose a dynamic shared context processing method based on
DSC (Dynamic Shared Context) model, applied in an e-collaborative learning
environment. Firstly, we present the model. This is a way to measure the
relevance between events and roles in collaborative environments. With this
method, we can share the most appropriate event information for each role
instead of sharing all information to all roles in a collaborative work
environment. Then, we apply and verify this method in our project with Google
App supported e-learning collaborative environment. During this experiment, we
compared DSC method measured relevance of events and roles to manual measured
relevance. And we describe the favorable points from this comparison and our
finding. Finally, we discuss our future research of a hybrid DSC method to make
dynamical information shared more effective in a collaborative work
environment.
|
1201.3900
|
Elasticity on Ontology Matching of Folksodriven Structure Network
|
cs.DL cs.IR
|
Nowadays folksonomy tags are used not just for personal organization, but for
communication and sharing between people sharing their own local interests. In
this paper is considered the new concept structure called "Folksodriven" to
represent folksonomies. The Folksodriven Structure Network (FSN) was thought as
folksonomy tags suggestions for the user on a dataset built on chosen websites
- based on Natural Language Processing (NLP). Morphological changes, such as
changes in folksonomy tags chose have direct impact on network connectivity
(structural plasticity) of the folksonomy tags considered. The goal of this
paper is on defining a base for a FSN plasticity theory to analyze. To perform
such goal it is necessary a systematic mathematical analysis on deformation and
fracture for the ontology matching on the FSN. The advantages of that approach
could be used on a new interesting method to be employed by a knowledge
management system.
|
1201.3901
|
On the Dispersions of Three Network Information Theory Problems
|
cs.IT math.IT
|
We analyze the dispersions of distributed lossless source coding (the
Slepian-Wolf problem), the multiple-access channel and the asymmetric broadcast
channel. For the two-encoder Slepian-Wolf problem, we introduce a quantity
known as the entropy dispersion matrix, which is analogous to the scalar
dispersions that have gained interest recently. We prove a global dispersion
result that can be expressed in terms of this entropy dispersion matrix and
provides intuition on the approximate rate losses at a given blocklength and
error probability. To gain better intuition about the rate at which the
non-asymptotic rate region converges to the Slepian-Wolf boundary, we define
and characterize two operational dispersions: the local dispersion and the
weighted sum-rate dispersion. The former represents the rate of convergence to
a point on the Slepian-Wolf boundary while the latter represents the fastest
rate for which a weighted sum of the two rates converges to its asymptotic
fundamental limit. Interestingly, when we approach either of the two corner
points, the local dispersion is characterized not by a univariate Gaussian but
a bivariate one as well as a subset of off-diagonal elements of the
aforementioned entropy dispersion matrix. Finally, we demonstrate the
versatility of our achievability proof technique by providing inner bounds for
the multiple-access channel and the asymmetric broadcast channel in terms of
dispersion matrices. All our proofs are unified a so-called vector rate
redundancy theorem which is proved using the multidimensional Berry-Esseen
theorem.
|
1201.3915
|
On Detection-Directed Estimation Approach for Noisy Compressive Sensing
|
cs.IT math.IT
|
In this paper, we investigate a Bayesian sparse reconstruction algorithm
called compressive sensing via Bayesian support detection (CS-BSD). This
algorithm is quite robust against measurement noise and achieves the
performance of a minimum mean square error (MMSE) estimator that has support
knowledge beyond a certain SNR threshold. The key idea behind CS-BSD is that
reconstruction takes a detection-directed estimation structure consisting of
two parts: support detection and signal value estimation. Belief propagation
(BP) and a Bayesian hypothesis test perform support detection, and an MMSE
estimator finds the signal values belonging to the support set. CS-BSD
converges faster than other BP-based algorithms, and it can be converted to a
parallel architecture to become much faster. Numerical results are provided to
verify the superiority of CS-BSD compared to recent algorithms.
|
1201.3972
|
A Novel Approach to Fast Image Filtering Algorithm of Infrared Images
based on Intro Sort Algorithm
|
cs.CV
|
In this study we investigate the fast image filtering algorithm based on
Intro sort algorithm and fast noise reduction of infrared images. Main feature
of the proposed approach is that no prior knowledge of noise required. It is
developed based on Stefan- Boltzmann law and the Fourier law. We also
investigate the fast noise reduction approach that has advantage of less
computation load. In addition, it can retain edges, details, text information
even if the size of the window increases. Intro sort algorithm begins with
Quick sort and switches to heap sort when the recursion depth exceeds a level
based on the number of elements being sorted. This approach has the advantage
of fast noise reduction by reducing the comparison time. It also significantly
speed up the noise reduction process and can apply to real-time image
processing. This approach will extend the Infrared images applications for
medicine and video conferencing.
|
1201.3979
|
The one-way unlocalizable quantum discord
|
quant-ph cs.IT math.IT
|
In this paper, we present the concept of the one-way unlocalizable quantum
discord and investigate its properties. We provide a polygamy inequality for it
in tripartite pure quantum system of arbitrary dimension. Several tradeoff
relations between the one-way unlocalizable quantum discord and other
correlations are given. If the von Neumann measurement is on a part of the
system, we give two expressions of the one-way unlocalizable quantum discord in
terms of partial distillable entanglement and quantum disturbance. Finally, we
also provide a lower bound for bipartite shareability of quantum correlation
beyond entanglement in a tripartite system.
|
1201.3982
|
Min-Sum algorithm for lattices constructed by Construction D
|
cs.CR cs.IT math.CO math.IT
|
The so-called min-sum algorithm has been applied for decoding lattices
constructed by Construction D'. We generalize this iterative decoding algorithm
to decode lattices constructed by Construction D. An upper bound on the
decoding complexity per iteration, in terms of coding gain, label group sizes
of the lattice and other factors is derived. We show that iterative decoding of
LDGM lattices has a reasonably low complexity such that lattices with
dimensions of a few thousands can be easily decoded.
|
1201.4002
|
Adaptive Policies for Sequential Sampling under Incomplete Information
and a Cost Constraint
|
stat.ML cs.LG math.OC
|
We consider the problem of sequential sampling from a finite number of
independent statistical populations to maximize the expected infinite horizon
average outcome per period, under a constraint that the expected average
sampling cost does not exceed an upper bound. The outcome distributions are not
known. We construct a class of consistent adaptive policies, under which the
average outcome converges with probability 1 to the true value under complete
information for all distributions with finite means. We also compare the rate
of convergence for various policies in this class using simulation.
|
1201.4013
|
Connectivity of Confined Dense Networks: Boundary Effects and Scaling
Laws
|
cs.NI cs.IT math.IT
|
In this paper, we study the probability that a dense network confined within
a given geometry is fully connected. We employ a cluster expansion approach
often used in statistical physics to analyze the effects that the boundaries of
the geometry have on connectivity. To maximize practicality and applicability,
we adopt four important point-to-point link models based on outage probability
in our analysis: single-input single-output (SISO), single-input
multiple-output (SIMO), multiple-input single-output (MISO), and multiple-input
multiple-output (MIMO). Furthermore, we derive diversity and power scaling laws
that dictate how boundary effects can be mitigated (to leading order) in
confined dense networks for each of these models. Finally, in order to
demonstrate the versatility of our theory, we analyze boundary effects for
dense networks comprising MIMO point-to-point links confined within a right
prism, a polyhedron that accurately models many geometries that can be found in
practice. We provide numerical results for this example, which verify our
analytical results.
|
1201.4044
|
Topological phase transition in a network model with preferential
attachment and node removal
|
cond-mat.stat-mech cs.SI physics.soc-ph
|
Preferential attachment is a popular model of growing networks. We consider a
generalized model with random node removal, and a combination of preferential
and random attachment. Using a high-degree expansion of the master equation, we
identify a topological phase transition depending on the rate of node removal
and the relative strength of preferential vs. random attachment, where the
degree distribution goes from a power law to one with an exponential tail.
|
1201.4049
|
Parameter Identification in a Probabilistic Setting
|
cs.NA cs.CE
|
Parameter identification problems are formulated in a probabilistic language,
where the randomness reflects the uncertainty about the knowledge of the true
values. This setting allows conceptually easily to incorporate new information,
e.g. through a measurement, by connecting it to Bayes's theorem. The unknown
quantity is modelled as a (may be high-dimensional) random variable. Such a
description has two constituents, the measurable function and the measure. One
group of methods is identified as updating the measure, the other group changes
the measurable function. We connect both groups with the relatively recent
methods of functional approximation of stochastic problems, and introduce
especially in combination with the second group of methods a new procedure
which does not need any sampling, hence works completely deterministically. It
also seems to be the fastest and more reliable when compared with other
methods. We show by example that it also works for highly nonlinear non-smooth
problems with non-Gaussian measures.
|
1201.4054
|
Sensor Networks: from Dependence Analysis Via Matroid Bases to Online
Synthesis
|
cs.IT cs.DS math.IT
|
Consider the two related problems of sensor selection and sensor fusion. In
the first, given a set of sensors, one wishes to identify a subset of the
sensors, which while small in size, captures the essence of the data gathered
by the sensors. In the second, one wishes to construct a fused sensor, which
utilizes the data from the sensors (possibly after discarding dependent ones)
in order to create a single sensor which is more reliable than each of the
individual ones. In this work, we rigorously define the dependence among
sensors in terms of joint empirical measures and incremental parsing. We show
that these measures adhere to a polymatroid structure, which in turn
facilitates the application of efficient algorithms for sensor selection. We
suggest both a random and a greedy algorithm for sensor selection. Given an
independent set, we then turn to the fusion problem, and suggest a novel
variant of the exponential weighting algorithm. In the suggested algorithm, one
competes against an augmented set of sensors, which allows it to converge to
the best fused sensor in a family of sensors, without having any prior data on
the sensors' performance.
|
1201.4080
|
Progress in animation of an EMA-controlled tongue model for
acoustic-visual speech synthesis
|
cs.AI
|
We present a technique for the animation of a 3D kinematic tongue model, one
component of the talking head of an acoustic-visual (AV) speech synthesizer.
The skeletal animation approach is adapted to make use of a deformable rig
controlled by tongue motion capture data obtained with electromagnetic
articulography (EMA), while the tongue surface is extracted from volumetric
magnetic resonance imaging (MRI) data. Initial results are shown and future
work outlined.
|
1201.4089
|
A Description Logic Primer
|
cs.AI cs.LO
|
This paper provides a self-contained first introduction to description logics
(DLs). The main concepts and features are explained with examples before syntax
and semantics of the DL SROIQ are defined in detail. Additional sections review
light-weight DL languages, discuss the relationship to the Web Ontology
Language OWL and give pointers to further reading.
|
1201.4106
|
Staircase Codes: FEC for 100 Gb/s OTN
|
cs.IT math.IT
|
Staircase codes, a new class of forward-error-correction (FEC) codes suitable
for high-speed optical communications, are introduced. An ITU-T
G.709-compatible staircase code with rate R=239/255 is proposed, and FPGA-based
simulation results are presented, exhibiting a net coding gain (NCG) of 9.41 dB
at an output error rate of 1E-15, an improvement of 0.42 dB relative to the
best code from the ITU-T G.975.1 recommendation. An error floor analysis
technique is presented, and the proposed code is shown to have an error floor
at 4.0E-21.
|
1201.4108
|
A Pragmatic Coded Modulation Scheme for High-Spectral-Efficiency
Fiber-Optic Communications
|
cs.IT math.IT
|
A pragmatic coded modulation system is presented that incorporates signal
shaping and exploits the excellent performance and efficient high-speed
decoding architecture of staircase codes. Reliable communication within 0.62
bits/s/Hz of the estimated capacity (per polarization) of a system with L=2000
km is provided by the proposed system, with an error floor below 1E-20. Also,
it is shown that digital backpropagation increases the achievable spectral
efficiencies---relative to linear equalization---by 0.55 to 0.75 bits/s/Hz per
polarization.
|
1201.4109
|
On the Multiple Access Channel with Asymmetric Noisy State Information
at the Encoders
|
cs.IT math.IT
|
We consider the problem of reliable communication over multiple-access
channels (MAC) where the channel is driven by an independent and identically
distributed state process and the encoders and the decoder are provided with
various degrees of asymmetric noisy channel state information (CSI). For the
case where the encoders observe causal, asymmetric noisy CSI and the decoder
observes complete CSI, we provide inner and outer bounds to the capacity
region, which are tight for the sum-rate capacity. We then observe that, under
a Markov assumption, similar capacity results also hold in the case where the
receiver observes noisy CSI. Furthermore, we provide a single letter
characterization for the capacity region when the CSI at the encoders are
asymmetric deterministic functions of the CSI at the decoder and the encoders
have non-causal noisy CSI (its causal version is recently solved in
\cite{como-yuksel}). When the encoders observe asymmetric noisy CSI with
asymmetric delays and the decoder observes complete CSI, we provide a single
letter characterization for the capacity region. Finally, we consider a
cooperative scenario with common and private messages, with asymmetric noisy
CSI at the encoders and complete CSI at the decoder. We provide a single letter
expression for the capacity region for such channels. For the cooperative
scenario, we also note that as soon as the common message encoder does not have
access to CSI, then in any noisy setup, covering the cases where no CSI or
noisy CSI at the decoder, it is possible to obtain a single letter
characterization for the capacity region. The main component in these results
is a generalization of a converse coding approach, recently introduced in [1]
for the MAC with asymmetric quantized CSI at the encoders and herein
considerably extended and adapted for the noisy CSI setup.
|
1201.4116
|
Analysis of Cell Load Coupling for LTE Network Planning and Optimization
|
cs.IT cs.NI math.IT
|
System-centric modeling and analysis are of key significance in planning and
optimizing cellular networks. In this paper, we provide a mathematical analysis
of performance modeling for LTE networks. The system model characterizes the
coupling relation between the cell load factors, taking into account
non-uniform traffic demand and interference between the cells with arbitrary
network topology. Solving the model enables a network-wide performance
evaluation in resource consumption. We develop and prove both sufficient and
necessary conditions for the feasibility of the load-coupling system, and
provide results related to computational aspects for numerically approaching
the solution. The theoretical findings are accompanied with experimental
results to instructively illustrate the application in optimizing LTE network
configuration.
|
1201.4118
|
Vertex Nomination via Content and Context
|
stat.AP cs.SI physics.soc-ph
|
If I know of a few persons of interest, how can a combination of human
language technology and graph theory help me find other people similarly
interesting? If I know of a few people committing a crime, how can I determine
their co-conspirators? Given a set of actors deemed interesting, we seek other
actors who are similarly interesting. We use a collection of communications
encoded as an attributed graph, where vertices represents actors and edges
connect pairs of actors that communicate. Attached to each edge is the set of
documents wherein that pair of actors communicate, providing content in context
- the communication topic in the context of who communicates with whom. In
these documents, our identified interesting actors communicate amongst each
other and with other actors whose interestingness is unknown. Our objective is
to nominate the most likely interesting vertex from all vertices with unknown
interestingness. As an illustrative example, the Enron email corpus consists of
communications between actors, some of which are allegedly committing fraud.
Some of their fraudulent activity is captured in emails, along with many
innocuous emails (both between the fraudsters and between the other employees
of Enron); we are given the identities of a few fraudster vertices and asked to
nominate other vertices in the graph as likely representing other actors
committing fraud. Foundational theory and initial experimental results indicate
that approaching this task with a joint model of content and context improves
the performance (as measured by standard information retrieval measures) over
either content or context alone.
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