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1011.2575
|
Complex sequencing rules of birdsong can be explained by simple hidden
Markov processes
|
q-bio.NC cs.CL
|
Complex sequencing rules observed in birdsongs provide an opportunity to
investigate the neural mechanism for generating complex sequential behaviors.
To relate the findings from studying birdsongs to other sequential behaviors,
it is crucial to characterize the statistical properties of the sequencing
rules in birdsongs. However, the properties of the sequencing rules in
birdsongs have not yet been fully addressed. In this study, we investigate the
statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura
striata var. domestica). Based on manual-annotated syllable sequences, we first
show that there are significant higher-order context dependencies in Bengalese
finch songs, that is, which syllable appears next depends on more than one
previous syllable. This property is shared with other complex sequential
behaviors. We then analyze acoustic features of the song and show that
higher-order context dependencies can be explained using first-order hidden
state transition dynamics with redundant hidden states. This model corresponds
to hidden Markov models (HMMs), well known statistical models with a large
range of application for time series modeling. The song annotation with these
models with first-order hidden state dynamics agreed well with manual
annotation, the score was comparable to that of a second-order HMM, and
surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does
not use context information. Our results imply that the hierarchical
representation with hidden state dynamics may underlie the neural
implementation for generating complex sequences with higher-order dependencies.
|
1011.2624
|
Clustering using Unsupervised Binary Trees: CUBT
|
stat.ME cs.LG stat.CO
|
We herein introduce a new method of interpretable clustering that uses
unsupervised binary trees. It is a three-stage procedure, the first stage of
which entails a series of recursive binary splits to reduce the heterogeneity
of the data within the new subsamples. During the second stage (pruning),
consideration is given to whether adjacent nodes can be aggregated. Finally,
during the third stage (joining), similar clusters are joined together, even if
they do not share the same parent originally. Consistency results are obtained,
and the procedure is used on simulated and real data sets.
|
1011.2644
|
Do AES encryptions act randomly?
|
cs.IT cs.CR math.IT
|
The Advanced Encryption Standard (AES) is widely recognized as the most
important block cipher in common use nowadays. This high assurance in AES is
given by its resistance to ten years of extensive cryptanalysis, that has shown
no weakness, not even any deviation from the statistical behaviour expected
from a random permutation. Only reduced versions of the ciphers have been
broken, but they are not usually implemented. In this paper we build a
distinguishing attack on the AES, exploiting the properties of a novel cipher
embedding. With our attack we give some statistical evidence that the set of
AES-$128$ encryptions acts on the message space in a way significantly
different than that of the set of random permutations acting on the same space.
While we feel that more computational experiments by independent third parties
are needed in order to validate our statistical results, we show that the
non-random behaviour is the same as we would predict using the property of our
embedding. Indeed, the embedding lowers the nonlinearity of the AES rounds and
therefore the AES encryptions tend, on average, to keep low the rank of
low-rank matrices constructed in the large space. Our attack needs $2^{23}$
plaintext-ciphertext pairs and costs the equivalent of $2^{48}$ encryptions.
We expect our attack to work also for AES-$192$ and AES-$256$, as confirmed
by preliminary experiments.
|
1011.2686
|
A Discrete Time Markov Chain Model for High Throughput Bidirectional
Fano Decoders
|
cs.IT math.IT
|
The bidirectional Fano algorithm (BFA) can achieve at least two times
decoding throughput compared to the conventional unidirectional Fano algorithm
(UFA). In this paper, bidirectional Fano decoding is examined from the queuing
theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model
the BFA decoder with a finite input buffer. The relationship between the input
data rate, the input buffer size and the clock speed of the BFA decoder is
established. The DTMC based modelling can be used in designing a high
throughput parallel BFA decoding system. It is shown that there is a tradeoff
between the number of BFA decoders and the input buffer size, and an optimal
input buffer size can be chosen to minimize the hardware complexity for a
target decoding throughput in designing a high throughput parallel BFA decoding
system.
|
1011.2689
|
Contact processes and moment closure on adaptive networks
|
physics.soc-ph cs.SI
|
Contact processes describe the transmission of distinct properties of nodes
via the links of a network. They provide a simple framework for many phenomena,
such as epidemic spreading and opinion formation. Combining contact processes
with rules for topological evolution yields an adaptive network in which the
states of the nodes can interact dynamically with the topological degrees of
freedom. By moment-closure approximation it is possible to derive
low-dimensional systems of ordinary differential equations that describe the
dynamics of the adaptive network on a coarse-grained level. In this chapter we
discuss the approximation technique itself as well as its applications to
adaptive networks. Thus, it can serve both as a tutorial as well as a review of
recent results.
|
1011.2719
|
Decidability Classes for Mobile Agents Computing
|
cs.DC cs.CC cs.MA
|
We establish a classification of decision problems that are to be solved by
mobile agents operating in unlabeled graphs, using a deterministic protocol.
The classification is with respect to the ability of a team of agents to solve
the problem, possibly with the aid of additional information. In particular,
our focus is on studying differences between the decidability of a decision
problem by agents and its verifiability when a certificate for a positive
answer is provided to the agents. We show that the class MAV of mobile agents
verifiable problems is much wider than the class MAD of mobile agents decidable
problems. Our main result shows that there exist natural MAV-complete problems:
the most difficult problems in this class, to which all problems in MAV are
reducible. Our construction of a MAV-complete problem involves two main
ingredients in mobile agents computability: the topology of the quotient graph
and the number of operating agents. Beyond the class MAV we show that, for a
single agent, three natural oracles yield a strictly increasing chain of
relative decidability classes.
|
1011.2740
|
Deterministic Compressed Sensing Matrices from Multiplicative Character
Sequences
|
cs.IT math.IT
|
Compressed sensing is a novel technique where one can recover sparse signals
from the undersampled measurements. In this paper, a $K \times N$ measurement
matrix for compressed sensing is deterministically constructed via
multiplicative character sequences. Precisely, a constant multiple of a cyclic
shift of an $M$-ary power residue or Sidelnikov sequence is arranged as a
column vector of the matrix, through modulating a primitive $M$-th root of
unity. The Weil bound is then used to show that the matrix has asymptotically
optimal coherence for large $K$ and $M$, and to present a sufficient condition
on the sparsity level for unique sparse solution. Also, the restricted isometry
property (RIP) is statistically studied for the deterministic matrix. Numerical
results show that the deterministic compressed sensing matrix guarantees
reliable matching pursuit recovery performance for both noiseless and noisy
measurements.
|
1011.2795
|
A Distributed Data Collection Algorithm for Wireless Sensor Networks
with Persistent Storage Nodes
|
cs.NI cs.IT math.IT
|
A distributed data collection algorithm to accurately store and forward
information obtained by wireless sensor networks is proposed. The proposed
algorithm does not depend on the sensor network topology, routing tables, or
geographic locations of sensor nodes, but rather makes use of uniformly
distributed storage nodes. Analytical and simulation results for this algorithm
show that, with high probability, the data disseminated by the sensor nodes can
be precisely collected by querying any small set of storage nodes.
|
1011.2797
|
When are microcircuits well-modeled by maximum entropy methods?
|
q-bio.NC cond-mat.dis-nn cs.IT math.IT physics.data-an
|
Describing the collective activity of neural populations is a daunting task:
the number of possible patterns grows exponentially with the number of cells,
resulting in practically unlimited complexity. Recent empirical studies,
however, suggest a vast simplification in how multi-neuron spiking occurs: the
activity patterns of some circuits are nearly completely captured by pairwise
interactions among neurons. Why are such pairwise models so successful in some
instances, but insufficient in others? Here, we study the emergence of
higher-order interactions in simple circuits with different architectures and
inputs. We quantify the impact of higher-order interactions by comparing the
responses of mechanistic circuit models vs. "null" descriptions in which all
higher-than-pairwise correlations have been accounted for by lower order
statistics, known as pairwise maximum entropy models.
We find that bimodal input signals produce larger deviations from pairwise
predictions than unimodal inputs for circuits with local and global
connectivity. Moreover, recurrent coupling can accentuate these deviations, if
coupling strengths are neither too weak nor too strong. A circuit model based
on intracellular recordings from ON parasol retinal ganglion cells shows that a
broad range of light signals induce unimodal inputs to spike generators, and
that coupling strengths produce weak effects on higher-order interactions. This
provides a novel explanation for the success of pairwise models in this system.
Overall, our findings identify circuit-level mechanisms that produce and fail
to produce higher-order spiking statistics in neural ensembles.
|
1011.2807
|
Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse
Data
|
cs.DB cs.DS
|
The K-Nearest Neighbor (KNN) join is an expensive but important operation in
many data mining algorithms. Several recent applications need to perform KNN
join for high dimensional sparse data. Unfortunately, all existing KNN join
algorithms are designed for low dimensional data. To fulfill this void, we
investigate the KNN join problem for high dimensional sparse data.
In this paper, we propose three KNN join algorithms: a brute force (BF)
algorithm, an inverted index-based(IIB) algorithm and an improved inverted
index-based(IIIB) algorithm. Extensive experiments on both synthetic and
real-world datasets were conducted to demonstrate the effectiveness of our
algorithms for high dimensional sparse data.
|
1011.2809
|
Multipath Parameter Estimation from OFDM Signals in Mobile Channels
|
cs.IT math.IT
|
We study multipath parameter estimation from orthogonal frequency division
multiplex signals transmitted over doubly dispersive mobile radio channels. We
are interested in cases where the transmission is long enough to suffer time
selectivity, but short enough such that the time variation can be accurately
modeled as depending only on per-tap linear phase variations due to Doppler
effects. We therefore concentrate on the estimation of the complex gain, delay
and Doppler offset of each tap of the multipath channel impulse response. We
show that the frequency domain channel coefficients for an entire packet can be
expressed as the superimposition of two-dimensional complex sinusoids. The
maximum likelihood estimate requires solution of a multidimensional non-linear
least squares problem, which is computationally infeasible in practice. We
therefore propose a low complexity suboptimal solution based on iterative
successive and parallel cancellation. First, initial delay/Doppler estimates
are obtained via successive cancellation. These estimates are then refined
using an iterative parallel cancellation procedure. We demonstrate via Monte
Carlo simulations that the root mean squared error statistics of our estimator
are very close to the Cramer-Rao lower bound of a single two-dimensional
sinusoid in Gaussian noise.
|
1011.2834
|
New Set of Codes for the Maximum-Likelihood Decoding Problem
|
cs.IT math.IT
|
The maximum-likelihood decoding problem is known to be NP-hard for general
linear and Reed-Solomon codes. In this paper, we introduce the notion of
A-covered codes, that is, codes that can be decoded through a polynomial time
algorithm A whose decoding bound is beyond the covering radius. For these
codes, we show that the maximum-likelihood decoding problem is reachable in
polynomial time in the code parameters. Focusing on bi- nary BCH codes, we were
able to find several examples of A-covered codes, including two codes for which
the maximum-likelihood decoding problem can be solved in quasi-quadratic time.
|
1011.2835
|
Approximately Optimal Wireless Broadcasting
|
cs.IT cs.NI math.IT
|
We study a wireless broadcast network, where a single source reliably
communicates independent messages to multiple destinations, with the aid of
relays and cooperation between destinations. The wireless nature of the medium
is captured by the broadcast nature of transmissions as well as the
superposition of all transmit signals plus independent Gaussian noise at the
received signal at any radio. We propose a scheme that can achieve rate tuples
within a constant gap away from the cut-set bound, where the constant is
independent of channel coefficients and power constraints.
The proposed scheme operates in two steps. The inner code, in which the
relays perform a quantize-and-encode operation, is constructed by lifting a
scheme designed for a corresponding discrete superposition network. The outer
code is a Marton code for the non-Gaussian vector broadcast channel induced by
the relaying scheme, and is constructed by adopting a ``receiver-centric''
viewpoint.
|
1011.2898
|
Reified unit resolution and the failed literal rule
|
cs.LO cs.AI
|
Unit resolution can simplify a CNF formula or detect an inconsistency by
repeatedly assign the variables occurring in unit clauses. Given any CNF
formula sigma, we show that there exists a satisfiable CNF formula psi with
size polynomially related to the size of sigma such that applying unit
resolution to psi simulates all the effects of applying it to sigma. The
formula psi is said to be the reified counterpart of sigma. This approach can
be used to prove that the failed literal rule, which is an inference rule used
by some SAT solvers, can be entirely simulated by unit resolution. More
generally, it sheds new light on the expressive power of unit resolution.
|
1011.2918
|
Mean field limit of a continuous time finite state game
|
math.OC cs.SY math.DS
|
Mean field games is a recent area of study introduced by Lions and Lasry in a
series of seminal papers in 2006. Mean field games model situations of
competition between large number of rational agents that play non-cooperative
dynamic games under certain symmetry assumptions. They key step is to develop a
mean field model, in a similar way that what is done in statistical physics in
order to construct a mathematically tractable model. A main question that
arises in the study of such mean field problems is the rigorous justification
of the mean field models by a limiting procedure. In this paper we consider the
mean field limit of two-state Markov decision problem as the number of players
$N\to \infty$. First we establish the existence and uniqueness of a symmetric
partial information Markov perfect equilibrium. Then we derive a mean field
model and characterize its main properties. This mean field limit is a system
of coupled ordinary differential equations with initial-terminal data. Our main
result is the convergence as $N\to \infty$ of the $N$ player game to the mean
field model and an estimate of the rate of convergence.
|
1011.2919
|
Hardware architectures for Successive Cancellation Decoding of Polar
Codes
|
cs.AR cs.IT math.IT
|
The recently-discovered polar codes are widely seen as a major breakthrough
in coding theory. These codes achieve the capacity of many important channels
under successive cancellation decoding. Motivated by the rapid progress in the
theory of polar codes, we propose a family of architectures for efficient
hardware implementation of successive cancellation decoders. We show that such
decoders can be implemented with O(n) processing elements and O(n) memory
elements, while providing constant throughput. We also propose a technique for
overlapping the decoding of several consecutive codewords, thereby achieving a
significant speed-up factor. We furthermore show that successive cancellation
decoding can be implemented in the logarithmic domain, thereby eliminating the
multiplication and division operations and greatly reducing the complexity of
each processing element.
|
1011.2922
|
Emoticonsciousness
|
cs.CL
|
A temporal analysis of emoticon use in Swedish, Italian, German and English
asynchronous electronic communication is reported. Emoticons are classified as
positive, negative and neutral. Postings to newsgroups over a 66 week period
are considered. The aggregate analysis of emoticon use in newsgroups for
science and politics tend on the whole to be consistent over the entire time
period. Where possible, events that coincide with divergences from trends in
language-subject pairs are noted. Political discourse in Italian over the
period shows marked use of negative emoticons, and in Swedish, positive
emoticons.
|
1011.2945
|
Phase transitions for the cavity approach to the clique problem on
random graphs
|
math.PR cond-mat.stat-mech cs.SI physics.soc-ph
|
We give a rigorous proof of two phase transitions for a disordered system
designed to find large cliques inside Erdos random graphs. Such a system is
associated with a conservative probabilistic cellular automaton inspired by the
cavity method originally introduced in spin glass theory.
|
1011.2989
|
A Decoding Approach to Fault Tolerant Control of Linear Systems with
Quantized Disturbance Input
|
math.OC cs.IT math.IT
|
The aim of this paper is to propose an alternative method to solve a Fault
Tolerant Control problem. The model is a linear system affected by a
disturbance term: this represents a large class of technological faulty
processes. The goal is to make the system able to tolerate the undesired
perturbation, i.e., to remove or at least reduce its negative effects; such a
task is performed in three steps: the detection of the fault, its
identification and the consequent process recovery. When the disturbance
function is known to be \emph{quantized} over a finite number of levels, the
detection can be successfully executed by a recursive \emph{decoding}
algorithm, arising from Information and Coding Theory and suitably adapted to
the control framework. This technique is analyzed and tested in a flight
control issue; both theoretical considerations and simulations are reported.
|
1011.2996
|
Large-deviation properties of largest component for random graphs
|
cond-mat.dis-nn cs.SI physics.data-an physics.soc-ph
|
Distributions of the size of the largest component, in particular the
large-deviation tail, are studied numerically for two graph ensembles, for
Erdoes-Renyi random graphs with finite connectivity and for two-dimensional
bond percolation. Probabilities as small as 10^-180 are accessed using an
artificial finite-temperature (Boltzmann) ensemble. The distributions for the
Erdoes-Renyi ensemble agree well with previously obtained analytical results.
The results for the percolation problem, where no analytical results are
available, are qualitatively similar, but the shapes of the distributions are
somehow different and the finite-size corrections are sometimes much larger.
Furthermore, for both problems, a first-order phase transition at low
temperatures T within the artificial ensemble is found in the percolating
regime, respectively.
|
1011.3019
|
Bounded Multivariate Surfaces On Monovariate Internal Functions
|
cs.CV
|
Combining the properties of monovariate internal functions as proposed in
Kolmogorov superimposition theorem, in tandem with the bounds wielded by the
multivariate formulation of Chebyshev inequality, a hybrid model is presented,
that decomposes images into homogeneous probabilistically bounded multivariate
surfaces. Given an image, the model shows a novel way of working on reduced
image representation while processing and capturing the interaction among the
multidimensional information that describes the content of the same. Further,
it tackles the practical issues of preventing leakage by bounding the growth of
surface and reducing the problem sample size. The model if used, also sheds
light on how the Chebyshev parameter relates to the number of pixels and the
dimensionality of the feature space that associates with a pixel. Initial
segmentation results on the Berkeley image segmentation benchmark indicate the
effectiveness of the proposed decomposition algorithm.
|
1011.3023
|
Classification with Scattering Operators
|
cs.CV
|
A scattering vector is a local descriptor including multiscale and
multi-direction co-occurrence information. It is computed with a cascade of
wavelet decompositions and complex modulus. This scattering representation is
locally translation invariant and linearizes deformations. A supervised
classification algorithm is computed with a PCA model selection on scattering
vectors. State of the art results are obtained for handwritten digit
recognition and texture classification.
|
1011.3062
|
Generalized Stable Matching in Bipartite Networks
|
math.OC cs.DM cs.GT cs.SI
|
In this paper we study the generalized version of weighted matching in
bipartite networks. Consider a weighted matching in a bipartite network in
which the nodes derive value from the split of the matching edge assigned to
them if they are matched. The value a node derives from the split depends both
on the split as well as the partner the node is matched to. We assume that the
value of a split to the node is continuous and strictly increasing in the part
of the split assigned to the node. A stable weighted matching is a matching and
splits on the edges in the matching such that no two adjacent nodes in the
network can split the edge between them so that both of them can derive a
higher value than in the matching. We extend the weighted matching problem to
this general case and study the existence of a stable weighted matching. We
also present an algorithm that converges to a stable weighted matching. The
algorithm generalizes the Hungarian algorithm for bipartite matching. Faster
algorithms can be made when there is more structure on the value functions.
|
1011.3074
|
Distributed Detection over Gaussian Multiple Access Channels with
Constant Modulus Signaling
|
cs.IT math.IT
|
A distributed detection scheme where the sensors transmit with constant
modulus signals over a Gaussian multiple access channel is considered. The
deflection coefficient of the proposed scheme is shown to depend on the
characteristic function of the sensing noise and the error exponent for the
system is derived using large deviation theory. Optimization of the deflection
coefficient and error exponent are considered with respect to a transmission
phase parameter for a variety of sensing noise distributions including
impulsive ones. The proposed scheme is also favorably compared with existing
amplify-and-forward and detect-and-forward schemes. The effect of fading is
shown to be detrimental to the detection performance through a reduction in the
deflection coefficient depending on the fading statistics. Simulations
corroborate that the deflection coefficient and error exponent can be
effectively used to optimize the error probability for a wide variety of
sensing noise distributions.
|
1011.3090
|
Regularization Strategies and Empirical Bayesian Learning for MKL
|
stat.ML cs.LG
|
Multiple kernel learning (MKL), structured sparsity, and multi-task learning
have recently received considerable attention. In this paper, we show how
different MKL algorithms can be understood as applications of either
regularization on the kernel weights or block-norm-based regularization, which
is more common in structured sparsity and multi-task learning. We show that
these two regularization strategies can be systematically mapped to each other
through a concave conjugate operation. When the kernel-weight-based regularizer
is separable into components, we can naturally consider a generative
probabilistic model behind MKL. Based on this model, we propose learning
algorithms for the kernel weights through the maximization of marginal
likelihood. We show through numerical experiments that $\ell_2$-norm MKL and
Elastic-net MKL achieve comparable accuracy to uniform kernel combination.
Although uniform kernel combination might be preferable from its simplicity,
$\ell_2$-norm MKL and Elastic-net MKL can learn the usefulness of the
information sources represented as kernels. In particular, Elastic-net MKL
achieves sparsity in the kernel weights.
|
1011.3115
|
Cyber-Physical Control over Wireless Sensor and Actuator Networks with
Packet Loss
|
cs.NI cs.SY
|
There is a growing interest in design and implementation of cyber-physical
control systems over wireless sensor and actuator networks (WSANs). Thanks to
the use of wireless communications and distributed architectures, these systems
encompass many advantages as compared to traditional networked control systems
using hard wirelines. While WSANs are enabling a new generation of control
systems, they also introduce considerable challenges for quality-of-service
(QoS) provisioning. In this chapter we examine some of the major QoS challenges
raised by WSANs, including resource constraints, platform heterogeneity,
dynamic network topology, and mixed traffic. These challenges make it difficult
to fulfill the requirements of cyber-physical control in terms of reliability
and real-time. The focus of this chapter is on addressing the problem of
network reliability. Specifically, we analyze the behavior of wireless channels
via simulations based on a realistic link-layer model. Packet loss rate (PLR)
is taken as a major metric for the analysis. The results confirm the
unreliability of wireless communications and the uncertainty of packet loss
over WSANs. To tackle packet loss, we present a simple solution that can take
advantage of existing prediction algorithms. Simulations are conducted to
evaluate the performance of several classical prediction algorithms used for
packet loss compensation. The results give some insights into how to deal with
packet loss in cyber-physical control systems over unreliable WSANs.
|
1011.3120
|
The Local Emergence and Global Diffusion of Research Technologies: An
Exploration of Patterns of Network Formation
|
cs.DL cs.SI
|
Grasping the fruits of "emerging technologies" is an objective of many
government priority programs in a knowledge-based and globalizing economy. We
use the publication records (in the Science Citation Index) of two emerging
technologies to study the mechanisms of diffusion in the case of two innovation
trajectories: small interference RNA (siRNA) and nano-crystalline solar cells
(NCSC). Methods for analyzing and visualizing geographical and cognitive
diffusion are specified as indicators of different dynamics. Geographical
diffusion is illustrated with overlays to Google Maps; cognitive diffusion is
mapped using an overlay to a map based on the ISI Subject Categories. The
evolving geographical networks show both preferential attachment and
small-world characteristics. The strength of preferential attachment decreases
over time, while the network evolves into an oligopolistic control structure
with small-world characteristics. The transition from disciplinary-oriented
("mode-1") to transfer-oriented ("mode-2") research is suggested as the crucial
difference in explaining the different rates of diffusion between siRNA and
NCSC.
|
1011.3152
|
On the Energy Efficiency of LT Codes in Proactive Wireless Sensor
Networks
|
cs.IT math.IT
|
This paper presents an in-depth analysis on the energy efficiency of Luby
Transform (LT) codes with Frequency Shift Keying (FSK) modulation in a Wireless
Sensor Network (WSN) over Rayleigh fading channels with pathloss. We describe a
proactive system model according to a flexible duty-cycling mechanism utilized
in practical sensor apparatus. The present analysis is based on realistic
parameters including the effect of channel bandwidth used in the IEEE 802.15.4
standard, active mode duration and computation energy. A comprehensive
analysis, supported by some simulation studies on the probability mass function
of the LT code rate and coding gain, shows that among uncoded FSK and various
classical channel coding schemes, the optimized LT coded FSK is the most
energy-efficient scheme for distance d greater than the pre-determined
threshold level d_T , where the optimization is performed over coding and
modulation parameters. In addition, although the optimized uncoded FSK
outperforms coded schemes for d < d_T , the energy gap between LT coded and
uncoded FSK is negligible for d < d_T compared to the other coded schemes.
These results come from the flexibility of the LT code to adjust its rate to
suit instantaneous channel conditions, and suggest that LT codes are beneficial
in practical low-power WSNs with dynamic position sensor nodes.
|
1011.3168
|
Online Learning: Beyond Regret
|
stat.ML cs.GT cs.LG
|
We study online learnability of a wide class of problems, extending the
results of (Rakhlin, Sridharan, Tewari, 2010) to general notions of performance
measure well beyond external regret. Our framework simultaneously captures such
well-known notions as internal and general Phi-regret, learning with
non-additive global cost functions, Blackwell's approachability, calibration of
forecasters, adaptive regret, and more. We show that learnability in all these
situations is due to control of the same three quantities: a martingale
convergence term, a term describing the ability to perform well if future is
known, and a generalization of sequential Rademacher complexity, studied in
(Rakhlin, Sridharan, Tewari, 2010). Since we directly study complexity of the
problem instead of focusing on efficient algorithms, we are able to improve and
extend many known results which have been previously derived via an algorithmic
construction.
|
1011.3174
|
Tensor-SIFT based Earth Mover's Distance for Contour Tracking
|
cs.CV math.OC
|
Contour tracking in adverse environments is a challenging problem due to
cluttered background, illumination variation, occlusion, and noise, among
others. This paper presents a robust contour tracking method by contributing to
some of the key issues involved, including (a) a region functional formulation
and its optimization; (b) design of a robust and effective feature; and (c)
development of an integrated tracking algorithm. First, we formulate a region
functional based on robust Earth Mover's distance (EMD) with kernel density for
distribution modeling, and propose a two-phase method for its optimization. In
the first phase, letting the candidate contour be fixed, we express EMD as the
transportation problem and solve it by the simplex algorithm. Next, using the
theory of shape derivative, we make a perturbation analysis of the contour
around the best solution to the transportation problem. This leads to a partial
differential equation (PDE) that governs the contour evolution. Second, we
design a novel and effective feature for tracking applications. We propose a
dimensionality reduction method by tensor decomposition, achieving a
low-dimensional description of SIFT features called Tensor-SIFT for
characterizing local image region properties. Applicable to both color and
gray-level images, Tensor-SIFT is very distinctive, insensitive to illumination
changes, and noise. Finally, we develop an integrated algorithm that combines
various techniques of the simplex algorithm, narrow-band level set and fast
marching algorithms. Particularly, we introduce an inter-frame initialization
method and a stopping criterion for the termination of PDE iteration.
Experiments in challenging image sequences show that the proposed work has
promising performance.
|
1011.3177
|
The Data Replication Method for the Classification with Reject Option
|
cs.CV
|
Classification is one of the most important tasks of machine learning.
Although the most well studied model is the two-class problem, in many
scenarios there is the opportunity to label critical items for manual revision,
instead of trying to automatically classify every item. In this paper we adapt
a paradigm initially proposed for the classification of ordinal data to address
the classification problem with reject option. The technique reduces the
problem of classifying with reject option to the standard two-class problem.
The introduced method is then mapped into support vector machines and neural
networks. Finally, the framework is extended to multiclass ordinal data with
reject option. An experimental study with synthetic and real data sets,
verifies the usefulness of the proposed approach.
|
1011.3189
|
Warping Peirce Quincuncial Panoramas
|
cs.CV cs.GR eess.IV
|
The Peirce quincuncial projection is a mapping of the surface of a sphere to
the interior of a square. It is a conformal map except for four points on the
equator. These points of non-conformality cause significant artifacts in
photographic applications. In this paper, we propose an algorithm and
user-interface to mitigate these artifacts. Moreover, in order to facilitate an
interactive user-interface, we present a fast algorithm for calculating the
Peirce quincuncial projection of spherical imagery. We then promote the Peirce
quincuncial projection as a viable alternative to the more popular
stereographic projection in some scenarios.
|
1011.3241
|
New Methods of Analysis of Narrative and Semantics in Support of
Interactivity
|
cs.AI cs.HC stat.AP
|
Our work has focused on support for film or television scriptwriting. Since
this involves potentially varied story-lines, we note the implicit or latent
support for interactivity. Furthermore the film, television, games, publishing
and other sectors are converging, so that cross-over and re-use of one form of
product in another of these sectors is ever more common. Technically our work
has been largely based on mathematical algorithms for data clustering and
display. Operationally, we also discuss how our algorithms can support
collective, distributed problem-solving.
|
1011.3244
|
"Meaning" as a sociological concept: A review of the modeling, mapping,
and simulation of the communication of knowledge and meaning
|
nlin.AO cs.AI physics.soc-ph
|
The development of discursive knowledge presumes the communication of meaning
as analytically different from the communication of information. Knowledge can
then be considered as a meaning which makes a difference. Whereas the
communication of information is studied in the information sciences and
scientometrics, the communication of meaning has been central to Luhmann's
attempts to make the theory of autopoiesis relevant for sociology. Analytical
techniques such as semantic maps and the simulation of anticipatory systems
enable us to operationalize the distinctions which Luhmann proposed as relevant
to the elaboration of Husserl's "horizons of meaning" in empirical research:
interactions among communications, the organization of meaning in
instantiations, and the self-organization of interhuman communication in terms
of symbolically generalized media such as truth, love, and power. Horizons of
meaning, however, remain uncertain orders of expectations, and one should
caution against reification from the meta-biological perspective of systems
theory.
|
1011.3257
|
Integration of Flexible Web Based GUI in I-SOAS
|
cs.HC cs.AI
|
It is necessary to improve the concepts of the present web based graphical
user interface for the development of more flexible and intelligent interface
to provide ease and increase the level of comfort at user end like most of the
desktop based applications. This research is conducted targeting the goal of
implementing flexible GUI consisting of a visual component manager with
different components by functionality, design and purpose. In this research
paper we present a Rich Internet Application (RIA) based graphical user
interface for web based product development, and going into the details we
present a comparison between existing RIA Technologies, adopted methodology in
the GUI development and developed prototype.
|
1011.3258
|
Integration of Agile Ontology Mapping towards NLP Search in I-SOAS
|
cs.CL cs.IR
|
In this research paper we address the importance of Product Data Management
(PDM) with respect to its contributions in industry. Moreover we also present
some currently available major challenges to PDM communities and targeting some
of these challenges we present an approach i.e. I-SOAS, and briefly discuss how
this approach can be helpful in solving the PDM community's faced problems.
Furthermore, limiting the scope of this research to one challenge, we focus on
the implementation of a semantic based search mechanism in PDM Systems. Going
into the details, at first we describe the respective field i.e. Language
Technology (LT), contributing towards natural language processing, to take
advantage in implementing a search engine capable of understanding the semantic
out of natural language based search queries. Then we discuss how can we
practically take advantage of LT by implementing its concepts in the form of
software application with the use of semantic web technology i.e. Ontology.
Later, in the end of this research paper, we briefly present a prototype
application developed with the use of concepts of LT towards semantic based
search.
|
1011.3272
|
Group-Decodable Space-Time Block Codes with Code Rate > 1
|
cs.IT math.IT
|
High-rate space-time block codes (STBC with code rate > 1) in multi-input
multi-output (MIMO) systems are able to provide both spatial multiplexing gain
and diversity gain, but have high maximum likelihood (ML) decoding complexity.
Since group-decodable (quasi-orthogonal) code structure can reduce the decoding
complexity, we present in this paper systematic methods to construct
group-decodable high-rate STBC with full symbol-wise diversity gain for
arbitrary transmit antenna number and code length. We show that the proposed
group-decodable STBC can achieve high code rate that increases almost linearly
with the transmit antenna number, and the slope of this near-linear dependence
increases with the code length. Comparisons with existing low-rate and
high-rate codes (such as orthogonal STBC and algebraic STBC) are conducted to
show the decoding complexity reduction and good code performance achieved by
the proposed codes.
|
1011.3315
|
Evolutionary method for finding communities in bipartite networks
|
physics.data-an cond-mat.stat-mech cs.NE cs.SI physics.soc-ph
|
An important step in unveiling the relation between network structure and
dynamics defined on networks is to detect communities, and numerous methods
have been developed separately to identify community structure in different
classes of networks, such as unipartite networks, bipartite networks, and
directed networks. We show that both unipartite and directed networks can be
represented as bipartite networks, and their modularity is completely
consistent with that for bipartite networks, the detection of modular structure
on which can be reformulated as modularity maximization. To optimize the
bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA),
which is shown to be especially efficient for community structure detection.
The high efficiency of the MAGA is based on the following three improvements we
make. First, we introduce a different measure for the informativeness of a
locus instead of the standard deviation, which can exactly determine which loci
mutate. This measure is the bias between the distribution of a locus over the
current population and the uniform distribution of the locus, i.e., the
Kullback-Leibler divergence between them. Second, we develop a reassignment
technique for differentiating the informative state a locus has attained from
the random state in the initial phase. Third, we present a modified mutation
rule which by incorporating related operation can guarantee the convergence of
the MAGA to the global optimum and can speed up the convergence process.
Experimental results show that the MAGA outperforms existing methods in terms
of modularity for both bipartite and unipartite networks.
|
1011.3347
|
On sizes of complete arcs in PG(2,q)
|
math.CO cs.IT math.IT
|
New upper bounds on the smallest size t_{2}(2,q) of a complete arc in the
projective plane PG(2,q) are obtained for 853 <= q <= 4561 and q\in T1\cup T2
where T1={173,181,193,229,243,257,271,277,293,343,373,409,443,449,457,
461,463,467,479,487,491,499,529,563,569,571,577,587,593,599,601,607,613,617,619,631,
641,661,673,677,683,691, 709},
T2={4597,4703,4723,4733,4789,4799,4813,4831,5003,5347,5641,5843,6011,8192}.
From these new bounds it follows that for q <= 2593 and q=2693,2753, the
relation t_{2}(2,q) < 4.5\sqrt{q} holds. Also, for q <= 4561 we have t_{2}(2,q)
< 4.75\sqrt{q}. It is showed that for 23 <= q <= 4561 and q\in T2\cup
{2^{14},2^{15},2^{18}}, the inequality t_{2}(2,q) < \sqrt{q}ln^{0.75}q is true.
Moreover, the results obtained allow us to conjecture that this estimate holds
for all q >= 23. The new upper bounds are obtained by finding new small
complete arcs with the help of a computer search using randomized greedy
algorithms. Also new constructions of complete arcs are proposed. These
constructions form families of k-arcs in PG(2,q) containing arcs of all sizes k
in a region k_{min} <= k <= k_{max} where k_{min} is of order q/3 or q/4 while
k_{max} has order q/2. The completeness of the arcs obtained by the new
constructions is proved for q <= 1367 and 2003 <= q <= 2063. There is reason to
suppose that the arcs are complete for all q > 1367. New sizes of complete arcs
in PG(2,q) are presented for 169 <= q <= 349 and q=1013,2003.
|
1011.3380
|
Achievable Rates over Doubly Selective Rician-Fading Channels under
Peak-Power Constraint
|
cs.IT math.IT
|
The goal of this paper is to obtain a better knowledge of the achievable data
rate over noncoherent Rician fading channel with time and frequency memory. We
assume that the average-power as well as the peak-power of the input signal are
finite and the peak-power limitation is applied in the time domain. Expression
for this rate is based on a lower bound on mutual information that assume
independent and identically distributed input data symbols. The lower bound is
expressed as a difference of two terms. The first term is the information rate
of the coherent channel with a weighted signal-to-noise ratio that results from
the peak-power limitation. The second term is a penalty term, explicit in the
Doppler spectrum of the channel, that captures the effect of the channel
uncertainty induced by the noncoherent setting. Impact of channel parameters,
such as delay and Doppler spread, on the information rate are discussed and
numerical applications on an experimental Rician channel surveyed in an
acoustic underwater environment are also provided.
|
1011.3397
|
The Inverse Task of the Reflexive Game Theory: Theoretical Matters,
Practical Applications and Relationship with Other Issues
|
cs.MA cs.AI cs.RO
|
The Reflexive Game Theory (RGT) has been recently proposed by Vladimir
Lefebvre to model behavior of individuals in groups. The goal of this study is
to introduce the Inverse task. We consider methods of solution together with
practical applications. We present a brief overview of the RGT for easy
understanding of the problem. We also develop the schematic representation of
the RGT inference algorithms to create the basis for soft- and hardware
solutions of the RGT tasks. We propose a unified hierarchy of schemas to
represent humans and robots. This hierarchy is considered as a unified
framework to solve the entire spectrum of the RGT tasks. We conclude by
illustrating how this framework can be applied for modeling of mixed groups of
humans and robots. All together this provides the exhaustive solution of the
Inverse task and clearly illustrates its role and relationships with other
issues considered in the RGT.
|
1011.3400
|
Prize insights in probability, and one goat of a recycled error: Jason
Rosenhouse's The Monty Hall Problem
|
math.HO cs.AI math.PR math.ST stat.TH
|
The Monty Hall problem is the TV game scenario where you, the contestant, are
presented with three doors, with a car hidden behind one and goats hidden
behind the other two. After you select a door, the host (Monty Hall) opens a
second door to reveal a goat. You are then invited to stay with your original
choice of door, or to switch to the remaining unopened door, and claim whatever
you find behind it. Assuming your objective is to win the car, is your best
strategy to stay or switch, or does it not matter? Jason Rosenhouse has
provided the definitive analysis of this game, along with several intriguing
variations, and discusses some of its psychological and philosophical
implications. This extended review examines several themes from the book in
some detail from a Bayesian perspective, and points out one apparently
inadvertent error.
|
1011.3466
|
Non-Existence of Linear Universal Drift Functions
|
cs.NE math.PR
|
Drift analysis has become a powerful tool to prove bounds on the runtime of
randomized search heuristics. It allows, for example, fairly simple proofs for
the classical problem how the (1+1) Evolutionary Algorithm (EA) optimizes an
arbitrary pseudo-Boolean linear function. The key idea of drift analysis is to
measure the progress via another pseudo-Boolean function (called drift
function) and use deeper results from probability theory to derive from this a
good bound for the runtime of the EA. Surprisingly, all these results manage to
use the same drift function for all linear objective functions.
In this work, we show that such universal drift functions only exist if the
mutation probability is close to the standard value of $1/n$.
|
1011.3494
|
Learning Planar Ising Models
|
stat.ML cs.AI
|
Inference and learning of graphical models are both well-studied problems in
statistics and machine learning that have found many applications in science
and engineering. However, exact inference is intractable in general graphical
models, which suggests the problem of seeking the best approximation to a
collection of random variables within some tractable family of graphical
models. In this paper, we focus our attention on the class of planar Ising
models, for which inference is tractable using techniques of statistical
physics [Kac and Ward; Kasteleyn]. Based on these techniques and recent methods
for planarity testing and planar embedding [Chrobak and Payne], we propose a
simple greedy algorithm for learning the best planar Ising model to approximate
an arbitrary collection of binary random variables (possibly from sample data).
Given the set of all pairwise correlations among variables, we select a planar
graph and optimal planar Ising model defined on this graph to best approximate
that set of correlations. We demonstrate our method in some simulations and for
the application of modeling senate voting records.
|
1011.3498
|
Effects of the Generation Size and Overlap on Throughput and Complexity
in Randomized Linear Network Coding
|
cs.IT cs.DM math.IT
|
To reduce computational complexity and delay in randomized network coded
content distribution, and for some other practical reasons, coding is not
performed simultaneously over all content blocks, but over much smaller,
possibly overlapping subsets of these blocks, known as generations. A penalty
of this strategy is throughput reduction. To analyze the throughput loss, we
model coding over generations with random generation scheduling as a coupon
collector's brotherhood problem. This model enables us to derive the expected
number of coded packets needed for successful decoding of the entire content as
well as the probability of decoding failure (the latter only when generations
do not overlap) and further, to quantify the tradeoff between computational
complexity and throughput. Interestingly, with a moderate increase in the
generation size, throughput quickly approaches link capacity. Overlaps between
generations can further improve throughput substantially for relatively small
generation sizes.
|
1011.3516
|
A statistical-mechanical view on source coding: physical compression and
data compression
|
cond-mat.stat-mech cs.IT math.IT
|
We draw a certain analogy between the classical information-theoretic problem
of lossy data compression (source coding) of memoryless information sources and
the statistical mechanical behavior of a certain model of a chain of connected
particles (e.g., a polymer) that is subjected to a contracting force. The free
energy difference pertaining to such a contraction turns out to be proportional
to the rate-distortion function in the analogous data compression model, and
the contracting force is proportional to the derivative this function. Beyond
the fact that this analogy may be interesting on its own right, it may provide
a physical perspective on the behavior of optimum schemes for lossy data
compression (and perhaps also, an information-theoretic perspective on certain
physical system models). Moreover, it triggers the derivation of lossy
compression performance for systems with memory, using analysis tools and
insights from statistical mechanics.
|
1011.3550
|
Overlay Protection Against Link Failures Using Network Coding
|
cs.IT math.IT
|
This paper introduces a network coding-based protection scheme against single
and multiple link failures. The proposed strategy ensures that in a connection,
each node receives two copies of the same data unit: one copy on the working
circuit, and a second copy that can be extracted from linear combinations of
data units transmitted on a shared protection path. This guarantees
instantaneous recovery of data units upon the failure of a working circuit. The
strategy can be implemented at an overlay layer, which makes its deployment
simple and scalable. While the proposed strategy is similar in spirit to the
work of Kamal '07 & '10, there are significant differences. In particular, it
provides protection against multiple link failures. The new scheme is simpler,
less expensive, and does not require the synchronization required by the
original scheme. The sharing of the protection circuit by a number of
connections is the key to the reduction of the cost of protection. The paper
also conducts a comparison of the cost of the proposed scheme to the 1+1 and
shared backup path protection (SBPP) strategies, and establishes the benefits
of our strategy.
|
1011.3557
|
A Probabilistic Approach for Learning Folksonomies from Structured Data
|
cs.AI cs.CY cs.LG
|
Learning structured representations has emerged as an important problem in
many domains, including document and Web data mining, bioinformatics, and image
analysis. One approach to learning complex structures is to integrate many
smaller, incomplete and noisy structure fragments. In this work, we present an
unsupervised probabilistic approach that extends affinity propagation to
combine the small ontological fragments into a collection of integrated,
consistent, and larger folksonomies. This is a challenging task because the
method must aggregate similar structures while avoiding structural
inconsistencies and handling noise. We validate the approach on a real-world
social media dataset, comprised of shallow personal hierarchies specified by
many individual users, collected from the photosharing website Flickr. Our
empirical results show that our proposed approach is able to construct deeper
and denser structures, compared to an approach using only the standard affinity
propagation algorithm. Additionally, the approach yields better overall
integration quality than a state-of-the-art approach based on incremental
relational clustering.
|
1011.3571
|
A Framework for Quantitative Analysis of Cascades on Networks
|
cs.SI cs.CY physics.soc-ph
|
How does information flow in online social networks? How does the structure
and size of the information cascade evolve in time? How can we efficiently mine
the information contained in cascade dynamics? We approach these questions
empirically and present an efficient and scalable mathematical framework for
quantitative analysis of cascades on networks. We define a cascade generating
function that captures the details of the microscopic dynamics of the cascades.
We show that this function can also be used to compute the macroscopic
properties of cascades, such as their size, spread, diameter, number of paths,
and average path length. We present an algorithm to efficiently compute cascade
generating function and demonstrate that while significantly compressing
information within a cascade, it nevertheless allows us to accurately
reconstruct its structure. We use this framework to study information dynamics
on the social network of Digg. Digg allows users to post and vote on stories,
and easily see the stories that friends have voted on. As a story spreads on
Digg through voting, it generates cascades. We extract cascades of more than
3,500 Digg stories and calculate their macroscopic and microscopic properties.
We identify several trends in cascade dynamics: spreading via chaining,
branching and community. We discuss how these affect the spread of the story
through the Digg social network. Our computational framework is general and
offers a practical solution to quantitative analysis of the microscopic
structure of even very large cascades.
|
1011.3588
|
Distributed Interference Cancellation in Multiple Access Channels
|
cs.IT math.IT
|
In this paper, we consider a Gaussian multiple access channel with multiple
independent additive white Gaussian interferences. Each interference is known
to exactly one transmitter non-causally. The capacity region is characterized
to within a constant gap regardless of channel parameters. These results are
based on a layered modulo-lattice scheme which realizes distributed
interference cancellation.
|
1011.3595
|
Optimizing real-time RDF data streams
|
cs.AI cs.PF
|
The Resource Description Framework (RDF) provides a common data model for the
integration of "real-time" social and sensor data streams with the Web and with
each other. While there exist numerous protocols and data formats for
exchanging dynamic RDF data, or RDF updates, these options should be examined
carefully in order to enable a Semantic Web equivalent of the high-throughput,
low-latency streams of typical Web 2.0, multimedia, and gaming applications.
This paper contains a brief survey of RDF update formats and a high-level
discussion of both TCP and UDP-based transport protocols for updates. Its main
contribution is the experimental evaluation of a UDP-based architecture which
serves as a real-world example of a high-performance RDF streaming application
in an Internet-scale distributed environment.
|
1011.3710
|
Accuracy of Mean-Field Theory for Dynamics on Real-World Networks
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Mean-field analysis is an important tool for understanding dynamics on
complex networks. However, surprisingly little attention has been paid to the
question of whether mean-field predictions are accurate, and this is
particularly true for real-world networks with clustering and modular
structure. In this paper, we compare mean-field predictions to numerical
simulation results for dynamical processes running on 21 real-world networks
and demonstrate that the accuracy of the theory depends not only on the mean
degree of the networks but also on the mean first-neighbor degree. We show that
mean-field theory can give (unexpectedly) accurate results for certain dynamics
on disassortative real-world networks even when the mean degree is as low as 4.
|
1011.3717
|
Random Beamforming over Quasi-Static and Fading Channels: A
Deterministic Equivalent Approach
|
cs.IT math.IT
|
In this work, we study the performance of random isometric precoders over
quasi-static and correlated fading channels. We derive deterministic
approximations of the mutual information and the
signal-to-interference-plus-noise ratio (SINR) at the output of the
minimum-mean-square-error (MMSE) receiver and provide simple provably
converging fixed-point algorithms for their computation. Although these
approximations are only proven exact in the asymptotic regime with infinitely
many antennas at the transmitters and receivers, simulations suggest that they
closely match the performance of small-dimensional systems. We exemplarily
apply our results to the performance analysis of multi-cellular communication
systems, multiple-input multiple-output multiple-access channels (MIMO-MAC),
and MIMO interference channels. The mathematical analysis is based on the
Stieltjes transform method. This enables the derivation of deterministic
equivalents of functionals of large-dimensional random matrices. In contrast to
previous works, our analysis does not rely on arguments from free probability
theory which enables the consideration of random matrix models for which
asymptotic freeness does not hold. Thus, the results of this work are also a
novel contribution to the field of random matrix theory and applicable to a
wide spectrum of practical systems.
|
1011.3722
|
Statistical mechanical analysis of a hierarchical random code ensemble
in signal processing
|
cond-mat.dis-nn cs.IT math.IT
|
We study a random code ensemble with a hierarchical structure, which is
closely related to the generalized random energy model with discrete energy
values. Based on this correspondence, we analyze the hierarchical random code
ensemble by using the replica method in two situations: lossy data compression
and channel coding. For both the situations, the exponents of large deviation
analysis characterizing the performance of the ensemble, the distortion rate of
lossy data compression and the error exponent of channel coding in Gallager's
formalism, are accessible by a generating function of the generalized random
energy model. We discuss that the transitions of those exponents observed in
the preceding work can be interpreted as phase transitions with respect to the
replica number. We also show that the replica symmetry breaking plays an
essential role in these transitions.
|
1011.3728
|
PADDLE: Proximal Algorithm for Dual Dictionaries LEarning
|
cs.LG cs.IT math.IT stat.ML
|
Recently, considerable research efforts have been devoted to the design of
methods to learn from data overcomplete dictionaries for sparse coding.
However, learned dictionaries require the solution of an optimization problem
for coding new data. In order to overcome this drawback, we propose an
algorithm aimed at learning both a dictionary and its dual: a linear mapping
directly performing the coding. By leveraging on proximal methods, our
algorithm jointly minimizes the reconstruction error of the dictionary and the
coding error of its dual; the sparsity of the representation is induced by an
$\ell_1$-based penalty on its coefficients. The results obtained on synthetic
data and real images show that the algorithm is capable of recovering the
expected dictionaries. Furthermore, on a benchmark dataset, we show that the
image features obtained from the dual matrix yield state-of-the-art
classification performance while being much less computational intensive.
|
1011.3754
|
Principles of Physical Layer Security in Multiuser Wireless Networks: A
Survey
|
cs.IT math.IT
|
This paper provides a comprehensive review of the domain of physical layer
security in multiuser wireless networks. The essential premise of
physical-layer security is to enable the exchange of confidential messages over
a wireless medium in the presence of unauthorized eavesdroppers without relying
on higher-layer encryption. This can be achieved primarily in two ways: without
the need for a secret key by intelligently designing transmit coding
strategies, or by exploiting the wireless communication medium to develop
secret keys over public channels. The survey begins with an overview of the
foundations dating back to the pioneering work of Shannon and Wyner on
information-theoretic security. We then describe the evolution of secure
transmission strategies from point-to-point channels to multiple-antenna
systems, followed by generalizations to multiuser broadcast, multiple-access,
interference, and relay networks. Secret-key generation and establishment
protocols based on physical layer mechanisms are subsequently covered.
Approaches for secrecy based on channel coding design are then examined, along
with a description of inter-disciplinary approaches based on game theory and
stochastic geometry. The associated problem of physical-layer message
authentication is also introduced briefly. The survey concludes with
observations on potential research directions in this area.
|
1011.3761
|
Lossy compression of discrete sources via Viterbi algorithm
|
cs.IT math.IT
|
We present a new lossy compressor for discrete-valued sources. For coding a
sequence $x^n$, the encoder starts by assigning a certain cost to each possible
reconstruction sequence. It then finds the one that minimizes this cost and
describes it losslessly to the decoder via a universal lossless compressor. The
cost of each sequence is a linear combination of its distance from the sequence
$x^n$ and a linear function of its $k^{\rm th}$ order empirical distribution.
The structure of the cost function allows the encoder to employ the Viterbi
algorithm to recover the minimizer of the cost. We identify a choice of the
coefficients comprising the linear function of the empirical distribution used
in the cost function which ensures that the algorithm universally achieves the
optimum rate-distortion performance of any stationary ergodic source in the
limit of large $n$, provided that $k$ diverges as $o(\log n)$. Iterative
techniques for approximating the coefficients, which alleviate the
computational burden of finding the optimal coefficients, are proposed and
studied.
|
1011.3768
|
Detecting and Tracking the Spread of Astroturf Memes in Microblog
Streams
|
cs.SI cs.CY
|
Online social media are complementing and in some cases replacing
person-to-person social interaction and redefining the diffusion of
information. In particular, microblogs have become crucial grounds on which
public relations, marketing, and political battles are fought. We introduce an
extensible framework that will enable the real-time analysis of meme diffusion
in social media by mining, visualizing, mapping, classifying, and modeling
massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect
astroturfing, smear campaigns, and other misinformation in the context of U.S.
political elections. We present some cases of abusive behaviors uncovered by
our service. Finally, we discuss promising preliminary results on the detection
of suspicious memes via supervised learning based on features extracted from
the topology of the diffusion networks, sentiment analysis, and crowdsourced
annotations.
|
1011.3812
|
Comments on Degrees of freedom region for $K$-user interference channel
with $M$ antennas
|
cs.IT math.IT
|
For a $K$-user interference channel with $M$ antenna at each transmitter and
each receiver, the maximum total DoF of this channel has been previously
determined to be $\max \sum_{k=1}^K d_k = MK/2$. However, the DoF region
remains to be unknown. In this short note, through a simple time-sharing
argument, we obtain the degrees of freedom (DoF) region of this channel.
|
1011.3834
|
Ising-like agent-based technology diffusion model: adoption patterns vs.
seeding strategies
|
physics.soc-ph cs.SI q-fin.TR
|
The well-known Ising model used in statistical physics was adapted to a
social dynamics context to simulate the adoption of a technological innovation.
The model explicitly combines (a) an individual's perception of the advantages
of an innovation and (b) social influence from members of the decision-maker's
social network. The micro-level adoption dynamics are embedded into an
agent-based model that allows exploration of macro-level patterns of technology
diffusion throughout systems with different configurations (number and
distributions of early adopters, social network topologies). In the present
work we carry out many numerical simulations. We find that when the gap between
the individual's perception of the options is high, the adoption speed
increases if the dispersion of early adopters grows. Another test was based on
changing the network topology by means of stochastic connections to a common
opinion reference (hub), which resulted in an increment in the adoption speed.
Finally, we performed a simulation of competition between options for both
regular and small world networks.
|
1011.3854
|
A probabilistic and RIPless theory of compressed sensing
|
cs.IT math.IT
|
This paper introduces a simple and very general theory of compressive
sensing. In this theory, the sensing mechanism simply selects sensing vectors
independently at random from a probability distribution F; it includes all
models - e.g. Gaussian, frequency measurements - discussed in the literature,
but also provides a framework for new measurement strategies as well. We prove
that if the probability distribution F obeys a simple incoherence property and
an isotropy property, one can faithfully recover approximately sparse signals
from a minimal number of noisy measurements. The novelty is that our recovery
results do not require the restricted isometry property (RIP) - they make use
of a much weaker notion - or a random model for the signal. As an example, the
paper shows that a signal with s nonzero entries can be faithfully recovered
from about s log n Fourier coefficients that are contaminated with noise.
|
1011.3867
|
Interference Alignment Through User Cooperation for Two-cell MIMO
Interfering Broadcast Channels
|
cs.IT math.IT
|
This paper focuses on two-cell multiple-input multiple-output (MIMO) Gaussian
interfering broadcast channels (MIMO-IFBC) with $K$ cooperating users on the
cell-boundary of each BS. It corresponds to a downlink scenario for cellular
networks with two base stations (BSs), and $K$ users equipped with Wi-Fi
interfaces enabling to cooperate among users on a peer-to-peer basis. In this
scenario, we propose a novel interference alignment (IA) technique exploiting
user cooperation. Our proposed algorithm obtains the achievable degrees of
freedom (DoF) of 2K when each BS and user have $M=K+1$ transmit antennas and
$N=K$ receive antennas, respectively. Furthermore, the algorithm requires only
a small amount of channel feedback information with the aid of the user
cooperation channels. The simulations demonstrate that not only are the
analytical results valid, but the achievable DoF of our proposed algorithm also
outperforms those of conventional techniques.
|
1011.3870
|
Network error correction with unequal link capacities
|
cs.IT math.IT
|
This paper studies the capacity of single-source single-sink noiseless
networks under adversarial or arbitrary errors on no more than z edges. Unlike
prior papers, which assume equal capacities on all links, arbitrary link
capacities are considered. Results include new upper bounds, network error
correction coding strategies, and examples of network families where our bounds
are tight. An example is provided of a network where the capacity is 50%
greater than the best rate that can be achieved with linear coding. While
coding at the source and sink suffices in networks with equal link capacities,
in networks with unequal link capacities, it is shown that intermediate nodes
may have to do coding, nonlinear error detection, or error correction in order
to achieve the network error correction capacity.
|
1011.3878
|
On the Critical Coupling for Kuramoto Oscillators
|
math.DS cs.SY math-ph math.MP math.OC nlin.CD
|
The Kuramoto model captures various synchronization phenomena in biological
and man-made systems of coupled oscillators. It is well-known that there exists
a critical coupling strength among the oscillators at which a phase transition
from incoherency to synchronization occurs. This paper features four
contributions. First, we characterize and distinguish the different notions of
synchronization used throughout the literature and formally introduce the
concept of phase cohesiveness as an analysis tool and performance index for
synchronization. Second, we review the vast literature providing necessary,
sufficient, implicit, and explicit estimates of the critical coupling strength
for finite and infinite-dimensional, and for first and second-order Kuramoto
models. Third, we present the first explicit necessary and sufficient condition
on the critical coupling to achieve synchronization in the finite-dimensional
Kuramoto model for an arbitrary distribution of the natural frequencies. The
multiplicative gap in the synchronization condition yields a practical
stability result determining the admissible initial and the guaranteed ultimate
phase cohesiveness as well as the guaranteed asymptotic magnitude of the order
parameter. Fourth and finally, we extend our analysis to multi-rate Kuramoto
models consisting of second-order Kuramoto oscillators with inertia and viscous
damping together with first-order Kuramoto oscillators with multiple time
constants. We prove that the multi-rate Kuramoto model is locally topologically
conjugate to a first-order Kuramoto model with scaled natural frequencies, and
we present necessary and sufficient conditions for almost global phase
synchronization and local frequency synchronization. Interestingly, these
conditions do not depend on the inertiae which contradicts prior observations
on the role of inertiae in synchronization of second-order Kuramoto models.
|
1011.3879
|
Algebraic Watchdog: Mitigating Misbehavior in Wireless Network Coding
|
cs.CR cs.IT cs.NI math.IT
|
We propose a secure scheme for wireless network coding, called the algebraic
watchdog. By enabling nodes to detect malicious behaviors probabilistically and
use overheard messages to police their downstream neighbors locally, the
algebraic watchdog delivers a secure global self-checking network. Unlike
traditional Byzantine detection protocols which are receiver-based, this
protocol gives the senders an active role in checking the node downstream. The
key idea is inspired by Marti et al.'s watchdog-pathrater, which attempts to
detect and mitigate the effects of routing misbehavior.
As an initial building block of a such system, we first focus on a two-hop
network. We present a graphical model to understand the inference process nodes
execute to police their downstream neighbors; as well as to compute, analyze,
and approximate the probabilities of misdetection and false detection. In
addition, we present an algebraic analysis of the performance using an
hypothesis testing framework that provides exact formulae for probabilities of
false detection and misdetection.
We then extend the algebraic watchdog to a more general network setting, and
propose a protocol in which we can establish trust in coded systems in a
distributed manner. We develop a graphical model to detect the presence of an
adversarial node downstream within a general multi-hop network. The structure
of the graphical model (a trellis) lends itself to well-known algorithms, such
as the Viterbi algorithm, which can compute the probabilities of misdetection
and false detection. We show analytically that as long as the min-cut is not
dominated by the Byzantine adversaries, upstream nodes can monitor downstream
neighbors and allow reliable communication with certain probability. Finally,
we present simulation results that support our analysis.
|
1011.3890
|
Optimal Distributed Beamforming for MISO Interference Channels
|
cs.IT math.IT
|
We consider the problem of quantifying the Pareto optimal boundary in the
achievable rate region over multiple-input single-output (MISO) interference
channels, where the problem boils down to solving a sequence of convex
feasibility problems after certain transformations. The feasibility problem is
solved by two new distributed optimal beamforming algorithms, where the first
one is to parallelize the computation based on the method of alternating
projections, and the second one is to localize the computation based on the
method of cyclic projections. Convergence proofs are established for both
algorithms.
|
1011.3912
|
Artificial Hormone Reaction Networks: Towards Higher Evolvability in
Evolutionary Multi-Modular Robotics
|
cs.RO cs.AI cs.NE
|
The semi-automatic or automatic synthesis of robot controller software is
both desirable and challenging. Synthesis of rather simple behaviors such as
collision avoidance by applying artificial evolution has been shown multiple
times. However, the difficulty of this synthesis increases heavily with
increasing complexity of the task that should be performed by the robot. We try
to tackle this problem of complexity with Artificial Homeostatic Hormone
Systems (AHHS), which provide both intrinsic, homeostatic processes and
(transient) intrinsic, variant behavior. By using AHHS the need for pre-defined
controller topologies or information about the field of application is
minimized. We investigate how the principle design of the controller and the
hormone network size affects the overall performance of the artificial
evolution (i.e., evolvability). This is done by comparing two variants of AHHS
that show different effects when mutated. We evolve a controller for a robot
built from five autonomous, cooperating modules. The desired behavior is a form
of gait resulting in fast locomotion by using the modules' main hinges.
|
1011.3970
|
From Social Simulation to Integrative System Design
|
cs.CY cs.CE physics.comp-ph physics.soc-ph
|
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.
|
1011.3985
|
Perfect Secrecy Using Compressed Sensing
|
cs.IT cs.CR math.IT
|
In this paper we consider the compressed sensing-based encryption and
proposed the conditions in which the perfect secrecy is obtained. We prove when
the Restricted Isometery Property (RIP) is hold and the number of measurements
is more than two times of sparsity level i.e. M \geq 2k, the perfect secrecy
condition introduced by Shannon is achievable if message block is not equal to
zero or we have infinite block length
|
1011.4028
|
On the approximation ability of evolutionary optimization with
application to minimum set cover
|
cs.NE
|
Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural
evolution. They are often used to obtain satisficing solutions in practice. In
this paper, we investigate a largely underexplored issue: the approximation
performance of EAs in terms of how close the solution obtained is to an optimal
solution. We study an EA framework named simple EA with isolated population
(SEIP) that can be implemented as a single- or multi-objective EA. We analyze
the approximation performance of SEIP using the partial ratio, which
characterizes the approximation ratio that can be guaranteed. Specifically, we
analyze SEIP using a set cover problem that is NP-hard. We find that in a
simple configuration, SEIP efficiently achieves an $H_n$-approximation ratio,
the asymptotic lower bound, for the unbounded set cover problem. We also find
that SEIP efficiently achieves an $(H_k-\frac{k-1}/{8k^9})$-approximation
ratio, the currently best-achievable result, for the k-set cover problem.
Moreover, for an instance class of the k-set cover problem, we disclose how
SEIP, using either one-bit or bit-wise mutation, can overcome the difficulty
that limits the greedy algorithm.
|
1011.4058
|
Modeling Image Structure with Factorized Phase-Coupled Boltzmann
Machines
|
cs.CV cond-mat.dis-nn q-bio.NC stat.ML
|
We describe a model for capturing the statistical structure of local
amplitude and local spatial phase in natural images. The model is based on a
recently developed, factorized third-order Boltzmann machine that was shown to
be effective at capturing higher-order structure in images by modeling
dependencies among squared filter outputs (Ranzato and Hinton, 2010). Here, we
extend this model to $L_p$-spherically symmetric subspaces. In order to model
local amplitude and phase structure in images, we focus on the case of two
dimensional subspaces, and the $L_2$-norm. When trained on natural images the
model learns subspaces resembling quadrature-pair Gabor filters. We then
introduce an additional set of hidden units that model the dependencies among
subspace phases. These hidden units form a combinatorial mixture of phase
coupling distributions, concentrated in the sum and difference of phase pairs.
When adapted to natural images, these distributions capture local spatial phase
structure in natural images.
|
1011.4071
|
Supervised Random Walks: Predicting and Recommending Links in Social
Networks
|
cs.SI cs.AI cs.DS physics.soc-ph stat.ML
|
Predicting the occurrence of links is a fundamental problem in networks. In
the link prediction problem we are given a snapshot of a network and would like
to infer which interactions among existing members are likely to occur in the
near future or which existing interactions are we missing. Although this
problem has been extensively studied, the challenge of how to effectively
combine the information from the network structure with rich node and edge
attribute data remains largely open.
We develop an algorithm based on Supervised Random Walks that naturally
combines the information from the network structure with node and edge level
attributes. We achieve this by using these attributes to guide a random walk on
the graph. We formulate a supervised learning task where the goal is to learn a
function that assigns strengths to edges in the network such that a random
walker is more likely to visit the nodes to which new links will be created in
the future. We develop an efficient training algorithm to directly learn the
edge strength estimation function.
Our experiments on the Facebook social graph and large collaboration networks
show that our approach outperforms state-of-the-art unsupervised approaches as
well as approaches that are based on feature extraction.
|
1011.4098
|
Understanding Cascading Failures in Power Grids
|
cs.SI math.PR stat.AP
|
In the past, we have observed several large blackouts, i.e. loss of power to
large areas. It has been noted by several researchers that these large
blackouts are a result of a cascade of failures of various components. As a
power grid is made up of several thousands or even millions of components
(relays, breakers, transformers, etc.), it is quite plausible that a few of
these components do not perform their function as desired. Their
failure/misbehavior puts additional burden on the working components causing
them to misbehave, and thus leading to a cascade of failures.
The complexity of the entire power grid makes it difficult to model each and
every individual component and study the stability of the entire system. For
this reason, it is often the case that abstract models of the working of the
power grid are constructed and then analyzed. These models need to be
computationally tractable while serving as a reasonable model for the entire
system. In this work, we construct one such model for the power grid, and
analyze it.
|
1011.4104
|
Clustering and Latent Semantic Indexing Aspects of the Singular Value
Decomposition
|
cs.LG cs.NA math.SP
|
This paper discusses clustering and latent semantic indexing (LSI) aspects of
the singular value decomposition (SVD). The purpose of this paper is twofold.
The first is to give an explanation on how and why the singular vectors can be
used in clustering. And the second is to show that the two seemingly unrelated
SVD aspects actually originate from the same source: related vertices tend to
be more clustered in the graph representation of lower rank approximate matrix
using the SVD than in the original semantic graph. Accordingly, the SVD can
improve retrieval performance of an information retrieval system since queries
made to the approximate matrix can retrieve more relevant documents and filter
out more irrelevant documents than the same queries made to the original
matrix. By utilizing this fact, we will devise an LSI algorithm that mimicks
SVD capability in clustering related vertices. Convergence analysis shows that
the algorithm is convergent and produces a unique solution for each input.
Experimental results using some standard datasets in LSI research show that
retrieval performances of the algorithm are comparable to the SVD's. In
addition, the algorithm is more practical and easier to use because there is no
need to determine decomposition rank which is crucial in driving retrieval
performance of the SVD.
|
1011.4109
|
Design and simulation of a sigma delta ADC
|
cs.IT cs.AR math.IT
|
In this report we describe the design and simulation of a Sigma Delta ADC in
Matlan/Simulink
|
1011.4155
|
Motifs de graphe pour le calcul de d\'ependances syntaxiques compl\`etes
|
cs.CL
|
This article describes a method to build syntactical dependencies starting
from the phrase structure parsing process. The goal is to obtain all the
information needed for a detailled semantical analysis. Interaction Grammars
are used for parsing; the saturation of polarities which is the core of this
formalism can be mapped to dependency relation. Formally, graph patterns are
used to express the set of constraints which control dependency creations.
|
1011.4161
|
Community characterization of heterogeneous complex systems
|
physics.soc-ph cs.SI physics.data-an
|
We introduce an analytical statistical method to characterize the communities
detected in heterogeneous complex systems. By posing a suitable null
hypothesis, our method makes use of the hypergeometric distribution to assess
the probability that a given property is over-expressed in the elements of a
community with respect to all the elements of the investigated set. We apply
our method to two specific complex networks, namely a network of world movies
and a network of physics preprints. The characterization of the elements and of
the communities is done in terms of languages and countries for the movie
network and of journals and subject categories for papers. We find that our
method is able to characterize clearly the identified communities. Moreover our
method works well both for large and for small communities.
|
1011.4199
|
Biologically Inspired Design Principles for Scalable, Robust, Adaptive,
Decentralized Search and Automated Response (RADAR)
|
cs.NE cs.DC cs.SY math.OC q-bio.QM
|
Distributed search problems are ubiquitous in Artificial Life (ALife). Many
distributed search problems require identifying a rare and previously unseen
event and producing a rapid response. This challenge amounts to finding and
removing an unknown needle in a very large haystack. Traditional computational
search models are unlikely to find, nonetheless, appropriately respond to,
novel events, particularly given data distributed across multiple platforms in
a variety of formats and sources with variable and unknown reliability.
Biological systems have evolved solutions to distributed search and response
under uncertainty. Immune systems and ant colonies efficiently scale up
massively parallel search with automated response in highly dynamic
environments, and both do so using distributed coordination without centralized
control. These properties are relevant to ALife, where distributed, autonomous,
robust and adaptive control is needed to design robot swarms, mobile computing
networks, computer security systems and other distributed intelligent systems.
They are also relevant for searching, tracking the spread of ideas, and
understanding the impact of innovations in online social networks. We review
design principles for Scalable Robust, Adaptive, Decentralized search with
Automated Response (Scalable RADAR) in biology. We discuss how biological RADAR
scales up efficiently, and then discuss in detail how modular search in the
immune system can be mimicked or built upon in ALife. Such search mechanisms
are particularly useful when components have limited capacity to communicate
and social or physical distance makes long distance communication more costly.
|
1011.4237
|
Variational and symplectic approach of the model-free control
|
cs.SY math.OC
|
We propose a theoretical development of the model-free control in order to
extend its robustness capabilities. The proposed method is based on the
auto-tuning of the model-free controller parameter using an optimal approach.
Some examples are discussed to illustrate our approach.
|
1011.4302
|
The Effects of Narrowband Interference on Finite-Resolution IR-UWB
Digital Receiver
|
cs.IT math.IT
|
Finite-resolution digital receiver is recently considered as a potential way
to Ultra Wide Band (UWB) communication systems due to its ability of mitigating
the challenge of Analog-Digital Converter (ADC). In this paper, the effects of
narrowband interference (NBI) are investigated when finite-resolution digital
receiver is used for Impulse Radio-UWB (IR-UWB) system. It is shown that
finite-resolution receiver enlarges the impact of NBI. The lower resolution of
the UWB receiver is, the more degradations NBI causes.
|
1011.4321
|
A Fuzzy Clustering Model for Fuzzy Data with Outliers
|
cs.CV
|
In this paper a fuzzy clustering model for fuzzy data with outliers is
proposed. The model is based on Wasserstein distance between interval valued
data which is generalized to fuzzy data. In addition, Keller's approach is used
to identify outliers and reduce their influences. We have also defined a
transformation to change our distance to the Euclidean distance. With the help
of this approach, the problem of fuzzy clustering of fuzzy data is reduced to
fuzzy clustering of crisp data. In order to show the performance of the
proposed clustering algorithm, two simulation experiments are discussed.
|
1011.4324
|
Moment-Based Spectral Analysis of Large-Scale Networks Using Local
Structural Information
|
cs.SI cs.SY math.DS math.OC physics.data-an physics.soc-ph
|
The eigenvalues of matrices representing the structure of large-scale complex
networks present a wide range of applications, from the analysis of dynamical
processes taking place in the network to spectral techniques aiming to rank the
importance of nodes in the network. A common approach to study the relationship
between the structure of a network and its eigenvalues is to use synthetic
random networks in which structural properties of interest, such as degree
distributions, are prescribed. Although very common, synthetic models present
two major flaws: (\emph{i}) These models are only suitable to study a very
limited range of structural properties, and (\emph{ii}) they implicitly induce
structural properties that are not directly controlled and can deceivingly
influence the network eigenvalue spectrum. In this paper, we propose an
alternative approach to overcome these limitations. Our approach is not based
on synthetic models, instead, we use algebraic graph theory and convex
optimization to study how structural properties influence the spectrum of
eigenvalues of the network. Using our approach, we can compute with low
computational overhead global spectral properties of a network from its local
structural properties. We illustrate our approach by studying how structural
properties of online social networks influence their eigenvalue spectra.
|
1011.4328
|
Graphical Models Concepts in Compressed Sensing
|
cs.IT math.IT
|
This paper surveys recent work in applying ideas from graphical models and
message passing algorithms to solve large scale regularized regression
problems. In particular, the focus is on compressed sensing reconstruction via
ell_1 penalized least-squares (known as LASSO or BPDN). We discuss how to
derive fast approximate message passing algorithms to solve this problem.
Surprisingly, the analysis of such algorithms allows to prove exact
high-dimensional limit results for the LASSO risk.
This paper will appear as a chapter in a book on `Compressed Sensing' edited
by Yonina Eldar and Gitta Kutyniok.
|
1011.4362
|
Should one compute the Temporal Difference fix point or minimize the
Bellman Residual? The unified oblique projection view
|
cs.AI
|
We investigate projection methods, for evaluating a linear approximation of
the value function of a policy in a Markov Decision Process context. We
consider two popular approaches, the one-step Temporal Difference fix-point
computation (TD(0)) and the Bellman Residual (BR) minimization. We describe
examples, where each method outperforms the other. We highlight a simple
relation between the objective function they minimize, and show that while BR
enjoys a performance guarantee, TD(0) does not in general. We then propose a
unified view in terms of oblique projections of the Bellman equation, which
substantially simplifies and extends the characterization of (schoknecht,2002)
and the recent analysis of (Yu & Bertsekas, 2008). Eventually, we describe some
simulations that suggest that if the TD(0) solution is usually slightly better
than the BR solution, its inherent numerical instability makes it very bad in
some cases, and thus worse on average.
|
1011.4394
|
Measuring the Hierarchy of Feedforward Networks
|
physics.data-an cond-mat.dis-nn cs.SI nlin.AO physics.soc-ph
|
In this paper we explore the concept of hierarchy as a quantifiable
descriptor of ordered structures, departing from the definition of three
conditions to be satisfied for a hierarchical structure: {\em order}, {\em
predictability} and {\em pyramidal structure}. According to these principles we
define a hierarchical index taking concepts from graph and information theory.
This estimator allows to quantify the hierarchical character of any system
susceptible to be abstracted in a feedforward causal graph, i.e., a directed
acyclic graph defined in a single connected structure. Our hierarchical index
is a balance between this predictability and pyramidal condition by the
definition of two entropies: one attending the onward flow and other for the
backward reversion. We show how this index allows to identify hierarchical,
anti-hierarchical and non hierarchical structures. Our formalism reveals that
departing from the defined conditions for a hierarchical structure, feedforward
trees and the inverted tree graphs emerge as the only causal structures of
maximal hierarchical and anti-hierarchical systems, respectively. Conversely,
null values of the hierarchical index are attributed to a number of different
configuration networks; from linear chains, due to their lack of pyramid
structure, to full-connected feedforward graphs where the diversity of onward
pathways is canceled by the uncertainty (lack of predictability) when going
backwards. Some illustrative examples are provided for the distinction among
these three types of hierarchical causal graphs.
|
1011.4445
|
Voter model with non-Poissonian interevent intervals
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Recent analysis of social communications among humans has revealed that the
interval between interactions for a pair of individuals and for an individual
often follows a long-tail distribution. We investigate the effect of such a
non-Poissonian nature of human behavior on dynamics of opinion formation. We
use a variant of the voter model and numerically compare the time to consensus
of all the voters with different distributions of interevent intervals and
different networks. Compared with the exponential distribution of interevent
intervals (i.e., the standard voter model), the power-law distribution of
interevent intervals slows down consensus on the ring. This is because of the
memory effect; in the power-law case, the expected time until the next update
event on a link is large if the link has not had an update event for a long
time. On the complete graph, the consensus time in the power-law case is close
to that in the exponential case. Regular graphs bridge these two results such
that the slowing down of the consensus in the power-law case as compared to the
exponential case is less pronounced as the degree increases.
|
1011.4532
|
New Algorithms on Wavelet Trees and Applications to Information
Retrieval
|
cs.DS cs.IR
|
Wavelet trees are widely used in the representation of sequences,
permutations, text collections, binary relations, discrete points, and other
succinct data structures. We show, however, that this still falls short of
exploiting all of the virtues of this versatile data structure. In particular
we show how to use wavelet trees to solve fundamental algorithmic problems such
as {\em range quantile} queries, {\em range next value} queries, and {\em range
intersection} queries. We explore several applications of these queries in
Information Retrieval, in particular {\em document retrieval} in hierarchical
and temporal documents, and in the representation of {\em inverted lists}.
|
1011.4597
|
Energy-Efficient Precoding for Multiple-Antenna Terminals
|
cs.IT math.IT
|
The problem of energy-efficient precoding is investigated when the terminals
in the system are equipped with multiple antennas. Considering static and
fast-fading multiple-input multiple-output (MIMO) channels, the
energy-efficiency is defined as the transmission rate to power ratio and shown
to be maximized at low transmit power. The most interesting case is the one of
slow fading MIMO channels. For this type of channels, the optimal precoding
scheme is generally not trivial. Furthermore, using all the available transmit
power is not always optimal in the sense of energy-efficiency (which, in this
case, corresponds to the communication-theoretic definition of the
goodput-to-power (GPR) ratio). Finding the optimal precoding matrices is shown
to be a new open problem and is solved in several special cases: 1. when there
is only one receive antenna; 2. in the low or high signal-to-noise ratio
regime; 3. when uniform power allocation and the regime of large numbers of
antennas are assumed. A complete numerical analysis is provided to illustrate
the derived results and stated conjectures. In particular, the impact of the
number of antennas on the energy-efficiency is assessed and shown to be
significant.
|
1011.4598
|
Power Allocation Games in Wireless Networks of Multi-antenna Terminals
|
cs.IT math.IT
|
We consider wireless networks that can be modeled by multiple access channels
in which all the terminals are equipped with multiple antennas. The propagation
model used to account for the effects of transmit and receive antenna
correlations is the unitary-invariant-unitary model, which is one of the most
general models available in the literature. In this context, we introduce and
analyze two resource allocation games. In both games, the mobile stations
selfishly choose their power allocation policies in order to maximize their
individual uplink transmission rates; in particular they can ignore some
specified centralized policies. In the first game considered, the base station
implements successive interference cancellation (SIC) and each mobile station
chooses his best space-time power allocation scheme; here, a coordination
mechanism is used to indicate to the users the order in which the receiver
applies SIC. In the second framework, the base station is assumed to implement
single-user decoding. For these two games a thorough analysis of the Nash
equilibrium is provided: the existence and uniqueness issues are addressed; the
corresponding power allocation policies are determined by exploiting random
matrix theory; the sum-rate efficiency of the equilibrium is studied
analytically in the low and high signal-to-noise ratio regimes and by
simulations in more typical scenarios. Simulations show that, in particular,
the sum-rate efficiency is high for the type of systems investigated and the
performance loss due to the use of the proposed suboptimum coordination
mechanism is very small.
|
1011.4602
|
Gaussian Broadcast Channels with an Orthogonal and Bidirectional
Cooperation Link
|
cs.IT math.IT
|
This paper considers a system where one transmitter broadcasts a single
common message to two receivers linked by a bidirectional cooperation channel,
which is assumed to be orthogonal to the downlink channel. Assuming a
simplified setup where, in particular, scalar relaying protocols are used and
channel coding is not exploited, we want to provide elements of response to
several questions of practical interest. Here are the main underlying issues:
1. The way of recombining the signals at the receivers; 2. The optimal number
of cooperation rounds; 3. The way of cooperating (symmetrically or
asymmetrically; which receiver should start cooperating in the latter case); 4.
The influence of spectral resources. These issues are considered by studying
the performance of the assumed system through analytical results when they are
derivable and through simulation results. For the particular choices we made,
the results sometimes do not coincide with those available for the discrete
counterpart of the studied channel.
|
1011.4609
|
Bounds from a Card Trick
|
cs.IT math.IT
|
We describe a new variation of a mathematical card trick, whose analysis
leads to new lower bounds for data compression and estimating the entropy of
Markov sources.
|
1011.4615
|
Generalized Tree-Based Wavelet Transform
|
cs.CV
|
In this paper we propose a new wavelet transform applicable to functions
defined on graphs, high dimensional data and networks. The proposed method
generalizes the Haar-like transform proposed in [1], and it is defined via a
hierarchical tree, which is assumed to capture the geometry and structure of
the input data. It is applied to the data using a modified version of the
common one-dimensional (1D) wavelet filtering and decimation scheme, which can
employ different wavelet filters. In each level of this wavelet decomposition
scheme, a permutation derived from the tree is applied to the approximation
coefficients, before they are filtered. We propose a tree construction method
that results in an efficient representation of the input function in the
transform domain. We show that the proposed transform is more efficient than
both the 1D and two-dimensional (2D) separable wavelet transforms in
representing images. We also explore the application of the proposed transform
to image denoising, and show that combined with a subimage averaging scheme, it
achieves denoising results which are similar to those obtained with the K-SVD
algorithm.
|
1011.4623
|
Opinion Polarity Identification through Adjectives
|
cs.CL
|
"What other people think" has always been an important piece of information
during various decision-making processes. Today people frequently make their
opinions available via the Internet, and as a result, the Web has become an
excellent source for gathering consumer opinions. There are now numerous Web
resources containing such opinions, e.g., product reviews forums, discussion
groups, and Blogs. But, due to the large amount of information and the wide
range of sources, it is essentially impossible for a customer to read all of
the reviews and make an informed decision on whether to purchase the product.
It is also difficult for the manufacturer or seller of a product to accurately
monitor customer opinions. For this reason, mining customer reviews, or opinion
mining, has become an important issue for research in Web information
extraction. One of the important topics in this research area is the
identification of opinion polarity. The opinion polarity of a review is usually
expressed with values 'positive', 'negative' or 'neutral'. We propose a
technique for identifying polarity of reviews by identifying the polarity of
the adjectives that appear in them. Our evaluation shows the technique can
provide accuracy in the area of 73%, which is well above the 58%-64% provided
by naive Bayesian classifiers.
|
1011.4632
|
Random Projections for $k$-means Clustering
|
cs.AI cs.DS
|
This paper discusses the topic of dimensionality reduction for $k$-means
clustering. We prove that any set of $n$ points in $d$ dimensions (rows in a
matrix $A \in \RR^{n \times d}$) can be projected into $t = \Omega(k / \eps^2)$
dimensions, for any $\eps \in (0,1/3)$, in $O(n d \lceil \eps^{-2} k/ \log(d)
\rceil )$ time, such that with constant probability the optimal $k$-partition
of the point set is preserved within a factor of $2+\eps$. The projection is
done by post-multiplying $A$ with a $d \times t$ random matrix $R$ having
entries $+1/\sqrt{t}$ or $-1/\sqrt{t}$ with equal probability. A numerical
implementation of our technique and experiments on a large face images dataset
verify the speed and the accuracy of our theoretical results.
|
1011.4644
|
Stochastic blockmodels with growing number of classes
|
math.ST cs.SI stat.ME stat.ML stat.TH
|
We present asymptotic and finite-sample results on the use of stochastic
blockmodels for the analysis of network data. We show that the fraction of
misclassified network nodes converges in probability to zero under maximum
likelihood fitting when the number of classes is allowed to grow as the root of
the network size and the average network degree grows at least
poly-logarithmically in this size. We also establish finite-sample confidence
bounds on maximum-likelihood blockmodel parameter estimates from data
comprising independent Bernoulli random variates; these results hold uniformly
over class assignment. We provide simulations verifying the conditions
sufficient for our results, and conclude by fitting a logit parameterization of
a stochastic blockmodel with covariates to a network data example comprising a
collection of Facebook profiles, resulting in block estimates that reveal
residual structure.
|
1011.4682
|
Analysis of attractor distances in Random Boolean Networks
|
cs.NE nlin.CD physics.bio-ph q-bio.QM
|
We study the properties of the distance between attractors in Random Boolean
Networks, a prominent model of genetic regulatory networks. We define three
distance measures, upon which attractor distance matrices are constructed and
their main statistic parameters are computed. The experimental analysis shows
that ordered networks have a very clustered set of attractors, while chaotic
networks' attractors are scattered; critical networks show, instead, a pattern
with characteristics of both ordered and chaotic networks.
|
1011.4725
|
Lossy Broadcasting in Two-Way Relay Networks with Common Reconstructions
|
cs.IT math.IT
|
The broadcast phase (downlink transmission) of the two-way relay network is
studied in the source coding and joint source-channel coding settings. The
rates needed for reliable communication are characterised for a number of
special cases including: small distortions, deterministic distortion measures,
and jointly Gaussian sources with quadratic distortion measures. The broadcast
problem is also studied with common-reconstruction decoding constraints, and
the rates needed for reliable communication are characterised for all discrete
memoryless sources and per-letter distortion measures.
|
1011.4748
|
Combinatorial Network Optimization with Unknown Variables: Multi-Armed
Bandits with Linear Rewards
|
math.OC cs.LG cs.NI math.PR
|
In the classic multi-armed bandits problem, the goal is to have a policy for
dynamically operating arms that each yield stochastic rewards with unknown
means. The key metric of interest is regret, defined as the gap between the
expected total reward accumulated by an omniscient player that knows the reward
means for each arm, and the expected total reward accumulated by the given
policy. The policies presented in prior work have storage, computation and
regret all growing linearly with the number of arms, which is not scalable when
the number of arms is large. We consider in this work a broad class of
multi-armed bandits with dependent arms that yield rewards as a linear
combination of a set of unknown parameters. For this general framework, we
present efficient policies that are shown to achieve regret that grows
logarithmically with time, and polynomially in the number of unknown parameters
(even though the number of dependent arms may grow exponentially). Furthermore,
these policies only require storage that grows linearly in the number of
unknown parameters. We show that this generalization is broadly applicable and
useful for many interesting tasks in networks that can be formulated as
tractable combinatorial optimization problems with linear objective functions,
such as maximum weight matching, shortest path, and minimum spanning tree
computations.
|
1011.4752
|
The Non-Bayesian Restless Multi-Armed Bandit: a Case of Near-Logarithmic
Regret
|
math.OC cs.LG cs.NI math.PR
|
In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are
$N$ arms, with rewards on all arms evolving at each time as Markov chains with
known parameters. A player seeks to activate $K \geq 1$ arms at each time in
order to maximize the expected total reward obtained over multiple plays. RMAB
is a challenging problem that is known to be PSPACE-hard in general. We
consider in this work the even harder non-Bayesian RMAB, in which the
parameters of the Markov chain are assumed to be unknown \emph{a priori}. We
develop an original approach to this problem that is applicable when the
corresponding Bayesian problem has the structure that, depending on the known
parameter values, the optimal solution is one of a prescribed finite set of
policies. In such settings, we propose to learn the optimal policy for the
non-Bayesian RMAB by employing a suitable meta-policy which treats each policy
from this finite set as an arm in a different non-Bayesian multi-armed bandit
problem for which a single-arm selection policy is optimal. We demonstrate this
approach by developing a novel sensing policy for opportunistic spectrum access
over unknown dynamic channels. We prove that our policy achieves
near-logarithmic regret (the difference in expected reward compared to a
model-aware genie), which leads to the same average reward that can be achieved
by the optimal policy under a known model. This is the first such result in the
literature for a non-Bayesian RMAB.
|
1011.4792
|
Pair-wise Markov Random Fields Applied to the Design of Low Complexity
MIMO Detectors
|
cs.IT math.IT
|
Pair-wise Markov random fields (MRF) are considered for application to the
development of low complexity, iterative MIMO detection. Specifically, we
consider two types of MRF, namely, the fully-connected and ring-type. For the
edge potentials, we use the bivariate Gaussian function obtained by
marginalizing the posterior joint probability density under the Gaussian
assumption. Since the corresponding factor graphs are sparse, in the sense that
the number of edges connected to a factor node (edge degree) is only 2, the
computations are much easier than that of ML, which is similar to the belief
propagation (BP), or sum-product, algorithm that is run over the fully
connected factor graph. The BER performances for non-Gaussian input are
evaluated via simulation, and the results show the validity of the proposed
algorithms. We also customize the algorithm for Gaussian input to obtain the
Gaussian BP that is run over the two MRF and proves its convergence in mean to
the linear MMSE estimates. The result lies on the same line of those in [16]
and [24], but with differences in its graphical model and the message passing
rule. Since the MAP estimator for the Gaussian input is equivalent to the
linear MMSE estimator, it shows the optimality, in mean, of the scheme for
Gaussian input.
|
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