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1208.4294
|
Guaranteeing Spatial Uniformity in Diffusively-Coupled Systems
|
math.DS cs.SY math.OC
|
We present a condition that guarantees spatially uniformity in the solution
trajectories of a diffusively-coupled compartmental ODE model, where each
compartment represents a spatial domain of components interconnected through
diffusion terms with like components in different compartments. Each set of
like components has its own weighted undirected graph describing the topology
of the interconnection between compartments. The condition makes use of the
Jacobian matrix to describe the dynamics of each compartment as well as the
Laplacian eigenvalues of each of the graphs. We discuss linear matrix
inequalities that can be used to verify the condition guaranteeing spatial
uniformity, and apply the result to a coupled oscillator network. Next we turn
to reaction-diffusion PDEs with Neumann boundary conditions, and derive an
analogous condition guaranteeing spatial uniformity of solutions. The paper
contributes a relaxed condition to check spatial uniformity that allows
individual components to have their own specific diffusion terms and
interconnection structures.
|
1208.4316
|
An Online Character Recognition System to Convert Grantha Script to
Malayalam
|
cs.CV
|
This paper presents a novel approach to recognize Grantha, an ancient script
in South India and converting it to Malayalam, a prevalent language in South
India using online character recognition mechanism. The motivation behind this
work owes its credit to (i) developing a mechanism to recognize Grantha script
in this modern world and (ii) affirming the strong connection among Grantha and
Malayalam. A framework for the recognition of Grantha script using online
character recognition is designed and implemented. The features extracted from
the Grantha script comprises mainly of time-domain features based on writing
direction and curvature. The recognized characters are mapped to corresponding
Malayalam characters. The framework was tested on a bed of medium length
manuscripts containing 9-12 sample lines and printed pages of a book titled
Soundarya Lahari writtenin Grantha by Sri Adi Shankara to recognize the words
and sentences. The manuscript recognition rates with the system are for Grantha
as 92.11%, Old Malayalam 90.82% and for new Malayalam script 89.56%. The
recognition rates of pages of the printed book are for Grantha as 96.16%, Old
Malayalam script 95.22% and new Malayalam script as 92.32% respectively. These
results show the efficiency of the developed system.
|
1208.4381
|
A thermodynamic counterpart of the Axelrod model of social influence:
The one-dimensional case
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We propose a thermodynamic version of the Axelrod model of social influence.
In one-dimensional (1D) lattices, the thermodynamic model becomes a coupled
Potts model with a bonding interaction that increases with the site matching
traits. We analytically calculate thermodynamic and critical properties for a
1D system and show that an order-disorder phase transition only occurs at T = 0
independent of the number of cultural traits q and features F. The 1D
thermodynamic Axelrod model belongs to the same universality class of the Ising
and Potts models, notwithstanding the increase of the internal dimension of the
local degree of freedom and the state-dependent bonding interaction. We suggest
a unifying proposal to compare exponents across different discrete 1D models.
The comparison with our Hamiltonian description reveals that in the
thermodynamic limit the original out-of-equilibrium 1D Axelrod model with noise
behaves like an ordinary thermodynamic 1D interacting particle system.
|
1208.4384
|
Iterative graph cuts for image segmentation with a nonlinear statistical
shape prior
|
cs.CV math.OC physics.data-an q-bio.QM stat.AP
|
Shape-based regularization has proven to be a useful method for delineating
objects within noisy images where one has prior knowledge of the shape of the
targeted object. When a collection of possible shapes is available, the
specification of a shape prior using kernel density estimation is a natural
technique. Unfortunately, energy functionals arising from kernel density
estimation are of a form that makes them impossible to directly minimize using
efficient optimization algorithms such as graph cuts. Our main contribution is
to show how one may recast the energy functional into a form that is
minimizable iteratively and efficiently using graph cuts.
|
1208.4386
|
Cooperative Communication Based on Random Beamforming Strategy in
Wireless Sensor Networks
|
cs.NI cs.IT math.IT
|
This paper presents a two-phase cooperative communication strategy and an
optimal power allocation strategy to transmit sensor observations to a fusion
center in a large-scale sensor network. Outage probability is used to evaluate
the performance of the proposed system. Simulation results demonstrate that: 1)
when signal-to-noise ratio is low, the performance of the proposed system is
better than that of the multiple-input and multiple-output system over
uncorrelated slow fading Rayleigh channels; 2) given the transmission rate and
the total transmission SNR, there exists an optimal power allocation that
minimizes the outage probability; 3) on correlated slow fading Rayleigh
channels, channel correlation will degrade the system performance in linear
proportion to the correlation level.
|
1208.4390
|
On secure network coding with uniform wiretap sets
|
cs.IT math.IT
|
This paper shows determining the secrecy capacity of a unicast network with
uniform wiretap sets is at least as difficult as the k-unicast problem. In
particular, we show that a general k-unicast problem can be reduced to the
problem of finding the secrecy capacity of a corresponding single unicast
network with uniform link capacities and one arbitrary wiretap link.
|
1208.4391
|
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev
Descent
|
cs.CV cs.SY
|
We present a method to track the precise shape of an object in video based on
new modeling and optimization on a new Riemannian manifold of parameterized
regions.
Joint dynamic shape and appearance models, in which a template of the object
is propagated to match the object shape and radiance in the next frame, are
advantageous over methods employing global image statistics in cases of complex
object radiance and cluttered background. In cases of 3D object motion and
viewpoint change, self-occlusions and dis-occlusions of the object are
prominent, and current methods employing joint shape and appearance models are
unable to adapt to new shape and appearance information, leading to inaccurate
shape detection. In this work, we model self-occlusions and dis-occlusions in a
joint shape and appearance tracking framework.
Self-occlusions and the warp to propagate the template are coupled, thus a
joint problem is formulated. We derive a coarse-to-fine optimization scheme,
advantageous in object tracking, that initially perturbs the template by coarse
perturbations before transitioning to finer-scale perturbations, traversing all
scales, seamlessly and automatically. The scheme is a gradient descent on a
novel infinite-dimensional Riemannian manifold that we introduce. The manifold
consists of planar parameterized regions, and the metric that we introduce is a
novel Sobolev-type metric defined on infinitesimal vector fields on regions.
The metric has the property of resulting in a gradient descent that
automatically favors coarse-scale deformations (when they reduce the energy)
before moving to finer-scale deformations.
Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and
background show that occlusion/dis-occlusion modeling leads to superior shape
accuracy compared to recent methods employing joint shape/appearance models or
employing global statistics.
|
1208.4398
|
A Unified Approach for Modeling and Recognition of Individual Actions
and Group Activities
|
cs.CV stat.ML
|
Recognizing group activities is challenging due to the difficulties in
isolating individual entities, finding the respective roles played by the
individuals and representing the complex interactions among the participants.
Individual actions and group activities in videos can be represented in a
common framework as they share the following common feature: both are composed
of a set of low-level features describing motions, e.g., optical flow for each
pixel or a trajectory for each feature point, according to a set of composition
constraints in both temporal and spatial dimensions. In this paper, we present
a unified model to assess the similarity between two given individual or group
activities. Our approach avoids explicit extraction of individual actors,
identifying and representing the inter-person interactions. With the proposed
approach, retrieval from a video database can be performed through
Query-by-Example; and activities can be recognized by querying videos
containing known activities. The suggested video matching process can be
performed in an unsupervised manner. We demonstrate the performance of our
approach by recognizing a set of human actions and football plays.
|
1208.4405
|
Delay-Doppler Channel Estimation with Almost Linear Complexity
|
cs.IT math.IT math.NT math.RT
|
A fundamental task in wireless communication is Channel Estimation: Compute
the channel parameters a signal undergoes while traveling from a transmitter to
a receiver. In the case of delay-Doppler channel, a widely used method is the
Matched Filter algorithm. It uses a pseudo-random sequence of length N, and, in
case of non-trivial relative velocity between transmitter and receiver, its
computational complexity is O(N^{2}log(N)). In this paper we introduce a novel
approach of designing sequences that allow faster channel estimation. Using
group representation techniques we construct sequences, which enable us to
introduce a new algorithm, called the flag method, that significantly improves
the matched filter algorithm. The flag method finds the channel parameters in
O(mNlog(N)) operations, for channel of sparsity m. We discuss applications of
the flag method to GPS, radar system, and mobile communication as well.
|
1208.4414
|
A Lattice of Gambles
|
math.PR cs.IT math.IT
|
A gambler walks into a hypothetical fair casino with a very real dollar bill,
but by the time he leaves he's exchanged the dollar for a random amount of
money. What is lost in the process? It may be that the gambler walks out at the
end of the day, after a roller-coaster ride of winning and losing, with his
dollar still intact, or maybe even with two dollars. But what the gambler loses
the moment he places his first bet is position. He exchanges one distribution
of money for a distribution of lesser quality, from which he cannot return. Our
first discussion in this work connects known results of economic inequality and
majorization to the probability theory of gambling and Martingales. We provide
a simple proof that fair gambles cannot increase the Lorenz curve, and we also
constructively demonstrate that any sequence of non-increasing Lorenz curves
corresponds to at least one Martingale.
We next consider the efficiency of gambles. If all fair gambles are available
then one can move down the lattice of distributions defined by the Lorenz
ordering. However, the step from one distribution to the next is not unique. Is
there a sense of efficiency with which one can move down the Lorenz stream? One
approach would be to minimize the average total volume of money placed on the
table. In this case, it turns out that implementing part of the strategy using
private randomness can help reduce the need for the casino's randomness,
resulting in less money on the table that the casino cannot get its hands on.
|
1208.4415
|
Distributed Channel Synthesis
|
cs.IT math.IT
|
Two familiar notions of correlation are rediscovered as the extreme operating
points for distributed synthesis of a discrete memoryless channel, in which a
stochastic channel output is generated based on a compressed description of the
channel input. Wyner's common information is the minimum description rate
needed. However, when common randomness independent of the input is available,
the necessary description rate reduces to Shannon's mutual information. This
work characterizes the optimal trade-off between the amount of common
randomness used and the required rate of description. We also include a number
of related derivations, including the effect of limited local randomness, rate
requirements for secrecy, applications to game theory, and new insights into
common information duality.
Our proof makes use of a soft covering lemma, known in the literature for its
role in quantifying the resolvability of a channel. The direct proof
(achievability) constructs a feasible joint distribution over all parts of the
system using a soft covering, from which the behavior of the encoder and
decoder is inferred, with no explicit reference to joint typicality or binning.
Of auxiliary interest, this work also generalizes and strengthens this soft
covering tool.
|
1208.4423
|
Estimation in Phase-Shift and Forward Wireless Sensor Networks
|
cs.IT math.IT
|
We consider a network of single-antenna sensors that observe an unknown
deterministic parameter. Each sensor applies a phase shift to the observation
and the sensors simultaneously transmit the result to a multi-antenna fusion
center (FC). Based on its knowledge of the wireless channel to the sensors, the
FC calculates values for the phase factors that minimize the variance of the
parameter estimate, and feeds this information back to the sensors. The use of
a phase-shift-only transmission scheme provides a simplified analog
implementation at the sensor, and also leads to a simpler algorithm design and
performance analysis. We propose two algorithms for this problem, a numerical
solution based on a relaxed semidefinite programming problem, and a closed-form
solution based on the analytic constant modulus algorithm. Both approaches are
shown to provide performance close to the theoretical bound. We derive
asymptotic performance analyses for cases involving large numbers of sensors or
large numbers of FC antennas, and we also study the impact of phase errors at
the sensor transmitters. Finally, we consider the sensor selection problem, in
which only a subset of the sensors is chosen to send their observations to the
FC.
|
1208.4434
|
Subdivision Shell Elements with Anisotropic Growth
|
cs.NA cs.CE physics.comp-ph
|
A thin shell finite element approach based on Loop's subdivision surfaces is
proposed, capable of dealing with large deformations and anisotropic growth. To
this end, the Kirchhoff-Love theory of thin shells is derived and extended to
allow for arbitrary in-plane growth. The simplicity and computational
efficiency of the subdivision thin shell elements is outstanding, which is
demonstrated on a few standard loading benchmarks. With this powerful tool at
hand, we demonstrate the broad range of possible applications by numerical
solution of several growth scenarios, ranging from the uniform growth of a
sphere, to boundary instabilities induced by large anisotropic growth. Finally,
it is shown that the problem of a slowly and uniformly growing sheet confined
in a fixed hollow sphere is equivalent to the inverse process where a sheet of
fixed size is slowly crumpled in a shrinking hollow sphere in the frictionless,
quasi-static, elastic limit.
|
1208.4455
|
Elusive Codes in Hamming Graphs
|
math.CO cs.IT math.IT
|
We consider a code to be a subset of the vertex set of a Hamming graph. We
examine elusive pairs, code-group pairs where the code is not determined by
knowledge of its set of neighbours. We construct a new infinite family of
elusive pairs, where the group in question acts transitively on the set of
neighbours of the code. In our examples, we find that the alphabet size always
divides the length of the code, and prove that there is no elusive pair for the
smallest set of parameters for which this is not the case. We also pose several
questions regarding elusive pairs.
|
1208.4469
|
On The Secrecy of the Cognitive Interference Channel with Channel State
|
cs.IT math.IT
|
In this paper the secrecy problem in the cognitive statedependent
interference channel is considered. In this scenario we have a primary and a
cognitive transmitter-receiver pairs. The cognitive transmitter has the message
of the primary sender as side information. In addition, the state of the
channel is known at the cognitive encoder. So, the cognitive encoder uses this
side information to cooperate with the primary transmitter and sends its
individual message confidentially. An achievable rate region and an outer bound
for the rate region in this channel are derived. The results are extended to
the previous works as special cases.
|
1208.4475
|
Information-Theoretic Measures of Influence Based on Content Dynamics
|
cs.SI physics.soc-ph stat.AP
|
The fundamental building block of social influence is for one person to
elicit a response in another. Researchers measuring a "response" in social
media typically depend either on detailed models of human behavior or on
platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most
content on social networks is difficult to model because the modes and
motivation of human expression are diverse and incompletely understood. We
introduce content transfer, an information-theoretic measure with a predictive
interpretation that directly quantifies the strength of the effect of one
user's content on another's in a model-free way. Estimating this measure is
made possible by combining recent advances in non-parametric entropy estimation
with increasingly sophisticated tools for content representation. We
demonstrate on Twitter data collected for thousands of users that content
transfer is able to capture non-trivial, predictive relationships even for
pairs of users not linked in the follower or mention graph. We suggest that
this measure makes large quantities of previously under-utilized social media
content accessible to rigorous statistical causal analysis.
|
1208.4503
|
Introduction of the weight edition errors in the Levenshtein distance
|
cs.CL
|
In this paper, we present a new approach dedicated to correcting the spelling
errors of the Arabic language. This approach corrects typographical errors like
inserting, deleting, and permutation. Our method is inspired from the
Levenshtein algorithm, and allows a finer and better scheduling than
Levenshtein. The results obtained are very satisfactory and encouraging, which
shows the interest of our new approach.
|
1208.4505
|
Compressive Source Separation: Theory and Methods for Hyperspectral
Imaging
|
cs.IT math.IT
|
With the development of numbers of high resolution data acquisition systems
and the global requirement to lower the energy consumption, the development of
efficient sensing techniques becomes critical. Recently, Compressed Sampling
(CS) techniques, which exploit the sparsity of signals, have allowed to
reconstruct signal and images with less measurements than the traditional
Nyquist sensing approach. However, multichannel signals like Hyperspectral
images (HSI) have additional structures, like inter-channel correlations, that
are not taken into account in the classical CS scheme. In this paper we exploit
the linear mixture of sources model, that is the assumption that the
multichannel signal is composed of a linear combination of sources, each of
them having its own spectral signature, and propose new sampling schemes
exploiting this model to considerably decrease the number of measurements
needed for the acquisition and source separation. Moreover, we give theoretical
lower bounds on the number of measurements required to perform reconstruction
of both the multichannel signal and its sources. We also proposed optimization
algorithms and extensive experimentation on our target application which is
HSI, and show that our approach recovers HSI with far less measurements and
computational effort than traditional CS approaches.
|
1208.4508
|
Optimal Spectrum Access for Cognitive Radios
|
cs.IT cs.NI math.IT
|
In this paper, we investigate a time-slotted cognitive setting with buffered
primary and secondary users. In order to alleviate the negative effects of
misdetection and false alarm probabilities, a novel design of spectrum access
mechanism is proposed. We propose two schemes. First, the SU senses primary
channel to exploit the periods of silence, if the PU is declared to be idle,
the SU randomly accesses the channel with some access probability $a_s$.
Second, in addition to accessing the channel if the PU is idle, the SU possibly
accesses the channel if it is declared to be busy with some access probability
$b_s$. The access probabilities as function of the misdetection, false alarm
and average primary arrival rate are obtained via solving an optimization
problem designed to maximize the secondary service rate given a constraint on
primary queue stability. In addition, we propose a variable sensing duration
schemes where the SU optimizes over the optimal sensing time to achieve the
maximum stable throughput of the network. The results reveal the performance
gains of the proposed schemes over the conventional sensing scheme. We propose
a method to estimate the mean arrival rate and the outage probability of the PU
based on the primary feedback channel, i.e., acknowledgments (ACKs) and
negative-acknowledgments (NACKs) messages.
|
1208.4552
|
Network-based information filtering algorithms: ranking and
recommendation
|
cs.SI cs.IR physics.data-an physics.soc-ph
|
After the Internet and the World Wide Web have become popular and
widely-available, the electronically stored online interactions of individuals
have fast emerged as a challenge for researchers and, perhaps even faster, as a
source of valuable information for entrepreneurs. We now have detailed records
of informal friendship relations in social networks, purchases on e-commerce
sites, various sorts of information being sent from one user to another, online
collections of web bookmarks, and many other data sets that allow us to pose
questions that are of interest from both academical and commercial point of
view. For example, which other users of a social network you might want to be
friend with? Which other items you might be interested to purchase? Who are the
most influential users in a network? Which web page you might want to visit
next? All these questions are not only interesting per se but the answers to
them may help entrepreneurs provide better service to their customers and,
ultimately, increase their profits.
|
1208.4571
|
In the Face (book) of Social Learning
|
cs.CY cs.SI
|
Social networks have risen to prominence over the last years as the
predominant form of electronic interaction between individuals. In an attempt
to harness the power of the large user base which they have managed to attract,
this study proposes an e-learning prototype which integrates concepts of the
social and semantic web. A selected set of services are deployed which have
been scientifically proven to positively impact the learning process of users
via electronic means. The integrability of these services into a social network
platform application is visualized through an exploratory prototype. The
Graphical User Interface (GUI) which is developed to implement these key
features is in alignment with User-Centered principles. The designed prototype
proves that a number of services can be integrated in a user-friendly
application and can potentially serve to gain feedback regarding additional
aspects that should be included.
|
1208.4583
|
A novel Hopfield neural network approach for minimizing total weighted
tardiness of jobs scheduled on identical machines
|
cs.NE
|
This paper explores fast, polynomial time heuristic approximate solutions to
the NP-hard problem of scheduling jobs on N identical machines. The jobs are
independent and are allowed to be stopped and restarted on another machine at a
later time. They have well-defined deadlines, and relative priorities
quantified by non-negative real weights. The objective is to find schedules
which minimize the total weighted tardiness (TWT) of all jobs. We show how this
problem can be mapped into quadratic form and present a polynomial time
heuristic solution based on the Hopfield Neural Network (HNN) approach. It is
demonstrated, through the results of extensive numerical simulations, that this
solution outperforms other popular heuristic methods. The proposed heuristic is
both theoretically and empirically shown to be scalable to large problem sizes
(over 100 jobs to be scheduled), which makes it applicable to grid computing
scheduling, arising in fields such as computational biology, chemistry and
finance.
|
1208.4586
|
Differentially Private Data Analysis of Social Networks via Restricted
Sensitivity
|
cs.CR cs.SI physics.soc-ph
|
We introduce the notion of restricted sensitivity as an alternative to global
and smooth sensitivity to improve accuracy in differentially private data
analysis. The definition of restricted sensitivity is similar to that of global
sensitivity except that instead of quantifying over all possible datasets, we
take advantage of any beliefs about the dataset that a querier may have, to
quantify over a restricted class of datasets. Specifically, given a query f and
a hypothesis H about the structure of a dataset D, we show generically how to
transform f into a new query f_H whose global sensitivity (over all datasets
including those that do not satisfy H) matches the restricted sensitivity of
the query f. Moreover, if the belief of the querier is correct (i.e., D is in
H) then f_H(D) = f(D). If the belief is incorrect, then f_H(D) may be
inaccurate.
We demonstrate the usefulness of this notion by considering the task of
answering queries regarding social-networks, which we model as a combination of
a graph and a labeling of its vertices. In particular, while our generic
procedure is computationally inefficient, for the specific definition of H as
graphs of bounded degree, we exhibit efficient ways of constructing f_H using
different projection-based techniques. We then analyze two important query
classes: subgraph counting queries (e.g., number of triangles) and local
profile queries (e.g., number of people who know a spy and a computer-scientist
who know each other). We demonstrate that the restricted sensitivity of such
queries can be significantly lower than their smooth sensitivity. Thus, using
restricted sensitivity we can maintain privacy whether or not D is in H, while
providing more accurate results in the event that H holds true.
|
1208.4634
|
A Provenance Tracking Model for Data Updates
|
cs.DC cs.DB
|
For data-centric systems, provenance tracking is particularly important when
the system is open and decentralised, such as the Web of Linked Data. In this
paper, a concise but expressive calculus which models data updates is
presented. The calculus is used to provide an operational semantics for a
system where data and updates interact concurrently. The operational semantics
of the calculus also tracks the provenance of data with respect to updates.
This provides a new formal semantics extending provenance diagrams which takes
into account the execution of processes in a concurrent setting. Moreover, a
sound and complete model for the calculus based on ideals of series-parallel
DAGs is provided. The notion of provenance introduced can be used as a
subjective indicator of the quality of data in concurrent interacting systems.
|
1208.4651
|
Throughput Maximization for an Energy Harvesting Communication System
with Processing Cost
|
cs.IT math.IT
|
In wireless networks, energy consumed for communication includes both the
transmission and the processing energy. In this paper, point-to-point
communication over a fading channel with an energy harvesting transmitter is
studied considering jointly the energy costs of transmission and processing.
Under the assumption of known energy arrival and fading profiles, optimal
transmission policy for throughput maximization is investigated. Assuming that
the transmitter has sufficient amount of data in its buffer at the beginning of
the transmission period, the average throughput by a given deadline is
maximized. Furthermore, a "directional glue pouring algorithm" that computes
the optimal transmission policy is described.
|
1208.4656
|
Capacity of Compound MIMO Gaussian Channels with Additive Uncertainty
|
cs.IT math.IT
|
This paper considers reliable communications over a multiple-input
multiple-output (MIMO) Gaussian channel, where the channel matrix is within a
bounded channel uncertainty region around a nominal channel matrix, i.e., an
instance of the compound MIMO Gaussian channel. We study the optimal transmit
covariance matrix design to achieve the capacity of compound MIMO Gaussian
channels, where the channel uncertainty region is characterized by the spectral
norm. This design problem is a challenging non-convex optimization problem.
However, in this paper, we reveal that this problem has a hidden convexity
property, which can be exploited to map the problem into a convex optimization
problem. We first prove that the optimal transmit design is to diagonalize the
nominal channel, and then show that the duality gap between the capacity of the
compound MIMO Gaussian channel and the min-max channel capacity is zero, which
proves the conjecture of Loyka and Charalambous (IEEE Trans. Inf. Theory, vol.
58, no. 4, pp. 2048-2063, 2012). The key tools for showing these results are a
new matrix determinant inequality and some unitarily invariant properties.
|
1208.4662
|
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of
Cells Using Multi-Resolution Community Detection
|
physics.med-ph cond-mat.stat-mech cs.CV physics.data-an
|
We have developed an automatic method for segmenting fluorescence lifetime
(FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution
community detection (MCD) based network segmentation method. The image
processing problem is framed as identifying segments with respective average
FLTs against a background in FLIM images. The proposed method segments a FLIM
image for a given resolution of the network composed using image pixels as the
nodes and similarity between the pixels as the edges. In the resulting
segmentation, low network resolution leads to larger segments and high network
resolution leads to smaller segments. Further, the mean-square error (MSE) in
estimating the FLT segments in a FLIM image using the proposed method was found
to be consistently decreasing with increasing resolution of the corresponding
network. The proposed MCD method outperformed a popular spectral clustering
based method in performing FLIM image segmentation. The spectral segmentation
method introduced noisy segments in its output at high resolution. It was
unable to offer a consistent decrease in MSE with increasing resolution.
|
1208.4692
|
Monte Carlo Search Algorithm Discovery for One Player Games
|
cs.AI cs.GT
|
Much current research in AI and games is being devoted to Monte Carlo search
(MCS) algorithms. While the quest for a single unified MCS algorithm that would
perform well on all problems is of major interest for AI, practitioners often
know in advance the problem they want to solve, and spend plenty of time
exploiting this knowledge to customize their MCS algorithm in a problem-driven
way. We propose an MCS algorithm discovery scheme to perform this in an
automatic and reproducible way. We first introduce a grammar over MCS
algorithms that enables inducing a rich space of candidate algorithms.
Afterwards, we search in this space for the algorithm that performs best on
average for a given distribution of training problems. We rely on multi-armed
bandits to approximately solve this optimization problem. The experiments,
generated on three different domains, show that our approach enables
discovering algorithms that outperform several well-known MCS algorithms such
as Upper Confidence bounds applied to Trees and Nested Monte Carlo search. We
also show that the discovered algorithms are generally quite robust with
respect to changes in the distribution over the training problems.
|
1208.4696
|
Typical $l_1$-recovery limit of sparse vectors represented by
concatenations of random orthogonal matrices
|
cs.IT cond-mat.dis-nn math.IT
|
We consider the problem of recovering an $N$-dimensional sparse vector
$\vm{x}$ from its linear transformation $\vm{y}=\vm{D} \vm{x}$ of $M(< N)$
dimension. Minimizing the $l_{1}$-norm of $\vm{x}$ under the constraint $\vm{y}
= \vm{D} \vm{x}$ is a standard approach for the recovery problem, and earlier
studies report that the critical condition for typically successful
$l_1$-recovery is universal over a variety of randomly constructed matrices
$\vm{D}$. For examining the extent of the universality, we focus on the case in
which $\vm{D}$ is provided by concatenating $\nb=N/M$ matrices $\vm{O}_{1},
\vm{O}_{2},..., \vm{O}_\nb$ drawn uniformly according to the Haar measure on
the $M \times M$ orthogonal matrices. By using the replica method in
conjunction with the development of an integral formula for handling the random
orthogonal matrices, we show that the concatenated matrices can result in
better recovery performance than what the universality predicts when the
density of non-zero signals is not uniform among the $\nb$ matrix modules. The
universal condition is reproduced for the special case of uniform non-zero
signal densities. Extensive numerical experiments support the theoretical
predictions.
|
1208.4773
|
Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree
Policies and Direct Policy Search
|
cs.SY cs.AI cs.LG
|
Direct policy search (DPS) and look-ahead tree (LT) policies are two widely
used classes of techniques to produce high performance policies for sequential
decision-making problems. To make DPS approaches work well, one crucial issue
is to select an appropriate space of parameterized policies with respect to the
targeted problem. A fundamental issue in LT approaches is that, to take good
decisions, such policies must develop very large look-ahead trees which may
require excessive online computational resources. In this paper, we propose a
new hybrid policy learning scheme that lies at the intersection of DPS and LT,
in which the policy is an algorithm that develops a small look-ahead tree in a
directed way, guided by a node scoring function that is learned through DPS.
The LT-based representation is shown to be a versatile way of representing
policies in a DPS scheme, while at the same time, DPS enables to significantly
reduce the size of the look-ahead trees that are required to take high-quality
decisions.
We experimentally compare our method with two other state-of-the-art DPS
techniques and four common LT policies on four benchmark domains and show that
it combines the advantages of the two techniques from which it originates. In
particular, we show that our method: (1) produces overall better performing
policies than both pure DPS and pure LT policies, (2) requires a substantially
smaller number of policy evaluations than other DPS techniques, (3) is easy to
tune and (4) results in policies that are quite robust with respect to
perturbations of the initial conditions.
|
1208.4777
|
Power Controlled Adaptive Sum-Capacity of Fading MACs with Distributed
CSI
|
cs.IT math.IT
|
We consider the problem of finding optimal, fair and distributed power-rate
strategies to achieve the sum capacity of the Gaussian multiple-access
block-fading channel. In here, the transmitters have access to only their own
fading coefficients, while the receiver has global access to all the fading
coefficients. Outage is not permitted in any communication block. The resulting
average sum-throughput is also known as `power-controlled adaptive
sum-capacity', which appears as an open problem in literature.
This paper presents the power-controlled adaptive sum-capacity of a
wide-class of popular MAC models. In particular, we propose a power-rate
strategy in the presence of distributed channel state information (CSI), which
is throughput optimal when all the users have identical channel statistics. The
proposed scheme also has an efficient implementation using successive
cancellation and rate-splitting. We propose an upperbound when the channel laws
are not identical. Furthermore, the optimal schemes are extended to situations
in which each transmitter has additional finite-rate partial CSI on the link
quality of others.
|
1208.4790
|
Worst-Case Expected-Capacity Loss of Slow-Fading Channels
|
cs.IT math.IT
|
For delay-limited communication over block-fading channels, the difference
between the ergodic capacity and the maximum achievable expected rate for
coding over a finite number of coherent blocks represents a fundamental measure
of the penalty incurred by the delay constraint. This paper introduces a notion
of worst-case expected-capacity loss. Focusing on the slow-fading scenario
(one-block delay), the worst-case additive and multiplicative expected-capacity
losses are precisely characterized for the point-to-point fading channel.
Extension to the problem of writing on fading paper is also considered, where
both the ergodic capacity and the additive expected-capacity loss over
one-block delay are characterized to within one bit per channel use.
|
1208.4809
|
Comparing N-Node Set Importance Representative results with Node
Importance Representative results for Categorical Clustering: An exploratory
study
|
cs.DB
|
The proportionate increase in the size of the data with increase in space
implies that clustering a very large data set becomes difficult and is a time
consuming process.Sampling is one important technique to scale down the size of
dataset and to improve the efficiency of clustering. After sampling allocating
unlabeled objects into proper clusters is impossible in the categorical
domain.To address the problem, Chen employed a method called MAximal
Representative Data Labeling to allocate each unlabeled data point to the
appropriate cluster based on Node Importance Representative and N-Node
Importance Representative algorithms. This paper took off from Chen s
investigation and analyzed and compared the results of NIR and NNIR leading to
the conclusion that the two processes contradict each other when it comes to
finding the resemblance between an unlabeled data point and a cluster.A new and
better way of solving the problem was arrived at that finds resemblance between
unlabeled data point within all clusters, while also providing maximal
resemblance for allocation of data in the required cluster.
|
1208.4842
|
The Segmentation Fusion Method On10 Multi-Sensors
|
cs.CV
|
The most significant problem may be undesirable effects for the spectral
signatures of fused images as well as the benefits of using fused images mostly
compared to their source images were acquired at the same time by one sensor.
They may or may not be suitable for the fusion of other images. It becomes
therefore increasingly important to investigate techniques that allow
multi-sensor, multi-date image fusion to make final conclusions can be drawn on
the most suitable method of fusion. So, In this study we present a new method
Segmentation Fusion method (SF) for remotely sensed images is presented by
considering the physical characteristics of sensors, which uses a feature level
processing paradigm. In a particularly, attempts to test the proposed method
performance on 10 multi-sensor images and comparing it with different fusion
techniques for estimating the quality and degree of information improvement
quantitatively by using various spatial and spectral metrics.
|
1208.4877
|
PIRATTE: Proxy-based Immediate Revocation of ATTribute-based Encryption
|
cs.CR cs.SI
|
Access control to data in traditional enterprises is typically enforced
through reference monitors. However, as more and more enterprise data is
outsourced, trusting third party storage servers is getting challenging. As a
result, cryptography, specifically Attribute-based encryption (ABE) is getting
popular for its expressiveness. The challenge of ABE is revocation.
To address this challenge, we propose PIRATTE, an architecture that supports
fine-grained access control policies and dynamic group membership. PIRATTE is
built using attribute-based encryption; a key and novel feature of our
architecture, however, is that it is possible to remove access from a user
without issuing new keys to other users or re-encrypting existing ciphertexts.
We achieve this by introducing a proxy that participates in the decryption
process and enforces revocation constraints. The proxy is minimally trusted and
cannot decrypt ciphertexts or provide access to previously revoked users. We
describe the PIRATTE construction and provide a security analysis along with
performance evaluation.We also describe an architecture for online social
network that can use PIRATTE, and prototype application of PIRATTE on Facebook.
|
1208.4895
|
Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs
|
cs.SY cs.DC
|
We study a general framework for broadcast gossip algorithms which use
companion variables to solve the average consensus problem. Each node maintains
an initial state and a companion variable. Iterative updates are performed
asynchronously whereby one random node broadcasts its current state and
companion variable and all other nodes receiving the broadcast update their
state and companion variable. We provide conditions under which this scheme is
guaranteed to converge to a consensus solution, where all nodes have the same
limiting values, on any strongly connected directed graph. Under stronger
conditions, which are reasonable when the underlying communication graph is
undirected, we guarantee that the consensus value is equal to the average, both
in expectation and in the mean-squared sense. Our analysis uses tools from
non-negative matrix theory and perturbation theory. The perturbation results
rely on a parameter being sufficiently small. We characterize the allowable
upper bound as well as the optimal setting for the perturbation parameter as a
function of the network topology, and this allows us to characterize the
worst-case rate of convergence. Simulations illustrate that, in comparison to
existing broadcast gossip algorithms, the approaches proposed in this paper
have the advantage that they simultaneously can be guaranteed to converge to
the average consensus and they converge in a small number of broadcasts.
|
1208.4899
|
The Effect of Macrodiversity on the Performance of Maximal Ratio
Combining in Flat Rayleigh Fading
|
cs.IT math.IT
|
The performance of maximal ratio combining (MRC) in Rayleigh channels with
co-channel interference (CCI) is well-known for receive arrays which are
co-located. Recent work in network MIMO, edge-excited cells and base station
collaboration is increasing interest in macrodiversity systems. Hence, in this
paper we consider the effect of macrodiversity on MRC performance in Rayleigh
fading channels with CCI. We consider the uncoded symbol error rate (SER) as
our performance measure of interest and investigate how different
macrodiversity power profiles affect SER performance. This is the first
analytical work in this area. We derive approximate and exact symbol error rate
results for M-QAM/BPSK modulations and use the analysis to provide a simple
power metric. Numerical results, verified by simulations, are used in
conjunction with the analysis to gain insight into the effects of the link
powers on performance.
|
1208.4901
|
Performance Analysis of Dual-User Macrodiversity MIMO Systems with
Linear Receivers in Flat Rayleigh Fading
|
cs.IT math.IT
|
The performance of linear receivers in the presence of co-channel
interference in Rayleigh channels is a fundamental problem in wireless
communications. Performance evaluation for these systems is well-known for
receive arrays where the antennas are close enough to experience equal average
SNRs from a source. In contrast, almost no analytical results are available for
macrodiversity systems where both the sources and receive antennas are widely
separated. Here, receive antennas experience unequal average SNRs from a source
and a single receive antenna receives a different average SNR from each source.
Although this is an extremely difficult problem, progress is possible for the
two-user scenario. In this paper, we derive closed form results for the
probability density function (pdf) and cumulative distribution function (cdf)
of the output signal to interference plus noise ratio (SINR) and signal to
noise ratio (SNR) of minimum mean squared error (MMSE) and zero forcing (ZF)
receivers in independent Rayleigh channels with arbitrary numbers of receive
antennas. The results are verified by Monte Carlo simulations and high SNR
approximations are also derived. The results enable further system analysis
such as the evaluation of outage probability, bit error rate (BER) and
capacity.
|
1208.4942
|
A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based
Approaches for E-GTSP
|
cs.AI
|
The Generalized Traveling Salesman Problem (GTSP) is one of the NP-hard
combinatorial optimization problems. A variant of GTSP is E-GTSP where E,
meaning equality, has the constraint: exactly one node from a cluster of a
graph partition is visited. The main objective of the E-GTSP is to find a
minimum cost tour passing through exactly one node from each cluster of an
undirected graph. Agent-based approaches involving are successfully used
nowadays for solving real life complex problems. The aim of the current paper
is to illustrate some variants of agent-based algorithms including ant-based
models with specific properties for solving E-GTSP.
|
1208.4945
|
Parallel ACO with a Ring Neighborhood for Dynamic TSP
|
cs.AI
|
The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.
|
1208.5003
|
Identification of Probabilities of Languages
|
cs.LG math.PR
|
We consider the problem of inferring the probability distribution associated
with a language, given data consisting of an infinite sequence of elements of
the languge. We do this under two assumptions on the algorithms concerned: (i)
like a real-life algorothm it has round-off errors, and (ii) it has no
round-off errors. Assuming (i) we (a) consider a probability mass function of
the elements of the language if the data are drawn independent identically
distributed (i.i.d.), provided the probability mass function is computable and
has a finite expectation. We give an effective procedure to almost surely
identify in the limit the target probability mass function using the Strong Law
of Large Numbers. Second (b) we treat the case of possibly incomputable
probabilistic mass functions in the above setting. In this case we can only
pointswize converge to the target probability mass function almost surely.
Third (c) we consider the case where the data are dependent assuming they are
typical for at least one computable measure and the language is finite. There
is an effective procedure to identify by infinite recurrence a nonempty subset
of the computable measures according to which the data is typical. Here we use
the theory of Kolmogorov complexity. Assuming (ii) we obtain the weaker result
for (a) that the target distribution is identified by infinite recurrence
almost surely; (b) stays the same as under assumption (i). We consider the
associated predictions.
|
1208.5012
|
Precoder Design for Orthogonal Space-Time Block Coding based Cognitive
Radio with Polarized Antennas
|
cs.NI cs.IT math.IT
|
The spectrum sharing has recently passed into a mainstream Cognitive Radio
(CR) strategy. We investigate the core issue in this strategy: interference
mitigation at Primary Receiver (PR).We propose a linear precoder design which
aims at alleviating the interference caused by Secondary User (SU) from the
source for Orthogonal Space-Time Block Coding (OSTBC) based CR. We resort to
Minimum Variance (MV) approach to contrive the precoding matrix at Secondary
Transmitter (ST) in order to maximize the Signal to Noise Ratio (SNR) at
Secondary Receiver (SR) on the premise that the orthogonality of OSTBC is kept,
the interference introduced to Primary Link (PL) by Secondary Link (SL) is
maintained under a tolerable level and the total transmitted power constraint
at ST is satisfied. Moreover, the selection of polarization mode for SL is
incorporated in the precoder design. In order to provide an analytic solution
with low computational cost, we put forward an original precoder design
algorithm which exploits an auxiliary variable to treat the optimization
problem with a mixture of linear and quadratic constraints. Numerical results
demonstrate that our proposed precoder design enable SR to have an agreeable
SNR on the prerequisite that the interference at PR is maintained below the
threshold.
|
1208.5016
|
WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity
|
cs.CV
|
This article presents a new distance for measuring shape dissimilarity
between objects. Recent publications introduced the use of eigenvalues of the
Laplace operator as compact shape descriptors. Here, we revisit the eigenvalues
to define a proper distance, called Weighted Spectral Distance (WESD), for
quantifying shape dissimilarity. The definition of WESD is derived through
analysing the heat-trace. This analysis provides the proposed distance an
intuitive meaning and mathematically links it to the intrinsic geometry of
objects. We analyse the resulting distance definition, present and prove its
important theoretical properties. Some of these properties include: i) WESD is
defined over the entire sequence of eigenvalues yet it is guaranteed to
converge, ii) it is a pseudometric, iii) it is accurately approximated with a
finite number of eigenvalues, and iv) it can be mapped to the [0,1) interval.
Lastly, experiments conducted on synthetic and real objects are presented.
These experiments highlight the practical benefits of WESD for applications in
vision and medical image analysis.
|
1208.5024
|
Brain-Computer Interface Controlled Robotic Gait Orthosis
|
cs.HC cs.RO
|
Reliance on wheelchairs after spinal cord injury (SCI) leads to many medical
co-morbidities. Treatment of these conditions contributes to the majority of
SCI health care costs. Restoring able-body-like ambulation after SCI may reduce
the incidence of these conditions, and increase independence and quality of
life. However, no biomedical solution exists that can reverse this lost
neurological function, and hence novel methods are needed. Brain-computer
interface (BCI) controlled lower extremity prosthesis may constitute one such
novel approach.
One subject with able-body and one with paraplegia due to SCI underwent
electroencephalogram (EEG) recording while engaged in alternating epochs of
idling and walking kinesthetic motor imagery (KMI). These data were analyzed to
generate an EEG prediction model for online BCI operation. A commercial robotic
gait orthosis (RoGO) system (treadmill suspended), was interfaced with the BCI
computer. In an online test, the subjects were tasked to ambulate using the
BCI-RoGO system when prompted by computerized cues. The performance of this
system was assessed with cross-correlation analysis, and omission and false
alarm rates.
The offline accuracy of the EEG prediction model averaged 86.3%. The
cross-correlation between instructional cues and BCI-RoGO walking epochs
averaged 0.812 +/- 0.048 (p-value<10^-4). There were on average 0.8 false
alarms per session and no omissions.
This is the first time a person with parapegia due to SCI regained basic
brain-controlled ambulation, thereby indicating that restoring brain-controlled
ambulation is feasible. Future work will test this system in a population of
individuals with SCI. If successful, this may justify future development of
invasive BCI-controlled lower extremity prostheses. This system may also be
applied to incomplete SCI to improve neurological outcomes beyond those of
standard physiotherapy.
|
1208.5052
|
Local multiresolution order in community detection
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Community detection algorithms attempt to find the best clusters of nodes in
an arbitrary complex network. Multi-scale ("multiresolution") community
detection extends the problem to identify the best network scale(s) for these
clusters. The latter task is generally accomplished by analyzing community
stability simultaneously for all clusters in the network. In the current work,
we extend this general approach to define local multiresolution methods, which
enable the extraction of well-defined local communities even if the global
community structure is vaguely defined in an average sense. Toward this end, we
propose measures analogous to variation of information and normalized mutual
information that are used to quantitatively identify the best resolution(s) at
the community level based on correlations between clusters in
independently-solved systems. We demonstrate our method on two constructed
networks as well as a real network and draw inferences about local community
strength. Our approach is independent of the applied community detection
algorithm save for the inherent requirement that the method be able to identify
communities across different network scales, with appropriate changes to
account for how different resolutions are evaluated or defined in a particular
community detection method. It should, in principle, easily adapt to
alternative community comparison measures.
|
1208.5062
|
Changepoint detection for high-dimensional time series with missing data
|
stat.ML cs.LG
|
This paper describes a novel approach to change-point detection when the
observed high-dimensional data may have missing elements. The performance of
classical methods for change-point detection typically scales poorly with the
dimensionality of the data, so that a large number of observations are
collected after the true change-point before it can be reliably detected.
Furthermore, missing components in the observed data handicap conventional
approaches. The proposed method addresses these challenges by modeling the
dynamic distribution underlying the data as lying close to a time-varying
low-dimensional submanifold embedded within the ambient observation space.
Specifically, streaming data is used to track a submanifold approximation,
measure deviations from this approximation, and calculate a series of
statistics of the deviations for detecting when the underlying manifold has
changed in a sharp or unexpected manner. The approach described in this paper
leverages several recent results in the field of high-dimensional data
analysis, including subspace tracking with missing data, multiscale analysis
techniques for point clouds, online optimization, and change-point detection
performance analysis. Simulations and experiments highlight the robustness and
efficacy of the proposed approach in detecting an abrupt change in an otherwise
slowly varying low-dimensional manifold.
|
1208.5071
|
On the Synergistic Benefits of Alternating CSIT for the MISO BC
|
cs.IT math.IT
|
The degrees of freedom (DoF) of the two-user multiple-input single-output
(MISO) broadcast channel (BC) are studied under the assumption that the form,
I_i, i=1,2, of the channel state information at the transmitter (CSIT) for each
user's channel can be either perfect (P), delayed (D) or not available (N),
i.e., I_1 and I_2 can take values of either P, D or N, and therefore the
overall CSIT can alternate between the 9 resulting states, each state denoted
as I_1I_2. The fraction of time associated with CSIT state I_1I_2 is denoted by
the parameter \lambda_{I_1I_2} and it is assumed throughout that
\lambda_{I_1I_2}=\lambda_{I_2I_1}, i.e., \lambda_{PN}=\lambda_{NP},
\lambda_{PD}=\lambda_{DP}, \lambda_{DN}=\lambda_{ND}. Under this assumption of
symmetry, the main contribution of this paper is a complete characterization of
the DoF region of the two user MISO BC with alternating CSIT. Surprisingly, the
DoF region is found to depend only on the marginal probabilities (\lambda_P,
\lambda_D,\lambda_N)=(\sum_{I_2}\lambda_{PI_2},\sum_{I_2}\lambda_{DI_2},
\sum_{I_2}\lambda_{NI_2}), I_2\in {P,D,N}, which represent the fraction of time
that any given user (e.g., user 1) is associated with perfect, delayed, or no
CSIT, respectively. As a consequence, the DoF region with all 9 CSIT states,
\mathcal{D}(\lambda_{I_1I_2}:I_1,I_2\in{P,D,N}), is the same as the DoF region
with only 3 CSIT states \mathcal{D}(\lambda_{PP}, \lambda_{DD}, \lambda_{NN}),
under the same marginal distribution of CSIT states, i.e., (\lambda_{PP},
\lambda_{DD},\lambda_{NN})=(\lambda_P,\lambda_D,\lambda_N). The results
highlight the synergistic benefits of alternating CSIT and the tradeoffs
between various forms of CSIT for any given DoF value.
|
1208.5076
|
Opinion Dynamics in Social Networks: A Local Interaction Game with
Stubborn Agents
|
cs.GT cs.SI
|
The process by which new ideas, innovations, and behaviors spread through a
large social network can be thought of as a networked interaction game: Each
agent obtains information from certain number of agents in his friendship
neighborhood, and adapts his idea or behavior to increase his benefit. In this
paper, we are interested in how opinions, about a certain topic, form in social
networks. We model opinions as continuous scalars ranging from 0 to 1 with 1(0)
representing extremely positive(negative) opinion. Each agent has an initial
opinion and incurs some cost depending on the opinions of his neighbors, his
initial opinion, and his stubbornness about his initial opinion. Agents
iteratively update their opinions based on their own initial opinions and
observing the opinions of their neighbors. The iterative update of an agent can
be viewed as a myopic cost-minimization response (i.e., the so-called best
response) to the others' actions. We study whether an equilibrium can emerge as
a result of such local interactions and how such equilibrium possibly depends
on the network structure, initial opinions of the agents, and the location of
stubborn agents and the extent of their stubbornness. We also study the
convergence speed to such equilibrium and characterize the convergence time as
a function of aforementioned factors. We also discuss the implications of such
results in a few well-known graphs such as Erdos-Renyi random graphs and
small-world graphs.
|
1208.5092
|
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
|
cs.CV cs.SI stat.ML
|
This paper proposes a simple but effective graph-based agglomerative
algorithm, for clustering high-dimensional data. We explore the different roles
of two fundamental concepts in graph theory, indegree and outdegree, in the
context of clustering. The average indegree reflects the density near a sample,
and the average outdegree characterizes the local geometry around a sample.
Based on such insights, we define the affinity measure of clusters via the
product of average indegree and average outdegree. The product-based affinity
makes our algorithm robust to noise. The algorithm has three main advantages:
good performance, easy implementation, and high computational efficiency. We
test the algorithm on two fundamental computer vision problems: image
clustering and object matching. Extensive experiments demonstrate that it
outperforms the state-of-the-arts in both applications.
|
1208.5130
|
Value production in a collaborative environment
|
physics.soc-ph cs.CY cs.SI physics.data-an
|
We review some recent endeavors and add some new results to characterize and
understand underlying mechanisms in Wikipedia (WP), the paradigmatic example of
collaborative value production. We analyzed the statistics of editorial
activity in different languages and observed typical circadian and weekly
patterns, which enabled us to estimate the geographical origins of
contributions to WPs in languages spoken in several time zones. Using a
recently introduced measure we showed that the editorial activities have
intrinsic dependencies in the burstiness of events. A comparison of the English
and Simple English WPs revealed important aspects of language complexity and
showed how peer cooperation solved the task of enhancing readability. One of
our focus issues was characterizing the conflicts or edit wars in WPs, which
helped us to automatically filter out controversial pages. When studying the
temporal evolution of the controversiality of such pages we identified typical
patterns and classified conflicts accordingly. Our quantitative analysis
provides the basis of modeling conflicts and their resolution in collaborative
environments and contribute to the understanding of this issue, which becomes
increasingly important with the development of information communication
technology.
|
1208.5154
|
Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial
Intelligence (2008)
|
cs.AI
|
This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in
Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
|
1208.5155
|
Proceedings of the Twenty-Third Conference on Uncertainty in Artificial
Intelligence (2007)
|
cs.AI
|
This is the Proceedings of the Twenty-Third Conference on Uncertainty in
Artificial Intelligence, which was held in Vancouver, British Columbia, July 19
- 22 2007.
|
1208.5159
|
Proceedings of the Twenty-First Conference on Uncertainty in Artificial
Intelligence (2005)
|
cs.AI
|
This is the Proceedings of the Twenty-First Conference on Uncertainty in
Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29
2005.
|
1208.5160
|
Proceedings of the Twenty-Second Conference on Uncertainty in Artificial
Intelligence (2006)
|
cs.AI
|
This is the Proceedings of the Twenty-Second Conference on Uncertainty in
Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006.
|
1208.5161
|
Proceedings of the Twentieth Conference on Uncertainty in Artificial
Intelligence (2004)
|
cs.AI
|
This is the Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004.
|
1208.5216
|
High-rate self-synchronizing codes
|
cs.IT math.CO math.IT
|
Self-synchronization under the presence of additive noise can be achieved by
allocating a certain number of bits of each codeword as markers for
synchronization. Difference systems of sets are combinatorial designs which
specify the positions of synchronization markers in codewords in such a way
that the resulting error-tolerant self-synchronizing codes may be realized as
cosets of linear codes. Ideally, difference systems of sets should sacrifice as
few bits as possible for a given code length, alphabet size, and
error-tolerance capability. However, it seems difficult to attain optimality
with respect to known bounds when the noise level is relatively low. In fact,
the majority of known optimal difference systems of sets are for exceptionally
noisy channels, requiring a substantial amount of bits for synchronization. To
address this problem, we present constructions for difference systems of sets
that allow for higher information rates while sacrificing optimality to only a
small extent. Our constructions utilize optimal difference systems of sets as
ingredients and, when applied carefully, generate asymptotically optimal ones
with higher information rates. We also give direct constructions for optimal
difference systems of sets with high information rates and error-tolerance that
generate binary and ternary self-synchronizing codes.
|
1208.5243
|
General Managers Role in Balancing Subsidiary Between Internal
Competition and Knowledge Sharing
|
cs.SI cs.CY
|
In our work we saw that during the last decades the environment that the MNCs
operate in has changed becoming more volatile and less pacedly growing. In this
environment the MNCs themselves have become more complex and also flexible. We
found that MNCs are essentially three dimensional, that is, they organize
around product, functional and geographical dimensions and exhibit
characteristics that having a common origin can be applied along any one
dimension. Therefore we depicted MNCs as having a divergent, partially
overlapping structural map. On that map there can, for instance, be
functionally oriented Centers Of Excellence, product dimension World Product
Mandates, and for capturing synergy of a big set of operations country
dimension based arrangements. Analyzing the development of organizational
aspects of MNC theories we saw different bodies of work pointing to a similar
direction. We followed developments of concepts Heterarchy, Transnational (and
the related Individualized Corporation), works of interorganizational theories
school (the multicentered MNC), works considering autonomous strategic
decisions, and works originating from subsidiary (host) country research. In
addition to the structural developments mentioned earlier these works also
point to a need to empower the frontline units. Outlining our research problem
the conceptualization of MNCs as operating (competitive) internal markets was
shown to rely on entrepreneurial, initiative taking behavior and result in the
development of the subsidiary. We classified the subsidiaries first selecting
operations substantial enough and then differentiating them based on the
autonomy level, as it has implications for the types of initiatives taken.
|
1208.5258
|
A Theory of Pricing Private Data
|
cs.CR cs.DB
|
Personal data has value to both its owner and to institutions who would like
to analyze it. Privacy mechanisms protect the owner's data while releasing to
analysts noisy versions of aggregate query results. But such strict protections
of individual's data have not yet found wide use in practice. Instead, Internet
companies, for example, commonly provide free services in return for valuable
sensitive information from users, which they exploit and sometimes sell to
third parties.
As the awareness of the value of the personal data increases, so has the
drive to compensate the end user for her private information. The idea of
monetizing private data can improve over the narrower view of hiding private
data, since it empowers individuals to control their data through financial
means.
In this paper we propose a theoretical framework for assigning prices to
noisy query answers, as a function of their accuracy, and for dividing the
price amongst data owners who deserve compensation for their loss of privacy.
Our framework adopts and extends key principles from both differential privacy
and query pricing in data markets. We identify essential properties of the
price function and micro-payments, and characterize valid solutions.
|
1208.5269
|
Support Recovery with Sparsely Sampled Free Random Matrices
|
cs.IT math.IT
|
Consider a Bernoulli-Gaussian complex $n$-vector whose components are $V_i =
X_i B_i$, with $X_i \sim \Cc\Nc(0,\Pc_x)$ and binary $B_i$ mutually independent
and iid across $i$. This random $q$-sparse vector is multiplied by a square
random matrix $\Um$, and a randomly chosen subset, of average size $n p$, $p
\in [0,1]$, of the resulting vector components is then observed in additive
Gaussian noise. We extend the scope of conventional noisy compressive sampling
models where $\Um$ is typically %A16 the identity or a matrix with iid
components, to allow $\Um$ satisfying a certain freeness condition. This class
of matrices encompasses Haar matrices and other unitarily invariant matrices.
We use the replica method and the decoupling principle of Guo and Verd\'u, as
well as a number of information theoretic bounds, to study the input-output
mutual information and the support recovery error rate in the limit of $n \to
\infty$. We also extend the scope of the large deviation approach of Rangan,
Fletcher and Goyal and characterize the performance of a class of estimators
encompassing thresholded linear MMSE and $\ell_1$ relaxation.
|
1208.5270
|
The Effects of Limited Channel Knowledge on Cognitive Radio System
Capacity
|
cs.IT math.IT
|
We examine the impact of limited channel knowledge on the secondary user (SU)
in a cognitive radio system. Under a minimum signal-to-interference-and-noise
ratio (SINR) constraint for the primary user (PU) receiver, we determine the SU
capacity under five channel knowledge scenarios. We derive analytical
expressions for the capacity cumulative distribution functions and the
probability of SU blocking as a function of allowable interference. We show
that imperfect knowledge of the PU-PU channel gain by the SU-Tx often prohibits
SU transmission or necessitates a high interference level at the PU. We also
show that errored knowledge of the PU-PU channel is more beneficial than
statistical channel knowledge and imperfect knowledge of the SU-Tx to PU-Rx
link has a limited impact on SU capacity.
|
1208.5273
|
Wave-Like Solutions of General One-Dimensional Spatially Coupled Systems
|
cs.IT math.IT
|
We establish the existence of wave-like solutions to spatially coupled
graphical models which, in the large size limit, can be characterized by a
one-dimensional real-valued state. This is extended to a proof of the threshold
saturation phenomenon for all such models, which includes spatially coupled
irregular LDPC codes over the BEC, but also addresses hard-decision decoding
for transmission over general channels, the CDMA multiple-access problem,
compressed sensing, and some statistical physics models.
For traditional uncoupled iterative coding systems with two components and
transmission over the BEC, the asymptotic convergence behavior is completely
characterized by the EXIT curves of the components. More precisely, the system
converges to the desired fixed point, which is the one corresponding to perfect
decoding, if and only if the two EXIT functions describing the components do
not cross. For spatially coupled systems whose state is one-dimensional a
closely related graphical criterion applies. Now the curves are allowed to
cross, but not by too much. More precisely, we show that the threshold
saturation phenomenon is related to the positivity of the (signed) area
enclosed by two EXIT-like functions associated to the component systems, a very
intuitive and easy-to-use graphical characterization.
In the spirit of EXIT functions and Gaussian approximations, we also show how
to apply the technique to higher dimensional and even infinite-dimensional
cases. In these scenarios the method is no longer rigorous, but it typically
gives accurate predictions. To demonstrate this application, we discuss
transmission over general channels using both the belief-propagation as well as
the min-sum decoder.
|
1208.5280
|
On the Peak-to-Average Power Ratio Reduction Problem for Orthogonal
Transmission Schemes
|
cs.IT math.IT
|
High peak values of transmission signals in wireless communication systems
lead to wasteful energy consumption and out-of-band radiation. However,
reducing peak values generally comes at the cost some other resource. We
provide a theoretical contribution towards understanding the relationship
between peak value reduction and the resulting cost in information rates. In
particular, we address the relationship between peak values and the proportion
of transmission signals allocated for information transmission when using a
strategy known as tone reservation. We show that when using tone reservation in
both OFDM and DS-CDMA systems, if a Peak-to-Average Power Ratio criterion is
always satisfied, then the proportion of transmission signals that may be
allocated for information transmission must tend to zero. We investigate
properties of these two systems for sets of both finite and infinite
cardinalities. We present properties that OFDM and DS-CDMA share in common as
well as ways in which they fundamentally differ.
|
1208.5281
|
Expected Supremum of a Random Linear Combination of Shifted Kernels
|
cs.IT math.IT
|
We address the expected supremum of a linear combination of shifts of the
sinc kernel with random coefficients. When the coefficients are Gaussian, the
expected supremum is of order \sqrt{\log n}, where n is the number of shifts.
When the coefficients are uniformly bounded, the expected supremum is of order
\log\log n. This is a noteworthy difference to orthonormal functions on the
unit interval, where the expected supremum is of order \sqrt{n\log n} for all
reasonable coefficient statistics.
|
1208.5316
|
How Non-linearity will Transform Information Systems
|
cs.CE q-fin.GN
|
One 'problem' with the 21st century world, particularly the economic and
business worlds, is the phenomenal and increasing number of interconnections
between economic agents (consumers, firms, banks, markets, national economies).
This implies that such agents are all interacting and consequently giving raise
to enormous degrees of non-linearity, a.k.a. complexity. Complexity often
brings with it unexpected phenomena, such as chaos and emerging behaviour, that
can become challenges for the survival of economic agents and systems.
Developing econophysics approaches are beginning to apply, to the 'economic
web', methods and models that have been used in physics and/or systems theory
to tackle non-linear domains. The paper gives an account of the research in
progress in this field and shows its implications for enteprise information
systems, anticipating the emergence of software that will allow to reflect the
complexity of the business world, as holistic risk management becomes a mandate
for financial institutions and business organizations.
|
1208.5333
|
A hybrid ACO approach to the Matrix Bandwidth Minimization Problem
|
cs.AI cs.NE
|
The evolution of the human society raises more and more difficult endeavors.
For some of the real-life problems, the computing time-restriction enhances
their complexity. The Matrix Bandwidth Minimization Problem (MBMP) seeks for a
simultaneous permutation of the rows and the columns of a square matrix in
order to keep its nonzero entries close to the main diagonal. The MBMP is a
highly investigated P-complete problem, as it has broad applications in
industry, logistics, artificial intelligence or information recovery. This
paper describes a new attempt to use the Ant Colony Optimization framework in
tackling MBMP. The introduced model is based on the hybridization of the Ant
Colony System technique with new local search mechanisms. Computational
experiments confirm a good performance of the proposed algorithm for the
considered set of MBMP instances.
|
1208.5340
|
New results of ant algorithms for the Linear Ordering Problem
|
cs.AI cs.NE
|
Ant-based algorithms are successful tools for solving complex problems. One
of these problems is the Linear Ordering Problem (LOP). The paper shows new
results on some LOP instances, using Ant Colony System (ACS) and the Step-Back
Sensitive Ant Model (SB-SAM).
|
1208.5341
|
Sensitive Ants in Solving the Generalized Vehicle Routing Problem
|
cs.AI cs.NE
|
The idea of sensitivity in ant colony systems has been exploited in hybrid
ant-based models with promising results for many combinatorial optimization
problems. Heterogeneity is induced in the ant population by endowing individual
ants with a certain level of sensitivity to the pheromone trail. The variable
pheromone sensitivity within the same population of ants can potentially
intensify the search while in the same time inducing diversity for the
exploration of the environment. The performance of sensitive ant models is
investigated for solving the generalized vehicle routing problem. Numerical
results and comparisons are discussed and analysed with a focus on emphasizing
any particular aspects and potential benefits related to hybrid ant-based
models.
|
1208.5365
|
A Missing and Found Recognition System for Hajj and Umrah
|
cs.CV cs.CY
|
This note describes an integrated recognition system for identifying missing
and found objects as well as missing, dead, and found people during Hajj and
Umrah seasons in the two Holy cities of Makkah and Madina in the Kingdom of
Saudi Arabia. It is assumed that the total estimated number of pilgrims will
reach 20 millions during the next decade. The ultimate goal of this system is
to integrate facial recognition and object identification solutions into the
Hajj and Umrah rituals. The missing and found computerized system is part of
the CrowdSensing system for Hajj and Umrah crowd estimation, management and
safety.
|
1208.5373
|
Distributed Pharaoh System for Network Routing
|
cs.AI
|
In this paper it is introduced a biobjective ant algorithm for constructing
low cost routing networks. The new algorithm is called the Distributed Pharaoh
System (DPS). DPS is based on AntNet algorithm. The algorithm is using Pharaoh
Ant System (PAS) with an extra-exploration phase and a 'no-entry' condition in
order to improve the solutions for the Low Cost Network Routing problem.
Additionally it is used a cost model for overlay network construction that
includes network traffic demands. The Pharaoh ants (Monomorium pharaonis)
includes negative pheromones with signals concentrated at decision points where
trails fork. The negative pheromones may complement positive pheromone or could
help ants to escape from an unnecessarily long route to food that is being
reinforced by attractive signals. Numerical experiments were made for a random
10-node network. The average node degree of the network tested was 4.0. The
results are encouraging. The algorithm converges to the shortest path while
converging on a low cost overlay routing network topology.
|
1208.5374
|
New Constructions of Zero-Correlation Zone Sequences
|
cs.IT math.IT
|
In this paper, we propose three classes of systematic approaches for
constructing zero correlation zone (ZCZ) sequence families. In most cases,
these approaches are capable of generating sequence families that achieve the
upper bounds on the family size ($K$) and the ZCZ width ($T$) for a given
sequence period ($N$).
Our approaches can produce various binary and polyphase ZCZ families with
desired parameters $(N,K,T)$ and alphabet size. They also provide additional
tradeoffs amongst the above four system parameters and are less constrained by
the alphabet size. Furthermore, the constructed families have nested-like
property that can be either decomposed or combined to constitute smaller or
larger ZCZ sequence sets. We make detailed comparisons with related works and
present some extended properties. For each approach, we provide examples to
numerically illustrate the proposed construction procedure.
|
1208.5394
|
The Japanese Smart Grid Initiatives, Investments, and Collaborations
|
cs.SY
|
A smart grid delivers power around the country and has an intelligent
monitoring system, which not only keeps track of all the energy coming in from
diverse sources but also can detect where energy is needed through a two-way
communication system that collects data about how and when consumers use power.
It is safer in many ways, compared with the current one-directional power
supply system that seems susceptible to either sabotage or natural disasters,
including being more resistant to attack and power outages. In such an
autonomic and advanced-grid environment, investing in a pilot study and knowing
the nation readiness to adopt a smart grid absolves the government of complex
intervention from any failure to bring Japan into the autonomic-grid
environment. This paper looks closely into the concept of the Japanese
government go green effort, the objective of which is to make Japan a leading
nation in environmental and energy sustainability through green innovation,
such as creating a low-carbon society and embracing the natural grid community.
This paper paints a clearer conceptual picture of how Japan smart grid effort
compares with that of the US.
|
1208.5413
|
New affine-invariant codes from lifting
|
cs.IT cs.CC math.IT
|
In this work we explore error-correcting codes derived from the "lifting" of
"affine-invariant" codes. Affine-invariant codes are simply linear codes whose
coordinates are a vector space over a field and which are invariant under
affine-transformations of the coordinate space. Lifting takes codes defined
over a vector space of small dimension and lifts them to higher dimensions by
requiring their restriction to every subspace of the original dimension to be a
codeword of the code being lifted. While the operation is of interest on its
own, this work focusses on new ranges of parameters that can be obtained by
such codes, in the context of local correction and testing. In particular we
present four interesting ranges of parameters that can be achieved by such
lifts, all of which are new in the context of affine-invariance and some may be
new even in general. The main highlight is a construction of high-rate codes
with sublinear time decoding. The only prior construction of such codes is due
to Kopparty, Saraf and Yekhanin \cite{KSY}. All our codes are extremely simple,
being just lifts of various parity check codes (codes with one symbol of
redundancy), and in the final case, the lift of a Reed-Solomon code.
We also present a simple connection between certain lifted codes and lower
bounds on the size of "Nikodym sets". Roughly, a Nikodym set in
$\mathbb{F}_q^m$ is a set $S$ with the property that every point has a line
passing through it which is almost entirely contained in $S$. While previous
lower bounds on Nikodym sets were roughly growing as $q^m/2^m$, we use our
lifted codes to prove a lower bound of $(1 - o(1))q^m$ for fields of constant
characteristic.
|
1208.5429
|
Fast Erasure-and-Error Decoding and Systematic Encoding of a Class of
Affine Variety Codes
|
cs.IT cs.DM math.IT
|
In this paper, a lemma in algebraic coding theory is established, which is
frequently appeared in the encoding and decoding for algebraic codes such as
Reed-Solomon codes and algebraic geometry codes. This lemma states that two
vector spaces, one corresponds to information symbols and the other is indexed
by the support of Grobner basis, are canonically isomorphic, and moreover, the
isomorphism is given by the extension through linear feedback shift registers
from Grobner basis and discrete Fourier transforms. Next, the lemma is applied
to fast unified system of encoding and decoding erasures and errors in a
certain class of affine variety codes.
|
1208.5438
|
Data mining the MNC like internal co-opetition duality in a university
context
|
cs.SI cs.CY
|
The goal of the paper is to quantify the simultaneous competition and
cooperation that takes place in organizations. As the concepts seem to be
dichotomous opposites at first, the term internal coopetition duality is put
forth. Parallels are drawn between coopetitive processes in big multinational
corporations (MNCs) and these taking place in universities, also the structural
solutions used in both are analyzed. Data mining is used while looking at how
specializations inside the university are in competition for better students.
We look at the profiles that students have and find natural divisions between
the specializations, by applying graph theory and modularity algorithms for
community detection. The competitive position of the specializations is evident
in the average grades of the detected communities. The ratio of intercommunity
ties to intracommunity ties (conductance) quantifies the cooperative stance,
though, as students with similar profiles express linkages in the curricula;
and the choices regarding career development undertaken become evident.
Managerial implications discussed include the imperative for actively managing
and financially rewarding the outcomes of the coopetitive duality.
|
1208.5443
|
A Framework for Extracting Semantic Guarantees from Privacy
|
cs.DB
|
Statistical privacy views privacy definitions as contracts that guide the
behavior of algorithms that take in sensitive data and produce sanitized data.
For most existing privacy definitions, it is not clear what they actually
guarantee.
In this paper, we propose the first (to the best of our knowledge) framework
for extracting semantic guarantees from privacy definitions. That is, instead
of answering narrow questions such as "does privacy definition Y protect X?"
the goal is to answer the more general question "what does privacy definition Y
protect?"
The privacy guarantees we can extract are Bayesian in nature and deal with
changes in an attacker's beliefs. The key to our framework is an object we call
the row cone. Every privacy definition has a row cone, which is a convex set
that describes all the ways an attacker's prior beliefs can be turned into
posterior beliefs after observing an output of an algorithm satisfying that
privacy definition.
The framework can be applied to privacy definitions or even to individual
algorithms to identify the types of inferences they defend against. We
illustrate the use of our framework with analyses of several definitions and
algorithms for which we can derive previously unknown semantics. These include
randomized response, FRAPP, and several algorithms that add integer-valued
noise to their inputs.
|
1208.5451
|
Are You Imitating Me? Unsupervised Sparse Modeling for Group Activity
Analysis from a Single Video
|
cs.CV
|
A framework for unsupervised group activity analysis from a single video is
here presented. Our working hypothesis is that human actions lie on a union of
low-dimensional subspaces, and thus can be efficiently modeled as sparse linear
combinations of atoms from a learned dictionary representing the action's
primitives. Contrary to prior art, and with the primary goal of spatio-temporal
action grouping, in this work only one single video segment is available for
both unsupervised learning and analysis without any prior training information.
After extracting simple features at a single spatio-temporal scale, we learn a
dictionary for each individual in the video during each short time lapse. These
dictionaries allow us to compare the individuals' actions by producing an
affinity matrix which contains sufficient discriminative information about the
actions in the scene leading to grouping with simple and efficient tools. With
diverse publicly available real videos, we demonstrate the effectiveness of the
proposed framework and its robustness to cluttered backgrounds, changes of
human appearance, and action variability.
|
1208.5464
|
Finding Communities in Site Web-Graphs and Citation Graphs
|
cs.IR cs.SI
|
The Web is a typical example of a social network. One of the most intriguing
features of the Web is its self-organization behavior, which is usually faced
through the existence of communities. The discovery of the communities in a
Web-graph can be used to improve the effectiveness of search engines, for
purposes of prefetching, bibliographic citation ranking, spam detection,
creation of road-maps and site graphs, etc. Correspondingly, a citation graph
is also a social network which consists of communities. The identification of
communities in citation graphs can enhance the bibliography search as well as
the data-mining. In this paper we will present a fast algorithm which can
identify the communities over a given unweighted/undirected graph. This graph
may represent a Web-graph or a citation graph.
|
1208.5537
|
Planning Random path distributions for ambush games in unstructured
environments
|
cs.RO cs.GT
|
Operating vehicles in adversarial environments require non-conventional
planning techniques. A two-player, zero-sum non-cooperative game is introduced,
which is solved via a linear program. An extension is proposed to construct
networks displaying good representations of the environment characteristics,
while offering acceptable results for the technique used. Sensitivity of the
solution to the LP solver algorithm is identified. The performances of the
planner are finally assessed by comparison with those of conventional planners.
Results are used to formulate secondary objectives to the problem.
|
1208.5554
|
Soft Computing approaches on the Bandwidth Problem
|
cs.AI
|
The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous
reordering of the rows and the columns of a square matrix such that the nonzero
entries are collected within a band of small width close to the main diagonal.
The MBMP is a NP-complete problem, with applications in many scientific
domains, linear systems, artificial intelligence, and real-life situations in
industry, logistics, information recovery. The complex problems are hard to
solve, that is why any attempt to improve their solutions is beneficent.
Genetic algorithms and ant-based systems are Soft Computing methods used in
this paper in order to solve some MBMP instances. Our approach is based on a
learning agent-based model involving a local search procedure. The algorithm is
compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic
algorithm, using several instances from Matrix Market collection. Computational
experiments confirm a good performance of the proposed algorithms for the
considered set of MBMP instances. On Soft Computing basis, we also propose a
new theoretical Reinforcement Learning model for solving the MBMP problem.
|
1208.5556
|
Minimizing the Time of Spam Mail Detection by Relocating Filtering
System to the Sender Mail Server
|
cs.IR cs.NI
|
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to
massive number of users. Spam emails consume the network resources and cause
lots of security uncertainties. As we studied, the location where the spam
filter operates in is an important parameter to preserve network resources.
Although there are many different methods to block spam emails, most of program
developers only intend to block spam emails from being delivered to their
clients. In this paper, we will introduce a new and efficient approach to
prevent spam emails from being transferred. The result shows that if we focus
on developing a filtering method for spams emails in the sender mail server
rather than the receiver mail server, we can detect the spam emails in the
shortest time consequently to avoid wasting network resources.
|
1208.5604
|
Optimal co-design of control, scheduling and routing in multi-hop
control networks
|
math.OC cs.SY
|
A Multi-hop Control Network consists of a plant where the communication
between sensors, actuators and computational units is supported by a (wireless)
multi-hop communication network, and data flow is performed using scheduling
and routing of sensing and actuation data. Given a SISO LTI plant, we will
address the problem of co-designing a digital controller and the network
parameters (scheduling and routing) in order to guarantee stability and
maximize a performance metric on the transient response to a step input, with
constraints on the control effort, on the output overshoot and on the bandwidth
of the communication channel. We show that the above optimization problem is a
polynomial optimization problem, which is generally NP-hard. We provide
sufficient conditions on the network topology, scheduling and routing such that
it is computationally feasible, namely such that it reduces to a convex
optimization problem.
|
1208.5616
|
Cooperative Cognitive Relaying with Ordered Cognitive Multiple Access
|
cs.NI cs.IT math.IT
|
We investigate a cognitive radio system with two secondary users who can
cooperate with the primary user in relaying its packets to the primary
receiver. In addition to its own queue, each secondary user has a queue to keep
the primary packets that are not received correctly by the primary receiver.
The secondary users accept the unreceived primary packets with a certain
probability and transmit randomly from either of their queues if both are
nonempty. These probabilities are optimized to expand the maximum stable
throughput region of the system. Moreover, we suggest a secondary multiple
access scheme in which one secondary user senses the channel for $\tau$ seconds
from the beginning of the time slot and transmits if the channel is found to be
free. The other secondary user senses the channel over the period $[0,2\tau]$
to detect the possible activity of the primary user and the first-ranked
secondary user. It transmits, if possible, starting after $2\tau$ seconds from
the beginning of the time slot. It compensates for the delayed transmission by
increasing its transmission rate so that it still transmits one packet during
the time slot. We show the potential advantage of this ordered system over the
conventional random access system. We also show the benefit of cooperation in
enhancing the network's throughput.
|
1208.5654
|
Document Clustering Evaluation: Divergence from a Random Baseline
|
cs.IR cs.AI
|
Divergence from a random baseline is a technique for the evaluation of
document clustering. It ensures cluster quality measures are performing work
that prevents ineffective clusterings from giving high scores to clusterings
that provide no useful result. These concepts are defined and analysed using
intrinsic and extrinsic approaches to the evaluation of document cluster
quality. This includes the classical clusters to categories approach and a
novel approach that uses ad hoc information retrieval. The divergence from a
random baseline approach is able to differentiate ineffective clusterings
encountered in the INEX XML Mining track. It also appears to perform a
normalisation similar to the Normalised Mutual Information (NMI) measure but it
can be applied to any measure of cluster quality. When it is applied to the
intrinsic measure of distortion as measured by RMSE, subtraction from a random
baseline provides a clear optimum that is not apparent otherwise. This approach
can be applied to any clustering evaluation. This paper describes its use in
the context of document clustering evaluation.
|
1208.5659
|
Optimal Random Access and Random Spectrum Sensing for an Energy
Harvesting Cognitive Radio
|
cs.NI cs.IT math.IT
|
We consider a secondary user with energy harvesting capability. We design
access schemes for the secondary user which incorporate random spectrum sensing
and random access, and which make use of the primary automatic repeat request
(ARQ) feedback. The sensing and access probabilities are obtained such that the
secondary throughput is maximized under the constraints that both the primary
and secondary queues are stable and that the primary queueing delay is kept
lower than a specified value needed to guarantee a certain quality of service
(QoS) for the primary user. We consider spectrum sensing errors and assume
multipacket reception (MPR) capabilities. Numerical results are presented to
show the enhanced performance of our proposed system over a random access
system, and to demonstrate the benefit of leveraging the primary feedback.
|
1208.5700
|
Dynamic Pricing of Power in Smart-Grid Networks
|
cs.SY
|
In this paper we introduce the problem of dynamic pricing of power for
smart-grid networks. This is studied within a network utility maximization
(NUM) framework in a deterministic setting with a single provider, multiple
users and a finite horizon. The provider produces power or buys power in a
(deterministic) spot market, and determines a dynamic price to charge the
users. The users then adjust their demand in response to the time-varying
prices. This is typically categorized as the demand response problem, and we
study a progression of related models by focusing on two aspects: 1) the
characterization of the structure of the optimal dynamic prices in the Smart
Grid and the optimal demand and supply under various interaction with a spot
market; 2) a greedy approach to facilitate the solution process of the
aggregate NUM problem and the optimality gap between the greedy solution and
the optimal one.
|
1208.5703
|
Skewless Network Clock Synchronization
|
math.OC cs.SY
|
This paper examines synchronization of computer clocks connected via a data
network and proposes a skewless algorithm to synchronize them. Unlike existing
solutions, which either estimate and compensate the frequency difference (skew)
among clocks or introduce offset corrections that can generate jitter and
possibly even backward jumps, our algorithm achieves synchronization without
these problems. We first analyze the convergence property of the algorithm and
provide necessary and sufficient conditions on the parameters to guarantee
synchronization. We then implement our solution on a cluster of IBM BladeCenter
servers running Linux and study its performance. In particular, both
analytically and experimentally, we show that our algorithm can converge in the
presence of timing loops. This marks a clear contrast with current standards
such as NTP and PTP, where timing loops are specifically avoided. Furthermore,
timing loops can even be beneficial in our scheme. For example, it is
demonstrated that highly connected subnetworks can collectively outperform
individual clients when the time source has large jitter. It is also
experimentally demonstrated that our algorithm outperforms other
well-established software-based solutions such as the NTPv4 and IBM Coordinated
Cluster Time (IBM CCT).
|
1208.5713
|
Distance Measures for Sequences
|
cs.IT cs.DS math.IT
|
Given a set of sequences, the distance between pairs of them helps us to find
their similarity and derive structural relationship amongst them. For genomic
sequences such measures make it possible to construct the evolution tree of
organisms. In this paper we compare several distance measures and examine a
method that involves circular shifting one sequence against the other for
finding good alignment to minimize Hamming distance. We also use run-length
encoding together with LZ77 to characterize information in a binary sequence.
|
1208.5745
|
Bayes Networks for Supporting Query Processing Over Incomplete
Autonomous Databases
|
cs.DB
|
As the information available to lay users through autonomous data sources
continues to increase, mediators become important to ensure that the wealth of
information available is tapped effectively. A key challenge that these
information mediators need to handle is the varying levels of incompleteness in
the underlying databases in terms of missing attribute values. Existing
approaches such as QPIAD aim to mine and use Approximate Functional
Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These
approaches make independence assumptions about missing values---which
critically hobbles their performance when there are tuples containing missing
values for multiple correlated attributes. In this paper, we present a
principled probabilistic alternative that views an incomplete tuple as defining
a distribution over the complete tuples that it stands for. We learn this
distribution in terms of Bayes networks. Our approach involves
mining/"learning" Bayes networks from a sample of the database, and using it to
do both imputation (predict a missing value) and query rewriting (retrieve
relevant results with incompleteness on the query-constrained attributes, when
the data sources are autonomous). We present empirical studies to demonstrate
that (i) at higher levels of incompleteness, when multiple attribute values are
missing, Bayes networks do provide a significantly higher classification
accuracy and (ii) the relevant possible answers retrieved by the queries
reformulated using Bayes networks provide higher precision and recall than AFDs
while keeping query processing costs manageable.
|
1208.5801
|
Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector
Fields
|
cs.LG
|
Scientists study trajectory data to understand trends in movement patterns,
such as human mobility for traffic analysis and urban planning. There is a
pressing need for scalable and efficient techniques for analyzing this data and
discovering the underlying patterns. In this paper, we introduce a novel
technique which we call vector-field $k$-means.
The central idea of our approach is to use vector fields to induce a
similarity notion between trajectories. Other clustering algorithms seek a
representative trajectory that best describes each cluster, much like $k$-means
identifies a representative "center" for each cluster. Vector-field $k$-means,
on the other hand, recognizes that in all but the simplest examples, no single
trajectory adequately describes a cluster. Our approach is based on the premise
that movement trends in trajectory data can be modeled as flows within multiple
vector fields, and the vector field itself is what defines each of the
clusters. We also show how vector-field $k$-means connects techniques for
scalar field design on meshes and $k$-means clustering.
We present an algorithm that finds a locally optimal clustering of
trajectories into vector fields, and demonstrate how vector-field $k$-means can
be used to mine patterns from trajectory data. We present experimental evidence
of its effectiveness and efficiency using several datasets, including
historical hurricane data, GPS tracks of people and vehicles, and anonymous
call records from a large phone company. We compare our results to previous
trajectory clustering techniques, and find that our algorithm performs faster
in practice than the current state-of-the-art in trajectory clustering, in some
examples by a large margin.
|
1208.5814
|
Minimum Complexity Pursuit for Universal Compressed Sensing
|
cs.IT math.IT
|
The nascent field of compressed sensing is founded on the fact that
high-dimensional signals with "simple structure" can be recovered accurately
from just a small number of randomized samples. Several specific kinds of
structures have been explored in the literature, from sparsity and group
sparsity to low-rankness. However, two fundamental questions have been left
unanswered, namely: What are the general abstract meanings of "structure" and
"simplicity"? And do there exist universal algorithms for recovering such
simple structured objects from fewer samples than their ambient dimension? In
this paper, we address these two questions. Using algorithmic information
theory tools such as the Kolmogorov complexity, we provide a unified definition
of structure and simplicity. Leveraging this new definition, we develop and
analyze an abstract algorithm for signal recovery motivated by Occam's
Razor.Minimum complexity pursuit (MCP) requires just O(3\kappa) randomized
samples to recover a signal of complexity \kappa and ambient dimension n. We
also discuss the performance of MCP in the presence of measurement noise and
with approximately simple signals.
|
1208.5842
|
Tenacious tagging of images via Mellin monomials
|
cs.CV math.CA
|
We describe a method for attaching persistent metadata to an image. The
method can be interpreted as a template-based blind watermarking scheme, robust
to common editing operations, namely: cropping, rotation, scaling, stretching,
shearing, compression, printing, scanning, noise, and color removal. Robustness
is achieved through the reciprocity of the embedding and detection invariants.
The embedded patterns are real onedimensional Mellin monomial patterns
distributed over two-dimensions. The embedded patterns are scale invariant and
can be directly embedded in an image by simple pixel addition. Detection
achieves rotation and general affine invariance by signal projection using
implicit Radon transformation. Embedded signals contract to one-dimension in
the two-dimensional Fourier polar domain. The real signals are detected by
correlation with complex Mellin monomial templates. Using a unique template of
4 chirp patterns we detect the affine signature with exquisite sensitivity and
moderate security. The practical implementation achieves efficiencies through
fast Fourier transform (FFT) correspondences such as the projection-slice
theorem, the FFT correlation relation, and fast resampling via the chirp-z
transform. The overall method utilizes orthodox spread spectrum patterns for
the payload and performs well in terms of the classic
robustness-capacity-visibility performance triangle. Tags are entirely
imperceptible with a mean SSIM greater than 0.988 in all cases tested.
Watermarked images survive almost all Stirmark attacks. The method is ideal for
attaching metadata robustly to both digital and analogue images.
|
1208.5855
|
Attraction-Based Receding Horizon Path Planning with Temporal Logic
Constraints
|
cs.RO
|
Our goal in this paper is to plan the motion of a robot in a partitioned
environment with dynamically changing, locally sensed rewards. We assume that
arbitrary assumptions on the reward dynamics can be given. The robot aims to
accomplish a high-level temporal logic surveillance mission and to locally
optimize the collection of the rewards in the visited regions. These two
objectives often conflict and only a compromise between them can be reached. We
address this issue by taking into consideration a user-defined preference
function that captures the trade-off between the importance of collecting high
rewards and the importance of making progress towards a surveyed region. Our
solution leverages ideas from the automata-based approach to model checking. We
demonstrate the utilization and benefits of the suggested framework in an
illustrative example.
|
1208.5894
|
Average Case Recovery Analysis of Tomographic Compressive Sensing
|
math.NA cs.IT math.IT
|
The reconstruction of three-dimensional sparse volume functions from few
tomographic projections constitutes a challenging problem in image
reconstruction and turns out to be a particular instance problem of compressive
sensing. The tomographic measurement matrix encodes the incidence relation of
the imaging process, and therefore is not subject to design up to small
perturbations of non-zero entries. We present an average case analysis of the
recovery properties and a corresponding tail bound to establish weak
thresholds, in excellent agreement with numerical experiments. Our result
improve the state-of-the-art of tomographic imaging in experimental fluid
dynamics by a factor of three.
|
1208.5913
|
Logic of Negation-Complete Interactive Proofs (Formal Theory of
Epistemic Deciders)
|
math.LO cs.CR cs.DC cs.LO cs.MA
|
We produce a decidable classical normal modal logic of internalised
negation-complete and thus disjunctive non-monotonic interactive proofs (LDiiP)
from an existing logical counterpart of non-monotonic or instant interactive
proofs (LiiP). LDiiP internalises agent-centric proof theories that are
negation-complete (maximal) and consistent (and hence strictly weaker than, for
example, Peano Arithmetic) and enjoy the disjunction property (like
Intuitionistic Logic). In other words, internalised proof theories are
ultrafilters and all internalised proof goals are definite in the sense of
being either provable or disprovable to an agent by means of disjunctive
internalised proofs (thus also called epistemic deciders). Still, LDiiP itself
is classical (monotonic, non-constructive), negation-incomplete, and does not
have the disjunction property. The price to pay for the negation completeness
of our interactive proofs is their non-monotonicity and non-communality (for
singleton agent communities only). As a normal modal logic, LDiiP enjoys a
standard Kripke-semantics, which we justify by invoking the Axiom of Choice on
LiiP's and then construct in terms of a concrete oracle-computable function.
LDiiP's agent-centric internalised notion of proof can also be viewed as a
negation-complete disjunctive explicit refinement of standard KD45-belief, and
yields a disjunctive but negation-incomplete explicit refinement of
S4-provability.
|
1208.5919
|
The Stationary Phase Approximation, Time-Frequency Decomposition and
Auditory Processing
|
cs.IT cs.SD math.IT
|
The principle of stationary phase (PSP) is re-examined in the context of
linear time-frequency (TF) decomposition using Gaussian, gammatone and
gammachirp filters at uniform, logarithmic and cochlear spacings in frequency.
This necessitates consideration of the use the PSP on non-asymptotic integrals
and leads to the introduction of a test for phase rate dominance. Regions of
the TF plane that pass the test and don't contain stationary phase points
contribute little or nothing to the final output. Analysis values that lie in
these regions can thus be set to zero, i.e. sparsity. In regions of the TF
plane that fail the test or are in the vicinity of stationary phase points,
synthesis is performed in the usual way. A new interpretation of the location
parameters associated with the synthesis filters leads to: (i) a new method for
locating stationary phase points in the TF plane; (ii) a test for phase rate
dominance in that plane. Together this is a TF stationary phase approximation
(TFSFA) for both analysis and synthesis. The stationary phase regions of
several elementary signals are identified theoretically and examples of
reconstruction given. An analysis of the TF phase rate characteristics for the
case of two simultaneous tones predicts and quantifies a form of simultaneous
masking similar to that which characterizes the auditory system.
|
1208.5946
|
On extracting common random bits from correlated sources on large
alphabets
|
cs.IT math.IT
|
Suppose Alice and Bob receive strings $X=(X_1,...,X_n)$ and $Y=(Y_1,...,Y_n)$
each uniformly random in $[s]^n$ but so that $X$ and $Y$ are correlated . For
each symbol $i$, we have that $Y_i = X_i$ with probability $1-\eps$ and
otherwise $Y_i$ is chosen independently and uniformly from $[s]$.
Alice and Bob wish to use their respective strings to extract a uniformly
chosen common sequence from $[s]^k$ but without communicating. How well can
they do? The trivial strategy of outputting the first $k$ symbols yields an
agreement probability of $(1 - \eps + \eps/s)^k$. In a recent work by Bogdanov
and Mossel it was shown that in the binary case where $s=2$ and $k = k(\eps)$
is large enough then it is possible to extract $k$ bits with a better agreement
probability rate. In particular, it is possible to achieve agreement
probability $(k\eps)^{-1/2} \cdot 2^{-k\eps/(2(1 - \eps/2))}$ using a random
construction based on Hamming balls, and this is optimal up to lower order
terms.
In the current paper we consider the same problem over larger alphabet sizes
$s$ and we show that the agreement probability rate changes dramatically as the
alphabet grows. In particular we show no strategy can achieve agreement
probability better than $(1-\eps)^k (1+\delta(s))^k$ where $\delta(s) \to 0$ as
$s \to \infty$. We also show that Hamming ball based constructions have {\em
much lower} agreement probability rate than the trivial algorithm as $s \to
\infty$. Our proofs and results are intimately related to subtle properties of
hypercontractive inequalities.
|
1208.5959
|
On optimal wavelet reconstructions from Fourier samples: linearity and
universality of the stable sampling rate
|
math.NA cs.IT math.IT
|
In this paper we study the problem of computing wavelet coefficients of
compactly supported functions from their Fourier samples. For this, we use the
recently introduced framework of generalized sampling. Our first result
demonstrates that using generalized sampling one obtains a stable and accurate
reconstruction, provided the number of Fourier samples grows linearly in the
number of wavelet coefficients recovered. For the class of Daubechies wavelets
we derive the exact constant of proportionality.
Our second result concerns the optimality of generalized sampling for this
problem. Under some mild assumptions we show that generalized sampling cannot
be outperformed in terms of approximation quality by more than a constant
factor. Moreover, for the class of so-called perfect methods, any attempt to
lower the sampling ratio below a certain critical threshold necessarily results
in exponential ill-conditioning. Thus generalized sampling provides a
nearly-optimal solution to this problem.
|
1208.6025
|
Feasibility of Genetic Algorithm for Textile Defect Classification Using
Neural Network
|
cs.NE
|
The global market for textile industry is highly competitive nowadays.
Quality control in production process in textile industry has been a key factor
for retaining existence in such competitive market. Automated textile
inspection systems are very useful in this respect, because manual inspection
is time consuming and not accurate enough. Hence, automated textile inspection
systems have been drawing plenty of attention of the researchers of different
countries in order to replace manual inspection. Defect detection and defect
classification are the two major problems that are posed by the research of
automated textile inspection systems. In this paper, we perform an extensive
investigation on the applicability of genetic algorithm (GA) in the context of
textile defect classification using neural network (NN). We observe the effect
of tuning different network parameters and explain the reasons. We empirically
find a suitable NN model in the context of textile defect classification. We
compare the performance of this model with that of the classification models
implemented by others.
|
1208.6028
|
Design of Low Noise Amplifiers Using Particle Swarm Optimization
|
cs.NE
|
This short paper presents a work on the design of low noise microwave
amplifiers using particle swarm optimization (PSO) technique. Particle Swarm
Optimization is used as a method that is applied to a single stage amplifier
circuit to meet two criteria: desired gain and desired low noise. The aim is to
get the best optimized design using the predefined constraints for gain and low
noise values. The code is written to apply the algorithm to meet the desired
goals and the obtained results are verified using different simulators. The
results obtained show that PSO can be applied very efficiently for this kind of
design problems with multiple constraints.
|
1208.6061
|
Robust Stability of Quantum Systems with a Nonlinear Coupling Operator
|
quant-ph cs.SY math.OC
|
This paper considers the problem of robust stability for a class of uncertain
quantum systems subject to unknown perturbations in the system coupling
operator. A general stability result is given for a class of perturbations to
the system coupling operator. Then, the special case of a nominal linear
quantum system is considered with non-linear perturbations to the system
coupling operator. In this case, a robust stability condition is given in terms
of a scaled strict bounded real condition.
|
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