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1108.3415
|
Frequency-Hopping Sequence Sets With Low Average and Maximum Hamming
Correlation
|
cs.IT math.IT
|
In frequency-hopping multiple-access (FHMA) systems, the average Hamming
correlation (AHC) among frequency-hopping sequences (FHSs) as well as the
maximum Hamming correlation (MHC) is an important performance measure.
Therefore, it is a challenging problem to design FHS sets with good AHC and MHC
properties for application. In this paper, we analyze the AHC properties of an
FHS set, and present new constructions for FHS sets with optimal AHC. We first
calculate the AHC of some known FHS sets with optimal MHC, and check their
optimalities. We then prove that any uniformly distributed FHS set has optimal
AHC. We also present two constructions of FHS sets with optimal AHC based on
cyclotomy. Finally, we show that if an FHS set is obtained from another FHS set
with optimal AHC by an interleaving, it has optimal AHC.
|
1108.3417
|
The Exponent of a Polarizing Matrix Constructed from the Kronecker
Product
|
cs.IT math.IT
|
The asymptotic performance of a polar code under successive cancellation
decoding is determined by the exponent of its polarizing matrix. We first prove
that the partial distances of a polarizing matrix constructed from the
Kronecker product are simply expressed as a product of those of its component
matrices. We then show that the exponent of the polarizing matrix is shown to
be a weighted sum of the exponents of its component matrices. These results may
be employed in the design of a large polarizing matrix with high exponent.
|
1108.3426
|
A Spatial Calculus of Wrapped Compartments
|
cs.LO cs.CE cs.ET q-bio.QM
|
The Calculus of Wrapped Compartments (CWC) is a recently proposed modelling
language for the representation and simulation of biological systems behaviour.
Although CWC has no explicit structure modelling a spatial geometry, its
compartment labelling feature can be exploited to model various examples of
spatial interactions in a natural way. However, specifying large networks of
compartments may require a long modelling phase. In this work we present a
surface language for CWC that provides basic constructs for modelling spatial
interactions. These constructs can be compiled away to obtain a standard CWC
model, thus exploiting the existing CWC simulation tool. A case study
concerning the modelling of Arbuscular Mychorrizal fungi growth is discussed.
|
1108.3436
|
Modelling of Genetic Regulatory Mechanisms with GReg
|
cs.LO cs.CE
|
Most available tools propose simulation frameworks to study models of
biological systems, but simulation only explores a few of the most probable
behaviours of the system. On the contrary, techniques such as model checking,
coming from IT-systems analysis, explore all the possible behaviours of the
modelled systems, thus helping to identify emergent properties. A main drawback
from most model checking tools in the life sciences domain is that they take as
input a language designed for computer scientists, that is not easily
understood by non-expert users. We propose in this article an approach based on
DSL. It provides a comprehensible language to describe the system while
allowing the use of complex and powerful underlying model checking techniques.
|
1108.3446
|
Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
|
cs.LG cs.AI
|
Smart premise selection is essential when using automated reasoning as a tool
for large-theory formal proof development. A good method for premise selection
in complex mathematical libraries is the application of machine learning to
large corpora of proofs. This work develops learning-based premise selection in
two ways. First, a newly available minimal dependency analysis of existing
high-level formal mathematical proofs is used to build a large knowledge base
of proof dependencies, providing precise data for ATP-based re-verification and
for training premise selection algorithms. Second, a new machine learning
algorithm for premise selection based on kernel methods is proposed and
implemented. To evaluate the impact of both techniques, a benchmark consisting
of 2078 large-theory mathematical problems is constructed,extending the older
MPTP Challenge benchmark. The combined effect of the techniques results in a
50% improvement on the benchmark over the Vampire/SInE state-of-the-art system
for automated reasoning in large theories.
|
1108.3462
|
A Multiagent Simulation for Traffic Flow Management with Evolutionary
Optimization
|
cs.MA nlin.AO
|
A traffic flow is one of the main transportation issues in nowadays
industrialized agglomerations. Configuration of traffic lights is among the key
aspects in traffic flow management. This paper proposes an evolutionary
optimization tool that utilizes multiagent simulator in order to obtain
accurate model. Even though more detailed studies are still necessary, a
preliminary research gives an expectation for promising results.
|
1108.3476
|
Structured Sparsity and Generalization
|
cs.LG stat.ML
|
We present a data dependent generalization bound for a large class of
regularized algorithms which implement structured sparsity constraints. The
bound can be applied to standard squared-norm regularization, the Lasso, the
group Lasso, some versions of the group Lasso with overlapping groups, multiple
kernel learning and other regularization schemes. In all these cases
competitive results are obtained. A novel feature of our bound is that it can
be applied in an infinite dimensional setting such as the Lasso in a separable
Hilbert space or multiple kernel learning with a countable number of kernels.
|
1108.3489
|
A Novel and Robust Evolution Algorithm for Optimizing Complicated
Functions
|
cs.NE
|
In this paper, a novel mutation operator of differential evolution algorithm
is proposed. A new algorithm called divergence differential evolution algorithm
(DDEA) is developed by combining the new mutation operator with divergence
operator and assimilation operator (divergence operator divides population,
and, assimilation operator combines population), which can detect multiple
solutions and robustness in noisy environment. The new algorithm is applied to
optimize Michalewicz Function and to track changing of rain-induced-attenuation
process. The results based on DDEA are compared with those based on
Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets
better results than DEA does in the same premise. The new algorithm is
significant for optimizing and tracking the characteristics of MIMO (Multiple
Input Multiple Output) channel at millimeter waves.
|
1108.3524
|
On deep holes of standard Reed-Solomon codes
|
math.NT cs.IT math.IT
|
Determining deep holes is an important open problem in decoding Reed-Solomon
codes. It is well known that the received word is trivially a deep hole if the
degree of its Lagrange interpolation polynomial equals the dimension of the
Reed-Solomon code. For the standard Reed-Solomon codes $[p-1, k]_p$ with $p$ a
prime, Cheng and Murray conjectured in 2007 that there is no other deep holes
except the trivial ones. In this paper, we show that this conjecture is not
true. In fact, we find a new class of deep holes for standard Reed-Solomon
codes $[q-1, k]_q$ with $q$ a prime power of $p$. Let $q \geq 4$ and $2 \leq
k\leq q-2$. We show that the received word $u$ is a deep hole if its Lagrange
interpolation polynomial is the sum of monomial of degree $q-2$ and a
polynomial of degree at most $k-1$. So there are at least $2(q-1)q^k$ deep
holes if $k \leq q-3$.
|
1108.3525
|
Hamiltonian Streamline Guided Feature Extraction with Applications to
Face Detection
|
cs.CV math.DS
|
We propose a new feature extraction method based on two dynamical systems
induced by intensity landscape: the negative gradient system and the
Hamiltonian system. We build features based on the Hamiltonian streamlines.
These features contain nice global topological information about the intensity
landscape, and can be used for object detection. We show that for training
images of same size, our feature space is much smaller than that generated by
Haar-like features. The training time is extremely short, and detection speed
and accuracy is similar to Haar-like feature based classifiers.
|
1108.3540
|
A theory of robust software synthesis
|
cs.SY cs.FL math.OC
|
A key property for systems subject to uncertainty in their operating
environment is robustness, ensuring that unmodelled, but bounded, disturbances
have only a proportionally bounded effect upon the behaviours of the system.
Inspired by ideas from robust control and dissipative systems theory, we
present a formal definition of robustness and algorithmic tools for the design
of optimally robust controllers for omega-regular properties on discrete
transition systems. Formally, we define metric automata - automata equipped
with a metric on states - and strategies on metric automata which guarantee
robustness for omega-regular properties. We present fixed point algorithms to
construct optimally robust strategies in polynomial time. In contrast to
strategies computed by classical graph theoretic approaches, the strategies
computed by our algorithm ensure that the behaviours of the controlled system
gracefully degrade under the action of disturbances; the degree of degradation
is parameterized by the magnitude of the disturbance. We show an application of
our theory to the design of controllers that tolerate infinitely many transient
errors provided they occur infrequently enough.
|
1108.3544
|
Secure Lossy Transmission of Vector Gaussian Sources
|
cs.IT cs.CR math.IT
|
We study the secure lossy transmission of a vector Gaussian source to a
legitimate user in the presence of an eavesdropper, where both the legitimate
user and the eavesdropper have vector Gaussian side information. The aim of the
transmitter is to describe the source to the legitimate user in a way that the
legitimate user can reconstruct the source within a certain distortion level
while the eavesdropper is kept ignorant of the source as much as possible as
measured by the equivocation. We obtain an outer bound for the rate,
equivocation and distortion region of this secure lossy transmission problem.
This outer bound is tight when the transmission rate constraint is removed. In
other words, we obtain the maximum equivocation at the eavesdropper when the
legitimate user needs to reconstruct the source within a fixed distortion level
while there is no constraint on the transmission rate. This characterization of
the maximum equivocation involves two auxiliary random variables. We show that
a non-trivial selection for both random variables may be necessary in general.
The necessity of two auxiliary random variables also implies that, in general,
Wyner-Ziv coding is suboptimal in the presence of an eavesdropper. In addition,
we show that, even when there is no rate constraint on the legitimate link,
uncoded transmission (deterministic or stochastic) is suboptimal; the presence
of an eavesdropper necessitates the use of a coded scheme to attain the maximum
equivocation.
|
1108.3558
|
Proceedings of the 5th Workshop on Membrane Computing and Biologically
Inspired Process Calculi (MeCBIC 2011)
|
cs.DC cs.CE cs.ET cs.FL cs.LO
|
This volume represents the proceedings of the 5th Workshop on Membrane
Computing and Biologically Inspired Process Calculi (MeCBIC 2011), held
together with the 12th International Conference on Membrane Computing on 23rd
August 2011 in Fontainebleau, France.
|
1108.3571
|
Gaussian Channel with Noisy Feedback and Peak Energy Constraint
|
cs.IT math.IT
|
Optimal coding over the additive white Gaussian noise channel under the peak
energy constraint is studied when there is noisy feedback over an orthogonal
additive white Gaussian noise channel. As shown by Pinsker, under the peak
energy constraint, the best error exponent for communicating an M-ary message,
M >= 3, with noise-free feedback is strictly larger than the one without
feedback. This paper extends Pinsker's result and shows that if the noise power
in the feedback link is sufficiently small, the best error exponent for
conmmunicating an M-ary message can be strictly larger than the one without
feedback. The proof involves two feedback coding schemes. One is motivated by a
two-stage noisy feedback coding scheme of Burnashev and Yamamoto for binary
symmetric channels, while the other is a linear noisy feedback coding scheme
that extends Pinsker's noise-free feedback coding scheme. When the feedback
noise power $\alpha$ is sufficiently small, the linear coding scheme
outperforms the two-stage (nonlinear) coding scheme, and is asymptotically
optimal as $\alpha$ tends to zero. By contrast, when $\alpha$ is relatively
larger, the two-stage coding scheme performs better.
|
1108.3599
|
Decode-forward and Compute-forward Coding Schemes for the Two-Way Relay
Channel
|
cs.IT math.IT
|
We consider the full-duplex two-way relay channel with direct link between
two users and propose two coding schemes: a partial decode-forward scheme, and
a combined decode-forward and compute-forward scheme. Both schemes use
rate-splitting and superposition coding at each user and generate codewords for
each node independently. When applied to the Gaussian channel, partial
decode-forward can strictly increase the rate region over decode-forward, which
is opposite to the one-way relay channel. The combined scheme uses
superposition coding of both Gaussian and lattice codes to allow the relay to
decode the Gaussian parts and compute the lattice parts. This scheme can also
achieve new rates and outperform both decode-forward and compute-forward
separately. These schemes are steps towards understanding the optimal coding.
|
1108.3605
|
Hierarchical Object Parsing from Structured Noisy Point Clouds
|
cs.CV
|
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.
|
1108.3614
|
Feature Reinforcement Learning In Practice
|
cs.AI cs.RO
|
Following a recent surge in using history-based methods for resolving
perceptual aliasing in reinforcement learning, we introduce an algorithm based
on the feature reinforcement learning framework called PhiMDP. To create a
practical algorithm we devise a stochastic search procedure for a class of
context trees based on parallel tempering and a specialized proposal
distribution. We provide the first empirical evaluation for PhiMDP. Our
proposed algorithm achieves superior performance to the classical U-tree
algorithm and the recent active-LZ algorithm, and is competitive with
MC-AIXI-CTW that maintains a bayesian mixture over all context trees up to a
chosen depth.We are encouraged by our ability to compete with this
sophisticated method using an algorithm that simply picks one single model, and
uses Q-learning on the corresponding MDP. Our PhiMDP algorithm is much simpler,
yet consumes less time and memory. These results show promise for our future
work on attacking more complex and larger problems.
|
1108.3636
|
Information theory: Sources, Dirichlet series, and realistic analyses of
data structures
|
cs.IT cs.DM cs.DS math.IT
|
Most of the text algorithms build data structures on words, mainly trees, as
digital trees (tries) or binary search trees (bst). The mechanism which
produces symbols of the words (one symbol at each unit time) is called a
source, in information theory contexts. The probabilistic behaviour of the
trees built on words emitted by the same source depends on two factors: the
algorithmic properties of the tree, together with the information-theoretic
properties of the source. Very often, these two factors are considered in a too
simplified way: from the algorithmic point of view, the cost of the Bst is only
measured in terms of the number of comparisons between words --from the
information theoretic point of view, only simple sources (memoryless sources or
Markov chains) are studied.
We wish to perform here a realistic analysis, and we choose to deal together
with a general source and a realistic cost for data structures: we take into
account comparisons between symbols, and we consider a general model of source,
related to a dynamical system, which is called a dynamical source. Our methods
are close to analytic combinatorics, and our main object of interest is the
generating function of the source Lambda(s), which is here of Dirichlet type.
Such an object transforms probabilistic properties of the source into analytic
properties. The tameness of the source, which is defined through analytic
properties of Lambda(s), appears to be central in the analysis, and is
precisely studied for the class of dynamical sources. We focus here on
arithmetical conditions, of diophantine type, which are sufficient to imply
tameness on a domain with hyperbolic shape.
|
1108.3652
|
Coordination using Implicit Communication
|
cs.IT math.IT
|
We explore a basic noise-free signaling scenario where coordination and
communication are naturally merged. A random signal X_1,...,X_n is processed to
produce a control signal or action sequence A_1,...,A_n, which is observed and
further processed (without access to X_1,...,X_n) to produce a third sequence
B_1,...,B_n. The object of interest is the set of empirical joint distributions
p(x,a,b) that can be achieved in this setting. We show that H(A) >= I(X;A,B) is
the necessary and sufficient condition for achieving p(x,a,b) when no causality
constraints are enforced on the encoders. We also give results for various
causality constraints.
This setting sheds light on the embedding of digital information in analog
signals, a concept that is exploited in digital watermarking, steganography,
cooperative communication, and strategic play in team games such as bridge.
|
1108.3691
|
Influence, originality and similarity in directed acyclic graphs
|
physics.soc-ph cs.DL cs.SI
|
We introduce a framework for network analysis based on random walks on
directed acyclic graphs where the probability of passing through a given node
is the key ingredient. We illustrate its use in evaluating the mutual influence
of nodes and discovering seminal papers in a citation network. We further
introduce a new similarity metric and test it in a simple personalized
recommendation process. This metric's performance is comparable to that of
classical similarity metrics, thus further supporting the validity of our
framework.
|
1108.3702
|
Model of skyscraper evacuation with the use of space symmetry and fluid
dynamic approximation
|
cs.MA
|
The simulation of evacuation of pedestrians from skyscraper is a situation
where the symmetry analysis method and equations of fluid dynamics finds to be
very useful. When applied, they strongly reduce the number of free parameters
used in simulations and in such a way speed up the calculations and make them
easier to manage by the programmer and what is even more important, they can
give a fresh insight into a problem of evacuation and help with incorporation
of "Ambient Intelligent Devices" into future real buildings. We have analyzed
various, simplified, cases of evacuation from skyscraper by employing improved
"Social Force Model". For each of them we obtained the average force acting on
the pedestrian as a function of the evacuation time. The results clearly show
that both methods mentioned above, can be successfully implemented in the
simulation process and return with satisfactory conclusions.
|
1108.3711
|
Doing Better Than UCT: Rational Monte Carlo Sampling in Trees
|
cs.AI
|
UCT, a state-of-the art algorithm for Monte Carlo tree sampling (MCTS), is
based on UCB, a sampling policy for the Multi-armed Bandit Problem (MAB) that
minimizes the accumulated regret. However, MCTS differs from MAB in that only
the final choice, rather than all arm pulls, brings a reward, that is, the
simple regret, as opposite to the cumulative regret, must be minimized. This
ongoing work aims at applying meta-reasoning techniques to MCTS, which is
non-trivial. We begin by introducing policies for multi-armed bandits with
lower simple regret than UCB, and an algorithm for MCTS which combines
cumulative and simple regret minimization and outperforms UCT. We also develop
a sampling scheme loosely based on a myopic version of perfect value of
information. Finite-time and asymptotic analysis of the policies is provided,
and the algorithms are compared empirically.
|
1108.3728
|
On Distribution Preserving Quantization
|
cs.IT math.IT
|
Upon compressing perceptually relevant signals, conventional quantization
generally results in unnatural outcomes at low rates. We propose distribution
preserving quantization (DPQ) to solve this problem. DPQ is a new quantization
concept that confines the probability space of the reconstruction to be
identical to that of the source. A distinctive feature of DPQ is that it
facilitates a seamless transition between signal synthesis and quantization. A
theoretical analysis of DPQ leads to a distribution preserving rate-distortion
function (DP-RDF), which serves as a lower bound on the rate of any DPQ scheme,
under a constraint on distortion. In general situations, the DP-RDF approaches
the classic rate-distortion function for the same source and distortion
measure, in the limit of an increasing rate. A practical DPQ scheme based on a
multivariate transformation is also proposed. This scheme asymptotically
achieves the DP-RDF for i.i.d. Gaussian sources and the mean squared error.
|
1108.3732
|
The Successive Approximation Approach for NUM Frameworks with Elastic
and Inelastic Traffic
|
cs.SY
|
The concave utility in the Network Utility Maximization (NUM) problem is only
suitable for elastic flows. However, the networks with the multiclass traffic,
the utility of inelastic traffic is usually represented by the sigmoidal
function which is a nonconcave function. Hence, the basic NUM problem becomes a
nonconvex optimization problem. Solving the nonconvex NUM distributively is a
difficult problem. The current works utilize the standard dual-based algorithm
for the convex NUM and find the criteria for the global optimal convergence of
the algorithm. It turns out that the link capacity must higher than a certain
value to achieve the global optimum.
We propose a new distributed algorithm that converges to the suboptimal
solution of the nonconvex NUM for all of link capacity. We approximate the
logarithm of the original problem to the convex problem which is solved
efficiently by the standard dual-base distributed algorithm. After a sequence
of approximations, the solutions converge to the KKT solution of the original
problem. In many of our experiments, it also converges to the global optimal
solution of the NUM. Moreover, we extend our work to solve the joint rate and
power NUM problem with elastic and inelastic traffic in a wireless network. Our
techniques can be applied to any log-concave utilities.
|
1108.3742
|
Degrees of Freedom of the Network MIMO Channel With Distributed CSI
|
cs.IT math.IT
|
In this work, we discuss the joint precoding with finite rate feedback in the
so-called network MIMO where the TXs share the knowledge of the data symbols to
be transmitted. We introduce a distributed channel state information (DCSI)
model where each TX has its own local estimate of the overall multi-user MIMO
channel and must make a precoding decision solely based on the available local
CSI. We refer to this channel as the DCSI-MIMO channel and the precoding
problem as distributed precoding. We extend to the DCSI setting the work from
Jindal for the conventional MIMO Broadcast Channel (BC) in which the number of
Degrees of Freedom (DoFs) achieved by Zero Forcing (ZF) was derived as a
function of the scaling in the logarithm of the Signal-to-Noise Ratio (SNR) of
the number of quantizing bits. Particularly, we show the seemingly pessimistic
result that the number of DoFs at each user is limited by the worst CSI across
all users and across all TXs. This is in contrast to the conventional MIMO BC
where the number of DoFs at one user is solely dependent on the quality of the
estimation of his own feedback. Consequently, we provide precoding schemes
improving on the achieved number of DoFs. For the two-user case, the derived
novel precoder achieves a number of DoFs limited by the best CSI accuracy
across the TXs instead of the worst with conventional ZF. We also advocate the
use of hierarchical quantization of the CSI, for which we show that
considerable gains are possible. Finally, we use the previous analysis to
derive the DoFs optimal allocation of the feedback bits to the various TXs
under a constraint on the size of the aggregate feedback in the network, in the
case where conventional ZF is used.
|
1108.3754
|
On Quasi-Cyclic Codes as a Generalization of Cyclic Codes
|
cs.IT math.IT
|
In this article we see quasi-cyclic codes as block cyclic codes. We
generalize some properties of cyclic codes to quasi-cyclic ones such as
generator polynomials and ideals. Indeed we show a one-to-one correspondence
between l-quasi-cyclic codes of length m and ideals of M_l(Fq)[X]/(X^m-1). This
permits to construct new classes of codes, namely quasi-BCH and
quasi-evaluation codes. We study the parameters of such codes and propose a
decoding algorithm up to half the designed minimum distance. We even found one
new quasi-cyclic code with better parameters than known [189, 11, 125]_F4 and
48 derivated codes beating the known bounds as well.
|
1108.3757
|
Self-Organizing Mixture Networks for Representation of Grayscale Digital
Images
|
cs.AI
|
Self-Organizing Maps are commonly used for unsupervised learning purposes.
This paper is dedicated to the certain modification of SOM called SOMN
(Self-Organizing Mixture Networks) used as a mechanism for representing
grayscale digital images. Any grayscale digital image regarded as a
distribution function can be approximated by the corresponding Gaussian
mixture. In this paper, the use of SOMN is proposed in order to obtain such
approximations for input grayscale images in unsupervised manner.
|
1108.3780
|
Performance Bounds and Associated Design Principles for Multi-Cellular
Wireless OFDMA Systems (with Detailed Proofs)
|
cs.IT math.IT
|
In this paper, we consider the downlink of large-scale multi-cellular
OFDMA-based networks and study performance bounds of the system as a function
of the number of users $K$, the number of base-stations $B$, and the number of
resource-blocks $N$. Here, a resource block is a collection of subcarriers such
that all such collections, that are disjoint have associated independently
fading channels. We derive novel upper and lower bounds on the sum-utility for
a general spatial geometry of base stations, a truncated path loss model, and a
variety of fading models (Rayleigh, Nakagami-$m$, Weibull, and LogNormal). We
also establish the associated scaling laws and show that, in the special case
of fixed number of resource blocks, a grid-based network of base stations, and
Rayleigh-fading channels, the sum information capacity of the system scales as
$\Theta(B \log\log K/B)$ for extended networks, and as $O(B \log\log K)$ and
$\Omega(\log \log K)$ for dense networks. Interpreting these results, we
develop some design principles for the service providers along with some
guidelines for the regulators in order to achieve provisioning of various QoS
guarantees for the end users and, at the same time, maximize revenue for the
service providers.
|
1108.3832
|
Coding in the Presence of Semantic Value of Information: Unequal Error
Protection Using Poset Decoders
|
cs.IT math.IT
|
In this work we explore possibilities for coding when information worlds have
different (semantic) values. We introduce a loss function that expresses the
overall performance of a coding scheme for discrete channels and exchange the
usual goal of minimizing the error probability to that of minimizing the
expected loss. In this environment we explore the possibilities of using
poset-decoders to make a message-wise unequal error protection (UEP), where the
most valuable information is protected by placing in its proximity information
words that differ by small valued information. Similar definitions and results
are shortly presented also for signal constellations in Euclidean space.
|
1108.3843
|
Using Inverse lambda and Generalization to Translate English to Formal
Languages
|
cs.CL
|
We present a system to translate natural language sentences to formulas in a
formal or a knowledge representation language. Our system uses two inverse
lambda-calculus operators and using them can take as input the semantic
representation of some words, phrases and sentences and from that derive the
semantic representation of other words and phrases. Our inverse lambda operator
works on many formal languages including first order logic, database query
languages and answer set programming. Our system uses a syntactic combinatorial
categorial parser to parse natural language sentences and also to construct the
semantic meaning of the sentences as directed by their parsing. The same parser
is used for both. In addition to the inverse lambda-calculus operators, our
system uses a notion of generalization to learn semantic representation of
words from the semantic representation of other words that are of the same
category. Together with this, we use an existing statistical learning approach
to assign weights to deal with multiple meanings of words. Our system produces
improved results on standard corpora on natural language interfaces for robot
command and control and database queries.
|
1108.3848
|
Language understanding as a step towards human level intelligence -
automatizing the construction of the initial dictionary from example
sentences
|
cs.CL
|
For a system to understand natural language, it needs to be able to take
natural language text and answer questions given in natural language with
respect to that text; it also needs to be able to follow instructions given in
natural language. To achieve this, a system must be able to process natural
language and be able to capture the knowledge within that text. Thus it needs
to be able to translate natural language text into a formal language. We
discuss our approach to do this, where the translation is achieved by composing
the meaning of words in a sentence. Our initial approach uses an inverse lambda
method that we developed (and other methods) to learn meaning of words from
meaning of sentences and an initial lexicon. We then present an improved method
where the initial lexicon is also learned by analyzing the training sentence
and meaning pairs. We evaluate our methods and compare them with other existing
methods on a corpora of database querying and robot command and control.
|
1108.3850
|
Solving puzzles described in English by automated translation to answer
set programming and learning how to do that translation
|
cs.CL cs.AI cs.LO
|
We present a system capable of automatically solving combinatorial logic
puzzles given in (simplified) English. It involves translating the English
descriptions of the puzzles into answer set programming(ASP) and using ASP
solvers to provide solutions of the puzzles. To translate the descriptions, we
use a lambda-calculus based approach using Probabilistic Combinatorial
Categorial Grammars (PCCG) where the meanings of words are associated with
parameters to be able to distinguish between multiple meanings of the same
word. Meaning of many words and the parameters are learned. The puzzles are
represented in ASP using an ontology which is applicable to a large set of
logic puzzles.
|
1108.3873
|
The Diversity Potential of Relay Selection with Practical Channel
Estimation
|
cs.IT math.IT
|
We investigate the diversity order of decode-and-forward relay selection in
Nakagami-m fading, in cases where practical channel estimation techniques are
applied. In this respect, we introduce a unified model for the imperfect
channel estimates, where the effects of noise, time-varying channels, and
feedback delays are jointly considered. Based on this model, the correlation
between the actual and the estimated channel values, \rho, is expressed as a
function of the signal-to-noise ratio (SNR), yielding closed-form expressions
for the overall outage probability as a function of \rho. The resulting
diversity order and power gain reveal a high dependence of the performance of
relay selection on the high SNR behavior of \rho, thus shedding light onto the
effect of channel estimation on the overall performance. It is shown that when
the channel estimates are not frequently updated in applications involving
time-varying channels, or when the amount of power allocated for channel
estimation is not sufficiently high, the diversity potential of relay selection
is severely degraded. In short, the main contribution of this paper lies in
answering the following question: How fast should \rho tend to one, as the SNR
tends to infinity, so that relay selection does not experience any diversity
loss?
|
1108.3883
|
Exact Regenerating Codes for Byzantine Fault Tolerance in Distributed
Storage
|
cs.IT math.IT
|
Due to the use of commodity software and hardware, crash-stop and Byzantine
failures are likely to be more prevalent in today's large-scale distributed
storage systems. Regenerating codes have been shown to be a more efficient way
to disperse information across multiple nodes and recover crash-stop failures
in the literature. In this paper, we present the design of regeneration codes
in conjunction with integrity check that allows exact regeneration of failed
nodes and data reconstruction in presence of Byzantine failures. A progressive
decoding mechanism is incorporated in both procedures to leverage computation
performed thus far. The fault-tolerance and security properties of the schemes
are also analyzed.
|
1108.3887
|
Hamming Weights in Irreducible Cyclic Codes
|
cs.IT math.IT
|
Irreducible cyclic codes are an interesting type of codes and have
applications in space communications. They have been studied for decades and a
lot of progress has been made. The objectives of this paper are to survey and
extend earlier results on the weight distributions of irreducible cyclic codes,
present a divisibility theorem and develop bounds on the weights in irreducible
cyclic codes.
|
1108.3915
|
City on the Sky: Flexible, Secure Data Sharing on the Cloud
|
cs.DB cs.NI
|
Sharing data from various sources and of diverse kinds, and fusing them
together for sophisticated analytics and mash-up applications are emerging
trends, and are prerequisites for grand visions such as that of cyber-physical
systems enabled smart cities. Cloud infrastructure can enable such data sharing
both because it can scale easily to an arbitrary volume of data and computation
needs on demand, as well as because of natural collocation of diverse such data
sets within the infrastructure. However, in order to convince data owners that
their data are well protected while being shared among cloud users, the cloud
platform needs to provide flexible mechanisms for the users to express the
constraints (access rules) subject to which the data should be shared, and
likewise, enforce them effectively. We study a comprehensive set of practical
scenarios where data sharing needs to be enforced by methods such as
aggregation, windowed frame, value constrains, etc., and observe that existing
basic access control mechanisms do not provide adequate flexibility to enable
effective data sharing in a secure and controlled manner. In this paper, we
thus propose a framework for cloud that extends popular XACML model
significantly by integrating flexible access control decisions and data access
in a seamless fashion. We have prototyped the framework and deployed it on
commercial cloud environment for experimental runs to test the efficacy of our
approach and evaluate the performance of the implemented prototype.
|
1108.3973
|
Implicit learning of object geometry by reducing contact forces and
increasing smoothness
|
cs.SY math.OC
|
Moving our hands smoothly is essential to execute ordinary tasks, such as
carrying a glass of water without spilling. Past studies have revealed a
natural tendency to generate smooth trajectories when moving the hand from one
point to another in free space. Here we provide a new perspective on movement
smoothness by showing that smoothness is also enforced when the hand maintains
contact with a curved surface. Maximally smooth motions over curved surfaces
occur along geodesic lines that depend on fundamental features of the surface,
such as its radius and center of curvature. Subjects were requested to execute
movements of the hand while in contact with a virtual sphere that they could
not see. We found that with practice, subjects tended to move their hand along
smooth trajectories, near geodesic pathways joining start to end positions, to
reduce contact forces with constrained boundary, variance of contact force,
tangential velocity profile error and sum of square jerk along the time span of
movement. Furthermore, after practicing movements in a region of the sphere,
subjects executed near-geodesic movements, less contact forces, less contact
force variance, less tangential velocity profile error and less sum of square
jerk in a different region. These findings suggest that the execution of smooth
movements while the hand is in contact with a surface is a means for extracting
information about the surface's geometrical features.
|
1108.3980
|
Three-dimensional Torques and Power of Horse Forelimb Joints at Trot
|
cs.RO
|
Reasons for Performing Study: Equine gait analysis has focused on 2D analysis
in the sagittal plane, while descriptions of 3D kinetics and ground reaction
force could provide more information on the Equine gait analysis. Hypothesis or
Objectives: The aim of this study was to characterize the 3D torques and powers
of the forelimb joints at trotting. Methods: Eight sound horses were used in
the study. A full 3D torque and power for elbow, carpus, fetlock, pastern and
coffin joints of right forelimb in horses at trot were obtained by calculating
the inverse kinetics of simplified link segmental model. Results: Over two
third of energy (70%) generated by all joints come from stance phase, and most
of energy generated was by elbow joint both in stance (77%) and sway (88%)
phases. Energy absorbed by all joints during stance (40%) and sway (60%) phases
respectively is not a big difference. During stance phase, all most two third
of energy (65%) absorbed was by fetlock joint, while over two third of energy
(74%) absorbed was by carpus joint during sway phase. Conclusions & Clinical
Relevance: This study presents a full 3D kinetic analysis of the relative
motion of the humerus, radius, cannon, pastern and coffin segments of the
forelimb at the trot. The results could provide for a more sensitive measure
for kinetic analysis.
|
1108.4034
|
Finding Community Structure with Performance Guarantees in Complex
Networks
|
cs.SI cs.DS physics.soc-ph
|
Many networks including social networks, computer networks, and biological
networks are found to divide naturally into communities of densely connected
individuals. Finding community structure is one of fundamental problems in
network science. Since Newman's suggestion of using \emph{modularity} as a
measure to qualify the goodness of community structures, many efficient methods
to maximize modularity have been proposed but without a guarantee of
optimality. In this paper, we propose two polynomial-time algorithms to the
modularity maximization problem with theoretical performance guarantees. The
first algorithm comes with a \emph{priori guarantee} that the modularity of
found community structure is within a constant factor of the optimal modularity
when the network has the power-law degree distribution. Despite being mainly of
theoretical interest, to our best knowledge, this is the first approximation
algorithm for finding community structure in networks. In our second algorithm,
we propose a \emph{sparse metric}, a substantially faster linear programming
method for maximizing modularity and apply a rounding technique based on this
sparse metric with a \emph{posteriori approximation guarantee}. Our experiments
show that the rounding algorithm returns the optimal solutions in most cases
and are very scalable, that is, it can run on a network of a few thousand nodes
whereas the LP solution in the literature only ran on a network of at most 235
nodes.
|
1108.4048
|
A graphical environment to express the semantics of control systems
|
cs.SY cs.PL math.OC
|
We present the concept of a unified graphical environment for expressing the
semantics of control systems. The graphical control system design environment
in Simulink already allows engineers to insert a variety of assertions aimed
the verification and validation of the control software. We propose extensions
to a Simulink-like environment's annotation capabilities to include formal
control system stability, performance properties and their proofs. We provide a
conceptual description of a tool, that takes in a Simulink-like diagram of the
control system as the input, and generates a graphically annotated control
system diagram as the output. The annotations can either be inserted by the
user or generated automatically by a third party control analysis software such
as IQC$\beta$ or $\mu$-tool. We finally describe how the graphical
representation of the system and its properties can be translated to annotated
programs in a programming language used in verification and validation such as
Lustre or C.
|
1108.4052
|
Query Expansion: Term Selection using the EWC Semantic Relatedness
Measure
|
cs.CL
|
This paper investigates the efficiency of the EWC semantic relatedness
measure in an ad-hoc retrieval task. This measure combines the Wikipedia-based
Explicit Semantic Analysis measure, the WordNet path measure and the mixed
collocation index. In the experiments, the open source search engine Terrier
was utilised as a tool to index and retrieve data. The proposed technique was
tested on the NTCIR data collection. The experiments demonstrated promising
results.
|
1108.4063
|
Backpressure with Adaptive Redundancy (BWAR)
|
cs.NI cs.SY math.OC
|
Backpressure scheduling and routing, in which packets are preferentially
transmitted over links with high queue differentials, offers the promise of
throughput-optimal operation for a wide range of communication networks.
However, when the traffic load is low, due to the corresponding low queue
occupancy, backpressure scheduling/routing experiences long delays. This is
particularly of concern in intermittent encounter-based mobile networks which
are already delay-limited due to the sparse and highly dynamic network
connectivity. While state of the art mechanisms for such networks have proposed
the use of redundant transmissions to improve delay, they do not work well when
the traffic load is high. We propose in this paper a novel hybrid approach that
we refer to as backpressure with adaptive redundancy (BWAR), which provides the
best of both worlds. This approach is highly robust and distributed and does
not require any prior knowledge of network load conditions. We evaluate BWAR
through both mathematical analysis and simulations based on cell-partitioned
model. We prove theoretically that BWAR does not perform worse than traditional
backpressure in terms of the maximum throughput, while yielding a better delay
bound. The simulations confirm that BWAR outperforms traditional backpressure
at low load, while outperforming a state of the art encounter-routing scheme
(Spray and Wait) at high load.
|
1108.4079
|
Toward Parts-Based Scene Understanding with Pixel-Support Parts-Sparse
Pictorial Structures
|
cs.CV stat.ML
|
Scene understanding remains a significant challenge in the computer vision
community. The visual psychophysics literature has demonstrated the importance
of interdependence among parts of the scene. Yet, the majority of methods in
computer vision remain local. Pictorial structures have arisen as a fundamental
parts-based model for some vision problems, such as articulated object
detection. However, the form of classical pictorial structures limits their
applicability for global problems, such as semantic pixel labeling. In this
paper, we propose an extension of the pictorial structures approach, called
pixel-support parts-sparse pictorial structures, or PS3, to overcome this
limitation. Our model extends the classical form in two ways: first, it defines
parts directly based on pixel-support rather than in a parametric form, and
second, it specifies a space of plausible parts-based scene models and permits
one to be used for inference on any given image. PS3 makes strides toward
unifying object-level and pixel-level modeling of scene elements. In this
report, we implement the first half of our model and rely upon external
knowledge to provide an initial graph structure for a given image. Our
experimental results on benchmark datasets demonstrate the capability of this
new parts-based view of scene modeling.
|
1108.4080
|
Convergence Properties of Two ({\mu} + {\lambda}) Evolutionary
Algorithms On OneMax and Royal Roads Test Functions
|
cs.NE
|
We present a number of bounds on convergence time for two elitist
population-based Evolutionary Algorithms using a recombination operator
k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the
effect of distribution of elite species and population size.
|
1108.4083
|
Convergence of a Recombination-Based Elitist Evolutionary Algorithm on
the Royal Roads Test Function
|
cs.NE
|
We present an analysis of the performance of an elitist Evolutionary
algorithm using a recombination operator known as 1-Bit-Swap on the Royal Roads
test function based on a population. We derive complete, approximate and
asymptotic convergence rates for the algorithm. The complete model shows the
benefit of the size of the population and re- combination pool.
|
1108.4096
|
A Deterministic Equivalent for the Analysis of Non-Gaussian Correlated
MIMO Multiple Access Channels
|
cs.IT math.IT
|
Large dimensional random matrix theory (RMT) has provided an efficient
analytical tool to understand multiple-input multiple-output (MIMO) channels
and to aid the design of MIMO wireless communication systems. However, previous
studies based on large dimensional RMT rely on the assumption that the transmit
correlation matrix is diagonal or the propagation channel matrix is Gaussian.
There is an increasing interest in the channels where the transmit correlation
matrices are generally nonnegative definite and the channel entries are
non-Gaussian. This class of channel models appears in several applications in
MIMO multiple access systems, such as small cell networks (SCNs). To address
these problems, we use the generalized Lindeberg principle to show that the
Stieltjes transforms of this class of random matrices with Gaussian or
non-Gaussian independent entries coincide in the large dimensional regime. This
result permits to derive the deterministic equivalents (e.g., the Stieltjes
transform and the ergodic mutual information) for non-Gaussian MIMO channels
from the known results developed for Gaussian MIMO channels, and is of great
importance in characterizing the spectral efficiency of SCNs.
|
1108.4098
|
Multisensor Images Fusion Based on Feature-Level
|
cs.CV
|
Until now, of highest relevance for remote sensing data processing and
analysis have been techniques for pixel level image fusion. So, This paper
attempts to undertake the study of Feature-Level based image fusion. For this
purpose, feature based fusion techniques, which are usually based on empirical
or heuristic rules, are employed. Hence, in this paper we consider feature
extraction (FE) for fusion. It aims at finding a transformation of the original
space that would produce such new features, which preserve or improve as much
as possible. This study introduces three different types of Image fusion
techniques including Principal Component Analysis based Feature Fusion (PCA),
Segment Fusion (SF) and Edge fusion (EF). This paper also devotes to
concentrate on the analytical techniques for evaluating the quality of image
fusion (F) by using various methods including (SD), (En), (CC), (SNR), (NRMSE)
and (DI) to estimate the quality and degree of information improvement of a
fused image quantitatively.
|
1108.4114
|
Collaborative Network Formation in Spatial Oligopolies
|
math.OC cs.SY
|
Recently, it has been shown that networks with an arbitrary degree sequence
may be a stable solution to a network formation game. Further, in recent years
there has been a rise in the number of firms participating in collaborative
efforts. In this paper, we show conditions under which a graph with an
arbitrary degree sequence is admitted as a stable firm collaboration graph.
|
1108.4115
|
The Calculation and Simulation of the Price of Anarchy for Network
Formation Games
|
math.OC cs.SY
|
We model the formation of networks as the result of a game where by players
act selfishly to get the portfolio of links they desire most. The integration
of player strategies into the network formation model is appropriate for
organizational networks because in these smaller networks, dynamics are not
random, but the result of intentional actions carried through by players
maximizing their own objectives. This model is a better framework for the
analysis of influences upon a network because it integrates the strategies of
the players involved. We present an Integer Program that calculates the price
of anarchy of this game by finding the worst stable graph and the best
coordinated graph for this game. We simulate the formation of the network and
calculated the simulated price of anarchy, which we find tends to be rather
low.
|
1108.4135
|
Complex-Valued Autoencoders
|
cs.NE math.RA
|
Autoencoders are unsupervised machine learning circuits whose learning goal
is to minimize a distortion measure between inputs and outputs. Linear
autoencoders can be defined over any field and only real-valued linear
autoencoder have been studied so far. Here we study complex-valued linear
autoencoders where the components of the training vectors and adjustable
matrices are defined over the complex field with the $L_2$ norm. We provide
simpler and more general proofs that unify the real-valued and complex-valued
cases, showing that in both cases the landscape of the error function is
invariant under certain groups of transformations. The landscape has no local
minima, a family of global minima associated with Principal Component Analysis,
and many families of saddle points associated with orthogonal projections onto
sub-space spanned by sub-optimal subsets of eigenvectors of the covariance
matrix. The theory yields several iterative, convergent, learning algorithms, a
clear understanding of the generalization properties of the trained
autoencoders, and can equally be applied to the hetero-associative case when
external targets are provided. Partial results on deep architecture as well as
the differential geometry of autoencoders are also presented. The general
framework described here is useful to classify autoencoders and identify
general common properties that ought to be investigated for each class,
illuminating some of the connections between information theory, unsupervised
learning, clustering, Hebbian learning, and autoencoders.
|
1108.4142
|
Dynamic Pricing with Limited Supply
|
cs.GT cs.DS cs.LG
|
We consider the problem of dynamic pricing with limited supply. A seller has
$k$ identical items for sale and is facing $n$ potential buyers ("agents") that
are arriving sequentially. Each agent is interested in buying one item. Each
agent's value for an item is an IID sample from some fixed distribution with
support $[0,1]$. The seller offers a take-it-or-leave-it price to each arriving
agent (possibly different for different agents), and aims to maximize his
expected revenue.
We focus on "prior-independent" mechanisms -- ones that do not use any
information about the distribution. They are desirable because knowing the
distribution is unrealistic in many practical scenarios. We study how the
revenue of such mechanisms compares to the revenue of the optimal offline
mechanism that knows the distribution ("offline benchmark").
We present a prior-independent dynamic pricing mechanism whose revenue is at
most $O((k \log n)^{2/3})$ less than the offline benchmark, for every
distribution that is regular. In fact, this guarantee holds without *any*
assumptions if the benchmark is relaxed to fixed-price mechanisms. Further, we
prove a matching lower bound. The performance guarantee for the same mechanism
can be improved to $O(\sqrt{k} \log n)$, with a distribution-dependent
constant, if $k/n$ is sufficiently small. We show that, in the worst case over
all demand distributions, this is essentially the best rate that can be
obtained with a distribution-specific constant.
On a technical level, we exploit the connection to multi-armed bandits (MAB).
While dynamic pricing with unlimited supply can easily be seen as an MAB
problem, the intuition behind MAB approaches breaks when applied to the setting
with limited supply. Our high-level conceptual contribution is that even the
limited supply setting can be fruitfully treated as a bandit problem.
|
1108.4152
|
On the Network-Wide Gain of Memory-Assisted Source Coding
|
cs.IT math.IT
|
Several studies have identified a significant amount of redundancy in the
network traffic. For example, it is demonstrated that there is a great amount
of redundancy within the content of a server over time. This redundancy can be
leveraged to reduce the network flow by the deployment of memory units in the
network. The question that arises is whether or not the deployment of memory
can result in a fundamental improvement in the performance of the network. In
this paper, we answer this question affirmatively by first establishing the
fundamental gains of memory-assisted source compression and then applying the
technique to a network. Specifically, we investigate the gain of
memory-assisted compression in random network graphs consisted of a single
source and several randomly selected memory units. We find a threshold value
for the number of memories deployed in a random graph and show that if the
number of memories exceeds the threshold we observe network-wide reduction in
the traffic.
|
1108.4168
|
Computational Complexity of Cyclotomic Fast Fourier Transforms over
Characteristic-2 Fields
|
cs.IT math.IT
|
Cyclotomic fast Fourier transforms (CFFTs) are efficient implementations of
discrete Fourier transforms over finite fields, which have widespread
applications in cryptography and error control codes. They are of great
interest because of their low multiplicative and overall complexities. However,
their advantages are shown by inspection in the literature, and there is no
asymptotic computational complexity analysis for CFFTs. Their high additive
complexity also incurs difficulties in hardware implementations. In this paper,
we derive the bounds for the multiplicative and additive complexities of CFFTs,
respectively. Our results confirm that CFFTs have the smallest multiplicative
complexities among all known algorithms while their additive complexities
render them asymptotically suboptimal. However, CFFTs remain valuable as they
have the smallest overall complexities for most practical lengths. Our additive
complexity analysis also leads to a structured addition network, which not only
has low complexity but also is suitable for hardware implementations.
|
1108.4191
|
Chains of Kinematic Points
|
math.OC cs.SY
|
In formulating the stability problem for an infinite chain of cars, state
space is traditionally taken to be the Hilbert space $\ell^2$, wherein the
displacements of cars from their equilibria, or the velocities from their
equilibria, are taken to be square summable. But this obliges the displacements
or velocity perturbations of cars that are far down the chain to be vanishingly
small and leads to anomalous behaviour. In this paper an alternative
formulation is proposed wherein state space is the Banach space $\ell^\infty$,
allowing the displacements or velocity perturbations of cars from their
equilibria to be merely bounded.
|
1108.4199
|
Biomimetic use of genetic algorithms
|
cs.AI cs.NE q-bio.PE
|
Genetic algorithms are considered as an original way to solve problems,
probably because of their generality and of their "blind" nature. But GAs are
also unusual since the features of many implementations (among all that could
be thought of) are principally led by the biological metaphor, while efficiency
measurements intervene only afterwards. We propose here to examine the
relevance of these biomimetic aspects, by pointing out some fundamental
similarities and divergences between GAs and the genome of living beings shaped
by natural selection. One of the main differences comes from the fact that GAs
rely principally on the so-called implicit parallelism, while giving to the
mutation/selection mechanism the second role. Such differences could suggest
new ways of employing GAs on complex problems, using complex codings and
starting from nearly homogeneous populations.
|
1108.4216
|
Coordination of passive systems under quantized measurements
|
math.OC cs.SY
|
In this paper we investigate a passivity approach to collective coordination
and synchronization problems in the presence of quantized measurements and show
that coordination tasks can be achieved in a practical sense for a large class
of passive systems.
|
1108.4220
|
A Dynamical Systems Approach for Static Evaluation in Go
|
cs.AI math.DS
|
In the paper arguments are given why the concept of static evaluation has the
potential to be a useful extension to Monte Carlo tree search. A new concept of
modeling static evaluation through a dynamical system is introduced and
strengths and weaknesses are discussed. The general suitability of this
approach is demonstrated.
|
1108.4224
|
On Sequences with a Perfect Linear Complexity Profile
|
cs.IT math.IT
|
We derive B\'ezout identities for the minimal polynomials of a finite
sequence and use them to prove a theorem of Wang and Massey on binary sequences
with a perfect linear complexity profile. We give a new proof of Rueppel's
conjecture and simplify Dai's original proof. We obtain short proofs of results
of Niederreiter relating the linear complexity of a sequence s and K(s), which
was defined using continued fractions. We give an upper bound for the sum of
the linear complexities of any sequence. This bound is tight for sequences with
a perfect linear complexity profile and we apply it to characterise these
sequences in two new ways.
|
1108.4226
|
Research on Wireless Multi-hop Networks: Current State and Challenges
|
cs.NI cs.IT math.IT
|
Wireless multi-hop networks, in various forms and under various names, are
being increasingly used in military and civilian applications. Studying
connectivity and capacity of these networks is an important problem. The
scaling behavior of connectivity and capacity when the network becomes
sufficiently large is of particular interest. In this position paper, we
briefly overview recent development and discuss research challenges and
opportunities in the area, with a focus on the network connectivity.
|
1108.4244
|
Limitation of multi-resolution methods in community detection
|
physics.soc-ph cs.SI
|
Recently, a type of multi-resolution methods in community detection was
introduced, which can adjust the resolution of modularity by modifying the
modularity function with tunable resolution parameters, such as those proposed
by Arenas, Fernandez and Gomez and by Reichardt and Bornholdt. In this paper,
we show that these methods still have the intrinsic limitation-large
communities may have been split before small communities become visible-because
it is at the cost of the community stability that the enhancement of the
modularity resolution is obtained. The theoretical results indicated that the
limitation depends on the degree of interconnectedness of small communities and
the difference between the sizes of small communities and of large communities,
while independent of the size of the whole network. These findings have been
confirmed in several example networks, where communities even are
full-completed sub-graphs.
|
1108.4257
|
Capacity Analysis of Linear Operator Channels over Finite Fields
|
cs.IT math.IT
|
Motivated by communication through a network employing linear network coding,
capacities of linear operator channels (LOCs) with arbitrarily distributed
transfer matrices over finite fields are studied. Both the Shannon capacity $C$
and the subspace coding capacity $C_{\text{SS}}$ are analyzed. By establishing
and comparing lower bounds on $C$ and upper bounds on $C_{\text{SS}}$, various
necessary conditions and sufficient conditions such that $C=C_{\text{SS}}$ are
obtained. A new class of LOCs such that $C=C_{\text{SS}}$ is identified, which
includes LOCs with uniform-given-rank transfer matrices as special cases. It is
also demonstrated that $C_{\text{SS}}$ is strictly less than $C$ for a broad
class of LOCs. In general, an optimal subspace coding scheme is difficult to
find because it requires to solve the maximization of a non-concave function.
However, for a LOC with a unique subspace degradation, $C_{\text{SS}}$ can be
obtained by solving a convex optimization problem over rank distribution.
Classes of LOCs with a unique subspace degradation are characterized. Since
LOCs with uniform-given-rank transfer matrices have unique subspace
degradations, some existing results on LOCs with uniform-given-rank transfer
matrices are explained from a more general way.
|
1108.4279
|
Detection and emergence
|
cs.AI
|
Two different conceptions of emergence are reconciled as two instances of the
phenomenon of detection. In the process of comparing these two conceptions, we
find that the notions of complexity and detection allow us to form a unified
definition of emergence that clearly delineates the role of the observer.
|
1108.4297
|
Why is language well-designed for communication? (Commentary on
Christiansen and Chater: 'Language as shaped by the brain')
|
cs.CL q-bio.NC
|
Selection through iterated learning explains no more than other
non-functional accounts, such as universal grammar, why language is so
well-designed for communicative efficiency. It does not predict several
distinctive features of language like central embedding, large lexicons or the
lack of iconicity, that seem to serve communication purposes at the expense of
learnability.
|
1108.4315
|
Edge detection based on morphological amoebas
|
cs.CV
|
Detecting the edges of objects within images is critical for quality image
processing. We present an edge-detecting technique that uses morphological
amoebas that adjust their shape based on variation in image contours. We
evaluate the method both quantitatively and qualitatively for edge detection of
images, and compare it to classic morphological methods. Our amoeba-based
edge-detection system performed better than the classic edge detectors.
|
1108.4327
|
On conditions for asymptotic stability of dissipative
infinite-dimensional systems with intermittent damping
|
math.OC cs.SY
|
We study the asymptotic stability of a dissipative evolution in a Hilbert
space subject to intermittent damping. We observe that, even if the
intermittence satisfies a persistent excitation condition, if the Hilbert space
is infinite-dimensional then the system needs not being asymptotically stable
(not even in the weak sense). Exponential stability is recovered under a
generalized observability inequality, allowing for time-domains that are not
intervals. Weak asymptotic stability is obtained under a similarly generalized
unique continuation principle. Finally, strong asymptotic stability is proved
for intermittences that do not necessarily satisfy some persistent excitation
condition, evaluating their total contribution to the decay of the trajectories
of the damped system. Our results are discussed using the example of the wave
equation, Schr\"odinger's equation and, for strong stability, also the special
case of finite-dimensional systems.
|
1108.4361
|
The relationship between acquaintanceship and coauthorship in scientific
collaboration networks
|
cs.CY cs.DL cs.SI physics.soc-ph
|
This article examines the relationship between acquaintanceship and
coauthorship patterns in a multi-disciplinary, multi-institutional,
geographically distributed research center. Two social networks are constructed
and compared: a network of coauthorship, representing how researchers write
articles with one another, and a network of acquaintanceship, representing how
those researchers know each other on a personal level, based on their responses
to an online survey. Statistical analyses of the topology and community
structure of these networks point to the importance of small-scale, local,
personal networks predicated upon acquaintanceship for accomplishing
collaborative work in scientific communities.
|
1108.4380
|
Determinantal Representations and the Hermite Matrix
|
math.AG cs.SY math.OC
|
We consider the problem of writing real polynomials as determinants of
symmetric linear matrix polynomials. This problem of algebraic geometry, whose
roots go back to the nineteenth century, has recently received new attention
from the viewpoint of convex optimization. We relate the question to sums of
squares decompositions of a certain Hermite matrix. If some power of a
polynomial admits a definite determinantal representation, then its Hermite
matrix is a sum of squares. Conversely, we show how a determinantal
representation can sometimes be constructed from a sums-of-squares
decomposition of the Hermite matrix. We finally show that definite
determinantal representations always exist, if one allows for denominators.
|
1108.4386
|
Tight Bounds on the Optimization Time of the (1+1) EA on Linear
Functions
|
cs.NE
|
The analysis of randomized search heuristics on classes of functions is
fundamental for the understanding of the underlying stochastic process and the
development of suitable proof techniques. Recently, remarkable progress has
been made in bounding the expected optimization time of the simple (1+1) EA on
the class of linear functions. We improve the best known bound in this setting
from $(1.39+o(1))en\ln n$ to $en\ln n+O(n)$ in expectation and with high
probability, which is tight up to lower-order terms. Moreover, upper and lower
bounds for arbitrary mutations probabilities $p$ are derived, which imply
expected polynomial optimization time as long as $p=O((\ln n)/n)$ and which are
tight if $p=c/n$ for a constant $c$. As a consequence, the standard mutation
probability $p=1/n$ is optimal for all linear functions, and the (1+1) EA is
found to be an optimal mutation-based algorithm. The proofs are based on
adaptive drift functions and the recent multiplicative drift theorem.
|
1108.4432
|
Exploiting the Passive Dynamics of a Compliant Leg to Develop Gait
Transitions
|
cs.RO cs.SY math.OC physics.comp-ph
|
In the area of bipedal locomotion, the spring loaded inverted pendulum (SLIP)
model has been proposed as a unified framework to explain the dynamics of a
wide variety of gaits. In this paper, we present a novel analysis of the
mathematical model and its dynamical properties. We use the perspective of
hybrid dynamical systems to study the dynamics and define concepts such as
partial stability and viability. With this approach, on the one hand, we
identified stable and unstable regions of locomotion. On the other hand, we
found ways to exploit the unstable regions of locomotion to induce gait
transitions at a constant energy regime. Additionally, we show that simple
non-constant angle of attack control policies can render the system almost
always stable.
|
1108.4440
|
Promoting scientific thinking with robots
|
physics.ed-ph cs.AI cs.RO
|
This article describes an exemplary robot exercise which was conducted in a
class for mechatronics students. The goal of this exercise was to engage
students in scientific thinking and reasoning, activities which do not always
play an important role in their curriculum. The robotic platform presented here
is simple in its construction and is customizable to the needs of the teacher.
Therefore, it can be used for exercises in many different fields of science,
not necessarily related to robotics. Here we present a situation where the
robot is used like an alien creature from which we want to understand its
behavior, resembling an ethological research activity. This robot exercise is
suited for a wide range of courses, from general introduction to science, to
hardware oriented lectures.
|
1108.4443
|
SNF Project Locomotion: Final report 2009-2010
|
cs.RO
|
Summary of results in last project period (1. 10. 2009 - 30. 9. 2010) of SNFS
Project "From locomotion to cognition"
The research that we have been involved in, and will continue to do, starts
from the insight that in order to understand and design intelligent behavior,
we must adopt an embodied perspective, i.e. we must take the entire agent,
including its shape or morphology, the materials out of which it is built, and
its interaction with the environment into account, in addition to the neural
control. A lot of our research in the past has been on relatively low-level
sensory-motor tasks such as locomotion (e.g. walking, running, jumping),
navigation, and grasping. While this research is of interest in itself, in the
context of artificial intelligence and cognitive science, this leads to the
question of what these kinds of tasks have to do with higher levels of
cognition, or to put it more provocatively, "What does walking have to do with
thinking?" This question is of course reminiscent of the notorious "symbol
grounding problem". In contrast to most of the research on symbol grounding, we
propose to exploit the dynamic interaction between the embodied agent and the
environment as the basis for grounding. We use the term "morphological
computation" to designate the fact that some of the control or computation can
be taken over by the dynamic interaction derived from morphological properties
(e.g. the passive forward swing of the leg in walking, the spring-like
properties of the muscles, and the weight distribution). By taking
morphological computation into account, an agent will be able to achieve not
only faster, more robust, and more energy-efficient behavior, but also more
situated exploration by the agent for the comprehensive understanding of the
environment.
|
1108.4445
|
SNF Project Locomotion: Progress report 2008-2009
|
cs.RO
|
Summary of results (project period 1. 10. 2008 - 30. 9. 2009) of SNFS Project
"From locomotion to cognition"
The research that we have been involved in, and will continue to do, starts
from the insight that in order to understand and design intelligent behavior,
we must adopt an embodied perspective, i.e. we must take the entire agent,
including its shape or morphology, the materials out of which it is built, and
its interaction with the environment into account, in addition to the neural
control. A lot of our research in the past has been on relatively low-level
sensory-motor tasks such as locomotion (e.g. walking, running, jumping),
navigation, and grasping. While this research is of interest in itself, in the
context of artificial intelligence and cognitive science, this leads to the
question of what these kinds of tasks have to do with higher levels of
cognition, or to put it more provocatively, "What does walking have to do with
thinking?" This question is of course reminiscent of the notorious "symbol
grounding problem". In contrast to most of the research on symbol grounding, we
propose to exploit the dynamic interaction between the embodied agent and the
environment as the basis for grounding. We use the term "morphological
computation" to designate the fact that some of the control or computation can
be taken over by the dynamic interaction derived from morphological properties
(e.g. the passive forward swing of the leg in walking, the spring-like
properties of the muscles, and the weight distribution). By taking
morphological computation into account, an agent will be able to achieve not
only faster, more robust, and more energy-efficient behavior, but also more
situated exploration by the agent for the comprehensive understanding of the
environment.
|
1108.4448
|
Magneto-mechanical actuation model for fin-based locomotion
|
cs.RO
|
In this paper, we report the results from the analysis of a numerical model
used for the design of a magnetic linear actuator with applications to
fin-based locomotion. Most of the current robotic fish generate bending motion
using rotary motors which implies at least one mechanical conversion of the
motion. We seek a solution that directly bends the fin and, at the same time,
is able to exploit the magneto-mechanical properties of the fin material. This
strong fin-actuator coupling blends the actuator and the body of the robot,
allowing cross optimization of the system's elements.
We study a simplified model of an elastic element, a spring-mass system
representing a flexible fin, subjected to nonlinear forcing, emulating magnetic
interaction. The dynamics of the system is studied under unforced and periodic
forcing conditions. The analysis is focused on the limit cycles present in the
system, which allows the periodic bending of the fin and the generation of
thrust. The frequency, maximum amplitude and center of the periodic orbits
(offset of the bending) depend directly on the stiffness of the fin and the
intensity of the forcing; we use this dependency to sketch a simple parameter
controller. Although the model is strongly simplified, it provides means to
estimate first values of the parameters for this kind of actuator and it is
useful to evaluate the feasibility of minimal actuation control of such
systems.
|
1108.4450
|
Linear Complexity of Ding-Helleseth Generalized Cyclotomic Binary
Sequences of Any Order
|
cs.IT math.IT
|
This paper gives the linear complexity of binary Ding-Helleseth generalized
cyclotomic sequences of any order.
|
1108.4475
|
Coordinated Beamforming for Multiuser MISO Interference Channel under
Rate Outage Constraints
|
cs.IT math.IT
|
This paper studies the coordinated beamforming design problem for the
multiple-input single-output (MISO) interference channel, assuming only channel
distribution information (CDI) at the transmitters. Under a given requirement
on the rate outage probability for receivers, we aim to maximize the system
utility (e.g., the weighted sum rate, weighted geometric mean rate, and the
weighed harmonic mean rate) subject to the rate outage constraints and
individual power constraints. The outage constraints, however, lead to a
complicated, nonconvex structure for the considered beamforming design problem
and make the optimization problem difficult to handle. {Although} this
nonconvex optimization problem can be solved in an exhaustive search manner,
this brute-force approach is only feasible when the number of
transmitter-receiver pairs is small. For a system with a large number of
transmitter-receiver pairs, computationally efficient alternatives are
necessary. The focus of this paper is hence on the design of such efficient
approximation methods. In particular, by employing semidefinite relaxation
(SDR) and first-order approximation techniques, we propose an efficient
successive convex approximation (SCA) algorithm that provides high-quality
approximate beamforming solutions via solving a sequence of convex
approximation problems. The solution thus obtained is further shown to be a
stationary point for the SDR of the original outage constrained beamforming
design problem. {Furthermore}, we propose a distributed SCA algorithm where
each transmitter optimizes its own beamformer using local CDI and information
obtained from limited message exchange with the other transmitters. Our
simulation results demonstrate that the proposed SCA algorithm and its
distributed counterpart indeed converge, and near-optimal performance can be
achieved for all the considered system utilities.
|
1108.4478
|
An Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes
|
cs.IT math.IT
|
This paper presents an efficient algorithm for finding the dominant trapping
sets of a low-density parity-check (LDPC) code. The algorithm can be used to
estimate the error floor of LDPC codes or to be part of the apparatus to design
LDPC codes with low error floors. For regular codes, the algorithm is initiated
with a set of short cycles as the input. For irregular codes, in addition to
short cycles, variable nodes with low degree and cycles with low approximate
cycle extrinsic message degree (ACE) are also used as the initial inputs. The
initial inputs are then expanded recursively to dominant trapping sets of
increasing size. At the core of the algorithm lies the analysis of the
graphical structure of dominant trapping sets and the relationship of such
structures to short cycles, low-degree variable nodes and cycles with low ACE.
The algorithm is universal in the sense that it can be used for an arbitrary
graph and that it can be tailored to find other graphical objects, such as
absorbing sets and Zyablov-Pinsker (ZP) trapping sets, known to dominate the
performance of LDPC codes in the error floor region over different channels and
for different iterative decoding algorithms. Simulation results on several LDPC
codes demonstrate the accuracy and efficiency of the proposed algorithm. In
particular, the algorithm is significantly faster than the existing search
algorithms for dominant trapping sets.
|
1108.4499
|
Predictor-Based Output Feedback for Nonlinear Delay Systems
|
math.OC cs.SY
|
We provide two solutions to the heretofore open problem of stabilization of
systems with arbitrarily long delays at the input and output of a nonlinear
system using output feedback only. Both of our solutions are global, employ the
predictor approach over the period that combines the input and output delays,
address nonlinear systems with sampled measurements and with control applied
using a zero-order hold, and require that the sampling/holding periods be
sufficiently short, though not necessarily constant. Our first approach
considers general nonlinear systems for which the solution map is available
explicitly and whose one-sample-period predictor-based discrete-time model
allows state reconstruction, in a finite number of steps, from the past values
of inputs and output measurements. Our second approach considers a class of
globally Lipschitz strict-feedback systems with disturbances and employs an
appropriately constructed successive approximation of the predictor map, a
high-gain sampled-data observer, and a linear stabilizing feedback for the
delay-free system. We specialize the second approach to linear systems, where
the predictor is available explicitly. We provide two illustrative examples-one
analytical for the first approach and one numerical for the second approach.
|
1108.4516
|
Scalable Continual Top-k Keyword Search in Relational Databases
|
cs.DB cs.IR
|
Keyword search in relational databases has been widely studied in recent
years because it does not require users neither to master a certain structured
query language nor to know the complex underlying database schemas. Most of
existing methods focus on answering snapshot keyword queries in static
databases. In practice, however, databases are updated frequently, and users
may have long-term interests on specific topics. To deal with such a situation,
it is necessary to build effective and efficient facility in a database system
to support continual keyword queries.
In this paper, we propose an efficient method for answering continual top-$k$
keyword queries over relational databases. The proposed method is built on an
existing scheme of keyword search on relational data streams, but incorporates
the ranking mechanisms into the query processing methods and makes two
improvements to support efficient top-$k$ keyword search in relational
databases. Compared to the existing methods, our method is more efficient both
in computing the top-$k$ results in a static database and in maintaining the
top-$k$ results when the database continually being updated. Experimental
results validate the effectiveness and efficiency of the proposed method.
|
1108.4531
|
Novel Analysis of Population Scalability in Evolutionary Algorithms
|
cs.NE
|
Population-based evolutionary algorithms (EAs) have been widely applied to
solve various optimization problems. The question of how the performance of a
population-based EA depends on the population size arises naturally. The
performance of an EA may be evaluated by different measures, such as the
average convergence rate to the optimal set per generation or the expected
number of generations to encounter an optimal solution for the first time.
Population scalability is the performance ratio between a benchmark EA and
another EA using identical genetic operators but a larger population size.
Although intuitively the performance of an EA may improve if its population
size increases, currently there exist only a few case studies for simple
fitness functions. This paper aims at providing a general study for discrete
optimisation. A novel approach is introduced to analyse population scalability
using the fundamental matrix. The following two contributions summarize the
major results of the current article. (1) We demonstrate rigorously that for
elitist EAs with identical global mutation, using a lager population size
always increases the average rate of convergence to the optimal set; and yet,
sometimes, the expected number of generations needed to find an optimal
solution (measured by either the maximal value or the average value) may
increase, rather than decrease. (2) We establish sufficient and/or necessary
conditions for the superlinear scalability, that is, when the average
convergence rate of a $(\mu+\mu)$ EA (where $\mu\ge2$) is bigger than $\mu$
times that of a $(1+1)$ EA.
|
1108.4545
|
The fuzzy gene filter: A classifier performance assesment
|
cs.LG cs.CE
|
The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed
to rank genes in order of differential expression, based on expression data
generated in a microarray experiment. This paper examines the effectiveness of
the FGF for feature selection using various classification architectures. The
FGF is compared to three of the most common gene ranking algorithms: t-test,
Wilcoxon test and ROC curve analysis. Four classification schemes are used to
compare the performance of the FGF vis-a-vis the standard approaches: K Nearest
Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC)
and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross
Validation scheme is used to identify the optimal number top ranking genes, as
well as the optimal classifier parameters. Two microarray data sets are used
for the comparison: a prostate cancer data set and a lymphoma data set.
|
1108.4548
|
Ant Colony Optimization of Rough Set for HV Bushings Fault Detection
|
cs.NE
|
Most transformer failures are attributed to bushings failures. Hence it is
necessary to monitor the condition of bushings. In this paper three methods are
developed to monitor the condition of oil filled bushing. Multi-layer
perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are
developed and combined through majority voting to form a committee. The MLP
performs better that the RBF and the RS is terms of classification accuracy.
The RBF is the fasted to train. The committee performs better than the
individual models. The diversity of models is measured to evaluate their
similarity when used in the committee.
|
1108.4551
|
Improving the performance of the ripper in insurance risk classification
: A comparitive study using feature selection
|
cs.LG cs.CE
|
The Ripper algorithm is designed to generate rule sets for large datasets
with many features. However, it was shown that the algorithm struggles with
classification performance in the presence of missing data. The algorithm
struggles to classify instances when the quality of the data deteriorates as a
result of increasing missing data. In this paper, a feature selection technique
is used to help improve the classification performance of the Ripper model.
Principal component analysis and evidence automatic relevance determination
techniques are used to improve the performance. A comparison is done to see
which technique helps the algorithm improve the most. Training datasets with
completely observable data were used to construct the model and testing
datasets with missing values were used for measuring accuracy. The results
showed that principal component analysis is a better feature selection for the
Ripper in improving the classification performance.
|
1108.4559
|
Optimal Algorithms for Ridge and Lasso Regression with Partially
Observed Attributes
|
cs.LG
|
We consider the most common variants of linear regression, including Ridge,
Lasso and Support-vector regression, in a setting where the learner is allowed
to observe only a fixed number of attributes of each example at training time.
We present simple and efficient algorithms for these problems: for Lasso and
Ridge regression they need the same total number of attributes (up to
constants) as do full-information algorithms, for reaching a certain accuracy.
For Support-vector regression, we require exponentially less attributes
compared to the state of the art. By that, we resolve an open problem recently
posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to
be justified by superior performance compared to the state of the art.
|
1108.4585
|
Social dynamics with peer support on heterogeneous networks: The "mafia
model"
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Human behavior often exhibit a scheme in which individuals adopt indifferent,
neutral, or radical positions on a given topic. The mechanisms leading to
community formation are strongly related with social pressure and the topology
of the contact network. Here, we discuss an approach to model social behavior
which accounts for the protection by alike peers proportional to their relative
abundance in the closest neighborhood. We explore the ensuing non-linear
dynamics emphasizing the role of the specific structure of the social network,
modeled by scale-free graphs. We find that both coexistence of opinions and
consensus on the default position are possible stationary states of the model.
In particular, we show how these states critically depend on the heterogeneity
of the social network and the specific distribution of external control
elements.
|
1108.4596
|
XML content warehousing: Improving sociological studies of mailing lists
and web data
|
cs.DB
|
In this paper, we present the guidelines for an XML-based approach for the
sociological study of Web data such as the analysis of mailing lists or
databases available online. The use of an XML warehouse is a flexible solution
for storing and processing this kind of data. We propose an implemented
solution and show possible applications with our case study of profiles of
experts involved in W3C standard-setting activity. We illustrate the
sociological use of semi-structured databases by presenting our XML Schema for
mailing-list warehousing. An XML Schema allows many adjunctions or crossings of
data sources, without modifying existing data sets, while allowing possible
structural evolution. We also show that the existence of hidden data implies
increased complexity for traditional SQL users. XML content warehousing allows
altogether exhaustive warehousing and recursive queries through contents, with
far less dependence on the initial storage. We finally present the possibility
of exporting the data stored in the warehouse to commonly-used advanced
software devoted to sociological analysis.
|
1108.4618
|
Artificial Neural Network and Rough Set for HV Bushings Condition
Monitoring
|
cs.NE
|
Most transformer failures are attributed to bushings failures. Hence it is
necessary to monitor the condition of bushings. In this paper three methods are
developed to monitor the condition of oil filled bushing. Multi-layer
perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are
developed and combined through majority voting to form a committee. The MLP
performs better that the RBF and the RS is terms of classification accuracy.
The RBF is the fasted to train. The committee performs better than the
individual models. The diversity of models is measured to evaluate their
similarity when used in the committee.
|
1108.4658
|
A Well-Behaved Alternative to the Modularity Index
|
physics.soc-ph cs.SI
|
This paper reviews the modularity index and suggests an alternative index of
the quality of a division of a network into subsets.
|
1108.4664
|
Sparse Approximation is Hard
|
cs.CC cs.IT math.IT
|
Given a redundant dictionary $\Phi$, represented by an $M \times N$ matrix
($\Phi \in \mathbb{R}^{M \times N}$) and a target signal $y \in \mathbb{R}^M$,
the \emph{sparse approximation problem} asks to find an approximate
representation of $y$ using a linear combination of at most $k$ atoms. In this
paper, a new complexity theoretic hardness result for sparse approximation
problem is presented via considering a different measure of quality for the
solution. It is argued that, from an algorithmic standpoint, the problem is
more meaningful if it asks to maximize the norm of the target signal's
projection onto the selected atoms which are represented by column vectors.
Then, a multiplicative inapproximability result is established with this new
measure, under a reasonable complexity theoretic assumption. This result in
turn implies additive inapproximability for the problem with the standard
measure. Specifically, if $ZPP \neq NP$, all polynomial time algorithms which
provide a $k$-sparse vector $x$ should satisfy
$$ {\|y-\Phi x\|}_2^2 \geq (1-c){\|y-\Phi x^*\|}_2^2 + c {\|y\|}_2^2, $$
\noindent for $1/4(1-1/e) > c \geq 0$ where $x^*$ is the optimal $k$-sparse
solution. This result provides a quantification of the hardness for the case
$y-\Phi x^* = 0$, revealing more details about the inherent structure of the
problem.
|
1108.4675
|
Category-Based Routing in Social Networks: Membership Dimension and the
Small-World Phenomenon (Short)
|
cs.SI cs.DS physics.soc-ph
|
A classic experiment by Milgram shows that individuals can route messages
along short paths in social networks, given only simple categorical information
about recipients (such as "he is a prominent lawyer in Boston" or "she is a
Freshman sociology major at Harvard"). That is, these networks have very short
paths between pairs of nodes (the so-called small-world phenomenon); moreover,
participants are able to route messages along these paths even though each
person is only aware of a small part of the network topology. Some sociologists
conjecture that participants in such scenarios use a greedy routing strategy in
which they forward messages to acquaintances that have more categories in
common with the recipient than they do, and similar strategies have recently
been proposed for routing messages in dynamic ad-hoc networks of mobile
devices. In this paper, we introduce a network property called membership
dimension, which characterizes the cognitive load required to maintain
relationships between participants and categories in a social network. We show
that any connected network has a system of categories that will support greedy
routing, but that these categories can be made to have small membership
dimension if and only if the underlying network exhibits the small-world
phenomenon.
|
1108.4698
|
Least Squares Temporal Difference Actor-Critic Methods with Applications
to Robot Motion Control
|
cs.RO cs.SY math.OC
|
We consider the problem of finding a control policy for a Markov Decision
Process (MDP) to maximize the probability of reaching some states while
avoiding some other states. This problem is motivated by applications in
robotics, where such problems naturally arise when probabilistic models of
robot motion are required to satisfy temporal logic task specifications. We
transform this problem into a Stochastic Shortest Path (SSP) problem and
develop a new approximate dynamic programming algorithm to solve it. This
algorithm is of the actor-critic type and uses a least-square temporal
difference learning method. It operates on sample paths of the system and
optimizes the policy within a pre-specified class parameterized by a
parsimonious set of parameters. We show its convergence to a policy
corresponding to a stationary point in the parameters' space. Simulation
results confirm the effectiveness of the proposed solution.
|
1108.4709
|
The Diversity-Multiplexing-Delay Tradeoff in MIMO Multihop Networks with
ARQ
|
cs.IT math.IT
|
We study the tradeoff between reliability, data rate, and delay for
half-duplex MIMO multihop networks that utilize the
automatic-retransmission-request (ARQ) protocol both in the asymptotic high
signal-to-noise ratio (SNR) regime and in the finite SNR regime. We propose
novel ARQ protocol designs that optimize these tradeoffs. We first derive the
diversity-multiplexing-delay tradeoff (DMDT) in the high SNR regime, where the
delay is caused only by retransmissions. This asymptotic DMDT shows that the
performance of an N node network is limited by the weakest three-node
sub-network, and the performance of a three-node sub-network is determined by
its weakest link, and, hence, the optimal ARQ protocol needs to equalize the
performance on each link by allocating ARQ window sizes optimally. This
equalization is captured through a novel Variable Block-Length (VBL) ARQ
protocol that we propose, which achieves the optimal DMDT.
We then consider the DMDT in the finite SNR regime, where the delay is caused
by both the ARQ retransmissions and queueing. We characterize the finite SNR
DMDT of the fixed ARQ protocol, when an end-to-end delay constraint is imposed,
by deriving the probability of message error using an approach that couples the
information outage analysis with the queueing network analysis. The exponent of
the probability of deadline violation demonstrates that the system performance
is again limited by the weakest three-node sub-network. The queueing delay
changes the consideration for optimal ARQ design: more retransmissions reduce
decoding error by lowering the information outage probability, but may also
increase message drop rate due to delay deadline violations. Hence, the optimal
ARQ should balance link performance while avoiding significant delay.
|
1108.4723
|
Self-Optimized OFDMA via Multiple Stackelberg Leader Equilibrium
|
cs.IT cs.GT math.IT math.OC nlin.AO
|
The challenge of self-optimization for orthogonal frequency-division
multiple-access (OFDMA) interference channels is that users inherently compete
harmfully and simultaneous water-filling (WF) would lead to a
Pareto-inefficient equilibrium. To overcome this, we first introduce the role
of environmental interference derivative in the WF optimization of the
interactive OFDMA game and then study the environmental interference derivative
properties of Stackelberg equilibrium (SE). Such properties provide important
insights to devise free OFDMA games for achieving various SEs, realizable by
simultaneous WF regulated by specifically chosen operational interference
derivatives. We also present a definition of all-Stackelberg-leader equilibrium
(ASE) where users are all foresighted to each other, albeit each with only
local channel state information (CSI), and can thus most effectively reconcile
their competition to maximize the user rates. We show that under certain
environmental conditions, the free games are both unique and optimal.
Simulation results reveal that our distributed ASE game achieves the
performance very close to the near-optimal centralized iterative spectrum
balancing (ISB) method in [5].
|
1108.4729
|
Self-organized network design by link survivals and shortcuts
|
physics.soc-ph cs.SI
|
One of the challenges for future infrastructures is how to design a network
with high efficiency and strong connectivity at low cost. We propose
self-organized geographical networks beyond the vulnerable scale-free structure
found in many real systems. The networks with spatially concentrated nodes
emerge through link survival and path reinforcement on routing flows in a
wireless environment with a constant transmission range of a node. In
particular, we show that adding some shortcuts induces both the small-world
effect and a significant improvement of the robustness to the same level as in
the optimal bimodal networks. Such a simple universal mechanism will open
prospective ways for several applications in wide-area ad hoc networks, smart
grids, and urban planning.
|
1108.4753
|
Differential properties of functions x -> x^{2^t-1} -- extended version
|
cs.CR cs.DM cs.IT math.IT
|
We provide an extensive study of the differential properties of the functions
$x\mapsto x^{2^t-1}$ over $\F$, for $2 \leq t \leq n-1$. We notably show that
the differential spectra of these functions are determined by the number of
roots of the linear polynomials $x^{2^t}+bx^2+(b+1)x$ where $b$ varies in
$\F$.We prove a strong relationship between the differential spectra of
$x\mapsto x^{2^t-1}$ and $x\mapsto x^{2^{s}-1}$ for $s= n-t+1$. As a direct
consequence, this result enlightens a connection between the differential
properties of the cube function and of the inverse function. We also determine
the complete differential spectra of $x \mapsto x^7$ by means of the value of
some Kloosterman sums, and of $x \mapsto x^{2^t-1}$ for $t \in \{\lfloor
n/2\rfloor, \lceil n/2\rceil+1, n-2\}$.
|
1108.4785
|
Searching for Nodes in Random Graphs
|
cond-mat.stat-mech cs.NI cs.SI physics.soc-ph
|
We consider the problem of searching for a node on a labelled random graph
according to a greedy algorithm that selects a route to the desired node using
metric information on the graph. Motivated by peer-to-peer networks two types
of random graph are proposed with properties particularly amenable to this kind
of algorithm. We derive equations for the probability that the search is
successful and also study the number of hops required, finding both numerical
and analytic evidence of a transition as the number of links is varied.
|
1108.4801
|
Supervised Rank Aggregation for Predicting Influence in Networks
|
cs.SI cs.GT cs.IR physics.soc-ph
|
Much work in Social Network Analysis has focused on the identification of the
most important actors in a social network. This has resulted in several
measures of influence and authority. While most of such sociometrics (e.g.,
PageRank) are driven by intuitions based on an actors location in a network,
asking for the "most influential" actors in itself is an ill-posed question,
unless it is put in context with a specific measurable task. Constructing a
predictive task of interest in a given domain provides a mechanism to
quantitatively compare different measures of influence. Furthermore, when we
know what type of actionable insight to gather, we need not rely on a single
network centrality measure. A combination of measures is more likely to capture
various aspects of the social network that are predictive and beneficial for
the task. Towards this end, we propose an approach to supervised rank
aggregation, driven by techniques from Social Choice Theory. We illustrate the
effectiveness of this method through experiments on Twitter and citation
networks.
|
1108.4804
|
dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation
|
cs.AI
|
The aim of this paper is to announce the release of a novel system for
abstract argumentation which is based on decomposition and dynamic programming.
We provide first experimental evaluations to show the feasibility of this
approach.
|
1108.4879
|
Using Supervised Learning to Improve Monte Carlo Integral Estimation
|
stat.ML cs.CE cs.NA stat.CO
|
Monte Carlo (MC) techniques are often used to estimate integrals of a
multivariate function using randomly generated samples of the function. In
light of the increasing interest in uncertainty quantification and robust
design applications in aerospace engineering, the calculation of expected
values of such functions (e.g. performance measures) becomes important.
However, MC techniques often suffer from high variance and slow convergence as
the number of samples increases. In this paper we present Stacked Monte Carlo
(StackMC), a new method for post-processing an existing set of MC samples to
improve the associated integral estimate. StackMC is based on the supervised
learning techniques of fitting functions and cross validation. It should reduce
the variance of any type of Monte Carlo integral estimate (simple sampling,
importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We
report on an extensive set of experiments confirming that the StackMC estimate
of an integral is more accurate than both the associated unprocessed Monte
Carlo estimate and an estimate based on a functional fit to the MC samples.
These experiments run over a wide variety of integration spaces, numbers of
sample points, dimensions, and fitting functions. In particular, we apply
StackMC in estimating the expected value of the fuel burn metric of future
commercial aircraft and in estimating sonic boom loudness measures. We compare
the efficiency of StackMC with that of more standard methods and show that for
negligible additional computational cost significant increases in accuracy are
gained.
|
1108.4891
|
Computing with Logic as Operator Elimination: The ToyElim System
|
cs.AI cs.LO
|
A prototype system is described whose core functionality is, based on
propositional logic, the elimination of second-order operators, such as Boolean
quantifiers and operators for projection, forgetting and circumscription. This
approach allows to express many representational and computational tasks in
knowledge representation - for example computation of abductive explanations
and models with respect to logic programming semantics - in a uniform
operational system, backed by a uniform classical semantic framework.
|
1108.4919
|
Numerical extraction of a macroscopic pde and a lifting operator from a
lattice Boltzmann model
|
cs.CE physics.comp-ph physics.flu-dyn
|
Lifting operators play an important role in starting a lattice Boltzmann
model from a given initial density. The density, a macroscopic variable, needs
to be mapped to the distribution functions, mesoscopic variables, of the
lattice Boltzmann model. Several methods proposed as lifting operators have
been tested and discussed in the literature. The most famous methods are an
analytically found lifting operator, like the Chapman-Enskog expansion, and a
numerical method, like the Constrained Runs algorithm, to arrive at an implicit
expression for the unknown distribution functions with the help of the density.
This paper proposes a lifting operator that alleviates several drawbacks of
these existing methods. In particular, we focus on the computational expense
and the analytical work that needs to be done. The proposed lifting operator, a
numerical Chapman-Enskog expansion, obtains the coefficients of the
Chapman-Enskog expansion numerically. Another important feature of the use of
lifting operators is found in hybrid models. There the lattice Boltzmann model
is spatially coupled with a model based on a more macroscopic description, for
example an advection-diffusion-reaction equation. In one part of the domain,
the lattice Boltzmann model is used, while in another part, the more
macroscopic model. Such a hybrid coupling results in missing data at the
interfaces between the different models. A lifting operator is then an
important tool since the lattice Boltzmann model is typically described by more
variables than a model based on a macroscopic partial differential equation.
|
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