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1206.1892
|
Computing the degree of a lattice ideal of dimension one
|
math.AC cs.IT math.AG math.IT
|
We show that the degree of a graded lattice ideal of dimension 1 is the order
of the torsion subgroup of the quotient group of the lattice. This gives an
efficient method to compute the degree of this type of lattice ideals.
|
1206.1898
|
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument
of a Noisy Function
|
stat.ML cs.AI math.ST stat.TH
|
We propose a novel Bayesian approach to solve stochastic optimization
problems that involve finding extrema of noisy, nonlinear functions. Previous
work has focused on representing possible functions explicitly, which leads to
a two-step procedure of first, doing inference over the function space and
second, finding the extrema of these functions. Here we skip the representation
step and directly model the distribution over extrema. To this end, we devise a
non-parametric conjugate prior based on a kernel regressor. The resulting
posterior distribution directly captures the uncertainty over the maximum of
the unknown function. We illustrate the effectiveness of our model by
optimizing a noisy, high-dimensional, non-convex objective function.
|
1206.1903
|
Mechanism Designs for Stochastic Resources for Renewable Energy
Integration
|
cs.GT cs.SI
|
Among the many challenges of integrating renewable energy sources into the
existing power grid, is the challenge of integrating renewable energy
generators into the power systems economy. Electricity markets currently are
run in a way that participating generators must supply contracted amounts. And
yet, renewable energy generators such as wind power generators cannot supply
contracted amounts with certainty. Thus, alternative market architectures must
be considered where there are aggregator entities who participate in the
electricity market by buying power from the renewable energy generators, and
assuming risk of any shortfall from contracted amounts. In this paper, we
propose auction mechanisms that can be used by the aggregators for procuring
stochastic resources, such as wind power. The nature of stochastic resources is
different from classical resources in that such a resource is only available
stochastically. The distribution of the generation is private information, and
the system objective is to truthfully elicit such information. We introduce a
variant of the VCG mechanism for this problem. We also propose a non-VCG
mechanism with a contracted-payment-plus-penalty payoff structure. We
generalize the basic mechanisms in various ways. We then consider the setting
where there are two classes of players to demonstrate the difficulty of auction
design in such scenarios. We also consider an alternative architecture where
the generators need to fulfill any shortfall from the contracted amount by
buying from the spot market.
|
1206.1926
|
The hardest logic puzzle ever becomes even tougher
|
math.LO cs.AI
|
"The hardest logic puzzle ever" presented by George Boolos became a target
for philosophers and logicians who tried to modify it and make it even tougher.
I propose further modification of the original puzzle where part of the
available information is eliminated but the solution is still possible. The
solution also gives interesting ideas on logic behind discovery of unknown
language.
|
1206.1932
|
Theoretical approach and impact of correlations on the critical packet
generation rate in traffic dynamics on complex networks
|
physics.soc-ph cs.SI
|
Using the formalism of the biased random walk in random uncorrelated networks
with arbitrary degree distributions, we develop theoretical approach to the
critical packet generation rate in traffic based on routing strategy with local
information. We explain microscopic origins of the transition from the flow to
the jammed phase and discuss how the node neighbourhood topology affects the
transport capacity in uncorrelated and correlated networks.
|
1206.1948
|
The Capacity of Less Noisy Cognitive Interference Channels
|
cs.IT math.IT
|
Fundamental limits of the cognitive interference channel (CIC) with two pairs
of transmitter-receiver has been under exploration for several years. In this
paper, we study the discrete memoryless cognitive interference channel (DM-CIC)
in which the cognitive transmitter non-causally knows the message of the
primary transmitter. The capacity of this channel is not known in general; it
is only known in some special cases. Inspired by the concept of less noisy
broadcast channel (BC), in this work we introduce the notion of less noisy
cognitive interference channel. Unlike BC, due to the inherent asymmetry of the
cognitive channel, two different less noisy channels are distinguishable; these
are named the primary-less-noisy and cognitive-less-noisy channels. We derive
capacity region for the latter case, by introducing inner and outer bounds on
the capacity of the DM-CIC and showing that these bounds coincide for the
cognitive-less-noisy channel. Having established the capacity region, we prove
that superposition coding is the optimal encoding technique.
|
1206.1953
|
Improvement of Loadability in Distribution System Using Genetic
Algorithm
|
cs.SY cs.NE
|
Generally during recent decades due to development of power systems, the
methods for delivering electrical energy to consumers, and because of voltage
variations is a very important problem, the power plants follow this criteria.
The good solution for improving transfer and distribution of electrical power
the majority of consumers prefer to use energy near the loads .So small units
that are connected to distribution system named "Decentralized Generation" or
"Dispersed Generation". Deregulated in power industry and development of
renewable energies are the most important factors in developing this type of
electricity generation. Today DG has a key role in electrical distribution
systems. For example we can refer to improving reliability indices, improvement
of stability and reduction of losses in power system. One of the key problems
in using DG's, is allocation of these sources in distribution networks. Load
ability in distribution systems and its improvement has an effective role in
the operation of power systems. However, placement of distributed generation
sources in order to improve the distribution system load ability index was not
considered, we show DG placement and allocation with genetic algorithm
optimization method maximize load ability of power systems .This method
implemented on the IEEE Standard bench marks. The results show the
effectiveness of the proposed algorithm .Another benefits of DG in selected
positions are also studied and compared.
|
1206.1971
|
A Connectionist Network Approach to Find Numerical Solutions of
Diophantine Equations
|
cs.NE
|
The paper introduces a connectionist network approach to find numerical
solutions of Diophantine equations as an attempt to address the famous
Hilbert's tenth problem. The proposed methodology uses a three layer feed
forward neural network with back propagation as sequential learning procedure
to find numerical solutions of a class of Diophantine equations. It uses a
dynamically constructed network architecture where number of nodes in the input
layer is chosen based on the number of variables in the equation. The powers of
the given Diophantine equation are taken as input to the input layer. The
training of the network starts with initial random integral weights. The
weights are updated based on the back propagation of the error values at the
output layer. The optimization of weights is augmented by adding a momentum
factor into the network. The optimized weights of the connection between the
input layer and the hidden layer are taken as numerical solution of the given
Diophantine equation. The procedure is validated using different Diophantine
Equations of different number of variables and different powers.
|
1206.1973
|
Communications-Inspired Projection Design with Application to
Compressive Sensing
|
cs.IT math.IT
|
We consider the recovery of an underlying signal x \in C^m based on
projection measurements of the form y=Mx+w, where y \in C^l and w is
measurement noise; we are interested in the case l < m. It is assumed that the
signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is
to design a projection matrix M \in C^(l x m) to maximize key
information-theoretic quantities with operational significance, including the
mutual information between the signal and the projections I(x;y) or the Renyi
entropy of the projections h_a(y) (Shannon entropy is a special case). By
capitalizing on explicit characterizations of the gradients of the information
measures with respect to the projections matrix, where we also partially extend
the well-known results of Palomar and Verdu from the mutual information to the
Renyi entropy domain, we unveil the key operations carried out by the optimal
projections designs: mode exposure and mode alignment. Experiments are
considered for the case of compressive sensing (CS) applied to imagery. In this
context, we provide a demonstration of the performance improvement possible
through the application of the novel projection designs in relation to
conventional ones, as well as justification for a fast online projections
design method with which state-of-the-art adaptive CS signal recovery is
achieved.
|
1206.2009
|
Developing a model for a text database indexed pedagogically for
teaching the Arabic language
|
cs.CL
|
In this memory we made the design of an indexing model for Arabic language
and adapting standards for describing learning resources used (the LOM and
their application profiles) with learning conditions such as levels education
of students, their levels of understanding...the pedagogical context with
taking into account the repre-sentative elements of the text, text's
length,...in particular, we highlight the specificity of the Arabic language
which is a complex language, characterized by its flexion, its voyellation and
its agglutination.
|
1206.2010
|
Temporal expression normalisation in natural language texts
|
cs.CL cs.IR
|
Automatic annotation of temporal expressions is a research challenge of great
interest in the field of information extraction. In this report, I describe a
novel rule-based architecture, built on top of a pre-existing system, which is
able to normalise temporal expressions detected in English texts. Gold standard
temporally-annotated resources are limited in size and this makes research
difficult. The proposed system outperforms the state-of-the-art systems with
respect to TempEval-2 Shared Task (value attribute) and achieves substantially
better results with respect to the pre-existing system on top of which it has
been developed. I will also introduce a new free corpus consisting of 2822
unique annotated temporal expressions. Both the corpus and the system are
freely available on-line.
|
1206.2027
|
Adaptive Fractional PID Controller for Robot Manipulator
|
nlin.AO cs.RO
|
A Fractional adaptive PID (FPID) controller for a robot manipulator will be
proposed. The PID parameters have been optimized by Genetic algorithm. The
proposed controller is found robust by means of simulation in a tracking job.
The validity of the proposed controller is shown by simulation of two-link
robot manipulator. The result then is compared with integer type adaptive PID
controller. It is found that when error signals in the learning stage are
bounded, the trajectory of the robot converges to the desired one
asymptotically.
|
1206.2032
|
Timely Coordination in a Multi-Agent System
|
cs.MA cs.GT cs.LO
|
In a distributed algorithm, multiple processes, or agents, work toward a
common goal. More often than not, the actions of some agents are dependent on
the previous execution (if not also on the outcome) of the actions of other
agents. The resulting interdependencies between the timings of the actions of
the various agents give rise to the study of methods for timely coordination of
these actions.
In this work, we formulate and mathematically analyze "Timely-Coordinated
Response" - a novel multi-agent coordination problem in which the time
difference between each pair of actions may be constrained by upper and/or
lower bounds. This problem generalizes coordination problems previously studied
by Halpern and Moses and by Ben-Zvi and Moses.
We optimally solve timely-coordinated response in two ways: using a
generalization of the fixed-point approach of Halpern and Moses, and using a
generalization of the "syncausality" approach of Ben-Zvi and Moses. We
constructively show the equivalence of the solutions yielded by both
approaches, and by combining them, derive strengthened versions of known
results for some previously-defined special cases of this problem.
Our analysis is conducted under minimal assumptions: we work in a
continuous-time model with possibly infinitely many agents. The general results
we obtain for this model reduce to stronger ones for discrete-time models with
only finitely many agents. In order to distill the properties of such models
that are significant to this reduction, we define several classes of
naturally-occurring models, which in a sense separate the different results. We
present both a more practical optimal solution, as well as a surprisingly
simple condition for solvability, for timely coordinated response under these
models.
Finally, we show how our results generalize the results known for
previously-studied special cases of this problem.
|
1206.2058
|
Dimension Reduction by Mutual Information Discriminant Analysis
|
cs.CV cs.IT cs.LG math.IT
|
In the past few decades, researchers have proposed many discriminant analysis
(DA) algorithms for the study of high-dimensional data in a variety of
problems. Most DA algorithms for feature extraction are based on
transformations that simultaneously maximize the between-class scatter and
minimize the withinclass scatter matrices. This paper presents a novel DA
algorithm for feature extraction using mutual information (MI). However, it is
not always easy to obtain an accurate estimation for high-dimensional MI. In
this paper, we propose an efficient method for feature extraction that is based
on one-dimensional MI estimations. We will refer to this algorithm as mutual
information discriminant analysis (MIDA). The performance of this proposed
method was evaluated using UCI databases. The results indicate that MIDA
provides robust performance over different data sets with different
characteristics and that MIDA always performs better than, or at least
comparable to, the best performing algorithms.
|
1206.2059
|
NP-hardness of polytope M-matrix testing and related problems
|
math.OC cs.CC cs.SY math.DS
|
In this note we prove NP-hardness of the following problem: Given a set of
matrices, is there a convex combination of those that is a nonsingular
M-matrix? Via known characterizations of M-matrices, our result establishes
NP-hardness of several fundamental problems in systems analysis and control,
such as testing the instability of an uncertain dynamical system, and
minimizing the spectral radius of an affine matrix function.
|
1206.2061
|
Comments on "On Approximating Euclidean Metrics by Weighted t-Cost
Distances in Arbitrary Dimension"
|
cs.NA cs.CV
|
Mukherjee (Pattern Recognition Letters, vol. 32, pp. 824-831, 2011) recently
introduced a class of distance functions called weighted t-cost distances that
generalize m-neighbor, octagonal, and t-cost distances. He proved that weighted
t-cost distances form a family of metrics and derived an approximation for the
Euclidean norm in $\mathbb{Z}^n$. In this note we compare this approximation to
two previously proposed Euclidean norm approximations and demonstrate that the
empirical average errors given by Mukherjee are significantly optimistic in
$\mathbb{R}^n$. We also propose a simple normalization scheme that improves the
accuracy of his approximation substantially with respect to both average and
maximum relative errors.
|
1206.2068
|
Revolvable Indoor Panoramas Using a Rectified Azimuthal Projection
|
cs.CV math.DG
|
We present an algorithm for converting an indoor spherical panorama into a
photograph with a simulated overhead view. The resulting image will have an
extremely wide field of view covering up to 4{\pi} steradians of the spherical
panorama. We argue that our method complements the stereographic projection
commonly used in the "little planet" effect. The stereographic projection works
well in creating little planets of outdoor scenes; whereas our method is a
well-suited counterpart for indoor scenes. The main innovation of our method is
the introduction of a novel azimuthal map projection that can smoothly blend
between the stereographic projection and the Lambert azimuthal equal-area
projection. Our projection has an adjustable parameter that allows one to
control and compromise between distortions in shape and distortions in size
within the projected panorama. This extra control parameter gives our
projection the ability to produce superior results over the stereographic
projection.
|
1206.2082
|
Dimension Independent Similarity Computation
|
cs.DS cs.AI cs.DC
|
We present a suite of algorithms for Dimension Independent Similarity
Computation (DISCO) to compute all pairwise similarities between very high
dimensional sparse vectors. All of our results are provably independent of
dimension, meaning apart from the initial cost of trivially reading in the
data, all subsequent operations are independent of the dimension, thus the
dimension can be very large. We study Cosine, Dice, Overlap, and the Jaccard
similarity measures. For Jaccard similiarity we include an improved version of
MinHash. Our results are geared toward the MapReduce framework. We empirically
validate our theorems at large scale using data from the social networking site
Twitter. At time of writing, our algorithms are live in production at
twitter.com.
|
1206.2097
|
Bursty communication patterns facilitate spreading in a threshold-based
epidemic dynamics
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Records of social interactions provide us with new sources of data for
understanding how interaction patterns affect collective dynamics. Such human
activity patterns are often bursty, i.e., they consist of short periods of
intense activity followed by long periods of silence. This burstiness has been
shown to affect spreading phenomena; it accelerates epidemic spreading in some
cases and slows it down in other cases. We investigate a model of
history-dependent contagion. In our model, repeated interactions between
susceptible and infected individuals in a short period of time is needed for a
susceptible individual to contract infection. We carry out numerical
simulations on real temporal network data to find that bursty activity patterns
facilitate epidemic spreading in our model.
|
1206.2123
|
Extending Term Suggestion with Author Names
|
cs.IR cs.DL
|
Term suggestion or recommendation modules can help users to formulate their
queries by mapping their personal vocabularies onto the specialized vocabulary
of a digital library. While we examined actual user queries of the social
sciences digital library Sowiport we could see that nearly one third of the
users were explicitly looking for author names rather than terms. Common term
recommenders neglect this fact. By picking up the idea of polyrepresentation we
could show that in a standardized IR evaluation setting we can significantly
increase the retrieval performances by adding topical-related author names to
the query. This positive effect only appears when the query is additionally
expanded with thesaurus terms. By just adding the author names to a query we
often observe a query drift which results in worse results.
|
1206.2126
|
Improving Retrieval Results with discipline-specific Query Expansion
|
cs.IR cs.DL
|
Choosing the right terms to describe an information need is becoming more
difficult as the amount of available information increases.
Search-Term-Recommendation (STR) systems can help to overcome these problems.
This paper evaluates the benefits that may be gained from the use of STRs in
Query Expansion (QE). We create 17 STRs, 16 based on specific disciplines and
one giving general recommendations, and compare the retrieval performance of
these STRs. The main findings are: (1) QE with specific STRs leads to
significantly better results than QE with a general STR, (2) QE with specific
STRs selected by a heuristic mechanism of topic classification leads to better
results than the general STR, however (3) selecting the best matching specific
STR in an automatic way is a major challenge of this process.
|
1206.2130
|
An information-theoretic proof of Nash's inequality
|
cs.IT math-ph math.FA math.IT math.MP
|
We show that an information-theoretic property of Shannon's entropy power,
known as concavity of entropy power, can be fruitfully employed to prove
inequalities in sharp form. In particular, the concavity of entropy power
implies the logarithmic Sobolev inequality, and Nash's inequality with the
sharp constant.
|
1206.2138
|
Comparative Analysis of Peak Correlation Characteristics of
Non-Orthogonal Spreading Codes for Wireless Systems
|
cs.IT math.IT
|
The performance of a CDMA based wireless system is largely dependent on the
characteristics of pseudo-random spreading codes. The spreading codes should be
carefully chosen to ensure highest possible peak value of auto-correlation
function and lower correlation peaks (side-lobes) at non-zero time-shifts.
Simultaneously, zero cross-correlation value at all time shifts is required in
order to eliminate the effect of multiple access interference at the receiver.
But no such code family exists which possess both characteristics
simultaneously. That's why an exhaustive effort has been made in this paper to
evaluate the peak correlation characteristics of various non-orthogonal
spreading codes and suggest a suitable solution.
|
1206.2145
|
The stability of networks --- towards a structural dynamical systems
theory
|
nlin.CD cs.SI physics.soc-ph
|
The need to build a link between the structure of a complex network and the
dynamical properties of the corresponding complex system (comprised of multiple
low dimensional systems) has recently become apparent. Several attempts to
tackle this problem have been made and all focus on either the controllability
or synchronisability of the network --- usually analyzed by way of the master
stability function, or the graph Laplacian. We take a different approach. Using
the basic tools from dynamical systems theory we show that the dynamical
stability of a network can easily be defined in terms of the eigenvalues of an
homologue of the network adjacency matrix. This allows us to compute the
stability of a network (a quantity derived from the eigenspectrum of the
adjacency matrix). Numerical experiments show that this quantity is very
closely related too, and can even be predicted from, the standard structural
network properties. Following from this we show that the stability of large
network systems can be understood via an analytic study of the eigenvalues of
their fixed points --- even for a very large number of fixed points.
|
1206.2190
|
Communication-Efficient Parallel Belief Propagation for Latent Dirichlet
Allocation
|
cs.LG
|
This paper presents a novel communication-efficient parallel belief
propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA).
Based on the synchronous belief propagation (BP) algorithm, we first develop a
parallel belief propagation (PBP) algorithm on the parallel architecture.
Because the extensive communication delay often causes a low efficiency of
parallel topic modeling, we further use Zipf's law to reduce the total
communication cost in PBP. Extensive experiments on different data sets
demonstrate that CE-PBP achieves a higher topic modeling accuracy and reduces
more than 80% communication cost than the state-of-the-art parallel Gibbs
sampling (PGS) algorithm.
|
1206.2197
|
Complex Orthogonal Matching Pursuit and Its Exact Recovery Conditions
|
cs.IT math.IT math.NA stat.ML
|
In this paper, we present new results on using orthogonal matching pursuit
(OMP), to solve the sparse approximation problem over redundant dictionaries
for complex cases (i.e., complex measurement vector, complex dictionary and
complex additive white Gaussian noise (CAWGN)). A sufficient condition that OMP
can recover the optimal representation of an exactly sparse signal in the
complex cases is proposed both in noiseless and bound Gaussian noise settings.
Similar to exact recovery condition (ERC) results in real cases, we extend them
to complex case and derivate the corresponding ERC in the paper. It leverages
this theory to show that OMP succeed for k-sparse signal from a class of
complex dictionary. Besides, an application with geometrical theory of
diffraction (GTD) model is presented for complex cases. Finally, simulation
experiments illustrate the validity of the theoretical analysis.
|
1206.2199
|
Predicting link directions via a recursive subgraph-based ranking
|
physics.soc-ph cs.SI
|
Link directions are essential to the functionality of networks and their
prediction is helpful towards a better knowledge of directed networks from
incomplete real-world data. We study the problem of predicting the directions
of some links by using the existence and directions of the rest of links. We
propose a solution by first ranking nodes in a specific order and then
predicting each link as stemming from a lower-ranked node towards a
higher-ranked one. The proposed ranking method works recursively by utilizing
local indicators on multiple scales, each corresponding to a subgraph extracted
from the original network. Experiments on real networks show that the
directions of a substantial fraction of links can be correctly recovered by our
method, which outperforms either purely local or global methods.
|
1206.2220
|
Representations of Genetic Tables, Bimagic Squares, Hamming Distances
and Shannon Entropy
|
cs.IT math.IT
|
In this paper we have established relations of the genetic tables with magic
and bimagic squares. Connections with Hamming distances, binomial coefficients
are established. The idea of Gray code is applied. Shannon entropy of magic
squares of order 4x4, 8x8 and 16x16 are also calculated. Some comparison is
also made. Symmetry among restriction enzymes having four letters is also
studied.
|
1206.2248
|
Fast Cross-Validation via Sequential Testing
|
cs.LG stat.ML
|
With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy.
|
1206.2262
|
Community-detection cellular automata with local and long-range
connectivity
|
nlin.CG cs.SI physics.soc-ph
|
We explore a community-detection cellular automata algorithm inspired by
human heuristics, based on information diffusion and a non-linear processing
phase with a dynamics inspired by human heuristics. The main point of the
methods is that of furnishing different "views" of the clustering levels from
an individual point of view. We apply the method to networks with local
connectivity and long-range rewiring.
|
1206.2276
|
Irregular Product Codes
|
cs.IT math.IT
|
We consider irregular product codes.In this class of codes, each codeword is
represented by a matrix. The entries in each row (column) of the matrix should
come from a component row (column) code. As opposed to (standard) product
codes, we do not require that all component row codes nor all component column
codes be the same. As we will see, relaxing this requirement can provide some
additional attractive features including 1) allowing some regions of the
codeword be more error-resilient 2) allowing a more refined spectrum of rates
for finite-lengths and improved performance in some of these rates 3) more
interaction between row and column codes during decoding. We study these codes
over erasure channels. We find that for any $0 < \epsilon < 1$, for many rate
distributions on component row codes, there is a matching rate distribution on
component column codes such that an irregular product code based on MDS codes
with those rate distributions on the component codes has asymptotic rate $1 -
\epsilon$ and can decode on erasure channels (of alphabet size equal the
alphabet size of the component MDS codes) with erasure probability $<
\epsilon$.
|
1206.2292
|
An Intercell Interference Model based on Scheduling for Future
Generation Wireless Networks (Part 1 and Part 2)
|
cs.IT math.IT math.PR
|
This technical report is divided into two parts. The first part of the
technical report presents a novel framework for modeling the uplink and
downlink intercell interference (ICI) in a multiuser cellular network. The
proposed framework assists in quantifying the impact of various fading channel
models and multiuser scheduling schemes on the uplink and downlink ICI.
Firstly, we derive a semi-analytical expression for the distribution of the
location of the scheduled user in a given cell considering a wide range of
scheduling schemes. Based on this, we derive the distribution and moment
generating function (MGF) of the ICI considering a single interfering cell.
Consequently, we determine the MGF of the cumulative ICI observed from all
interfering cells and derive explicit MGF expressions for three typical fading
models. Finally, we utilize the obtained expressions to evaluate important
network performance metrics such as the outage probability, ergodic capacity
and average fairness numerically. Monte-Carlo simulation results are provided
to demonstrate the efficacy of the derived analytical expressions {\bf The
first part of the technical report is currently submitted to IEEE Transactions
on Wireless Communications}. The second part of the technical report deals with
the statistical modeling of uplink inter-cell interference (ICI) considering
greedy scheduling with power adaptation based on channel conditions. The
derived model is utilized to evaluate important network performance metrics
such as ergodic capacity, average fairness and average power preservation
numerically. In parallel to the literature, we have shown that greedy
scheduling with power adaptation reduces the ICI, average power consumption of
users, and enhances the average fairness among users, compared to the case
without power adaptation.
|
1206.2322
|
A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model
|
cs.IT math.IT
|
To achieve high range resolution profile (HRRP), the geometric theory of
diffraction (GTD) parametric model is widely used in stepped-frequency radar
system. In the paper, a fast synthetic range profile algorithm, called
orthogonal matching pursuit with sensing dictionary (OMP-SD), is proposed. It
formulates the traditional HRRP synthetic to be a sparse approximation problem
over redundant dictionary. As it employs a priori information that targets are
sparsely distributed in the range space, the synthetic range profile (SRP) can
be accomplished even in presence of data lost. Besides, the computational
complexity is reduced by introducing sensing dictionary (SD) and it mitigates
the model mismatch at the same time. The computation complexity decreases from
O(MNDK) flops for OMP to O(M(N +D)K) flops for OMP-SD. Simulation experiments
illustrate its advantages both in additive white Gaussian noise (AWGN) and
noiseless situation, respectively.
|
1206.2347
|
Uncertain and Approximative Knowledge Representation to Reasoning on
Classification with a Fuzzy Networks Based System
|
cs.AI
|
The approach described here allows to use the fuzzy Object Based
Representation of imprecise and uncertain knowledge. This representation has a
great practical interest due to the possibility to realize reasoning on
classification with a fuzzy semantic network based system. For instance, the
distinction between necessary, possible and user classes allows to take into
account exceptions that may appear on fuzzy knowledge-base and facilitates
integration of user's Objects in the base. This approach describes the
theoretical aspects of the architecture of the whole experimental A.I. system
we built in order to provide effective on-line assistance to users of new
technological systems: the understanding of "how it works" and "how to complete
tasks" from queries in quite natural languages. In our model, procedural
semantic networks are used to describe the knowledge of an "ideal" expert while
fuzzy sets are used both to describe the approximative and uncertain knowledge
of novice users in fuzzy semantic networks which intervene to match fuzzy
labels of a query with categories from our "ideal" expert.
|
1206.2362
|
Applying Compression to a Game's Network Protocol
|
cs.IT math.IT
|
This report presents the results of applying different compression algorithms
to the network protocol of an online game. The algorithm implementations
compared are zlib, liblzma and my own implementation based on LZ77 and a
variation of adaptive Huffman coding. The comparison data was collected from
the game TomeNET. The results show that adaptive coding is especially useful
for compressing large amounts of very small packets.
|
1206.2369
|
Networks in Motion
|
physics.soc-ph cond-mat.dis-nn cs.SI nlin.AO q-bio.MN
|
Feature article on how networks that govern communication, growth, herd
behavior, and other key processes in nature and society are becoming
increasingly amenable to modeling, forecast, and control.
|
1206.2372
|
PRISMA: PRoximal Iterative SMoothing Algorithm
|
math.OC cs.LG
|
Motivated by learning problems including max-norm regularized matrix
completion and clustering, robust PCA and sparse inverse covariance selection,
we propose a novel optimization algorithm for minimizing a convex objective
which decomposes into three parts: a smooth part, a simple non-smooth Lipschitz
part, and a simple non-smooth non-Lipschitz part. We use a time variant
smoothing strategy that allows us to obtain a guarantee that does not depend on
knowing in advance the total number of iterations nor a bound on the domain.
|
1206.2437
|
A Novel Windowing Technique for Efficient Computation of MFCC for
Speaker Recognition
|
cs.CV
|
In this paper, we propose a novel family of windowing technique to compute
Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition
from speech. The proposed method is based on fundamental property of discrete
time Fourier transform (DTFT) related to differentiation in frequency domain.
Classical windowing scheme such as Hamming window is modified to obtain
derivatives of discrete time Fourier transform coefficients. It has been
mathematically shown that the slope and phase of power spectrum are inherently
incorporated in newly computed cepstrum. Speaker recognition systems based on
our proposed family of window functions are shown to attain substantial and
consistent performance improvement over baseline single tapered Hamming window
as well as recently proposed multitaper windowing technique.
|
1206.2459
|
R\'enyi Divergence and Kullback-Leibler Divergence
|
cs.IT math.IT math.ST stat.ML stat.TH
|
R\'enyi divergence is related to R\'enyi entropy much like Kullback-Leibler
divergence is related to Shannon's entropy, and comes up in many settings. It
was introduced by R\'enyi as a measure of information that satisfies almost the
same axioms as Kullback-Leibler divergence, and depends on a parameter that is
called its order. In particular, the R\'enyi divergence of order 1 equals the
Kullback-Leibler divergence.
We review and extend the most important properties of R\'enyi divergence and
Kullback-Leibler divergence, including convexity, continuity, limits of
$\sigma$-algebras and the relation of the special order 0 to the Gaussian
dichotomy and contiguity. We also show how to generalize the Pythagorean
inequality to orders different from 1, and we extend the known equivalence
between channel capacity and minimax redundancy to continuous channel inputs
(for all orders) and present several other minimax results.
|
1206.2465
|
Search Strategies of Library Search Experts
|
cs.IR cs.DL
|
Search engines like Google, Yahoo or Bing are an excellent support for
finding documents, but this strength also imposes a limitation. As they are
optimized for document retrieval tasks, they perform less well when it comes to
more complex search needs. Complex search tasks are usually described as
open-ended, abstract and poorly defined information needs with a multifaceted
character. In this paper we will present the results of an experiment carried
out with information professionals from libraries and museums in the course of
a search contest. The aim of the experiment was to analyze the search
strategies of experienced information workers trying to tackle search tasks of
varying complexity and get qualitative results on the impact of time pressure
on such an experiment.
|
1206.2478
|
On the Exact BER of Bit-Wise Demodulators for One-Dimensional
Constellations
|
cs.IT math.IT
|
The optimal bit-wise demodulator for M-ary pulse amplitude modulation (PAM)
over the additive white Gaussian noise channel is analyzed in terms of uncoded
bit-error rate (BER). New closed-form BER expressions for 4-PAM with any
labeling are developed. Moreover, closed-form BER expressions for 11 out of 23
possible bit patterns for 8-PAM are presented, which enable us to obtain the
BER for 8-PAM with some of the most popular labelings, including the binary
reflected Gray code and the natural binary code. Numerical results show that,
regardless of the labeling, there is no difference between the optimal
demodulator and the symbol-wise demodulator for any BER of practical interest
(below 0.1).
|
1206.2484
|
Architecture for Automated Tagging and Clustering of Song Files
According to Mood
|
cs.IR cs.MM
|
Music is one of the basic human needs for recreation and entertainment. As
song files are digitalized now a days, and digital libraries are expanding
continuously, which makes it difficult to recall a song. Thus need of a new
classification system other than genre is very obvious and mood based
classification system serves the purpose very well. In this paper we will
present a well-defined architecture to classify songs into different mood-based
categories, using audio content analysis, affective value of song lyrics to map
a song onto a psychological-based emotion space and information from online
sources. In audio content analysis we will use music features such as
intensity, timbre and rhythm including their subfeatures to map music in a
2-Dimensional emotional space. In lyric based classification 1-Dimensional
emotional space is used. Both the results are merged onto a 2-Dimensional
emotional space, which will classify song into a particular mood category.
Finally clusters of mood based song files are formed and arranged according to
data acquired from various Internet sources.
|
1206.2491
|
Rewritable storage channels with hidden state
|
cs.IT math.IT
|
Many storage channels admit reading and rewriting of the content at a given
cost. We consider rewritable channels with a hidden state which models the
unknown characteristics of the memory cell. In addition to mitigating the
effect of the write noise, rewrites can help the write controller obtain a
better estimate of the hidden state. The paper has two contributions. The first
is a lower bound on the capacity of a general rewritable channel with hidden
state. The lower bound is obtained using a coding scheme that combines
Gelfand-Pinsker coding with superposition coding. The rewritable AWGN channel
is discussed as an example. The second contribution is a simple coding scheme
for a rewritable channel where the write noise and hidden state are both
uniformly distributed. It is shown that this scheme is asymptotically optimal
as the number of rewrites gets large.
|
1206.2510
|
Generic Subsequence Matching Framework: Modularity, Flexibility,
Efficiency
|
cs.MM cs.DS cs.IR
|
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.
|
1206.2517
|
Assessing the Quality of Wikipedia Pages Using Edit Longevity and
Contributor Centrality
|
cs.SI cs.CY
|
In this paper we address the challenge of assessing the quality of Wikipedia
pages using scores derived from edit contribution and contributor
authoritativeness measures. The hypothesis is that pages with significant
contributions from authoritative contributors are likely to be high-quality
pages. Contributions are quantified using edit longevity measures and
contributor authoritativeness is scored using centrality metrics in either the
Wikipedia talk or co-author networks. The results suggest that it is useful to
take into account the contributor authoritativeness when assessing the
information quality of Wikipedia content. The percentile visualization of the
quality scores provides some insights about the anomalous articles, and can be
used to help Wikipedia editors to identify Start and Stub articles that are of
relatively good quality.
|
1206.2523
|
Binary Jumbled String Matching for Highly Run-Length Compressible Texts
|
cs.DS cs.IR
|
The Binary Jumbled String Matching problem is defined as: Given a string $s$
over $\{a,b\}$ of length $n$ and a query $(x,y)$, with $x,y$ non-negative
integers, decide whether $s$ has a substring $t$ with exactly $x$ $a$'s and $y$
$b$'s. Previous solutions created an index of size O(n) in a pre-processing
step, which was then used to answer queries in constant time. The fastest
algorithms for construction of this index have running time $O(n^2/\log n)$
[Burcsi et al., FUN 2010; Moosa and Rahman, IPL 2010], or $O(n^2/\log^2 n)$ in
the word-RAM model [Moosa and Rahman, JDA 2012]. We propose an index
constructed directly from the run-length encoding of $s$. The construction time
of our index is $O(n+\rho^2\log \rho)$, where O(n) is the time for computing
the run-length encoding of $s$ and $\rho$ is the length of this encoding---this
is no worse than previous solutions if $\rho = O(n/\log n)$ and better if $\rho
= o(n/\log n)$. Our index $L$ can be queried in $O(\log \rho)$ time. While
$|L|= O(\min(n, \rho^{2}))$ in the worst case, preliminary investigations have
indicated that $|L|$ may often be close to $\rho$. Furthermore, the algorithm
for constructing the index is conceptually simple and easy to implement. In an
attempt to shed light on the structure and size of our index, we characterize
it in terms of the prefix normal forms of $s$ introduced in [Fici and Lipt\'ak,
DLT 2011].
|
1206.2526
|
Analysis of Inpainting via Clustered Sparsity and Microlocal Analysis
|
math.FA cs.IT math.IT math.NA
|
Recently, compressed sensing techniques in combination with both wavelet and
directional representation systems have been very effectively applied to the
problem of image inpainting. However, a mathematical analysis of these
techniques which reveals the underlying geometrical content is completely
missing. In this paper, we provide the first comprehensive analysis in the
continuum domain utilizing the novel concept of clustered sparsity, which
besides leading to asymptotic error bounds also makes the superior behavior of
directional representation systems over wavelets precise. First, we propose an
abstract model for problems of data recovery and derive error bounds for two
different recovery schemes, namely l_1 minimization and thresholding. Second,
we set up a particular microlocal model for an image governed by edges inspired
by seismic data as well as a particular mask to model the missing data, namely
a linear singularity masked by a horizontal strip. Applying the abstract
estimate in the case of wavelets and of shearlets we prove that -- provided the
size of the missing part is asymptotically to the size of the analyzing
functions -- asymptotically precise inpainting can be obtained for this model.
Finally, we show that shearlets can fill strictly larger gaps than wavelets in
this model.
|
1206.2528
|
Ordinary Search Engine Users assessing Difficulty, Effort, and Outcome
for Simple and Complex Search Tasks
|
cs.IR
|
Search engines are the preferred tools for finding information on the Web.
They are advancing to be the common helpers to answer any of our search needs.
We use them to carry out simple look-up tasks and also to work on rather time
consuming and more complex search tasks. Yet, we do not know very much about
the user performance while carrying out those tasks -- especially not for
ordinary users. The aim of this study was to get more insight into whether Web
users manage to assess difficulty, time effort, query effort, and task outcome
of search tasks, and if their judging performance relates to task complexity.
Our study was conducted with a systematically selected sample of 56 people with
a wide demographic background. They carried out a set of 12 search tasks with
commercial Web search engines in a laboratory environment. The results confirm
that it is hard for normal Web users to judge the difficulty and effort to
carry out complex search tasks. The judgments are more reliable for simple
tasks than for complex ones. Task complexity is an indicator for judging
performance.
|
1206.2544
|
Education in Conflict Zones: a Web and Mobility Approach
|
cs.CY cs.SI
|
We propose a new framework for education in conflict zones, considering the
explosive growth of social media, web services, and mobile Internet over the
past decade. Moreover, we focus on one conflict zone, Afghanistan, as a case
study, because of its alarmingly high illiteracy rate, lack of qualified
teachers, rough terrain, and relatively high mobile penetration of over 50%. In
several of Afghanistan's provinces, it is hard to currently sustain the
traditional bricks-and-mortar school model, due to numerous incidents of
schools, teachers, and students being attacked because of the ongoing
insurgency and political instability. Our model improves the virtual school
model, by addressing most of its disadvantages, to provide students in
Afghanistan with an opportunity to achieve standardised education, even when
the security situation does not allow them to attend traditional schools. One
of the biggest advantages of this model is that it is sufficiently robust to
deal with gender discrimination, imposed by culture or politics of the region.
|
1206.2550
|
Core percolation on complex networks
|
cond-mat.stat-mech cs.SI physics.soc-ph
|
As a fundamental structural transition in complex networks, core percolation
is related to a wide range of important problems. Yet, previous theoretical
studies of core percolation have been focusing on the classical
Erd\H{o}s-R\'enyi random networks with Poisson degree distribution, which are
quite unlike many real-world networks with scale-free or fat-tailed degree
distributions. Here we show that core percolation can be analytically studied
for complex networks with arbitrary degree distributions. We derive the
condition for core percolation and find that purely scale-free networks have no
core for any degree exponents. We show that for undirected networks if core
percolation occurs then it is always continuous while for directed networks it
becomes discontinuous when the in- and out-degree distributions are different.
We also apply our theory to real-world directed networks and find,
surprisingly, that they often have much larger core sizes as compared to random
models. These findings would help us better understand the interesting
interplay between the structural and dynamical properties of complex networks.
|
1206.2568
|
LP decoding of expander codes: a simpler proof
|
cs.IT math.IT
|
A code $C \subseteq \F_2^n$ is a $(c,\epsilon,\delta)$-expander code if it
has a Tanner graph, where every variable node has degree $c$, and every subset
of variable nodes $L_0$ such that $|L_0|\leq \delta n$ has at least $\epsilon c
|L_0|$ neighbors. Feldman et al. (IEEE IT, 2007) proved that LP decoding
corrects $\frac{3\epsilon-2}{2\epsilon-1} \cdot (\delta n-1)$ errors of
$(c,\epsilon,\delta)$-expander code, where $\epsilon > 2/3+\frac{1}{3c}$. In
this paper, we provide a simpler proof of their result and show that this
result holds for every expansion parameter $\epsilon > 2/3$.
|
1206.2587
|
Exploiting Particle Swarm Optimization in Multiple Faults Fuzzy
Detection
|
cs.NE cs.SY
|
In this paper an on-line multiple faults detection approach is first of all
proposed. For efficiency, an optimal design of membership functions is
required. Thus, the proposed approach is improved using Particle Swarm
Optimization (PSO) technique. The inputs of the proposed approaches are
residuals representing the numerical evaluation of Analytical Redundancy
Relations. These residuals are generated due to the use of bond graph modeling.
The results of the fuzzy detection modules are displayed as a colored causal
graph. A comparison between the results obtained by using PSO and those given
by the use of Genetic Algorithms (GA) is finally made. The experiments focus on
a simulation of the three-tank hydraulic system, a benchmark in the diagnosis
domain.
|
1206.2599
|
A tale of two cities. Vulnerabilities of the London and Paris transit
networks
|
physics.soc-ph cs.SI physics.data-an
|
This paper analyses the impact of random failure or attack on the public
transit networks of London and Paris in a comparative study. In particular we
analyze how the dysfunction or removal of sets of stations or links (rails,
roads, etc.) affects the connectivity properties within these networks. We show
how accumulating dysfunction leads to emergent phenomena that cause the
transportation system to break down as a whole. Simulating different directed
attack strategies, we find minimal strategies with high impact and identify
a-priory criteria that correlate with the resilience of these networks. To
demonstrate our approach, we choose the London and Paris public transit
networks. Our quantitative analysis is performed in the frames of the complex
network theory - a methodological tool that has emerged recently as an
interdisciplinary approach joining methods and concepts of the theory of random
graphs, percolation, and statistical physics. In conclusion we demonstrate that
taking into account cascading effects the network integrity is controlled for
both networks by less than 0.5 % of the stations i.e. 19 for Paris and 34 for
London.
|
1206.2627
|
Image Similarity Using Sparse Representation and Compression Distance
|
cs.CV
|
A new line of research uses compression methods to measure the similarity
between signals. Two signals are considered similar if one can be compressed
significantly when the information of the other is known. The existing
compression-based similarity methods, although successful in the discrete one
dimensional domain, do not work well in the context of images. This paper
proposes a sparse representation-based approach to encode the information
content of an image using information from the other image, and uses the
compactness (sparsity) of the representation as a measure of its
compressibility (how much can the image be compressed) with respect to the
other image. The more sparse the representation of an image, the better it can
be compressed and the more it is similar to the other image. The efficacy of
the proposed measure is demonstrated through the high accuracies achieved in
image clustering, retrieval and classification.
|
1206.2656
|
A Construction of Quantum LDPC Codes from Cayley Graphs
|
cs.IT math.CO math.IT
|
We study a construction of Quantum LDPC codes proposed by MacKay, Mitchison
and Shokrollahi. It is based on the Cayley graph of Fn together with a set of
generators regarded as the columns of the parity-check matrix of a classical
code. We give a general lower bound on the minimum distance of the Quantum code
in $\mathcal{O}(dn^2)$ where d is the minimum distance of the classical code.
When the classical code is the $[n, 1, n]$ repetition code, we are able to
compute the exact parameters of the associated Quantum code which are $[[2^n,
2^{\frac{n+1}{2}}, 2^{\frac{n-1}{2}}]]$.
|
1206.2669
|
Information-Theoretically Secure Three-Party Computation with One
Corrupted Party
|
cs.CR cs.IT math.IT
|
The problem in which one of three pairwise interacting parties is required to
securely compute a function of the inputs held by the other two, when one party
may arbitrarily deviate from the computation protocol (active behavioral
model), is studied. An information-theoretic characterization of
unconditionally secure computation protocols under the active behavioral model
is provided. A protocol for Hamming distance computation is provided and shown
to be unconditionally secure under both active and passive behavioral models
using the information-theoretic characterization. The difference between the
notions of security under the active and passive behavioral models is
illustrated through the BGW protocol for computing quadratic and Hamming
distances; this protocol is secure under the passive model, but is shown to be
not secure under the active model.
|
1206.2691
|
IDS: An Incremental Learning Algorithm for Finite Automata
|
cs.LG cs.DS cs.FL
|
We present a new algorithm IDS for incremental learning of deterministic
finite automata (DFA). This algorithm is based on the concept of distinguishing
sequences introduced in (Angluin81). We give a rigorous proof that two versions
of this learning algorithm correctly learn in the limit. Finally we present an
empirical performance analysis that compares these two algorithms, focussing on
learning times and different types of learning queries. We conclude that IDS is
an efficient algorithm for software engineering applications of automata
learning, such as testing and model inference.
|
1206.2720
|
Temporal percolation of the susceptible network in an epidemic spreading
|
physics.soc-ph cs.SI
|
In this work, we study the evolution of the susceptible individuals during
the spread of an epidemic modeled by the susceptible-infected-recovered (SIR)
process spreading on the top of complex networks. Using an edge-based
compartmental approach and percolation tools, we find that a time-dependent
quantity $\Phi_S(t)$, namely, the probability that a given neighbor of a node
is susceptible at time $t$, is the control parameter of a node void percolation
process involving those nodes on the network not-reached by the disease. We
show that there exists a critical time $t_c$ above which the giant susceptible
component is destroyed. As a consequence, in order to preserve a macroscopic
connected fraction of the network composed by healthy individuals which
guarantee its functionality, any mitigation strategy should be implemented
before this critical time $t_c$. Our theoretical results are confirmed by
extensive simulations of the SIR process.
|
1206.2728
|
Effect of Closed Paths in Complex networks on Six Degrees of Separation
and Disorder
|
physics.soc-ph cs.SI
|
Milgram Condition proposed by Aoyama et al. plays an important role on the
analysis of "six degrees of separation". We have shown that the relations
between Milgram condition and the generalized clustering coefficient, which was
introduced as an index for measuring the number of closed paths by us, are
absolutely different in scale free networks (Barabasi and Albert) and small
world networks (Watts and Strogatz, Watts). This fact implies that the effect
of closed paths on information propagation is different in both networks. In
this article, we first investigate the difference and pursuit what is a crucial
mathematical quantity for information propagation. As a result we find that a
sort of "disorder" plays more important role for information propagation than
partially closed paths included in a network. Next we inquired into it in more
detail by introducing two types of intermediate networks. Then we find that the
average of the local clustering coefficient and the generalized clustering
coefficients $C_{(q)}$ have some different functions and important meanings,
respectively. We also find that $C_{(q)}$ is close to the propagation of
information on networks. Lastly, we show that realizability of six degrees of
separation in networks can be understood in a unified way by disorder.
|
1206.2733
|
Parrondo Paradox in Scale Free Networks
|
physics.soc-ph cs.SI
|
Parrondo's paradox occurs in sequences of games in which a winning
expectation may be obtained by playing the games in a random order, even though
each game in the sequence may be lost when played individually. Several
variations of Parrondo's games with paradoxical property have been introduced.
In this paper, I examine whether Parrondo's paradox occurs or not in scale free
networks. Two models are discussed by some theoretical analyses and computer
simulations. As a result, I prove that Parrondo's paradox occurs only in the
second model.
|
1206.2742
|
Online open neuroimaging mass meta-analysis
|
cs.DL cs.AI stat.AP
|
We describe a system for meta-analysis where a wiki stores numerical data in
a simple format and a web service performs the numerical computation.
We initially apply the system on multiple meta-analyses of structural
neuroimaging data results. The described system allows for mass meta-analysis,
e.g., meta-analysis across multiple brain regions and multiple mental
disorders.
|
1206.2802
|
Critical behavior in a cross-situational lexicon learning scenario
|
physics.soc-ph cond-mat.stat-mech cs.AI
|
The associationist account for early word-learning is based on the
co-occurrence between objects and words. Here we examine the performance of a
simple associative learning algorithm for acquiring the referents of words in a
cross-situational scenario affected by noise produced by out-of-context words.
We find a critical value of the noise parameter $\gamma_c$ above which learning
is impossible. We use finite-size scaling to show that the sharpness of the
transition persists across a region of order $\tau^{-1/2}$ about $\gamma_c$,
where $\tau$ is the number of learning trials, as well as to obtain the
learning error (scaling function) in the critical region. In addition, we show
that the distribution of durations of periods when the learning error is zero
is a power law with exponent -3/2 at the critical point.
|
1206.2807
|
An efficient hierarchical graph based image segmentation
|
cs.CV
|
Hierarchical image segmentation provides region-oriented scalespace, i.e., a
set of image segmentations at different detail levels in which the
segmentations at finer levels are nested with respect to those at coarser
levels. Most image segmentation algorithms, such as region merging algorithms,
rely on a criterion for merging that does not lead to a hierarchy, and for
which the tuning of the parameters can be difficult. In this work, we propose a
hierarchical graph based image segmentation relying on a criterion popularized
by Felzenzwalb and Huttenlocher. We illustrate with both real and synthetic
images, showing efficiency, ease of use, and robustness of our method.
|
1206.2866
|
Efficient scheduling using complex networks
|
physics.soc-ph cs.CE cs.SY
|
We consider the problem of efficiently scheduling the production of goods for
a model steel manufacturing company. We propose a new approach for solving this
classic problem, using techniques from the statistical physics of complex
networks in conjunction with depth-first search to generate a successful,
flexible, schedule. The schedule generated by our algorithm is more efficient
and outperforms schedules selected at random from those observed in real steel
manufacturing processes. Finally, we explore whether the proposed approach
could be beneficial for long term planning.
|
1206.2898
|
Long-Range Navigation on Complex Networks using L\'evy Random Walks
|
cond-mat.stat-mech cs.SI physics.soc-ph
|
We introduce a strategy of navigation in undirected networks, including
regular, random, and complex networks, that is inspired by L\'evy random walks,
generalizing previous navigation rules. We obtained exact expressions for the
stationary probability distribution, the occupation probability, the mean first
passage time, and the average time to reach a node on the network. We found
that the long-range navigation using the L\'evy random walk strategy, compared
with the normal random walk strategy, is more efficient at reducing the time to
cover the network. The dynamical effect of using the L\'evy walk strategy is to
transform a large-world network into a small world. Our exact results provide a
general framework that connects two important fields: L\'evy navigation
strategies and dynamics on complex networks.
|
1206.2944
|
Practical Bayesian Optimization of Machine Learning Algorithms
|
stat.ML cs.LG
|
Machine learning algorithms frequently require careful tuning of model
hyperparameters, regularization terms, and optimization parameters.
Unfortunately, this tuning is often a "black art" that requires expert
experience, unwritten rules of thumb, or sometimes brute-force search. Much
more appealing is the idea of developing automatic approaches which can
optimize the performance of a given learning algorithm to the task at hand. In
this work, we consider the automatic tuning problem within the framework of
Bayesian optimization, in which a learning algorithm's generalization
performance is modeled as a sample from a Gaussian process (GP). The tractable
posterior distribution induced by the GP leads to efficient use of the
information gathered by previous experiments, enabling optimal choices about
what parameters to try next. Here we show how the effects of the Gaussian
process prior and the associated inference procedure can have a large impact on
the success or failure of Bayesian optimization. We show that thoughtful
choices can lead to results that exceed expert-level performance in tuning
machine learning algorithms. We also describe new algorithms that take into
account the variable cost (duration) of learning experiments and that can
leverage the presence of multiple cores for parallel experimentation. We show
that these proposed algorithms improve on previous automatic procedures and can
reach or surpass human expert-level optimization on a diverse set of
contemporary algorithms including latent Dirichlet allocation, structured SVMs
and convolutional neural networks.
|
1206.2959
|
Collaborative High Accuracy Localization in Mobile Multipath
Environments
|
cs.NI cs.IT cs.RO math.IT
|
We study the problem of high accuracy localization of mobile nodes in a
multipath-rich environment where sub-meter accuracies are required. We employ a
peer-to-peer framework where the vehicles/nodes can get pairwise
multipath-degraded ranging estimates in local neighborhoods together with a
fixed number of anchor nodes. The challenge is to overcome the
multipath-barrier with redundancy in order to provide the desired accuracies
especially under severe multipath conditions when the fraction of received
signals corrupted by multipath is dominating. We invoke a analytical graphical
model framework based on particle filtering and reveal its high accuracy
localization promise through simulations. We also address design questions such
as "How many anchors and what fraction of line-of-sight (LOS) measurements are
needed to achieve a specified target accuracy?", by analytically characterizing
the performance improvement in localization accuracy as a function of the
number of nodes in the network and the fraction of LOS measurements. In
particular, for a static node placement, we show that the Cramer-Rao Lower
Bound (CRLB), a fundamental lower bound on the localization accuracy, can be
expressed as a product of two factors - a scalar function that depends only on
the parameters of the noise distribution and a matrix that depends only on the
geometry of node locations and the underlying connectivity graph. Further, a
simplified expression is obtained for the CRLB that helps deduce the scaling
behavior of the estimation error as a function of the number of agents and
anchors in the network. The bound suggests that even a small fraction of LOS
measurements can provide significant improvements. Conversely, a small fraction
of NLOS measurements can significantly degrade the performance. The analysis is
extended to the mobile setting and the performance is compared with the derived
CRLB.
|
1206.2960
|
Modeling two-language competition dynamics
|
physics.soc-ph cs.SI
|
During the last decade, much attention has been paid to language competition
in the complex systems community, that is, how the fractions of speakers of
several competing languages evolve in time. In this paper we review recent
advances in this direction and focus on three aspects. First we consider the
shift from two-state models to three state models that include the possibility
of bilingual individuals. The understanding of the role played by bilingualism
is essential in sociolinguistics. In particular, the question addressed is
whether bilingualism facilitates the coexistence of languages. Second, we will
analyze the effect of social interaction networks and physical barriers.
Finally, we will show how to analyze the issue of bilingualism from a game
theoretical perspective.
|
1206.2961
|
Epistemic view of quantum states and communication complexity of quantum
channels
|
quant-ph cs.IT math.IT
|
The communication complexity of a quantum channel is the minimal amount of
classical communication required for classically simulating a process of state
preparation, transmission through the channel and subsequent measurement. It
establishes a limit on the power of quantum communication in terms of classical
resources. We show that classical simulations employing a finite amount of
communication can be derived from a special class of hidden variable theories
where quantum states represent statistical knowledge about the classical state
and not an element of reality. This special class has attracted strong interest
very recently. The communication cost of each derived simulation is given by
the mutual information between the quantum state and the classical state of the
parent hidden variable theory. Finally, we find that the communication
complexity for single qubits is smaller than 1.28 bits. The previous known
upper bound was 1.85 bits.
|
1206.2974
|
On Constrained Randomized Quantization
|
cs.IT math.IT
|
Randomized (dithered) quantization is a method capable of achieving white
reconstruction error independent of the source. Dithered quantizers have
traditionally been considered within their natural setting of uniform
quantization. In this paper we extend conventional dithered quantization to
nonuniform quantization, via a subterfage: dithering is performed in the
companded domain. Closed form necessary conditions for optimality of the
compressor and expander mappings are derived for both fixed and variable rate
randomized quantization. Numerically, mappings are optimized by iteratively
imposing these necessary conditions. The framework is extended to include an
explicit constraint that deterministic or randomized quantizers yield
reconstruction error that is uncorrelated with the source. Surprising
theoretical results show direct and simple connection between the optimal
constrained quantizers and their unconstrained counterparts. Numerical results
for the Gaussian source provide strong evidence that the proposed constrained
randomized quantizer outperforms the conventional dithered quantizer, as well
as the constrained deterministic quantizer. Moreover, the proposed constrained
quantizer renders the reconstruction error nearly white. In the second part of
the paper, we investigate whether uncorrelated reconstruction error requires
random coding to achieve asymptotic optimality. We show that for a Gaussian
source, the optimal vector quantizer of asymptotically high dimension whose
quantization error is uncorrelated with the source, is indeed random. Thus,
random encoding in this setting of rate-distortion theory, is not merely a tool
to characterize performance bounds, but a required property of quantizers that
approach such bounds.
|
1206.2994
|
Towards Optimality in Transform Coding
|
cs.IT math.IT
|
It is well-known for transform coding of multivariate Gaussian sources, that
the Karhunen-Lo\`eve transform (KLT) minimizes the mean square error
distortion. However, finding the optimal transform for general non-Gaussian
sources has been an open problem for decades, despite several important
advances that provide some partial answers regarding KLT optimality. In this
paper, we present a necessary and sufficient condition for optimality of a
transform when high resolution, variable rate quantizers are employed. We hence
present not only a complete characterization of when KLT is optimal, but also a
determining condition for optimality of a general (non-KLT) transform. This
necessary and sufficient condition is shown to have direct connections to the
well studied source separation problem. This observation can impact source
separation itself, as illustrated with a new optimality result. We combine the
transform optimality condition with algorithmic tools from source separation,
to derive a practical numerical method to search for the optimal transform in
source coding. Then, we focus on multiterminal settings, for which {\it
conditional} KLT was shown to possess certain optimality properties for
Gaussian sources. We derive the optimal orthogonal transform for the setting
where side information is only available to the decoder, along with new
specialized results specific to the conditions for optimality of conditional
KLT. Finally, we consider distributed source coding where two correlated
sources are to be transform coded separately but decoded jointly. We derive the
necessary and sufficient condition of optimality of the orthogonal transforms.
We specialize to find the optimal orthogonal transforms, in this setting, for
specific source densities, including jointly Gaussian sources.
|
1206.3002
|
Study of the Importance of Adequacy to Robot Verbal and Non Verbal
Communication in Human-Robot interaction
|
cs.RO cs.HC
|
The Robadom project aims at creating a homecare robot that help and assist
people in their daily life, either in doing task for the human or in managing
day organization. A robot could have this kind of role only if it is accepted
by humans. Before thinking about the robot appearance, we decided to evaluate
the importance of the relation between verbal and nonverbal communication
during a human-robot interaction in order to determine the situation where the
robot is accepted. We realized two experiments in order to study this
acceptance. The first experiment studied the importance of having robot
nonverbal behavior in relation of its verbal behavior. The second experiment
studied the capability of a robot to provide a correct human-robot interaction.
|
1206.3014
|
Round-Robin Streaming with Generations
|
cs.IT math.IT
|
We consider three types of application layer coding for streaming over lossy
links: random linear coding, systematic random linear coding, and structured
coding. The file being streamed is divided into sub-blocks (generations). Code
symbols are formed by combining data belonging to the same generation, and
transmitted in a round-robin fashion. We compare the schemes based on delivery
packet count, net throughput, and energy consumption for a range of generation
sizes. We determine these performance measures both analytically and in an
experimental configuration. We find our analytical predictions to match the
experimental results. We show that coding at the application layer brings about
a significant increase in net data throughput, and thereby reduction in energy
consumption due to reduced communication time. On the other hand, on devices
with constrained computing resources, heavy coding operations cause packet
drops in higher layers and negatively affect the net throughput. We find from
our experimental results that low-rate MDS codes are best for small generation
sizes, whereas systematic random linear coding has the best net throughput and
lowest energy consumption for larger generation sizes due to its low decoding
complexity.
|
1206.3027
|
Social Networks, Functional Differentiation of Society, and Data
Protection
|
cs.CY cs.SI physics.soc-ph
|
Most scholars, politicians, and activists are following individualistic
theories of privacy and data protection. In contrast, some of the pioneers of
the data protection legislation in Germany like Adalbert Podlech, Paul J.
M\"uller, and Ulrich Dammann used a systems theory approach. Following Niklas
Luhmann, the aim of data protection is (1) maintaining the functional
differentiation of society against the threats posed by the possibilities of
modern information processing, and (2) countering undue information power by
organized social players. It could be, therefore, no surprise that the first
data protection law in the German state of Hesse contained rules to protect the
individual as well as the balance of power between the legislative and the
executive body of the state. Social networks like Facebook or Google+ do not
only endanger their users by exposing them to other users or the public. They
constitute, first and foremost, a threat to society as a whole by collecting
information about individuals, groups, and organizations from different social
systems and combining them in a centralized data bank. They transgress the
boundaries between social systems that act as a shield against total visibility
and transparency of the individual and protect the freedom and the autonomy of
the people. Without enforcing structural limitations on the organizational use
of collected data by the social network itself or the company behind it, social
networks pose the worst totalitarian peril for western societies since the fall
of the Soviet Union.
|
1206.3029
|
Asymptotic Outage Probability Analysis for General Fixed-Gain
Amplify-and-Forward Multihop Relay Systems
|
cs.IT math.IT
|
In this paper, we present an analysis of the outage probability for
fixed-gain amplify-and-forward (AF) multihop relay links operating in the high
SNR regime. Our analysis exploits properties of Mellin transforms to derive an
asymptotic approximation that is accurate even when the per-hop channel gains
adhere to completely different fading models. The main result contained in the
paper is a general expression for the outage probability, which is a functional
of the Mellin transforms of the per-hop channel gains. Furthermore, we
explicitly calculate the asymptotic outage probability for four different
systems, whereby in each system the per-hop channels adhere to either a
Nakagami-m, Weibull, Rician, or Hoyt fading profile, but where the
distributional parameters may differ from hop to hop. This analysis leads to
our second main result, which is a semi-general closed-form formula for the
outage probability of general fixed-gain AF multihop systems. We exploit this
formula to analyze an example scenario for a four-hop system where the per-hop
channels follow the four aforementioned fading models, i.e., the first channel
is Nakagami-m fading, the second is Weibull fading, and so on. Finally, we
provide simulation results to corroborate our analysis.
|
1206.3037
|
Generalized voter-like models on heterogeneous networks
|
physics.soc-ph cs.SI
|
We describe a generalization of the voter model on complex networks that
encompasses different sources of degree-related heterogeneity and that is
amenable to direct analytical solution by applying the standard methods of
heterogeneous mean-field theory. Our formalism allows for a compact description
of previously proposed heterogeneous voter-like models, and represents a basic
framework within which we can rationalize the effects of heterogeneity in
voter-like models, as well as implement novel sources of heterogeneity, not
previously considered in the literature.
|
1206.3038
|
On the Covering Radius of Some Modular Codes
|
cs.IT math.IT math.RA
|
This paper gives lower and upper bounds on the covering radius of codes over
$\Z_{2^s}$ with respect to homogenous distance. We also determine the covering
radius of various Repetition codes, Simplex codes (Type $\alpha$ and Type
$\beta$) and their dual and give bounds on the covering radii for MacDonald
codes of both types over $\Z_4$.
|
1206.3043
|
A Metapopulation Model for Chikungunya Including Populations Mobility on
a Large-Scale Network
|
cs.SI math.DS physics.soc-ph
|
In this work we study the influence of populations mobility on the spread of
a vector-borne disease. We focus on the chikungunya epidemic event that
occurred in 2005-2006 on the R\'eunion Island, Indian Ocean, France, and
validate our models with real epidemic data from the event. We propose a
metapopulation model to represent both a high-resolution patch model of the
island with realistic population densities and also mobility models for humans
(based on real-motion data) and mosquitoes. In this metapopulation network, two
models are coupled: one for the dynamics of the mosquito population and one for
the transmission of the disease. A high-resolution numerical model is created
out from real geographical, demographical and mobility data. The Island is
modeled with an 18 000-nodes metapopulation network. Numerical results show the
impact of the geographical environment and populations' mobility on the spread
of the disease. The model is finally validated against real epidemic data from
the R\'eunion event.
|
1206.3065
|
Stability Analysis and Controller Design for a Linear System with Duhem
Hysteresis Nonlinearity
|
math.OC cs.SY
|
In this paper, we investigate the stability of a feedback interconnection
between a linear system and a Duhem hysteresis operator, where the linear
system and the Duhem hysteresis operator satisfy either the counter-clockwise
(CCW) or clockwise (CW) input-output dynamics. More precisely, we present
sufficient conditions for the stability of the interconnected system that
depend on the CW or CCW properties of the linear system and the Duhem operator.
Based on these results we introduce a control design methodology for
stabilizing a linear plant with a hysteretic actuator or sensor without
requiring precise information on the hysteresis operator.
|
1206.3072
|
Statistical Consistency of Finite-dimensional Unregularized Linear
Classification
|
cs.LG stat.ML
|
This manuscript studies statistical properties of linear classifiers obtained
through minimization of an unregularized convex risk over a finite sample.
Although the results are explicitly finite-dimensional, inputs may be passed
through feature maps; in this way, in addition to treating the consistency of
logistic regression, this analysis also handles boosting over a finite weak
learning class with, for instance, the exponential, logistic, and hinge losses.
In this finite-dimensional setting, it is still possible to fit arbitrary
decision boundaries: scaling the complexity of the weak learning class with the
sample size leads to the optimal classification risk almost surely.
|
1206.3075
|
Cascades on clique-based graphs
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We present an analytical approach to determining the expected cascade size in
a broad range of dynamical models on the class of highly-clustered random
graphs introduced by Gleeson [J. P. Gleeson, Phys. Rev. E 80, 036107 (2009)]. A
condition for the existence of global cascades is also derived. Applications of
this approach include analyses of percolation, and Watts's model. We show how
our techniques can be used to study the effects of in-group bias in cascades on
social networks.
|
1206.3078
|
Mining Educational Data Using Classification to Decrease Dropout Rate of
Students
|
cs.IR
|
In the last two decades, number of Higher Education Institutions (HEI) grows
rapidly in India. Since most of the institutions are opened in private mode
therefore, a cut throat competition rises among these institutions while
attracting the student to got admission. This is the reason for institutions to
focus on the strength of students not on the quality of education. This paper
presents a data mining application to generate predictive models for
engineering student's dropout management. Given new records of incoming
students, the predictive model can produce short accurate prediction list
identifying students who tend to need the support from the student dropout
program most. The results show that the machine learning algorithm is able to
establish effective predictive model from the existing student dropout data.
|
1206.3099
|
Sparse Distributed Learning Based on Diffusion Adaptation
|
cs.LG cs.DC
|
This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery.
|
1206.3111
|
The third open Answer Set Programming competition
|
cs.AI
|
Answer Set Programming (ASP) is a well-established paradigm of declarative
programming in close relationship with other declarative formalisms such as SAT
Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since
its first informal editions, ASP systems have been compared in the now
well-established ASP Competition. The Third (Open) ASP Competition, as the
sequel to the ASP Competitions Series held at the University of Potsdam in
Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took
place at the University of Calabria (Italy) in the first half of 2011.
Participants competed on a pre-selected collection of benchmark problems, taken
from a variety of domains as well as real world applications. The Competition
ran on two tracks: the Model and Solve (M&S) Track, based on an open problem
encoding, and open language, and open to any kind of system based on a
declarative specification paradigm; and the System Track, run on the basis of
fixed, public problem encodings, written in a standard ASP language. This paper
discusses the format of the Competition and the rationale behind it, then
reports the results for both tracks. Comparison with the second ASP competition
and state-of-the-art solutions for some of the benchmark domains is eventually
discussed.
To appear in Theory and Practice of Logic Programming (TPLP).
|
1206.3120
|
Convexity Conditions for 802.11 WLANs
|
cs.NI cs.IT math.IT
|
In this paper we characterise the maximal convex subsets of the (non-convex)
rate region in 802.11 WLANs. In addition to being of intrinsic interest as a
fundamental property of 802.11 WLANs, this characterisation can be exploited to
allow the wealth of convex optimisation approaches to be applied to 802.11
WLANs.
|
1206.3133
|
Multi-terminal Secrecy in a Linear Non-coherent Packetized Networks
|
cs.IT cs.CR math.IT
|
We consider a group of m+1 trusted nodes that aim to create a shared secret
key K over a network in the presence of a passive eavesdropper, Eve. We assume
a linear non-coherent network coding broadcast channel (over a finite field
F_q) from one of the honest nodes (i.e., Alice) to the rest of them including
Eve. All of the trusted nodes can also discuss over a cost-free public channel
which is also overheard by Eve.
For this setup, we propose upper and lower bounds for the secret key
generation capacity assuming that the field size q is very large. For the case
of two trusted terminals (m = 1) our upper and lower bounds match and we have
complete characterization for the secrecy capacity in the large field size
regime.
|
1206.3137
|
Identifiability and Unmixing of Latent Parse Trees
|
stat.ML cs.LG
|
This paper explores unsupervised learning of parsing models along two
directions. First, which models are identifiable from infinite data? We use a
general technique for numerically checking identifiability based on the rank of
a Jacobian matrix, and apply it to several standard constituency and dependency
parsing models. Second, for identifiable models, how do we estimate the
parameters efficiently? EM suffers from local optima, while recent work using
spectral methods cannot be directly applied since the topology of the parse
tree varies across sentences. We develop a strategy, unmixing, which deals with
this additional complexity for restricted classes of parsing models.
|
1206.3138
|
On Modulo-Sum Computation over an Erasure Multiple Access Channel
|
cs.IT math.IT
|
We study computation of a modulo-sum of two binary source sequences over a
two-user erasure multiple access channel. The channel is modeled as a
binary-input, erasure multiple access channel, which can be in one of three
states - either the channel output is a modulo-sum of the two input symbols, or
the channel output equals the input symbol on the first link and an erasure on
the second link, or vice versa. The associated state sequence is independent
and identically distributed. We develop a new upper bound on the sum-rate by
revealing only part of the state sequence to the transmitters. Our coding
scheme is based on the compute and forward and the decode and forward
techniques. When a (strictly) causal feedback of the channel state is available
to the encoders, we show that the modulo-sum capacity is increased. Extensions
to the case of lossy reconstruction of the modulo-sum and to channels involving
additional states are also treated briefly.
|
1206.3189
|
A New Representation for the Symbol Error Rate
|
cs.IT math.IT
|
The symbol error rate of the minimum distance detector for an arbitrary
multi-dimensional constellation impaired by additive white Gaussian noise is
characterized as the product of a completely monotone function with a
non-negative power of the signal to noise ratio. This representation is also
shown to apply to cases when the impairing noise is compound Gaussian. Using
this general result, it is proved that the symbol error rate is completely
monotone if the rank of its constellation matrix is either one or two. Further,
a necessary and sufficient condition for the complete monotonicity of the
symbol error rate of a constellation of any dimension is also obtained.
Applications to stochastic ordering of wireless system performance are also
discussed.
|
1206.3204
|
Improved Spectral-Norm Bounds for Clustering
|
cs.LG cs.DS
|
Aiming to unify known results about clustering mixtures of distributions
under separation conditions, Kumar and Kannan[2010] introduced a deterministic
condition for clustering datasets. They showed that this single deterministic
condition encompasses many previously studied clustering assumptions. More
specifically, their proximity condition requires that in the target
$k$-clustering, the projection of a point $x$ onto the line joining its cluster
center $\mu$ and some other center $\mu'$, is a large additive factor closer to
$\mu$ than to $\mu'$. This additive factor can be roughly described as $k$
times the spectral norm of the matrix representing the differences between the
given (known) dataset and the means of the (unknown) target clustering.
Clearly, the proximity condition implies center separation -- the distance
between any two centers must be as large as the above mentioned bound.
In this paper we improve upon the work of Kumar and Kannan along several
axes. First, we weaken the center separation bound by a factor of $\sqrt{k}$,
and secondly we weaken the proximity condition by a factor of $k$. Using these
weaker bounds we still achieve the same guarantees when all points satisfy the
proximity condition. We also achieve better guarantees when only
$(1-\epsilon)$-fraction of the points satisfy the weaker proximity condition.
The bulk of our analysis relies only on center separation under which one can
produce a clustering which (i) has low error, (ii) has low $k$-means cost, and
(iii) has centers very close to the target centers.
Our improved separation condition allows us to match the results of the
Planted Partition Model of McSherry[2001], improve upon the results of
Ostrovsky et al[2006], and improve separation results for mixture of Gaussian
models in a particular setting.
|
1206.3231
|
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
|
cs.LG stat.ML
|
Continuous state spaces and stochastic, switching dynamics characterize a
number of rich, realworld domains, such as robot navigation across varying
terrain. We describe a reinforcementlearning algorithm for learning in these
domains and prove for certain environments the algorithm is probably
approximately correct with a sample complexity that scales polynomially with
the state-space dimension. Unfortunately, no optimal planning techniques exist
in general for such problems; instead we use fitted value iteration to solve
the learned MDP, and include the error due to approximate planning in our
bounds. Finally, we report an experiment using a robotic car driving over
varying terrain to demonstrate that these dynamics representations adequately
capture real-world dynamics and that our algorithm can be used to efficiently
solve such problems.
|
1206.3232
|
AND/OR Importance Sampling
|
cs.AI
|
The paper introduces AND/OR importance sampling for probabilistic graphical
models. In contrast to importance sampling, AND/OR importance sampling caches
samples in the AND/OR space and then extracts a new sample mean from the stored
samples. We prove that AND/OR importance sampling may have lower variance than
importance sampling; thereby providing a theoretical justification for
preferring it over importance sampling. Our empirical evaluation demonstrates
that AND/OR importance sampling is far more accurate than importance sampling
in many cases.
|
1206.3233
|
Speeding Up Planning in Markov Decision Processes via Automatically
Constructed Abstractions
|
cs.AI
|
In this paper, we consider planning in stochastic shortest path (SSP)
problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size
problems whose state space can be fully enumerated. This problem has numerous
important applications, such as navigation and planning under uncertainty. We
propose a new approach for constructing a multi-level hierarchy of
progressively simpler abstractions of the original problem. Once computed, the
hierarchy can be used to speed up planning by first finding a policy for the
most abstract level and then recursively refining it into a solution to the
original problem. This approach is fully automated and delivers a speed-up of
two orders of magnitude over a state-of-the-art MDP solver on sample problems
while returning near-optimal solutions. We also prove theoretical bounds on the
loss of solution optimality resulting from the use of abstractions.
|
1206.3234
|
Adaptive Inference on General Graphical Models
|
cs.DS cs.AI
|
Many algorithms and applications involve repeatedly solving variations of the
same inference problem; for example we may want to introduce new evidence to
the model or perform updates to conditional dependencies. The goal of adaptive
inference is to take advantage of what is preserved in the model and perform
inference more rapidly than from scratch. In this paper, we describe techniques
for adaptive inference on general graphs that support marginal computation and
updates to the conditional probabilities and dependencies in logarithmic time.
We give experimental results for an implementation of our algorithm, and
demonstrate its potential performance benefit in the study of protein
structure.
|
1206.3235
|
Identifying reasoning patterns in games
|
cs.GT cs.AI
|
We present an algorithm that identifies the reasoning patterns of agents in a
game, by iteratively examining the graph structure of its Multi-Agent Influence
Diagram (MAID) representation. If the decision of an agent participates in no
reasoning patterns, then we can effectively ignore that decision for the
purpose of calculating a Nash equilibrium for the game. In some cases, this can
lead to exponential time savings in the process of equilibrium calculation.
Moreover, our algorithm can be used to enumerate the reasoning patterns in a
game, which can be useful for constructing more effective computerized agents
interacting with humans.
|
1206.3236
|
Learning Inclusion-Optimal Chordal Graphs
|
cs.LG cs.DS stat.ML
|
Chordal graphs can be used to encode dependency models that are representable
by both directed acyclic and undirected graphs. This paper discusses a very
simple and efficient algorithm to learn the chordal structure of a
probabilistic model from data. The algorithm is a greedy hill-climbing search
algorithm that uses the inclusion boundary neighborhood over chordal graphs. In
the limit of a large sample size and under appropriate hypotheses on the
scoring criterion, we prove that the algorithm will find a structure that is
inclusion-optimal when the dependency model of the data-generating distribution
can be represented exactly by an undirected graph. The algorithm is evaluated
on simulated datasets.
|
1206.3237
|
Clique Matrices for Statistical Graph Decomposition and Parameterising
Restricted Positive Definite Matrices
|
cs.DM cs.LG stat.ML
|
We introduce Clique Matrices as an alternative representation of undirected
graphs, being a generalisation of the incidence matrix representation. Here we
use clique matrices to decompose a graph into a set of possibly overlapping
clusters, de ned as well-connected subsets of vertices. The decomposition is
based on a statistical description which encourages clusters to be well
connected and few in number. Inference is carried out using a variational
approximation. Clique matrices also play a natural role in parameterising
positive de nite matrices under zero constraints on elements of the matrix. We
show that clique matrices can parameterise all positive de nite matrices
restricted according to a decomposable graph and form a structured Factor
Analysis approximation in the non-decomposable case.
|
1206.3238
|
Greedy Block Coordinate Descent for Large Scale Gaussian Process
Regression
|
cs.LG stat.ML
|
We propose a variable decomposition algorithm -greedy block coordinate
descent (GBCD)- in order to make dense Gaussian process regression practical
for large scale problems. GBCD breaks a large scale optimization into a series
of small sub-problems. The challenge in variable decomposition algorithms is
the identification of a subproblem (the active set of variables) that yields
the largest improvement. We analyze the limitations of existing methods and
cast the active set selection into a zero-norm constrained optimization problem
that we solve using greedy methods. By directly estimating the decrease in the
objective function, we obtain not only efficient approximate solutions for
GBCD, but we are also able to demonstrate that the method is globally
convergent. Empirical comparisons against competing dense methods like
Conjugate Gradient or SMO show that GBCD is an order of magnitude faster.
Comparisons against sparse GP methods show that GBCD is both accurate and
capable of handling datasets of 100,000 samples or more.
|
1206.3239
|
On Identifying Total Effects in the Presence of Latent Variables and
Selection bias
|
stat.ME cs.AI stat.AP
|
Assume that cause-effect relationships between variables can be described as
a directed acyclic graph and the corresponding linear structural equation
model.We consider the identification problem of total effects in the presence
of latent variables and selection bias between a treatment variable and a
response variable. Pearl and his colleagues provided the back door criterion,
the front door criterion (Pearl, 2000) and the conditional instrumental
variable method (Brito and Pearl, 2002) as identifiability criteria for total
effects in the presence of latent variables, but not in the presence of
selection bias. In order to solve this problem, we propose new graphical
identifiability criteria for total effects based on the identifiable factor
models. The results of this paper are useful to identify total effects in
observational studies and provide a new viewpoint to the identification
conditions of factor models.
|
1206.3240
|
Complexity of Inference in Graphical Models
|
cs.DS cs.AI
|
It is well-known that inference in graphical models is hard in the worst
case, but tractable for models with bounded treewidth. We ask whether treewidth
is the only structural criterion of the underlying graph that enables tractable
inference. In other words, is there some class of structures with unbounded
treewidth in which inference is tractable? Subject to a combinatorial
hypothesis due to Robertson et al. (1994), we show that low treewidth is indeed
the only structural restriction that can ensure tractability. Thus, even for
the "best case" graph structure, there is no inference algorithm with
complexity polynomial in the treewidth.
|
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