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1111.6372
|
Nested Inequalities Among Divergence Measures
|
cs.IT math.IT
|
In this paper we have considered a single inequality having 11 known
divergence measures. This inequality include measures like:
Jeffryes-Kullback-Leiber J-divergence, Jensen-Shannon divergence (Burbea-Rao,
1982), arithmetic-geometric mean divergence (Taneja, 1995), Hellinger
discrimination, symmetric chi-square divergence, triangular discrimination,
etc. All these measures are well-known in the literature on Information theory
and Statistics. This sequence of 11 measures also include measures due to Kumar
and Johnson (2005) and Jain and Srivastava (2007). Three measures arising due
to some mean divergences also appears in this inequality. Based on non-negative
differences arising due to this single inequality of 11 measures, we have put
more than 40 divergence measures in nested or sequential form. Idea of reverse
inequalities is also introduced.
|
1111.6387
|
3D Model Retrieval Based on Semantic and Shape Indexes
|
cs.IR cs.AI cs.CV
|
The size of 3D models used on the web or stored in databases is becoming
increasingly high. Then, an efficient method that allows users to find similar
3D objects for a given 3D model query has become necessary. Keywords and the
geometry of a 3D model cannot meet the needs of users' retrieval because they
do not include the semantic information. In this paper, a new method has been
proposed to 3D models retrieval using semantic concepts combined with shape
indexes. To obtain these concepts, we use the machine learning methods to label
3D models by k-means algorithm in measures and shape indexes space. Moreover,
semantic concepts have been organized and represented by ontology language OWL
and spatial relationships are used to disambiguate among models of similar
appearance. The SPARQL query language has been used to question the information
displayed in this language and to compute the similarity between two 3D models.
We interpret our results using the Princeton Shape Benchmark Database and the
results show the performance of the proposed new approach to retrieval 3D
models. Keywords: 3D Model, 3D retrieval, measures, shape indexes, semantic,
ontology
|
1111.6401
|
Graph based E-Government web service composition
|
cs.AI
|
Nowadays, e-government has emerged as a government policy to improve the
quality and efficiency of public administrations. By exploiting the potential
of new information and communication technologies, government agencies are
providing a wide spectrum of online services. These services are composed of
several web services that comply with well defined processes. One of the big
challenges is the need to optimize the composition of the elementary web
services. In this paper, we present a solution for optimizing the computation
effort in web service composition. Our method is based on Graph Theory. We
model the semantic relationship between the involved web services through a
directed graph. Then, we compute all shortest paths using for the first time,
an extended version of the Floyd-Warshall algorithm.
|
1111.6414
|
Capacity-Approaching Signal Constellations for the Additive Exponential
Noise Channel
|
cs.IT math.IT
|
We present a new family of signal constellations, called log constellations,
that can be used to design near-capacity coded modulation schemes over additive
exponential noise (AEN) channels. Log constellations are designed by
geometrically approximating the input distribution that maximizes the AEN
channel capacity. The mutual information achievable over AEN channels with both
coded modulation (CM) and bit-interleaved coded modulation (BICM) approaches is
evaluated for various signal sets. In the case of CM, the proposed log
constellations outperform, sometimes by over half a decibel, the best existing
signal sets available from the literature, and can display error performance
within only 0.12 dB of the AEN channel capacity. In the context of BICM, log
constellations do not offer significant performance advantages over the best
existing constellations. As the potential performance degradation resulting
from the use of BICM instead of CM is larger than 1 dB, BICM may however not be
a suitable design approach over AEN channels.
|
1111.6453
|
Learning with Submodular Functions: A Convex Optimization Perspective
|
cs.LG math.OC
|
Submodular functions are relevant to machine learning for at least two
reasons: (1) some problems may be expressed directly as the optimization of
submodular functions and (2) the lovasz extension of submodular functions
provides a useful set of regularization functions for supervised and
unsupervised learning. In this monograph, we present the theory of submodular
functions from a convex analysis perspective, presenting tight links between
certain polyhedra, combinatorial optimization and convex optimization problems.
In particular, we show how submodular function minimization is equivalent to
solving a wide variety of convex optimization problems. This allows the
derivation of new efficient algorithms for approximate and exact submodular
function minimization with theoretical guarantees and good practical
performance. By listing many examples of submodular functions, we review
various applications to machine learning, such as clustering, experimental
design, sensor placement, graphical model structure learning or subset
selection, as well as a family of structured sparsity-inducing norms that can
be derived and used from submodular functions.
|
1111.6473
|
A kernel-based framework for learning graded relations from data
|
stat.ML cs.LG
|
Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches typically consider datasets containing crisp relations, so
that standard classification methods can be adopted. However, relations between
objects like similarities and preferences are often expressed in a graded
manner in real-world applications. A general kernel-based framework for
learning relations from data is introduced here. It extends existing approaches
because both crisp and graded relations are considered, and it unifies existing
approaches because different types of graded relations can be modeled,
including symmetric and reciprocal relations. This framework establishes
important links between recent developments in fuzzy set theory and machine
learning. Its usefulness is demonstrated through various experiments on
synthetic and real-world data.
|
1111.6502
|
Optimal Offline Broadcast Scheduling with an Energy Harvesting
Transmitter
|
cs.IT math.IT
|
We consider an energy harvesting transmitter broadcasting data to two
receivers. Energy and data arrivals are assumed to occur at arbitrary but known
instants. The goal is to minimize the total transmission time of the packets
arriving within a certain time window, using the energy that becomes available
during this time. An achievable rate region with structural properties
satisfied by the two-user AWGN BC capacity region is assumed. Structural
properties of power and rate allocation in an optimal policy are established,
as well as the uniqueness of the optimal policy under the condition that all
the data of the "weaker" user are available at the beginning. An iterative
algorithm, DuOpt, based on block coordinate descent that achieves the same
structural properties as the optimal is described. Investigating the ways to
have the optimal schedule of two consecutive epochs in terms of energy
efficiency and minimum transmission duration, it has been shown that DuOpt
achieves best performance under the same special condition of uniqueness.
|
1111.6552
|
Nouvelle repr\'esentation concise exacte des motifs corr\'el\'es rares :
Application \`a la d\'etection d'intrusions
|
cs.DB
|
Correlated rare pattern mining is an interesting issue in Data mining. In
this respect, the set of correlated rare patterns w.r.t. to the bond
correlation measure was studied in a recent work, in which the RCPR concise
exact representation of the set of correlated rare patterns was proposed.
However, none algorithm was proposed in order to mine this representation and
none experiment was carried out to evaluate it. In this paper, we introduce the
new RcprMiner algorithm allowing an efficient extraction of RCPR. We also
present the IsRCP algorithm allowing the query of the RCPR representation in
addition to the RCPRegeneration algorithm allowing the regeneration of the
whole set RCP of rare correlated patterns starting from this representation.
The carried out experiments highlight interesting compactness rates offered by
RCPR. The effectiveness of the proposed classification method, based on generic
rare correlated association rules derived from RCPR, has also been proved in
the context of intrusion detection.
|
1111.6553
|
Exploring Twitter Hashtags
|
cs.CL
|
Twitter messages often contain so-called hashtags to denote keywords related
to them. Using a dataset of 29 million messages, I explore relations among
these hashtags with respect to co-occurrences. Furthermore, I present an
attempt to classify hashtags into five intuitive classes, using a
machine-learning approach. The overall outcome is an interactive Web
application to explore Twitter hashtags.
|
1111.6563
|
Perception of Motion and Architectural Form: Computational Relationships
between Optical Flow and Perspective
|
q-bio.NC cs.NE
|
Perceptual geometry refers to the interdisciplinary research whose objectives
focuses on study of geometry from the perspective of visual perception, and in
turn, applies such geometric findings to the ecological study of vision.
Perceptual geometry attempts to answer fundamental questions in perception of
form and representation of space through synthesis of cognitive and biological
theories of visual perception with geometric theories of the physical world.
Perception of form, space and motion are among fundamental problems in vision
science. In cognitive and computational models of human perception, the
theories for modeling motion are treated separately from models for perception
of form.
|
1111.6631
|
Mathematical Analysis and Computational Integration of Massive
Heterogeneous Data from the Human Retina
|
q-bio.QM cs.IR math.SP
|
Modern epidemiology integrates knowledge from heterogeneous collections of
data consisting of numerical, descriptive and imaging. Large-scale
epidemiological studies use sophisticated statistical analysis, mathematical
models using differential equations and versatile analytic tools that handle
numerical data. In contrast, knowledge extraction from images and descriptive
information in the form of text and diagrams remain a challenge for most
fields, in particular, for diseases of the eye. In this article we provide a
roadmap towards extraction of knowledge from text and images with focus on
forthcoming applications to epidemiological investigation of retinal diseases,
especially from existing massive heterogeneous collections of data distributed
around the globe.
|
1111.6640
|
A Markov Random Field Topic Space Model for Document Retrieval
|
cs.IR
|
This paper proposes a novel statistical approach to intelligent document
retrieval. It seeks to offer a more structured and extensible mathematical
approach to the term generalization done in the popular Latent Semantic
Analysis (LSA) approach to document indexing. A Markov Random Field (MRF) is
presented that captures relationships between terms and documents as
probabilistic dependence assumptions between random variables. From there, it
uses the MRF-Gibbs equivalence to derive joint probabilities as well as local
probabilities for document variables. A parameter learning method is proposed
that utilizes rank reduction with singular value decomposition in a matter
similar to LSA to reduce dimensionality of document-term relationships to that
of a latent topic space. Experimental results confirm the ability of this
approach to effectively and efficiently retrieve documents from substantial
data sets.
|
1111.6664
|
Generalized Orthogonal Matching Pursuit
|
cs.IT math.IT
|
As a greedy algorithm to recover sparse signals from compressed measurements,
orthogonal matching pursuit (OMP) algorithm has received much attention in
recent years. In this paper, we introduce an extension of the OMP for pursuing
efficiency in reconstructing sparse signals. Our approach, henceforth referred
to as generalized OMP (gOMP), is literally a generalization of the OMP in the
sense that multiple $N$ indices are identified per iteration. Owing to the
selection of multiple ''correct'' indices, the gOMP algorithm is finished with
much smaller number of iterations when compared to the OMP. We show that the
gOMP can perfectly reconstruct any $K$-sparse signals ($K > 1$), provided that
the sensing matrix satisfies the RIP with $\delta_{NK} <
\frac{\sqrt{N}}{\sqrt{K} + 3 \sqrt{N}}$. We also demonstrate by empirical
simulations that the gOMP has excellent recovery performance comparable to
$\ell_1$-minimization technique with fast processing speed and competitive
computational complexity.
|
1111.6677
|
Publishing Location Dataset Differential Privately with Isotonic
Regression
|
cs.CR cs.DB
|
We consider the problem of publishing location datasets, in particular 2D
spatial pointsets, in a differentially private manner. Many existing mechanisms
focus on frequency counts of the points in some a priori partition of the
domain that is difficult to determine. We propose an approach that adds noise
directly to the point, or to a group of neighboring points. Our approach is
based on the observation that, the sensitivity of sorting, as a function on
sets of real numbers, can be bounded. Together with isotonic regression, the
dataset can be accurately reconstructed. To extend the mechanism to higher
dimension, we employ locality preserving function to map the dataset to a
bounded interval. Although there are fundamental limits on the performance of
locality preserving functions, fortunately, our problem only requires distance
preservation in the "easier" direction, and the well-known Hilbert
space-filling curve suffices to provide high accuracy. The publishing process
is simple from the publisher's point of view: the publisher just needs to map
the data, sort them, group them, add Laplace noise and publish the dataset. The
only parameter to determine is the group size which can be chosen based on
predicted generalization errors. Empirical study shows that the published
dataset can also exploited to answer other queries, for example, range query
and median query, accurately.
|
1111.6682
|
A General Robust Linear Transceiver Design for Multi-Hop
Amplify-and-Forward MIMO Relaying Systems
|
cs.IT math.IT
|
In this paper, linear transceiver design for multi-hop amplify-and-forward
(AF) multiple-input multiple-out (MIMO) relaying systems with Gaussian
distributed channel estimation errors is investigated. Commonly used
transceiver design criteria including weighted mean-square-error (MSE)
minimization, capacity maximization, worst-MSE/MAX-MSE minimization and
weighted sum-rate maximization, are considered and unified into a single
matrix-variate optimization problem. A general robust design algorithm is
proposed to solve the unified problem. Specifically, by exploiting majorization
theory and properties of matrix-variate functions, the optimal structure of the
robust transceiver is derived when either the covariance matrix of channel
estimation errors seen from the transmitter side or the corresponding
covariance matrix seen from the receiver side is proportional to an identity
matrix. Based on the optimal structure, the original transceiver design
problems are reduced to much simpler problems with only scalar variables whose
solutions are readily obtained by iterative water-filling algorithm. A number
of existing transceiver design algorithms are found to be special cases of the
proposed solution. The differences between our work and the existing related
work are also discussed in detail. The performance advantages of the proposed
robust designs are demonstrated by simulation results.
|
1111.6685
|
Some Results on the Target Set Selection Problem
|
math.CO cs.CC cs.DM cs.DS cs.SI
|
In this paper we consider a fundamental problem in the area of viral
marketing, called T{\scriptsize ARGET} S{\scriptsize ET} S{\scriptsize
ELECTION} problem. We study the problem when the underlying graph is a
block-cactus graph, a chordal graph or a Hamming graph. We show that if $G$ is
a block-cactus graph, then the T{\scriptsize ARGET} S{\scriptsize ET}
S{\scriptsize ELECTION} problem can be solved in linear time, which generalizes
Chen's result \cite{chen2009} for trees, and the time complexity is much better
than the algorithm in \cite{treewidth} (for bounded treewidth graphs) when
restricted to block-cactus graphs. We show that if the underlying graph $G$ is
a chordal graph with thresholds $\theta(v)\leq 2$ for each vertex $v$ in $G$,
then the problem can be solved in linear time. For a Hamming graph $G$ having
thresholds $\theta(v)=2$ for each vertex $v$ of $G$, we precisely determine an
optimal target set $S$ for $(G,\theta)$. These results partially answer an open
problem raised by Dreyer and Roberts \cite{Dreyer2009}.
|
1111.6695
|
Optimal Shape-Gain Quantization for Multiuser MIMO Systems with Linear
Precoding
|
cs.IT math.IT
|
This paper studies the optimal bit allocation for shape-gain vector
quantization of wireless channels in multiuser (MU) multiple-input
multiple-output (MIMO) downlink systems based on linear precoding. Our design
minimizes the mean squared-error between the original and quantized channels
through optimal bit allocation across shape (direction) and gain (magnitude)
for a fixed feedback overhead per user. This is shown to significantly reduce
the quantization error, which in turn, decreases the MU interference. This
paper makes three main contributions: first, we focus on channel gain
quantization and derive the quantization distortion, based on a Euclidean
distance measure, corresponding to singular values of a MIMO channel. Second,
we show that the Euclidean distance-based distortion of a unit norm complex
channel, due to shape quantization, is proportional to \frac{2^{-2Bs}}{2M-1},
where, Bs is the number of shape quantization bits and M is the number of
transmit antennas. Finally, we show that for channels in complex space and
allowing for a large feedback overhead, the number of direction quantization
bits should be approximately (2M - 1) times the number of channel magnitude
quantization bits.
|
1111.6713
|
An Enhanced Indexing And Ranking Technique On The Semantic Web
|
cs.AI
|
With the fast growth of the Internet, more and more information is available
on the Web. The Semantic Web has many features which cannot be handled by using
the traditional search engines. It extracts metadata for each discovered Web
documents in RDF or OWL formats, and computes relations between documents. We
proposed a hybrid indexing and ranking technique for the Semantic Web which
finds relevant documents and computes the similarity among a set of documents.
First, it returns with the most related document from the repository of
Semantic Web Documents (SWDs) by using a modified version of the ObjectRank
technique. Then, it creates a sub-graph for the most related SWDs. Finally, It
returns the hubs and authorities of these document by using the HITS algorithm.
Our technique increases the quality of the results and decreases the execution
time of processing the user's query.
|
1111.6771
|
Autonomic Management for Multi-agent Systems
|
cs.MA
|
Autonomic computing is a computing system that can manage itself by
self-configuration, self-healing, self-optimizing and self-protection.
Researchers have been emphasizing the strong role that multi agent systems can
play progressively towards the design and implementation of complex autonomic
systems. The important of autonomic computing is to create computing systems
capable of managing themselves to a far greater extent than they do today. With
the nature of autonomy, reactivity, sociality and pro-activity, software agents
are promising to make autonomic computing system a reality. This paper mixed
multi-agent system with autonomic feature that completely hides its complexity
from users/services. Mentioned Java Application Development Framework as
platform example of this environment, could applied to web services as front
end to users. With multi agent support it also provides adaptability,
intelligence, collaboration, goal oriented interactions, flexibility, mobility
and persistence in software systems
|
1111.6790
|
Constraining the Size Growth of the Task Space with Socially Guided
Intrinsic Motivation using Demonstrations
|
cs.AI
|
This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.
|
1111.6804
|
Betweenness Centrality as a Driver of Preferential Attachment in the
Evolution of Research Collaboration Networks
|
cs.SI physics.soc-ph
|
We analyze whether preferential attachment in scientific coauthorship
networks is different for authors with different forms of centrality. Using a
complete database for the scientific specialty of research about "steel
structures," we show that betweenness centrality of an existing node is a
significantly better predictor of preferential attachment by new entrants than
degree or closeness centrality. During the growth of a network, preferential
attachment shifts from (local) degree centrality to betweenness centrality as a
global measure. An interpretation is that supervisors of PhD projects and
postdocs broker between new entrants and the already existing network, and thus
become focal to preferential attachment. Because of this mediation, scholarly
networks can be expected to develop differently from networks which are
predicated on preferential attachment to nodes with high degree centrality.
|
1111.6807
|
On the problem of reversibility of the entropy power inequality
|
math.FA cs.IT math.IT math.PR
|
As was shown recently by the authors, the entropy power inequality can be
reversed for independent summands with sufficiently concave densities, when the
distributions of the summands are put in a special position. In this note it is
proved that reversibility is impossible over the whole class of convex
probability distributions. Related phenomena for identically distributed
summands are also discussed.
|
1111.6822
|
Optimal Phase Transitions in Compressed Sensing
|
cs.IT math.IT math.ST stat.TH
|
Compressed sensing deals with efficient recovery of analog signals from
linear encodings. This paper presents a statistical study of compressed sensing
by modeling the input signal as an i.i.d. process with known distribution.
Three classes of encoders are considered, namely optimal nonlinear, optimal
linear and random linear encoders. Focusing on optimal decoders, we investigate
the fundamental tradeoff between measurement rate and reconstruction fidelity
gauged by error probability and noise sensitivity in the absence and presence
of measurement noise, respectively. The optimal phase transition threshold is
determined as a functional of the input distribution and compared to suboptimal
thresholds achieved by popular reconstruction algorithms. In particular, we
show that Gaussian sensing matrices incur no penalty on the phase transition
threshold with respect to optimal nonlinear encoding. Our results also provide
a rigorous justification of previous results based on replica heuristics in the
weak-noise regime.
|
1111.6825
|
A Fuzzy Realistic Mobility Model For Ad hoc Networks
|
cs.AI cs.NI
|
Realistic mobility models can demonstrate more precise evaluation results
because their parameters are closer to the reality. In this paper a realistic
Fuzzy Mobility Model has been proposed. This model has rules which is
changeable depending on nodes and environment conditions. This model is more
complete and precise than the other mobility models and this is the advantage
of this model. After simulation, it was found out that not only considering
nodes movement as being imprecise (fuzzy) has a positive effects on most of ad
hoc network parameters, but also, more importantly as they are closer to the
real world condition, they can have a more positive effect on the
implementation of ad hoc network protocols.
|
1111.6828
|
Bayesian Estimation of a Gaussian source in Middleton's Class-A
Impulsive Noise
|
cs.IT math.IT stat.AP
|
The paper focuses on minimum mean square error (MMSE) Bayesian estimation for
a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In
addition to the optimal Bayesian estimator, the paper considers also the
soft-limiter and the blanker, which are two popular suboptimal estimators
characterized by very low complexity. The MMSE-optimum thresholds for such
suboptimal estimators are obtained by practical iterative algorithms with fast
convergence. The paper derives also the optimal thresholds according to a
maximum-SNR (MSNR) criterion, and establishes connections with the MMSE
criterion. Furthermore, closed form analytic expressions are derived for the
MSE and the SNR of all the suboptimal estimators, which perfectly match
simulation results. Noteworthy, these results can be applied to characterize
the receiving performance of any multicarrier system impaired by a
Gaussian-mixture noise, such as asymmetric digital subscriber lines (ADSL) and
power-line communications (PLC).
|
1111.6842
|
Fast Private Data Release Algorithms for Sparse Queries
|
cs.DS cs.CR cs.LG
|
We revisit the problem of accurately answering large classes of statistical
queries while preserving differential privacy. Previous approaches to this
problem have either been very general but have not had run-time polynomial in
the size of the database, have applied only to very limited classes of queries,
or have relaxed the notion of worst-case error guarantees. In this paper we
consider the large class of sparse queries, which take non-zero values on only
polynomially many universe elements. We give efficient query release algorithms
for this class, in both the interactive and the non-interactive setting. Our
algorithms also achieve better accuracy bounds than previous general techniques
do when applied to sparse queries: our bounds are independent of the universe
size. In fact, even the runtime of our interactive mechanism is independent of
the universe size, and so can be implemented in the "infinite universe" model
in which no finite universe need be specified by the data curator.
|
1111.6843
|
Understanding the Social Cascading of Geekspeak and the Upshots for
Social Cognitive Systems
|
cs.AI cs.MA
|
Barring swarm robotics, a substantial share of current machine-human and
machine-machine learning and interaction mechanisms are being developed and fed
by results of agent-based computer simulations, game-theoretic models, or
robotic experiments based on a dyadic communication pattern. Yet, in real life,
humans no less frequently communicate in groups, and gain knowledge and take
decisions basing on information cumulatively gleaned from more than one single
source. These properties should be taken into consideration in the design of
autonomous artificial cognitive systems construed to interact with learn from
more than one contact or 'neighbour'. To this end, significant practical import
can be gleaned from research applying strict science methodology to human and
social phenomena, e.g. to discovery of realistic creativity potential spans, or
the 'exposure thresholds' after which new information could be accepted by a
cognitive agent. The results will be presented of a project analysing the
social propagation of neologisms in a microblogging service. From local,
low-level interactions and information flows between agents inventing and
imitating discrete lexemes we aim to describe the processes of the emergence of
more global systemic order and dynamics, using the latest methods of complexity
science. Whether in order to mimic them, or to 'enhance' them, parameters
gleaned from complexity science approaches to humans' social and humanistic
behaviour should subsequently be incorporated as points of reference in the
field of robotics and human-machine interaction.
|
1111.6849
|
Neuropsychological constraints to human data production on a global
scale
|
cs.SI cs.CC
|
Which are the factors underlying human information production on a global
level? In order to gain an insight into this question we study a corpus of
252-633 Million publicly available data files on the Internet corresponding to
an overall storage volume of 284-675 Terabytes. Analyzing the file size
distribution for several distinct data types we find indications that the
neuropsychological capacity of the human brain to process and record
information may constitute the dominant limiting factor for the overall growth
of globally stored information, with real-world economic constraints having
only a negligible influence. This supposition draws support from the
observation that the files size distributions follow a power law for data
without a time component, like images, and a log-normal distribution for
multimedia files, for which time is a defining qualia.
|
1111.6857
|
Multivariate information measures: an experimentalist's perspective
|
cs.IT cs.LG math.IT physics.data-an stat.AP
|
Information theory is widely accepted as a powerful tool for analyzing
complex systems and it has been applied in many disciplines. Recently, some
central components of information theory - multivariate information measures -
have found expanded use in the study of several phenomena. These information
measures differ in subtle yet significant ways. Here, we will review the
information theory behind each measure, as well as examine the differences
between these measures by applying them to several simple model systems. In
addition to these systems, we will illustrate the usefulness of the information
measures by analyzing neural spiking data from a dissociated culture through
early stages of its development. We hope that this work will aid other
researchers as they seek the best multivariate information measure for their
specific research goals and system. Finally, we have made software available
online which allows the user to calculate all of the information measures
discussed within this paper.
|
1111.6883
|
Dynamics of Knowledge in DeLP through Argument Theory Change
|
cs.AI cs.LO
|
This article is devoted to the study of methods to change defeasible logic
programs (de.l.p.s) which are the knowledge bases used by the Defeasible Logic
Programming (DeLP) interpreter. DeLP is an argumentation formalism that allows
to reason over potentially inconsistent de.l.p.s. Argument Theory Change (ATC)
studies certain aspects of belief revision in order to make them suitable for
abstract argumentation systems. In this article, abstract arguments are
rendered concrete by using the particular rule-based defeasible logic adopted
by DeLP. The objective of our proposal is to define prioritized argument
revision operators \`a la ATC for de.l.p.s, in such a way that the newly
inserted argument ends up undefeated after the revision, thus warranting its
conclusion. In order to ensure this warrant, the de.l.p. has to be changed in
concordance with a minimal change principle. To this end, we discuss different
minimal change criteria that could be adopted. Finally, an algorithm is
presented, implementing the argument revision operations.
|
1111.6923
|
Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned
Dictionaries
|
stat.ML cs.CV cs.IT math.IT math.PR stat.AP
|
Recent breakthrough results in compressed sensing (CS) have established that
many high dimensional objects can be accurately recovered from a relatively
small number of non- adaptive linear projection observations, provided that the
objects possess a sparse representation in some basis. Subsequent efforts have
shown that the performance of CS can be improved by exploiting the structure in
the location of the non-zero signal coefficients (structured sparsity) or using
some form of online measurement focusing (adaptivity) in the sensing process.
In this paper we examine a powerful hybrid of these two techniques. First, we
describe a simple adaptive sensing procedure and show that it is a provably
effective method for acquiring sparse signals that exhibit structured sparsity
characterized by tree-based coefficient dependencies. Next, employing
techniques from sparse hierarchical dictionary learning, we show that
representations exhibiting the appropriate form of structured sparsity can be
learned from collections of training data. The combination of these techniques
results in an effective and efficient adaptive compressive acquisition
procedure.
|
1111.6925
|
Structure Learning of Probabilistic Graphical Models: A Comprehensive
Survey
|
stat.ML cs.LG
|
Probabilistic graphical models combine the graph theory and probability
theory to give a multivariate statistical modeling. They provide a unified
description of uncertainty using probability and complexity using the graphical
model. Especially, graphical models provide the following several useful
properties:
- Graphical models provide a simple and intuitive interpretation of the
structures of probabilistic models. On the other hand, they can be used to
design and motivate new models.
- Graphical models provide additional insights into the properties of the
model, including the conditional independence properties.
- Complex computations which are required to perform inference and learning
in sophisticated models can be expressed in terms of graphical manipulations,
in which the underlying mathematical expressions are carried along implicitly.
The graphical models have been applied to a large number of fields, including
bioinformatics, social science, control theory, image processing, marketing
analysis, among others. However, structure learning for graphical models
remains an open challenge, since one must cope with a combinatorial search over
the space of all possible structures.
In this paper, we present a comprehensive survey of the existing structure
learning algorithms.
|
1111.6937
|
Efficient Discovery of Association Rules and Frequent Itemsets through
Sampling with Tight Performance Guarantees
|
cs.DS cs.DB cs.LG
|
The tasks of extracting (top-$K$) Frequent Itemsets (FI's) and Association
Rules (AR's) are fundamental primitives in data mining and database
applications. Exact algorithms for these problems exist and are widely used,
but their running time is hindered by the need of scanning the entire dataset,
possibly multiple times. High quality approximations of FI's and AR's are
sufficient for most practical uses, and a number of recent works explored the
application of sampling for fast discovery of approximate solutions to the
problems. However, these works do not provide satisfactory performance
guarantees on the quality of the approximation, due to the difficulty of
bounding the probability of under- or over-sampling any one of an unknown
number of frequent itemsets. In this work we circumvent this issue by applying
the statistical concept of \emph{Vapnik-Chervonenkis (VC) dimension} to develop
a novel technique for providing tight bounds on the sample size that guarantees
approximation within user-specified parameters. Our technique applies both to
absolute and to relative approximations of (top-$K$) FI's and AR's. The
resulting sample size is linearly dependent on the VC-dimension of a range
space associated with the dataset to be mined. The main theoretical
contribution of this work is a proof that the VC-dimension of this range space
is upper bounded by an easy-to-compute characteristic quantity of the dataset
which we call \emph{d-index}, and is the maximum integer $d$ such that the
dataset contains at least $d$ transactions of length at least $d$ such that no
one of them is a superset of or equal to another. We show that this bound is
strict for a large class of datasets.
|
1111.6983
|
Aggregation of Composite Solutions: strategies, models, examples
|
cs.SE cs.AI math.OC
|
The paper addresses aggregation issues for composite (modular) solutions. A
systemic view point is suggested for various aggregation problems. Several
solution structures are considered: sets, set morphologies, trees, etc. Mainly,
the aggregation approach is targeted to set morphologies. The aggregation
problems are based on basic structures as substructure, superstructure,
median/consensus, and extended median/consensus. In the last case, preliminary
structure is built (e.g., substructure, median/consensus) and addition of
solution elements is considered while taking into account profit of the
additional elements and total resource constraint. Four aggregation strategies
are examined: (i) extension strategy (designing a substructure of initial
solutions as "system kernel" and extension of the substructure by additional
elements); (ii) compression strategy (designing a superstructure of initial
solutions and deletion of some its elements); (iii) combined strategy; and (iv)
new design strategy to build a new solution over an extended domain of solution
elements. Numerical real-world examples (e.g., telemetry system, communication
protocol, student plan, security system, Web-based information system,
investment, educational courses) illustrate the suggested aggregation approach.
|
1111.7025
|
Task Interaction in an HTN Planner
|
cs.AI cs.DC
|
Hierarchical Task Network (HTN) planning uses task decomposition to plan for
an executable sequence of actions as a solution to a problem. In order to
reason effectively, an HTN planner needs expressive domain knowledge. For
instance, a simplified HTN planning system such as JSHOP2 uses such
expressivity and avoids some task interactions due to the increased complexity
of the planning process. We address the possibility of simplifying the domain
representation needed for an HTN planner to find good solutions, especially in
real-world domains describing home and building automation environments. We
extend the JSHOP2 planner to reason about task interaction that happens when
task's effects are already achieved by other tasks. The planner then prunes
some of the redundant searches that can occur due to the planning process's
interleaving nature. We evaluate the original and our improved planner on two
benchmark domains. We show that our planner behaves better by using simplified
domain knowledge and outperforms JSHOP2 in a number of relevant cases.
|
1111.7033
|
Stability of Evolving Multi-Agent Systems
|
cs.MA cs.NE
|
A Multi-Agent System is a distributed system where the agents or nodes
perform complex functions that cannot be written down in analytic form.
Multi-Agent Systems are highly connected, and the information they contain is
mostly stored in the connections. When agents update their state, they take
into account the state of the other agents, and they have access to those
states via the connections. There is also external, user-generated input into
the Multi-Agent System. As so much information is stored in the connections,
agents are often memory-less. This memory-less property, together with the
randomness of the external input, has allowed us to model Multi-Agent Systems
using Markov chains. In this paper, we look at Multi-Agent Systems that evolve,
i.e. the number of agents varies according to the fitness of the individual
agents. We extend our Markov chain model, and define stability. This is the
start of a methodology to control Multi-Agent Systems. We then build upon this
to construct an entropy-based definition for the degree of instability (entropy
of the limit probabilities), which we used to perform a stability analysis. We
then investigated the stability of evolving agent populations through
simulation, and show that the results are consistent with the original
definition of stability in non-evolving Multi-Agent Systems, proposed by Chli
and De Wilde. This paper forms the theoretical basis for the construction of
Digital Business Ecosystems, and applications have been reported elsewhere.
|
1111.7069
|
Differential Modulation for Bi-directional Relaying with Analog Network
Coding
|
cs.IT math.IT
|
In this paper, we propose an analog network coding scheme with differential
modulation (ANC-DM) using amplify-and-forward protocol for bidirectional relay
networks when neither the source nodes nor the relay knows the channel state
information (CSI). The performance of the proposed ANC-DM scheme is analyzed
and a simple asymptotic bit error rate (BER) expression is derived. The
analytical results are verified through simulations. It is shown that the BER
performance of the proposed differential scheme is about 3 dB away from that of
the coherent detection scheme. To improve the system performance, the optimum
power allocation between the sources and the relay is determined based on the
simplified BER. Simulation results indicate that the proposed differential
scheme with optimum power allocation yields 1-2 dB performance improvement over
an equal power allocation scheme.
|
1111.7076
|
Relay Selection for Two-way Relaying with Amplify-and-Forward Protocols
|
cs.IT math.IT
|
In this paper, we propose a relay selection amplify-and-forward (RS-AF)
protocol in general bi-directional relay networks with two sources and $N$
relays. In the proposed scheme, the two sources first transmit to all the
relays simultaneously, and then a single relay with a minimum sum symbol error
rate (SER) will be selected to broadcast the received signals back to both
sources. To facilitate the selection process, we propose a simple sub-optimal
Min-Max criterion for relay selection, where a single relay which minimizes the
maximum SER of two source nodes will be selected. Simulation results show that
the proposed Min-Max selection has almost the same performance as the optimal
selection with lower complexity. We also present a simple asymptotic SER
expression and make comparison with the conventional all-participate
amplify-and-forward (AP-AF) relaying scheme. The analytical results are
verified through simulations. To improve the system performance, optimum power
allocation (OPA) between the sources and the relay is determined based on the
asymptotic SER. Simulation results indicate that the proposed RS-AF scheme with
OPA yields considerable performance improvement over an equal power allocation
(EPA) scheme, specially with large number of relay nodes.
|
1111.7078
|
Joint Relay Selection and Analog Network Coding using Differential
Modulation in Two-Way Relay Channels
|
cs.IT math.IT
|
In this paper, we consider a general bi-directional relay network with two
sources and N relays when neither the source nodes nor the relays know the
channel state information (CSI). A joint relay selection and analog network
coding using differential modulation (RS-ANC-DM) is proposed. In the proposed
scheme, the two sources employ differential modulations and transmit the
differential modulated symbols to all relays at the same time. The signals
received at the relay is a superposition of two transmitted symbols, which we
call the analog network coded symbols. Then a single relay which has minimum
sum SER is selected out of N relays to forward the ANC signals to both sources.
To facilitate the selection process, in this paper we also propose a simple
sub-optimal Min-Max criterion for relay selection, where a single relay which
minimizes the maximum SER of two source nodes is selected. Simulation results
show that the proposed Min-Max selection has almost the same performance as the
optimal selection, but is much simpler. The performance of the proposed
RS-ANC-DM scheme is analyzed, and a simple asymptotic SER expression is
derived. The analytical results are verified through simulations.
|
1111.7088
|
Uniqueness Analysis of Non-Unitary Matrix Joint Diagonalization
|
cs.IT math.IT
|
Matrix Joint Diagonalization (MJD) is a powerful approach for solving the
Blind Source Separation (BSS) problem. It relies on the construction of
matrices which are diagonalized by the unknown demixing matrix. Their joint
diagonalizer serves as a correct estimate of this demixing matrix only if it is
uniquely determined. Thus, a critical question is under what conditions a joint
diagonalizer is unique. In the present work we fully answer this question about
the identifiability of MJD based BSS approaches and provide a general result on
uniqueness conditions of matrix joint diagonalization. It unifies all existing
results which exploit the concepts of non-circularity, non-stationarity,
non-whiteness, and non-Gaussianity. As a corollary, we propose a solution for
complex BSS, which can be formulated in a closed form in terms of an eigenvalue
and a singular value decomposition of two matrices.
|
1111.7094
|
Multi-Gateway Cooperation in Multibeam Satellite Systems
|
cs.IT math.IT
|
Multibeam systems with hundreds of beams have been recently deployed in order
to provide higher capacities by employing fractional frequency reuse.
Furthermore, employing full frequency reuse and precoding over multiple beams
has shown great throughput potential in literature. However, feeding all this
data from a single gateway is not feasible based on the current frequency
allocations. In this context, we investigate a range of scenarios involving
beam clusters where each cluster is managed by a single gateway. More
specifically, the following cases are considered for handling intercluster
interference: a) conventional frequency colouring, b) joint processing within
cluster, c) partial CSI sharing among clusters, d) partial CSI and data sharing
among clusters. CSI sharing does not provide considerable performance gains
with respect to b) but combined with data sharing offers roughly a 40%
improvement over a) and a 15% over b).
|
1111.7100
|
Determining a rotation of a tetrahedron from a projection
|
math.MG cs.CG cs.CV
|
The following problem, arising from medical imaging, is addressed: Suppose
that $T$ is a known tetrahedron in $\R^3$ with centroid at the origin. Also
known is the orthogonal projection $U$ of the vertices of the image $\phi T$ of
$T$ under an unknown rotation $\phi$ about the origin. Under what circumstances
can $\phi$ be determined from $T$ and $U$?
|
1111.7104
|
On the Minimum Differential Feedback for Time-Correlated MIMO Rayleigh
Block-Fading Channels
|
cs.IT math.IT
|
In this paper, we consider a general multiple input multiple output (MIMO)
system with channel state information (CSI) feedback over time-correlated
Rayleigh block-fading channels. Specifically, we first derive the closed-form
expression of the minimum differential feedback rate to achieve the maximum
erdodic capacity in the presence of channel estimation errors and quantization
distortion at the receiver. With the feedback-channel transmission rate
constraint, in the periodic feedback system, we further investigate the
relationship of the ergodic capacity and the differential feedback interval,
and we find by theoretical analysis that there exists an optimal differential
feedback interval to maximize ergodic capacity. Finally, analytical results are
verified through simulations in a practical periodic differential feedback
system using Lloyd's quantization algorithm.
|
1111.7108
|
Joint Relay and Jammer Selection for Secure Two-Way Relay Networks
|
cs.IT math.IT
|
In this paper, we investigate joint relay and jammer selection in two-way
cooperative networks, consisting of two sources, a number of intermediate
nodes, and one eavesdropper, with the constraints of physical layer security.
Specifically, the proposed algorithms select two or three intermediate nodes to
enhance security against the malicious eavesdropper. The first selected node
operates in the conventional relay mode and assists the sources to deliver
their data to the corresponding destinations using an amplify-and-forward
protocol. The second and third nodes are used in different communication phases
as jammers in order to create intentional interference upon the eavesdropper
node. Firstly, we find that in a topology where the intermediate nodes are
randomly and sparsely distributed, the proposed schemes with cooperative
jamming outperform the conventional non-jamming schemes within a certain
transmitted power regime. We also find that, in the scenario in which the
intermediate nodes gather as a close cluster, the jamming schemes may be less
effective than their non-jamming counterparts. Therefore, we introduce a hybrid
scheme to switch between jamming and non-jamming modes. Simulation results
validate our theoretical analysis and show that the hybrid switching scheme
further improves the secrecy rate.
|
1111.7164
|
PARIS: Probabilistic Alignment of Relations, Instances, and Schema
|
cs.DB
|
One of the main challenges that the Semantic Web faces is the integration of
a growing number of independently designed ontologies. In this work, we present
PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not
only instances, but also relations and classes. Alignments at the instance
level cross-fertilize with alignments at the schema level. Thereby, our system
provides a truly holistic solution to the problem of ontology alignment. The
heart of the approach is probabilistic, i.e., we measure degrees of matchings
based on probability estimates. This allows PARIS to run without any parameter
tuning. We demonstrate the efficiency of the algorithm and its precision
through extensive experiments. In particular, we obtain a precision of around
90% in experiments with some of the world's largest ontologies.
|
1111.7165
|
Answering Top-k Queries Over a Mixture of Attractive and Repulsive
Dimensions
|
cs.DB
|
In this paper, we formulate a top-k query that compares objects in a database
to a user-provided query object on a novel scoring function. The proposed
scoring function combines the idea of attractive and repulsive dimensions into
a general framework to overcome the weakness of traditional distance or
similarity measures. We study the properties of the proposed class of scoring
functions and develop efficient and scalable index structures that index the
isolines of the function. We demonstrate various scenarios where the query
finds application. Empirical evaluation demonstrates a performance gain of one
to two orders of magnitude on querying time over existing state-of-the-art
top-k techniques. Further, a qualitative analysis is performed on a real
dataset to highlight the potential of the proposed query in discovering hidden
data characteristics.
|
1111.7166
|
PIQL: Success-Tolerant Query Processing in the Cloud
|
cs.DB
|
Newly-released web applications often succumb to a "Success Disaster," where
overloaded database machines and resulting high response times destroy a
previously good user experience. Unfortunately, the data independence provided
by a traditional relational database system, while useful for agile
development, only exacerbates the problem by hiding potentially expensive
queries under simple declarative expressions. As a result, developers of these
applications are increasingly abandoning relational databases in favor of
imperative code written against distributed key/value stores, losing the many
benefits of data independence in the process. Instead, we propose PIQL, a
declarative language that also provides scale independence by calculating an
upper bound on the number of key/value store operations that will be performed
for any query. Coupled with a service level objective (SLO) compliance
prediction model and PIQL's scalable database architecture, these bounds make
it easy for developers to write success-tolerant applications that support an
arbitrarily large number of users while still providing acceptable performance.
In this paper, we present the PIQL query processing system and evaluate its
scale independence on hundreds of machines using two benchmarks, TPC-W and
SCADr.
|
1111.7167
|
gSketch: On Query Estimation in Graph Streams
|
cs.DB
|
Many dynamic applications are built upon large network infrastructures, such
as social networks, communication networks, biological networks and the Web.
Such applications create data that can be naturally modeled as graph streams,
in which edges of the underlying graph are received and updated sequentially in
a form of a stream. It is often necessary and important to summarize the
behavior of graph streams in order to enable effective query processing.
However, the sheer size and dynamic nature of graph streams present an enormous
challenge to existing graph management techniques. In this paper, we propose a
new graph sketch method, gSketch, which combines well studied synopses for
traditional data streams with a sketch partitioning technique, to estimate and
optimize the responses to basic queries on graph streams. We consider two
different scenarios for query estimation: (1) A graph stream sample is
available; (2) Both a graph stream sample and a query workload sample are
available. Algorithms for different scenarios are designed respectively by
partitioning a global sketch to a group of localized sketches in order to
optimize the query estimation accuracy. We perform extensive experimental
studies on both real and synthetic data sets and demonstrate the power and
robustness of gSketch in comparison with the state-of-the-art global sketch
method.
|
1111.7168
|
Indexing the Earth Mover's Distance Using Normal Distributions
|
cs.DB
|
Querying uncertain data sets (represented as probability distributions)
presents many challenges due to the large amount of data involved and the
difficulties comparing uncertainty between distributions. The Earth Mover's
Distance (EMD) has increasingly been employed to compare uncertain data due to
its ability to effectively capture the differences between two distributions.
Computing the EMD entails finding a solution to the transportation problem,
which is computationally intensive. In this paper, we propose a new lower bound
to the EMD and an index structure to significantly improve the performance of
EMD based K-nearest neighbor (K-NN) queries on uncertain databases. We propose
a new lower bound to the EMD that approximates the EMD on a projection vector.
Each distribution is projected onto a vector and approximated by a normal
distribution, as well as an accompanying error term. We then represent each
normal as a point in a Hough transformed space. We then use the concept of
stochastic dominance to implement an efficient index structure in the
transformed space. We show that our method significantly decreases K-NN query
time on uncertain databases. The index structure also scales well with database
cardinality. It is well suited for heterogeneous data sets, helping to keep EMD
based queries tractable as uncertain data sets become larger and more complex.
|
1111.7169
|
Size-l Object Summaries for Relational Keyword Search
|
cs.DB
|
A previously proposed keyword search paradigm produces, as a query result, a
ranked list of Object Summaries (OSs). An OS is a tree structure of related
tuples that summarizes all data held in a relational database about a
particular Data Subject (DS). However, some of these OSs are very large in size
and therefore unfriendly to users that initially prefer synoptic information
before proceeding to more comprehensive information about a particular DS. In
this paper, we investigate the effective and efficient retrieval of concise and
informative OSs. We argue that a good size-l OS should be a stand-alone and
meaningful synopsis of the most important information about the particular DS.
More precisely, we define a size-l OS as a partial OS composed of l important
tuples. We propose three algorithms for the efficient generation of size-l OSs
(in addition to the optimal approach which requires exponential time).
Experimental evaluation on DBLP and TPC-H databases verifies the effectiveness
and efficiency of our approach.
|
1111.7170
|
REX: Explaining Relationships between Entity Pairs
|
cs.DB
|
Knowledge bases of entities and relations (either constructed manually or
automatically) are behind many real world search engines, including those at
Yahoo!, Microsoft, and Google. Those knowledge bases can be viewed as graphs
with nodes representing entities and edges representing (primary)
relationships, and various studies have been conducted on how to leverage them
to answer entity seeking queries. Meanwhile, in a complementary direction,
analyses over the query logs have enabled researchers to identify entity pairs
that are statistically correlated. Such entity relationships are then presented
to search users through the "related searches" feature in modern search
engines. However, entity relationships thus discovered can often be "puzzling"
to the users because why the entities are connected is often indescribable. In
this paper, we propose a novel problem called "entity relationship
explanation", which seeks to explain why a pair of entities are connected, and
solve this challenging problem by integrating the above two complementary
approaches, i.e., we leverage the knowledge base to "explain" the connections
discovered between entity pairs. More specifically, we present REX, a system
that takes a pair of entities in a given knowledge base as input and
efficiently identifies a ranked list of relationship explanations. We formally
define relationship explanations and analyze their desirable properties.
Furthermore, we design and implement algorithms to efficiently enumerate and
rank all relationship explanations based on multiple measures of
"interestingness." We perform extensive experiments over real web-scale data
gathered from DBpedia and a commercial search engine, demonstrating the
efficiency and scalability of REX. We also perform user studies to corroborate
the effectiveness of explanations generated by REX.
|
1111.7171
|
PASS-JOIN: A Partition-based Method for Similarity Joins
|
cs.DB
|
As an essential operation in data cleaning, the similarity join has attracted
considerable attention from the database community. In this paper, we study
string similarity joins with edit-distance constraints, which find similar
string pairs from two large sets of strings whose edit distance is within a
given threshold. Existing algorithms are efficient either for short strings or
for long strings, and there is no algorithm that can efficiently and adaptively
support both short strings and long strings. To address this problem, we
propose a partition-based method called Pass-Join. Pass-Join partitions a
string into a set of segments and creates inverted indices for the segments.
Then for each string, Pass-Join selects some of its substrings and uses the
selected substrings to find candidate pairs using the inverted indices. We
devise efficient techniques to select the substrings and prove that our method
can minimize the number of selected substrings. We develop novel pruning
techniques to efficiently verify the candidate pairs. Experimental results show
that our algorithms are efficient for both short strings and long strings, and
outperform state-of-the-art methods on real datasets.
|
1111.7190
|
Developing Embodied Multisensory Dialogue Agents
|
cs.AI cs.CL
|
A few decades of work in the AI field have focused efforts on developing a
new generation of systems which can acquire knowledge via interaction with the
world. Yet, until very recently, most such attempts were underpinned by
research which predominantly regarded linguistic phenomena as separated from
the brain and body. This could lead one into believing that to emulate
linguistic behaviour, it suffices to develop 'software' operating on abstract
representations that will work on any computational machine. This picture is
inaccurate for several reasons, which are elucidated in this paper and extend
beyond sensorimotor and semantic resonance. Beginning with a review of
research, I list several heterogeneous arguments against disembodied language,
in an attempt to draw conclusions for developing embodied multisensory agents
which communicate verbally and non-verbally with their environment. Without
taking into account both the architecture of the human brain, and embodiment,
it is unrealistic to replicate accurately the processes which take place during
language acquisition, comprehension, production, or during non-linguistic
actions. While robots are far from isomorphic with humans, they could benefit
from strengthened associative connections in the optimization of their
processes and their reactivity and sensitivity to environmental stimuli, and in
situated human-machine interaction. The concept of multisensory integration
should be extended to cover linguistic input and the complementary information
combined from temporally coincident sensory impressions.
|
1111.7219
|
Optoelectronic Reservoir Computing
|
cs.ET cs.LG cs.NE nlin.CD physics.optics
|
Reservoir computing is a recently introduced, highly efficient bio-inspired
approach for processing time dependent data. The basic scheme of reservoir
computing consists of a non linear recurrent dynamical system coupled to a
single input layer and a single output layer. Within these constraints many
implementations are possible. Here we report an opto-electronic implementation
of reservoir computing based on a recently proposed architecture consisting of
a single non linear node and a delay line. Our implementation is sufficiently
fast for real time information processing. We illustrate its performance on
tasks of practical importance such as nonlinear channel equalization and speech
recognition, and obtain results comparable to state of the art digital
implementations.
|
1111.7221
|
An Optimal Controller Architecture for Poset-Causal Systems
|
math.OC cs.SY
|
We propose a novel and natural architecture for decentralized control that is
applicable whenever the underlying system has the structure of a partially
ordered set (poset). This controller architecture is based on the concept of
Moebius inversion for posets, and enjoys simple and appealing separation
properties, since the closed-loop dynamics can be analyzed in terms of
decoupled subsystems. The controller structure provides rich and interesting
connections between concepts from order theory such as Moebius inversion and
control-theoretic concepts such as state prediction, correction, and
separability. In addition, using our earlier results on H_2-optimal
decentralized control for arbitrary posets, we prove that the H_2-optimal
controller in fact possesses the proposed structure, thereby establishing the
optimality of the new controller architecture.
|
1111.7224
|
Generating Exact- and Ranked Partially-Matched Answers to Questions in
Advertisements
|
cs.DB
|
Taking advantage of the Web, many advertisements (ads for short) websites,
which aspire to increase client's transactions and thus profits, offer
searching tools which allow users to (i) post keyword queries to capture their
information needs or (ii) invoke form-based interfaces to create queries by
selecting search options, such as a price range, filled-in entries, check
boxes, or drop-down menus. These search mechanisms, however, are inadequate,
since they cannot be used to specify a natural-language query with rich
syntactic and semantic content, which can only be handled by a question
answering (QA) system. Furthermore, existing ads websites are incapable of
evaluating arbitrary Boolean queries or retrieving partiallymatched answers
that might be of interest to the user whenever a user's search yields only a
few or no results at all. In solving these problems, we present a QA system for
ads, called CQAds, which (i) allows users to post a natural-language question Q
for retrieving relevant ads, if they exist, (ii) identifies ads as answers that
partially-match the requested information expressed in Q, if insufficient or no
answers to Q can be retrieved, which are ordered using a similarity-ranking
approach, and (iii) analyzes incomplete or ambiguous questions to perform the
"best guess" in retrieving answers that "best match" the selection criteria
specified in Q. CQAds is also equipped with a Boolean model to evaluate Boolean
operators that are either explicitly or implicitly specified in Q, i.e., with
or without Boolean operators specified by the users, respectively. CQAds is
easy to use, scalable to all ads domains, and more powerful than search tools
provided by existing ads websites, since its query-processing strategy
retrieves relevant ads of higher quality and quantity. We have verified the
accuracy of CQAds in retrieving ads on eight ads domains and compared
it...[truncated].
|
1111.7265
|
Linear Correction of Mismatched L-values in BICM receivers
|
cs.IT math.IT
|
In this work we analyze the problem of linear correction of the reliability
metrics (L-values) in BICM receivers. We want to find the correction factors
that minimize the probability of error of a maximum likelihood decoder that
uses the corrected L-values. To this end, we use the efficient approximation of
the pairwise error probability in the domain of the cumulant generating
functions (CGF) of the L-values and conclude that the optimal correction
factors are equal to the twice of the saddlepoint of the CGF. We provide a
simple numerical example of transmission in the presence of interference where
we demonstrate a notable improvement attainable with the proposed method. The
proposed method is compared with the one based on the maximization of
generalized mutual information.
|
1111.7271
|
Invariant texture analysis through Local Binary Patterns
|
cs.CV
|
In many image processing applications, such as segmentation and
classification, the selection of robust features descriptors is crucial to
improve the discrimination capabilities in real world scenarios. In particular,
it is well known that image textures constitute power visual cues for feature
extraction and classification. In the past few years the local binary pattern
(LBP) approach, a texture descriptor method proposed by Ojala et al., has
gained increased acceptance due to its computational simplicity and more
importantly for encoding a powerful signature for describing textures. However,
the original algorithm presents some limitations such as noise sensitivity and
its lack of rotational invariance which have led to many proposals or
extensions in order to overcome such limitations. In this paper we performed a
quantitative study of the Ojala's original LBP proposal together with other
recently proposed LBP extensions in the presence of rotational, illumination
and noisy changes. In the experiments we have considered two different
databases: Brodatz and CUReT for different sizes of LBP masks. Experimental
results demonstrated the effectiveness and robustness of the described texture
descriptors for images that are subjected to geometric or radiometric changes.
|
1111.7295
|
A Learning Framework for Self-Tuning Histograms
|
cs.DB cs.LG
|
In this paper, we consider the problem of estimating self-tuning histograms
using query workloads. To this end, we propose a general learning theoretic
formulation. Specifically, we use query feedback from a workload as training
data to estimate a histogram with a small memory footprint that minimizes the
expected error on future queries. Our formulation provides a framework in which
different approaches can be studied and developed. We first study the simple
class of equi-width histograms and present a learning algorithm, EquiHist, that
is competitive in many settings. We also provide formal guarantees for
equi-width histograms that highlight scenarios in which equi-width histograms
can be expected to succeed or fail. We then go beyond equi-width histograms and
present a novel learning algorithm, SpHist, for estimating general histograms.
Here we use Haar wavelets to reduce the problem of learning histograms to that
of learning a sparse vector. Both algorithms have multiple advantages over
existing methods: 1) simple and scalable extensions to multi-dimensional data,
2) scalability with number of histogram buckets and size of query feedback, 3)
natural extensions to incorporate new feedback and handle database updates. We
demonstrate these advantages over the current state-of-the-art, ISOMER, through
detailed experiments on real and synthetic data. In particular, we show that
SpHist obtains up to 50% less error than ISOMER on real-world multi-dimensional
datasets.
|
1112.0031
|
Neighborhoods are good communities
|
cs.SI cs.DM cs.DS physics.soc-ph
|
The communities of a social network are sets of vertices with more
connections inside the set than outside. We theoretically demonstrate that two
commonly observed properties of social networks, heavy-tailed degree
distributions and large clustering coefficients, imply the existence of vertex
neighborhoods (also known as egonets) that are themselves good communities. We
evaluate these neighborhood communities on a range of graphs. What we find is
that the neighborhood communities often exhibit conductance scores that are as
good as the Fiedler cut. Also, the conductance of neighborhood communities
shows similar behavior as the network community profile computed with a
personalized PageRank community detection method. The latter requires sweeping
over a great many starting vertices, which can be expensive. By using a small
and easy-to-compute set of neighborhood communities as seeds for these PageRank
communities, however, we find communities that precisely capture the behavior
of the network community profile when seeded everywhere in the graph, and at a
significant reduction in total work.
|
1112.0032
|
A model of Cross Language Retrieval for IT domain papers through a map
of ACM Computing Classification System
|
cs.DL cs.HC cs.IR
|
This article presents a concept model, and the associated tool to help
advanced learners to find adapted bibliography. The purpose is the use of an IT
representation as educational research software for newcomers in research. We
use an ontology based on the ACM's Computing Classification System in order to
find scientific articles directly related to the new researcher's domain
without any formal request. An ontology translation in French is automatically
proposed and can be based on Web 2.0 enhanced by a community of users. A
visualization and navigation model is proposed to make it more accessible and
examples are given to show the interface of our tool: Ontology Navigator.
|
1112.0038
|
Information Theoretic Authentication and Secrecy Codes in the Splitting
Model
|
cs.CR cs.IT math.IT
|
In the splitting model, information theoretic authentication codes allow
non-deterministic encoding, that is, several messages can be used to
communicate a particular plaintext. Certain applications require that the
aspect of secrecy should hold simultaneously. Ogata-Kurosawa-Stinson-Saido
(2004) have constructed optimal splitting authentication codes achieving
perfect secrecy for the special case when the number of keys equals the number
of messages. In this paper, we establish a construction method for optimal
splitting authentication codes with perfect secrecy in the more general case
when the number of keys may differ from the number of messages. To the best
knowledge, this is the first result of this type.
|
1112.0045
|
CytoITMprobe: a network information flow plugin for Cytoscape
|
q-bio.QM cs.DB q-bio.MN
|
To provide the Cytoscape users the possibility of integrating ITM Probe into
their workflows, we developed CytoITMprobe, a new Cytoscape plugin.
CytoITMprobe maintains all the desirable features of ITM Probe and adds
additional flexibility not achievable through its web service version. It
provides access to ITM Probe either through a web server or locally. The input,
consisting of a Cytoscape network, together with the desired origins and/or
destinations of information and a dissipation coefficient, is specified through
a query form. The results are shown as a subnetwork of significant nodes and
several summary tables. Users can control the composition and appearance of the
subnetwork and interchange their ITM Probe results with other software tools
through tab-delimited files.
The main strength of CytoITMprobe is its flexibility. It allows the user to
specify as input any Cytoscape network, rather than being restricted to the
pre-compiled protein-protein interaction networks available through the ITM
Probe web service. Users may supply their own edge weights and
directionalities. Consequently, as opposed to ITM Probe web service,
CytoITMprobe can be applied to many other domains of network-based research
beyond protein-networks. It also enables seamless integration of ITM Probe
results with other Cytoscape plugins having complementary functionality for
data analysis.
|
1112.0049
|
Popularity-Driven Networking
|
cond-mat.stat-mech cs.SI math.PR physics.soc-ph
|
We investigate the growth of connectivity in a network. In our model,
starting with a set of disjoint nodes, links are added sequentially. Each link
connects two nodes, and the connection rate governing this random process is
proportional to the degrees of the two nodes. Interestingly, this network
exhibits two abrupt transitions, both occurring at finite times. The first is a
percolation transition in which a giant component, containing a finite fraction
of all nodes, is born. The second is a condensation transition in which the
entire system condenses into a single, fully connected, component. We derive
the size distribution of connected components as well as the degree
distribution, which is purely exponential throughout the evolution.
Furthermore, we present a criterion for the emergence of sudden condensation
for general homogeneous connection rates.
|
1112.0052
|
Query Optimization Using Genetic Algorithms in the Vector Space Model
|
cs.IR
|
In information retrieval research; Genetic Algorithms (GA) can be used to
find global solutions in many difficult problems. This study used different
similarity measures (Dice, Inner Product) in the VSM, for each similarity
measure we compared ten different GA approaches based on different fitness
functions, different mutations and different crossover strategies to find the
best strategy and fitness function that can be used when the data collection is
the Arabic language. Our results shows that the GA approach which uses
one-point crossover operator, point mutation and Inner Product similarity as a
fitness function is the best IR system in VSM.
|
1112.0054
|
Improving the User Query for the Boolean Model Using Genetic Algorithms
|
cs.IR
|
The Use of genetic algorithms in the Information retrieval (IR) area,
especially in optimizing a user query in Arabic data collections is presented
in this paper. Very little research has been carried out on Arabic text
collections. Boolean model have been used in this research. To optimize the
query using GA we used different fitness functions, different mutation
strategies to find which is the best strategy and fitness function that can be
used with Boolean model when the data collection is the Arabic language. Our
results show that the best GA strategy for the Boolean model is the GA (M2,
Precision) method.
|
1112.0057
|
Flip-OFDM for Unipolar Communication Systems
|
cs.IT math.IT
|
Unipolar communications systems can transmit information using only real and
positive signals. This includes a variety of physical channels ranging from
optical (fiber or free-space), to RF wireless using amplitude modulation with
non-coherent reception, to baseband single wire communications. Unipolar OFDM
techniques enable to efficiently compensate frequency selective distortion in
the unipolar communication systems. One of the leading examples of unipolar
OFDM is asymmetric clipped optical OFDM (ACO-OFDM) originally proposed for
optical communications. Flip-OFDM is an alternative approach that was proposed
in a patent, but its performance and full potentials have never been
investigated in the literature. In this paper, we first compare Flip-OFDM and
ACO-OFDM, and show that both techniques have the same performance but different
complexities (Flip-OFDM offers 50% saving). We then propose a new detection
scheme, which enables to reduce the noise at the Flip-OFDM receiver by almost
3dB. The analytical performance of the noise filtering schemes is supported by
the simulation results.
|
1112.0059
|
Local Naive Bayes Nearest Neighbor for Image Classification
|
cs.CV
|
We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN
image classification algorithm that increases classification accuracy and
improves its ability to scale to large numbers of object classes. The key
observation is that only the classes represented in the local neighborhood of a
descriptor contribute significantly and reliably to their posterior probability
estimates. Instead of maintaining a separate search structure for each class,
we merge all of the reference data together into one search structure, allowing
quick identification of a descriptor's local neighborhood. We show an increase
in classification accuracy when we ignore adjustments to the more distant
classes and show that the run time grows with the log of the number of classes
rather than linearly in the number of classes as did the original. This gives a
100 times speed-up over the original method on the Caltech 256 dataset. We also
provide the first head-to-head comparison of NBNN against spatial pyramid
methods using a common set of input features. We show that local NBNN
outperforms all previous NBNN based methods and the original spatial pyramid
model. However, we find that local NBNN, while competitive with, does not beat
state-of-the-art spatial pyramid methods that use local soft assignment and
max-pooling.
|
1112.0061
|
On the Entropy Region of Gaussian Random Variables
|
cs.IT math.IT
|
Given n (discrete or continuous) random variables X_i, the
(2^n-1)-dimensional vector obtained by evaluating the joint entropy of all
non-empty subsets of {X_1,...,X_n} is called an entropic vector. Determining
the region of entropic vectors is an important open problem with many
applications in information theory. Recently, it has been shown that the
entropy regions for discrete and continuous random variables, though different,
can be determined from one another. An important class of continuous random
variables are those that are vector-valued and jointly Gaussian. In this paper
we give a full characterization of the convex cone of the entropy region of
three jointly Gaussian vector-valued random variables and prove that it is the
same as the convex cone of three scalar-valued Gaussian random variables and
further that it yields the entire entropy region of 3 arbitrary random
variables. We further determine the actual entropy region of 3 vector-valued
jointly Gaussian random variables through a conjecture. For n>=4 number of
random variables, we point out a set of 2^n-1-n(n+1)/2 minimal necessary and
sufficient conditions that 2^n-1 numbers must satisfy in order to correspond to
the entropy vector of n scalar jointly Gaussian random variables. This improves
on a result of Holtz and Sturmfels which gave a nonminimal set of conditions.
These constraints are related to Cayley's hyperdeterminant and hence with an
eye towards characterizing the entropy region of jointly Gaussian random
variables, we also present some new results in this area. We obtain a new
(determinant) formula for the 2*2*2 hyperdeterminant and we also give a new
(transparent) proof of the fact that the principal minors of an n*n symmetric
matrix satisfy the 2*2*...*2 (up to n times) hyperdeterminant relations.
|
1112.0062
|
A new class of hyper-bent Boolean functions in binomial forms
|
cs.IT math.IT
|
Bent functions, which are maximally nonlinear Boolean functions with even
numbers of variables and whose Hamming distance to the set of all affine
functions equals $2^{n-1}\pm 2^{\frac{n}{2}-1}$, were introduced by Rothaus in
1976 when he considered problems in combinatorics. Bent functions have been
extensively studied due to their applications in cryptography, such as S-box,
block cipher and stream cipher. Further, they have been applied to coding
theory, spread spectrum and combinatorial design. Hyper-bent functions, as a
special class of bent functions, were introduced by Youssef and Gong in 2001,
which have stronger properties and rarer elements. Many research focus on the
construction of bent and hyper-bent functions. In this paper, we consider
functions defined over $\mathbb{F}_{2^n}$ by
$f_{a,b}:=\mathrm{Tr}_{1}^{n}(ax^{(2^m-1)})+\mathrm{Tr}_{1}^{4}(bx^{\frac{2^n-1}{5}})$,
where $n=2m$, $m\equiv 2\pmod 4$, $a\in \mathbb{F}_{2^m}$ and
$b\in\mathbb{F}_{16}$. When $a\in \mathbb{F}_{2^m}$ and $(b+1)(b^4+b+1)=0$,
with the help of Kloosterman sums and the factorization of $x^5+x+a^{-1}$, we
present a characterization of hyper-bentness of $f_{a,b}$. Further, we use
generalized Ramanujan-Nagell equations to characterize hyper-bent functions of
$f_{a,b}$ in the case $a\in\mathbb{F}_{2^{\frac{m}{2}}}$.
|
1112.0071
|
Robustly Stable Signal Recovery in Compressed Sensing with Structured
Matrix Perturbation
|
cs.IT math.IT
|
The sparse signal recovery in the standard compressed sensing (CS) problem
requires that the sensing matrix be known a priori. Such an ideal assumption
may not be met in practical applications where various errors and fluctuations
exist in the sensing instruments. This paper considers the problem of
compressed sensing subject to a structured perturbation in the sensing matrix.
Under mild conditions, it is shown that a sparse signal can be recovered by
$\ell_1$ minimization and the recovery error is at most proportional to the
measurement noise level, which is similar to the standard CS result. In the
special noise free case, the recovery is exact provided that the signal is
sufficiently sparse with respect to the perturbation level. The formulated
structured sensing matrix perturbation is applicable to the direction of
arrival estimation problem, so has practical relevance. Algorithms are proposed
to implement the $\ell_1$ minimization problem and numerical simulations are
carried out to verify the result obtained.
|
1112.0077
|
Immunization for complex network based on the effective degree of vertex
|
physics.soc-ph cs.SI
|
The basic idea of many effective immunization strategies is first to rank the
importance of vertices according to the degrees of vertices and then remove the
vertices from highest importance to lowest until the network becomes
disconnected. Here we define the effective degrees of vertex, i.e., the number
of its connections linking to un-immunized nodes in current network during the
immunization procedure, to rank the importance of vertex, and modify these
strategies by using the effective degrees of vertices. Simulations on both the
scale-free network models with various degree correlations and two real
networks have revealed that the immunization strategies based on the effective
degrees are often more effective than those based on the degrees in the initial
network.
|
1112.0101
|
Dynamic Intrusion Detection in Resource-Constrained Cyber Networks
|
cs.SY math.DS math.OC
|
We consider a large-scale cyber network with N components (e.g., paths,
servers, subnets). Each component is either in a healthy state (0) or an
abnormal state (1). Due to random intrusions, the state of each component
transits from 0 to 1 over time according to certain stochastic process. At each
time, a subset of K (K < N) components are checked and those observed in
abnormal states are fixed. The objective is to design the optimal scheduling
for intrusion detection such that the long-term network cost incurred by all
abnormal components is minimized. We formulate the problem as a special class
of Restless Multi-Armed Bandit (RMAB) process. A general RMAB suffers from the
curse of dimensionality (PSPACE-hard) and numerical methods are often
inapplicable. We show that, for this class of RMAB, Whittle index exists and
can be obtained in closed form, leading to a low-complexity implementation of
Whittle index policy with a strong performance. For homogeneous components,
Whittle index policy is shown to have a simple structure that does not require
any prior knowledge on the intrusion processes. Based on this structure,
Whittle index policy is further shown to be optimal over a finite time horizon
with an arbitrary length. Beyond intrusion detection, these results also find
applications in queuing networks with finite-size buffers.
|
1112.0126
|
An automaton approach for waiting times in DNA evolution
|
cs.DM cs.CE cs.FL q-bio.PE
|
In a recent article, Behrens and Vingron (JCB 17, 12, 2010) compute waiting
times for k-mers to appear during DNA evolution under the assumption that the
considered k-mers do not occur in the initial DNA sequence, an issue arising
when studying the evolution of regulatory DNA sequences with regard to
transcription factor (TF) binding site emergence. The mathematical analysis
underlying their computation assumes that occurrences of words under interest
do not overlap. We relax here this assumption by use of an automata approach.
In an alphabet of size 4 like the DNA alphabet, most words have no or a low
autocorrelation; therefore, globally, our results confirm those of Behrens and
Vingron. The outcome is quite different when considering highly autocorrelated
k-mers; in this case, the autocorrelation pushes down the probability of
occurrence of these k-mers at generation 1 and, consequently, increases the
waiting time for apparition of these k-mers up to 40%. An analysis of existing
TF binding sites unveils a significant proportion of k-mers exhibiting
autocorrelation. Thus, our computations based on automata greatly improve the
accuracy of predicting waiting times for the emergence of TF binding sites to
appear during DNA evolution. We do the computation in the Bernoulli or M0
model; computations in the M1 model, a Markov model of order 1, are more costly
in terms of time and memory but should produce similar results. While Behrens
and Vingron considered specifically promoters of length 1000, we extend the
results to promoters of any size; we exhibit the property that the probability
that a k-mer occurs at generation time 1 while being absent at time 0 behaves
linearly with respect to the length of the promoter, which induces a hyperbolic
behaviour of the waiting time of any k-mer with respect to the length of the
promoter.
|
1112.0136
|
Sampling High-Dimensional Bandlimited Fields on Low-Dimensional
Manifolds
|
cs.IT math.IT
|
Consider the task of sampling and reconstructing a bandlimited spatial field
in $\Re^2$ using moving sensors that take measurements along their path. It is
inexpensive to increase the sampling rate along the paths of the sensors but
more expensive to increase the total distance traveled by the sensors per unit
area, which we call the \emph{path density}. In this paper we introduce the
problem of designing sensor trajectories that are minimal in path density
subject to the condition that the measurements of the field on these
trajectories admit perfect reconstruction of bandlimited fields. We study
various possible designs of sampling trajectories. Generalizing some ideas from
the classical theory of sampling on lattices, we obtain necessary and
sufficient conditions on the trajectories for perfect reconstruction. We show
that a single set of equispaced parallel lines has the lowest path density from
certain restricted classes of trajectories that admit perfect reconstruction.
We then generalize some of our results to higher dimensions. We first obtain
results on designing sampling trajectories in higher dimensional fields.
Further, interpreting trajectories as 1-dimensional manifolds, we extend some
of our ideas to higher dimensional sampling manifolds. We formulate the problem
of designing $\kappa$-dimensional sampling manifolds for $d$-dimensional
spatial fields that are minimal in \emph{manifold density}, a natural
generalization of the path density. We show that our results on sampling
trajectories for fields in $\Re^2$ can be generalized to analogous results on
$d-1$-dimensional sampling manifolds for $d$-dimensional spatial fields.
|
1112.0147
|
Q-Adapted Quantum Stochastic Integrals and Differentials in Fock Scale
|
math-ph cs.IT math.IT math.MP math.QA quant-ph
|
In this paper we first introduce the Fock-Guichardet formalism for the
quantum stochastic integration, then the four fundamental processes of the
dynamics are introduced in the canonical basis as the operator-valued measures
of the QS integration over a space-time. Then rigorous analysis of the QS
integrals is carried out, and continuity of the QS derivative is proved.
Finally, Q-adapted dynamics is discussed, including Bosonic Q=1, Fermionic
Q=-1, and monotone Q=0 quantum dynamics. These may be of particular interest to
quantum field theory, quantum open systems, and quantum theory of stochastic
processes.
|
1112.0168
|
Statistical Sign Language Machine Translation: from English written text
to American Sign Language Gloss
|
cs.CL
|
This works aims to design a statistical machine translation from English text
to American Sign Language (ASL). The system is based on Moses tool with some
modifications and the results are synthesized through a 3D avatar for
interpretation. First, we translate the input text to gloss, a written form of
ASL. Second, we pass the output to the WebSign Plug-in to play the sign.
Contributions of this work are the use of a new couple of language English/ASL
and an improvement of statistical machine translation based on string matching
thanks to Jaro-distance.
|
1112.0195
|
Cooperative Beamforming for Dual-Hop Amplify-and-Forward Multi-Antenna
Relaying Cellular Networks
|
cs.IT math.IT
|
In this paper, linear beamforming design for amplify-and-forward relaying
cellular networks is considered, in which base station, relay station and
mobile terminals are all equipped with multiple antennas. The design is based
on minimum mean-square-error criterion, and both uplink and downlink scenarios
are considered. It is found that the downlink and uplink beamforming design
problems are in the same form, and iterative algorithms with the same structure
can be used to solve the design problems. For the specific cases of fully
loaded or overloaded uplink systems, a novel algorithm is derived and its
relationships with several existing beamforming design algorithms for
conventional MIMO or multiuser systems are revealed. Simulation results are
presented to demonstrate the performance advantage of the proposed design
algorithms.
|
1112.0204
|
Digital Ecosystems: Ecosystem-Oriented Architectures
|
cs.NI cs.MA cs.NE
|
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.
|
1112.0210
|
Mesoscopic approach to minority games in herd regime
|
nlin.AO cs.MA math.DS q-fin.TR stat.AP
|
We study minority games in efficient regime. By incorporating the utility
function and aggregating agents with similar strategies we develop an effective
mesoscale notion of state of the game. Using this approach, the game can be
represented as a Markov process with substantially reduced number of states
with explicitly computable probabilities. For any payoff, the finiteness of the
number of states is proved. Interesting features of an extensive random
variable, called aggregated demand, viz. its strong inhomogeneity and presence
of patterns in time, can be easily interpreted. Using Markov theory and
quenched disorder approach, we can explain important macroscopic
characteristics of the game: behavior of variance per capita and predictability
of the aggregated demand. We prove that in case of linear payoff many
attractors in the state space are possible.
|
1112.0213
|
Supervised Learning of Logical Operations in Layered Spiking Neural
Networks with Spike Train Encoding
|
cs.NE q-bio.NC
|
Few algorithms for supervised training of spiking neural networks exist that
can deal with patterns of multiple spikes, and their computational properties
are largely unexplored. We demonstrate in a set of simulations that the ReSuMe
learning algorithm can be successfully applied to layered neural networks.
Input and output patterns are encoded as spike trains of multiple precisely
timed spikes, and the network learns to transform the input trains into target
output trains. This is done by combining the ReSuMe learning algorithm with
multiplicative scaling of the connections of downstream neurons.
We show in particular that layered networks with one hidden layer can learn
the basic logical operations, including Exclusive-Or, while networks without
hidden layer cannot, mirroring an analogous result for layered networks of rate
neurons.
While supervised learning in spiking neural networks is not yet fit for
technical purposes, exploring computational properties of spiking neural
networks advances our understanding of how computations can be done with spike
trains.
|
1112.0241
|
Degree heterogeneity in spatial networks with total cost constraint
|
physics.soc-ph cs.SI stat.CO
|
Recently, In [Phys. Rev. Lett. 104, 018701 (2010)] the authors studied a
spatial network which is constructed from a regular lattice by adding
long-range edges (shortcuts) with probability $P_{ij}\sim r_{ij}^{-\alpha}$,
where $r_{ij}$ is the Manhattan length of the long-range edges. The total
length of the additional edges is subject to a cost constraint ($\sum r=C$).
These networks have fixed optimal exponent $\alpha$ for transportation
(measured by the average shortest-path length). However, we observe that the
degree in such spatial networks is homogenously distributed, which is far
different from real networks such as airline systems. In this paper, we propose
a method to introduce degree heterogeneity in spatial networks with total cost
constraint. Results show that with degree heterogeneity the optimal exponent
shifts to a smaller value and the average shortest-path length can further
decrease. Moreover, we consider the synchronization on the spatial networks and
related results are discussed. Our new model may better reproduce the features
of many real transportation systems.
|
1112.0253
|
Singularities and global stability of decentralized formations in the
plane
|
math.OC cs.SY
|
Formation control is concerned with the design of control laws that stabilize
agents at given distances from each other, with the constraint that an agent's
dynamics can depend only on a subset of other agents. When the information flow
graph of the system, which encodes this dependency, is acyclic, simple control
laws are known to globally stabilize the system, save for a set of measure zero
of initial conditions. The situation has proven to be more complex when the
graph contains cycles; in fact, with the exception of the cyclic formation with
three agents, which is stabilized with laws similar to the ones of the acyclic
case, very little is known about formations with cycles. Moreover, all of the
control laws used in the acyclic case fail at stabilizing more complex cyclic
formations. In this paper, we explain why this is the case and show that a
large class of planar formations with cycles cannot be globally stabilized,
even up to sets of measure zero of initial conditions. The approach rests on
relating the information flow to singularities in the dynamics of formations.
These singularities are in turn shown to make the existence of stable
configurations that do not satisfy the prescribed edge lengths generic.
|
1112.0262
|
Fairness in society
|
physics.soc-ph cs.SI
|
Models that explain the economical and political realities of nowadays
societies should help all the world's citizens. Yet, the last four years showed
that the current models are missing. Here we develop a dynamical
society-deciders model showing that the long lasting economical stress can be
solved when increasing fairness in nations. fairness is computed for each
nation using indicators from economy and politics. Rather than austerity versus
spending, the dynamical model suggests that solving crises in western societies
is possible with regulations that reduce the stability of the deciders, while
shifting wealth in the direction of the people. This shall increase the
dynamics among socio-economic classes, further increasing fairness.
|
1112.0296
|
AWGN Channel under Time-Varying Amplitude Constraints with Causal
Information at the Transmitter
|
cs.IT cs.NI math.IT
|
We consider the classical AWGN channel where the channel input is constrained
to an amplitude constraint that stochastically varies at each channel use,
independent of the message. This is an abstraction of an energy harvesting
transmitter where the code symbol energy at each channel use is determined by
an exogenous energy arrival process and there is no battery for energy storage.
At each channel use, an independent realization of the amplitude constraint
process is observed by the transmitter causally. This scenario is a
state-dependent channel with perfect causal state information at the
transmitter. We derive the capacity of this channel using Shannon's coding
scheme with causal state information. We prove that the code symbols must be
selected from a finite set in the capacity achieving scheme, as in the case of
Smith. We numerically study the binary on-off energy arrivals where the
amplitude constraint is either zero or a non-zero constant.
|
1112.0311
|
Anisotropic Nonlocal Means Denoising
|
math.ST cs.IT math.IT stat.TH
|
It has recently been proved that the popular nonlocal means (NLM) denoising
algorithm does not optimally denoise images with sharp edges. Its weakness lies
in the isotropic nature of the neighborhoods it uses to set its smoothing
weights. In response, in this paper we introduce several theoretical and
practical anisotropic nonlocal means (ANLM) algorithms and prove that they are
near minimax optimal for edge-dominated images from the Horizon class. On
real-world test images, an ANLM algorithm that adapts to the underlying image
gradients outperforms NLM by a significant margin.
|
1112.0343
|
Ontological Queries: Rewriting and Optimization (Extended Version)
|
cs.DB cs.LO
|
Ontological queries are evaluated against an ontology rather than directly on
a database. The evaluation and optimization of such queries is an intriguing
new problem for database research.
In this paper we discuss two important aspects of this problem: query
rewriting and query optimization. Query rewriting consists of the compilation
of an ontological query into an equivalent query against the underlying
relational database. The focus here is on soundness and completeness. We review
previous results and present a new rewriting algorithm for rather general types
of ontological constraints.
In particular, we show how a conjunctive query against an ontology can be
compiled into a union of conjunctive queries against the underlying database.
Ontological query optimization, in this context, attempts to improve this
process so to produce possibly small and cost-effective UCQ rewritings for an
input query. We review existing optimization methods, and propose an effective
new method that works for linear Datalog+/-, a class of Datalog-based rules
that encompasses well-known description logics of the DL-Lite family.
|
1112.0348
|
Explicit Characterization of Stability Region for Stationary Multi-Queue
Multi-Server Systems
|
math.OC cs.IT cs.SY math.IT
|
In this paper, we characterize the network stability region (capacity region)
of multi-queue multi-server (MQMS) queueing systems with stationary channel
distribution and stationary arrival processes. The stability region is
specified by a finite set of linear inequalities. We first show that the
stability region is a polytope characterized by the finite set of its facet
defining hyperplanes. We explicitly determine the coefficients of the linear
inequalities describing the facet defining hyperplanes of the stability region
polytope. We further derive the necessary and sufficient conditions for the
stability of the system for general arrival processes with finite first and
second moments. For the case of stationary arrival processes, the derived
conditions characterize the system stability region. Furthermore, we obtain an
upper bound for the average queueing delay of Maximum Weight (MW) server
allocation policy which has been shown in the literature to be a throughput
optimal policy for MQMS systems. Using a similar approach, we can characterize
the stability region for a fluid model MQMS system. However, the stability
region of the fluid model system is described by an infinite number of linear
inequalities since in this case the stability region is a convex surface. We
present an example where we show that in some cases depending on the channel
distribution, the stability region can be characterized by a finite set of
non-linear inequalities instead of an infinite number of linear inequalities.
|
1112.0371
|
Zigzag Codes: MDS Array Codes with Optimal Rebuilding
|
cs.IT math.IT
|
MDS array codes are widely used in storage systems to protect data against
erasures. We address the \emph{rebuilding ratio} problem, namely, in the case
of erasures, what is the fraction of the remaining information that needs to be
accessed in order to rebuild \emph{exactly} the lost information? It is clear
that when the number of erasures equals the maximum number of erasures that an
MDS code can correct then the rebuilding ratio is 1 (access all the remaining
information). However, the interesting and more practical case is when the
number of erasures is smaller than the erasure correcting capability of the
code. For example, consider an MDS code that can correct two erasures: What is
the smallest amount of information that one needs to access in order to correct
a single erasure? Previous work showed that the rebuilding ratio is bounded
between 1/2 and 3/4, however, the exact value was left as an open problem. In
this paper, we solve this open problem and prove that for the case of a single
erasure with a 2-erasure correcting code, the rebuilding ratio is 1/2. In
general, we construct a new family of $r$-erasure correcting MDS array codes
that has optimal rebuilding ratio of $\frac{e}{r}$ in the case of $e$ erasures,
$1 \le e \le r$. Our array codes have efficient encoding and decoding
algorithms (for the case $r=2$ they use a finite field of size 3) and an
optimal update property.
|
1112.0383
|
Bounds on and Constructions of Unit Time-Phase Signal Sets
|
cs.IT math.IT
|
Digital signals are complex-valued functions on $\Z_n$. Signal sets with
certain properties are required in various communication systems. Traditional
signal sets consider only the time distortion during transmission. Recently,
signal sets against both the time and phase distortion have been studied, and
are called {\em time-phase} signal sets. Several constructions of time-phase
signal sets are available in the literature. There are a number of bounds on
time signal sets (also called codebooks). They are automatically bounds on
time-phase signal sets, but are bad bounds. The first objective of this paper
is to develop better bounds on time-phase signal sets from known bounds on time
signal sets. The second objective of this paper is to construct two series of
time-phase signal sets, one of which is optimal.
|
1112.0391
|
Robust Lasso with missing and grossly corrupted observations
|
math.ST cs.IT math.IT stat.TH
|
This paper studies the problem of accurately recovering a sparse vector
$\beta^{\star}$ from highly corrupted linear measurements $y = X \beta^{\star}
+ e^{\star} + w$ where $e^{\star}$ is a sparse error vector whose nonzero
entries may be unbounded and $w$ is a bounded noise. We propose a so-called
extended Lasso optimization which takes into consideration sparse prior
information of both $\beta^{\star}$ and $e^{\star}$. Our first result shows
that the extended Lasso can faithfully recover both the regression as well as
the corruption vector. Our analysis relies on the notion of extended restricted
eigenvalue for the design matrix $X$. Our second set of results applies to a
general class of Gaussian design matrix $X$ with i.i.d rows $\oper N(0,
\Sigma)$, for which we can establish a surprising result: the extended Lasso
can recover exact signed supports of both $\beta^{\star}$ and $e^{\star}$ from
only $\Omega(k \log p \log n)$ observations, even when the fraction of
corruption is arbitrarily close to one. Our analysis also shows that this
amount of observations required to achieve exact signed support is indeed
optimal.
|
1112.0396
|
Grammatical Relations of Myanmar Sentences Augmented by
Transformation-Based Learning of Function Tagging
|
cs.CL
|
In this paper we describe function tagging using Transformation Based
Learning (TBL) for Myanmar that is a method of extensions to the previous
statistics-based function tagger. Contextual and lexical rules (developed using
TBL) were critical in achieving good results. First, we describe a method for
expressing lexical relations in function tagging that statistical function
tagging are currently unable to express. Function tagging is the preprocessing
step to show grammatical relations of the sentences. Then we use the context
free grammar technique to clarify the grammatical relations in Myanmar
sentences or to output the parse trees. The grammatical relations are the
functional structure of a language. They rely very much on the function tag of
the tokens. We augment the grammatical relations of Myanmar sentences with
transformation-based learning of function tagging.
|
1112.0404
|
A Cyclic Representation of Discrete Coordination Procedures
|
cs.MA cs.DM cs.SY math.OC
|
We show that any discrete opinion pooling procedure with positive weights can
be asymptotically approximated by DeGroot's procedure whose communication
digraph is a Hamiltonian cycle with loops. In this cycle, the weight of each
arc (which is not a loop) is inversely proportional to the influence of the
agent the arc leads to.
|
1112.0463
|
Mask Iterative Hard Thresholding Algorithms for Sparse Image
Reconstruction of Objects with Known Contour
|
stat.ML cs.IT math.IT
|
We develop mask iterative hard thresholding algorithms (mask IHT and mask
DORE) for sparse image reconstruction of objects with known contour. The
measurements follow a noisy underdetermined linear model common in the
compressive sampling literature. Assuming that the contour of the object that
we wish to reconstruct is known and that the signal outside the contour is
zero, we formulate a constrained residual squared error minimization problem
that incorporates both the geometric information (i.e. the knowledge of the
object's contour) and the signal sparsity constraint. We first introduce a mask
IHT method that aims at solving this minimization problem and guarantees
monotonically non-increasing residual squared error for a given signal sparsity
level. We then propose a double overrelaxation scheme for accelerating the
convergence of the mask IHT algorithm. We also apply convex mask reconstruction
approaches that employ a convex relaxation of the signal sparsity constraint.
In X-ray computed tomography (CT), we propose an automatic scheme for
extracting the convex hull of the inspected object from the measured sinograms;
the obtained convex hull is used to capture the object contour information. We
compare the proposed mask reconstruction schemes with the existing large-scale
sparse signal reconstruction methods via numerical simulations and demonstrate
that, by exploiting both the geometric contour information of the underlying
image and sparsity of its wavelet coefficients, we can reconstruct this image
using a significantly smaller number of measurements than the existing methods.
|
1112.0467
|
Merging Belief Propagation and the Mean Field Approximation: A Free
Energy Approach
|
cs.IT math.IT stat.ML
|
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system.
|
1112.0508
|
Label Ranking with Abstention: Predicting Partial Orders by Thresholding
Probability Distributions (Extended Abstract)
|
cs.AI
|
We consider an extension of the setting of label ranking, in which the
learner is allowed to make predictions in the form of partial instead of total
orders. Predictions of that kind are interpreted as a partial abstention: If
the learner is not sufficiently certain regarding the relative order of two
alternatives, it may abstain from this decision and instead declare these
alternatives as being incomparable. We propose a new method for learning to
predict partial orders that improves on an existing approach, both
theoretically and empirically. Our method is based on the idea of thresholding
the probabilities of pairwise preferences between labels as induced by a
predicted (parameterized) probability distribution on the set of all rankings.
|
1112.0539
|
Maximal Scheduling in Wireless Networks with Priorities
|
cs.IT math.IT
|
We consider a general class of low complexity distributed scheduling
algorithms in wireless networks, maximal scheduling with priorities, where a
maximal set of transmitting links in each time slot are selected according to
certain pre-specified static priorities. The proposed scheduling scheme is
simple, which is easily amendable for distributed implementation in practice,
such as using inter-frame space (IFS) parameters under the ubiquitous 802.11
protocols. To obtain throughput guarantees, we first analyze the case of
maximal scheduling with a fixed priority vector, and formulate a lower bound on
its stability region and scheduling efficiency. We further propose a low
complexity priority assignment algorithm, which can stabilize any arrival rate
that is in the union of the lower bound regions of all priorities. The
stability result is proved using fluid limits, and can be applied to very
general stochastic arrival processes. Finally, the performance of the proposed
prioritized maximal scheduling scheme is verified by simulation results.
|
1112.0617
|
Quantum social networks
|
physics.soc-ph cs.SI quant-ph
|
We introduce a physical approach to social networks (SNs) in which each actor
is characterized by a yes-no test on a physical system. This allows us to
consider SNs beyond those originated by interactions based on pre-existing
properties, as in a classical SN (CSN). As an example of SNs beyond CSNs, we
introduce quantum SNs (QSNs) in which actor is characterized by a test of
whether or not the system is in a quantum state. We show that QSNs outperform
CSNs for a certain task and some graphs. We identify the simplest of these
graphs and show that graphs in which QSNs outperform CSNs are increasingly
frequent as the number of vertices increases. We also discuss more general SNs
and identify the simplest graphs in which QSNs cannot be outperformed.
|
1112.0655
|
A Biomimetic Model of the Outer Plexiform Layer by Incorporating
Memristive Devices
|
cs.CV
|
In this paper we present a biorealistic model for the first part of the early
vision processing by incorporating memristive nanodevices. The architecture of
the proposed network is based on the organisation and functioning of the outer
plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive
devices are indeed a valuable building block for neuromorphic architectures, as
their highly non-linear and adaptive response could be exploited for
establishing ultra-dense networks with similar dynamics to their biological
counterparts. We particularly show that hexagonal memristive grids can be
employed for faithfully emulating the smoothing-effect occurring at the OPL for
enhancing the dynamic range of the system. In addition, we employ a
memristor-based thresholding scheme for detecting the edges of grayscale
images, while the proposed system is also evaluated for its adaptation and
fault tolerance capacity against different light or noise conditions as well as
distinct device yields.
|
1112.0665
|
Generalized Thresholding and Online Sparsity-Aware Learning in a Union
of Subspaces
|
cs.IT math.IT
|
This paper studies a sparse signal recovery task in time-varying
(time-adaptive) environments. The contribution of the paper to sparsity-aware
online learning is threefold; first, a Generalized Thresholding (GT) operator,
which relates to both convex and non-convex penalty functions, is introduced.
This operator embodies, in a unified way, the majority of well-known
thresholding rules which promote sparsity. Second, a non-convexly constrained,
sparsity-promoting, online learning scheme, namely the Adaptive
Projection-based Generalized Thresholding (APGT), is developed that
incorporates the GT operator with a computational complexity that scales
linearly to the number of unknowns. Third, the novel family of partially
quasi-nonexpansive mappings is introduced as a functional analytic tool for
treating the GT operator. By building upon the rich fixed point theory, the
previous class of mappings helps us, also, to establish a link between the GT
operator and a union of linear subspaces; a non-convex object which lies at the
heart of any sparsity promoting technique, batch or online. Based on such a
functional analytic framework, a convergence analysis of the APGT is provided.
Furthermore, extensive experiments suggest that the APGT exhibits competitive
performance when compared to computationally more demanding alternatives, such
as the sparsity-promoting Affine Projection Algorithm (APA)- and Recursive
Least Squares (RLS)-based techniques.
|
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