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1106.3684
|
Exploratory simulation of an Intelligent Iris Verifier Distributed
System
|
cs.CV cs.ET cs.LO
|
This paper discusses some topics related to the latest trends in the field of
evolutionary approaches to iris recognition. It presents the results of an
exploratory experimental simulation whose goal was to analyze the possibility
of establishing an Interchange Protocol for Digital Identities evolved in
different geographic locations interconnected through and into an Intelligent
Iris Verifier Distributed System (IIVDS) based on multi-enrollment. Finding a
logically consistent model for the Interchange Protocol is the key factor in
designing the future large-scale iris biometric networks. Therefore, the
logical model of such a protocol is also investigated here. All tests are made
on Bath Iris Database and prove that outstanding power of discrimination
between the intra- and the inter-class comparisons can be achieved by an IIVDS,
even when practicing 52.759.182 inter-class and 10.991.943 intra-class
comparisons. Still, the test results confirm that inconsistent enrollment can
change the logic of recognition from a fuzzified 2-valent consistent logic of
biometric certitudes to a fuzzified 3-valent inconsistent possibilistic logic
of biometric beliefs justified through experimentally determined probabilities,
or to a fuzzified 8-valent logic which is almost consistent as a biometric
theory - this quality being counterbalanced by an absolutely reasonable loss in
the user comfort level.
|
1106.3685
|
Embedding and Automating Conditional Logics in Classical Higher-Order
Logic
|
cs.AI cs.LO math.LO
|
A sound and complete embedding of conditional logics into classical
higher-order logic is presented. This embedding enables the application of
off-the-shelf higher-order automated theorem provers and model finders for
reasoning within and about conditional logics.
|
1106.3693
|
Perfect Reconstruction Two-Channel Wavelet Filter-Banks for Graph
Structured Data
|
cs.DC cs.SI
|
In this work we propose the construction of two-channel wavelet filterbanks
for analyzing functions defined on the vertices of any arbitrary finite
weighted undirected graph. These graph based functions are referred to as
graph-signals as we build a framework in which many concepts from the classical
signal processing domain, such as Fourier decomposition, signal filtering and
downsampling can be extended to graph domain. Especially, we observe a spectral
folding phenomenon in bipartite graphs which occurs during downsampling of
these graphs and produces aliasing in graph signals. This property of bipartite
graphs, allows us to design critically sampled two-channel filterbanks, and we
propose quadrature mirror filters (referred to as graph-QMF) for bipartite
graph which cancel aliasing and lead to perfect reconstruction. For arbitrary
graphs we present a bipartite subgraph decomposition which produces an
edge-disjoint collection of bipartite subgraphs. Graph-QMFs are then
constructed on each bipartite subgraph leading to "multi-dimensional" separable
wavelet filterbanks on graphs. Our proposed filterbanks are critically sampled
and we state necessary and sufficient conditions for orthogonality, aliasing
cancellation and perfect reconstruction. The filterbanks are realized by
Chebychev polynomial approximations.
|
1106.3703
|
Prediction and Modularity in Dynamical Systems
|
nlin.AO cs.AI cs.IT cs.LG cs.SY math.IT q-bio.QM stat.ME
|
Identifying and understanding modular organizations is centrally important in
the study of complex systems. Several approaches to this problem have been
advanced, many framed in information-theoretic terms. Our treatment starts from
the complementary point of view of statistical modeling and prediction of
dynamical systems. It is known that for finite amounts of training data,
simpler models can have greater predictive power than more complex ones. We use
the trade-off between model simplicity and predictive accuracy to generate
optimal multiscale decompositions of dynamical networks into weakly-coupled,
simple modules. State-dependent and causal versions of our method are also
proposed.
|
1106.3711
|
Sidelobe Suppression for Capon Beamforming with Mainlobe to Sidelobe
Power Ratio Maximization
|
cs.IT math.IT
|
High sidelobe level is a major disadvantage of the Capon beamforming. To
suppress the sidelobe, this paper introduces a mainlobe to sidelobe power ratio
constraint to the Capon beamforming. it minimizes the sidelobe power while
keeping the mainlobe power constant. Simulations show that the obtained
beamformer outperforms the Capon beamformer.
|
1106.3713
|
Source-Channel Coding Theorems for the Multiple-Access Relay Channel
|
cs.IT math.IT
|
We study reliable transmission of arbitrarily correlated sources over
multiple-access relay channels (MARCs) and multiple-access broadcast relay
channels (MABRCs). In MARCs only the destination is interested in
reconstructing the sources, while in MABRCs both the relay and the destination
want to reconstruct them. In addition to arbitrary correlation among the source
signals at the users, both the relay and the destination have side information
correlated with the source signals. Our objective is to determine whether a
given pair of sources can be losslessly transmitted to the destination for a
given number of channel symbols per source sample, defined as the
source-channel rate. Sufficient conditions for reliable communication based on
operational separation, as well as necessary conditions on the achievable
source-channel rates are characterized. Since operational separation is
generally not optimal for MARCs and MABRCs, sufficient conditions for reliable
communication using joint source-channel coding schemes based on a combination
of the correlation preserving mapping technique with Slepian-Wolf source coding
are also derived. For correlated sources transmitted over fading Gaussian MARCs
and MABRCs, we present conditions under which separation (i.e., separate and
stand-alone source and channel codes) is optimal. This is the first time
optimality of separation is proved for MARCs and MABRCs.
|
1106.3725
|
Learning XML Twig Queries
|
cs.DB cs.LG
|
We investigate the problem of learning XML queries, path queries and tree
pattern queries, from examples given by the user. A learning algorithm takes on
the input a set of XML documents with nodes annotated by the user and returns a
query that selects the nodes in a manner consistent with the annotation. We
study two learning settings that differ with the types of annotations. In the
first setting the user may only indicate required nodes that the query must
return. In the second, more general, setting, the user may also indicate
forbidden nodes that the query must not return. The query may or may not return
any node with no annotation. We formalize what it means for a class of queries
to be \emph{learnable}. One requirement is the existence of a learning
algorithm that is sound i.e., always returns a query consistent with the
examples given by the user. Furthermore, the learning algorithm should be
complete i.e., able to produce every query with a sufficiently rich example.
Other requirements involve tractability of learning and its robustness to
nonessential examples. We show that the classes of simple path queries and
path-subsumption-free tree queries are learnable from positive examples. The
learnability of the full class of tree pattern queries (and the full class of
path queries) remains an open question. We show also that adding negative
examples to the picture renders the learning unfeasible.
Published in ICDT 2012, Berlin.
|
1106.3740
|
The Asymptotic Mandelbrot Law of Some Evolution Networks
|
physics.data-an cs.SI physics.soc-ph
|
In this letter, we study some evolution networks that grow with linear
preferential attachment. Based upon some recent results on the quotient Gamma
function, we give a rigorous proof of the asymptotic Mandelbrot law for the
degree distribution $p_k \propto (k + c)^{-\gamma}$ in certain conditions. We
also analytically derive the best fitting values for the scaling exponent
$\gamma$ and the shifting coefficient $c$.
|
1106.3745
|
Composition with Target Constraints
|
cs.DB
|
It is known that the composition of schema mappings, each specified by
source-to-target tgds (st-tgds), can be specified by a second-order tgd (SO
tgd). We consider the question of what happens when target constraints are
allowed. Specifically, we consider the question of specifying the composition
of standard schema mappings (those specified by st-tgds, target egds, and a
weakly acyclic set of target tgds). We show that SO tgds, even with the
assistance of arbitrary source constraints and target constraints, cannot
specify in general the composition of two standard schema mappings. Therefore,
we introduce source-to-target second-order dependencies (st-SO dependencies),
which are similar to SO tgds, but allow equations in the conclusion. We show
that st-SO dependencies (along with target egds and target tgds) are sufficient
to express the composition of every finite sequence of standard schema
mappings, and further, every st-SO dependency specifies such a composition. In
addition to this expressive power, we show that st-SO dependencies enjoy other
desirable properties. In particular, they have a polynomial-time chase that
generates a universal solution. This universal solution can be used to find the
certain answers to unions of conjunctive queries in polynomial time. It is easy
to show that the composition of an arbitrary number of standard schema mappings
is equivalent to the composition of only two standard schema mappings. We show
that surprisingly, the analogous result holds also for schema mappings
specified by just st-tgds (no target constraints). This is proven by showing
that every SO tgd is equivalent to an unnested SO tgd (one where there is no
nesting of function symbols). Similarly, we prove unnesting results for st-SO
dependencies, with the same types of consequences.
|
1106.3754
|
Families of graph-different Hamilton paths
|
math.CO cs.IT math.IT
|
Let D be an arbitrary subset of the natural numbers. For every n, let M(n;D)
be the maximum of the cardinality of a set of Hamiltonian paths in the complete
graph K_n such that the union of any two paths from the family contains a not
necessarily induced cycle of some length from D. We determine or bound the
asymptotics of M(n;D) in various special cases. This problem is closely related
to that of the permutation capacity of graphs and constitutes a further
extension of the problem area around Shannon capacity. We also discuss how to
generalize our cycle-difference problems and present an example where cycles
are replaced by 4-cliques. These problems are in a natural duality to those of
graph intersection, initiated by Erd\"os, Simonovits and S\'os. The lack of
kernel structure as a natural candidate for optimum makes our problems quite
challenging.
|
1106.3759
|
Frequency Theorem for discrete time stochastic system with
multiplicative noise
|
math.OC cs.SY
|
In this paper we consider the problem of minimizing a quadratic functional
for a discrete-time linear stochastic system with multiplicative noise, on a
standard probability space, in infinite time horizon. We show that the
necessary and sufficient conditions for the existence of the optimal control
can be formulated as matrix inequalities in frequency domain. Furthermore, we
show that if the optimal control exists, then certain Lyapunov equations must
have a solution. The optimal control is obtained by solving a deterministic
linear-quadratic optimal control problem whose functional depends on the
solution to the Lyapunov equations. Moreover, we show that under certain
conditions, solvability of the Lyapunov equations is guaranteed. We also show
that, if the frequency inequalities are strict, then the solution is unique up
to equivalence.
|
1106.3767
|
Rewriting Ontological Queries into Small Nonrecursive Datalog Programs
|
cs.AI cs.DB cs.LO
|
We consider the setting of ontological database access, where an Abox is
given in form of a relational database D and where a Boolean conjunctive query
q has to be evaluated against D modulo a Tbox T formulated in DL-Lite or Linear
Datalog+/-. It is well-known that (T,q) can be rewritten into an equivalent
nonrecursive Datalog program P that can be directly evaluated over D. However,
for Linear Datalog? or for DL-Lite versions that allow for role inclusion, the
rewriting methods described so far result in a nonrecursive Datalog program P
of size exponential in the joint size of T and q. This gives rise to the
interesting question of whether such a rewriting necessarily needs to be of
exponential size. In this paper we show that it is actually possible to
translate (T,q) into a polynomially sized equivalent nonrecursive Datalog
program P.
|
1106.3791
|
Reference Sequence Construction for Relative Compression of Genomes
|
q-bio.QM cs.CE cs.IT math.IT
|
Relative compression, where a set of similar strings are compressed with
respect to a reference string, is a very effective method of compressing DNA
datasets containing multiple similar sequences. Relative compression is fast to
perform and also supports rapid random access to the underlying data. The main
difficulty of relative compression is in selecting an appropriate reference
sequence. In this paper, we explore using the dictionary of repeats generated
by Comrad, Re-pair and Dna-x algorithms as reference sequences for relative
compression. We show this technique allows better compression and supports
random access just as well. The technique also allows more general repetitive
datasets to be compressed using relative compression.
|
1106.3809
|
Fisher Information in Flow Size Distribution
|
cs.IT cs.NI math.IT
|
The flow size distribution is a useful metric for traffic modeling and
management. Its estimation based on sampled data, however, is problematic.
Previous work has shown that flow sampling (FS) offers enormous statistical
benefits over packet sampling but high resource requirements precludes its use
in routers. We present Dual Sampling (DS), a two-parameter family, which, to a
large extent, provide FS-like statistical performance by approaching FS
continuously, with just packet-sampling-like computational cost. Our work
utilizes a Fisher information based approach recently used to evaluate a number
of sampling schemes, excluding FS, for TCP flows. We revise and extend the
approach to make rigorous and fair comparisons between FS, DS and others. We
show how DS significantly outperforms other packet based methods, including
Sample and Hold, the closest packet sampling-based competitor to FS. We
describe a packet sampling-based implementation of DS and analyze its key
computational costs to show that router implementation is feasible. Our
approach offers insights into numerous issues, including the notion of `flow
quality' for understanding the relative performance of methods, and how and
when employing sequence numbers is beneficial. Our work is theoretical with
some simulation support and case studies on Internet data.
|
1106.3826
|
On the Non-Progressive Spread of Influence through Social Networks
|
cs.SI cs.GT physics.soc-ph
|
The spread of influence in social networks is studied in two main categories:
the progressive model and the non-progressive model (see e.g. the seminal work
of Kempe, Kleinberg, and Tardos in KDD 2003). While the progressive models are
suitable for modeling the spread of influence in monopolistic settings,
non-progressive are more appropriate for modeling non-monopolistic settings,
e.g., modeling diffusion of two competing technologies over a social network.
Despite the extensive work on the progressive model, non-progressive models
have not been studied well. In this paper, we study the spread of influence in
the non-progressive model under the strict majority threshold: given a graph
$G$ with a set of initially infected nodes, each node gets infected at time
$\tau$ iff a majority of its neighbors are infected at time $\tau-1$. Our goal
in the \textit{MinPTS} problem is to find a minimum-cardinality initial set of
infected nodes that would eventually converge to a steady state where all nodes
of $G$ are infected.
We prove that while the MinPTS is NP-hard for a restricted family of graphs,
it admits an improved constant-factor approximation algorithm for power-law
graphs. We do so by proving lower and upper bounds in terms of the minimum and
maximum degree of nodes in the graph. The upper bound is achieved in turn by
applying a natural greedy algorithm. Our experimental evaluation of the greedy
algorithm also shows its superior performance compared to other algorithms for
a set of real-world graphs as well as the random power-law graphs. Finally, we
study the convergence properties of these algorithms and show that the
non-progressive model converges in at most $O(|E(G)|)$ steps.
|
1106.3834
|
Dimensionally Constrained Symbolic Regression
|
stat.ML cs.NE physics.comp-ph
|
We describe dimensionally constrained symbolic regression which has been
developed for mass measurement in certain classes of events in high-energy
physics (HEP). With symbolic regression, we can derive equations that are well
known in HEP. However, in problems with large number of variables, we find that
by constraining the terms allowed in the symbolic regression, convergence
behavior is improved. Dimensionally constrained symbolic regression (DCSR)
finds solutions with much better fitness than is normally possible with
symbolic regression. In some cases, novel solutions are found.
|
1106.3862
|
On Kinds of Indiscernibility in Logic and Metaphysics
|
physics.hist-ph cs.AI quant-ph
|
Using the Hilbert-Bernays account as a spring-board, we first define four
ways in which two objects can be discerned from one another, using the
non-logical vocabulary of the language concerned. (These definitions are based
on definitions made by Quine and Saunders.) Because of our use of the
Hilbert-Bernays account, these definitions are in terms of the syntax of the
language. But we also relate our definitions to the idea of permutations on the
domain of quantification, and their being symmetries. These relations turn out
to be subtle---some natural conjectures about them are false. We will see in
particular that the idea of symmetry meshes with a species of indiscernibility
that we will call `absolute indiscernibility'. We then report all the logical
implications between our four kinds of discernibility. We use these four kinds
as a resource for stating four metaphysical theses about identity. Three of
these theses articulate two traditional philosophical themes: viz. the
principle of the identity of indiscernibles (which will come in two versions),
and haecceitism. The fourth is recent. Its most notable feature is that it
makes diversity (i.e. non-identity) weaker than what we will call individuality
(being an individual): two objects can be distinct but not individuals. For
this reason, it has been advocated both for quantum particles and for spacetime
points. Finally, we locate this fourth metaphysical thesis in a broader
position, which we call structuralism. We conclude with a discussion of the
semantics suitable for a structuralist, with particular reference to physical
theories as well as elementary model theory.
|
1106.3876
|
Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion
Applications
|
cs.AI
|
Nowadays ontologies present a growing interest in Data Fusion applications.
As a matter of fact, the ontologies are seen as a semantic tool for describing
and reasoning about sensor data, objects, relations and general domain
theories. In addition, uncertainty is perhaps one of the most important
characteristics of the data and information handled by Data Fusion. However,
the fundamental nature of ontologies implies that ontologies describe only
asserted and veracious facts of the world. Different probabilistic, fuzzy and
evidential approaches already exist to fill this gap; this paper recaps the
most popular tools. However none of the tools meets exactly our purposes.
Therefore, we constructed a Dempster-Shafer ontology that can be imported into
any specific domain ontology and that enables us to instantiate it in an
uncertain manner. We also developed a Java application that enables reasoning
about these uncertain ontological instances.
|
1106.3932
|
Coincidences and the encounter problem: A formal account
|
cs.AI
|
Individuals have an intuitive perception of what makes a good coincidence.
Though the sensitivity to coincidences has often been presented as resulting
from an erroneous assessment of probability, it appears to be a genuine
competence, based on non-trivial computations. The model presented here
suggests that coincidences occur when subjects perceive complexity drops.
Co-occurring events are, together, simpler than if considered separately. This
model leads to a possible redefinition of subjective probability.
|
1106.3940
|
Cooperative spectrum sensing over unreliable reporting channel
|
stat.OT cs.IT math.IT
|
This article aims to analyze a cooperative spectrum sensing scheme using a
centralized approach with unreliable reporting channel. The spectrum sensing is
applied to a cognitive radio system, where each cognitive radio performs a
simple energy detection and send the decision to a fusion center through a
reporting channel. When the decisions are available at the fusion center, a
n-out-of-K rule is applied. The impact of the choice of the parameter n in the
cognitive radio system performance is analyzed in the case where the reporting
channel introduces errors.
|
1106.3951
|
Optimal rate list decoding via derivative codes
|
cs.IT cs.CC cs.DS math.IT
|
The classical family of $[n,k]_q$ Reed-Solomon codes over a field $\F_q$
consist of the evaluations of polynomials $f \in \F_q[X]$ of degree $< k$ at
$n$ distinct field elements. In this work, we consider a closely related family
of codes, called (order $m$) {\em derivative codes} and defined over fields of
large characteristic, which consist of the evaluations of $f$ as well as its
first $m-1$ formal derivatives at $n$ distinct field elements. For large enough
$m$, we show that these codes can be list-decoded in polynomial time from an
error fraction approaching $1-R$, where $R=k/(nm)$ is the rate of the code.
This gives an alternate construction to folded Reed-Solomon codes for achieving
the optimal trade-off between rate and list error-correction radius. Our
decoding algorithm is linear-algebraic, and involves solving a linear system to
interpolate a multivariate polynomial, and then solving another structured
linear system to retrieve the list of candidate polynomials $f$. The algorithm
for derivative codes offers some advantages compared to a similar one for
folded Reed-Solomon codes in terms of efficient unique decoding in the presence
of side information.
|
1106.3967
|
Intelligent Self-Repairable Web Wrappers
|
cs.AI cs.IR
|
The amount of information available on the Web grows at an incredible high
rate. Systems and procedures devised to extract these data from Web sources
already exist, and different approaches and techniques have been investigated
during the last years. On the one hand, reliable solutions should provide
robust algorithms of Web data mining which could automatically face possible
malfunctioning or failures. On the other, in literature there is a lack of
solutions about the maintenance of these systems. Procedures that extract Web
data may be strictly interconnected with the structure of the data source
itself; thus, malfunctioning or acquisition of corrupted data could be caused,
for example, by structural modifications of data sources brought by their
owners. Nowadays, verification of data integrity and maintenance are mostly
manually managed, in order to ensure that these systems work correctly and
reliably. In this paper we propose a novel approach to create procedures able
to extract data from Web sources -- the so called Web wrappers -- which can
face possible malfunctioning caused by modifications of the structure of the
data source, and can automatically repair themselves.
|
1106.3977
|
Models, Calculation and Optimization of Gas Networks, Equipment and
Contracts for Design, Operation, Booking and Accounting
|
cs.CE
|
There are proposed models of contracts, technological equipment and gas
networks and methods of their optimization. The flow in network undergoes
restrictions of contracts and equipment to be operated. The values of sources
and sinks are provided by contracts. The contract models represent (sub-)
networks. The simplest contracts represent either nodes or edges. Equipment is
modeled by edges. More sophisticated equipment is represented by sub-networks.
Examples of such equipment are multi-poles and compressor stations with many
entries and exits. The edges can be of different types corresponding to
equipment and contracts. On such edges, there are given systems of equation and
inequalities simulating the contracts and equipment. On this base, the methods
proposed that allow: calculation and control of contract values for booking on
future days and for accounting of sales and purchases; simulation and
optimization of design and of operation of gas networks. These models and
methods are implemented in software systems ACCORD and Graphicord as well as in
the distributed control system used by Wingas, Germany. As numerical example,
the industrial computations are presented.
|
1106.3981
|
Group Codes and the Schreier matrix form
|
cs.IT math.IT
|
In a group trellis, the sequence of branches that split from the identity
path and merge to the identity path form two normal chains. The Schreier
refinement theorem can be applied to these two normal chains. The refinement of
the two normal chains can be written in the form of a matrix, called the
Schreier matrix form, with rows and columns determined by the two normal
chains.
Based on the Schreier matrix form, we give an encoder structure for a group
code which is an estimator. The encoder uses the important idea of shortest
length generator sequences previously explained by Forney and Trott. In this
encoder the generator sequences are shown to have an additional property: the
components of the generators are coset representatives in a chain coset
decomposition of the branch group B of the code. Therefore this encoder appears
to be a natural form for a group code encoder. The encoder has a register
implementation which is somewhat different from the classical shift register
structure.
This form of the encoder can be extended. We find a composition chain of the
branch group B and give an encoder which uses coset representatives in the
composition chain of B. When B is solvable, the generators are constructed
using coset representatives taken from prime cyclic groups.
|
1106.4058
|
Experimental Support for a Categorical Compositional Distributional
Model of Meaning
|
cs.CL math.CT
|
Modelling compositional meaning for sentences using empirical distributional
methods has been a challenge for computational linguists. We implement the
abstract categorical model of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) using
data from the BNC and evaluate it. The implementation is based on unsupervised
learning of matrices for relational words and applying them to the vectors of
their arguments. The evaluation is based on the word disambiguation task
developed by Mitchell and Lapata (2008) for intransitive sentences, and on a
similar new experiment designed for transitive sentences. Our model matches the
results of its competitors in the first experiment, and betters them in the
second. The general improvement in results with increase in syntactic
complexity showcases the compositional power of our model.
|
1106.4064
|
Algorithmic Programming Language Identification
|
cs.LG
|
Motivated by the amount of code that goes unidentified on the web, we
introduce a practical method for algorithmically identifying the programming
language of source code. Our work is based on supervised learning and
intelligent statistical features. We also explored, but abandoned, a
grammatical approach. In testing, our implementation greatly outperforms that
of an existing tool that relies on a Bayesian classifier. Code is written in
Python and available under an MIT license.
|
1106.4075
|
On the Inclusion Relation of Reproducing Kernel Hilbert Spaces
|
math.FA cs.LG
|
To help understand various reproducing kernels used in applied sciences, we
investigate the inclusion relation of two reproducing kernel Hilbert spaces.
Characterizations in terms of feature maps of the corresponding reproducing
kernels are established. A full table of inclusion relations among widely-used
translation invariant kernels is given. Concrete examples for Hilbert-Schmidt
kernels are presented as well. We also discuss the preservation of such a
relation under various operations of reproducing kernels. Finally, we briefly
discuss the special inclusion with a norm equivalence.
|
1106.4083
|
Symmetry-Based Search Space Reduction For Grid Maps
|
cs.AI cs.RO
|
In this paper we explore a symmetry-based search space reduction technique
which can speed up optimal pathfinding on undirected uniform-cost grid maps by
up to 38 times. Our technique decomposes grid maps into a set of empty
rectangles, removing from each rectangle all interior nodes and possibly some
from along the perimeter. We then add a series of macro-edges between selected
pairs of remaining perimeter nodes to facilitate provably optimal traversal
through each rectangle. We also develop a novel online pruning technique to
further speed up search. Our algorithm is fast, memory efficient and retains
the same optimality and completeness guarantees as searching on an unmodified
grid map.
|
1106.4090
|
Discovery of Invariants through Automated Theory Formation
|
cs.LO cs.AI cs.SE
|
Refinement is a powerful mechanism for mastering the complexities that arise
when formally modelling systems. Refinement also brings with it additional
proof obligations -- requiring a developer to discover properties relating to
their design decisions. With the goal of reducing this burden, we have
investigated how a general purpose theory formation tool, HR, can be used to
automate the discovery of such properties within the context of Event-B. Here
we develop a heuristic approach to the automatic discovery of invariants and
report upon a series of experiments that we undertook in order to evaluate our
approach. The set of heuristics developed provides systematic guidance in
tailoring HR for a given Event-B development. These heuristics are based upon
proof-failure analysis, and have given rise to some promising results.
|
1106.4128
|
Percolation in Interdependent and Interconnected Networks: Abrupt Change
from Second to First Order Transition
|
physics.soc-ph cs.SI
|
Robustness of two coupled networks system has been studied only for
dependency coupling (S. Buldyrev et. al., Nature, 2010) and only for
connectivity coupling (E. A. Leicht and R. M. D'Souza, arxiv:09070894). Here we
study, using a percolation approach, a more realistic coupled networks system
where both interdependent and interconnected links exist. We find a rich and
unusual phase transition phenomena including hybrid transition of mixed first
and second order i.e., discontinuities like a first order transition of the
giant component followed by a continuous decrease to zero like a second order
transition. Moreover, we find unusual discontinuous changes from second order
to first order transition as a function of the dependency coupling between the
two networks.
|
1106.4131
|
Repeaters in relativistic communications
|
physics.class-ph cs.IT math.IT
|
The communication efficiency between a transmitter and a receiver is affected
by motion and the presence of gravitational fields. We study the effect of
regenerating the signal in intermediate repeaters in different relativistic
scenarios and comment the differences with respect to nonrelativistic
repeaters.
|
1106.4215
|
Heterogenous mean-field analysis of a generalized voter-like model on
networks
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We propose a generalized framework for the study of voter models in complex
networks at the the heterogeneous mean-field (HMF) level that (i) yields a
unified picture for existing copy/invasion processes and (ii) allows for the
introduction of further heterogeneity through degree-selectivity rules. In the
context of the HMF approximation, our model is capable of providing
straightforward estimates for central quantities such as the exit probability
and the consensus/fixation time, based on the statistical properties of the
complex network alone. The HMF approach has the advantage of being readily
applicable also in those cases in which exact solutions are difficult to work
out. Finally, the unified formalism allows one to understand previously
proposed voter-like processes as simple limits of the generalized model.
|
1106.4218
|
Rooting opinions in the minds: a cognitive model and a formal account of
opinions and their dynamics
|
cs.AI
|
The study of opinions, their formation and change, is one of the defining
topics addressed by social psychology, but in recent years other disciplines,
like computer science and complexity, have tried to deal with this issue.
Despite the flourishing of different models and theories in both fields,
several key questions still remain unanswered. The understanding of how
opinions change and the way they are affected by social influence are
challenging issues requiring a thorough analysis of opinion per se but also of
the way in which they travel between agents' minds and are modulated by these
exchanges. To account for the two-faceted nature of opinions, which are mental
entities undergoing complex social processes, we outline a preliminary model in
which a cognitive theory of opinions is put forward and it is paired with a
formal description of them and of their spreading among minds. Furthermore,
investigating social influence also implies the necessity to account for the
way in which people change their minds, as a consequence of interacting with
other people, and the need to explain the higher or lower persistence of such
changes.
|
1106.4221
|
Understanding opinions. A cognitive and formal account
|
cs.AI
|
The study of opinions, their formation and change, is one of the defining
topics addressed by social psychology, but in recent years other disciplines,
as computer science and complexity, have addressed this challenge. Despite the
flourishing of different models and theories in both fields, several key
questions still remain unanswered. The aim of this paper is to challenge the
current theories on opinion by putting forward a cognitively grounded model
where opinions are described as specific mental representations whose main
properties are put forward. A comparison with reputation will be also
presented.
|
1106.4232
|
Approximate controllability for linear degenerate parabolic problems
with bilinear control
|
math.AP cs.SY math.OC
|
In this work we study the global approximate multiplicative controllability
for the linear degenerate parabolic Cauchy-Neumann problem $$ \{{array}{l}
\displaystyle{v_t-(a(x) v_x)_x =\alpha (t,x)v\,\,\qquad {in} \qquad Q_T
\,=\,(0,T)\times(-1,1)} [2.5ex] \displaystyle{a(x)v_x(t,x)|_{x=\pm 1} =
0\,\,\qquad\qquad\qquad\,\, t\in (0,T)} [2.5ex] \displaystyle{v(0,x)=v_0 (x)
\,\qquad\qquad\qquad\qquad\quad\,\, x\in (-1,1)}, {array}. $$ with the bilinear
control $\alpha(t,x)\in L^\infty (Q_T).$ The problem is strongly degenerate in
the sense that $a\in C^1([-1,1]),$ positive on $(-1,1),$ is allowed to vanish
at $\pm 1$ provided that a certain integrability condition is fulfilled. We
will show that the above system can be steered in $L^2(\Omega)$ from any
nonzero, nonnegative initial state into any neighborhood of any desirable
nonnegative target-state by bilinear static controls. Moreover, we extend the
above result relaxing the sign constraint on $v_0$.
|
1106.4251
|
Learning with the Weighted Trace-norm under Arbitrary Sampling
Distributions
|
cs.LG stat.ML
|
We provide rigorous guarantees on learning with the weighted trace-norm under
arbitrary sampling distributions. We show that the standard weighted trace-norm
might fail when the sampling distribution is not a product distribution (i.e.
when row and column indexes are not selected independently), present a
corrected variant for which we establish strong learning guarantees, and
demonstrate that it works better in practice. We provide guarantees when
weighting by either the true or empirical sampling distribution, and suggest
that even if the true distribution is known (or is uniform), weighting by the
empirical distribution may be beneficial.
|
1106.4286
|
Multi-receiver Wiretap Channel with Public and Confidential Messages
|
cs.IT cs.CR math.IT
|
We study the multi-receiver wiretap channel with public and confidential
messages. In this channel, there is a transmitter that wishes to communicate
with two legitimate users in the presence of an external eavesdropper. The
transmitter sends a pair of public and confidential messages to each legitimate
user. While there are no secrecy constraints on the public messages,
confidential messages need to be transmitted in perfect secrecy. We study the
discrete memoryless multi-receiver wiretap channel as well as its Gaussian
multi-input multi-output (MIMO) instance. First, we consider the degraded
discrete memoryless channel, and obtain an inner bound for the capacity region
by using an achievable scheme that uses superposition coding and binning. Next,
we obtain an outer bound, and show that this outer bound partially matches the
inner bound, providing a partial characterization for the capacity region of
the degraded channel model. Second, we obtain an inner bound for the general,
not necessarily degraded, discrete memoryless channel by using Marton's inner
bound, superposition coding, rate-splitting and binning. Third, we consider the
degraded Gaussian MIMO channel, and show that, to evaluate both the inner and
outer bounds, considering only jointly Gaussian auxiliary random variables and
channel input is sufficient. Since the inner and outer bounds partially match,
these sufficiency results provide a partial characterization of the capacity
region of the degraded Gaussian MIMO channel. Finally, we provide an inner
bound for the capacity region of the general, not necessarily degraded,
Gaussian MIMO multi-receiver wiretap channel.
|
1106.4288
|
Continuum Limits of Markov Chains with Application to Network Modeling
|
cs.NI cs.IT math.AP math.IT
|
In this paper we investigate the continuum limits of a class of Markov
chains. The investigation of such limits is motivated by the desire to model
very large networks. We show that under some conditions, a sequence of Markov
chains converges in some sense to the solution of a partial differential
equation. Based on such convergence we approximate Markov chains modeling
networks with a large number of components by partial differential equations.
While traditional Monte Carlo simulation for very large networks is practically
infeasible, partial differential equations can be solved with reasonable
computational overhead using well-established mathematical tools.
|
1106.4300
|
Human as Real-Time Sensors of Social and Physical Events: A Case Study
of Twitter and Sports Games
|
cs.SI physics.soc-ph
|
In this work, we study how Twitter can be used as a sensor to detect frequent
and diverse social and physical events in real-time. We devise efficient data
collection and event recognition solutions that work despite various limits on
free access to Twitter data. We describe a web service implementation of our
solution and report our experience with the 2010-2011 US National Football
League (NFL) games. The service was able to recognize NFL game events within 40
seconds and with accuracy up to 90%. This capability will be very useful for
not only real-time electronic program guide for live broadcast programs but
also refined auction of advertisement slots. More importantly, it demonstrates
for the first time the feasibility of using Twitter for real-time social and
physical event detection for ubiquitous computing.
|
1106.4333
|
Residual Component Analysis
|
stat.ML cs.AI math.ST stat.CO stat.TH
|
Probabilistic principal component analysis (PPCA) seeks a low dimensional
representation of a data set in the presence of independent spherical Gaussian
noise, Sigma = (sigma^2)*I. The maximum likelihood solution for the model is an
eigenvalue problem on the sample covariance matrix. In this paper we consider
the situation where the data variance is already partially explained by other
factors, e.g. covariates of interest, or temporal correlations leaving some
residual variance. We decompose the residual variance into its components
through a generalized eigenvalue problem, which we call residual component
analysis (RCA). We show that canonical covariates analysis (CCA) is a special
case of our algorithm and explore a range of new algorithms that arise from the
framework. We illustrate the ideas on a gene expression time series data set
and the recovery of human pose from silhouette.
|
1106.4337
|
Speed of complex network synchronization
|
cond-mat.dis-nn cs.SI nlin.CD physics.soc-ph
|
Synchrony is one of the most common dynamical states emerging on networks.
The speed of convergence towards synchrony provides a fundamental collective
time scale for synchronizing systems. Here we study the asymptotic
synchronization times for directed networks with topologies ranging from
completely ordered, grid-like, to completely disordered, random, including
intermediate, partially disordered topologies. We extend the approach of Master
Stability Functions to quantify synchronization times. We find that the
synchronization times strongly and systematically depend on the network
topology. In particular, at fixed in-degree, stronger topological randomness
induces faster synchronization, whereas at fixed path length, synchronization
is slowest for intermediate randomness in the small-world regime. Randomly
rewiring real-world neural, social and transport networks confirms this
picture.
|
1106.4346
|
Average-Consensus Algorithms in a Deterministic Framework
|
cs.DC cs.SY math.OC
|
We consider the average-consensus problem in a multi-node network of finite
size. Communication between nodes is modeled by a sequence of directed signals
with arbitrary communication delays. Four distributed algorithms that achieve
average-consensus are proposed. Necessary and sufficient communication
conditions are given for each algorithm to achieve average-consensus. Resource
costs for each algorithm are derived based on the number of scalar values that
are required for communication and storage at each node. Numerical examples are
provided to illustrate the empirical convergence rate of the four algorithms in
comparison with a well-known "gossip" algorithm as well as a randomized
information spreading algorithm when assuming a fully connected random graph
with instantaneous communication.
|
1106.4355
|
Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals
|
stat.ML cs.LG
|
Standard compressive sensing results state that to exactly recover an s
sparse signal in R^p, one requires O(s. log(p)) measurements. While this bound
is extremely useful in practice, often real world signals are not only sparse,
but also exhibit structure in the sparsity pattern. We focus on
group-structured patterns in this paper. Under this model, groups of signal
coefficients are active (or inactive) together. The groups are predefined, but
the particular set of groups that are active (i.e., in the signal support) must
be learned from measurements. We show that exploiting knowledge of groups can
further reduce the number of measurements required for exact signal recovery,
and derive universal bounds for the number of measurements needed. The bound is
universal in the sense that it only depends on the number of groups under
consideration, and not the particulars of the groups (e.g., compositions,
sizes, extents, overlaps, etc.). Experiments show that our result holds for a
variety of overlapping group configurations.
|
1106.4386
|
Optimal Rate Scheduling via Utility-Maximization for J-User MIMO Markov
Fading Wireless Channels with Cooperation
|
math.PR cs.IT math.IT math.OC math.ST stat.TH
|
We design a dynamic rate scheduling policy of Markov type via the solution (a
social optimal Nash equilibrium point) to a utility-maximization problem over a
randomly evolving capacity set for a class of generalized processor-sharing
queues living in a random environment, whose job arrivals to each queue follow
a doubly stochastic renewal process (DSRP). Both the random environment and the
random arrival rate of each DSRP are driven by a finite state continuous time
Markov chain (FS-CTMC). Whereas the scheduling policy optimizes in a greedy
fashion with respect to each queue and environmental state and since the
closed-form solution for the performance of such a queueing system under the
policy is difficult to obtain, we establish a reflecting diffusion with
regime-switching (RDRS) model for its measures of performance and justify its
asymptotic optimality through deriving the stochastic fluid and diffusion
limits for the corresponding system under heavy traffic and identifying a cost
function related to the utility function, which is minimized through minimizing
the workload process in the diffusion limit. More importantly, our queueing
model includes both J-user multi-input multi-output (MIMO) multiple access
channel (MAC) and broadcast channel (BC) with cooperation and admission control
as special cases. In these wireless systems, data from the J users in the MAC
or data to the J users in the BC is transmitted over a common channel that is
fading according to the FS-CTMC. The J-user capacity region for the MAC or the
BC is a set-valued stochastic process that switches with the FS-CTMC fading. In
any particular channel state, we show that each of the J-user capacity regions
is a convex set bounded by a number of linear or smooth curved facets.
Therefore our queueing model can perfectly match the dynamics of these wireless
systems.
|
1106.4399
|
Motif based hierarchical random graphs: structural properties and
critical points of an Ising model
|
math-ph cond-mat.stat-mech cs.SI math.MP physics.soc-ph
|
A class of random graphs is introduced and studied. The graphs are
constructed in an algorithmic way from five motifs which were found in [Milo
R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D., Alon U., Science,
2002, 298, 824-827]. The construction scheme resembles that used in [Hinczewski
M., A. Nihat Berker, Phys. Rev. E, 2006, 73, 066126], according to which the
short-range bonds are non-random, whereas the long-range bonds appear
independently with the same probability. A number of structural properties of
the graphs have been described, among which there are degree distributions,
clustering, amenability, small-world property. For one of the motifs, the
critical point of the Ising model defined on the corresponding graph has been
studied.
|
1106.4475
|
Interesting Multi-Relational Patterns
|
cs.DB cs.DS cs.SI
|
Mining patterns from multi-relational data is a problem attracting increasing
interest within the data mining community. Traditional data mining approaches
are typically developed for highly simplified types of data, such as an
attribute-value table or a binary database, such that those methods are not
directly applicable to multi-relational data. Nevertheless, multi-relational
data is a more truthful and therefore often also a more powerful representation
of reality. Mining patterns of a suitably expressive syntax directly from this
representation, is thus a research problem of great importance. In this paper
we introduce a novel approach to mining patterns in multi-relational data. We
propose a new syntax for multi-relational patterns as complete connected
subgraphs in a representation of the database as a K-partite graph. We show how
this pattern syntax is generally applicable to multirelational data, while it
reduces to well-known tiles [7] when the data is a simple binary or
attribute-value table. We propose RMiner, an efficient algorithm to mine such
patterns, and we introduce a method for quantifying their interestingness when
contrasted with prior information of the data miner. Finally, we illustrate the
usefulness of our approach by discussing results on real-world and synthetic
databases.
|
1106.4487
|
Natural Evolution Strategies
|
stat.ML cs.NE
|
This paper presents Natural Evolution Strategies (NES), a recent family of
algorithms that constitute a more principled approach to black-box optimization
than established evolutionary algorithms. NES maintains a parameterized
distribution on the set of solution candidates, and the natural gradient is
used to update the distribution's parameters in the direction of higher
expected fitness. We introduce a collection of techniques that address issues
of convergence, robustness, sample complexity, computational complexity and
sensitivity to hyperparameters. This paper explores a number of implementations
of the NES family, ranging from general-purpose multi-variate normal
distributions to heavy-tailed and separable distributions tailored towards
global optimization and search in high dimensional spaces, respectively.
Experimental results show best published performance on various standard
benchmarks, as well as competitive performance on others.
|
1106.4507
|
OFDM pilot allocation for sparse channel estimation
|
cs.IT math.IT
|
In communication systems, efficient use of the spectrum is an indispensable
concern. Recently the use of compressed sensing for the purpose of estimating
Orthogonal Frequency Division Multiplexing (OFDM) sparse multipath channels has
been proposed to decrease the transmitted overhead in form of the pilot
subcarriers which are essential for channel estimation. In this paper, we
investigate the problem of deterministic pilot allocation in OFDM systems. The
method is based on minimizing the coherence of the submatrix of the unitary
Discrete Fourier Transform (DFT) matrix associated with the pilot subcarriers.
Unlike the usual case of equidistant pilot subcarriers, we show that
non-uniform patterns based on cyclic difference sets are optimal. In cases
where there are no difference sets, we perform a greedy search method for
finding a suboptimal solution. We also investigate the performance of the
recovery methods such as Orthogonal Matching Pursuit (OMP) and Iterative Method
with Adaptive Thresholding (IMAT) for estimation of the channel taps.
|
1106.4509
|
Machine Learning Markets
|
cs.AI cs.MA cs.NE q-fin.TR stat.ML
|
Prediction markets show considerable promise for developing flexible
mechanisms for machine learning. Here, machine learning markets for
multivariate systems are defined, and a utility-based framework is established
for their analysis. This differs from the usual approach of defining static
betting functions. It is shown that such markets can implement model
combination methods used in machine learning, such as product of expert and
mixture of expert approaches as equilibrium pricing models, by varying agent
utility functions. They can also implement models composed of local potentials,
and message passing methods. Prediction markets also allow for more flexible
combinations, by combining multiple different utility functions. Conversely,
the market mechanisms implement inference in the relevant probabilistic models.
This means that market mechanism can be utilized for implementing parallelized
model building and inference for probabilistic modelling.
|
1106.4514
|
Sub-Nyquist Sampling: Bridging Theory and Practice
|
cs.IT cs.ET math.IT
|
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.
|
1106.4557
|
Learning When Training Data are Costly: The Effect of Class Distribution
on Tree Induction
|
cs.AI
|
For large, real-world inductive learning problems, the number of training
examples often must be limited due to the costs associated with procuring,
preparing, and storing the training examples and/or the computational costs
associated with learning from them. In such circumstances, one question of
practical importance is: if only n training examples can be selected, in what
proportion should the classes be represented? In this article we help to answer
this question by analyzing, for a fixed training-set size, the relationship
between the class distribution of the training data and the performance of
classification trees induced from these data. We study twenty-six data sets
and, for each, determine the best class distribution for learning. The
naturally occurring class distribution is shown to generally perform well when
classifier performance is evaluated using undifferentiated error rate (0/1
loss). However, when the area under the ROC curve is used to evaluate
classifier performance, a balanced distribution is shown to perform well. Since
neither of these choices for class distribution always generates the
best-performing classifier, we introduce a budget-sensitive progressive
sampling algorithm for selecting training examples based on the class
associated with each example. An empirical analysis of this algorithm shows
that the class distribution of the resulting training set yields classifiers
with good (nearly-optimal) classification performance.
|
1106.4561
|
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
|
cs.AI
|
In recent years research in the planning community has moved increasingly
toward s application of planners to realistic problems involving both time and
many typ es of resources. For example, interest in planning demonstrated by the
space res earch community has inspired work in observation scheduling,
planetary rover ex ploration and spacecraft control domains. Other temporal and
resource-intensive domains including logistics planning, plant control and
manufacturing have also helped to focus the community on the modelling and
reasoning issues that must be confronted to make planning technology meet the
challenges of application. The International Planning Competitions have acted
as an important motivating fo rce behind the progress that has been made in
planning since 1998. The third com petition (held in 2002) set the planning
community the challenge of handling tim e and numeric resources. This
necessitated the development of a modelling langua ge capable of expressing
temporal and numeric properties of planning domains. In this paper we describe
the language, PDDL2.1, that was used in the competition. We describe the syntax
of the language, its formal semantics and the validation of concurrent plans.
We observe that PDDL2.1 has considerable modelling power --- exceeding the
capabilities of current planning technology --- and presents a number of
important challenges to the research community.
|
1106.4569
|
The Communicative Multiagent Team Decision Problem: Analyzing Teamwork
Theories and Models
|
cs.AI
|
Despite the significant progress in multiagent teamwork, existing research
does not address the optimality of its prescriptions nor the complexity of the
teamwork problem. Without a characterization of the optimality-complexity
tradeoffs, it is impossible to determine whether the assumptions and
approximations made by a particular theory gain enough efficiency to justify
the losses in overall performance. To provide a tool for use by multiagent
researchers in evaluating this tradeoff, we present a unified framework, the
COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model
combines and extends existing multiagent theories, such as decentralized
partially observable Markov decision processes and economic team theory. In
addition to their generality of representation, COM-MTDPs also support the
analysis of both the optimality of team performance and the computational
complexity of the agents' decision problem. In analyzing complexity, we present
a breakdown of the computational complexity of constructing optimal teams under
various classes of problem domains, along the dimensions of observability and
communication cost. In analyzing optimality, we exploit the COM-MTDP's ability
to encode existing teamwork theories and models to encode two instantiations of
joint intentions theory taken from the literature. Furthermore, the COM-MTDP
model provides a basis for the development of novel team coordination
algorithms. We derive a domain-independent criterion for optimal communication
and provide a comparative analysis of the two joint intentions instantiations
with respect to this optimal policy. We have implemented a reusable,
domain-independent software package based on COM-MTDPs to analyze teamwork
coordination strategies, and we demonstrate its use by encoding and evaluating
the two joint intentions strategies within an example domain.
|
1106.4570
|
Competitive Safety Analysis: Robust Decision-Making in Multi-Agent
Systems
|
cs.GT cs.AI
|
Much work in AI deals with the selection of proper actions in a given (known
or unknown) environment. However, the way to select a proper action when facing
other agents is quite unclear. Most work in AI adopts classical game-theoretic
equilibrium analysis to predict agent behavior in such settings. This approach
however does not provide us with any guarantee for the agent. In this paper we
introduce competitive safety analysis. This approach bridges the gap between
the desired normative AI approach, where a strategy should be selected in order
to guarantee a desired payoff, and equilibrium analysis. We show that a safety
level strategy is able to guarantee the value obtained in a Nash equilibrium,
in several classical computer science settings. Then, we discuss the concept of
competitive safety strategies, and illustrate its use in a decentralized load
balancing setting, typical to network problems. In particular, we show that
when we have many agents, it is possible to guarantee an expected payoff which
is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion
of competitive safety analysis for decentralized load balancing is further
developed to deal with many communication links and arbitrary speeds. Finally,
we discuss the extension of the above concepts to Bayesian games, and
illustrate their use in a basic auctions setup.
|
1106.4571
|
Acquiring Word-Meaning Mappings for Natural Language Interfaces
|
cs.CL cs.AI
|
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance.
|
1106.4572
|
Specific-to-General Learning for Temporal Events with Application to
Learning Event Definitions from Video
|
cs.AI cs.LG
|
We develop, analyze, and evaluate a novel, supervised, specific-to-general
learner for a simple temporal logic and use the resulting algorithm to learn
visual event definitions from video sequences. First, we introduce a simple,
propositional, temporal, event-description language called AMA that is
sufficiently expressive to represent many events yet sufficiently restrictive
to support learning. We then give algorithms, along with lower and upper
complexity bounds, for the subsumption and generalization problems for AMA
formulas. We present a positive-examples--only specific-to-general learning
method based on these algorithms. We also present a polynomial-time--computable
``syntactic'' subsumption test that implies semantic subsumption without being
equivalent to it. A generalization algorithm based on syntactic subsumption can
be used in place of semantic generalization to improve the asymptotic
complexity of the resulting learning algorithm. Finally, we apply this
algorithm to the task of learning relational event definitions from video and
show that it yields definitions that are competitive with hand-coded ones.
|
1106.4573
|
Towards Adjustable Autonomy for the Real World
|
cs.AI
|
Adjustable autonomy refers to entities dynamically varying their own
autonomy, transferring decision-making control to other entities (typically
agents transferring control to human users) in key situations. Determining
whether and when such transfers-of-control should occur is arguably the
fundamental research problem in adjustable autonomy. Previous work has
investigated various approaches to addressing this problem but has often
focused on individual agent-human interactions. Unfortunately, domains
requiring collaboration between teams of agents and humans reveal two key
shortcomings of these previous approaches. First, these approaches use rigid
one-shot transfers of control that can result in unacceptable coordination
failures in multiagent settings. Second, they ignore costs (e.g., in terms of
time delays or effects on actions) to an agent's team due to such
transfers-of-control. To remedy these problems, this article presents a novel
approach to adjustable autonomy, based on the notion of a transfer-of-control
strategy. A transfer-of-control strategy consists of a conditional sequence of
two types of actions: (i) actions to transfer decision-making control (e.g.,
from an agent to a user or vice versa) and (ii) actions to change an agent's
pre-specified coordination constraints with team members, aimed at minimizing
miscoordination costs. The goal is for high-quality individual decisions to be
made with minimal disruption to the coordination of the team. We present a
mathematical model of transfer-of-control strategies. The model guides and
informs the operationalization of the strategies using Markov Decision
Processes, which select an optimal strategy, given an uncertain environment and
costs to the individuals and teams. The approach has been carefully evaluated,
including via its use in a real-world, deployed multi-agent system that assists
a research group in its daily activities.
|
1106.4574
|
Better Mini-Batch Algorithms via Accelerated Gradient Methods
|
cs.LG
|
Mini-batch algorithms have been proposed as a way to speed-up stochastic
convex optimization problems. We study how such algorithms can be improved
using accelerated gradient methods. We provide a novel analysis, which shows
how standard gradient methods may sometimes be insufficient to obtain a
significant speed-up and propose a novel accelerated gradient algorithm, which
deals with this deficiency, enjoys a uniformly superior guarantee and works
well in practice.
|
1106.4575
|
An Analysis of Phase Transition in NK Landscapes
|
cs.AI
|
In this paper, we analyze the decision version of the NK landscape model from
the perspective of threshold phenomena and phase transitions under two random
distributions, the uniform probability model and the fixed ratio model. For the
uniform probability model, we prove that the phase transition is easy in the
sense that there is a polynomial algorithm that can solve a random instance of
the problem with the probability asymptotic to 1 as the problem size tends to
infinity. For the fixed ratio model, we establish several upper bounds for the
solubility threshold, and prove that random instances with parameters above
these upper bounds can be solved polynomially. This, together with our
empirical study for random instances generated below and in the phase
transition region, suggests that the phase transition of the fixed ratio model
is also easy.
|
1106.4576
|
Expert-Guided Subgroup Discovery: Methodology and Application
|
cs.AI
|
This paper presents an approach to expert-guided subgroup discovery. The main
step of the subgroup discovery process, the induction of subgroup descriptions,
is performed by a heuristic beam search algorithm, using a novel parametrized
definition of rule quality which is analyzed in detail. The other important
steps of the proposed subgroup discovery process are the detection of
statistically significant properties of selected subgroups and subgroup
visualization: statistically significant properties are used to enrich the
descriptions of induced subgroups, while the visualization shows subgroup
properties in the form of distributions of the numbers of examples in the
subgroups. The approach is illustrated by the results obtained for a medical
problem of early detection of patient risk groups.
|
1106.4577
|
Interactive Execution Monitoring of Agent Teams
|
cs.MA cs.AI
|
There is an increasing need for automated support for humans monitoring the
activity of distributed teams of cooperating agents, both human and machine. We
characterize the domain-independent challenges posed by this problem, and
describe how properties of domains influence the challenges and their
solutions. We will concentrate on dynamic, data-rich domains where humans are
ultimately responsible for team behavior. Thus, the automated aid should
interactively support effective and timely decision making by the human. We
present a domain-independent categorization of the types of alerts a plan-based
monitoring system might issue to a user, where each type generally requires
different monitoring techniques. We describe a monitoring framework for
integrating many domain-specific and task-specific monitoring techniques and
then using the concept of value of an alert to avoid operator overload. We use
this framework to describe an execution monitoring approach we have used to
implement Execution Assistants (EAs) in two different dynamic, data-rich,
real-world domains to assist a human in monitoring team behavior. One domain
(Army small unit operations) has hundreds of mobile, geographically distributed
agents, a combination of humans, robots, and vehicles. The other domain (teams
of unmanned ground and air vehicles) has a handful of cooperating robots. Both
domains involve unpredictable adversaries in the vicinity. Our approach
customizes monitoring behavior for each specific task, plan, and situation, as
well as for user preferences. Our EAs alert the human controller when reported
events threaten plan execution or physically threaten team members. Alerts were
generated in a timely manner without inundating the user with too many alerts
(less than 10 percent of alerts are unwanted, as judged by domain experts).
|
1106.4578
|
Propositional Independence - Formula-Variable Independence and
Forgetting
|
cs.AI
|
Independence -- the study of what is relevant to a given problem of reasoning
-- has received an increasing attention from the AI community. In this paper,
we consider two basic forms of independence, namely, a syntactic one and a
semantic one. We show features and drawbacks of them. In particular, while the
syntactic form of independence is computationally easy to check, there are
cases in which things that intuitively are not relevant are not recognized as
such. We also consider the problem of forgetting, i.e., distilling from a
knowledge base only the part that is relevant to the set of queries constructed
from a subset of the alphabet. While such process is computationally hard, it
allows for a simplification of subsequent reasoning, and can thus be viewed as
a form of compilation: once the relevant part of a knowledge base has been
extracted, all reasoning tasks to be performed can be simplified.
|
1106.4600
|
Perturbed and Permuted: Signal Integration in Network-Structured Dynamic
Systems
|
q-bio.QM cond-mat.dis-nn cs.SY math.DS q-bio.MN
|
Biological systems (among others) may respond to a large variety of distinct
external stimuli, or signals. These perturbations will generally be presented
to the system not singly, but in various combinations, so that a proper
understanding of the system response requires assessment of the degree to which
the effects of one signal modulate the effects of another. This paper develops
a pair of structural metrics for sparse differential equation models of complex
dynamic systems and demonstrates that said metrics correlate with proxies of
the susceptibility of one signal-response to be altered in the context of a
second signal. One of these metrics may be interpreted as a normalized arc
density in the neighborhood of certain influential nodes; this metric appears
to correlate with increased independence of signal response.
|
1106.4632
|
Inferring 3D Articulated Models for Box Packaging Robot
|
cs.RO cs.AI cs.CV
|
Given a point cloud, we consider inferring kinematic models of 3D articulated
objects such as boxes for the purpose of manipulating them. While previous work
has shown how to extract a planar kinematic model (often represented as a
linear chain), such planar models do not apply to 3D objects that are composed
of segments often linked to the other segments in cyclic configurations. We
present an approach for building a model that captures the relation between the
input point cloud features and the object segment as well as the relation
between the neighboring object segments. We use a conditional random field that
allows us to model the dependencies between different segments of the object.
We test our approach on inferring the kinematic structure from partial and
noisy point cloud data for a wide variety of boxes including cake boxes, pizza
boxes, and cardboard cartons of several sizes. The inferred structure enables
our robot to successfully close these boxes by manipulating the flaps.
|
1106.4649
|
Space-Efficient Data-Analysis Queries on Grids
|
cs.DS cs.CG cs.DB
|
We consider various data-analysis queries on two-dimensional points. We give
new space/time tradeoffs over previous work on geometric queries such as
dominance and rectangle visibility, and on semigroup and group queries such as
sum, average, variance, minimum and maximum. We also introduce new solutions to
queries less frequently considered in the literature such as two-dimensional
quantiles, majorities, successor/predecessor, mode, and various top-$k$
queries, considering static and dynamic scenarios.
|
1106.4692
|
Early Phishing
|
cs.CR cs.CY cs.SI
|
The history of phishing traces back in important ways to the mid-1990s when
hacking software facilitated the mass targeting of people in password stealing
scams on America Online (AOL). The first of these software programs was mine,
called AOHell, and it was where the word phishing was coined. The software
provided an automated password and credit card-stealing mechanism starting in
January 1995. Though the practice of tricking users in order to steal passwords
or information possibly goes back to the earliest days of computer networking,
AOHell's phishing system was the first automated tool made publicly available
for this purpose. The program influenced the creation of many other automated
phishing systems that were made over a number of years. These tools were
available to amateurs who used them to engage in a countless number of phishing
attacks. By the later part of the decade, the activity moved from AOL to other
networks and eventually grew to involve professional criminals on the internet.
What began as a scheme by rebellious teenagers to steal passwords evolved into
one of the top computer security threats affecting people, corporations, and
governments.
|
1106.4728
|
Large Zero Autocorrelation Zone of Golay Sequences and $4^q$-QAM Golay
Complementary Sequences
|
cs.IT math.IT
|
Sequences with good correlation properties have been widely adopted in modern
communications, radar and sonar applications. In this paper, we present our new
findings on some constructions of single $H$-ary Golay sequence and $4^q$-QAM
Golay complementary sequence with a large zero autocorrelation zone, where
$H\ge 2$ is an arbitrary even integer and $q\ge 2$ is an arbitrary integer.
Those new results on Golay sequences and QAM Golay complementary sequences can
be explored during synchronization and detection at the receiver end and thus
improve the performance of the communication system.
|
1106.4862
|
Translation of Pronominal Anaphora between English and Spanish:
Discrepancies and Evaluation
|
cs.CL cs.AI
|
This paper evaluates the different tasks carried out in the translation of
pronominal anaphora in a machine translation (MT) system. The MT interlingua
approach named AGIR (Anaphora Generation with an Interlingua Representation)
improves upon other proposals presented to date because it is able to translate
intersentential anaphors, detect co-reference chains, and translate Spanish
zero pronouns into English---issues hardly considered by other systems. The
paper presents the resolution and evaluation of these anaphora problems in AGIR
with the use of different kinds of knowledge (lexical, morphological,
syntactic, and semantic). The translation of English and Spanish anaphoric
third-person personal pronouns (including Spanish zero pronouns) into the
target language has been evaluated on unrestricted corpora. We have obtained a
precision of 80.4% and 84.8% in the translation of Spanish and English
pronouns, respectively. Although we have only studied the Spanish and English
languages, our approach can be easily extended to other languages such as
Portuguese, Italian, or Japanese.
|
1106.4863
|
Monte Carlo Methods for Tempo Tracking and Rhythm Quantization
|
cs.AI
|
We present a probabilistic generative model for timing deviations in
expressive music performance. The structure of the proposed model is equivalent
to a switching state space model. The switch variables correspond to discrete
note locations as in a musical score. The continuous hidden variables denote
the tempo. We formulate two well known music recognition problems, namely tempo
tracking and automatic transcription (rhythm quantization) as filtering and
maximum a posteriori (MAP) state estimation tasks. Exact computation of
posterior features such as the MAP state is intractable in this model class, so
we introduce Monte Carlo methods for integration and optimization. We compare
Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated
annealing and iterative improvement) and sequential Monte Carlo methods
(particle filters). Our simulation results suggest better results with
sequential methods. The methods can be applied in both online and batch
scenarios such as tempo tracking and transcription and are thus potentially
useful in a number of music applications such as adaptive automatic
accompaniment, score typesetting and music information retrieval.
|
1106.4864
|
Exploiting Contextual Independence In Probabilistic Inference
|
cs.AI
|
Bayesian belief networks have grown to prominence because they provide
compact representations for many problems for which probabilistic inference is
appropriate, and there are algorithms to exploit this compactness. The next
step is to allow compact representations of the conditional probabilities of a
variable given its parents. In this paper we present such a representation that
exploits contextual independence in terms of parent contexts; which variables
act as parents may depend on the value of other variables. The internal
representation is in terms of contextual factors (confactors) that is simply a
pair of a context and a table. The algorithm, contextual variable elimination,
is based on the standard variable elimination algorithm that eliminates the
non-query variables in turn, but when eliminating a variable, the tables that
need to be multiplied can depend on the context. This algorithm reduces to
standard variable elimination when there is no contextual independence
structure to exploit. We show how this can be much more efficient than variable
elimination when there is structure to exploit. We explain why this new method
can exploit more structure than previous methods for structured belief network
inference and an analogous algorithm that uses trees.
|
1106.4865
|
Bound Propagation
|
cs.AI
|
In this article we present an algorithm to compute bounds on the marginals of
a graphical model. For several small clusters of nodes upper and lower bounds
on the marginal values are computed independently of the rest of the network.
The range of allowed probability distributions over the surrounding nodes is
restricted using earlier computed bounds. As we will show, this can be
considered as a set of constraints in a linear programming problem of which the
objective function is the marginal probability of the center nodes. In this way
knowledge about the maginals of neighbouring clusters is passed to other
clusters thereby tightening the bounds on their marginals. We show that sharp
bounds can be obtained for undirected and directed graphs that are used for
practical applications, but for which exact computations are infeasible.
|
1106.4866
|
On Polynomial Sized MDP Succinct Policies
|
cs.AI
|
Policies of Markov Decision Processes (MDPs) determine the next action to
execute from the current state and, possibly, the history (the past states).
When the number of states is large, succinct representations are often used to
compactly represent both the MDPs and the policies in a reduced amount of
space. In this paper, some problems related to the size of succinctly
represented policies are analyzed. Namely, it is shown that some MDPs have
policies that can only be represented in space super-polynomial in the size of
the MDP, unless the polynomial hierarchy collapses. This fact motivates the
study of the problem of deciding whether a given MDP has a policy of a given
size and reward. Since some algorithms for MDPs work by finding a succinct
representation of the value function, the problem of deciding the existence of
a succinct representation of a value function of a given size and reward is
also considered.
|
1106.4867
|
Compiling Causal Theories to Successor State Axioms and STRIPS-Like
Systems
|
cs.AI
|
We describe a system for specifying the effects of actions. Unlike those
commonly used in AI planning, our system uses an action description language
that allows one to specify the effects of actions using domain rules, which are
state constraints that can entail new action effects from old ones.
Declaratively, an action domain in our language corresponds to a nonmonotonic
causal theory in the situation calculus. Procedurally, such an action domain is
compiled into a set of logical theories, one for each action in the domain,
from which fully instantiated successor state-like axioms and STRIPS-like
systems are then generated. We expect the system to be a useful tool for
knowledge engineers writing action specifications for classical AI planning
systems, GOLOG systems, and other systems where formal specifications of
actions are needed.
|
1106.4868
|
VHPOP: Versatile Heuristic Partial Order Planner
|
cs.AI
|
VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP.
It draws from the experience gained in the early to mid 1990's on flaw
selection strategies for POCL planning, and combines this with more recent
developments in the field of domain independent planning such as distance based
heuristics and reachability analysis. We present an adaptation of the additive
heuristic for plan space planning, and modify it to account for possible reuse
of existing actions in a plan. We also propose a large set of novel flaw
selection strategies, and show how these can help us solve more problems than
previously possible by POCL planners. VHPOP also supports planning with
durative actions by incorporating standard techniques for temporal constraint
reasoning. We demonstrate that the same heuristic techniques used to boost the
performance of classical POCL planning can be effective in domains with
durative actions as well. The result is a versatile heuristic POCL planner
competitive with established CSP-based and heuristic state space planners.
|
1106.4869
|
SHOP2: An HTN Planning System
|
cs.AI
|
The SHOP2 planning system received one of the awards for distinguished
performance in the 2002 International Planning Competition. This paper
describes the features of SHOP2 which enabled it to excel in the competition,
especially those aspects of SHOP2 that deal with temporal and metric planning
domains.
|
1106.4871
|
An Architectural Approach to Ensuring Consistency in Hierarchical
Execution
|
cs.AI
|
Hierarchical task decomposition is a method used in many agent systems to
organize agent knowledge. This work shows how the combination of a hierarchy
and persistent assertions of knowledge can lead to difficulty in maintaining
logical consistency in asserted knowledge. We explore the problematic
consequences of persistent assumptions in the reasoning process and introduce
novel potential solutions. Having implemented one of the possible solutions,
Dynamic Hierarchical Justification, its effectiveness is demonstrated with an
empirical analysis.
|
1106.4872
|
Wrapper Maintenance: A Machine Learning Approach
|
cs.AI
|
The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task.
|
1106.4880
|
Semantic Inference using Chemogenomics Data for Drug Discovery
|
q-bio.QM cs.DL cs.IR
|
Background Semantic Web Technology (SWT) makes it possible to integrate and
search the large volume of life science datasets in the public domain, as
demonstrated by well-known linked data projects such as LODD, Bio2RDF, and
Chem2Bio2RDF. Integration of these sets creates large networks of information.
We have previously described a tool called WENDI for aggregating information
pertaining to new chemical compounds, effectively creating evidence paths
relating the compounds to genes, diseases and so on. In this paper we examine
the utility of automatically inferring new compound-disease associations (and
thus new links in the network) based on semantically marked-up versions of
these evidence paths, rule-sets and inference engines.
Results Through the implementation of a semantic inference algorithm, rule
set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have
created a new tool called Chemogenomic Explorer that uses networks of
ontologically annotated RDF statements along with deductive reasoning tools to
infer new associations between the query structure and genes and diseases from
WENDI results. The tool then permits interactive clustering and filtering of
these evidence paths.
Conclusions We present a new aggregate approach to inferring links between
chemical compounds and diseases using semantic inference. This approach allows
multiple evidence paths between compounds and diseases to be identified using a
rule-set and semantically annotated data, and for these evidence paths to be
clustered to show overall evidence linking the compound to a disease. We
believe this is a powerful approach, because it allows compound-disease
relationships to be ranked by the amount of evidence supporting them.
|
1106.4907
|
Face Identification from Manipulated Facial Images using SIFT
|
cs.CV
|
Editing on digital images is ubiquitous. Identification of deliberately
modified facial images is a new challenge for face identification system. In
this paper, we address the problem of identification of a face or person from
heavily altered facial images. In this face identification problem, the input
to the system is a manipulated or transformed face image and the system reports
back the determined identity from a database of known individuals. Such a
system can be useful in mugshot identification in which mugshot database
contains two views (frontal and profile) of each criminal. We considered only
frontal view from the available database for face identification and the query
image is a manipulated face generated by face transformation software tool
available online. We propose SIFT features for efficient face identification in
this scenario. Further comparative analysis has been given with well known
eigenface approach. Experiments have been conducted with real case images to
evaluate the performance of both methods.
|
1106.4925
|
Belief-propagation algorithm and the Ising model on networks with
arbitrary distributions of motifs
|
cond-mat.dis-nn cs.AI
|
We generalize the belief-propagation algorithm to sparse random networks with
arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in
these networks belongs to a given set of motifs (generalization of the
configuration model). These networks can be treated as sparse uncorrelated
hypergraphs in which hyperedges represent motifs. Here a hypergraph is a
generalization of a graph, where a hyperedge can connect any number of
vertices. These uncorrelated hypergraphs are tree-like (hypertrees), which
crucially simplify the problem and allow us to apply the belief-propagation
algorithm to these loopy networks with arbitrary motifs. As natural examples,
we consider motifs in the form of finite loops and cliques. We apply the
belief-propagation algorithm to the ferromagnetic Ising model on the resulting
random networks. We obtain an exact solution of this model on networks with
finite loops or cliques as motifs. We find an exact critical temperature of the
ferromagnetic phase transition and demonstrate that with increasing the
clustering coefficient and the loop size, the critical temperature increases
compared to ordinary tree-like complex networks. Our solution also gives the
birth point of the giant connected component in these loopy networks.
|
1106.4987
|
The Cosparse Analysis Model and Algorithms
|
math.NA cs.IT math.IT
|
After a decade of extensive study of the sparse representation synthesis
model, we can safely say that this is a mature and stable field, with clear
theoretical foundations, and appealing applications. Alongside this approach,
there is an analysis counterpart model, which, despite its similarity to the
synthesis alternative, is markedly different. Surprisingly, the analysis model
did not get a similar attention, and its understanding today is shallow and
partial. In this paper we take a closer look at the analysis approach, better
define it as a generative model for signals, and contrast it with the synthesis
one. This work proposes effective pursuit methods that aim to solve inverse
problems regularized with the analysis-model prior, accompanied by a
preliminary theoretical study of their performance. We demonstrate the
effectiveness of the analysis model in several experiments.
|
1106.4988
|
The uniform controllability property of semidiscrete approximations for
the parabolic distributed parameter systems in Banach spaces
|
math.OC cs.SY
|
The problem we consider in this work is to minimize the L^q-norm (q > 2) of
the semidiscrete controls. As shown in [LT06], under the main approximation
assumptions that the discretized semigroup is uniformly analytic and that the
degree of unboundedness of control operator is lower than 1/2, the uniform
controllability property of semidiscrete approximations for the parabolic
systems is achieved in L^2. In the present paper, we show that the uniform
controllability property still continue to be asserted in L^q. (q > 2) even
with the con- dition that the degree of unboundedness of control operator is
greater than 1/2. Moreover, the minimization procedure to compute the ap-
proximation controls is provided. An example of application is imple- mented
for the one dimensional heat equation with Dirichlet boundary control.
|
1106.5003
|
Voltage Collapse and ODE Approach to Power Flows: Analysis of a Feeder
Line with Static Disorder in Consumption/Production
|
nlin.AO cs.CE physics.soc-ph
|
We consider a model of a distribution feeder connecting multiple loads to the
sub-station. Voltage is controlled directly at the head of the line
(sub-station), however, voltage anywhere further down the line is subject to
fluctuations, caused by irregularities of real and reactive distributed power
consumption/generation. The lack of a direct control of voltage along the line
may result in the voltage instability, also called voltage collapse -
phenomenon well known and documented in the power engineering literature.
Motivated by emerging photo-voltaic technology, which brings a new source of
renewable generation but also contributes significant increase in power flow
fluctuations, we reexamine the phenomenon of voltage stability and collapse. In
the limit where the number of consumers is large and spatial variations in
power flows are smooth functions of position along the feeder, we derive a set
of the power flow Ordinary Differential Equations (ODE), verify phenomenon of
voltage collapse, and study the effect of disorder and irregularity in
injection and consumption on the voltage profile by simulating the stochastic
ODE. We observe that disorder leads to nonlinear amplification of the voltage
variations at the end of the line as the point of voltage collapse is
approached. We also find that the disorder, when correlated on a scale
sufficiently small compared to the length of the line, self-averages, i.e. the
voltage profile remains spatially smooth for any individual realization of the
disorder and is correlated only at scales comparable to the length of the line.
Finally, we explain why the integrated effect of disorder on the voltage at the
end of the line cannot be described within a naive one-generator-one-load
model.
|
1106.5037
|
Fast and Efficient Compressive Sensing using Structurally Random
Matrices
|
cs.IT math.IT
|
This paper introduces a new framework of fast and efficient sensing matrices
for practical compressive sensing, called Structurally Random Matrix (SRM). In
the proposed framework, we pre-randomize a sensing signal by scrambling its
samples or flipping its sample signs and then fast-transform the randomized
samples and finally, subsample the transform coefficients as the final sensing
measurements. SRM is highly relevant for large-scale, real-time compressive
sensing applications as it has fast computation and supports block-based
processing. In addition, we can show that SRM has theoretical sensing
performance comparable with that of completely random sensing matrices.
Numerical simulation results verify the validity of the theory as well as
illustrate the promising potentials of the proposed sensing framework.
|
1106.5039
|
The Capacity of MIMO Channels with Per-Antenna Power Constraint
|
cs.IT math.IT math.OC
|
We establish the optimal input signaling and the capacity of MIMO channels
under per-antenna power constraint. While admitting a linear eigenbeam
structure, the optimal input is no longer diagonalizable by the channel right
singular vectors as with sum power constraint. We formulate the capacity
optimization as an SDP problem and solve in closed-form the optimal input
covariance as a function of the dual variable. We then design an efficient
algorithm to find this optimal input signaling for all channel sizes. The
proposed algorithm allows for straightforward implementation in practical
systems in real time. Simulation results show that with equal constraint per
antenna, capacity with per-antenna power can be close to capacity with sum
power, but as the constraint becomes more skew, the two capacities diverge.
Forcing input eigenbeams to match the channel right singular vectors achieves
no improvement over independent signaling and can even be detrimental to
capacity.
|
1106.5040
|
Optimal High Frequency Trading with limit and market orders
|
q-fin.TR cs.SY math.OC q-fin.CP
|
We propose a framework for studying optimal market making policies in a limit
order book (LOB). The bid-ask spread of the LOB is modelled by a Markov chain
with finite values, multiple of the tick size, and subordinated by the Poisson
process of the tick-time clock. We consider a small agent who continuously
submits limit buy/sell orders and submits market orders at discrete dates. The
objective of the market maker is to maximize her expected utility from revenue
over a short term horizon by a tradeoff between limit and market orders, while
controlling her inventory position. This is formulated as a mixed regime
switching regular/ impulse control problem that we characterize in terms of
quasi-variational system by dynamic programming methods. In the case of a
mean-variance criterion with martingale reference price or when the asset price
follows a Levy process and with exponential utility criterion, the dynamic
programming system can be reduced to a system of simple equations involving
only the inventory and spread variables. Calibration procedures are derived for
estimating the transition matrix and intensity parameters for the spread and
for Cox processes modelling the execution of limit orders. Several
computational tests are performed both on simulated and real data, and
illustrate the impact and profit when considering execution priority in limit
orders and market orders
|
1106.5053
|
Modeling Social Networks with Node Attributes using the Multiplicative
Attribute Graph Model
|
cs.SI physics.soc-ph
|
Networks arising from social, technological and natural domains exhibit rich
connectivity patterns and nodes in such networks are often labeled with
attributes or features. We address the question of modeling the structure of
networks where nodes have attribute information. We present a Multiplicative
Attribute Graph (MAG) model that considers nodes with categorical attributes
and models the probability of an edge as the product of individual attribute
link formation affinities. We develop a scalable variational expectation
maximization parameter estimation method. Experiments show that MAG model
reliably captures network connectivity as well as provides insights into how
different attributes shape the network structure.
|
1106.5063
|
On-line Decentralized Charging of Plug-In Electric Vehicles in Power
Systems
|
math.OC cs.SY
|
The concept of plug-in electric vehicles (PEV) are gaining increasing
popularity in recent years, due to the growing societal awareness of reducing
greenhouse gas (GHG) emissions, and gaining independence on foreign oil or
petroleum. Large-scale deployment of PEVs currently faces many challenges. One
particular concern is that the PEV charging can potentially cause significant
impacts on the existing power distribution system, due to the increase in peak
load. As such, this work tries to mitigate the impacts of PEV charging by
proposing a decentralized smart PEV charging algorithm to minimize the
distribution system load variance, so that a `flat' total load profile can be
obtained. The charging algorithm is myopic, in that it controls the PEV
charging processes in each time slot based entirely on the current power system
states, without knowledge about future system dynamics. We provide theoretical
guarantees on the asymptotic optimality of the proposed charging algorithm.
Thus, compared to other forecast based smart charging approaches in the
literature, the charging algorithm not only achieves optimality asymptotically
in an on-line, and decentralized manner, but also is robust against various
uncertainties in the power system, such as random PEV driving patterns and
distributed generation (DG) with highly intermittent renewable energy sources.
|
1106.5111
|
Exploiting Reputation in Distributed Virtual Environments
|
cs.AI
|
The cognitive research on reputation has shown several interesting properties
that can improve both the quality of services and the security in distributed
electronic environments. In this paper, the impact of reputation on
decision-making under scarcity of information will be shown. First, a cognitive
theory of reputation will be presented, then a selection of simulation
experimental results from different studies will be discussed. Such results
concern the benefits of reputation when agents need to find out good sellers in
a virtual market-place under uncertainty and informational cheating.
|
1106.5112
|
The All Relevant Feature Selection using Random Forest
|
cs.AI
|
In this paper we examine the application of the random forest classifier for
the all relevant feature selection problem. To this end we first examine two
recently proposed all relevant feature selection algorithms, both being a
random forest wrappers, on a series of synthetic data sets with varying size.
We show that reasonable accuracy of predictions can be achieved and that
heuristic algorithms that were designed to handle the all relevant problem,
have performance that is close to that of the reference ideal algorithm. Then,
we apply one of the algorithms to four families of semi-synthetic data sets to
assess how the properties of particular data set influence results of feature
selection. Finally we test the procedure using a well-known gene expression
data set. The relevance of nearly all previously established important genes
was confirmed, moreover the relevance of several new ones is discovered.
|
1106.5113
|
Secure Mining of Association Rules in Horizontally Distributed Databases
|
cs.DB cs.CR cs.DC
|
We propose a protocol for secure mining of association rules in horizontally
distributed databases. The current leading protocol is that of Kantarcioglu and
Clifton (TKDE 2004). Our protocol, like theirs, is based on the Fast
Distributed Mining (FDM) algorithm of Cheung et al. (PDIS 1996), which is an
unsecured distributed version of the Apriori algorithm. The main ingredients in
our protocol are two novel secure multi-party algorithms --- one that computes
the union of private subsets that each of the interacting players hold, and
another that tests the inclusion of an element held by one player in a subset
held by another. Our protocol offers enhanced privacy with respect to the
protocol of Kantarcioglu and Clifton. In addition, it is simpler and is
significantly more efficient in terms of communication rounds, communication
cost and computational cost.
|
1106.5122
|
Clustering with Prototype Extraction for Census Data Analysis
|
cs.DB cs.SI physics.soc-ph
|
Not long ago primary census data became available to publicity. It opened
qualitatively new perspectives not only for researchers in demography and
sociology, but also for those people, who somehow face processes occurring in
society. In this paper authors propose using Data Mining methods for searching
hidden patterns in census data. A novel clustering-based technique is described
as well. It allows determining factors which influence people behavior, in
particular decision-making process (as an example, a decision whether to have a
baby or not). Proposed technique is based on clustering a set of respondents,
for whom a certain event have already happened (for instance, a baby was born),
and discovering clusters' prototypes from a set of respondents, for whom this
event hasn't occurred yet. By means of analyzing clusters' and their
prototypes' characteristics it is possible to identify which factors influence
the decision-making process. Authors also provide an experimental example of
the described approach usage.
|
1106.5130
|
Some Properties of R\'{e}nyi Entropy over Countably Infinite Alphabets
|
cs.IT math.IT
|
In this paper we study certain properties of R\'{e}nyi entropy functionals
$H_\alpha(\mathcal{P})$ on the space of probability distributions over
$\mathbb{Z}_+$. Primarily, continuity and convergence issues are addressed.
Some properties shown parallel those known in the finite alphabet case, while
others illustrate a quite different behaviour of R\'enyi entropy in the
infinite case. In particular, it is shown that, for any distribution $\mathcal
P$ and any $r\in[0,\infty]$, there exists a sequence of distributions
$\mathcal{P}_n$ converging to $\mathcal{P}$ with respect to the total variation
distance, such that $\lim_{n\to\infty}\lim_{\alpha\to{1+}}
H_\alpha(\mathcal{P}_n) = \lim_{\alpha\to{1+}}\lim_{n\to\infty}
H_\alpha(\mathcal{P}_n) + r$.
|
1106.5150
|
All scale-free networks are sparse
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We study the realizability of scale free-networks with a given degree
sequence, showing that the fraction of realizable sequences undergoes two
first-order transitions at the values 0 and 2 of the power-law exponent. We
substantiate this finding by analytical reasoning and by a numerical method,
proposed here, based on extreme value arguments, which can be applied to any
given degree distribution. Our results reveal a fundamental reason why large
scale-free networks without constraints on minimum and maximum degree must be
sparse.
|
1106.5156
|
Morphological Reconstruction for Word Level Script Identification
|
cs.CV
|
A line of a bilingual document page may contain text words in regional
language and numerals in English. For Optical Character Recognition (OCR) of
such a document page, it is necessary to identify different script forms before
running an individual OCR system. In this paper, we have identified a tool of
morphological opening by reconstruction of an image in different directions and
regional descriptors for script identification at word level, based on the
observation that every text has a distinct visual appearance. The proposed
system is developed for three Indian major bilingual documents, Kannada, Telugu
and Devnagari containing English numerals. The nearest neighbour and k-nearest
neighbour algorithms are applied to classify new word images. The proposed
algorithm is tested on 2625 words with various font styles and sizes. The
results obtained are quite encouraging
|
1106.5174
|
A Game Theoretical Approach to Broadcast Information Diffusion in Social
Networks
|
cs.SI cs.GT physics.soc-ph
|
One major function of social networks (e.g., massive online social networks)
is the dissemination of information, such as scientific knowledge, news, and
rumors. Information can be propagated by the users of the network via natural
connections in written, oral or electronic form. The information passing from a
sender to receivers and back (in the form of comments) involves all of the
actors considering their knowledge, trust, and popularity, which shape their
publishing and commenting strategies. To understand such human aspects of the
information dissemination, we propose a game theoretical model of a one-way
information forwarding and feedback mechanism in a star-shaped social network
that takes into account the personalities of the communicating actors.
|
1106.5177
|
Coherence-Pattern Guided Compressive Sensing with Unresolved Grids
|
cs.IT math.IT math.NA
|
Highly coherent sensing matrices arise in discretization of continuum imaging
problems such as radar and medical imaging when the grid spacing is below the
Rayleigh threshold.
Algorithms based on techniques of band exclusion (BE) and local optimization
(LO) are proposed to deal with such coherent sensing matrices. These techniques
are embedded in the existing compressed sensing algorithms such as Orthogonal
Matching Pursuit (OMP), Subspace Pursuit (SP), Iterative Hard Thresholding
(IHT), Basis Pursuit (BP) and Lasso, and result in the modified algorithms
BLOOMP, BLOSP, BLOIHT, BP-BLOT and Lasso-BLOT, respectively.
Under appropriate conditions, it is proved that BLOOMP can reconstruct
sparse, widely separated objects up to one Rayleigh length in the Bottleneck
distance {\em independent} of the grid spacing. One of the most distinguishing
attributes of BLOOMP is its capability of dealing with large dynamic ranges.
The BLO-based algorithms are systematically tested with respect to four
performance metrics: dynamic range, noise stability, sparsity and resolution.
With respect to dynamic range and noise stability, BLOOMP is the best
performer. With respect to sparsity, BLOOMP is the best performer for high
dynamic range while for dynamic range near unity BP-BLOT and Lasso-BLOT with
the optimized regularization parameter have the best performance. In the
noiseless case, BP-BLOT has the highest resolving power up to certain dynamic
range.
The algorithms BLOSP and BLOIHT are good alternatives to
BLOOMP and BP/Lasso-BLOT: they are faster than both BLOOMP and BP/Lasso-BLOT
and shares, to a lesser degree, BLOOMP's amazing attribute with respect to
dynamic range.
Detailed comparisons with existing algorithms such as Spectral Iterative Hard
Thresholding (SIHT) and the frame-adapted BP are given.
|
1106.5186
|
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities
for CAD Systems
|
cs.CV
|
Although radiologists can employ CAD systems to characterize malignancies,
pulmonary fibrosis and other chronic diseases; the design of imaging techniques
to quantify infectious diseases continue to lag behind. There exists a need to
create more CAD systems capable of detecting and quantifying characteristic
patterns often seen in respiratory tract infections such as influenza,
bacterial pneumonia, or tuborculosis. One of such patterns is Tree-in-bud (TIB)
which presents \textit{thickened} bronchial structures surrounding by clusters
of \textit{micro-nodules}. Automatic detection of TIB patterns is a challenging
task because of their weak boundary, noisy appearance, and small lesion size.
In this paper, we present two novel methods for automatically detecting TIB
patterns: (1) a fast localization of candidate patterns using information from
local scale of the images, and (2) a M\"{o}bius invariant feature extraction
method based on learned local shape and texture properties. A comparative
evaluation of the proposed methods is presented with a dataset of 39 laboratory
confirmed viral bronchiolitis human parainfluenza (HPIV) CTs and 21 normal lung
CTs. Experimental results demonstrate that the proposed CAD system can achieve
high detection rate with an overall accuracy of 90.96%.
|
1106.5213
|
Personalised Travel Recommendation based on Location Co-occurrence
|
cs.IR
|
We propose a new task of recommending touristic locations based on a user's
visiting history in a geographically remote region. This can be used to plan a
touristic visit to a new city or country, or by travel agencies to provide
personalised travel deals.
A set of geotags is used to compute a location similarity model between two
different regions. The similarity between two landmarks is derived from the
number of users that have visited both places, using a Gaussian density
estimation of the co-occurrence space of location visits to cluster related
geotags. The standard deviation of the kernel can be used as a scale parameter
that determines the size of the recommended landmarks.
A personalised recommendation based on the location similarity model is
evaluated on city and country scale and is able to outperform a location
ranking based on popularity. Especially when a tourist filter based on visit
duration is enforced, the prediction can be accurately adapted to the
preference of the user. An extensive evaluation based on manual annotations
shows that more strict ranking methods like cosine similarity and a proposed
RankDiff algorithm provide more serendipitous recommendations and are able to
link similar locations on opposite sides of the world.
|
1106.5236
|
A General Framework for Structured Sparsity via Proximal Optimization
|
cs.LG stat.ML
|
We study a generalized framework for structured sparsity. It extends the
well-known methods of Lasso and Group Lasso by incorporating additional
constraints on the variables as part of a convex optimization problem. This
framework provides a straightforward way of favouring prescribed sparsity
patterns, such as orderings, contiguous regions and overlapping groups, among
others. Existing optimization methods are limited to specific constraint sets
and tend to not scale well with sample size and dimensionality. We propose a
novel first order proximal method, which builds upon results on fixed points
and successive approximations. The algorithm can be applied to a general class
of conic and norm constraints sets and relies on a proximity operator
subproblem which can be computed explicitly. Experiments on different
regression problems demonstrate the efficiency of the optimization algorithm
and its scalability with the size of the problem. They also demonstrate state
of the art statistical performance, which improves over Lasso and StructOMP.
|
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