id
stringlengths 9
16
| title
stringlengths 4
278
| categories
stringlengths 5
104
| abstract
stringlengths 6
4.09k
|
|---|---|---|---|
1105.1733
|
Linear Hybrid System Falsification With Descent
|
cs.SY math.OC
|
In this paper, we address the problem of local search for the falsification
of hybrid automata with affine dynamics. Namely, if we are given a sequence of
locations and a maximum simulation time, we return the trajectory that comes
the closest to the unsafe set. In order to solve this problem, we formulate it
as a differentiable optimization problem which we solve using Sequential
Quadratic Programming. The purpose of developing such a local search method is
to combine it with high level stochastic optimization algorithms in order to
falsify hybrid systems with complex discrete dynamics and high dimensional
continuous spaces. Experimental results indicate that indeed the local search
procedure improves upon the results of pure stochastic optimization algorithms.
|
1105.1745
|
Analysis of Alternative Metrics for the PAPR Problem in OFDM
Transmission
|
cs.IT math.IT
|
The effective PAPR of the transmit signal is the standard metric to capture
the effect of nonlinear distortion in OFDM transmission. A common rule of thumb
is the log$(N)$ barrier where $N$ is the number of subcarriers which has been
theoretically analyzed by many authors. Recently, new alternative metrics have
been proposed in practice leading potentially to different system design rules
which are theoretically analyzed in this paper. One of the main findings is
that, most surprisingly, the log$(N)$ barrier turns out to be much too
conservative: e.g. for the so-called amplifier-oriented metric the scaling is
rather $\log[ \log(N)]$. To prove this result, new upper bounds on the PAPR
distribution for coded systems are presented as well as a theorem relating PAPR
results to these alternative metrics.
|
1105.1749
|
A Real-Time Model-Based Reinforcement Learning Architecture for Robot
Control
|
cs.AI cs.RO cs.SE
|
Reinforcement Learning (RL) is a method for learning decision-making tasks
that could enable robots to learn and adapt to their situation on-line. For an
RL algorithm to be practical for robotic control tasks, it must learn in very
few actions, while continually taking those actions in real-time. Existing
model-based RL methods learn in relatively few actions, but typically take too
much time between each action for practical on-line learning. In this paper, we
present a novel parallel architecture for model-based RL that runs in real-time
by 1) taking advantage of sample-based approximate planning methods and 2)
parallelizing the acting, model learning, and planning processes such that the
acting process is sufficiently fast for typical robot control cycles. We
demonstrate that algorithms using this architecture perform nearly as well as
methods using the typical sequential architecture when both are given unlimited
time, and greatly out-perform these methods on tasks that require real-time
actions such as controlling an autonomous vehicle.
|
1105.1767
|
A projected gradient dynamical system modeling the dynamics of
bargaining
|
q-fin.TR cs.SY math.DS math.OC
|
We propose a projected gradient dynamical system as a model for a bargaining
scheme for an asset for which the two interested agents have personal
valuations which do not initially coincide. The personal valuations are formed
using subjective beliefs concerning the future states of the world and the
reservation prices are calculated using expected utility theory. The agents are
not rigid concerning their subjective probabilities and are willing to update
them under the pressure to reach finally an agreement concerning the asset. The
proposed projected dynamical system, on the space of probability measures,
provides a model for the evolution of the agents beliefs during the bargaining
period and is constructed so that agreement is reached under the minimum
possible deviation of both agents from their initial beliefs. The convergence
results are shown using techniques from convex dynamics and Lyapunov function
theory.
|
1105.1822
|
On the Preliminary Design of Multiple Gravity-Assist Trajectories
|
math.OC cs.NE cs.SY physics.space-ph
|
In this paper the preliminary design of multiple gravity-assist trajectories
is formulated as a global optimization problem. An analysis of the structure of
the solution space reveals a strong multimodality, which is strictly dependent
on the complexity of the model. On the other hand it is shown how an
oversimplification could prevent finding potentially interesting solutions. A
trajectory model, which represents a compromise between model completeness and
optimization problem complexity is then presented. The exploration of the
resulting solution space is performed through a novel global search approach,
which hybridizes an evolutionary based algorithm with a systematic branching
strategy. This approach allows an efficient exploration of complex solution
domains by automatically balancing local convergence and global search. A
number of difficult multiple gravity-assist trajectory design cases
demonstrates the effectiveness of the proposed methodology.
|
1105.1823
|
Design of Low-Thrust Gravity Assist Trajectories to Europa
|
math.OC cs.SY
|
This paper presents the design of a mission to Europa using solar electric
propulsion as main source of thrust. A direct transcription method based on
Finite Elements in Time was used for the design and optimisation of the entire
low-thrust gravity assist transfer from the Earth to Europa. Prior to that, a
global search algorithm was used to generate a set of suitable first guess
solutions for the transfer to Jupiter, and for the capture in the Jovian
system. In particular, a fast deterministic search algorithm was developed to
find the most promising set of swing-bys to reach Jupiter A second fast search
algorithm was developed to find the best sequence of swing-bys of the Jovian
moons. After introducing the global search algorithms and the direct
transcription through Finite Elements in Time, the paper presents a number of
first guess Solutions and a fully optimised transfer from the Earth to Europa.
|
1105.1829
|
Optimizing low-thrust and gravity assist maneuvers to design
interplanetary trajectories
|
math.OC cs.SY
|
In this paper a direct method based on a transcription by finite elements in
time has been used to design optimal interplanetary trajectories, exploiting a
combination of gravity assist maneuvers and low-thrust propulsion. A multiphase
parametric approach has been used to introduce swing-bys, treated as coast
phases between two thrusted or coasting trajectory arcs. Gravity maneuvers are
at first modeled with a linked-conic approximation and then introduced through
a full three-dimensional propagation including perturbations by the Sun. The
method is successfully applied to the design of a mission to planet Mercury,
for which different options corresponding to different sequences of gravity
maneuvers or launch opportunities are presented.
|
1105.1853
|
Feedback Message Passing for Inference in Gaussian Graphical Models
|
stat.ML cs.AI
|
While loopy belief propagation (LBP) performs reasonably well for inference
in some Gaussian graphical models with cycles, its performance is
unsatisfactory for many others. In particular for some models LBP does not
converge, and in general when it does converge, the computed variances are
incorrect (except for cycle-free graphs for which belief propagation (BP) is
non-iterative and exact). In this paper we propose {\em feedback message
passing} (FMP), a message-passing algorithm that makes use of a special set of
vertices (called a {\em feedback vertex set} or {\em FVS}) whose removal
results in a cycle-free graph. In FMP, standard BP is employed several times on
the cycle-free subgraph excluding the FVS while a special message-passing
scheme is used for the nodes in the FVS. The computational complexity of exact
inference is $O(k^2n)$, where $k$ is the number of feedback nodes, and $n$ is
the total number of nodes. When the size of the FVS is very large, FMP is
intractable. Hence we propose {\em approximate FMP}, where a pseudo-FVS is used
instead of an FVS, and where inference in the non-cycle-free graph obtained by
removing the pseudo-FVS is carried out approximately using LBP. We show that,
when approximate FMP converges, it yields exact means and variances on the
pseudo-FVS and exact means throughout the remainder of the graph. We also
provide theoretical results on the convergence and accuracy of approximate FMP.
In particular, we prove error bounds on variance computation. Based on these
theoretical results, we design efficient algorithms to select a pseudo-FVS of
bounded size. The choice of the pseudo-FVS allows us to explicitly trade off
between efficiency and accuracy. Experimental results show that using a
pseudo-FVS of size no larger than $\log(n)$, this procedure converges much more
often, more quickly, and provides more accurate results than LBP on the entire
graph.
|
1105.1894
|
Decoding Cyclic Codes up to a New Bound on the Minimum Distance
|
cs.IT math.IT
|
A new lower bound on the minimum distance of q-ary cyclic codes is proposed.
This bound improves upon the Bose-Chaudhuri-Hocquenghem (BCH) bound and, for
some codes, upon the Hartmann-Tzeng (HT) bound. Several Boston bounds are
special cases of our bound. For some classes of codes the bound on the minimum
distance is refined. Furthermore, a quadratic-time decoding algorithm up to
this new bound is developed. The determination of the error locations is based
on the Euclidean Algorithm and a modified Chien search. The error evaluation is
done by solving a generalization of Forney's formula.
|
1105.1901
|
Convergence Analysis of Differential Evolution Variants on Unconstrained
Global Optimization Functions
|
cs.NE
|
In this paper, we present an empirical study on convergence nature of
Differential Evolution (DE) variants to solve unconstrained global optimization
problems. The aim is to identify the competitive nature of DE variants in
solving the problem at their hand and compare. We have chosen fourteen
benchmark functions grouped by feature: unimodal and separable, unimodal and
nonseparable, multimodal and separable, and multimodal and nonseparable.
Fourteen variants of DE were implemented and tested on fourteen benchmark
problems for dimensions of 30. The competitiveness of the variants are
identified by the Mean Objective Function value, they achieved in 100 runs. The
convergence nature of the best and worst performing variants are analyzed by
measuring their Convergence Speed (Cs) and Quality Measure (Qm).
|
1105.1917
|
How citation boosts promote scientific paradigm shifts and Nobel Prizes
|
physics.soc-ph cs.DL cs.SI
|
Nobel Prizes are commonly seen to be among the most prestigious achievements
of our times. Based on mining several million citations, we quantitatively
analyze the processes driving paradigm shifts in science. We find that
groundbreaking discoveries of Nobel Prize Laureates and other famous scientists
are not only acknowledged by many citations of their landmark papers.
Surprisingly, they also boost the citation rates of their previous
publications. Given that innovations must outcompete the rich-gets-richer
effect for scientific citations, it turns out that they can make their way only
through citation cascades. A quantitative analysis reveals how and why they
happen. Science appears to behave like a self-organized critical system, in
which citation cascades of all sizes occur, from continuous scientific progress
all the way up to scientific revolutions, which change the way we see our
world. Measuring the "boosting effect" of landmark papers, our analysis reveals
how new ideas and new players can make their way and finally triumph in a world
dominated by established paradigms. The underlying "boost factor" is also
useful to discover scientific breakthroughs and talents much earlier than
through classical citation analysis, which by now has become a widespread
method to measure scientific excellence, influencing scientific careers and the
distribution of research funds. Our findings reveal patterns of collective
social behavior, which are also interesting from an attention economics
perspective. Understanding the origin of scientific authority may therefore
ultimately help to explain, how social influence comes about and why the value
of goods depends so strongly on the attention they attract.
|
1105.1922
|
Numerical Construction of LISS Lyapunov Functions under a Small Gain
Condition
|
math.NA cs.SY math.OC
|
In the stability analysis of large-scale interconnected systems it is
frequently desirable to be able to determine a decay point of the gain
operator, i.e., a point whose image under the monotone operator is strictly
smaller than the point itself. The set of such decay points plays a crucial
role in checking, in a semi-global fashion, the local input-to-state stability
of an interconnected system and in the numerical construction of a LISS
Lyapunov function. We provide a homotopy algorithm that computes a decay point
of a monotone op- erator. For this purpose we use a fixed point algorithm and
provide a function whose fixed points correspond to decay points of the
monotone operator. The advantage to an earlier algorithm is demonstrated.
Furthermore an example is given which shows how to analyze a given perturbed
interconnected system.
|
1105.1929
|
The Hidden Web, XML and Semantic Web: A Scientific Data Management
Perspective
|
cs.AI
|
The World Wide Web no longer consists just of HTML pages. Our work sheds
light on a number of trends on the Internet that go beyond simple Web pages.
The hidden Web provides a wealth of data in semi-structured form, accessible
through Web forms and Web services. These services, as well as numerous other
applications on the Web, commonly use XML, the eXtensible Markup Language. XML
has become the lingua franca of the Internet that allows customized markups to
be defined for specific domains. On top of XML, the Semantic Web grows as a
common structured data source. In this work, we first explain each of these
developments in detail. Using real-world examples from scientific domains of
great interest today, we then demonstrate how these new developments can assist
the managing, harvesting, and organization of data on the Web. On the way, we
also illustrate the current research avenues in these domains. We believe that
this effort would help bridge multiple database tracks, thereby attracting
researchers with a view to extend database technology.
|
1105.1930
|
Emerging multidisciplinary research across database management systems
|
cs.DB
|
The database community is exploring more and more multidisciplinary avenues:
Data semantics overlaps with ontology management; reasoning tasks venture into
the domain of artificial intelligence; and data stream management and
information retrieval shake hands, e.g., when processing Web click-streams.
These new research avenues become evident, for example, in the topics that
doctoral students choose for their dissertations. This paper surveys the
emerging multidisciplinary research by doctoral students in database systems
and related areas. It is based on the PIKM 2010, which is the 3rd Ph.D.
workshop at the International Conference on Information and Knowledge
Management (CIKM). The topics addressed include ontology development, data
streams, natural language processing, medical databases, green energy, cloud
computing, and exploratory search. In addition to core ideas from the workshop,
we list some open research questions in these multidisciplinary areas.
|
1105.1943
|
Asymptotic Analysis of Double-Scattering Channels
|
cs.IT math.IT
|
We consider a multiple-input multiple-output (MIMO) multiple access channel
(MAC), where the channel between each transmitter and the receiver is modeled
by the doubly-scattering channel model. Based on novel techniques from random
matrix theory, we derive deterministic approximations of the mutual
information, the signal-to-noise-plus-interference-ratio (SINR) at the output
of the minimum-mean-square-error (MMSE) detector and the sum-rate with MMSE
detection which are almost surely tight in the large system limit. Moreover, we
derive the asymptotically optimal transmit covariance matrices. Our simulation
results show that the asymptotic analysis provides very close approximations
for realistic system dimensions.
|
1105.1950
|
An analytical framework for data stream mining techniques based on
challenges and requirements
|
cs.DB
|
A growing number of applications that generate massive streams of data need
intelligent data processing and online analysis. Real-time surveillance
systems, telecommunication systems, sensor networks and other dynamic
environments are such examples. The imminent need for turning such data into
useful information and knowledge augments the development of systems,
algorithms and frameworks that address streaming challenges. The storage,
querying and mining of such data sets are highly computationally challenging
tasks. Mining data streams is concerned with extracting knowledge structures
represented in models and patterns in non stopping streams of information.
Generally, two main challenges are designing fast mining methods for data
streams and need to promptly detect changing concepts and data distribution
because of highly dynamic nature of data streams. The goal of this article is
to analyze and classify the application of diverse data mining techniques in
different challenges of data stream mining. In this paper, we present the
theoretical foundations of data stream analysis and propose an analytical
framework for data stream mining techniques.
|
1105.1951
|
Self-configuration from a Machine-Learning Perspective
|
nlin.AO cs.LG stat.ML
|
The goal of machine learning is to provide solutions which are trained by
data or by experience coming from the environment. Many training algorithms
exist and some brilliant successes were achieved. But even in structured
environments for machine learning (e.g. data mining or board games), most
applications beyond the level of toy problems need careful hand-tuning or human
ingenuity (i.e. detection of interesting patterns) or both. We discuss several
aspects how self-configuration can help to alleviate these problems. One aspect
is the self-configuration by tuning of algorithms, where recent advances have
been made in the area of SPO (Sequen- tial Parameter Optimization). Another
aspect is the self-configuration by pattern detection or feature construction.
Forming multiple features (e.g. random boolean functions) and using algorithms
(e.g. random forests) which easily digest many fea- tures can largely increase
learning speed. However, a full-fledged theory of feature construction is not
yet available and forms a current barrier in machine learning. We discuss
several ideas for systematic inclusion of feature construction. This may lead
to partly self-configuring machine learning solutions which show robustness,
flexibility, and fast learning in potentially changing environments.
|
1105.1969
|
Capacity of Discrete Molecular Diffusion Channels
|
cs.IT math.IT
|
In diffusion-based molecular communications, messages can be conveyed via the
variation in the concentration of molecules in the medium. In this paper, we
intend to analyze the achievable capacity in transmission of information from
one node to another in a diffusion channel. We observe that because of the
molecular diffusion in the medium, the channel possesses memory. We then model
the memory of the channel by a two-step Markov chain and obtain the equations
describing the capacity of the diffusion channel. By performing a numerical
analysis, we obtain the maximum achievable rate for different levels of the
transmitter power, i.e., the molecule production rate.
|
1105.2013
|
Weyl theory and explicit solutions of direct and inverse problems for a
Dirac system with rectangular matrix potential
|
math.SP cs.SY math.CA math.OC
|
A non-classical Weyl theory is developed for Dirac systems with rectangular
matrix potentials. The notion of the Weyl function is introduced and the
corresponding direct problem is treated. Furthermore, explicit solutions of the
direct and inverse problems are obtained for the case of rational Weyl matrix
functions.
|
1105.2017
|
Potential Games for Energy-Efficient Resource Allocation in
Multipoint-to-Multipoint CDMA Wireless Data Networks
|
cs.IT math.IT
|
The problem of noncooperative resource allocation in a
multipoint-to-multipoint cellular network is considered in this paper. The
considered scenario is general enough to represent several key instances of
modern wireless networks such as a multicellular network, a peer-to-peer
network (interference channel), and a wireless network equipped with
femtocells. In particular, the problem of joint transmit waveforms adaptation,
linear receiver design, and transmit power control is examined. Several utility
functions to be maximized are considered, and, among them, we cite the received
SINR, and the transmitter energy efficiency, which is measured in bit/Joule,
and represents the number of successfully delivered bits for each energy unit
used for transmission. Resorting to the theory of potential games,
noncooperative games admitting Nash equilibria in multipoint-to-multipoint
cellular networks regardless of the channel coefficient realizations are
designed. Computer simulations confirm that the considered games are
convergent, and show the huge benefits that resource allocation schemes can
bring to the performance of wireless data networks.
|
1105.2054
|
Generalized Boosting Algorithms for Convex Optimization
|
cs.LG stat.ML
|
Boosting is a popular way to derive powerful learners from simpler hypothesis
classes. Following previous work (Mason et al., 1999; Friedman, 2000) on
general boosting frameworks, we analyze gradient-based descent algorithms for
boosting with respect to any convex objective and introduce a new measure of
weak learner performance into this setting which generalizes existing work. We
present the weak to strong learning guarantees for the existing gradient
boosting work for strongly-smooth, strongly-convex objectives under this new
measure of performance, and also demonstrate that this work fails for
non-smooth objectives. To address this issue, we present new algorithms which
extend this boosting approach to arbitrary convex loss functions and give
corresponding weak to strong convergence results. In addition, we demonstrate
experimental results that support our analysis and demonstrate the need for the
new algorithms we present.
|
1105.2062
|
Scalar Quantization with Random Thresholds
|
cs.IT math.IT
|
The distortion-rate performance of certain randomly-designed scalar
quantizers is determined. The central results are the mean-squared error
distortion and output entropy for quantizing a uniform random variable with
thresholds drawn independently from a uniform distribution. The distortion is
at most 6 times that of an optimal (deterministically-designed) quantizer, and
for a large number of levels the output entropy is reduced by approximately
(1-gamma)/(ln 2) bits, where gamma is the Euler-Mascheroni constant. This shows
that the high-rate asymptotic distortion of these quantizers in an
entropy-constrained context is worse than the optimal quantizer by at most a
factor of 6 exp(-2(1-gamma)).
|
1105.2096
|
Sum Capacity of Gaussian Interfering Multiple Access Channels in the Low
Interference Regime
|
cs.IT math.IT
|
This paper has been withdrawn due to an incorrect proof.
|
1105.2114
|
An algebraic look into MAC-DMT of lattice space-time codes
|
cs.IT math.IT math.NT
|
In this paper we are concentrating on the diversity-multiplexing gain
trade-off (DMT) of some space-time lattice codes. First we give a DMT bound for
lattice codes having restricted dimension. We then recover the well known
results of the DMT of algebraic number field codes and the Alamouti code by
using the union bound and see that these codes do achieve the previously
mentioned bound. During our analysis interesting connections to the Dedekind's
zeta-function and to the unit group of algebraic number fields are revealed.
Finally we prove that both the number field codes and Alamouti code are in some
sense optimal codes in the multiple access channel (MAC).
|
1105.2176
|
A Framework for Optimization under Limited Information
|
math.OC cs.IT cs.LG cs.SY math.IT
|
In many real world problems, optimization decisions have to be made with
limited information. The decision maker may have no a priori or posteriori data
about the often nonconvex objective function except from on a limited number of
points that are obtained over time through costly observations. This paper
presents an optimization framework that takes into account the information
collection (observation), estimation (regression), and optimization
(maximization) aspects in a holistic and structured manner. Explicitly
quantifying the information acquired at each optimization step using the
entropy measure from information theory, the (nonconvex) objective function to
be optimized (maximized) is modeled and estimated by adopting a Bayesian
approach and using Gaussian processes as a state-of-the-art regression method.
The resulting iterative scheme allows the decision maker to solve the problem
by expressing preferences for each aspect quantitatively and concurrently.
|
1105.2211
|
Dual Control with Active Learning using Gaussian Process Regression
|
math.OC cs.IT cs.LG cs.SY math.IT
|
In many real world problems, control decisions have to be made with limited
information. The controller may have no a priori (or even posteriori) data on
the nonlinear system, except from a limited number of points that are obtained
over time. This is either due to high cost of observation or the highly
non-stationary nature of the system. The resulting conflict between information
collection (identification, exploration) and control (optimization,
exploitation) necessitates an active learning approach for iteratively
selecting the control actions which concurrently provide the data points for
system identification. This paper presents a dual control approach where the
information acquired at each control step is quantified using the entropy
measure from information theory and serves as the training input to a
state-of-the-art Gaussian process regression (Bayesian learning) method. The
explicit quantification of the information obtained from each data point allows
for iterative optimization of both identification and control objectives. The
approach developed is illustrated with two examples: control of logistic map as
a chaotic system and position control of a cart with inverted pendulum.
|
1105.2214
|
An improved mathematical model of social group competition
|
physics.soc-ph cs.SI
|
An improved mathematical model of social group competition is proposed. The
utility obtained by a member of a certain group from each other member is
assumed to be group size-dependent. Obtained results are close to available
census data. It is shown that a significant fraction of population can be
affiliated in a group with lower maximal specific utility.
|
1105.2254
|
Symmetries in observer design: review of some recent results and
applications to EKF-based SLAM
|
math.OC cs.RO cs.SY
|
In this paper, we first review the theory of symmetry-preserving observers
and we mention some recent results. Then, we apply the theory to Extended
Kalman Filter-based Simultaneous Localization and Mapping (EKF SLAM). It allows
to derive a new (symmetry-preserving) Extended Kalman Filter for the non-linear
SLAM problem that possesses convergence properties. We also prove a special
choice of the gains ensures global exponential convergence.
|
1105.2255
|
On the Limitations of Provenance for Queries With Difference
|
cs.DB
|
The annotation of the results of database transformations was shown to be
very effective for various applications. Until recently, most works in this
context focused on positive query languages. The provenance semirings is a
particular approach that was proven effective for these languages, and it was
shown that when propagating provenance with semirings, the expected equivalence
axioms of the corresponding query languages are satisfied. There have been
several attempts to extend the framework to account for relational algebra
queries with difference. We show here that these suggestions fail to satisfy
some expected equivalence axioms (that in particular hold for queries on
"standard" set and bag databases). Interestingly, we show that this is not a
pitfall of these particular attempts, but rather every such attempt is bound to
fail in satisfying these axioms, for some semirings. Finally, we show
particular semirings for which an extension for supporting difference is
(im)possible.
|
1105.2257
|
From brain to earth and climate systems: Small-world interaction
networks or not?
|
physics.data-an cs.SI physics.soc-ph
|
We consider recent reports on small-world topologies of interaction networks
derived from the dynamics of spatially extended systems that are investigated
in diverse scientific fields such as neurosciences, geophysics, or meteorology.
With numerical simulations that mimic typical experimental situations we have
identified an important constraint when characterizing such networks:
indications of a small-world topology can be expected solely due to the spatial
sampling of the system along with commonly used time series analysis based
approaches to network characterization.
|
1105.2264
|
Distributed Semantic Web Data Management in HBase and MySQL Cluster
|
cs.DB cs.PF
|
Various computing and data resources on the Web are being enhanced with
machine-interpretable semantic descriptions to facilitate better search,
discovery and integration. This interconnected metadata constitutes the
Semantic Web, whose volume can potentially grow the scale of the Web. Efficient
management of Semantic Web data, expressed using the W3C's Resource Description
Framework (RDF), is crucial for supporting new data-intensive,
semantics-enabled applications. In this work, we study and compare two
approaches to distributed RDF data management based on emerging cloud computing
technologies and traditional relational database clustering technologies. In
particular, we design distributed RDF data storage and querying schemes for
HBase and MySQL Cluster and conduct an empirical comparison of these approaches
on a cluster of commodity machines using datasets and queries from the Third
Provenance Challenge and Lehigh University Benchmark. Our study reveals
interesting patterns in query evaluation, shows that our algorithms are
promising, and suggests that cloud computing has a great potential for scalable
Semantic Web data management.
|
1105.2274
|
Data-Distributed Weighted Majority and Online Mirror Descent
|
cs.LG cs.DC
|
In this paper, we focus on the question of the extent to which online
learning can benefit from distributed computing. We focus on the setting in
which $N$ agents online-learn cooperatively, where each agent only has access
to its own data. We propose a generic data-distributed online learning
meta-algorithm. We then introduce the Distributed Weighted Majority and
Distributed Online Mirror Descent algorithms, as special cases. We show, using
both theoretical analysis and experiments, that compared to a single agent:
given the same computation time, these distributed algorithms achieve smaller
generalization errors; and given the same generalization errors, they can be
$N$ times faster.
|
1105.2283
|
The Deterministic Sum Capacity of a Multiple Access Channel Interfering
with a Point to Point Link
|
cs.IT math.IT
|
In this paper, we use the linear deterministic approximation model to study a
two user multiple access channel mutually interfering with a point to point
link, which represents a basic setup of a cellular system. We derive outer
bounds on the achievable sum rate and construct coding schemes achieving the
outer bounds. For a large parameter range, the sum capacity is identical to the
sum capacity of the interference channel obtained by silencing the weaker user
in the multiple access channel. For other interference configurations, the sum
rate can be increased using interference alignment, which exploits the channel
gain difference of the users in the multiple access channel. From these
results, lower bounds on the generalized degrees of freedom for the Gaussian
counterpart are derived.
|
1105.2291
|
Proof of a Conjecture of Helleseth: Maximal Linear Recursive Sequences
of Period $2^{2^n}-1$ Never Have Three-Valued Cross-Correlation
|
math.CO cs.IT math.IT
|
We prove a conjecture of Helleseth that claims that for any $n \geq 0$, a
pair of binary maximal linear sequences of period $2^{2^n}-1$ can not have a
three-valued cross-correlation function.
|
1105.2311
|
An Achievable Rate Region for the Broadcast Channel with Feedback
|
cs.IT math.IT
|
A single-letter achievable rate region is proposed for the two-receiver
discrete memoryless broadcast channel with generalized feedback. The coding
strategy involves block-Markov superposition coding, using Marton's coding
scheme for the broadcast channel without feedback as the starting point. If the
message rates in the Marton scheme are too high to be decoded at the end of a
block, each receiver is left with a list of messages compatible with its
output. Resolution information is sent in the following block to enable each
receiver to resolve its list. The key observation is that the resolution
information of the first receiver is correlated with that of the second. This
correlated information is efficiently transmitted via joint source-channel
coding, using ideas similar to the Han-Costa coding scheme. Using the result,
we obtain an achievable rate region for the stochastically degraded AWGN
broadcast channel with noisy feedback from only one receiver. It is shown that
this region is strictly larger than the no-feedback capacity region.
|
1105.2361
|
A standard form for generator matrices with respect to the
Niederreiter-Rosenbloom-Tsfasman metric
|
cs.IT math.IT math.NT
|
In this note, we present an analogue for codes in vector spaces with a
Rosenbloom-Tsfasman metric of the well-known standard form of generator
matrices for codes in spaces with the Hamming metric.
|
1105.2375
|
On the DMT of TDD-SIMO Systems with Channel-Dependent Reverse Channel
Training
|
cs.IT math.IT
|
This paper investigates the Diversity-Multiplexing gain Trade-off (DMT) of a
training based reciprocal Single Input Multiple Output (SIMO) system, with (i)
perfect Channel State Information (CSI) at the Receiver (CSIR) and noisy CSI at
the Transmitter (CSIT), and (ii) noisy CSIR and noisy CSIT. In both the cases,
the CSIT is acquired through Reverse Channel Training (RCT), i.e., by sending a
training sequence from the receiver to the transmitter. A channel-dependent
fixed-power training scheme is proposed for acquiring CSIT, along with a
forward-link data transmit power control scheme. With perfect CSIR, the
proposed scheme is shown to achieve a diversity order that is quadratically
increasing with the number of receive antennas. This is in contrast with
conventional orthogonal RCT schemes, where the diversity order is known to
saturate as the number of receive antennas is increased, for a given channel
coherence time. Moreover, the proposed scheme can achieve a larger DMT compared
to the orthogonal training scheme. With noisy CSIR and noisy CSIT, a three-way
training scheme is proposed and its DMT performance is analyzed. It is shown
that nearly the same diversity order is achievable as in the perfect CSIR case.
The time-overhead in the training schemes is explicitly accounted for in this
work, and the results show that the proposed channel-dependent RCT and data
power control schemes offer a significant improvement in terms of the DMT,
compared to channel-agnostic orthogonal RCT schemes. The outage performance of
the proposed scheme is illustrated through Monte-Carlo simulations.
|
1105.2377
|
Entropy rate calculations of algebraic measures
|
cs.IT math.IT
|
Let $K = \{0,1,...,q-1\}$. We use a special class of translation invariant
measures on $K^\mathbb{Z}$ called algebraic measures to study the entropy rate
of a hidden Markov processes. Under some irreducibility assumptions of the
Markov transition matrix we derive exact formulas for the entropy rate of a
general $q$ state hidden Markov process derived from a Markov source corrupted
by a specific noise model. We obtain upper bounds on the error when using an
approximation to the formulas and numerically compute the entropy rates of two
and three state hidden Markov models.
|
1105.2416
|
PAC-Bayesian Analysis of Martingales and Multiarmed Bandits
|
cs.LG stat.ML
|
We present two alternative ways to apply PAC-Bayesian analysis to sequences
of dependent random variables. The first is based on a new lemma that enables
to bound expectations of convex functions of certain dependent random variables
by expectations of the same functions of independent Bernoulli random
variables. This lemma provides an alternative tool to Hoeffding-Azuma
inequality to bound concentration of martingale values. Our second approach is
based on integration of Hoeffding-Azuma inequality with PAC-Bayesian analysis.
We also introduce a way to apply PAC-Bayesian analysis in situation of limited
feedback. We combine the new tools to derive PAC-Bayesian generalization and
regret bounds for the multiarmed bandit problem. Although our regret bound is
not yet as tight as state-of-the-art regret bounds based on other
well-established techniques, our results significantly expand the range of
potential applications of PAC-Bayesian analysis and introduce a new analysis
tool to reinforcement learning and many other fields, where martingales and
limited feedback are encountered.
|
1105.2422
|
Joint Network and LDPC Coding for Bi-directional Relaying
|
cs.IT math.IT
|
In this paper, we consider joint network and LDPC coding for practically
implementing the denosie-and-forward protocol over bi-directional relaying. the
closed-form expressions for computing the log-likelihood ratios of the
network-coded codewords have been derived for both real and complex
multiple-access channels. It is revealed that the equivalent channel observed
at the relay is an asymmetrical channel, where the channel input is the XOR
form of the two source nodes.
|
1105.2434
|
Diffusion in Social Networks with Competing Products
|
cs.SI cs.DS physics.soc-ph
|
We introduce a new threshold model of social networks, in which the nodes
influenced by their neighbours can adopt one out of several alternatives. We
characterize the graphs for which adoption of a product by the whole network is
possible (respectively necessary) and the ones for which a unique outcome is
guaranteed. These characterizations directly yield polynomial time algorithms
that allow us to determine whether a given social network satisfies one of the
above properties.
We also study algorithmic questions for networks without unique outcomes. We
show that the problem of computing the minimum possible spread of a product is
NP-hard to approximate with an approximation ratio better than $\Omega(n)$, in
contrast to the maximum spread, which is efficiently computable. We then move
on to questions regarding the behavior of a node with respect to adopting some
(resp. a given) product. We show that the problem of determining whether a
given node has to adopt some (resp. a given) product in all final networks is
co-NP-complete.
|
1105.2441
|
Science Models as Value-Added Services for Scholarly Information Systems
|
cs.DL cs.IR
|
The paper introduces scholarly Information Retrieval (IR) as a further
dimension that should be considered in the science modeling debate. The IR use
case is seen as a validation model of the adequacy of science models in
representing and predicting structure and dynamics in science. Particular
conceptualizations of scholarly activity and structures in science are used as
value-added search services to improve retrieval quality: a co-word model
depicting the cognitive structure of a field (used for query expansion), the
Bradford law of information concentration, and a model of co-authorship
networks (both used for re-ranking search results). An evaluation of the
retrieval quality when science model driven services are used turned out that
the models proposed actually provide beneficial effects to retrieval quality.
From an IR perspective, the models studied are therefore verified as expressive
conceptualizations of central phenomena in science. Thus, it could be shown
that the IR perspective can significantly contribute to a better understanding
of scholarly structures and activities.
|
1105.2443
|
Comparing webometric with web-independent rankings: a case study with
German universities
|
cs.SI cs.DL physics.soc-ph
|
In this paper we examine if hyperlink-based (webometric) indicators can be
used to rank academic websites. Therefore we analyzed the interlinking
structure of German university websites and compared our simple hyperlink-based
ranking with official and web-independent rankings of universities. We found
that link impact could not easily be seen as a prestige factor for
universities.
|
1105.2447
|
LUNES: Agent-based Simulation of P2P Systems (Extended Version)
|
cs.DC cs.MA cs.NI
|
We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which
allows to simulate complex networks composed of a high number of nodes. LUNES
is modular, since it splits the three phases of network topology creation,
protocol simulation and performance evaluation. This permits to easily
integrate external software tools into the main software architecture. The
simulation of the interaction protocols among network nodes is performed via a
simulation middleware that supports both the sequential and the
parallel/distributed simulation approaches. In the latter case, a specific
mechanism for the communication overhead-reduction is used; this guarantees
high levels of performance and scalability. To demonstrate the efficiency of
LUNES, we test the simulator with gossip protocols executed on top of networks
(representing peer-to-peer overlays), generated with different topologies.
Results demonstrate the effectiveness of the proposed approach.
|
1105.2459
|
Deciphering Network Community Structure by Surprise
|
q-bio.MN cs.SI physics.soc-ph
|
The analysis of complex networks permeates all sciences, from biology to
sociology. A fundamental, unsolved problem is how to characterize the community
structure of a network. Here, using both standard and novel benchmarks, we show
that maximization of a simple global parameter, which we call Surprise (S),
leads to a very efficient characterization of the community structure of
complex synthetic networks. Particularly, S qualitatively outperforms the most
commonly used criterion to define communities, Newman and Girvan's modularity
(Q). Applying S maximization to real networks often provides natural,
well-supported partitions, but also sometimes counterintuitive solutions that
expose the limitations of our previous knowledge. These results indicate that
it is possible to define an effective global criterion for community structure
and open new routes for the understanding of complex networks.
|
1105.2461
|
Optimal grid exploration by asynchronous oblivious robots
|
cs.DC cs.DM cs.NI cs.RO
|
We consider a team of {\em autonomous weak robots} that are endowed with
visibility sensors and motion actuators. Autonomous means that the team cannot
rely on any kind of central coordination mechanism or scheduler. By weak we
mean that the robots are devoid of (1) any (observable) IDs allowing to
differentiate them (anonymous), (2) means of communication allowing them to
communicate directly, and (3) any way to remember any previous observation nor
computation performed in any previous step (oblivious). Robots asynchronously
operate in cycles of three phases: Look, Compute, and Move. Furthermore, the
network is an anonymous unoriented grid. In such settings, the robots must
collaborate to solve a collective task, here the terminating grid exploration
(exploration for short), despite being limited with respect to input from the
environment, asymmetry, memory, etc. Exploration requires that robots explore
the grid and stop when the task is complete. We propose optimal (w.r.t. the
number of robots) solutions for the deterministic terminating exploration of a
grid shaped network by a team of $k$ asynchronous oblivious robots in the fully
asynchronous and non-atomic model, so called CORDA. In more details, we first
assume the ATOM model in which each Look-Compute-Move cycle execution is
executed atomically, ie every robot that is activated at instant t
instantaneously executes a full cycle between t and t+1. ATOM being strictly
stronger than CORDA, all impossibility results in ATOM also hold in CORDA. We
show that it is impossible to explore a grid of at least three nodes with less
than three robots in ATOM. (This first result holds for both deterministic and
probabilistic settings.) Next, we show that it is impossible to
deterministically explore a (2,2)-Grid with less than 4 robots, and a
(3,3)-Grid with less than 5 robots, respectively. Then, we propose
deterministic algorithms in CORDA to exhibit the optimal number of robots
allowing to explore of a given grid. Our results show that except in two
particular cases, 3 robots are necessary and sufficient to deterministically
explore a grid of at least three nodes. The optimal number of robots for the
two remaining cases is: 4 for the (2,2)-Grid and 5 for the (3,3)-Grid.
|
1105.2470
|
The game of go as a complex network
|
cs.GT cond-mat.stat-mech cs.SI physics.soc-ph
|
We study the game of go from a complex network perspective. We construct a
directed network using a suitable definition of tactical moves including local
patterns, and study this network for different datasets of professional
tournaments and amateur games. The move distribution follows Zipf's law and the
network is scale free, with statistical peculiarities different from other real
directed networks, such as e. g. the World Wide Web. These specificities
reflect in the outcome of ranking algorithms applied to it. The fine study of
the eigenvalues and eigenvectors of matrices used by the ranking algorithms
singles out certain strategic situations. Our results should pave the way to a
better modelization of board games and other types of human strategic scheming.
|
1105.2491
|
A Multiple Component Matching Framework for Person Re-Identification
|
cs.CV
|
Person re-identification consists in recognizing an individual that has
already been observed over a network of cameras. It is a novel and challenging
research topic in computer vision, for which no reference framework exists yet.
Despite this, previous works share similar representations of human body based
on part decomposition and the implicit concept of multiple instances. Building
on these similarities, we propose a Multiple Component Matching (MCM) framework
for the person re-identification problem, which is inspired by Multiple
Component Learning, a framework recently proposed for object detection. We show
that previous techniques for person re-identification can be considered
particular implementations of our MCM framework. We then present a novel person
re-identification technique as a direct, simple implementation of our
framework, focused in particular on robustness to varying lighting conditions,
and show that it can attain state of the art performances.
|
1105.2526
|
Deconvolution of mixing time series on a graph
|
stat.ME cs.SI
|
In many applications we are interested in making inference on latent time
series from indirect measurements, which are often low-dimensional projections
resulting from mixing or aggregation. Positron emission tomography,
super-resolution, and network traffic monitoring are some examples. Inference
in such settings requires solving a sequence of ill-posed inverse problems,
y_t= A x_t, where the projection mechanism provides information on A. We
consider problems in which A specifies mixing on a graph of times series that
are bursty and sparse. We develop a multilevel state-space model for mixing
times series and an efficient approach to inference. A simple model is used to
calibrate regularization parameters that lead to efficient inference in the
multilevel state-space model. We apply this method to the problem of estimating
point-to-point traffic flows on a network from aggregate measurements. Our
solution outperforms existing methods for this problem, and our two-stage
approach suggests an efficient inference strategy for multilevel models of
dependent time series.
|
1105.2541
|
Rearranging trees for robust consensus
|
math.OC cs.SY
|
In this paper, we use the H2 norm associated with a communication graph to
characterize the robustness of consensus to noise. In particular, we restrict
our attention to trees and by systematic attention to the effect of local
changes in topology, we derive a partial ordering for undirected trees
according to the H2 norm. Our approach for undirected trees provides a
constructive method for deriving an ordering for directed trees. Further, our
approach suggests a decentralized manner in which trees can be rearranged in
order to improve their robustness.
|
1105.2550
|
A Maximal Large Deviation Inequality for Sub-Gaussian Variables
|
cs.LG
|
In this short note we prove a maximal concentration lemma for sub-Gaussian
random variables stating that for independent sub-Gaussian random variables we
have \[P<(\max_{1\le i\le N}S_{i}>\epsilon>)
\le\exp<(-\frac{1}{N^2}\sum_{i=1}^{N}\frac{\epsilon^{2}}{2\sigma_{i}^{2}}>), \]
where $S_i$ is the sum of $i$ zero mean independent sub-Gaussian random
variables and $\sigma_i$ is the variance of the $i$th random variable.
|
1105.2614
|
Growth and Optimality in Network Evolution
|
cond-mat.dis-nn cs.SI nlin.AO physics.bio-ph
|
In this paper we investigate networks whose evolution is governed by the
interaction of a random assembly process and an optimization process. In the
first process, new nodes are added one at a time and form connections to
randomly selected old nodes. In between node additions, the network is rewired
to minimize its pathlength. For timescales, at which neither the assembly nor
the optimization processes are dominant, we find a rich variety of complex
networks with power law tails in the degree distributions. These networks also
exhibit non-trivial clustering, a hierarchical organization and interesting
degree mixing patterns.
|
1105.2621
|
A Compressed Sensing Wire-Tap Channel
|
cs.IT math.IT
|
A multiplicative Gaussian wire-tap channel inspired by compressed sensing is
studied. Lower and upper bounds on the secrecy capacity are derived, and shown
to be relatively tight in the large system limit for a large class of
compressed sensing matrices. Surprisingly, it is shown that the secrecy
capacity of this channel is nearly equal to the capacity without any secrecy
constraint provided that the channel of the eavesdropper is strictly worse than
the channel of the intended receiver. In other words, the eavesdropper can see
almost everything and yet learn almost nothing. This behavior, which contrasts
sharply with that of many commonly studied wiretap channels, is made possible
by the fact that a small number of linear projections can make a crucial
difference in the ability to estimate sparse vectors.
|
1105.2631
|
On Pseudocodewords and Decision Regions of Linear Programming Decoding
of HDPC Codes
|
cs.IT math.IT
|
In this paper we explore the decision regions of Linear Programming (LP)
decoding. We compare the decision regions of an LP decoder, a Belief
Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. We
study the effect of minimal-weight pseudocodewords on LP decoding. We present
global optimization as a method for finding the minimal pseudoweight of a given
code as well as the number of minimal-weight generators. We present a complete
pseudoweight distribution for the [24; 12; 8] extended Golay code, and provide
justifications of why the pseudoweight distribution alone cannot be used for
obtaining a tight upper bound on the error probability.
|
1105.2651
|
A Note on the Entropy/Influence Conjecture
|
math.CO cs.LG
|
The entropy/influence conjecture, raised by Friedgut and Kalai in 1996, seeks
to relate two different measures of concentration of the Fourier coefficients
of a Boolean function. Roughly saying, it claims that if the Fourier spectrum
is "smeared out", then the Fourier coefficients are concentrated on "high"
levels. In this note we generalize the conjecture to biased product measures on
the discrete cube, and prove a variant of the conjecture for functions with an
extremely low Fourier weight on the "high" levels.
|
1105.2707
|
Generalized Symmetric Divergence Measures and Metric Spaces
|
cs.IT math.IT
|
Recently, Taneja studied two one parameter generalizations of J-divergence,
Jensen-Shannon divergence and Arithmetic-Geometric divergence. These two
generalizations in particular contain measures like: Hellinger discrimination,
symmetric chi-square divergence, and triangular discrimination. These measures
are well known in the literature of Statistics and Information theory. In this
paper our aim is to prove metric space properties for square root of these two
symmetric generalized divergence measures.
|
1105.2760
|
Amplify-and-Forward in Wireless Relay Networks
|
cs.IT math.IT
|
A general class of wireless relay networks with a single source-destination
pair is considered. Intermediate nodes in the network employ an
amplify-and-forward scheme to relay their input signals. In this case the
overall input-output channel from the source via the relays to the destination
effectively behaves as an intersymbol interference channel with colored noise.
Unlike previous work we formulate the problem of the maximum achievable rate in
this setting as an optimization problem with no assumption on the network size,
topology, and received signal-to-noise ratio. Previous work considered only
scenarios wherein relays use all their power to amplify their received signals.
We demonstrate that this may not always maximize the maximal achievable rate in
amplify-and-forward relay networks. The proposed formulation allows us to not
only recover known results on the performance of the amplify-and-forward
schemes for some simple relay networks but also characterize the performance of
more complex amplify-and-forward relay networks which cannot be addressed in a
straightforward manner using existing approaches.
Using cut-set arguments, we derive simple upper bounds on the capacity of
general wireless relay networks. Through various examples, we show that a large
class of amplify-and-forward relay networks can achieve rates within a constant
factor of these upper bounds asymptotically in network parameters.
|
1105.2782
|
$\ell_0$ Minimization for Wavelet Frame Based Image Restoration
|
cs.CV math.FA math.OC
|
The theory of (tight) wavelet frames has been extensively studied in the past
twenty years and they are currently widely used for image restoration and other
image processing and analysis problems. The success of wavelet frame based
models, including balanced approach and analysis based approach, is due to
their capability of sparsely approximating piecewise smooth functions like
images. Motivated by the balanced approach and analysis based approach, we
shall propose a wavelet frame based $\ell_0$ minimization model, where the
$\ell_0$ "norm" of the frame coefficients is penalized. We adapt the penalty
decomposition (PD) method to solve the proposed optimization problem. Numerical
results showed that the proposed model solved by the PD method can generate
images with better quality than those obtained by either analysis based
approach or balanced approach in terms of restoring sharp features as well as
maintaining smoothness of the recovered images. Some convergence analysis of
the PD method will also be provided.
|
1105.2783
|
$p$-ary sequences with six-valued cross-correlation function: a new
decimation of Niho type
|
cs.IT cs.DM math.IT
|
For an odd prime $p$ and $n=2m$, a new decimation
$d=\frac{(p^{m}-1)^{2}}{2}+1$ of Niho type of $m$-sequences is presented. Using
generalized Niho's Theorem, we show that the cross-correlation function between
a $p$-ary $m$-sequence of period $p^{n}-1$ and its decimated sequence by the
above $d$ is at most six-valued and we can easily know that the magnitude of
the cross correlation is upper bounded by $4\sqrt{p^{n}}-1$.
|
1105.2786
|
On the Cross-Correlation of a Ternary $m$-sequence of Period $3^{4k}-1$
and Its Decimated Sequence by $\frac{(3^{2k}+1)^{2}}{20}$
|
cs.IT math.IT
|
Let $d=\frac{(3^{2k}+1)^{2}}{20}$, where $k$ is an odd integer. We show that
the magnitude of the cross-correlation values of a ternary $m$-sequence
$\{s_{t}\}$ of period $3^{4k}-1$ and its decimated sequence $\{s_{dt}\}$ is
upper bounded by $5\sqrt{3^{n}}+1$, where $n=4k$.
|
1105.2790
|
On the equivalence of Hopfield Networks and Boltzmann Machines
|
cond-mat.dis-nn cs.AI
|
A specific type of neural network, the Restricted Boltzmann Machine (RBM), is
implemented for classification and feature detection in machine learning. RBM
is characterized by separate layers of visible and hidden units, which are able
to learn efficiently a generative model of the observed data. We study a
"hybrid" version of RBM's, in which hidden units are analog and visible units
are binary, and we show that thermodynamics of visible units are equivalent to
those of a Hopfield network, in which the N visible units are the neurons and
the P hidden units are the learned patterns. We apply the method of stochastic
stability to derive the thermodynamics of the model, by considering a formal
extension of this technique to the case of multiple sets of stored patterns,
which may act as a benchmark for the study of correlated sets. Our results
imply that simulating the dynamics of a Hopfield network, requiring the update
of N neurons and the storage of N(N-1)/2 synapses, can be accomplished by a
hybrid Boltzmann Machine, requiring the update of N+P neurons but the storage
of only NP synapses. In addition, the well known glass transition of the
Hopfield network has a counterpart in the Boltzmann Machine: It corresponds to
an optimum criterion for selecting the relative sizes of the hidden and visible
layers, resolving the trade-off between flexibility and generality of the
model. The low storage phase of the Hopfield model corresponds to few hidden
units and hence a overly constrained RBM, while the spin-glass phase (too many
hidden units) corresponds to unconstrained RBM prone to overfitting of the
observed data.
|
1105.2795
|
View subspaces for indexing and retrieval of 3D models
|
cs.CV cs.MM
|
View-based indexing schemes for 3D object retrieval are gaining popularity
since they provide good retrieval results. These schemes are coherent with the
theory that humans recognize objects based on their 2D appearances. The
viewbased techniques also allow users to search with various queries such as
binary images, range images and even 2D sketches. The previous view-based
techniques use classical 2D shape descriptors such as Fourier invariants,
Zernike moments, Scale Invariant Feature Transform-based local features and 2D
Digital Fourier Transform coefficients. These methods describe each object
independent of others. In this work, we explore data driven subspace models,
such as Principal Component Analysis, Independent Component Analysis and
Nonnegative Matrix Factorization to describe the shape information of the
views. We treat the depth images obtained from various points of the view
sphere as 2D intensity images and train a subspace to extract the inherent
structure of the views within a database. We also show the benefit of
categorizing shapes according to their eigenvalue spread. Both the shape
categorization and data-driven feature set conjectures are tested on the PSB
database and compared with the competitor view-based 3D shape retrieval
algorithms
|
1105.2796
|
Salient Local 3D Features for 3D Shape Retrieval
|
cs.CV cs.MM
|
In this paper we describe a new formulation for the 3D salient local features
based on the voxel grid inspired by the Scale Invariant Feature Transform
(SIFT). We use it to identify the salient keypoints (invariant points) on a 3D
voxelized model and calculate invariant 3D local feature descriptors at these
keypoints. We then use the bag of words approach on the 3D local features to
represent the 3D models for shape retrieval. The advantages of the method are
that it can be applied to rigid as well as to articulated and deformable 3D
models. Finally, this approach is applied for 3D Shape Retrieval on the McGill
articulated shape benchmark and then the retrieval results are presented and
compared to other methods.
|
1105.2797
|
Face Recognition using 3D Facial Shape and Color Map Information:
Comparison and Combination
|
cs.CV
|
In this paper, we investigate the use of 3D surface geometry for face
recognition and compare it to one based on color map information. The 3D
surface and color map data are from the CAESAR anthropometric database. We find
that the recognition performance is not very different between 3D surface and
color map information using a principal component analysis algorithm. We also
discuss the different techniques for the combination of the 3D surface and
color map information for multi-modal recognition by using different fusion
approaches and show that there is significant improvement in results. The
effectiveness of various techniques is compared and evaluated on a dataset with
200 subjects in two different positions.
|
1105.2800
|
Retrieval and Clustering from a 3D Human Database based on Body and Head
Shape
|
cs.CV cs.CG
|
In this paper, we describe a framework for similarity based retrieval and
clustering from a 3D human database. Our technique is based on both body and
head shape representation and the retrieval is based on similarity of both of
them. The 3D human database used in our study is the CAESAR anthropometric
database which contains approximately 5000 bodies. We have developed a
web-based interface for specifying the queries to interact with the retrieval
system. Our approach performs the similarity based retrieval in a reasonable
amount of time and is a practical approach.
|
1105.2813
|
Optimal Upper and Lower Bounds for Boolean Expressions by Dissociation
|
cs.AI cs.DB cs.LO
|
This paper develops upper and lower bounds for the probability of Boolean
expressions by treating multiple occurrences of variables as independent and
assigning them new individual probabilities. Our technique generalizes and
extends the underlying idea of a number of recent approaches which are
varyingly called node splitting, variable renaming, variable splitting, or
dissociation for probabilistic databases. We prove that the probabilities we
assign to new variables are the best possible in some sense.
|
1105.2831
|
Planar Pixelations and Image Recognition
|
math.DG cs.CG cs.CV
|
Any subset of the plane can be approximated by a set of square pixels. This
transition from a shape to its pixelation is rather brutal since it destroys
geometric and topological information about the shape. Using a technique
inspired by Morse Theory, we algorithmically produce a PL approximation of the
original shape using only information from its pixelation. This approximation
converges to the original shape in a very strong sense: as the size of the
pixels goes to zero we can recover important geometric and topological
invariants of the original shape such as Betti numbers, area, perimeter and
curvature measures.
|
1105.2858
|
A Reconstruction Method for Band-Limited Signals on the Hyperbolic Plane
|
math.FA cs.IT math.IT
|
A notion of band limited functions is considered in the case of the
hyperbolic plane in its Poincare upper half-plane $\mathbb{H}$ realization. The
concept of band-limitedness is based on the existence of the Helgason-Fourier
transform on $\mathbb{H}$. An iterative algorithm is presented, which allows to
reconstruct band-limited functions from some countable sets of their values. It
is shown that for sufficiently dense metric lattices a geometric rate of
convergence can be guaranteed as long as the sampling density is high enough
compared to the band-width of the sampled function.
|
1105.2864
|
The Rate-Distortion Function for Product of Two Sources with
Side-Information at Decoders
|
cs.IT math.IT
|
This paper investigates a lossy source coding problem in which two decoders
can access their side-information respectively. The correlated sources are a
product of two component correlated sources, and we exclusively investigate the
case such that each component is degraded. We show the rate-distortion function
for that case, and give the following observations. When the components are
degraded in matched order, the rate distortion function of the product sources
is equal to the sum of the component-wise rate distortion functions. On the
otherhand, the former is strictly smaller than the latter when the component
sources are degraded in mismatched order. The converse proof for the mismatched
case is motivated by the enhancement technique used for broadcast channels. For
binary Hamming and Gaussian examples, we evaluate the rate-distortion
functions.
|
1105.2865
|
Error Correction for Index Coding with Side Information
|
cs.IT math.IT
|
A problem of index coding with side information was first considered by Y.
Birk and T. Kol (IEEE INFOCOM, 1998). In the present work, a generalization of
index coding scheme, where transmitted symbols are subject to errors, is
studied. Error-correcting methods for such a scheme, and their parameters, are
investigated. In particular, the following question is discussed: given the
side information hypergraph of index coding scheme and the maximal number of
erroneous symbols $\delta$, what is the shortest length of a linear index code,
such that every receiver is able to recover the required information? This
question turns out to be a generalization of the problem of finding a
shortest-length error-correcting code with a prescribed error-correcting
capability in the classical coding theory.
The Singleton bound and two other bounds, referred to as the $\alpha$-bound
and the $\kappa$-bound, for the optimal length of a linear error-correcting
index code (ECIC) are established. For large alphabets, a construction based on
concatenation of an optimal index code with an MDS classical code, is shown to
attain the Singleton bound. For smaller alphabets, however, this construction
may not be optimal. A random construction is also analyzed. It yields another
implicit bound on the length of an optimal linear ECIC.
Further, the problem of error-correcting decoding by a linear ECIC is
studied. It is shown that in order to decode correctly the desired symbol, the
decoder is required to find one of the vectors, belonging to an affine space
containing the actual error vector. The syndrome decoding is shown to produce
the correct output if the weight of the error pattern is less or equal to the
error-correcting capability of the corresponding ECIC.
Finally, the notion of static ECIC, which is suitable for use with a family
of instances of an index coding problem, is introduced.
|
1105.2868
|
Semantic Vector Machines
|
cs.LG cs.AI
|
We first present our work in machine translation, during which we used
aligned sentences to train a neural network to embed n-grams of different
languages into an $d$-dimensional space, such that n-grams that are the
translation of each other are close with respect to some metric. Good n-grams
to n-grams translation results were achieved, but full sentences translation is
still problematic. We realized that learning semantics of sentences and
documents was the key for solving a lot of natural language processing
problems, and thus moved to the second part of our work: sentence compression.
We introduce a flexible neural network architecture for learning embeddings of
words and sentences that extract their semantics, propose an efficient
implementation in the Torch framework and present embedding results comparable
to the ones obtained with classical neural language models, while being more
powerful.
|
1105.2894
|
Ant Colony Optimization and Hypergraph Covering Problems
|
cs.NE
|
Ant Colony Optimization (ACO) is a very popular metaheuristic for solving
computationally hard combinatorial optimization problems. Runtime analysis of
ACO with respect to various pseudo-boolean functions and different graph based
combinatorial optimization problems has been taken up in recent years. In this
paper, we investigate the runtime behavior of an MMAS*(Max-Min Ant System) ACO
algorithm on some well known hypergraph covering problems that are NP-Hard. In
particular, we have addressed the Minimum Edge Cover problem, the Minimum
Vertex Cover problem and the Maximum Weak- Independent Set problem. The
influence of pheromone values and heuristic information on the running time is
analysed. The results indicate that the heuristic information has greater
impact towards improving the expected optimization time as compared to
pheromone values. For certain instances of hypergraphs, we show that the MMAS*
algorithm gives a constant order expected optimization time when the dominance
of heuristic information is suitably increased.
|
1105.2902
|
A Multi-Purpose Scenario-based Simulator for Smart House Environments
|
cs.AI
|
Developing smart house systems has been a great challenge for researchers and
engineers in this area because of the high cost of implementation and
evaluation process of these systems, while being very time consuming. Testing a
designed smart house before actually building it is considered as an obstacle
towards an efficient smart house project. This is because of the variety of
sensors, home appliances and devices available for a real smart environment. In
this paper, we present the design and implementation of a multi-purpose smart
house simulation system for designing and simulating all aspects of a smart
house environment. This simulator provides the ability to design the house plan
and different virtual sensors and appliances in a two dimensional model of the
virtual house environment. This simulator can connect to any external smart
house remote controlling system, providing evaluation capabilities to their
system much easier than before. It also supports detailed adding of new
emerging sensors and devices to help maintain its compatibility with future
simulation needs. Scenarios can also be defined for testing various possible
combinations of device states; so different criteria and variables can be
simply evaluated without the need of experimenting on a real environment.
|
1105.2943
|
Feature Selection for MAUC-Oriented Classification Systems
|
cs.LG cs.AI
|
Feature selection is an important pre-processing step for many pattern
classification tasks. Traditionally, feature selection methods are designed to
obtain a feature subset that can lead to high classification accuracy. However,
classification accuracy has recently been shown to be an inappropriate
performance metric of classification systems in many cases. Instead, the Area
Under the receiver operating characteristic Curve (AUC) and its multi-class
extension, MAUC, have been proved to be better alternatives. Hence, the target
of classification system design is gradually shifting from seeking a system
with the maximum classification accuracy to obtaining a system with the maximum
AUC/MAUC. Previous investigations have shown that traditional feature selection
methods need to be modified to cope with this new objective. These methods most
often are restricted to binary classification problems only. In this study, a
filter feature selection method, namely MAUC Decomposition based Feature
Selection (MDFS), is proposed for multi-class classification problems. To the
best of our knowledge, MDFS is the first method specifically designed to select
features for building classification systems with maximum MAUC. Extensive
empirical results demonstrate the advantage of MDFS over several compared
feature selection methods.
|
1105.2952
|
Bounds on the Bayes Error Given Moments
|
stat.ML cs.IT math.IT
|
We show how to compute lower bounds for the supremum Bayes error if the
class-conditional distributions must satisfy moment constraints, where the
supremum is with respect to the unknown class-conditional distributions. Our
approach makes use of Curto and Fialkow's solutions for the truncated moment
problem. The lower bound shows that the popular Gaussian assumption is not
robust in this regard. We also construct an upper bound for the supremum Bayes
error by constraining the decision boundary to be linear.
|
1105.2965
|
Generating Similar Graphs From Spherical Features
|
cs.SI physics.soc-ph stat.AP stat.ME stat.ML
|
We propose a novel model for generating graphs similar to a given example
graph. Unlike standard approaches that compute features of graphs in Euclidean
space, our approach obtains features on a surface of a hypersphere. We then
utilize a von Mises-Fisher distribution, an exponential family distribution on
the surface of a hypersphere, to define a model over possible feature values.
While our approach bears similarity to a popular exponential random graph model
(ERGM), unlike ERGMs, it does not suffer from degeneracy, a situation when a
significant probability mass is placed on unrealistic graphs. We propose a
parameter estimation approach for our model, and a procedure for drawing
samples from the distribution. We evaluate the performance of our approach both
on the small domain of all 8-node graphs as well as larger real-world social
networks.
|
1105.2988
|
Anatomy of a Bit: Information in a Time Series Observation
|
cs.IT cond-mat.stat-mech math.IT math.ST nlin.AO stat.TH
|
Appealing to several multivariate information measures---some familiar, some
new here---we analyze the information embedded in discrete-valued stochastic
time series. We dissect the uncertainty of a single observation to demonstrate
how the measures' asymptotic behavior sheds structural and semantic light on
the generating process's internal information dynamics. The measures scale with
the length of time window, which captures both intensive (rates of growth) and
subextensive components. We provide interpretations for the components,
developing explicit relationships between them. We also identify the
informational component shared between the past and the future that is not
contained in a single observation. The existence of this component directly
motivates the notion of a process's effective (internal) states and indicates
why one must build models.
|
1105.2989
|
Worst-Case Robust Distributed Power Allocation in Shared Unlicensed
Spectrum
|
cs.IT math.IT
|
This paper considers non-cooperative and fully-distributed power-allocation
for selfish transmitter-receiver pairs in shared unlicensed spectrum when
normalized-interference to each receiver is uncertain. We model each uncertain
parameter by the sum of its nominal (estimated) value and a bounded additive
error in a convex set, and show that the allocated power always converges to
its equilibrium, called robust Nash equilibrium (RNE). In the case of a bounded
and symmetric uncertainty region, we show that the power allocation problem for
each user is simplified, and can be solved in a distributed manner. We derive
the conditions for RNE's uniqueness and for convergence of the distributed
algorithm; and show that the total throughput (social utility) is less than
that at NE when RNE is unique. We also show that for multiple RNEs, the social
utility may be higher at a RNE as compared to that at the corresponding NE, and
demonstrate that this is caused by users' orthogonal utilization of bandwidth
at RNE. Simulations confirm our analysis.
|
1105.3006
|
The approximate maximum-likelihood certificate
|
cs.IT math.IT
|
A new property which relies on the linear programming (LP) decoder, the
approximate maximum-likelihood certificate (AMLC), is introduced. When using
the belief propagation decoder, this property is a measure of how close the
decoded codeword is to the LP solution. Using upper bounding techniques, it is
demonstrated that the conditional frame error probability given that the AMLC
holds is, with some degree of confidence, below a threshold. In channels with
low noise, this threshold is several orders of magnitude lower than the
simulated frame error rate, and our bound holds with very high degree of
confidence. In contrast, showing this error performance by simulation would
require very long Monte Carlo runs. When the AMLC holds, our approach thus
provides the decoder with extra error detection capability, which is especially
important in applications requiring high data integrity.
|
1105.3037
|
Horizon Adaptation for Nonlinear Model Predictive Controllers with
guaranteed Degree of Suboptimality
|
math.OC cs.SY
|
We propose adaptation strategies to modify the standard constrained model
predictive controller scheme in order to guarantee a certain lower bound on the
degree of suboptimality. Within this analysis, the length of the optimization
horizon is the parameter we wish to adapt. We develop and prove several
shortening and prolongation strategies which also allow for an effective
implementation. Moreover, extensions of stability results and suboptimality
estimates to model predictive controllers with varying optimization horizon are
shown.
|
1105.3040
|
The transversality conditions for infinite-horizon optimal control
problem with a free right endpoint and the stability of the adjoint variable
(in Russian)
|
math.OC cs.SY
|
An infinite-horizon optimal control problem with a free right endpoint is
considered. In this paper we proved that Lyapunov stability of the adjoint
variable implying the vanishing of the adjoint variable at infinity along
optimal solution.
|
1105.3042
|
Parallelizing a State Exchange Strategy for Noncooperative Distributed
NMPC
|
math.OC cs.SY math.DS
|
We consider a distributed non cooperative control setting in which systems
are interconnected via state constraints. Each of these systems is governed by
an agent which is responsible for exchanging information with its neighbours
and computing a feedback law using a nonlinear model predictive controller to
avoid violations of constraints. For this setting we present an algorithm which
generates a parallelizable hierarchy among the systems. Moreover, we show both
feasibility and stability of the closed loop using only abstract properties of
this algorithm. To this end, we utilize a trajectory based stability result
which we extend to the distributed setting.
|
1105.3068
|
On the Capacity of Noisy Computations
|
cs.IT math.IT
|
This paper presents an analysis of the concept of capacity for noisy
computations, i.e. algorithms implemented by unreliable computing devices (e.g.
noisy Turing Machines). The capacity of a noisy computation is defined and
justified by companion coding theorems. Under some constraints on the encoding
process, capacity is the upper bound of input rates allowing reliable
computation, i.e. decodability of noisy outputs into expected outputs. A model
of noisy computation of a perfect function f thanks to an unreliable device F
is given together with a model of reliable computation based on input encoding
and output decoding. A coding lemma (extending the Feinstein's theorem to noisy
computations), a joint source-computation coding theorem and its converse are
proved. They apply if the input source, the function f, the noisy device F and
the cascade f^{-1}F induce AMS and ergodic one-sided random processes.
|
1105.3106
|
Collective stability of networks of winner-take-all circuits
|
q-bio.NC cond-mat.dis-nn cs.NE
|
The neocortex has a remarkably uniform neuronal organization, suggesting that
common principles of processing are employed throughout its extent. In
particular, the patterns of connectivity observed in the superficial layers of
the visual cortex are consistent with the recurrent excitation and inhibitory
feedback required for cooperative-competitive circuits such as the soft
winner-take-all (WTA). WTA circuits offer interesting computational properties
such as selective amplification, signal restoration, and decision making. But,
these properties depend on the signal gain derived from positive feedback, and
so there is a critical trade-off between providing feedback strong enough to
support the sophisticated computations, while maintaining overall circuit
stability. We consider the question of how to reason about stability in very
large distributed networks of such circuits. We approach this problem by
approximating the regular cortical architecture as many interconnected
cooperative-competitive modules. We demonstrate that by properly understanding
the behavior of this small computational module, one can reason over the
stability and convergence of very large networks composed of these modules. We
obtain parameter ranges in which the WTA circuit operates in a high-gain
regime, is stable, and can be aggregated arbitrarily to form large stable
networks. We use nonlinear Contraction Theory to establish conditions for
stability in the fully nonlinear case, and verify these solutions using
numerical simulations. The derived bounds allow modes of operation in which the
WTA network is multi-stable and exhibits state-dependent persistent activities.
Our approach is sufficiently general to reason systematically about the
stability of any network, biological or technological, composed of networks of
small modules that express competition through shared inhibition.
|
1105.3107
|
Learning to Place New Objects
|
cs.RO
|
The ability to place objects in the environment is an important skill for a
personal robot. An object should not only be placed stably, but should also be
placed in its preferred location/orientation. For instance, a plate is
preferred to be inserted vertically into the slot of a dish-rack as compared to
be placed horizontally in it. Unstructured environments such as homes have a
large variety of object types as well as of placing areas. Therefore our
algorithms should be able to handle placing new object types and new placing
areas. These reasons make placing a challenging manipulation task. In this
work, we propose a supervised learning algorithm for finding good placements
given the point-clouds of the object and the placing area. It learns to combine
the features that capture support, stability and preferred placements using a
shared sparsity structure in the parameters. Even when neither the object nor
the placing area is seen previously in the training set, our algorithm predicts
good placements. In extensive experiments, our method enables the robot to
stably place several new objects in several new placing areas with 98%
success-rate; and it placed the objects in their preferred placements in 92% of
the cases.
|
1105.3144
|
Asynchronous Physical-layer Network Coding
|
cs.IT cs.NI math.IT
|
A key issue in physical-layer network coding (PNC) is how to deal with the
asynchrony between signals transmitted by multiple transmitters. That is,
symbols transmitted by different transmitters could arrive at the receiver with
symbol misalignment as well as relative carrier-phase offset. A second
important issue is how to integrate channel coding with PNC to achieve reliable
communication. This paper investigates these two issues and makes the following
contributions: 1) We propose and investigate a general framework for decoding
at the receiver based on belief propagation (BP). The framework can effectively
deal with symbol and phase asynchronies while incorporating channel coding at
the same time. 2) For unchannel-coded PNC, we show that for BPSK and QPSK
modulations, our BP method can significantly reduce the asynchrony penalties
compared with prior methods. 3) For unchannel-coded PNC, with half symbol
offset between the transmitters, our BP method can drastically reduce the
performance penalty due to phase asynchrony, from more than 6 dB to no more
than 1 dB. 4) For channel-coded PNC, with our BP method, both symbol and phase
asynchronies actually improve the system performance compared with the
perfectly synchronous case. Furthermore, the performance spread due to
different combinations of symbol and phase offsets between the transmitters in
channel-coded PNC is only around 1 dB. The implication of 3) is that if we
could control the symbol arrival times at the receiver, it would be
advantageous to deliberately introduce a half symbol offset in unchannel-coded
PNC. The implication of 4) is that when channel coding is used, symbol and
phase asynchronies are not major performance concerns in PNC.
|
1105.3168
|
Lassoing Line Outages in the Smart Power Grid
|
cs.SY math.OC stat.AP
|
Fast and accurate unveiling of power line outages is of paramount importance
not only for preventing faults that may lead to blackouts, but also for routine
monitoring and control tasks of the smart grid, including state estimation and
optimal power flow. Existing approaches are either challenged by the
\emph{combinatorial complexity} issues involved, and are thus limited to
identifying single- and double-line outages; or, they invoke less pragmatic
assumptions such as \emph{conditionally independent} phasor angle measurements
available across the grid. Using only a subset of voltage phasor angle data,
the present paper develops a near real-time algorithm for identifying multiple
line outages at the affordable complexity of solving a quadratic program via
block coordinate descent iterations. The novel approach relies on reformulating
the DC linear power flow model as a \emph{sparse} overcomplete expansion, and
leveraging contemporary advances in compressive sampling and variable selection
using the least-absolute shrinkage and selection operator (Lasso). Analysis and
simulated tests on the standard IEEE 118-bus system confirm the effectiveness
of lassoing line changes in the smart power grid.
|
1105.3228
|
The formation of share market prices under heterogeneous beliefs and
common knowledge
|
physics.soc-ph cs.SI q-fin.PR
|
Financial economic models often assume that investors know (or agree on) the
fundamental value of the shares of the firm, easing the passage from the
individual to the collective dimension of the financial system generated by the
Share Exchange over time. Our model relaxes that heroic assumption of one
unique "true value" and deals with the formation of share market prices through
the dynamic formation of individual and social opinions (or beliefs) based upon
a fundamental signal of economic performance and position of the firm, the
forecast revision by heterogeneous individual investors, and their social mood
or sentiment about the ongoing state of the market pricing process. Market
clearing price formation is then featured by individual and group dynamics that
make its collective dimension irreducible to its individual level. This dynamic
holistic approach can be applied to better understand the market exuberance
generated by the Share Exchange over time.
|
1105.3259
|
On R\'enyi and Tsallis entropies and divergences for exponential
families
|
cs.IT cs.LG math.IT
|
Many common probability distributions in statistics like the Gaussian,
multinomial, Beta or Gamma distributions can be studied under the unified
framework of exponential families. In this paper, we prove that both R\'enyi
and Tsallis divergences of distributions belonging to the same exponential
family admit a generic closed form expression. Furthermore, we show that
R\'enyi and Tsallis entropies can also be calculated in closed-form for
sub-families including the Gaussian or exponential distributions, among others.
|
1105.3264
|
Community Detection Using A Neighborhood Strength Driven Label
Propagation Algorithm
|
cs.SI physics.soc-ph
|
Studies of community structure and evolution in large social networks require
a fast and accurate algorithm for community detection. As the size of analyzed
communities grows, complexity of the community detection algorithm needs to be
kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of
nearly-linear running time and easy implementation, thus it forms a good basis
for efficient community detection methods. In this paper, we propose new update
rule and label propagation criterion in LPA to improve both its computational
efficiency and the quality of communities that it detects. The speed is
optimized by avoiding unnecessary updates performed by the original algorithm.
This change reduces significantly (by order of magnitude for large networks)
the number of iterations that the algorithm executes. We also evaluate our
generalization of the LPA update rule that takes into account, with varying
strength, connections to the neighborhood of a node considering a new label.
Experiments on computer generated networks and a wide range of social networks
show that our new rule improves the quality of the detected communities
compared to those found by the original LPA. The benefit of considering
positive neighborhood strength is pronounced especially on real-world networks
containing sufficiently large fraction of nodes with high clustering
coefficient.
|
1105.3266
|
Stability of Constrained Adaptive Model Predictive Control Algorithms
|
math.OC cs.SY
|
Recently, suboptimality estimates for model predictive controllers (MPC) have
been derived for the case without additional stabilizing endpoint constraints
or a Lyapunov function type endpoint weight. The proposed methods yield a
posteriori and a priori estimates of the degree of suboptimality with respect
to the infinite horizon optimal control and can be evaluated at runtime of the
MPC algorithm. Our aim is to design automatic adaptation strategies of the
optimization horizon in order to guarantee stability and a predefined degree of
suboptimality for the closed loop solution. Here, we present a stability proof
for an arbitrary adaptation scheme and state a simple shortening and
prolongation strategy which can be used for adapting the optimization horizon.
|
1105.3267
|
Reducing the Prediction Horizon in NMPC: An Algorithm Based Approach
|
math.OC cs.SY math.NA
|
In order to guarantee stability, known results for MPC without additional
terminal costs or endpoint constraints often require rather large prediction
horizons. Still, stable behavior of closed loop solutions can often be observed
even for shorter horizons. Here, we make use of the recent observation that
stability can be guaranteed for smaller prediction horizons via Lyapunov
arguments if more than only the first control is implemented. Since such a
procedure may be harmful in terms of robustness, we derive conditions which
allow to increase the rate at which state measurements are used for feedback
while maintaining stability and desired performance specifications. Our main
contribution consists in developing two algorithms based on the deduced
conditions and a corresponding stability theorem which ensures asymptotic
stability for the MPC closed loop for significantly shorter prediction
horizons.
|
1105.3268
|
Robustness of Prediction Based Delay Compensation for Nonlinear Systems
|
math.OC cs.SY
|
Control of systems where the information between the controller, actuator,
and sensor can be lost or delayed can be challenging with respect to stability
and performance. One way to overcome the resulting problems is the use of
prediction based compensation schemes. Instead of a single input, a sequence of
(predicted) future controls is submitted and implemented at the actuator. If
suitable, so-called prediction consistent compensation and control schemes,
such as certain predictive control approaches, are used, stability of the
closed loop in the presence of delays and packet losses can be guaranteed. In
this paper, we show that control schemes employing prediction based delay
compensation approaches do posses inherent robustness properties. Specifically,
if the nominal closed loop system without delay compensation is ISS with
respect to perturbation and measurement errors, then the closed loop system
employing prediction based delay compensation techniques is robustly stable. We
analyze the influence of the prediction horizon on the robustness gains and
illustrate the results in simulation.
|
1105.3270
|
Optimal Camera Placement to measure Distances Conservativly Regarding
Static and Dynamic Obstacles
|
cs.CV cs.RO math.OC
|
In modern production facilities industrial robots and humans are supposed to
interact sharing a common working area. In order to avoid collisions, the
distances between objects need to be measured conservatively which can be done
by a camera network. To estimate the acquired distance, unmodelled objects,
e.g., an interacting human, need to be modelled and distinguished from
premodelled objects like workbenches or robots by image processing such as the
background subtraction method.
The quality of such an approach massively depends on the settings of the
camera network, that is the positions and orientations of the individual
cameras. Of particular interest in this context is the minimization of the
error of the distance using the objects modelled by the background subtraction
method instead of the real objects. Here, we show how this minimization can be
formulated as an abstract optimization problem. Moreover, we state various
aspects on the implementation as well as reasons for the selection of a
suitable optimization method, analyze the complexity of the proposed method and
present a basic version used for extensive experiments.
|
1105.3272
|
Stability of Observer Based Predictive Control for Nonlinear
Sampled-data Systems
|
math.OC cs.SY math.FA
|
We propose a new model predictive control (MPC) approach which is completely
based on an observer for the state system. For this, we show semiglobally
practically asymptotic stability of the closed loop for an abstract observer
and illustrate our results for a numerical example.
|
1105.3298
|
Graphical model approximations of random finite set filters
|
cs.SY math.OC
|
Random finite sets (RFSs) has been a fruitful area of research in recent
years, yielding new approximate filters such as the probability hypothesis
density (PHD), cardinalised PHD (CPHD), and multiple target multi-Bernoulli
(MeMBer). These new methods have largely been based on approximations that
side-step the need for measurement-to-track association. Comparably, RFS
methods that incorporate data association, such as Morelande and Challa's (M-C)
method, have received little attention. This paper provides a RFS algorithm
that incorporates data association similarly to the M-C method, but retains
computational tractability via a recently developed approximation of marginal
association weights. We describe an efficient method for resolving the track
coalescence phenomenon which is problematic for joint probabilistic data
association (JPDA) and related methods (including M-C). The method utilises a
network flow optimisation, and thus is tractable for large numbers of targets.
Finally, our derivation also shows that it is natural for the multi-target
density to incorporate both a Poisson point process (PPP) component
(representing targets that have never been detected) and a multi-Bernoulli
component (representing targets under track). We describe a method of
recycling, in which tracks with a low probability existence are transferred
from the multi-Bernoulli component to the PPP component, effectively yielding a
hybrid of M-C and PHD.
|
1105.3299
|
Compressed Sensing with coherent tight frames via $l_q$-minimization for
$0<q\leq1$
|
math.NA cs.IT math.IT
|
Our aim of this article is to reconstruct a signal from undersampled data in
the situation that the signal is sparse in terms of a tight frame. We present a
condition, which is independent of the coherence of the tight frame, to
guarantee accurate recovery of signals which are sparse in the tight frame,
from undersampled data with minimal $l_1$-norm of transform coefficients. This
improves the result in [1]. Also, the $l_q$-minimization $(0<q<1)$ approaches
are introduced. We show that under a suitable condition, there exists a value
$q_0\in(0,1]$ such that for any $q\in(0,q_0)$, each solution of the
$l_q$-minimization is approximately well to the true signal. In particular,
when the tight frame is an identity matrix or an orthonormal basis, all results
obtained in this paper appeared in [13] and [26].
|
1105.3316
|
Affinity Paths and Information Diffusion in Social Networks
|
physics.soc-ph cs.SI
|
Widespread interest in the diffusion of information through social networks
has produced a large number of Social Dynamics models. A majority of them use
theoretical hypothesis to explain their diffusion mechanisms while the few
empirically based ones average out their measures over many messages of
different content. Our empirical research tracking the step-by-step email
propagation of an invariable viral marketing message delves into the content
impact and has discovered new and striking features. The topology and dynamics
of the propagation cascades display patterns not inherited from the email
networks carrying the message. Their disconnected, low transitivity, tree-like
cascades present positive correlation between their nodes probability to
forward the message and the average number of neighbors they target and show
increased participants' involvement as the propagation paths length grows. Such
patterns not described before, nor replicated by any of the existing models of
information diffusion, can be explained if participants make their pass-along
decisions based uniquely on local knowledge of their network neighbors affinity
with the message content. We prove the plausibility of such mechanism through a
stylized, agent-based model that replicates the \emph{Affinity Paths} observed
in real information diffusion cascades.
|
1105.3326
|
Xampling at the Rate of Innovation
|
cs.IT math.IT
|
We address the problem of recovering signals from samples taken at their rate
of innovation. Our only assumption is that the sampling system is such that the
parameters defining the signal can be stably determined from the samples, a
condition that lies at the heart of every sampling theorem. Consequently, our
analysis subsumes previously studied nonlinear acquisition devices and
nonlinear signal classes. In particular, we do not restrict attention to
memoryless nonlinear distortions or to union-of-subspace models. This allows
treatment of various finite-rate-of-innovation (FRI) signals that were not
previously studied, including, for example, continuous phase modulation
transmissions. Our strategy relies on minimizing the error between the measured
samples and those corresponding to our signal estimate. This least-squares (LS)
objective is generally non-convex and might possess many local minima.
Nevertheless, we prove that under the stability hypothesis, any optimization
method designed to trap a stationary point of the LS criterion necessarily
converges to the true solution. We demonstrate our approach in the context of
recovering pulse streams in settings that were not previously treated.
Furthermore, in situations for which other algorithms are applicable, we show
that our method is often preferable in terms of noise robustness.
|
1105.3338
|
The Power Grid as a Complex Network: a Survey
|
physics.soc-ph cs.DM cs.SI
|
The statistical tools of Complex Network Analysis are of great use to
understand salient properties of complex systems, may these be natural or
pertaining human engineered infrastructures. One of these that is receiving
growing attention for its societal relevance is that of electricity
distribution. In this paper, we present a survey of the most important
scientific studies investigating the properties of different Power Grids
infrastructures using Complex Network Analysis techniques and methodologies. We
categorize and explore the most relevant literature works considering general
topological properties, differences between the various graph-related
indicators and reliability aspects.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.