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1006.3021
|
A General Framework for Equivalences in Answer-Set Programming by
Countermodels in the Logic of Here-and-There
|
cs.AI
|
Different notions of equivalence, such as the prominent notions of strong and
uniform equivalence, have been studied in Answer-Set Programming, mainly for
the purpose of identifying programs that can serve as substitutes without
altering the semantics, for instance in program optimization. Such semantic
comparisons are usually characterized by various selections of models in the
logic of Here-and-There (HT). For uniform equivalence however, correct
characterizations in terms of HT-models can only be obtained for finite
theories, respectively programs. In this article, we show that a selection of
countermodels in HT captures uniform equivalence also for infinite theories.
This result is turned into coherent characterizations of the different notions
of equivalence by countermodels, as well as by a mixture of HT-models and
countermodels (so-called equivalence interpretations). Moreover, we generalize
the so-called notion of relativized hyperequivalence for programs to
propositional theories, and apply the same methodology in order to obtain a
semantic characterization which is amenable to infinite settings. This allows
for a lifting of the results to first-order theories under a very general
semantics given in terms of a quantified version of HT. We thus obtain a
general framework for the study of various notions of equivalence for theories
under answer-set semantics. Moreover, we prove an expedient property that
allows for a simplified treatment of extended signatures, and provide further
results for non-ground logic programs. In particular, uniform equivalence
coincides under open and ordinary answer-set semantics, and for finite
non-ground programs under these semantics, also the usual characterization of
uniform equivalence in terms of maximal and total HT-models of the grounding is
correct, even for infinite domains, when corresponding ground programs are
infinite.
|
1006.3033
|
Extension of Wirtinger's Calculus to Reproducing Kernel Hilbert Spaces
and the Complex Kernel LMS
|
cs.LG
|
Over the last decade, kernel methods for nonlinear processing have
successfully been used in the machine learning community. The primary
mathematical tool employed in these methods is the notion of the Reproducing
Kernel Hilbert Space. However, so far, the emphasis has been on batch
techniques. It is only recently, that online techniques have been considered in
the context of adaptive signal processing tasks. Moreover, these efforts have
only been focussed on real valued data sequences. To the best of our knowledge,
no adaptive kernel-based strategy has been developed, so far, for complex
valued signals. Furthermore, although the real reproducing kernels are used in
an increasing number of machine learning problems, complex kernels have not,
yet, been used, in spite of their potential interest in applications that deal
with complex signals, with Communications being a typical example. In this
paper, we present a general framework to attack the problem of adaptive
filtering of complex signals, using either real reproducing kernels, taking
advantage of a technique called \textit{complexification} of real RKHSs, or
complex reproducing kernels, highlighting the use of the complex gaussian
kernel. In order to derive gradients of operators that need to be defined on
the associated complex RKHSs, we employ the powerful tool of Wirtinger's
Calculus, which has recently attracted attention in the signal processing
community. To this end, in this paper, the notion of Wirtinger's calculus is
extended, for the first time, to include complex RKHSs and use it to derive
several realizations of the Complex Kernel Least-Mean-Square (CKLMS) algorithm.
Experiments verify that the CKLMS offers significant performance improvements
over several linear and nonlinear algorithms, when dealing with nonlinearities.
|
1006.3035
|
Products of Weighted Logic Programs
|
cs.AI cs.PL
|
Weighted logic programming, a generalization of bottom-up logic programming,
is a well-suited framework for specifying dynamic programming algorithms. In
this setting, proofs correspond to the algorithm's output space, such as a path
through a graph or a grammatical derivation, and are given a real-valued score
(often interpreted as a probability) that depends on the real weights of the
base axioms used in the proof. The desired output is a function over all
possible proofs, such as a sum of scores or an optimal score. We describe the
PRODUCT transformation, which can merge two weighted logic programs into a new
one. The resulting program optimizes a product of proof scores from the
original programs, constituting a scoring function known in machine learning as
a ``product of experts.'' Through the addition of intuitive constraining side
conditions, we show that several important dynamic programming algorithms can
be derived by applying PRODUCT to weighted logic programs corresponding to
simpler weighted logic programs. In addition, we show how the computation of
Kullback-Leibler divergence, an information-theoretic measure, can be
interpreted using PRODUCT.
|
1006.3056
|
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian
Mixture Models to Structured Sparsity
|
cs.CV
|
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.
|
1006.3128
|
The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in
Compressed Sensing
|
cs.IT math.IT
|
Recovery of the sparsity pattern (or support) of an unknown sparse vector
from a limited number of noisy linear measurements is an important problem in
compressed sensing. In the high-dimensional setting, it is known that recovery
with a vanishing fraction of errors is impossible if the measurement rate and
the per-sample signal-to-noise ratio (SNR) are finite constants, independent of
the vector length. In this paper, it is shown that recovery with an arbitrarily
small but constant fraction of errors is, however, possible, and that in some
cases computationally simple estimators are near-optimal. Bounds on the
measurement rate needed to attain a desired fraction of errors are given in
terms of the SNR and various key parameters of the unknown vector for several
different recovery algorithms. The tightness of the bounds, in a scaling sense,
as a function of the SNR and the fraction of errors, is established by
comparison with existing information-theoretic necessary bounds. Near
optimality is shown for a wide variety of practically motivated signal models.
|
1006.3151
|
Channel Tracking for Relay Networks via Adaptive Particle MCMC
|
cs.IT math.IT
|
This paper presents a new approach for channel tracking and parameter
estimation in cooperative wireless relay networks. We consider a system with
multiple relay nodes operating under an amplify and forward relay function. We
develop a novel algorithm to efficiently solve the challenging problem of joint
channel tracking and parameters estimation of the Jakes' system model within a
mobile wireless relay network. This is based on \textit{particle Markov chain
Monte Carlo} (PMCMC) method. In particular, it first involves developing a
Bayesian state space model, then estimating the associated high dimensional
posterior using an adaptive Markov chain Monte Carlo (MCMC) sampler relying on
a proposal built using a Rao-Blackwellised Sequential Monte Carlo (SMC) filter.
|
1006.3154
|
Spectrum Sensing in Cooperative Cognitive Radio Networks with Partial
CSI
|
cs.IT math.IT
|
We develop an efficient algorithm for cooperative spectrum sensing in a relay
based cognitive radio network. We consider a stochastic model where data is
sent from the Base Station (BS) of the Primary User (PU). The data is relayed
by the Secondary Users (SU) to the SU BS. The SU BS has only partial CSI
knowledge of the wireless channels. In order to obtain the optimal decision
rule based on Likelihood Ratio Test (LRT), the marginal likelihood under each
hypothesis needs to be evaluated pointwise. These, however, cannot be obtained
analytically due to the intractability of the integrals. Instead, we
approximate these quantities by utilising the Laplace method. Performance is
evaluated via numerical simulations and it is shown that the proposed spectrum
sensing scheme can achieve superior results to the energy detection scheme.
|
1006.3155
|
Blind Spectrum Sensing in Cognitive Radio over Fading Channels and
Frequency Offsets
|
cs.IT math.IT
|
This paper deals with the challenging problem of spectrum sensing in
cognitive radio. We consider a stochastic system model where the the Primary
User (PU) transmits a periodic signal over fading channels. The effect of
frequency offsets due to oscillator mismatch, and Doppler offset is studied. We
show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated
poitnwise. We present a novel approach to approximate the marginilisation of
the frequency offset using a single point estimate. This is obtained via a low
complexity Constrained Adaptive Notch Filter (CANF) to estimate the frequency
offset. Performance is evaluated via numerical simulations and it is shown that
the proposed spectrum sensing scheme can achieve the same performance as the
near-optimal scheme, that is based on a bank of matched filters, using only a
fraction of the complexity required.
|
1006.3156
|
Decoding of Convolutional Codes over the Erasure Channel
|
cs.IT math.IT
|
In this paper we study the decoding capabilities of convolutional codes over
the erasure channel. Of special interest will be maximum distance profile (MDP)
convolutional codes. These are codes which have a maximum possible column
distance increase. We show how this strong minimum distance condition of MDP
convolutional codes help us to solve error situations that maximum distance
separable (MDS) block codes fail to solve. Towards this goal, we define two
subclasses of MDP codes: reverse-MDP convolutional codes and complete-MDP
convolutional codes. Reverse-MDP codes have the capability to recover a maximum
number of erasures using an algorithm which runs backward in time. Complete-MDP
convolutional codes are both MDP and reverse-MDP codes. They are capable to
recover the state of the decoder under the mildest condition. We show that
complete-MDP convolutional codes perform in certain sense better than MDS block
codes of the same rate over the erasure channel.
|
1006.3180
|
H2O: An Autonomic, Resource-Aware Distributed Database System
|
cs.DB cs.DC
|
This paper presents the design of an autonomic, resource-aware distributed
database which enables data to be backed up and shared without complex manual
administration. The database, H2O, is designed to make use of unused resources
on workstation machines. Creating and maintaining highly-available, replicated
database systems can be difficult for untrained users, and costly for IT
departments. H2O reduces the need for manual administration by autonomically
replicating data and load-balancing across machines in an enterprise.
Provisioning hardware to run a database system can be unnecessarily costly as
most organizations already possess large quantities of idle resources in
workstation machines. H2O is designed to utilize this unused capacity by using
resource availability information to place data and plan queries over
workstation machines that are already being used for other tasks. This paper
discusses the requirements for such a system and presents the design and
implementation of H2O.
|
1006.3215
|
Solving Functional Constraints by Variable Substitution
|
cs.AI cs.LO cs.PL
|
Functional constraints and bi-functional constraints are an important
constraint class in Constraint Programming (CP) systems, in particular for
Constraint Logic Programming (CLP) systems. CP systems with finite domain
constraints usually employ CSP-based solvers which use local consistency, for
example, arc consistency. We introduce a new approach which is based instead on
variable substitution. We obtain efficient algorithms for reducing systems
involving functional and bi-functional constraints together with other
non-functional constraints. It also solves globally any CSP where there exists
a variable such that any other variable is reachable from it through a sequence
of functional constraints. Our experiments on random problems show that
variable elimination can significantly improve the efficiency of solving
problems with functional constraints.
|
1006.3222
|
MIMO Detection Algorithms for High Data Rate Wireless Transmission
|
cs.OH cs.IT math.IT
|
Motivated by MIMO broad-band fading channel model, in this section a
comparative study is presented regarding various uncoded adaptive and
non-adaptive MIMO detection algorithms with respect to BER/PER performance, and
hardware complexity. All the simulations are conducted within MIMO-OFDM
framework and with a packet structure similar to that of IEEE 802.11a/g
standard. As the comparison results show, the RLS algorithm appears to be an
affordable solution for wideband MIMO system targeting at Giga-bit wireless
transmission. So MIMO can overcome huge processing power required for MIMO
detection by using optimizing channel coding and MIMO detection.
|
1006.3271
|
The probabilistic analysis of language acquisition: Theoretical,
computational, and experimental analysis
|
cs.CL physics.data-an q-bio.NC
|
There is much debate over the degree to which language learning is governed
by innate language-specific biases, or acquired through cognition-general
principles. Here we examine the probabilistic language acquisition hypothesis
on three levels: We outline a novel theoretical result showing that it is
possible to learn the exact generative model underlying a wide class of
languages, purely from observing samples of the language. We then describe a
recently proposed practical framework, which quantifies natural language
learnability, allowing specific learnability predictions to be made for the
first time. In previous work, this framework was used to make learnability
predictions for a wide variety of linguistic constructions, for which
learnability has been much debated. Here, we present a new experiment which
tests these learnability predictions. We find that our experimental results
support the possibility that these linguistic constructions are acquired
probabilistically from cognition-general principles.
|
1006.3275
|
Normalized Information Distance is Not Semicomputable
|
cs.CC cs.CV physics.data-an
|
Normalized information distance (NID) uses the theoretical notion of
Kolmogorov complexity, which for practical purposes is approximated by the
length of the compressed version of the file involved, using a real-world
compression program. This practical application is called 'normalized
compression distance' and it is trivially computable. It is a parameter-free
similarity measure based on compression, and is used in pattern recognition,
data mining, phylogeny, clustering, and classification. The complexity
properties of its theoretical precursor, the NID, have been open. We show that
the NID is neither upper semicomputable nor lower semicomputable.
|
1006.3301
|
Codebook-Based SDMA for Coexistence with Fixed Wireless Service
|
cs.IT cs.NI math.IT
|
A portion of frequency band for International Mobile Telecommunications
(IMT)-Advanced is currently allocated to Fixed Wireless Service (FWS) such as
Fixed Service (FS), Fixed Satellite Service (FSS), or Fixed Wireless Access
(FWA), which requires frequency sharing between both the systems. SDMA, due to
its high throughput nature, is candidate for IMT-Advanced. This paper proposes
a systematic design of a precoder codebook for SDMA sharing spectrum with
existing FWS. Based on an estimated direction angle of a victim FWS system, an
interfering transmitter adaptively constructs a codebook forming a transmit
null in the direction angle while satisfying orthogonal beamforming constraint.
We derive not only asymptotic throughput scaling laws, but also an upperbound
on throughput loss to analyze performance loss of the proposed SDMA relative to
the popular SDMA called per-user unitary rate control (PU2RC). Furthermore, we
develop a method of evaluating protection distance in order to analyze the
spectrum sharing performance of the proposed approach. The simulation results
of protection distance confirm that the proposed SDMA efficiently shares
spectrum with FWS systems by reducing protection distance to more than 66%.
Although our proposed SDMA always has lower throughput compared to PU2RC in
non-coexistence scenario, it offers an intriguing opportunity to reuse spectrum
already allocated to FWS.
|
1006.3360
|
Base station cooperation on the downlink: Large system analysis
|
cs.IT math.IT
|
This paper considers maximizing the network-wide minimum supported rate in
the downlink of a two-cell system, where each base station (BS) is endowed with
multiple antennas. This is done for different levels of cell cooperation. At
one extreme, we consider single cell processing where the BS is oblivious to
the interference it is creating at the other cell. At the other extreme, we
consider full cooperative macroscopic beamforming. In between, we consider
coordinated beamforming, which takes account of inter-cell interference, but
does not require full cooperation between the BSs. We combine elements of
Lagrangian duality and large system analysis to obtain limiting SINRs and
bit-rates, allowing comparison between the considered schemes. The main
contributions of the paper are theorems which provide concise formulas for
optimal transmit power, beamforming vectors, and achieved signal to
interference and noise ratio (SINR) for the considered schemes. The formulas
obtained are valid for the limit in which the number of users per cell, K, and
the number of antennas per base station, N, tend to infinity, with fixed ratio.
These theorems also provide expressions for the effective bandwidths occupied
by users, and the effective interference caused in the adjacent cell, which
allow direct comparisons between the considered schemes.
|
1006.3385
|
A Fixed Precoding Approach to Achieve the Degrees of Freedom in X
channel
|
cs.IT math.IT
|
This paper aims to provide a fixed precoding scheme to achieve the Degrees of
Freedom DoF of the generalized ergodic X channel. This is achieved through
using the notion of ergodic interference alignment technique. Accordingly, in
the proposed method the transmitters do not require to know the full channel
state information, while this assumption is the integral part of existing
methods. Instead, a finite-rate feed-back channel is adequate to achieve the
DoF. In other words, it is demonstrated that quantized versions of channel
gains are adequate to achieve theDOF. To get an insight regarding the
functionality of the proposed method, first we rely on finite field channel
models, and then extend the terminology to more realistic cases, including
dispersive fading channels in the presence of quantizer. Accordingly, in a
Rayliegh fading environment, it is shown a feedback rate of
2log(p)+Theta(log(log(p))) can provide the DoF, where $p$ is the total transmit
power.
|
1006.3403
|
Image processing of a spectrogram produced by Spectrometer Airglow
Temperature Imager
|
physics.comp-ph cs.CV physics.ins-det
|
The Spectral Airglow Temperature Imager is an instrument, specially designed
for investigation of the wave processes in the Mesosphere-Lower Thermosphere.
In order to determine the kinematics parameters of a wave, the values of a
physical quantity in different space points and their changes in the time
should be known. An approach for image processing of registered spectrograms is
proposed. A detailed description is made of the steps of this approach, related
to recovering CCD pixel values, influenced by cosmic particles, dark image
correction and filter parameters determination.
|
1006.3417
|
Fictitious Play with Time-Invariant Frequency Update for Network
Security
|
cs.GT cs.CR cs.LG
|
We study two-player security games which can be viewed as sequences of
nonzero-sum matrix games played by an Attacker and a Defender. The evolution of
the game is based on a stochastic fictitious play process, where players do not
have access to each other's payoff matrix. Each has to observe the other's
actions up to present and plays the action generated based on the best response
to these observations. In a regular fictitious play process, each player makes
a maximum likelihood estimate of her opponent's mixed strategy, which results
in a time-varying update based on the previous estimate and current action. In
this paper, we explore an alternative scheme for frequency update, whose mean
dynamic is instead time-invariant. We examine convergence properties of the
mean dynamic of the fictitious play process with such an update scheme, and
establish local stability of the equilibrium point when both players are
restricted to two actions. We also propose an adaptive algorithm based on this
time-invariant frequency update.
|
1006.3424
|
Porting Decision Tree Algorithms to Multicore using FastFlow
|
cs.DC cs.DB
|
The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.
|
1006.3425
|
Power law in website ratings
|
cs.IR cs.IT math.IT physics.soc-ph
|
In the practical work of websites popularization, analysis of their
efficiency and downloading it is of key importance to take into account
web-ratings data. The main indicators of website traffic include the number of
unique hosts from which the analyzed website was addressed and the number of
granted web pages (hits) per unit time (for example, day, month or year). Of
certain interest is the ratio between the number of hits (S) and hosts (H). In
practice there is even used such a concept as "average number of viewed pages"
(S/H), which on default supposes a linear dependence of S on H. What actually
happens is that linear dependence is observed only as a partial case of power
dependence, and not always. Another new power law has been discovered on the
Internet, in particular, on the WWW.
|
1006.3448
|
Orthogonal Persistence Revisited
|
cs.PL cs.DB
|
The social and economic importance of large bodies of programs and data that
are potentially long-lived has attracted much attention in the commercial and
research communities. Here we concentrate on a set of methodologies and
technologies called persistent programming. In particular we review programming
language support for the concept of orthogonal persistence, a technique for the
uniform treatment of objects irrespective of their types or longevity. While
research in persistent programming has become unfashionable, we show how the
concept is beginning to appear as a major component of modern systems. We
relate these attempts to the original principles of orthogonal persistence and
give a few hints about how the concept may be utilised in the future.
|
1006.3455
|
An External Description for MIMO Systems Sampled in an Aperiodic Way
|
cs.DM cs.IT math.IT
|
An external description for aperiodically sampled MIMO linear systems has
been developed. Emphasis is on the sampling period sequence, included among the
variables to be handled. The computational procedure is simple and no use of
polynomial matrix theory is required. This input/output description is believed
to be a basic formulation for its later application to the problem of optimal
control and/or identification of linear dynamical systems.
|
1006.3468
|
Algorithm for Sector Spectra Calculation from Images Registered by the
Spectral Airglow Temperature Imager
|
physics.data-an cs.CV
|
The Spectral Airglow Temperature Imager is an instrument, specially designed
for investigation of the wave processes in the Mesosphere-Lower Thermosphere.
In order to determine the kinematic parameters of a wave, the values of a
physical quantity in different space points and their changes in the time
should be known. As a result of the possibilities of the SATI instrument for
space scanning, different parts of the images (sectors of spectrograms)
correspond to the respective mesopause areas (where the radiation is
generated). Algorithms for sector spectra calculation are proposed. In contrast
to the original algorithms where twelve sectors with angles of 30 degrees are
only determined now sectors with arbitrary orientation and angles are
calculated. An algorithm is presented for sector calculation based on pixel
division into sub pixels. A comparative results are shown.
|
1006.3498
|
Fast and accurate annotation of short texts with Wikipedia pages
|
cs.IR
|
We address the problem of cross-referencing text fragments with Wikipedia
pages, in a way that synonymy and polysemy issues are resolved accurately and
efficiently. We take inspiration from a recent flow of work [Cucerzan 2007,
Mihalcea and Csomai 2007, Milne and Witten 2008, Chakrabarti et al 2009], and
extend their scenario from the annotation of long documents to the annotation
of short texts, such as snippets of search-engine results, tweets, news, blogs,
etc.. These short and poorly composed texts pose new challenges in terms of
efficiency and effectiveness of the annotation process, that we address by
designing and engineering TAGME, the first system that performs an accurate and
on-the-fly annotation of these short textual fragments. A large set of
experiments shows that TAGME outperforms state-of-the-art algorithms when they
are adapted to work on short texts and it results fast and competitive on long
texts.
|
1006.3506
|
Action Recognition in Videos: from Motion Capture Labs to the Web
|
cs.CV
|
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.
|
1006.3514
|
Similarity Search and Locality Sensitive Hashing using TCAMs
|
cs.DB cs.IR
|
Similarity search methods are widely used as kernels in various machine
learning applications. Nearest neighbor search (NNS) algorithms are often used
to retrieve similar entries, given a query. While there exist efficient
techniques for exact query lookup using hashing, similarity search using exact
nearest neighbors is known to be a hard problem and in high dimensions, best
known solutions offer little improvement over a linear scan. Fast solutions to
the approximate NNS problem include Locality Sensitive Hashing (LSH) based
techniques, which need storage polynomial in $n$ with exponent greater than
$1$, and query time sublinear, but still polynomial in $n$, where $n$ is the
size of the database. In this work we present a new technique of solving the
approximate NNS problem in Euclidean space using a Ternary Content Addressable
Memory (TCAM), which needs near linear space and has O(1) query time. In fact,
this method also works around the best known lower bounds in the cell probe
model for the query time using a data structure near linear in the size of the
data base. TCAMs are high performance associative memories widely used in
networking applications such as access control lists. A TCAM can query for a
bit vector within a database of ternary vectors, where every bit position
represents $0$, $1$ or $*$. The $*$ is a wild card representing either a $0$ or
a $1$. We leverage TCAMs to design a variant of LSH, called Ternary Locality
Sensitive Hashing (TLSH) wherein we hash database entries represented by
vectors in the Euclidean space into $\{0,1,*\}$. By using the added
functionality of a TLSH scheme with respect to the $*$ character, we solve an
instance of the approximate nearest neighbor problem with 1 TCAM access and
storage nearly linear in the size of the database. We believe that this work
can open new avenues in very high speed data mining.
|
1006.3520
|
Information Distance
|
cs.IT math.IT math.PR physics.data-an
|
While Kolmogorov complexity is the accepted absolute measure of information
content in an individual finite object, a similarly absolute notion is needed
for the information distance between two individual objects, for example, two
pictures. We give several natural definitions of a universal information
metric, based on length of shortest programs for either ordinary computations
or reversible (dissipationless) computations. It turns out that these
definitions are equivalent up to an additive logarithmic term. We show that the
information distance is a universal cognitive similarity distance. We
investigate the maximal correlation of the shortest programs involved, the
maximal uncorrelation of programs (a generalization of the Slepian-Wolf theorem
of classical information theory), and the density properties of the discrete
metric spaces induced by the information distances. A related distance measures
the amount of nonreversibility of a computation. Using the physical theory of
reversible computation, we give an appropriate (universal, anti-symmetric, and
transitive) measure of the thermodynamic work required to transform one object
in another object by the most efficient process. Information distance between
individual objects is needed in pattern recognition where one wants to express
effective notions of "pattern similarity" or "cognitive similarity" between
individual objects and in thermodynamics of computation where one wants to
analyse the energy dissipation of a computation from a particular input to a
particular output.
|
1006.3537
|
Fastest Distributed Consensus Averaging Problem on Chain of Rhombus
Networks
|
cs.IT cs.DC cs.DM math.IT
|
Distributed consensus has appeared as one of the most important and primary
problems in the context of distributed computation and it has received renewed
interest in the field of sensor networks (due to recent advances in wireless
communications), where solving fastest distributed consensus averaging problem
over networks with different topologies is one of the primary problems in this
issue. Here in this work analytical solution for the problem of fastest
distributed consensus averaging algorithm over Chain of Rhombus networks is
provided, where the solution procedure consists of stratification of associated
connectivity graph of the network and semidefinite programming, particularly
solving the slackness conditions, where the optimal weights are obtained by
inductive comparing of the characteristic polynomials initiated by slackness
conditions. Also characteristic polynomial together with its roots
corresponding to eigenvalues of weight matrix including SLEM of network is
determined inductively. Moreover to see the importance of rhombus graphs it is
indicated that convergence rate of path network increases by replacing a single
node by a rhombus sub graph within the path network.
|
1006.3573
|
Nested Polar Codes for Wiretap and Relay Channels
|
cs.IT math.IT
|
We show that polar codes asymptotically achieve the whole
capacity-equivocation region for the wiretap channel when the wiretapper's
channel is degraded with respect to the main channel, and the weak secrecy
notion is used. Our coding scheme also achieves the capacity of the physically
degraded receiver-orthogonal relay channel. We show simulation results for
moderate block length for the binary erasure wiretap channel, comparing polar
codes and two edge type LDPC codes.
|
1006.3650
|
The Use of Probabilistic Systems to Mimic the Behaviour of Idiotypic AIS
Robot Controllers
|
cs.AI cs.NE cs.RO
|
Previous work has shown that robot navigation systems that employ an
architecture based upon the idiotypic network theory of the immune system have
an advantage over control techniques that rely on reinforcement learning only.
This is thought to be a result of intelligent behaviour selection on the part
of the idiotypic robot. In this paper an attempt is made to imitate idiotypic
dynamics by creating controllers that use reinforcement with a number of
different probabilistic schemes to select robot behaviour. The aims are to show
that the idiotypic system is not merely performing some kind of periodic random
behaviour selection, and to try to gain further insight into the processes that
govern the idiotypic mechanism. Trials are carried out using simulated Pioneer
robots that undertake navigation exercises. Results show that a scheme that
boosts the probability of selecting highly-ranked alternative behaviours to 50%
during stall conditions comes closest to achieving the properties of the
idiotypic system, but remains unable to match it in terms of all round
performance.
|
1006.3652
|
Modelling Reactive and Proactive Behaviour in Simulation
|
cs.AI cs.CE cs.MA
|
This research investigated the simulation model behaviour of a traditional
and combined discrete event as well as agent based simulation models when
modelling human reactive and proactive behaviour in human centric complex
systems. A departmental store was chosen as human centric complex case study
where the operation system of a fitting room in WomensWear department was
investigated. We have looked at ways to determine the efficiency of new
management policies for the fitting room operation through simulating the
reactive and proactive behaviour of staff towards customers. Once development
of the simulation models and their verification had been done, we carried out a
validation experiment in the form of a sensitivity analysis. Subsequently, we
executed a statistical analysis where the mixed reactive and proactive
behaviour experimental results were compared with some reactive experimental
results from previously published works. Generally, this case study discovered
that simple proactive individual behaviour could be modelled in both simulation
models. In addition, we found the traditional discrete event model performed
similar in the simulation model output compared to the combined discrete event
and agent based simulation when modelling similar human behaviour.
|
1006.3654
|
Detecting Anomalous Process Behaviour using Second Generation Artificial
Immune Systems
|
cs.AI cs.CR cs.NE
|
Artificial Immune Systems have been successfully applied to a number of
problem domains including fault tolerance and data mining, but have been shown
to scale poorly when applied to computer intrusion detec- tion despite the fact
that the biological immune system is a very effective anomaly detector. This
may be because AIS algorithms have previously been based on the adaptive immune
system and biologically-naive mod- els. This paper focuses on describing and
testing a more complex and biologically-authentic AIS model, inspired by the
interactions between the innate and adaptive immune systems. Its performance on
a realistic process anomaly detection problem is shown to be better than
standard AIS methods (negative-selection), policy-based anomaly detection
methods (systrace), and an alternative innate AIS approach (the DCA). In
addition, it is shown that runtime information can be used in combination with
system call information to enhance detection capability.
|
1006.3678
|
Functional Answer Set Programming
|
cs.LO cs.AI
|
In this paper we propose an extension of Answer Set Programming (ASP), and in
particular, of its most general logical counterpart, Quantified Equilibrium
Logic (QEL), to deal with partial functions. Although the treatment of equality
in QEL can be established in different ways, we first analyse the choice of
decidable equality with complete functions and Herbrand models, recently
proposed in the literature. We argue that this choice yields some
counterintuitive effects from a logic programming and knowledge representation
point of view. We then propose a variant called QELF where the set of functions
is partitioned into partial and Herbrand functions (we also call constructors).
In the rest of the paper, we show a direct connection to Scott's Logic of
Existence and present a practical application, proposing an extension of normal
logic programs to deal with partial functions and equality, so that they can be
translated into function-free normal programs, being possible in this way to
compute their answer sets with any standard ASP solver.
|
1006.3679
|
Segmentation of Natural Images by Texture and Boundary Compression
|
cs.CV cs.IT cs.LG math.IT
|
We present a novel algorithm for segmentation of natural images that
harnesses the principle of minimum description length (MDL). Our method is
based on observations that a homogeneously textured region of a natural image
can be well modeled by a Gaussian distribution and the region boundary can be
effectively coded by an adaptive chain code. The optimal segmentation of an
image is the one that gives the shortest coding length for encoding all
textures and boundaries in the image, and is obtained via an agglomerative
clustering process applied to a hierarchy of decreasing window sizes as
multi-scale texture features. The optimal segmentation also provides an
accurate estimate of the overall coding length and hence the true entropy of
the image. We test our algorithm on the publicly available Berkeley
Segmentation Dataset. It achieves state-of-the-art segmentation results
compared to other existing methods.
|
1006.3726
|
Diamond Dicing
|
cs.DB
|
In OLAP, analysts often select an interesting sample of the data. For
example, an analyst might focus on products bringing revenues of at least 100
000 dollars, or on shops having sales greater than 400 000 dollars. However,
current systems do not allow the application of both of these thresholds
simultaneously, selecting products and shops satisfying both thresholds. For
such purposes, we introduce the diamond cube operator, filling a gap among
existing data warehouse operations.
Because of the interaction between dimensions the computation of diamond
cubes is challenging. We compare and test various algorithms on large data sets
of more than 100 million facts. We find that while it is possible to implement
diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a
hundred times faster than popular database engines (including a row-store and a
column-store).
|
1006.3780
|
Least Squares Superposition Codes of Moderate Dictionary Size, Reliable
at Rates up to Capacity
|
cs.IT cs.LG math.IT math.ST stat.TH
|
For the additive white Gaussian noise channel with average codeword power
constraint, new coding methods are devised in which the codewords are sparse
superpositions, that is, linear combinations of subsets of vectors from a given
design, with the possible messages indexed by the choice of subset. Decoding is
by least squares, tailored to the assumed form of linear combination.
Communication is shown to be reliable with error probability exponentially
small for all rates up to the Shannon capacity.
|
1006.3782
|
Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless
Networks
|
cs.NI cs.IT math.IT
|
Distributed medium access control (MAC) protocols are essential for the
proliferation of low cost, decentralized wireless local area networks (WLANs).
Most MAC protocols are designed with the presumption that nodes comply with
prescribed rules. However, selfish nodes have natural motives to manipulate
protocols in order to improve their own performance. This often degrades the
performance of other nodes as well as that of the overall system. In this work,
we propose a class of protocols that limit the performance gain which nodes can
obtain through selfish manipulation while incurring only a small efficiency
loss. The proposed protocols are based on the idea of a review strategy, with
which nodes collect signals about the actions of other nodes over a period of
time, use a statistical test to infer whether or not other nodes are following
the prescribed protocol, and trigger a punishment if a departure from the
protocol is perceived. We consider the cases of private and public signals and
provide analytical and numerical results to demonstrate the properties of the
proposed protocols.
|
1006.3787
|
Complete Complementary Results Report of the MARF's NLP Approach to the
DEFT 2010 Competition
|
cs.CL
|
This companion paper complements the main DEFT'10 article describing the MARF
approach (arXiv:0905.1235) to the DEFT'10 NLP challenge (described at
http://www.groupes.polymtl.ca/taln2010/deft.php in French). This paper is aimed
to present the complete result sets of all the conducted experiments and their
settings in the resulting tables highlighting the approach and the best
results, but also showing the worse and the worst and their subsequent
analysis. This particular work focuses on application of the MARF's classical
and NLP pipelines to identification tasks within various francophone corpora to
identify decades when certain articles were published for the first track
(Piste 1) and place of origin of a publication (Piste 2), such as the journal
and location (France vs. Quebec). This is the sixth iteration of the release of
the results.
|
1006.3855
|
Impact of Channel Asymmetry on Performance of Channel Estimation and
Precoding for Downlink Base Station Cooperative Transmission
|
cs.IT math.IT
|
Base station (BS) cooperative transmission can improve the spectrum
efficiency of cellular systems, whereas using which the channels will become
asymmetry. In this paper, we study the impact of the asymmetry on the
performance of channel estimation and precoding in downlink BS cooperative
multiple-antenna multiple-carrier systems. We first present three linear
estimators which jointly estimate the channel coefficients from users in
different cells with minimum mean square error, robust design and least square
criterion, and then study the impact of uplink channel asymmetry on their
performance. It is shown that when the large scale channel information is
exploited for channel estimation, using non-orthogonal training sequences among
users in different cells leads to minor performance loss. Next, we analyze the
impact of downlink channel asymmetry on the performance of precoding with
channel estimation errors. Our analysis shows that although the estimation
errors of weak cross links are large, the resulting rate loss is minor because
their contributions are weighted by the receive signal to noise ratio. The
simulation results verify our analysis and show that the rate loss per user is
almost constant no matter where the user is located, when the channel
estimators exploiting the large scale fading gains.
|
1006.3870
|
Toward Fast Reliable Communication at Rates Near Capacity with Gaussian
Noise
|
cs.IT cs.LG math.IT math.ST stat.TH
|
For the additive Gaussian noise channel with average codeword power
constraint, sparse superposition codes and adaptive successive decoding is
developed. Codewords are linear combinations of subsets of vectors, with the
message indexed by the choice of subset. A feasible decoding algorithm is
presented. Communication is reliable with error probability exponentially small
for all rates below the Shannon capacity.
|
1006.3959
|
Molecular Communication Using Brownian Motion with Drift
|
physics.bio-ph cond-mat.mes-hall cond-mat.soft cs.IT math.IT
|
Inspired by biological communication systems, molecular communication has
been proposed as a viable scheme to communicate between nano-sized devices
separated by a very short distance. Here, molecules are released by the
transmitter into the medium, which are then sensed by the receiver. This paper
develops a preliminary version of such a communication system focusing on the
release of either one or two molecules into a fluid medium with drift. We
analyze the mutual information between transmitter and the receiver when
information is encoded in the time of release of the molecule. Simplifying
assumptions are required in order to calculate the mutual information, and
theoretical results are provided to show that these calculations are upper
bounds on the true mutual information. Furthermore, optimized degree
distributions are provided, which suggest transmission strategies for a variety
of drift velocities.
|
1006.4026
|
A proof of two conjectures on APN functions
|
math.NT cs.IT math.IT
|
Dobbertin, Mills, M\"uller, Pott and Willems conjecture that two families of
power mapping are families of APN functions. Here we prove those two
conjectures.
|
1006.4030
|
A Novel VLSI Architecture of Fixed-complexity Sphere Decoder
|
cs.IT math.IT
|
Fixed-complexity Sphere Decoder (FSD) is a recently proposed technique for
Multiple-Input Multiple-Output (MIMO) detection. It has several outstanding
features such as constant throughput and large potential parallelism, which
makes it suitable for efficient VLSI implementation. However, to our best
knowledge, no VLSI implementation of FSD has been reported in the literature,
although some FPGA prototypes of FSD with pipeline architecture have been
developed. These solutions achieve very high throughput but at very high cost
of hardware resources, making them impractical in real applications. In this
paper, we present a novel four-nodes-per-cycle parallel architecture of FSD,
with a breadth-first processing that allows for short critical path. The
implementation achieves a throughput of 213.3 Mbps at 400 MHz clock frequency,
at a cost of 0.18 mm2 Silicon area on 0.13{\mu}m CMOS technology. The proposed
solution is much more economical compared with the existing FPGA
implementations, and very suitable for practicl applications because of its
balanced performance and hardware-complexity; moreover it has the flexibility
to be expanded into an eight-nodes-per-cycle version in order to double the
throughput.
|
1006.4035
|
Towards the Development of a Simulator for Investigating the Impact of
People Management Practices on Retail Performance
|
cs.AI cs.CE cs.MA
|
Often models for understanding the impact of management practices on retail
performance are developed under the assumption of stability, equilibrium and
linearity, whereas retail operations are considered in reality to be dynamic,
non-linear and complex. Alternatively, discrete event and agent-based modelling
are approaches that allow the development of simulation models of heterogeneous
non-equilibrium systems for testing out different scenarios. When developing
simulation models one has to abstract and simplify from the real world, which
means that one has to try and capture the 'essence' of the system required for
developing a representation of the mechanisms that drive the progression in the
real system. Simulation models can be developed at different levels of
abstraction. To know the appropriate level of abstraction for a specific
application is often more of an art than a science. We have developed a retail
branch simulation model to investigate which level of model accuracy is
required for such a model to obtain meaningful results for practitioners.
|
1006.4039
|
Distributed Autonomous Online Learning: Regrets and Intrinsic
Privacy-Preserving Properties
|
cs.LG cs.AI
|
Online learning has become increasingly popular on handling massive data. The
sequential nature of online learning, however, requires a centralized learner
to store data and update parameters. In this paper, we consider online learning
with {\em distributed} data sources. The autonomous learners update local
parameters based on local data sources and periodically exchange information
with a small subset of neighbors in a communication network. We derive the
regret bound for strongly convex functions that generalizes the work by Ram et
al. (2010) for convex functions. Most importantly, we show that our algorithm
has \emph{intrinsic} privacy-preserving properties, and we prove the sufficient
and necessary conditions for privacy preservation in the network. These
conditions imply that for networks with greater-than-one connectivity, a
malicious learner cannot reconstruct the subgradients (and sensitive raw data)
of other learners, which makes our algorithm appealing in privacy sensitive
applications.
|
1006.4046
|
Online Identification and Tracking of Subspaces from Highly Incomplete
Information
|
cs.IT cs.SY math.IT math.OC stat.ML
|
This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation),
an efficient online algorithm for tracking subspaces from highly incomplete
observations. GROUSE requires only basic linear algebraic manipulations at each
iteration, and each subspace update can be performed in linear time in the
dimension of the subspace. The algorithm is derived by analyzing incremental
gradient descent on the Grassmannian manifold of subspaces. With a slight
modification, GROUSE can also be used as an online incremental algorithm for
the matrix completion problem of imputing missing entries of a low-rank matrix.
GROUSE performs exceptionally well in practice both in tracking subspaces and
as an online algorithm for matrix completion.
|
1006.4088
|
The Stability of Low-Rank Matrix Reconstruction: a Constrained Singular
Value View
|
cs.IT math.IT
|
The stability of low-rank matrix reconstruction with respect to noise is
investigated in this paper. The $\ell_*$-constrained minimal singular value
($\ell_*$-CMSV) of the measurement operator is shown to determine the recovery
performance of nuclear norm minimization based algorithms. Compared with the
stability results using the matrix restricted isometry constant, the
performance bounds established using $\ell_*$-CMSV are more concise, and their
derivations are less complex. Isotropic and subgaussian measurement operators
are shown to have $\ell_*$-CMSVs bounded away from zero with high probability,
as long as the number of measurements is relatively large. The $\ell_*$-CMSV
for correlated Gaussian operators are also analyzed and used to illustrate the
advantage of $\ell_*$-CMSV compared with the matrix restricted isometry
constant. We also provide a fixed point characterization of $\ell_*$-CMSV that
is potentially useful for its computation.
|
1006.4114
|
How to build a DNA search engine like Google?
|
q-bio.GN cs.ET cs.IR
|
This paper proposed a new method to build the large scale DNA sequences
search system based on web search engine technology. We give a very brief
introduction for the methods used in search engine first. Then how to build a
DNA search system like Google is illustrated in detail. Since there is no local
alignment process, this system is able to provide the ms level search services
for billions of DNA sequences in a typical server.
|
1006.4173
|
Better size estimation for sparse matrix products
|
cs.DS cs.DB
|
We consider the problem of doing fast and reliable estimation of the number
of non-zero entries in a sparse boolean matrix product. This problem has
applications in databases and computer algebra. Let n denote the total number
of non-zero entries in the input matrices. We show how to compute a 1 +-
epsilon approximation (with small probability of error) in expected time O(n)
for any epsilon > 4/\sqrt[4]{z}. The previously best estimation algorithm, due
to Cohen (JCSS 1997), uses time O(n/epsilon^2). We also present a variant using
O(sort(n)) I/Os in expectation in the cache-oblivious model. In contrast to
these results, the currently best algorithms for computing a sparse boolean
matrix product use time omega(n^{4/3}) (resp. omega(n^{4/3}/B) I/Os), even if
the result matrix has only z=O(n) nonzero entries. Our algorithm combines the
size estimation technique of Bar-Yossef et al. (RANDOM 2002) with a particular
class of pairwise independent hash functions that allows the sketch of a set of
the form A x C to be computed in expected time O(|A|+|C|) and O(sort(|A|+|C|))
I/Os. We then describe how sampling can be used to maintain (independent)
sketches of matrices that allow estimation to be performed in time o(n) if z is
sufficiently large. This gives a simpler alternative to the sketching technique
of Ganguly et al. (PODS 2005), and matches a space lower bound shown in that
paper. Finally, we present experiments on real-world data sets that show the
accuracy of both our methods to be significantly better than the worst-case
analysis predicts.
|
1006.4175
|
Optimization of Weighted Curvature for Image Segmentation
|
cs.CV
|
Minimization of boundary curvature is a classic regularization technique for
image segmentation in the presence of noisy image data. Techniques for
minimizing curvature have historically been derived from descent methods which
could be trapped in a local minimum and therefore required a good
initialization. Recently, combinatorial optimization techniques have been
applied to the optimization of curvature which provide a solution that achieves
nearly a global optimum. However, when applied to image segmentation these
methods required a meaningful data term. Unfortunately, for many images,
particularly medical images, it is difficult to find a meaningful data term.
Therefore, we propose to remove the data term completely and instead weight the
curvature locally, while still achieving a global optimum.
|
1006.4255
|
Polar codes for the two-user multiple-access channel
|
cs.IT math.IT
|
Arikan's polar coding method is extended to two-user multiple-access
channels. It is shown that if the two users of the channel use the Arikan
construction, the resulting channels will polarize to one of five possible
extremals, on each of which uncoded transmission is optimal. The sum rate
achieved by this coding technique is the one that correponds to uniform input
distributions. The encoding and decoding complexities and the error performance
of these codes are as in the single-user case: $O(n\log n)$ for encoding and
decoding, and $o(\exp(-n^{1/2-\epsilon}))$ for block error probability, where
$n$ is the block length.
|
1006.4270
|
Two-dimensional ranking of Wikipedia articles
|
cs.IR physics.soc-ph
|
The Library of Babel, described by Jorge Luis Borges, stores an enormous
amount of information. The Library exists {\it ab aeterno}. Wikipedia, a free
online encyclopaedia, becomes a modern analogue of such a Library. Information
retrieval and ranking of Wikipedia articles become the challenge of modern
society. While PageRank highlights very well known nodes with many ingoing
links, CheiRank highlights very communicative nodes with many outgoing links.
In this way the ranking becomes two-dimensional. Using CheiRank and PageRank we
analyze the properties of two-dimensional ranking of all Wikipedia English
articles and show that it gives their reliable classification with rich and
nontrivial features. Detailed studies are done for countries, universities,
personalities, physicists, chess players, Dow-Jones companies and other
categories.
|
1006.4330
|
Large gaps imputation in remote sensed imagery of the environment
|
stat.AP cs.CV
|
Imputation of missing data in large regions of satellite imagery is necessary
when the acquired image has been damaged by shadows due to clouds, or
information gaps produced by sensor failure.
The general approach for imputation of missing data, that could not be
considered missed at random, suggests the use of other available data. Previous
work, like local linear histogram matching, take advantage of a co-registered
older image obtained by the same sensor, yielding good results in filling
homogeneous regions, but poor results if the scenes being combined have radical
differences in target radiance due, for example, to the presence of sun glint
or snow.
This study proposes three different alternatives for filling the data gaps.
The first two involves merging radiometric information from a lower resolution
image acquired at the same time, in the Fourier domain (Method A), and using
linear regression (Method B). The third method consider segmentation as the
main target of processing, and propose a method to fill the gaps in the map of
classes, avoiding direct imputation (Method C).
All the methods were compared by means of a large simulation study,
evaluating performance with a multivariate response vector with four measures:
Q, RMSE, Kappa and Overall Accuracy coefficients. Difference in performance
were tested with a MANOVA mixed model design with two main effects, imputation
method and type of lower resolution extra data, and a blocking third factor
with a nested sub-factor, introduced by the real Landsat image and the
sub-images that were used. Method B proved to be the best for all criteria.
|
1006.4358
|
Combining Channel Output Feedback and CSI Feedback for MIMO Wireless
Systems
|
cs.IT math.IT
|
The use of channel output feedback to improve the reliability of fading
channels has received scant attention in the literature. In most work on
feedback for fading channels, only channel state information (CSI) feedback has
been exploited for coding at the transmitter. In this work, the design of a
coding scheme for multiple-input multiple-output (MIMO) fading systems with
channel output and channel state feedback at the transmitter is considered.
Under the assumption of additive white Gaussian noise and an independent and
identically distributed fading process, a simple linear coding strategy that
achieves any rate up to capacity is proposed. The framework assumes perfect CSI
at the transmitter and receiver. This simple linear processing scheme can
provide a doubly exponential probability of error decay with blocklength for
all rates less than capacity. Remarkably, this encoding scheme actually
consists of two separate encoding blocks: one that adapts to the current CSI
and one that adapts to the previous channel output feedback. This scheme is
extended to the case when the CSI is quantized at the receiver and conveyed to
the transmitter over a limited rate feedback channel; for multiple-input
single-output (MISO) fading systems it is shown the doubly exponential
probability of error decay is achieved as the blocklength increases.
|
1006.4386
|
Collaborative Relay Beamforming for Secrecy
|
cs.IT math.IT
|
In this paper, collaborative use of relays to form a beamforming system and
provide physical-layer security is investigated. In particular,
decode-and-forward (DF) and amplify-and-forward (AF) relay beamforming designs
under total and individual relay power constraints are studied with the goal of
maximizing the secrecy rates when perfect channel state information (CSI) is
available. In the DF scheme, the total power constraint leads to a closed-form
solution, and in this case, the optimal beamforming structure is identified in
the low and high signal-to-noise ratio (SNR) regimes. The beamforming design
under individual relay power constraints is formulated as an optimization
problem which is shown to be easily solved using two different approaches,
namely semidefinite programming and second-order cone programming. A simplified
and suboptimal technique which reduces the computation complexity under
individual power constraints is also presented. In the AF scheme, not having
analytical solutions for the optimal beamforming design under both total and
individual power constraints, an iterative algorithm is proposed to numerically
obtain the optimal beamforming structure and maximize the secrecy rates.
Finally, robust beamforming designs in the presence of imperfect CSI are
investigated for DF-based relay beamforming, and optimization frameworks are
provided
|
1006.4425
|
On-the-fly Uniformization of Time-Inhomogeneous Infinite Markov
Population Models
|
math.PR cs.CE cs.NA
|
This paper presents an on-the-fly uniformization technique for the analysis
of time-inhomogeneous Markov population models. This technique is applicable to
models with infinite state spaces and unbounded rates, which are, for instance,
encountered in the realm of biochemical reaction networks. To deal with the
infinite state space, we dynamically maintain a finite subset of the states
where most of the probability mass is located. This approach yields an
underapproximation of the original, infinite system. We present experimental
results to show the applicability of our technique.
|
1006.4442
|
On the Implementation of the Probabilistic Logic Programming Language
ProbLog
|
cs.PL cs.LG cs.LO
|
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.
|
1006.4458
|
Few Algorithms for ascertaining merit of a document and their
applications
|
cs.IR
|
Existing models for ranking documents(mostly in world wide web) are prestige
based. In this article, three algorithms to objectively judge the merit of a
document are proposed - 1) Citation graph maxflow 2) Recursive Gloss Overlap
based intrinsic merit scoring and 3) Interview algorithm. A short discussion on
generic judgement and its mathematical treatment is presented in introduction
to motivate these algorithms.
|
1006.4474
|
sTeX+ - a System for Flexible Formalization of Linked Data
|
cs.SE cs.AI
|
We present the sTeX+ system, a user-driven advancement of sTeX - a semantic
extension of LaTeX that allows for producing high-quality PDF documents for
(proof)reading and printing, as well as semantic XML/OMDoc documents for the
Web or further processing. Originally sTeX had been created as an invasive,
semantic frontend for authoring XML documents. Here, we used sTeX in a Software
Engineering case study as a formalization tool. In order to deal with modular
pre-semantic vocabularies and relations, we upgraded it to sTeX+ in a
participatory design process. We present a tool chain that starts with an sTeX+
editor and ultimately serves the generated documents as XHTML+RDFa Linked Data
via an OMDoc-enabled, versioned XML database. In the final output, all
structural annotations are preserved in order to enable semantic information
retrieval services.
|
1006.4484
|
Interactive Reconciliation with Low-Density Parity-Check Codes
|
cs.IT math.IT
|
Efficient information reconciliation is crucial in several scenarios, being
quantum key distribution a remarkable example. However, efficiency is not the
only requirement for determining the quality of the information reconciliation
process. In some of these scenarios we find other relevant parameters such as
the interactivity or the adaptability to different channel statistics. We
propose an interactive protocol for information reconciliation based on
low-density parity-check codes. The coding rate is adapted in real time by
using simultaneously puncturing and shortening strategies, allowing it to cover
a predefined error rate range with just a single code. The efficiency of the
information reconciliation process using the proposed protocol is considerably
better than the efficiency of its non-interactive version.
|
1006.4509
|
Receive Diversity and Ergodic Performance of Interference Alignment on
the MIMO Gaussian Interference Channel
|
cs.IT math.IT
|
We consider interference alignment (IA) over K-user Gaussian MIMO
interference channel (MIMO-IC) when the SNR is not asymptotically high. We
introduce a generalization of IA which enables receive diversity inside the
interference-free subspace. We generalize the existence criterion of an IA
solution proposed by Yetis et al. to this case, thereby establishing a
multi-user diversity-multiplexing trade-off (DMT) for the interference channel.
Furthermore, we derive a closed-form tight lower-bound for the ergodic mutual
information achievable using IA over a Gaussian MIMO-IC with Gaussian i.i.d.
channel coefficients at arbitrary SNR, when the transmitted signals are white
inside the subspace defined by IA. Finally, as an application of the previous
results, we compare the performance achievable by IA at various operating
points allowed by the DMT, to a recently introduced distributed method based on
game theory.
|
1006.4524
|
Fundamental Rate-Reliability-Complexity Limits in Outage Limited MIMO
Communications
|
cs.IT cs.CC math.IT math.ST stat.TH
|
The work establishes fundamental limits with respect to rate, reliability and
computational complexity, for a general setting of outage-limited MIMO
communications. In the high-SNR regime, the limits are optimized over all
encoders, all decoders, and all complexity regulating policies. The work then
proceeds to explicitly identify encoder-decoder designs and policies, that meet
this optimal tradeoff. In practice, the limits aim to meaningfully quantify
different pertinent measures, such as the optimal rate-reliability capabilities
per unit complexity and power, the optimal diversity gains per complexity
costs, or the optimal number of numerical operations (i.e., flops) per bit.
Finally the tradeoff's simple nature, renders it useful for insightful
comparison of the rate-reliability-complexity capabilities for different
encoders-decoders.
|
1006.4535
|
Studies on Relevance, Ranking and Results Display
|
cs.IR
|
This study considers the extent to which users with the same query agree as
to what is relevant, and how what is considered relevant may translate into a
retrieval algorithm and results display. To combine user perceptions of
relevance with algorithm rank and to present results, we created a prototype
digital library of scholarly literature. We confine studies to one population
of scientists (paleontologists), one domain of scholarly scientific articles
(paleo-related), and a prototype system (PaleoLit) that we built for the
purpose. Based on the principle that users do not pre-suppose answers to a
given query but that they will recognize what they want when they see it, our
system uses a rules-based algorithm to cluster results into fuzzy categories
with three relevance levels. Our system matches at least 1/3 of our
participants' relevancy ratings 87% of the time. Our subsequent usability study
found that participants trusted our uncertainty labels but did not value our
color-coded horizontal results layout above a standard retrieval list. We posit
that users make such judgments in limited time, and that time optimization per
task might help explain some of our findings.
|
1006.4540
|
A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee
Colony Optimization
|
cs.LG cs.AI cs.NE
|
Feature selection refers to the problem of selecting relevant features which
produce the most predictive outcome. In particular, feature selection task is
involved in datasets containing huge number of features. Rough set theory has
been one of the most successful methods used for feature selection. However,
this method is still not able to find optimal subsets. This paper proposes a
new feature selection method based on Rough set theory hybrid with Bee Colony
Optimization (BCO) in an attempt to combat this. This proposed work is applied
in the medical domain to find the minimal reducts and experimentally compared
with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods
such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle
Swarm Optimization (PSO).
|
1006.4544
|
Human Disease Diagnosis Using a Fuzzy Expert System
|
cs.AI
|
Human disease diagnosis is a complicated process and requires high level of
expertise. Any attempt of developing a web-based expert system dealing with
human disease diagnosis has to overcome various difficulties. This paper
describes a project work aiming to develop a web-based fuzzy expert system for
diagnosing human diseases. Now a days fuzzy systems are being used successfully
in an increasing number of application areas; they use linguistic rules to
describe systems. This research project focuses on the research and development
of a web-based clinical tool designed to improve the quality of the exchange of
health information between health care professionals and patients.
Practitioners can also use this web-based tool to corroborate diagnosis. The
proposed system is experimented on various scenarios in order to evaluate it's
performance. In all the cases, proposed system exhibits satisfactory results.
|
1006.4551
|
Vagueness of Linguistic variable
|
cs.AI
|
In the area of computer science focusing on creating machines that can engage
on behaviors that humans consider intelligent. The ability to create
intelligent machines has intrigued humans since ancient times and today with
the advent of the computer and 50 years of research into various programming
techniques, the dream of smart machines is becoming a reality. Researchers are
creating systems which can mimic human thought, understand speech, beat the
best human chessplayer, and countless other feats never before possible.
Ability of the human to estimate the information is most brightly shown in
using of natural languages. Using words of a natural language for valuation
qualitative attributes, for example, the person pawns uncertainty in form of
vagueness in itself estimations. Vague sets, vague judgments, vague conclusions
takes place there and then, where and when the reasonable subject exists and
also is interested in something. The vague sets theory has arisen as the answer
to an illegibility of language the reasonable subject speaks. Language of a
reasonable subject is generated by vague events which are created by the reason
and which are operated by the mind. The theory of vague sets represents an
attempt to find such approximation of vague grouping which would be more
convenient, than the classical theory of sets in situations where the natural
language plays a significant role. Such theory has been offered by known
American mathematician Gau and Buehrer .In our paper we are describing how
vagueness of linguistic variables can be solved by using the vague set
theory.This paper is mainly designed for one of directions of the eventology
(the theory of the random vague events), which has arisen within the limits of
the probability theory and which pursue the unique purpose to describe
eventologically a movement of reason.
|
1006.4553
|
Evolution of Biped Walking Using Neural Oscillators Controller and
Harmony Search Algorithm Optimizer
|
cs.RO
|
In this paper, a simple Neural controller has been used to achieve stable
walking in a NAO biped robot, with 22 degrees of freedom that implemented in a
virtual physics-based simulation environment of Robocup soccer simulation
environment. The algorithm uses a Matsuoka base neural oscillator to generate
control signal for the biped robot. To find the best angular trajectory and
optimize network parameters, a new population-based search algorithm, called
the Harmony Search (HS) algorithm, has been used. The algorithm conceptualized
a group of musicians together trying to search for better state of harmony.
Simulation results demonstrate that the modification of the step period and the
walking motion due to the sensory feedback signals improves the stability of
the walking motion.
|
1006.4561
|
An Efficient Technique for Similarity Identification between Ontologies
|
cs.AI
|
Ontologies usually suffer from the semantic heterogeneity when simultaneously
used in information sharing, merging, integrating and querying processes.
Therefore, the similarity identification between ontologies being used becomes
a mandatory task for all these processes to handle the problem of semantic
heterogeneity. In this paper, we propose an efficient technique for similarity
measurement between two ontologies. The proposed technique identifies all
candidate pairs of similar concepts without omitting any similar pair. The
proposed technique can be used in different types of operations on ontologies
such as merging, mapping and aligning. By analyzing its results a reasonable
improvement in terms of completeness, correctness and overall quality of the
results has been found.
|
1006.4563
|
The State of the Art: Ontology Web-Based Languages: XML Based
|
cs.AI
|
Many formal languages have been proposed to express or represent Ontologies,
including RDF, RDFS, DAML+OIL and OWL. Most of these languages are based on XML
syntax, but with various terminologies and expressiveness. Therefore, choosing
a language for building an Ontology is the main step. The main point of
choosing language to represent Ontology is based mainly on what the Ontology
will represent or be used for. That language should have a range of quality
support features such as ease of use, expressive power, compatibility, sharing
and versioning, internationalisation. This is because different kinds of
knowledge-based applications need different language features. The main
objective of these languages is to add semantics to the existing information on
the web. The aims of this paper is to provide a good knowledge of existing
language and understanding of these languages and how could be used.
|
1006.4567
|
Understanding Semantic Web and Ontologies: Theory and Applications
|
cs.AI
|
Semantic Web is actually an extension of the current one in that it
represents information more meaningfully for humans and computers alike. It
enables the description of contents and services in machine-readable form, and
enables annotating, discovering, publishing, advertising and composing services
to be automated. It was developed based on Ontology, which is considered as the
backbone of the Semantic Web. In other words, the current Web is transformed
from being machine-readable to machine-understandable. In fact, Ontology is a
key technique with which to annotate semantics and provide a common,
comprehensible foundation for resources on the Semantic Web. Moreover, Ontology
can provide a common vocabulary, a grammar for publishing data, and can supply
a semantic description of data which can be used to preserve the Ontologies and
keep them ready for inference. This paper provides basic concepts of web
services and the Semantic Web, defines the structure and the main applications
of ontology, and provides many relevant terms are explained in order to provide
a basic understanding of ontologies.
|
1006.4568
|
Approaches, Challenges and Future Direction of Image Retrieval
|
cs.IR
|
This paper attempts to discuss the evolution of the retrieval approaches
focusing on development, challenges and future direction of the image
retrieval. It highlights both the already addressed and outstanding issues. The
explosive growth of image data leads to the need of research and development of
Image Retrieval. However, Image retrieval researches are moving from keyword,
to low level features and to semantic features. Drive towards semantic features
is due to the problem of the keywords which can be very subjective and time
consuming while low level features cannot always describe high level concepts
in the users' mind. Hence, introducing an interpretation inconsistency between
image descriptors and high level semantics that known as the semantic gap. This
paper also discusses the semantic gap issues, user query mechanisms as well as
common ways used to bridge the gap in image retrieval.
|
1006.4588
|
Efficient Region-Based Image Querying
|
cs.CV
|
Retrieving images from large and varied repositories using visual contents
has been one of major research items, but a challenging task in the image
management community. In this paper we present an efficient approach for
region-based image classification and retrieval using a fast multi-level neural
network model. The advantages of this neural model in image classification and
retrieval domain will be highlighted. The proposed approach accomplishes its
goal in three main steps. First, with the help of a mean-shift based
segmentation algorithm, significant regions of the image are isolated.
Secondly, color and texture features of each region are extracted by using
color moments and 2D wavelets decomposition technique. Thirdly the multi-level
neural classifier is trained in order to classify each region in a given image
into one of five predefined categories, i.e., "Sky", "Building", "SandnRock",
"Grass" and "Water". Simulation results show that the proposed method is
promising in terms of classification and retrieval accuracy results. These
results compare favorably with the best published results obtained by other
state-of-the-art image retrieval techniques.
|
1006.4645
|
SPOT: An R Package For Automatic and Interactive Tuning of Optimization
Algorithms by Sequential Parameter Optimization
|
cs.NE cs.AI math.OC stat.AP
|
The sequential parameter optimization (SPOT) package for R is a toolbox for
tuning and understanding simulation and optimization algorithms. Model-based
investigations are common approaches in simulation and optimization. Sequential
parameter optimization has been developed, because there is a strong need for
sound statistical analysis of simulation and optimization algorithms. SPOT
includes methods for tuning based on classical regression and analysis of
variance techniques; tree-based models such as CART and random forest; Gaussian
process models (Kriging), and combinations of different meta-modeling
approaches. This article exemplifies how SPOT can be used for automatic and
interactive tuning.
|
1006.4703
|
A construction of universal secure network coding
|
cs.IT cs.CR math.IT
|
We construct a universal secure network coding. Our construction just
modifies the transmission scheme at the source node and works with every linear
coding at an intermediate node. We relax the security criterion such that the
mutual information between the message and the eavesdropped signal is
sufficiently small instead of strictly zero. Our construction allows the set of
eavesdropped links to change at each time slot.
|
1006.4754
|
Active Sites model for the B-Matrix Approach
|
cs.NE
|
This paper continues on the work of the B-Matrix approach in hebbian learning
proposed by Dr. Kak. It reports the results on methods of improving the memory
retrieval capacity of the hebbian neural network which implements the B-Matrix
approach. Previously, the approach to retrieving the memories from the network
was to clamp all the individual neurons separately and verify the integrity of
these memories. Here we present a network with the capability to identify the
"active sites" in the network during the training phase and use these "active
sites" to generate the memories retrieved from these neurons. Three methods are
proposed for obtaining the update order of the network from the proximity
matrix when multiple neurons are to be clamped. We then present a comparison
between the new methods to the classical case and also among the methods
themselves.
|
1006.4786
|
Compressive Direction Finding Based on Amplitude Comparison
|
cs.IT math.IT
|
This paper exploits recent developments in compressive sensing (CS) to
efficiently perform the direction finding via amplitude comprarison. The new
method is proposed based on unimodal characteristic of antenna pattern and
sparse property of received data. Unlike the conventional methods based
peak-searching and symmetric constraint, the sparse reconstruction algorithm
requires less pulse and takes advantage of CS. Simulation results validate the
performance of the proposed method is better than the conventional methods.
|
1006.4801
|
Noise Invalidation Denoising
|
stat.ME cs.CV math.ST stat.TH
|
A denoising technique based on noise invalidation is proposed. The adaptive
approach derives a noise signature from the noise order statistics and utilizes
the signature to denoise the data. The novelty of this approach is in
presenting a general-purpose denoising in the sense that it does not need to
employ any particular assumption on the structure of the noise-free signal,
such as data smoothness or sparsity of the coefficients. An advantage of the
method is in denoising the corrupted data in any complete basis transformation
(orthogonal or non-orthogonal). Experimental results show that the proposed
method, called Noise Invalidation Denoising (NIDe), outperforms existing
denoising approaches in terms of Mean Square Error (MSE).
|
1006.4804
|
The General Solutions of Linear ODE and Riccati Equation
|
math.CA cs.SY math-ph math.AP math.MP math.OC nlin.SI
|
This paper gives out the general solutions of variable coefficients ODE and
Riccati equation by way of integral series E(X) and F(X). Such kinds of
integral series are the generalized form of exponential function, and keep the
properties of convergent and reversible.
|
1006.4818
|
Stability (over time) of Modified-CS and LS-CS for Recursive Causal
Sparse Reconstruction
|
cs.IT math.IT stat.ME
|
In this work, we obtain sufficient conditions for the ``stability" of our
recently proposed algorithms, modified-CS (for noisy measurements) and Least
Squares CS-residual (LS-CS), designed for recursive reconstruction of sparse
signal sequences from noisy measurements. By ``stability" we mean that the
number of misses from the current support estimate and the number of extras in
it remain bounded by a time-invariant value at all times. The concept is
meaningful only if the bound is small compared to the current signal support
size. A direct corollary is that the reconstruction errors are also bounded by
a time-invariant and small value.
|
1006.4832
|
MINLIP for the Identification of Monotone Wiener Systems
|
cs.LG
|
This paper studies the MINLIP estimator for the identification of Wiener
systems consisting of a sequence of a linear FIR dynamical model, and a
monotonically increasing (or decreasing) static function. Given $T$
observations, this algorithm boils down to solving a convex quadratic program
with $O(T)$ variables and inequality constraints, implementing an inference
technique which is based entirely on model complexity control. The resulting
estimates of the linear submodel are found to be almost consistent when no
noise is present in the data, under a condition of smoothness of the true
nonlinearity and local Persistency of Excitation (local PE) of the data. This
result is novel as it does not rely on classical tools as a 'linearization'
using a Taylor decomposition, nor exploits stochastic properties of the data.
It is indicated how to extend the method to cope with noisy data, and empirical
evidence contrasts performance of the estimator against other recently proposed
techniques.
|
1006.4833
|
A Generic Storage API
|
cs.DB
|
We present a generic API suitable for provision of highly generic storage
facilities that can be tailored to produce various individually customised
storage infrastructures. The paper identifies a candidate set of minimal
storage system building blocks, which are sufficiently simple to avoid
encapsulating policy where it cannot be customised by applications, and
composable to build highly flexible storage architectures. Four main generic
components are defined: the store, the namer, the caster and the interpreter.
It is hypothesised that these are sufficiently general that they could act as
building blocks for any information storage and retrieval system. The essential
characteristics of each are defined by an interface, which may be implemented
by multiple implementing classes.
|
1006.4910
|
Kalman Filters and Homography: Utilizing the Matrix $A$
|
cs.CV
|
Many problems in Computer Vision can be reduced to either working around a
known transform, or given a model for the transform computing the inverse
problem of the transform itself. We will look at two ways of working with the
matrix $A$ and see how transforms are at the root of image processing and
vision problems.
|
1006.4925
|
Simulating information creation in social Semantic Web applications
|
cs.CE
|
Appropriate ranking algorithms and incentive mechanisms are essential to the
creation of high-quality information by users of a social network. However,
evaluating such mechanisms in a quantifiable way is a difficult problem.
Studies of live social networks of limited utility, due to the subjective
nature of ranking and the lack of experimental control. Simulation provides a
valuable alternative: insofar as the simulation resembles the live social
network, fielding a new algorithm within a simulated network can predict the
effect it will have on the live network. In this paper, we propose a simulation
model based on the actor-conceptinstance model of semantic social networks,
then we evaluate the model against a number of common ranking algorithms.We
observe their effects on information creation in such a network, and we extend
our results to the evaluation of generic ranking algorithms and incentive
mechanisms.
|
1006.4948
|
Automatic Music Composition using Answer Set Programming
|
cs.LO cs.AI
|
Music composition used to be a pen and paper activity. These these days music
is often composed with the aid of computer software, even to the point where
the computer compose parts of the score autonomously. The composition of most
styles of music is governed by rules. We show that by approaching the
automation, analysis and verification of composition as a knowledge
representation task and formalising these rules in a suitable logical language,
powerful and expressive intelligent composition tools can be easily built. This
application paper describes the use of answer set programming to construct an
automated system, named ANTON, that can compose melodic, harmonic and rhythmic
music, diagnose errors in human compositions and serve as a computer-aided
composition tool. The combination of harmonic, rhythmic and melodic composition
in a single framework makes ANTON unique in the growing area of algorithmic
composition. With near real-time composition, ANTON reaches the point where it
can not only be used as a component in an interactive composition tool but also
has the potential for live performances and concerts or automatically generated
background music in a variety of applications. With the use of a fully
declarative language and an "off-the-shelf" reasoning engine, ANTON provides
the human composer a tool which is significantly simpler, more compact and more
versatile than other existing systems. This paper has been accepted for
publication in Theory and Practice of Logic Programming (TPLP).
|
1006.4949
|
Artificial Immune Systems (2010)
|
cs.AI cs.MA cs.NE
|
The human immune system has numerous properties that make it ripe for
exploitation in the computational domain, such as robustness and fault
tolerance, and many different algorithms, collectively termed Artificial Immune
Systems (AIS), have been inspired by it. Two generations of AIS are currently
in use, with the first generation relying on simplified immune models and the
second generation utilising interdisciplinary collaboration to develop a deeper
understanding of the immune system and hence produce more complex models. Both
generations of algorithms have been successfully applied to a variety of
problems, including anomaly detection, pattern recognition, optimisation and
robotics. In this chapter an overview of AIS is presented, its evolution is
discussed, and it is shown that the diversification of the field is linked to
the diversity of the immune system itself, leading to a number of algorithms as
opposed to one archetypal system. Two case studies are also presented to help
provide insight into the mechanisms of AIS; these are the idiotypic network
approach and the Dendritic Cell Algorithm.
|
1006.4953
|
Large scale link based latent Dirichlet allocation for web document
classification
|
cs.IR
|
In this paper we demonstrate the applicability of latent Dirichlet allocation
(LDA) for classifying large Web document collections. One of our main results
is a novel influence model that gives a fully generative model of the document
content taking linkage into account. In our setup, topics propagate along links
in such a way that linked documents directly influence the words in the linking
document. As another main contribution we develop LDA specific boosting of
Gibbs samplers resulting in a significant speedup in our experiments. The
inferred LDA model can be applied for classification as dimensionality
reduction similarly to latent semantic indexing. In addition, the model yields
link weights that can be applied in algorithms to process the Web graph; as an
example we deploy LDA link weights in stacked graphical learning. By using
Weka's BayesNet classifier, in terms of the AUC of classification, we achieve
4% improvement over plain LDA with BayesNet and 18% over tf.idf with SVM. Our
Gibbs sampling strategies yield about 5-10 times speedup with less than 1%
decrease in accuracy in terms of likelihood and AUC of classification.
|
1006.4959
|
Open-Ended Evolutionary Robotics: an Information Theoretic Approach
|
cs.RO
|
This paper is concerned with designing self-driven fitness functions for
Embedded Evolutionary Robotics. The proposed approach considers the entropy of
the sensori-motor stream generated by the robot controller. This entropy is
computed using unsupervised learning; its maximization, achieved by an on-board
evolutionary algorithm, implements a "curiosity instinct", favouring
controllers visiting many diverse sensori-motor states (sms). Further, the set
of sms discovered by an individual can be transmitted to its offspring, making
a cultural evolution mode possible. Cumulative entropy (computed from ancestors
and current individual visits to the sms) defines another self-driven fitness;
its optimization implements a "discovery instinct", as it favours controllers
visiting new or rare sensori-motor states. Empirical results on the benchmark
problems proposed by Lehman and Stanley (2008) comparatively demonstrate the
merits of the approach.
|
1006.4990
|
GraphLab: A New Framework for Parallel Machine Learning
|
cs.LG cs.DC
|
Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting common patterns in ML, we developed GraphLab, which
improves upon abstractions like MapReduce by compactly expressing asynchronous
iterative algorithms with sparse computational dependencies while ensuring data
consistency and achieving a high degree of parallel performance. We demonstrate
the expressiveness of the GraphLab framework by designing and implementing
parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and
Compressed Sensing. We show that using GraphLab we can achieve excellent
parallel performance on large scale real-world problems.
|
1006.5008
|
Detecting Danger: The Dendritic Cell Algorithm
|
cs.AI cs.CR cs.NE
|
The Dendritic Cell Algorithm (DCA) is inspired by the function of the
dendritic cells of the human immune system. In nature, dendritic cells are the
intrusion detection agents of the human body, policing the tissue and organs
for potential invaders in the form of pathogens. In this research, and abstract
model of DC behaviour is developed and subsequently used to form an algorithm,
the DCA. The abstraction process was facilitated through close collaboration
with laboratory- based immunologists, who performed bespoke experiments, the
results of which are used as an integral part of this algorithm. The DCA is a
population based algorithm, with each agent in the system represented as an
'artificial DC'. Each DC has the ability to combine multiple data streams and
can add context to data suspected as anomalous. In this chapter the abstraction
process and details of the resultant algorithm are given. The algorithm is
applied to numerous intrusion detection problems in computer security including
the detection of port scans and botnets, where it has produced impressive
results with relatively low rates of false positives.
|
1006.5036
|
Performance evaluation for ML sequence detection in ISI channels with
Gauss Markov Noise
|
cs.IT math.IT
|
Inter-symbol interference (ISI) channels with data dependent Gauss Markov
noise have been used to model read channels in magnetic recording and other
data storage systems. The Viterbi algorithm can be adapted for performing
maximum likelihood sequence detection in such channels. However, the problem of
finding an analytical upper bound on the bit error rate of the Viterbi detector
in this case has not been fully investigated. Current techniques rely on an
exhaustive enumeration of short error events and determine the BER using a
union bound. In this work, we consider a subset of the class of ISI channels
with data dependent Gauss-Markov noise. We derive an upper bound on the
pairwise error probability (PEP) between the transmitted bit sequence and the
decoded bit sequence that can be expressed as a product of functions depending
on current and previous states in the (incorrect) decoded sequence and the
(correct) transmitted sequence. In general, the PEP is asymmetric. The average
BER over all possible bit sequences is then determined using a pairwise state
diagram. Simulations results which corroborate the analysis of upper bound,
demonstrate that analytic bound on BER is tight in high SNR regime. In the high
SNR regime, our proposed upper bound obviates the need for computationally
expensive simulation.
|
1006.5040
|
The comparison of Wiktionary thesauri transformed into the
machine-readable format
|
cs.IR
|
Wiktionary is a unique, peculiar, valuable and original resource for natural
language processing (NLP). The paper describes an open-source Wiktionary
parser: its architecture and requirements followed by a description of
Wiktionary features to be taken into account, some open problems of Wiktionary
and the parser. The current implementation of the parser extracts the
definitions, semantic relations, and translations from English and Russian
Wiktionaries. The paper's goal is to interest researchers (1) in using the
constructed machine-readable dictionary for different NLP tasks, (2) in
extending the software to parse 170 still unused Wiktionaries. The comparison
of a number and types of semantic relations, a number of definitions, and a
number of translations in the English Wiktionary and the Russian Wiktionary has
been carried out. It was found that the number of semantic relations in the
English Wiktionary is larger by 1.57 times than in Russian (157 and 100
thousands). But the Russian Wiktionary has more "rich" entries (with a big
number of semantic relations), e.g. the number of entries with three or more
semantic relations is larger by 1.63 times than in the English Wiktionary. Upon
comparison, it was found out the methodological shortcomings of the Wiktionary.
|
1006.5041
|
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
|
cs.AI
|
Finding the structure of a graphical model has been received much attention
in many fields. Recently, it is reported that the non-Gaussianity of data
enables us to identify the structure of a directed acyclic graph without any
prior knowledge on the structure. In this paper, we propose a novel
non-Gaussianity based algorithm for more general type of models; chain graphs.
The algorithm finds an ordering of the disjoint subsets of variables by
iteratively evaluating the independence between the variable subset and the
residuals when the remaining variables are regressed on those. However, its
computational cost grows exponentially according to the number of variables.
Therefore, we further discuss an efficient approximate approach for applying
the algorithm to large sized graphs. We illustrate the algorithm with
artificial and real-world datasets.
|
1006.5051
|
Fast ABC-Boost for Multi-Class Classification
|
cs.LG stat.ML
|
Abc-boost is a new line of boosting algorithms for multi-class
classification, by utilizing the commonly used sum-to-zero constraint. To
implement abc-boost, a base class must be identified at each boosting step.
Prior studies used a very expensive procedure based on exhaustive search for
determining the base class at each boosting step. Good testing performances of
abc-boost (implemented as abc-mart and abc-logitboost) on a variety of datasets
were reported.
For large datasets, however, the exhaustive search strategy adopted in prior
abc-boost algorithms can be too prohibitive. To overcome this serious
limitation, this paper suggests a heuristic by introducing Gaps when computing
the base class during training. That is, we update the choice of the base class
only for every $G$ boosting steps (i.e., G=1 in prior studies). We test this
idea on large datasets (Covertype and Poker) as well as datasets of moderate
sizes. Our preliminary results are very encouraging. On the large datasets,
even with G=100 (or larger), there is essentially no loss of test accuracy. On
the moderate datasets, no obvious loss of test accuracy is observed when G<=
20~50. Therefore, aided by this heuristic, it is promising that abc-boost will
be a practical tool for accurate multi-class classification.
|
1006.5059
|
Capacity Planning for Vertical Search Engines
|
cs.IR
|
Vertical search engines focus on specific slices of content, such as the Web
of a single country or the document collection of a large corporation. Despite
this, like general open web search engines, they are expensive to maintain,
expensive to operate, and hard to design. Because of this, predicting the
response time of a vertical search engine is usually done empirically through
experimentation, requiring a costly setup. An alternative is to develop a model
of the search engine for predicting performance. However, this alternative is
of interest only if its predictions are accurate. In this paper we propose a
methodology for analyzing the performance of vertical search engines. Applying
the proposed methodology, we present a capacity planning model based on a
queueing network for search engines with a scale typically suitable for the
needs of large corporations. The model is simple and yet reasonably accurate
and, in contrast to previous work, considers the imbalance in query service
times among homogeneous index servers. We discuss how we tune up the model and
how we apply it to predict the impact on the query response time when
parameters such as CPU and disk capacities are changed. This allows a manager
of a vertical search engine to determine a priori whether a new configuration
of the system might keep the query response under specified performance
constraints.
|
1006.5060
|
Learning sparse gradients for variable selection and dimension reduction
|
stat.ML cs.LG stat.ME
|
Variable selection and dimension reduction are two commonly adopted
approaches for high-dimensional data analysis, but have traditionally been
treated separately. Here we propose an integrated approach, called sparse
gradient learning (SGL), for variable selection and dimension reduction via
learning the gradients of the prediction function directly from samples. By
imposing a sparsity constraint on the gradients, variable selection is achieved
by selecting variables corresponding to non-zero partial derivatives, and
effective dimensions are extracted based on the eigenvectors of the derived
sparse empirical gradient covariance matrix. An error analysis is given for the
convergence of the estimated gradients to the true ones in both the Euclidean
and the manifold setting. We also develop an efficient forward-backward
splitting algorithm to solve the SGL problem, making the framework practically
scalable for medium or large datasets. The utility of SGL for variable
selection and feature extraction is explicitly given and illustrated on
artificial data as well as real-world examples. The main advantages of our
method include variable selection for both linear and nonlinear predictions,
effective dimension reduction with sparse loadings, and an efficient algorithm
for large p, small n problems.
|
1006.5061
|
Optimal Bandwidth and Power Allocation for Sum Ergodic Capacity under
Fading Channels in Cognitive Radio Networks
|
cs.IT math.IT
|
This paper studies optimal bandwidth and power allocation in a cognitive
radio network where multiple secondary users (SUs) share the licensed spectrum
of a primary user (PU) under fading channels using the frequency division
multiple access scheme. The sum ergodic capacity of all the SUs is taken as the
performance metric of the network. Besides all combinations of the peak/average
transmit power constraints at the SUs and the peak/average interference power
constraint imposed by the PU, total bandwidth constraint of the licensed
spectrum is also taken into account. Optimal bandwidth allocation is derived in
closed-form for any given power allocation. The structures of optimal power
allocations are also derived under all possible combinations of the
aforementioned power constraints. These structures indicate the possible
numbers of users that transmit at nonzero power but below their corresponding
peak powers, and show that other users do not transmit or transmit at their
corresponding peak power. Based on these structures, efficient algorithms are
developed for finding the optimal power allocations.
|
1006.5066
|
Power Allocation Strategies across N Orthogonal Channels at Both Source
and Relay
|
cs.IT math.IT
|
We consider a wireless relay network with one source, one relay and one
destination, where communications between nodes are preformed via N orthogonal
channels. This, for example, is the case when orthogonal frequency division
multiplexing is employed for data communications. Since the power available at
the source and relay is limited, we study optimal power allocation strategies
at the source and relay in order to maximize the overall source-destination
capacity under individual power constraints at the source and/or the relay.
Depending on the availability of the channel state information at the source
and rely, optimal power allocation strategies are performed at both the source
and relay or only at the relay. Considering different setups for the problem,
various optimization problems are formulated and solved. Some properties of the
optimal solution are also proved.
|
1006.5086
|
Split Bregman method for large scale fused Lasso
|
stat.CO cs.LG math.OC
|
rdering of regression or classification coefficients occurs in many
real-world applications. Fused Lasso exploits this ordering by explicitly
regularizing the differences between neighboring coefficients through an
$\ell_1$ norm regularizer. However, due to nonseparability and nonsmoothness of
the regularization term, solving the fused Lasso problem is computationally
demanding. Existing solvers can only deal with problems of small or medium
size, or a special case of the fused Lasso problem in which the predictor
matrix is identity matrix. In this paper, we propose an iterative algorithm
based on split Bregman method to solve a class of large-scale fused Lasso
problems, including a generalized fused Lasso and a fused Lasso support vector
classifier. We derive our algorithm using augmented Lagrangian method and prove
its convergence properties. The performance of our method is tested on both
artificial data and real-world applications including proteomic data from mass
spectrometry and genomic data from array CGH. We demonstrate that our method is
many times faster than the existing solvers, and show that it is especially
efficient for large p, small n problems.
|
1006.5087
|
Gaussian Z-Interference Channel with a Relay Link: Achievability Region
and Asymptotic Sum Capacity
|
cs.IT math.IT
|
This paper studies a Gaussian Z-interference channel with a rate-limited
digital relay link from one receiver to another. Achievable rate regions are
derived based on a combination of Han-Kobayashi common-private information
splitting technique and several different relay strategies including
compress-and-forward and a partial decode-and-forward strategy, in which the
interference is partially decoded then binned and forwarded through the digital
link for subtraction at the other end. For the Gaussian Z-interference channel
with a digital link from the interference-free receiver to the interfered
receiver, the capacity region is established in the strong interference regime;
an achievable rate region is established in the weak interference regime. In
the weak interference regime, the partial decode-and-forward strategy is shown
to be asymptotically sum-capacity achieving in the high signal-to-noise ratio
and high interference-to-noise ratio limit. In this case, each relay bit
asymptotically improves the sum capacity by one bit. For the Gaussian
Z-interference channel with a digital link from the interfered receiver to the
interference-free receiver, the capacity region is established in the strong
interference regime; achievable rate regions are established in the moderately
strong and weak interference regimes. In addition, the asymptotically sum
capacity is established in the limit of large relay link rate. In this case,
the sum capacity improvement due to the digital link is bounded by half a bit
when the interference link is weaker than certain threshold, but the sum
capacity improvement becomes unbounded as the interference link becomes
stronger.
|
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