id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1210.6912 | Enhancing the functional content of protein interaction networks | q-bio.MN cs.CE cs.LG q-bio.GN stat.ML | Protein interaction networks are a promising type of data for studying
complex biological systems. However, despite the rich information embedded in
these networks, they face important data quality challenges of noise and
incompleteness that adversely affect the results obtained from their analysis.
Here, we explore the use of the concept of common neighborhood similarity
(CNS), which is a form of local structure in networks, to address these issues.
Although several CNS measures have been proposed in the literature, an
understanding of their relative efficacies for the analysis of interaction
networks has been lacking. We follow the framework of graph transformation to
convert the given interaction network into a transformed network corresponding
to a variety of CNS measures evaluated. The effectiveness of each measure is
then estimated by comparing the quality of protein function predictions
obtained from its corresponding transformed network with those from the
original network. Using a large set of S. cerevisiae interactions, and a set of
136 GO terms, we find that several of the transformed networks produce more
accurate predictions than those obtained from the original network. In
particular, the $HC.cont$ measure proposed here performs particularly well for
this task. Further investigation reveals that the two major factors
contributing to this improvement are the abilities of CNS measures, especially
$HC.cont$, to prune out noisy edges and introduce new links between
functionally related proteins.
|
1210.6927 | Plug-and-Play Model Predictive Control based on robust control invariant
sets | cs.SY math.OC | In this paper we consider a linear system represented by a coupling graph
between subsystems and propose a distributed control scheme capable to
guarantee asymptotic stability and satisfaction of constraints on system inputs
and states. Most importantly, as in Riverso et al., 2012 our design procedure
enables plug-and-play (PnP) operations, meaning that (i) the addition or
removal of subsystems triggers the design of local controllers associated to
successors to the subsystem only and (ii) the synthesis of a local controller
for a subsystem requires information only from predecessors of the subsystem
and it can be performed using only local computational resources. Our method
hinges on local tube MPC controllers based on robust control invariant sets and
it advances the PnP design procedure proposed in Riverso et al., 2012 in
several directions. Quite notably, using recent results in the computation of
robust control invariant sets, we show how critical steps in the design of a
local controller can be solved through linear programming. Finally, an
application of the proposed control design procedure to frequency control in
power networks is presented.
|
1210.6954 | Optimal Locally Repairable and Secure Codes for Distributed Storage
Systems | cs.IT math.IT | This paper aims to go beyond resilience into the study of security and
local-repairability for distributed storage systems (DSS). Security and
local-repairability are both important as features of an efficient storage
system, and this paper aims to understand the trade-offs between resilience,
security, and local-repairability in these systems. In particular, this paper
first investigates security in the presence of colluding eavesdroppers, where
eavesdroppers are assumed to work together in decoding stored information.
Second, the paper focuses on coding schemes that enable optimal local repairs.
It further brings these two concepts together, to develop locally repairable
coding schemes for DSS that are secure against eavesdroppers.
The main results of this paper include: a. An improved bound on the secrecy
capacity for minimum storage regenerating codes, b. secure coding schemes that
achieve the bound for some special cases, c. a new bound on minimum distance
for locally repairable codes, d. code construction for locally repairable codes
that attain the minimum distance bound, and e. repair-bandwidth-efficient
locally repairable codes with and without security constraints.
|
1210.6956 | Vortexje - An Open-Source Panel Method for Co-Simulation | cs.CE physics.flu-dyn | This paper discusses the use of the 3-dimensional panel method for dynamical
system simulation. Specifically, the advantages and disadvantages of model
exchange versus co-simulation of the aerodynamics and the dynamical system
model are discussed. Based on a trade-off analysis, a set of recommendations
for a panel method implementation and for a co-simulation environment is
proposed. These recommendations are implemented in a C++ library, offered
on-line under an open source license. This code is validated against XFLR5, and
its suitability for co-simulation is demonstrated with an example of a tethered
wing, i.e, a kite. The panel method implementation and the co-simulation
environment are shown to be able to solve this stiff problem in a stable
fashion.
|
1210.6962 | Quantum-to-classical rate distortion coding | quant-ph cs.IT math.IT | We establish a theory of quantum-to-classical rate distortion coding. In this
setting, a sender Alice has many copies of a quantum information source. Her
goal is to transmit classical information about the source, obtained by
performing a measurement on it, to a receiver Bob, up to some specified level
of distortion. We derive a single-letter formula for the minimum rate of
classical communication needed for this task. We also evaluate this rate in the
case in which Bob has some quantum side information about the source. Our
results imply that, in general, Alice's best strategy is a non-classical one,
in which she performs a collective measurement on successive outputs of the
source.
|
1210.6963 | Schulze and Ranked-Pairs Voting are Fixed-Parameter Tractable to Bribe,
Manipulate, and Control | cs.GT cs.DS cs.MA | Schulze and ranked-pairs elections have received much attention recently, and
the former has quickly become a quite widely used election system. For many
cases these systems have been proven resistant to bribery, control, or
manipulation, with ranked pairs being particularly praised for being NP-hard
for all three of those. Nonetheless, the present paper shows that with respect
to the number of candidates, Schulze and ranked-pairs elections are
fixed-parameter tractable to bribe, control, and manipulate: we obtain uniform,
polynomial-time algorithms whose degree does not depend on the number of
candidates. We also provide such algorithms for some weighted variants of these
problems.
|
1210.7002 | A Biomimetic Approach Based on Immune Systems for Classification of
Unstructured Data | cs.AI | In this paper we present the results of unstructured data clustering in this
case a textual data from Reuters 21578 corpus with a new biomimetic approach
using immune system. Before experimenting our immune system, we digitalized
textual data by the n-grams approach. The novelty lies on hybridization of
n-grams and immune systems for clustering. The experimental results show that
the recommended ideas are promising and prove that this method can solve the
text clustering problem.
|
1210.7009 | A symbol-based algorithm for decoding bar codes | math.NA cs.IT math.IT math.OC | We investigate the problem of decoding a bar code from a signal measured with
a hand-held laser-based scanner. Rather than formulating the inverse problem as
one of binary image reconstruction, we instead incorporate the symbology of the
bar code into the reconstruction algorithm directly, and search for a sparse
representation of the UPC bar code with respect to this known dictionary. Our
approach significantly reduces the degrees of freedom in the problem, allowing
for accurate reconstruction that is robust to noise and unknown parameters in
the scanning device. We propose a greedy reconstruction algorithm and provide
robust reconstruction guarantees. Numerical examples illustrate the
insensitivity of our symbology-based reconstruction to both imprecise model
parameters and noise on the scanned measurements.
|
1210.7014 | Computer vision tools for the non-invasive assessment of autism-related
behavioral markers | cs.CV | The early detection of developmental disorders is key to child outcome,
allowing interventions to be initiated that promote development and improve
prognosis. Research on autism spectrum disorder (ASD) suggests behavioral
markers can be observed late in the first year of life. Many of these studies
involved extensive frame-by-frame video observation and analysis of a child's
natural behavior. Although non-intrusive, these methods are extremely
time-intensive and require a high level of observer training; thus, they are
impractical for clinical and large population research purposes. Diagnostic
measures for ASD are available for infants but are only accurate when used by
specialists experienced in early diagnosis. This work is a first milestone in a
long-term multidisciplinary project that aims at helping clinicians and general
practitioners accomplish this early detection/measurement task automatically.
We focus on providing computer vision tools to measure and identify ASD
behavioral markers based on components of the Autism Observation Scale for
Infants (AOSI). In particular, we develop algorithms to measure three critical
AOSI activities that assess visual attention. We augment these AOSI activities
with an additional test that analyzes asymmetrical patterns in unsupported
gait. The first set of algorithms involves assessing head motion by tracking
facial features, while the gait analysis relies on joint foreground
segmentation and 2D body pose estimation in video. We show results that provide
insightful knowledge to augment the clinician's behavioral observations
obtained from real in-clinic assessments.
|
1210.7038 | Full Object Boundary Detection by Applying Scale Invariant Features in a
Region Merging Segmentation Algorithm | cs.CV cs.AI | Object detection is a fundamental task in computer vision and has many
applications in image processing. This paper proposes a new approach for object
detection by applying scale invariant feature transform (SIFT) in an automatic
segmentation algorithm. SIFT is an invariant algorithm respect to scale,
translation and rotation. The features are very distinct and provide stable
keypoints that can be used for matching an object in different images. At
first, an object is trained with different aspects for finding best keypoints.
The object can be recognized in the other images by using achieved keypoints.
Then, a robust segmentation algorithm is used to detect the object with full
boundary based on SIFT keypoints. In segmentation algorithm, a merging role is
defined to merge the regions in image with the assistance of keypoints. The
results show that the proposed approach is reliable for object detection and
can extract object boundary well.
|
1210.7044 | Quotients of Orders in Cyclic Algebras and Space-Time Codes | cs.IT math.IT | Let $F$ be a number field with ring of integers $\Oc_F$ and $\Dc$ a division
$F$-algebra with a maximal cyclic subfield $K$. We study rings occurring as
quotients of a natural $\Oc_F$-order $\Lambda$ in $\Dc$ by two-sided ideals. We
reduce the problem to studying the ideal structure of $\Lambda/\qf^s\Lambda$,
where $\qf$ is a prime ideal in $\Oc_F$, $s\geq 1$. We study the case where
$\qf$ remains unramified in $K$, both when $s=1$ and $s>1$. This work is
motivated by its applications to space-time coded modulation.
|
1210.7047 | User-level Weibo Recommendation incorporating Social Influence based on
Semi-Supervised Algorithm | cs.SI cs.CY cs.LG | Tencent Weibo, as one of the most popular micro-blogging services in China,
has attracted millions of users, producing 30-60 millions of weibo (similar as
tweet in Twitter) daily. With the overload problem of user generate content,
Tencent users find it is more and more hard to browse and find valuable
information at the first time. In this paper, we propose a Factor Graph based
weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo
Recommendation), which could help Tencent users to find most suitable
information. The main innovation is that we consider both direct and indirect
social influence from topic level based on social balance theory. The main
advantages of adopting this strategy are that it could first build a more
accurate description of latent relationship between two users with weak
connections, which could help to solve the data sparsity problem; second
provide a more accurate recommendation for a certain user from a wider range.
Other meaningful contextual information is also combined into our model, which
include: Users profile, Users influence, Content of weibos, Topic information
of weibos and etc. We also design a semi-supervised algorithm to further reduce
the influence of data sparisty. The experiments show that all the selected
variables are important and the proposed model outperforms several baseline
methods.
|
1210.7053 | Managing sparsity, time, and quality of inference in topic models | stat.ML cs.AI cs.CV stat.ME | Inference is an integral part of probabilistic topic models, but is often
non-trivial to derive an efficient algorithm for a specific model. It is even
much more challenging when we want to find a fast inference algorithm which
always yields sparse latent representations of documents. In this article, we
introduce a simple framework for inference in probabilistic topic models,
denoted by FW. This framework is general and flexible enough to be easily
adapted to mixture models. It has a linear convergence rate, offers an easy way
to incorporate prior knowledge, and provides us an easy way to directly trade
off sparsity against quality and time. We demonstrate the goodness and
flexibility of FW over existing inference methods by a number of tasks.
Finally, we show how inference in topic models with nonconjugate priors can be
done efficiently.
|
1210.7054 | Large-Scale Sparse Principal Component Analysis with Application to Text
Data | stat.ML cs.LG math.OC | Sparse PCA provides a linear combination of small number of features that
maximizes variance across data. Although Sparse PCA has apparent advantages
compared to PCA, such as better interpretability, it is generally thought to be
computationally much more expensive. In this paper, we demonstrate the
surprising fact that sparse PCA can be easier than PCA in practice, and that it
can be reliably applied to very large data sets. This comes from a rigorous
feature elimination pre-processing result, coupled with the favorable fact that
features in real-life data typically have exponentially decreasing variances,
which allows for many features to be eliminated. We introduce a fast block
coordinate ascent algorithm with much better computational complexity than the
existing first-order ones. We provide experimental results obtained on text
corpora involving millions of documents and hundreds of thousands of features.
These results illustrate how Sparse PCA can help organize a large corpus of
text data in a user-interpretable way, providing an attractive alternative
approach to topic models.
|
1210.7056 | Selective Transfer Learning for Cross Domain Recommendation | cs.LG cs.IR stat.ML | Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring knowledge from some manually selected source domains,
the data sparseness problem could be mitigated. However for most cases, parts
of source domain data are not consistent with the observations in the target
domain, which may misguide the target domain model building. In this paper, we
propose a novel criterion based on empirical prediction error and its variance
to better capture the consistency across domains in CF settings. Consequently,
we embed this criterion into a boosting framework to perform selective
knowledge transfer. Comparing to several state-of-the-art methods, we show that
our proposed selective transfer learning framework can significantly improve
the accuracy of rating prediction tasks on several real-world recommendation
tasks.
|
1210.7070 | A Multiscale Framework for Challenging Discrete Optimization | cs.CV cs.LG math.OC stat.ML | Current state-of-the-art discrete optimization methods struggle behind when
it comes to challenging contrast-enhancing discrete energies (i.e., favoring
different labels for neighboring variables). This work suggests a multiscale
approach for these challenging problems. Deriving an algebraic representation
allows us to coarsen any pair-wise energy using any interpolation in a
principled algebraic manner. Furthermore, we propose an energy-aware
interpolation operator that efficiently exposes the multiscale landscape of the
energy yielding an effective coarse-to-fine optimization scheme. Results on
challenging contrast-enhancing energies show significant improvement over
state-of-the-art methods.
|
1210.7102 | 3D Face Recognition using Significant Point based SULD Descriptor | cs.CV | In this work, we present a new 3D face recognition method based on Speeded-Up
Local Descriptor (SULD) of significant points extracted from the range images
of faces. The proposed model consists of a method for extracting distinctive
invariant features from range images of faces that can be used to perform
reliable matching between different poses of range images of faces. For a given
3D face scan, range images are computed and the potential interest points are
identified by searching at all scales. Based on the stability of the interest
point, significant points are extracted. For each significant point we compute
the SULD descriptor which consists of vector made of values from the convolved
Haar wavelet responses located on concentric circles centred on the significant
point, and where the amount of Gaussian smoothing is proportional to the radii
of the circles. Experimental results show that the newly proposed method
provides higher recognition rate compared to other existing contemporary models
developed for 3D face recognition.
|
1210.7137 | Alberti's letter counts | math.HO cs.CL | Four centuries before modern statistical linguistics was born, Leon Battista
Alberti (1404--1472) compared the frequency of vowels in Latin poems and
orations, making the first quantified observation of a stylistic difference
ever. Using a corpus of 20 Latin texts (over 5 million letters), Alberti's
observations are statistically assessed. Letter counts prove that poets used
significantly more a's, e's, and y's, whereas orators used more of the other
vowels. The sample sizes needed to justify the assertions are studied, and
proved to be within reach for Alberti's scholarship.
|
1210.7154 | Get my pizza right: Repairing missing is-a relations in ALC ontologies
(extended version) | cs.AI | With the increased use of ontologies in semantically-enabled applications,
the issue of debugging defects in ontologies has become increasingly important.
These defects can lead to wrong or incomplete results for the applications.
Debugging consists of the phases of detection and repairing. In this paper we
focus on the repairing phase of a particular kind of defects, i.e. the missing
relations in the is-a hierarchy. Previous work has dealt with the case of
taxonomies. In this work we extend the scope to deal with ALC ontologies that
can be represented using acyclic terminologies. We present algorithms and
discuss a system.
|
1210.7190 | Subspace Fuzzy Vault | cs.IT cs.CR math.IT | Fuzzy vault is a scheme providing secure authentication based on fuzzy
matching of sets. A major application is the use of biometric features for
authentication, whereby unencrypted storage of these features is not an option
because of security concerns. While there is still ongoing research around the
practical implementation of such schemes, we propose and analyze here an
alternative construction based on subspace codes. This offers some advantages
in terms of security, as an eventual discovery of the key does not provide an
obvious access to the features. Crucial for an efficient implementation are the
computational complexity and the choice of good code parameters. The parameters
depend on the particular application, e.g. the biometric feature to be stored
and the rate one wants to allow for false acceptance. The developed theory is
closely linked to constructions of subspace codes studied in the area of random
network coding.
|
1210.7282 | The Hangulphabet: A Descriptive Alphabet | cs.CL | This paper describes the Hangulphabet, a new writing system that should prove
useful in a number of contexts. Using the Hangulphabet, a user can instantly
see voicing, manner and place of articulation of any phoneme found in human
language. The Hangulphabet places consonant graphemes on a grid with the x-axis
representing the place of articulation and the y-axis representing manner of
articulation. Each individual grapheme contains radicals from both axes where
the points intersect. The top radical represents manner of articulation where
the bottom represents place of articulation. A horizontal line running through
the middle of the bottom radical represents voicing. For vowels, place of
articulation is located on a grid that represents the position of the tongue in
the mouth. This grid is similar to that of the IPA vowel chart (International
Phonetic Association, 1999). The difference with the Hangulphabet being the
trapezoid representing the vocal apparatus is on a slight tilt. Place of
articulation for a vowel is represented by a breakout figure from the grid.
This system can be used as an alternative to the International Phonetic
Alphabet (IPA) or as a complement to it. Beginning students of linguistics may
find it particularly useful. A Hangulphabet font has been created to facilitate
switching between the Hangulphabet and the IPA.
|
1210.7292 | Optimized M2L Kernels for the Chebyshev Interpolation based Fast
Multipole Method | cs.NA cs.CE cs.MS math.NA | A fast multipole method (FMM) for asymptotically smooth kernel functions
(1/r, 1/r^4, Gauss and Stokes kernels, radial basis functions, etc.) based on a
Chebyshev interpolation scheme has been introduced in [Fong et al., 2009]. The
method has been extended to oscillatory kernels (e.g., Helmholtz kernel) in
[Messner et al., 2012]. Beside its generality this FMM turns out to be
favorable due to its easy implementation and its high performance based on
intensive use of highly optimized BLAS libraries. However, one of its
bottlenecks is the precomputation of the multiple-to-local (M2L) operator, and
its higher number of floating point operations (flops) compared to other FMM
formulations. Here, we present several optimizations for that operator, which
is known to be the costliest FMM operator. The most efficient ones do not only
reduce the precomputation time by a factor up to 340 but they also speed up the
matrix-vector product. We conclude with comparisons and numerical validations
of all presented optimizations.
|
1210.7295 | Analysis and Control of Period-Doubling Bifurcation in Buck Converters
Using Harmonic Balance | cs.SY math.DS nlin.CD | Period doubling bifurcation in buck converters is studied by using the
harmonic balance method. A simple dynamic model of a buck converter in
continuous conduction mode under voltage mode or current mode control is
derived. This model consists of the feedback connection of a linear system and
a nonlinear one. An exact harmonic balance analysis is used to obtain a
necessary and sufficient condition for a period doubling bifurcation to occur.
If such a bifurcation occurs, the analysis also provides information on its
exact location. Using the condition for bifurcation, a feedforward control is
designed to eliminate the period doubling bifurcation. This results in a wider
range of allowed source voltage, and also in improved line regulation.
|
1210.7325 | Solving Sequences of Generalized Least-Squares Problems on
Multi-threaded Architectures | cs.MS cs.CE q-bio.GN | Generalized linear mixed-effects models in the context of genome-wide
association studies (GWAS) represent a formidable computational challenge: the
solution of millions of correlated generalized least-squares problems, and the
processing of terabytes of data. We present high performance in-core and
out-of-core shared-memory algorithms for GWAS: By taking advantage of
domain-specific knowledge, exploiting multi-core parallelism, and handling data
efficiently, our algorithms attain unequalled performance. When compared to
GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor
we obtain 50-fold speedups. As a consequence, our routines enable genome
studies of unprecedented size.
|
1210.7335 | Professional diversity and the productivity of cities | physics.soc-ph cs.SI physics.data-an | The relationships between diversity, productivity and scale determine much of
the structure and robustness of complex biological and social systems. While
arguments for the link between specialization and productivity are common,
diversity has often been invoked as a hedging strategy, allowing systems to
evolve in response to environmental change. Despite their general appeal, these
arguments have not typically produced quantitative predictions for optimal
levels of functional diversity consistent with observations. One important
reason why these relationships have resisted formalization is the idiosyncratic
nature of diversity measures, which depend on given classification schemes.
Here, we address these issues by analyzing the statistics of professions in
cities and show how their probability distribution takes a universal
scale-invariant form, common to all cities, obtained in the limit of infinite
resolution of given taxonomies. We propose a model that generates the form and
parameters of this distribution via the introduction of new occupations at a
rate leading to individual specialization subject to the preservation of access
to overall function via their ego social networks. This perspective unifies
ideas about the importance of network structure in ecology and of innovation as
a recombinatory process with economic concepts of productivity gains obtained
through the division and coordination of labor, stimulated by scale.
|
1210.7341 | Subset Codes for Packet Networks | cs.IT math.IT | In this paper, we present a coding-theoretic framework for message
transmission over packet-switched networks. Network is modeled as a channel
which can induce packet errors, deletions, insertions, and out of order
delivery of packets. The proposed approach can be viewed as an extension of the
one introduced by Koetter and Kschischang for networks based on random linear
network coding. Namely, while their framework is based on subspace codes and
designed for networks in which network nodes perform random linear combining of
the packets, ours is based on the so-called subset codes, and is designed for
networks employing routing in network nodes.
|
1210.7350 | Fast Data in the Era of Big Data: Twitter's Real-Time Related Query
Suggestion Architecture | cs.IR cs.DB | We present the architecture behind Twitter's real-time related query
suggestion and spelling correction service. Although these tasks have received
much attention in the web search literature, the Twitter context introduces a
real-time "twist": after significant breaking news events, we aim to provide
relevant results within minutes. This paper provides a case study illustrating
the challenges of real-time data processing in the era of "big data". We tell
the story of how our system was built twice: our first implementation was built
on a typical Hadoop-based analytics stack, but was later replaced because it
did not meet the latency requirements necessary to generate meaningful
real-time results. The second implementation, which is the system deployed in
production, is a custom in-memory processing engine specifically designed for
the task. This experience taught us that the current typical usage of Hadoop as
a "big data" platform, while great for experimentation, is not well suited to
low-latency processing, and points the way to future work on data analytics
platforms that can handle "big" as well as "fast" data.
|
1210.7362 | Discrete Energy Minimization, beyond Submodularity: Applications and
Approximations | cs.CV cs.LG math.OC stat.ML | In this thesis I explore challenging discrete energy minimization problems
that arise mainly in the context of computer vision tasks. This work motivates
the use of such "hard-to-optimize" non-submodular functionals, and proposes
methods and algorithms to cope with the NP-hardness of their optimization.
Consequently, this thesis revolves around two axes: applications and
approximations. The applications axis motivates the use of such
"hard-to-optimize" energies by introducing new tasks. As the energies become
less constrained and structured one gains more expressive power for the
objective function achieving more accurate models. Results show how
challenging, hard-to-optimize, energies are more adequate for certain computer
vision applications. To overcome the resulting challenging optimization tasks
the second axis of this thesis proposes approximation algorithms to cope with
the NP-hardness of the optimization. Experiments show that these new methods
yield good results for representative challenging problems.
|
1210.7375 | Tractable and Consistent Random Graph Models | physics.soc-ph cs.SI | We define a general class of network formation models, Statistical
Exponential Random Graph Models (SERGMs), that nest standard exponential random
graph models (ERGMs) as a special case. We provide the first general results on
when these models' (including ERGMs) parameters estimated from the observation
of a single network are consistent (i.e., become accurate as the number of
nodes grows). Next, addressing the problem that standard techniques of
estimating ERGMs have been shown to have exponentially slow mixing times for
many specifications, we show that by reformulating network formation as a
distribution over the space of sufficient statistics instead of the space of
networks, the size of the space of estimation can be greatly reduced, making
estimation practical and easy. We also develop a related, but distinct, class
of models that we call subgraph generation models (SUGMs) that are useful for
modeling sparse networks and whose parameter estimates are also directly and
easily estimable, consistent, and asymptotically normally distributed. Finally,
we show how choice-based (strategic) network formation models can be written as
SERGMs and SUGMs, and apply our models and techniques to network data from
rural Indian villages.
|
1210.7397 | Optimal Sensor Placement for Target Localization and Tracking in 2D and
3D | math.OC cs.SY | This paper analytically characterizes optimal sensor placements for target
localization and tracking in 2D and 3D. Three types of sensors are considered:
bearing-only, range-only, and received-signal-strength. The optimal placement
problems of the three sensor types are formulated as an identical parameter
optimization problem and consequently analyzed in a unified framework. Recently
developed frame theory is applied to the optimality analysis. We prove
necessary and sufficient conditions for optimal placements in 2D and 3D. A
number of important analytical properties of optimal placements are further
explored. In order to verify the analytical analysis, we present a gradient
control law that can numerically construct generic optimal placements.
|
1210.7399 | One-Step Quantized Network Coding for Near Sparse Gaussian Messages | cs.IT math.IT | In this paper, mathematical bases for non-adaptive joint source network
coding of correlated messages in a Bayesian scenario are studied. Specifically,
we introduce one-step Quantized Network Coding (QNC), which is a hybrid
combination of network coding and packet forwarding for transmission. Motivated
by the work on Bayesian compressed sensing, we derive theoretical guarantees on
robust recovery in a one-step QNC scenario. Our mathematical derivations for
Gaussian messages express the opportunity of distributed compression by using
one-step QNC, as a simplified version of QNC scenario. Our simulation results
show an improvement in terms of quality-delay performance over routing based
packet forwarding.
|
1210.7401 | Joint Doppler frequency shift compensation and data detection method
using 2-D unitary ESPRIT algorithm for SIMO-OFDM railway communication
systems | cs.IT math.IT | In this paper, we present a joint Doppler frequency shift compensation and
data detection method using 2-D unitary ESPRIT algorithm for SIMO-OFDM railway
communication systems over fast time-varying sparse multipath channels. By
creating the spatio-temporal array data matrix utilizing the ISI-free part of
the CP (cyclic prefix), we first propose a novel algorithm for obtaining
auto-paired joint DOA and Doppler frequency shift estimates of all paths via
2-D unitary ESPRIT algorithm. Thereafter, based on the obtained estimates, a
joint Doppler frequency shift compensation and data detection method is
developed. This method consists of three parts: (a) the received signal is
spatially filtered to get the signal corresponding to each path, and the signal
corresponding to each path is compensated for the Doppler frequency shift in
time domain, (b) the Doppler frequency shift-compensated signals of all paths
are summed together, and (c) the desired information is detected by performing
FFT on the summed signal after excluding the CP. Moreover, we prove that the
channel matrix becomes time-invariant after Doppler frequency shift
compensation and the ICI is effectively avoided. Finally, simulation results
are presented to demonstrate the performance of the proposed method and compare
it with the conventional method.
|
1210.7403 | Resolution Enhancement of Range Images via Color-Image Segmentation | cs.CV | We report a method for super-resolution of range images. Our approach
leverages the interpretation of LR image as sparse samples on the HR grid.
Based on this interpretation, we demonstrate that our recently reported
approach, which reconstructs dense range images from sparse range data by
exploiting a registered colour image, can be applied for the task of resolution
enhancement of range images. Our method only uses a single colour image in
addition to the range observation in the super-resolution process. Using the
proposed approach, we demonstrate super-resolution results for large factors
(e.g. 4) with good localization accuracy.
|
1210.7410 | Distributed Control of Angle-constrained Circular Formations using
Bearing-only Measurements | cs.SY math.OC | This paper studies distributed formation control of multiple agents in the
plane using bearing-only measurements. It is assumed that each agent only
measures the local bearings of their neighbor agents. The target formation
considered in this paper is a circular formation, where each agent has exactly
two neighbors. In the target formation, the angle subtended at each agent by
their two neighbors is specified. We propose a distributed control law that
stabilizes angle-constrained target formations merely using local bearing
measurements. The stability of the target formation is analyzed based on
Lyapunov approaches. We present a unified proof to show that our control law
not only can ensure local exponential stability but also can give local
finite-time stability. The exponential or finite-time stability can be easily
switched by tuning a parameter in the control law.
|
1210.7420 | Complexity of Ten Decision Problems in Continuous Time Dynamical Systems | math.OC cs.CC cs.SY | We show that for continuous time dynamical systems described by polynomial
differential equations of modest degree (typically equal to three), the
following decision problems which arise in numerous areas of systems and
control theory cannot have a polynomial time (or even pseudo-polynomial time)
algorithm unless P=NP: local attractivity of an equilibrium point, stability of
an equilibrium point in the sense of Lyapunov, boundedness of trajectories,
convergence of all trajectories in a ball to a given equilibrium point,
existence of a quadratic Lyapunov function, invariance of a ball, invariance of
a quartic semialgebraic set under linear dynamics, local collision avoidance,
and existence of a stabilizing control law. We also extend our earlier
NP-hardness proof of testing local asymptotic stability for polynomial vector
fields to the case of trigonometric differential equations of degree four.
|
1210.7422 | Sensor networks security based on sensitive robots agents. A conceptual
model | cs.MA | Multi-agent systems are currently applied to solve complex problems. The
security of networks is an eloquent example of a complex and difficult problem.
A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion
Detection is introduced in the current paper. The proposed technique could be
used with machine learning based intrusion detection techniques. The new model
uses the reaction of virtual sensitive robots to different stigmergic variables
in order to keep the tracks of the intruders when securing a sensor network.
|
1210.7443 | A Better Understanding of the Performance of Rate-1/2 Binary Turbo Codes
that Use Odd-Even Interleavers | cs.IT math.IT | The effects of the odd-even constraint - as an interleaver design criterion -
on the performance of rate-1/2 binary turbo codes are revisited. According to
the current understanding, its adoption is favored because it makes the
information bits be uniformly protected, each one by its own parity bit. In
this paper, we provide instances that contradict this point of view suggesting
for a different explanation of the constraint's behavior, in terms of distance
spectrum.
|
1210.7461 | Recognizing Static Signs from the Brazilian Sign Language: Comparing
Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines
and Artificial Neural Networks | cs.CV cs.LG stat.ML | In this paper, we explore and detail our experiments in a
high-dimensionality, multi-class image classification problem often found in
the automatic recognition of Sign Languages. Here, our efforts are directed
towards comparing the characteristics, advantages and drawbacks of creating and
training Support Vector Machines disposed in a Directed Acyclic Graph and
Artificial Neural Networks to classify signs from the Brazilian Sign Language
(LIBRAS). We explore how the different heuristics, hyperparameters and
multi-class decision schemes affect the performance, efficiency and ease of use
for each classifier. We provide hyperparameter surface maps capturing accuracy
and efficiency, comparisons between DDAGs and 1-vs-1 SVMs, and effects of
heuristics when training ANNs with Resilient Backpropagation. We report
statistically significant results using Cohen's Kappa statistic for contingency
tables.
|
1210.7473 | Comments on "Nonextensive Entropies derived from Form Invariance of
Pseudoadditivity" | cs.IT math-ph math.IT math.MP | Recently, Suyari has defined nonextensive information content measure with
unique class of functions which satisfies certain set of axioms. Nonextensive
entropy is then defined as the appropriate expectation value of nonextensive
information content [H. Suyari, Phys. Rev E 65 066118 (2002)]. In this comment
we show that the class of functions determined by Suyari's axioms is actually
wider than the one given by Suyari and we determine the class. Particularly, an
information content corresponding to Havrda-Charvat entropy satisfies Suyari's
axioms and does not belong to the class given by Suyari but belongs to our
class. Moreover, some of the conditions from Suyari's set of axioms are
redundant, and some of them can be replaced with more intuitive weaker ones. We
give a modification of Suyari's axiomatic system with these weaker assumptions
and define the corresponding information content measure.
|
1210.7495 | Illustrating a neural model of logic computations: The case of Sherlock
Holmes' old maxim | q-bio.NC cs.AI | Natural languages can express some logical propositions that humans are able
to understand. We illustrate this fact with a famous text that Conan Doyle
attributed to Holmes: 'It is an old maxim of mine that when you have excluded
the impossible, whatever remains, however improbable, must be the truth'. This
is a subtle logical statement usually felt as an evident truth. The problem we
are trying to solve is the cognitive reason for such a feeling. We postulate
here that we accept Holmes' maxim as true because our adult brains are equipped
with neural modules that naturally perform modal logical computations.
|
1210.7498 | Percolation on interacting, antagonistic networks | physics.soc-ph cond-mat.dis-nn cond-mat.stat-mech cs.SI | Recently, new results on percolation of interdependent networks have shown
that the percolation transition can be first order. In this paper we show that,
when considering antagonistic interactions between interacting networks, the
percolation process might present a bistability of the equilibrium solution. To
this end, we introduce antagonistic interactions for which the functionality,
or activity, of a node in a network is incompatible with the functionality, of
the linked nodes in the other interacting networks. In particular, we study the
percolation transition in two interacting networks with purely antagonistic
interaction and different topology. For two antagonistic Poisson networks of
different average degree we found a large region in the phase diagram in which
there is a bistability of the steady state solutions of the percolation
process, i.e. we can find that either one of the two networks might percolate.
For two antagonistic scale-free networks we found that there is a region in the
phase diagram in which, despite the antagonistic interactions, both networks
are percolating. Finally we characterize the rich phase diagram of the
percolation problems on two antagonistic networks, the first one of the two
being a Poisson network and the second one being a scale-free network.
|
1210.7506 | Convolutional Compressed Sensing Using Deterministic Sequences | cs.IT cs.MM math.IT | In this paper, a new class of circulant matrices built from deterministic
sequences is proposed for convolution-based compressed sensing (CS). In
contrast to random convolution, the coefficients of the underlying filter are
given by the discrete Fourier transform of a deterministic sequence with good
autocorrelation. Both uniform recovery and non-uniform recovery of sparse
signals are investigated, based on the coherence parameter of the proposed
sensing matrices. Many examples of the sequences are investigated, particularly
the Frank-Zadoff-Chu (FZC) sequence, the \textit{m}-sequence and the Golay
sequence. A salient feature of the proposed sensing matrices is that they can
not only handle sparse signals in the time domain, but also those in the
frequency and/or or discrete-cosine transform (DCT) domain.
|
1210.7515 | Rewriting Codes for Flash Memories | cs.IT math.IT | Flash memory is a non-volatile computer memory comprising blocks of cells,
wherein each cell can take on q different values or levels. While increasing
the cell level is easy, reducing the level of a cell can be accomplished only
by erasing an entire block. Since block erasures are highly undesirable, coding
schemes - known as floating codes (or flash codes) and buffer codes - have been
designed in order to maximize the number of times that information stored in a
flash memory can be written (and re-written) prior to incurring a block
erasure.
An (n,k,t)q flash code C is a coding scheme for storing k information bits in
$n$ cells in such a way that any sequence of up to t writes can be accommodated
without a block erasure. The total number of available level transitions in n
cells is n(q-1), and the write deficiency of C, defined as \delta(C) =
n(q-1)-t, is a measure of how close the code comes to perfectly utilizing all
these transitions. In this paper, we show a construction of flash codes with
write deficiency O(qk\log k) if q \geq \log_2k, and at most O(k\log^2 k)
otherwise.
An (n,r,\ell,t)q buffer code is a coding scheme for storing a buffer of r
\ell-ary symbols such that for any sequence of t symbols it is possible to
successfully decode the last r symbols that were written. We improve upon a
previous upper bound on the maximum number of writes t in the case where there
is a single cell to store the buffer. Then, we show how to improve a
construction by Jiang et al. that uses multiple cells, where n\geq 2r.
|
1210.7533 | Joint Viterbi Decoding and Decision Feedback Equalization for Monobit
Digital Receivers | cs.IT math.IT | In ultra-wideband (UWB) communication systems with impulse radio (IR)
modulation, the bandwidth is usually 1GHz or more. To process the received
signal digitally, high sampling rate analog-digital-converters (ADC) are
required. Due to the high complexity and large power consumption, monobit ADC
is appropriate. The optimal monobit receiver has been derived. But it is not
efficient to combat intersymbol interference (ISI). Decision feedback
equalization (DFE) is an effect way dealing with ISI. In this paper, we
proposed a algorithm that combines Viterbi decoding and DFE together for
monobit receivers. In this way, we suppress the impact of ISI effectively, thus
improving the bit error rate (BER) performance. By state expansion, we achieve
better performance. The simulation results show that the algorithm has about
1dB SNR gain compared to separate demodulation and decoding method and 1dB loss
compared to the BER performance in the channel without ISI. Compare to the full
resolution detection in fading channel without ISI, it has 3dB SNR loss after
state expansion.
|
1210.7539 | Feedback Allocation For OFDMA Systems With Slow Frequency-domain
Scheduling | cs.IT math.IT | We study the problem of allocating limited feedback resources across multiple
users in an orthogonal-frequency-division-multiple-access downlink system with
slow frequency-domain scheduling. Many flavors of slow frequency-domain
scheduling (e.g., persistent scheduling, semi-persistent scheduling), that
adapt user-sub-band assignments on a slower time-scale, are being considered in
standards such as 3GPP Long-Term Evolution. In this paper, we develop a
feedback allocation algorithm that operates in conjunction with any arbitrary
slow frequency-domain scheduler with the goal of improving the throughput of
the system. Given a user-sub-band assignment chosen by the scheduler, the
feedback allocation algorithm involves solving a weighted sum-rate maximization
at each (slow) scheduling instant. We first develop an optimal
dynamic-programming-based algorithm to solve the feedback allocation problem
with pseudo-polynomial complexity in the number of users and in the total
feedback bit budget. We then propose two approximation algorithms with
complexity further reduced, for scenarios where the problem exhibits additional
structure.
|
1210.7543 | Exploiting Sparse Dynamics For Bandwidth Reduction In Cooperative
Sensing Systems | cs.IT math.IT | Recently, there has been a significant interest in developing cooperative
sensing systems for certain types of wireless applications. In such systems, a
group of sensing nodes periodically collect measurements about the signals
being observed in the given geographical region and transmit these measurements
to a central node, which in turn processes this information to recover the
signals. For example, in cognitive radio networks, the signals of interest are
those generated by the primary transmitters and the sensing nodes are the
secondary users. In such networks, it is critically important to be able to
reliably determine the presence or absence of primary transmitters in order to
avoid causing interference. The standard approach to transmit these
measurements from sensor the nodes to the fusion center has been to use
orthogonal channels. Such an approach quickly places a burden on the
control-channel-capacity of the network that would scale linearly in the number
of cooperating sensing nodes. In this paper, we show that as long as one
condition is satisfied: the dynamics of the observed signals are sparse, i.e.,
the observed signals do not change their values very rapidly in relation to the
time-scale at which the measurements are collected, we can significantly reduce
the control bandwidth of the system while achieving the full (linear) bandwidth
performance.
|
1210.7559 | Tensor decompositions for learning latent variable models | cs.LG math.NA stat.ML | This work considers a computationally and statistically efficient parameter
estimation method for a wide class of latent variable models---including
Gaussian mixture models, hidden Markov models, and latent Dirichlet
allocation---which exploits a certain tensor structure in their low-order
observable moments (typically, of second- and third-order). Specifically,
parameter estimation is reduced to the problem of extracting a certain
(orthogonal) decomposition of a symmetric tensor derived from the moments; this
decomposition can be viewed as a natural generalization of the singular value
decomposition for matrices. Although tensor decompositions are generally
intractable to compute, the decomposition of these specially structured tensors
can be efficiently obtained by a variety of approaches, including power
iterations and maximization approaches (similar to the case of matrices). A
detailed analysis of a robust tensor power method is provided, establishing an
analogue of Wedin's perturbation theorem for the singular vectors of matrices.
This implies a robust and computationally tractable estimation approach for
several popular latent variable models.
|
1210.7599 | The automatic creation of concept maps from documents written using
morphologically rich languages | cs.IR cs.AI cs.CL | Concept map is a graphical tool for representing knowledge. They have been
used in many different areas, including education, knowledge management,
business and intelligence. Constructing of concept maps manually can be a
complex task; an unskilled person may encounter difficulties in determining and
positioning concepts relevant to the problem area. An application that
recommends concept candidates and their position in a concept map can
significantly help the user in that situation. This paper gives an overview of
different approaches to automatic and semi-automatic creation of concept maps
from textual and non-textual sources. The concept map mining process is
defined, and one method suitable for the creation of concept maps from
unstructured textual sources in highly inflected languages such as the Croatian
language is described in detail. Proposed method uses statistical and data
mining techniques enriched with linguistic tools. With minor adjustments, that
method can also be used for concept map mining from textual sources in other
morphologically rich languages.
|
1210.7600 | Study on the Availability Prediction of the Reconfigurable Networked
Software System | cs.MA cs.SE | This paper describes multi-agent based availability prediction approach for
the reconfigurable networked software system.
|
1210.7631 | The fortresses of Ejin: an example of outlining a site from satellite
images | cs.CV | From 1960's to 1970's, the Chinese Army built some fortified artificial
hills. Some of them are located in the Inner Mongolia, Western China. These
large fortresses are surrounded by moats. For some of them it is still possible
to see earthworks, trenches and ditches, the planning of which could have a
symbolic meaning. We can argue this result form their digital outlining,
obtained after an image processing of satellite images, based on edge
detection.
|
1210.7657 | Text Classification with Compression Algorithms | cs.LG | This work concerns a comparison of SVM kernel methods in text categorization
tasks. In particular I define a kernel function that estimates the similarity
between two objects computing by their compressed lengths. In fact, compression
algorithms can detect arbitrarily long dependencies within the text strings.
Data text vectorization looses information in feature extractions and is highly
sensitive by textual language. Furthermore, these methods are language
independent and require no text preprocessing. Moreover, the accuracy computed
on the datasets (Web-KB, 20ng and Reuters-21578), in some case, is greater than
Gaussian, linear and polynomial kernels. The method limits are represented by
computational time complexity of the Gram matrix and by very poor performance
on non-textual datasets.
|
1210.7659 | The Objective Indefiniteness Interpretation of Quantum Mechanics | quant-ph cs.IT math.IT math.LO physics.hist-ph | The common-sense view of reality is expressed logically in Boolean subset
logic (each element is either definitely in or not in a subset, i.e., either
definitely has or does not have a property). But quantum mechanics does not
agree with this "properties all the way down" picture of micro-reality. Are
there other coherent alternative views of reality? A logic of partitions, dual
to the Boolean logic of subsets (partitions are dual to subsets), was recently
developed along with a logical version of information theory. In view of the
subset-partition duality, partition logic is the alternative to Boolean subset
logic and thus it abstractly describes the alternative dual view of
micro-reality. Perhaps QM is compatible with this dual view? Indeed, when the
mathematics of partitions using sets is "lifted" from sets to vector spaces,
then it yields the mathematics and relations of quantum mechanics. Thus the
vision of micro-reality abstractly characterized by partition logic matches
that described by quantum mechanics. The key concept explicated by partition
logic is the old idea of "objective indefiniteness" (emphasized by Shimony).
Thus partition logic, logical information theory, and the lifting program
provide the back story so that the old idea then yields the objective
indefiniteness interpretation of quantum mechanics.
|
1210.7669 | Performance Evaluation of Different Techniques for texture
Classification | cs.CV | Texture is the term used to characterize the surface of a given object or
phenomenon and is an important feature used in image processing and pattern
recognition. Our aim is to compare various Texture analyzing methods and
compare the results based on time complexity and accuracy of classification.
The project describes texture classification using Wavelet Transform and Co
occurrence Matrix. Comparison of features of a sample texture with database of
different textures is performed. In wavelet transform we use the Haar, Symlets
and Daubechies wavelets. We find that, thee Haar wavelet proves to be the most
efficient method in terms of performance assessment parameters mentioned above.
Comparison of Haar wavelet and Co-occurrence matrix method of classification
also goes in the favor of Haar. Though the time requirement is high in the
later method, it gives excellent results for classification accuracy except if
the image is rotated.
|
1210.7683 | Computing Petaflops over Terabytes of Data: The Case of Genome-Wide
Association Studies | cs.MS cs.CE cs.PF q-bio.GN q-bio.QM | In many scientific and engineering applications, one has to solve not one but
a sequence of instances of the same problem. Often times, the problems in the
sequence are linked in a way that allows intermediate results to be reused. A
characteristic example for this class of applications is given by the
Genome-Wide Association Studies (GWAS), a widely spread tool in computational
biology. GWAS entails the solution of up to trillions ($10^{12}$) of correlated
generalized least-squares problems, posing a daunting challenge: the
performance of petaflops ($10^{15}$ floating-point operations) over terabytes
of data.
In this paper, we design an algorithm for performing GWAS on multi-core
architectures. This is accomplished in three steps. First, we show how to
exploit the relation among successive problems, thus reducing the overall
computational complexity. Then, through an analysis of the required data
transfers, we identify how to eliminate any overhead due to input/output
operations. Finally, we study how to decompose computation into tasks to be
distributed among the available cores, to attain high performance and
scalability. With our algorithm, a GWAS that currently requires the use of a
supercomputer may now be performed in matter of hours on a single multi-core
node.
The discussion centers around the methodology to develop the algorithm rather
than the specific application. We believe the paper contributes valuable
guidelines of general applicability for computational scientists on how to
develop and optimize numerical algorithms.
|
1210.7711 | Refined support and entropic uncertainty inequalities | cs.IT math.IT | Generalized versions of the entropic (Hirschman-Beckner) and support
(Elad-Bruckstein) uncertainty principle are presented for frames
representations. Moreover, a sharpened version of the support inequality has
been obtained by introducing a generalization of the coherence. In the finite
dimensional case and under certain conditions, minimizers of this inequalities
are given as constant functions on their support. In addition, $\ell^p$-norms
inequalities are introduced as byproducts of the entropic inequalities.
|
1210.7719 | Robustness, Canalyzing Functions and Systems Design | math.PR cs.SY | We study a notion of robustness of a Markov kernel that describes a system of
several input random variables and one output random variable. Robustness
requires that the behaviour of the system does not change if one or several of
the input variables are knocked out. If the system is required to be robust
against too many knockouts, then the output variable cannot distinguish
reliably between input states and must be independent of the input. We study
how many input states the output variable can distinguish as a function of the
required level of robustness.
Gibbs potentials allow a mechanistic description of the behaviour of the
system after knockouts. Robustness imposes structural constraints on these
potentials. We show that interaction families of Gibbs potentials allow to
describe robust systems.
Given a distribution of the input random variables and the Markov kernel
describing the system, we obtain a joint probability distribution. Robustness
implies a number of conditional independence statements for this joint
distribution. The set of all probability distributions corresponding to robust
systems can be decomposed into a finite union of components, and we find
parametrizations of the components. The decomposition corresponds to a primary
decomposition of the conditional independence ideal and can be derived from
more general results about generalized binomial edge ideals.
|
1210.7752 | Phase retrieval with polarization | cs.IT math.FA math.IT | In many areas of imaging science, it is difficult to measure the phase of
linear measurements. As such, one often wishes to reconstruct a signal from
intensity measurements, that is, perform phase retrieval. In this paper, we
provide a novel measurement design which is inspired by interferometry and
exploits certain properties of expander graphs. We also give an efficient phase
retrieval procedure, and use recent results in spectral graph theory to produce
a stable performance guarantee which rivals the guarantee for PhaseLift in
[Candes et al. 2011]. We use numerical simulations to illustrate the
performance of our phase retrieval procedure, and we compare reconstruction
error and runtime with a common alternating-projections-type procedure.
|
1210.7828 | Exponential random graph models | physics.soc-ph cond-mat.dis-nn cs.SI | Nowadays, exponential random graphs (ERGs) are among the most widely-studied
network models. Different analytical and numerical techniques for ERG have been
developed that resulted in the well-established theory with true predictive
power. An excellent basic discussion of exponential random graphs addressed to
social science students and researchers is given in [Anderson et al.,
1999][Robins et al., 2007]. This essay is intentionally designed to be more
theoretical in comparison with the well-known primers just mentioned. Given the
interdisciplinary character of the new emerging science of complex networks,
the essay aims to give a contribution upon which network scientists and
practitioners, who represent different research areas, could build a common
area of understanding.
|
1210.7859 | Stochastic Games on a Multiple Access Channel | cs.SY cs.GT | We consider a scenario where N users try to access a common base station.
Associated with each user is its channel state and a finite queue which varies
with time. Each user chooses his power and the admission control variable in a
dynamic manner so as to maximize his expected throughput. The throughput of
each user is a function of the actions and states of all users. The scenario
considers the situation where each user knows his channel and buffer state but
is unaware of the states and actions taken by the other users. We consider the
scenario when each user is saturated (i.e., always has a packet to transmit) as
well as the case when each user is unsaturated. We formulate the problem as a
Markov game and show connections with strategic form games. We then consider
various throughput functions associated with the multiple user channel and
provide algorithms for finding these equilibria.
|
1210.7906 | Synthesis-by-analysis of BCH Codes | cs.IT math.IT | In this paper we propose a technique to blindly synthesize the generator
polynomial of BCH codes. The proposed technique involves finding Greatest
Common Divisor (GCD) among different codewords and block lengths. Based on this
combinatorial GCD calculation, correlation values are found. For a valid block
length, the iterative GCD calculation results either into generator polynomial
or some of its higher order multiples. These higher order polynomials are
factorized under modulo-2 operation, and one of the resulting factors is always
the generator polynomial which further increases the correlation value. The
resulting correlation plot for different polynomials shows very high values for
correct block length and valid generator polynomial. Knowing the valid block
length and generator polynomial, all other parameters including number of
parity-check digits (n-k), minimum distance dmin and error correcting
capability t are readily exposed.
|
1210.7917 | The Model of Semantic Concepts Lattice For Data Mining Of Microblogs | cs.CL cs.IR | The model of semantic concept lattice for data mining of microblogs has been
proposed in this work. It is shown that the use of this model is effective for
the semantic relations analysis and for the detection of associative rules of
key words.
|
1210.7931 | Polymatroids and polyquantoids | cs.IT cs.CR math.CO math.IT | When studying entropy functions of multivariate probability distributions,
polymatroids and matroids emerge. Entropy functions of pure multiparty quantum
states give rise to analogous notions, called here polyquantoids and quantoids.
Polymatroids and polyquantoids are related via linear mappings and duality.
Quantum secret sharing schemes that are ideal are described by selfdual
matroids. Expansions of integer polyquantoids to quantoids are studied and
linked to that of polymatroids.
|
1210.7940 | Transmission of information via the non-linear Scroedinger equation: The
random Gaussian input case | cs.IT math.IT nlin.SI | The explosion of demand for ultra-high information transmission rates over
the last decade has necessitated the usage of increasingly high light
intensities for fiber optical transmissions. As a result, the fiber
non-linearities need to be treated non-perturbatively. Similar analyses in the
past have focused on the effects of non-linearities on existing transmission
technologies, e.g. WDM. In this paper we take advantage of the fact that, under
certain assumptions, light transmission through optical fibers can be described
using the non-linear Schroedinger equation, which is exactly integrable. As a
particular example, we show that in the low Gaussian noise limit, the Gaussian
input distribution has a higher mutual information than the transmission using
WDM over the same available bandwidth.
|
1210.7956 | Implementation of a Vision System for a Landmine Detecting Robot Using
Artificial Neural Network | cs.NE cs.CV | Landmines, specifically anti-tank mines, cluster bombs, and unexploded
ordnance form a serious problem in many countries. Several landmine sweeping
techniques are used for minesweeping. This paper presents the design and the
implementation of the vision system of an autonomous robot for landmines
localization. The proposed work develops state-of-the-art techniques in digital
image processing for pre-processing captured images of the contaminated area.
After enhancement, Artificial Neural Network (ANN) is used in order to
identify, recognize and classify the landmines' make and model. The
Back-Propagation algorithm is used for training the network. The proposed work
proved to be able to identify and classify different types of landmines under
various conditions (rotated landmine, partially covered landmine) with a
success rate of up to 90%.
|
1210.7959 | Algorithm Selection for Combinatorial Search Problems: A Survey | cs.AI | The Algorithm Selection Problem is concerned with selecting the best
algorithm to solve a given problem on a case-by-case basis. It has become
especially relevant in the last decade, as researchers are increasingly
investigating how to identify the most suitable existing algorithm for solving
a problem instead of developing new algorithms. This survey presents an
overview of this work focusing on the contributions made in the area of
combinatorial search problems, where Algorithm Selection techniques have
achieved significant performance improvements. We unify and organise the vast
literature according to criteria that determine Algorithm Selection systems in
practice. The comprehensive classification of approaches identifies and
analyses the different directions from which Algorithm Selection has been
approached. This paper contrasts and compares different methods for solving the
problem as well as ways of using these solutions. It closes by identifying
directions of current and future research.
|
1210.7961 | Osculating Spaces of Varieties and Linear Network Codes | math.AG cs.IT math.IT | We present a general theory to obtain good linear network codes utilizing the
osculating nature of algebraic varieties. In particular, we obtain from the
osculating spaces of Veronese varieties explicit families of equidimensional
vector spaces, in which any pair of distinct vector spaces intersects in the
same dimension.
Linear network coding transmits information in terms of a basis of a vector
space and the information is received as a basis of a possible altered vector
space. Ralf Koetter and Frank R. Kschischang introduced a metric on the set af
vector spaces and showed that a minimal distance decoder for this metric
achieves correct decoding if the dimension of the intersection of the
transmitted and received vector space is sufficiently large.
The obtained osculating spaces of Veronese varieties are equidistant in the
above metric. The parameters of the resulting linear network codes are
determined.
|
1210.8083 | A Note on the Dimensions of the Structural Invariant Subspaces of the
Discrete-Time Singular Hamiltonian Systems | cs.SY | The structural invariant subspaces of the discrete-time singular Hamiltonian
system are used in 1] to give an analytic nonrecursive expression of all the
admissible trajectories. A deeper insight into the features of these subspaces,
particularly focused on the dimensionality issue, is the object of this note.
|
1210.8099 | An Atypical Survey of Typical-Case Heuristic Algorithms | cs.CC cs.AI cs.DS | Heuristic approaches often do so well that they seem to pretty much always
give the right answer. How close can heuristic algorithms get to always giving
the right answer, without inducing seismic complexity-theoretic consequences?
This article first discusses how a series of results by Berman, Buhrman,
Hartmanis, Homer, Longpr\'{e}, Ogiwara, Sch\"{o}ening, and Watanabe, from the
early 1970s through the early 1990s, explicitly or implicitly limited how well
heuristic algorithms can do on NP-hard problems. In particular, many desirable
levels of heuristic success cannot be obtained unless severe, highly unlikely
complexity class collapses occur. Second, we survey work initiated by Goldreich
and Wigderson, who showed how under plausible assumptions deterministic
heuristics for randomized computation can achieve a very high frequency of
correctness. Finally, we consider formal ways in which theory can help explain
the effectiveness of heuristics that solve NP-hard problems in practice.
|
1210.8116 | On U-Statistics and Compressed Sensing I: Non-Asymptotic Average-Case
Analysis | cs.IT math.IT | Hoeffding's U-statistics model combinatorial-type matrix parameters
(appearing in CS theory) in a natural way. This paper proposes using these
statistics for analyzing random compressed sensing matrices, in the
non-asymptotic regime (relevant to practice). The aim is to address certain
pessimisms of "worst-case" restricted isometry analyses, as observed by both
Blanchard & Dossal, et. al.
We show how U-statistics can obtain "average-case" analyses, by relating to
statistical restricted isometry property (StRIP) type recovery guarantees.
However unlike standard StRIP, random signal models are not required; the
analysis here holds in the almost sure (probabilistic) sense. For
Gaussian/bounded entry matrices, we show that both l1-minimization and LASSO
essentially require on the order of k \cdot [\log((n-k)/u) + \sqrt{2(k/n)
\log(n/k)}] measurements to respectively recover at least 1-5u fraction, and
1-4u fraction, of the signals. Noisy conditions are considered. Empirical
evidence suggests our analysis to compare well to Donoho & Tanner's recent
large deviation bounds for l0/l1-equivalence, in the regime of block lengths
1000-3000 with high undersampling (50-150 measurements); similar system sizes
are found in recent CS implementation.
In this work, it is assumed throughout that matrix columns are independently
sampled.
|
1210.8117 | On U-Statistics and Compressed Sensing II: Non-Asymptotic Worst-Case
Analysis | cs.IT math.IT | In another related work, U-statistics were used for non-asymptotic
"average-case" analysis of random compressed sensing matrices. In this
companion paper the same analytical tool is adopted differently - here we
perform non-asymptotic "worst-case" analysis.
Simple union bounds are a natural choice for "worst-case" analyses, however
their tightness is an issue (and questioned in previous works). Here we focus
on a theoretical U-statistical result, which potentially allows us to prove
that these union bounds are tight. To our knowledge, this kind of (powerful)
result is completely new in the context of CS. This general result applies to a
wide variety of parameters, and is related to (Stein-Chen) Poisson
approximation. In this paper, we consider i) restricted isometries, and ii)
mutual coherence. For the bounded case, we show that k-th order restricted
isometry constants have tight union bounds, when the measurements m =
\mathcal{O}(k (1 + \log(n/k))). Here we require the restricted isometries to
grow linearly in k, however we conjecture that this result can be improved to
allow them to be fixed. Also, we show that mutual coherence (with the standard
estimate \sqrt{(4\log n)/m}) have very tight union bounds.
For coherence, the normalization complicates general discussion, and we
consider only Gaussian and Bernoulli cases here.
|
1210.8124 | Hierarchical Learning Algorithm for the Beta Basis Function Neural
Network | cs.NE cs.AI | The paper presents a two-level learning method for the design of the Beta
Basis Function Neural Network BBFNN. A Genetic Algorithm is employed at the
upper level to construct BBFNN, while the key learning parameters :the width,
the centers and the Beta form are optimised using the gradient algorithm at the
lower level. In order to demonstrate the effectiveness of this hierarchical
learning algorithm HLABBFNN, we need to validate our algorithm for the
approximation of non-linear function.
|
1210.8129 | Compact Support Biorthogonal Wavelet Filterbanks for Arbitrary
Undirected Graphs | cs.IT cs.DC math.IT | In our recent work, we proposed the design of perfect reconstruction
orthogonal wavelet filterbanks, called graph- QMF, for arbitrary undirected
weighted graphs. In that formulation we first designed "one-dimensional"
two-channel filterbanks on bipartite graphs, and then extended them to
"multi-dimensional" separable two-channel filterbanks for arbitrary graphs via
a bipartite subgraph decomposition. We specifically designed wavelet filters
based on the spectral decomposition of the graph, and stated necessary and
sufficient conditions for a two-channel graph filter-bank on bipartite graphs
to provide aliasing-cancellation, perfect reconstruction and orthogonal set of
basis (orthogonality). While, the exact graph-QMF designs satisfy all the above
conditions, they are not exactly k-hop localized on the graph. In this paper,
we relax the condition of orthogonality to design a biorthogonal pair of
graph-wavelets that can have compact spatial spread and still satisfy the
perfect reconstruction conditions. The design is analogous to the standard
Cohen-Daubechies-Feauveau's (CDF) construction of factorizing a maximally-flat
Daubechies half-band filter. Preliminary results demonstrate that the proposed
filterbanks can be useful for both standard signal processing applications as
well as for signals defined on arbitrary graphs.
Note: Code examples from this paper are available at
http://biron.usc.edu/wiki/index.php/Graph Filterbanks
|
1210.8176 | Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple
Antennas | cs.PF cs.IT math.IT stat.AP | In this paper, we propose a signal-selective spectrum sensing method for
cognitive radio networks and specifically targeted for receivers with
multiple-antenna capability. This method is used for detecting the presence or
absence of primary users based on the eigenvalues of the cyclic covariance
matrix of received signals. In particular, the cyclic correlation significance
test is used to detect a specific signal-of-interest by exploiting knowledge of
its cyclic frequencies. The analytical threshold for achieving constant false
alarm rate using this detection method is presented, verified through
simulations, and shown to be independent of both the number of samples used and
the noise variance, effectively eliminating the dependence on accurate noise
estimation. The proposed method is also shown, through numerical simulations,
to outperform existing multiple-antenna cyclostationary-based spectrum sensing
algorithms under a quasi-static Rayleigh fading channel, in both spatially
correlated and uncorrelated noise environments. The algorithm also has
significantly lower computational complexity than these other approaches.
|
1210.8182 | Discovering Social Circles in Ego Networks | cs.SI physics.soc-ph | People's personal social networks are big and cluttered, and currently there
is no good way to automatically organize them. Social networking sites allow
users to manually categorize their friends into social circles (e.g. 'circles'
on Google+, and 'lists' on Facebook and Twitter), however they are laborious to
construct and must be updated whenever a user's network grows. In this paper,
we study the novel task of automatically identifying users' social circles. We
pose this task as a multi-membership node clustering problem on a user's
ego-network, a network of connections between her friends. We develop a model
for detecting circles that combines network structure as well as user profile
information. For each circle we learn its members and the circle-specific user
profile similarity metric. Modeling node membership to multiple circles allows
us to detect overlapping as well as hierarchically nested circles. Experiments
show that our model accurately identifies circles on a diverse set of data from
Facebook, Google+, and Twitter, for all of which we obtain hand-labeled
ground-truth.
|
1210.8184 | A stopping criterion for Markov chains when generating independent
random graphs | cs.SI cs.DM physics.soc-ph | Markov chains are convenient means of generating realizations of networks
with a given (joint or otherwise) degree distribution, since they simply
require a procedure for rewiring edges. The major challenge is to find the
right number of steps to run such a chain, so that we generate truly
independent samples. Theoretical bounds for mixing times of these Markov chains
are too large to be practically useful. Practitioners have no useful guide for
choosing the length, and tend to pick numbers fairly arbitrarily. We give a
principled mathematical argument showing that it suffices for the length to be
proportional to the number of desired number of edges. We also prescribe a
method for choosing this proportionality constant. We run a series of
experiments showing that the distributions of common graph properties converge
in this time, providing empirical evidence for our claims.
|
1210.8188 | Relative Value Iteration for Stochastic Differential Games | math.OC cs.SY | We study zero-sum stochastic differential games with player dynamics governed
by a nondegenerate controlled diffusion process. Under the assumption of
uniform stability, we establish the existence of a solution to the Isaac's
equation for the ergodic game and characterize the optimal stationary
strategies. The data is not assumed to be bounded, nor do we assume geometric
ergodicity. Thus our results extend previous work in the literature. We also
study a relative value iteration scheme that takes the form of a parabolic
Isaac's equation. Under the hypothesis of geometric ergodicity we show that the
relative value iteration converges to the elliptic Isaac's equation as time
goes to infinity. We use these results to establish convergence of the relative
value iteration for risk-sensitive control problems under an asymptotic
flatness assumption.
|
1210.8191 | Performance Indicator for MIMO MMSE Receivers in the Presence of Channel
Estimation Error | cs.PF cs.IT cs.NI math.IT | We present the derivation of post-processing SNR for
Minimum-Mean-Squared-Error (MMSE) receivers with imperfect channel estimates,
and show that it is an accurate indicator of the error rate performance of MIMO
systems in the presence of channel estimation error. Simulation results show
the tightness of the analysis.
|
1210.8193 | Decision dynamics in complex networks subject to mass media and social
contact transmission mechanisms | physics.soc-ph cs.SI | The dynamics of decisions in complex networks is studied within a Markov
process framework using numerical simulations combined with mathematical
insight into the process mechanisms. A mathematical discrete-time model is
derived based on a set of basic assumptions on the convincing mechanisms
associated to two opinions. The model is analyzed with respect to multiplicity
of critical points, illustrating in this way the main behavior to be expected
in the network. Particular interest is focussed on the effect of social network
and exogenous mass media-based influences on the decision behavior. A set of
numerical simulation results is provided illustrating how these mechanisms
impact the final decision results. The analysis reveals (i) the presence of
fixed-point multiplicity (with a maximum of four different fixed points),
multistability, and sensitivity with respect to process parameters, and (ii)
that mass media have a strong impact on the decision behavior.
|
1210.8194 | Extending the Concept of Analog Butterworth Filter for Fractional Order
Systems | cs.SY | This paper proposes the design of Fractional Order (FO) Butterworth filter in
complex w-plane (w=sq; q being any real number) considering the presence of
under-damped, hyper-damped, ultra-damped poles. This is the first attempt to
design such fractional Butterworth filters in complex w-plane instead of
complex s-plane, as conventionally done for integer order filters. Firstly, the
concept of fractional derivatives and w-plane stability of linear fractional
order systems are discussed. Detailed mathematical formulation for the design
of fractional Butterworth-like filter (FBWF) in w-plane is then presented.
Simulation examples are given along with a practical example to design the FO
Butterworth filter with given specifications in frequency domain to show the
practicability of the proposed formulation.
|
1210.8196 | Optimized Quality Factor of Fractional Order Analog Filters with
Band-Pass and Band-Stop Characteristics | cs.SY math.OC | Fractional order (FO) filters have been investigated in this paper, with
band-pass (BP) and band-stop (BS) characteristics, which can not be achieved
with conventional integer order filters with orders lesser then two. The
quality factors for symmetric and asymmetric magnitude response have been
optimized using real coded Genetic Algorithm (GA) for a user specified center
frequency. Parametric influence of the FO filters on the magnitude response is
also illustrated with credible numerical simulations.
|
1210.8197 | Stabilization Based Networked Predictive Controller Design for Switched
Plants | cs.SY math.OC | Stabilizing state feedback controller has been designed in this paper for a
switched DC motor plant, controlled over communication network. The switched
system formulation for the networked control system (NCS) with additional
switching in a plant parameter along with the switching due to random packet
losses, have been formulated as few set of non-strict Linear Matrix
Inequalities (LMIs). In order to solve non-strict LMIs using standard LMI
solver and to design the stabilizing state feedback controller, the Cone
Complementary Linearization (CCL) technique has been adopted. Simulation
studies have been carried out for a DC motor plant, operating at two different
sampling times with random switching in the moment of inertia, representing
sudden jerks.
|
1210.8220 | Closed-loop Reference Models for Output-Feedback Adaptive Systems | math.OC cs.SY nlin.AO | Closed-loop reference models have recently been proposed for states
accessible adaptive systems. They have been shown to have improved transient
response over their open loop counter parts. The results in the states
accessible case are extended to single input single output plants of arbitrary
relative degree.
|
1210.8223 | On the Existence of Retransmission Permutation Arrays | math.CO cs.IT math.IT | We investigate retransmission permutation arrays (RPAs) that are motivated by
applications in overlapping channel transmissions. An RPA is an $n\times n$
array in which each row is a permutation of ${1, ..., n}$, and for $1\leq i\leq
n$, all $n$ symbols occur in each $i\times\lceil\frac{n}{i}\rceil$ rectangle in
specified corners of the array. The array has types 1, 2, 3 and 4 if the stated
property holds in the top left, top right, bottom left and bottom right
corners, respectively. It is called latin if it is a latin square. We show that
for all positive integers $n$, there exists a type-$1,2,3,4$ $\RPA(n)$ and a
type-1,2 latin $\RPA(n)$.
|
1210.8229 | Top Down Approach to find Maximal Frequent Item Sets using Subset
Creation | cs.DB | Association rule has been an area of active research in the field of
knowledge discovery. Data mining researchers had improved upon the quality of
association rule mining for business development by incorporating influential
factors like value (utility), quantity of items sold (weight) and more for the
mining of association patterns. In this paper, we propose an efficient approach
to find maximal frequent itemset first. Most of the algorithms in literature
used to find minimal frequent item first, then with the help of minimal
frequent itemsets derive the maximal frequent itemsets. These methods consume
more time to find maximal frequent itemsets. To overcome this problem, we
propose a navel approach to find maximal frequent itemset directly using the
concepts of subsets. The proposed method is found to be efficient in finding
maximal frequent itemsets.
|
1210.8242 | Pipelined Workflow in Hybrid MPI/Pthread runtime for External Memory
Graph Construction | cs.DB cs.DC | Graph construction from a given set of edges is a data-intensive operator
that appears in social network analysis, ontology enabled databases, and, other
analytics processing. The operator represents an edge list to compressed sparse
row (CSR) representation (or sometimes in adjacency list, or as clustered
B-Tree storage). In this work, we show how to scale CSR construction to massive
scale on SSD-enabled supercomputers such as Gordon using pipelined processing.
We develop several abstraction and operations for external memory and parallel
edge list and integer array processing that are utilized towards building a
scalable algorithm for creating CSR representation.
Our experiments demonstrate that this scheme is four to six times faster than
currently available implementation. Moreover, our scheme can handle up to 8
billion edges (128GB) by using external memory as compared to prior schemes
where performance degrades considerably for edge list size 26 million and
beyond.
|
1210.8253 | Ranks of propelinear perfect binary codes | math.CO cs.IT math.IT | It is proven that for any numbers n=2^m-1, m >= 4 and r, such that n -
log(n+1)<= r <= n excluding n = r = 63, n = 127, r in {126,127} and n = r =
2047 there exists a propelinear perfect binary code of length n and rank r.
|
1210.8260 | Mean Field Theory of Dynamical Systems Driven by External Signals | nlin.CD cond-mat.dis-nn cs.AI | Dynamical systems driven by strong external signals are ubiquituous in nature
and engineering. Here we study "echo state networks", networks of a large
number of randomly connected nodes, which represent a simple model of a neural
network, and have important applications in machine learning. We develop a mean
field theory of echo state networks. The dynamics of the network is captured by
the evolution law, similar to a logistic map, for a single collective variable.
When the network is driven by many independent external signals, this
collective variable reaches a steady state. But when the network is driven by a
single external signal, the collective variable is nonstationnary but can be
characterised by its time averaged distribution. The predictions of the mean
field theory, including the value of the largest Lyaponuov exponent, are
compared with the numerical integration of the equations of motion.
|
1210.8262 | On the Relation Between the Common Labelling and the Median Graph | cs.CV | In structural pattern recognition, given a set of graphs, the computation of
a Generalized Median Graph is a well known problem. Some methods approach the
problem by assuming a relation between the Generalized Median Graph and the
Common Labelling problem. However, this relation has still not been formally
proved. In this paper, we analyse such relation between both problems. The main
result proves that the cost of the common labelling upper-bounds the cost of
the median with respect to the given set. In addition, we show that the two
problems are equivalent in some cases.
|
1210.8267 | Detecting Linear Block Codes in Noise using the GLRT | cs.IT math.IT | In this paper, we consider the problem of distinguishing the noisy codewords
of a known binary linear block code from a random bit sequence. We propose to
use the generalized likelihood ratio test (GLRT) to solve this problem. We also
give a formula to find approximate number of codewords required and compare our
results with an existing method.
|
1210.8291 | Learning in the Model Space for Fault Diagnosis | cs.LG cs.AI | The emergence of large scaled sensor networks facilitates the collection of
large amounts of real-time data to monitor and control complex engineering
systems. However, in many cases the collected data may be incomplete or
inconsistent, while the underlying environment may be time-varying or
un-formulated. In this paper, we have developed an innovative cognitive fault
diagnosis framework that tackles the above challenges. This framework
investigates fault diagnosis in the model space instead of in the signal space.
Learning in the model space is implemented by fitting a series of models using
a series of signal segments selected with a rolling window. By investigating
the learning techniques in the fitted model space, faulty models can be
discriminated from healthy models using one-class learning algorithm. The
framework enables us to construct fault library when unknown faults occur,
which can be regarded as cognitive fault isolation. This paper also
theoretically investigates how to measure the pairwise distance between two
models in the model space and incorporates the model distance into the learning
algorithm in the model space. The results on three benchmark applications and
one simulated model for the Barcelona water distribution network have confirmed
the effectiveness of the proposed framework.
|
1210.8293 | Lyapunov Control of Quantum Systems with Applications to Quantum
Computing | cs.SY math.OC | In the design of complex quantum systems like ion traps for quantum
computing, it is usually desired to stabilize a particular system state or make
the system state track a desired trajectory. Several control theoretical
approaches based on feedback seem attractive to solve such problems. But the
uncertain dynamics introduced by measurement on quantum systems makes the
synthesis of feedback control laws very complicated. Although we have not
explicitly modeled the change in system dynamics due to measurement (we have
assumed weak measurements), this is a first step towards a more detailed
analysis and closed-loop feedback design. Here, we present a Lyapunov-based
control approach on the lines of that developed by Mirrahimi, Rouchon, Turnici
(2005). The states are assumed to be obtained from weak measurements. The
Lyapunov control technique has not been applied to realistic quantum systems so
far. We have extended and applied the technique to two realistic physical
systems - the quantum harmonic oscillator and the n-qubit system. We also
propose to extend this concept to ion traps.
|
1210.8296 | Parameter Estimation of Switched Hammerstein Systems | cs.SY math.OC | This paper deals with the parameter estimation problem of the
Single-Input-Single-Output (SISO) switched Hammerstein system. Suppose that the
switching law is arbitrary but can be observed online. All subsystems are
parameterized and the Recursive Least Squares (RLS) algorithm is applied to
estimate their parameters. To overcome the difficulty caused by coupling of
data from different subsystems, the concept "intrinsic switch" is introduced.
Two cases are considered: i) The input is taken to be a sequence of independent
identically distributed (i.i.d.) random variables when identification is the
only purpose; ii) A diminishingly excited signal is superimposed on the control
when the adaptive control law is given. The strong consistency of the estimates
in both cases is established and a simulation example is given to verify the
theoretical analysis.
|
1210.8318 | Mugshot Identification from Manipulated Facial Images | cs.CV cs.MM | Editing on digital images is ubiquitous. Identification of deliberately
modified facial images is a new challenge for face identification system. In
this paper, we address the problem of identification of a face or person from
heavily altered facial images. In this face identification problem, the input
to the system is a manipulated or transformed face image and the system reports
back the determined identity from a database of known individuals. Such a
system can be useful in mugshot identification in which mugshot database
contains two views (frontal and profile) of each criminal. We considered only
frontal view from the available database for face identification and the query
image is a manipulated face generated by face transformation software tool
available online. We propose SIFT features for efficient face identification in
this scenario. Further comparative analysis has been given with well known
eigenface approach. Experiments have been conducted with real case images to
evaluate the performance of both methods.
|
1210.8326 | General BER Expression for One-Dimensional Constellations | cs.IT math.IT | A novel general ready-to-use bit-error rate (BER) expression for
one-dimensional constellations is developed. The BER analysis is performed for
bit patterns that form a labeling. The number of patterns for equally spaced
M-PAM constellations with different BER is analyzed.
|
1210.8353 | Temporal Autoencoding Restricted Boltzmann Machine | stat.ML cs.AI cs.LG | Much work has been done refining and characterizing the receptive fields
learned by deep learning algorithms. A lot of this work has focused on the
development of Gabor-like filters learned when enforcing sparsity constraints
on a natural image dataset. Little work however has investigated how these
filters might expand to the temporal domain, namely through training on natural
movies. Here we investigate exactly this problem in established temporal deep
learning algorithms as well as a new learning paradigm suggested here, the
Temporal Autoencoding Restricted Boltzmann Machine (TARBM).
|
1210.8378 | Development of a Dual Sensor Heat Control System | cs.SY | Convenience and safeguarding our home appliances have become an important
issue when dealing with an advancement and growth of an economy. This research
focuses on the design and construction of a Dual Sensor heat-monitoring system.
The circuit works by monitoring temperature from an external input and
comparing the temperature level with that of a preset temperature value. The
power output of the circuit is cut off or switched OFF or an alarm is triggered
ON if the temperature of the external input is equal to or, greater than the
preset temperature value. The methodology involves the application of linear
precision temperature sensors i.e., they generate a voltage that is directly
proportional to the temperature. Basically the system is constructed using
temperature sensors and comparators. The system is powered using a 12V power
supply. The results of the tests showed that the power output of the circuit is
switched OFF hence switching OFF the heating device or an alarm is triggered ON
when the device exceeded a preset temperature level. The general operation of
the system and performance is dependent on the temperature difference between
the preset temperature value and external temperature intended to be monitored.
The overall system was tested and found perfectly functional.
|
1210.8385 | First Experiments with PowerPlay | cs.AI cs.LG | Like a scientist or a playing child, PowerPlay not only learns new skills to
solve given problems, but also invents new interesting problems by itself. By
design, it continually comes up with the fastest to find, initially novel, but
eventually solvable tasks. It also continually simplifies or compresses or
speeds up solutions to previous tasks. Here we describe first experiments with
PowerPlay. A self-delimiting recurrent neural network SLIM RNN is used as a
general computational problem solving architecture. Its connection weights can
encode arbitrary, self-delimiting, halting or non-halting programs affecting
both environment (through effectors) and internal states encoding abstractions
of event sequences. Our PowerPlay-driven SLIM RNN learns to become an
increasingly general solver of self-invented problems, continually adding new
problem solving procedures to its growing skill repertoire. Extending a recent
conference paper, we identify interesting, emerging, developmental stages of
our open-ended system. We also show how it automatically self-modularizes,
frequently re-using code for previously invented skills, always trying to
invent novel tasks that can be quickly validated because they do not require
too many weight changes affecting too many previous tasks.
|
1210.8398 | An Alignment Algorithm for Sequences | cs.IT math.IT | This paper describes a new alignment algorithm for sequences that can be used
for determination of deletions and substitutions. It provides several solutions
out of which the best one can be chosen on the basis of minimization of gaps or
other considerations. The algorithm does not use similarity tables and it
performs aspects of both global and local alignment. The algorithm is compared
with other sequence alignment algorithms.
|
1210.8400 | Distributed Quantization Networks | cs.IT math.IT | Several key results in distributed source coding offer the intuition that
little improvement in compression can be gained from intersensor communication
when the information is coded in long blocks. However, when sensors are
restricted to code their observations in small blocks (e.g., 1), intelligent
collaboration between sensors can greatly reduce distortion. For networks where
sensors are allowed to "chat" using a side channel that is unobservable at the
fusion center, we provide asymptotically-exact characterization of distortion
performance and optimal quantizer design in the high-resolution
(low-distortion) regime using a framework called distributed functional scalar
quantization (DFSQ). The key result is that chatting can dramatically improve
performance even when intersensor communication is at very low rate, especially
if the fusion center desires fidelity of a nonlinear computation applied to
source realizations rather than fidelity in representing the sources
themselves. We also solve the rate allocation problem when communication links
have heterogeneous costs and provide a detailed example to demonstrate the
theoretical and practical gains from chatting. This example for maximum
computation gives insight on the gap between chatting and distributed networks,
and how to optimize the intersensor communication.
|
1210.8436 | Optimal size, freshness and time-frame for voice search vocabulary | cs.CL cs.IR | In this paper, we investigate how to optimize the vocabulary for a voice
search language model. The metric we optimize over is the out-of-vocabulary
(OoV) rate since it is a strong indicator of user experience. In a departure
from the usual way of measuring OoV rates, web search logs allow us to compute
the per-session OoV rate and thus estimate the percentage of users that
experience a given OoV rate. Under very conservative text normalization, we
find that a voice search vocabulary consisting of 2 to 2.5 million words
extracted from 1 week of search query data will result in an aggregate OoV rate
of 1%; at that size, the same OoV rate will also be experienced by 90% of
users. The number of words included in the vocabulary is a stable indicator of
the OoV rate. Altering the freshness of the vocabulary or the duration of the
time window over which the training data is gathered does not significantly
change the OoV rate. Surprisingly, a significantly larger vocabulary
(approximately 10 million words) is required to guarantee OoV rates below 1%
for 95% of the users.
|
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