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0907.2393
|
Multiscale Network Reduction Methodologies: Bistochastic and Disparity
Filtering of Human Migration Flows between 3,000+ U. S. Counties
|
physics.soc-ph cs.SI physics.data-an stat.AP
|
To control for multiscale effects in networks, one can transform the matrix
of (in general) weighted, directed internodal flows to bistochastic
(doubly-stochastic) form, using the iterative proportional fitting
(Sinkhorn-Knopp) procedure, which alternatively scales row and column sums to
all equal 1. The dominant entries in the bistochasticized table can then be
employed for network reduction, using strong component hierarchical clustering.
We illustrate various facets of this well-established, widely-applied two-stage
algorithm with the 3, 107 x 3, 107 (asymmetric) 1995-2000 intercounty migration
flow table for the United States. We compare the results obtained with ones
using the disparity filter, for "extracting the "multiscale backbone of complex
weighted networks", recently put forth by Serrano, Boguna and Vespignani (SBV)
(Proc. Natl. Acad. Sci. 106 [2009], 6483), upon which we have briefly commented
(Proc. Natl. Acad. Sci. 106 [2009], E66). The performance of the bistochastic
filter appears to be superior-at least in this specific case-in two respects:
(1) it requires far fewer links to complete a stongly-connected network
backbone; and (2) it "belittles" small flows and nodes less-a principal
desideratum of SBV-in the sense that the correlations of the nonzero raw flows
are considerably weaker with the corresponding bistochastized links than with
the significance levels yielded by the disparity filter. Additional comparative
studies--as called for by SBV-of these two filtering procedures, in particular
as regards their topological properties, should be of considerable interest.
Relatedly, in its many geographic applications, the two-stage procedure
has--with rare exceptions-clustered contiguous areas, often reconstructing
traditional regions (islands, for example), even though no contiguity
constraints, at all, are imposed beforehand.
|
0907.2412
|
Design of Pulse Shapes Based on Sampling with Gaussian Prefilter
|
cs.IT math.IT
|
Two new pulse shapes for communications are presented. The first pulse shape
generates a set of pulses without intersymbol interferenc (ISI) or is ISI-free
for short. In the neighbourhood of the origin it is similar in shape to the
classical cardinal sine function but is of exponential decay at infinity. This
pulse shape is identical to the interpolating function of a recent sampling
theorem with Gaussian prefilter. The second pulse shape is obtained from the
first pulse shape by spectral factorization. Besides being also of exponential
decay at infinity, it has a causal appearance since it is of superexponential
decay for negative times. It is closely related to the orthonormal generating
function considered earlier by Unser in the context of shift-invariant spaces.
This pulse shape is not ISI-free but it generates a set of orthonormal pulses.
The second pulse shape may also be used to define a receive matched filter so
that at the filter output the ISI-free pulses of the first kind are recovered.
|
0907.2452
|
Pattern Based Term Extraction Using ACABIT System
|
cs.CL
|
In this paper, we propose a pattern-based term extraction approach for
Japanese, applying ACABIT system originally developed for French. The proposed
approach evaluates termhood using morphological patterns of basic terms and
term variants. After extracting term candidates, ACABIT system filters out
non-terms from the candidates based on log-likelihood. This approach is
suitable for Japanese term extraction because most of Japanese terms are
compound nouns or simple phrasal patterns.
|
0907.2455
|
Parallel Opportunistic Routing in Wireless Networks
|
cs.IT math.IT
|
We study benefits of opportunistic routing in a large wireless ad hoc network
by examining how the power, delay, and total throughput scale as the number of
source- destination pairs increases up to the operating maximum. Our
opportunistic routing is novel in a sense that it is massively parallel, i.e.,
it is performed by many nodes simultaneously to maximize the opportunistic gain
while controlling the inter-user interference. The scaling behavior of
conventional multi-hop transmission that does not employ opportunistic routing
is also examined for comparison. Our results indicate that our opportunistic
routing can exhibit a net improvement in overall power--delay trade-off over
the conventional routing by providing up to a logarithmic boost in the scaling
law. Such a gain is possible since the receivers can tolerate more interference
due to the increased received signal power provided by the multi-user diversity
gain, which means that having more simultaneous transmissions is possible.
|
0907.2465
|
The Transactional Nature of Quantum Information
|
quant-ph cs.IT math.IT
|
Information, in its communications sense, is a transactional property. If the
received signals communicate choices made by the sender of the signals, then
information has been transmitter by the sender to the receiver. Given this
reality, the potential information in an unknown pure quantum state should be
non-zero. We examine transactional quantum information, which unlike von
Neumann entropy, depends on the mutuality of the relationship between the
sender and the receiver, associating information with an unknown pure state.
The information that can be obtained from a pure state in repeated experiments
is potentially infinite.
|
0907.2471
|
Benchmarking Declarative Approximate Selection Predicates
|
cs.DB cs.IR
|
Declarative data quality has been an active research topic. The fundamental
principle behind a declarative approach to data quality is the use of
declarative statements to realize data quality primitives on top of any
relational data source. A primary advantage of such an approach is the ease of
use and integration with existing applications. Several similarity predicates
have been proposed in the past for common quality primitives (approximate
selections, joins, etc.) and have been fully expressed using declarative SQL
statements. In this thesis, new similarity predicates are proposed along with
their declarative realization, based on notions of probabilistic information
retrieval. Then, full declarative specifications of previously proposed
similarity predicates in the literature are presented, grouped into classes
according to their primary characteristics. Finally, a thorough performance and
accuracy study comparing a large number of similarity predicates for data
cleaning operations is performed.
|
0907.2510
|
Capacity of a Class of Linear Binary Field Multi-source Relay Networks
|
cs.IT math.IT
|
Characterizing the capacity region of multi-source wireless relay networks is
one of the fundamental issues in network information theory. The problem is,
however, quite challenging due to inter-user interference when there exist
multiple source--destination (S--D) pairs in the network. By focusing on a
special class of networks, we show that the capacity can be found. Namely, we
study a layered linear binary field network with time-varying channels, which
is a simplified model reflecting broadcast, interference, and fading natures of
wireless communications. We observe that fading can play an important role in
mitigating inter-user interference effectively for both single-hop and
multi-hop networks. We propose new encoding and relaying schemes with
randomized channel pairing, which exploit such channel variations, and derive
their achievable rates. By comparing them with the cut-set upper bound, the
capacity region of single-hop networks and the sum capacity of multi-hop
networks can be characterized for some classes of channel distributions and
network topologies. For these classes, we show that the capacity region or sum
capacity can be interpreted as the max-flow min-cut theorem.
|
0907.2599
|
Multiple-Input Multiple-Output Gaussian Broadcast Channels with Common
and Confidential Messages
|
cs.IT math.IT
|
This paper considers the problem of the multiple-input multiple-output (MIMO)
Gaussian broadcast channel with two receivers (receivers 1 and 2) and two
messages: a common message intended for both receivers and a confidential
message intended only for receiver 1 but needing to be kept asymptotically
perfectly secure from receiver 2. A matrix characterization of the secrecy
capacity region is established via a channel enhancement argument. The enhanced
channel is constructed by first splitting receiver 1 into two virtual receivers
and then enhancing only the virtual receiver that decodes the confidential
message. The secrecy capacity region of the enhanced channel is characterized
using an extremal entropy inequality previously established for characterizing
the capacity region of a degraded compound MIMO Gaussian broadcast channel.
|
0907.2601
|
Decompounding on compact Lie groups
|
cs.IT math.IT math.ST stat.TH
|
Noncommutative harmonic analysis is used to solve a nonparametric estimation
problem stated in terms of compound Poisson processes on compact Lie groups.
This problem of decompounding is a generalization of a similar classical
problem. The proposed solution is based on a char- acteristic function method.
The treated problem is important to recent models of the physical inverse
problem of multiple scattering.
|
0907.2682
|
Permutation Arrays Under the Chebyshev Distance
|
cs.IT math.IT
|
An (n,d) permutation array (PA) is a set of permutations of length n with the
property that the distance (under some metric) between any two permutations in
the array is at least d. They became popular recently for communication over
power lines. Motivated by an application to flash memories, in this paper the
metric used is the Chebyshev metric. A number of different constructions are
given as well as bounds on the size of such PA.
|
0907.2702
|
Interference Channels with Destination Cooperation
|
cs.IT math.IT
|
Interference is a fundamental feature of the wireless channel. To better
understand the role of cooperation in interference management, the two-user
Gaussian interference channel where the destination nodes can cooperate by
virtue of being able to both transmit and receive is studied. The sum-capacity
of this channel is characterized up to a constant number of bits. The coding
scheme employed builds up on the superposition scheme of Han and Kobayashi
(1981) for two-user interference channels without cooperation. New upperbounds
to the sum-capacity are also derived.
|
0907.2759
|
On Cyclic and Nearly Cyclic Multiagent Interactions in the Plane
|
cs.MA cs.RO
|
We discuss certain types of cyclic and nearly cyclic interactions among N
"point"-agents in the plane, leading to formations of interesting limiting
geometric configurations. Cyclic pursuit and local averaging interactions have
been analyzed in the context of multi-agent gathering. In this paper, we
consider some nearly cyclic interactions that break symmetry leading to factor
circulants rather than circulant interaction matrices.
|
0907.2775
|
Modelling Concurrent Behaviors in the Process Specification Language
|
cs.AI
|
In this paper, we propose a first-order ontology for generalized stratified
order structure. We then classify the models of the theory using
model-theoretic techniques. An ontology mapping from this ontology to the core
theory of Process Specification Language is also discussed.
|
0907.2859
|
General Spectrum Sensing in Cognitive Radio Networks
|
cs.IT math.IT
|
The successful operation of cognitive radio (CR) between CR transmitter and
CR receiver (CR link) relies on reliable spectrum sensing. To network CRs
requires spectrum sensing at CR transmitter and further information regarding
the spectrum availability at CR receiver. Redefining the spectrum sensing along
with statistical inference suitable for cognitive radio networks (CRN), we
mathematically derive conditions to allow CR transmitter forwarding packets to
CR receiver under guaranteed outage probability, and prove that the correlation
of localized spectrum availability between a cooperative node and CR receiver
determines effectiveness of the cooperative scheme. Applying our novel
mathematical model to potential hidden terminals in CRN, we illustrate that the
allowable transmission region of a CR, defined as neighborhood, is no longer
circular shape even in a pure path loss channel model. This results in
asymmetric CR links to make bidirectional links generally inappropriate in CRN,
though this challenge can be alleviated by cooperative sensing. Therefore,
spectrum sensing capability determines CRN topology. For multiple cooperative
nodes, to fully utilize spectrum availability, the selection methodology of
cooperative nodes is developed due to limited overhead of information exchange.
Defining reliability as information of spectrum availability at CR receiver
provided by a cooperative node and by applying neighborhood area, we can
compare sensing capability of cooperative nodes from both link and network
perspectives. In addition, due to lack of centralized coordination in dynamic
CRN, CRs can only acquire local and partial information within limited sensing
duration, robust spectrum sensing is therefore proposed. Limits of cooperative
schemes and their impacts on network operation are also derived.
|
0907.2868
|
Scalable Probabilistic Similarity Ranking in Uncertain Databases
(Technical Report)
|
cs.DB cs.IR
|
This paper introduces a scalable approach for probabilistic top-k similarity
ranking on uncertain vector data. Each uncertain object is represented by a set
of vector instances that are assumed to be mutually-exclusive. The objective is
to rank the uncertain data according to their distance to a reference object.
We propose a framework that incrementally computes for each object instance and
ranking position, the probability of the object falling at that ranking
position. The resulting rank probability distribution can serve as input for
several state-of-the-art probabilistic ranking models. Existing approaches
compute this probability distribution by applying a dynamic programming
approach of quadratic complexity. In this paper we theoretically as well as
experimentally show that our framework reduces this to a linear-time complexity
while having the same memory requirements, facilitated by incremental accessing
of the uncertain vector instances in increasing order of their distance to the
reference object. Furthermore, we show how the output of our method can be used
to apply probabilistic top-k ranking for the objects, according to different
state-of-the-art definitions. We conduct an experimental evaluation on
synthetic and real data, which demonstrates the efficiency of our approach.
|
0907.2896
|
Decentralized Admission Control for Power-Controlled Wireless Links
|
cs.IT math.IT
|
This paper deals with the problem of admission control/channel access in
power-controlled decentralized wireless networks, in which the
quality-of-service (QoS) is expressed in terms of the signal-to-interference
ratio (SIR). We analyze a previously proposed admission control algorithm,
which was designed to maintain the SIR of operational (active) links above some
given threshold at all times (protection of active links). This protection
property ensures that as new users attempt to join the network, the already
established links sustain their quality. The considered scheme may be thus
applicable in some cognitive radio networks, where the fundamental premise is
that secondary users may be granted channel access only if it does not cause
disturbance to primary users.
The admission control algorithm was previously analyzed under the assumption
of affine interference functions. This paper extends all the previous results
to arbitrary standard interference functions, which capture many important
receiver designs, including optimal linear reception in the sense of maximizing
the SIR and the worst-case receiver design. Furthermore, we provide novel
conditions for protection of active users under the considered control scheme
when individual power constraints are imposed on each link. Finally, we
consider the possibility of a joint optimization of transmitters and receivers
in networks with linear transceivers, which includes linear beamforming in
multiple antenna systems. Transmitter optimization is performed alternately
with receiver optimization to generate non-decreasing sequences of SIRs.
Numerical evaluations show that additional transmitter side optimization has
potential for significant performance gains.
|
0907.2951
|
Untangling the Braid: Finding Outliers in a Set of Streams
|
cs.DB cs.DS
|
Monitoring the performance of large shared computing systems such as the
cloud computing infrastructure raises many challenging algorithmic problems.
One common problem is to track users with the largest deviation from the norm
(outliers), for some measure of performance. Taking a stream-computing
perspective, we can think of each user's performance profile as a stream of
numbers (such as response times), and the aggregate performance profile of the
shared infrastructure as a "braid" of these intermixed streams. The monitoring
system's goal then is to untangle this braid sufficiently to track the top k
outliers. This paper investigates the space complexity of one-pass algorithms
for approximating outliers of this kind, proves lower bounds using multi-party
communication complexity, and proposes small-memory heuristic algorithms. On
one hand, stream outliers are easily tracked for simple measures, such as max
or min, but our theoretical results rule out even good approximations for most
of the natural measures such as average, median, or the quantiles. On the other
hand, we show through simulation that our proposed heuristics perform quite
well for a variety of synthetic data.
|
0907.2955
|
General Deviants: An Analysis of Perturbations in Compressed Sensing
|
cs.IT math.IT
|
We analyze the Basis Pursuit recovery of signals with general perturbations.
Previous studies have only considered partially perturbed observations Ax + e.
Here, x is a signal which we wish to recover, A is a full-rank matrix with more
columns than rows, and e is simple additive noise. Our model also incorporates
perturbations E to the matrix A which result in multiplicative noise. This
completely perturbed framework extends the prior work of Candes, Romberg and
Tao on stable signal recovery from incomplete and inaccurate measurements. Our
results show that, under suitable conditions, the stability of the recovered
signal is limited by the noise level in the observation. Moreover, this
accuracy is within a constant multiple of the best-case reconstruction using
the technique of least squares. In the absence of additive noise numerical
simulations essentially confirm that this error is a linear function of the
relative perturbation.
|
0907.2984
|
Fountain Communication using Concatenated Codes
|
cs.IT math.IT
|
This paper extends linear-complexity concatenated coding schemes to fountain
communication over the discrete-time memoryless channel. Achievable fountain
error exponents for one-level and multi-level concatenated fountain codes are
derived. It is also shown that concatenated coding schemes possess interesting
properties in several multi-user fountain communication scenarios.
|
0907.2990
|
The Single Machine Total Weighted Tardiness Problem - Is it (for
Metaheuristics) a Solved Problem ?
|
cs.AI
|
The article presents a study of rather simple local search heuristics for the
single machine total weighted tardiness problem (SMTWTP), namely hillclimbing
and Variable Neighborhood Search. In particular, we revisit these approaches
for the SMTWTP as there appears to be a lack of appropriate/challenging
benchmark instances in this case. The obtained results are impressive indeed.
Only few instances remain unsolved, and even those are approximated within 1%
of the optimal/best known solutions. Our experiments support the claim that
metaheuristics for the SMTWTP are very likely to lead to good results, and
that, before refining search strategies, more work must be done with regard to
the proposition of benchmark data. Some recommendations for the construction of
such data sets are derived from our investigations.
|
0907.2993
|
Improvements for multi-objective flow shop scheduling by Pareto Iterated
Local Search
|
cs.AI
|
The article describes the proposition and application of a local search
metaheuristic for multi-objective optimization problems. It is based on two
main principles of heuristic search, intensification through variable
neighborhoods, and diversification through perturbations and successive
iterations in favorable regions of the search space. The concept is
successfully tested on permutation flow shop scheduling problems under multiple
objectives and compared to other local search approaches. While the obtained
results are encouraging in terms of their quality, another positive attribute
of the approach is its simplicity as it does require the setting of only very
few parameters.
|
0907.3099
|
Graph Theory and Optimization Problems for Very Large Networks
|
cs.NI cs.AI
|
Graph theory provides a primary tool for analyzing and designing computer
communication networks. In the past few decades, Graph theory has been used to
study various types of networks, including the Internet, wide Area Networks,
Local Area Networks, and networking protocols such as border Gateway Protocol,
Open shortest Path Protocol, and Networking Networks. In this paper, we present
some key graph theory concepts used to represent different types of networks.
Then we describe how networks are modeled to investigate problems related to
network protocols. Finally, we present some of the tools used to generate graph
for representing practical networks.
|
0907.3183
|
Why Did My Query Slow Down?
|
cs.DB
|
Many enterprise environments have databases running on network-attached
server-storage infrastructure (referred to as Storage Area Networks or SANs).
Both the database and the SAN are complex systems that need their own separate
administrative teams. This paper puts forth the vision of an innovative
management framework to simplify administrative tasks that require an in-depth
understanding of both the database and the SAN. As a concrete instance, we
consider the task of diagnosing the slowdown in performance of a database query
that is executed multiple times (e.g., in a periodic report-generation
setting). This task is very challenging because the space of possible causes
includes problems specific to the database, problems specific to the SAN, and
problems that arise due to interactions between the two systems. In addition,
the monitoring data available from these systems can be noisy.
We describe the design of DIADS which is an integrated diagnosis tool for
database and SAN administrators. DIADS generates and uses a powerful
abstraction called Annotated Plan Graphs (APGs) that ties together the
execution path of queries in the database and the SAN. Using an innovative
workflow that combines domain-specific knowledge with machine-learning
techniques, DIADS was applied successfully to diagnose query slowdowns caused
by complex combinations of events across a PostgreSQL database and a production
SAN.
|
0907.3200
|
A Mathematical Unification of Geometric Crossovers Defined on Phenotype
Space
|
cs.NE cs.DM
|
Geometric crossover is a representation-independent definition of crossover
based on the distance of the search space interpreted as a metric space. It
generalizes the traditional crossover for binary strings and other important
recombination operators for the most frequently used representations. Using a
distance tailored to the problem at hand, the abstract definition of crossover
can be used to design new problem specific crossovers that embed problem
knowledge in the search. This paper is motivated by the fact that
genotype-phenotype mapping can be theoretically interpreted using the concept
of quotient space in mathematics. In this paper, we study a metric
transformation, the quotient metric space, that gives rise to the notion of
quotient geometric crossover. This turns out to be a very versatile notion. We
give many example applications of the quotient geometric crossover.
|
0907.3202
|
Mathematical Interpretation between Genotype and Phenotype Spaces and
Induced Geometric Crossovers
|
cs.NE cs.DM
|
In this paper, we present that genotype-phenotype mapping can be
theoretically interpreted using the concept of quotient space in mathematics.
Quotient space can be considered as mathematically-defined phenotype space in
the evolutionary computation theory. The quotient geometric crossover has the
effect of reducing the search space actually searched by geometric crossover,
and it introduces problem knowledge in the search by using a distance better
tailored to the specific solution interpretation. Quotient geometric crossovers
are directly applied to the genotype space but they have the effect of the
crossovers performed on phenotype space. We give many example applications of
the quotient geometric crossover.
|
0907.3209
|
Registration of Standardized Histological Images in Feature Space
|
cs.CV
|
In this paper, we propose three novel and important methods for the
registration of histological images for 3D reconstruction. First, possible
intensity variations and nonstandardness in images are corrected by an
intensity standardization process which maps the image scale into a standard
scale where the similar intensities correspond to similar tissues meaning.
Second, 2D histological images are mapped into a feature space where continuous
variables are used as high confidence image features for accurate registration.
Third, we propose an automatic best reference slice selection algorithm that
improves reconstruction quality based on both image entropy and mean square
error of the registration process. We demonstrate that the choice of reference
slice has a significant impact on registration error, standardization, feature
space and entropy information. After 2D histological slices are registered
through an affine transformation with respect to an automatically chosen
reference, the 3D volume is reconstructed by co-registering 2D slices
elastically.
|
0907.3215
|
Fully Automatic 3D Reconstruction of Histological Images
|
cs.CV
|
In this paper, we propose a computational framework for 3D volume
reconstruction from 2D histological slices using registration algorithms in
feature space. To improve the quality of reconstructed 3D volume, first,
intensity variations in images are corrected by an intensity standardization
process which maps image intensity scale to a standard scale where similar
intensities correspond to similar tissues. Second, a subvolume approach is
proposed for 3D reconstruction by dividing standardized slices into groups.
Third, in order to improve the quality of the reconstruction process, an
automatic best reference slice selection algorithm is developed based on an
iterative assessment of image entropy and mean square error of the registration
process. Finally, we demonstrate that the choice of the reference slice has a
significant impact on registration quality and subsequent 3D reconstruction.
|
0907.3218
|
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face
Recognition
|
cs.CV
|
In this paper, the problem of automatic Gabor wavelet selection for face
recognition is tackled by introducing an automatic algorithm based on Parallel
AdaBoosting method. Incorporating mutual information into the algorithm leads
to the selection procedure not only based on classification accuracy but also
on efficiency. Effective image features are selected by using properly chosen
Gabor wavelets optimised with Parallel AdaBoost method and mutual information
to get high recognition rates with low computational cost. Experiments are
conducted using the well-known FERET face database. In proposed framework,
memory and computation costs are reduced significantly and high classification
accuracy is obtained.
|
0907.3220
|
Inter Genre Similarity Modelling For Automatic Music Genre
Classification
|
cs.SD cs.AI stat.ML
|
Music genre classification is an essential tool for music information
retrieval systems and it has been finding critical applications in various
media platforms. Two important problems of the automatic music genre
classification are feature extraction and classifier design. This paper
investigates inter-genre similarity modelling (IGS) to improve the performance
of automatic music genre classification. Inter-genre similarity information is
extracted over the mis-classified feature population. Once the inter-genre
similarity is modelled, elimination of the inter-genre similarity reduces the
inter-genre confusion and improves the identification rates. Inter-genre
similarity modelling is further improved with iterative IGS modelling(IIGS) and
score modelling for IGS elimination(SMIGS). Experimental results with promising
classification improvements are provided.
|
0907.3291
|
The Compound Capacity of Polar Codes
|
cs.IT math.IT
|
We consider the compound capacity of polar codes under successive
cancellation decoding for a collection of binary-input memoryless
output-symmetric channels. By deriving a sequence of upper and lower bounds, we
show that in general the compound capacity under successive decoding is
strictly smaller than the unrestricted compound capacity.
|
0907.3315
|
Effective Personalized Recommendation in Collaborative Tagging Systems
|
cs.IR
|
Recently, collaborative tagging systems have attracted more and more
attention and have been widely applied in web systems. Tags provide highly
abstracted information about personal preferences and item content, and are
therefore potential to help in improving better personalized recommendations.
In this paper, we propose a tag-based recommendation algorithm considering the
personal vocabulary and evaluate it in a real-world dataset: Del.icio.us.
Experimental results demonstrate that the usage of tag information can
significantly improve the accuracy of personalized recommendations.
|
0907.3340
|
A Barzilai-Borwein $l_1$-Regularized Least Squares Algorithm for
Compressed Sensing
|
cs.NA cs.IT math.IT
|
Problems in signal processing and medical imaging often lead to calculating
sparse solutions to under-determined linear systems. Methodologies for solving
this problem are presented as background to the method used in this work where
the problem is reformulated as an unconstrained convex optimization problem.
The least squares approach is modified by an $l_1$-regularization term. A
sparse solution is sought using a Barzilai-Borwein type projection algorithm
with an adaptive step length. New insight into the choice of step length is
provided through a study of the special structure of the underlying problem.
Numerical experiments are conducted and results given, comparing this algorithm
with a number of other current algorithms.
|
0907.3341
|
Opportunistic Secrecy with a Strict Delay Constraint
|
cs.IT math.IT
|
We investigate the delay limited secrecy capacity of the flat fading channel
under two different assumptions on the available transmitter channel state
information (CSI). The first scenario assumes perfect prior knowledge of both
the main and eavesdropper channel gains. Here, upper and lower bounds on the
delay limited secrecy capacity are derived, and shown to be tight in the high
signal-to-noise ratio (SNR) regime. In the second scenario, only the main
channel CSI is assumed to be available at the transmitter where, remarkably, we
establish the achievability of a non-zero delay-limited secure rate, for a wide
class of channel distributions, with a high probability. In the two cases, our
achievability arguments are based on a novel two-stage key-sharing approach
that overcomes the secrecy outage phenomenon observed in earlier works.
|
0907.3342
|
Neural Modeling and Control of Diesel Engine with Pollution Constraints
|
cs.LG cs.NE
|
The paper describes a neural approach for modelling and control of a
turbocharged Diesel engine. A neural model, whose structure is mainly based on
some physical equations describing the engine behaviour, is built for the
rotation speed and the exhaust gas opacity. The model is composed of three
interconnected neural submodels, each of them constituting a nonlinear
multi-input single-output error model. The structural identification and the
parameter estimation from data gathered on a real engine are described. The
neural direct model is then used to determine a neural controller of the
engine, in a specialized training scheme minimising a multivariable criterion.
Simulations show the effect of the pollution constraint weighting on a
trajectory tracking of the engine speed. Neural networks, which are flexible
and parsimonious nonlinear black-box models, with universal approximation
capabilities, can accurately describe or control complex nonlinear systems,
with little a priori theoretical knowledge. The presented work extends optimal
neuro-control to the multivariable case and shows the flexibility of neural
optimisers. Considering the preliminary results, it appears that neural
networks can be used as embedded models for engine control, to satisfy the more
and more restricting pollutant emission legislation. Particularly, they are
able to model nonlinear dynamics and outperform during transients the control
schemes based on static mappings.
|
0907.3387
|
Correcting Limited-Magnitude Errors in the Rank-Modulation Scheme
|
cs.IT math.IT
|
We study error-correcting codes for permutations under the infinity norm,
motivated by a novel storage scheme for flash memories call rank modulation. In
this scheme, a set of $n$ flash cells are combined to create a single virtual
multi-level cell. Information is stored in the permutation induced by the cell
charge levels. Spike errors, which are characterized by a limited-magnitude
change in cell charge levels, correspond to a low-distance change under the
infinity norm.
We define codes protecting against spike errors, called limited-magnitude
rank-modulation codes (LMRM codes), and present several constructions for these
codes, some resulting in optimal codes. These codes admit simple recursive, and
sometimes direct, encoding and decoding procedures.
We also provide lower and upper bounds on the maximal size of LMRM codes both
in the general case, and in the case where the codes form a subgroup of the
symmetric group. In the asymptotic analysis, the codes we construct out-perform
the Gilbert-Varshamov-like bound estimate.
|
0907.3397
|
The Gray Image of Codes over Finite Chain Rings
|
math.RA cs.IT math.IT
|
The results of J. F. Qiann et al. [4] on $(1-\gamma)$-cyclic codes over
finite chain rings of nilpotency index 2 are extended to $(1-\gamma^e)$-cyclic
codes over finite chain rings of arbitrary nilpotency index $e+1$. The Gray map
is introduced for this type of rings. We prove that the Gray image of a linear
$(1 - \gamma^{e})$-cyclic code over a finite chain ring is a distance-invariant
quasi-cyclic code over its residue field. When the length of codes and the
characteristic of a ring are relatively prime, the Gray images of a linear
cyclic code and a linear $(1+\gamma^e)$-cyclic code are permutatively to
quasi-cyclic codes over its residue field.
|
0907.3445
|
Investigating the Change of Web Pages' Titles Over Time
|
cs.IR cs.DL
|
Inaccessible web pages are part of the browsing experience. The content of
these pages however is often not completely lost but rather missing. Lexical
signatures (LS) generated from the web pages' textual content have been shown
to be suitable as search engine queries when trying to discover a (missing) web
page. Since LSs are expensive to generate, we investigate the potential of web
pages' titles as they are available at a lower cost. We present the results
from studying the change of titles over time. We take titles from copies
provided by the Internet Archive of randomly sampled web pages and show the
frequency of change as well as the degree of change in terms of the Levenshtein
score. We found very low frequencies of change and high Levenshtein scores
indicating that titles, on average, change little from their original, first
observed values (rooted comparison) and even less from the values of their
previous observation (sliding).
|
0907.3493
|
Secure Network Coding for Wiretap Networks of Type II
|
cs.IT math.IT
|
We consider the problem of securing a multicast network against a wiretapper
that can intercept the packets on a limited number of arbitrary network edges
of its choice. We assume that the network employs the network coding technique
to simultaneously deliver the packets available at the source to all the
receivers.
We show that this problem can be looked at as a network generalization of the
wiretap channel of type II introduced in a seminal paper by Ozarow and Wyner.
In particular, we show that the transmitted information can be secured by using
the Ozarow-Wyner approach of coset coding at the source on top of the existing
network code. This way, we quickly and transparently recover some of the
results available in the literature on secure network coding for wiretap
networks. Moreover, we derive new bounds on the required alphabet size that are
independent of the network size and devise an algorithm for the construction of
secure network codes. We also look at the dual problem and analyze the amount
of information that can be gained by the wiretapper as a function of the number
of wiretapped edges.
|
0907.3574
|
Message Passing Algorithms for Compressed Sensing
|
cs.IT cond-mat.dis-nn math.IT stat.CO
|
Compressed sensing aims to undersample certain high-dimensional signals, yet
accurately reconstruct them by exploiting signal characteristics. Accurate
reconstruction is possible when the object to be recovered is sufficiently
sparse in a known basis. Currently, the best known sparsity-undersampling
tradeoff is achieved when reconstructing by convex optimization -- which is
expensive in important large-scale applications. Fast iterative thresholding
algorithms have been intensively studied as alternatives to convex optimization
for large-scale problems. Unfortunately known fast algorithms offer
substantially worse sparsity-undersampling tradeoffs than convex optimization.
We introduce a simple costless modification to iterative thresholding making
the sparsity-undersampling tradeoff of the new algorithms equivalent to that of
the corresponding convex optimization procedures. The new
iterative-thresholding algorithms are inspired by belief propagation in
graphical models. Our empirical measurements of the sparsity-undersampling
tradeoff for the new algorithms agree with theoretical calculations. We show
that a state evolution formalism correctly derives the true
sparsity-undersampling tradeoff. There is a surprising agreement between
earlier calculations based on random convex polytopes and this new, apparently
very different theoretical formalism.
|
0907.3576
|
Recovering Signals from Lowpass Data
|
cs.IT math.IT
|
The problem of recovering a signal from its low frequency components occurs
often in practical applications due to the lowpass behavior of many physical
systems. Here we study in detail conditions under which a signal can be
determined from its low-frequency content. We focus on signals in
shift-invariant spaces generated by multiple generators. For these signals, we
derive necessary conditions on the cutoff frequency of the lowpass filter as
well as necessary and sufficient conditions on the generators such that signal
recovery is possible. When the lowpass content is not sufficient to determine
the signal, we propose appropriate pre-processing that can improve the
reconstruction ability. In particular, we show that modulating the signal with
one or more mixing functions prior to lowpass filtering, can ensure the
recovery of the signal in many cases, and reduces the necessary bandwidth of
the filter.
|
0907.3604
|
Image Sampling with Quasicrystals
|
cs.CV cs.GR
|
We investigate the use of quasicrystals in image sampling. Quasicrystals
produce space-filling, non-periodic point sets that are uniformly discrete and
relatively dense, thereby ensuring the sample sites are evenly spread out
throughout the sampled image. Their self-similar structure can be attractive
for creating sampling patterns endowed with a decorative symmetry. We present a
brief general overview of the algebraic theory of cut-and-project quasicrystals
based on the geometry of the golden ratio. To assess the practical utility of
quasicrystal sampling, we evaluate the visual effects of a variety of
non-adaptive image sampling strategies on photorealistic image reconstruction
and non-photorealistic image rendering used in multiresolution image
representations. For computer visualization of point sets used in image
sampling, we introduce a mosaic rendering technique.
|
0907.3616
|
Optimal Routing and Power Control for a Single Cell, Dense, Ad Hoc
Wireless Network
|
cs.NI cs.IT math.IT
|
We consider a dense, ad hoc wireless network, confined to a small region. The
wireless network is operated as a single cell, i.e., only one successful
transmission is supported at a time. Data packets are sent between
sourcedestination pairs by multihop relaying. We assume that nodes
self-organise into a multihop network such that all hops are of length d
meters, where d is a design parameter. There is a contention based multiaccess
scheme, and it is assumed that every node always has data to send, either
originated from it or a transit packet (saturation assumption). In this
scenario, we seek to maximize a measure of the transport capacity of the
network (measured in bit-meters per second) over power controls (in a fading
environment) and over the hop distance d, subject to an average power
constraint. We first argue that for a dense collection of nodes confined to a
small region, single cell operation is efficient for single user decoding
transceivers. Then, operating the dense ad hoc wireless network (described
above) as a single cell, we study the hop length and power control that
maximizes the transport capacity for a given network power constraint.
|
0907.3654
|
Optimization of Synthesis Oversampled Complex Filter Banks
|
cs.IT cs.SY eess.SY math.IT math.OC
|
An important issue with oversampled FIR analysis filter banks (FBs) is to
determine inverse synthesis FBs, when they exist. Given any complex oversampled
FIR analysis FB, we first provide an algorithm to determine whether there
exists an inverse FIR synthesis system. We also provide a method to ensure the
Hermitian symmetry property on the synthesis side, which is serviceable to
processing real-valued signals. As an invertible analysis scheme corresponds to
a redundant decomposition, there is no unique inverse FB. Given a particular
solution, we parameterize the whole family of inverses through a null space
projection. The resulting reduced parameter set simplifies design procedures,
since the perfect reconstruction constrained optimization problem is recast as
an unconstrained optimization problem. The design of optimized synthesis FBs
based on time or frequency localization criteria is then investigated, using a
simple yet efficient gradient algorithm.
|
0907.3666
|
Various thresholds for $\ell_1$-optimization in compressed sensing
|
cs.IT math.IT
|
Recently, \cite{CRT,DonohoPol} theoretically analyzed the success of a
polynomial $\ell_1$-optimization algorithm in solving an under-determined
system of linear equations. In a large dimensional and statistical context
\cite{CRT,DonohoPol} proved that if the number of equations (measurements in
the compressed sensing terminology) in the system is proportional to the length
of the unknown vector then there is a sparsity (number of non-zero elements of
the unknown vector) also proportional to the length of the unknown vector such
that $\ell_1$-optimization succeeds in solving the system. In this paper, we
provide an alternative performance analysis of $\ell_1$-optimization and obtain
the proportionality constants that in certain cases match or improve on the
best currently known ones from \cite{DonohoPol,DT}.
|
0907.3679
|
Block-length dependent thresholds in block-sparse compressed sensing
|
cs.IT math.IT
|
One of the most basic problems in compressed sensing is solving an
under-determined system of linear equations. Although this problem seems rather
hard certain $\ell_1$-optimization algorithm appears to be very successful in
solving it. The recent work of \cite{CRT,DonohoPol} rigorously proved (in a
large dimensional and statistical context) that if the number of equations
(measurements in the compressed sensing terminology) in the system is
proportional to the length of the unknown vector then there is a sparsity
(number of non-zero elements of the unknown vector) also proportional to the
length of the unknown vector such that $\ell_1$-optimization algorithm succeeds
in solving the system. In more recent papers
\cite{StojnicICASSP09block,StojnicJSTSP09} we considered the setup of the
so-called \textbf{block}-sparse unknown vectors. In a large dimensional and
statistical context, we determined sharp lower bounds on the values of
allowable sparsity for any given number (proportional to the length of the
unknown vector) of equations such that an $\ell_2/\ell_1$-optimization
algorithm succeeds in solving the system. The results established in
\cite{StojnicICASSP09block,StojnicJSTSP09} assumed a fairly large block-length
of the block-sparse vectors. In this paper we consider the block-length to be a
parameter of the system. Consequently, we then establish sharp lower bounds on
the values of the allowable block-sparsity as functions of the block-length.
|
0907.3781
|
Un syst\`eme modulaire d'acquisition automatique de traductions \`a
partir du Web
|
cs.CL
|
We present a method of automatic translation (French/English) of Complex
Lexical Units (CLU) for aiming at extracting a bilingual lexicon. Our modular
system is based on linguistic properties (compositionality, polysemy, etc.).
Different aspects of the multilingual Web are used to validate candidate
translations and collect new terms. We first build a French corpus of Web pages
to collect CLU. Three adapted processing stages are applied for each linguistic
property : compositional and non polysemous translations, compositional
polysemous translations and non compositional translations. Our evaluation on a
sample of CLU shows that our technique based on the Web can reach a very high
precision.
|
0907.3819
|
Self-adaptive web intrusion detection system
|
cs.NI cs.AI cs.MA
|
The evolution of the web server contents and the emergence of new kinds of
intrusions make necessary the adaptation of the intrusion detection systems
(IDS). Nowadays, the adaptation of the IDS requires manual -- tedious and
unreactive -- actions from system administrators. In this paper, we present a
self-adaptive intrusion detection system which relies on a set of local
model-based diagnosers. The redundancy of diagnoses is exploited, online, by a
meta-diagnoser to check the consistency of computed partial diagnoses, and to
trigger the adaptation of defective diagnoser models (or signatures) in case of
inconsistency. This system is applied to the intrusion detection from a stream
of HTTP requests. Our results show that our system 1) detects intrusion
occurrences sensitively and precisely, 2) accurately self-adapts diagnoser
model, thus improving its detection accuracy.
|
0907.3823
|
USUM: Update Summary Generation System
|
cs.IR
|
Huge amount of information is present in the World Wide Web and a large
amount is being added to it frequently. A query-specific summary of multiple
documents is very helpful to the user in this context. Currently, few systems
have been proposed for query-specific, extractive multi-document summarization.
If a summary is available for a set of documents on a given query and if a new
document is added to the corpus, generating an updated summary from the scratch
is time consuming and many a times it is not practical/possible. In this paper
we propose a solution to this problem. This is especially useful in a scenario
where the source documents are not accessible. We cleverly embed the sentences
of the current summary into the new document and then perform query-specific
summary generation on that document. Our experimental results show that the
performance of the proposed approach is good in terms of both quality and
efficiency.
|
0907.3867
|
Artificial Dendritic Cells: Multi-faceted Perspectives
|
cs.AI cs.CR cs.MA
|
Dendritic cells are the crime scene investigators of the human immune system.
Their function is to correlate potentially anomalous invading entities with
observed damage to the body. The detection of such invaders by dendritic cells
results in the activation of the adaptive immune system, eventually leading to
the removal of the invader from the host body. This mechanism has provided
inspiration for the development of a novel bio-inspired algorithm, the
Dendritic Cell Algorithm. This algorithm processes information at multiple
levels of resolution, resulting in the creation of information granules of
variable structure. In this chapter we examine the multi-faceted nature of
immunology and how research in this field has shaped the function of the
resulting Dendritic Cell Algorithm. A brief overview of the algorithm is given
in combination with the details of the processes used for its development. The
chapter is concluded with a discussion of the parallels between our
understanding of the human immune system and how such knowledge influences the
design of artificial immune systems.
|
0907.3970
|
Infinite Families of Recursive Formulas Generating Power Moments of
Kloosterman Sums: Symplectic Case
|
math.NT cs.IT math.IT
|
In this paper, we construct two infinite families of binary linear codes
associated with double cosets with respect to certain maximal parabolic
subgroup of the symplectic group Sp(2n,q) Here q is a power of two. Then we
obtain an infinite family of recursive formulas for the power moments of
Kloosterman sums and those of 2-dimensional Kloosterman sums in terms of the
frequencies of weights in the codes. This is done via Pless power moment
identity and by utilizing the explicit expressions of exponential sums over
those double cosets related to the evaluations of "Gauss sums" for the
symplectic groups Sp(2n,q).
|
0907.3972
|
Infinite Families of Recursive Formulas Generating Power Moments of
Kloosterman Sums: O\^{+}(2n, 2\^{r}) Case
|
math.NT cs.IT math.IT
|
In this paper, we construct four infinite families of binary linear codes
associated with double cosets with respect to certain maximal parabolic
subgroup of the orthogonal group O^+(2n,2^r). Here q is a power of two. Then we
obtain two infinite families of recursive formulas for the power moments of
Kloosterman sums and those of 2-dimensional Kloosterman sums in terms of the
frequencies of weights in the codes. This is done via Pless power moment
identity and by utilizing the explicit expressions of exponential sums over
those double cosets related to the evaluations of "Gauss sums" for the
orthogonal groups O^+(2n,2^r).
|
0907.3974
|
Simple Recursive Formulas Generating Power Moments of Kloosterman Sums
|
math.NT cs.IT math.IT
|
In this paper, we construct four binary linear codes closely connected with
certain exponential sums over the finite field F_q and F_q-{0,1}. Here q is a
power of two. Then we obtain four recursive formulas for the power moments of
Kloosterman sums in terms of the frequencies of weights in the codes. This is
done via Pless power moment identity and by utilizing the explicit expressions
of the exponential sums obtained earlier.
|
0907.3986
|
Contextual Bandits with Similarity Information
|
cs.DS cs.LG
|
In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence
of choices. In each round it chooses from a time-invariant set of alternatives
and receives the payoff associated with this alternative. While the case of
small strategy sets is by now well-understood, a lot of recent work has focused
on MAB problems with exponentially or infinitely large strategy sets, where one
needs to assume extra structure in order to make the problem tractable. In
particular, recent literature considered information on similarity between
arms.
We consider similarity information in the setting of "contextual bandits", a
natural extension of the basic MAB problem where before each round an algorithm
is given the "context" -- a hint about the payoffs in this round. Contextual
bandits are directly motivated by placing advertisements on webpages, one of
the crucial problems in sponsored search. A particularly simple way to
represent similarity information in the contextual bandit setting is via a
"similarity distance" between the context-arm pairs which gives an upper bound
on the difference between the respective expected payoffs.
Prior work on contextual bandits with similarity uses "uniform" partitions of
the similarity space, which is potentially wasteful. We design more efficient
algorithms that are based on adaptive partitions adjusted to "popular" context
and "high-payoff" arms.
|
0907.4031
|
Cognitive MAC Protocols for General Primary Network Models
|
cs.NI cs.IT math.IT
|
We consider the design of cognitive Medium Access Control (MAC) protocols
enabling a secondary (unlicensed) transmitter-receiver pair to communicate over
the idle periods of a set of primary (licensed) channels. More specifically, we
propose cognitive MAC protocols optimized for both slotted and un-slotted
primary networks. For the slotted structure, the objective is to maximize the
secondary throughput while maintaining synchronization between the secondary
pair and not causing interference to the primary network. Our investigations
differentiate between two sensing scenarios. In the first, the secondary
transmitter is capable of sensing all the primary channels, whereas it senses
only a subset of the primary channels in the second scenario. In both cases, we
propose blind MAC protocols that efficiently learn the statistics of the
primary traffic on-line and asymptotically achieve the throughput obtained when
prior knowledge of primary traffic statistics is available. For the un-slotted
structure, the objective is to maximize the secondary throughput while
satisfying an interference constraint on the primary network. Sensing-dependent
periods are optimized for each primary channel yielding a MAC protocol which
outperforms previously proposed techniques that rely on a single sensing period
optimization.
|
0907.4100
|
Beyond Turing Machines
|
cs.AI
|
This paper discusses "computational" systems capable of "computing" functions
not computable by predefined Turing machines if the systems are not isolated
from their environment. Roughly speaking, these systems can change their finite
descriptions by interacting with their environment.
|
0907.4128
|
Relativized hyperequivalence of logic programs for modular programming
|
cs.AI cs.LO
|
A recent framework of relativized hyperequivalence of programs offers a
unifying generalization of strong and uniform equivalence. It seems to be
especially well suited for applications in program optimization and modular
programming due to its flexibility that allows us to restrict, independently of
each other, the head and body alphabets in context programs. We study
relativized hyperequivalence for the three semantics of logic programs given by
stable, supported and supported minimal models. For each semantics, we identify
four types of contexts, depending on whether the head and body alphabets are
given directly or as the complement of a given set. Hyperequivalence relative
to contexts where the head and body alphabets are specified directly has been
studied before. In this paper, we establish the complexity of deciding
relativized hyperequivalence with respect to the three other types of context
programs.
To appear in Theory and Practice of Logic Programming (TPLP).
|
0907.4354
|
Learning Object Location Predictors with Boosting and Grammar-Guided
Feature Extraction
|
cs.CV
|
We present BEAMER: a new spatially exploitative approach to learning object
detectors which shows excellent results when applied to the task of detecting
objects in greyscale aerial imagery in the presence of ambiguous and noisy
data. There are four main contributions used to produce these results. First,
we introduce a grammar-guided feature extraction system, enabling the
exploration of a richer feature space while constraining the features to a
useful subset. This is specified with a rule-based generative grammar crafted
by a human expert. Second, we learn a classifier on this data using a newly
proposed variant of AdaBoost which takes into account the spatially correlated
nature of the data. Third, we perform another round of training to optimize the
method of converting the pixel classifications generated by boosting into a
high quality set of (x, y) locations. Lastly, we carefully define three common
problems in object detection and define two evaluation criteria that are
tightly matched to these problems. Major strengths of this approach are: (1) a
way of randomly searching a broad feature space, (2) its performance when
evaluated on well-matched evaluation criteria, and (3) its use of the location
prediction domain to learn object detectors as well as to generate detections
that perform well on several tasks: object counting, tracking, and target
detection. We demonstrate the efficacy of BEAMER with a comprehensive
experimental evaluation on a challenging data set.
|
0907.4385
|
The Cost of Stability in Coalitional Games
|
cs.GT cs.AI cs.CC
|
A key question in cooperative game theory is that of coalitional stability,
usually captured by the notion of the \emph{core}--the set of outcomes such
that no subgroup of players has an incentive to deviate. However, some
coalitional games have empty cores, and any outcome in such a game is unstable.
In this paper, we investigate the possibility of stabilizing a coalitional
game by using external payments. We consider a scenario where an external
party, which is interested in having the players work together, offers a
supplemental payment to the grand coalition (or, more generally, a particular
coalition structure). This payment is conditional on players not deviating from
their coalition(s). The sum of this payment plus the actual gains of the
coalition(s) may then be divided among the agents so as to promote stability.
We define the \emph{cost of stability (CoS)} as the minimal external payment
that stabilizes the game.
We provide general bounds on the cost of stability in several classes of
games, and explore its algorithmic properties. To develop a better intuition
for the concepts we introduce, we provide a detailed algorithmic study of the
cost of stability in weighted voting games, a simple but expressive class of
games which can model decision-making in political bodies, and cooperation in
multiagent settings. Finally, we extend our model and results to games with
coalition structures.
|
0907.4426
|
Evolution of Digital Logic Functionality via a Genetic Algorithm
|
cs.NE
|
Digital logic forms the functional basics of most modern electronic equipment
and as such the creation of novel digital logic circuits is an active area of
computer engineering research. This study demonstrates that genetic algorithms
can be used to evolve functionally useful sets of logic gate interconnections
to create useful digital logic circuits. The efficacy of this approach is
illustrated via the evolution of AND, OR, XOR, NOR, and XNOR functionality from
sets of NAND gates, thereby illustrating that evolutionary methods have the
potential be applied to the design of digital electronics.
|
0907.4447
|
Graphical Probabilistic Routing Model for OBS Networks with Realistic
Traffic Scenario
|
cs.NI cs.AI
|
Burst contention is a well-known challenging problem in Optical Burst
Switching (OBS) networks. Contention resolution approaches are always reactive
and attempt to minimize the BLR based on local information available at the
core node. On the other hand, a proactive approach that avoids burst losses
before they occur is desirable. To reduce the probability of burst contention,
a more robust routing algorithm than the shortest path is needed. This paper
proposes a new routing mechanism for JET-based OBS networks, called Graphical
Probabilistic Routing Model (GPRM) that selects less utilized links, on a
hop-by-hop basis by using a bayesian network. We assume no wavelength
conversion and no buffering to be available at the core nodes of the OBS
network. We simulate the proposed approach under dynamic load to demonstrate
that it reduces the Burst Loss Ratio (BLR) compared to static approaches by
using Network Simulator 2 (ns-2) on NSFnet network topology and with realistic
traffic matrix. Simulation results clearly show that the proposed approach
outperforms static approaches in terms of BLR.
|
0907.4471
|
Strategies and performances of Soft Input Decryption
|
cs.IT cs.CR math.IT
|
This paper analyzes performance aspects of Soft Input Decryption and L
values. Soft Input Decryption is a novel method which uses L values (soft
output) of a SISO channel decoder for the correction of input of Soft Input
Decryption (SID blocks) which have been modified during the transmission over a
noisy channel. The method is based on the combination of cryptography and
channel coding improving characteristics of both of them. The algorithm,
strategies and scenarios of Soft Input Decryption are described.
|
0907.4509
|
Pattern Recognition Theory of Mind
|
cs.AI
|
I propose that pattern recognition, memorization and processing are key
concepts that can be a principle set for the theoretical modeling of the mind
function. Most of the questions about the mind functioning can be answered by a
descriptive modeling and definitions from these principles. An understandable
consciousness definition can be drawn based on the assumption that a pattern
recognition system can recognize its own patterns of activity. The principles,
descriptive modeling and definitions can be a basis for theoretical and applied
research on cognitive sciences, particularly at artificial intelligence
studies.
|
0907.4521
|
Grassmannian Beamforming for MIMO-OFDM Systems with Frequency and
Spatially Correlated Channels Using Huffman Coding
|
cs.IT math.IT
|
Multiple input multiple output (MIMO) precoding is an efficient scheme that
may significantly enhance the communication link. However, this enhancement
comes with a cost. Many precoding schemes require channel knowledge at the
transmitter that is obtained through feedback from the receiver. Focusing on
the natural common fusion of orthogonal frequency division multiplexing (OFDM)
and MIMO, we exploit the channel correlation in the frequency and spatial
domain to reduce the required feedback rate in a frequency division duplex
(FDD) system. The proposed feedback method is based on Huffman coding and is
employed here for the single stream case. The method leads to a significant
reduction in the required feedback rate, without any loss in performance. The
proposed method may be extended to the multi-stream case.
|
0907.4561
|
Fact Sheet on Semantic Web
|
cs.AI
|
The report gives an overview about activities on the topic Semantic Web. It
has been released as technical report for the project "KTweb -- Connecting
Knowledge Technologies Communities" in 2003.
|
0907.4622
|
Aneka: A Software Platform for .NET-based Cloud Computing
|
cs.DC cs.CE cs.NI cs.OS cs.PL cs.SE
|
Aneka is a platform for deploying Clouds developing applications on top of
it. It provides a runtime environment and a set of APIs that allow developers
to build .NET applications that leverage their computation on either public or
private clouds. One of the key features of Aneka is the ability of supporting
multiple programming models that are ways of expressing the execution logic of
applications by using specific abstractions. This is accomplished by creating a
customizable and extensible service oriented runtime environment represented by
a collection of software containers connected together. By leveraging on these
architecture advanced services including resource reservation, persistence,
storage management, security, and performance monitoring have been implemented.
On top of this infrastructure different programming models can be plugged to
provide support for different scenarios as demonstrated by the engineering,
life science, and industry applications.
|
0907.4653
|
Distributed MIMO radar using compressive sampling
|
cs.IT math.IT
|
A distributed MIMO radar is considered, in which the transmit and receive
antennas belong to nodes of a small scale wireless network. The transmit
waveforms could be uncorrelated, or correlated in order to achieve a desirable
beampattern. The concept of compressive sampling is employed at the receive
nodes in order to perform direction of arrival (DOA) estimation. According to
the theory of compressive sampling, a signal that is sparse in some domain can
be recovered based on far fewer samples than required by the Nyquist sampling
theorem. The DOAs of targets form a sparse vector in the angle space, and
therefore, compressive sampling can be applied for DOA estimation. The proposed
approach achieves the superior resolution of MIMO radar with far fewer samples
than other approaches. This is particularly useful in a distributed scenario,
in which the results at each receive node need to be transmitted to a fusion
center.
|
0907.4697
|
Unsupervised and Non Parametric Iterative Soft Bit Error Rate Estimation
for Any Communications System
|
cs.IT math.IT
|
This paper addresses the problem of unsupervised soft bit error rate (BER)
estimation for any communications system, where no prior knowledge either about
transmitted information bits, or the transceiver scheme is available. We show
that the problem of BER estimation is equivalent to estimating the conditional
probability density functions (pdf)s of soft channel/receiver outputs. Assuming
that the receiver has no analytical model of soft observations, we propose a
non parametric Kernel-based pdf estimation technique, and show that the
resulting BER estimator is asymptotically unbiased and point-wise consistent.
We then introduce an iterative Stochastic Expectation Maximization (EM)
algorithm for the estimation of both a priori and a posteriori probabilities of
transmitted information bits, and the classification of soft observations
according to transmitted bit values. These inputs serve in the iterative
Kernel-based estimation procedure of conditional pdfs. We analyze the
performance of the proposed unsupervised and non parametric BER estimator in
the framework of a multiuser code division multiple access (CDMA) system with
single user detection, and show that attractive performance are achieved
compared with conventional Monte Carlo (MC)-aided techniques.
|
0907.4705
|
Compressive Sensing for MIMO Radar
|
cs.IT math.IT
|
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing.
|
0907.4885
|
Growth Rate of the Weight Distribution of Doubly-Generalized LDPC Codes:
General Case and Efficient Evaluation
|
cs.IT math.IT
|
The growth rate of the weight distribution of irregular doubly-generalized
LDPC (D-GLDPC) codes is developed and in the process, a new efficient numerical
technique for its evaluation is presented. The solution involves simultaneous
solution of a 4 x 4 system of polynomial equations. This represents the first
efficient numerical technique for exact evaluation of the growth rate, even for
LDPC codes. The technique is applied to two example D-GLDPC code ensembles.
|
0907.4960
|
Ezhil: A Tamil Programming Language
|
cs.PL cs.CL
|
Ezhil is a Tamil language based interpreted procedural programming language.
Tamil keywords and grammar are chosen to make the native Tamil speaker write
programs in the Ezhil system. Ezhil allows easy representation of computer
program closer to the Tamil language logical constructs equivalent to the
conditional, branch and loop statements in modern English based programming
languages. Ezhil is a compact programming language aimed towards Tamil speaking
novice computer users. Grammar for Ezhil and a few example programs are
reported here, from the initial proof-of-concept implementation using the
Python programming language1. To the best of our knowledge, Ezhil language is
the first freely available Tamil programming language.
|
0907.4984
|
Automatic local Gabor Features extraction for face recognition
|
cs.CV
|
We present in this paper a biometric system of face detection and recognition
in color images. The face detection technique is based on skin color
information and fuzzy classification. A new algorithm is proposed in order to
detect automatically face features (eyes, mouth and nose) and extract their
correspondent geometrical points. These fiducial points are described by sets
of wavelet components which are used for recognition. To achieve the face
recognition, we use neural networks and we study its performances for different
inputs. We compare the two types of features used for recognition: geometric
distances and Gabor coefficients which can be used either independently or
jointly. This comparison shows that Gabor coefficients are more powerful than
geometric distances. We show with experimental results how the importance
recognition ratio makes our system an effective tool for automatic face
detection and recognition.
|
0907.4996
|
Cooperative Jamming for Wireless Physical Layer Security
|
cs.IT math.IT
|
Cooperative jamming is an approach that has been recently proposed for
improving physical layer based security for wireless networks in the presence
of an eavesdropper. While the source transmits its message to its destination,
a relay node transmits a jamming signal to create interference at the
eavesdropper. In this paper, a scenario in which the relay is equipped with
multiple antennas is considered. A novel system design is proposed for
determining the antenna weights and transmit power of source and relay, so that
the system secrecy rate is maximized subject to a total transmit power
constraint, or, the transmit power is minimized subject to a secrecy rate
constraint. Since the optimal solutions to these problems are difficult to
obtain, suboptimal closed-form solutions are proposed that introduce an
additional constraint, i.e., the complete nulling of jamming signal at the
destination.
|
0907.5024
|
Living at the Edge: A Large Deviations Approach to the Outage MIMO
Capacity
|
cs.IT cond-mat.stat-mech math.IT stat.AP
|
Using a large deviations approach we calculate the probability distribution
of the mutual information of MIMO channels in the limit of large antenna
numbers. In contrast to previous methods that only focused at the distribution
close to its mean (thus obtaining an asymptotically Gaussian distribution), we
calculate the full distribution, including its tails which strongly deviate
from the Gaussian behavior near the mean. The resulting distribution
interpolates seamlessly between the Gaussian approximation for rates $R$ close
to the ergodic value of the mutual information and the approach of Zheng and
Tse for large signal to noise ratios $\rho$. This calculation provides us with
a tool to obtain outage probabilities analytically at any point in the $(R,
\rho, N)$ parameter space, as long as the number of antennas $N$ is not too
small. In addition, this method also yields the probability distribution of
eigenvalues constrained in the subspace where the mutual information per
antenna is fixed to $R$ for a given $\rho$. Quite remarkably, this eigenvalue
density is of the form of the Marcenko-Pastur distribution with square-root
singularities, and it depends on the values of $R$ and $\rho$.
|
0907.5030
|
Existence of new inequalities for representable polymatroids
|
cs.IT math.IT
|
An Ingletonian polymatroid satisfies, in addition to the polymatroid axioms,
the inequalities of Ingleton (Combin. Math. Appln., 1971). These inequalities
are required for a polymatroid to be representable. It is has been an open
question as to whether these inequalities are also sufficient. Representable
polymatroids are of interest in their own right. They also have a strong
connection to network coding. In particular, the problem of finding the linear
network coding capacity region is equivalent to the characterization of all
representable, entropic polymatroids. In this paper, we describe a new approach
to adhere two polymatroids together to produce a new polymatroid. Using this
approach, we can construct a polymatroid that is not inside the minimal closed
and convex cone containing all representable polymatroids. This polymatroid is
proved to satisfy not only the Ingleton inequalities, but also the recently
reported inequalities of Dougherty, Freiling and Zeger. A direct consequence is
that these inequalities are not sufficient to characterize representable
polymatroids.
|
0907.5032
|
Restart Strategy Selection using Machine Learning Techniques
|
cs.AI
|
Restart strategies are an important factor in the performance of
conflict-driven Davis Putnam style SAT solvers. Selecting a good restart
strategy for a problem instance can enhance the performance of a solver.
Inspired by recent success applying machine learning techniques to predict the
runtime of SAT solvers, we present a method which uses machine learning to
boost solver performance through a smart selection of the restart strategy.
Based on easy to compute features, we train both a satisfiability classifier
and runtime models. We use these models to choose between restart strategies.
We present experimental results comparing this technique with the most commonly
used restart strategies. Our results demonstrate that machine learning is
effective in improving solver performance.
|
0907.5033
|
Online Search Cost Estimation for SAT Solvers
|
cs.AI
|
We present two different methods for estimating the cost of solving SAT
problems. The methods focus on the online behaviour of the backtracking solver,
as well as the structure of the problem. Modern SAT solvers present several
challenges to estimate search cost including coping with nonchronological
backtracking, learning and restarts. Our first method adapt an existing
algorithm for estimating the size of a search tree to deal with these
challenges. We then suggest a second method that uses a linear model trained on
data gathered online at the start of search. We compare the effectiveness of
these two methods using random and structured problems. We also demonstrate
that predictions made in early restarts can be used to improve later
predictions. We conclude by showing that the cost of solving a set of problems
can be reduced by selecting a solver from a portfolio based on such cost
estimations.
|
0907.5043
|
Online-offline activities and game-playing behaviors of avatars in a
massive multiplayer online role-playing game
|
physics.pop-ph cs.MA physics.soc-ph
|
Massive multiplayer online role-playing games (MMORPGs) are very popular in
China, which provides a potential platform for scientific research. We study
the online-offline activities of avatars in an MMORPG to understand their
game-playing behavior. The statistical analysis unveils that the active avatars
can be classified into three types. The avatars of the first type are owned by
game cheaters who go online and offline in preset time intervals with the
online duration distributions dominated by pulses. The second type of avatars
is characterized by a Weibull distribution in the online durations, which is
confirmed by statistical tests. The distributions of online durations of the
remaining individual avatars differ from the above two types and cannot be
described by a simple form. These findings have potential applications in the
game industry.
|
0907.5063
|
On Measuring Non-Recursive Trade-Offs
|
cs.FL cs.IT math.IT
|
We investigate the phenomenon of non-recursive trade-offs between
descriptional systems in an abstract fashion. We aim at categorizing
non-recursive trade-offs by bounds on their growth rate, and show how to deduce
such bounds in general. We also identify criteria which, in the spirit of
abstract language theory, allow us to deduce non-recursive tradeoffs from
effective closure properties of language families on the one hand, and
differences in the decidability status of basic decision problems on the other.
We develop a qualitative classification of non-recursive trade-offs in order to
obtain a better understanding of this very fundamental behaviour of
descriptional systems.
|
0907.5083
|
Serializing the Parallelism in Parallel Communicating Pushdown Automata
Systems
|
cs.FL cs.CL cs.DC
|
We consider parallel communicating pushdown automata systems (PCPA) and
define a property called known communication for it. We use this property to
prove that the power of a variant of PCPA, called returning centralized
parallel communicating pushdown automata (RCPCPA), is equivalent to that of
multi-head pushdown automata. The above result presents a new sub-class of
returning parallel communicating pushdown automata systems (RPCPA) called
simple-RPCPA and we show that it can be written as a finite intersection of
multi-head pushdown automata systems.
|
0907.5119
|
On the Size Complexity of Non-Returning Context-Free PC Grammar Systems
|
cs.FL cs.DC cs.MA
|
Improving the previously known best bound, we show that any recursively
enumerable language can be generated with a non-returning parallel
communicating (PC) grammar system having six context-free components. We also
present a non-returning universal PC grammar system generating unary languages,
that is, a system where not only the number of components, but also the number
of productions and the number of nonterminals are limited by certain constants,
and these size parameters do not depend on the generated language.
|
0907.5141
|
Cooperative Training for Attribute-Distributed Data: Trade-off Between
Data Transmission and Performance
|
cs.DC cs.MA
|
This paper introduces a modeling framework for distributed regression with
agents/experts observing attribute-distributed data (heterogeneous data). Under
this model, a new algorithm, the iterative covariance optimization algorithm
(ICOA), is designed to reshape the covariance matrix of the training residuals
of individual agents so that the linear combination of the individual
estimators minimizes the ensemble training error. Moreover, a scheme (Minimax
Protection) is designed to provide a trade-off between the number of data
instances transmitted among the agents and the performance of the ensemble
estimator without undermining the convergence of the algorithm. This scheme
also provides an upper bound (with high probability) on the test error of the
ensemble estimator. The efficacy of ICOA combined with Minimax Protection and
the comparison between the upper bound and actual performance are both
demonstrated by simulations.
|
0907.5155
|
On Classification from Outlier View
|
cs.AI
|
Classification is the basis of cognition. Unlike other solutions, this study
approaches it from the view of outliers. We present an expanding algorithm to
detect outliers in univariate datasets, together with the underlying
foundation. The expanding algorithm runs in a holistic way, making it a rather
robust solution. Synthetic and real data experiments show its power.
Furthermore, an application for multi-class problems leads to the introduction
of the oscillator algorithm. The corresponding result implies the potential
wide use of the expanding algorithm.
|
0907.5165
|
Interference alignment-based sum capacity bounds for random dense
Gaussian interference networks
|
cs.IT math.IT
|
We consider a dense $K$ user Gaussian interference network formed by paired
transmitters and receivers placed independently at random in a fixed spatial
region. Under natural conditions on the node position distributions and signal
attenuation, we prove convergence in probability of the average per-user
capacity $\csum/K$ to $\half \ep \log(1 + 2 \SNR)$. The achievability result
follows directly from results based on an interference alignment scheme
presented in recent work of Nazer et al. Our main contribution comes through an
upper bound, motivated by ideas of `bottleneck capacity' developed in recent
work of Jafar. By controlling the physical location of transmitter--receiver
pairs, we can match a large proportion of these pairs to form so-called
$\epsilon$-bottleneck links, with consequent control of the sum capacity.
|
0907.5168
|
Collaborative Training in Sensor Networks: A graphical model approach
|
cs.DC cs.MA
|
Graphical models have been widely applied in solving distributed inference
problems in sensor networks. In this paper, the problem of coordinating a
network of sensors to train a unique ensemble estimator under communication
constraints is discussed. The information structure of graphical models with
specific potential functions is employed, and this thus converts the
collaborative training task into a problem of local training plus global
inference. Two important classes of algorithms of graphical model inference,
message-passing algorithm and sampling algorithm, are employed to tackle
low-dimensional, parametrized and high-dimensional, non-parametrized problems
respectively. The efficacy of this approach is demonstrated by concrete
examples.
|
0907.5287
|
Propelinear structure of Z_{2k}-linear codes
|
cs.IT math.IT
|
Let C be an additive subgroup of $\Z_{2k}^n$ for any $k\geq 1$. We define a
Gray map $\Phi:\Z_{2k}^n \longrightarrow \Z_2^{kn}$ such that $\Phi(\codi)$ is
a binary propelinear code and, hence, a Hamming-compatible group code.
Moreover, $\Phi$ is the unique Gray map such that $\Phi(C)$ is
Hamming-compatible group code. Using this Gray map we discuss about the
nonexistence of 1-perfect binary mixed group code.
|
0907.5321
|
Multiple pattern classification by sparse subspace decomposition
|
cs.CV
|
A robust classification method is developed on the basis of sparse subspace
decomposition. This method tries to decompose a mixture of subspaces of
unlabeled data (queries) into class subspaces as few as possible. Each query is
classified into the class whose subspace significantly contributes to the
decomposed subspace. Multiple queries from different classes can be
simultaneously classified into their respective classes. A practical greedy
algorithm of the sparse subspace decomposition is designed for the
classification. The present method achieves high recognition rate and robust
performance exploiting joint sparsity.
|
0907.5388
|
Providing Secrecy With Structured Codes: Tools and Applications to
Two-User Gaussian Channels
|
cs.IT math.IT
|
Recent results have shown that structured codes can be used to construct good
channel codes, source codes and physical layer network codes for Gaussian
channels. For Gaussian channels with secrecy constraints, however, efforts to
date rely on random codes. In this work, we advocate that structured codes are
useful for providing secrecy, and show how to compute the secrecy rate when
structured codes are used. In particular, we solve the problem of bounding
equivocation rates with one important class of structured codes, i.e., nested
lattice codes. Having established this result, we next demonstrate the use of
structured codes for secrecy in two-user Gaussian channels. In particular, with
structured codes, we prove that a positive secure degree of freedom is
achievable for a large class of fully connected Gaussian channels as long as
the channel is not degraded. By way of this, for these channels, we establish
that structured codes outperform Gaussian random codes at high SNR. This class
of channels include the two-user multiple access wiretap channel, the two-user
interference channel with confidential messages and the two-user interference
wiretap channel. A notable consequence of this result is that, unlike the case
with Gaussian random codes, using structured codes for both transmission and
cooperative jamming, it is possible to achieve an arbitrary large secrecy rate
given enough power.
|
0907.5397
|
Telescoping Recursive Representations and Estimation of Gauss-Markov
Random Fields
|
cs.IT math.IT math.ST stat.TH
|
We present \emph{telescoping} recursive representations for both continuous
and discrete indexed noncausal Gauss-Markov random fields. Our recursions start
at the boundary (a hypersurface in $\R^d$, $d \ge 1$) and telescope inwards.
For example, for images, the telescoping representation reduce recursions from
$d = 2$ to $d = 1$, i.e., to recursions on a single dimension. Under
appropriate conditions, the recursions for the random field are linear
stochastic differential/difference equations driven by white noise, for which
we derive recursive estimation algorithms, that extend standard algorithms,
like the Kalman-Bucy filter and the Rauch-Tung-Striebel smoother, to noncausal
Markov random fields.
|
0907.5433
|
Efficient Web Log Mining using Doubly Linked Tree
|
cs.IR cs.IT math.IT
|
World Wide Web is a huge data repository and is growing with the explosive
rate of about 1 million pages a day. As the information available on World Wide
Web is growing the usage of the web sites is also growing. Web log records each
access of the web page and number of entries in the web logs is increasing
rapidly. These web logs, when mined properly can provide useful information for
decision-making. The designer of the web site, analyst and management
executives are interested in extracting this hidden information from web logs
for decision making. Web access pattern, which is the frequently used sequence
of accesses, is one of the important information that can be mined from the web
logs. This information can be used to gather business intelligence to improve
sales and advertisement, personalization for a user, to analyze system
performance and to improve the web site organization. There exist many
techniques to mine access patterns from the web logs. This paper describes the
powerful algorithm that mines the web logs efficiently. Proposed algorithm
firstly converts the web access data available in a special doubly linked tree.
Each access is called an event. This tree keeps the critical mining related
information in very compressed form based on the frequent event count. Proposed
recursive algorithm uses this tree to efficiently find all access patterns that
satisfy user specified criteria. To prove that our algorithm is efficient from
the other GSP (Generalized Sequential Pattern) algorithms we have done
experimental studies on sample data.
|
0907.5442
|
On Computing Compression Trees for Data Collection in Sensor Networks
|
cs.NI cs.IT math.IT
|
We address the problem of efficiently gathering correlated data from a wired
or a wireless sensor network, with the aim of designing algorithms with
provable optimality guarantees, and understanding how close we can get to the
known theoretical lower bounds. Our proposed approach is based on finding an
optimal or a near-optimal {\em compression tree} for a given sensor network: a
compression tree is a directed tree over the sensor network nodes such that the
value of a node is compressed using the value of its parent. We consider this
problem under different communication models, including the {\em broadcast
communication} model that enables many new opportunities for energy-efficient
data collection. We draw connections between the data collection problem and a
previously studied graph concept, called {\em weakly connected dominating
sets}, and we use this to develop novel approximation algorithms for the
problem. We present comparative results on several synthetic and real-world
datasets showing that our algorithms construct near-optimal compression trees
that yield a significant reduction in the data collection cost.
|
0907.5538
|
A preliminary XML-based search system for planetary data
|
cs.IR astro-ph.EP cs.DB physics.geo-ph
|
Planetary sciences can benefit from several different sources of information,
i.e. ground-based or near Earth-based observations, space missions and
laboratory experiments. The data collected from these sources, however, are
spread over a number of smaller, separate communities and stored through
different facilities: this makes it difficult to integrate them. The IDIS
initiative, born in the context of the Europlanet project, performed a pilot
study of the viability and the issues to be overcome in order to create an
integrated search system for planetary data. As part of the results of such
pilot study, the IDIS Small Bodies and Dust node developed a search system
based on a preliminary XML data model. Here we introduce the goals of the IDIS
initiative and describe the structure and the working of this search system.
The source code of the search system is released under GPL license to allow
people interested in participating to the IDIS initiative both as developers
and as data providers to familiarise with the search environment and to allow
the creation of volunteer nodes to be integrated into the existing network.
|
0907.5598
|
Convergence of Expected Utility for Universal AI
|
cs.AI
|
We consider a sequence of repeated interactions between an agent and an
environment. Uncertainty about the environment is captured by a probability
distribution over a space of hypotheses, which includes all computable
functions. Given a utility function, we can evaluate the expected utility of
any computational policy for interaction with the environment. After making
some plausible assumptions (and maybe one not-so-plausible assumption), we show
that if the utility function is unbounded, then the expected utility of any
policy is undefined.
|
0908.0014
|
Keys through ARQ
|
cs.IT cs.CR math.IT
|
This paper develops a novel framework for sharing secret keys using the
well-known Automatic Repeat reQuest (ARQ) protocol. The proposed key sharing
protocol does not assume any prior knowledge about the channel state
information (CSI), but, harnesses the available opportunistic secrecy gains
using only the one bit feedback, in the form of ACK/NACK. The distribution of
key bits among multiple ARQ epochs, in our approach, allows for mitigating the
secrecy outage phenomenon observed in earlier works. We characterize the
information theoretic limits of the proposed scheme, under different
assumptions on the channel spatial and temporal correlation function, and
develop low complexity explicit implementations. Our analysis reveals a novel
role of "dumb antennas" in overcoming the negative impact of spatial
correlation, between the legitimate and eavesdropper channels, on the
achievable secrecy rates. We further develop an adaptive rate allocation policy
which achieves higher secrecy rates by exploiting the channel temporal
correlation. Finally, our theoretical claims are validated by numerical results
that establish the achievability of non-zero secrecy rates even when the
eavesdropper channel is less noisy, on the average, than the legitimate
channel.
|
0908.0045
|
On Random Construction of a Bipolar Sensing Matrix with Compact
Representation
|
cs.IT math.IT
|
A random construction of bipolar sensing matrices based on binary linear
codes is introduced and its RIP (Restricted Isometry Property) is analyzed
based on an argument on the ensemble average of the weight distribution of
binary linear codes.
|
0908.0050
|
Online Learning for Matrix Factorization and Sparse Coding
|
stat.ML cs.LG math.OC
|
Sparse coding--that is, modelling data vectors as sparse linear combinations
of basis elements--is widely used in machine learning, neuroscience, signal
processing, and statistics. This paper focuses on the large-scale matrix
factorization problem that consists of learning the basis set, adapting it to
specific data. Variations of this problem include dictionary learning in signal
processing, non-negative matrix factorization and sparse principal component
analysis. In this paper, we propose to address these tasks with a new online
optimization algorithm, based on stochastic approximations, which scales up
gracefully to large datasets with millions of training samples, and extends
naturally to various matrix factorization formulations, making it suitable for
a wide range of learning problems. A proof of convergence is presented, along
with experiments with natural images and genomic data demonstrating that it
leads to state-of-the-art performance in terms of speed and optimization for
both small and large datasets.
|
0908.0051
|
High-Rate, Distributed Training-Embedded Complex Orthogonal Designs for
Relay Networks
|
cs.IT math.IT
|
Distributed Space-Time Block Codes (DSTBCs) from Complex Orthogonal Designs
(CODs) (both square and non-square CODs other than the Alamouti design) are
known to lose their single-symbol ML decodable (SSD) property when used in
two-hop wireless relay networks using amplify and forward protocol. For such a
network, in this paper, a new class of high rate, training-embedded (TE) SSD
DSTBCs are constructed from TE-CODs. The proposed codes include the training
symbols in the structure of the code which is shown to be the key point to
obtain high rate as well as the SSD property. TE-CODs are shown to offer
full-diversity for arbitrary complex constellations. Non-square TE-CODs are
shown to provide higher rates (in symbols per channel use) compared to the
known SSD DSTBCs for relay networks with number of relays less than $10.$
|
0908.0078
|
On Algebraic Traceback in Dynamic Networks
|
cs.IT cs.NI math.IT
|
This paper introduces the concept of incremental traceback for determining
changes in the trace of a network as it evolves with time. A distributed
algorithm, based on the methodology of algebraic traceback developed by Dean et
al, is proposed which can completely determine a path of d nodes/routers using
O(d) marked packets, and subsequently determine the changes in its topology
using O(log d) marked packets with high probability. The algorithm is
established to be order-wise optimal i.e., no other distributed algorithm can
determine changes in the path topology using lesser order of bits (i.e., marked
packets). The algorithm is shown to have a computational complexity of O(d log
d), which is significantly less than that of any existing non-incremental
algorithm of algebraic traceback. Extensions of this algorithm to settings with
node identity spoofing and network coding are also presented.
|
0908.0089
|
Knowledge Discovery of Hydrocyclone s Circuit Based on SONFIS and SORST
|
cs.AI
|
This study describes application of some approximate reasoning methods to
analysis of hydrocyclone performance. In this manner, using a combining of Self
Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS)-SONFIS- and Rough Set
Theory (RST)-SORST-crisp and fuzzy granules are obtained. Balancing of crisp
granules and non-crisp granules can be implemented in close-open iteration.
Using different criteria and based on granulation level balance point
(interval) or a pseudo-balance point is estimated. Validation of the proposed
methods, on the data set of the hydrocyclone is rendered.
|
0908.0100
|
A Class of DSm Conditional Rules
|
cs.AI
|
In this paper we introduce two new DSm fusion conditioning rules with
example, and as a generalization of them a class of DSm fusion conditioning
rules, and then extend them to a class of DSm conditioning rules.
|
0908.0163
|
An Improvement of Cover/El Gamal's Compress-and-Forward Relay Scheme
|
cs.IT math.IT
|
The compress-and-forward relay scheme developed by (Cover and El Gamal, 1979)
is improved with a modification on the decoding process. The improvement
follows as a result of realizing that it is not necessary for the destination
to decode the compressed observation of the relay; and even if the compressed
observation is to be decoded, it can be more easily done by joint decoding with
the original message, rather than in a successive way. An extension to multiple
relays is also discussed.
|
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