id
stringlengths 9
16
| title
stringlengths 4
278
| categories
stringlengths 5
104
| abstract
stringlengths 6
4.09k
|
|---|---|---|---|
1102.5559
|
Support-Predicted Modified-CS for Recursive Robust Principal Components'
Pursuit
|
cs.IT math.IT
|
This work proposes a causal and recursive algorithm for solving the "robust"
principal components' analysis (PCA) problem. We primarily focus on robustness
to correlated outliers. In recent work, we proposed a new way to look at this
problem and showed how a key part of its solution strategy involves solving a
noisy compressive sensing(CS) problem. However, if the support size of the
outliers becomes too large, for a given dimension of the current PC space, then
the number of "measurements" available for CS may become too small. In this
work, we show how to address this issue by utilizing the correlation of the
outliers to predict their support at the current time; and using this as
"partial support knowledge" for solving Modified-CS instead of CS.
|
1102.5561
|
Decision Making Agent Searching for Markov Models in Near-Deterministic
World
|
cs.AI cs.LG
|
Reinforcement learning has solid foundations, but becomes inefficient in
partially observed (non-Markovian) environments. Thus, a learning agent -born
with a representation and a policy- might wish to investigate to what extent
the Markov property holds. We propose a learning architecture that utilizes
combinatorial policy optimization to overcome non-Markovity and to develop
efficient behaviors, which are easy to inherit, tests the Markov property of
the behavioral states, and corrects against non-Markovity by running a
deterministic factored Finite State Model, which can be learned. We illustrate
the properties of architecture in the near deterministic Ms. Pac-Man game. We
analyze the architecture from the point of view of evolutionary, individual,
and social learning.
|
1102.5586
|
Covert channel detection using Information Theory
|
cs.CR cs.IT math.IT
|
This paper presents an information theory based detection framework for
covert channels. We first show that the usual notion of interference does not
characterize the notion of deliberate information flow of covert channels. We
then show that even an enhanced notion of "iterated multivalued interference"
can not capture flows with capacity lower than one bit of information per
channel use. We then characterize and compute the capacity of covert channels
that use control flows for a class of systems.
|
1102.5593
|
Low Complexity Kolmogorov-Smirnov Modulation Classification
|
cs.IT cs.LG math.IT
|
Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness
of fit, is applied for automatic modulation classification (AMC) in this paper.
The basic procedure involves computing the empirical cumulative distribution
function (ECDF) of some decision statistic derived from the received signal,
and comparing it with the CDFs of the signal under each candidate modulation
format. The K-S-based modulation classifier is first developed for AWGN
channel, then it is applied to OFDM-SDMA systems to cancel multiuser
interference. Regarding the complexity issue of K-S modulation classification,
we propose a low-complexity method based on the robustness of the K-S
classifier. Extensive simulation results demonstrate that compared with the
traditional cumulant-based classifiers, the proposed K-S classifier offers
superior classification performance and requires less number of signal samples
(thus is fast).
|
1102.5597
|
Fast and Faster: A Comparison of Two Streamed Matrix Decomposition
Algorithms
|
cs.NA cs.LG
|
With the explosion of the size of digital dataset, the limiting factor for
decomposition algorithms is the \emph{number of passes} over the input, as the
input is often stored out-of-core or even off-site. Moreover, we're only
interested in algorithms that operate in \emph{constant memory} w.r.t. to the
input size, so that arbitrarily large input can be processed. In this paper, we
present a practical comparison of two such algorithms: a distributed method
that operates in a single pass over the input vs. a streamed two-pass
stochastic algorithm. The experiments track the effect of distributed
computing, oversampling and memory trade-offs on the accuracy and performance
of the two algorithms. To ensure meaningful results, we choose the input to be
a real dataset, namely the whole of the English Wikipedia, in the application
settings of Latent Semantic Analysis.
|
1102.5599
|
Consensus of Discrete-Time Linear Multi-Agent Systems with Observer-Type
Protocols
|
cs.SY math.OC
|
This paper concerns the consensus of discrete-time multi-agent systems with
linear or linearized dynamics. An observer-type protocol based on the relative
outputs of neighboring agents is proposed. The consensus of such a multi-agent
system with a directed communication topology can be cast into the stability of
a set of matrices with the same low dimension as that of a single agent. The
notion of discrete-time consensus region is then introduced and analyzed. For
neurally stable agents, it is shown that there exists an observer-type protocol
having a bounded consensus region in the form of an open unit disk, provided
that each agent is stabilizable and detectable. An algorithm is further
presented to construct a protocol to achieve consensus with respect to all the
communication topologies containing a spanning tree. Moreover, for the case
where the agents have no poles outside the unit circle,an algorithm is proposed
to construct a protocol having an origin-centered disk of radius $\delta$
($0<\delta<1$) as its consensus region, where $\delta$ has to further satisfy a
constraint related to the unstable eigenvalues of a single agent for the case
where each agent has a least one eigenvalue outside the unit circle. Finally,
the consensus algorithms are applied to solve formation control problems of
multi-agent systems.
|
1102.5603
|
Distributed Adaptive Attitude Synchronization of Multiple Spacecraft
|
cs.SY math.OC
|
This paper addresses the distributed attitude synchronization problem of
multiple spacecraft with unknown inertia matrices. Two distributed adaptive
controllers are proposed for the cases with and without a virtual leader to
which a time-varying reference attitude is assigned. The first controller
achieves attitude synchronization for a group of spacecraft with a leaderless
communication topology having a directed spanning tree. The second controller
guarantees that all spacecraft track the reference attitude if the virtual
leader has a directed path to all other spacecraft. Simulation examples are
presented to illustrate the effectiveness of the results.
|
1102.5635
|
Practical inventory routing: A problem definition and an optimization
method
|
cs.AI
|
The global objective of this work is to provide practical optimization
methods to companies involved in inventory routing problems, taking into
account this new type of data. Also, companies are sometimes not able to deal
with changing plans every period and would like to adopt regular structures for
serving customers.
|
1102.5641
|
Coherent Optical DFT-Spread OFDM
|
cs.IT math.IT
|
We consider application of the discrete Fourier transform-spread orthogonal
frequency-division multiplexing (DFT-spread OFDM) technique to high-speed fiber
optic communications. The DFT-spread OFDM is a form of single-carrier technique
that possesses almost all advantages of the multicarrier OFDM technique (such
as high spectral efficiency, flexible bandwidth allocation, low sampling rate
and low-complexity equalization). In particular, we consider the optical
DFT-spread OFDM system with polarization division multiplexing (PDM) that
employs a tone-by-tone linear minimum mean square error (MMSE) equalizer. We
show that such a system offers a much lower peak-to-average power ratio (PAPR)
performance as well as better bit error rate (BER) performance compared with
the optical OFDM system that employs amplitude clipping.
|
1102.5643
|
Joint Beamforming and Power Allocation for MIMO Relay Broadcast Channel
with Individual SINR Constraints
|
cs.IT math.IT
|
In this paper, system design for the multi-input multi-output (MIMO) relay
broadcast channel with individual signal-to-interference-plus-noise ratio
(SINR) constraints at the mobile stations (MS) is considered. By exploring the
structure of downlink (DL) uplink (UL) duality at either the base station (BS)
or the relay station (RS), we propose two schemes of joint power allocation and
beamforming design at the BS and the RS. The problem of existence of feasible
solutions under practical power constraints at the BS and the RS with given
SINR targets is considered first. Then the problem of sum power minimization is
considered. Each design problem can be solved efficiently using optimal joint
power allocation and beamforming under the framework of convex optimization. We
also show that with subchannel pairing at the RS, the transmission power can be
reduced by channel compensation at either hop. Finally, an extension to more
general multi-hop applications is provided to further improve the power
efficiency.
|
1102.5673
|
Interference Alignment for the MIMO Interference Channel with Delayed
Local CSIT
|
cs.IT math.IT
|
We consider the MIMO (multiple-input multiple-output) Gaussian interference
channel with i.i.d. fading across antennas and channel uses and with the
delayed local channel state information at the transmitters (CSIT). For the
two-user case, achievability results for the degrees of freedom (DoF) region of
this channel are provided. We also prove the tightness of our achievable DoF
region for some antenna configurations. Interestingly, there are some cases in
which the DoF region with delayed local CSIT is identical to the DoF region
with perfect CSIT and that is strictly larger than the DoF region with no CSIT.
We then consider the $K$-user MISO (multiple-input single-output) IC and show
that the degrees of freedom of this channel could be greater than one with
delayed local CSIT.
|
1102.5688
|
A novel super resolution reconstruction of low reoslution images
progressively using dct and zonal filter based denoising
|
cs.CV
|
Due to the factors like processing power limitations and channel capabilities
images are often down sampled and transmitted at low bit rates resulting in a
low resolution compressed image. High resolution images can be reconstructed
from several blurred, noisy and down sampled low resolution images using a
computational process know as super resolution reconstruction. Super-resolution
is the process of combining multiple aliased low-quality images to produce a
high resolution, high-quality image. The problem of recovering a high
resolution image progressively from a sequence of low resolution compressed
images is considered. In this paper we propose a novel DCT based progressive
image display algorithm by stressing on the encoding and decoding process. At
the encoder we consider a set of low resolution images which are corrupted by
additive white Gaussian noise and motion blur. The low resolution images are
compressed using 8 by 8 blocks DCT and noise is filtered using our proposed
novel zonal filter. Multiframe fusion is performed in order to obtain a single
noise free image. At the decoder the image is reconstructed progressively by
transmitting the coarser image first followed by the detail image. And finally
a super resolution image is reconstructed by applying our proposed novel
adaptive interpolation technique. We have performed both objective and
subjective analysis of the reconstructed image, and the resultant image has
better super resolution factor, and a higher ISNR and PSNR. A comparative study
done with Iterative Back Projection (IBP) and Projection on to Convex Sets
(POCS),Papoulis Grechberg, FFT based Super resolution Reconstruction shows that
our method has out performed the previous contributions.
|
1102.5713
|
Quantum feedback for rapid state preparation in the presence of control
imperfections
|
quant-ph cs.SY math.OC
|
Quantum feedback control protocols can improve the operation of quantum
devices. Here we examine the performance of a purification protocol when there
are imperfections in the controls. The ideal feedback protocol produces an $x$
eigenstate from a mixed state in the minimum time, and is known as rapid state
preparation. The imperfections we examine include time delays in the feedback
loop, finite strength feedback, calibration errors, and inefficient detection.
We analyse these imperfections using the Wiseman-Milburn feedback master
equation and related formalism. We find that the protocol is most sensitive to
time delays in the feedback loop. For systems with slow dynamics, however, our
analysis suggests that inefficient detection would be the bigger problem. We
also show how system imperfections, such as dephasing and damping, can be
included in model via the feedback master equation.
|
1102.5724
|
Reliable Physical Layer Network Coding
|
cs.IT math.IT
|
When two or more users in a wireless network transmit simultaneously, their
electromagnetic signals are linearly superimposed on the channel. As a result,
a receiver that is interested in one of these signals sees the others as
unwanted interference. This property of the wireless medium is typically viewed
as a hindrance to reliable communication over a network. However, using a
recently developed coding strategy, interference can in fact be harnessed for
network coding. In a wired network, (linear) network coding refers to each
intermediate node taking its received packets, computing a linear combination
over a finite field, and forwarding the outcome towards the destinations. Then,
given an appropriate set of linear combinations, a destination can solve for
its desired packets. For certain topologies, this strategy can attain
significantly higher throughputs over routing-based strategies. Reliable
physical layer network coding takes this idea one step further: using
judiciously chosen linear error-correcting codes, intermediate nodes in a
wireless network can directly recover linear combinations of the packets from
the observed noisy superpositions of transmitted signals. Starting with some
simple examples, this survey explores the core ideas behind this new technique
and the possibilities it offers for communication over interference-limited
wireless networks.
|
1102.5728
|
Named Entity Recognition Using Web Document Corpus
|
cs.IR cs.LG
|
This paper introduces a named entity recognition approach in textual corpus.
This Named Entity (NE) can be a named: location, person, organization, date,
time, etc., characterized by instances. A NE is found in texts accompanied by
contexts: words that are left or right of the NE. The work mainly aims at
identifying contexts inducing the NE's nature. As such, The occurrence of the
word "President" in a text, means that this word or context may be followed by
the name of a president as President "Obama". Likewise, a word preceded by the
string "footballer" induces that this is the name of a footballer. NE
recognition may be viewed as a classification method, where every word is
assigned to a NE class, regarding the context. The aim of this study is then to
identify and classify the contexts that are most relevant to recognize a NE,
those which are frequently found with the NE. A learning approach using
training corpus: web documents, constructed from learning examples is then
suggested. Frequency representations and modified tf-idf representations are
used to calculate the context weights associated to context frequency, learning
example frequency, and document frequency in the corpus.
|
1102.5750
|
Neyman-Pearson classification, convexity and stochastic constraints
|
stat.ML cs.LG math.ST stat.TH
|
Motivated by problems of anomaly detection, this paper implements the
Neyman-Pearson paradigm to deal with asymmetric errors in binary classification
with a convex loss. Given a finite collection of classifiers, we combine them
and obtain a new classifier that satisfies simultaneously the two following
properties with high probability: (i) its probability of type I error is below
a pre-specified level and (ii), it has probability of type II error close to
the minimum possible. The proposed classifier is obtained by solving an
optimization problem with an empirical objective and an empirical constraint.
New techniques to handle such problems are developed and have consequences on
chance constrained programming.
|
1102.5755
|
Normal Factor Graphs: A Diagrammatic Approach to Linear Algebra
|
cs.IT math.IT
|
Inspired by some new advances on normal factor graphs (NFGs), we introduce
NFGs as a simple and intuitive diagrammatic approach towards encoding some
concepts from linear algebra. We illustrate with examples the workings of such
an approach and settle a conjecture of Peterson on the Pfaffian.
|
1102.5757
|
Improving the character recognition efficiency of feed forward BP neural
network
|
cs.NE
|
This work is focused on improving the character recognition capability of
feed-forward back-propagation neural network by using one, two and three hidden
layers and the modified additional momentum term. 182 English letters were
collected for this work and the equivalent binary matrix form of these
characters was applied to the neural network as training patterns. While the
network was getting trained, the connection weights were modified at each epoch
of learning. For each training sample, the error surface was examined for
minima by computing the gradient descent. We started the experiment by using
one hidden layer and the number of hidden layers was increased up to three and
it has been observed that accuracy of the network was increased with low mean
square error but at the cost of training time. The recognition accuracy was
improved further when modified additional momentum term was used.
|
1103.0038
|
On the Sum-Capacity with Successive Decoding in Interference Channels
|
cs.IT math.IT
|
In this paper, we investigate the sum-capacity of the two-user Gaussian
interference channel with Gaussian superposition coding and successive
decoding. We first examine an approximate deterministic formulation of the
problem, and introduce the complementarity conditions that capture the use of
Gaussian coding and successive decoding. In the deterministic channel problem,
we find the constrained sum-capacity and its achievable schemes with the
minimum number of messages, first in symmetric channels, and then in general
asymmetric channels. We show that the constrained sum-capacity oscillates as a
function of the cross link gain parameters between the information theoretic
sum-capacity and the sum-capacity with interference treated as noise.
Furthermore, we show that if the number of messages of either of the two users
is fewer than the minimum number required to achieve the constrained
sum-capacity, the maximum achievable sum-rate drops to that with interference
treated as noise. We provide two algorithms (a simple one and a finer one) to
translate the optimal schemes in the deterministic channel model to the
Gaussian channel model. We also derive two upper bounds on the sum-capacity of
the Gaussian Han-Kobayashi schemes, which automatically upper bound the
sum-capacity using successive decoding of Gaussian codewords. Numerical
evaluations show that, similar to the deterministic channel results, the
constrained sum-capacity in the Gaussian channels oscillates between the
sum-capacity with Han-Kobayashi schemes and that with single message schemes.
|
1103.0048
|
On the structural properties of small-world networks with finite range
of shortcut links
|
physics.soc-ph cond-mat.dis-nn cs.SI
|
We explore a new variant of Small-World Networks (SWNs), in which an
additional parameter ($r$) sets the length scale over which shortcuts are
uniformly distributed. When $r=0$ we have an ordered network, whereas $r=1$
corresponds to the original SWN model. These short-range SWNs have a similar
degree distribution and scaling properties as the original SWN model. We
observe the small-world phenomenon for $r \ll 1$ indicating that global
shortcuts are not necessary for the small-world effect. For short-range SWNs,
the average path length changes nonmonotonically with system size, whereas for
the original SWN model it increases monotonically. We propose an expression for
the average path length for short-range SWNs based on numerical simulations and
analytical approximations.
|
1103.0056
|
Exact solutions for social and biological contagion models on mixed
directed and undirected, degree-correlated random networks
|
physics.soc-ph cond-mat.dis-nn cs.SI
|
We derive analytic expressions for the possibility, probability, and expected
size of global spreading events starting from a single infected seed for a
broad collection of contagion processes acting on random networks with both
directed and undirected edges and arbitrary degree-degree correlations. Our
work extends previous theoretical developments for the undirected case, and we
provide numerical support for our findings by investigating an example class of
networks for which we are able to obtain closed-form expressions.
|
1103.0083
|
Mining Target-Oriented Fuzzy Correlation Rules to Optimize Telecom
Service Management
|
cs.DB
|
To optimize telecom service management, it is necessary that information
about telecom services is highly related to the most popular telecom service.
To this end, we propose an algorithm for mining target-oriented fuzzy
correlation rules. In this paper, we show that by using the fuzzy statistics
analysis and the data mining technology, the target-oriented fuzzy correlation
rules can be obtained from a given database. We conduct an experiment by using
a sample database from a telecom service provider in Taiwan. Our work can be
used to assist the telecom service provider in providing the appropriate
services to the customers for better customer relationship management.
|
1103.0086
|
A generic trust framework for large-scale open systems using machine
learning
|
cs.DC cs.CR cs.LG
|
In many large scale distributed systems and on the web, agents need to
interact with other unknown agents to carry out some tasks or transactions. The
ability to reason about and assess the potential risks in carrying out such
transactions is essential for providing a safe and reliable environment. A
traditional approach to reason about the trustworthiness of a transaction is to
determine the trustworthiness of the specific agent involved, derived from the
history of its behavior. As a departure from such traditional trust models, we
propose a generic, machine learning approach based trust framework where an
agent uses its own previous transactions (with other agents) to build a
knowledge base, and utilize this to assess the trustworthiness of a transaction
based on associated features, which are capable of distinguishing successful
transactions from unsuccessful ones. These features are harnessed using
appropriate machine learning algorithms to extract relationships between the
potential transaction and previous transactions. The trace driven experiments
using real auction dataset show that this approach provides good accuracy and
is highly efficient compared to other trust mechanisms, especially when
historical information of the specific agent is rare, incomplete or inaccurate.
|
1103.0087
|
Cost effective approach on feature selection using genetic algorithms
and fuzzy logic for diabetes diagnosis
|
cs.NE
|
A way to enhance the performance of a model that combines genetic algorithms
and fuzzy logic for feature selection and classification is proposed. Early
diagnosis of any disease with less cost is preferable. Diabetes is one such
disease. Diabetes has become the fourth leading cause of death in developed
countries and there is substantial evidence that it is reaching epidemic
proportions in many developing and newly industrialized nations. In medical
diagnosis, patterns consist of observable symptoms along with the results of
diagnostic tests. These tests have various associated costs and risks. In the
automated design of pattern classification, the proposed system solves the
feature subset selection problem. It is a task of identifying and selecting a
useful subset of pattern-representing features from a larger set of features.
Using fuzzy rule-based classification system, the proposed system proves to
improve the classification accuracy.
|
1103.0089
|
Capacity Bounds for Relay Channels with Inter-symbol Interference and
Colored Gaussian Noise
|
cs.IT math.IT
|
The capacity of a relay channel with inter-symbol interference (ISI) and
additive colored Gaussian noise is examined under an input power constraint.
Prior results are used to show that the capacity of this channel can be
computed by examining the circular degraded relay channel in the limit of
infinite block length. The current work provides single letter expressions for
the achievable rates with decodeand- forward (DF) and compress-and-forward (CF)
processing employed at the relay. Additionally, the cut-set bound for the relay
channel is generalized for the ISI/colored Gaussian noise scenario. All results
hinge on showing the optimality of the decomposition of the relay channel with
ISI/colored Gaussian noise into an equivalent collection of coupled parallel,
scalar, memoryless relay channels. The region of optimality of the DF and CF
achievable rates are also discussed. Optimal power allocation strategies are
also discussed for the two lower bounds and the cut-set upper bound. As the
maximizing power allocations for DF and CF appear to be intractable, the
desired cost functions are modified and then optimized. The resulting rates are
illustrated through the computation of numerical examples.
|
1103.0102
|
Multi-label Learning via Structured Decomposition and Group Sparsity
|
cs.LG stat.ML
|
In multi-label learning, each sample is associated with several labels.
Existing works indicate that exploring correlations between labels improve the
prediction performance. However, embedding the label correlations into the
training process significantly increases the problem size. Moreover, the
mapping of the label structure in the feature space is not clear. In this
paper, we propose a novel multi-label learning method "Structured Decomposition
+ Group Sparsity (SDGS)". In SDGS, we learn a feature subspace for each label
from the structured decomposition of the training data, and predict the labels
of a new sample from its group sparse representation on the multi-subspace
obtained from the structured decomposition. In particular, in the training
stage, we decompose the data matrix $X\in R^{n\times p}$ as
$X=\sum_{i=1}^kL^i+S$, wherein the rows of $L^i$ associated with samples that
belong to label $i$ are nonzero and consist a low-rank matrix, while the other
rows are all-zeros, the residual $S$ is a sparse matrix. The row space of $L_i$
is the feature subspace corresponding to label $i$. This decomposition can be
efficiently obtained via randomized optimization. In the prediction stage, we
estimate the group sparse representation of a new sample on the multi-subspace
via group \emph{lasso}. The nonzero representation coefficients tend to
concentrate on the subspaces of labels that the sample belongs to, and thus an
effective prediction can be obtained. We evaluate SDGS on several real datasets
and compare it with popular methods. Results verify the effectiveness and
efficiency of SDGS.
|
1103.0120
|
Automatic Detection of Ringworm using Local Binary Pattern (LBP)
|
cs.CV
|
In this paper we present a novel approach for automatic recognition of ring
worm skin disease based on LBP (Local Binary Pattern) feature extracted from
the affected skin images. The proposed method is evaluated by extensive
experiments on the skin images collected from internet. The dataset is tested
using three different classifiers i.e. Bayesian, MLP and SVM. Experimental
results show that the proposed methodology efficiently discriminates between a
ring worm skin and a normal skin. It is a low cost technique and does not
require any special imaging devices.
|
1103.0127
|
Fuzzy Approach to Critical Bus Ranking under Normal and Line Outage
Contingencies
|
cs.AI
|
Identification of critical or weak buses for a given operating condition is
an important task in the load dispatch centre. It has become more vital in view
of the threat of voltage instability leading to voltage collapse. This paper
presents a fuzzy approach for ranking critical buses in a power system under
normal and network contingencies based on Line Flow index and voltage profiles
at load buses. The Line Flow index determines the maximum load that is possible
to be connected to a bus in order to maintain stability before the system
reaches its bifurcation point. Line Flow index (LF index) along with voltage
profiles at the load buses are represented in Fuzzy Set notation. Further they
are evaluated using fuzzy rules to compute Criticality Index. Based on this
index, critical buses are ranked. The bus with highest rank is the weakest bus
as it can withstand a small amount of load before causing voltage collapse. The
proposed method is tested on Five Bus Test System.
|
1103.0135
|
Capacity results for compound wiretap channels
|
cs.IT math.IT
|
We derive a lower bound on the secrecy capacity of the compound wiretap
channel with channel state information at the transmitter which matches the
general upper bound on the secrecy capacity of general compound wiretap
channels given by Liang et al. and thus establishing a full coding theorem in
this case. We achieve this with a quite strong secrecy criterion and with a
decoder that is robust against the effect of randomisation in the encoding.
This relieves us from the need of decoding the randomisation parameter which is
in general not possible within this model. Moreover we prove a lower bound on
the secrecy capacity of the compound wiretap channel without channel state
information.
|
1103.0171
|
Finite Dimensional Infinite Constellations
|
cs.IT math.IT
|
In the setting of a Gaussian channel without power constraints, proposed by
Poltyrev, the codewords are points in an n-dimensional Euclidean space (an
infinite constellation) and the tradeoff between their density and the error
probability is considered. The capacity in this setting is the highest
achievable normalized log density (NLD) with vanishing error probability. This
capacity as well as error exponent bounds for this setting are known. In this
work we consider the optimal performance achievable in the fixed blocklength
(dimension) regime. We provide two new achievability bounds, and extend the
validity of the sphere bound to finite dimensional infinite constellations. We
also provide asymptotic analysis of the bounds: When the NLD is fixed, we
provide asymptotic expansions for the bounds that are significantly tighter
than the previously known error exponent results. When the error probability is
fixed, we show that as n grows, the gap to capacity is inversely proportional
(up to the first order) to the square-root of n where the proportion constant
is given by the inverse Q-function of the allowed error probability, times the
square root of 1/2. In an analogy to similar result in channel coding, the
dispersion of infinite constellations is 1/2nat^2 per channel use. All our
achievability results use lattices and therefore hold for the maximal error
probability as well. Connections to the error exponent of the power constrained
Gaussian channel and to the volume-to-noise ratio as a figure of merit are
discussed. In addition, we demonstrate the tightness of the results numerically
and compare to state-of-the-art coding schemes.
|
1103.0172
|
Inverse Queries For Multidimensional Spaces
|
cs.DB
|
Traditional spatial queries return, for a given query object $q$, all
database objects that satisfy a given predicate, such as epsilon range and
$k$-nearest neighbors. This paper defines and studies {\em inverse} spatial
queries, which, given a subset of database objects $Q$ and a query predicate,
return all objects which, if used as query objects with the predicate, contain
$Q$ in their result. We first show a straightforward solution for answering
inverse spatial queries for any query predicate. Then, we propose a
filter-and-refinement framework that can be used to improve efficiency. We show
how to apply this framework on a variety of inverse queries, using appropriate
space pruning strategies. In particular, we propose solutions for inverse
epsilon range queries, inverse $k$-nearest neighbor queries, and inverse
skyline queries. Our experiments show that our framework is significantly more
efficient than naive approaches.
|
1103.0205
|
Nearest Neighbour Decoding and Pilot-Aided Channel Estimation in
Stationary Gaussian Flat-Fading Channels
|
cs.IT math.IT
|
We study the information rates of non-coherent, stationary, Gaussian,
multiple-input multiple-output (MIMO) flat-fading channels that are achievable
with nearest neighbour decoding and pilot-aided channel estimation. In
particular, we analyse the behaviour of these achievable rates in the limit as
the signal-to-noise ratio (SNR) tends to infinity. We demonstrate that nearest
neighbour decoding and pilot-aided channel estimation achieves the capacity
pre-log - which is defined as the limiting ratio of the capacity to the
logarithm of SNR as the SNR tends to infinity - of non-coherent multiple-input
single-output (MISO) flat-fading channels, and it achieves the best so far
known lower bound on the capacity pre-log of non-coherent MIMO flat-fading
channels.
|
1103.0248
|
DB Category: Denotational Semantics for View-based Database Mappings
|
cs.DB cs.LO math.CT
|
We present a categorical denotational semantics for a database mapping, based
on views, in the most general framework of a database integration/exchange.
Developed database category DB, for databases (objects) and view-based mappings
(morphisms) between them, is different from Set category: the morphisms (based
on a set of complex query computations) are not functions, while the objects
are database instances (sets of relations). The logic based schema mappings
between databases, usually written in a highly expressive logical language (ex.
LAV, GAV, GLAV mappings, or tuple generating dependency) may be functorially
translated into this "computation" category DB. A new approach is adopted,
based on the behavioral point of view for databases, and behavioral
equivalences for databases and their mappings are established. By introduction
of view-based observations for databases, which are computations without
side-effects, we define a fundamental (Universal algebra) monad with a
power-view endofunctor T. The resulting 2-category DB is symmetric, so that any
mapping can be represented as an object (database instance) as well, where a
higher-level mapping between mappings is a 2-cell morphism. Database category
DB has the following properties: it is equal to its dual, complete and
cocomplete. Special attention is devoted to practical examples: a query
definition, a query rewriting in GAV Database-integration environment, and the
fixpoint solution of a canonical data integration model.
|
1103.0266
|
On the Order Optimality of Large-scale Underwater Networks
|
cs.IT math.IT
|
Capacity scaling laws are analyzed in an underwater acoustic network with $n$
regularly located nodes on a square, in which both bandwidth and received
signal power can be limited significantly. A narrow-band model is assumed where
the carrier frequency is allowed to scale as a function of $n$. In the network,
we characterize an attenuation parameter that depends on the frequency scaling
as well as the transmission distance. Cut-set upper bounds on the throughput
scaling are then derived in both extended and dense networks having unit node
density and unit area, respectively. It is first analyzed that under extended
networks, the upper bound is inversely proportional to the attenuation
parameter, thus resulting in a highly power-limited network. Interestingly, it
is seen that the upper bound for extended networks is intrinsically related to
the attenuation parameter but not the spreading factor. On the other hand, in
dense networks, we show that there exists either a bandwidth or power
limitation, or both, according to the path-loss attenuation regimes, thus
yielding the upper bound that has three fundamentally different operating
regimes. Furthermore, we describe an achievable scheme based on the simple
nearest-neighbor multi-hop (MH) transmission. We show that under extended
networks, the MH scheme is order-optimal for all the operating regimes. An
achievability result is also presented in dense networks, where the operating
regimes that guarantee the order optimality are identified. It thus turns out
that frequency scaling is instrumental towards achieving the order optimality
in the regimes. Finally, these scaling results are extended to a random network
realization. As a result, vital information for fundamental limits of a variety
of underwater network scenarios is provided by showing capacity scaling laws.
|
1103.0270
|
Interference Alignment and Degrees of Freedom Region of Cellular Sigma
Channel
|
cs.IT math.IT
|
We investigate the Degrees of Freedom (DoF) Region of a cellular network,
where the cells can have overlapping areas. Within an overlapping area, the
mobile users can access multiple base stations. We consider a case where there
are two base stations both equipped with multiple antennas. The mobile stations
are all equipped with single antenna and each mobile station can belong to
either a single cell or both cells. We completely characterize the DoF region
for the uplink channel assuming that global channel state information is
available at the transmitters. The achievability scheme is based on
interference alignment at the base stations.
|
1103.0305
|
GLRT-Based Spectrum Sensing with Blindly Learned Feature under Rank-1
Assumption
|
cs.IT math.IT
|
Prior knowledge can improve the performance of spectrum sensing. Instead of
using universal features as prior knowledge, we propose to blindly learn the
localized feature at the secondary user. Motivated by pattern recognition in
machine learning, we define signal feature as the leading eigenvector of the
signal's sample covariance matrix. Feature learning algorithm (FLA) for blind
feature learning and feature template matching algorithm (FTM) for spectrum
sensing are proposed. Furthermore, we implement the FLA and FTM in hardware.
Simulations and hardware experiments show that signal feature can be learned
blindly. In addition, by using signal feature as prior knowledge, the detection
performance can be improved by about 2 dB. Motivated by experimental results,
we derive several GLRT based spectrum sensing algorithms under rank-1
assumption, considering signal feature, signal power and noise power as the
available parameters. The performance of our proposed algorithms is tested on
both synthesized rank-1 signal and captured DTV data, and compared to other
state-of-the-art covariance matrix based spectrum sensing algorithms. In
general, our GLRT based algorithms have better detection performance. In
addition, algorithms with signal feature as prior knowledge are about 2 dB
better than algorithms without prior knowledge.
|
1103.0311
|
Consensus Problem under Diffusion-based Molecular Communication
|
cs.IT math.IT nlin.AO
|
We investigate the consensus problem in a network where nodes communicate via
diffusion-based molecular communication (DbMC). In DbMC, messages are conveyed
via the variation in the concentration of molecules in the medium. Every node
acquires sensory information about the environment. Communication enables the
nodes to reach the best estimate for that measurement, e.g., the average of the
initial estimates by all nodes. We consider an iterative method for
communication among nodes that enables information spreading and averaging in
the network. We show that the consensus can be attained after a finite number
of iterations and variance of estimates of nodes can be made arbitrarily small
via communication.
|
1103.0317
|
Generalized Gray Codes for Local Rank Modulation
|
cs.IT math.IT
|
We consider the local rank-modulation scheme in which a sliding window going
over a sequence of real-valued variables induces a sequence of permutations.
Local rank-modulation is a generalization of the rank-modulation scheme, which
has been recently suggested as a way of storing information in flash memory. We
study Gray codes for the local rank-modulation scheme in order to simulate
conventional multi-level flash cells while retaining the benefits of rank
modulation. Unlike the limited scope of previous works, we consider code
constructions for the entire range of parameters including the code length,
sliding window size, and overlap between adjacent windows. We show our
constructed codes have asymptotically-optimal rate. We also provide efficient
encoding, decoding, and next-state algorithms.
|
1103.0326
|
On the Achievable Rate of Stationary Rayleigh Flat-Fading Channels with
Gaussian Inputs
|
cs.IT math.IT
|
In this work, we consider a discrete-time stationary Rayleigh flat-fading
channel with unknown channel state information at transmitter and receiver. The
law of the channel is presumed to be known to the receiver. In addition, we
assume the power spectral density (PSD) of the fading process to be compactly
supported. For i.i.d. zero-mean proper Gaussian input distributions, we
investigate the achievable rate. One of the main contributions is the
derivation of two new upper bounds on the achievable rate with zero-mean proper
Gaussian input symbols. The first one holds only for the special case of a
rectangular PSD and depends on the SNR and the spread of the PSD. Together with
a lower bound on the achievable rate, which is achievable with i.i.d. zero-mean
proper Gaussian input symbols, we have found a set of bounds which is tight in
the sense that their difference is bounded. Furthermore, we show that the high
SNR slope is characterized by a pre-log of 1-2f_d, where f_d is the normalized
maximum Doppler frequency. This pre-log is equal to the high SNR pre-log of the
peak power constrained capacity. Furthermore, we derive an alternative upper
bound on the achievable rate with i.i.d. input symbols which is based on the
one-step channel prediction error variance. The novelty lies in the fact that
this bound is not restricted to peak power constrained input symbols like known
bounds, e.g. in [1]. Therefore, the derived upper bound can also be used to
evaluate the achievable rate with i.i.d. proper Gaussian input symbols. We
compare the derived bounds on the achievable rate with i.i.d. zero-mean proper
Gaussian input symbols with bounds on the peak power constrained capacity given
in [1-3]. Finally, we compare the achievable rate with i.i.d. zero-mean proper
Gaussian input symbols with the achievable rate using synchronized detection in
combination with a solely pilot based channel estimation.
|
1103.0358
|
On Network Coding Capacity - Matroidal Networks and Network Capacity
Regions
|
cs.IT math.IT
|
One fundamental problem in the field of network coding is to determine the
network coding capacity of networks under various network coding schemes. In
this thesis, we address the problem with two approaches: matroidal networks and
capacity regions.
In our matroidal approach, we prove the converse of the theorem which states
that, if a network is scalar-linearly solvable then it is a matroidal network
associated with a representable matroid over a finite field. As a consequence,
we obtain a correspondence between scalar-linearly solvable networks and
representable matroids over finite fields in the framework of matroidal
networks. We prove a theorem about the scalar-linear solvability of networks
and field characteristics. We provide a method for generating scalar-linearly
solvable networks that are potentially different from the networks that we
already know are scalar-linearly solvable.
In our capacity region approach, we define a multi-dimensional object, called
the network capacity region, associated with networks that is analogous to the
rate regions in information theory. For the network routing capacity region, we
show that the region is a computable rational polytope and provide exact
algorithms and approximation heuristics for computing the region. For the
network linear coding capacity region, we construct a computable rational
polytope, with respect to a given finite field, that inner bounds the linear
coding capacity region and provide exact algorithms and approximation
heuristics for computing the polytope. The exact algorithms and approximation
heuristics we present are not polynomial time schemes and may depend on the
output size.
|
1103.0361
|
Computing Bounds on Network Capacity Regions as a Polytope
Reconstruction Problem
|
cs.IT math.IT
|
We define a notion of network capacity region of networks that generalizes
the notion of network capacity defined by Cannons et al. and prove its notable
properties such as closedness, boundedness and convexity when the finite field
is fixed. We show that the network routing capacity region is a computable
rational polytope and provide exact algorithms and approximation heuristics for
computing the region. We define the semi-network linear coding capacity region,
with respect to a fixed finite field, that inner bounds the corresponding
network linear coding capacity region, show that it is a computable rational
polytope, and provide exact algorithms and approximation heuristics. We show
connections between computing these regions and a polytope reconstruction
problem and some combinatorial optimization problems, such as the minimum cost
directed Steiner tree problem. We provide an example to illustrate our results.
The algorithms are not necessarily polynomial-time.
|
1103.0365
|
Diagonal Based Feature Extraction for Handwritten Alphabets Recognition
System using Neural Network
|
stat.CO cs.NE
|
An off-line handwritten alphabetical character recognition system using
multilayer feed forward neural network is described in the paper. A new method,
called, diagonal based feature extraction is introduced for extracting the
features of the handwritten alphabets. Fifty data sets, each containing 26
alphabets written by various people, are used for training the neural network
and 570 different handwritten alphabetical characters are used for testing. The
proposed recognition system performs quite well yielding higher levels of
recognition accuracy compared to the systems employing the conventional
horizontal and vertical methods of feature extraction. This system will be
suitable for converting handwritten documents into structural text form and
recognizing handwritten names.
|
1103.0368
|
Computing an Aggregate Edge-Weight Function for Clustering Graphs with
Multiple Edge Types
|
cs.SI cs.DS physics.soc-ph
|
We investigate the community detection problem on graphs in the existence of
multiple edge types. Our main motivation is that similarity between objects can
be defined by many different metrics and aggregation of these metrics into a
single one poses several important challenges, such as recovering this
aggregation function from ground-truth, investigating the space of different
clusterings, etc. In this paper, we address how to find an aggregation function
to generate a composite metric that best resonates with the ground-truth. We
describe two approaches: solving an inverse problem where we try to find
parameters that generate a graph whose clustering gives the ground-truth
clustering, and choosing parameters to maximize the quality of the ground-truth
clustering. We present experimental results on real and synthetic benchmarks.
|
1103.0377
|
On Properties of the Minimum Entropy Sub-tree to Compute Lower Bounds on
the Partition Function
|
stat.AP cs.IT math.IT physics.comp-ph
|
Computing the partition function and the marginals of a global probability
distribution are two important issues in any probabilistic inference problem.
In a previous work, we presented sub-tree based upper and lower bounds on the
partition function of a given probabilistic inference problem. Using the
entropies of the sub-trees we proved an inequality that compares the lower
bounds obtained from different sub-trees. In this paper we investigate the
properties of one specific lower bound, namely the lower bound computed by the
minimum entropy sub-tree. We also investigate the relationship between the
minimum entropy sub-tree and the sub-tree that gives the best lower bound.
|
1103.0398
|
Natural Language Processing (almost) from Scratch
|
cs.LG cs.CL
|
We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements.
|
1103.0414
|
Convergence analysis of a proximal Gauss-Newton method
|
math.OC cs.SY math.NA
|
An extension of the Gauss-Newton algorithm is proposed to find local
minimizers of penalized nonlinear least squares problems, under generalized
Lipschitz assumptions. Convergence results of local type are obtained, as well
as an estimate of the radius of the convergence ball. Some applications for
solving constrained nonlinear equations are discussed and the numerical
performance of the method is assessed on some significant test problems.
|
1103.0461
|
Paranoid Secondary: Waterfilling in a Cognitive Interference Channel
with Partial Information
|
cs.IT math.IT
|
We study a two-user cognitive channel, where the primary flow is sporadic,
cannot be re-designed and operating below its link capacity. To study the
impact of primary traffic uncertainty, we propose a block activity model that
captures the random on-off periods of primary's transmissions. Each block in
the model can be split into parallel Gaussian-mixture channels, such that each
channel resembles a multiple user channel (MAC) from the point of view of the
secondary user. The secondary senses the current state of the primary at the
start of each block. We show that the optimal power transmitted depends on the
sensed state and the optimal power profile is paranoid, i.e. either growing or
decaying in power as a function of time. We show that such a scheme achieves
capacity when there is no noise in the sensing. The optimal transmission for
the secondary performs rate splitting and follows a layered water-filling power
allocation for each parallel channel to achieve capacity. The secondary rate
approaches a genie-aided scheme for large block-lengths. Additionally, if the
fraction of time primary uses the channel tends to one, the paranoid scheme and
the genie-aided upper bound get arbitrarily close to a no-sensing scheme.
|
1103.0484
|
Algebraic Hybrid Satellite-Terrestrial Space-Time Codes for Digital
Broadcasting in SFN
|
cs.IT math.IT
|
Lately, different methods for broadcasting future digital TV in a single
frequency network (SFN) have been under an intensive study. To improve the
transmission to also cover suburban and rural areas, a hybrid scheme may be
used. In hybrid transmission, the signal is transmitted both from a satellite
and from a terrestrial site. In 2008, Y. Nasser et al. proposed to use a double
layer 3D space-time (ST) code in the hybrid 4 x 2 MIMO transmission of digital
TV. In this paper, alternative codes with simpler structure are proposed for
the 4 x 2 hybrid system, and new codes are constructed for the 3 x 2 system.
The performance of the proposed codes is analyzed through computer simulations,
showing a significant improvement over simple repetition schemes. The proposed
codes prove in addition to be very robust in the presence of power imbalance
between the two sites.
|
1103.0486
|
Exploiting symmetries in SDP-relaxations for polynomial optimization
|
math.OC cs.SY
|
In this paper we study various approaches for exploiting symmetries in
polynomial optimization problems within the framework of semi definite
programming relaxations. Our special focus is on constrained problems
especially when the symmetric group is acting on the variables. In particular,
we investigate the concept of block decomposition within the framework of
constrained polynomial optimization problems, show how the degree principle for
the symmetric group can be computationally exploited and also propose some
methods to efficiently compute in the geometric quotient.
|
1103.0490
|
Sound and Complete Query Answering in Intensional P2P Data Integration
|
cs.DB cs.LO
|
Contemporary use of the term 'intension' derives from the traditional logical
doctrine that an idea has both an extension and an intension. In this paper we
introduce an intensional FOL (First-order-logic) for P2P systems by fusing the
Bealer's intensional algebraic FOL with the S5 possible-world semantics of the
Montague, we define the intensional equivalence relation for this logic and the
weak deductive inference for it. The notion of ontology has become widespread
in semantic Web. The meaning of concepts and views defined over some database
ontology can be considered as intensional objects which have particular
extension in some possible world: for instance in the actual world. Thus, non
invasive mapping between completely independent peer databases in a P2P systems
can be naturally specified by the set of couples of intensionally equivalent
views, which have the same meaning (intension), over two different peers. Such
a kind of mapping has very different semantics from the standard view-based
mappings based on the material implication commonly used for Data Integration.
We show how a P2P database system may be embedded into this intensional modal
FOL, and how we are able to obtain a weak non-omniscient inference, which can
be effectively implemented. For a query answering we consider non omniscient
query agents and we define object-oriented class for them which implements as
method the query rewriting algorithm. Finally, we show that this query
answering algorithm is sound and complete w.r.t. the weak deduction of the P2P
intensional logic.
|
1103.0494
|
Outage Probability in {\eta}-{\mu}/{\eta}-{\mu} Interference-limited
Scenarios
|
cs.IT math.IT
|
In this paper exact closed-form expressions are derived for the outage
probability (OP) in scenarios where both the signal of interest (SOI) and the
interfering signals experience {\eta}-{\mu} fading and the background noise can
be neglected. With the only assumption that the {\mu} parameter is a positive
integer number for the interfering signals, the derived expressions are given
in elementary terms for maximal ratio combining (MRC) with independent
branches. The analysis is also valid when the {\mu} parameters of the
pre-combining SOI power envelopes are positive integer or half-integer numbers
and the SOI is formed at the receiver from spatially correlated MRC.
|
1103.0502
|
Unified Analysis of the Average Gaussian Error Probability for a Class
of Fading Channels
|
cs.IT math.IT
|
This paper focuses on the analysis of average Gaussian error probabilities in
certain fading channels, i.e. we are interested in E[Q((p {\gamma})^(1/2))]
where Q(.) is the Gaussian Q-function, p is a positive real number and {\gamma}
is a nonnegative random variable. We present a unified analysis of the average
Gaussian error probability, derive a compact expression in terms of the
Lauricella FD^(n) function that is applicable to a broad class of fading
channels, and discuss the relation of this expression and expressions of this
type recently appeared in literature. As an intermediate step in our
derivations, we also obtain a compact expression for the outage probability of
the same class of fading channels. Finally, we show how this unified analysis
allows us to obtain novel performance analytical results.
|
1103.0505
|
A Note on the Sum of Correlated Gamma Random Variables
|
cs.IT math.IT
|
The sum of correlated gamma random variables appears in the analysis of many
wireless communications systems, e.g. in systems under Nakagami-m fading. In
this Letter we obtain exact expressions for the probability density function
(PDF) and the cumulative distribution function (CDF) of the sum of arbitrarily
correlated gamma variables in terms of certain Lauricella functions.
|
1103.0512
|
Dynamics of bounded confidence opinion in heterogeneous social networks:
concord against partial antagonism
|
physics.soc-ph cs.SI nlin.AO nlin.PS
|
Bounded confidence models of opinion dynamics have been actively studied in
recent years, in particular, opinion formation and extremism propagation along
with other aspects of social dynamics. In this work, after an analysis of
limitations of the Deffuant-Weisbuch (DW) bounded confidence, relative
agreement model, we propose the Mixed model that takes into account two
psychological types of individuals. Concord agents (C-agents) are friendly
people; they interact in a way that their opinions get closer always. Agents of
the other psychological type show partial antagonism in their interaction
(PA-agents). Opinion dynamics in heterogeneous social groups, consisting of
agents of the two types, was studied on different social networks. Limit cases
of the mixed model, pure C- and PA-societies, were also studied. We found that
group opinion formation is, qualitatively, almost independent of the topology
of networks used in this work. Opinion fragmentation, polarization and
consensus are observed in the mixed model at different proportions of PA- and
C-agents, depending on the value of initial opinion tolerance of agents. As for
the opinion formation and arising of "dissidents", the opinion dynamics of the
C-agents society was found to be similar to that of the DW model, except for
the rate of opinion convergence. Nevertheless, mixed societies showed dynamics
and bifurcation patterns notably different to those of the DW model. The
influence of biased initial conditions over opinion formation in heterogeneous
social groups was also studied versus the initial value of opinion uncertainty,
varying the proportion of the PA- to C-agents. Bifurcation diagrams showed
impressive evolution of collective opinion, in particular, radical change of
left to right consensus or vice versa at an opinion uncertainty value equal to
0.7 in the model with the PA/C mixture of population near 50/50.
|
1103.0538
|
Stochatic Perron's method and verification without smoothness using
viscosity comparison: the linear case
|
math.PR cs.SY math.AP math.OC
|
We introduce a probabilistic version of the classical Perron's method to
construct viscosity solutions to linear parabolic equations associated to
stochastic differential equations. Using this method, we construct easily two
viscosity (sub and super) solutions that squeeze in between the expected
payoff. If a comparison result holds true, then there exists a unique viscosity
solution which is a martingale along the solutions of the stochastic
differential equation. The unique viscosity solution is actually equal to the
expected payoff. This amounts to a verification result (Ito's Lemma) for
non-smooth viscosity solutions of the linear parabolic equation. This is the
first step in a larger program to prove verification for viscosity solutions
and the Dynamic Programming Principle for stochastic control problems and games
|
1103.0540
|
An Algorithm for Repairing Low-Quality Video Enhancement Techniques
Based on Trained Filter
|
cs.CV cs.MM
|
Multifarious image enhancement algorithms have been used in different
applications. Still, some algorithms or modules are imperfect for practical
use. When the image enhancement modules have been fixed or combined by a series
of algorithms, we need to repair them as a whole part without changing the
inside. This report aims to find an algorithm based on trained filters to
repair low-quality image enhancement modules. A brief review on basic image
enhancement techniques and pixel classification methods will be presented, and
the procedure of trained filters will be described step by step. The
experiments and result comparisons for this algorithm will be described in
detail.
|
1103.0561
|
Downlink SDMA with Limited Feedback in Interference-Limited Wireless
Networks
|
cs.IT cs.NI math.IT
|
The tremendous capacity gains promised by space division multiple access
(SDMA) depend critically on the accuracy of the transmit channel state
information. In the broadcast channel, even without any network interference,
it is known that such gains collapse due to interstream interference if the
feedback is delayed or low rate. In this paper, we investigate SDMA in the
presence of interference from many other simultaneously active transmitters
distributed randomly over the network. In particular we consider zero-forcing
beamforming in a decentralized (ad hoc) network where each receiver provides
feedback to its respective transmitter. We derive closed-form expressions for
the outage probability, network throughput, transmission capacity, and average
achievable rate and go on to quantify the degradation in network performance
due to residual self-interference as a function of key system parameters. One
particular finding is that as in the classical broadcast channel, the per-user
feedback rate must increase linearly with the number of transmit antennas and
SINR (in dB) for the full multiplexing gains to be preserved with limited
feedback. We derive the throughput-maximizing number of streams, establishing
that single-stream transmission is optimal in most practically relevant
settings. In short, SDMA does not appear to be a prudent design choice for
interference-limited wireless networks.
|
1103.0579
|
Distributed Estimation via Iterative Projections with Application to
Power Network Monitoring
|
math.OC cs.SY
|
This work presents a distributed method for control centers to monitor the
operating condition of a power network, i.e., to estimate the network state,
and to ultimately determine the occurrence of threatening situations. State
estimation has been recognized to be a fundamental task for network control
centers to ensure correct and safe functionalities of power grids. We consider
(static) state estimation problems, in which the state vector consists of the
voltage magnitude and angle at all network buses. We consider the state to be
linearly related to network measurements, which include power flows, current
injections, and voltages phasors at some buses. We admit the presence of
several cooperating control centers, and we design two distributed methods for
them to compute the minimum variance estimate of the state given the network
measurements. The two distributed methods rely on different modes of
cooperation among control centers: in the first method an incremental mode of
cooperation is used, whereas, in the second method, a diffusive interaction is
implemented. Our procedures, which require each control center to know only the
measurements and structure of a subpart of the whole network, are
computationally efficient and scalable with respect to the network dimension,
provided that the number of control centers also increases with the network
cardinality. Additionally, a finite-memory approximation of our diffusive
algorithm is proposed, and its accuracy is characterized. Finally, our
estimation methods are exploited to develop a distributed algorithm to detect
corrupted data among the network measurements.
|
1103.0598
|
Learning transformed product distributions
|
cs.LG
|
We consider the problem of learning an unknown product distribution $X$ over
$\{0,1\}^n$ using samples $f(X)$ where $f$ is a \emph{known} transformation
function. Each choice of a transformation function $f$ specifies a learning
problem in this framework.
Information-theoretic arguments show that for every transformation function
$f$ the corresponding learning problem can be solved to accuracy $\eps$, using
$\tilde{O}(n/\eps^2)$ examples, by a generic algorithm whose running time may
be exponential in $n.$ We show that this learning problem can be
computationally intractable even for constant $\eps$ and rather simple
transformation functions. Moreover, the above sample complexity bound is nearly
optimal for the general problem, as we give a simple explicit linear
transformation function $f(x)=w \cdot x$ with integer weights $w_i \leq n$ and
prove that the corresponding learning problem requires $\Omega(n)$ samples.
As our main positive result we give a highly efficient algorithm for learning
a sum of independent unknown Bernoulli random variables, corresponding to the
transformation function $f(x)= \sum_{i=1}^n x_i$. Our algorithm learns to
$\eps$-accuracy in poly$(n)$ time, using a surprising poly$(1/\eps)$ number of
samples that is independent of $n.$ We also give an efficient algorithm that
uses $\log n \cdot \poly(1/\eps)$ samples but has running time that is only
$\poly(\log n, 1/\eps).$
|
1103.0605
|
Loopy Belief Propagation, Bethe Free Energy and Graph Zeta Function
|
cs.AI cs.DM
|
We propose a new approach to the theoretical analysis of Loopy Belief
Propagation (LBP) and the Bethe free energy (BFE) by establishing a formula to
connect LBP and BFE with a graph zeta function. The proposed approach is
applicable to a wide class of models including multinomial and Gaussian types.
The connection derives a number of new theoretical results on LBP and BFE. This
paper focuses two of such topics. One is the analysis of the region where the
Hessian of the Bethe free energy is positive definite, which derives the
non-convexity of BFE for graphs with multiple cycles, and a condition of
convexity on a restricted set. This analysis also gives a new condition for the
uniqueness of the LBP fixed point. The other result is to clarify the relation
between the local stability of a fixed point of LBP and local minima of the
BFE, which implies, for example, that a locally stable fixed point of the
Gaussian LBP is a local minimum of the Gaussian Bethe free energy.
|
1103.0632
|
An Agent Based Architecture (Using Planning) for Dynamic and Semantic
Web Services Composition in an EBXML Context
|
cs.AI
|
The process-based semantic composition of Web Services is gaining a
considerable momentum as an approach for the effective integration of
distributed, heterogeneous, and autonomous applications. To compose Web
Services semantically, we need an ontology. There are several ways of inserting
semantics in Web Services. One of them consists of using description languages
like OWL-S. In this paper, we introduce our work which consists in the
proposition of a new model and the use of semantic matching technology for
semantic and dynamic composition of ebXML business processes.
|
1103.0633
|
RDBNorma: - A semi-automated tool for relational database schema
normalization up to third normal form
|
cs.DB
|
In this paper a tool called RDBNorma is proposed, that uses a novel approach
to represent a relational database schema and its functional dependencies in
computer memory using only one linked list and used for semi-automating the
process of relational database schema normalization up to third normal form.
This paper addresses all the issues of representing a relational schema along
with its functional dependencies using one linked list along with the
algorithms to convert a relation into second and third normal form by using
above representation. We have compared performance of RDBNorma with existing
tool called Micro using standard relational schemas collected from various
resources. It is observed that proposed tool is at least 2.89 times faster than
the Micro and requires around half of the space than Micro to represent a
relation. Comparison is done by entering all the attributes and functional
dependencies holds on a relation in the same order and implementing both the
tools in same language and on same machine.
|
1103.0680
|
First-order Logic: Modality and Intensionality
|
cs.LO cs.GL cs.IT math.IT
|
Contemporary use of the term 'intension' derives from the traditional logical
Frege-Russell's doctrine that an idea (logic formula) has both an extension and
an intension. From the Montague's point of view, the meaning of an idea can be
considered as particular extensions in different possible worlds. In this paper
we analyze the minimal intensional semantic enrichment of the syntax of the FOL
language, by unification of different views: Tarskian extensional semantics of
the FOL, modal interpretation of quantifiers, and a derivation of the Tarskian
theory of truth from unified semantic theory based on a single meaning
relation. We show that not all modal predicate logics are intensional, and that
an equivalent modal Kripke's interpretation of logic quantifiers in FOL results
in a particular pure extensional modal predicate logic (as is the standard
Tarskian semantics of the FOL). This minimal intensional enrichment is obtained
by adopting the theory of properties, relations and propositions (PRP) as the
universe or domain of the FOL, composed by particulars and universals (or
concepts), with the two-step interpretation of the FOL that eliminates the weak
points of the Montague's intensional semantics. Differently from the Bealer's
intensional FOL, we show that it is not necessary the introduction of the
intensional abstraction in order to obtain the full intensional properties of
the FOL. Final result of this paper is represented by the commutative
homomorphic diagram that holds in each given possible world of this new
intensional FOL, from the free algebra of the FOL syntax, toward its
intensional algebra of concepts, and, successively, to the new extensional
relational algebra (different from Cylindric algebras), and we show that it
corresponds to the Tarski's interpretation of the standard extensional FOL in
this possible world.
|
1103.0686
|
Querying and Manipulating Temporal Databases
|
cs.DB
|
Many works have focused, for over twenty five years, on the integration of
the time dimension in databases (DB). However, the standard SQL3 does not yet
allow easy definition, manipulation and querying of temporal DBs. In this
paper, we study how we can simplify querying and manipulating temporal facts in
SQL3, using a model that integrates time in a native manner. To do this, we
propose new keywords and syntax to define different temporal versions for many
relational operators and functions used in SQL. It then becomes possible to
perform various queries and updates appropriate to temporal facts. We
illustrate the use of these proposals on many examples from a real application.
|
1103.0697
|
A Wiki for Business Rules in Open Vocabulary, Executable English
|
cs.AI
|
The problem of business-IT alignment is of widespread economic concern.
As one way of addressing the problem, this paper describes an online system
that functions as a kind of Wiki -- one that supports the collaborative writing
and running of business and scientific applications, as rules in open
vocabulary, executable English, using a browser.
Since the rules are in English, they are indexed by Google and other search
engines. This is useful when looking for rules for a task that one has in mind.
The design of the system integrates the semantics of data, with a semantics
of an inference method, and also with the meanings of English sentences. As
such, the system has functionality that may be useful for the Rules, Logic,
Proof and Trust requirements of the Semantic Web.
The system accepts rules, and small numbers of facts, typed or copy-pasted
directly into a browser. One can then run the rules, again using a browser. For
larger amounts of data, the system uses information in the rules to
automatically generate and run SQL over networked databases. From a few highly
declarative rules, the system typically generates SQL that would be too
complicated to write reliably by hand. However, the system can explain its
results in step-by-step hypertexted English, at the business or scientific
level
As befits a Wiki, shared use of the system is free.
|
1103.0701
|
Analytical maximum-likelihood method to detect patterns in real networks
|
physics.data-an cs.SI physics.soc-ph
|
In order to detect patterns in real networks, randomized graph ensembles that
preserve only part of the topology of an observed network are systematically
used as fundamental null models. However, their generation is still
problematic. The existing approaches are either computationally demanding and
beyond analytic control, or analytically accessible but highly approximate.
Here we propose a solution to this long-standing problem by introducing an
exact and fast method that allows to obtain expectation values and standard
deviations of any topological property analytically, for any binary, weighted,
directed or undirected network. Remarkably, the time required to obtain the
expectation value of any property is as short as that required to compute the
same property on the single original network. Our method reveals that the null
behavior of various correlation properties is different from what previously
believed, and highly sensitive to the particular network considered. Moreover,
our approach shows that important structural properties (such as the modularity
used in community detection problems) are currently based on incorrect
expressions, and provides the exact quantities that should replace them.
|
1103.0711
|
Class Schema Evolution for Persistent Object-Oriented Software: Model,
Empirical Study, and Automated Support
|
cs.SE cs.DB
|
With the wide support for object serialization in object-oriented programming
languages, persistent objects have become common place and most large
object-oriented software systems rely on extensive amounts of persistent data.
Such systems also evolve over time. Retrieving previously persisted objects
from classes whose schema has changed is however difficult, and may lead to
invalidating the consistency of the application. The ESCHER framework addresses
these issues through an IDE-integrated approach that handles class schema
evolution by managing versions of the code and generating transformation
functions automatically. The infrastructure also enforces class invariants to
prevent the introduction of potentially corrupt objects. This article describes
a model for class attribute changes, a measure for class evolution robustness,
four empirical studies, and the design and implementation of the ESCHER system.
|
1103.0733
|
Scalable Approach to Uncertainty Quantification and Robust Design of
Interconnected Dynamical Systems
|
cs.SY
|
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks.
|
1103.0738
|
A Medial Axis Based Thinning Strategy for Character Images
|
cs.CV cs.DL
|
Thinning of character images is a big challenge. Removal of strokes or
deformities in thinning is a difficult problem. In this paper, we have proposed
a medial axis based thinning strategy used for performing skeletonization of
printed and handwritten character images. In this method, we have used shape
characteristics of text to get skeleton of nearly same as the true character
shape. This approach helps to preserve the local features and true shape of the
character images. The proposed algorithm produces one pixel width thin
skeleton. As a by-product of our thinning approach, the skeleton also gets
segmented into strokes in vector form. Hence further stroke segmentation is not
required. Experiment is done on printed English and Bengali characters and we
obtain less spurious branches comparing with other thinning methods without any
post processing.
|
1103.0744
|
Model Identification of a Network as Compressing Sensing
|
math.DS cs.SY math.GN math.OC
|
In many applications, it is important to derive information about the
topology and the internal connections of dynamical systems interacting
together. Examples can be found in fields as diverse as Economics, Neuroscience
and Biochemistry. The paper deals with the problem of deriving a descriptive
model of a network, collecting the node outputs as time series with no use of a
priori insight on the topology, and unveiling an unknown structure as the
estimate of a "sparse Wiener filter". A geometric interpretation of the problem
in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast
the problem as the optimization of a cost function where a set of parameters
are used to operate a trade-off between accuracy and complexity in the final
model. The problem of reducing the complexity is addressed by fixing a certain
degree of sparsity and finding the solution that "better" satisfies the
constraints according to the criterion of approximation. Applications starting
from real data and numerical simulations are provided.
|
1103.0769
|
Sparse Volterra and Polynomial Regression Models: Recoverability and
Estimation
|
cs.LG cs.IT math.IT stat.ML
|
Volterra and polynomial regression models play a major role in nonlinear
system identification and inference tasks. Exciting applications ranging from
neuroscience to genome-wide association analysis build on these models with the
additional requirement of parsimony. This requirement has high interpretative
value, but unfortunately cannot be met by least-squares based or kernel
regression methods. To this end, compressed sampling (CS) approaches, already
successful in linear regression settings, can offer a viable alternative. The
viability of CS for sparse Volterra and polynomial models is the core theme of
this work. A common sparse regression task is initially posed for the two
models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type
algorithm is developed for sparse polynomial regressions. The identifiability
of polynomial models is critically challenged by dimensionality. However,
following the CS principle, when these models are sparse, they could be
recovered by far fewer measurements. To quantify the sufficient number of
measurements for a given level of sparsity, restricted isometry properties
(RIP) are investigated in commonly met polynomial regression settings,
generalizing known results for their linear counterparts. The merits of the
novel (weighted) adaptive CS algorithms to sparse polynomial modeling are
verified through synthetic as well as real data tests for genotype-phenotype
analysis.
|
1103.0784
|
Happiness is assortative in online social networks
|
cs.SI cs.CL physics.soc-ph
|
Social networks tend to disproportionally favor connections between
individuals with either similar or dissimilar characteristics. This propensity,
referred to as assortative mixing or homophily, is expressed as the correlation
between attribute values of nearest neighbour vertices in a graph. Recent
results indicate that beyond demographic features such as age, sex and race,
even psychological states such as "loneliness" can be assortative in a social
network. In spite of the increasing societal importance of online social
networks it is unknown whether assortative mixing of psychological states takes
place in situations where social ties are mediated solely by online networking
services in the absence of physical contact. Here, we show that general
happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6
month record of their individual tweets, is indeed assortative across the
Twitter social network. To our knowledge this is the first result that shows
assortative mixing in online networks at the level of SWB. Our results imply
that online social networks may be equally subject to the social mechanisms
that cause assortative mixing in real social networks and that such assortative
mixing takes place at the level of SWB. Given the increasing prevalence of
online social networks, their propensity to connect users with similar levels
of SWB may be an important instrument in better understanding how both positive
and negative sentiments spread through online social ties. Future research may
focus on how event-specific mood states can propagate and influence user
behavior in "real life".
|
1103.0795
|
Decimation-Enhanced Finite Alphabet Iterative Decoders for LDPC codes on
the BSC
|
cs.IT math.IT
|
Finite alphabet iterative decoders (FAID) with multilevel messages that can
surpass BP in the error floor region for LDPC codes on the BSC were previously
proposed. In this paper, we propose decimation-enhanced decoders. The technique
of decimation which is incorporated into the message update rule, involves
fixing certain bits of the code to a particular value. Under appropriately
chosen rules, decimation can significantly reduce the number of iterations
required to correct a fixed number of errors, while maintaining the good
performance of the original decoder in the error floor region. At the same
time, the algorithm is much more amenable to analysis. We shall provide a
simple decimation scheme for a particularly good 7-level FAID for column-weight
three codes on the BSC, that helps to correct a fixed number of errors in fewer
iterations, and provide insights into the analysis of the decoder. We shall
also examine the conditions under which the decimation-enhanced 7-level FAID
performs at least as good as the 7-level FAID.
|
1103.0800
|
Synthesizing Switching Logic to Minimize Long-Run Cost
|
cs.SY math.OC
|
Given a multi-modal dynamical system, optimal switching logic synthesis
involves generating the conditions for switching between the system modes such
that the resulting hybrid system satisfies a quantitative specification. We
formalize and solve the problem of optimal switching logic synthesis for
quantitative specifications over long run behavior. Each trajectory of the
system, and each state of the system, is associated with a cost. Our goal is to
synthesize a system that minimizes this cost from each initial state. Our paper
generalizes earlier work on synthesis for safety as safety specifications can
be encoded as quantitative specifications. We present an approach for
specifying quantitative measures using reward and penalty functions, and
illustrate its effectiveness using several examples. We present an automated
technique to synthesize switching logic for such quantitative measures. Our
algorithm is based on reducing the synthesis problem to an unconstrained
numerical optimization problem which can be solved by any off-the-shelf
numerical optimization engines. We demonstrate the effectiveness of this
approach with experimental results.
|
1103.0801
|
Two-Bit Bit Flipping Decoding of LDPC Codes
|
cs.IT math.IT
|
In this paper, we propose a new class of bit flipping algorithms for
low-density parity-check (LDPC) codes over the binary symmetric channel (BSC).
Compared to the regular (parallel or serial) bit flipping algorithms, the
proposed algorithms employ one additional bit at a variable node to represent
its "strength." The introduction of this additional bit increases the
guaranteed error correction capability by a factor of at least 2. An additional
bit can also be employed at a check node to capture information which is
beneficial to decoding. A framework for failure analysis of the proposed
algorithms is described. These algorithms outperform the Gallager A/B algorithm
and the min-sum algorithm at much lower complexity. Concatenation of two-bit
bit flipping algorithms show a potential to approach the performance of belief
propagation (BP) decoding in the error floor region, also at lower complexity.
|
1103.0825
|
Differentially Private Publication of Sparse Data
|
cs.DB
|
The problem of privately releasing data is to provide a version of a dataset
without revealing sensitive information about the individuals who contribute to
the data. The model of differential privacy allows such private release while
providing strong guarantees on the output. A basic mechanism achieves
differential privacy by adding noise to the frequency counts in the contingency
tables (or, a subset of the count data cube) derived from the dataset. However,
when the dataset is sparse in its underlying space, as is the case for most
multi-attribute relations, then the effect of adding noise is to vastly
increase the size of the published data: it implicitly creates a huge number of
dummy data points to mask the true data, making it almost impossible to work
with.
We present techniques to overcome this roadblock and allow efficient private
release of sparse data, while maintaining the guarantees of differential
privacy. Our approach is to release a compact summary of the noisy data.
Generating the noisy data and then summarizing it would still be very costly,
so we show how to shortcut this step, and instead directly generate the summary
from the input data, without materializing the vast intermediate noisy data. We
instantiate this outline for a variety of sampling and filtering methods, and
show how to use the resulting summary for approximate, private, query
answering. Our experimental study shows that this is an effective, practical
solution, with comparable and occasionally improved utility over the costly
materialization approach.
|
1103.0875
|
Generic Feasibility of Perfect Reconstruction with Short FIR Filters in
Multi-channel Systems
|
cs.IT math.IT math.NA math.PR
|
We study the feasibility of short finite impulse response (FIR) synthesis for
perfect reconstruction (PR) in generic FIR filter banks. Among all PR synthesis
banks, we focus on the one with the minimum filter length. For filter banks
with oversampling factors of at least two, we provide prescriptions for the
shortest filter length of the synthesis bank that would guarantee PR almost
surely. The prescribed length is as short or shorter than the analysis filters
and has an approximate inverse relationship with the oversampling factor. Our
results are in form of necessary and sufficient statements that hold
generically, hence only fail for elaborately-designed nongeneric examples. We
provide extensive numerical verification of the theoretical results and
demonstrate that the gap between the derived filter length prescriptions and
the true minimum is small. The results have potential applications in synthesis
FB design problems, where the analysis bank is given, and for analysis of
fundamental limitations in blind signals reconstruction from data collected by
unknown subsampled multi-channel systems.
|
1103.0890
|
Efficient Multi-Template Learning for Structured Prediction
|
cs.LG cs.CL
|
Conditional random field (CRF) and Structural Support Vector Machine
(Structural SVM) are two state-of-the-art methods for structured prediction
which captures the interdependencies among output variables. The success of
these methods is attributed to the fact that their discriminative models are
able to account for overlapping features on the whole input observations. These
features are usually generated by applying a given set of templates on labeled
data, but improper templates may lead to degraded performance. To alleviate
this issue, in this paper, we propose a novel multiple template learning
paradigm to learn structured prediction and the importance of each template
simultaneously, so that hundreds of arbitrary templates could be added into the
learning model without caution. This paradigm can be formulated as a special
multiple kernel learning problem with exponential number of constraints. Then
we introduce an efficient cutting plane algorithm to solve this problem in the
primal, and its convergence is presented. We also evaluate the proposed
learning paradigm on two widely-studied structured prediction tasks,
\emph{i.e.} sequence labeling and dependency parsing. Extensive experimental
results show that the proposed method outperforms CRFs and Structural SVMs due
to exploiting the importance of each template. Our complexity analysis and
empirical results also show that our proposed method is more efficient than
OnlineMKL on very sparse and high-dimensional data. We further extend this
paradigm for structured prediction using generalized $p$-block norm
regularization with $p>1$, and experiments show competitive performances when
$p \in [1,2)$.
|
1103.0920
|
Reduction of Many-valued into Two-valued Modal Logics
|
cs.LO cs.IT math.IT
|
In this paper we develop a 2-valued reduction of many-valued logics, into
2-valued multi-modal logics. Such an approach is based on the contextualization
of many-valued logics with the introduction of higher-order Herbrand
interpretation types, where we explicitly introduce the coexistence of a set of
algebraic truth values of original many-valued logic, transformed as parameters
(or possible worlds), and the set of classic two logic values. This approach is
close to the approach used in annotated logics, but offers the possibility of
using the standard semantics based on Herbrand interpretations. Moreover, it
uses the properties of the higher-order Herbrand types, as their fundamental
nature is based on autoreferential Kripke semantics where the possible worlds
are algebraic truth-values of original many-valued logic. This autoreferential
Kripke semantics, which has the possibility of flattening higher-order Herbrand
interpretations into ordinary 2-valued Herbrand interpretations, gives us a
clearer insight into the relationship between many-valued and 2-valued
multi-modal logics. This methodology is applied to the class of many-valued
Logic Programs, where reduction is done in a structural way, based on the logic
structure (logic connectives) of original many-valued logics. Following this,
we generalize the reduction to general structural many-valued logics, in an
abstract way, based on Suszko's informal non-constructive idea. In all cases,
by using developed 2-valued reductions we obtain a kind of non truth-valued
modal meta-logics, where two-valued formulae are modal sentences obtained by
application of particular modal operators to original many-valued formulae.
|
1103.0921
|
Managing and Querying Web Services Communities: A Survey
|
cs.DB
|
With the advance of Web Services technologies and the emergence of Web
Services into the information space, tremendous opportunities for empowering
users and organizations appear in various application domains including
electronic commerce, travel, intelligence information gathering and analysis,
health care, digital government, etc. However, the technology to organize,
search, integrate these Web Services has not kept pace with the rapid growth of
the available information space. The number of Web Services to be integrated
may be large and continuously changing. To ease and improve the process of Web
services discovery in an open environment like the Internet, it is suggested to
gather similar Web services into groups known as communities. Although Web
services are intensively investigated, the community management issues have not
been addressed yet In this paper we draw an overview of several Web services
Communities' management approaches based on some currently existing communities
platforms and frameworks. We also discuss different approaches for querying and
selecting Web services under the umbrella of Web services communities'. We
compare the current approaches among each others with respect to some key
requirements.
|
1103.0941
|
Estimating $\beta$-mixing coefficients
|
stat.ML cs.LG math.PR
|
The literature on statistical learning for time series assumes the asymptotic
independence or ``mixing' of the data-generating process. These mixing
assumptions are never tested, nor are there methods for estimating mixing rates
from data. We give an estimator for the $\beta$-mixing rate based on a single
stationary sample path and show it is $L_1$-risk consistent.
|
1103.0942
|
Generalization error bounds for stationary autoregressive models
|
stat.ML cs.LG
|
We derive generalization error bounds for stationary univariate
autoregressive (AR) models. We show that imposing stationarity is enough to
control the Gaussian complexity without further regularization. This lets us
use structural risk minimization for model selection. We demonstrate our
methods by predicting interest rate movements.
|
1103.0949
|
Adapting to Non-stationarity with Growing Expert Ensembles
|
stat.ML cs.LG physics.data-an stat.ME
|
When dealing with time series with complex non-stationarities, low
retrospective regret on individual realizations is a more appropriate goal than
low prospective risk in expectation. Online learning algorithms provide
powerful guarantees of this form, and have often been proposed for use with
non-stationary processes because of their ability to switch between different
forecasters or ``experts''. However, existing methods assume that the set of
experts whose forecasts are to be combined are all given at the start, which is
not plausible when dealing with a genuinely historical or evolutionary system.
We show how to modify the ``fixed shares'' algorithm for tracking the best
expert to cope with a steadily growing set of experts, obtained by fitting new
models to new data as it becomes available, and obtain regret bounds for the
growing ensemble.
|
1103.0967
|
Intensionality and Two-steps Interpretations
|
cs.LO cs.IT math.IT
|
In this paper we considered the extension of the First-order Logic (FOL) by
Bealer's intensional abstraction operator. Contemporary use of the term
'intension' derives from the traditional logical Frege-Russell's doctrine that
an idea (logic formula) has both an extension and an intension. Although there
is divergence in formulation, it is accepted that the extension of an idea
consists of the subjects to which the idea applies, and the intension consists
of the attributes implied by the idea. From the Montague's point of view, the
meaning of an idea can be considered as particular extensions in different
possible worlds. In the case of the pure FOL we obtain commutative homomorphic
diagram that holds in each given possible world of the intensional FOL, from
the free algebra of the FOL syntax, toward its intensional algebra of concepts,
and, successively, to the new extensional relational algebra (different from
Cylindric algebras). Then we show that it corresponds to the Tarski's
interpretation of the standard extensional FOL in this possible world.
|
1103.0973
|
Diffusion processes through social groups' dynamics
|
physics.soc-ph cs.SI
|
Axelrod's model describes the dissemination of a set of cultural traits in a
society constituted by individual agents. In a social context, nevertheless,
individual choices toward a specific attitude are also at the basis of the
formation of communities, groups and parties. The membership in a group changes
completely the behavior of single agents who start acting according to a social
identity. Groups act and interact among them as single entities, but still
conserve an internal dynamics. We show that, under certain conditions of social
dynamics, the introduction of group dynamics in a cultural dissemination
process avoids the flattening of the culture into a single entity and preserves
the multiplicity of cultural attitudes. We also considered diffusion processes
on this dynamical background, showing the conditions under which information as
well as innovation can spread through the population in a scenario where the
groups' choices determine the social structure.
|
1103.0996
|
Communication with Disturbance Constraints
|
cs.IT math.IT
|
Motivated by the broadcast view of the interference channel, the new problem
of communication with disturbance constraints is formulated. The
rate-disturbance region is established for the single constraint case and the
optimal encoding scheme turns out to be the same as the Han-Kobayashi scheme
for the two user-pair interference channel. This result is extended to the
Gaussian vector (MIMO) case. For the case of communication with two disturbance
constraints, inner and outer bounds on the rate-disturbance region for a
deterministic model are established. The inner bound is achieved by an encoding
scheme that involves rate splitting, Marton coding, and superposition coding,
and is shown to be optimal in several nontrivial cases. This encoding scheme
can be readily applied to discrete memoryless interference channels and
motivates a natural extension of the Han-Kobayashi scheme to more than two user
pairs.
|
1103.0999
|
Deterministic Network Model Revisited: An Algebraic Network Coding
Approach
|
cs.IT math.IT
|
The capacity of multiuser networks has been a long-standing problem in
information theory. Recently, Avestimehr et al. have proposed a deterministic
network model to approximate multiuser wireless networks. This model, known as
the ADT network model, takes into account the broadcast nature of wireless
medium and interference.
We show that the ADT network model can be described within the algebraic
network coding framework introduced by Koetter and Medard. We prove that the
ADT network problem can be captured by a single matrix, and show that the
min-cut of an ADT network is the rank of this matrix; thus, eliminating the
need to optimize over exponential number of cuts between two nodes to compute
the min-cut of an ADT network. We extend the capacity characterization for ADT
networks to a more general set of connections, including single
unicast/multicast connection and non-multicast connections such as multiple
multicast, disjoint multicast, and two-level multicast. We also provide
sufficiency conditions for achievability in ADT networks for any general
connection set. In addition, we show that random linear network coding, a
randomized distributed algorithm for network code construction, achieves the
capacity for the connections listed above. Furthermore, we extend the ADT
networks to those with random erasures and cycles (thus, allowing
bi-directional links).
In addition, we propose an efficient linear code construction for the
deterministic wireless multicast relay network model. Avestimehr et al.'s
proposed code construction is not guaranteed to be efficient and may
potentially involve an infinite block length. Unlike several previous coding
schemes, we do not attempt to find flows in the network. Instead, for a layered
network, we maintain an invariant where it is required that at each stage of
the code construction, certain sets of codewords are linearly independent.
|
1103.1001
|
Two-step differentiator for delayed signal
|
cs.SY math.DS math.OC
|
This paper presents a high-order differentiator for delayed measurement
signal. The proposed differentiator not only can correct the delay in signal,
but aslo can estimate the undelayed derivatives. The differentiator consists of
two-step algorithms with the delayed time instant. Conditions are given
ensuring convergence of the estimation error for the given delay in the
signals. The merits of method include its simple implementation and interesting
application. Numerical simulations illustrate the effectiveness of the proposed
differentiator.
|
1103.1003
|
Teraflop-scale Incremental Machine Learning
|
cs.AI
|
We propose a long-term memory design for artificial general intelligence
based on Solomonoff's incremental machine learning methods. We use R5RS Scheme
and its standard library with a few omissions as the reference machine. We
introduce a Levin Search variant based on Stochastic Context Free Grammar
together with four synergistic update algorithms that use the same grammar as a
guiding probability distribution of programs. The update algorithms include
adjusting production probabilities, re-using previous solutions, learning
programming idioms and discovery of frequent subprograms. Experiments with two
training sequences demonstrate that our approach to incremental learning is
effective.
|
1103.1013
|
A Feature Selection Method for Multivariate Performance Measures
|
cs.LG
|
Feature selection with specific multivariate performance measures is the key
to the success of many applications, such as image retrieval and text
classification. The existing feature selection methods are usually designed for
classification error. In this paper, we propose a generalized sparse
regularizer. Based on the proposed regularizer, we present a unified feature
selection framework for general loss functions. In particular, we study the
novel feature selection paradigm by optimizing multivariate performance
measures. The resultant formulation is a challenging problem for
high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed
to solve this problem, and the convergence is presented. In addition, we adapt
the proposed method to optimize multivariate measures for multiple instance
learning problems. The analyses by comparing with the state-of-the-art feature
selection methods show that the proposed method is superior to others.
Extensive experiments on large-scale and high-dimensional real world datasets
show that the proposed method outperforms $l_1$-SVM and SVM-RFE when choosing a
small subset of features, and achieves significantly improved performances over
SVM$^{perf}$ in terms of $F_1$-score.
|
1103.1077
|
Submodular Decomposition Framework for Inference in Associative Markov
Networks with Global Constraints
|
cs.CV cs.DM math.OC
|
In the paper we address the problem of finding the most probable state of
discrete Markov random field (MRF) with associative pairwise terms. Although of
practical importance, this problem is known to be NP-hard in general. We
propose a new type of MRF decomposition, submodular decomposition (SMD). Unlike
existing decomposition approaches SMD decomposes the initial problem into
subproblems corresponding to a specific class label while preserving the graph
structure of each subproblem. Such decomposition enables us to take into
account several types of global constraints in an efficient manner. We study
theoretical properties of the proposed approach and demonstrate its
applicability on a number of problems.
|
1103.1124
|
Fluid flow analysis in a rough fracture (type II) using complex networks
and lattice Boltzmann method
|
physics.flu-dyn cs.CE
|
Complexity of fluid flow in a rough fracture is induced by the complex
configurations of opening areas between the fracture planes. In this study, we
model fluid flow in an evolvable real rock joint structure, which under certain
normal load is sheared. In an experimental study, information regarding about
apertures of the rock joint during consecutive 20 mm displacements and fluid
flow (permeability) in different pressure heads have been recorded by a scanner
laser. Our aim in this study is to simulate the fluid flow in the mentioned
complex geometries using the lattice Boltzmann method (LBM), while the
characteristics of the aperture field will be compared with the modeled fluid
flow permeability To characterize the aperture, we use a new concept in the
graph theory, namely: complex networks and motif analysis of the corresponding
networks. In this approach, the similar aperture profile along the fluid flow
direction is mapped in to a network space. The modeled permeability using the
LBM shows good correlation with the experimental measured values. Furthermore,
the two main characters of the obtained networks, i.e., characteristic length
and number of edges show the same evolutionary trend with the modeled
permeability values. Analysis of motifs through the obtained networks showed
the most transient sub-graphs are much more frequent in residual stages. This
coincides with nearly stable fluid flow and high permeability values.
|
1103.1130
|
Periodic excitations of bilinear quantum systems
|
math.OC cs.SY math-ph math.AP math.MP quant-ph
|
A well-known method of transferring the population of a quantum system from
an eigenspace of the free Hamiltonian to another is to use a periodic control
law with an angular frequency equal to the difference of the eigenvalues. For
finite dimensional quantum systems, the classical theory of averaging provides
a rigorous explanation of this experimentally validated result. This paper
extends this finite dimensional result, known as the Rotating Wave
Approximation, to infinite dimensional systems and provides explicit
convergence estimates.
|
1103.1156
|
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware
Implementation
|
cs.AI cs.NE
|
In this paper a novel neuro-fuzzy system is proposed where its learning is
based on the creation of fuzzy relations by using new implication method
without utilizing any exact mathematical techniques. Then, a simple memristor
crossbar-based analog circuit is designed to implement this neuro-fuzzy system
which offers very interesting properties. In addition to high connectivity
between neurons and being fault-tolerant, all synaptic weights in our proposed
method are always non-negative and there is no need to precisely adjust them.
Finally, this structure is hierarchically expandable and can compute operations
in real time since it is implemented through analog circuits. Simulation
results show the efficiency and applicability of our neuro-fuzzy computing
system. They also indicate that this system can be a good candidate to be used
for creating artificial brain.
|
1103.1157
|
GRASP and path-relinking for Coalition Structure Generation
|
cs.AI
|
In Artificial Intelligence with Coalition Structure Generation (CSG) one
refers to those cooperative complex problems that require to find an optimal
partition, maximising a social welfare, of a set of entities involved in a
system into exhaustive and disjoint coalitions. The solution of the CSG problem
finds applications in many fields such as Machine Learning (covering machines,
clustering), Data Mining (decision tree, discretization), Graph Theory, Natural
Language Processing (aggregation), Semantic Web (service composition), and
Bioinformatics. The problem of finding the optimal coalition structure is
NP-complete. In this paper we present a greedy adaptive search procedure
(GRASP) with path-relinking to efficiently search the space of coalition
structures. Experiments and comparisons to other algorithms prove the validity
of the proposed method in solving this hard combinatorial problem.
|
1103.1168
|
An Alternating Direction Algorithm for Matrix Completion with
Nonnegative Factors
|
cs.IT cs.NA math.IT math.NA
|
This paper introduces an algorithm for the nonnegative matrix
factorization-and-completion problem, which aims to find nonnegative low-rank
matrices X and Y so that the product XY approximates a nonnegative data matrix
M whose elements are partially known (to a certain accuracy). This problem
aggregates two existing problems: (i) nonnegative matrix factorization where
all entries of M are given, and (ii) low-rank matrix completion where
nonnegativity is not required. By taking the advantages of both nonnegativity
and low-rankness, one can generally obtain superior results than those of just
using one of the two properties. We propose to solve the non-convex constrained
least-squares problem using an algorithm based on the classic alternating
direction augmented Lagrangian method. Preliminary convergence properties of
the algorithm and numerical simulation results are presented. Compared to a
recent algorithm for nonnegative matrix factorization, the proposed algorithm
produces factorizations of similar quality using only about half of the matrix
entries. On tasks of recovering incomplete grayscale and hyperspectral images,
the proposed algorithm yields overall better qualities than those produced by
two recent matrix-completion algorithms that do not exploit nonnegativity.
|
1103.1178
|
A Simplified Approach to Recovery Conditions for Low Rank Matrices
|
math.OC cs.IT cs.SY math.IT
|
Recovering sparse vectors and low-rank matrices from noisy linear
measurements has been the focus of much recent research. Various reconstruction
algorithms have been studied, including $\ell_1$ and nuclear norm minimization
as well as $\ell_p$ minimization with $p<1$. These algorithms are known to
succeed if certain conditions on the measurement map are satisfied. Proofs of
robust recovery for matrices have so far been much more involved than in the
vector case.
In this paper, we show how several robust classes of recovery conditions can
be extended from vectors to matrices in a simple and transparent way, leading
to the best known restricted isometry and nullspace conditions for matrix
recovery. Our results rely on the ability to "vectorize" matrices through the
use of a key singular value inequality.
|
1103.1205
|
A Directional Feature with Energy based Offline Signature Verification
Network
|
cs.AI
|
Signature used as a biometric is implemented in various systems as well as
every signature signed by each person is distinct at the same time. So, it is
very important to have a computerized signature verification system. In offline
signature verification system dynamic features are not available obviously, but
one can use a signature as an image and apply image processing techniques to
make an effective offline signature verification system. Author proposes a
intelligent network used directional feature and energy density both as inputs
to the same network and classifies the signature. Neural network is used as a
classifier for this system. The results are compared with both the very basic
energy density method and a simple directional feature method of offline
signature verification system and this proposed new network is found very
effective as compared to the above two methods, specially for less number of
training samples, which can be implemented practically.
|
1103.1224
|
Accidental Politicians: How Randomly Selected Legislators Can Improve
Parliament Efficiency
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
We study a prototypical model of a Parliament with two Parties or two
Political Coalitions and we show how the introduction of a variable percentage
of randomly selected independent legislators can increase the global efficiency
of a Legislature, in terms of both the number of laws passed and the average
social welfare obtained. We also analytically find an "efficiency golden rule"
which allows to fix the optimal number of legislators to be selected at random
after that regular elections have established the relative proportion of the
two Parties or Coalitions. These results are in line with both the ancient
Greek democratic system and the recent discovery that the adoption of random
strategies can improve the efficiency of hierarchical organizations.
|
1103.1243
|
Randomizing world trade. I. A binary network analysis
|
physics.soc-ph cond-mat.stat-mech cs.SI physics.data-an q-fin.GN
|
The international trade network (ITN) has received renewed multidisciplinary
interest due to recent advances in network theory. However, it is still unclear
whether a network approach conveys additional, nontrivial information with
respect to traditional international-economics analyses that describe world
trade only in terms of local (first-order) properties. In this and in a
companion paper, we employ a recently proposed randomization method to assess
in detail the role that local properties have in shaping higher-order patterns
of the ITN in all its possible representations (binary/weighted,
directed/undirected, aggregated/disaggregated by commodity) and across several
years. Here we show that, remarkably, the properties of all binary projections
of the network can be completely traced back to the degree sequence, which is
therefore maximally informative. Our results imply that explaining the observed
degree sequence of the ITN, which has not received particular attention in
economic theory, should instead become one the main focuses of models of trade.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.