id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1301.5006 | Blind Adaptive Algorithms for Decision Feedback DS-CDMA Receivers in
Multipath Channels | cs.IT math.IT | In this work we examine blind adaptive and iterative decision feedback (DF)
receivers for direct sequence code division multiple access (DS-CDMA) systems
in frequency selective channels. Code-constrained minimum variance (CMV) and
constant modulus (CCM) design criteria for DF receivers based on constrained
optimization techniques are investigated for scenarios subject to multipath.
Computationally efficient blind adaptive stochastic gradient (SG) and recursive
least squares (RLS) algorithms are developed for estimating the parameters of
DF detectors along with successive, parallel and iterative DF structures. A
novel successive parallel arbitrated DF scheme is presented and combined with
iterative techniques for use with cascaded DF stages in order to mitigate the
deleterious effects of error propagation. Simulation results for an uplink
scenario assess the algorithms, the blind adaptive DF detectors against linear
receivers and evaluate the effects of error propagation of the new
cancellations techniques against previously reported approaches.
|
1301.5011 | Adaptive Space-Time Decision Feedback Neural Detectors with Data
Selection for High-Data Rate Users in DS-CDMA Systems | cs.IT math.IT | A space-time adaptive decision feedback (DF) receiver using recurrent neural
networks (RNN) is proposed for joint equalization and interference suppression
in direct-sequence code-division-multiple-access (DS-CDMA) systems equipped
with antenna arrays. The proposed receiver structure employs dynamically driven
RNNs in the feedforward section for equalization and multi-access interference
suppression and a finite impulse response (FIR) linear filter in the feedback
section for performing interference cancellation. A data selective gradient
algorithm, based upon the set-membership design framework, is proposed for the
estimation of the coefficients of RNN structures and is applied to the
estimation of the parameters of the proposed neural receiver structure.
Simulation results show that the proposed techniques achieve significant
performance gains over existing schemes.
|
1301.5022 | A formalization of re-identification in terms of compatible
probabilities | cs.CR cs.AI cs.IT math.IT | Re-identification algorithms are used in data privacy to measure disclosure
risk. They model the situation in which an adversary attacks a published
database by means of linking the information of this adversary with the
database.
In this paper we formalize this type of algorithm in terms of true
probabilities and compatible belief functions. The purpose of this work is to
leave aside as re-identification algorithms those algorithms that do not
satisfy a minimum requirement.
|
1301.5033 | On the Distribution of MIMO Mutual Information: An In-Depth Painlev\'{e}
Based Characterization | cs.IT math.IT | This paper builds upon our recent work which computed the moment generating
function of the MIMO mutual information exactly in terms of a Painlev\'{e} V
differential equation. By exploiting this key analytical tool, we provide an
in-depth characterization of the mutual information distribution for
sufficiently large (but finite) antenna numbers. In particular, we derive
systematic closed-form expansions for the high order cumulants. These results
yield considerable new insight, such as providing a technical explanation as to
why the well known Gaussian approximation is quite robust to large SNR for the
case of unequal antenna arrays, whilst it deviates strongly for equal antenna
arrays. In addition, by drawing upon our high order cumulant expansions, we
employ the Edgeworth expansion technique to propose a refined Gaussian
approximation which is shown to give a very accurate closed-form
characterization of the mutual information distribution, both around the mean
and for moderate deviations into the tails (where the Gaussian approximation
fails remarkably). For stronger deviations where the Edgeworth expansion
becomes unwieldy, we employ the saddle point method and asymptotic integration
tools to establish new analytical characterizations which are shown to be very
simple and accurate. Based on these results we also recover key well
established properties of the tail distribution, including the
diversity-multiplexing-tradeoff.
|
1301.5034 | Downlink MIMO HetNets: Modeling, Ordering Results and Performance
Analysis | cs.IT cs.NI math.IT | We develop a general downlink model for multi-antenna heterogeneous cellular
networks (HetNets), where base stations (BSs) across tiers may differ in terms
of transmit power, target signal-to-interference-ratio (SIR), deployment
density, number of transmit antennas and the type of multi-antenna
transmission. In particular, we consider and compare space division multiple
access (SDMA), single user beamforming (SU-BF), and baseline single-input
single-output (SISO) transmission. For this general model, the main
contributions are: (i) ordering results for both coverage probability and per
user rate in closed form for any BS distribution for the three considered
techniques, using novel tools from stochastic orders, (ii) upper bounds on the
coverage probability assuming a Poisson BS distribution, and (iii) a comparison
of the area spectral efficiency (ASE). The analysis concretely demonstrates,
for example, that for a given total number of transmit antennas in the network,
it is preferable to spread them across many single-antenna BSs vs. fewer
multi-antenna BSs. Another observation is that SU-BF provides higher coverage
and per user data rate than SDMA, but SDMA is in some cases better in terms of
ASE.
|
1301.5044 | Performance Analysis of Heterogeneous Feedback Design in an OFDMA
Downlink with Partial and Imperfect Feedback | cs.IT math.IT | Current OFDMA systems group resource blocks into subband to form the basic
feedback unit. Homogeneous feedback design with a common subband size is not
aware of the heterogeneous channel statistics among users. Under a general
correlated channel model, we demonstrate the gain of matching the subband size
to the underlying channel statistics motivating heterogeneous feedback design
with different subband sizes and feedback resources across clusters of users.
Employing the best-M partial feedback strategy, users with smaller subband size
would convey more partial feedback to match the frequency selectivity. In order
to develop an analytical framework to investigate the impact of partial
feedback and potential imperfections, we leverage the multi-cluster subband
fading model. The perfect feedback scenario is thoroughly analyzed, and the
closed form expression for the average sum rate is derived for the
heterogeneous partial feedback system. We proceed to examine the effect of
imperfections due to channel estimation error and feedback delay, which leads
to additional consideration of system outage. Two transmission strategies: the
fix rate and the variable rate, are considered for the outage analysis. We also
investigate how to adapt to the imperfections in order to maximize the average
goodput under heterogeneous partial feedback.
|
1301.5047 | Asymptotically Efficient Distributed Estimation With Exponential Family
Statistics | math.PR cs.IT math.IT math.OC | The paper studies the problem of distributed parameter estimation in
multi-agent networks with exponential family observation statistics. A
certainty-equivalence type distributed estimator of the consensus + innovations
form is proposed in which, at each each observation sampling epoch agents
update their local parameter estimates by appropriately combining the data
received from their neighbors and the locally sensed new information
(innovation). Under global observability of the networked sensing model, i.e.,
the ability to distinguish between different instances of the parameter value
based on the joint observation statistics, and mean connectivity of the
inter-agent communication network, the proposed estimator is shown to yield
consistent parameter estimates at each network agent. Further, it is shown that
the distributed estimator is asymptotically efficient, in that, the asymptotic
covariances of the agent estimates coincide with that of the optimal
centralized estimator, i.e., the inverse of the centralized Fisher information
rate. From a technical viewpoint, the proposed distributed estimator leads to
non-Markovian mixed timescale stochastic recursions and the analytical methods
developed in the paper contribute to the general theory of distributed
stochastic approximation.
|
1301.5061 | Capacity Region Bounds and Resource Allocation for Two-Way OFDM Relay
Channels | cs.IT math.IT | In this paper, we consider two-way orthogonal frequency division multiplexing
(OFDM) relay channels, where the direct link between the two terminal nodes is
too weak to be used for data transmission. The widely known per-subcarrier
decode-and-forward (DF) relay strategy, treats each subcarrier as a separate
channel, and performs independent channel coding over each subcarrier. We show
that this per-subcarrier DF relay strategy is only a suboptimal DF relay
strategy, and present a multi-subcarrier DF relay strategy which utilizes
cross-subcarrier channel coding to achieve a larger rate region. We then
propose an optimal resource allocation algorithm to characterize the achievable
rate region of the multi-subcarrier DF relay strategy. The computational
complexity of this algorithm is much smaller than that of standard Lagrangian
duality optimization algorithms. We further analyze the asymptotic performance
of two-way relay strategies including the above two DF relay strategies and an
amplify-and-forward (AF) relay strategy. The analysis shows that the
multi-subcarrier DF relay strategy tends to achieve the capacity region of the
two-way OFDM relay channels in the low signal-to-noise ratio (SNR) regime,
while the AF relay strategy tends to achieve the multiplexing gain region of
the two-way OFDM relay channels in the high SNR regime. Numerical results are
provided to justify all the analytical results and the efficacy of the proposed
optimal resource allocation algorithm.
|
1301.5063 | Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity
Estimation from Facial Images | cs.CV cs.LG stat.ML | We propose a novel method for automatic pain intensity estimation from facial
images based on the framework of kernel Conditional Ordinal Random Fields
(KCORF). We extend this framework to account for heteroscedasticity on the
output labels(i.e., pain intensity scores) and introduce a novel dynamic
features, dynamic ranks, that impose temporal ordinal constraints on the static
ranks (i.e., intensity scores). Our experimental results show that the proposed
approach outperforms state-of-the art methods for sequence classification with
ordinal data and other ordinal regression models. The approach performs
significantly better than other models in terms of Intra-Class Correlation
measure, which is the most accepted evaluation measure in the tasks of facial
behaviour intensity estimation.
|
1301.5069 | Secrecy without one-way functions | cs.CR cs.IT math.IT | We show that some problems in information security can be solved without
using one-way functions. The latter are usually regarded as a central concept
of cryptography, but the very existence of one-way functions depends on
difficult conjectures in complexity theory, most notably on the notorious "$P
\ne NP$" conjecture.
In this paper, we suggest protocols for secure computation of the sum,
product, and some other functions, without using any one-way functions. A new
input that we offer here is that, in contrast with other proposals, we conceal
"intermediate results" of a computation. For example, when we compute the sum
of $k$ numbers, only the final result is known to the parties; partial sums are
not known to anybody. Other applications of our method include voting/rating
over insecure channels and a rather elegant and efficient solution of Yao's
"millionaires' problem".
Then, while it is fairly obvious that a secure (bit) commitment between two
parties is impossible without a one-way function, we show that it is possible
if the number of parties is at least 3. We also show how our (bit) commitment
scheme for 3 parties can be used to arrange an unconditionally secure (bit)
commitment between just two parties if they use a "dummy" (e.g., a computer) as
the third party. We explain how our concept of a "dummy" is different from a
well-known concept of a "trusted third party".
We also suggest a protocol, without using a one-way function, for "mental
poker", i.e., a fair card dealing (and playing) over distance. We also propose
a secret sharing scheme where an advantage over Shamir's and other known secret
sharing schemes is that nobody, including the dealer, ends up knowing the
shares owned by any particular player.
It should be mentioned that computational cost of our protocols is negligible
to the point that all of them can be executed without a computer.
|
1301.5083 | Improved Asymptotic Key Rate of the B92 Protocol | quant-ph cs.IT math.IT | We analyze the asymptotic key rate of the single photon B92 protocol by using
Renner's security analysis given in 2005. The new analysis shows that the B92
protocol can securely generate key at 6.5% depolarizing rate, while the
previous analyses cannot guarantee the secure key generation at 4.2%
depolarizing rate.
|
1301.5088 | Piecewise Linear Multilayer Perceptrons and Dropout | stat.ML cs.LG | We propose a new type of hidden layer for a multilayer perceptron, and
demonstrate that it obtains the best reported performance for an MLP on the
MNIST dataset.
|
1301.5096 | Minimax Filtering via Relations between Information and Estimation | cs.IT math.IT | We investigate the problem of continuous-time causal estimation under a
minimax criterion. Let $X^T = \{X_t,0\leq t\leq T\}$ be governed by the
probability law $P_{\theta}$ from a class of possible laws indexed by $\theta
\in \Lambda$, and $Y^T$ be the noise corrupted observations of $X^T$ available
to the estimator. We characterize the estimator minimizing the worst case
regret, where regret is the difference between the causal estimation loss of
the estimator and that of the optimum estimator.
One of the main contributions of this paper is characterizing the minimax
estimator, showing that it is in fact a Bayesian estimator. We then relate
minimax regret to the channel capacity when the channel is either Gaussian or
Poisson. In this case, we characterize the minimax regret and the minimax
estimator more explicitly. If we further assume that the uncertainty set
consists of deterministic signals, the worst case regret is exactly equal to
the corresponding channel capacity, namely the maximal mutual information
attainable across the channel among all possible distributions on the
uncertainty set of signals. The corresponding minimax estimator is the Bayesian
estimator assuming the capacity-achieving prior. Using this relation, we also
show that the capacity achieving prior coincides with the least favorable
input. Moreover, we show that this minimax estimator is not only minimizing the
worst case regret but also essentially minimizing regret for "most" of the
other sources in the uncertainty set.
We present a couple of examples for the construction of an minimax filter via
an approximation of the associated capacity achieving distribution.
|
1301.5108 | Balanced Sparsest Generator Matrices for MDS Codes | cs.IT math.IT | We show that given $n$ and $k$, for $q$ sufficiently large, there always
exists an $[n, k]_q$ MDS code that has a generator matrix $G$ satisfying the
following two conditions: (C1) Sparsest: each row of $G$ has Hamming weight $n
- k + 1$; (C2) Balanced: Hamming weights of the columns of $G$ differ from each
other by at most one.
|
1301.5109 | Constrained Source Coding with Side Information | cs.IT math.IT | The source-coding problem with side information at the decoder is studied
subject to a constraint that the encoder---to whom the side information is
unavailable---be able to compute the decoder's reconstruction sequence to
within some distortion. For discrete memoryless sources and finite
single-letter distortion measures, an expression is given for the minimal
description rate as a function of the joint law of the source and side
information and of the allowed distortions at the encoder and at the decoder.
The minimal description rate is also computed for a memoryless Gaussian source
with squared-error distortion measures. A solution is also provided to a more
general problem where there are more than two distortion constraints and each
distortion function may be a function of three arguments: the source symbol,
the encoder's reconstruction symbol, and the decoder's reconstruction symbol.
|
1301.5112 | Active Learning on Trees and Graphs | cs.LG stat.ML | We investigate the problem of active learning on a given tree whose nodes are
assigned binary labels in an adversarial way. Inspired by recent results by
Guillory and Bilmes, we characterize (up to constant factors) the optimal
placement of queries so to minimize the mistakes made on the non-queried nodes.
Our query selection algorithm is extremely efficient, and the optimal number of
mistakes on the non-queried nodes is achieved by a simple and efficient mincut
classifier. Through a simple modification of the query selection algorithm we
also show optimality (up to constant factors) with respect to the trade-off
between number of queries and number of mistakes on non-queried nodes. By using
spanning trees, our algorithms can be efficiently applied to general graphs,
although the problem of finding optimal and efficient active learning
algorithms for general graphs remains open. Towards this end, we provide a
lower bound on the number of mistakes made on arbitrary graphs by any active
learning algorithm using a number of queries which is up to a constant fraction
of the graph size.
|
1301.5121 | Partitioning Graph Databases - A Quantitative Evaluation | cs.DB cs.DC | Electronic data is growing at increasing rates, in both size and
connectivity: the increasing presence of, and interest in, relationships
between data. An example is the Twitter social network graph. Due to this
growth demand is increasing for technologies that can process such data.
Currently relational databases are the predominant technology, but they are
poorly suited to processing connected data as they are optimized for
index-intensive operations. Conversely, graph databases are optimized for graph
computation. They link records by direct references, avoiding index lookups,
and enabling retrieval of adjacent elements in constant time, regardless of
graph size. However, as data volume increases these databases outgrow the
resources of one computer and data partitioning becomes necessary. We evaluate
the viability of using graph partitioning algorithms to partition graph
databases. A prototype partitioned database was developed. Three partitioning
algorithms explored and one implemented. Three graph datasets were used: two
real and one synthetically generated. These were partitioned in various ways
and the impact on database performance measured. We defined one synthetic
access pattern per dataset and executed each on the partitioned datasets.
Evaluation took place in a simulation environment, ensuring repeatability and
allowing measurement of metrics like network traffic and load balance. Results
show that compared to random partitioning the partitioning algorithm reduced
traffic by 40-90%. Executing the algorithm intermittently during usage
maintained partition quality, while requiring only 1% the computation of
initial partitioning. Strong correlations were found between theoretic quality
metrics and generated network traffic under non-uniform access patterns.
|
1301.5154 | A Rational and Efficient Algorithm for View Revision in Databases | cs.LO cs.AI cs.DB | The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In this paper, we argue that to apply rationality result of belief dynamics
theory to various practical problems, it should be generalized in two respects:
first of all, it should allow a certain part of belief to be declared as
immutable; and second, the belief state need not be deductively closed. Such a
generalization of belief dynamics, referred to as base dynamics, is presented,
along with the concept of a generalized revision algorithm for Horn knowledge
bases. We show that Horn knowledge base dynamics has interesting connection
with kernel change and abduction. Finally, we also show that both variants are
rational in the sense that they satisfy certain rationality postulates stemming
from philosophical works on belief dynamics.
|
1301.5159 | International collaboration clusters in Africa | cs.DL cs.SI physics.soc-ph | Recent discussion about the increase in international research collaboration
suggests a comprehensive global network centred around a group of core
countries and driven by generic socio-economic factors where the global system
influences all national and institutional outcomes. In counterpoint, we
demonstrate that the collaboration pattern for countries in Africa is far from
universal. Instead, it exhibits layers of internal clusters and external links
that are explained not by monotypic global influences but by regional geography
and, perhaps even more strongly, by history, culture and language. Analysis of
these bottom-up, subjective, human factors is required in order to provide the
fuller explanation useful for policy and management purposes.
|
1301.5160 | See the Tree Through the Lines: The Shazoo Algorithm -- Full Version -- | cs.LG | Predicting the nodes of a given graph is a fascinating theoretical problem
with applications in several domains. Since graph sparsification via spanning
trees retains enough information while making the task much easier, trees are
an important special case of this problem. Although it is known how to predict
the nodes of an unweighted tree in a nearly optimal way, in the weighted case a
fully satisfactory algorithm is not available yet. We fill this hole and
introduce an efficient node predictor, Shazoo, which is nearly optimal on any
weighted tree. Moreover, we show that Shazoo can be viewed as a common
nontrivial generalization of both previous approaches for unweighted trees and
weighted lines. Experiments on real-world datasets confirm that Shazoo performs
well in that it fully exploits the structure of the input tree, and gets very
close to (and sometimes better than) less scalable energy minimization methods.
|
1301.5177 | "Seed+Expand": A validated methodology for creating high quality
publication oeuvres of individual researchers | cs.DL cs.IR | The study of science at the individual micro-level frequently requires the
disambiguation of author names. The creation of author's publication oeuvres
involves matching the list of unique author names to names used in publication
databases. Despite recent progress in the development of unique author
identifiers, e.g., ORCID, VIVO, or DAI, author disambiguation remains a key
problem when it comes to large-scale bibliometric analysis using data from
multiple databases. This study introduces and validates a new methodology
called seed+expand for semi-automatic bibliographic data collection for a given
set of individual authors. Specifically, we identify the oeuvre of a set of
Dutch full professors during the period 1980-2011. In particular, we combine
author records from the National Research Information System (NARCIS) with
publication records from the Web of Science. Starting with an initial list of
8,378 names, we identify "seed publications" for each author using five
different approaches. Subsequently, we "expand" the set of publication in three
different approaches. The different approaches are compared and resulting
oeuvres are evaluated on precision and recall using a "gold standard" dataset
of authors for which verified publications in the period 2001-2010 are
available.
|
1301.5201 | Models of Social Groups in Blogosphere Based on Information about
Comment Addressees and Sentiments | cs.SI physics.soc-ph | This work concerns the analysis of number, sizes and other characteristics of
groups identified in the blogosphere using a set of models identifying social
relations. These models differ regarding identification of social relations,
influenced by methods of classifying the addressee of the comments (they are
either the post author or the author of a comment on which this comment is
directly addressing) and by a sentiment calculated for comments considering the
statistics of words present and connotation. The state of a selected blog
portal was analyzed in sequential, partly overlapping time intervals. Groups in
each interval were identified using a version of the CPM algorithm, on the
basis of them, stable groups, existing for at least a minimal assumed duration
of time, were identified.
|
1301.5220 | Properties of the Least Squares Temporal Difference learning algorithm | stat.ML cs.LG | This paper presents four different ways of looking at the well-known Least
Squares Temporal Differences (LSTD) algorithm for computing the value function
of a Markov Reward Process, each of them leading to different insights: the
operator-theory approach via the Galerkin method, the statistical approach via
instrumental variables, the linear dynamical system view as well as the limit
of the TD iteration. We also give a geometric view of the algorithm as an
oblique projection. Furthermore, there is an extensive comparison of the
optimization problem solved by LSTD as compared to Bellman Residual
Minimization (BRM). We then review several schemes for the regularization of
the LSTD solution. We then proceed to treat the modification of LSTD for the
case of episodic Markov Reward Processes.
|
1301.5258 | Extremality Properties for the Basic Polarization Transformations | cs.IT math.IT | We study the extremality of the BEC and the BSC for Gallager's reliability
function $E_0$ evaluated under the uniform input distribution for binary input
DMCs from the aspect of channel polarization. In particular, we show that
amongst all B-DMCs of a given $E_0(\rho)$ value, for a fixed $\rho \geq 0$, the
BEC and BSC are extremal in the evolution of $E_0$ under the one-step
polarization transformations.
|
1301.5273 | Using Periodicity of Nucleotide Sequences | q-bio.GN cs.CE | Withdrawn by arXiv administrators due to content entirely plagiarized from
other authors (not in arXiv).
|
1301.5288 | The connection between Bayesian estimation of a Gaussian random field
and RKHS | stat.ML cs.LG math.ST stat.TH | Reconstruction of a function from noisy data is often formulated as a
regularized optimization problem over an infinite-dimensional reproducing
kernel Hilbert space (RKHS). The solution describes the observed data and has a
small RKHS norm. When the data fit is measured using a quadratic loss, this
estimator has a known statistical interpretation. Given the noisy measurements,
the RKHS estimate represents the posterior mean (minimum variance estimate) of
a Gaussian random field with covariance proportional to the kernel associated
with the RKHS. In this paper, we provide a statistical interpretation when more
general losses are used, such as absolute value, Vapnik or Huber. Specifically,
for any finite set of sampling locations (including where the data were
collected), the MAP estimate for the signal samples is given by the RKHS
estimate evaluated at these locations.
|
1301.5309 | Capacity Results for Binary Fading Interference Channels with Delayed
CSIT | cs.IT math.IT | To study the effect of lack of up-to-date channel state information at the
transmitters (CSIT), we consider two-user binary fading interference channels
with Delayed-CSIT. We characterize the capacity region for such channels under
homogeneous assumption where channel gains have identical and independent
distributions across time and space, eliminating the possibility of exploiting
time/space correlation. We introduce and discuss several novel coding
opportunities created by outdated CSIT that can enlarge the achievable rate
region. The capacity-achieving scheme relies on accurate combination,
concatenation, and merging of these opportunities, depending on the channel
statistics. The outer-bounds are based on an extremal inequality we develop for
a binary broadcast channel with Delayed-CSIT. We further extend the results and
characterize the capacity region when output feedback links are available from
the receivers to the transmitters in addition to the delayed knowledge of the
channel state information. We also discuss the extension of our results to the
non-homogeneous setting.
|
1301.5332 | Online Learning with Pairwise Loss Functions | stat.ML cs.LG | Efficient online learning with pairwise loss functions is a crucial component
in building large-scale learning system that maximizes the area under the
Receiver Operator Characteristic (ROC) curve. In this paper we investigate the
generalization performance of online learning algorithms with pairwise loss
functions. We show that the existing proof techniques for generalization bounds
of online algorithms with a univariate loss can not be directly applied to
pairwise losses. In this paper, we derive the first result providing
data-dependent bounds for the average risk of the sequence of hypotheses
generated by an arbitrary online learner in terms of an easily computable
statistic, and show how to extract a low risk hypothesis from the sequence. We
demonstrate the generality of our results by applying it to two important
problems in machine learning. First, we analyze two online algorithms for
bipartite ranking; one being a natural extension of the perceptron algorithm
and the other using online convex optimization. Secondly, we provide an
analysis for the risk bound for an online algorithm for supervised metric
learning.
|
1301.5334 | Generalized Cut-Set Bounds for Broadcast Networks | cs.IT math.IT | A broadcast network is a classical network with all source messages
collocated at a single source node. For broadcast networks, the standard
cut-set bounds, which are known to be loose in general, are closely related to
union as a specific set operation to combine the basic cuts of the network.
This paper provides a new set of network coding bounds for general broadcast
networks. These bounds combine the basic cuts of the network via a variety of
set operations (not just the union) and are established via only the
submodularity of Shannon entropy. The tightness of these bounds are
demonstrated via applications to combination networks.
|
1301.5348 | Why Size Matters: Feature Coding as Nystrom Sampling | cs.LG cs.CV | Recently, the computer vision and machine learning community has been in
favor of feature extraction pipelines that rely on a coding step followed by a
linear classifier, due to their overall simplicity, well understood properties
of linear classifiers, and their computational efficiency. In this paper we
propose a novel view of this pipeline based on kernel methods and Nystrom
sampling. In particular, we focus on the coding of a data point with a local
representation based on a dictionary with fewer elements than the number of
data points, and view it as an approximation to the actual function that would
compute pair-wise similarity to all data points (often too many to compute in
practice), followed by a Nystrom sampling step to select a subset of all data
points.
Furthermore, since bounds are known on the approximation power of Nystrom
sampling as a function of how many samples (i.e. dictionary size) we consider,
we can derive bounds on the approximation of the exact (but expensive to
compute) kernel matrix, and use it as a proxy to predict accuracy as a function
of the dictionary size, which has been observed to increase but also to
saturate as we increase its size. This model may help explaining the positive
effect of the codebook size and justifying the need to stack more layers (often
referred to as deep learning), as flat models empirically saturate as we add
more complexity.
|
1301.5349 | Toward the Automatic Generation of a Semantic VRML Model from
Unorganized 3D Point Clouds | cs.CG cs.AI | This paper presents our experience regarding the creation of 3D semantic
facility model out of unorganized 3D point clouds. Thus, a knowledge-based
detection approach of objects using the OWL ontology language is presented.
This knowledge is used to define SWRL detection rules. In addition, the
combination of 3D processing built-ins and topological Built-Ins in SWRL rules
aims at combining geometrical analysis of 3D point clouds and specialist's
knowledge. This combination allows more flexible and intelligent detection and
the annotation of objects contained in 3D point clouds. The created WiDOP
prototype takes a set of 3D point clouds as input, and produces an indexed
scene of colored objects visualized within VRML language as output. The context
of the study is the detection of railway objects materialized within the
Deutsche Bahn scene such as signals, technical cupboards, electric poles, etc.
Therefore, the resulting enriched and populated domain ontology, that contains
the annotations of objects in the point clouds, is used to feed a GIS system.
|
1301.5356 | Efficient MRF Energy Propagation for Video Segmentation via Bilateral
Filters | cs.CV | Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.
|
1301.5359 | Local Graph Coloring and Index Coding | cs.IT cs.DM math.IT | We present a novel upper bound for the optimal index coding rate. Our bound
uses a graph theoretic quantity called the local chromatic number. We show how
a good local coloring can be used to create a good index code. The local
coloring is used as an alignment guide to assign index coding vectors from a
general position MDS code. We further show that a natural LP relaxation yields
an even stronger index code. Our bounds provably outperform the state of the
art on index coding but at most by a constant factor.
|
1301.5434 | Design of Compandor Based on Approximate the First-Degree Spline
Function | cs.IT math.IT | In this paper, the approximation of the optimal compressor function using
spline function of the first-degree is done. For the companding quantizer
designed on the basis of the approximative spline function of the first-degree,
the support region is numerically optimized to provide the minimum of the total
distortion for the last segment. It is shown that the companding quantizer with
the optimized support region threshold provides the signal to quantization
noise ratio that is very close to the one of the optimal companding quantizer
having an equal number of levels.
|
1301.5451 | Spread spectrum compressed sensing MRI using chirp radio frequency
pulses | cs.CV math.OC physics.med-ph | Compressed sensing has shown great potential in reducing data acquisition
time in magnetic resonance imaging (MRI). Recently, a spread spectrum
compressed sensing MRI method modulates an image with a quadratic phase. It
performs better than the conventional compressed sensing MRI with variable
density sampling, since the coherence between the sensing and sparsity bases
are reduced. However, spread spectrum in that method is implemented via a shim
coil which limits its modulation intensity and is not convenient to operate. In
this letter, we propose to apply chirp (linear frequency-swept) radio frequency
pulses to easily control the spread spectrum. To accelerate the image
reconstruction, an alternating direction algorithm is modified by exploiting
the complex orthogonality of the quadratic phase encoding. Reconstruction on
the acquired data demonstrates that more image features are preserved using the
proposed approach than those of conventional CS-MRI.
|
1301.5482 | Relative Generalized Rank Weight of Linear Codes and Its Applications to
Network Coding | cs.IT cs.CR math.CO math.IT | By extending the notion of minimum rank distance, this paper introduces two
new relative code parameters of a linear code C_1 of length n over a field
extension and its subcode C_2. One is called the relative
dimension/intersection profile (RDIP), and the other is called the relative
generalized rank weight (RGRW). We clarify their basic properties and the
relation between the RGRW and the minimum rank distance. As applications of the
RDIP and the RGRW, the security performance and the error correction capability
of secure network coding, guaranteed independently of the underlying network
code, are analyzed and clarified. We propose a construction of secure network
coding scheme, and analyze its security performance and error correction
capability as an example of applications of the RDIP and the RGRW. Silva and
Kschischang showed the existence of a secure network coding in which no part of
the secret message is revealed to the adversary even if any dim C_1-1 links are
wiretapped, which is guaranteed over any underlying network code. However, the
explicit construction of such a scheme remained an open problem. Our new
construction is just one instance of secure network coding that solves this
open problem.
|
1301.5488 | Multi-class Generalized Binary Search for Active Inverse Reinforcement
Learning | cs.LG cs.AI stat.ML | This paper addresses the problem of learning a task from demonstration. We
adopt the framework of inverse reinforcement learning, where tasks are
represented in the form of a reward function. Our contribution is a novel
active learning algorithm that enables the learning agent to query the expert
for more informative demonstrations, thus leading to more sample-efficient
learning. For this novel algorithm (Generalized Binary Search for Inverse
Reinforcement Learning, or GBS-IRL), we provide a theoretical bound on sample
complexity and illustrate its applicability on several different tasks. To our
knowledge, GBS-IRL is the first active IRL algorithm with provable sample
complexity bounds. We also discuss our method in light of other existing
methods in the literature and its general applicability in multi-class
classification problems. Finally, motivated by recent work on learning from
demonstration in robots, we also discuss how different forms of human feedback
can be integrated in a transparent manner in our learning framework.
|
1301.5491 | ChESS - Quick and Robust Detection of Chess-board Features | cs.CV | Localization of chess-board vertices is a common task in computer vision,
underpinning many applications, but relatively little work focusses on
designing a specific feature detector that is fast, accurate and robust. In
this paper the `Chess-board Extraction by Subtraction and Summation' (ChESS)
feature detector, designed to exclusively respond to chess-board vertices, is
presented. The method proposed is robust against noise, poor lighting and poor
contrast, requires no prior knowledge of the extent of the chess-board pattern,
is computationally very efficient, and provides a strength measure of detected
features. Such a detector has significant application both in the key field of
camera calibration, as well as in Structured Light 3D reconstruction. Evidence
is presented showing its robustness, accuracy, and efficiency in comparison to
other commonly used detectors both under simulation and in experimental 3D
reconstruction of flat plate and cylindrical objects
|
1301.5522 | On Gaussian Half-Duplex Relay Networks | cs.IT math.IT | This paper considers Gaussian relay networks where a source transmits a
message to a sink terminal with the help of one or more relay nodes. The relays
work in half-duplex mode, in the sense that they can not transmit and receive
at the same time. For the case of one relay, the generalized Degrees-of-Freedom
is characterized first and then it is shown that capacity can be achieved to
within a constant gap regardless of the actual value of the channel parameters.
Different achievable schemes are presented with either deterministic or random
switch for the relay node. It is shown that random switch in general achieves
higher rates than deterministic switch. For the case of K relays, it is shown
that the generalized Degrees-of-Freedom can be obtained by solving a linear
program and that capacity can be achieved to within a constant gap of
K/2log(4K). This gap may be further decreased by considering more structured
networks such as, for example, the diamond network.
|
1301.5535 | On the Achievable Rate-Regions for State-Dependent Gaussian Interference
Channel | cs.IT math.IT | In this paper, we study a general additive state-dependent Gaussian
interference channel (ASD-GIC) where we consider two-user interference channel
with two independent states known non-causally at both transmitters, but
unknown to either of the receivers. An special case, where the additive states
over the two links are the same is studied in [1], [2], in which it is shown
that the gap between the achievable symmetric rate and the upper bound is less
than 1/4 bit for the strong interference case. Here, we also consider the case
where each channel state has unbounded variance [3], which is referred to as
the strong interferences. We first obtain an outer bound on the capacity
region. By utilizing lattice-based coding schemes, we obtain four achievable
rate regions. Depend on noise variance and channel power constraint, achievable
rate regions can coincide with the channel capacity region. For the symmetric
model, the achievable sum-rate reaches to within 0.661 bit of the channel
capacity for signal to noise ratio (SNR) greater than one.
|
1301.5536 | On the Correlation Between Polarized BECs | cs.IT math.IT | We consider the $2^n$ channels synthesized by the $n$-fold application of
Ar\i{}kan's polar transform to a binary erasure channel (BEC). The synthetic
channels are BECs themselves, and we show that, asymptotically for almost all
these channels, the pairwise correlations between their erasure events are
extremely small: the correlation coefficients vanish faster than any
exponential in $n$. Such a fast decay of correlations allows us to conclude
that the union bound on the block error probability of polar codes is very
tight.
|
1301.5582 | Multi-Class Detection and Segmentation of Objects in Depth | cs.CV cs.RO | The quality of life of many people could be improved by autonomous humanoid
robots in the home. To function in the human world, a humanoid household robot
must be able to locate itself and perceive the environment like a human; scene
perception, object detection and segmentation, and object spatial localization
in 3D are fundamental capabilities for such humanoid robots. This paper
presents a 3D multi-class object detection and segmentation method. The
contributions are twofold. Firstly, we present a multi-class detection method,
where a minimal joint codebook is learned in a principled manner. Secondly, we
incorporate depth information using RGB-D imagery, which increases the
robustness of the method and gives the 3D location of objects -- necessary
since the robot reasons in 3D space. Experiments show that the multi-class
extension improves the detection efficiency with respect to the number of
classes and the depth extension improves the detection robustness and give
sufficient natural 3D location of the objects.
|
1301.5586 | Measuring the Significance of the Geographic Flow of Music | cs.SI physics.soc-ph | In previous work, our results suggested that some cities tend to be ahead of
others in their musical preferences. We concluded that work by noting that to
properly test this claim, we would try to exploit the leader-follower
relationships that we identified to make predictions. Here we present the
results of our predictive evaluation. We find that information on the past
musical preferences in other cities allows a linear model to improve its
predictions by approx. 5% over a simple baseline. This suggests that at best,
previously found leader-follower relationships are rather weak.
|
1301.5593 | A Packetized Direct Load Control Mechanism for Demand Side Management | cs.SY | Electricity peaks can be harmful to grid stability and result in additional
generation costs to balance supply with demand. By developing a network of
smart appliances together with a quasi-decentralized control protocol, direct
load control (DLC) provides an opportunity to reduce peak consumption by
directly controlling the on/off switch of the networked appliances. This paper
proposes a packetized DLC (PDLC) solution that is illustrated by an application
to air conditioning temperature control. Here the term packetized refers to a
fixed time energy usage authorization. The consumers in each room choose their
preferred set point, and then an operator of the local appliance pool will
determine the comfort band around the set point. We use a thermal dynamic model
to investigate the duty cycle of thermostatic appliances. Three theorems are
proposed in this paper. The first two theorems evaluate the performance of the
PDLC in both transient and steady state operation. The first theorem proves
that the average room temperature would converge to the average room set point
with fixed number of packets applied in each discrete interval. The second
theorem proves that the PDLC solution guarantees to control the temperature of
all the rooms within their individual comfort bands. The third theorem proposes
an allocation method to link the results in theorem 1 and assumptions in
theorem 2 such that the overall PDLC solution works. The direct result of the
theorems is that we can reduce the consumption oscillation that occurs when no
control is applied. Simulation is provided to verify theoretical results.
|
1301.5595 | A discrete analysis of metal-v belt drive | cs.CE | The metal-V belt drive includes a large number of parts which interact
between them to transmit power from the input to the output pulleys. A
compression belt composed of a great number of struts is maintained by a
tension flat belt. Power is them shared into the two belts that moves generally
in opposite directions. Due to the particular geometry of the elements and to
the great number of parts, a numerical approach achieves the global equilibrium
of the mechanism from the elementary part equilibrium. Sliding arc on each
pulley can be thus defined both for the compression and tension belts. Finally,
power sharing can be calculated as differential motion between the belts, is
defined. The first part of the paper will present the different steps of the
quasi-static mechanical analysis and their numerical implementations. Load
distributions, speed profiles and sliding angle values will be discussed. The
second part of the paper will deal to a systematic use of the computer
software. Speed ratio, transmitted torque, strut geometry and friction
coefficients effect will be analysed with the output parameter variations.
Finally, the effect pulley deformable flanges will be discussed.
|
1301.5596 | Systems of MDS codes from units and idempotents | cs.IT math.IT | Algebraic systems are constructed from which series of maximum distance
separable (mds) codes are derived. The methods use unit and idempotent schemes.
|
1301.5607 | Information as Distinctions: New Foundations for Information Theory | cs.IT math.IT math.LO | The logical basis for information theory is the newly developed logic of
partitions that is dual to the usual Boolean logic of subsets. The key concept
is a "distinction" of a partition, an ordered pair of elements in distinct
blocks of the partition. The logical concept of entropy based on partition
logic is the normalized counting measure of the set of distinctions of a
partition on a finite set--just as the usual logical notion of probability
based on the Boolean logic of subsets is the normalized counting measure of the
subsets (events). Thus logical entropy is a measure on the set of ordered
pairs, and all the compound notions of entropy (join entropy, conditional
entropy, and mutual information) arise in the usual way from the measure (e.g.,
the inclusion-exclusion principle)--just like the corresponding notions of
probability. The usual Shannon entropy of a partition is developed by replacing
the normalized count of distinctions (dits) by the average number of binary
partitions (bits) necessary to make all the distinctions of the partition.
|
1301.5650 | Regularization and nonlinearities for neural language models: when are
they needed? | stat.ML cs.LG | Neural language models (LMs) based on recurrent neural networks (RNN) are
some of the most successful word and character-level LMs. Why do they work so
well, in particular better than linear neural LMs? Possible explanations are
that RNNs have an implicitly better regularization or that RNNs have a higher
capacity for storing patterns due to their nonlinearities or both. Here we
argue for the first explanation in the limit of little training data and the
second explanation for large amounts of text data. We show state-of-the-art
performance on the popular and small Penn dataset when RNN LMs are regularized
with random dropout. Nonetheless, we show even better performance from a
simplified, much less expressive linear RNN model without off-diagonal entries
in the recurrent matrix. We call this model an impulse-response LM (IRLM).
Using random dropout, column normalization and annealed learning rates, IRLMs
develop neurons that keep a memory of up to 50 words in the past and achieve a
perplexity of 102.5 on the Penn dataset. On two large datasets however, the
same regularization methods are unsuccessful for both models and the RNN's
expressivity allows it to overtake the IRLM by 10 and 20 percent perplexity,
respectively. Despite the perplexity gap, IRLMs still outperform RNNs on the
Microsoft Research Sentence Completion (MRSC) task. We develop a slightly
modified IRLM that separates long-context units (LCUs) from short-context units
and show that the LCUs alone achieve a state-of-the-art performance on the MRSC
task of 60.8%. Our analysis indicates that a fruitful direction of research for
neural LMs lies in developing more accessible internal representations, and
suggests an optimization regime of very high momentum terms for effectively
training such models.
|
1301.5655 | Achievable rate region based on coset codes for multiple access channel
with states | cs.IT math.IT | We prove that the ensemble the nested coset codes built on finite fields
achieves the capacity of arbitrary discrete memoryless point-to-point channels.
Exploiting it's algebraic structure, we develop a coding technique for
communication over general discrete multiple access channel with channel state
information distributed at the transmitters. We build an algebraic coding
framework for this problem using the ensemble of Abelian group codes and
thereby derive a new achievable rate region. We identify non-additive and
non-symmteric examples for which the proposed achievable rate region is
strictly larger than the one achievable using random unstructured codes.
|
1301.5676 | Spatial Coupling as a Proof Technique | cs.IT math.IT | The aim of this paper is to show that spatial coupling can be viewed not only
as a means to build better graphical models, but also as a tool to better
understand uncoupled models. The starting point is the observation that some
asymptotic properties of graphical models are easier to prove in the case of
spatial coupling. In such cases, one can then use the so-called interpolation
method to transfer known results for the spatially coupled case to the
uncoupled one.
Our main use of this framework is for LDPC codes, where we use interpolation
to show that the average entropy of the codeword conditioned on the observation
is asymptotically the same for spatially coupled as for uncoupled ensembles.
We give three applications of this result for a large class of LDPC
ensembles. The first one is a proof of the so-called Maxwell construction
stating that the MAP threshold is equal to the Area threshold of the BP GEXIT
curve. The second is a proof of the equality between the BP and MAP GEXIT
curves above the MAP threshold. The third application is the intimately related
fact that the replica symmetric formula for the conditional entropy in the
infinite block length limit is exact.
|
1301.5684 | Computing sum of sources over an arbitrary multiple access channel | cs.IT math.IT | The problem of computing sum of sources over a multiple access channel (MAC)
is considered. Building on the technique of linear computation coding (LCC)
proposed by Nazer and Gastpar [2007], we employ the ensemble of nested coset
codes to derive a new set of sufficient conditions for computing the sum of
sources over an \textit{arbitrary} MAC. The optimality of nested coset codes
[Padakandla, Pradhan 2011] enables this technique outperform LCC even for
linear MAC with a structural match. Examples of nonadditive MAC for which the
technique proposed herein outperforms separation and systematic based
computation are also presented. Finally, this technique is enhanced by
incorporating separation based strategy, leading to a new set of sufficient
conditions for computing the sum over a MAC.
|
1301.5686 | Transfer Topic Modeling with Ease and Scalability | cs.CL cs.LG stat.ML | The increasing volume of short texts generated on social media sites, such as
Twitter or Facebook, creates a great demand for effective and efficient topic
modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it
is not optimal due to its weakness in handling short texts with fast-changing
topics and scalability concerns. In this paper, we propose a transfer learning
approach that utilizes abundant labeled documents from other domains (such as
Yahoo! News or Wikipedia) to improve topic modeling, with better model fitting
and result interpretation. Specifically, we develop Transfer Hierarchical LDA
(thLDA) model, which incorporates the label information from other domains via
informative priors. In addition, we develop a parallel implementation of our
model for large-scale applications. We demonstrate the effectiveness of our
thLDA model on both a microblogging dataset and standard text collections
including AP and RCV1 datasets.
|
1301.5687 | Outage Probability of Wireless Ad Hoc Networks with Cooperative Relaying | cs.IT math.IT | In this paper, we analyze the performance of cooperative transmissions in
wireless ad hoc networks with random node locations. According to a contention
probability for message transmission, each source node can either transmits its
own message signal or acts as a potential relay for others. Hence, each
destination node can potentially receive two copies of the message signal, one
from the direct link and the other from the relay link. Taking the random node
locations and interference into account, we derive closed-form expressions for
the outage probability with different combining schemes at the destination
nodes. In particular, the outage performance of optimal combining, maximum
ratio combining, and selection combining strategies are studied and quantified.
|
1301.5695 | Optimal Amplify-and-Forward Schemes for Relay Channels with Correlated
Relay Noise | cs.IT math.IT | This paper investigates amplify-and-forward (AF) schemes for both one and
two-way relay channels. Unlike most existing works assuming independent noise
at the relays, we consider a more general scenario with correlated relay noise.
We first propose an approach to efficiently solve a class of quadratically
constrained fractional problems via second-order cone programming (SOCP). Then
it is shown that the AF relay optimization problems studied in this paper can
be incorporated into such quadratically constrained fractional problems. As a
consequence, the proposed approach can be used as a unified framework to solve
the optimal AF rate for the one-way relay channel and the optimal AF rate
region for the two-way relay channel under both sum and individual relay power
constraints.
In particular, for one-way relay channel under individual relay power
constraints, we propose two suboptimal AF schemes in closed-form. It is shown
that they are approximately optimal in certain conditions of interest.
Furthermore, we find an interesting result that, on average, noise correlation
is beneficial no matter the relays know the noise covariance matrix or not for
such scenario. Overall, the obtained results recover and generalize several
existing results for the uncorrelated counterpart. (unsubmitted)
|
1301.5701 | Sequential and Decentralized Estimation of Linear Regression Parameters
in Wireless Sensor Networks | stat.AP cs.IT math.IT math.OC math.PR stat.ME | Sequential estimation of a vector of linear regression coefficients is
considered under both centralized and decentralized setups. In sequential
estimation, the number of observations used for estimation is determined by the
observed samples, hence is random, as opposed to fixed-sample-size estimation.
Specifically, after receiving a new sample, if a target accuracy level is
reached, we stop and estimate using the samples collected so far; otherwise we
continue to receive another sample. It is known that finding an optimum
sequential estimator, which minimizes the average sample number for a given
target accuracy level, is an intractable problem with a general stopping rule
that depends on the complete observation history. By properly restricting the
search space to stopping rules that depend on a specific subset of the complete
observation history, we derive the optimum sequential estimator in the
centralized case via optimal stopping theory. However, finding the optimum
stopping rule in this case requires numerical computations that {\em
quadratically} scales with the number of parameters to be estimated. For the
decentralized setup with stringent energy constraints, under an alternative
problem formulation that is conditional on the observed regressors, we first
derive a simple optimum scheme whose computational complexity is {\em constant}
with respect to the number of parameters. Then, following this simple optimum
scheme we propose a decentralized sequential estimator whose computational
complexity and energy consumption scales {\em linearly} with the number of
parameters. Specifically, in the proposed decentralized scheme a
close-to-optimum average stopping time performance is achieved by infrequently
transmitting a single pulse with very short duration.
|
1301.5728 | A Potential Theory of General Spatially-Coupled Systems via a Continuum
Approximation | cs.IT math.IT | This paper analyzes general spatially-coupled (SC) systems with
multi-dimensional coupling. A continuum approximation is used to derive
potential functions that characterize the performance of the SC systems. For
any dimension of coupling, it is shown that, if the boundary of the SC systems
is fixed to the unique stable solution that minimizes the potential over all
stationary solutions, the systems can approach the optimal performance as the
number of coupled systems tends to infinity.
|
1301.5734 | Reinforcement learning from comparisons: Three alternatives is enough,
two is not | math.OC cs.LG math.PR | The paper deals with the problem of finding the best alternatives on the
basis of pairwise comparisons when these comparisons need not be transitive. In
this setting, we study a reinforcement urn model. We prove convergence to the
optimal solution when reinforcement of a winning alternative occurs each time
after considering three random alternatives. The simpler process, which
reinforces the winner of a random pair does not always converges: it may cycle.
|
1301.5765 | High Capacity Indoor & Hotspot Wireless System in Shared Spectrum - A
Techno-Economic Analysis | cs.NI cs.IT math.IT | Predictions for wireless and mobile Internet access suggest exponential
traffic increase particularly in inbuilding environments. Non-traditional
actors such as facility owners have a growing interest in deploying and
operating their own indoor networks to fulfill the capacity demand. Such local
operators will need spectrum sharing with neighboring networks because they are
not likely to have their own dedicated spectrum. Management of inter-network
interference then becomes a key issue for high capacity provision. Tight
operator-wise cooperation provides superior performance, but at the expense of
high infrastructure cost and business-related barriers. Limited coordination on
the other hand causes harmful interference between operators which in turn will
require even denser networks. In this paper, we propose a techno-economic
analysis framework for investigating and comparing the strategies of the indoor
operators. We refine a traditional network cost model by introducing new
inter-operator cost factors. Then, we present a numerical example to
demonstrate how the proposed framework can help us comparing different operator
strategies. Finally, we suggest areas for future research.
|
1301.5809 | Producing a Unified Graph Representation from Multiple Social Network
Views | cs.SI physics.soc-ph | In many social networks, several different link relations will exist between
the same set of users. Additionally, attribute or textual information will be
associated with those users, such as demographic details or user-generated
content. For many data analysis tasks, such as community finding and data
visualisation, the provision of multiple heterogeneous types of user data makes
the analysis process more complex. We propose an unsupervised method for
integrating multiple data views to produce a single unified graph
representation, based on the combination of the k-nearest neighbour sets for
users derived from each view. These views can be either relation-based or
feature-based. The proposed method is evaluated on a number of annotated
multi-view Twitter datasets, where it is shown to support the discovery of the
underlying community structure in the data.
|
1301.5831 | Canalization and control in automata networks: body segmentation in
Drosophila melanogaster | q-bio.MN cs.CE cs.DM cs.FL nlin.AO | We present schema redescription as a methodology to characterize canalization
in automata networks used to model biochemical regulation and signalling. In
our formulation, canalization becomes synonymous with redundancy present in the
logic of automata. This results in straightforward measures to quantify
canalization in an automaton (micro-level), which is in turn integrated into a
highly scalable framework to characterize the collective dynamics of
large-scale automata networks (macro-level). This way, our approach provides a
method to link micro- to macro-level dynamics -- a crux of complexity. Several
new results ensue from this methodology: uncovering of dynamical modularity
(modules in the dynamics rather than in the structure of networks),
identification of minimal conditions and critical nodes to control the
convergence to attractors, simulation of dynamical behaviour from incomplete
information about initial conditions, and measures of macro-level canalization
and robustness to perturbations. We exemplify our methodology with a well-known
model of the intra- and inter cellular genetic regulation of body segmentation
in Drosophila melanogaster. We use this model to show that our analysis does
not contradict any previous findings. But we also obtain new knowledge about
its behaviour: a better understanding of the size of its wild-type attractor
basin (larger than previously thought), the identification of novel minimal
conditions and critical nodes that control wild-type behaviour, and the
resilience of these to stochastic interventions. Our methodology is applicable
to any complex network that can be modelled using automata, but we focus on
biochemical regulation and signalling, towards a better understanding of the
(decentralized) control that orchestrates cellular activity -- with the
ultimate goal of explaining how do cells and tissues 'compute'.
|
1301.5848 | Decentralized Coded Caching Attains Order-Optimal Memory-Rate Tradeoff | cs.IT cs.NI math.IT | Replicating or caching popular content in memories distributed across the
network is a technique to reduce peak network loads. Conventionally, the main
performance gain of this caching was thought to result from making part of the
requested data available closer to end users. Instead, we recently showed that
a much more significant gain can be achieved by using caches to create
coded-multicasting opportunities, even for users with different demands,
through coding across data streams. These coded-multicasting opportunities are
enabled by careful content overlap at the various caches in the network,
created by a central coordinating server.
In many scenarios, such a central coordinating server may not be available,
raising the question if this multicasting gain can still be achieved in a more
decentralized setting. In this paper, we propose an efficient caching scheme,
in which the content placement is performed in a decentralized manner. In other
words, no coordination is required for the content placement. Despite this lack
of coordination, the proposed scheme is nevertheless able to create
coded-multicasting opportunities and achieves a rate close to the optimal
centralized scheme.
|
1301.5852 | On a Multiple-Access in a Vector Disjunctive Channel | cs.IT math.IT | We address the problem of increasing the sum rate in a multiple-access system
from [1] for small number of users. We suggest an improved signal-code
construction in which in case of a small number of users we give more resources
to them. For the resulting multiple-access system a lower bound on the relative
sum rate is derived. It is shown to be very close to the maximal value of
relative sum rate in [1] even for small number of users. The bound is obtained
for the case of decoding by exhaustive search. We also suggest
reduced-complexity decoding and compare the maximal number of users in this
case and in case of decoding by exhaustive search.
|
1301.5871 | Towards a faster symbolic aggregate approximation method | cs.DB cs.IR | The similarity search problem is one of the main problems in time series data
mining. Traditionally, this problem was tackled by sequentially comparing the
given query against all the time series in the database, and returning all the
time series that are within a predetermined threshold of that query. But the
large size and the high dimensionality of time series databases that are in use
nowadays make that scenario inefficient. There are many representation
techniques that aim at reducing the dimensionality of time series so that the
search can be handled faster at a lower-dimensional space level. The symbolic
aggregate approximation (SAX) is one of the most competitive methods in the
literature. In this paper we present a new method that improves the performance
of SAX by adding to it another exclusion condition that increases the exclusion
power. This method is based on using two representations of the time series:
one of SAX and the other is based on an optimal approximation of the time
series. Pre-computed distances are calculated and stored offline to be used
online to exclude a wide range of the search space using two exclusion
conditions. We conduct experiments which show that the new method is faster
than SAX.
|
1301.5887 | Counting Triangles in Massive Graphs with MapReduce | cs.SI cs.DC | Graphs and networks are used to model interactions in a variety of contexts.
There is a growing need to quickly assess the characteristics of a graph in
order to understand its underlying structure. Some of the most useful metrics
are triangle-based and give a measure of the connectedness of mutual friends.
This is often summarized in terms of clustering coefficients, which measure the
likelihood that two neighbors of a node are themselves connected. Computing
these measures exactly for large-scale networks is prohibitively expensive in
both memory and time. However, a recent wedge sampling algorithm has proved
successful in efficiently and accurately estimating clustering coefficients. In
this paper, we describe how to implement this approach in MapReduce to deal
with massive graphs. We show results on publicly-available networks, the
largest of which is 132M nodes and 4.7B edges, as well as artificially
generated networks (using the Graph500 benchmark), the largest of which has
240M nodes and 8.5B edges. We can estimate the clustering coefficient by degree
bin (e.g., we use exponential binning) and the number of triangles per bin, as
well as the global clustering coefficient and total number of triangles, in an
average of 0.33 seconds per million edges plus overhead (approximately 225
seconds total for our configuration). The technique can also be used to study
triangle statistics such as the ratio of the highest and lowest degree, and we
highlight differences between social and non-social networks. To the best of
our knowledge, these are the largest triangle-based graph computations
published to date.
|
1301.5898 | Phase Diagram and Approximate Message Passing for Blind Calibration and
Dictionary Learning | cs.IT cond-mat.stat-mech cs.LG math.IT | We consider dictionary learning and blind calibration for signals and
matrices created from a random ensemble. We study the mean-squared error in the
limit of large signal dimension using the replica method and unveil the
appearance of phase transitions delimiting impossible, possible-but-hard and
possible inference regions. We also introduce an approximate message passing
algorithm that asymptotically matches the theoretical performance, and show
through numerical tests that it performs very well, for the calibration
problem, for tractable system sizes.
|
1301.5912 | Resource Allocation and Interference Mitigation Techniques for
Cooperative Multi-Antenna and Spread Spectrum Wireless Networks | cs.IT math.IT | This chapter presents joint interference suppression and power allocation
algorithms for DS-CDMA and MIMO networks with multiple hops and
amplify-and-forward and decode-and-forward (DF) protocols. A scheme for joint
allocation of power levels across the relays and linear interference
suppression is proposed. We also consider another strategy for joint
interference suppression and relay selection that maximizes the diversity
available in the system. Simulations show that the proposed cross-layer
optimization algorithms obtain significant gains in capacity and performance
over existing schemes.
|
1301.5915 | The Packing Radius of a Code and Partitioning Problems: the Case for
Poset Metrics | cs.IT math.CO math.IT | Until this work, the packing radius of a poset code was only known in the
cases where the poset was a chain, a hierarchy, a union of disjoint chains of
the same size, and for some families of codes. Our objective is to approach the
general case of any poset. To do this, we will divide the problem into two
parts.
The first part consists in finding the packing radius of a single vector. We
will show that this is equivalent to a generalization of a famous NP-hard
problem known as "the partition problem". Then, we will review the main results
known about this problem giving special attention to the algorithms to solve
it. The main ingredient to these algorithms is what is known as the
differentiating method, and therefore, we will extend it to the general case.
The second part consists in finding the vector that determines the packing
radius of the code. For this, we will show how it is sometimes possible to
compare the packing radius of two vectors without calculating them explicitly.
|
1301.5937 | A Tight Lower Bound on the Mutual Information of a Binary and an
Arbitrary Finite Random Variable in Dependence of the Variational Distance | cs.IT math.IT | In this paper a numerical method is presented, which finds a lower bound for
the mutual information between a binary and an arbitrary finite random variable
with joint distributions that have a variational distance not greater than a
known value to a known joint distribution. This lower bound can be applied to
mutual information estimation with confidence intervals.
|
1301.5938 | Evolution of the Internet k-dense structure | cs.SI cs.NI physics.soc-ph | As the Internet AS-level topology grows over time, some of its structural
properties remain unchanged. Such time- invariant properties are generally
interesting, because they tend to reflect some fundamental processes or
constraints behind Internet growth. As has been shown before, the
time-invariant structural properties of the Internet include some most basic
ones, such as the degree distribution or clustering. Here we add to this
time-invariant list a non-trivial property - k-dense decomposition. This
property is derived from a recursive form of edge multiplicity, defined as the
number of triangles that share a given edge. We show that after proper
normalization, the k- dense decomposition of the Internet has remained stable
over the last decade, even though the Internet size has approximately doubled,
and so has the k-density of its k-densest core. This core consists mostly of
content providers peering at Internet eXchange Points, and it only loosely
overlaps with the high-degree or high-rank AS core, consisting mostly of tier-1
transit providers. We thus show that high degrees and high k-densities reflect
two different Internet-specific properties of ASes (transit versus content
providers). As a consequence, even though degrees and k-densities of nodes are
correlated, the relative fluctuations are strong, and related to that, random
graphs with the same degree distribution or even degree correlations as in the
Internet, do not reproduce its k-dense decomposition. Therefore an interesting
open question is what Internet topology models or generators can fully explain
or at least reproduce the k-dense properties of the Internet.
|
1301.5942 | Confidence Intervals for the Mutual Information | cs.IT math.IT | By combining a bound on the absolute value of the difference of mutual
information between two joint probablity distributions with a fixed variational
distance, and a bound on the probability of a maximal deviation in variational
distance between a true joint probability distribution and an empirical joint
probability distribution, confidence intervals for the mutual information of
two random variables with finite alphabets are established. Different from
previous results, these intervals do not need any assumptions on the
distribution and the sample size.
|
1301.5943 | Identifying Player\'s Strategies in No Limit Texas Hold\'em Poker
through the Analysis of Individual Moves | cs.AI cs.GT | The development of competitive artificial Poker playing agents has proven to
be a challenge, because agents must deal with unreliable information and
deception which make it essential to model the opponents in order to achieve
good results. This paper presents a methodology to develop opponent modeling
techniques for Poker agents. The approach is based on applying clustering
algorithms to a Poker game database in order to identify player types based on
their actions. First, common game moves were identified by clustering all
players\' moves. Then, player types were defined by calculating the frequency
with which the players perform each type of movement. With the given dataset, 7
different types of players were identified with each one having at least one
tactic that characterizes him. The identification of player types may improve
the overall performance of Poker agents, because it helps the agents to predict
the opponent\'s moves, by associating each opponent to a distinct cluster.
|
1301.5946 | Computer Poker Research at LIACC | cs.AI | Computer Poker's unique characteristics present a well-suited challenge for
research in artificial intelligence. For that reason, and due to the Poker's
market increase in popularity in Portugal since 2008, several members of LIACC
have researched in this field. Several works were published as papers and
master theses and more recently a member of LIACC engaged on a research in this
area as a Ph.D. thesis in order to develop a more extensive and in-depth work.
This paper describes the existing research in LIACC about Computer Poker, with
special emphasis on the completed master's theses and plans for future work.
This paper means to present a summary of the lab's work to the research
community in order to encourage the exchange of ideas with other labs /
individuals. LIACC hopes this will improve research in this area so as to reach
the goal of creating an agent that surpasses the best human players.
|
1301.5952 | Deterministic Constructions of Binary Measurement Matrices from Finite
Geometry | cs.IT math.IT | Deterministic constructions of measurement matrices in compressed sensing
(CS) are considered in this paper. The constructions are inspired by the recent
discovery of Dimakis, Smarandache and Vontobel which says that parity-check
matrices of good low-density parity-check (LDPC) codes can be used as
{provably} good measurement matrices for compressed sensing under
$\ell_1$-minimization. The performance of the proposed binary measurement
matrices is mainly theoretically analyzed with the help of the analyzing
methods and results from (finite geometry) LDPC codes. Particularly, several
lower bounds of the spark (i.e., the smallest number of columns that are
linearly dependent, which totally characterizes the recovery performance of
$\ell_0$-minimization) of general binary matrices and finite geometry matrices
are obtained and they improve the previously known results in most cases.
Simulation results show that the proposed matrices perform comparably to,
sometimes even better than, the corresponding Gaussian random matrices.
Moreover, the proposed matrices are sparse, binary, and most of them have
cyclic or quasi-cyclic structure, which will make the hardware realization
convenient and easy.
|
1301.5954 | QoS-Aware Transmission Policies for OFDM Bidirectional
Decode-and-Forward Relaying | cs.IT math.IT | Two-way relaying can considerably improve spectral efficiency in
relay-assisted bidirectional communications. However, the benefits and flexible
structure of orthogonal frequency division multiplexing (OFDM)-based two-way
decode-and-forward (DF) relay systems is much less exploited. Moreover, most of
existing works have not considered quality-of-service (QoS) provisioning for
two-way relaying. In this paper, we consider the OFDM-based bidirectional
transmission where a pair of users exchange information with or without the
assistance of a single DF relay. Each user can communicate with the other via
three transmission modes: direct transmission, one-way relaying, and two-way
relaying. We jointly optimize the transmission policies, including power
allocation, transmission mode selection, and subcarrier assignment for
maximizing the weighted sum rates of the two users with diverse
quality-of-service (QoS) guarantees. We formulate the joint optimization
problem as a mixed integer programming problem. By using the dual method, we
efficiently solve the problem in an asymptotically optimal manner. Moreover, we
derive the capacity region of two-way DF relaying in parallel channels.
Simulation results show that the proposed resource-allocation scheme can
substantially improve system performance compared with the conventional
schemes. A number of interesting insights are also provided via comprehensive
simulations.
|
1301.5961 | New Lower Bounds for Constant Dimension Codes | cs.IT math.IT | This paper provides new constructive lower bounds for constant dimension
codes, using different techniques such as Ferrers diagram rank metric codes and
pending blocks. Constructions for two families of parameters of constant
dimension codes are presented. The examples of codes obtained by these
constructions are the largest known constant dimension codes for the given
parameters.
|
1301.5973 | Non-Adaptive Distributed Compression in Networks | cs.IT math.IT | In this paper, we discuss non-adaptive distributed compression of inter-node
correlated real-valued messages. To do so, we discuss the performance of
conventional packet forwarding via routing, in terms of the total network load
versus the resulting quality of service (distortion level). As a better
alternative for packet forwarding, we briefly describe our previously proposed
one-step Quantized Network Coding (QNC), and make motivating arguments on its
advantage when the appropriate marginal rates for distributed source coding are
not available at the encoder source nodes. We also derive analytic guarantees
on the resulting distortion of our one-step QNC scenario. Finally, we conclude
the paper by providing a mathematical comparison between the total network
loads of one-step QNC and conventional packet forwarding, showing a significant
reduction in the case of one-step QNC.
|
1301.5979 | Understanding metropolitan patterns of daily encounters | physics.soc-ph cs.SI physics.data-an | Understanding of the mechanisms driving our daily face-to-face encounters is
still limited; the field lacks large-scale datasets describing both individual
behaviors and their collective interactions. However, here, with the help of
travel smart card data, we uncover such encounter mechanisms and structures by
constructing a time-resolved in-vehicle social encounter network on public
buses in a city (about 5 million residents). This is the first time that such a
large network of encounters has been identified and analyzed. Using a
population scale dataset, we find physical encounters display reproducible
temporal patterns, indicating that repeated encounters are regular and
identical. On an individual scale, we find that collective regularities
dominate distinct encounters' bounded nature. An individual's encounter
capability is rooted in his/her daily behavioral regularity, explaining the
emergence of "familiar strangers" in daily life. Strikingly, we find
individuals with repeated encounters are not grouped into small communities,
but become strongly connected over time, resulting in a large, but
imperceptible, small-world contact network or "structure of co-presence" across
the whole metropolitan area. Revealing the encounter pattern and identifying
this large-scale contact network are crucial to understanding the dynamics in
patterns of social acquaintances, collective human behaviors, and --
particularly -- disclosing the impact of human behavior on various
diffusion/spreading processes.
|
1301.5986 | Autocorrelation and Linear Complexity of Quaternary Sequences of Period
2p Based on Cyclotomic Classes of Order Four | cs.IT math.IT | We examine the linear complexity and the autocorrelation properties of new
quaternary cyclotomic sequences of period 2p. The sequences are constructed via
the cyclotomic classes of order four.
|
1301.6011 | A Framework for Intelligent Medical Diagnosis using Rough Set with
Formal Concept Analysis | cs.AI | Medical diagnosis process vary in the degree to which they attempt to deal
with different complicating aspects of diagnosis such as relative importance of
symptoms, varied symptom pattern and the relation between diseases them selves.
Based on decision theory, in the past many mathematical models such as crisp
set, probability distribution, fuzzy set, intuitionistic fuzzy set were
developed to deal with complicating aspects of diagnosis. But, many such models
are failed to include important aspects of the expert decisions. Therefore, an
effort has been made to process inconsistencies in data being considered by
Pawlak with the introduction of rough set theory. Though rough set has major
advantages over the other methods, but it generates too many rules that create
many difficulties while taking decisions. Therefore, it is essential to
minimize the decision rules. In this paper, we use two processes such as pre
process and post process to mine suitable rules and to explore the relationship
among the attributes. In pre process we use rough set theory to mine suitable
rules, whereas in post process we use formal concept analysis from these
suitable rules to explore better knowledge and most important factors affecting
the decision making.
|
1301.6022 | Improving the lifecycle of robotics components using Domain-Specific
Languages | cs.RO cs.SE | There is currently a large amount of robotics software using the
component-oriented programming paradigm. However, the rapid growth in number
and complexity of components may compromise the scalability and the whole
lifecycle of robotics software systems. Model-Driven Engineering can be used to
mitigate these problems. This paper describes how using Domain-Specific
Languages to generate and describe critical parts of robotic systems helps
developers to perform component managerial tasks such as component creation,
modification, monitoring and deployment. Four different DSLs are proposed in
this paper: i) CDSL for specifying the structure of the components, ii) IDSL
for the description of their interfaces, iii) DDSL for describing the
deployment process of component networks and iv) PDSL to define and configure
component parameters. Their benefits have been demonstrated after their
implementation in RoboComp, a general-purpose and component-based robotics
framework. Examples of the usage of these DSLs are shown along with experiments
that demonstrate the benefits they bring to the lifecycle of the components.
|
1301.6039 | Recycling Proof Patterns in Coq: Case Studies | cs.AI cs.LG cs.LO | Development of Interactive Theorem Provers has led to the creation of big
libraries and varied infrastructures for formal proofs. However, despite (or
perhaps due to) their sophistication, the re-use of libraries by non-experts or
across domains is a challenge. In this paper, we provide detailed case studies
and evaluate the machine-learning tool ML4PG built to interactively data-mine
the electronic libraries of proofs, and to provide user guidance on the basis
of proof patterns found in the existing libraries.
|
1301.6058 | Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic
Regret | cs.LG | In online learning the performance of an algorithm is typically compared to
the performance of a fixed function from some class, with a quantity called
regret. Forster proposed a last-step min-max algorithm which was somewhat
simpler than the algorithm of Vovk, yet with the same regret. In fact the
algorithm he analyzed assumed that the choices of the adversary are bounded,
yielding artificially only the two extreme cases. We fix this problem by
weighing the examples in such a way that the min-max problem will be well
defined, and provide analysis with logarithmic regret that may have better
multiplicative factor than both bounds of Forster and Vovk. We also derive a
new bound that may be sub-logarithmic, as a recent bound of Orabona et.al, but
may have better multiplicative factor. Finally, we analyze the algorithm in a
weak-type of non-stationary setting, and show a bound that is sub-linear if the
non-stationarity is sub-linear as well.
|
1301.6063 | Arbitrarily Small Amounts of Correlation for Arbitrarily Varying Quantum
Channels | quant-ph cs.IT math-ph math.IT math.MP | As our main result we show that, in order to achieve the randomness assisted
message - and entanglement transmission capacities of a finite arbitrarily
varying quantum channel it is not necessary that sender and receiver share
(asymptotically perfect) common randomness. Rather, it is sufficient that they
each have access to an unlimited amount of uses of one part of a correlated
bipartite source. This access might be restricted to an arbitrary small
(nonzero) fraction per channel use, without changing the main result. We
investigate the notion of common randomness. It turns out that this is a very
costly resource - generically, it cannot be obtained just by local processing
of a bipartite source. This result underlines the importance of our main
result. Also, the asymptotic equivalence of the maximal- and average error
criterion for classical message transmission over finite arbitrarily varying
quantum channels is proven. At last, we prove a simplifed symmetrizability
condition for finite arbitrarily varying quantum channels.
|
1301.6111 | A Proof of Threshold Saturation for Spatially-Coupled LDPC Codes on BMS
Channels | cs.IT math.IT | Low-density parity-check (LDPC) convolutional codes have been shown to
exhibit excellent performance under low-complexity belief-propagation decoding
[1], [2]. This phenomenon is now termed threshold saturation via spatial
coupling. The underlying principle behind this appears to be very general and
spatially-coupled (SC) codes have been successfully applied in numerous areas.
Recently, SC regular LDPC codes have been proven to achieve capacity
universally, over the class of binary memoryless symmetric (BMS) channels,
under belief-propagation decoding [3], [4].
In [5], [6], potential functions are used to prove that the BP threshold of
SC irregular LDPC ensembles saturates, for the binary erasure channel, to the
conjectured MAP threshold (known as the Maxwell threshold) of the underlying
irregular ensembles. In this paper, that proof technique is generalized to BMS
channels, thereby extending some results of [4] to irregular LDPC ensembles. We
also believe that this approach can be expanded to cover a wide class of
graphical models whose message-passing rules are associated with a Bethe free
energy.
|
1301.6117 | Higher genus universally decodable matrices (UDMG) | cs.IT math.IT | We introduce the notion of Universally Decodable Matrices of Genus g (UDMG),
which for g=0 reduces to the notion of Universally Decodable Matrices (UDM)
introduced in [8]. A UDMG is a set of L matrices over a finite field, each with
K rows, and a linear independence condition satisfied by collections of K+g
columns formed from the initial segments of the matrices. We consider the
mathematical structure of UDMGs and their relation to linear vector codes. We
then give a construction of UDMG based on curves of genus g over the finite
field, which is a natural generalization of the UDM constructed in [8]. We
provide upper (and constructable lower) bounds for L in terms of K, q, g, and
the number of columns of the matrices. We will show there is a fundamental
trade off (Theorem 5.4) between L and g, akin to the Singleton bound for the
minimal Hamming distance of linear vector codes.
|
1301.6118 | X THEN X: Manipulation of Same-System Runoff Elections | cs.GT cs.CC cs.MA | Do runoff elections, using the same voting rule as the initial election but
just on the winning candidates, increase or decrease the complexity of
manipulation? Does allowing revoting in the runoff increase or decrease the
complexity relative to just having a runoff without revoting? For both weighted
and unweighted voting, we show that even for election systems with simple
winner problems the complexity of manipulation, manipulation with runoffs, and
manipulation with revoting runoffs are independent, in the abstract. On the
other hand, for some important, well-known election systems we determine what
holds for each of these cases. For no such systems do we find runoffs lowering
complexity, and for some we find that runoffs raise complexity. Ours is the
first paper to show that for natural, unweighted election systems, runoffs can
increase the manipulation complexity.
|
1301.6120 | A Rate-Splitting Approach to Fading Channels with Imperfect
Channel-State Information | cs.IT math.IT | As shown by M\'edard, the capacity of fading channels with imperfect
channel-state information (CSI) can be lower-bounded by assuming a Gaussian
channel input $X$ with power $P$ and by upper-bounding the conditional entropy
$h(X|Y,\hat{H})$ by the entropy of a Gaussian random variable with variance
equal to the linear minimum mean-square error in estimating $X$ from
$(Y,\hat{H})$. We demonstrate that, using a rate-splitting approach, this lower
bound can be sharpened: by expressing the Gaussian input $X$ as the sum of two
independent Gaussian variables $X_1$ and $X_2$ and by applying M\'edard's lower
bound first to bound the mutual information between $X_1$ and $Y$ while
treating $X_2$ as noise, and by applying it a second time to the mutual
information between $X_2$ and $Y$ while assuming $X_1$ to be known, we obtain a
capacity lower bound that is strictly larger than M\'edard's lower bound. We
then generalize this approach to an arbitrary number $L$ of layers, where $X$
is expressed as the sum of $L$ independent Gaussian random variables of
respective variances $P_{\ell}$, $\ell = 1,\dotsc,L$ summing up to $P$. Among
all such rate-splitting bounds, we determine the supremum over power
allocations $P_\ell$ and total number of layers $L$. This supremum is achieved
for $L\to\infty$ and gives rise to an analytically expressible capacity lower
bound. For Gaussian fading, this novel bound is shown to converge to the
Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows,
provided that the variance of the channel estimation error $H-\hat{H}$ tends to
zero as the SNR tends to infinity.
|
1301.6125 | Flaglets: Exact Wavelets on the Ball | cs.IT astro-ph.IM math.IT | We summarise the construction of exact axisymmetric scale-discretised
wavelets on the sphere and on the ball. The wavelet transform on the ball
relies on a novel 3D harmonic transform called the Fourier-Laguerre transform
which combines the spherical harmonic transform with damped Laguerre
polynomials on the radial half-line. The resulting wavelets, called flaglets,
extract scale-dependent, spatially localised features in three-dimensions while
treating the tangential and radial structures separately. Both the
Fourier-Laguerre and the flaglet transforms are theoretically exact thanks to a
novel sampling theorem on the ball. Our implementation of these methods is
publicly available and achieves floating-point accuracy when applied to
band-limited signals.
|
1301.6150 | Polar Codes For Broadcast Channels | cs.IT math.IT | Polar codes are introduced for discrete memoryless broadcast channels. For
$m$-user deterministic broadcast channels, polarization is applied to map
uniformly random message bits from $m$ independent messages to one codeword
while satisfying broadcast constraints. The polarization-based codes achieve
rates on the boundary of the private-message capacity region. For two-user
noisy broadcast channels, polar implementations are presented for two
information-theoretic schemes: i) Cover's superposition codes; ii) Marton's
codes. Due to the structure of polarization, constraints on the auxiliary and
channel-input distributions are identified to ensure proper alignment of
polarization indices in the multi-user setting. The codes achieve rates on the
capacity boundary of a few classes of broadcast channels (e.g., binary-input
stochastically degraded). The complexity of encoding and decoding is $O(n*log
n)$ where $n$ is the block length. In addition, polar code sequences obtain a
stretched-exponential decay of $O(2^{-n^{\beta}})$ of the average block error
probability where $0 < \beta < 0.5$.
|
1301.6157 | High-Rate Regenerating Codes Through Layering | cs.IT math.IT | In this paper, we provide explicit constructions for a class of exact-repair
regenerating codes that possess a layered structure. These regenerating codes
correspond to interior points on the storage-repair-bandwidth tradeoff, and
compare very well in comparison to scheme that employs space-sharing between
MSR and MBR codes. For the parameter set $(n,k,d=k)$ with $n < 2k-1$, we
construct a class of codes with an auxiliary parameter $w$, referred to as
canonical codes. With $w$ in the range $n-k < w < k$, these codes operate in
the region between the MSR point and the MBR point, and perform significantly
better than the space-sharing line. They only require a field size greater than
$w+n-k$. For the case of $(n,n-1,n-1)$, canonical codes can also be shown to
achieve an interior point on the line-segment joining the MSR point and the
next point of slope-discontinuity on the storage-repair-bandwidth tradeoff.
Thus we establish the existence of exact-repair codes on a point other than the
MSR and the MBR point on the storage-repair-bandwidth tradeoff. We also
construct layered regenerating codes for general parameter set $(n,k<d,k)$,
which we refer to as non-canonical codes. These codes also perform
significantly better than the space-sharing line, though they require a
significantly higher field size. All the codes constructed in this paper are
high-rate, can repair multiple node-failures and do not require any computation
at the helper nodes. We also construct optimal codes with locality in which the
local codes are layered regenerating codes.
|
1301.6190 | Blahut-Arimoto Algorithm and Code Design for Action-Dependent Source
Coding Problems | cs.IT math.IT | The source coding problem with action-dependent side information at the
decoder has recently been introduced to model data acquisition in
resource-constrained systems. In this paper, an efficient algorithm for
numerical computation of the rate-distortion-cost function for this problem is
proposed, and a convergence proof is provided. Moreover, a two-stage code
design based on multiplexing is put forth, whereby the first stage encodes the
actions and the second stage is composed of an array of classical Wyner-Ziv
codes, one for each action. Specific coding/decoding strategies are designed
based on LDGM codes and message passing. Through numerical examples, the
proposed code design is shown to achieve performance close to the lower bound
dictated by the rate-distortion-cost function.
|
1301.6191 | Reuse, Temporal Dynamics, Interest Sharing, and Collaboration in Social
Tagging Systems | cs.IR cs.DL cs.SI physics.soc-ph | User-generated content is shaping the dynamics of the World Wide Web. Indeed,
an increasingly large number of systems provide mechanisms to support the
growing demand for content creation, sharing, and management. Tagging systems
are a particular class of these systems where users share and collaboratively
annotate content such as photos and URLs. This collaborative behavior and the
pool of user-generated metadata create opportunities to improve existing
systems and to design new mechanisms. However, to realize this potential, it is
necessary to understand the usage characteristics of current systems. This work
addresses this issue characterizing three tagging systems (CiteULike, Connotea
and del.icio.us) while focusing on three aspects: i) the patterns of
information (tags and items) production; ii) the temporal dynamics of users'
tag vocabularies; and, iii) the social aspects of tagging systems.
|
1301.6196 | On the Number of Interference Alignment Solutions for the K-User MIMO
Channel with Constant Coefficients | cs.IT math.IT | In this paper, we study the number of different interference alignment (IA)
solutions in a K-user multiple-input multiple-output (MIMO) interference
channel, when the alignment is performed via beamforming and no symbol
extensions are allowed. We focus on the case where the number of IA equations
matches the number of variables. In this situation, the number of IA solutions
is finite and constant for any channel realization out of a zero-measure set
and, as we prove in the paper, it is given by an integral formula that can be
numerically approximated using Monte Carlo integration methods. More precisely,
the number of alignment solutions is the scaled average of the determinant of a
certain Hermitian matrix related to the geometry of the problem. Interestingly,
while the value of this determinant at an arbitrary point can be used to check
the feasibility of the IA problem, its average (properly scaled) gives the
number of solutions. For single-beam systems the asymptotic growth rate of the
number of solutions is analyzed and some connections with classical
combinatorial problems are presented. Nonetheless, our results can be applied
to arbitrary interference MIMO networks, with any number of users, antennas and
streams per user.
|
1301.6198 | Approximate Sum-Capacity of K-user Cognitive Interference Channels with
Cumulative Message Sharing | cs.IT math.IT | This paper considers the K user cognitive interference channel with one
primary and K-1 secondary/cognitive transmitters with a cumulative message
sharing structure, i.e cognitive transmitter $i\in [2:K]$ knows non-causally
all messages of the users with index less than i. We propose a computable outer
bound valid for any memoryless channel. We first evaluate the sum-rate outer
bound for the high- SNR linear deterministic approximation of the Gaussian
noise channel. This is shown to be capacity for the 3-user channel with
arbitrary channel gains and the sum-capacity for the symmetric K-user channel.
Interestingly. for the K user channel having only the K th cognitive know all
the other messages is sufficient to achieve capacity i.e cognition at
transmitter 2 to K-1 is not needed. Next the sum capacity of the symmetric
Gaussian noise channel is characterized to within a constant additive and
multiplicative gap. The proposed achievable scheme for the additive gap is
based on Dirty paper coding and can be thought of as a MIMO-broadcast scheme
where only one encoding order is possible due to the message sharing structure.
As opposed to other multiuser interference channel models, a single scheme
suffices for both the weak and strong interference regimes. With this scheme
the generalized degrees of freedom (gDOF) is shown to be a function of K, in
contrast to the non cognitive case and the broadcast channel case.
Interestingly, it is show that as the number of users grows to infinity the
gDoF of the K-user cognitive interference channel with cumulative message
sharing tends to the gDoF of a broadcast channel with a K-antenna transmitter
and K single-antenna receivers. The analytical additive additive and
multiplicative gaps are a function of the number of users. Numerical
evaluations of inner and outer bounds show that the actual gap is less than the
analytical one.
|
1301.6199 | Sample Complexity of Bayesian Optimal Dictionary Learning | cs.LG cond-mat.dis-nn cond-mat.stat-mech cs.IT math.IT | We consider a learning problem of identifying a dictionary matrix D (M times
N dimension) from a sample set of M dimensional vectors Y = N^{-1/2} DX, where
X is a sparse matrix (N times P dimension) in which the density of non-zero
entries is 0<rho< 1. In particular, we focus on the minimum sample size P_c
(sample complexity) necessary for perfectly identifying D of the optimal
learning scheme when D and X are independently generated from certain
distributions. By using the replica method of statistical mechanics, we show
that P_c=O(N) holds as long as alpha = M/N >rho is satisfied in the limit of N
to infinity. Our analysis also implies that the posterior distribution given Y
is condensed only at the correct dictionary D when the compression rate alpha
is greater than a certain critical value alpha_M(rho). This suggests that
belief propagation may allow us to learn D with a low computational complexity
using O(N) samples.
|
1301.6209 | On the achievable region for interference networks with point-to-point
codes | cs.IT math.IT | This paper studies evaluation of the capacity region for interference
networks with point-to-point (p2p) capacity-achieving codes. Such capacity
region has recently been characterized as union of several sub-regions each of
which has distinctive operational characteristics. Detailed evaluation of this
region, therefore, can be accomplished in a very simple manner by acknowledging
such characteristics, which, in turn, provides an insight for a simple
implementation scenario. Completely generalized message assignment which is
also practically relevant is considered in this paper, and it is shown to
provide strictly larger achievable rates than what traditional message
assignment does when a receiver with joint decoding capability is used.
|
1301.6230 | Numerical homotopy continuation for control and online identification of
nonlinear systems: the survey of selected results | math.OC cs.SY | The article gives an overview of the parameter numerical continuation
methodology applied to setpoint control and parameter identification of
nonlinear systems. The control problems for affine systems as well as general
(nonaffine) nonlinear systems are considered. Online parameter identification
is also presented in two versions: with linear and nonlinear nonconvex
parameterization. Simulation results for illustrative examples are shown.
|
1301.6231 | Generalizing Bounds on the Minimum Distance of Cyclic Codes Using Cyclic
Product Codes | cs.IT math.IT | Two generalizations of the Hartmann--Tzeng (HT) bound on the minimum distance
of q-ary cyclic codes are proposed. The first one is proven by embedding the
given cyclic code into a cyclic product code. Furthermore, we show that unique
decoding up to this bound is always possible and outline a quadratic-time
syndrome-based error decoding algorithm. The second bound is stronger and the
proof is more involved.
Our technique of embedding the code into a cyclic product code can be applied
to other bounds, too and therefore generalizes them.
|
1301.6236 | Multi-Trial Guruswami--Sudan Decoding for Generalised Reed--Solomon
Codes | cs.IT math.IT | An iterated refinement procedure for the Guruswami--Sudan list decoding
algorithm for Generalised Reed--Solomon codes based on Alekhnovich's module
minimisation is proposed. The method is parametrisable and allows variants of
the usual list decoding approach. In particular, finding the list of
\emph{closest} codewords within an intermediate radius can be performed with
improved average-case complexity while retaining the worst-case complexity.
|
1301.6255 | Information Loss due to Finite Block Length in a Gaussian Line Network:
An Improved Bound | cs.IT math.IT math.PR stat.AP | A bound on the maximum information transmission rate through a cascade of
Gaussian links is presented. The network model consists of a source node
attempting to send a message drawn from a finite alphabet to a sink, through a
cascade of Additive White Gaussian Noise links each having an input power
constraint. Intermediate nodes are allowed to perform arbitrary
encoding/decoding operations, but the block length and the encoding rate are
fixed. The bound presented in this paper is fundamental and depends only on the
design parameters namely, the network size, block length, transmission rate,
and signal-to-noise ratio.
|
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