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1101.4458
|
Remarks on the Restricted Isometry Property in Orthogonal Matching
Pursuit algorithm
|
cs.IT math.IT
|
This paper demonstrates theoretically that if the restricted isometry
constant $\delta_K$ of the compressed sensing matrix satisfies $$ \delta_{K+1}
< \frac{1}{\sqrt{K}+1}, $$ then a greedy algorithm called Orthogonal Matching
Pursuit (OMP) can recover a signal with $K$ nonzero entries in $K$ iterations.
In contrast, matrices are also constructed with restricted isometry constant $$
\delta_{K+1} = \frac{1}{\sqrt{K}} $$ such that OMP can not recover $K$-sparse
$x$ in $K$ iterations. This result shows that the conjecture given by Dai and
Milenkovic is ture.
|
1101.4477
|
Limited Feedback Over Temporally Correlated Channels for the Downlink of
a Femtocell Network
|
cs.IT math.IT
|
Heterogeneous networks are a flexible deployment model that rely on low power
nodes to improve the user broadband experience in a cost effective manner.
Femtocells are an integral part of heterogeneous networks, whose main purpose
is to improve the indoor capacity. When restricting access to home users,
femtocells cause a substantial interference problem that cannot be mitigated
through coordination with the macrocell base station. In this paper, we analyze
multiple antenna communication on the downlink of a macrocell network, with
femtocell overlay. We evaluate the feasibility of limited feedback beamforming
given delay on the feedback channel, quantization error and uncoordinated
interference from the femtocells. We model the femtocell spatial distribution
as a Poisson point process and the temporal correlation of the channel
according to a Gauss-Markov model. We derive the probability of outage at the
macrocell users as a function of the temporal correlation, the femtocell
density, and the feedback rate. We propose rate backoff to maximize the average
achievable rate in the network. Simulation results show that limited feedback
beamforming is a viable solution for femtocell networks despite the CSI
inaccuracy and the interference. They illustrate how properly designed rate
backoff improves the achievable rate of the macrocell system.
|
1101.4479
|
A Context-theoretic Framework for Compositionality in Distributional
Semantics
|
cs.CL cs.AI
|
Techniques in which words are represented as vectors have proved useful in
many applications in computational linguistics, however there is currently no
general semantic formalism for representing meaning in terms of vectors. We
present a framework for natural language semantics in which words, phrases and
sentences are all represented as vectors, based on a theoretical analysis which
assumes that meaning is determined by context.
In the theoretical analysis, we define a corpus model as a mathematical
abstraction of a text corpus. The meaning of a string of words is assumed to be
a vector representing the contexts in which it occurs in the corpus model.
Based on this assumption, we can show that the vector representations of words
can be considered as elements of an algebra over a field. We note that in
applications of vector spaces to representing meanings of words there is an
underlying lattice structure; we interpret the partial ordering of the lattice
as describing entailment between meanings. We also define the context-theoretic
probability of a string, and, based on this and the lattice structure, a degree
of entailment between strings.
We relate the framework to existing methods of composing vector-based
representations of meaning, and show that our approach generalises many of
these, including vector addition, component-wise multiplication, and the tensor
product.
|
1101.4486
|
High-rate Space-Time-Frequency Codes Achieving Full-Diversity with
Partial Interference Cancellation Group Decoding
|
cs.IT math.IT
|
The partial interference cancellation (PIC) group decoding has recently been
proposed to deal with the decoding complexity and code rate trade-off on the
basis of space-time block code (STBC) design criterion when full diversity is
achieved. It provides a framework to arrange the rate-complexity-performance
tradeoff by choosing a suitable size of information symbol groups. In this
paper, a simple design of a linear dispersive space-time-frequency (STF) code
is proposed with a design criterion to achieve high rate for
frequency-selective channels in terms of multipath when the PIC group decoding
is applied at receiver. With an appropriate grouping scheme as well as the PIC
group decoding, the proposed STF code is shown to obtain the similar diversity
gain as the maximum likelihood (ML) decoding, namely full-dimensional sphere
decoding, but have a low decoding complexity. It seems as an intermediate
decoding between the ML receiver and zero-forcing (ZF) receiver. The proposed
grouping design criterion for the PIC group decoding to achieve full diversity
deploying the orthogonal-frequency-division multiplexing (OFDM) technique is
also an intermediate condition between the loosest ML full rank criterion of
codewords and the strongest ZF linear independence condition of the column
vectors for the equivalent frequency-selective channel matrix. It can achieves
full diversity with the PIC group decoding for any number of sub-carriers and
the data rate can be made high. Several code design examples are illustrated
for the feasibility of this coding scheme. Simulation results show that the
proposed STF code can well address the rate-performance-complexity tradeoff of
the multiple-input multiple-output orthogonal frequency division multiplexing
(MIMO-OFDM) communication system.
|
1101.4505
|
Interplay between telecommunications and face-to-face interactions - a
study using mobile phone data
|
physics.soc-ph cs.SI
|
In this study we analyze one year of anonymized telecommunications data for
over one million customers from a large European cellphone operator, and we
investigate the relationship between people's calls and their physical
location. We discover that more than 90% of users who have called each other
have also shared the same space (cell tower), even if they live far apart.
Moreover, we find that close to 70% of users who call each other frequently (at
least once per month on average) have shared the same space at the same time -
an instance that we call co-location. Co-locations appear indicative of
coordination calls, which occur just before face-to-face meetings. Their number
is highly predictable based on the amount of calls between two users and the
distance between their home locations - suggesting a new way to quantify the
interplay between telecommunications and face-to-face interactions.
|
1101.4573
|
Finding undetected protein associations in cell signaling by belief
propagation
|
q-bio.MN cond-mat.stat-mech cs.AI cs.CE
|
External information propagates in the cell mainly through signaling cascades
and transcriptional activation, allowing it to react to a wide spectrum of
environmental changes. High throughput experiments identify numerous molecular
components of such cascades that may, however, interact through unknown
partners. Some of them may be detected using data coming from the integration
of a protein-protein interaction network and mRNA expression profiles. This
inference problem can be mapped onto the problem of finding appropriate optimal
connected subgraphs of a network defined by these datasets. The optimization
procedure turns out to be computationally intractable in general. Here we
present a new distributed algorithm for this task, inspired from statistical
physics, and apply this scheme to alpha factor and drug perturbations data in
yeast. We identify the role of the COS8 protein, a member of a gene family of
previously unknown function, and validate the results by genetic experiments.
The algorithm we present is specially suited for very large datasets, can run
in parallel, and can be adapted to other problems in systems biology. On
renowned benchmarks it outperforms other algorithms in the field.
|
1101.4603
|
Evaluation Codes from smooth Quadric Surfaces and Twisted Segre
Varieties
|
cs.IT math.AG math.IT math.NT
|
We give the parameters of any evaluation code on a smooth quadric surface.
For hyperbolic quadrics the approach uses elementary results on product codes
and the parameters of codes on elliptic quadrics are obtained by detecting a
BCH structure of these codes and using the BCH bound. The elliptic quadric is a
twist of the surface P^1 x P^1 and we detect a similar BCH structure on twists
of the Segre embedding of a product of any d copies of the projective line.
|
1101.4617
|
Applications of Stochastic Ordering to Wireless Communications
|
cs.IT math.IT
|
Stochastic orders are binary relations defined on probability distributions
which capture intuitive notions like being larger or being more variable. This
paper introduces stochastic ordering of instantaneous SNRs of fading channels
as a tool to compare the performance of communication systems over different
channels. Stochastic orders unify existing performance metrics such as ergodic
capacity, and metrics based on error rate functions for commonly used
modulation schemes through their relation with convex, and completely monotonic
(c.m.) functions. Toward this goal, performance metrics such as instantaneous
error rates of M-QAM and M-PSK modulations are shown to be c.m. functions of
the instantaneous SNR, while metrics such as the instantaneous capacity are
seen to have a completely monotonic derivative (c.m.d.). It is shown that the
commonly used parametric fading distributions for modeling line of sight (LoS),
exhibit a monotonicity in the LoS parameter with respect to the stochastic
Laplace transform order. Using stochastic orders, average performance of
systems involving multiple random variables are compared over different
channels, even when closed form expressions for such averages are not
tractable. These include diversity combining schemes, relay networks, and
signal detection over fading channels with non-Gaussian additive noise, which
are investigated herein. Simulations are also provided to corroborate our
results.
|
1101.4620
|
Unconditionally Secure Bit Commitment with Flying Qudits
|
quant-ph cs.CR cs.IT math.IT
|
In the task cryptographers call bit commitment, one party encrypts a
prediction in a way that cannot be decrypted until they supply a key, but has
only one valid key. Bit commitment has many applications, and has been much
studied, but completely and provably secure schemes have remained elusive. Here
we report a new development in physics-based cryptography which gives a
completely new way of implementing bit commitment that is perfectly secure. The
technique involves sending a quantum state (for instance one or more photons)
at light speed in one of two or more directions, either along a secure channel
or by quantum teleportation. Its security proof relies on the no-cloning
theorem of quantum theory and the no superluminal signalling principle of
special relativity.
|
1101.4681
|
Close the Gaps: A Learning-while-Doing Algorithm for a Class of
Single-Product Revenue Management Problems
|
cs.LG
|
We consider a retailer selling a single product with limited on-hand
inventory over a finite selling season. Customer demand arrives according to a
Poisson process, the rate of which is influenced by a single action taken by
the retailer (such as price adjustment, sales commission, advertisement
intensity, etc.). The relationship between the action and the demand rate is
not known in advance. However, the retailer is able to learn the optimal action
"on the fly" as she maximizes her total expected revenue based on the observed
demand reactions.
Using the pricing problem as an example, we propose a dynamic
"learning-while-doing" algorithm that only involves function value estimation
to achieve a near-optimal performance. Our algorithm employs a series of
shrinking price intervals and iteratively tests prices within that interval
using a set of carefully chosen parameters. We prove that the convergence rate
of our algorithm is among the fastest of all possible algorithms in terms of
asymptotic "regret" (the relative loss comparing to the full information
optimal solution). Our result closes the performance gaps between parametric
and non-parametric learning and between a post-price mechanism and a
customer-bidding mechanism. Important managerial insight from this research is
that the values of information on both the parametric form of the demand
function as well as each customer's exact reservation price are less important
than prior literature suggests. Our results also suggest that firms would be
better off to perform dynamic learning and action concurrently rather than
sequentially.
|
1101.4711
|
Von Neumann Normalisation of a Quantum Random Number Generator
|
cs.IT math.IT quant-ph
|
In this paper we study von Neumann un-biasing normalisation for ideal and
real quantum random number generators, operating on finite strings or infinite
bit sequences. In the ideal cases one can obtain the desired un-biasing. This
relies critically on the independence of the source, a notion we rigorously
define for our model. In real cases, affected by imperfections in measurement
and hardware, one cannot achieve a true un-biasing, but, if the bias "drifts
sufficiently slowly", the result can be arbitrarily close to un-biasing. For
infinite sequences, normalisation can both increase or decrease the
(algorithmic) randomness of the generated sequences. A successful application
of von Neumann normalisation---in fact, any un-biasing transformation---does
exactly what it promises, un-biasing, one (among infinitely many) symptoms of
randomness; it will not produce "true" randomness.
|
1101.4724
|
A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse
Channels
|
cs.IT math.IT
|
We propose a factor-graph-based approach to joint
channel-estimation-and-decoding (JCED) of bit- interleaved coded orthogonal
frequency division multiplexing (BICM-OFDM). In contrast to existing designs,
ours is capable of exploiting not only sparsity in sampled channel taps but
also clustering among the large taps, behaviors which are known to manifest at
larger communication bandwidths. In order to exploit these channel-tap
structures, we adopt a two-state Gaussian mixture prior in conjunction with a
Markov model on the hidden state. For loopy belief propagation, we exploit a
"generalized approximate message passing" (GAMP) algorithm recently developed
in the context of compressed sensing, and show that it can be successfully
coupled with soft-input soft-output decoding, as well as hidden Markov
inference, through the standard sum-product framework. For N subcarriers and
any channel length L < N, the resulting JCED-GAMP scheme has a computational
complexity of only O(N log2 N + N|S|), where |S| is the constellation size.
Numerical experiments using IEEE 802.15.4a channels show that our scheme yields
BER performance within 1 dB of the known-channel bound and 3-4 dB better than
soft equalization based on LMMSE and LASSO.
|
1101.4730
|
Dynamic scaling, data-collapse and self-similarity in
Barab\'{a}si-Albert networks
|
cond-mat.stat-mech cond-mat.dis-nn cs.SI physics.soc-ph
|
In this article, we show that if each node of the Barab\'{a}si-Albert (BA)
network is characterized by the generalized degree $q$, i.e. the product of
their degree $k$ and the square root of their respective birth time, then the
distribution function $F(q,t)$ exhibits dynamic scaling $F(q,t\rightarrow
\infty)\sim t^{-1/2}\phi(q/t^{1/2})$ where $\phi(x)$ is the scaling function.
We verified it by showing that a series of distinct $F(q,t)$ vs $q$ curves for
different network sizes $N$ collapse onto a single universal curve if we plot
$t^{1/2}F(q,t)$ vs $q/t^{1/2}$ instead. Finally, we show that the BA network
falls into two universality classes depending on whether new nodes arrive with
single edge ($m=1$) or with multiple edges ($m>1$).
|
1101.4749
|
Online Adaptive Decision Fusion Framework Based on Entropic Projections
onto Convex Sets with Application to Wildfire Detection in Video
|
cs.CV cs.LG
|
In this paper, an Entropy functional based online Adaptive Decision Fusion
(EADF) framework is developed for image analysis and computer vision
applications. In this framework, it is assumed that the compound algorithm
consists of several sub-algorithms each of which yielding its own decision as a
real number centered around zero, representing the confidence level of that
particular sub-algorithm. Decision values are linearly combined with weights
which are updated online according to an active fusion method based on
performing entropic projections onto convex sets describing sub-algorithms. It
is assumed that there is an oracle, who is usually a human operator, providing
feedback to the decision fusion method. A video based wildfire detection system
is developed to evaluate the performance of the algorithm in handling the
problems where data arrives sequentially. In this case, the oracle is the
security guard of the forest lookout tower verifying the decision of the
combined algorithm. Simulation results are presented. The EADF framework is
also tested with a standard dataset.
|
1101.4752
|
A Primal-Dual Convergence Analysis of Boosting
|
cs.LG math.OC
|
Boosting combines weak learners into a predictor with low empirical risk. Its
dual constructs a high entropy distribution upon which weak learners and
training labels are uncorrelated. This manuscript studies this primal-dual
relationship under a broad family of losses, including the exponential loss of
AdaBoost and the logistic loss, revealing:
- Weak learnability aids the whole loss family: for any {\epsilon}>0,
O(ln(1/{\epsilon})) iterations suffice to produce a predictor with empirical
risk {\epsilon}-close to the infimum;
- The circumstances granting the existence of an empirical risk minimizer may
be characterized in terms of the primal and dual problems, yielding a new proof
of the known rate O(ln(1/{\epsilon}));
- Arbitrary instances may be decomposed into the above two, granting rate
O(1/{\epsilon}), with a matching lower bound provided for the logistic loss.
|
1101.4795
|
Numerical Evaluation of Algorithmic Complexity for Short Strings: A
Glance into the Innermost Structure of Randomness
|
cs.IT cs.CC math.IT
|
We describe an alternative method (to compression) that combines several
theoretical and experimental results to numerically approximate the algorithmic
(Kolmogorov-Chaitin) complexity of all $\sum_{n=1}^82^n$ bit strings up to 8
bits long, and for some between 9 and 16 bits long. This is done by an
exhaustive execution of all deterministic 2-symbol Turing machines with up to 4
states for which the halting times are known thanks to the Busy Beaver problem,
that is 11019960576 machines. An output frequency distribution is then
computed, from which the algorithmic probability is calculated and the
algorithmic complexity evaluated by way of the (Levin-Zvonkin-Chaitin) coding
theorem.
|
1101.4815
|
Source Optimization in MISO Relaying with Channel Mean Feedback: A
Stochastic Ordering Approach
|
cs.IT math.IT
|
This paper investigates the optimum source transmission strategy to maximize
the capacity of a multiple-input single-output (MISO) amplify-and-forward relay
channel, assuming source-relay channel mean feedback at the source. The
challenge here is that relaying introduces a nonconvex structure in the
objective function, thereby excluding the possible use of previous methods
dealing with mean feedback that generally rely on the concavity of the
objective function. A novel method is employed, which divides the feasible set
into two subsets and establishes the optimum from one of them by comparison. As
such, the optimization is transformed into the comparison of two nonnegative
random variables in the Laplace transform order, which is one of the important
stochastic orders. It turns out that the optimum transmission strategy is to
transmit along the known channel mean and its orthogonal eigenchannels. The
condition for rank-one precoding (beamforming) to achieve capacity is also
determined. Our results subsume those for traditional MISO precoding with mean
feedback.
|
1101.4849
|
A Maximum Entropy solution of the Covariance Extension Problem for
Reciprocal Processes
|
math.OC cs.IT cs.SY math.IT math.PR
|
Stationary reciprocal processes defined on a finite interval of the integer
line can be seen as a special class of Markov random fields restricted to one
dimension. Non stationary reciprocal processes have been extensively studied in
the past especially by Jamison, Krener, Levy and co-workers. The specialization
of the non-stationary theory to the stationary case, however, does not seem to
have been pursued in sufficient depth in the literature. Stationary reciprocal
processes (and reciprocal stochastic models) are potentially useful for
describing signals which naturally live in a finite region of the time (or
space) line. Estimation or identification of these models starting from
observed data seems still to be an open problem which can lead to many
interesting applications in signal and image processing. In this paper, we
discuss a class of reciprocal processes which is the acausal analog of
auto-regressive (AR) processes, familiar in control and signal processing. We
show that maximum likelihood identification of these processes leads to a
covariance extension problem for block-circulant covariance matrices. This
generalizes the famous covariance band extension problem for stationary
processes on the integer line. As in the usual stationary setting on the
integer line, the covariance extension problem turns out to be a basic
conceptual and practical step in solving the identification problem. We show
that the maximum entropy principle leads to a complete solution of the problem.
|
1101.4918
|
Using Feature Weights to Improve Performance of Neural Networks
|
cs.LG cs.AI cs.CV
|
Different features have different relevance to a particular learning problem.
Some features are less relevant; while some very important. Instead of
selecting the most relevant features using feature selection, an algorithm can
be given this knowledge of feature importance based on expert opinion or prior
learning. Learning can be faster and more accurate if learners take feature
importance into account. Correlation aided Neural Networks (CANN) is presented
which is such an algorithm. CANN treats feature importance as the correlation
coefficient between the target attribute and the features. CANN modifies normal
feed-forward Neural Network to fit both correlation values and training data.
Empirical evaluation shows that CANN is faster and more accurate than applying
the two step approach of feature selection and then using normal learning
algorithms.
|
1101.4924
|
A Generalized Method for Integrating Rule-based Knowledge into Inductive
Methods Through Virtual Sample Creation
|
cs.LG cs.AI cs.CV
|
Hybrid learning methods use theoretical knowledge of a domain and a set of
classified examples to develop a method for classification. Methods that use
domain knowledge have been shown to perform better than inductive learners.
However, there is no general method to include domain knowledge into all
inductive learning algorithms as all hybrid methods are highly specialized for
a particular algorithm. We present an algorithm that will take domain knowledge
in the form of propositional rules, generate artificial examples from the rules
and also remove instances likely to be flawed. This enriched dataset then can
be used by any learning algorithm. Experimental results of different scenarios
are shown that demonstrate this method to be more effective than simple
inductive learning.
|
1101.4957
|
Aircraft Proximity Maps Based on Data-Driven Flow Modeling
|
cs.SY physics.data-an
|
With the forecast increase in air traffic demand over the next decades, it is
imperative to develop tools to provide traffic flow managers with the
information required to support decision making. In particular,
decision-support tools for traffic flow management should aid in limiting
controller workload and complexity, while supporting increases in air traffic
throughput. While many decision-support tools exist for short-term traffic
planning, few have addressed the strategic needs for medium- and long-term
planning for time horizons greater than 30 minutes. This paper seeks to address
this gap through the introduction of 3D aircraft proximity maps that evaluate
the future probability of presence of at least one or two aircraft at any given
point of the airspace. Three types of proximity maps are presented: presence
maps that indicate the local density of traffic; conflict maps that determine
locations and probabilities of potential conflicts; and outliers maps that
evaluate the probability of conflict due to aircraft not belonging to dominant
traffic patterns. These maps provide traffic flow managers with information
relating to the complexity and difficulty of managing an airspace. The intended
purpose of the maps is to anticipate how aircraft flows will interact, and how
outliers impact the dominant traffic flow for a given time period. This
formulation is able to predict which "critical" regions may be subject to
conflicts between aircraft, thereby requiring careful monitoring. These
probabilities are computed using a generative aircraft flow model. Time-varying
flow characteristics, such as geometrical configuration, speed, and probability
density function of aircraft spatial distribution within the flow, are
determined from archived Enhanced Traffic Management System data, using a
tailored clustering algorithm. Aircraft not belonging to flows are identified
as outliers.
|
1101.4989
|
Opportunistic Buffered Decode-Wait-and-Forward (OBDWF) Protocol for
Mobile Wireless Relay Networks
|
cs.IT math.IT
|
In this paper, we propose an opportunistic buffered decode-wait-and-forward
(OBDWF) protocol to exploit both relay buffering and relay mobility to enhance
the system throughput and the end-to-end packet delay under bursty arrivals. We
consider a point-to-point communication link assisted by K mobile relays. We
illustrate that the OBDWF protocol could achieve a better throughput and delay
performance compared with existing baseline systems such as the conventional
dynamic decode-and-forward (DDF) and amplified-and-forward (AF) protocol. In
addition to simulation performance, we also derived closed-form asymptotic
throughput and delay expressions of the OBDWF protocol. Specifically, the
proposed OBDWF protocol achieves an asymptotic throughput O(logK) with O(1)
total transmit power in the relay network. This is a significant gain compared
with the best known performance in conventional protocols (O(logK) throughput
with O(K) total transmit power). With bursty arrivals, we show that both the
stability region and average delay of the proposed OBDWF protocol can achieve
order-wise performance gain O(K) compared with conventional DDF protocol.
|
1101.4999
|
List decoding of a class of affine variety codes
|
cs.IT math.IT
|
Consider a polynomial $F$ in $m$ variables and a finite point ensemble $S=S_1
\times ... \times S_m$. When given the leading monomial of $F$ with respect to
a lexicographic ordering we derive improved information on the possible number
of zeros of $F$ of multiplicity at least $r$ from $S$. We then use this
information to design a list decoding algorithm for a large class of affine
variety codes.
|
1101.5025
|
Order Statistics Based List Decoding Techniques for Linear Binary Block
Codes
|
cs.IT math.IT
|
The order statistics based list decoding techniques for linear binary block
codes of small to medium block length are investigated. The construction of the
list of the test error patterns is considered. The original order statistics
decoding is generalized by assuming segmentation of the most reliable
independent positions of the received bits. The segmentation is shown to
overcome several drawbacks of the original order statistics decoding. The
complexity of the order statistics based decoding is further reduced by
assuming a partial ordering of the received bits in order to avoid the complex
Gauss elimination. The probability of the test error patterns in the decoding
list is derived. The bit error rate performance and the decoding complexity
trade-off of the proposed decoding algorithms is studied by computer
simulations. Numerical examples show that, in some cases, the proposed decoding
schemes are superior to the original order statistics decoding in terms of both
the bit error rate performance as well as the decoding complexity.
|
1101.5039
|
A Novel Template-Based Learning Model
|
cs.LG
|
This article presents a model which is capable of learning and abstracting
new concepts based on comparing observations and finding the resemblance
between the observations. In the model, the new observations are compared with
the templates which have been derived from the previous experiences. In the
first stage, the objects are first represented through a geometric description
which is used for finding the object boundaries and a descriptor which is
inspired by the human visual system and then they are fed into the model. Next,
the new observations are identified through comparing them with the
previously-learned templates and are used for producing new templates. The
comparisons are made based on measures like Euclidean or correlation distance.
The new template is created by applying onion-pealing algorithm. The algorithm
consecutively uses convex hulls which are made by the points representing the
objects. If the new observation is remarkably similar to one of the observed
categories, it is no longer utilized in creating a new template. The existing
templates are used to provide a description of the new observation. This
description is provided in the templates space. Each template represents a
dimension of the feature space. The degree of the resemblance each template
bears to each object indicates the value associated with the object in that
dimension of the templates space. In this way, the description of the new
observation becomes more accurate and detailed as the time passes and the
experiences increase. We have used this model for learning and recognizing the
new polygons in the polygon space. Representing the polygons was made possible
through employing a geometric method and a method inspired by human visual
system. Various implementations of the model have been compared. The evaluation
results of the model prove its efficiency in learning and deriving new
templates.
|
1101.5048
|
Reinforced communication and social navigation: remember your friends
and remember yourself
|
physics.soc-ph cs.SI
|
In social systems, people communicate with each other and form groups based
on their interests. The pattern of interactions, the network, and the ideas
that flow on the network naturally evolve together. Researchers use simple
models to capture the feedback between changing network patterns and ideas on
the network, but little is understood about the role of past events in the
feedback process. Here we introduce a simple agent-based model to study the
coupling between peoples' ideas and social networks, and better understand the
role of history in dynamic social networks. We measure how information about
ideas can be recovered from information about network structure and, the other
way around, how information about network structure can be recovered from
information about ideas. We find that it is in general easier to recover ideas
from the network structure than vice versa.
|
1101.5058
|
Impact of link deletions on public cooperation in scale-free networks
|
physics.soc-ph cs.SI q-bio.PE
|
Working together in groups may be beneficial if compared to isolated efforts.
Yet this is true only if all group members contribute to the success. If not,
group efforts may act detrimentally on the fitness of their members. Here we
study the evolution of cooperation in public goods games on scale-free networks
that are subject to deletion of links that are connected to the highest-degree
individuals, i.e., on networks that are under attack. We focus on the case
where all groups a player belongs to are considered for the determination of
payoffs; the so-called multi-group public goods games. We find that the effect
of link deletions on the evolution of cooperation is predominantly detrimental,
although there exist regions of the multiplication factor where the existence
of an "optimal" number of removed links for deterioration of cooperation can
also be demonstrated. The findings are explained by means of wealth
distributions and analytical approximations, confirming that socially diverse
states are crucial for the successful evolution of cooperation.
|
1101.5076
|
Geometric representations for minimalist grammars
|
cs.CL
|
We reformulate minimalist grammars as partial functions on term algebras for
strings and trees. Using filler/role bindings and tensor product
representations, we construct homomorphisms for these data structures into
geometric vector spaces. We prove that the structure-building functions as well
as simple processors for minimalist languages can be realized by piecewise
linear operators in representation space. We also propose harmony, i.e. the
distance of an intermediate processing step from the final well-formed state in
representation space, as a measure of processing complexity. Finally, we
illustrate our findings by means of two particular arithmetic and fractal
representations.
|
1101.5079
|
Compressive Sensing Using the Entropy Functional
|
cs.IT math.IT
|
In most compressive sensing problems l1 norm is used during the signal
reconstruction process. In this article the use of entropy functional is
proposed to approximate the l1 norm. A modified version of the entropy
functional is continuous, differentiable and convex. Therefore, it is possible
to construct globally convergent iterative algorithms using Bregman's row
action D-projection method for compressive sensing applications. Simulation
examples are presented.
|
1101.5088
|
On Sharing Viral Video over an Ad Hoc Wireless Network
|
cs.SI
|
We consider the problem of broadcasting a viral video (a large file) over an
ad hoc wireless network (e.g., students in a campus). Many smartphones are GPS
enabled, and equipped with peer-to-peer (ad hoc) transmission mode, allowing
them to wirelessly exchange files over short distances rather than use the
carrier's WAN. The demand for the file however is transmitted through the
social network (e.g., a YouTube link posted on Facebook).
To address this coupled-network problem (demand on the social network;
bandwidth on the wireless network) where the two networks have different
topologies, we propose a file dissemination algorithm. In our scheme, users
query their social network to find geographically nearby friends that have the
desired file, and utilize the underlying ad hoc network to route the data via
multi-hop transmissions. We show that for many popular models for social
networks, the file dissemination time scales sublinearly with n; the number of
users, compared to the linear scaling required if each user who wants the file
must download it from the carrier's WAN.
|
1101.5097
|
Infinite Multiple Membership Relational Modeling for Complex Networks
|
cs.SI cs.LG physics.soc-ph
|
Learning latent structure in complex networks has become an important problem
fueled by many types of networked data originating from practically all fields
of science. In this paper, we propose a new non-parametric Bayesian
multiple-membership latent feature model for networks. Contrary to existing
multiple-membership models that scale quadratically in the number of vertices
the proposed model scales linearly in the number of links admitting
multiple-membership analysis in large scale networks. We demonstrate a
connection between the single membership relational model and multiple
membership models and show on "real" size benchmark network data that
accounting for multiple memberships improves the learning of latent structure
as measured by link prediction while explicitly accounting for multiple
membership result in a more compact representation of the latent structure of
networks.
|
1101.5108
|
Causal Dependence Tree Approximations of Joint Distributions for
Multiple Random Processes
|
cs.IT math.IT
|
We investigate approximating joint distributions of random processes with
causal dependence tree distributions. Such distributions are particularly
useful in providing parsimonious representation when there exists causal
dynamics among processes. By extending the results by Chow and Liu on
dependence tree approximations, we show that the best causal dependence tree
approximation is the one which maximizes the sum of directed informations on
its edges, where best is defined in terms of minimizing the KL-divergence
between the original and the approximate distribution. Moreover, we describe a
low-complexity algorithm to efficiently pick this approximate distribution.
|
1101.5120
|
Temporal patterns of happiness and information in a global social
network: Hedonometrics and Twitter
|
physics.soc-ph cs.SI
|
Individual happiness is a fundamental societal metric. Normally measured
through self-report, happiness has often been indirectly characterized and
overshadowed by more readily quantifiable economic indicators such as gross
domestic product. Here, we examine expressions made on the online, global
microblog and social networking service Twitter, uncovering and explaining
temporal variations in happiness and information levels over timescales ranging
from hours to years. Our data set comprises over 46 billion words contained in
nearly 4.6 billion expressions posted over a 33 month span by over 63 million
unique users. In measuring happiness, we use a real-time, remote-sensing,
non-invasive, text-based approach---a kind of hedonometer. In building our
metric, made available with this paper, we conducted a survey to obtain
happiness evaluations of over 10,000 individual words, representing a tenfold
size improvement over similar existing word sets. Rather than being ad hoc, our
word list is chosen solely by frequency of usage and we show how a highly
robust metric can be constructed and defended.
|
1101.5130
|
Analytical Evaluation of Fractional Frequency Reuse for OFDMA Cellular
Networks
|
cs.IT cs.NI math.IT math.PR
|
Fractional frequency reuse (FFR) is an interference management technique
well-suited to OFDMA-based cellular networks wherein the cells are partitioned
into spatial regions with different frequency reuse factors. To date, FFR
techniques have been typically been evaluated through system-level simulations
using a hexagonal grid for the base station locations. This paper instead
focuses on analytically evaluating the two main types of FFR deployments -
Strict FFR and Soft Frequency Reuse (SFR) - using a Poisson point process to
model the base station locations. The results are compared with the standard
grid model and an actual urban deployment. Under reasonable special cases for
modern cellular networks, our results reduce to simple closed-form expressions,
which provide insight into system design guidelines and the relative merits of
Strict FFR, SFR, universal reuse, and fixed frequency reuse. We observe that
FFR provides an increase in the sum-rate as well as the well-known benefit of
improved coverage for cell-edge users. Finally, a SINR-proportional resource
allocation strategy is proposed based on the analytical expressions, showing
that Strict FFR provides greater overall network throughput at low traffic
loads, while SFR better balances the requirements of interference reduction and
resource efficiency when the traffic load is high.
|
1101.5141
|
A Complex Networks Approach for Data Clustering
|
physics.data-an cs.LG cs.SI physics.soc-ph
|
Many methods have been developed for data clustering, such as k-means,
expectation maximization and algorithms based on graph theory. In this latter
case, graphs are generally constructed by taking into account the Euclidian
distance as a similarity measure, and partitioned using spectral methods.
However, these methods are not accurate when the clusters are not well
separated. In addition, it is not possible to automatically determine the
number of clusters. These limitations can be overcome by taking into account
network community identification algorithms. In this work, we propose a
methodology for data clustering based on complex networks theory. We compare
different metrics for quantifying the similarity between objects and take into
account three community finding techniques. This approach is applied to two
real-world databases and to two sets of artificially generated data. By
comparing our method with traditional clustering approaches, we verify that the
proximity measures given by the Chebyshev and Manhattan distances are the most
suitable metrics to quantify the similarity between objects. In addition, the
community identification method based on the greedy optimization provides the
smallest misclassification rates.
|
1101.5151
|
Simulation of Self-Assembly in the Abstract Tile Assembly Model with ISU
TAS
|
cs.MS cs.CE
|
Since its introduction by Erik Winfree in 1998, the abstract Tile Assembly
Model (aTAM) has inspired a wealth of research. As an abstract model for tile
based self-assembly, it has proven to be remarkably powerful and expressive in
terms of the structures which can self-assemble within it. As research has
progressed in the aTAM, the self-assembling structures being studied have
become progressively more complex. This increasing complexity, along with a
need for standardization of definitions and tools among researchers, motivated
the development of the Iowa State University Tile Assembly Simulator (ISU TAS).
ISU TAS is a graphical simulator and tile set editor for designing and building
2-D and 3-D aTAM tile assembly systems and simulating their self-assembly. This
paper reviews the features and functionality of ISU TAS and describes how it
can be used to further research into the complexities of the aTAM. Software and
source code are available at http://www.cs.iastate.edu/~lnsa.
|
1101.5207
|
Hybrid Digital-Analog Codes for Source-Channel Broadcast of Gaussian
Sources over Gaussian Channels
|
cs.IT math.IT
|
The problem of broadcasting a parallel Gaussian source over an additive white
Gaussian noise broadcast channel under the mean-squared error distortion
criterion is studied. A hybrid digital-analog coding strategy which combines
source coding with side information, channel coding with side information,
layered source coding, and superposition broadcast channel coding is presented.
When specialized to the open problem of broadcasting a white Gaussian source
over an additive white Gaussian noise broadcast channel with bandwidth mismatch
which has been the subject of several previous investigations, this coding
scheme strictly improves on the state-of-the-art.
|
1101.5257
|
Cooperative Regenerating Codes for Distributed Storage Systems
|
cs.IT cs.DC math.IT
|
When there are multiple node failures in a distributed storage system,
regenerating the failed storage nodes individually in a one-by-one manner is
suboptimal as far as repair-bandwidth minimization is concerned. If data
exchange among the newcomers is enabled, we can get a better tradeoff between
repair bandwidth and the storage per node. An explicit and optimal construction
of cooperative regenerating code is illustrated.
|
1101.5308
|
Parsimonious Flooding in Geometric Random-Walks
|
cs.SI cs.DM
|
We study the information spreading yielded by the \emph{(Parsimonious)
$1$-Flooding Protocol} in geometric Mobile Ad-Hoc Networks. We consider $n$
agents on a convex plane region of diameter $D$ performing independent random
walks with move radius $\rho$. At any time step, every active agent $v$ informs
every non-informed agent which is within distance $R$ from $v$ ($R>0$ is the
transmission radius). An agent is only active at the time step immediately
after the one in which has been informed and, after that, she is removed. At
the initial time step, a source agent is informed and we look at the
\emph{completion time} of the protocol, i.e., the first time step (if any) in
which all agents are informed. This random process is equivalent to the
well-known \emph{Susceptible-Infective-Removed ($SIR$}) infection process in
Mathematical Epidemiology. No analytical results are available for this random
process over any explicit mobility model. The presence of removed agents makes
this process much more complex than the (standard) flooding. We prove optimal
bounds on the completion time depending on the parameters $n$, $D$, $R$, and
$\rho$. The obtained bounds hold with high probability. We remark that our
method of analysis provides a clear picture of the dynamic shape of the
information spreading (or infection wave) over the time.
|
1101.5317
|
A Novel Unified Expression for the Capacity and Bit Error Probability of
Wireless Communication Systems over Generalized Fading Channels
|
cs.IT cs.PF math.IT
|
Analysis of the average binary error probabilities (ABEP) and average
capacity (AC) of wireless communications systems over generalized fading
channels have been considered separately in the past. This paper introduces a
novel moment generating function (MGF)-based \emph{unified expression} for the
ABEP and AC of single and multiple link communication with maximal ratio
combining. In addition, this paper proposes the hyper-Fox's H fading model as a
unified fading distribution of a majority of the well-known generalized fading
models. As such, we offer a generic unified performance expression that can be
easily calculated and that is applicable to a wide variety of fading scenarios.
The mathematical formalism is illustrated with some selected numerical examples
that validate the correctness of our newly derived results.
|
1101.5320
|
A Panorama on Multiscale Geometric Representations, Intertwining
Spatial, Directional and Frequency Selectivity
|
cs.CV
|
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.
|
1101.5322
|
Defecting or not defecting: how to "read" human behavior during
cooperative games by EEG measurements
|
physics.soc-ph cs.SI q-bio.NC
|
Understanding the neural mechanisms responsible for human social interactions
is difficult, since the brain activities of two or more individuals have to be
examined simultaneously and correlated with the observed social patterns. We
introduce the concept of hyper-brain network, a connectivity pattern
representing at once the information flow among the cortical regions of a
single brain as well as the relations among the areas of two distinct brains.
Graph analysis of hyper-brain networks constructed from the EEG scanning of 26
couples of individuals playing the Iterated Prisoner's Dilemma reveals the
possibility to predict non-cooperative interactions during the decision-making
phase. The hyper-brain networks of two-defector couples have significantly less
inter-brain links and overall higher modularity - i.e. the tendency to form two
separate subgraphs - than couples playing cooperative or tit-for-tat
strategies. The decision to defect can be "read" in advance by evaluating the
changes of connectivity pattern in the hyper-brain network.
|
1101.5334
|
SmartInt: Using Mined Attribute Dependencies to Integrate Fragmented Web
Databases
|
cs.DB cs.IR
|
Many web databases can be seen as providing partial and overlapping
information about entities in the world. To answer queries effectively, we need
to integrate the information about the individual entities that are fragmented
over multiple sources. At first blush this is just the inverse of traditional
database normalization problem - rather than go from a universal relation to
normalized tables, we want to reconstruct the universal relation given the
tables (sources). The standard way of reconstructing the entities will involve
joining the tables. Unfortunately, because of the autonomous and decentralized
way in which the sources are populated, they often do not have Primary Key -
Foreign Key relations. While tables may share attributes, naive joins over
these shared attributes can result in reconstruction of many spurious entities
thus seriously compromising precision. Our system, \smartint\ is aimed at
addressing the problem of data integration in such scenarios. Given a query,
our system uses the Approximate Functional Dependencies (AFDs) to piece
together a tree of relevant tables to answer it. The result tuples produced by
our system are able to strike a favorable balance between precision and recall.
|
1101.5336
|
On the existence of a (2,3)-spread in V(7,2)
|
math.CO cs.IT math.IT
|
An $(s,t)$-spread in a finite vector space $V=V(n,q)$ is a collection
$\mathcal F$ of $t$-dimensional subspaces of $V$ with the property that every
$s$-dimensional subspace of $V$ is contained in exactly one member of $\mathcal
F$. It is remarkable that no $(s,t)$-spreads has been found yet, except in the
case $s=1$. In this note, the concept $\alpha$-point to a $(2,3)$-spread
$\mathcal F$ in {$V=V(7,2)$} is introduced. A classical result of Thomas,
applied to the vector space $V$, states that all points of $V$ cannot be
$\alpha$-points to a given $(2,3)$-spread $\mathcal F$ in $V$. {In this note,
we strengthened this result by proving that} every 6-dimensional subspace of
$V$ must contain at least one point that is not an $\alpha$-point to a given
$(2,3)$-spread of $V$.
|
1101.5379
|
How Many Nodes are Effectively Accessed in Complex Networks?
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
The measurement called accessibility has been proposed as a means to quantify
the efficiency of the communication between nodes in complex networks. This
article reports important results regarding the properties of the
accessibility, including its relationship with the average minimal time to
visit all nodes reachable after $h$ steps along a random walk starting from a
source, as well as the number of nodes that are visited after a finite period
of time. We characterize the relationship between accessibility and the average
number of walks required in order to visit all reachable nodes (the exploration
time), conjecture that the maximum accessibility implies the minimal
exploration time, and confirm the relationship between the accessibility values
and the number of nodes visited after a basic time unit. The latter
relationship is investigated with respect to three types of dynamics, namely:
traditional random walks, self-avoiding random walks, and preferential random
walks.
|
1101.5428
|
The Computing of Digital Ecosystems
|
cs.DC cs.MA cs.NE
|
A primary motivation for our research in digital ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the computing technologies that contribute
to these properties have not been made explicit in digital ecosystems research.
Here, we discuss how different computing technologies can contribute to
providing the necessary self-organising features, including Multi-Agent Systems
(MASs), Service-Oriented Architectures (SOAs), and distributed evolutionary
computing (DEC). The potential for exploiting these properties in digital
ecosystems is considered, suggesting how several key features of biological
ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking
these features may assist in developing robust, scalable self-organising
architectures. An example architecture, the Digital Ecosystem, is considered in
detail. The Digital Ecosystem is then measured experimentally through
simulations, considering the self-organised diversity of its evolving agent
populations relative to the user request behaviour.
|
1101.5460
|
A Human-Centric Approach to Group-Based Context-Awareness
|
cs.AI cs.HC
|
The emerging need for qualitative approaches in context-aware information
processing calls for proper modeling of context information and efficient
handling of its inherent uncertainty resulted from human interpretation and
usage. Many of the current approaches to context-awareness either lack a solid
theoretical basis for modeling or ignore important requirements such as
modularity, high-order uncertainty management and group-based
context-awareness. Therefore, their real-world application and extendability
remains limited. In this paper, we present f-Context as a service-based
context-awareness framework, based on language-action perspective (LAP) theory
for modeling. Then we identify some of the complex, informational parts of
context which contain high-order uncertainties due to differences between
members of the group in defining them. An agent-based perceptual computer
architecture is proposed for implementing f-Context that uses computing with
words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed
using a realistic scenario involving a group of mobile users. We believe that
the proposed approach can open the door to future research on context-awareness
by offering a theoretical foundation based on human communication, and a
service-based layered architecture which exploits CWW for context-aware,
group-based and platform-independent access to information systems.
|
1101.5463
|
Walking on a Graph with a Magnifying Glass: Stratified Sampling via
Weighted Random Walks
|
cs.SI cs.NI physics.soc-ph stat.ME
|
Our objective is to sample the node set of a large unknown graph via
crawling, to accurately estimate a given metric of interest. We design a random
walk on an appropriately defined weighted graph that achieves high efficiency
by preferentially crawling those nodes and edges that convey greater
information regarding the target metric. Our approach begins by employing the
theory of stratification to find optimal node weights, for a given estimation
problem, under an independence sampler. While optimal under independence
sampling, these weights may be impractical under graph crawling due to
constraints arising from the structure of the graph. Therefore, the edge
weights for our random walk should be chosen so as to lead to an equilibrium
distribution that strikes a balance between approximating the optimal weights
under an independence sampler and achieving fast convergence. We propose a
heuristic approach (stratified weighted random walk, or S-WRW) that achieves
this goal, while using only limited information about the graph structure and
the node properties. We evaluate our technique in simulation, and
experimentally, by collecting a sample of Facebook college users. We show that
S-WRW requires 13-15 times fewer samples than the simple re-weighted random
walk (RW) to achieve the same estimation accuracy for a range of metrics.
|
1101.5494
|
Developing a New Approach for Arabic Morphological Analysis and
Generation
|
cs.CL
|
Arabic morphological analysis is one of the essential stages in Arabic
Natural Language Processing. In this paper we present an approach for Arabic
morphological analysis. This approach is based on Arabic morphological
automaton (AMAUT). The proposed technique uses a morphological database
realized using XMODEL language. Arabic morphology represents a special type of
morphological systems because it is based on the concept of scheme to represent
Arabic words. We use this concept to develop the Arabic morphological automata.
The proposed approach has development standardization aspect. It can be
exploited by NLP applications such as syntactic and semantic analysis,
information retrieval, machine translation and orthographical correction. The
proposed approach is compared with Xerox Arabic Analyzer and Smrz Arabic
Analyzer.
|
1101.5591
|
Direct, physically-motivated derivation of the contagion condition for
spreading processes on generalized random networks
|
cond-mat.dis-nn cs.SI physics.soc-ph
|
For a broad range single-seed contagion processes acting on generalized
random networks, we derive a unifying analytic expression for the possibility
of global spreading events in a straightforward, physically intuitive fashion.
Our reasoning lays bare a direct mechanical understanding of an archetypal
spreading phenomena that is not evident in circuitous extant mathematical
approaches.
|
1101.5632
|
Active Markov Information-Theoretic Path Planning for Robotic
Environmental Sensing
|
cs.LG cs.AI cs.MA cs.RO
|
Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequently scale poorly with the length of history of
observations. Hence, it becomes computationally impractical to use these
strategies for in situ, real-time active sampling. To ease this computational
burden, this paper presents a Markov-based approach to efficient
information-theoretic path planning for active sampling of GP-based fields. We
analyze the time complexity of solving the Markov-based path planning problem,
and demonstrate analytically that it scales better than that of deriving the
non-Markovian strategies with increasing length of planning horizon. For a
class of exploration tasks called the transect sampling task, we provide
theoretical guarantees on the active sampling performance of our Markov-based
policy, from which ideal environmental field conditions and sampling task
settings can be established to limit its performance degradation due to
violation of the Markov assumption. Empirical evaluation on real-world
temperature and plankton density field data shows that our Markov-based policy
can generally achieve active sampling performance comparable to that of the
widely-used non-Markovian greedy policies under less favorable realistic field
conditions and task settings while enjoying significant computational gain over
them.
|
1101.5668
|
Analysis of Web Logs and Web User in Web Mining
|
cs.DB
|
Log files contain information about User Name, IP Address, Time Stamp, Access
Request, number of Bytes Transferred, Result Status, URL that Referred and User
Agent. The log files are maintained by the web servers. By analysing these log
files gives a neat idea about the user. This paper gives a detailed discussion
about these log files, their formats, their creation, access procedures, their
uses, various algorithms used and the additional parameters that can be used in
the log files which in turn gives way to an effective mining. It also provides
the idea of creating an extended log file and learning the user behaviour.
|
1101.5672
|
On the Local Correctness of L^1 Minimization for Dictionary Learning
|
cs.IT cs.LG math.IT
|
The idea that many important classes of signals can be well-represented by
linear combinations of a small set of atoms selected from a given dictionary
has had dramatic impact on the theory and practice of signal processing. For
practical problems in which an appropriate sparsifying dictionary is not known
ahead of time, a very popular and successful heuristic is to search for a
dictionary that minimizes an appropriate sparsity surrogate over a given set of
sample data. While this idea is appealing, the behavior of these algorithms is
largely a mystery; although there is a body of empirical evidence suggesting
they do learn very effective representations, there is little theory to
guarantee when they will behave correctly, or when the learned dictionary can
be expected to generalize. In this paper, we take a step towards such a theory.
We show that under mild hypotheses, the dictionary learning problem is locally
well-posed: the desired solution is indeed a local minimum of the $\ell^1$
norm. Namely, if $\mb A \in \Re^{m \times n}$ is an incoherent (and possibly
overcomplete) dictionary, and the coefficients $\mb X \in \Re^{n \times p}$
follow a random sparse model, then with high probability $(\mb A,\mb X)$ is a
local minimum of the $\ell^1$ norm over the manifold of factorizations $(\mb
A',\mb X')$ satisfying $\mb A' \mb X' = \mb Y$, provided the number of samples
$p = \Omega(n^3 k)$. For overcomplete $\mb A$, this is the first result showing
that the dictionary learning problem is locally solvable. Our analysis draws on
tools developed for the problem of completing a low-rank matrix from a small
subset of its entries, which allow us to overcome a number of technical
obstacles; in particular, the absence of the restricted isometry property.
|
1101.5687
|
A correspondence-less approach to matching of deformable shapes
|
cs.CV cs.CG
|
Finding a match between partially available deformable shapes is a
challenging problem with numerous applications. The problem is usually
approached by computing local descriptors on a pair of shapes and then
establishing a point-wise correspondence between the two. In this paper, we
introduce an alternative correspondence-less approach to matching fragments to
an entire shape undergoing a non-rigid deformation. We use diffusion geometric
descriptors and optimize over the integration domains on which the integral
descriptors of the two parts match. The problem is regularized using the
Mumford-Shah functional. We show an efficient discretization based on the
Ambrosio-Tortorelli approximation generalized to triangular meshes. Experiments
demonstrating the success of the proposed method are presented.
|
1101.5716
|
Zero-Delay Joint Source-Channel Coding for a Bivariate Gaussian on a
Gaussian MAC
|
cs.IT math.IT
|
In this paper, delay-free, low complexity, joint source-channel coding (JSCC)
for transmission of two correlated Gaussian memoryless sources over a Gaussian
Multiple Access Channel (GMAC) is considered. The main contributions of the
paper are two distributed JSCC schemes: one discrete scheme based on nested
scalar quantization, and one hybrid discrete-analog scheme based on a scalar
quantizer and a linear continuous mapping. The proposed schemes show promising
performance which improve with increasing correlation and are robust against
variations in noise level. Both schemes exhibit a constant gap to the
performance upper bound when the channel signal-to-noise ratio gets large.
|
1101.5731
|
Minimizing Hidden-Node Network Interference by Optimizing SISO and MIMO
Spectral Efficiency
|
cs.IT math.IT
|
In this paper, the optimal spectral efficiency (data rate divided by the
message bandwidth) that minimizes the probability of causing disruptive
interference for ad hoc wireless networks or cognitive radios is investigated.
Two basic problem constraints are considered: a given message size, or fixed
data rate. Implicitly, the trade being optimized is between longer transmit
duration and wider bandwidth versus higher transmit power. Both single-input
single-output (SISO) and multiple-input multiple-output (MIMO) links are
considered. Here, a link optimizes its spectral efficiency to be a "good
neighbor." The probability of interference is characterized by the probability
that the signal power received by a hidden node in a wireless network exceeds
some threshold. The optimized spectral efficiency is a function of the
transmitter-to-hidden-node channel exponent, exclusively. It is shown that for
typical channel exponents a spectral efficiency of slightly greater than
1~b/s/Hz per antenna is optimal. It is also shown that the optimal spectral
efficiency is valid in the environment with multiple hidden nodes. Also
explicit evaluations of the probability of collisions is presented as a
function of spectral efficiency.
|
1101.5755
|
2D Sparse Signal Recovery via 2D Orthogonal Matching Pursuit
|
cs.IT cs.MM math.IT
|
Recovery algorithms play a key role in compressive sampling (CS). Most of
current CS recovery algorithms are originally designed for one-dimensional (1D)
signal, while many practical signals are two-dimensional (2D). By utilizing 2D
separable sampling, 2D signal recovery problem can be converted into 1D signal
recovery problem so that ordinary 1D recovery algorithms, e.g. orthogonal
matching pursuit (OMP), can be applied directly. However, even with 2D
separable sampling, the memory usage and complexity at the decoder is still
high. This paper develops a novel recovery algorithm called 2D-OMP, which is an
extension of 1D-OMP. In the 2D-OMP, each atom in the dictionary is a matrix. At
each iteration, the decoder projects the sample matrix onto 2D atoms to select
the best matched atom, and then renews the weights for all the already selected
atoms via the least squares. We show that 2D-OMP is in fact equivalent to
1D-OMP, but it reduces recovery complexity and memory usage significantly.
What's more important, by utilizing the same methodology used in this paper,
one can even obtain higher dimensional OMP (say 3D-OMP, etc.) with ease.
|
1101.5757
|
Polarized Montagovian Semantics for the Lambek-Grishin calculus
|
cs.CL
|
Grishin proposed enriching the Lambek calculus with multiplicative
disjunction (par) and coresiduals. Applications to linguistics were discussed
by Moortgat, who spoke of the Lambek-Grishin calculus (LG). In this paper, we
adapt Girard's polarity-sensitive double negation embedding for classical logic
to extract a compositional Montagovian semantics from a display calculus for
focused proof search in LG. We seize the opportunity to illustrate our approach
alongside an analysis of extraction, providing linguistic motivation for linear
distributivity of tensor over par, thus answering a question of
Kurtonina&Moortgat. We conclude by comparing our proposal to the continuation
semantics of Bernardi&Moortgat, corresponding to call-by- name and
call-by-value evaluation strategies.
|
1101.5763
|
A New Semantic Web Approach for Constructing, Searching and Modifying
Ontology Dynamically
|
cs.IR
|
Semantic web is the next generation web, which concerns the meaning of web
documents It has the immense power to pull out the most relevant information
from the web pages, which is also meaningful to any user, using software
agents. In today's world, agent communication is not possible if concerned
ontology is changed a little. We have pointed out this very problem and
developed an Ontology Purification System to help agent communication. In our
system you can send queries and view the search results. If it can't meet the
criteria then it finds out the mismatched elements. Modification is done within
a second and you can see the difference. That's why we emphasis on the word
dynamic. When Administrator is updating the system, at the same time that
updation is visible to the user.
|
1101.5766
|
Geometric Models with Co-occurrence Groups
|
cs.CV cs.IT math.IT
|
A geometric model of sparse signal representations is introduced for classes
of signals. It is computed by optimizing co-occurrence groups with a maximum
likelihood estimate calculated with a Bernoulli mixture model. Applications to
face image compression and MNIST digit classification illustrate the
applicability of this model.
|
1101.5785
|
Statistical Compressed Sensing of Gaussian Mixture Models
|
cs.CV cs.LG
|
A novel framework of compressed sensing, namely statistical compressed
sensing (SCS), that aims at efficiently sampling a collection of signals that
follow a statistical distribution, and achieving accurate reconstruction on
average, is introduced. SCS based on Gaussian models is investigated in depth.
For signals that follow a single Gaussian model, with Gaussian or Bernoulli
sensing matrices of O(k) measurements, considerably smaller than the O(k
log(N/k)) required by conventional CS based on sparse models, where N is the
signal dimension, and with an optimal decoder implemented via linear filtering,
significantly faster than the pursuit decoders applied in conventional CS, the
error of SCS is shown tightly upper bounded by a constant times the best k-term
approximation error, with overwhelming probability. The failure probability is
also significantly smaller than that of conventional sparsity-oriented CS.
Stronger yet simpler results further show that for any sensing matrix, the
error of Gaussian SCS is upper bounded by a constant times the best k-term
approximation with probability one, and the bound constant can be efficiently
calculated. For Gaussian mixture models (GMMs), that assume multiple Gaussian
distributions and that each signal follows one of them with an unknown index, a
piecewise linear estimator is introduced to decode SCS. The accuracy of model
selection, at the heart of the piecewise linear decoder, is analyzed in terms
of the properties of the Gaussian distributions and the number of sensing
measurements. A maximum a posteriori expectation-maximization algorithm that
iteratively estimates the Gaussian models parameters, the signals model
selection, and decodes the signals, is presented for GMM-based SCS. In real
image sensing applications, GMM-based SCS is shown to lead to improved results
compared to conventional CS, at a considerably lower computational cost.
|
1101.5805
|
The VC-Dimension of Queries and Selectivity Estimation Through Sampling
|
cs.DB cs.DS cs.LG
|
We develop a novel method, based on the statistical concept of the
Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality)
of SQL queries - a crucial step in optimizing the execution of large scale
database and data-mining operations. The major theoretical contribution of this
work, which is of independent interest, is an explicit bound to the
VC-dimension of a range space defined by all possible outcomes of a collection
(class) of queries. We prove that the VC-dimension is a function of the maximum
number of Boolean operations in the selection predicate and of the maximum
number of select and join operations in any individual query in the collection,
but it is neither a function of the number of queries in the collection nor of
the size (number of tuples) of the database. We leverage on this result and
develop a method that, given a class of queries, builds a concise random sample
of a database, such that with high probability the execution of any query in
the class on the sample provides an accurate estimate for the selectivity of
the query on the original large database. The error probability holds
simultaneously for the selectivity estimates of all queries in the collection,
thus the same sample can be used to evaluate the selectivity of multiple
queries, and the sample needs to be refreshed only following major changes in
the database. The sample representation computed by our method is typically
sufficiently small to be stored in main memory. We present extensive
experimental results, validating our theoretical analysis and demonstrating the
advantage of our technique when compared to complex selectivity estimation
techniques used in PostgreSQL and the Microsoft SQL Server.
|
1101.5809
|
The Degrees of Freedom Region and Interference Alignment for the MIMO
Interference Channel with Delayed CSI
|
cs.IT math.IT
|
The degrees of freedom (DoF) region of the 2-user multiple-antenna or MIMO
(multiple-input, multiple-output) interference channel (IC) is studied under
fast fading and the assumption of {\em delayed} channel state information (CSI)
wherein all terminals know all (or certain) channel matrices perfectly, but
with a delay, and each receiver in addition knows its own incoming channels
instantaneously. The general MIMO IC is considered with an arbitrary number of
antennas at each of the four terminals. Dividing it into several classes
depending on the relation between the numbers of antennas at the four
terminals, the fundamental DoF regions are characterized under the delayed CSI
assumption for {\em all} possible values of number of antennas at the four
terminals. In particular, an outer bound on the DoF region of the general MIMO
IC is derived. This bound is then shown to be tight for all MIMO ICs by
developing interference alignment based achievability schemes for each class. A
comparison of these DoF regions under the delayed CSI assumption is made with
those of the idealistic `perfect CSI' assumption where perfect and
instantaneous CSI is available at all terminals on the one hand and with the
DoF regions of the conservative `no CSI' assumption on the other, where CSI is
available at the receivers but not at all at the transmitters.
|
1101.5858
|
Simultaneous Code/Error-Trellis Reduction for Convolutional Codes Using
Shifted Code/Error-Subsequences
|
cs.IT math.IT
|
In this paper, we show that the code-trellis and the error-trellis for a
convolutional code can be reduced simultaneously, if reduction is possible.
Assume that the error-trellis can be reduced using shifted error-subsequences.
In this case, if the identical shifts occur in the subsequences of each code
path, then the code-trellis can also be reduced. First, we obtain pairs of
transformations which generate the identical shifts both in the subsequences of
the code-path and in those of the error-path. Next, by applying these
transformations to the generator matrix and the parity-check matrix, we show
that reduction of these matrices is accomplished simultaneously, if it is
possible. Moreover, it is shown that the two associated trellises are also
reduced simultaneously.
|
1101.5888
|
Predicted and Verified Deviations from Zipf's law in Ecology of
Competing Products
|
physics.soc-ph cs.SI
|
Zipf's power-law distribution is a generic empirical statistical regularity
found in many complex systems. However, rather than universality with a single
power-law exponent (equal to 1 for Zipf's law), there are many reported
deviations that remain unexplained. A recently developed theory finds that the
interplay between (i) one of the most universal ingredients, namely stochastic
proportional growth, and (ii) birth and death processes, leads to a generic
power-law distribution with an exponent that depends on the characteristics of
each ingredient. Here, we report the first complete empirical test of the
theory and its application, based on the empirical analysis of the dynamics of
market shares in the product market. We estimate directly the average growth
rate of market shares and its standard deviation, the birth rates and the
"death" (hazard) rate of products. We find that temporal variations and product
differences of the observed power-law exponents can be fully captured by the
theory with no adjustable parameters. Our results can be generalized to many
systems for which the statistical properties revealed by power law exponents
are directly linked to the underlying generating mechanism.
|
1101.5913
|
Path lengths, correlations, and centrality in temporal networks
|
physics.soc-ph cond-mat.dis-nn cs.SI physics.data-an
|
In temporal networks, where nodes interact via sequences of temporary events,
information or resources can only flow through paths that follow the
time-ordering of events. Such temporal paths play a crucial role in dynamic
processes. However, since networks have so far been usually considered static
or quasi-static, the properties of temporal paths are not yet well understood.
Building on a definition and algorithmic implementation of the average temporal
distance between nodes, we study temporal paths in empirical networks of human
communication and air transport. Although temporal distances correlate with
static graph distances, there is a large spread, and nodes that appear close
from the static network view may be connected via slow paths or not at all.
Differences between static and temporal properties are further highlighted in
studies of the temporal closeness centrality. In addition, correlations and
heterogeneities in the underlying event sequences affect temporal path lengths,
increasing temporal distances in communication networks and decreasing them in
the air transport network.
|
1101.5915
|
Dynamic Monopolies in Colored Tori
|
cs.SI cs.DC cs.DS physics.soc-ph
|
The {\em information diffusion} has been modeled as the spread of an
information within a group through a process of social influence, where the
diffusion is driven by the so called {\em influential network}. Such a process,
which has been intensively studied under the name of {\em viral marketing}, has
the goal to select an initial good set of individuals that will promote a new
idea (or message) by spreading the "rumor" within the entire social network
through the word-of-mouth. Several studies used the {\em linear threshold
model} where the group is represented by a graph, nodes have two possible
states (active, non-active), and the threshold triggering the adoption
(activation) of a new idea to a node is given by the number of the active
neighbors.
The problem of detecting in a graph the presence of the minimal number of
nodes that will be able to activate the entire network is called {\em target
set selection} (TSS). In this paper we extend TSS by allowing nodes to have
more than two colors. The multicolored version of the TSS can be described as
follows: let $G$ be a torus where every node is assigned a color from a finite
set of colors. At each local time step, each node can recolor itself, depending
on the local configurations, with the color held by the majority of its
neighbors. We study the initial distributions of colors leading the system to a
monochromatic configuration of color $k$, focusing on the minimum number of
initial $k$-colored nodes. We conclude the paper by providing the time
complexity to achieve the monochromatic configuration.
|
1101.5938
|
Dialog interface for dynamic data models
|
cs.SE cs.DB
|
In this paper, the new information system development methodology will be
proposed. This methodology will enable the whole data model to be built and
adjusted at the run time, without rebuilding the application. This will make
the user much more powerful and independent on the manufacturer of the system.
It will also cut the price and shorten the development time of the information
systems dramatically, because common business logic will not have to be
implemented for each individual table and the major part of the user interface
will be generated automatically.
|
1101.5966
|
On the Analysis of Weighted Nonbinary Repeat Multiple-Accumulate Codes
|
cs.IT math.IT
|
In this paper, we consider weighted nonbinary repeat multiple-accumulate
(WNRMA) code ensembles obtained from the serial concatenation of a nonbinary
rate-1/n repeat code and the cascade of L>= 1 accumulators, where each encoder
is followed by a nonbinary random weighter. The WNRMA codes are assumed to be
iteratively decoded using the turbo principle with maximum a posteriori
constituent decoders. We derive the exact weight enumerator of nonbinary
accumulators and subsequently give the weight enumerators for WNRMA code
ensembles. We formally prove that the symbol-wise minimum distance of WNRMA
code ensembles asymptotically grows linearly with the block length when L >= 3
and n >= 2, and L=2 and n >= 3, for all powers of primes q >= 3 considered,
where q is the field size. Thus, WNRMA code ensembles are asymptotically good
for these parameters. We also give iterative decoding thresholds, computed by
an extrinsic information transfer chart analysis, on the q-ary symmetric
channel to show the convergence properties. Finally, we consider the binary
image of WNRMA code ensembles and compare the asymptotic minimum distance
growth rates with those of binary repeat multiple-accumulate code ensembles.
|
1101.5972
|
Hidden Tree Structure is a Key to the Emergence of Scaling in the World
Wide Web
|
cs.SI physics.soc-ph
|
Preferential attachment is the most popular explanation for the emergence of
scaling behavior in the World Wide Web, but this explanation has been
challenged by the global information hypothesis, the existence of linear
preference and the emergence of new big internet companies in the real world.
We notice that most websites have an obvious feature that their pages are
organized as a tree (namely hidden tree) and hence propose a new model that
introduces a hidden tree structure into the Erd\H{o}s-R\'e}yi model by adding a
new rule: when one node connects to another, it should also connect to all
nodes in the path between these two nodes in the hidden tree. The experimental
results show that the degree distribution of the generated graphs would obey
power law distributions and have variable high clustering coefficients and
variable small average lengths of shortest paths. The proposed model provides
an alternative explanation to the emergence of scaling in the World Wide Web
without the above-mentioned difficulties, and also explains the "preferential
attachment" phenomenon.
|
1101.5984
|
Optimality of Binning for Distributed Hypothesis Testing
|
cs.IT math.IT
|
We study a hypothesis testing problem in which data is compressed
distributively and sent to a detector that seeks to decide between two possible
distributions for the data. The aim is to characterize all achievable encoding
rates and exponents of the type 2 error probability when the type 1 error
probability is at most a fixed value. For related problems in distributed
source coding, schemes based on random binning perform well and often optimal.
For distributed hypothesis testing, however, the use of binning is hindered by
the fact that the overall error probability may be dominated by errors in
binning process. We show that despite this complication, binning is optimal for
a class of problems in which the goal is to "test against conditional
independence." We then use this optimality result to give an outer bound for a
more general class of instances of the problem.
|
1101.5985
|
Multi-Edge type Unequal Error Protection LDPC codes
|
cs.IT math.IT
|
Irregular low-density parity check (LDPC) codes are particularly well-suited
for transmission schemes that require unequal error protection (UEP) of the
transmitted data due to the different connection degrees of its variable nodes.
However, this UEP capability is strongly dependent on the connection profile
among the protection classes. This paper applies a multi-edge type analysis of
LDPC codes for optimizing such connection profile according to the performance
requirements of each protection class. This allows the construction of UEP-LDPC
codes where the difference between the performance of the protection classes
can be adjusted and with an UEP capability that does not vanish as the number
of decoding iterations grows.
|
1101.5997
|
New Model for Multi-Objective Evolutionary Algorithms
|
cs.NE
|
Multi-Objective Evolutionary Algorithms (MOEAs) have been proved efficient to
deal with Multi-objective Optimization Problems (MOPs). Until now tens of MOEAs
have been proposed. The unified mode would provide a more systematic approach
to build new MOEAs. Here a new model is proposed which includes two sub-models
based on two classes of different schemas of MOEAs. According to the new model,
some representatives algorithms are decomposed and some interesting issues are
discussed.
|
1101.6001
|
Boolean network robotics: a proof of concept
|
cs.AI cs.NE cs.RO
|
Dynamical systems theory and complexity science provide powerful tools for
analysing artificial agents and robots. Furthermore, they have been recently
proposed also as a source of design principles and guidelines. Boolean networks
are a prominent example of complex dynamical systems and they have been shown
to effectively capture important phenomena in gene regulation. From an
engineering perspective, these models are very compelling, because they can
exhibit rich and complex behaviours, in spite of the compactness of their
description. In this paper, we propose the use of Boolean networks for
controlling robots' behaviour. The network is designed by means of an automatic
procedure based on stochastic local search techniques. We show that this
approach makes it possible to design a network which enables the robot to
accomplish a task that requires the capability of navigating the space using a
light stimulus, as well as the formation and use of an internal memory.
|
1101.6009
|
Solving the Satisfiability Problem Through Boolean Networks
|
cs.AI cs.NE nlin.CG
|
In this paper we present a new approach to solve the satisfiability problem
(SAT), based on boolean networks (BN). We define a mapping between a SAT
instance and a BN, and we solve SAT problem by simulating the BN dynamics. We
prove that BN fixed points correspond to the SAT solutions. The mapping
presented allows to develop a new class of algorithms to solve SAT. Moreover,
this new approach suggests new ways to combine symbolic and connectionist
computation and provides a general framework for local search algorithms.
|
1101.6018
|
Boolean Networks Design by Genetic Algorithms
|
cs.NE nlin.AO
|
We present and discuss the results of an experimental analysis in the design
of Boolean networks by means of genetic algorithms. A population of networks is
evolved with the aim of finding a network such that the attractor it reaches is
of required length $l$. In general, any target can be defined, provided that it
is possible to model the task as an optimisation problem over the space of
networks. We experiment with different initial conditions for the networks,
namely in ordered, chaotic and critical regions, and also with different target
length values. Results show that all kinds of initial networks can attain the
desired goal, but with different success ratios: initial populations composed
of critical or chaotic networks are more likely to reach the target. Moreover,
the evolution starting from critical networks achieves the best overall
performance. This study is the first step toward the use of search algorithms
as tools for automatically design Boolean networks with required properties.
|
1101.6022
|
Tailored graph ensembles as proxies or null models for real networks II:
results on directed graphs
|
q-bio.QM cond-mat.dis-nn cs.SI physics.soc-ph
|
We generate new mathematical tools with which to quantify the macroscopic
topological structure of large directed networks. This is achieved via a
statistical mechanical analysis of constrained maximum entropy ensembles of
directed random graphs with prescribed joint distributions for in- and
outdegrees and prescribed degree-degree correlation functions. We calculate
exact and explicit formulae for the leading orders in the system size of the
Shannon entropies and complexities of these ensembles, and for
information-theoretic distances. The results are applied to data on gene
regulation networks.
|
1101.6030
|
Power Allocation in Team Jamming Games in Wireless Ad Hoc Networks
|
cs.GT cs.CR cs.IT cs.SY math.IT math.OC
|
In this work, we study the problem of power allocation in teams. Each team
consists of two agents who try to split their available power between the tasks
of communication and jamming the nodes of the other team. The agents have
constraints on their total energy and instantaneous power usage. The cost
function is the difference between the rates of erroneously transmitted bits of
each team. We model the problem as a zero-sum differential game between the two
teams and use {\it{Isaacs'}} approach to obtain the necessary conditions for
the optimal trajectories. This leads to a continuous-kernel power allocation
game among the players. Based on the communications model, we present
sufficient conditions on the physical parameters of the agents for the
existence of a pure strategy Nash equilibrium (PSNE). Finally, we present
simulation results for the case when the agents are holonomic.
|
1101.6033
|
Some More Functions That Are Not APN Infinitely Often. The Case of
Kasami exponents
|
cs.IT math.IT math.NT
|
We prove a necessary condition for some polynomials of Kasami degree to be
APN over F_{q^n} for large n.
|
1101.6052
|
Stochastic Homogenization for Some Nonlinear Integro-Differential
Equations
|
math.AP cs.SY math.OC math.PR
|
In this note we extend to the random, stationary ergodic setting previous
results of periodic homogenization for a particular family of nonlinear
nonlocal "elliptic" equations with oscillatory coefficients. Such equations
include, but are not limited to Bellman equations and the Isaacs equations for
the control and differential games of some pure jump processes. The existence
of an effective equation and convergence the solutions of the family of the
original equations is obtained. Even in the linear case of the equations
contained herein the results appear to be new.
|
1102.0026
|
Spatially-Aware Comparison and Consensus for Clusterings
|
cs.LG cs.CG cs.DB
|
This paper proposes a new distance metric between clusterings that
incorporates information about the spatial distribution of points and clusters.
Our approach builds on the idea of a Hilbert space-based representation of
clusters as a combination of the representations of their constituent points.
We use this representation and the underlying metric to design a
spatially-aware consensus clustering procedure. This consensus procedure is
implemented via a novel reduction to Euclidean clustering, and is both simple
and efficient. All of our results apply to both soft and hard clusterings. We
accompany these algorithms with a detailed experimental evaluation that
demonstrates the efficiency and quality of our techniques.
|
1102.0033
|
Control of Multi-Agent Formations with Only Shape Constraints
|
cs.SY
|
This paper considers a novel problem of how to choose an appropriate geometry
for a group of agents with only shape constraints but with a flexible scale.
Instead of assigning the formation system with a specific geometry, here the
only requirement on the desired geometry is a shape without any location,
rotation and, most importantly, scale constraints. Optimal rigid transformation
between two different geometries is discussed with especial focus on the
scaling operation, and the cooperative performance of the system is evaluated
by what we call the geometries degrees of similarity (DOS) with respect to the
desired shape during the entire convergence process. The design of the scale
when measuring the DOS is discussed from constant value and time-varying
function perspectives respectively. Fixed structured nonlinear control laws
that are functions on the scale are developed to guarantee the exponential
convergence of the system to the assigned shape. Our research is originated
from a three-agent formation system and is further extended to multiple (n > 3)
agents by defining a triangular complement graph. Simulations demonstrate that
formation system with the time-varying scale function outperforms the one with
an arbitrary constant scale, and the relationship between underlying topology
and the system performance is further discussed based on the simulation
observations. Moveover, the control scheme is applied to bearing-only
sensor-target localization to show its application potentials.
|
1102.0040
|
On the Zero-Error Capacity Threshold for Deletion Channels
|
cs.IT math.IT
|
We consider the zero-error capacity of deletion channels. Specifically, we
consider the setting where we choose a codebook ${\cal C}$ consisting of
strings of $n$ bits, and our model of the channel corresponds to an adversary
who may delete up to $pn$ of these bits for a constant $p$. Our goal is to
decode correctly without error regardless of the actions of the adversary. We
consider what values of $p$ allow non-zero capacity in this setting. We suggest
multiple approaches, one of which makes use of the natural connection between
this problem and the problem of finding the expected length of the longest
common subsequence of two random sequences.
|
1102.0043
|
The Gaussian Interference Relay Channel: Improved Achievable Rates and
Sum Rate Upperbounds Using a Potent Relay
|
cs.IT math.IT
|
We consider the Gaussian interference channel with an intermediate relay as a
main building block for cooperative interference networks. On the achievability
side, we consider compress-and-forward based strategies. Specifically, a
generalized compress-and-forward strategy, where the destinations jointly
decode the compression indices and the source messages, is shown to improve
upon the compress-and-forward strategy which sequentially decodes the
compression indices and source messages, and the recently proposed generalized
hash-and-forward strategy. We also construct a nested lattice code based
compute-and-forward relaying scheme, which outperforms other relaying schemes
when the direct link is weak. In this case, it is shown that, with a relay, the
interference link can be useful for decoding the source messages. Noting the
need for upperbounding the capacity for this channel, we propose a new
technique with which the sum rate can be bounded. In particular, the sum
capacity is upperbounded by considering the channel when the relay node has
abundant power and is named potent for that reason. For the Gaussian
interference relay channel with potent relay, we study the strong and the weak
interference regimes and establish the sum capacity, which, in turn, serve as
upperbounds for the sum capacity of the GIFRC with finite relay power.
Numerical results demonstrate that upperbounds are tighter than the cut-set
bound, and coincide with known achievable sum rates for many scenarios of
interest. Additionally, the degrees of freedom of the GIFRC are shown to be 2
when the relay has large power, achievable using compress-and-forward.
|
1102.0048
|
Smart depth of field optimization applied to a robotised view camera
|
math.OC cs.CV cs.RO
|
The great flexibility of a view camera allows to take high quality
photographs that would not be possible any other way. But making a given object
into focus is a long and tedious task, although the underlying laws are well
known. This paper presents the result of a project which has lead to the design
of a computer controlled view camera and to its companion software. Thanks to
the high precision machining of its components, and to the known optical
parameters of lenses and sensor, we have been able to consider a reliable
mathematical model of the view camera, allowing the acquisition of 3D
coordinates to build a geometrical model of the object. Then many problems can
be solved, e.g. minimizing the f-number while maintaining the object within the
depth of field, which takes the form of a constrained optimization problem. All
optimization algorithms have been validated on a virtual view camera before
implementation on the prototype
|
1102.0059
|
Statistical methods for tissue array images - algorithmic scoring and
co-training
|
stat.ME cs.CE cs.CV cs.LG q-bio.QM
|
Recent advances in tissue microarray technology have allowed
immunohistochemistry to become a powerful medium-to-high throughput analysis
tool, particularly for the validation of diagnostic and prognostic biomarkers.
However, as study size grows, the manual evaluation of these assays becomes a
prohibitive limitation; it vastly reduces throughput and greatly increases
variability and expense. We propose an algorithm - Tissue Array Co-Occurrence
Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on
textural regularity summarized by local inter-pixel relationships. The
algorithm can be easily trained for any staining pattern, is absent of
sensitive tuning parameters and has the ability to report salient pixels in an
image that contribute to its score. Pathologists' input via informative
training patches is an important aspect of the algorithm that allows the
training for any specific marker or cell type. With co-training, the error rate
of TACOMA can be reduced substantially for a very small training sample (e.g.,
with size 30). We give theoretical insights into the success of co-training via
thinning of the feature set in a high-dimensional setting when there is
"sufficient" redundancy among the features. TACOMA is flexible, transparent and
provides a scoring process that can be evaluated with clarity and confidence.
In a study based on an estrogen receptor (ER) marker, we show that TACOMA is
comparable to, or outperforms, pathologists' performance in terms of accuracy
and repeatability.
|
1102.0079
|
Information-theoretic measures associated with rough set approximations
|
cs.AI
|
Although some information-theoretic measures of uncertainty or granularity
have been proposed in rough set theory, these measures are only dependent on
the underlying partition and the cardinality of the universe, independent of
the lower and upper approximations. It seems somewhat unreasonable since the
basic idea of rough set theory aims at describing vague concepts by the lower
and upper approximations. In this paper, we thus define new
information-theoretic entropy and co-entropy functions associated to the
partition and the approximations to measure the uncertainty and granularity of
an approximation space. After introducing the novel notions of entropy and
co-entropy, we then examine their properties. In particular, we discuss the
relationship of co-entropies between different universes. The theoretical
development is accompanied by illustrative numerical examples.
|
1102.0099
|
Automatic Network Fingerprinting through Single-Node Motifs
|
physics.soc-ph cs.SI q-bio.QM
|
Complex networks have been characterised by their specific connectivity
patterns (network motifs), but their building blocks can also be identified and
described by node-motifs---a combination of local network features. One
technique to identify single node-motifs has been presented by Costa et al. (L.
D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett.,
87, 1, 2009). Here, we first suggest improvements to the method including how
its parameters can be determined automatically. Such automatic routines make
high-throughput studies of many networks feasible. Second, the new routines are
validated in different network-series. Third, we provide an example of how the
method can be used to analyse network time-series. In conclusion, we provide a
robust method for systematically discovering and classifying characteristic
nodes of a network. In contrast to classical motif analysis, our approach can
identify individual components (here: nodes) that are specific to a network.
Such special nodes, as hubs before, might be found to play critical roles in
real-world networks.
|
1102.0115
|
Business Intelligence for Small and Middle-Sized Entreprises
|
cs.DB
|
Data warehouses are the core of decision support sys- tems, which nowadays
are used by all kind of enter- prises in the entire world. Although many
studies have been conducted on the need of decision support systems (DSSs) for
small businesses, most of them adopt ex- isting solutions and approaches, which
are appropriate for large-scaled enterprises, but are inadequate for small and
middle-sized enterprises. Small enterprises require cheap, lightweight
architec- tures and tools (hardware and software) providing on- line data
analysis. In order to ensure these features, we review web-based business
intelligence approaches. For real-time analysis, the traditional OLAP
architecture is cumbersome and storage-costly; therefore, we also re- view
in-memory processing. Consequently, this paper discusses the existing approa-
ches and tools working in main memory and/or with web interfaces (including
freeware tools), relevant for small and middle-sized enterprises in decision
making.
|
1102.0160
|
Optimal Band Allocation for Cognitive Cellular Networks
|
cs.IT math.IT
|
FCC new regulation for cognitive use of the TV white space spectrum provides
a new means for improving traditional cellular network performance. But it also
introduces a number of technical challenges. This letter studies one of the
challenges, that is, given the significant differences in the propagation
property and the transmit power limitations between the cellular band and the
TV white space, how to jointly utilize both bands such that the benefit from
the TV white space for improving cellular network performance is maximized.
Both analytical and simulation results are provided.
|
1102.0183
|
High-Performance Neural Networks for Visual Object Classification
|
cs.AI cs.NE
|
We present a fast, fully parameterizable GPU implementation of Convolutional
Neural Network variants. Our feature extractors are neither carefully designed
nor pre-wired, but rather learned in a supervised way. Our deep hierarchical
architectures achieve the best published results on benchmarks for object
classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with
error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple
back-propagation perform better than more shallow ones. Learning is
surprisingly rapid. NORB is completely trained within five epochs. Test error
rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs,
respectively.
|
1102.0204
|
Repairing Multiple Failures with Coordinated and Adaptive Regenerating
Codes
|
cs.IT cs.DC math.IT
|
Erasure correcting codes are widely used to ensure data persistence in
distributed storage systems. This paper addresses the simultaneous repair of
multiple failures in such codes. We go beyond existing work (i.e., regenerating
codes by Dimakis et al.) by describing (i) coordinated regenerating codes (also
known as cooperative regenerating codes) which support the simultaneous repair
of multiple devices, and (ii) adaptive regenerating codes which allow adapting
the parameters at each repair. Similarly to regenerating codes by Dimakis et
al., these codes achieve the optimal tradeoff between storage and the repair
bandwidth. Based on these extended regenerating codes, we study the impact of
lazy repairs applied to regenerating codes and conclude that lazy repairs
cannot reduce the costs in term of network bandwidth but allow reducing the
disk-related costs (disk bandwidth and disk I/O).
|
1102.0230
|
Speeding up SAT solver by exploring CNF symmetries : Revisited
|
math.CO cs.AI
|
Boolean Satisfiability solvers have gone through dramatic improvements in
their performances and scalability over the last few years by considering
symmetries. It has been shown that by using graph symmetries and generating
symmetry breaking predicates (SBPs) it is possible to break symmetries in
Conjunctive Normal Form (CNF). The SBPs cut down the search space to the
nonsymmetric regions of the space without affecting the satisfiability of the
CNF formula. The symmetry breaking predicates are created by representing the
formula as a graph, finding the graph symmetries and using some symmetry
extraction mechanism (Crawford et al.). Here in this paper we take one
non-trivial CNF and explore its symmetries. Finally, we generate the SBPs and
adding it to CNF we show how it helps to prune the search tree, so that SAT
solver would take short time. Here we present the pruning procedure of the
search tree from scratch, starting from the CNF and its graph representation.
As we explore the whole mechanism by a non-trivial example, it would be easily
comprehendible. Also we have given a new idea of generating symmetry breaking
predicates for breaking symmetry in CNF, not derived from Crawford's
conditions. At last we propose a backtrack SAT solver with inbuilt SBP
generator.
|
1102.0250
|
Information-Theoretic Viewpoints on Optimal Causal Coding-Decoding
Problems
|
cs.IT math.IT
|
In this paper we consider an interacting two-agent sequential decision-making
problem consisting of a Markov source process, a causal encoder with feedback,
and a causal decoder. Motivated by a desire to foster links between control and
information theory, we augment the standard formulation by considering general
alphabets and a cost function operating on current and previous symbols. Using
dynamic programming, we provide a structural result whereby an optimal scheme
exists that operates on appropriate sufficient statistics. We emphasize an
example where the decoder alphabet lies in a space of beliefs on the source
alphabet, and the additive cost function is a log likelihood ratio pertaining
to sequential information gain. We also consider the inverse optimal control
problem, where a fixed encoder/decoder pair satisfying statistical conditions
is shown to be optimal for some cost function, using probabilistic matching. We
provide examples of the applicability of this framework to communication with
feedback, hidden Markov models and the nonlinear filter, decentralized control,
brain-machine interfaces, and queuing theory.
|
1102.0257
|
Emergence through Selection: The Evolution of a Scientific Challenge
|
cs.SI cs.AI math.DS physics.soc-ph
|
One of the most interesting scientific challenges nowadays deals with the
analysis and the understanding of complex networks' dynamics and how their
processes lead to emergence according to the interactions among their
components. In this paper we approach the definition of new methodologies for
the visualization and the exploration of the dynamics at play in real dynamic
social networks. We present a recently introduced formalism called TVG (for
time-varying graphs), which was initially developed to model and analyze
highly-dynamic and infrastructure-less communication networks such as mobile
ad-hoc networks, wireless sensor networks, or vehicular networks. We discuss
its applicability to complex networks in general, and social networks in
particular, by showing how it enables the specification and analysis of complex
dynamic phenomena in terms of temporal interactions, and allows to easily
switch the perspective between local and global dynamics. As an example, we
chose the case of scientific communities by analyzing portion of the ArXiv
repository (ten years of publications in physics) focusing on the social
determinants (e.g. goals and potential interactions among individuals) behind
the emergence and the resilience of scientific communities. We consider that
scientific communities are at the same time communities of practice (through
co-authorship) and that they exist also as representations in the scientists'
mind, since references to other scientists' works is not merely an objective
link to a relevant work, but it reveals social objects that one manipulates,
select and refers to. In the paper we show the emergence/selection of a
community as a goal-driven preferential attachment toward a set of authors
among which there are some key scientists (Nobel prizes).
|
1102.0267
|
The Capacity Region of the MIMO Interference Channel and its Reciprocity
to Within a Constant Gap
|
cs.IT math.IT
|
The capacity region of the 2-user multi-input multi-output (MIMO) Gaussian
interference channel (IC) is characterized to within a constant gap that is
independent of the channel matrices for the general case of the MIMO IC with an
arbitrary number of antennas at each node. An achievable rate region and an
outer bound to the capacity region of a class of interference channels were
obtained in previous work by Telatar and Tse as unions over all possible input
distributions. In contrast to that previous work on the MIMO IC, a simple and
an explicit achievable coding scheme are obtained here and shown to have the
constant-gap-to-capacity property and in which the sub-rates of the common and
private messages of each user are explicitly specified for each achievable rate
pair. The constant-gap-to-capacity results are thus proved in this work by
first establishing explicit upper and lower bounds to the capacity region. A
reciprocity result is also proved which is that the capacity of the reciprocal
MIMO IC is within a constant gap of the capacity region of the forward MIMO IC.
|
1102.0309
|
`Lassoing' a phylogenetic tree I: Basic properties, shellings, and
covers
|
q-bio.PE cs.CE cs.DS
|
A classical result, fundamental to evolutionary biology, states that an
edge-weighted tree $T$ with leaf set $X$, positive edge weights, and no
vertices of degree 2 can be uniquely reconstructed from the set of leaf-to-leaf
distances between any two elements of $X$. In biology, $X$ corresponds to a set
of taxa (e.g. extant species), the tree $T$ describes their phylogenetic
relationships, the edges correspond to earlier species evolving for a time
until splitting in two or more species by some speciation/bifurcation event,
and their length corresponds to the genetic change accumulating over that time
in such a species. In this paper, we investigate which subsets of
$\binom{X}{2}$ suffice to determine (`lasso') a tree from the leaf-to-leaf
distances induced by that tree. The question is particularly topical since
reliable estimates of genetic distance - even (if not in particular) by modern
mass-sequencing methods - are, in general, available only for certain
combinations of taxa.
|
1102.0316
|
Partition Functions of Normal Factor Graphs
|
cs.IT math.IT
|
One of the most common types of functions in mathematics, physics, and
engineering is a sum of products, sometimes called a partition function. After
"normalization," a sum of products has a natural graphical representation,
called a normal factor graph (NFG), in which vertices represent factors, edges
represent internal variables, and half-edges represent the external variables
of the partition function. In physics, so-called trace diagrams share similar
features. We believe that the conceptual framework of representing sums of
products as partition functions of NFGs is an important and intuitive paradigm
that, surprisingly, does not seem to have been introduced explicitly in the
previous factor graph literature. Of particular interest are NFG modifications
that leave the partition function invariant. A simple subclass of such NFG
modifications offers a unifying view of the Fourier transform, tree-based
reparameterization, loop calculus, and the Legendre transform.
|
1102.0365
|
Limit Theorems in Hidden Markov Models
|
cs.IT math.IT
|
In this paper, under mild assumptions, we derive a law of large numbers, a
central limit theorem with an error estimate, an almost sure invariance
principle and a variant of Chernoff bound in finite-state hidden Markov models.
These limit theorems are of interest in certain ares in statistics and
information theory. Particularly, we apply the limit theorems to derive the
rate of convergence of the maximum likelihood estimator in finite-state hidden
Markov models.
|
1102.0371
|
Synthese des Controleurs Optimaux pour les Systemes a Evenements
Discrets
|
cs.FL cs.SY
|
In this paper, we introduce the problem of synthesizing optimal controllers
for discrete event systems and we propose a procedure for solving this problem,
where the method and specifications are represented by finite state automata
and with increasing complexity. We will subscribe to the synthetic methodology
by the control theory initiated by supervision by Ramadge and Wonham. For an
illustration on a simple example, then a model with a complexity high. In this
spirit, languages, methods and tools development used to specify and
development must reach a level of quality to meet the requirements expressed.
Face this situation, we are helping in this work the systematic use of formal
methods in systems development cycles in the equipping and adapting the UML
(Unified Modeling Language) which is the most exploited in industrial projects.
|
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