id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1308.3785 | Implementation Of Back-Propagation Neural Network For Isolated Bangla
Speech Recognition | cs.CL cs.NE | This paper is concerned with the development of Back-propagation Neural
Network for Bangla Speech Recognition. In this paper, ten bangla digits were
recorded from ten speakers and have been recognized. The features of these
speech digits were extracted by the method of Mel Frequency Cepstral
Coefficient (MFCC) analysis. The mfcc features of five speakers were used to
train the network with Back propagation algorithm. The mfcc features of ten
bangla digit speeches, from 0 to 9, of another five speakers were used to test
the system. All the methods and algorithms used in this research were
implemented using the features of Turbo C and C++ languages. From our
investigation it is seen that the developed system can successfully encode and
analyze the mfcc features of the speech signal to recognition. The developed
system achieved recognition rate about 96.332% for known speakers (i.e.,
speaker dependent) and 92% for unknown speakers (i.e., speaker independent).
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1308.3799 | Permutation Enhanced Parallel Reconstruction with A Linear Compressive
Sampling Device | cs.IT math.IT | In this letter, a permutation enhanced parallel reconstruction architecture
for compressive sampling is proposed. In this architecture, a measurement
matrix is constructed from a block-diagonal sensing matrix and the sparsifying
basis of the target signal. In this way, the projection of the signal onto the
sparsifying basis can be divided into several segments and all segments can be
reconstructed in parallel. Thus, the computational complexity and the time for
reconstruction can be reduced significantly. This feature is especially
appealing for big data processing. Furthermore, to reduce the number of
measurements needed to achieve the desired reconstruction error performance,
permutation is introduced for the projection of the signal. It is shown that
the permutation can be performed implicitly by using a pre-designed measurement
matrix. Thus, the permutation enhanced parallel reconstruction can be achieved
with a linear compressive sampling device.
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1308.3818 | Reference Distance Estimator | cs.LG stat.ML | A theoretical study is presented for a simple linear classifier called
reference distance estimator (RDE), which assigns the weight of each feature j
as P(r|j)-P(r), where r is a reference feature relevant to the target class y.
The analysis shows that if r performs better than random guess in predicting y
and is conditionally independent with each feature j, the RDE will have the
same classification performance as that from P(y|j)-P(y), a classifier trained
with the gold standard y. Since the estimation of P(r|j)-P(r) does not require
labeled data, under the assumption above, RDE trained with a large number of
unlabeled examples would be close to that trained with infinite labeled
examples. For the case the assumption does not hold, we theoretically analyze
the factors that influence the closeness of the RDE to the perfect one under
the assumption, and present an algorithm to select reference features and
combine multiple RDEs from different reference features using both labeled and
unlabeled data. The experimental results on 10 text classification tasks show
that the semi-supervised learning method improves supervised methods using
5,000 labeled examples and 13 million unlabeled ones, and in many tasks, its
performance is even close to a classifier trained with 13 million labeled
examples. In addition, the bounds in the theorems provide good estimation of
the classification performance and can be useful for new algorithm design.
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1308.3827 | Layered Constructions for Low-Delay Streaming Codes | cs.IT math.IT | We propose a new class of error correction codes for low-delay streaming
communication. We consider an online setup where a source packet arrives at the
encoder every $M$ channel uses, and needs to be decoded with a maximum delay of
$T$ packets. We consider a sliding-window erasure channel --- $\cC(N,B,W)$ ---
which introduces either up to $N$ erasures in arbitrary positions, or $B$
erasures in a single burst, in any window of length $W$. When $M=1$, the case
where source-arrival and channel-transmission rates are equal, we propose a
class of codes --- MiDAS codes --- that achieve a near optimal rate. Our
construction is based on a {\em layered} approach. We first construct an
optimal code for the $\cC(N=1,B,W)$ channel, and then concatenate an additional
layer of parity-check symbols to deal with $N>1$. When $M > 1$, the case where
source-arrival and channel-transmission rates are unequal, we characterize the
capacity when $N=1$ and $W \ge M(T+1),$ and for $N>1$, we propose a
construction based on a layered approach. Numerical simulations over
Gilbert-Elliott and Fritchman channel models indicate significant gains in the
residual loss probability over baseline schemes. We also discuss the connection
between the error correction properties of the MiDAS codes and their underlying
column distance and column span.
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1308.3830 | Natural Language Web Interface for Database (NLWIDB) | cs.CL cs.DB cs.HC | It is a long term desire of the computer users to minimize the communication
gap between the computer and a human. On the other hand, almost all ICT
applications store information in to databases and retrieve from them.
Retrieving information from the database requires knowledge of technical
languages such as Structured Query Language. However majority of the computer
users who interact with the databases do not have a technical background and
are intimidated by the idea of using languages such as SQL. For above reasons,
a Natural Language Web Interface for Database (NLWIDB) has been developed. The
NLWIDB allows the user to query the database in a language more like English,
through a convenient interface over the Internet.
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1308.3831 | Strict majority bootstrap percolation in the r-wheel | cs.SI math.PR | In this paper we study the strict majority bootstrap percolation process on
graphs. Vertices may be active or passive. Initially, active vertices are
chosen independently with probability p. Each passive vertex becomes active if
at least half of its neighbors are active (and thereafter never changes its
state). If at the end of the process all vertices become active then we say
that the initial set of active vertices percolates on the graph. We address the
problem of finding graphs for which percolation is likely to occur for small
values of p. Specifically, we study a graph that we call r-wheel: a ring of n
vertices augmented with a universal vertex where each vertex in the ring is
connected to its r closest neighbors to the left and to its r closest neighbors
to the right. We prove that the critical probability is 1/4. In other words, if
p>1/4 then for large values of r percolation occurs with probability
arbitrarily close to 1 as n goes to infinity. On the other hand, if p<1/4 then
the probability of percolation is bounded away from 1.
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1308.3839 | Consensus Sequence Segmentation | cs.CL | In this paper we introduce a method to detect words or phrases in a given
sequence of alphabets without knowing the lexicon. Our linear time unsupervised
algorithm relies entirely on statistical relationships among alphabets in the
input sequence to detect location of word boundaries. We compare our algorithm
to previous approaches from unsupervised sequence segmentation literature and
provide superior segmentation over number of benchmarks.
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1308.3847 | Exploiting Binary Floating-Point Representations for Constraint
Propagation: The Complete Unabridged Version | cs.AI cs.SE | Floating-point computations are quickly finding their way in the design of
safety- and mission-critical systems, despite the fact that designing
floating-point algorithms is significantly more difficult than designing
integer algorithms. For this reason, verification and validation of
floating-point computations is a hot research topic. An important verification
technique, especially in some industrial sectors, is testing. However,
generating test data for floating-point intensive programs proved to be a
challenging problem. Existing approaches usually resort to random or
search-based test data generation, but without symbolic reasoning it is almost
impossible to generate test inputs that execute complex paths controlled by
floating-point computations. Moreover, as constraint solvers over the reals or
the rationals do not natively support the handling of rounding errors, the need
arises for efficient constraint solvers over floating-point domains. In this
paper, we present and fully justify improved algorithms for the propagation of
arithmetic IEEE 754 binary floating-point constraints. The key point of these
algorithms is a generalization of an idea by B. Marre and C. Michel that
exploits a property of the representation of floating-point numbers.
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1308.3874 | Alert-BDI: BDI Model with Adaptive Alertness through Situational
Awareness | cs.MA | In this paper, we address the problems faced by a group of agents that
possess situational awareness, but lack a security mechanism, by the
introduction of a adaptive risk management system. The Belief-Desire-Intention
(BDI) architecture lacks a framework that would facilitate an adaptive risk
management system that uses the situational awareness of the agents. We extend
the BDI architecture with the concept of adaptive alertness. Agents can modify
their level of alertness by monitoring the risks faced by them and by their
peers. Alert-BDI enables the agents to detect and assess the risks faced by
them in an efficient manner, thereby increasing operational efficiency and
resistance against attacks.
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1308.3876 | Detection and Filtering of Collaborative Malicious Users in Reputation
System using Quality Repository Approach | cs.SI cs.IR physics.soc-ph | Online reputation system is gaining popularity as it helps a user to be sure
about the quality of a product/service he wants to buy. Nonetheless online
reputation system is not immune from attack. Dealing with malicious ratings in
reputation systems has been recognized as an important but difficult task. This
problem is challenging when the number of true user's ratings is relatively
small and unfair ratings plays majority in rated values. In this paper, we have
proposed a new method to find malicious users in online reputation systems
using Quality Repository Approach (QRA). We mainly concentrated on anomaly
detection in both rating values and the malicious users. QRA is very efficient
to detect malicious user ratings and aggregate true ratings. The proposed
reputation system has been evaluated through simulations and it is concluded
that the QRA based system significantly reduces the impact of unfair ratings
and improve trust on reputation score with lower false positive as compared to
other method used for the purpose.
|
1308.3892 | Do the rich get richer? An empirical analysis of the BitCoin transaction
network | physics.soc-ph cs.SI q-fin.GN | The possibility to analyze everyday monetary transactions is limited by the
scarcity of available data, as this kind of information is usually considered
highly sensitive. Present econophysics models are usually employed on presumed
random networks of interacting agents, and only macroscopic properties (e.g.
the resulting wealth distribution) are compared to real-world data. In this
paper, we analyze BitCoin, which is a novel digital currency system, where the
complete list of transactions is publicly available. Using this dataset, we
reconstruct the network of transactions, and extract the time and amount of
each payment. We analyze the structure of the transaction network by measuring
network characteristics over time, such as the degree distribution, degree
correlations and clustering. We find that linear preferential attachment drives
the growth of the network. We also study the dynamics taking place on the
transaction network, i.e. the flow of money. We measure temporal patterns and
the wealth accumulation. Investigating the microscopic statistics of money
movement, we find that sublinear preferential attachment governs the evolution
of the wealth distribution. We report a scaling relation between the degree and
wealth associated to individual nodes.
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1308.3898 | Firefly Algorithm: Recent Advances and Applications | math.OC cs.AI | Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.
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1308.3900 | Bat Algorithm: Literature Review and Applications | cs.AI math.OC | Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.
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1308.3916 | Robust Supervisory Control for Uniting Two Output-Feedback Hybrid
Controllers with Different Objectives | cs.SY | The problem of robustly, asymptotically stabilizing a point (or a set) with
two output-feedback hybrid controllers is considered. These control laws may
have different objectives, e.g., the closed-loop systems resulting with each
controller may have different attractors. We provide a control algorithm that
combines the two hybrid controllers to accomplish the stabilization task. The
algorithm consists of a hybrid supervisor that, based on the values of plant's
outputs and (norm) state estimates, selects the hybrid controller that should
be applied to the plant. The accomplishment of the stabilization task relies on
an output-to-state stability property induced by the controllers, which enables
the construction of an estimator for the norm of the plant's state. The
algorithm is motivated by and applied to robust, semi-global stabilization
problems uniting two controllers.
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1308.3946 | Optimal Algorithms for Testing Closeness of Discrete Distributions | cs.DS cs.IT cs.LG math.IT | We study the question of closeness testing for two discrete distributions.
More precisely, given samples from two distributions $p$ and $q$ over an
$n$-element set, we wish to distinguish whether $p=q$ versus $p$ is at least
$\eps$-far from $q$, in either $\ell_1$ or $\ell_2$ distance. Batu et al. gave
the first sub-linear time algorithms for these problems, which matched the
lower bounds of Valiant up to a logarithmic factor in $n$, and a polynomial
factor of $\eps.$
In this work, we present simple (and new) testers for both the $\ell_1$ and
$\ell_2$ settings, with sample complexity that is information-theoretically
optimal, to constant factors, both in the dependence on $n$, and the dependence
on $\eps$; for the $\ell_1$ testing problem we establish that the sample
complexity is $\Theta(\max\{n^{2/3}/\eps^{4/3}, n^{1/2}/\eps^2 \}).$
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1308.3956 | Target Assignment in Robotic Networks: Distance Optimality Guarantees
and Hierarchical Strategies | cs.RO | We study the problem of multi-robot target assignment to minimize the total
distance traveled by the robots until they all reach an equal number of static
targets. In the first half of the paper, we present a necessary and sufficient
condition under which true distance optimality can be achieved for robots with
limited communication and target-sensing ranges. Moreover, we provide an
explicit, non-asymptotic formula for computing the number of robots needed to
achieve distance optimality in terms of the robots' communication and
target-sensing ranges with arbitrary guaranteed probabilities. The same bounds
are also shown to be asymptotically tight.
In the second half of the paper, we present suboptimal strategies for use
when the number of robots cannot be chosen freely. Assuming first that all
targets are known to all robots, we employ a hierarchical communication model
in which robots communicate only with other robots in the same partitioned
region. This hierarchical communication model leads to constant approximations
of true distance-optimal solutions under mild assumptions. We then revisit the
limited communication and sensing models. By combining simple rendezvous-based
strategies with a hierarchical communication model, we obtain decentralized
hierarchical strategies that achieve constant approximation ratios with respect
to true distance optimality. Results of simulation show that the approximation
ratio is as low as 1.4.
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1308.3957 | Iterative Multiuser Detection and Decoding with Spatially Coupled
Interleaving | cs.IT math.IT | Spatially coupled (SC) interleaving is proposed to improve the performance of
iterative multiuser detection and decoding (MUDD) for quasi-static fading
multiple-input multiple-output systems. The linear minimum mean-squared error
(LMMSE) demodulator is used to reduce the complexity and to avoid error
propagation. Furthermore, sliding window MUDD is proposed to circumvent an
increase of the decoding latency due to SC interleaving. Theoretical and
numerical analyses show that SC interleaving can improve the performance of the
iterative LMMSE MUDD for regular low-density parity-check codes.
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1308.3985 | Remarks on criteria for achieving the optimal diversity-multiplexing
gain trade-off | cs.IT math.IT | In this short note we will prove that non-vanishing determinant (NVD)
criterion is not enough for an asymmetric space-time block code (STBC) to
achieve the optimal diversity-multiplexing gain trade-off (DMT). This result is
in contrast to the recent result made by Srinath and Rajan. In order to clarify
the issue further the approximately universality criterion by Tavildar and
Viswanath is translated into language of lattice theory and some conjectures
are given.
|
1308.3995 | A Comparison of Hybridized and Standard DG Methods for Target-Based
hp-Adaptive Simulation of Compressible Flow | cs.CE cs.NA math.NA | We present a comparison between hybridized and non-hybridized discontinuous
Galerkin methods in the context of target-based hp-adaptation for compressible
flow problems. The aim is to provide a critical assessment of the computational
efficiency of hybridized DG methods. Hybridization of finite element
discretizations has the main advantage, that the resulting set of algebraic
equations has globally coupled degrees of freedom only on the skeleton of the
computational mesh. Consequently, solving for these degrees of freedom involves
the solution of a potentially much smaller system. This not only reduces
storage requirements, but also allows for a faster solution with iterative
solvers. Using a discrete-adjoint approach, sensitivities with respect to
output functionals are computed to drive the adaptation. From the error
distribution given by the adjoint-based error estimator, h- or p-refinement is
chosen based on the smoothness of the solution which can be quantified by
properly-chosen smoothness indicators. Numerical results are shown for
subsonic, transonic, and supersonic flow around the NACA0012 airfoil.
hp-adaptation proves to be superior to pure h-adaptation if discontinuous or
singular flow features are involved. In all cases, a higher polynomial degree
turns out to be beneficial. We show that for polynomial degree of approximation
p=2 and higher, and for a broad range of test cases, HDG performs better than
DG in terms of runtime and memory requirements.
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1308.4002 | Topological bifurcations in a model society of reasonable contrarians | nlin.CG cs.SI nlin.CD physics.soc-ph | People are often divided into conformists and contrarians, the former tending
to align to the majority opinion in their neighborhood and the latter tending
to disagree with that majority. In practice, however, the contrarian tendency
is rarely followed when there is an overwhelming majority with a given opinion,
which denotes a social norm. Such reasonable contrarian behavior is often
considered a mark of independent thought, and can be a useful strategy in
financial markets.
We present the opinion dynamics of a society of reasonable contrarian agents.
The model is a cellular automaton of Ising type, with antiferromagnetic pair
interactions modeling contrarianism and plaquette terms modeling social norms.
We introduce the entropy of the collective variable as a way of comparing
deterministic (mean-field) and probabilistic (simulations) bifurcation
diagrams.
In the mean field approximation the model exhibits bifurcations and a chaotic
phase, interpreted as coherent oscillations of the whole society. However, in a
one-dimensional spatial arrangement one observes incoherent oscillations and a
constant average.
In simulations on Watts-Strogatz networks with a small-world effect the mean
field behavior is recovered, with a bifurcation diagram that resembles the
mean-field one, but using the rewiring probability as the control parameter.
Similar bifurcation diagrams are found for scale free networks, and we are able
to compute an effective connectivity for such networks.
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1308.4004 | A balanced k-means algorithm for weighted point sets | math.OC cs.LG stat.ML | The classical $k$-means algorithm for partitioning $n$ points in
$\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread
clustering methods. The need to respect prescribed lower bounds on the cluster
sizes has been observed in many scientific and business applications.
In this paper, we present and analyze a generalization of $k$-means that is
capable of handling weighted point sets and prescribed lower and upper bounds
on the cluster sizes. We call it weight-balanced $k$-means. The key difference
to existing models lies in the ability to handle the combination of weighted
point sets with prescribed bounds on the cluster sizes. This imposes the need
to perform partial membership clustering, and leads to significant differences.
For example, while finite termination of all $k$-means variants for
unweighted point sets is a simple consequence of the existence of only finitely
many partitions of a given set of points, the situation is more involved for
weighted point sets, as there are infinitely many partial membership
clusterings. Using polyhedral theory, we show that the number of iterations of
weight-balanced $k$-means is bounded above by $n^{O(dk)}$, so in particular it
is polynomial for fixed $k$ and $d$. This is similar to the known worst-case
upper bound for classical $k$-means for unweighted point sets and unrestricted
cluster sizes, despite the much more general framework. We conclude with the
discussion of some additional favorable properties of our method.
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1308.4008 | A Literature Survey of Benchmark Functions For Global Optimization
Problems | cs.AI math.OC | Test functions are important to validate and compare the performance of
optimization algorithms. There have been many test or benchmark functions
reported in the literature; however, there is no standard list or set of
benchmark functions. Ideally, test functions should have diverse properties so
that can be truly useful to test new algorithms in an unbiased way. For this
purpose, we have reviewed and compiled a rich set of 175 benchmark functions
for unconstrained optimization problems with diverse properties in terms of
modality, separability, and valley landscape. This is by far the most complete
set of functions so far in the literature, and tt can be expected this complete
set of functions can be used for validation of new optimization in the future.
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1308.4013 | Incentives for Privacy Tradeoff in Community Sensing | cs.GT cs.AI | Community sensing, fusing information from populations of privately-held
sensors, presents a great opportunity to create efficient and cost-effective
sensing applications. Yet, reasonable privacy concerns often limit the access
to such data streams. How should systems valuate and negotiate access to
private information, for example in return for monetary incentives? How should
they optimally choose the participants from a large population of strategic
users with privacy concerns, and compensate them for information shared? In
this paper, we address these questions and present a novel mechanism,
SeqTGreedy, for budgeted recruitment of participants in community sensing. We
first show that privacy tradeoffs in community sensing can be cast as an
adaptive submodular optimization problem. We then design a budget feasible,
incentive compatible (truthful) mechanism for adaptive submodular maximization,
which achieves near-optimal utility for a large class of sensing applications.
This mechanism is general, and of independent interest. We demonstrate the
effectiveness of our approach in a case study of air quality monitoring, using
data collected from the Mechanical Turk platform. Compared to the state of the
art, our approach achieves up to 30% reduction in cost in order to achieve a
desired level of utility.
|
1308.4014 | Epidemic Spreading on Weighted Complex Networks | physics.soc-ph cs.SI | Nowadays, the emergence of online services provides various multi-relation
information to support the comprehensive understanding of the epidemic
spreading process. In this Letter, we consider the edge weights to represent
such multi-role relations. In addition, we perform detailed analysis of two
representative metrics, outbreak threshold and epidemic prevalence, on SIS and
SIR models. Both theoretical and simulation results find good agreements with
each other. Furthermore, experiments show that, on fully mixed networks, the
weight distribution on edges would not affect the epidemic results once the
average weight of whole network is fixed. This work may shed some light on the
in-depth understanding of epidemic spreading on multi-relation and weighted
networks.
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1308.4027 | Combined-Semantics Equivalence Is Decidable for a Practical Class of
Conjunctive Queries | cs.DB | In this paper, we focus on the problem of determining whether two conjunctive
("CQ") queries posed on relational data are combined-semantics equivalent [9].
We continue the tradition of [2,5,9] of studying this problem using the tool of
containment between queries. We introduce a syntactic necessary and sufficient
condition for equivalence of queries belonging to a large natural language of
"explicit-wave" combined-semantics CQ queries; this language encompasses (but
is not limited to) all set, bag, and bag-set queries, and appears to cover all
combined-semantics CQ queries that are expressible in SQL. Our result solves in
the positive the decidability problem of determining combined-semantics
equivalence for pairs of explicit-wave CQ queries. That is, for an arbitrary
pair of combined-semantics CQ queries, it is decidable (i) to determine whether
each of the queries is explicit wave, and (ii) to determine, in case both
queries are explicit wave, whether or not they are combined-semantics
equivalent, by using our syntactic criterion. (The problem of determining
equivalence for general combined-semantics CQ queries remains open. Even so,
our syntactic sufficient containment condition could still be used to determine
that two general CQ queries are combined-semantics equivalent.) Our equivalence
test, as well as our general sufficient condition for containment of
combined-semantics CQ queries, reduce correctly to the special cases reported
in [2,5] for set, bag, and bag-set semantics. Our containment and equivalence
conditions also properly generalize the results of [9], provided that the
latter are restricted to the language of (combined-semantics) CQ queries.
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1308.4048 | Gcube Indexing | cs.DB | Spatial Online Analytical Processing System involves the non-categorical
attribute information also whereas standard Online Analytical Processing System
deals with only categorical attributes. Providing spatial information to the
data warehouse (DW); two major challenges faced are;1.Defining and Aggregation
of Spatial/Continues values and 2.Representation, indexing, updating and
efficient query processing. In this paper, we present GCUBE(Geographical Cube)
storage and indexing procedure to aggregate the spatial information/Continuous
values. We employed the proposed approach storing and indexing using synthetic
and real data sets and evaluated its build, update and Query time. It is
observed that the proposed procedure offers significant performance advantage.
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1308.4067 | The S-metric, the Beichl-Cloteaux approximation, and preferential
attachment | math.CO cs.SI physics.soc-ph | The S-metric has grown popular in network studies, as a measure of
``scale-freeness'' restricted to the collection G(D) of connected graphs with a
common degree sequence D=(d_1,\ldots,d_n). The calculation of S depends on the
maximum possible degree assortativity r among graphs in G(D). The original
method involves a heuristic construction of a maximally assortative graph g*.
The approximation by Beichl and Cloteaux involves constructing a possibly
disconnected graph g' with r(g') >= r(g*) and requires O(n^2) tests for the
graphicality of a degree sequence. The present paper uses the Tripathi-Vijay
test to streamline this approximation, and thereby to investigate two
collections of graphs: Barabasi-Albert trees and coauthorship graphs of
mathematical sciences researchers. Long-term trends in the coauthorship graphs
are discussed, and contextualized by insights derived from the BA trees. It is
known that greater degree-based preferential attachment produces greater
variance in degree sequences, and these trees exhibited assortativities
restricted to a narrow band. In contrast, variance in degree rose over time in
the coauthorship graphs in spite of weakening degree-based preferential
attachment. These observations and their implications are discussed and avenues
of future work are suggested.
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1308.4077 | Support Recovery for the Drift Coefficient of High-Dimensional
Diffusions | cs.IT cs.LG math.IT math.PR math.ST stat.TH | Consider the problem of learning the drift coefficient of a $p$-dimensional
stochastic differential equation from a sample path of length $T$. We assume
that the drift is parametrized by a high-dimensional vector, and study the
support recovery problem when both $p$ and $T$ can tend to infinity. In
particular, we prove a general lower bound on the sample-complexity $T$ by
using a characterization of mutual information as a time integral of
conditional variance, due to Kadota, Zakai, and Ziv. For linear stochastic
differential equations, the drift coefficient is parametrized by a $p\times p$
matrix which describes which degrees of freedom interact under the dynamics. In
this case, we analyze a $\ell_1$-regularized least squares estimator and prove
an upper bound on $T$ that nearly matches the lower bound on specific classes
of sparse matrices.
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1308.4123 | A Likelihood Ratio Approach for Probabilistic Inequalities | math.PR cs.LG math.ST stat.TH | We propose a new approach for deriving probabilistic inequalities based on
bounding likelihood ratios. We demonstrate that this approach is more general
and powerful than the classical method frequently used for deriving
concentration inequalities such as Chernoff bounds. We discover that the
proposed approach is inherently related to statistical concepts such as
monotone likelihood ratio, maximum likelihood, and the method of moments for
parameter estimation. A connection between the proposed approach and the large
deviation theory is also established. We show that, without using moment
generating functions, tightest possible concentration inequalities may be
readily derived by the proposed approach. We have derived new concentration
inequalities using the proposed approach, which cannot be obtained by the
classical approach based on moment generating functions.
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1308.4189 | Seeing What You're Told: Sentence-Guided Activity Recognition In Video | cs.CV cs.AI cs.CL | We present a system that demonstrates how the compositional structure of
events, in concert with the compositional structure of language, can interplay
with the underlying focusing mechanisms in video action recognition, thereby
providing a medium, not only for top-down and bottom-up integration, but also
for multi-modal integration between vision and language. We show how the roles
played by participants (nouns), their characteristics (adjectives), the actions
performed (verbs), the manner of such actions (adverbs), and changing spatial
relations between participants (prepositions) in the form of whole sentential
descriptions mediated by a grammar, guides the activity-recognition process.
Further, the utility and expressiveness of our framework is demonstrated by
performing three separate tasks in the domain of multi-activity videos:
sentence-guided focus of attention, generation of sentential descriptions of
video, and query-based video search, simply by leveraging the framework in
different manners.
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1308.4200 | Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with
Implicit Low-rank Transformations | cs.CV cs.LG stat.ML | Images seen during test time are often not from the same distribution as
images used for learning. This problem, known as domain shift, occurs when
training classifiers from object-centric internet image databases and trying to
apply them directly to scene understanding tasks. The consequence is often
severe performance degradation and is one of the major barriers for the
application of classifiers in real-world systems. In this paper, we show how to
learn transform-based domain adaptation classifiers in a scalable manner. The
key idea is to exploit an implicit rank constraint, originated from a
max-margin domain adaptation formulation, to make optimization tractable.
Experiments show that the transformation between domains can be very
efficiently learned from data and easily applied to new categories. This begins
to bridge the gap between large-scale internet image collections and object
images captured in everyday life environments.
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1308.4201 | Full-Diversity Space-Time Block Codes for Integer-Forcing Linear
Receivers | cs.IT math.IT | In multiple-input multiple-output (MIMO) fading channels, the design
criterion for full-diversity space-time block codes (STBCs) is primarily
determined by the decoding method at the receiver. Although constructions of
STBCs have predominantly matched the maximum-likelihood (ML) decoder, design
criteria and constructions of full-diversity STBCs have also been reported for
low-complexity linear receivers. A new receiver architecture called
Integer-Forcing (IF) linear receiver has been proposed to MIMO channels by Zhan
et al. which showed promising results for the high-rate V-BLAST encoding
scheme. In this paper, we address the design of full-diversity STBCs for IF
linear receivers. In particular, we are interested in characterizing the
structure of STBCs that provide full-diversity with the IF receiver. Along that
direction, we derive an upper bound on the probability of decoding error, and
show that STBCs that satisfy the restricted non-vanishing singular value (RNVS)
property provide full-diversity for the IF receiver. Furthermore, we prove that
all known STBCs with the non-vanishing determinant property provide
full-diversity with IF receivers, as they guarantee the RNVS property. By using
the formulation of RNVS property, we also prove the existence of a
full-diversity STBC outside the class of perfect STBCs, thereby adding
significant insights compared to the existing works on STBCs with IF decoding.
Finally, we present extensive simulation results to demonstrate that linear
designs with RNVS property provide full-diversity for IF receiver.
|
1308.4206 | Nested Nonnegative Cone Analysis | stat.ME cs.LG | Motivated by the analysis of nonnegative data objects, a novel Nested
Nonnegative Cone Analysis (NNCA) approach is proposed to overcome some
drawbacks of existing methods. The application of traditional PCA/SVD method to
nonnegative data often cause the approximation matrix leave the nonnegative
cone, which leads to non-interpretable and sometimes nonsensical results. The
nonnegative matrix factorization (NMF) approach overcomes this issue, however
the NMF approximation matrices suffer several drawbacks: 1) the factorization
may not be unique, 2) the resulting approximation matrix at a specific rank may
not be unique, and 3) the subspaces spanned by the approximation matrices at
different ranks may not be nested. These drawbacks will cause troubles in
determining the number of components and in multi-scale (in ranks)
interpretability. The NNCA approach proposed in this paper naturally generates
a nested structure, and is shown to be unique at each rank. Simulations are
used in this paper to illustrate the drawbacks of the traditional methods, and
the usefulness of the NNCA method.
|
1308.4214 | Pylearn2: a machine learning research library | stat.ML cs.LG cs.MS | Pylearn2 is a machine learning research library. This does not just mean that
it is a collection of machine learning algorithms that share a common API; it
means that it has been designed for flexibility and extensibility in order to
facilitate research projects that involve new or unusual use cases. In this
paper we give a brief history of the library, an overview of its basic
philosophy, a summary of the library's architecture, and a description of how
the Pylearn2 community functions socially.
|
1308.4216 | Triple Point in Correlated Interdependent Networks | physics.soc-ph cs.SI | Many real-world networks depend on other networks, often in non-trivial ways,
to maintain their functionality. These interdependent "networks of networks"
are often extremely fragile. When a fraction $1-p$ of nodes in one network
randomly fails, the damage propagates to nodes in networks that are
interdependent and a dynamic failure cascade occurs that affects the entire
system. We present dynamic equations for two interdependent networks that allow
us to reproduce the failure cascade for an arbitrary pattern of
interdependency. We study the "rich club" effect found in many real
interdependent network systems in which the high-degree nodes are extremely
interdependent, correlating a fraction $\alpha$ of the higher degree nodes on
each network. We find a rich phase diagram in the plane $p-\alpha$, with a
triple point reminiscent of the triple point of liquids that separates a
non-functional phase from two functional phases.
|
1308.4227 | A Computational Framework for the Mixing Times in the QBD Processes with
Infinitely-Many Levels | math.PR cs.PF cs.SY math.OC | In this paper, we develop some matrix Poisson's equations satisfied by the
mean and variance of the mixing time in an irreducible positive-recurrent
discrete-time Markov chain with infinitely-many levels, and provide a
computational framework for the solution to the matrix Poisson's equations by
means of the UL-type of $RG$-factorization as well as the generalized inverses.
In an important special case: the level-dependent QBD processes, we provide a
detailed computation for the mean and variance of the mixing time. Based on
this, we give new highlight on computation of the mixing time in the
block-structured Markov chains with infinitely-many levels through the
matrix-analytic method.
|
1308.4259 | Time Development of Early Social Networks: Link analysis and group
dynamics | physics.soc-ph cs.SI physics.ed-ph | Empirical data on early network history are rare. Students beginning their
studies at a university with no or few prior connections to each other offer a
unique opportunity to investigate the formation and early development of social
networks. During a nine week introductory physics course, first year physics
students were asked to identify those with whom they communicated about problem
solving in physics during the preceding week. We use these students' self
reports to produce time dependent student interaction networks. These networks
have also been investigated to elucidate possible effects of gender and
students' final course grade. Changes in the weekly number of links are
investigated to show that while roughly half of all links change from week to
week, students also reestablish a growing number of links as they progress
through their first weeks of study. To investigate how students group, Infomap
is used to establish groups. Further, student group flow is examined using
alluvial diagrams, showing that many students jump between group each week.,
Finally, a segregation measure is developed which shows that students structure
themselves according to gender and laboratory exercise groups and not according
to end-of-course grade. The results show the behavior of an early
social-educational network, and may have implications for theoretical network
models as well as for physics education.
|
1308.4268 | Multirate Digital Signal Processing via Sampled-Data H-infinity
Optimization | cs.IT cs.SY math.IT math.OC | In this thesis, we present a new method for designing multirate signal
processing and digital communication systems via sampled-data H-infinity
control theory. The difference between our method and conventional ones is in
the signal spaces. Conventional designs are executed in the discrete-time
domain, while our design takes account of both the discrete-time and the
continuous-time signals. Namely, our method can take account of the
characteristic of the original analog signal and the influence of the A/D and
D/A conversion. While the conventional method often indicates that an ideal
digital low-pass filter is preferred, we show that the optimal solution need
not be an ideal low-pass when the original analog signal is not completely
band-limited. This fact can not be recognized only in the discrete-time domain.
Moreover, we consider quantization effects. We discuss the stability and the
performance of quantized sampled-data control systems. We justify H-infinity
control to reduce distortion caused by the quantizer. Then we apply it to
differential pulse code modulation. While the conventional Delta modulator is
not optimal and besides not stable, our modulator is stable and optimal with
respect to the H-infinity-norm. We also give an LMI (Linear Matrix Inequality)
solution to the optimal H-infinity approximation of IIR (Infinite Impulse
Response) filters via FIR (Finite Impulse Response) filters. A comparison with
the Nehari shuffle is made with a numerical example, and it is observed that
the LMI solution generally performs better. Another numerical study also
indicates that there is a trade-off between the pass-band and stop-band
approximation characteristics.
|
1308.4273 | Adaptive matching pursuit for off-grid compressed sensing | eess.SP cs.IT math.IT | Compressive sensing (CS) can effectively recover a signal when it is sparse
in some discrete atoms. However, in some applications, signals are sparse in a
continuous parameter space, e.g., frequency space, rather than discrete atoms.
Usually, we divide the continuous parameter into finite discrete grid points
and build a dictionary from these grid points. However, the actual targets may
not exactly lie on the grid points no matter how densely the parameter is
grided, which introduces mismatch between the predefined dictionary and the
actual one. In this article, a novel method, namely adaptive matching pursuit
with constrained total least squares (AMP-CTLS), is proposed to find actual
atoms even if they are not included in the initial dictionary. In AMP-CTLS, the
grid and the dictionary are adaptively updated to better agree with
measurements. The convergence of the algorithm is discussed, and numerical
experiments demonstrate the advantages of AMP-CTLS.
|
1308.4274 | Chaotic Characteristic of Discrete-time Linear Inclusion Dynamical
Systems | cs.SY math.DS math.OC | In this paper, we study the fiber-chaos of switched linear dynamical systems.
|
1308.4294 | Expanding the Knowledge Horizon in Underwater Robot Swarms | cs.RO | In this paper we study the time delays affecting the diffusion of information
in an underwater heterogeneous robot swarm, considering a time-sensitive
environment. In many situations each member of the swarm must update its
knowledge about the environment as soon as possible, thus every effort to
expand the knowledge horizon is useful. Otherwise critical information may not
reach nodes far from the source causing dangerous misbehaviour of the swarm. We
consider two extreme situations. In the first scenario we have an unique
probabilistic delay distribution. In the second scenario, each agent is subject
to a different truncated gaussian distribution, meaning local conditions are
significantly different from link to link. We study how several swarm
topologies react to the two scenarios and how to allocate the more efficient
transmission resources in order to expand the horizon. Results show that
significant time savings under a gossip-like protocol are possible properly
allocating the resources. Moreover, methods to determine the fastest swarm
topologies and the most important nodes are suggested.
|
1308.4316 | Decentralized Charging of Plug-In Electric Vehicles with Distribution
Feeder Overload Control | cs.SY | As the number of charging Plug-in Electric Vehicles (PEVs) increase, due to
the limited power capacity of the distribution feeders and the sensitivity of
the mid-way distribution transformers to the excessive load, it is crucial to
control the amount of power through each specific distribution feeder to avoid
system overloads that may lead to breakdowns. In this paper we develop, analyze
and evaluate charging algorithms for PEVs with feeder overload constraints in
the distribution grid. The algorithms we propose jointly minimize the variance
of the aggregate load and prevent overloading of the distribution feeders.
|
1308.4338 | SAR Image Despeckling Algorithms using Stochastic Distances and Nonlocal
Means | cs.IT cs.CV cs.GR math.IT stat.AP stat.ML | This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences within the Information Theory framework. The
technique is applied to intensity Synthetic Aperture Radar (SAR) data with
homogeneous regions using the Gamma model. The first approach uses a
Nagao-Matsuyama-type procedure for setting the overlapping samples, and the
second uses the nonlocal method. The proposals are compared with the Improved
Sigma filter and with anisotropic diffusion for speckled data (SRAD) using a
protocol based on Monte Carlo simulation. Among the criteria used to quantify
the quality of filters, we employ the equivalent number of looks, and line and
edge preservation. Moreover, we also assessed the filters by the Universal
Image Quality Index and by the Pearson correlation between edges. Applications
to real images are also discussed. The proposed methods show good results.
|
1308.4398 | Understanding recurrent crime as system-immanent collective behavior | physics.soc-ph cs.SI q-bio.PE | Containing the spreading of crime is a major challenge for society. Yet,
since thousands of years, no effective strategy has been found to overcome
crime. To the contrary, empirical evidence shows that crime is recurrent, a
fact that is not captured well by rational choice theories of crime. According
to these, strong enough punishment should prevent crime from happening. To gain
a better understanding of the relationship between crime and punishment, we
consider that the latter requires prior discovery of illicit behavior and study
a spatial version of the inspection game. Simulations reveal the spontaneous
emergence of cyclic dominance between ''criminals'', ''inspectors'', and
''ordinary people'' as a consequence of spatial interactions. Such cycles
dominate the evolutionary process, in particular when the temptation to commit
crime or the cost of inspection are low or moderate. Yet, there are also
critical parameter values beyond which cycles cease to exist and the population
is dominated either by a stable mixture of criminals and inspectors or one of
these two strategies alone. Both continuous and discontinuous phase transitions
to different final states are possible, indicating that successful strategies
to contain crime can be very much counter-intuitive and complex. Our results
demonstrate that spatial interactions are crucial for the evolutionary outcome
of the inspection game, and they also reveal why criminal behavior is likely to
be recurrent rather than evolving towards an equilibrium with monotonous
parameter dependencies.
|
1308.4440 | Influences Combination of Multi-Sensor Images on Classification Accuracy | cs.CV | This paper focuses on two main issues; first one is the impact of combination
of multi-sensor images on the supervised learning classification accuracy using
segment Fusion (SF). The second issue attempts to undertake the study of
supervised machine learning classification technique of remote sensing images
by using four classifiers like Parallelepiped (Pp), Mahalanobis Distance (MD),
Maximum-Likelihood (ML) and Euclidean Distance(ED) classifiers, and their
accuracies have been evaluated on their respected classification to choose the
best technique for classification of remote sensing images. QuickBird
multispectral data (MS) and panchromatic data (PAN) have been used in this
study to demonstrate the enhancement and accuracy assessment of fused image
over the original images using ALwassaiProcess software. According to
experimental result of this study, is that the test results indicate the
supervised classification results of fusion image, which generated better than
the MS did. As well as the result with Euclidean classifier is robust and
provides better results than the other classifiers do, despite of the popular
belief that the maximum-likelihood classifier is the most accurate classifier.
|
1308.4479 | An Investigation of the Sampling-Based Alignment Method and Its
Contributions | cs.CL | By investigating the distribution of phrase pairs in phrase translation
tables, the work in this paper describes an approach to increase the number of
n-gram alignments in phrase translation tables output by a sampling-based
alignment method. This approach consists in enforcing the alignment of n-grams
in distinct translation subtables so as to increase the number of n-grams.
Standard normal distribution is used to allot alignment time among translation
subtables, which results in adjustment of the distribution of n- grams. This
leads to better evaluation results on statistical machine translation tasks
than the original sampling-based alignment approach. Furthermore, the
translation quality obtained by merging phrase translation tables computed from
the sampling-based alignment method and from MGIZA++ is examined.
|
1308.4499 | On a question of Babadi and Tarokh II | cs.IT math.IT | In this paper we continue to study a question proposed by Babadi and Tarokh
\cite{ba2} on the mysterious randomness of Gold sequences. Upon improving their
result, we establish the randomness of product of pseudorandom matrices formed
from two linear block codes with respect to the empirical spectral
distribution, if the dual distance of both codes is at least 5, hence providing
an affirmative answer to the question.
|
1308.4506 | A study of retrieval algorithms of sparse messages in networks of neural
cliques | cs.NE | Associative memories are data structures addressed using part of the content
rather than an index. They offer good fault reliability and biological
plausibility. Among different families of associative memories, sparse ones are
known to offer the best efficiency (ratio of the amount of bits stored to that
of bits used by the network itself). Their retrieval process performance has
been shown to benefit from the use of iterations. However classical algorithms
require having prior knowledge about the data to retrieve such as the number of
nonzero symbols. We introduce several families of algorithms to enhance the
retrieval process performance in recently proposed sparse associative memories
based on binary neural networks. We show that these algorithms provide better
performance, along with better plausibility than existing techniques. We also
analyze the required number of iterations and derive corresponding curves.
|
1308.4526 | Formalization, Mechanization and Automation of G\"odel's Proof of God's
Existence | cs.LO cs.AI math.LO | G\"odel's ontological proof has been analysed for the first-time with an
unprecedent degree of detail and formality with the help of higher-order
theorem provers. The following has been done (and in this order): A detailed
natural deduction proof. A formalization of the axioms, definitions and
theorems in the TPTP THF syntax. Automatic verification of the consistency of
the axioms and definitions with Nitpick. Automatic demonstration of the
theorems with the provers LEO-II and Satallax. A step-by-step formalization
using the Coq proof assistant. A formalization using the Isabelle proof
assistant, where the theorems (and some additional lemmata) have been automated
with Sledgehammer and Metis.
|
1308.4560 | On the Throughput and Energy Efficiency of Cognitive MIMO Transmissions | cs.IT math.IT | In this paper, throughput and energy efficiency of cognitive multiple-input
multiple-output (MIMO) systems operating under quality-of-service (QoS)
constraints, interference limitations, and imperfect channel sensing, are
studied. It is assumed that transmission power and covariance of the input
signal vectors are varied depending on the sensed activities of primary users
(PUs) in the system. Interference constraints are applied on the transmission
power levels of cognitive radios (CRs) to provide protection for the PUs whose
activities are modeled as a Markov chain. Considering the reliability of the
transmissions and channel sensing results, a state-transition model is
provided. Throughput is determined by formulating the effective capacity. First
derivative of the effective capacity is derived in the low-power regime and the
minimum bit energy requirements in the presence of QoS limitations and
imperfect sensing results are identified. Minimum energy per bit is shown to be
achieved by beamforming in the maximal-eigenvalue eigenspace of certain
matrices related to the channel matrix. In a special case, wideband slope is
determined for more refined analysis of energy efficiency. Numerical results
are provided for the throughput for various levels of buffer constraints and
different number of transmit and receive antennas. The impact of interference
constraints and benefits of multiple-antenna transmissions are determined. It
is shown that increasing the number of antennas when the interference power
constraint is stringent is generally beneficial. On the other hand, it is shown
that under relatively loose interference constraints, increasing the number of
antennas beyond a certain level does not lead to much increase in the
throughput.
|
1308.4565 | Decentralized Online Big Data Classification - a Bandit Framework | cs.LG cs.MA | Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We assume that the classification functions available to each
processing element are fixed, but their prediction accuracy for various types
of incoming data are unknown and can change dynamically over time, and thus
they need to be learned online. We model the problem of joint classification by
the distributed and heterogeneous learners from multiple data sources as a
distributed contextual bandit problem where each data is characterized by a
specific context. We develop distributed online learning algorithms for which
we can prove that they have sublinear regret. Compared to prior work in
distributed online data mining, our work is the first to provide analytic
regret results characterizing the performance of the proposed algorithms.
|
1308.4568 | Distributed Online Learning via Cooperative Contextual Bandits | cs.LG stat.ML | In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems.
|
1308.4572 | Codeword or noise? Exact random coding exponents for slotted
asynchronism | cs.IT math.IT | We consider the problem of slotted asynchronous coded communication, where in
each time frame (slot), the transmitter is either silent or transmits a
codeword from a given (randomly selected) codebook. The task of the decoder is
to decide whether transmission has taken place, and if so, to decode the
message. We derive the optimum detection/decoding rule in the sense of the best
trade-off among the probabilities of decoding error, false alarm, and
misdetection. For this detection/decoding rule, we then derive single-letter
characterizations of the exact exponential rates of these three probabilities
for the average code in the ensemble.
|
1308.4577 | Network Reliability: The effect of local network structure on diffusive
processes | physics.soc-ph cs.SI physics.comp-ph | This paper re-introduces the network reliability polynomial - introduced by
Moore and Shannon in 1956 -- for studying the effect of network structure on
the spread of diseases. We exhibit a representation of the polynomial that is
well-suited for estimation by distributed simulation. We describe a collection
of graphs derived from Erd\H{o}s-R\'enyi and scale-free-like random graphs in
which we have manipulated assortativity-by-degree and the number of triangles.
We evaluate the network reliability for all these graphs under a reliability
rule that is related to the expected size of a connected component. Through
these extensive simulations, we show that for positively or neutrally
assortative graphs, swapping edges to increase the number of triangles does not
increase the network reliability. Also, positively assortative graphs are more
reliable than neutral or disassortative graphs with the same number of edges.
Moreover, we show the combined effect of both assortativity-by-degree and the
presence of triangles on the critical point and the size of the smallest
subgraph that is reliable.
|
1308.4618 | Can inferred provenance and its visualisation be used to detect
erroneous annotation? A case study using UniProtKB | cs.CL cs.CE cs.DL q-bio.QM | A constant influx of new data poses a challenge in keeping the annotation in
biological databases current. Most biological databases contain significant
quantities of textual annotation, which often contains the richest source of
knowledge. Many databases reuse existing knowledge, during the curation process
annotations are often propagated between entries. However, this is often not
made explicit. Therefore, it can be hard, potentially impossible, for a reader
to identify where an annotation originated from. Within this work we attempt to
identify annotation provenance and track its subsequent propagation.
Specifically, we exploit annotation reuse within the UniProt Knowledgebase
(UniProtKB), at the level of individual sentences. We describe a visualisation
approach for the provenance and propagation of sentences in UniProtKB which
enables a large-scale statistical analysis. Initially levels of sentence reuse
within UniProtKB were analysed, showing that reuse is heavily prevalent, which
enables the tracking of provenance and propagation. By analysing sentences
throughout UniProtKB, a number of interesting propagation patterns were
identified, covering over 100, 000 sentences. Over 8000 sentences remain in the
database after they have been removed from the entries where they originally
occurred. Analysing a subset of these sentences suggest that approximately 30%
are erroneous, whilst 35% appear to be inconsistent. These results suggest that
being able to visualise sentence propagation and provenance can aid in the
determination of the accuracy and quality of textual annotation. Source code
and supplementary data are available from the authors website.
|
1308.4643 | Topological security assessment of technological networks | cs.SI physics.soc-ph | The spreading of dangerous malware or faults in inter-dependent networks of
electronics devices has raised deep concern, because from the ICT networks
infections may propagate to other Critical Infrastructures producing the
well-known domino or cascading effect. Researchers are attempting to develop a
high level analysis of malware propagation discarding software details, in
order to generalize to the maximum extent the defensive strategies. For
example, it has been suggested that the maximum eigenvalue of the network
adjacency matrix could act as a threshold for the malware's spreading. This
leads naturally to use the spectral graph theory to identify the most critical
and influential nodes in technological networks. Many well-known graph
parameters have been studied in the past years to accomplish the task. Here we
test our AV11 algorithm showing that outperforms degree, closeness, betweenness
centrality and the dynamical importance
|
1308.4648 | PACE: Pattern Accurate Computationally Efficient Bootstrapping for
Timely Discovery of Cyber-Security Concepts | cs.IR cs.CL | Public disclosure of important security information, such as knowledge of
vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and
other online sources months before proper classification into structured
databases. In order to facilitate timely discovery of such knowledge, we
propose a novel semi-supervised learning algorithm, PACE, for identifying and
classifying relevant entities in text sources. The main contribution of this
paper is an enhancement of the traditional bootstrapping method for entity
extraction by employing a time-memory trade-off that simultaneously circumvents
a costly corpus search while strengthening pattern nomination, which should
increase accuracy. An implementation in the cyber-security domain is discussed
as well as challenges to Natural Language Processing imposed by the security
domain.
|
1308.4675 | Genetic Algorithm for Solving Simple Mathematical Equality Problem | cs.NE | This paper explains genetic algorithm for novice in this field. Basic
philosophy of genetic algorithm and its flowchart are described. Step by step
numerical computation of genetic algorithm for solving simple mathematical
equality problem will be briefly explained
|
1308.4687 | Query Processing Performance and Searching Over Encrypted Data By Using
An Efficient Algorithm | cs.DB cs.CR | Data is the central asset of today's dynamically operating organization and
their business. This data is usually stored in database. A major consideration
is applied on the security of that data from the unauthorized access and
intruders. Data encryption is a strong option for security of data in database
and especially in those organizations where security risks are high. But there
is a potential disadvantage of performance degradation. When we apply
encryption on database then we should compromise between the security and
efficient query processing. The work of this paper tries to fill this gap. It
allows the users to query over the encrypted column directly without decrypting
all the records. It's improves the performance of the system. The proposed
algorithm works well in the case of range and fuzzy match queries.
|
1308.4718 | Invertibility and Robustness of Phaseless Reconstruction | math.FA cs.CV stat.ML | This paper is concerned with the question of reconstructing a vector in a
finite-dimensional real Hilbert space when only the magnitudes of the
coefficients of the vector under a redundant linear map are known. We analyze
various Lipschitz bounds of the nonlinear analysis map and we establish
theoretical performance bounds of any reconstruction algorithm. We show that
robust and stable reconstruction requires additional redundancy than the
critical threshold.
|
1308.4757 | Online and stochastic Douglas-Rachford splitting method for large scale
machine learning | cs.NA cs.LG stat.ML | Online and stochastic learning has emerged as powerful tool in large scale
optimization. In this work, we generalize the Douglas-Rachford splitting (DRs)
method for minimizing composite functions to online and stochastic settings (to
our best knowledge this is the first time DRs been generalized to sequential
version). We first establish an $O(1/\sqrt{T})$ regret bound for batch DRs
method. Then we proved that the online DRs splitting method enjoy an $O(1)$
regret bound and stochastic DRs splitting has a convergence rate of
$O(1/\sqrt{T})$. The proof is simple and intuitive, and the results and
technique can be served as a initiate for the research on the large scale
machine learning employ the DRs method. Numerical experiments of the proposed
method demonstrate the effectiveness of the online and stochastic update rule,
and further confirm our regret and convergence analysis.
|
1308.4761 | Matching Demand with Supply in the Smart Grid using Agent-Based
Multiunit Auction | cs.AI cs.GT | Recent work has suggested reducing electricity generation cost by cutting the
peak to average ratio (PAR) without reducing the total amount of the loads.
However, most of these proposals rely on consumer's willingness to act. In this
paper, we propose an approach to cut PAR explicitly from the supply side. The
resulting cut loads are then distributed among consumers by the means of a
multiunit auction which is done by an intelligent agent on behalf of the
consumer. This approach is also in line with the future vision of the smart
grid to have the demand side matched with the supply side. Experiments suggest
that our approach reduces overall system cost and gives benefit to both
consumers and the energy provider.
|
1308.4764 | On the Zero-freeness of Tall Multirate Linear Systems | cs.SY | In this paper, tall discrete-time linear systems with multirate outputs are
studied. In particular, we focus on their zeros. In systems and control
literature zeros of multirate systems are defined as those of their
corresponding time-invariant blocked systems. Hence, the zeros of tall blocked
systems resulting from blocking of linear systems with multirate outputs are
mainly explored in this work. We specifically investigate zeros of tall blocked
systems formed by blocking tall multirate linear systems with generic parameter
matrices. It is demonstrated that tall blocked systems generically have no
finite nonzero zeros; however, they may have zeros at the origin or at infinity
depending on the choice of blocking delay and the input, state and output
dimensions.
|
1308.4774 | Bit Rate of Programs | cs.SE cs.IT math.IT | A program can be considered as a device that generates discrete time signals,
where a signal is an execution. Shannon information rate, or bit rate, of the
signals may not be uniformly distributed. When the program is specified by a
finite state transition system, algorithms are provided in identifying
information-rich components. For a black-box program that has a partial
specification or does not even have a specification, a bit rate signal and its
spectrum are studied, which make use of data compression and the Fourier
transform. The signal provides a bit-rate coverage for testing the black-box
while its spectrum indicates a visual representation for execution's
information characteristics.
|
1308.4777 | Adaptive Multi-objective Optimization for Energy Efficient Interference
Coordination in Multi-Cell Networks | cs.IT math.IT | In this paper, we investigate the distributed power allocation for multi-cell
OFDMA networks taking both energy efficiency and inter-cell interference (ICI)
mitigation into account. A performance metric termed as throughput contribution
is exploited to measure how ICI is effectively coordinated. To achieve a
distributed power allocation scheme for each base station (BS), the throughput
contribution of each BS to the network is first given based on a pricing
mechanism. Different from existing works, a biobjective problem is formulated
based on multi-objective optimization theory, which aims at maximizing the
throughput contribution of the BS to the network and minimizing its total power
consumption at the same time. Using the method of Pascoletti and Serafini
scalarization, the relationship between the varying parameters and minimal
solutions is revealed. Furthermore, to exploit the relationship an algorithm is
proposed based on which all the solutions on the boundary of the efficient set
can be achieved by adaptively adjusting the involved parameters. With the
obtained solution set, the decision maker has more choices on power allocation
schemes in terms of both energy consumption and throughput. Finally, the
performance of the algorithm is assessed by the simulation results.
|
1308.4786 | An Investigation On Fuzzy Logic Controllers (TAKAGI-SUGENO & MAMDANI) In
Inverse Pendulum System | cs.SY | The concept of controlling non-linear systems is one the significant fields
in scientific researches for the purpose of which intelligent approaches can
provide desirable applicability. Fuzzy systems are systems with ambiguous
definition and fuzzy control is an especial type of non-linear control. Inverse
pendulum system is one the most widely popular non-linear systems which is
known for its specific characteristics such as being intrinsically non-linear
and unsteady. Therefore, a controller is required for maintaining stability of
the system Present study tries to compare the obtained results from designing
fuzzy intelligent controllers in similar conditions and also identify the
appropriate controller for holding the inverse pendulum in vertical position on
the cart.
|
1308.4791 | Multipath Matching Pursuit | cs.IT math.IT | In this paper, we propose an algorithm referred to as multipath matching
pursuit that investigates multiple promising candidates to recover sparse
signals from compressed measurements. Our method is inspired by the fact that
the problem to find the candidate that minimizes the residual is readily
modeled as a combinatoric tree search problem and the greedy search strategy is
a good fit for solving this problem. In the empirical results as well as the
restricted isometry property (RIP) based performance guarantee, we show that
the proposed MMP algorithm is effective in reconstructing original sparse
signals for both noiseless and noisy scenarios.
|
1308.4801 | The Mapping of Simulated Climate-Dependent Building Innovations | cs.CE | Performances of building energy innovations are most of the time dependent on
the external climate conditions. This means a high performance of a specific
innovation in a certain part of Europe, does not imply the same performances in
other regions. The mapping of simulated building performances at the EU scale
could prevent the waste of potential good ideas by identifying the best region
for a specific innovation. This paper presents a methodology for obtaining maps
of performances of building innovations that are virtually spread over whole
Europe. It is concluded that these maps are useful for finding regions at the
EU where innovations have the highest expected performances.
|
1308.4809 | Block Markov Superposition Transmission: Construction of Big
Convolutional Codes from Short Codes | cs.IT math.IT | A construction of big convolutional codes from short codes called block
Markov superposition transmission (BMST) is proposed. The BMST is very similar
to superposition blockMarkov encoding (SBME), which has been widely used to
prove multiuser coding theorems. The encoding process of BMST can be as fast as
that of the involved short code, while the decoding process can be implemented
as an iterative sliding-window decoding algorithm with a tunable delay. More
importantly, the performance of BMST can be simply lower-bounded in terms of
the transmission memory given that the performance of the short code is
available. Numerical results show that, 1) the lower bounds can be matched with
a moderate decoding delay in the low bit-error-rate (BER) region, implying that
the iterative slidingwindow decoding algorithm is near optimal; 2) BMST with
repetition codes and single parity-check codes can approach the Shannon limit
within 0.5 dB at BER of 10^{-5} for a wide range of code rates; and 3) BMST can
also be applied to nonlinear codes.
|
1308.4828 | The Sample-Complexity of General Reinforcement Learning | cs.LG | We present a new algorithm for general reinforcement learning where the true
environment is known to belong to a finite class of N arbitrary models. The
algorithm is shown to be near-optimal for all but O(N log^2 N) time-steps with
high probability. Infinite classes are also considered where we show that
compactness is a key criterion for determining the existence of uniform
sample-complexity bounds. A matching lower bound is given for the finite case.
|
1308.4839 | Diversification Based Static Index Pruning - Application to Temporal
Collections | cs.IR | Nowadays, web archives preserve the history of large portions of the web. As
medias are shifting from printed to digital editions, accessing these huge
information sources is drawing increasingly more attention from national and
international institutions, as well as from the research community. These
collections are intrinsically big, leading to index files that do not fit into
the memory and an increase query response time. Decreasing the index size is a
direct way to decrease this query response time.
Static index pruning methods reduce the size of indexes by removing a part of
the postings. In the context of web archives, it is necessary to remove
postings while preserving the temporal diversity of the archive. None of the
existing pruning approaches take (temporal) diversification into account.
In this paper, we propose a diversification-based static index pruning
method. It differs from the existing pruning approaches by integrating
diversification within the pruning context. We aim at pruning the index while
preserving retrieval effectiveness and diversity by pruning while maximizing a
given IR evaluation metric like DCG. We show how to apply this approach in the
context of web archives. Finally, we show on two collections that search
effectiveness in temporal collections after pruning can be improved using our
approach rather than diversity oblivious approaches.
|
1308.4840 | Power Control in Networks With Heterogeneous Users: A Quasi-Variational
Inequality Approach | cs.IT math.IT | This work deals with the power allocation problem in a
multipoint-to-multipoint network, which is heterogenous in the sense that each
transmit and receiver pair can arbitrarily choose whether to selfishly maximize
its own rate or energy efficiency. This is achieved by modeling the transmit
and receiver pairs as rational players that engage in a non-cooperative game in
which the utility function changes according to each player's nature. The
underlying game is reformulated as a quasi variational inequality (QVI) problem
using convex fractional program theory. The equivalence between the QVI and the
non-cooperative game provides us with all the mathematical tools to study the
uniqueness of its Nash equilibrium (NE) points and to derive novel algorithms
that allow the network to converge to these points in an iterative manner both
with and without the need for a centralized processing. A small-cell network is
considered as a possible case study of this heterogeneous scenario. Numerical
results are used to validate the proposed solutions in different operating
conditions.
|
1308.4846 | POMDPs under Probabilistic Semantics | cs.AI | We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold {\lambda} in (0, 1]; and (ii) qualitative constraint which is a
special case of quantitative constraint with {\lambda} = 1. We consider the
computation of the almost-sure winning set, where the controller needs to
ensure that the path constraint is satisfied with probability 1. Our main
results for qualitative path constraint are as follows: (i) the problem of
deciding the existence of a finite-memory controller is EXPTIME-complete; and
(ii) the problem of deciding the existence of an infinite-memory controller is
undecidable. For quantitative path constraint we show that the problem of
deciding the existence of a finite-memory controller is undecidable.
|
1308.4847 | The dynamic pattern of human attention | physics.soc-ph cs.SI | A mass of traces of human activities show diverse dynamic patterns. In this
paper, we comprehensively investigate the dynamic pattern of human attention
defined by the quantity of interests on subdisciplines in an online academic
communication forum. Both the expansion and exploration of human attention have
a power-law scaling relation with browsing actions, of which the exponent is
close to that in one-dimension random walk. Furthermore, the memory effect of
human attention is characterized by the power-law distributions of both the
return interval time and return interval steps, which is reinforced by studying
the attention shift that monotonically increase with the interval order between
pairs of continuously segmental sequences of expansion. At last, the observing
dynamic pattern of human attention in the browsing process is analytically
described by a dynamic model whose generic mechanism is analogy to that of
human spatial mobility. Thus, our work not only enlarges the research scope of
human dynamics, but also provides an insight to understand the relationship
between the interest transitivity in online activities and human spatial
mobility in real world.
|
1308.4880 | May the Best Meme Win!: New Exploration of Competitive Epidemic
Spreading over Arbitrary Multi-Layer Networks | physics.soc-ph cs.SI | This study extends the SIS epidemic model for single virus propagation over
an arbitrary graph to an SI1SI2S epidemic model of two exclusive, competitive
viruses over a two-layer network with generic structure, where network layers
represent the distinct transmission routes of the viruses. We find analytical
results determining extinction, mutual exclusion, and coexistence of the
viruses by introducing the concepts of survival threshold and winning
threshold. Furthermore, we show the possibility of coexistence in SIS-type
competitive spreading over multilayer networks. Not only do we rigorously prove
a region of coexistence, we quantitate it via interrelation of central nodes
across the network layers. Little to no overlapping of layers central nodes is
the key determinant of coexistence. Specifically, we show coexistence is
impossible if network layers are identical yet possible if the network layers
have distinct dominant eigenvectors and node degree vectors. For example, we
show both analytically and numerically that positive correlation of network
layers makes it difficult for a virus to survive while in a network with
negatively correlated layers survival is easier but total removal of the other
virus is more difficult. We believe our methodology has great potentials for
application to broader classes of multi-pathogen spreading over multi-layer and
interconnected networks.
|
1308.4902 | A review on handwritten character and numeral recognition for Roman,
Arabic, Chinese and Indian scripts | cs.CV | There are a lot of intensive researches on handwritten character recognition
(HCR) for almost past four decades. The research has been done on some of
popular scripts such as Roman, Arabic, Chinese and Indian. In this paper we
present a review on HCR work on the four popular scripts. We have summarized
most of the published paper from 2005 to recent and also analyzed the various
methods in creating a robust HCR system. We also added some future direction of
research on HCR.
|
1308.4904 | Proceedings Third International Workshop on Hybrid Autonomous Systems | cs.SY cs.CE | The interest on autonomous systems is increasing both in industry and
academia. Such systems must operate with limited human intervention in a
changing environment and must be able to compensate for significant system
failures without external intervention. The most appropriate models of
autonomous systems can be found in the class of hybrid systems (which study
continuous-state dynamic processes via discrete-state controllers) that
interact with their environment. This workshop brings together researchers
interested in all aspects of autonomy and resilience of hybrid systems.
|
1308.4908 | A Unified Framework for Multi-Sensor HDR Video Reconstruction | cs.CV cs.GR cs.MM | One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and allows reconstruction to an
arbitrary resolution and output mapping. We present an implementation in CUDA
and show real-time performance for an experimental 4 Mpixel multi-sensor HDR
video system. We further show that our algorithm has clear advantages over
existing methods, both in terms of flexibility and reconstruction quality.
|
1308.4915 | Minimal Dirichlet energy partitions for graphs | math.OC cs.LG stat.ML | Motivated by a geometric problem, we introduce a new non-convex graph
partitioning objective where the optimality criterion is given by the sum of
the Dirichlet eigenvalues of the partition components. A relaxed formulation is
identified and a novel rearrangement algorithm is proposed, which we show is
strictly decreasing and converges in a finite number of iterations to a local
minimum of the relaxed objective function. Our method is applied to several
clustering problems on graphs constructed from synthetic data, MNIST
handwritten digits, and manifold discretizations. The model has a
semi-supervised extension and provides a natural representative for the
clusters as well.
|
1308.4922 | Learning Deep Representation Without Parameter Inference for Nonlinear
Dimensionality Reduction | cs.LG stat.ML | Unsupervised deep learning is one of the most powerful representation
learning techniques. Restricted Boltzman machine, sparse coding, regularized
auto-encoders, and convolutional neural networks are pioneering building blocks
of deep learning. In this paper, we propose a new building block -- distributed
random models. The proposed method is a special full implementation of the
product of experts: (i) each expert owns multiple hidden units and different
experts have different numbers of hidden units; (ii) the model of each expert
is a k-center clustering, whose k-centers are only uniformly sampled examples,
and whose output (i.e. the hidden units) is a sparse code that only the
similarity values from a few nearest neighbors are reserved. The relationship
between the pioneering building blocks, several notable research branches and
the proposed method is analyzed. Experimental results show that the proposed
deep model can learn better representations than deep belief networks and
meanwhile can train a much larger network with much less time than deep belief
networks.
|
1308.4941 | Automatic Labeling for Entity Extraction in Cyber Security | cs.IR cs.CL | Timely analysis of cyber-security information necessitates automated
information extraction from unstructured text. While state-of-the-art
extraction methods produce extremely accurate results, they require ample
training data, which is generally unavailable for specialized applications,
such as detecting security related entities; moreover, manual annotation of
corpora is very costly and often not a viable solution. In response, we develop
a very precise method to automatically label text from several data sources by
leveraging related, domain-specific, structured data and provide public access
to a corpus annotated with cyber-security entities. Next, we implement a
Maximum Entropy Model trained with the average perceptron on a portion of our
corpus ($\sim$750,000 words) and achieve near perfect precision, recall, and
accuracy, with training times under 17 seconds.
|
1308.4942 | A Multiscale Pyramid Transform for Graph Signals | cs.IT cs.SI math.FA math.IT | Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.
|
1308.4943 | David Poole's Specificity Revised | cs.AI | In the middle of the 1980s, David Poole introduced a semantical,
model-theoretic notion of specificity to the artificial-intelligence community.
Since then it has found further applications in non-monotonic reasoning, in
particular in defeasible reasoning. Poole tried to approximate the intuitive
human concept of specificity, which seems to be essential for reasoning in
everyday life with its partial and inconsistent information. His notion,
however, turns out to be intricate and problematic, which --- as we show ---
can be overcome to some extent by a closer approximation of the intuitive human
concept of specificity. Besides the intuitive advantages of our novel
specificity ordering over Poole's specificity relation in the classical
examples of the literature, we also report some hard mathematical facts:
Contrary to what was claimed before, we show that Poole's relation is not
transitive. The present means to decide our novel specificity relation,
however, show only a slight improvement over the known ones for Poole's
relation, and further work is needed in this aspect.
|
1308.4965 | A proposal for a Chinese keyboard for cellphones, smartphones, ipads and
tablets | cs.HC cs.CL | In this paper, we investigate the possibility to use two tilings of the
hyperbolic plane as basic frame for devising a way to input texts in Chinese
characters into messages of cellphones, smartphones, ipads and tablets.
|
1308.4969 | Optimal interdependence between networks for the evolution of
cooperation | physics.soc-ph cond-mat.stat-mech cs.SI q-bio.PE | Recent research has identified interactions between networks as crucial for
the outcome of evolutionary games taking place on them. While the consensus is
that interdependence does promote cooperation by means of organizational
complexity and enhanced reciprocity that is out of reach on isolated networks,
we here address the question just how much interdependence there should be.
Intuitively, one might assume the more the better. However, we show that in
fact only an intermediate density of sufficiently strong interactions between
networks warrants an optimal resolution of social dilemmas. This is due to an
intricate interplay between the heterogeneity that causes an asymmetric
strategy flow because of the additional links between the networks, and the
independent formation of cooperative patterns on each individual network.
Presented results are robust to variations of the strategy updating rule, the
topology of interdependent networks, and the governing social dilemma, thus
suggesting a high degree of universality.
|
1308.4994 | Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds &
Theoretical Guarantees | cs.IT math.IT | It was recently shown that low rank matrix completion theory can be employed
for designing new sampling schemes in the context of MIMO radars, which can
lead to the reduction of the high volume of data typically required for
accurate target detection and estimation. Employing random samplers at each
reception antenna, a partially observed version of the received data matrix is
formulated at the fusion center, which, under certain conditions, can be
recovered using convex optimization. This paper presents the theoretical
analysis regarding the performance of matrix completion in colocated MIMO radar
systems, exploiting the particular structure of the data matrix. Both Uniform
Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for
transmission and reception. Especially for the ULA case, under some mild
assumptions on the directions of arrival of the targets, it is explicitly shown
that the coherence of the data matrix is both asymptotically and approximately
optimal with respect to the number of antennas of the arrays involved and
further, the data matrix is recoverable using a subset of its entries with
minimal cardinality. Sufficient conditions guaranteeing low matrix coherence
and consequently satisfactory matrix completion performance are also presented,
including the arbitrary 2-dimensional array case.
|
1308.4999 | Incorporating Text Analysis into Evolution of Social Groups in
Blogosphere | cs.SI physics.soc-ph | Data reflecting social and business relations has often form of network of
connections between entities (called social network). In such network important
and influential users can be identified as well as groups of strongly connected
users. Finding such groups and observing their evolution becomes an
increasingly important research problem. One of the significant problems is to
develop method incorporating not only information about connections between
entities but also information obtained from text written by the users. Method
presented in this paper combine social network analysis and text mining in
order to understand groups evolution.
|
1308.5000 | Smoothing and Decomposition for Analysis Sparse Recovery | math.OC cs.IT math.IT | We consider algorithms and recovery guarantees for the analysis sparse model
in which the signal is sparse with respect to a highly coherent frame. We
consider the use of a monotone version of the fast iterative shrinkage-
thresholding algorithm (MFISTA) to solve the analysis sparse recovery problem.
Since the proximal operator in MFISTA does not have a closed-form solution for
the analysis model, it cannot be applied directly. Instead, we examine two
alternatives based on smoothing and decomposition transformations that relax
the original sparse recovery problem, and then implement MFISTA on the relaxed
formulation. We refer to these two methods as smoothing-based and
decomposition-based MFISTA. We analyze the convergence of both algorithms, and
establish that smoothing- based MFISTA converges more rapidly when applied to
general nonsmooth optimization problems. We then derive a performance bound on
the reconstruction error using these techniques. The bound proves that our
methods can recover a signal sparse in a redundant tight frame when the
measurement matrix satisfies a properly adapted restricted isometry property.
Numerical examples demonstrate the performance of our methods and show that
smoothing-based MFISTA converges faster than the decomposition-based
alternative in real applications, such as MRI image reconstruction.
|
1308.5010 | Sentiment in New York City: A High Resolution Spatial and Temporal View | physics.soc-ph cs.CL cs.CY | Measuring public sentiment is a key task for researchers and policymakers
alike. The explosion of available social media data allows for a more
time-sensitive and geographically specific analysis than ever before. In this
paper we analyze data from the micro-blogging site Twitter and generate a
sentiment map of New York City. We develop a classifier specifically tuned for
140-character Twitter messages, or tweets, using key words, phrases and
emoticons to determine the mood of each tweet. This method, combined with
geotagging provided by users, enables us to gauge public sentiment on extremely
fine-grained spatial and temporal scales. We find that public mood is generally
highest in public parks and lowest at transportation hubs, and locate other
areas of strong sentiment such as cemeteries, medical centers, a jail, and a
sewage facility. Sentiment progressively improves with proximity to Times
Square. Periodic patterns of sentiment fluctuate on both a daily and a weekly
scale: more positive tweets are posted on weekends than on weekdays, with a
daily peak in sentiment around midnight and a nadir between 9:00 a.m. and noon.
|
1308.5015 | The Simple Rules of Social Contagion | cs.SI physics.soc-ph | It is commonly believed that information spreads between individuals like a
pathogen, with each exposure by an informed friend potentially resulting in a
naive individual becoming infected. However, empirical studies of social media
suggest that individual response to repeated exposure to information is
significantly more complex than the prediction of the pathogen model. As a
proxy for intervention experiments, we compare user responses to multiple
exposures on two different social media sites, Twitter and Digg. We show that
the position of the exposing messages on the user-interface strongly affects
social contagion. Accounting for this visibility significantly simplifies the
dynamics of social contagion. The likelihood an individual will spread
information increases monotonically with exposure, while explicit feedback
about how many friends have previously spread it increases the likelihood of a
response. We apply our model to real-time forecasting of user behavior.
|
1308.5032 | How Did Humans Become So Creative? A Computational Approach | cs.NE cs.AI cs.MA q-bio.NC | This paper summarizes efforts to computationally model two transitions in the
evolution of human creativity: its origins about two million years ago, and the
'big bang' of creativity about 50,000 years ago. Using a computational model of
cultural evolution in which neural network based agents evolve ideas for
actions through invention and imitation, we tested the hypothesis that human
creativity began with onset of the capacity for recursive recall. We compared
runs in which agents were limited to single-step actions to runs in which they
used recursive recall to chain simple actions into complex ones. Chaining
resulted in higher diversity, open-ended novelty, no ceiling on the mean
fitness of actions, and greater ability to make use of learning. Using a
computational model of portrait painting, we tested the hypothesis that the
explosion of creativity in the Middle/Upper Paleolithic was due to onset of
con-textual focus: the capacity to shift between associative and analytic
thought. This resulted in faster convergence on portraits that resembled the
sitter, employed painterly techniques, and were rated as preferable. We
conclude that recursive recall and contextual focus provide a computationally
plausible explanation of how humans evolved the means to transform this planet.
|
1308.5033 | A hybrid evolutionary algorithm with importance sampling for
multi-dimensional optimization | cs.NE | A hybrid evolutionary algorithm with importance sampling method is proposed
for multi-dimensional optimization problems in this paper. In order to make use
of the information provided in the search process, a set of visited solutions
is selected to give scores for intervals in each dimension, and they are
updated as algorithm proceeds. Those intervals with higher scores are regarded
as good intervals, which are used to estimate the joint distribution of optimal
solutions through an interaction between the pool of good genetics, which are
the individuals with smaller fitness values. And the sampling probabilities for
good genetics are determined through an interaction between those estimated
good intervals. It is a cross validation mechanism which determines the
sampling probabilities for good intervals and genetics, and the resulted
probabilities are used to design crossover, mutation and other stochastic
operators with importance sampling method. As the selection of genetics and
intervals is not directly dependent on the values of fitness, the resulted
offsprings may avoid the trap of local optima. And a purely random EA is also
combined into the proposed algorithm to maintain the diversity of population.
30 benchmark test functions are used to evaluate the performance of the
proposed algorithm, and it is found that the proposed hybrid algorithm is an
efficient algorithm for multi-dimensional optimization problems considered in
this paper.
|
1308.5038 | Group-Sparse Signal Denoising: Non-Convex Regularization, Convex
Optimization | cs.CV cs.LG stat.ML | Convex optimization with sparsity-promoting convex regularization is a
standard approach for estimating sparse signals in noise. In order to promote
sparsity more strongly than convex regularization, it is also standard practice
to employ non-convex optimization. In this paper, we take a third approach. We
utilize a non-convex regularization term chosen such that the total cost
function (consisting of data consistency and regularization terms) is convex.
Therefore, sparsity is more strongly promoted than in the standard convex
formulation, but without sacrificing the attractive aspects of convex
optimization (unique minimum, robust algorithms, etc.). We use this idea to
improve the recently developed 'overlapping group shrinkage' (OGS) algorithm
for the denoising of group-sparse signals. The algorithm is applied to the
problem of speech enhancement with favorable results in terms of both SNR and
perceptual quality.
|
1308.5045 | Network Coding meets Decentralized Control: Network Linearization and
Capacity-Stabilizablilty Equivalence | math.OC cs.IT math.IT | We take a unified view of network coding and decentralized control. Precisely
speaking, we consider both as linear time-invariant systems by appropriately
restricting channels and coding schemes of network coding to be linear
time-invariant, and the plant and controllers of decentralized control to be
linear time-invariant as well. First, we apply linear system theory to network
coding. This gives a novel way of converting an arbitrary relay network to an
equivalent acyclic single-hop relay network, which we call Network
Linearization. Based on network linearization, we prove that the fundamental
design limit, mincut, is achievable by a linear time-invariant network-coding
scheme regardless of the network topology.
Then, we use the network-coding to view decentralized linear systems. We
argue that linear time-invariant controllers in a decentralized linear system
"communicate" via linear network coding to stabilize the plant. To justify this
argument, we give an algorithm to "externalize" the implicit communication
between the controllers that we believe must be occurring to stabilize the
plant. Based on this, we show that the stabilizability condition for
decentralized linear systems comes from an underlying communication limit,
which can be described by the algebraic mincut-maxflow theorem. With this
re-interpretation in hand, we also consider stabilizability over LTI networks
to emphasize the connection with network coding. In particular, in broadcast
and unicast problems, unintended messages at the receivers will be modeled as
secrecy constraints.
|
1308.5046 | The Fractal Dimension of SAT Formulas | cs.AI | Modern SAT solvers have experienced a remarkable progress on solving
industrial instances. Most of the techniques have been developed after an
intensive experimental testing process. Recently, there have been some attempts
to analyze the structure of these formulas in terms of complex networks, with
the long-term aim of explaining the success of these SAT solving techniques,
and possibly improving them.
We study the fractal dimension of SAT formulas, and show that most industrial
families of formulas are self-similar, with a small fractal dimension. We also
show that this dimension is not affected by the addition of learnt clauses. We
explore how the dimension of a formula, together with other graph properties
can be used to characterize SAT instances. Finally, we give empirical evidence
that these graph properties can be used in state-of-the-art portfolios.
|
1308.5053 | Delay Optimal Scheduling for Energy Harvesting Based Communications | cs.ET cs.IT math.IT | Green communication attracts increasing research interest recently. Equipped
with a rechargeable battery, a source node can harvest energy from ambient
environments and rely on this free and regenerative energy supply to transmit
packets. Due to the uncertainty of available energy from harvesting, however,
intolerably large latency and packet loss could be induced, if the source
always waits for harvested energy. To overcome this problem, one Reliable
Energy Source (RES) can be resorted to for a prompt delivery of backlogged
packets. Naturally, there exists a tradeoff between the packet delivery delay
and power consumption from the RES. In this paper, we address the delay optimal
scheduling problem for a bursty communication link powered by a
capacity-limited battery storing harvested energy together with one RES. The
proposed scheduling scheme gives priority to the usage of harvested energy, and
resorts to the RES when necessary based on the data and energy queueing
processes, with an average power constraint from the RES. Through
twodimensional Markov chain modeling and linear programming formulation, we
derive the optimal threshold-based scheduling policy together with the
corresponding transmission parameters. Our study includes three exemplary cases
that capture some important relations between the data packet arrival process
and energy harvesting capability. Our theoretical analysis is corroborated by
simulation results.
|
1308.5063 | Suspicious Object Recognition Method in Video Stream Based on Visual
Attention | cs.CV | We propose a state of the art method for intelligent object recognition and
video surveillance based on human visual attention. Bottom up and top down
attention are applied respectively in the process of acquiring interested
object(saliency map) and object recognition. The revision of 4 channel PFT
method is proposed for bottom up attention and enhances the speed and accuracy.
Inhibit of return (IOR) is applied in judging the sequence of saliency object
pop out. Euclidean distance of color distribution, object center coordinates
and speed are considered in judging whether the target is match and suspicious.
The extensive tests on videos and images show that our method in video analysis
has high accuracy and fast speed compared with traditional method. The method
can be applied into many fields such as video surveillance and security.
|
1308.5094 | Complexity of evolutionary equilibria in static fitness landscapes | q-bio.PE cs.NE | A fitness landscape is a genetic space -- with two genotypes adjacent if they
differ in a single locus -- and a fitness function. Evolutionary dynamics
produce a flow on this landscape from lower fitness to higher; reaching
equilibrium only if a local fitness peak is found. I use computational
complexity to question the common assumption that evolution on static fitness
landscapes can quickly reach a local fitness peak. I do this by showing that
the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2;
the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are
NK fitness landscapes where every adaptive path from some vertices is of
exponential length. Alternatively -- under the standard complexity theoretic
assumption that there are problems in PLS not solvable in polynomial time --
this means that there are no evolutionary dynamics (known, or to be discovered,
and not necessarily following adaptive paths) that can converge to a local
fitness peak on all NK landscapes with K = 2. Applying results from the
analysis of simplex algorithms, I show that there exist single-peaked
landscapes with no reciprocal sign epistasis where the expected length of an
adaptive path following strong selection weak mutation dynamics is
$e^{O(n^{1/3})}$ even though an adaptive path to the optimum of length less
than n is available from every vertex. The technical results are written to be
accessible to mathematical biologists without a computer science background,
and the biological literature is summarized for the convenience of
non-biologists with the aim to open a constructive dialogue between the two
disciplines.
|
1308.5121 | Voter Model with Arbitrary Degree Dependence: Clout, Confidence and
Irreversibility | physics.soc-ph cond-mat.stat-mech cs.MA cs.SI | In this paper, we consider the voter model with popularity bias. The
influence of each node on its neighbors depends on its degree. We find the
consensus probabilities and expected consensus times for each of the states. We
also find the fixation probability, which is the probability that a single node
whose state differs from every other node imposes its state on the entire
system. In addition, we find the expected fixation time. Then two extensions to
the model are proposed and the motivations behind them are discussed. The first
one is confidence, where in addition to the states of neighbors, nodes take
their own state into account at each update. We repeat the calculations for the
augmented model and investigate the effects of adding confidence to the model.
The second proposed extension is irreversibility, where one of the states is
given the property that once nodes adopt it, they cannot switch back. The
dynamics of densities, fixation times and consensus times are obtained.
|
1308.5125 | Discovering Latent Patterns from the Analysis of User-Curated Movie
Lists | cs.SI physics.soc-ph | User content curation is becoming an important source of preference data, as
well as providing information regarding the items being curated. One popular
approach involves the creation of lists. On Twitter, these lists might contain
accounts relevant to a particular topic, whereas on a community site such as
the Internet Movie Database (IMDb), this might take the form of lists of movies
sharing common characteristics. While list curation involves substantial
combined effort on the part of users, researchers have rarely looked at mining
the outputs of this kind of crowdsourcing activity. Here we study a large
collection of movie lists from IMDb. We apply network analysis methods to a
graph that reflects the degree to which pairs of movies are "co-listed", that
is, assigned to the same lists. This allows us to uncover a more nuanced
grouping of movies that goes beyond categorisation schemes based on attributes
such as genre or director.
|
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