id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1308.2705 | Stochastic Models Predict User Behavior in Social Media | cs.CY cs.SI physics.soc-ph | User response to contributed content in online social media depends on many
factors. These include how the site lays out new content, how frequently the
user visits the site, how many friends the user follows, how active these
friends are, as well as how interesting or useful the content is to the user.
We present a stochastic modeling framework that relates a user's behavior to
details of the site's user interface and user activity and describe a procedure
for estimating model parameters from available data. We apply the model to
study discussions of controversial topics on Twitter, specifically, to predict
how followers of an advocate for a topic respond to the advocate's posts. We
show that a model of user behavior that explicitly accounts for a user
transitioning through a series of states before responding to an advocate's
post better predicts response than models that fail to take these states into
account. We demonstrate other benefits of stochastic models, such as their
ability to identify users who are highly interested in advocate's posts.
|
1308.2725 | Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection
Algorithms for MIMO Systems | cs.IT math.IT | In this work, decision feedback (DF) detection algorithms based on multiple
processing branches for multi-input multi-output (MIMO) spatial multiplexing
systems are proposed. The proposed detector employs multiple cancellation
branches with receive filters that are obtained from a common matrix inverse
and achieves a performance close to the maximum likelihood detector (MLD).
Constrained minimum mean-squared error (MMSE) receive filters designed with
constraints on the shape and magnitude of the feedback filters for the
multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive
implementation of the proposed MB-MMSE-DF detector is developed along with a
recursive least squares-type algorithm for estimating the parameters of the
receive filters when the channel is time-varying. A soft-output version of the
MB-MMSE-DF detector is also proposed as a component of an iterative detection
and decoding receiver structure. A computational complexity analysis shows that
the MB-MMSE-DF detector does not require a significant additional complexity
over the conventional MMSE-DF detector, whereas a diversity analysis discusses
the diversity order achieved by the MB-MMSE-DF detector. Simulation results
show that the MB-MMSE-DF detector achieves a performance superior to existing
suboptimal detectors and close to the MLD, while requiring significantly lower
complexity.
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1308.2737 | H-infinity Optimal Approximation for Causal Spline Interpolation | cs.IT cs.SY math.IT math.OC | In this paper, we give a causal solution to the problem of spline
interpolation using H-infinity optimal approximation. Generally speaking,
spline interpolation requires filtering the whole sampled data, the past and
the future, to reconstruct the inter-sample values. This leads to non-causality
of the filter, and this becomes a critical issue for real-time applications.
Our objective here is to derive a causal system which approximates spline
interpolation by H-infinity optimization for the filter. The advantage of
H-infinity optimization is that it can address uncertainty in the input signals
to be interpolated in design, and hence the optimized system has robustness
property against signal uncertainty. We give a closed-form solution to the
H-infinity optimization in the case of the cubic splines. For higher-order
splines, the optimal filter can be effectively solved by a numerical
computation. We also show that the optimal FIR (Finite Impulse Response) filter
can be designed by an LMI (Linear Matrix Inequality), which can also be
effectively solved numerically. A design example is presented to illustrate the
result.
|
1308.2743 | H-infinity Design of Periodically Nonuniform Interpolation and
Decimation for Non-Band-Limited Signals | cs.IT cs.SY math.IT math.OC | In this paper, we consider signal interpolation of discrete-time signals
which are decimated nonuniformly. A conventional interpolation method is based
on the sampling theorem, and the resulting system consists of an ideal filter
with complex-valued coefficients. While the conventional method assumes band
limitation of signals, we propose a new method by sampled-data H-infinity
optimization. By this method, we can remove the band-limiting assumption and
the optimal filter can be with real-valued coefficients. Moreover, we show that
without band-limited assumption, there can be the optimal decimation patterns
among ones with the same ratio. By examples, we show the effectiveness of our
method.
|
1308.2747 | Closed-Loop Beam Alignment for Massive MIMO Channel Estimation | cs.IT math.IT | Training sequences are designed to probe wireless channels in order to obtain
channel state information for block-fading channels. Optimal training sounds
the channel using orthogonal beamforming vectors to find an estimate that
optimizes some cost function, such as mean square error. As the number of
transmit antennas increases, however, the training overhead becomes
significant. This creates a need for alternative channel estimation schemes for
increasingly large transmit arrays. In this work, we relax the orthogonal
restriction on sounding vectors. The use of a feedback channel after each
forward channel use during training enables closed-loop sounding vector design.
A misalignment cost function is introduced, which provides a metric to
sequentially design sounding vectors. In turn, the structure of the sounding
vectors aligns the transmit beamformer with the true channel direction, thereby
increasing beamforming gain. This beam alignment scheme for massive MIMO is
shown to improve beamforming gain over conventional orthogonal training for a
MISO channel.
|
1308.2762 | An efficient ant based qos aware intelligent temporally ordered routing
algorithm for manets | cs.NI cs.NE | A Mobile Ad hoc network (MANET) is a self configurable network connected by
wireless links. This type of network is only suitable for temporary
communication links as it is infrastructure-less and there is no centralised
control. Providing QoS aware routing is a challenging task in this type of
network due to dynamic topology and limited resources. The main purpose of QoS
aware routing is to find a feasible path from source to destination which will
satisfy two or more end to end QoS constrains. Therefore, the task of designing
an efficient routing algorithm which will satisfy all the quality of service
requirements and be robust and adaptive is considered as a highly challenging
problem. In this work we have designed a new efficient and energy aware
multipath routing algorithm based on ACO framework, inspired by the behaviours
of biological ants. Basically by considering QoS constraints and artificial
ants we have designed an intelligent version of classical Temporally Ordered
Routing Algorithm (TORA) which will increase network lifetime and decrease
packet loss and average end to end delay that makes this algorithm suitable for
real time and multimedia applications.
|
1308.2772 | Extended Distributed Learning Automata:A New Method for Solving
Stochastic Graph Optimization Problems | cs.AI | In this paper, a new structure of cooperative learning automata so-called
extended learning automata (eDLA) is introduced. Based on the proposed
structure, a new iterative randomized heuristic algorithm for finding optimal
sub-graph in a stochastic edge-weighted graph through sampling is proposed. It
has been shown that the proposed algorithm based on new networked-structure can
be to solve the optimization problems on stochastic graph through less number
of sampling in compare to standard sampling. Stochastic graphs are graphs in
which the edges have an unknown distribution probability weights. Proposed
algorithm uses an eDLA to find a policy that leads to an induced sub-graph that
satisfies some restrictions such as minimum or maximum weight (length). At each
stage of the proposed algorithm, eDLA determines which edges to be sampled.
This eDLA-based proposed sampling method may result in decreasing unnecessary
samples and hence decreasing the time that algorithm requires for finding the
optimal sub-graph. It has been shown that proposed method converge to optimal
solution, furthermore the probability of this convergence can be made
arbitrarily close to 1 by using a sufficiently small learning rate. A new
variance-aware threshold value was proposed that can be improving significantly
convergence rate of the proposed eDLA-based algorithm. It has been shown that
the proposed algorithm is competitive in terms of the quality of the solution
|
1308.2773 | Wind Speed Data Analysis for Various Seasons during a Decade by Wavelet
and S transform | cs.CE | The appropriate weather prediction is a challenging task and it can be
feasible with proper wind speed fluctuation analysis. In this current paper
daubechies-4 wavelet is used to analyze the winter wind speed fluctuations due
to lesser agitated wind data samples of winter. In summer abrupt changes in
wind speed occurs which creates difficulty for wavelets to keep proper track of
wind speed fluctuations. So, in that case the concept of the S-transform is
introduced.
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1308.2787 | Accelerating R-based Analytics on the Cloud | cs.DC cs.CE cs.SE | This paper addresses how the benefits of cloud-based infrastructure can be
harnessed for analytical workloads. Often the software handling analytical
workloads is not developed by a professional programmer, but on an ad hoc basis
by Analysts in high-level programming environments such as R or Matlab. The
goal of this research is to allow Analysts to take an analytical job that
executes on their personal workstations, and with minimum effort execute it on
cloud infrastructure and manage both the resources and the data required by the
job. If this can be facilitated gracefully, then the Analyst benefits from
on-demand resources, low maintenance cost and scalability of computing
resources, all of which are offered by the cloud. In this paper, a Platform for
Parallel R-based Analytics on the Cloud (P2RAC) that is placed between an
Analyst and a cloud infrastructure is proposed and implemented. P2RAC offers a
set of command-line tools for managing the resources, such as instances and
clusters, the data and the execution of the software on the Amazon Elastic
Computing Cloud infrastructure. Experimental studies are pursued using two
parallel problems and the results obtained confirm the feasibility of employing
P2RAC for solving large-scale analytical problems on the cloud.
|
1308.2798 | Effective Construction of a Class of Bent Quadratic Boolean Functions | cs.IT math.IT | In this paper, we consider the characterization of the bentness of quadratic
Boolean functions of the form $f(x)=\sum_{i=1}^{\frac{m}{2}-1}
Tr^n_1(c_ix^{1+2^{ei}})+ Tr_1^{n/2}(c_{m/2}x^{1+2^{n/2}}) ,$ where $n=me$, $m$
is even and $c_i\in GF(2^e)$. For a general $m$, it is difficult to determine
the bentness of these functions. We present the bentness of quadratic Boolean
function for two cases: $m=2^vp^r$ and $m=2^vpq$, where $p$ and $q$ are two
distinct primes. Further, we give the enumeration of quadratic bent functions
for the case $m=2^vpq$.
|
1308.2808 | Discretized Gabor Frames of Totally Positive Functions | cs.IT math.IT math.NA | In this paper a large class of universal windows for Gabor frames
(Weyl-Heisenberg frames) is constructed. These windows have the fundamental
property that every overcritical rectangular lattice generates a Gabor frame.
Likewise, every undercritical rectangular lattice generates a Riesz sequence.
|
1308.2833 | Asymptotically Optimal Power Allocation for Energy Harvesting
Communication Networks | cs.IT math.IT | For a general energy harvesting (EH) communication network, i.e., a network
where the nodes generate their transmit power through EH, we derive the
asymptotically optimal online power allocation solution which optimizes a
general utility function when the number of transmit time slots, $N$, and the
battery capacities of the EH nodes, $B_{\rm max}$, satisfy $N\to\infty$ and
$B_{\rm max}\to\infty$. The considered family of utility functions is general
enough to include the most important performance measures in communication
theory such as the average data rate, outage probability, average bit error
probability, and average signal-to-noise ratio. The proposed power allocation
solution is very simple. Namely, the asymptotically optimal power allocation
for the EH network is identical to the optimal power allocation for an
equivalent non-EH network whose nodes have infinite energy available but their
average transmit power is constrained to be equal to the average harvested
power and/or the maximum average transmit power of the corresponding nodes in
the EH network. Moreover, the maximum average performance of a general EH
network converges to the maximum average performance of the corresponding
equivalent non-EH network, when $N\to\infty$ and $B_{\rm max}\to\infty$.
Although the proposed solution is asymptotic in nature, it is applicable to EH
systems transmitting in a large but finite number of time slots and having a
battery capacity much larger than the average harvested power and/or the
maximum average transmit power.
|
1308.2838 | Wireless Information and Energy Transfer in Multi-Antenna Interference
Channel | cs.IT math.IT | This paper considers the transmitter design for wireless information and
energy transfer (WIET) in a multiple-input single-output (MISO) interference
channel (IFC). The design problem is to maximize the system throughput (i.e.,
the weighted sum rate) subject to individual energy harvesting constraints and
power constraints. Different from the conventional IFCs without energy
harvesting, the cross-link signals in the considered scenario play two opposite
roles in information detection (ID) and energy harvesting (EH). It is observed
that the ideal scheme, where the receivers can simultaneously perform ID and EH
from the received signal, may not always achieve the best tradeoff between
information transfer and energy harvesting, but simple practical schemes based
on time splitting may perform better. We therefore propose two practical time
splitting schemes, namely time division mode switching (TDMS) and time division
multiple access (TDMA), in addition to a power splitting (PS) scheme which
separates the received signal into two parts for ID and EH, respectively. In
the two-user scenario, we show that beamforming is optimal to all the schemes.
Moreover, the design problems associated with the TDMS and TDMA schemes admit
semi-analytical solutions. In the general K-user scenario, a successive convex
approximation method is proposed to handle the WIET problems associated with
the ideal scheme and the PS scheme, which are known to be NP-hard in general.
The K-user TDMS and TDMA schemes are shown efficiently solvable as convex
problems. Simulation results show that stronger cross-link channel powers
actually improve the information sum rate under energy harvesting constraints.
Moreover, none of the schemes under consideration can dominate another in terms
of the sum rate performance.
|
1308.2853 | When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor
Tucker Decompositions with Structured Sparsity | cs.LG cs.IR math.NA math.ST stat.ML stat.TH | Overcomplete latent representations have been very popular for unsupervised
feature learning in recent years. In this paper, we specify which overcomplete
models can be identified given observable moments of a certain order. We
consider probabilistic admixture or topic models in the overcomplete regime,
where the number of latent topics can greatly exceed the size of the observed
word vocabulary. While general overcomplete topic models are not identifiable,
we establish generic identifiability under a constraint, referred to as topic
persistence. Our sufficient conditions for identifiability involve a novel set
of "higher order" expansion conditions on the topic-word matrix or the
population structure of the model. This set of higher-order expansion
conditions allow for overcomplete models, and require the existence of a
perfect matching from latent topics to higher order observed words. We
establish that random structured topic models are identifiable w.h.p. in the
overcomplete regime. Our identifiability results allows for general
(non-degenerate) distributions for modeling the topic proportions, and thus, we
can handle arbitrarily correlated topics in our framework. Our identifiability
results imply uniqueness of a class of tensor decompositions with structured
sparsity which is contained in the class of Tucker decompositions, but is more
general than the Candecomp/Parafac (CP) decomposition.
|
1308.2857 | Engagement in the electoral processes: scaling laws and the role of the
political positions | physics.soc-ph cs.SI physics.data-an | We report on a statistical analysis of the engagement in the electoral
processes of all Brazilian cities by considering the number of party
memberships and the number of candidates for mayor and councillor. By
investigating the relationships between the number of party members and the
population of voters, we have found that the functional form of these
relationships are well described by sub-linear power laws (allometric scaling)
surrounded by a multiplicative log-normal noise. We have observed that this
pattern is quite similar to those previously-reported for the relationships
between the number candidates (mayor and councillor) and population of voters
[EPL 96, 48001 (2011)], suggesting that similar universal laws may be ruling
the engagement in the electoral processes. We also note that the power law
exponents display a clear hierarchy, where the more influential is the
political position the smaller is the value of the exponent. We have also
investigated the probability distributions of the number of candidates (mayor
and councilor), party memberships and voters. The results indicate that the
most influential positions are characterized by distributions with very
short-tails, while less influential positions display an intermediate power law
decay before showing an exponential-like cutoff. We discuss that, in addition
to the political power of the position, limitations in the number of available
seats can also be connected with this changing of behavior. We further believe
that our empirical findings point out to an underrepresentation effect, where
the larger city is, the larger are the obstacles for more individuals to become
directly engaged in the electoral process.
|
1308.2867 | Composite Self-Concordant Minimization | stat.ML cs.LG math.OC | We propose a variable metric framework for minimizing the sum of a
self-concordant function and a possibly non-smooth convex function, endowed
with an easily computable proximal operator. We theoretically establish the
convergence of our framework without relying on the usual Lipschitz gradient
assumption on the smooth part. An important highlight of our work is a new set
of analytic step-size selection and correction procedures based on the
structure of the problem. We describe concrete algorithmic instances of our
framework for several interesting applications and demonstrate them numerically
on both synthetic and real data.
|
1308.2872 | Can Agent Intelligence be used to Achieve Fault Tolerant Parallel
Computing Systems? | cs.DC cs.MA | The work reported in this paper is motivated towards validating an
alternative approach for fault tolerance over traditional methods like
checkpointing that constrain efficacious fault tolerance. Can agent
intelligence be used to achieve fault tolerant parallel computing systems? If
so, "What agent capabilities are required for fault tolerance?", "What parallel
computational tasks can benefit from such agent capabilities?" and "How can
agent capabilities be implemented for fault tolerance?" need to be addressed.
Cognitive capabilities essential for achieving fault tolerance through agents
are considered. Parallel reduction algorithms are identified as a class of
algorithms that can benefit from cognitive agent capabilities. The Message
Passing Interface is utilized for implementing an intelligent agent based
approach. Preliminary results obtained from the experiments validate the
feasibility of an agent based approach for achieving fault tolerance in
parallel computing systems.
|
1308.2893 | Multiclass learnability and the ERM principle | cs.LG | We study the sample complexity of multiclass prediction in several learning
settings. For the PAC setting our analysis reveals a surprising phenomenon: In
sharp contrast to binary classification, we show that there exist multiclass
hypothesis classes for which some Empirical Risk Minimizers (ERM learners) have
lower sample complexity than others. Furthermore, there are classes that are
learnable by some ERM learners, while other ERM learners will fail to learn
them. We propose a principle for designing good ERM learners, and use this
principle to prove tight bounds on the sample complexity of learning {\em
symmetric} multiclass hypothesis classes---classes that are invariant under
permutations of label names. We further provide a characterization of mistake
and regret bounds for multiclass learning in the online setting and the bandit
setting, using new generalizations of Littlestone's dimension.
|
1308.2894 | Low-Complexity Sphere Decoding of Polar Codes based on Optimum Path
Metric | cs.IT math.IT | Sphere decoding (SD) of polar codes is an efficient method to achieve the
error performance of maximum likelihood (ML) decoding. But the complexity of
the conventional sphere decoder is still high, where the candidates in a target
sphere are enumerated and the radius is decreased gradually until no available
candidate is in the sphere. In order to reduce the complexity of SD, a stack SD
(SSD) algorithm with an efficient enumeration is proposed in this paper. Based
on a novel path metric, SSD can effectively narrow the search range when
enumerating the candidates within a sphere. The proposed metric follows an
exact ML rule and takes the full usage of the whole received sequence.
Furthermore, another very simple metric is provided as an approximation of the
ML metric in the high signal-to-noise ratio regime. For short polar codes,
simulation results over the additive white Gaussian noise channels show that
the complexity of SSD based on the proposed metrics is up to 100 times lower
than that of the conventional SD.
|
1308.2923 | Robotic Message Ferrying for Wireless Networks using Coarse-Grained
Backpressure Control | cs.NI cs.RO cs.SY math.OC | We formulate the problem of robots ferrying messages between
statically-placed source and sink pairs that they can communicate with
wirelessly. We first analyze the capacity region for this problem under both
ideal (arbitrarily high velocity, long scheduling periods) and realistic
conditions. We indicate how robots could be scheduled optimally to satisfy any
arrival rate in the capacity region, given prior knowledge about arrival rates.
We find that if the number of robots allocated grows proportionally with the
number of source-sink pairs, then the capacity of the network scales as
$\Theta(1)$, similar to what was shown previously by Grossglauser and Tse for
uncontrolled mobility; however, in contrast to that prior result, we also find
that with controlled mobility this constant capacity scaling can be obtained
while ensuring finite delay. We then consider the setting where the arrival
rates are unknown and present a coarse-grained backpressure message ferrying
algorithm (CBMF) for it. In CBMF, the robots are matched to sources and sinks
once every epoch to maximize a queue-differential-based weight. The matching
controls both motion and transmission for each robot: if a robot is matched to
a source, it moves towards that source and collects data from it; and if it is
matched to a sink, it moves towards that sink and transmits data to it. We show
through analysis and simulations the conditions under which CBMF can stabilize
the network. We show that the maximum achievable stable throughput with this
policy tends to the ideal capacity as the schedule duration and robot velocity
increase.
|
1308.2930 | Semistability-Based Convergence Analysis for Paracontracting Multiagent
Coordination Optimization | cs.SY cs.NE math.OC | This sequential technical report extends some of the previous results we
posted at arXiv:1306.0225.
|
1308.2938 | ERP projects Internal Stakeholder network and how it influences the
projects outcome | cs.SI cs.CY | So far little effort has been put into researching the importance of internal
ERP project stakeholders mutual interactions,realizing the projects
complexity,influence on the whole organization, and high risk for a useful
final outcome. This research analyzes the stakeholders interactions and
positions in the project network, their criticality, potential bottlenecks and
conflicts. The main methods used are Social Network Analysis, and the
elicitation of drivers for the individual players. Information was collected
from several stakeholders from three large ERP projects all in global companies
headquartered in Finland, together with representatives from two different ERP
vendors, and with two experienced ERP consultants. The analysis gives
quantitative as well as qualitative characterization of stakeholder criticality
(mostly the Project Manager(s), the Business Owner(s) and the Process
Owner(s)), degree of centrality, closeness, mediating or bottleneck roles,
relational ties and conflicts (individual, besides those between business and
project organizations), and clique formations. A generic internal stakeholder
network model is established as well as the criticality of the project phases.
The results are summarized in the form of a list of recommendations for future
ERP projects to address the internal stakeholder impacts .Project management
should utilize the latest technology to provide tools to increase the
interaction between the stakeholders and to monitor the strength of these
relations. Social network analysis tools could be used in the projects to
visualize the stakeholder relations in order to better understand the possible
risks related to the relations (or lack of them).
|
1308.2944 | Smart business networks and business genetics with a high tech
communications supplier selection industry case | cs.CY cs.SI | Despite the emergence of event driven business process management, smart
business networks, social networks, etc. as important research areas in
management, for all the attractiveness of these concepts, two major challenges
remain around their design and the partner selection rules while learning from
interaction events.While smart business networks should provide advantages due
to the quick connect of business partners for selected functions in a process
common to several parties, literature does not provide constructive methods
whereby the selection of temporary partners and functions can be done. Most
discussions only rely solely on human judgment. This paper introduces both
computational geometry, and genetic programming, as systematic methods whereby
to identify, characterize, and then display on a continuing basis from event
monitoring such possible partnerships; such techniques also allow to plan for
their effect on the organizations and thus to carry out selection. The two
methods are being put in the context of emergence theory. Tessellations address
the identification and categorization issues; business maps address the display
and monitoring challenge with the use of Voronoii diagrams. Cellular automata
mimicking living bodies, with genetic algorithms of which parameters are
estimated by learning, address the selection and effect issues. To illustrate
the approach, some experimental results from the sourcing function in a high
tech industry, are discussed; they address the case of how to determine the
selection process for a systems integrator to set up joint ventures with
smaller technology suppliers.
|
1308.2952 | Subadditivity of Matrix phi-Entropy and Concentration of Random Matrices | cs.IT math.IT math.PR | Matrix concentration inequalities provide a direct way to bound the typical
spectral norm of a random matrix. The methods for establishing these results
often parallel classical arguments, such as the Laplace transform method. This
work develops a matrix extension of the entropy method, and it applies these
ideas to obtain some matrix concentration inequalities.
|
1308.2954 | Trace Complexity of Network Inference | cs.DS cs.SI | The network inference problem consists of reconstructing the edge set of a
network given traces representing the chronology of infection times as
epidemics spread through the network. This problem is a paradigmatic
representative of prediction tasks in machine learning that require deducing a
latent structure from observed patterns of activity in a network, which often
require an unrealistically large number of resources (e.g., amount of available
data, or computational time). A fundamental question is to understand which
properties we can predict with a reasonable degree of accuracy with the
available resources, and which we cannot. We define the trace complexity as the
number of distinct traces required to achieve high fidelity in reconstructing
the topology of the unobserved network or, more generally, some of its
properties. We give algorithms that are competitive with, while being simpler
and more efficient than, existing network inference approaches. Moreover, we
prove that our algorithms are nearly optimal, by proving an
information-theoretic lower bound on the number of traces that an optimal
inference algorithm requires for performing this task in the general case.
Given these strong lower bounds, we turn our attention to special cases, such
as trees and bounded-degree graphs, and to property recovery tasks, such as
reconstructing the degree distribution without inferring the network. We show
that these problems require a much smaller (and more realistic) number of
traces, making them potentially solvable in practice.
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1308.3009 | Structural Changes in Data Communication in Wireless Sensor Networks | physics.data-an cs.IT cs.NI math.IT | Wireless sensor networks are an important technology for making distributed
autonomous measures in hostile or inaccessible environments. Among the
challenges they pose, the way data travel among them is a relevant issue since
their structure is quite dynamic. The operational topology of such devices can
often be described by complex networks. In this work, we assess the variation
of measures commonly employed in the complex networks literature applied to
wireless sensor networks. Four data communication strategies were considered:
geometric, random, small-world, and scale-free models, along with the shortest
path length measure. The sensitivity of this measure was analyzed with respect
to the following perturbations: insertion and removal of nodes in the geometric
strategy; and insertion, removal and rewiring of links in the other models. The
assessment was performed using the normalized Kullback-Leibler divergence and
Hellinger distance quantifiers, both deriving from the Information Theory
framework. The results reveal that the shortest path length is sensitive to
perturbations.
|
1308.3015 | On Generalized Bayesian Data Fusion with Complex Models in Large Scale
Networks | cs.RO cs.SY stat.CO stat.ME | Recent advances in communications, mobile computing, and artificial
intelligence have greatly expanded the application space of intelligent
distributed sensor networks. This in turn motivates the development of
generalized Bayesian decentralized data fusion (DDF) algorithms for robust and
efficient information sharing among autonomous agents using probabilistic
belief models. However, DDF is significantly challenging to implement for
general real-world applications requiring the use of dynamic/ad hoc network
topologies and complex belief models, such as Gaussian mixtures or hybrid
Bayesian networks. To tackle these issues, we first discuss some new key
mathematical insights about exact DDF and conservative approximations to DDF.
These insights are then used to develop novel generalized DDF algorithms for
complex beliefs based on mixture pdfs and conditional factors. Numerical
examples motivated by multi-robot target search demonstrate that our methods
lead to significantly better fusion results, and thus have great potential to
enhance distributed intelligent reasoning in sensor networks.
|
1308.3025 | Effect of assessment error and private information on stern-judging in
indirect reciprocity | physics.soc-ph cs.SI q-bio.PE | Stern-judging is one of the best-known assessment rules in indirect
reciprocity. Indirect reciprocity is a fundamental mechanism for the evolution
of cooperation. It relies on mutual monitoring and assessments, i.e.,
individuals judge, following their own assessment rules, whether other
individuals are "good" or "bad" according to information on their past
behaviors. Among many assessment rules, stern-judging is known to provide
stable cooperation in a population, as observed when all members in the
population know all about others' behaviors (public information case) and when
the members never commit an assessment error. In this paper, the effect of
assessment error and private information on stern-judging is investigated. By
analyzing the image matrix, which describes who is good in the eyes of whom in
the population, we analytically show that private information and assessment
error cause the collapse of stern-judging: all individuals assess other
individuals as "good" at random with a probability of 1/2.
|
1308.3052 | Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual
Image Quality Index | cs.CV | It is an important task to faithfully evaluate the perceptual quality of
output images in many applications such as image compression, image restoration
and multimedia streaming. A good image quality assessment (IQA) model should
not only deliver high quality prediction accuracy but also be computationally
efficient. The efficiency of IQA metrics is becoming particularly important due
to the increasing proliferation of high-volume visual data in high-speed
networks. We present a new effective and efficient IQA model, called gradient
magnitude similarity deviation (GMSD). The image gradients are sensitive to
image distortions, while different local structures in a distorted image suffer
different degrees of degradations. This motivates us to explore the use of
global variation of gradient based local quality map for overall image quality
prediction. We find that the pixel-wise gradient magnitude similarity (GMS)
between the reference and distorted images combined with a novel pooling
strategy the standard deviation of the GMS map can predict accurately
perceptual image quality. The resulting GMSD algorithm is much faster than most
state-of-the-art IQA methods, and delivers highly competitive prediction
accuracy.
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1308.3058 | Phase Retrieval for Sparse Signals: Uniqueness Conditions | cs.IT math.IT | In a variety of fields, in particular those involving imaging and optics, we
often measure signals whose phase is missing or has been irremediably
distorted. Phase retrieval attempts the recovery of the phase information of a
signal from the magnitude of its Fourier transform to enable the reconstruction
of the original signal. A fundamental question then is: "Under which conditions
can we uniquely recover the signal of interest from its measured magnitudes?"
In this paper, we assume the measured signal to be sparse. This is a natural
assumption in many applications, such as X-ray crystallography, speckle imaging
and blind channel estimation. In this work, we derive a sufficient condition
for the uniqueness of the solution of the phase retrieval (PR) problem for both
discrete and continuous domains, and for one and multi-dimensional domains.
More precisely, we show that there is a strong connection between PR and the
turnpike problem, a classic combinatorial problem. We also prove that the
existence of collisions in the autocorrelation function of the signal may
preclude the uniqueness of the solution of PR. Then, assuming the absence of
collisions, we prove that the solution is almost surely unique on 1-dimensional
domains. Finally, we extend this result to multi-dimensional signals by solving
a set of 1-dimensional problems. We show that the solution of the
multi-dimensional problem is unique when the autocorrelation function has no
collisions, significantly improving upon a previously known result.
|
1308.3059 | Membership in social networks and the application in information
filtering | cs.IR cs.SI physics.soc-ph | During the past a few years, users' membership in the online system (i.e. the
social groups that online users joined) are wildly investigated. Most of these
works focus on the detection, formulation and growth of online communities. In
this paper, we study users' membership in a coupled system which contains
user-group and user-object bipartite networks. By linking users' membership
information and their object selection, we find that the users who have
collected only a few objects are more likely to be "influenced" by the
membership when choosing objects. Moreover, we observe that some users may join
many online communities though they collected few objects. Based on these
findings, we design a social diffusion recommendation algorithm which can
effectively solve the user cold-start problem. Finally, we propose a
personalized combination of our method and the hybrid method in [PNAS 107, 4511
(2010)], which leads to a further improvement in the overall recommendation
performance.
|
1308.3060 | Information filtering in sparse online systems: recommendation via
semi-local diffusion | cs.IR | With the rapid growth of the Internet and overwhelming amount of information
and choices that people are confronted with, recommender systems have been
developed to effectively support users' decision-making process in the online
systems. However, many recommendation algorithms suffer from the data sparsity
problem, i.e. the user-object bipartite networks are so sparse that algorithms
cannot accurately recommend objects for users. This data sparsity problem makes
many well-known recommendation algorithms perform poorly. To solve the problem,
we propose a recommendation algorithm based on the semi-local diffusion process
on a user-object bipartite network. The numerical simulation on two sparse
datasets, Amazon and Bookcross, show that our method significantly outperforms
the state-of-the-art methods especially for those small-degree users. Two
personalized semi-local diffusion methods are proposed which further improve
the recommendation accuracy. Finally, our work indicates that sparse online
systems are essentially different from the dense online systems, all the
algorithms and conclusions based on dense data should be rechecked again in
sparse data.
|
1308.3080 | Average Drift Analysis and Population Scalability | cs.NE | This paper aims to study how the population size affects the computation time
of evolutionary algorithms in a rigorous way. The computation time of an
evolutionary algorithm can be measured by either the expected number of
generations (hitting time) or the expected number of fitness evaluations
(running time) to find an optimal solution. Population scalability is the ratio
of the expected hitting time between a benchmark algorithm and an algorithm
using a larger population size. Average drift analysis is presented for
comparing the expected hitting time of two algorithms and estimating lower and
upper bounds on population scalability. Several intuitive beliefs are
rigorously analysed. It is prove that (1) using a population sometimes
increases rather than decreases the expected hitting time; (2) using a
population cannot shorten the expected running time of any elitist evolutionary
algorithm on unimodal functions in terms of the time-fitness landscape, but
this is not true in terms of the distance-based fitness landscape; (3) using a
population cannot always reduce the expected running time on fully-deceptive
functions, which depends on the benchmark algorithm using elitist selection or
random selection.
|
1308.3101 | Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise
Linear Priors | cs.CV cs.LG stat.ML | Label assignment problems with large state spaces are important tasks
especially in computer vision. Often the pairwise interaction (or smoothness
prior) between labels assigned at adjacent nodes (or pixels) can be described
as a function of the label difference. Exact inference in such labeling tasks
is still difficult, and therefore approximate inference methods based on a
linear programming (LP) relaxation are commonly used in practice. In this work
we study how compact linear programs can be constructed for general piecwise
linear smoothness priors. The number of unknowns is O(LK) per pairwise clique
in terms of the state space size $L$ and the number of linear segments K. This
compares to an O(L^2) size complexity of the standard LP relaxation if the
piecewise linear structure is ignored. Our compact construction and the
standard LP relaxation are equivalent and lead to the same (approximate) label
assignment.
|
1308.3106 | System and Methods for Converting Speech to SQL | cs.CL cs.DB | This paper concerns with the conversion of a Spoken English Language Query
into SQL for retrieving data from RDBMS. A User submits a query as speech
signal through the user interface and gets the result of the query in the text
format. We have developed the acoustic and language models using which a speech
utterance can be converted into English text query and thus natural language
processing techniques can be applied on this English text query to generate an
equivalent SQL query. For conversion of speech into English text HTK and Julius
tools have been used and for conversion of English text query into SQL query we
have implemented a System which uses rule based translation to translate
English Language Query into SQL Query. The translation uses lexical analyzer,
parser and syntax directed translation techniques like in compilers. JFLex and
BYACC tools have been used to build lexical analyzer and parser respectively.
System is domain independent i.e. system can run on different database as it
generates lex files from the underlying database.
|
1308.3112 | Nonlinearity measures of random Boolean functions | math.CO cs.IT math.IT | The r-th order nonlinearity of a Boolean function is the minimum number of
elements that have to be changed in its truth table to arrive at a Boolean
function of degree at most r. It is shown that the (suitably normalised) r-th
order nonlinearity of a random Boolean function converges strongly for all r\ge
1. This extends results by Rodier for r=1 and by Dib for r=2. The methods in
the present paper are mostly of elementary combinatorial nature and also lead
to simpler proofs in the cases that r=1 or 2.
|
1308.3127 | Performance Analysis of Connection Admission Control Scheme in IEEE
802.16 OFDMA Networks | cs.PF cs.IT cs.NI cs.SY math.IT | IEEE 802.16 OFDMA (Orthogonal Frequency Division Multiple Access) technology
has emerged as a promising technology for broadband access in a Wireless
Metropolitan Area Network (WMAN) environment. In this paper, we address the
problem of queueing theoretic performance modeling and analysis of OFDMA under
broad-band wireless networks. We consider a single-cell IEEE 802.16 environment
in which the base station allocates subchannels to the subscriber stations in
its coverage area. The subchannels allocated to a subscriber station are shared
by multiple connections at that subscriber station. To ensure the Quality of
Service (QoS) performances, a Connection Admission Control (CAC) scheme is
considered at a subscriber station. A queueing analytical framework for these
admission control schemes is presented considering OFDMA-based transmission at
the physical layer. Then, based on the queueing model, both the
connection-level and the packet-level performances are studied and compared
with their analogues in the case without CAC. The connection arrival is modeled
by a Poisson process and the packet arrival for a connection by a two-state
Markov Modulated Poisson Process (MMPP). We determine analytically and
numerically different performance parameters, such as connection blocking
probability, average number of ongoing connections, average queue length,
packet dropping probability, queue throughput and average packet delay.
|
1308.3136 | Toward the Coevolution of Novel Vertical-Axis Wind Turbines | cs.NE cs.AI cs.CE | The production of renewable and sustainable energy is one of the most
important challenges currently facing mankind. Wind has made an increasing
contribution to the world's energy supply mix, but still remains a long way
from reaching its full potential. In this paper, we investigate the use of
artificial evolution to design vertical-axis wind turbine prototypes that are
physically instantiated and evaluated under fan generated wind conditions.
Initially a conventional evolutionary algorithm is used to explore the design
space of a single wind turbine and later a cooperative coevolutionary algorithm
is used to explore the design space of an array of wind turbines. Artificial
neural networks are used throughout as surrogate models to assist learning and
found to reduce the number of fabrications required to reach a higher
aerodynamic efficiency. Unlike in other approaches, such as computational fluid
dynamics simulations, no mathematical formulations are used and no model
assumptions are made.
|
1308.3155 | Percolation on the Information-Theoretically Secure Signal to
Interference Ratio Graph | cs.IT math.IT math.PR | We consider a continuum percolation model consisting of two types of nodes,
namely legitimate and eavesdropper nodes, distributed according to independent
Poisson point processes (PPPs) in $\bbR ^2$ of intensities $\lambda$ and
$\lambda_E$ respectively. A directed edge from one legitimate node $A$ to
another legitimate node $B$ exists provided the strength of the {\it signal}
transmitted from node $A$ that is received at node $B$ is higher than that
received at any eavesdropper node. The strength of the received signal at a
node from a legitimate node depends not only on the distance between these
nodes, but also on the location of the other legitimate nodes and an
interference suppression parameter $\gamma$. The graph is said to percolate
when there exists an infinite connected component. We show that for any finite
intensity $\lambda_E$ of eavesdropper nodes, there exists a critical intensity
$\lambda_c < \infty$ such that for all $\lambda > \lambda_c$ the graph
percolates for sufficiently small values of the interference parameter.
Furthermore, for the sub-critical regime, we show that there exists a
$\lambda_0$ such that for all $\lambda < \lambda_0 \leq \lambda_c$ a suitable
graph defined over eavesdropper node connections percolates that precludes
percolation in the graphs formed by the legitimate nodes.
|
1308.3174 | Communication Network Design: Balancing Modularity and Mixing via
Optimal Graph Spectra | cs.SI cs.GT cs.MA | By leveraging information technologies, organizations now have the ability to
design their communication networks and crowdsourcing platforms to pursue
various performance goals, but existing research on network design does not
account for the specific features of social networks, such as the notion of
teams. We fill this gap by demonstrating how desirable aspects of
organizational structure can be mapped parsimoniously onto the spectrum of the
graph Laplacian allowing the specification of structural objectives and build
on recent advances in non-convex programming to optimize them. This design
framework is general, but we focus here on the problem of creating graphs that
balance high modularity and low mixing time, and show how "liaisons" rather
than brokers maximize this objective.
|
1308.3177 | Normalized Google Distance of Multisets with Applications | cs.IR cs.LG | Normalized Google distance (NGD) is a relative semantic distance based on the
World Wide Web (or any other large electronic database, for instance Wikipedia)
and a search engine that returns aggregate page counts. The earlier NGD between
pairs of search terms (including phrases) is not sufficient for all
applications. We propose an NGD of finite multisets of search terms that is
better for many applications. This gives a relative semantics shared by a
multiset of search terms. We give applications and compare the results with
those obtained using the pairwise NGD. The derivation of NGD method is based on
Kolmogorov complexity.
|
1308.3182 | Structural measures for multiplex networks | physics.soc-ph cs.SI | Many real-world complex systems consist of a set of elementary units
connected by relationships of different kinds. All such systems are better
described in terms of multiplex networks, where the links at each layer
represent a different type of interaction between the same set of nodes, rather
than in terms of (single-layer) networks. In this paper we present a general
framework to describe and study multiplex networks, whose links are either
unweighted or weighted. In particular we propose a series of measures to
characterize the multiplexicity of the systems in terms of: i) basic node and
link properties such as the node degree, and the edge overlap and
reinforcement, ii) local properties such as the clustering coefficient and the
transitivity, iii) global properties related to the navigability of the
multiplex across the different layers. The measures we introduce are validated
on a genuine multiplex data set of Indonesian terrorists, where information
among 78 individuals are recorded with respect to mutual trust, common
operations, exchanged communications and business relationships.
|
1308.3185 | To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies
in Cellular Networks | cs.GT cs.MA cs.NI | We consider a cellular network where mobile transceiver devices that are
owned by self-interested users are incentivized to cooperate with each other
using tokens, which they exchange electronically to "buy" and "sell" downlink
relay services, thereby increasing the network's capacity compared to a network
that only supports base station-to-device (B2D) communications. We investigate
how an individual device in the network can learn its optimal cooperation
policy online, which it uses to decide whether or not to provide downlink relay
services for other devices in exchange for tokens. We propose a supervised
learning algorithm that devices can deploy to learn their optimal cooperation
strategies online given their experienced network environment. We then
systematically evaluate the learning algorithm in various deployment scenarios.
Our simulation results suggest that devices have the greatest incentive to
cooperate when the network contains (i) many devices with high energy budgets
for relaying, (ii) many highly mobile users (e.g., users in motor vehicles),
and (iii) neither too few nor too many tokens. Additionally, within the token
system, self-interested devices can effectively learn to cooperate online, and
achieve over 20% higher throughput on average than with B2D communications
alone, all while selfishly maximizing their own utilities.
|
1308.3200 | An Upper Bound On the Size of Locally Recoverable Codes | cs.IT math.IT | In a {\em locally recoverable} or {\em repairable} code, any symbol of a
codeword can be recovered by reading only a small (constant) number of other
symbols. The notion of local recoverability is important in the area of
distributed storage where a most frequent error-event is a single storage node
failure (erasure). A common objective is to repair the node by downloading data
from as few other storage node as possible. In this paper, we bound the minimum
distance of a code in terms of its length, size and locality. Unlike previous
bounds, our bound follows from a significantly simple analysis and depends on
the size of the alphabet being used. It turns out that the binary Simplex codes
satisfy our bound with equality; hence the Simplex codes are the first example
of a optimal binary locally repairable code family. We also provide
achievability results based on random coding and concatenated codes that are
numerically verified to be close to our bounds.
|
1308.3217 | Can Visible Light Communications Provide Gb/s Service? | cs.IT math.IT | Visible light communications (VLC) that use the infrastructure of the indoor
illumination system have been envisioned as a compact, safe, and green
alternative to WiFi for the downlink of an indoor wireless mobile communication
system. Although the optical spectrum is typically well-suited to high
throughput applications, combining communications with indoor lighting in a
commercially viable system imposes severe limitations both in bandwidth and
received power. Clever techniques are needed to achieve Gb/s transmission, and
to do it in a cost effective manner so as to successfully compete with other
high-capacity alternatives for indoor access, such as millimeter-wave
radio-frequency (RF). This article presents modulation schemes that have the
potential to overcome the many challenges faced by VLC in providing multi Gb/s
indoor wireless connectivity.
|
1308.3225 | An interactive engine for multilingual video browsing using semantic
content | cs.MM cs.CV cs.IR | The amount of audio-visual information has increased dramatically with the
advent of High Speed Internet. Furthermore, technological advances in recent
years in the field of information technology, have simplified the use of video
data in various fields by the general public. This made it possible to store
large collections of video documents into computer systems. To enable efficient
use of these collections, it is necessary to develop tools to facilitate access
to these documents and handling them. In this paper we propose a method for
indexing and retrieval of video sequences in a video database of large
dimension, based on a weighting technique to calculate the degree of membership
of a concept in a video also a structuring of the data of the audio-visual
(context / concept / video) and a relevance feedback mechanism.
|
1308.3229 | The Quest for Sustainable Smart Grids | cs.SY physics.soc-ph | This letter is my comment about the opinion paper: Transdisciplinary electric
power grid science (PNAS, 2013 -
http://www.pnas.org/content/110/30/12159.full). [arXiv:1307.7305].
|
1308.3239 | Orthogonality and Cooperation in Collaborative Spectrum Sensing through
MIMO Decision Fusion | cs.IT math.IT | This paper deals with spectrum sensing for cognitive radio scenarios where
the decision fusion center (DFC) exploits array processing. More specifically,
we explore the impact of user cooperation and orthogonal transmissions among
secondary users (SUs) on the reporting channel. To this aim four protocols are
considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and
non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and
cooperative. The DFC employs maximum ratio combining (MRC) rule and performance
are evaluated in terms of complementary receiver operating characteristic
(CROC). Analytical results, coupled with Monte Carlo simulations, are
presented.
|
1308.3243 | Arabic Text Recognition in Video Sequences | cs.MM cs.CL cs.CV | In this paper, we propose a robust approach for text extraction and
recognition from Arabic news video sequence. The text included in video
sequences is an important needful for indexing and searching system. However,
this text is difficult to detect and recognize because of the variability of
its size, their low resolution characters and the complexity of the
backgrounds. To solve these problems, we propose a system performing in two
main tasks: extraction and recognition of text. Our system is tested on a
varied database composed of different Arabic news programs and the obtained
results are encouraging and show the merits of our approach.
|
1308.3272 | Space-Time Interference Alignment and Degrees of Freedom Regions for the
MISO Broadcast Channel with Periodic CSI Feedback | cs.IT math.IT | This paper characterizes the degrees of freedom (DoF) regions for the
multi-user vector broadcast channel with periodic channel state information
(CSI) feedback. As a part of the characterization, a new transmission method
called space-time interference alignment is proposed, which exploits both the
current and past CSI jointly. Using the proposed alignment technique, an inner
bound of the sum-DoF region is characterized as a function of a normalized CSI
feedback frequency, which measures CSI feedback speed compared to the speed of
user's channel variations. One consequence of the result is that the achievable
sum-DoF gain is improved significantly when a user sends back both current and
outdated CSI compared to the case where the user sends back current CSI only.
Then, a trade-off between CSI feedback delay and the sum-DoF gain is
characterized for the multi-user vector broadcast channel in terms of a
normalized CSI feedback delay that measures CSI obsoleteness compared to
channel coherence time. A crucial insight is that it is possible to achieve the
optimal DoF gain if the feedback delay is less than a derived fraction of the
channel coherence time. This precisely characterizes the intuition that a small
delay should be negligible.
|
1308.3282 | Complete stability analysis of a heuristic ADP control design | cs.NE cs.SY | This paper provides new stability results for Action-Dependent Heuristic
Dynamic Programming (ADHDP), using a control algorithm that iteratively
improves an internal model of the external world in the autonomous system based
on its continuous interaction with the environment. We extend previous results
by ADHDP control to the case of general multi-layer neural networks with deep
learning across all layers. In particular, we show that the introduced control
approach is uniformly ultimately bounded (UUB) under specific conditions on the
learning rates, without explicit constraints on the temporal discount factor.
We demonstrate the benefit of our results to the control of linear and
nonlinear systems, including the cart-pole balancing problem. Our results show
significantly improved learning and control performance as compared to the
state-of-art.
|
1308.3294 | A Secure and Comparable Text Encryption Algorithm | cs.CR cs.CL cs.CY cs.SI | This paper discloses a simple algorithm for encrypting text messages, based
on the NP-completeness of the subset sum problem, such that the similarity
between encryptions is roughly proportional to the semantic similarity between
their generating messages. This allows parties to compare encrypted messages
for semantic overlap without trusting an intermediary and might be applied, for
example, as a means of finding scientific collaborators over the Internet.
|
1308.3297 | Estimating Clique Composition and Size Distributions from Sampled
Network Data | cs.SI physics.data-an physics.soc-ph | Cliques are defined as complete graphs or subgraphs; they are the strongest
form of cohesive subgroup, and are of interest in both social science and
engineering contexts. In this paper we show how to efficiently estimate the
distribution of clique sizes from a probability sample of nodes obtained from a
graph (e.g., by independence or link-trace sampling). We introduce two types of
unbiased estimators, one of which exploits labeling of sampled nodes neighbors
and one of which does not require this information. We compare the estimators
on a variety of real-world graphs and provide suggestions for their use. We
generalize our estimators to cases in which cliques are distinguished not only
by size but also by node attributes, allowing us to estimate clique composition
by size. Finally, we apply our methodology to a sample of Facebook users to
estimate the clique size distribution by gender over the social graph.
|
1308.3300 | Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm | cs.IT cs.SY math.IT math.OC | Analysis and design of filtered-x adaptive algorithms are conventionally done
by assuming that the transfer function in the secondary path is a discrete-time
system. However, in real systems such as active noise control, the secondary
path is a continuous-time system. Therefore, such a system should be analyzed
and designed as a hybrid system including discrete- and continuous- time
systems and AD/DA devices. In this article, we propose a hybrid design taking
account of continuous-time behavior of the secondary path via lifting
(continuous-time polyphase decomposition) technique in sampled-data control
theory.
|
1308.3302 | YY Filter - A Paradigm of Digital Signal Processing | cs.IT cs.SY math.IT math.OC | YY filter, named after the founder Prof. Yutaka Yamamoto, is a digital filter
designed by sampled-data control theory, which can optimize the analog
performance of the signal processing system with AD/DA converters. This article
discusses problems in conventional signal processing and introduces advantages
of the YY filter.
|
1308.3303 | Upper Bounds On the ML Decoding Error Probability of General Codes over
AWGN Channels | cs.IT math.IT | In this paper, parameterized Gallager's first bounding technique (GFBT) is
presented by introducing nested Gallager regions, to derive upper bounds on the
ML decoding error probability of general codes over AWGN channels. The three
well-known bounds, namely, the sphere bound (SB) of Herzberg and Poltyrev, the
tangential bound (TB) of Berlekamp, and the tangential-sphere bound (TSB) of
Poltyrev, are generalized to general codes without the properties of
geometrical uniformity and equal energy. When applied to the binary linear
codes, the three generalized bounds are reduced to the conventional ones. The
new derivation also reveals that the SB of Herzberg and Poltyrev is equivalent
to the SB of Kasami et al., which was rarely cited in the literatures.
|
1308.3309 | Search-Space Characterization for Real-time Heuristic Search | cs.AI | Recent real-time heuristic search algorithms have demonstrated outstanding
performance in video-game pathfinding. However, their applications have been
thus far limited to that domain. We proceed with the aim of facilitating wider
applications of real-time search by fostering a greater understanding of the
performance of recent algorithms. We first introduce eight
algorithm-independent complexity measures for search spaces and correlate their
values with algorithm performance. The complexity measures are statistically
shown to be significant predictors of algorithm performance across a set of
commercial video-game maps. We then extend this analysis to a wider variety of
search spaces in the first application of database-driven real-time search to
domains outside of video-game pathfinding. In doing so, we gain insight into
algorithm performance and possible enhancement as well as into search space
complexity.
|
1308.3310 | On the Capacity and Degrees of Freedom Regions of MIMO Interference
Channels with Limited Receiver Cooperation | cs.IT math.IT | This paper gives the approximate capacity region of a two-user MIMO
interference channel with limited receiver cooperation, where the gap between
the inner and outer bounds is in terms of the total number of receive antennas
at the two receivers and is independent of the actual channel values. The
approximate capacity region is then used to find the degrees of freedom region.
For the special case of symmetric interference channels, we also find the
amount of receiver cooperation in terms of the backhaul capacity beyond which
the degrees of freedom do not improve. Further, the generalized degrees of
freedom are found for MIMO interference channels with equal number of antennas
at all nodes. It is shown that the generalized degrees of freedom improve
gradually from a "W" curve to a "V" curve with increase in cooperation in terms
of the backhaul capacity.
|
1308.3314 | The algorithm of noisy k-means | stat.ML cs.LG | In this note, we introduce a new algorithm to deal with finite dimensional
clustering with errors in variables. The design of this algorithm is based on
recent theoretical advances (see Loustau (2013a,b)) in statistical learning
with errors in variables. As the previous mentioned papers, the algorithm mixes
different tools from the inverse problem literature and the machine learning
community. Coarsely, it is based on a two-step procedure: (1) a deconvolution
step to deal with noisy inputs and (2) Newton's iterations as the popular
k-means.
|
1308.3324 | History Based Coalition Formation in Hedonic Context Using Trust | cs.MA cs.AI | In this paper we address the problem of coalition formation in hedonic
context. Our modelling tries to be as realistic as possible. In previous
models, once an agent joins a coalition it would not be able to leave the
coalition and join the new one; in this research we made it possible to leave a
coalition but put some restrictions to control the behavior of agents. Leaving
or staying of an agent in a coalition will affect on the trust of the other
agents included in this coalition. Agents will use the trust values in
computing the expected utility of coalitions. Three different risk behaviors
are introduced for agents that want to initiate a coalition. Using these risk
behaviors, some simulations are made and results are analyzed.
|
1308.3340 | Overlapping modularity at the critical point of k-clique percolation | physics.soc-ph cs.SI | One of the most remarkable social phenomena is the formation of communities
in social networks corresponding to families, friendship circles, work teams,
etc. Since people usually belong to several different communities at the same
time, the induced overlaps result in an extremely complicated web of the
communities themselves. Thus, uncovering the intricate community structure of
social networks is a non-trivial task with great potential for practical
applications, gaining a notable interest in the recent years. The Clique
Percolation Method (CPM) is one of the earliest overlapping community finding
methods, which was already used in the analysis of several different social
networks. In this approach the communities correspond to k-clique percolation
clusters, and the general heuristic for setting the parameters of the method is
to tune the system just below the critical point of k-clique percolation.
However, this rule is based on simple physical principles and its validity was
never subject to quantitative analysis. Here we examine the quality of the
partitioning in the vicinity of the critical point using recently introduced
overlapping modularity measures. According to our results on real social- and
other networks, the overlapping modularities show a maximum close to the
critical point, justifying the original criteria for the optimal parameter
settings.
|
1308.3357 | The Entity Registry System: Implementing 5-Star Linked Data Without the
Web | cs.DB cs.CY | Linked Data applications often assume that connectivity to data repositories
and entity resolution services are always available. This may not be a valid
assumption in many cases. Indeed, there are about 4.5 billion people in the
world who have no or limited Web access. Many data-driven applications may have
a critical impact on the life of those people, but are inaccessible to those
populations due to the architecture of today's data registries. In this paper,
we propose and evaluate a new open-source system that can be used as a
general-purpose entity registry suitable for deployment in poorly-connected or
ad-hoc environments.
|
1308.3372 | Objective Information Theory: A Sextuple Model and 9 Kinds of Metrics | cs.IT math.IT | In the contemporary era, the importance of information is undisputed, but
there has never been a common understanding of information, nor a unanimous
conclusion to the researches on information metrics. Based on the previous
studies, this paper analyzes the important achievements in the researches of
the properties and metrics of information as well as their main
insufficiencies, and explores the essence and connotation, the mathematical
expressions and other basic problems related to information. On the basis of
the understanding of the objectivity of information, it proposes the
definitions and a Sextuple model of information; discusses the basic properties
of information, and brings forward the definitions and mathematical expressions
of nine kinds of metrics of information, i.e., extensity, detailedness,
sustainability, containability, delay, richness, distribution, validity and
matchability. Through these, this paper establishes a basic theory frame of
Objective Information Theory to support the analysis and research on
information and information system systematically and comprehensively.
|
1308.3374 | Utilization of Noise-Only Samples in Array Processing With Prior
Knowledge | math.ST cs.IT math.IT stat.TH | For array processing, we consider the problem of estimating signals of
interest, and their directions of arrival (DOA), in unknown colored noise
fields. We develop an estimator that efficiently utilizes a set of noise-only
samples and, further, can incorporate prior knowledge of the DOAs with varying
degrees of certainty. The estimator is compared with state of the art
estimators that utilize noise-only samples, and the Cram\'{e}r-Rao bound,
exhibiting improved performance for smaller sample sets and in poor signal
conditions.
|
1308.3381 | High dimensional Sparse Gaussian Graphical Mixture Model | stat.ML cs.LG | This paper considers the problem of networks reconstruction from
heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well
known that parameter estimation in this context is challenging due to large
numbers of variables coupled with the degeneracy of the likelihood. We propose
as a solution a penalized maximum likelihood technique by imposing an $l_{1}$
penalty on the precision matrix. Our approach shrinks the parameters thereby
resulting in better identifiability and variable selection. We use the
Expectation Maximization (EM) algorithm which involves the graphical LASSO to
estimate the mixing coefficients and the precision matrices. We show that under
certain regularity conditions the Penalized Maximum Likelihood (PML) estimates
are consistent. We demonstrate the performance of the PML estimator through
simulations and we show the utility of our method for high dimensional data
analysis in a genomic application.
|
1308.3383 | Axioms for graph clustering quality functions | cs.CV cs.LG stat.ML | We investigate properties that intuitively ought to be satisfied by graph
clustering quality functions, that is, functions that assign a score to a
clustering of a graph. Graph clustering, also known as network community
detection, is often performed by optimizing such a function. Two axioms
tailored for graph clustering quality functions are introduced, and the four
axioms introduced in previous work on distance based clustering are
reformulated and generalized for the graph setting. We show that modularity, a
standard quality function for graph clustering, does not satisfy all of these
six properties. This motivates the derivation of a new family of quality
functions, adaptive scale modularity, which does satisfy the proposed axioms.
Adaptive scale modularity has two parameters, which give greater flexibility in
the kinds of clusterings that can be found. Standard graph clustering quality
functions, such as normalized cut and unnormalized cut, are obtained as special
cases of adaptive scale modularity.
In general, the results of our investigation indicate that the considered
axiomatic framework covers existing `good' quality functions for graph
clustering, and can be used to derive an interesting new family of quality
functions.
|
1308.3388 | Models of on-line social networks | cs.SI physics.soc-ph | We present a deterministic model for on-line social networks (OSNs) based on
transitivity and local knowledge in social interactions. In the Iterated Local
Transitivity (ILT) model, at each time-step and for every existing node $x$, a
new node appears which joins to the closed neighbour set of $x.$ The ILT model
provably satisfies a number of both local and global properties that were
observed in OSNs and other real-world complex networks, such as a densification
power law, decreasing average distance, and higher clustering than in random
graphs with the same average degree. Experimental studies of social networks
demonstrate poor expansion properties as a consequence of the existence of
communities with low number of inter-community edges. Bounds on the spectral
gap for both the adjacency and normalized Laplacian matrices are proved for
graphs arising from the ILT model, indicating such bad expansion properties.
The cop and domination number are shown to remain the same as the graph from
the initial time-step $G_0$, and the automorphism group of $G_0$ is a subgroup
of the automorphism group of graphs generated at all later time-steps. A
randomized version of the ILT model is presented, which exhibits a tuneable
densification power law exponent, and maintains several properties of the
deterministic model.
|
1308.3400 | Guiding Designs of Self-Organizing Swarms: Interactive and Automated
Approaches | cs.NE nlin.AO | Self-organization of heterogeneous particle swarms is rich in its dynamics
but hard to design in a traditional top-down manner, especially when many types
of kinetically distinct particles are involved. In this chapter, we discuss how
we have been addressing this problem by (1) utilizing and enhancing interactive
evolutionary design methods and (2) realizing spontaneous evolution of self
organizing swarms within an artificial ecosystem.
|
1308.3432 | Estimating or Propagating Gradients Through Stochastic Neurons for
Conditional Computation | cs.LG | Stochastic neurons and hard non-linearities can be useful for a number of
reasons in deep learning models, but in many cases they pose a challenging
problem: how to estimate the gradient of a loss function with respect to the
input of such stochastic or non-smooth neurons? I.e., can we "back-propagate"
through these stochastic neurons? We examine this question, existing
approaches, and compare four families of solutions, applicable in different
settings. One of them is the minimum variance unbiased gradient estimator for
stochatic binary neurons (a special case of the REINFORCE algorithm). A second
approach, introduced here, decomposes the operation of a binary stochastic
neuron into a stochastic binary part and a smooth differentiable part, which
approximates the expected effect of the pure stochatic binary neuron to first
order. A third approach involves the injection of additive or multiplicative
noise in a computational graph that is otherwise differentiable. A fourth
approach heuristically copies the gradient with respect to the stochastic
output directly as an estimator of the gradient with respect to the sigmoid
argument (we call this the straight-through estimator). To explore a context
where these estimators are useful, we consider a small-scale version of {\em
conditional computation}, where sparse stochastic units form a distributed
representation of gaters that can turn off in combinatorially many ways large
chunks of the computation performed in the rest of the neural network. In this
case, it is important that the gating units produce an actual 0 most of the
time. The resulting sparsity can be potentially be exploited to greatly reduce
the computational cost of large deep networks for which conditional computation
would be useful.
|
1308.3438 | Identification of hybrid node and link communities in complex networks | cs.SI physics.soc-ph | Identification of communities in complex networks has become an effective
means to analysis of complex systems. It has broad applications in diverse
areas such as social science, engineering, biology and medicine. Finding
communities of nodes and finding communities of links are two popular schemes
for network structure analysis. These schemes, however, have inherent drawbacks
and are often inadequate to properly capture complex organizational structures
in real networks. We introduce a new scheme and effective approach for
identifying complex network structures using a mixture of node and link
communities, called hybrid node-link communities. A central piece of our
approach is a probabilistic model that accommodates node, link and hybrid
node-link communities. Our extensive experiments on various real-world
networks, including a large protein-protein interaction network and a large
semantic association network of commonly used words, illustrated that the
scheme for hybrid communities is superior in revealing network characteristics.
Moreover, the new approach outperformed the existing methods for finding node
or link communities separately.
|
1308.3485 | Information sharing promotes prosocial behaviour | physics.soc-ph cond-mat.stat-mech cs.SI q-bio.PE | More often than not, bad decisions are bad regardless of where and when they
are made. Information sharing might thus be utilized to mitigate them. Here we
show that sharing the information about strategy choice between players
residing on two different networks reinforces the evolution of cooperation. In
evolutionary games the strategy reflects the action of each individual that
warrants the highest utility in a competitive setting. We therefore assume that
identical strategies on the two networks reinforce themselves by lessening
their propensity to change. Besides network reciprocity working in favour of
cooperation on each individual network, we observe the spontaneous emerge of
correlated behaviour between the two networks, which further deters defection.
If information is shared not just between individuals but also between groups,
the positive effect is even stronger, and this despite the fact that
information sharing is implemented without any assumptions with regards to
content.
|
1308.3506 | Computational Rationalization: The Inverse Equilibrium Problem | cs.GT cs.LG stat.ML | Modeling the purposeful behavior of imperfect agents from a small number of
observations is a challenging task. When restricted to the single-agent
decision-theoretic setting, inverse optimal control techniques assume that
observed behavior is an approximately optimal solution to an unknown decision
problem. These techniques learn a utility function that explains the example
behavior and can then be used to accurately predict or imitate future behavior
in similar observed or unobserved situations.
In this work, we consider similar tasks in competitive and cooperative
multi-agent domains. Here, unlike single-agent settings, a player cannot
myopically maximize its reward; it must speculate on how the other agents may
act to influence the game's outcome. Employing the game-theoretic notion of
regret and the principle of maximum entropy, we introduce a technique for
predicting and generalizing behavior.
|
1308.3508 | A General Optimization Technique for High Quality Community Detection in
Complex Networks | cs.SI physics.soc-ph | Recent years have witnessed the development of a large body of algorithms for
community detection in complex networks. Most of them are based upon the
optimization of objective functions, among which modularity is the most common,
though a number of alternatives have been suggested in the scientific
literature. We present here an effective general search strategy for the
optimization of various objective functions for community detection purposes.
When applied to modularity, on both real-world and synthetic networks, our
search strategy substantially outperforms the best existing algorithms in terms
of final scores of the objective function; for description length, its
performance is on par with the original Infomap algorithm. The execution time
of our algorithm is on par with non-greedy alternatives present in literature,
and networks of up to 10,000 nodes can be analyzed in time spans ranging from
minutes to a few hours on average workstations, making our approach readily
applicable to tasks which require the quality of partitioning to be as high as
possible, and are not limited by strict time constraints. Finally, based on the
most effective of the available optimization techniques, we compare the
performance of modularity and code length as objective functions, in terms of
the quality of the partitions one can achieve by optimizing them. To this end,
we evaluated the ability of each objective function to reconstruct the
underlying structure of a large set of synthetic and real-world networks.
|
1308.3509 | Stochastic Optimization for Machine Learning | cs.LG | It has been found that stochastic algorithms often find good solutions much
more rapidly than inherently-batch approaches. Indeed, a very useful rule of
thumb is that often, when solving a machine learning problem, an iterative
technique which relies on performing a very large number of
relatively-inexpensive updates will often outperform one which performs a
smaller number of much "smarter" but computationally-expensive updates.
In this thesis, we will consider the application of stochastic algorithms to
two of the most important machine learning problems. Part i is concerned with
the supervised problem of binary classification using kernelized linear
classifiers, for which the data have labels belonging to exactly two classes
(e.g. "has cancer" or "doesn't have cancer"), and the learning problem is to
find a linear classifier which is best at predicting the label. In Part ii, we
will consider the unsupervised problem of Principal Component Analysis, for
which the learning task is to find the directions which contain most of the
variance of the data distribution.
Our goal is to present stochastic algorithms for both problems which are,
above all, practical--they work well on real-world data, in some cases better
than all known competing algorithms. A secondary, but still very important,
goal is to derive theoretical bounds on the performance of these algorithms
which are at least competitive with, and often better than, those known for
other approaches.
|
1308.3513 | Hidden Parameter Markov Decision Processes: A Semiparametric Regression
Approach for Discovering Latent Task Parametrizations | cs.LG cs.AI | Control applications often feature tasks with similar, but not identical,
dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),
a framework that parametrizes a family of related dynamical systems with a
low-dimensional set of latent factors, and introduce a semiparametric
regression approach for learning its structure from data. In the control
setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a
new task instance, allowing an agent to flexibly adapt to task variations.
|
1308.3521 | A New Distributed DC-Programming Method and its Applications | cs.IT math.IT math.OC | We propose a novel decomposition framework for the distributed optimization
of Difference Convex (DC)-type nonseparable sum-utility functions subject to
coupling convex constraints. A major contribution of the paper is to develop
for the first time a class of (inexact) best-response-like algorithms with
provable convergence, where a suitably convexified version of the original DC
program is iteratively solved. The main feature of the proposed successive
convex approximation method is its decomposability structure across the users,
which leads naturally to distributed algorithms in the primal and/or dual
domain. The proposed framework is applicable to a variety of multiuser DC
problems in different areas, ranging from signal processing, to communications
and networking. As a case study, in the second part of the paper we focus on
two examples, namely: i) a novel resource allocation problem in the emerging
area of cooperative physical layer security; ii) and the renowned sum-rate
maximization of MIMO Cognitive Radio networks. Our contribution in this context
is to devise a class of easy-to-implement distributed algorithms with provable
convergence to stationary solution of such problems. Numerical results show
that the proposed distributed schemes reach performance close to (and sometimes
better than) that of centralized methods.
|
1308.3524 | Innovative Second-Generation Wavelets Construction With Recurrent Neural
Networks for Solar Radiation Forecasting | cs.NE | Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature.
|
1308.3536 | Evasion Paths in Mobile Sensor Networks | math.AT cs.RO | Suppose that ball-shaped sensors wander in a bounded domain. A sensor doesn't
know its location but does know when it overlaps a nearby sensor. We say that
an evasion path exists in this sensor network if a moving intruder can avoid
detection. In "Coordinate-free coverage in sensor networks with controlled
boundaries via homology", Vin deSilva and Robert Ghrist give a necessary
condition, depending only on the time-varying connectivity data of the sensors,
for an evasion path to exist. Using zigzag persistent homology, we provide an
equivalent condition that moreover can be computed in a streaming fashion.
However, no method with time-varying connectivity data as input can give
necessary and sufficient conditions for the existence of an evasion path.
Indeed, we show that the existence of an evasion path depends not only on the
fibrewise homotopy type of the region covered by sensors but also on its
embedding in spacetime. For planar sensors that also measure weak rotation and
distance information, we provide necessary and sufficient conditions for the
existence of an evasion path.
|
1308.3541 | Knapsack Constrained Contextual Submodular List Prediction with
Application to Multi-document Summarization | cs.LG | We study the problem of predicting a set or list of options under knapsack
constraint. The quality of such lists are evaluated by a submodular reward
function that measures both quality and diversity. Similar to DAgger (Ross et
al., 2010), by a reduction to online learning, we show how to adapt two
sequence prediction models to imitate greedy maximization under knapsack
constraint problems: CONSEQOPT (Dey et al., 2012) and SCP (Ross et al., 2013).
Experiments on extractive multi-document summarization show that our approach
outperforms existing state-of-the-art methods.
|
1308.3548 | Distributed Ranging and Localization for Wireless Networks via
Compressed Sensing | cs.NI cs.IT math.IT | Location-based services in a wireless network require nodes to know their
locations accurately. Conventional solutions rely on contention-based medium
access, where only one node can successfully transmit at any time in any
neighborhood. In this paper, a novel, complete, distributed ranging and
localization solution is proposed, which let all nodes in the network broadcast
their location estimates and measure distances to all neighbors simultaneously.
An on-off signaling is designed to overcome the physical half-duplex
constraint. In each iteration, all nodes transmit simultaneously, each
broadcasting codewords describing the current location estimate. From the
superposed signals from all neighbors, each node decodes their neighbors'
locations and also estimates their distances using the signal strengths. The
node then broadcasts its improved location estimates in the subsequent
iteration. Simulations demonstrate accurate localization throughout a large
network over a few thousand symbol intervals, suggesting much higher efficiency
than conventional schemes based on ALOHA or CSMA.
|
1308.3553 | Application of Analog Network Coding to MIMO Two-Way Relay Channel in
Cellular Systems | cs.IT math.IT | An efficient analog network coding transmission protocol is proposed in this
letter for a MIMO two way cellular network. Block signal alignment is first
proposed to null the inter-user interference for multi-antenna users, which
makes the dimensions of aligned space larger compared with the existing signal
alignment. Two algorithms are developed to jointly design the precoding
matrices at the relay and BS for outage optimization. Especially, the last
algorithm is designed to maximize the effective channel gain to the effective
noise gain ratio. The performance of this transmission protocol is also
verified by simulations.
|
1308.3554 | Source Code Retrieval Using Sequence Based Similarity | cs.SE cs.IR | Duplicated code has a negative impact on the quality of software systems and
should be detected at least. In this paper, we discuss an approach that
improves source code retrieval using the structural information about the
programs. We developed a lexical parser to extract control statements and
method identifiers from Java programs. We propose a similarity measure that is
defined by the ratio of the number of sequentially full matching statements to
the number of sequentially partial matching ones. The similarity measure is
considered to be an extension of a set based similarity index, e.g.,
Sorensen-Dice index. Our key contribution of this research is the development
of a similarity retrieval algorithm that derives meaningful search conditions
from a given sequence, and then performs retrieval using all of the derived
conditions. Experiments show that our retrieval model outperforms the other
retrieval models up to 90.9% in the number of retrieved methods.
|
1308.3558 | Fast Stochastic Alternating Direction Method of Multipliers | cs.LG cs.NA | In this paper, we propose a new stochastic alternating direction method of
multipliers (ADMM) algorithm, which incrementally approximates the full
gradient in the linearized ADMM formulation. Besides having a low per-iteration
complexity as existing stochastic ADMM algorithms, the proposed algorithm
improves the convergence rate on convex problems from $O(\frac 1 {\sqrt{T}})$
to $O(\frac 1 T)$, where $T$ is the number of iterations. This matches the
convergence rate of the batch ADMM algorithm, but without the need to visit all
the samples in each iteration. Experiments on the graph-guided fused lasso
demonstrate that the new algorithm is significantly faster than
state-of-the-art stochastic and batch ADMM algorithms.
|
1308.3565 | Fast prediction and evaluation of gravitational waveforms using
surrogate models | gr-qc cs.CE | [Abridged] We propose a solution to the problem of quickly and accurately
predicting gravitational waveforms within any given physical model. The method
is relevant for both real-time applications and in more traditional scenarios
where the generation of waveforms using standard methods can be prohibitively
expensive. Our approach is based on three offline steps resulting in an
accurate reduced-order model that can be used as a surrogate for the
true/fiducial waveform family. First, a set of m parameter values is determined
using a greedy algorithm from which a reduced basis representation is
constructed. Second, these m parameters induce the selection of m time values
for interpolating a waveform time series using an empirical interpolant. Third,
a fit in the parameter dimension is performed for the waveform's value at each
of these m times. The cost of predicting L waveform time samples for a generic
parameter choice is of order m L + m c_f online operations where c_f denotes
the fitting function operation count and, typically, m << L. We generate
accurate surrogate models for Effective One Body (EOB) waveforms of
non-spinning binary black hole coalescences with durations as long as 10^5 M,
mass ratios from 1 to 10, and for multiple harmonic modes. We find that these
surrogates are three orders of magnitude faster to evaluate as compared to the
cost of generating EOB waveforms in standard ways. Surrogate model building for
other waveform models follow the same steps and have the same low online
scaling cost. For expensive numerical simulations of binary black hole
coalescences we thus anticipate large speedups in generating new waveforms with
a surrogate. As waveform generation is one of the dominant costs in parameter
estimation algorithms and parameter space exploration, surrogate models offer a
new and practical way to dramatically accelerate such studies without impacting
accuracy.
|
1308.3575 | Euclidean and Hermitian Self-orthogonal Algebraic Geometry Codes and
Their Application to Quantum Codes | cs.IT math.IT | In the present paper, we show that if the dimension of an arbitrary algebraic
geometry code over a finite field of even characters is slightly less than half
of its length, then it is equivalent to an Euclidean self-orthogonal code.
However, in the literatures, a strong contrition about existence of certain
differential is required to obtain such a result. We also show a similar result
on Hermitian self-orthogonal algebraic geometry codes. As a consequence, we can
apply our result to quantum codes and obtain quantum codes with good asymptotic
bounds.
|
1308.3577 | A Construction of Quantum Codes via A Class of Classical Polynomial
Codes | cs.IT math.IT | There have been various constructions of classical codes from polynomial
valuations in literature \cite{ARC04, LNX01,LX04,XF04,XL00}. In this paper, we
present a construction of classical codes based on polynomial construction
again. One of the features of this construction is that not only the classical
codes arisen from the construction have good parameters, but also quantum codes
with reasonably good parameters can be produced from these classical codes. In
particular, some new quantum codes are constructed (see Examples \ref{5.5} and
\ref{5.6}).
|
1308.3578 | Quantum Gilbert-Varshamov Bound Through Symplectic Self-Orthogonal Codes | cs.IT math.IT | It is well known that quantum codes can be constructed through classical
symplectic self-orthogonal codes. In this paper, we give a kind of
Gilbert-Varshamov bound for symplectic self-orthogonal codes first and then
obtain the Gilbert-Varshamov bound for quantum codes. The idea of obtaining the
Gilbert-Varshamov bound for symplectic self-orthogonal codes follows from
counting arguments.
|
1308.3600 | Random Walks on Directed Networks: Inference and Respondent-driven
Sampling | stat.ME cs.SI physics.soc-ph | Respondent driven sampling (RDS) is a method often used to estimate
population properties (e.g. sexual risk behavior) in hard-to-reach populations.
It combines an effective modified snowball sampling methodology with an
estimation procedure that yields unbiased population estimates under the
assumption that the sampling process behaves like a random walk on the social
network of the population. Current RDS estimation methodology assumes that the
social network is undirected, i.e. that all edges are reciprocal. However,
empirical social networks in general also have non-reciprocated edges. To
account for this fact, we develop a new estimation method for RDS in the
presence of directed edges on the basis of random walks on directed networks.
We distinguish directed and undirected edges and consider the possibility that
the random walk returns to its current position in two steps through an
undirected edge. We derive estimators of the selection probabilities of
individuals as a function of the number of outgoing edges of sampled
individuals. We evaluate the performance of the proposed estimators on
artificial and empirical networks to show that they generally perform better
than existing methods. This is in particular the case when the fraction of
directed edges in the network is large.
|
1308.3615 | QuPARA: Query-Driven Large-Scale Portfolio Aggregate Risk Analysis on
MapReduce | cs.DC cs.CE | Stochastic simulation techniques are used for portfolio risk analysis. Risk
portfolios may consist of thousands of reinsurance contracts covering millions
of insured locations. To quantify risk each portfolio must be evaluated in up
to a million simulation trials, each capturing a different possible sequence of
catastrophic events over the course of a contractual year. In this paper, we
explore the design of a flexible framework for portfolio risk analysis that
facilitates answering a rich variety of catastrophic risk queries. Rather than
aggregating simulation data in order to produce a small set of high-level risk
metrics efficiently (as is often done in production risk management systems),
the focus here is on allowing the user to pose queries on unaggregated or
partially aggregated data. The goal is to provide a flexible framework that can
be used by analysts to answer a wide variety of unanticipated but natural ad
hoc queries. Such detailed queries can help actuaries or underwriters to better
understand the multiple dimensions (e.g., spatial correlation, seasonality,
peril features, construction features, and financial terms) that can impact
portfolio risk. We implemented a prototype system, called QuPARA (Query-Driven
Large-Scale Portfolio Aggregate Risk Analysis), using Hadoop, which is Apache's
implementation of the MapReduce paradigm. This allows the user to take
advantage of large parallel compute servers in order to answer ad hoc risk
analysis queries efficiently even on very large data sets typically encountered
in practice. We describe the design and implementation of QuPARA and present
experimental results that demonstrate its feasibility. A full portfolio risk
analysis run consisting of a 1,000,000 trial simulation, with 1,000 events per
trial, and 3,200 risk transfer contracts can be completed on a 16-node Hadoop
cluster in just over 20 minutes.
|
1308.3616 | Complexity in animal communication: Estimating the size of N-Gram
structures | q-bio.PE cs.IT math.IT q-bio.QM | In this paper, new techniques that allow conditional entropy to estimate the
combinatorics of symbols are applied to animal communication studies to
estimate the communication's repertoire size. By using the conditional entropy
estimates at multiple orders, the paper estimates the total repertoire sizes
for animal communication across bottlenose dolphins, humpback whales, and
several species of birds for N-grams length one to three. In addition to
discussing the impact of this method on studies of animal communication
complexity, the reliability of these estimates is compared to other methods
through simulation. While entropy does undercount the total repertoire size due
to rare N-grams, it gives a more accurate picture of the most frequently used
repertoire than just repertoire size alone.
|
1308.3657 | Hoodsquare: Modeling and Recommending Neighborhoods in Location-based
Social Networks | cs.CY cs.SI physics.soc-ph | Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.
|
1308.3662 | A Convex Framework for Optimal Investment on Disease Awareness in Social
Networks | cs.SI cs.SY math.OC physics.soc-ph | We consider the problem of controlling the propagation of an epidemic
outbreak in an arbitrary network of contacts by investing on disease awareness
throughout the network. We model the effect of agent awareness on the dynamics
of an epidemic using the SAIS epidemic model, an extension of the SIS epidemic
model that includes a state of "awareness". This model allows to derive a
condition to control the spread of an epidemic outbreak in terms of the
eigenvalues of a matrix that depends on the network structure and the
parameters of the model. We study the problem of finding the cost-optimal
investment on disease awareness throughout the network when the cost function
presents some realistic properties. We propose a convex framework to find
cost-optimal allocation of resources. We validate our results with numerical
simulations in a real online social network.
|
1308.3679 | Just In Time Indexing | cs.DB | One of the major challenges being faced by Database managers today is to
manage the performance of complex SQL queries which are dynamic in nature.
Since it is not possible to tune each and every query because of its dynamic
nature, there is a definite possibility that these queries may cause serious
database performance issues if left alone. Conventional indexes are useful only
for those queries which are frequently executed or those columns which are
frequently joined in SQL queries. This proposal is regarding a method, a query
optimizer for optimizing database queries in a database management system. Just
In Time(JIT) indexes are On Demand, temporary indexes created on the fly based
on current needs so that they would be able to satisfy any kind of queries. JIT
indexes are created only when the configured threshold values for resource
consumption are exceeded for a query. JIT indexes will be stored in a temporary
basis and will get replaced by new JIT indexes in course of time. The proposal
is substantiated with the help of experimental programs and with various test
cases. The idea of parallel programming is also brought into picture as it can
be effectively used in a multiprocessor system. Multiple threads are employed
while one set of threads proceed in the conventional way and the other set of
threads proceed with the proposed way. A live switch over is made when a
suitable stage is reached and from then onwards the proposed method will only
come into picture.
|
1308.3689 | Evolving a Behavioral Repertoire for a Walking Robot | cs.RO | Numerous algorithms have been proposed to allow legged robots to learn to
walk. However, the vast majority of these algorithms is devised to learn to
walk in a straight line, which is not sufficient to accomplish any real-world
mission. Here we introduce the Transferability-based Behavioral Repertoire
Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that
simultaneously discovers several hundreds of simple walking controllers, one
for each possible direction. By taking advantage of solutions that are usually
discarded by evolutionary processes, TBR-Evolution is substantially faster than
independently evolving each controller. Our technique relies on two methods:
(1) novelty search with local competition, which searches for both
high-performing and diverse solutions, and (2) the transferability approach,
which com-bines simulations and real tests to evolve controllers for a physical
robot. We evaluate this new technique on a hexapod robot. Results show that
with only a few dozen short experiments performed on the robot, the algorithm
learns a repertoire of con-trollers that allows the robot to reach every point
in its reachable space. Overall, TBR-Evolution opens a new kind of learning
algorithm that simultaneously optimizes all the achievable behaviors of a
robot.
|
1308.3700 | Sailfish: Alignment-free Isoform Quantification from RNA-seq Reads using
Lightweight Algorithms | q-bio.GN cs.CE | RNA-seq has rapidly become the de facto technique to measure gene expression.
However, the time required for analysis has not kept up with the pace of data
generation. Here we introduce Sailfish, a novel computational method for
quantifying the abundance of previously annotated RNA isoforms from RNA-seq
data. Sailfish entirely avoids mapping reads, which is a time-consuming step in
all current methods. Sailfish provides quantification estimates much faster
than existing approaches (typically 20-times faster) without loss of accuracy.
|
1308.3740 | Standardizing Interestingness Measures for Association Rules | stat.AP cs.LG stat.ML | Interestingness measures provide information that can be used to prune or
select association rules. A given value of an interestingness measure is often
interpreted relative to the overall range of the values that the
interestingness measure can take. However, properties of individual association
rules restrict the values an interestingness measure can achieve. An
interesting measure can be standardized to take this into account, but this has
only been done for one interestingness measure to date, i.e., the lift.
Standardization provides greater insight than the raw value and may even alter
researchers' perception of the data. We derive standardized analogues of three
interestingness measures and use real and simulated data to compare them to
their raw versions, each other, and the standardized lift.
|
1308.3750 | Comment on "robustness and regularization of support vector machines" by
H. Xu, et al., (Journal of Machine Learning Research, vol. 10, pp. 1485-1510,
2009, arXiv:0803.3490) | cs.LG | This paper comments on the published work dealing with robustness and
regularization of support vector machines (Journal of Machine Learning
Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc. They
proposed a theorem to show that it is possible to relate robustness in the
feature space and robustness in the sample space directly. In this paper, we
propose a counter example that rejects their theorem.
|
1308.3772 | Joint Phase Noise Estimation and Data Detection in Coded MIMO Systems | cs.IT math.IT | In this paper, the problem of joint oscillator phase noise (PHN) estimation
and data detection for multi-input multi-output (MIMO) systems using
bit-interleaved coded modulation (BICM) is analyzed. A new MIMO receiver that
iterates between the estimator and the detector, based on the
expectation-maximization (EM) framework, is proposed. It is shown that at high
signal-to-noise ratios, a maximum a posteriori estimator (MAP) can be used to
carry out the maximization step of the EM algorithm. Moreover, to reduce the
computational complexity of the proposed EM algorithm, a soft decision-directed
extended Kalman filter-smoother (EKFS) is applied instead of the MAP estimator
to track the PHN parameters. Numerical results show that by combining the
proposed EKFS based approach with an iterative detector that employs low
density parity check (LDPC) codes, PHN can be accurately tracked. Simulations
also demonstrate that compared to existing algorithms, the proposed iterative
receiver can significantly enhance the performance of MIMO systems in the
presence of PHN.
|
1308.3780 | Decision Theory with Resource-Bounded Agents | cs.GT cs.AI | There have been two major lines of research aimed at capturing
resource-bounded players in game theory. The first, initiated by Rubinstein,
charges an agent for doing costly computation; the second, initiated by Neyman,
does not charge for computation, but limits the computation that agents can do,
typically by modeling agents as finite automata. We review recent work on
applying both approaches in the context of decision theory. For the first
approach, we take the objects of choice in a decision problem to be Turing
machines, and charge players for the ``complexity'' of the Turing machine
chosen (e.g., its running time). This approach can be used to explain
well-known phenomena like first-impression-matters biases (i.e., people tend to
put more weight on evidence they hear early on) and belief polarization (two
people with different prior beliefs, hearing the same evidence, can end up with
diametrically opposed conclusions) as the outcomes of quite rational decisions.
For the second approach, we model people as finite automata, and provide a
simple algorithm that, on a problem that captures a number of settings of
interest, provably performs optimally as the number of states in the automaton
increases.
|
1308.3784 | Graph Colouring Problem Based on Discrete Imperialist Competitive
Algorithm | cs.AI cs.NE | In graph theory, Graph Colouring Problem (GCP) is an assignment of colours to
vertices of any given graph such that the colours on adjacent vertices are
different. The GCP is known to be an optimization and NP-hard problem.
Imperialist Competitive Algorithm (ICA) is a meta-heuristic optimization and
stochastic search strategy which is inspired from socio-political phenomenon of
imperialistic competition. The ICA contains two main operators: the
assimilation and the imperialistic competition. The ICA has excellent
capabilities such as high convergence rate and better global optimum
achievement. In this research, a discrete version of ICA is proposed to deal
with the solution of GCP. We call this algorithm as the DICA. The performance
of the proposed method is compared with Genetic Algorithm (GA) on seven
well-known graph colouring benchmarks. Experimental results demonstrate the
superiority of the DICA for the benchmarks. This means DICA can produce optimal
and valid solutions for different GCP instances.
|
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