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1105.0208
|
Algorithmic entropy, thermodynamics, and game interpretation
|
cs.IT math-ph math.IT math.LO math.MP math.PR
|
Basic relations for the mean length and algorithmic entropy are obtained by
solving a new extremal problem. Using this extremal problem, they are obtained
in a most simple and general way. The length and entropy are considered as two
players of a new type of a game, in which we follow the scheme of our previous
work on thermodynamic characteristics in quantum and classical approaches.
|
1105.0214
|
Comparing the Topological and Electrical Structure of the North American
Electric Power Infrastructure
|
physics.soc-ph cs.SI
|
The topological (graph) structure of complex networks often provides valuable
information about the performance and vulnerability of the network. However,
there are multiple ways to represent a given network as a graph. Electric power
transmission and distribution networks have a topological structure that is
straightforward to represent and analyze as a graph. However, simple graph
models neglect the comprehensive connections between components that result
from Ohm's and Kirchhoff's laws. This paper describes the structure of the
three North American electric power interconnections, from the perspective of
both topological and electrical connectivity. We compare the simple topology of
these networks with that of random (Erdos and Renyi, 1959),
preferential-attachment (Barabasi and Albert, 1999) and small-world (Watts and
Strogatz, 1998) networks of equivalent sizes and find that power grids differ
substantially from these abstract models in degree distribution, clustering,
diameter and assortativity, and thus conclude that these topological forms may
be misleading as models of power systems. To study the electrical connectivity
of power systems, we propose a new method for representing electrical structure
using electrical distances rather than geographic connections. Comparisons of
these two representations of the North American power networks reveal notable
differences between the electrical and topological structure of electric power
networks.
|
1105.0240
|
Optimal Function Computation in Directed and Undirected Graphs
|
cs.IT cs.NI math.IT
|
We consider the problem of information aggregation in sensor networks, where
one is interested in computing a function of the sensor measurements. We allow
for block processing and study in-network function computation in directed
graphs and undirected graphs. We study how the structure of the function
affects the encoding strategies, and the effect of interactive information
exchange.
We begin by considering a directed graph G = (V, E) on the sensor nodes,
where the goal is to determine the optimal encoders on each edge which achieve
function computation at the collector node. Our goal is to characterize the
rate region in R^{|E|}, i.e., the set of points for which there exist feasible
encoders with given rates which achieve zero-error computation for
asymptotically large block length. We determine the solution for directed
trees, specifying the optimal encoder and decoder for each edge. For general
directed acyclic graphs, we provide an outer bound on the rate region by
finding the disambiguation requirements for each cut, and describe examples
where this outer bound is tight.
Next, we address the scenario where nodes are connected in an undirected tree
network, and every node wishes to compute a given symmetric Boolean function of
the sensor data. Undirected edges permit interactive computation, and we
therefore study the effect of interaction on the aggregation and communication
strategies. We focus on sum-threshold functions, and determine the minimum
worst-case total number of bits to be exchanged on each edge. The optimal
strategy involves recursive in-network aggregation which is reminiscent of
message passing. In the case of general graphs, we present a cutset lower
bound, and an achievable scheme based on aggregation along trees. For complete
graphs, we prove that the complexity of this scheme is no more than twice that
of the optimal scheme.
|
1105.0247
|
Liquidation in Limit Order Books with Controlled Intensity
|
q-fin.TR cs.SY math.OC
|
We consider a framework for solving optimal liquidation problems in limit
order books. In particular, order arrivals are modeled as a point process whose
intensity depends on the liquidation price. We set up a stochastic control
problem in which the goal is to maximize the expected revenue from liquidating
the entire position held. We solve this optimal liquidation problem for
power-law and exponential-decay order book models and discuss several
extensions. We also consider the continuous selling (or fluid) limit when the
trading units are ever smaller and the intensity is ever larger. This limit
provides an analytical approximation to the value function and the optimal
solution. Using techniques from viscosity solutions we show that the discrete
state problem and its optimal solution converge to the corresponding quantities
in the continuous selling limit uniformly on compacts.
|
1105.0256
|
Easy-to-compute parameterizations of all wavelet filters: input-output
and state-space
|
math.CV cs.SY math.OC
|
We here use notions from the theory linear shift-invariant dynamical systems
to provide an easy-to-compute characterization of all rational wavelet filters.
For a given N bigger or equql to 2, the number of inputs, the construction is
based on a factorization to an elementary wavelet filter along with of m
elementary unitary matrices. We shall call this m the index of the filter. It
turns out that the resulting wavelet filter is of McMillan degree
$N((N-1)/2+m).
Rational wavelet filters bounded at infinity, admit state space realization.
The above input-output parameterization is exploited for a step-by-step
construction (where in each the index m is increased by one) of state space
model of wavelet filters.
|
1105.0257
|
Map equation for link community
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Community structure exists in many real-world networks and has been reported
being related to several functional properties of the networks. The
conventional approach was partitioning nodes into communities, while some
recent studies start partitioning links instead of nodes to find overlapping
communities of nodes efficiently. We extended the map equation method, which
was originally developed for node communities, to find link communities in
networks. This method is tested on various kinds of networks and compared with
the metadata of the networks, and the results show that our method can identify
the overlapping role of nodes effectively. The advantage of this method is that
the node community scheme and link community scheme can be compared
quantitatively by measuring the unknown information left in the networks
besides the community structure. It can be used to decide quantitatively
whether or not the link community scheme should be used instead of the node
community scheme. Furthermore, this method can be easily extended to the
directed and weighted networks since it is based on the random walk.
|
1105.0259
|
On the provable security of BEAR and LION schemes
|
cs.CR cs.IT math.CO math.IT
|
BEAR, LION and LIONESS are block ciphers presented by Biham and Anderson
(1996), inspired by the famous Luby-Rackoff constructions of block ciphers from
other cryptographic primitives (1988). The ciphers proposed by Biham and
Anderson are based on one stream cipher and one hash function. Good properties
of the primitives ensure good properties of the block cipher. In particular,
they are able to prove that their ciphers are immune to any efficient
known-plaintext key-recovery attack that can use as input only one
plaintext-ciphertext pair. Our contribution is showing that these ciphers are
actually immune to any efficient known-plaintext key-recovery attack that can
use as input any number of plaintext-ciphertext pairs. We are able to get this
improvement by using slightly weaker hypotheses on the primitives. We also
discuss the attack by Morin (1996).
|
1105.0275
|
Robustness of Complex Networks against Attacks Guided by Damage
|
physics.soc-ph cs.SI
|
Extensive researches have been dedicated to investigating the performance of
real networks and synthetic networks against random failures or intentional
attack guided by degree (degree attack). Degree is one of straightforward
measures to characterize the vitality of a vertex in maintaining the integrity
of the network but not the only one. Damage, the decrease of the largest
component size that was caused by the removal of a vertex, intuitively is a
more destructive guide for intentional attack on networks since the network
functionality is usually measured by the largest component size. However, it is
surprising to find that little is known about behaviors of real networks or
synthetic networks against intentional attack guided by damage (damage attack),
in which adversaries always choose the vertex with the largest damage to
attack.
In this article, we dedicate our efforts to understanding damage attack and
behaviors of real networks as well as synthetic networks against this attack.
To this end, existing attacking models, statistical properties of damage in
complex networks are first revisited. Then, we present the empirical analysis
results about behaviors of complex networks against damage attack with the
comparisons to degree attack. It is surprising to find a cross-point for
diverse networks before which damage attack is more destructive than degree
attack. Further investigation shows that the existence of cross-point can be
attributed to the fact that: degree attack tends produce networks with more
heterogenous damage distribution than damage attack. Results in this article
strongly suggest that damage attack is one of most destructive attacks and
deserves our research efforts.Our understandings about damage attack may also
shed light on efficient solutions to protect real networks against damage
attack.
|
1105.0285
|
WSR Maximized Resource Allocation in Multiple DF Relays Aided OFDMA
Downlink Transmission
|
cs.IT cs.SY math.IT math.OC
|
This paper considers the weighted sum rate (WSR) maximized resource
allocation (RA) constrained by a system sum power in an orthogonal frequency
division multiple access (OFDMA) downlink transmission system assisted by
multiple decode-and-forward (DF) relays. In particular, multiple relays may
cooperate with the source for every relay-aided transmission. A two-step
algorithm is proposed to find the globally optimum RA. In the first step, the
optimum source/relay power and assisting relays that maximize the rate is found
for every combination of subcarrier and destination, assuming a sum power is
allocated to the transmission at that subcarrier to that destination in the
relay-aided transmission mode and the direct mode, respectively. In the second
step, a convex-optimization based algorithm is designed to find the globally
optimum assignment of destination, transmission mode, and sum power for each
subcarrier to maximize the WSR. Combining the RAs found in the two steps, the
globally optimum RA can be found. In addition, we show that the optimum RA in
the second step can readily be derived when the system sum power is very high.
The effectiveness of the proposed algorithm is illustrated by numerical
experiments.
|
1105.0286
|
Dynamic Interference Mitigation for Generalized Partially Connected
Quasi-static MIMO Interference Channel
|
cs.IT math.IT
|
Recent works on MIMO interference channels have shown that interference
alignment can significantly increase the achievable degrees of freedom (DoF) of
the network. However, most of these works have assumed a fully connected
interference graph. In this paper, we investigate how the partial connectivity
can be exploited to enhance system performance in MIMO interference networks.
We propose a novel interference mitigation scheme which introduces constraints
for the signal subspaces of the precoders and decorrelators to mitigate "many"
interference nulling constraints at a cost of "little" freedoms in precoder and
decorrelator design so as to extend the feasibility region of the interference
alignment scheme. Our analysis shows that the proposed algorithm can
significantly increase system DoF in symmetric partially connected MIMO
interference networks. We also compare the performance of the proposed scheme
with various baselines and show via simulations that the proposed algorithms
could achieve significant gain in the system performance of randomly connected
interference networks.
|
1105.0288
|
Splitting and Updating Hybrid Knowledge Bases (Extended Version)
|
cs.AI
|
Over the years, nonmonotonic rules have proven to be a very expressive and
useful knowledge representation paradigm. They have recently been used to
complement the expressive power of Description Logics (DLs), leading to the
study of integrative formal frameworks, generally referred to as hybrid
knowledge bases, where both DL axioms and rules can be used to represent
knowledge. The need to use these hybrid knowledge bases in dynamic domains has
called for the development of update operators, which, given the substantially
different way Description Logics and rules are usually updated, has turned out
to be an extremely difficult task.
In [SL10], a first step towards addressing this problem was taken, and an
update operator for hybrid knowledge bases was proposed. Despite its
significance -- not only for being the first update operator for hybrid
knowledge bases in the literature, but also because it has some applications -
this operator was defined for a restricted class of problems where only the
ABox was allowed to change, which considerably diminished its applicability.
Many applications that use hybrid knowledge bases in dynamic scenarios require
both DL axioms and rules to be updated.
In this paper, motivated by real world applications, we introduce an update
operator for a large class of hybrid knowledge bases where both the DL
component as well as the rule component are allowed to dynamically change. We
introduce splitting sequences and splitting theorem for hybrid knowledge bases,
use them to define a modular update semantics, investigate its basic
properties, and illustrate its use on a realistic example about cargo imports.
|
1105.0319
|
The Arbitrarily Varying Multiple-Access Channel with Conferencing
Encoders
|
cs.IT math.IT
|
We derive the capacity region of arbitrarily varying multiple-access channels
with conferencing encoders for both deterministic and random coding. For a
complete description it is sufficient that one conferencing capacity is
positive. We obtain a dichotomy: either the channel's deterministic capacity
region is zero or it equals the two-dimensional random coding region. We
determine exactly when either case holds. We also discuss the benefits of
conferencing. We give the example of an AV-MAC which does not achieve any
non-zero rate pair without encoder cooperation, but the two-dimensional random
coding capacity region if conferencing is possible. Unlike compound
multiple-access channels, arbitrarily varying multiple-access channels may
exhibit a discontinuous increase of the capacity region when conferencing in at
least one direction is enabled.
|
1105.0324
|
Community detection based on "clumpiness" matrix in complex networks
|
physics.soc-ph cs.SI
|
The "clumpiness" matrix of a network is used to develop a method to identify
its community structure. A "projection space" is constructed from the
eigenvectors of the clumpiness matrix and a border line is defined using some
kind of angular distance in this space. The community structure of the network
is identified using this borderline and/or hierarchical clustering methods. The
performance of our algorithm is tested on some computer-generated and
real-world networks. The accuracy of the results is checked using normalized
mutual information. The effect of community size heterogeneity on the accuracy
of the method is also discussed.
|
1105.0332
|
Recalling of Images using Hopfield Neural Network Model
|
cs.NE
|
In the present paper, an effort has been made for storing and recalling
images with Hopfield Neural Network Model of auto-associative memory. Images
are stored by calculating a corresponding weight matrix. Thereafter, starting
from an arbitrary configuration, the memory will settle on exactly that stored
image, which is nearest to the starting configuration in terms of Hamming
distance. Thus given an incomplete or corrupted version of a stored image, the
network is able to recall the corresponding original image. The storing of the
objects has been performed according to the Hopfield algorithm explained below.
Once the net has completely learnt this set of input patterns, a set of testing
patterns containing degraded images will be given to the net. Then the Hopfield
net will tend to recall the closest matching pattern for the given degraded
image. The simulated results show that Hopfield model is the best for storing
and recalling images.
|
1105.0350
|
Preprocessing: A Prerequisite for Discovering Patterns in Web Usage
Mining Process
|
cs.DB
|
Web log data is usually diverse and voluminous. This data must be assembled
into a consistent, integrated and comprehensive view, in order to be used for
pattern discovery. Without properly cleaning, transforming and structuring the
data prior to the analysis, one cannot expect to find meaningful patterns. As
in most data mining applications, data preprocessing involves removing and
filtering redundant and irrelevant data, removing noise, transforming and
resolving any inconsistencies. In this paper, a complete preprocessing
methodology having merging, data cleaning, user/session identification and data
formatting and summarization activities to improve the quality of data by
reducing the quantity of data has been proposed. To validate the efficiency of
the proposed preprocessing methodology, several experiments are conducted and
the results show that the proposed methodology reduces the size of Web access
log files down to 73-82% of the initial size and offers richer logs that are
structured for further stages of Web Usage Mining (WUM). So preprocessing of
raw data in this WUM process is the central theme of this paper.
|
1105.0355
|
A Novel Crossover Operator for Genetic Algorithms: Ring Crossover
|
cs.NE
|
The genetic algorithm (GA) is an optimization and search technique based on
the principles of genetics and natural selection. A GA allows a population
composed of many individuals to evolve under specified selection rules to a
state that maximizes the "fitness" function. In that process, crossover
operator plays an important role. To comprehend the GAs as a whole, it is
necessary to understand the role of a crossover operator. Today, there are a
number of different crossover operators that can be used in GAs. However, how
to decide what operator to use for solving a problem? A number of test
functions with various levels of difficulty has been selected as a test polygon
for determine the performance of crossover operators. In this paper, a novel
crossover operator called 'ring crossover' is proposed. In order to evaluate
the efficiency and feasibility of the proposed operator, a comparison between
the results of this study and results of different crossover operators used in
GAs is made through a number of test functions with various levels of
difficulty. Results of this study clearly show significant differences between
the proposed operator and the other crossover operators.
|
1105.0377
|
WiMAX Based 60 GHz Millimeter-Wave Communication for Intelligent
Transport System Applications
|
cs.IT math.IT
|
With the successful worldwide deployment of 3rd generation mobile
communication, security aspects are ensured partly. Researchers are now looking
for 4G mobile for its deployment with high data rate, enhanced security and
reliability so that world should look for CALM, Continuous Air interface for
Long and Medium range communication. This CALM will be a reliable high data
rate secured mobile communication to be deployed for car to car communication
(C2C) for safety application. This paper reviewed the WiMAX ,& 60 GHz RF
carrier for C2C. The system is tested at SMIT laboratory with multimedia
transmission and reception. With proper deployment of this 60 GHz system on
vehicles, the existing commercial products for 802.11P will be required to be
replaced or updated soon .
|
1105.0379
|
Self-Repairing Codes for Distributed Storage - A Projective Geometric
Construction
|
cs.DC cs.IT math.IT
|
Self-Repairing Codes (SRC) are codes designed to suit the need of coding for
distributed networked storage: they not only allow stored data to be recovered
even in the presence of node failures, they also provide a repair mechanism
where as little as two live nodes can be contacted to regenerate the data of a
failed node. In this paper, we propose a new instance of self-repairing codes,
based on constructions of spreads coming from projective geometry. We study
some of their properties to demonstrate the suitability of these codes for
distributed networked storage.
|
1105.0381
|
Parallel and Distributed Simulation: Five W's (and One H)
|
cs.DC cs.MA
|
A well known golden rule of journalism (and many other fields too) is that if
you want to know the full story about something you have to answer all the five
W's (Who, What, When, Where, Why) and the H (How). This extended abstract is
about what is missing in parallel and distributed simulation and how this
affects its popularity.
|
1105.0382
|
Rapid Learning with Stochastic Focus of Attention
|
cs.LG stat.ML
|
We present a method to stop the evaluation of a decision making process when
the result of the full evaluation is obvious. This trait is highly desirable
for online margin-based machine learning algorithms where a classifier
traditionally evaluates all the features for every example. We observe that
some examples are easier to classify than others, a phenomenon which is
characterized by the event when most of the features agree on the class of an
example. By stopping the feature evaluation when encountering an easy to
classify example, the learning algorithm can achieve substantial gains in
computation. Our method provides a natural attention mechanism for learning
algorithms. By modifying Pegasos, a margin-based online learning algorithm, to
include our attentive method we lower the number of attributes computed from
$n$ to an average of $O(\sqrt{n})$ features without loss in prediction
accuracy. We demonstrate the effectiveness of Attentive Pegasos on MNIST data.
|
1105.0393
|
Universally Typical Sets for Ergodic Sources of Multidimensional Data
|
cs.IT math.IT
|
We lift important results about universally typical sets, typically sampled
sets, and empirical entropy estimation in the theory of samplings of discrete
ergodic information sources from the usual one-dimensional discrete-time
setting to a multidimensional lattice setting. We use techniques of packings
and coverings with multidimensional windows to construct sequences of
multidimensional array sets which in the limit build the generated samples of
any ergodic source of entropy rate below an $h_0$ with probability one and
whose cardinality grows at most at exponential rate $h_0$.
|
1105.0401
|
On-Demand Based Wireless Resources Trading for Green Communications
|
cs.IT math.IT
|
The purpose of Green Communications is to reduce the energy consumption of
the communication system as much as possible without compromising the quality
of service (QoS) for users. An effective approach for Green Wireless
Communications is On-Demand strategy, which scales power consumption with the
volume and location of user demand. Applying the On-Demand Communications
model, we propose a novel scheme -- Wireless Resource Trading, which
characterizes the trading relationship among different wireless resources for a
given number of performance metrics. According to wireless resource trading
relationship, different wireless resources can be consumed for the same set of
performance metrics. Therefore, to minimize the energy consumption for given
performance metrics, we can trade the other type of wireless resources for the
energy resource under the demanded performance metrics. Based on the wireless
resource trading relationship, we derive the optimal energy-bandwidth and
energy-time wireless resource trading relationship for green wireless
communications. We also develop an adaptive trading strategy by using different
bandwidths or different delays for different transmission distances with
available bandwidths and acceptable delay bounds in wireless networks. Our
conducted simulations show that the energy consumption of wireless networks can
be significantly reduced with our proposed wireless resources trading scheme.
|
1105.0417
|
Cone Schedules for Processing Systems in Fluctuating Environments
|
cs.NI cs.SY math.OC
|
We consider a generalized processing system having several queues, where the
available service rate combinations are fluctuating over time due to
reliability and availability variations. The objective is to allocate the
available resources, and corresponding service rates, in response to both
workload and service capacity considerations, in order to maintain the long
term stability of the system. The service configurations are completely
arbitrary, including negative service rates which represent forwarding and
service-induced cross traffic. We employ a trace-based trajectory asymptotic
technique, which requires minimal assumptions about the arrival dynamics of the
system.
We prove that cone schedules, which leverage the geometry of the queueing
dynamics, maximize the system throughput for a broad class of processing
systems, even under adversarial arrival processes. We study the impact of
fluctuating service availability, where resources are available only some of
the time, and the schedule must dynamically respond to the changing available
service rates, establishing both the capacity of such systems and the class of
schedules which will stabilize the system at full capacity. The rich geometry
of the system dynamics leads to important insights for stability, performance
and scalability, and substantially generalizes previous findings.
The processing system studied here models a broad variety of computer,
communication and service networks, including varying channel conditions and
cross-traffic in wireless networking, and call centers with fluctuating
capacity. The findings have implications for bandwidth and processor allocation
in communication networks and workforce scheduling in congested call centers.
|
1105.0442
|
On State Estimation with Bad Data Detection
|
cs.IT math.IT
|
In this paper, we consider the problem of state estimation through
observations possibly corrupted with both bad data and additive observation
noises. A mixed $\ell_1$ and $\ell_2$ convex programming is used to separate
both sparse bad data and additive noises from the observations. Through using
the almost Euclidean property for a linear subspace, we derive a new
performance bound for the state estimation error under sparse bad data and
additive observation noises. Our main contribution is to provide sharp bounds
on the almost Euclidean property of a linear subspace, using the
"escape-through-a-mesh" theorem from geometric functional analysis. We also
propose and numerically evaluate an iterative convex programming approach to
performing bad data detections in nonlinear electrical power networks problems.
|
1105.0452
|
Relay-Assisted Multiple Access with Multi-Packet Reception Capability
and Simultaneous Transmission and Reception
|
cs.IT math.IT
|
In this work we examine the operation of a node relaying packets from a
number of users to a destination node. We assume multi-packet reception
capabilities for the relay and the destination node. The relay node can
transmit and receive at the same time, so the problem of self interference
arises. The relay does not have packets of its own and the traffic at the
source nodes is considered saturated. The relay node stores a source packet
that it receives successfully in its queue when the transmission to the
destination node has failed. We obtain analytical expressions for the
characteristics of the relay's queue (such as arrival and service rate of the
relay's queue), the stability condition and the average length of the queue as
functions of the probabilities of transmissions, the self interference
coefficient and the outage probabilities of the links. We study the impact of
the relay node and the self interference coefficient on the throughput per
user-source as well as the aggregate throughput.
|
1105.0469
|
Rationality, irrationality and escalating behavior in lowest unique bid
auctions
|
physics.soc-ph cs.SI
|
Information technology has revolutionized the traditional structure of
markets. The removal of geographical and time constraints has fostered the
growth of online auction markets, which now include millions of economic agents
worldwide and annual transaction volumes in the billions of dollars. Here, we
analyze bid histories of a little studied type of online auctions --- lowest
unique bid auctions. Similarly to what has been reported for foraging animals
searching for scarce food, we find that agents adopt Levy flight search
strategies in their exploration of "bid space". The Levy regime, which is
characterized by a power-law decaying probability distribution of step lengths,
holds over nearly three orders of magnitude. We develop a quantitative model
for lowest unique bid online auctions that reveals that agents use nearly
optimal bidding strategies. However, agents participating in these auctions do
not optimize their financial gain. Indeed, as long as there are many auction
participants, a rational profit optimizing agent would choose not to
participate in these auction markets.
|
1105.0471
|
Suboptimal Solution Path Algorithm for Support Vector Machine
|
cs.LG
|
We consider a suboptimal solution path algorithm for the Support Vector
Machine. The solution path algorithm is an effective tool for solving a
sequence of a parametrized optimization problems in machine learning. The path
of the solutions provided by this algorithm are very accurate and they satisfy
the optimality conditions more strictly than other SVM optimization algorithms.
In many machine learning application, however, this strict optimality is often
unnecessary, and it adversely affects the computational efficiency. Our
algorithm can generate the path of suboptimal solutions within an arbitrary
user-specified tolerance level. It allows us to control the trade-off between
the accuracy of the solution and the computational cost. Moreover, We also show
that our suboptimal solutions can be interpreted as the solution of a
\emph{perturbed optimization problem} from the original one. We provide some
theoretical analyses of our algorithm based on this novel interpretation. The
experimental results also demonstrate the effectiveness of our algorithm.
|
1105.0473
|
A Sensing Error Aware MAC Protocol for Cognitive Radio Networks
|
cs.IT math.IT
|
Cognitive radios (CR) are intelligent radio devices that can sense the radio
environment and adapt to changes in the radio environment. Spectrum sensing and
spectrum access are the two key CR functions. In this paper, we present a
spectrum sensing error aware MAC protocol for a CR network collocated with
multiple primary networks. We explicitly consider both types of sensing errors
in the CR MAC design, since such errors are inevitable for practical spectrum
sensors and more important, such errors could have significant impact on the
performance of the CR MAC protocol. Two spectrum sensing polices are presented,
with which secondary users collaboratively sense the licensed channels. The
sensing policies are then incorporated into p-Persistent CSMA to coordinate
opportunistic spectrum access for CR network users. We present an analysis of
the interference and throughput performance of the proposed CR MAC, and find
the analysis highly accurate in our simulation studies. The proposed sensing
error aware CR MAC protocol outperforms two existing approaches with
considerable margins in our simulations, which justify the importance of
considering spectrum sensing errors in CR MAC design.
|
1105.0476
|
Downlink Power Allocation for Stored Variable-Bit-Rate Videos
|
cs.IT math.IT
|
In this paper, we study the problem of power allocation for streaming
multiple variable-bit-rate (VBR) videos in the downlink of a cellular network.
We consider a deterministic model for VBR video traffic and finite playout
buffer at the mobile users. The objective is to derive the optimal downlink
power allocation for the VBR video sessions, such that the video data can be
delivered in a timely fashion without causing playout buffer overflow and
underflow. The formulated problem is a nonlinear nonconvex optimization
problem. We analyze the convexity conditions for the formulated problem and
propose a two-step greedy approach to solve the problem. We also develop a
distributed algorithm based on the dual decomposition technique. The
performance of the proposed algorithms are validated with simulations using VBR
video traces under realistic scenarios.
|
1105.0510
|
Voting in a Stochastic Environment: The Case of Two Groups
|
cs.MA cs.SI cs.SY math.OC physics.soc-ph
|
Social dynamics determined by voting in a stochastic environment is analyzed
for a society composed of two cohesive groups of similar size. Within the model
of random walks determined by voting, explicit formulas are derived for the
capital increments of the groups against the parameters of the environment and
"claim thresholds" of the groups. The "unanimous acceptance" and "unanimous
rejection" group rules are considered as the voting procedures. Claim
thresholds are evaluated that are most beneficial to the participants of the
groups and to the society as a whole.
|
1105.0515
|
Core-Periphery Segregation in Evolving Prisoner's Dilemma Networks
|
q-bio.PE cs.SI physics.soc-ph
|
Dense cooperative networks are an essential element of social capital for a
prosperous society. These networks enable individuals to overcome collective
action dilemmas by enhancing trust. In many biological and social settings,
network structures evolve endogenously as agents exit relationships and build
new ones. However, the process by which evolutionary dynamics lead to
self-organization of dense cooperative networks has not been explored. Our
large group prisoner's dilemma experiments with exit and partner choice options
show that core-periphery segregation of cooperators and defectors drives the
emergence of cooperation. Cooperators' Quit-for-Tat and defectors' Roving
strategy lead to a highly asymmetric core and periphery structure. Densely
connected to each other, cooperators successfully isolate defectors and earn
larger payoffs than defectors. Our analysis of the topological characteristics
of evolving networks illuminates how social capital is generated.
|
1105.0540
|
Pruning nearest neighbor cluster trees
|
stat.ML cs.LG
|
Nearest neighbor (k-NN) graphs are widely used in machine learning and data
mining applications, and our aim is to better understand what they reveal about
the cluster structure of the unknown underlying distribution of points.
Moreover, is it possible to identify spurious structures that might arise due
to sampling variability?
Our first contribution is a statistical analysis that reveals how certain
subgraphs of a k-NN graph form a consistent estimator of the cluster tree of
the underlying distribution of points. Our second and perhaps most important
contribution is the following finite sample guarantee. We carefully work out
the tradeoff between aggressive and conservative pruning and are able to
guarantee the removal of all spurious cluster structures at all levels of the
tree while at the same time guaranteeing the recovery of salient clusters. This
is the first such finite sample result in the context of clustering.
|
1105.0569
|
Random Beamforming over Correlated Fading Channels
|
cs.IT math.IT
|
We study a multiple-input multiple-output (MIMO) multiple access channel
(MAC) from several multi-antenna transmitters to a multi-antenna receiver. The
fading channels between the transmitters and the receiver are modeled by random
matrices, composed of independent column vectors with zero mean and different
covariance matrices. Each transmitter is assumed to send multiple data streams
with a random precoding matrix extracted from a Haar-distributed matrix. For
this general channel model, we derive deterministic approximations of the
normalized mutual information, the normalized sum-rate with
minimum-mean-square-error (MMSE) detection and the
signal-to-interference-plus-noise-ratio (SINR) of the MMSE decoder, which
become arbitrarily tight as all system parameters grow infinitely large at the
same speed. In addition, we derive the asymptotically optimal power allocation
under individual or sum-power constraints. Our results allow us to tackle the
problem of optimal stream control in interference channels which would be
intractable in any finite setting. Numerical results corroborate our analysis
and verify its accuracy for realistic system dimensions. Moreover, the
techniques applied in this paper constitute a novel contribution to the field
of large random matrix theory and could be used to study even more involved
channel models.
|
1105.0611
|
Minimal symmetric Darlington synthesis
|
math.OC cs.SY
|
We consider the symmetric Darlington synthesis of a p x p rational symmetric
Schur function S with the constraint that the extension is of size 2p x 2p.
Under the assumption that S is strictly contractive in at least one point of
the imaginary axis, we determine the minimal McMillan degree of the extension.
In particular, we show that it is generically given by the number of zeros of
odd multiplicity of I-SS*. A constructive characterization of all such
extensions is provided in terms of a symmetric realization of S and of the
outer spectral factor of I-SS*. The authors's motivation for the problem stems
from Surface Acoustic Wave filters where physical constraints on the
electro-acoustic scattering matrix naturally raise this mathematical issue.
|
1105.0649
|
Minimal-memory, non-catastrophic, polynomial-depth quantum convolutional
encoders
|
quant-ph cs.IT math.IT
|
Quantum convolutional coding is a technique for encoding a stream of quantum
information before transmitting it over a noisy quantum channel. Two important
goals in the design of quantum convolutional encoders are to minimize the
memory required by them and to avoid the catastrophic propagation of errors. In
a previous paper, we determined minimal-memory, non-catastrophic,
polynomial-depth encoders for a few exemplary quantum convolutional codes. In
this paper, we elucidate a general technique for finding an encoder of an
arbitrary quantum convolutional code such that the encoder possesses these
desirable properties. We also provide an elementary proof that these encoders
are non-recursive. Finally, we apply our technique to many quantum
convolutional codes from the literature.
|
1105.0650
|
Transition Systems for Model Generators - A Unifying Approach
|
cs.AI
|
A fundamental task for propositional logic is to compute models of
propositional formulas. Programs developed for this task are called
satisfiability solvers. We show that transition systems introduced by
Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers
can be adapted for solvers developed for two other propositional formalisms:
logic programming under the answer-set semantics, and the logic PC(ID). We show
that in each case the task of computing models can be seen as "satisfiability
modulo answer-set programming," where the goal is to find a model of a theory
that also is an answer set of a certain program. The unifying perspective we
develop shows, in particular, that solvers CLASP and MINISATID are closely
related despite being developed for different formalisms, one for answer-set
programming and the latter for the logic PC(ID).
|
1105.0673
|
Mark My Words! Linguistic Style Accommodation in Social Media
|
cs.CL cs.SI
|
The psycholinguistic theory of communication accommodation accounts for the
general observation that participants in conversations tend to converge to one
another's communicative behavior: they coordinate in a variety of dimensions
including choice of words, syntax, utterance length, pitch and gestures. In its
almost forty years of existence, this theory has been empirically supported
exclusively through small-scale or controlled laboratory studies. Here we
address this phenomenon in the context of Twitter conversations. Undoubtedly,
this setting is unlike any other in which accommodation was observed and, thus,
challenging to the theory. Its novelty comes not only from its size, but also
from the non real-time nature of conversations, from the 140 character length
restriction, from the wide variety of social relation types, and from a design
that was initially not geared towards conversation at all. Given such
constraints, it is not clear a priori whether accommodation is robust enough to
occur given the constraints of this new environment. To investigate this, we
develop a probabilistic framework that can model accommodation and measure its
effects. We apply it to a large Twitter conversational dataset specifically
developed for this task. This is the first time the hypothesis of linguistic
style accommodation has been examined (and verified) in a large scale, real
world setting. Furthermore, when investigating concepts such as stylistic
influence and symmetry of accommodation, we discover a complexity of the
phenomenon which was never observed before. We also explore the potential
relation between stylistic influence and network features commonly associated
with social status.
|
1105.0697
|
Uncovering the Temporal Dynamics of Diffusion Networks
|
cs.SI cs.DS cs.IR physics.soc-ph
|
Time plays an essential role in the diffusion of information, influence and
disease over networks. In many cases we only observe when a node copies
information, makes a decision or becomes infected -- but the connectivity,
transmission rates between nodes and transmission sources are unknown.
Inferring the underlying dynamics is of outstanding interest since it enables
forecasting, influencing and retarding infections, broadly construed. To this
end, we model diffusion processes as discrete networks of continuous temporal
processes occurring at different rates. Given cascade data -- observed
infection times of nodes -- we infer the edges of the global diffusion network
and estimate the transmission rates of each edge that best explain the observed
data. The optimization problem is convex. The model naturally (without
heuristics) imposes sparse solutions and requires no parameter tuning. The
problem decouples into a collection of independent smaller problems, thus
scaling easily to networks on the order of hundreds of thousands of nodes.
Experiments on real and synthetic data show that our algorithm both recovers
the edges of diffusion networks and accurately estimates their transmission
rates from cascade data.
|
1105.0703
|
Adaptive Cut Generation Algorithm for Improved Linear Programming
Decoding of Binary Linear Codes
|
cs.IT math.IT
|
Linear programming (LP) decoding approximates maximum-likelihood (ML)
decoding of a linear block code by relaxing the equivalent ML integer
programming (IP) problem into a more easily solved LP problem. The LP problem
is defined by a set of box constraints together with a set of linear
inequalities called "parity inequalities" that are derived from the constraints
represented by the rows of a parity-check matrix of the code and can be added
iteratively and adaptively. In this paper, we first derive a new necessary
condition and a new sufficient condition for a violated parity inequality
constraint, or "cut," at a point in the unit hypercube. Then, we propose a new
and effective algorithm to generate parity inequalities derived from certain
additional redundant parity check (RPC) constraints that can eliminate
pseudocodewords produced by the LP decoder, often significantly improving the
decoder error-rate performance. The cut-generating algorithm is based upon a
specific transformation of an initial parity-check matrix of the linear block
code. We also design two variations of the proposed decoder to make it more
efficient when it is combined with the new cut-generating algorithm. Simulation
results for several low-density parity-check (LDPC) codes demonstrate that the
proposed decoding algorithms significantly narrow the performance gap between
LP decoding and ML decoding.
|
1105.0707
|
Parameterized Complexity of Problems in Coalitional Resource Games
|
cs.AI cs.CC cs.GT
|
Coalition formation is a key topic in multi-agent systems. Coalitions enable
agents to achieve goals that they may not have been able to achieve on their
own. Previous work has shown problems in coalitional games to be
computationally hard. Wooldridge and Dunne (Artificial Intelligence 2006)
studied the classical computational complexity of several natural decision
problems in Coalitional Resource Games (CRG) - games in which each agent is
endowed with a set of resources and coalitions can bring about a set of goals
if they are collectively endowed with the necessary amount of resources. The
input of coalitional resource games bundles together several elements, e.g.,
the agent set Ag, the goal set G, the resource set R, etc. Shrot, Aumann and
Kraus (AAMAS 2009) examine coalition formation problems in the CRG model using
the theory of Parameterized Complexity. Their refined analysis shows that not
all parts of input act equal - some instances of the problem are indeed
tractable while others still remain intractable.
We answer an important question left open by Shrot, Aumann and Kraus by
showing that the SC Problem (checking whether a Coalition is Successful) is
W[1]-hard when parameterized by the size of the coalition. Then via a single
theme of reduction from SC, we are able to show that various problems related
to resources, resource bounds and resource conflicts introduced by Wooldridge
et al are 1. W[1]-hard or co-W[1]-hard when parameterized by the size of the
coalition. 2. para-NP-hard or co-para-NP-hard when parameterized by |R|. 3. FPT
when parameterized by either |G| or |Ag|+|R|.
|
1105.0725
|
Exploiting Correlation in Sparse Signal Recovery Problems: Multiple
Measurement Vectors, Block Sparsity, and Time-Varying Sparsity
|
stat.CO cs.IT math.IT stat.ML
|
A trend in compressed sensing (CS) is to exploit structure for improved
reconstruction performance. In the basic CS model, exploiting the clustering
structure among nonzero elements in the solution vector has drawn much
attention, and many algorithms have been proposed. However, few algorithms
explicitly consider correlation within a cluster. Meanwhile, in the multiple
measurement vector (MMV) model correlation among multiple solution vectors is
largely ignored. Although several recently developed algorithms consider the
exploitation of the correlation, these algorithms need to know a priori the
correlation structure, thus limiting their effectiveness in practical problems.
Recently, we developed a sparse Bayesian learning (SBL) algorithm, namely
T-SBL, and its variants, which adaptively learn the correlation structure and
exploit such correlation information to significantly improve reconstruction
performance. Here we establish their connections to other popular algorithms,
such as the group Lasso, iterative reweighted $\ell_1$ and $\ell_2$ algorithms,
and algorithms for time-varying sparsity. We also provide strategies to improve
these existing algorithms.
|
1105.0728
|
Structured Sparsity via Alternating Direction Methods
|
math.OC cs.AI stat.ML
|
We consider a class of sparse learning problems in high dimensional feature
space regularized by a structured sparsity-inducing norm which incorporates
prior knowledge of the group structure of the features. Such problems often
pose a considerable challenge to optimization algorithms due to the
non-smoothness and non-separability of the regularization term. In this paper,
we focus on two commonly adopted sparsity-inducing regularization terms, the
overlapping Group Lasso penalty $l_1/l_2$-norm and the $l_1/l_\infty$-norm. We
propose a unified framework based on the augmented Lagrangian method, under
which problems with both types of regularization and their variants can be
efficiently solved. As the core building-block of this framework, we develop
new algorithms using an alternating partial-linearization/splitting technique,
and we prove that the accelerated versions of these algorithms require
$O(\frac{1}{\sqrt{\epsilon}})$ iterations to obtain an $\epsilon$-optimal
solution. To demonstrate the efficiency and relevance of our algorithms, we
test them on a collection of data sets and apply them to two real-world
problems to compare the relative merits of the two norms.
|
1105.0745
|
Weak Dynamic Programming for Generalized State Constraints
|
math.OC cs.SY math.AP math.PR q-fin.RM
|
We provide a dynamic programming principle for stochastic optimal control
problems with expectation constraints. A weak formulation, using test functions
and a probabilistic relaxation of the constraint, avoids restrictions related
to a measurable selection but still implies the Hamilton-Jacobi-Bellman
equation in the viscosity sense. We treat open state constraints as a special
case of expectation constraints and prove a comparison theorem to obtain the
equation for closed state constraints.
|
1105.0769
|
Complex-Valued Random Vectors and Channels: Entropy, Divergence, and
Capacity
|
cs.IT math.IT
|
Recent research has demonstrated significant achievable performance gains by
exploiting circularity/non-circularity or propeness/improperness of
complex-valued signals. In this paper, we investigate the influence of these
properties on important information theoretic quantities such as entropy,
divergence, and capacity. We prove two maximum entropy theorems that strengthen
previously known results. The proof of the former theorem is based on the
so-called circular analog of a given complex-valued random vector. Its
introduction is supported by a characterization theorem that employs a minimum
Kullback-Leibler divergence criterion. In the proof of latter theorem, on the
other hand, results about the second-order structure of complex-valued random
vectors are exploited. Furthermore, we address the capacity of multiple-input
multiple-output (MIMO) channels. Regardless of the specific distribution of the
channel parameters (noise vector and channel matrix, if modeled as random), we
show that the capacity-achieving input vector is circular for a broad range of
MIMO channels (including coherent and noncoherent scenarios). Finally, we
investigate the situation of an improper and Gaussian distributed noise vector.
We compute both capacity and capacity-achieving input vector and show that
improperness increases capacity, provided that the complementary covariance
matrix is exploited. Otherwise, a capacity loss occurs, for which we derive an
explicit expression.
|
1105.0785
|
Coupled Graphical Models and Their Thresholds
|
cs.IT cond-mat.stat-mech cs.DM math.IT
|
The excellent performance of convolutional low-density parity-check codes is
the result of the spatial coupling of individual underlying codes across a
window of growing size, but much smaller than the length of the individual
codes. Remarkably, the belief-propagation threshold of the coupled ensemble is
boosted to the maximum-a-posteriori one of the individual system. We
investigate the generality of this phenomenon beyond coding theory: we couple
general graphical models into a one-dimensional chain of large individual
systems. For the later we take the Curie-Weiss, random field Curie-Weiss,
$K$-satisfiability, and $Q$-coloring models. We always find, based on
analytical as well as numerical calculations, that the message passing
thresholds of the coupled systems come very close to the static ones of the
individual models. The remarkable properties of convolutional low-density
parity-check codes are a manifestation of this very general phenomenon.
|
1105.0807
|
Chains of Mean Field Models
|
cs.DM cond-mat.stat-mech cs.IT math.IT
|
We consider a collection of Curie-Weiss (CW) spin systems, possibly with a
random field, each of which is placed along the positions of a one-dimensional
chain. The CW systems are coupled together by a Kac-type interaction in the
longitudinal direction of the chain and by an infinite range interaction in the
direction transverse to the chain. Our motivations for studying this model come
from recent findings in the theory of error correcting codes based on spatially
coupled graphs. We find that, although much simpler than the codes, the model
studied here already displays similar behaviors. We are interested in the van
der Waals curve in a regime where the size of each Curie-Weiss model tends to
infinity, and the length of the chain and range of the Kac interaction are
large but finite. Below the critical temperature, and with appropriate boundary
conditions, there appears a series of equilibrium states representing kink-like
interfaces between the two equilibrium states of the individual system. The van
der Waals curve oscillates periodically around the Maxwell plateau. These
oscillations have a period inversely proportional to the chain length and an
amplitude exponentially small in the range of the interaction; in other words
the spinodal points of the chain model lie exponentially close to the phase
transition threshold. The amplitude of the oscillations is closely related to a
Peierls-Nabarro free energy barrier for the motion of the kink along the chain.
Analogies to similar phenomena and their possible algorithmic significance for
graphical models of interest in coding theory and theoretical computer science
are pointed out.
|
1105.0812
|
Compression of Flow Can Reveal Overlapping-Module Organization in
Networks
|
physics.soc-ph cs.IT cs.SI math.IT
|
To better understand the overlapping modular organization of large networks
with respect to flow, here we introduce the map equation for overlapping
modules. In this information-theoretic framework, we use the correspondence
between compression and regularity detection. The generalized map equation
measures how well we can compress a description of flow in the network when we
partition it into modules with possible overlaps. When we minimize the
generalized map equation over overlapping network partitions, we detect modules
that capture flow and determine which nodes at the boundaries between modules
should be classified in multiple modules and to what degree. With a novel
greedy search algorithm, we find that some networks, for example, the neural
network of C. Elegans, are best described by modules dominated by hard
boundaries, but that others, for example, the sparse European road network,
have a highly overlapping modular organization.
|
1105.0821
|
Considerations and Results in Multimedia and DVB Application Development
on Philips Nexperia Platform
|
cs.CV
|
This paper presents some experiments regarding applications development on
high performance media processors included in Philips Nexperia Family. The
PNX1302 dedicated DVB-T kit used has some limitations. Our work has succeeded
to overcome these limitations and to make possible a general-purpose use of
this kit. For exemplification two typical applications, important both for
multimedia and DVB, are analyzed: MPEG2 video stream decoding and MP3 audio
decoding. These original implementations are compared (in speed, memory
requirements and costs) with Philips Nexperia Library.
|
1105.0826
|
Streaming Multimedia Information Using the Features of the DVB-S Card
|
cs.MM cs.CV
|
This paper presents a study of audio-video streaming using the additional
possibilities of a DVB-S card. The board used for experiments (Technisat
SkyStar 2) is one of the most frequently used cards for this purpose. Using the
main blocks of the board's software support it is possible the implement a
really useful and full functional system for audio-video streaming. The
streaming is possible to be implemented either for decoded MPEG stream or for
transport stream. In this last case it is possible to view not only a program,
but any program from the same multiplex. This allows us to implement
|
1105.0830
|
Maximum Gain Round Trips with Cost Constraints
|
cs.SI physics.soc-ph
|
Searching for optimal ways in a network is an important task in multiple
application areas such as social networks, co-citation graphs or road networks.
In the majority of applications, each edge in a network is associated with a
certain cost and an optimal way minimizes the cost while fulfilling a certain
property, e.g connecting a start and a destination node. In this paper, we want
to extend pure cost networks to so-called cost-gain networks. In this type of
network, each edge is additionally associated with a certain gain. Thus, a way
having a certain cost additionally provides a certain gain. In the following,
we will discuss the problem of finding ways providing maximal gain while
costing less than a certain budget. An application for this type of problem is
the round trip problem of a traveler: Given a certain amount of time, which is
the best round trip traversing the most scenic landscape or visiting the most
important sights? In the following, we distinguish two cases of the problem.
The first does not control any redundant edges and the second allows a more
sophisticated handling of edges occurring more than once. To answer the maximum
round trip queries on a given graph data set, we propose unidirectional and
bidirectional search algorithms. Both types of algorithms are tested for the
use case named above on real world spatial networks.
|
1105.0857
|
Domain Adaptation: Overfitting and Small Sample Statistics
|
cs.LG
|
We study the prevalent problem when a test distribution differs from the
training distribution. We consider a setting where our training set consists of
a small number of sample domains, but where we have many samples in each
domain. Our goal is to generalize to a new domain. For example, we may want to
learn a similarity function using only certain classes of objects, but we
desire that this similarity function be applicable to object classes not
present in our training sample (e.g. we might seek to learn that "dogs are
similar to dogs" even though images of dogs were absent from our training set).
Our theoretical analysis shows that we can select many more features than
domains while avoiding overfitting by utilizing data-dependent variance
properties. We present a greedy feature selection algorithm based on using
T-statistics. Our experiments validate this theory showing that our T-statistic
based greedy feature selection is more robust at avoiding overfitting than the
classical greedy procedure.
|
1105.0881
|
A New Class of Backward Stochastic Partial Differential Equations with
Jumps and Applications
|
math.PR cs.SY math-ph math.AP math.MP math.OC math.ST stat.TH
|
We formulate a new class of stochastic partial differential equations
(SPDEs), named high-order vector backward SPDEs (B-SPDEs) with jumps, which
allow the high-order integral-partial differential operators into both drift
and diffusion coefficients. Under certain type of Lipschitz and linear growth
conditions, we develop a method to prove the existence and uniqueness of
adapted solution to these B-SPDEs with jumps. Comparing with the existing
discussions on conventional backward stochastic (ordinary) differential
equations (BSDEs), we need to handle the differentiability of adapted triplet
solution to the B-SPDEs with jumps, which is a subtle part in justifying our
main results due to the inconsistency of differential orders on two sides of
the B-SPDEs and the partial differential operator appeared in the diffusion
coefficient. In addition, we also address the issue about the B-SPDEs under
certain Markovian random environment and employ a B-SPDE with strongly
nonlinear partial differential operator in the drift coefficient to illustrate
the usage of our main results in finance.
|
1105.0902
|
Modeling Network Evolution Using Graph Motifs
|
stat.ME cs.SI physics.soc-ph stat.CO
|
Network structures are extremely important to the study of political science.
Much of the data in its subfields are naturally represented as networks. This
includes trade, diplomatic and conflict relationships. The social structure of
several organization is also of interest to many researchers, such as the
affiliations of legislators or the relationships among terrorist. A key aspect
of studying social networks is understanding the evolutionary dynamics and the
mechanism by which these structures grow and change over time. While current
methods are well suited to describe static features of networks, they are less
capable of specifying models of change and simulating network evolution. In the
following paper I present a new method for modeling network growth and
evolution. This method relies on graph motifs to generate simulated network
data with particular structural characteristic. This technique departs notably
from current methods both in form and function. Rather than a closed-form
model, or stochastic implementation from a single class of graphs, the proposed
"graph motif model" provides a framework for building flexible and complex
models of network evolution. The paper proceeds as follows: first a brief
review of the current literature on network modeling is provided to place the
graph motif model in context. Next, the graph motif model is introduced, and a
simple example is provided. As a proof of concept, three classic random graph
models are recovered using the graph motif modeling method: the Erdos-Renyi
binomial random graph, the Watts-Strogatz "small world" model, and the
Barabasi-Albert preferential attachment model. In the final section I discuss
the results of these simulations and subsequent advantage and disadvantages
presented by using this technique to model social networks.
|
1105.0903
|
A Month in the Life of Groupon
|
cs.SI
|
Groupon has become the latest Internet sensation, providing daily deals to
customers in the form of discount offers for restaurants, ticketed events,
appliances, services, and other items. We undertake a study of the economics of
daily deals on the web, based on a dataset we compiled by monitoring Groupon
over several weeks. We use our dataset to characterize Groupon deal purchases,
and to glean insights about Groupon's operational strategy. Our focus is on
purchase incentives. For the primary purchase incentive, price, our regression
model indicates that demand for coupons is relatively inelastic, allowing room
for price-based revenue optimization. More interestingly, mining our dataset,
we find evidence that Groupon customers are sensitive to other, "soft",
incentives, e.g., deal scheduling and duration, deal featuring, and limited
inventory. Our analysis points to the importance of considering incentives
other than price in optimizing deal sites and similar systems.
|
1105.0934
|
Stochastic programs without duality gaps
|
math.OC cs.SY q-fin.PR
|
This paper studies dynamic stochastic optimization problems parametrized by a
random variable. Such problems arise in many applications in operations
research and mathematical finance. We give sufficient conditions for the
existence of solutions and the absence of a duality gap. Our proof uses
extended dynamic programming equations, whose validity is established under new
relaxed conditions that generalize certain no-arbitrage conditions from
mathematical finance.
|
1105.0972
|
Rapid Feature Learning with Stacked Linear Denoisers
|
cs.LG cs.AI stat.ML
|
We investigate unsupervised pre-training of deep architectures as feature
generators for "shallow" classifiers. Stacked Denoising Autoencoders (SdA),
when used as feature pre-processing tools for SVM classification, can lead to
significant improvements in accuracy - however, at the price of a substantial
increase in computational cost. In this paper we create a simple algorithm
which mimics the layer by layer training of SdAs. However, in contrast to SdAs,
our algorithm requires no training through gradient descent as the parameters
can be computed in closed-form. It can be implemented in less than 20 lines of
MATLABTMand reduces the computation time from several hours to mere seconds. We
show that our feature transformation reliably improves the results of SVM
classification significantly on all our data sets - often outperforming SdAs
and even deep neural networks in three out of four deep learning benchmarks.
|
1105.0974
|
GANC: Greedy Agglomerative Normalized Cut
|
cs.AI
|
This paper describes a graph clustering algorithm that aims to minimize the
normalized cut criterion and has a model order selection procedure. The
performance of the proposed algorithm is comparable to spectral approaches in
terms of minimizing normalized cut. However, unlike spectral approaches, the
proposed algorithm scales to graphs with millions of nodes and edges. The
algorithm consists of three components that are processed sequentially: a
greedy agglomerative hierarchical clustering procedure, model order selection,
and a local refinement.
For a graph of n nodes and O(n) edges, the computational complexity of the
algorithm is O(n log^2 n), a major improvement over the O(n^3) complexity of
spectral methods. Experiments are performed on real and synthetic networks to
demonstrate the scalability of the proposed approach, the effectiveness of the
model order selection procedure, and the performance of the proposed algorithm
in terms of minimizing the normalized cut metric.
|
1105.0985
|
On the controllability of the Vlasov-Poisson system in the presence of
external force fields
|
math.AP cs.SY math.OC
|
In this work, we are interested in the controllability of Vlasov-Poisson
systems in the presence of an external force field (namely a bounded force
field or a magnetic field), by means of a local interior control. We are able
to extend the results of [7], where the only present force was the
self-consistent electric field.
|
1105.1028
|
Patient-Specific Prosthetic Fingers by Remote Collaboration - A Case
Study
|
cs.RO physics.med-ph
|
The concealment of amputation through prosthesis usage can shield an amputee
from social stigma and help improve the emotional healing process especially at
the early stages of hand or finger loss. However, the traditional techniques in
prosthesis fabrication defy this as the patients need numerous visits to the
clinics for measurements, fitting and follow-ups. This paper presents a method
for constructing a prosthetic finger through online collaboration with the
designer. The main input from the amputee comes from the Computer Tomography
(CT) data in the region of the affected and the non-affected fingers. These
data are sent over the internet and the prosthesis is constructed using
visualization, computer-aided design and manufacturing tools. The finished
product is then shipped to the patient. A case study with a single patient
having an amputated ring finger at the proximal interphalangeal joint shows
that the proposed method has a potential to address the patient's psychosocial
concerns and minimize the exposure of the finger loss to the public.
|
1105.1033
|
Adaptively Learning the Crowd Kernel
|
cs.LG
|
We introduce an algorithm that, given n objects, learns a similarity matrix
over all n^2 pairs, from crowdsourced data alone. The algorithm samples
responses to adaptively chosen triplet-based relative-similarity queries. Each
query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen
to be maximally informative given the preceding responses. The output is an
embedding of the objects into Euclidean space (like MDS); we refer to this as
the "crowd kernel." SVMs reveal that the crowd kernel captures prominent and
subtle features across a number of domains, such as "is striped" among neckties
and "vowel vs. consonant" among letters.
|
1105.1058
|
Formal vs self-organised knowledge systems: a network approach
|
physics.soc-ph cs.SI physics.data-an
|
In this work we consider the topological analysis of symbolic formal systems
in the framework of network theory. In particular we analyse the network
extracted by Principia Mathematica of B. Russell and A.N. Whitehead, where the
vertices are the statements and two statements are connected with a directed
link if one statement is used to demonstrate the other one. We compare the
obtained network with other directed acyclic graphs, such as a scientific
citation network and a stochastic model. We also introduce a novel topological
ordering for directed acyclic graphs and we discuss its properties in respect
to the classical one. The main result is the observation that formal systems of
knowledge topologically behave similarly to self-organised systems.
|
1105.1062
|
Universal Emergence of PageRank
|
cs.IR cond-mat.stat-mech nlin.CD
|
The PageRank algorithm enables to rank the nodes of a network through a
specific eigenvector of the Google matrix, using a damping parameter $\alpha
\in ]0,1[$. Using extensive numerical simulations of large web networks, with a
special accent on British University networks, we determine numerically and
analytically the universal features of PageRank vector at its emergence when
$\alpha \rightarrow 1$. The whole network can be divided into a core part and a
group of invariant subspaces. For $ \alpha \rightarrow 1$ the PageRank
converges to a universal power law distribution on the invariant subspaces
whose size distribution also follows a universal power law. The convergence of
PageRank at $ \alpha \rightarrow 1$ is controlled by eigenvalues of the core
part of the Google matrix which are extremely close to unity leading to large
relaxation times as for example in spin glasses.
|
1105.1072
|
English-Lithuanian-English Machine Translation lexicon and engine:
current state and future work
|
cs.CL
|
This article overviews the current state of the English-Lithuanian-English
machine translation system. The first part of the article describes the
problems that system poses today and what actions will be taken to solve them
in the future. The second part of the article tackles the main issue of the
translation process. Article briefly overviews the word sense disambiguation
for MT technique using Google.
|
1105.1117
|
Collective Animal Behavior from Bayesian Estimation and Probability
Matching
|
q-bio.QM cs.SI nlin.AO physics.data-an physics.soc-ph q-bio.NC
|
Animals living in groups make movement decisions that depend, among other
factors, on social interactions with other group members. Our present
understanding of social rules in animal collectives is mainly based on
empirical fits to observations, with less emphasis in obtaining
first-principles approaches that allow their derivation. Here we show that
patterns of collective decisions can be derived from the basic ability of
animals to make probabilistic estimations in the presence of uncertainty. We
build a decision-making model with two stages: Bayesian estimation and
probabilistic matching. In the first stage, each animal makes a Bayesian
estimation of which behavior is best to perform taking into account personal
information about the environment and social information collected by observing
the behaviors of other animals. In the probability matching stage, each animal
chooses a behavior with a probability equal to the Bayesian-estimated
probability that this behavior is the most appropriate one. This model derives
very simple rules of interaction in animal collectives that depend only on two
types of reliability parameters, one that each animal assigns to the other
animals and another given by the quality of the non-social information. We test
our model by obtaining theoretically a rich set of observed collective patterns
of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling
fish species. The quantitative link shown between probabilistic estimation and
collective rules of behavior allows a better contact with other fields such as
foraging, mate selection, neurobiology and psychology, and gives predictions
for experiments directly testing the relationship between estimation and
collective behavior.
|
1105.1178
|
Interpreting Graph Cuts as a Max-Product Algorithm
|
cs.LG cs.DS stat.ML
|
The maximum a posteriori (MAP) configuration of binary variable models with
submodular graph-structured energy functions can be found efficiently and
exactly by graph cuts. Max-product belief propagation (MP) has been shown to be
suboptimal on this class of energy functions by a canonical counterexample
where MP converges to a suboptimal fixed point (Kulesza & Pereira, 2008).
In this work, we show that under a particular scheduling and damping scheme,
MP is equivalent to graph cuts, and thus optimal. We explain the apparent
contradiction by showing that with proper scheduling and damping, MP always
converges to an optimal fixed point. Thus, the canonical counterexample only
shows the suboptimality of MP with a particular suboptimal choice of schedule
and damping. With proper choices, MP is optimal.
|
1105.1186
|
Sampling-based Algorithms for Optimal Motion Planning
|
cs.RO
|
During the last decade, sampling-based path planning algorithms, such as
Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have
been shown to work well in practice and possess theoretical guarantees such as
probabilistic completeness. However, little effort has been devoted to the
formal analysis of the quality of the solution returned by such algorithms,
e.g., as a function of the number of samples. The purpose of this paper is to
fill this gap, by rigorously analyzing the asymptotic behavior of the cost of
the solution returned by stochastic sampling-based algorithms as the number of
samples increases. A number of negative results are provided, characterizing
existing algorithms, e.g., showing that, under mild technical conditions, the
cost of the solution returned by broadly used sampling-based algorithms
converges almost surely to a non-optimal value. The main contribution of the
paper is the introduction of new algorithms, namely, PRM* and RRT*, which are
provably asymptotically optimal, i.e., such that the cost of the returned
solution converges almost surely to the optimum. Moreover, it is shown that the
computational complexity of the new algorithms is within a constant factor of
that of their probabilistically complete (but not asymptotically optimal)
counterparts. The analysis in this paper hinges on novel connections between
stochastic sampling-based path planning algorithms and the theory of random
geometric graphs.
|
1105.1187
|
Error Probability Bounds for Balanced Binary Relay Trees
|
cs.IT math.IT stat.AP
|
We study the detection error probability associated with a balanced binary
relay tree, where the leaves of the tree correspond to $N$ identical and
independent detectors. The root of the tree represents a fusion center that
makes the overall detection decision. Each of the other nodes in the tree are
relay nodes that combine two binary messages to form a single output binary
message. In this way, the information from the detectors is aggregated into the
fusion center via the intermediate relay nodes. In this context, we describe
the evolution of Type I and Type II error probabilities of the binary data as
it propagates from the leaves towards the root. Tight upper and lower bounds
for the total error probability at the fusion center as functions of $N$ are
derived. These characterize how fast the total error probability converges to 0
with respect to $N$, even if the individual sensors have error probabilities
that converge to 1/2.
|
1105.1226
|
Multilingual lexicon design tool and database management system for MT
|
cs.CL
|
The paper presents the design and development of English-Lithuanian-English
dictionarylexicon tool and lexicon database management system for MT. The
system is oriented to support two main requirements: to be open to the user and
to describe much more attributes of speech parts as a regular dictionary that
are required for the MT. Programming language Java and database management
system MySql is used to implement the designing tool and lexicon database
respectively. This solution allows easily deploying this system in the
Internet. The system is able to run on various OS such as: Windows, Linux, Mac
and other OS where Java Virtual Machine is supported. Since the modern lexicon
database managing system is used, it is not a problem accessing the same
database for several users.
|
1105.1242
|
Optimal Computation of Symmetric Boolean Functions in Collocated
Networks
|
cs.IT cs.DC cs.NI math.IT
|
We consider collocated wireless sensor networks, where each node has a
Boolean measurement and the goal is to compute a given Boolean function of
these measurements. We first consider the worst case setting and study optimal
block computation strategies for computing symmetric Boolean functions. We
study three classes of functions: threshold functions, delta functions and
interval functions. We provide exactly optimal strategies for the first two
classes, and a scaling law order-optimal strategy with optimal preconstant for
interval functions. We also extend the results to the case of integer
measurements and certain integer-valued functions. We use lower bounds from
communication complexity theory, and provide an achievable scheme using
information theoretic tools.
Next, we consider the case where nodes measurements are random and drawn from
independent Bernoulli distributions. We address the problem of optimal function
computation so as to minimize the expected total number of bits that are
transmitted. In the case of computing a single instance of a Boolean threshold
function, we show the surprising result that the optimal order of transmissions
depends in an extremely simple way on the values of previously transmitted
bits, and the ordering of the marginal probabilities of the Boolean variables.
The approach presented can be generalized to the case where each node has a
block of measurements, though the resulting problem is somewhat harder, and we
conjecture the optimal strategy. We further show how to generalize to a pulse
model of communication. One can also consider the related problem of
approximate computation given a fixed number of bits. In this case, the optimal
strategy is significantly different, and lacks an elegant characterization.
However, for the special case of the parity function, we show that the greedy
strategy is optimal.
|
1105.1246
|
High-SNR Capacity of Wireless Communication Channels in the Noncoherent
Setting: A Primer
|
cs.IT math.IT
|
This paper, mostly tutorial in nature, deals with the problem of
characterizing the capacity of fading channels in the high signal-to-noise
ratio (SNR) regime. We focus on the practically relevant noncoherent setting,
where neither transmitter nor receiver know the channel realizations, but both
are aware of the channel law. We present, in an intuitive and accessible form,
two tools, first proposed by Lapidoth & Moser (2003), of fundamental importance
to high-SNR capacity analysis: the duality approach and the escape-to-infinity
property of capacity-achieving distributions. Furthermore, we apply these tools
to refine some of the results that appeared previously in the literature and to
simplify the corresponding proofs.
|
1105.1247
|
Machine-Part cell formation through visual decipherable clustering of
Self Organizing Map
|
cs.AI
|
Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.
|
1105.1261
|
Pruned Continuous Haar Transform of 2D Polygonal Patterns with
Application to VLSI Layouts
|
cs.CE cs.CG cs.DS
|
We introduce an algorithm for the efficient computation of the continuous
Haar transform of 2D patterns that can be described by polygons. These patterns
are ubiquitous in VLSI processes where they are used to describe design and
mask layouts. There, speed is of paramount importance due to the magnitude of
the problems to be solved and hence very fast algorithms are needed. We show
that by techniques borrowed from computational geometry we are not only able to
compute the continuous Haar transform directly, but also to do it quickly. This
is achieved by massively pruning the transform tree and thus dramatically
decreasing the computational load when the number of vertices is small, as is
the case for VLSI layouts. We call this new algorithm the pruned continuous
Haar transform. We implement this algorithm and show that for patterns found in
VLSI layouts the proposed algorithm was in the worst case as fast as its
discrete counterpart and up to 12 times faster.
|
1105.1279
|
Wireless MIMO Switching with Network Coding
|
cs.IT cs.NI math.IT
|
In a generic switching problem, a switching pattern consists of a one-to-one
mapping from a set of inputs to a set of outputs (i.e., a permutation). We
propose and investigate a wireless switching framework in which a multi-antenna
relay is responsible for switching traffic among a set of $N$ stations. We
refer to such a relay as a MIMO switch. With beamforming and linear detection,
the MIMO switch controls which stations are connected to which other stations.
Each beamforming matrix realizes a permutation pattern among the stations. We
refer to the corresponding permutation matrix as a switch matrix. By scheduling
a set of different switch matrices, full connectivity among the stations can be
established. In this paper, we focus on "fair switching" in which equal amounts
of traffic are to be delivered for all $N(N-1)$ ordered pairs of stations. In
particular, we investigate how the system throughput can be maximized. In
general, for large $N$ the number of possible switch matrices (i.e.,
permutations) is huge, making the scheduling problem combinatorially
challenging. We show that for the cases of N=4 and 5, only a subset of $N-1$
switch matrices need to be considered in the scheduling problem to achieve good
throughput. We conjecture that this will be the case for large $N$ as well.
This conjecture, if valid, implies that for practical purposes, fair-switching
scheduling is not an intractable problem. We also investigate MIMO switching
with physical-layer network coding in this paper. We find that it can improve
throughput appreciably.
|
1105.1302
|
A Modified Cross Correlation Algorithm for Reference-free Image
Alignment of Non-Circular Projections in Single-Particle Electron Microscopy
|
q-bio.QM cs.CV math.NA
|
In this paper we propose a modified cross correlation method to align images
from the same class in single-particle electron microscopy of highly
non-spherical structures. In this new method, First we coarsely align
projection images, and then re-align the resulting images using the cross
correlation (CC) method. The coarse alignment is obtained by matching the
centers of mass and the principal axes of the images. The distribution of
misalignment in this coarse alignment can be quantified based on the
statistical properties of the additive background noise. As a consequence, the
search space for re-alignment in the cross correlation method can be reduced to
achieve better alignment. In order to overcome problems associated with false
peaks in the cross correlations function, we use artificially blurred images
for the early stage of the iterative cross correlation method and segment the
intermediate class average from every iteration step. These two additional
manipulations combined with the reduced search space size in the cross
correlation method yield better alignments for low signal-to-noise ratio images
than both classical cross correlation and maximum likelihood(ML) methods.
|
1105.1306
|
Excess entropy in natural language: present state and perspectives
|
cs.IT cs.CL math.IT
|
We review recent progress in understanding the meaning of mutual information
in natural language. Let us define words in a text as strings that occur
sufficiently often. In a few previous papers, we have shown that a power-law
distribution for so defined words (a.k.a. Herdan's law) is obeyed if there is a
similar power-law growth of (algorithmic) mutual information between adjacent
portions of texts of increasing length. Moreover, the power-law growth of
information holds if texts describe a complicated infinite (algorithmically)
random object in a highly repetitive way, according to an analogous power-law
distribution. The described object may be immutable (like a mathematical or
physical constant) or may evolve slowly in time (like cultural heritage). Here
we reflect on the respective mathematical results in a less technical way. We
also discuss feasibility of deciding to what extent these results apply to the
actual human communication.
|
1105.1361
|
Data-Efficient Quickest Change Detection with On-Off Observation Control
|
math.ST cs.IT math.IT math.OC stat.TH
|
In this paper we extend the Shiryaev's quickest change detection formulation
by also accounting for the cost of observations used before the change point.
The observation cost is captured through the average number of observations
used in the detection process before the change occurs. The objective is to
select an on-off observation control policy, that decides whether or not to
take a given observation, along with the stopping time at which the change is
declared, so as to minimize the average detection delay, subject to constraints
on both the probability of false alarm and the observation cost. By considering
a Lagrangian relaxation of the constraint problem, and using dynamic
programming arguments, we obtain an \textit{a posteriori} probability based
two-threshold algorithm that is a generalized version of the classical Shiryaev
algorithm. We provide an asymptotic analysis of the two-threshold algorithm and
show that the algorithm is asymptotically optimal, i.e., the performance of the
two-threshold algorithm approaches that of the Shiryaev algorithm, for a fixed
observation cost, as the probability of false alarm goes to zero. We also show,
using simulations, that the two-threshold algorithm has good observation
cost-delay trade-off curves, and provides significant reduction in observation
cost as compared to the naive approach of fractional sampling, where samples
are skipped randomly. Our analysis reveals that, for practical choices of
constraints, the two thresholds can be set independent of each other: one based
on the constraint of false alarm and another based on the observation cost
constraint alone.
|
1105.1363
|
Heavy traffic limit theorems for a queue with Poisson ON/OFF long-range
dependent sources and general service time distribution
|
math.PR cs.IT math.IT math.ST stat.TH
|
In Internet environment, traffic flow to a link is typically modeled by
superposition of ON/OFF based sources. During each ON-period for a particular
source, packets arrive according to a Poisson process and packet sizes (hence
service times) can be generally distributed. In this paper, we establish heavy
traffic limit theorems to provide suitable approximations for the system under
first-in first-out (FIFO) and work conserving service discipline, which state
that, when the lengths of both ON- and OFF-periods are lightly tailed, the
sequences of the scaled queue length and workload processes converge weakly to
short-range dependent reflecting Gaussian processes, and when the lengths of
ON- and/or OFF periods are heavily tailed with infinite variance, the sequences
converge weakly to either reflecting fractional Brownian motions (FBMs) or
certain type of long-range dependent reflecting Gaussian processes depending on
the choice of scaling as the number of superposed sources tends to infinity.
Moreover, the sequences exhibit a state space collapse-like property when the
number of sources is large enough, which is a kind of extension of the
well-known Little's law for M/M/1 queueing system. Theory to justify the
approximations is based on appropriate heavy traffic conditions which
essentially mean that the service rate closely approaches the arrival rate when
the number of input sources tends to infinity.
|
1105.1364
|
Achieving Data Privacy through Secrecy Views and Null-Based Virtual
Updates
|
cs.DB cs.LO
|
There may be sensitive information in a relational database, and we might
want to keep it hidden from a user or group thereof. In this work, sensitive
data is characterized as the contents of a set of secrecy views. For a user
without permission to access that sensitive data, the database instance he
queries is updated to make the contents of the views empty or contain only
tuples with null values. In particular, if this user poses a query about any of
these views, no meaningful information is returned. Since the database is not
expected to be physically changed to produce this result, the updates are only
virtual. And also minimal in a precise way. These minimal updates are reflected
in the secrecy view contents, and also in the fact that query answers, while
being privacy preserving, are also maximally informative. Virtual updates are
based on the use of null values as used in the SQL standard. We provide the
semantics of secrecy views and the virtual updates. The different ways in which
the underlying database is virtually updated are specified as the models of a
logic program with stable model semantics. The program becomes the basis for
the computation of the "secret answers" to queries, i.e. those that do not
reveal the sensitive information.
|
1105.1386
|
Self-organized adaptation of a simple neural circuit enables complex
robot behaviour
|
cond-mat.dis-nn cs.AI cs.RO nlin.CD q-bio.NC
|
Controlling sensori-motor systems in higher animals or complex robots is a
challenging combinatorial problem, because many sensory signals need to be
simultaneously coordinated into a broad behavioural spectrum. To rapidly
interact with the environment, this control needs to be fast and adaptive.
Current robotic solutions operate with limited autonomy and are mostly
restricted to few behavioural patterns. Here we introduce chaos control as a
new strategy to generate complex behaviour of an autonomous robot. In the
presented system, 18 sensors drive 18 motors via a simple neural control
circuit, thereby generating 11 basic behavioural patterns (e.g., orienting,
taxis, self-protection, various gaits) and their combinations. The control
signal quickly and reversibly adapts to new situations and additionally enables
learning and synaptic long-term storage of behaviourally useful motor
responses. Thus, such neural control provides a powerful yet simple way to
self-organize versatile behaviours in autonomous agents with many degrees of
freedom.
|
1105.1406
|
Comparison Latent Semantic and WordNet Approach for Semantic Similarity
Calculation
|
cs.IR
|
Information exchange among many sources in Internet is more autonomous,
dynamic and free. The situation drive difference view of concepts among
sources. For example, word 'bank' has meaning as economic institution for
economy domain, but for ecology domain it will be defined as slope of river or
lake. In this aper, we will evaluate latent semantic and WordNet approach to
calculate semantic similarity. The evaluation will be run for some concepts
from different domain with reference by expert or human. Result of the
evaluation can provide a contribution for mapping of concept, query rewriting,
interoperability, etc.
|
1105.1421
|
An Empirical Investigation on Important Subgraphs in
Cooperation-Competition networks
|
physics.soc-ph cs.SI
|
Subgraphs are very important for understanding structure and function of
complex networks. Dyad and triad are the elementary subgraphs. We focus on the
distribution of their act degree defined as the number of activities, events or
organizations they join, which indicates the importance of the subgraphs. The
empirical studies show that, in a lot of real world systems, the dyad or triad
act degree distributions follow "shifted power law" (SPL), where {\alpha} and
{\gamma} are constants. We defined a "heterogeneity index", H, to describe how
it is uneven and analytically deduced the correlation between H and {\alpha}
and {\gamma}. This manuscript, which shows the details of the empirical
studies, serves as an online supplement of a paper submitted to a journal.
|
1105.1436
|
Solving Rubik's Cube Using SAT Solvers
|
cs.AI
|
Rubik's Cube is an easily-understood puzzle, which is originally called the
"magic cube". It is a well-known planning problem, which has been studied for a
long time. Yet many simple properties remain unknown. This paper studies
whether modern SAT solvers are applicable to this puzzle. To our best
knowledge, we are the first to translate Rubik's Cube to a SAT problem. To
reduce the number of variables and clauses needed for the encoding, we replace
a naive approach of 6 Boolean variables to represent each color on each facelet
with a new approach of 3 or 2 Boolean variables. In order to be able to solve
quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer
encoding of 18-subtype turns based on 6-type turns. To speed up the solving
further, we encode some properties of two-phase algorithm as an additional
constraint, and restrict some move sequences by adding some constraint clauses.
Using only efficient encoding cannot solve this puzzle. For this reason, we
improve the existing SAT solvers, and develop a new SAT solver based on
PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver
replaces the lookahead solving strategy with an ALO (\emph{at-least-one})
solving strategy, and decomposes the original problem into sub-problems. Each
sub-problem is solved by PrecoSAT. The empirical results demonstrate both our
SAT translation and new solving technique are efficient. Without the efficient
SAT encoding and the new solving technique, Rubik's Cube will not be able to be
solved still by any SAT solver. Using the improved SAT solver, we can find
always a solution of length 20 in a reasonable time. Although our solver is
slower than Kociemba's algorithm using lookup tables, but does not require a
huge lookup table.
|
1105.1472
|
The regularized blind tip reconstruction algorithm as a scanning probe
microscopy tip metrology method
|
physics.ins-det cs.CE
|
The problem of an accurate tip radius and shape characterization is very
important for determination of surface mechanical and chemical properties on
the basis of the scanning probe microscopy measurements. We think that the most
favorable methods for this purpose are blind tip reconstruction methods, since
they do not need any calibrated characterizers and might be performed on an
ordinary SPM setup. As in many other inverse problems also in case of these
methods the stability of the solution in presence of vibrational and electronic
noise needs application of so called regularization techniques. In this paper
the novel regularization technique (Regularized Blind Tip Reconstruction -
RBTR) for blind tip reconstruction algorithm is presented. It improves the
quality of the solution in presence of isotropic and anisotropic noise. The
superiority of our approach is proved on the basis of computer simulations and
analysis of images of the Budget Sensors TipCheck calibration standard. In case
of characterization of real AFM probes as a reference method the high
resolution scanning electron microscopy was chosen and we obtain good
qualitative correspondence of both methods.
|
1105.1482
|
Efficient Soft-Input Soft-Output Tree Detection Via an Improved Path
Metric
|
cs.IT math.IT
|
Tree detection techniques are often used to reduce the complexity of a
posteriori probability (APP) detection in high dimensional multi-antenna
wireless communication systems. In this paper, we introduce an efficient
soft-input soft-output tree detection algorithm that employs a new type of
look-ahead path metric in the computation of its branch pruning (or sorting).
While conventional path metrics depend only on symbols on a visited path, the
new path metric accounts for unvisited parts of the tree in advance through an
unconstrained linear estimator and adds a bias term that reflects the
contribution of as-yet undecided symbols. By applying the linear estimate-based
look-ahead path metric to an M-algorithm that selects the best M paths for each
level of the tree we develop a new soft-input soft-output tree detector, called
an improved soft-input soft-output M-algorithm (ISS-MA). Based on an analysis
of the probability of correct path loss, we show that the improved path metric
offers substantial performance gain over the conventional path metric. We also
demonstrate through simulations that the ISS-MA provides a better
performance-complexity trade-off than existing soft-input soft-output detection
algorithms.
|
1105.1488
|
The structure of optimal portfolio strategies for continuous time
markets
|
q-fin.PM cs.SY math.OC math.PR
|
The paper studies problem of continuous time optimal portfolio selection for
a incom- plete market diffusion model. It is shown that, under some mild
conditions, near optimal strategies for investors with different performance
criteria can be constructed using a limited number of fixed processes (mutual
funds), for a market with a larger number of available risky stocks. In other
words, a dimension reduction is achieved via a relaxed version of the Mutual
Fund Theorem.
|
1105.1505
|
Generating Dependent Random Variables Over Networks
|
cs.IT math.IT
|
In this paper we study the problem of generation of dependent random
variables, known as the "coordination capacity" [4,5], in multiterminal
networks. In this model $m$ nodes of the network are observing i.i.d.
repetitions of $X^{(1)}$, $X^{(2)}$,..., $X^{(m)}$ distributed according to
$q(x^{(1)},...,x^{(m)})$. Given a joint distribution
$q(x^{(1)},...,x^{(m)},y^{(1)},...,y^{(m)})$, the final goal of the $i^{th}$
node is to construct the i.i.d. copies of $Y^{(i)}$ after the communication
over the network where $X^{(1)}$, $X^{(2)}$,..., $X^{(m)}, Y^{(1)}$,
$Y^{(2)}$,..., $Y^{(m)}$ are jointly distributed according to
$q(x^{(1)},...,x^{(m)},y^{(1)},...,y^{(m)})$. To do this, the nodes can
exchange messages over the network at rates not exceeding the capacity
constraints of the links. This problem is difficult to solve even for the
special case of two nodes. In this paper we prove new inner and outer bounds on
the achievable rates for networks with two nodes.
|
1105.1520
|
Linear Analog Codes: The Good and The Bad
|
cs.IT math.IT
|
This paper studies the theory of linear analog error correction coding. Since
classical concepts of minimum Hamming distance and minimum Euclidean distance
fail in the analog context, a new metric, termed the "minimum (squared
Euclidean) distance ratio," is defined. It is shown that linear analog codes
that achieve the largest possible value of minimum distance ratio also achieve
the smallest possible mean square error (MSE). Based on this achievability, a
concept of "maximum distance ratio expansible (MDRE)" is established, in a
spirit similar to maximum distance separable (MDS). Existing codes are
evaluated, and it is shown that MDRE and MDS can be simultaneously achieved
through careful design.
|
1105.1534
|
Taking the redpill: Artificial Evolution in native x86 systems
|
cs.NE q-bio.PE
|
In analogon to successful artificial evolution simulations as Tierra or
avida, this text presents a way to perform artificial evolution in a native x86
system. The implementation of the artificial chemistry and first results of
statistical experiments are presented.
|
1105.1562
|
A New Class of MDS Erasure Codes Based on Graphs
|
cs.IT math.IT
|
Maximum distance separable (MDS) array codes are XOR-based optimal erasure
codes that are particularly suitable for use in disk arrays. This paper
develops an innovative method to build MDS array codes from an elegant class of
nested graphs, termed \textit{complete-graph-of-rings (CGR)}. We discuss a
systematic and concrete way to transfer these graphs to array codes, unveil an
interesting relation between the proposed map and the renowned perfect
1-factorization, and show that the proposed CGR codes subsume B-codes as their
"contracted" codes. These new codes, termed \textit{CGR codes}, and their dual
codes are simple to describe, and require minimal encoding and decoding
complexity.
|
1105.1564
|
Complex Adaptive Digital EcoSystems
|
cs.MA
|
We investigate an abstract conceptualisation of DigitalEcosystems from a
computer science perspective. We then provide a conceptual framework for the
cross pollination of ideas, concepts and understanding between different
classes of ecosystems through the universally applicable principles of Complex
Adaptive Systems (CAS) modelling. A framework to assist the cross-disciplinary
collaboration of research into Digital Ecosystems, including Digital
BusinessEcosystems (DBEs) and Digital Knowledge Ecosystems (DKEs). So, we have
defined the key steps towards a theoretical framework for Digital Ecosystems,
that is compatible with the diverse theoretical views prevalent. Therefore, a
theoretical edifice that can unify the diverse efforts within Digital
Ecosystems research.
|
1105.1574
|
A Dynamic Programming Approach to Finite-horizon Coherent Quantum LQG
Control
|
quant-ph cs.SY math.DS math.OC
|
The paper is concerned with the coherent quantum Linear Quadratic Gaussian
(CQLQG) control problem for time-varying quantum plants governed by linear
quantum stochastic differential equations over a bounded time interval. A
controller is sought among quantum linear systems satisfying physical
realizability (PR) conditions. The latter describe the dynamic equivalence of
the system to an open quantum harmonic oscillator and relate its state-space
matrices to the free Hamiltonian, coupling and scattering operators of the
oscillator. Using the Hamiltonian parameterization of PR controllers, the CQLQG
problem is recast into an optimal control problem for a deterministic system
governed by a differential Lyapunov equation. The state of this subsidiary
system is the symmetric part of the quantum covariance matrix of the
plant-controller state vector. The resulting covariance control problem is
treated using dynamic programming and Pontryagin's minimum principle. The
associated Hamilton-Jacobi-Bellman equation for the minimum cost function
involves Frechet differentiation with respect to matrix-valued variables. The
gain matrices of the CQLQG optimal controller are shown to satisfy a
quasi-separation property as a weaker quantum counterpart of the
filtering/control decomposition of classical LQG controllers.
|
1105.1601
|
Code Reverse Engineering problem for Identification Codes
|
cs.CR cs.IT math.IT
|
At ITW'10, Bringer et al. suggest to strengthen their previous identification
protocol by extending the Code Reverse Engineering (CRE) problem to
identification codes. We first extend security results by Tillich et al. on
this very problem. We then prove the security of this protocol using
information theoretical arguments.
|
1105.1641
|
Neural network to identify individuals at health risk
|
cs.NE
|
The risk of diseases such as heart attack and high blood pressure could be
reduced by adequate physical activity. However, even though majority of general
population claims to perform some physical exercise, only a minority exercises
enough to keep a healthy living style. Thus, physical inactivity has become one
of the major concerns of public health in the past decade. Research shows that
the highest decrease in physical activity is noticed from high school to
college. Thus, it is of great importance to quickly identify college students
at health risk due to physical inactivity. Research also shows that the level
of physical activity of an individual is highly correlated to demographic
features such as race and gender, as well as self motivation and support from
family and friends. This information could be collected from each student via a
20 minute questionnaire, but the time needed to distribute and analyze each
questionnaire is infeasible on a collegiate campus. Thus, we propose an
automatic identifier of students at risk, so that these students could easier
be targeted by collegiate campuses and physical activity promotion departments.
We present in this paper preliminary results of a supervised backpropagation
multilayer neural network for classifying students into at-risk or not at-risk
group.
|
1105.1651
|
Combined local search strategy for learning in networks of binary
synapses
|
cond-mat.dis-nn cond-mat.stat-mech cs.IT math.IT
|
Learning in networks of binary synapses is known to be an NP-complete
problem. A combined stochastic local search strategy in the synaptic weight
space is constructed to further improve the learning performance of a single
random walker. We apply two correlated random walkers guided by their Hamming
distance and associated energy costs (the number of unlearned patterns) to
learn a same large set of patterns. Each walker first learns a small part of
the whole pattern set (partially different for both walkers but with the same
amount of patterns) and then both walkers explore their respective weight
spaces cooperatively to find a solution to classify the whole pattern set
correctly. The desired solutions locate at the common parts of weight spaces
explored by these two walkers. The efficiency of this combined strategy is
supported by our extensive numerical simulations and the typical Hamming
distance as well as energy cost is estimated by an annealed computation.
|
1105.1658
|
Secure Multiterminal Source Coding with Side Information at the
Eavesdropper
|
cs.IT math.IT
|
The problem of secure multiterminal source coding with side information at
the eavesdropper is investigated. This scenario consists of a main encoder
(referred to as Alice) that wishes to compress a single source but
simultaneously satisfying the desired requirements on the distortion level at a
legitimate receiver (referred to as Bob) and the equivocation rate --average
uncertainty-- at an eavesdropper (referred to as Eve). It is further assumed
the presence of a (public) rate-limited link between Alice and Bob. In this
setting, Eve perfectly observes the information bits sent by Alice to Bob and
has also access to a correlated source which can be used as side information. A
second encoder (referred to as Charlie) helps Bob in estimating Alice's source
by sending a compressed version of its own correlated observation via a
(private) rate-limited link, which is only observed by Bob. For instance, the
problem at hands can be seen as the unification between the Berger-Tung and the
secure source coding setups. Inner and outer bounds on the so called
rates-distortion-equivocation region are derived. The inner region turns to be
tight for two cases: (i) uncoded side information at Bob and (ii) lossless
reconstruction of both sources at Bob --secure distributed lossless
compression. Application examples to secure lossy source coding of Gaussian and
binary sources in the presence of Gaussian and binary/ternary (resp.) side
informations are also considered. Optimal coding schemes are characterized for
some cases of interest where the statistical differences between the side
information at the decoders and the presence of a non-zero distortion at Bob
can be fully exploited to guarantee secrecy.
|
1105.1668
|
Convergence Time Analysis of Quantized Gossip Consensus on Digraphs
|
cs.SY math.DS
|
We have recently proposed quantized gossip algorithms which solve the
consensus and averaging problems on directed graphs with the least restrictive
connectivity requirements. In this paper we study the convergence time of these
algorithms. To this end, we investigate the shrinking time of the smallest
interval that contains all states for the consensus algorithm, and the decay
time of a suitable Lyapunov function for the averaging algorithm. The
investigation leads us to characterizing the convergence time by the hitting
time in certain special Markov chains. We simplify the structures of state
transition by considering the special case of complete graphs, where every edge
can be activated with an equal probability, and derive polynomial upper bounds
on convergence time.
|
1105.1697
|
Vine copulas as a mean for the construction of high dimensional
probability distribution associated to a Markov Network
|
math.ST cs.IT math.IT stat.TH
|
Building higher-dimensional copulas is generally recognized as a difficult
problem. Regular-vines using bivariate copulas provide a flexible class of
high-dimensional dependency models. In large dimensions, the drawback of the
model is the exponentially increasing complexity. Recognizing some of the
conditional independences is a possibility for reducing the number of levels of
the pair-copula decomposition, and hence to simplify its construction Aas et al
(2009). The idea of using conditional independences was already performed under
elliptical copula assumptions Hanea, Kurowicka and Cooke (2006), Kurowicka and
Cooke (2002) and in the case of DAGs in a recent work Bauer, Czado and Klein
(2011). We provide a method which uses some of the conditional independences
encoded by the Markov network underlying the variables. We give a theorem which
under some graph conditions makes possible to derive pair-copula decomposition
of the probability density function associated to a Markov network. As the
underlying Markov network is usually unknown, we first have to discover it from
the sample data. Using our results published in Szantai and Kovacs (2008) and
Kovacs and Szantai (2010a) we will show how to derive a multidimensional copula
model exploiting the information on conditional independences hidden in the
sample data.
|
1105.1702
|
A Compositional Distributional Semantics, Two Concrete Constructions,
and some Experimental Evaluations
|
cs.CL math.CT
|
We provide an overview of the hybrid compositional distributional model of
meaning, developed in Coecke et al. (arXiv:1003.4394v1 [cs.CL]), which is based
on the categorical methods also applied to the analysis of information flow in
quantum protocols. The mathematical setting stipulates that the meaning of a
sentence is a linear function of the tensor products of the meanings of its
words. We provide concrete constructions for this definition and present
techniques to build vector spaces for meaning vectors of words, as well as that
of sentences. The applicability of these methods is demonstrated via a toy
vector space as well as real data from the British National Corpus and two
disambiguation experiments.
|
1105.1720
|
Software Vulnerabilities, Banking Threats, Botnets and Malware
Self-Protection Technologies
|
cs.NI cs.CR cs.IT math.IT
|
Information security is the protection of information from a wide range of
threats in order to ensure success business continuity by minimizing risks and
maximizing the return of investments and business opportunities. In this paper,
we study and discuss the software vulnerabilities, banking threats, botnets and
propose the malware self-protection technologies.
|
1105.1728
|
Controllability of the cubic Schroedinger equation via a low-dimensional
source term
|
math.OC cs.SY math.AP
|
We study controllability of $d$-dimensional defocusing cubic Schroedinger
equation under periodic boundary conditions. The control is applied additively,
via a source term, which is a linear combination of few complex exponentials
(modes) with time-variant coefficients - controls. We manage to prove that
controlling at most $2^d$ modes one can achieve controllability of the equation
in any finite-dimensional projection of the evolution space
$H^{s}(\mathbb{T}^d), \ s>d/2$, as well as approximate controllability in
$H^{s}(\mathbb{T}^d)$. We also present negative result regarding exact
controllability of cubic Schroedinger equation via a finite-dimensional source
term.
|
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