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1010.4854
|
Implicit and explicit communication in decentralized control
|
cs.IT cs.SY math.IT math.OC
|
There has been substantial progress recently in understanding toy problems of
purely implicit signaling. These are problems where the source and the channel
are implicit -- the message is generated endogenously by the system, and the
plant itself is used as a channel. In this paper, we explore how implicit and
explicit communication can be used synergistically to reduce control costs. The
setting is an extension of Witsenhausen's counterexample where a rate-limited
external channel connects the two controllers. Using a semi-deterministic
version of the problem, we arrive at a binning-based strategy that can
outperform the best known strategies by an arbitrarily large factor. We also
show that our binning-based strategy attains within a constant factor of the
optimal cost for an asymptotically infinite-length version of the problem
uniformly over all problem parameters and all rates on the external channel.
For the scalar case, although our results yield approximate optimality for each
fixed rate, we are unable to prove approximately-optimality uniformly over all
rates.
|
1010.4855
|
Towards a communication-theoretic understanding of system-level power
consumption
|
cs.IT cs.CC math.IT
|
Traditional communication theory focuses on minimizing transmit power.
However, communication links are increasingly operating at shorter ranges where
transmit power can be significantly smaller than the power consumed in
decoding. This paper models the required decoding power and investigates the
minimization of total system power from two complementary perspectives.
First, an isolated point-to-point link is considered. Using new lower bounds
on the complexity of message-passing decoding, lower bounds are derived on
decoding power. These bounds show that 1) there is a fundamental tradeoff
between transmit and decoding power; 2) unlike the implications of the
traditional "waterfall" curve which focuses on transmit power, the total power
must diverge to infinity as error probability goes to zero; 3) Regular LDPCs,
and not their known capacity-achieving irregular counterparts, can be shown to
be power order optimal in some cases; and 4) the optimizing transmit power is
bounded away from the Shannon limit.
Second, we consider a collection of links. When systems both generate and
face interference, coding allows a system to support a higher density of
transmitter-receiver pairs (assuming interference is treated as noise).
However, at low densities, uncoded transmission may be more power-efficient in
some cases.
|
1010.4858
|
S-MATE: Secure Coding-based Multipath Adaptive Traffic Engineering
|
cs.NI cs.CR cs.IT math.IT
|
There have been several approaches to provisioning traffic between core
network nodes in Internet Service Provider (ISP) networks. Such approaches aim
to minimize network delay, increase network capacity, and enhance network
security services. MATE (Multipath Adaptive Traffic Engineering) protocol has
been proposed for multipath adaptive traffic engineering between an ingress
node (source) and an egress node (destination). Its novel idea is to avoid
network congestion and attacks that might exist in edge and node disjoint paths
between two core network nodes.
This paper builds an adaptive, robust, and reliable traffic engineering
scheme for better performance of communication network operations. This will
also provision quality of service (QoS) and protection of traffic engineering
to maximize network efficiency. Specifically, we present a new approach, S-MATE
(secure MATE) is developed to protect the network traffic between two core
nodes (routers or switches) in a cloud network. S-MATE secures against a single
link attack/failure by adding redundancy in one of the operational paths
between the sender and receiver. The proposed scheme can be built to secure
core networks such as optical and IP networks.
|
1010.4876
|
Optimal Packet Scheduling on an Energy Harvesting Broadcast Link
|
cs.IT math.IT
|
The minimization of transmission completion time for a given number of bits
per user in an energy harvesting communication system, where energy harvesting
instants are known in an offline manner is considered. An achievable rate
region with structural properties satisfied by the 2-user AWGN Broadcast
Channel capacity region is assumed. It is shown that even though all data are
available at the beginning, a non-negative amount of energy from each energy
harvest is deferred for later use such that the transmit power starts at its
lowest value and rises as time progresses. The optimal scheduler ends the
transmission to both users at the same time. Exploiting the special structure
in the problem, the iterative offline algorithm, FlowRight, from earlier
literature, is adapted and proved to solve this problem. The solution has
polynomial complexity in the number of harvests used, and is observed to
converge quickly on numerical examples.
|
1010.4893
|
Collaborative Sources Identification in Mixed Signals via Hierarchical
Sparse Modeling
|
cs.CV
|
A collaborative framework for detecting the different sources in mixed
signals is presented in this paper. The approach is based on C-HiLasso, a
convex collaborative hierarchical sparse model, and proceeds as follows. First,
we build a structured dictionary for mixed signals by concatenating a set of
sub-dictionaries, each one of them learned to sparsely model one of a set of
possible classes. Then, the coding of the mixed signal is performed by
efficiently solving a convex optimization problem that combines standard
sparsity with group and collaborative sparsity. The present sources are
identified by looking at the sub-dictionaries automatically selected in the
coding. The collaborative filtering in C-HiLasso takes advantage of the
temporal/spatial redundancy in the mixed signals, letting collections of
samples collaborate in identifying the classes, while allowing individual
samples to have different internal sparse representations. This collaboration
is critical to further stabilize the sparse representation of signals, in
particular the class/sub-dictionary selection. The internal sparsity inside the
sub-dictionaries, as naturally incorporated by the hierarchical aspects of
C-HiLasso, is critical to make the model consistent with the essence of the
sub-dictionaries that have been trained for sparse representation of each
individual class. We present applications from speaker and instrument
identification and texture separation. In the case of audio signals, we use
sparse modeling to describe the short-term power spectrum envelopes of harmonic
sounds. The proposed pitch independent method automatically detects the number
of sources on a recording.
|
1010.4911
|
The Capacity Region of the 3-User Gaussian Interference Channel with
Mixed Strong-Very Strong Interference
|
cs.IT math.IT
|
We consider the 3-user Gaussian interference channel and provide an outer
bound on its capacity region. Under some conditions, which we call the mixed
strong-very strong interference conditions, this outer bound is achievable.
These conditions correspond to the case where at each receiver, one transmitter
is causing strong interference and the other is causing very strong
interference. Therefore, we characterize the capacity region of the 3-user
interference channel with mixed strong-very strong interference.
|
1010.4920
|
Jointly Optimal Channel Pairing and Power Allocation for Multichannel
Multihop Relaying
|
cs.IT cs.NI cs.PF math.IT
|
We study the problem of channel pairing and power allocation in a
multichannel multihop relay network to enhance the end-to-end data rate. Both
amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are
considered. Given fixed power allocation to the channels, we show that channel
pairing over multiple hops can be decomposed into independent pairing problems
at each relay, and a sorted-SNR channel pairing strategy is sum-rate optimal,
where each relay pairs its incoming and outgoing channels by their SNR order.
For the joint optimization of channel pairing and power allocation under both
total and individual power constraints, we show that the problem can be
decoupled into two subproblems solved separately. This separation principle is
established by observing the equivalence between sorting SNRs and sorting
channel gains in the jointly optimal solution. It significantly reduces the
computational complexity in finding the jointly optimal solution. It follows
that the channel pairing problem in joint optimization can be again decomposed
into independent pairing problems at each relay based on sorted channel gains.
The solution for optimizing power allocation for DF relaying is also provided,
as well as an asymptotically optimal solution for AF relaying. Numerical
results are provided to demonstrate substantial performance gain of the jointly
optimal solution over some suboptimal alternatives. It is also observed that
more gain is obtained from optimal channel pairing than optimal power
allocation through judiciously exploiting the variation among multiple
channels. Impact of the variation of channel gain, the number of channels, and
the number of hops on the performance gain is also studied through numerical
examples.
|
1010.4951
|
Local Component Analysis for Nonparametric Bayes Classifier
|
cs.CV cs.LG
|
The decision boundaries of Bayes classifier are optimal because they lead to
maximum probability of correct decision. It means if we knew the prior
probabilities and the class-conditional densities, we could design a classifier
which gives the lowest probability of error. However, in classification based
on nonparametric density estimation methods such as Parzen windows, the
decision regions depend on the choice of parameters such as window width.
Moreover, these methods suffer from curse of dimensionality of the feature
space and small sample size problem which severely restricts their practical
applications. In this paper, we address these problems by introducing a novel
dimension reduction and classification method based on local component
analysis. In this method, by adopting an iterative cross-validation algorithm,
we simultaneously estimate the optimal transformation matrices (for dimension
reduction) and classifier parameters based on local information. The proposed
method can classify the data with complicated boundary and also alleviate the
course of dimensionality dilemma. Experiments on real data show the superiority
of the proposed algorithm in term of classification accuracies for pattern
classification applications like age, facial expression and character
recognition. Keywords: Bayes classifier, curse of dimensionality dilemma,
Parzen window, pattern classification, subspace learning.
|
1010.4965
|
Dually flat structure with escort probability and its application to
alpha-Voronoi diagrams
|
cond-mat.stat-mech cs.IT math.DG math.IT
|
This paper studies geometrical structure of the manifold of escort
probability distributions and shows its new applicability to information
science. In order to realize escort probabilities we use a conformal
transformation that flattens so-called alpha-geometry of the space of discrete
probability distributions, which well characterizes nonadditive statistics on
the space. As a result escort probabilities are proved to be flat coordinates
of the usual probabilities for the derived dually flat structure. Finally, we
demonstrate that escort probabilities with the new structure admits a simple
algorithm to compute Voronoi diagrams and centroids with respect to
alpha-divergences.
|
1010.4971
|
Correlated couplings and robustness of coupled networks
|
physics.data-an cond-mat.stat-mech cs.SI physics.soc-ph
|
Most real-world complex systems can be modelled by coupled networks with
multiple layers. How and to what extent the pattern of couplings between
network layers may influence the interlaced structure and function of coupled
networks are not clearly understood. Here we study the impact of correlated
inter-layer couplings on the network robustness of coupled networks using
percolation concept. We found that the positive correlated inter-layer coupling
enhaces network robustness in the sense that it lowers the percolation
threshold of the interlaced network than the negative correlated coupling case.
At the same time, however, positive inter-layer correlation leads to smaller
giant component size in the well-connected region, suggesting potential
disadvantage for network connectivity, as demonstrated also with some
real-world coupled network structures.
|
1010.4980
|
On Design of Collaborative Beamforming for Two-Way Relay Networks
|
cs.IT math.IT
|
We consider a two-way relay network, where two source nodes, S1 and S2,
exchange information through a cluster of relay nodes. The relay nodes receive
the sum signal from S1 and S2 in the first time slot. In the second time slot,
each relay node multiplies its received signal by a complex coefficient and
retransmits the signal to the two source nodes, which leads to a collaborative
two-way beamforming system. By applying the principle of analog network coding,
each receiver at S1 and S2 cancels the "self-interference" in the received
signal from the relay cluster and decodes the message. This paper studies the
2-dimensional achievable rate region for such a two-way relay network with
collaborative beamforming. With different assumptions of channel reciprocity
between the source-relay and relay-source channels, the achievable rate region
is characterized under two setups. First, with reciprocal channels, we
investigate the achievable rate regions when the relay cluster is subject to a
sum-power constraint or individual-power constraints. We show that the optimal
beamforming vectors obtained from solving the weighted sum inverse-SNR
minimization (WSISMin) problems are sufficient to characterize the
corresponding achievable rate region. Furthermore, we derive the closed form
solutions for those optimal beamforming vectors and consequently propose the
partially distributed algorithms to implement the optimal beamforming, where
each relay node only needs the local channel information and one global
parameter. Second, with the non-reciprocal channels, the achievable rate
regions are also characterized for both the sum-power constraint case and the
individual-power constraint case. Although no closed-form solutions are
available under this setup, we present efficient numerical algorithms.
|
1010.4999
|
On the Stability of Swarm Consensus Under Noisy Control
|
nlin.AO cs.MA
|
Representation of a swarm of independent robotic agents under graph-theoretic
constructs allows for more formal analysis of convergence properties. We
consider the local and global convergence behavior of an N-member swarm of
agents in a modified consensus problem wherein the connectivity of agents is
governed by probabilistic functions. The addition of a random walk control
ensures Lyapunov stability of the swarm consensus. Simulation results are given
and planned experiments are described.
|
1010.5051
|
Complex Networks: effect of subtle changes in nature of randomness
|
cond-mat.stat-mech cs.SI physics.soc-ph
|
In two different classes of network models, namely, the Watts Strogatz type
and the Euclidean type, subtle changes have been introduced in the randomness.
In the Watts Strogatz type network, rewiring has been done in different ways
and although the qualitative results remain same, finite differences in the
exponents are observed. In the Euclidean type networks, where at least one
finite phase transition occurs, two models differing in a similar way have been
considered. The results show a possible shift in one of the phase transition
points but no change in the values of the exponents. The WS and Euclidean type
models are equivalent for extreme values of the parameters; we compare their
behaviour for intermediate values.
|
1010.5092
|
The Value of Information for Populations in Varying Environments
|
q-bio.PE cond-mat.stat-mech cs.IT math.IT
|
The notion of information pervades informal descriptions of biological
systems, but formal treatments face the problem of defining a quantitative
measure of information rooted in a concept of fitness, which is itself an
elusive notion. Here, we present a model of population dynamics where this
problem is amenable to a mathematical analysis. In the limit where any
information about future environmental variations is common to the members of
the population, our model is equivalent to known models of financial
investment. In this case, the population can be interpreted as a portfolio of
financial assets and previous analyses have shown that a key quantity of
Shannon's communication theory, the mutual information, sets a fundamental
limit on the value of information. We show that this bound can be violated when
accounting for features that are irrelevant in finance but inherent to
biological systems, such as the stochasticity present at the individual level.
This leads us to generalize the measures of uncertainty and information usually
encountered in information theory.
|
1010.5113
|
Coarse-Grained Analysis of Microscopic Neuronal Simulators on Networks:
Bifurcation and Rare-events computations
|
cs.SI nlin.AO physics.bio-ph q-bio.NC
|
We show how the Equation-Free approach for mutliscale computations can be
exploited to extract, in a computational strict and systematic way the emergent
dynamical attributes, from detailed large-scale microscopic stochastic models,
of neurons that interact on complex networks. In particular we show how the
Equation-Free approach can be exploited to perform system-level tasks such as
bifurcation, stability analysis and estimation of mean appearance times of rare
events, bypassing the need for obtaining analytical approximations, providing
an "on-demand" model reduction. Using the detailed simulator as a black-box
timestepper, we compute the coarse-grained equilibrium bifurcation diagrams,
examine the stability of the solution branches and perform a rare-events
analysis with respect to certain characteristics of the underlying network
topology such as the connectivity degree
|
1010.5141
|
Generalized Approximate Message Passing for Estimation with Random
Linear Mixing
|
cs.IT math.IT
|
We consider the estimation of an i.i.d.\ random vector observed through a
linear transform followed by a componentwise, probabilistic (possibly
nonlinear) measurement channel. A novel algorithm, called generalized
approximate message passing (GAMP), is presented that provides computationally
efficient approximate implementations of max-sum and sum-problem loopy belief
propagation for such problems. The algorithm extends earlier approximate
message passing methods to incorporate arbitrary distributions on both the
input and output of the transform and can be applied to a wide range of
problems in nonlinear compressed sensing and learning.
Extending an analysis by Bayati and Montanari, we argue that the asymptotic
componentwise behavior of the GAMP method under large, i.i.d. Gaussian
transforms is described by a simple set of state evolution (SE) equations. From
the SE equations, one can \emph{exactly} predict the asymptotic value of
virtually any componentwise performance metric including mean-squared error or
detection accuracy. Moreover, the analysis is valid for arbitrary input and
output distributions, even when the corresponding optimization problems are
non-convex. The results match predictions by Guo and Wang for relaxed belief
propagation on large sparse matrices and, in certain instances, also agree with
the optimal performance predicted by the replica method. The GAMP methodology
thus provides a computationally efficient methodology, applicable to a large
class of non-Gaussian estimation problems with precise asymptotic performance
guarantees.
|
1010.5163
|
Distributed Detection over Time Varying Networks: Large Deviations
Analysis
|
cs.IT math.IT
|
We apply large deviations theory to study asymptotic performance of running
consensus distributed detection in sensor networks. Running consensus is a
stochastic approximation type algorithm, recently proposed. At each time step
k, the state at each sensor is updated by a local averaging of the sensor's own
state and the states of its neighbors (consensus) and by accounting for the new
observations (innovation). We assume Gaussian, spatially correlated
observations. We allow the underlying network be time varying, provided that
the graph that collects the union of links that are online at least once over a
finite time window is connected. This paper shows through large deviations
that, under stated assumptions on the network connectivity and sensors'
observations, the running consensus detection asymptotically approaches in
performance the optimal centralized detection. That is, the Bayes probability
of detection error (with the running consensus detector) decays exponentially
to zero as k goes to infinity at the Chernoff information rate-the best
achievable rate of the asymptotically optimal centralized detector.
|
1010.5278
|
Analysis and Design of Tuned Turbo Codes
|
cs.IT math.IT
|
It has been widely observed that there exists a fundamental trade-off between
the minimum (Hamming) distance properties and the iterative decoding
convergence behavior of turbo-like codes. While capacity achieving code
ensembles typically are asymptotically bad in the sense that their minimum
distance does not grow linearly with block length, and they therefore exhibit
an error floor at moderate-to-high signal to noise ratios, asymptotically good
codes usually converge further away from channel capacity. In this paper, we
introduce the concept of tuned turbo codes, a family of asymptotically good
hybrid concatenated code ensembles, where asymptotic minimum distance growth
rates, convergence thresholds, and code rates can be traded-off using two
tuning parameters, {\lambda} and {\mu}. By decreasing {\lambda}, the asymptotic
minimum distance growth rate is reduced in exchange for improved iterative
decoding convergence behavior, while increasing {\lambda} raises the asymptotic
minimum distance growth rate at the expense of worse convergence behavior, and
thus the code performance can be tuned to fit the desired application. By
decreasing {\mu}, a similar tuning behavior can be achieved for higher rate
code ensembles.
|
1010.5290
|
Converged Algorithms for Orthogonal Nonnegative Matrix Factorizations
|
cs.LG cs.NA
|
This paper proposes uni-orthogonal and bi-orthogonal nonnegative matrix
factorization algorithms with robust convergence proofs. We design the
algorithms based on the work of Lee and Seung [1], and derive the converged
versions by utilizing ideas from the work of Lin [2]. The experimental results
confirm the theoretical guarantees of the convergences.
|
1010.5291
|
New Class of Optimal Frequency-Hopping Sequences by Polynomial Residue
Class Rings
|
cs.IT math.IT
|
In this paper, using the theory of polynomial residue class rings, a new
construction is proposed for frequency hopping patterns having optimal Hamming
autocorrelation with respect to the well-known $Lempel$-$Greenberger$ bound.
Based on the proposed construction, many new $Peng$-$Fan$ optimal families of
frequency hopping sequences are obtained. The parameters of these sets of
frequency hopping sequences are new and flexible.
|
1010.5308
|
Proceedings Third Interaction and Concurrency Experience: Guaranteed
Interaction
|
cs.LO cs.DC cs.MA
|
This volume contains the proceedings of the 3rd Interaction and Concurrency
Experience (ICE 2010) workshop, which was held in Amsterdam, Netherlands on
10th of June 2010 as a satellite event of DisCoTec'10. Each year, the workshop
focuses on a specific topic: the topic of ICE 2010 was Guaranteed Interactions,
by which we mean, for example, guaranteeing safety, reactivity, quality of
service or satisfaction of analysis hypotheses.
|
1010.5377
|
Estimating Network Parameters for Selecting Community Detection
Algorithms
|
cs.SI physics.soc-ph
|
This paper considers the problem of algorithm selection for community
detection. The aim of community detection is to identify sets of nodes in a
network which are more interconnected relative to their connectivity to the
rest of the network. A large number of algorithms have been developed to tackle
this problem, but as with any machine learning task there is no
"one-size-fits-all" and each algorithm excels in a specific part of the problem
space. This paper examines the performance of algorithms developed for weighted
networks against those using unweighted networks for different parts of the
problem space (parameterised by the intra/inter community links). It is then
demonstrated how the choice of algorithm (weighted/unweighted) can be made
based only on the observed network.
|
1010.5382
|
It takes half the energy of a photon to send one bit reliably on the
Poisson channel with feedback
|
cs.IT math.IT
|
We consider the transmission of a single bit over the continuous-time Poisson
channel with noiseless feedback. We show that to send the bit reliably
requires, on the average, half the energy of a photon. In the absence of
peak-power constraints this holds irrespective of the intensity of the dark
current. We also solve for the energy required to send $log_{2} M$ bits.
|
1010.5388
|
Helstrom's Theory on Quantum Binary Decision Revisited
|
cs.IT math.IT quant-ph
|
For a binary system specified by the density operators r0 and r1 and by the
prior probabilities q0 and q1, Helstrom's theory permits the evaluation of the
optimal measurement operators and of the corresponding maximum correct
detection probability. The theory is based on the eigendecomposition (EID) of
the operator, given by the difference of the weighted density operators, namely
D = q1r1-q0r0. In general, this EID is obtained explicitly only with pure
states, whereas with mixed states it must be carried out numerically. In this
letter we show that the same evaluation can be performed on the basis of a
modified version of the Gram matrix. The advantage is due to the fact that the
outer products of density operators are replaced by inner product, with a
considerable dimensionality reduction. At the limit, in quantum optical
communications the density operators have infinite dimensions, whereas the
inner products are simply scalar quantities. The Gram matrix approach permits
the explicit (not numerical) evaluation of a binary system performance in cases
not previously developed.
|
1010.5412
|
On optimizing over lift-and-project closures
|
cs.RO math.OC
|
The lift-and-project closure is the relaxation obtained by computing all
lift-and-project cuts from the initial formulation of a mixed integer linear
program or equivalently by computing all mixed integer Gomory cuts read from
all tableau's corresponding to feasible and infeasible bases. In this paper, we
present an algorithm for approximating the value of the lift-and-project
closure. The originality of our method is that it is based on a very simple cut
generation linear programming problem which is obtained from the original
linear relaxation by simply modifying the bounds on the variables and
constraints. This separation LP can also be seen as the dual of the cut
generation LP used in disjunctive programming procedures with a particular
normalization. We study some properties of this separation LP in particular
relating it to the equivalence between lift-and-project cuts and Gomory cuts
shown by Balas and Perregaard. Finally, we present some computational
experiments and comparisons with recent related works.
|
1010.5416
|
Capacity of Fading Gaussian Channel with an Energy Harvesting Sensor
Node
|
cs.IT math.IT
|
Network life time maximization is becoming an important design goal in
wireless sensor networks. Energy harvesting has recently become a preferred
choice for achieving this goal as it provides near perpetual operation. We
study such a sensor node with an energy harvesting source and compare various
architectures by which the harvested energy is used. We find its Shannon
capacity when it is transmitting its observations over a fading AWGN channel
with perfect/no channel state information provided at the transmitter. We
obtain an achievable rate when there are inefficiencies in energy storage and
the capacity when energy is spent in activities other than transmission.
|
1010.5426
|
Translation-Invariant Representation for Cumulative Foot Pressure Images
|
cs.AI
|
Human can be distinguished by different limb movements and unique ground
reaction force. Cumulative foot pressure image is a 2-D cumulative ground
reaction force during one gait cycle. Although it contains pressure spatial
distribution information and pressure temporal distribution information, it
suffers from several problems including different shoes and noise, when putting
it into practice as a new biometric for pedestrian identification. In this
paper, we propose a hierarchical translation-invariant representation for
cumulative foot pressure images, inspired by the success of Convolutional deep
belief network for digital classification. Key contribution in our approach is
discriminative hierarchical sparse coding scheme which helps to learn useful
discriminative high-level visual features. Based on the feature representation
of cumulative foot pressure images, we develop a pedestrian recognition system
which is invariant to three different shoes and slight local shape change.
Experiments are conducted on a proposed open dataset that contains more than
2800 cumulative foot pressure images from 118 subjects. Evaluations suggest the
effectiveness of the proposed method and the potential of cumulative foot
pressure images as a biometric.
|
1010.5445
|
Theory and Applications of Robust Optimization
|
math.OC cs.CE
|
In this paper we survey the primary research, both theoretical and applied,
in the area of Robust Optimization (RO). Our focus is on the computational
attractiveness of RO approaches, as well as the modeling power and broad
applicability of the methodology. In addition to surveying prominent
theoretical results of RO, we also present some recent results linking RO to
adaptable models for multi-stage decision-making problems. Finally, we
highlight applications of RO across a wide spectrum of domains, including
finance, statistics, learning, and various areas of engineering.
|
1010.5470
|
Resource-bounded Dimension in Computational Learning Theory
|
cs.CC cs.LG
|
This paper focuses on the relation between computational learning theory and
resource-bounded dimension. We intend to establish close connections between
the learnability/nonlearnability of a concept class and its corresponding size
in terms of effective dimension, which will allow the use of powerful dimension
techniques in computational learning and viceversa, the import of learning
results into complexity via dimension. Firstly, we obtain a tight result on the
dimension of online mistake-bound learnable classes. Secondly, in relation with
PAC learning, we show that the polynomial-space dimension of PAC learnable
classes of concepts is zero. This provides a hypothesis on effective dimension
that implies the inherent unpredictability of concept classes (the classes that
verify this property are classes not efficiently PAC learnable using any
hypothesis). Thirdly, in relation to space dimension of classes that are
learnable by membership query algorithms, the main result proves that
polynomial-space dimension of concept classes learnable by a membership-query
algorithm is zero.
|
1010.5478
|
Consequences of fluctuating group size for the evolution of cooperation
|
q-bio.PE cs.SI physics.soc-ph
|
Studies of cooperation have traditionally focused on discrete games such as
the well-known prisoner's dilemma, in which players choose between two pure
strategies: cooperation and defection. Increasingly, however, cooperation is
being studied in continuous games that feature a continuum of strategies
determining the level of cooperative investment. For the continuous snowdrift
game, it has been shown that a gradually evolving monomorphic population may
undergo evolutionary branching, resulting in the emergence of a defector
strategy that coexists with a cooperator strategy. This phenomenon has been
dubbed the 'tragedy of the commune'. Here we study the effects of fluctuating
group size on the tragedy of the commune and derive analytical conditions for
evolutionary branching. Our results show that the effects of fluctuating group
size on evolutionary dynamics critically depend on the structure of payoff
functions. For games with additively separable benefits and costs, fluctuations
in group size make evolutionary branching less likely, and sufficiently large
fluctuations in group size can always turn an evolutionary branching point into
a locally evolutionarily stable strategy. For games with multiplicatively
separable benefits and costs, fluctuations in group size can either prevent or
induce the tragedy of the commune. For games with general interactions between
benefits and costs, we derive a general classification scheme based on second
derivatives of the payoff function, to elucidate when fluctuations in group
size help or hinder cooperation.
|
1010.5497
|
Multiparty Equality Function Computation in Networks with Point-to-Point
Links
|
cs.IT cs.DC math.IT
|
In this report, we study the multiparty communication complexity problem of
the multiparty equality function (MEQ): EQ(x_1,...,x_n) = 1 if x_1=...=x_n, and
0 otherwise. The input vector (x_1,...,x_n) is distributed among n>=2 nodes,
with x_i known to node i, where x_i is chosen from the set {1,...,M}, for some
integer M>0.
Instead of the "number on the forehand" model, we consider a point-to-point
communication model (similar to the message passing model), which we believe is
more realistic in networking settings. We assume a synchronous fully connected
network of n nodes, the node IDs (identifiers) are common knowledge. We assume
that all point-to-point communication channels/links are private such that when
a node transmits, only the designated recipient can receive the message. The
identity of the sender is known to the recipient.
We demonstrate that traditional techniques generalized from two-party
communication complexity problem are not sufficient to obtain tight bounds
under the point-to-point communication model. We then introduce techniques
which significantly reduce the space of protocols to study. These techniques
are used to study some instances of the MEQ problem.
|
1010.5504
|
On the Convexity of Latent Social Network Inference
|
cs.SI physics.soc-ph
|
In many real-world scenarios, it is nearly impossible to collect explicit
social network data. In such cases, whole networks must be inferred from
underlying observations. Here, we formulate the problem of inferring latent
social networks based on network diffusion or disease propagation data. We
consider contagions propagating over the edges of an unobserved social network,
where we only observe the times when nodes became infected, but not who
infected them. Given such node infection times, we then identify the optimal
network that best explains the observed data. We present a maximum likelihood
approach based on convex programming with a l1-like penalty term that
encourages sparsity. Experiments on real and synthetic data reveal that our
method near-perfectly recovers the underlying network structure as well as the
parameters of the contagion propagation model. Moreover, our approach scales
well as it can infer optimal networks of thousands of nodes in a matter of
minutes.
|
1010.5506
|
Dualities and Identities for Entanglement-Assisted Quantum Codes
|
quant-ph cs.IT math.IT
|
The dual of an entanglement-assisted quantum error-correcting (EAQEC) code is
the code resulting from exchanging the original code's information qubits with
its ebits. To introduce this notion, we show how entanglement-assisted (EA)
repetition codes and accumulator codes are dual to each other, much like their
classical counterparts, and we give an explicit, general quantum shift-register
circuit that encodes both classes of codes.We later show that our constructions
are optimal, and this result completes our understanding of these dual classes
of codes. We also establish the Gilbert-Varshamov bound and the Plotkin bound
for EAQEC codes, and we use these to examine the existence of some EAQEC codes.
Finally, we provide upper bounds on the block error probability when
transmitting maximal-entanglement EAQEC codes over the depolarizing channel,
and we derive variations of the hashing bound for EAQEC codes, which is a lower
bound on the maximum rate at which reliable communication over Pauli channels
is possible with the use of pre-shared entanglement.
|
1010.5511
|
Efficient Minimization of Decomposable Submodular Functions
|
cs.LG math.OC
|
Many combinatorial problems arising in machine learning can be reduced to the
problem of minimizing a submodular function. Submodular functions are a natural
discrete analog of convex functions, and can be minimized in strongly
polynomial time. Unfortunately, state-of-the-art algorithms for general
submodular minimization are intractable for larger problems. In this paper, we
introduce a novel subclass of submodular minimization problems that we call
decomposable. Decomposable submodular functions are those that can be
represented as sums of concave functions applied to modular functions. We
develop an algorithm, SLG, that can efficiently minimize decomposable
submodular functions with tens of thousands of variables. Our algorithm
exploits recent results in smoothed convex minimization. We apply SLG to
synthetic benchmarks and a joint classification-and-segmentation task, and show
that it outperforms the state-of-the-art general purpose submodular
minimization algorithms by several orders of magnitude.
|
1010.5524
|
Analysis of 1-bit Output Noncoherent Fading Channels in the Low SNR
Regime
|
cs.IT math.IT
|
We consider general multi-antenna fading channels with coarsely quantized
outputs, where the channel is unknown to the transmitter and receiver. This
analysis is of interest in the context of sensor network communication where
low power and low cost are key requirements (e.g. standard IEEE 802.15.4
applications). This is also motivated by highly energy constrained
communications devices where sampling the signal may be more energy consuming
than processing or transmitting it. Therefore the analog-to-digital converters
(ADCs) for such applications should be low-resolution, in order to reduce their
cost and power consumption. In this paper, we consider the extreme case of only
1-bit ADC for each receive signal component. We derive asymptotics of the
mutual information up to the second order in the signal-to-noise ratio (SNR)
under average and peak power constraints and study the impact of quantization.
We show that up to second order in SNR, the mutual information of a system with
two-level (sign) output signals incorporates only a power penalty factor of
almost 1.96 dB compared to the system with infinite resolution for all channels
of practical interest. This generalizes a recent result for the coherent case.
|
1010.5526
|
Achieving near-Capacity on Large Discrete Memoryless Channels
|
cs.IT math.IT
|
We propose a method to increase the capacity achieved by uniform prior in
discrete memoryless channels (DMC) with high input cardinality. It consists in
appropriately reducing the input set. Different design criteria of the input
subset are discussed. We develop an efficient algorithm to solve this problem
based on the maximization of the cut-off rate. The method is applied to a
mono-bit transceiver MIMO system, and it is shown that the capacity can be
approached within tenths of a dB by employing standard binary codes while
avoiding the use of distribution shapers.
|
1010.5529
|
Belief Propagation based MIMO Detection Operating on Quantized Channel
Output
|
cs.IT math.IT
|
In multiple-antenna communications, as bandwidth and modulation order
increase, system components must work with demanding tolerances. In particular,
high resolution and high sampling rate analog-to-digital converters (ADCs) are
often prohibitively challenging to design. Therefore ADCs for such applications
should be low-resolution. This paper provides new insights into the problem of
optimal signal detection based on quantized received signals for multiple-input
multiple-output (MIMO) channels. It capitalizes on previous works which
extensively analyzed the unquantized linear vector channel using graphical
inference methods. In particular, a "loopy" belief propagation-like (BP) MIMO
detection algorithm, operating on quantized data with low complexity, is
proposed. In addition, we study the impact of finite receiver resolution in
fading channels in the large-system limit by means of a state evolution
analysis of the BP algorithm, which refers to the limit where the number of
transmit and receive antennas go to infinity with a fixed ratio. Simulations
show that the theoretical findings might give accurate results even with
moderate number of antennas.
|
1010.5532
|
Multiple Parameter Estimation With Quantized Channel Output
|
cs.IT math.IT
|
We present a general problem formulation for optimal parameter estimation
based on quantized observations, with application to antenna array
communication and processing (channel estimation, time-of-arrival (TOA) and
direction-of-arrival (DOA) estimation). The work is of interest in the case
when low resolution A/D-converters (ADCs) have to be used to enable higher
sampling rate and to simplify the hardware. An Expectation-Maximization (EM)
based algorithm is proposed for solving this problem in a general setting.
Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of
quantization and the optimal choice of the ADC characteristic. Numerical and
analytical analysis reveals that reliable estimation may still be possible even
when the quantization is very coarse.
|
1010.5537
|
Using entropy measures for comparison of software traces
|
cs.SE cs.IT math.IT
|
The analysis of execution paths (also known as software traces) collected
from a given software product can help in a number of areas including software
testing, software maintenance and program comprehension. The lack of a scalable
matching algorithm operating on detailed execution paths motivates the search
for an alternative solution.
This paper proposes the use of word entropies for the classification of
software traces. Using a well-studied defective software as an example, we
investigate the application of both Shannon and extended entropies
(Landsberg-Vedral, R\'{e}nyi and Tsallis) to the classification of traces
related to various software defects. Our study shows that using entropy
measures for comparisons gives an efficient and scalable method for comparing
traces. The three extended entropies, with parameters chosen to emphasize rare
events, all perform similarly and are superior to the Shannon entropy.
|
1010.5545
|
Many Roads to Synchrony: Natural Time Scales and Their Algorithms
|
nlin.CD cs.FL cs.IT math.DS math.IT
|
We consider two important time scales---the Markov and cryptic orders---that
monitor how an observer synchronizes to a finitary stochastic process. We show
how to compute these orders exactly and that they are most efficiently
calculated from the epsilon-machine, a process's minimal unifilar model.
Surprisingly, though the Markov order is a basic concept from stochastic
process theory, it is not a probabilistic property of a process. Rather, it is
a topological property and, moreover, it is not computable from any
finite-state model other than the epsilon-machine. Via an exhaustive survey, we
close by demonstrating that infinite Markov and infinite cryptic orders are a
dominant feature in the space of finite-memory processes. We draw out the roles
played in statistical mechanical spin systems by these two complementary length
scales.
|
1010.5562
|
Fast Continuous Haar and Fourier Transforms of Rectilinear Polygons from
VLSI Layouts
|
cs.CE cs.CG cs.DS
|
We develop the pruned continuous Haar transform and the fast continuous
Fourier series, two fast and efficient algorithms for rectilinear polygons.
Rectilinear polygons are used in VLSI processes to describe design and mask
layouts of integrated circuits. The Fourier representation is at the heart of
many of these processes and the Haar transform is expected to play a major role
in techniques envisioned to speed up VLSI design. To ensure correct printing of
the constantly shrinking transistors and simultaneously handle their
increasingly large number, ever more computationally intensive techniques are
needed. Therefore, efficient algorithms for the Haar and Fourier transforms are
vital. We derive the complexity of both algorithms and compare it to that of
discrete transforms traditionally used in VLSI. We find a significant reduction
in complexity when the number of vertices of the polygons is small, as is the
case in VLSI layouts. This analysis is completed by an implementation and a
benchmark of the continuous algorithms and their discrete counterpart. We show
that on tested VLSI layouts the pruned continuous Haar transform is 5 to 25
times faster, while the fast continuous Fourier series is 1.5 to 3 times
faster.
|
1010.5584
|
A derivational rephrasing experiment for question answering
|
cs.IR
|
In Knowledge Management, variations in information expressions have proven a
real challenge. In particular, classical semantic relations (e.g. synonymy) do
not connect words with different parts-of-speech. The method proposed tries to
address this issue. It consists in building a derivational resource from a
morphological derivation tool together with derivational guidelines from a
dictionary in order to store only correct derivatives. This resource, combined
with a syntactic parser, a semantic disambiguator and some derivational
patterns, helps to reformulate an original sentence while keeping the initial
meaning in a convincing manner This approach has been evaluated in three
different ways: the precision of the derivatives produced from a lemma; its
ability to provide well-formed reformulations from an original sentence,
preserving the initial meaning; its impact on the results coping with a real
issue, ie a question answering task . The evaluation of this approach through a
question answering system shows the pros and cons of this system, while
foreshadowing some interesting future developments.
|
1010.5608
|
A Generalized Coupon Collector Problem
|
cs.IT cs.DM cs.PF math.IT
|
This paper provides analysis to a generalized version of the coupon collector
problem, in which the collector gets $d$ distinct coupons each run and she
chooses the one that she has the least so far. On the asymptotic case when the
number of coupons $n$ goes to infinity, we show that on average $\frac{n\log
n}{d} + \frac{n}{d}(m-1)\log\log{n}+O(mn)$ runs are needed to collect $m$ sets
of coupons. An efficient exact algorithm is also developed for any finite case
to compute the average needed runs exactly. Numerical examples are provided to
verify our theoretical predictions.
|
1010.5610
|
Selective Image Super-Resolution
|
cs.CV
|
In this paper we propose a vision system that performs image Super Resolution
(SR) with selectivity. Conventional SR techniques, either by multi-image fusion
or example-based construction, have failed to capitalize on the intrinsic
structural and semantic context in the image, and performed "blind" resolution
recovery to the entire image area. By comparison, we advocate example-based
selective SR whereby selectivity is exemplified in three aspects: region
selectivity (SR only at object regions), source selectivity (object SR with
trained object dictionaries), and refinement selectivity (object boundaries
refinement using matting). The proposed system takes over-segmented
low-resolution images as inputs, assimilates recent learning techniques of
sparse coding (SC) and grouped multi-task lasso (GMTL), and leads eventually to
a framework for joint figure-ground separation and interest object SR. The
efficiency of our framework is manifested in our experiments with subsets of
the VOC2009 and MSRC datasets. We also demonstrate several interesting vision
applications that can build on our system.
|
1010.5644
|
Fast-Decodable Asymmetric Space-Time Codes from Division Algebras
|
cs.IT math.IT math.RA
|
Multiple-input double-output (MIDO) codes are important in the near-future
wireless communications, where the portable end-user device is physically small
and will typically contain at most two receive antennas. Especially tempting is
the 4 x 2 channel due to its immediate applicability in the digital video
broadcasting (DVB). Such channels optimally employ rate-two space-time (ST)
codes consisting of (4 x 4) matrices. Unfortunately, such codes are in general
very complex to decode, hence setting forth a call for constructions with
reduced complexity.
Recently, some reduced complexity constructions have been proposed, but they
have mainly been based on different ad hoc methods and have resulted in
isolated examples rather than in a more general class of codes. In this paper,
it will be shown that a family of division algebra based MIDO codes will always
result in at least 37.5% worst-case complexity reduction, while maintaining
full diversity and, for the first time, the non-vanishing determinant (NVD)
property. The reduction follows from the fact that, similarly to the Alamouti
code, the codes will be subsets of matrix rings of the Hamiltonian quaternions,
hence allowing simplified decoding. At the moment, such reductions are among
the best known for rate-two MIDO codes. Several explicit constructions are
presented and shown to have excellent performance through computer simulations.
|
1010.5661
|
The Wideband Slope of Interference Channels: The Large Bandwidth Case
|
cs.IT math.IT
|
It is well known that minimum received energy per bit in the interference
channel is -1.59dB as if there were no interference. Thus, the best way to
mitigate interference is to operate the interference channel in the low-SNR
regime. However, when the SNR is small but non-zero, minimum energy per bit
alone does not characterize performance. Verdu introduced the wideband slope
S_0 to characterize the performance in this regime. We show that a wideband
slope of S_0/S_{0,no interference}=1/2 is achievable. This result is similar to
recent results on degrees of freedom in the high SNR regime, and we use a type
of interference alignment using delays to obtain the result. We also show that
in many cases the wideband slope is upper bounded by S_0/S_{0,no
interference}<=1/2 for large number of users K .
|
1010.5691
|
A Bio-Inspired Robust Adaptive Random Search Algorithm for Distributed
Beamforming
|
cs.IT math.IT
|
A bio-inspired robust adaptive random search algorithm (BioRARSA), designed
for distributed beamforming for sensor and relay networks, is proposed in this
work. It has been shown via a systematic framework that BioRARSA converges in
probability and its convergence time scales linearly with the number of
distributed transmitters. More importantly, extensive simulation results
demonstrate that the proposed BioRARSA outperforms existing adaptive
distributed beamforming schemes by as large as 29.8% on average. This increase
in performance results from the fact that BioRARSA can adaptively adjust its
sampling stepsize via the "swim" behavior inspired by the bacterial foraging
mechanism. Hence, the convergence time of BioRARSA is insensitive to the
initial sampling stepsize of the algorithm, which makes it robust against the
dynamic nature of distributed wireless networks.
|
1010.5694
|
Events! (Reactivity in urbiscript)
|
cs.PL cs.RO
|
Urbi SDK is a software platform for the development of portable robotic
applications. It features the Urbi UObject C++ middleware, to manage hardware
drivers and/or possibly remote software components, and urbiscript, a domain
specific programming language to orchestrate them. Reactivity is a key feature
of Urbi SDK, embodied in events in urbiscript. This paper presents the support
for events in urbiscript.
|
1010.5720
|
Information-theoretic inference of common ancestors
|
cs.IT math.IT
|
A directed acyclic graph (DAG) partially represents the conditional
independence structure among observations of a system if the local Markov
condition holds, that is, if every variable is independent of its
non-descendants given its parents. In general, there is a whole class of DAGs
that represents a given set of conditional independence relations. We are
interested in properties of this class that can be derived from observations of
a subsystem only. To this end, we prove an information theoretic inequality
that allows for the inference of common ancestors of observed parts in any DAG
representing some unknown larger system. More explicitly, we show that a large
amount of dependence in terms of mutual information among the observations
implies the existence of a common ancestor that distributes this information.
Within the causal interpretation of DAGs our result can be seen as a
quantitative extension of Reichenbach's Principle of Common Cause to more than
two variables. Our conclusions are valid also for non-probabilistic
observations such as binary strings, since we state the proof for an
axiomatized notion of mutual information that includes the stochastic as well
as the algorithmic version.
|
1010.5734
|
Exploiting Statistical Dependencies in Sparse Representations for Signal
Recovery
|
cs.IT math.IT
|
Signal modeling lies at the core of numerous signal and image processing
applications. A recent approach that has drawn considerable attention is sparse
representation modeling, in which the signal is assumed to be generated as a
combination of a few atoms from a given dictionary. In this work we consider a
Bayesian setting and go beyond the classic assumption of independence between
the atoms. The main goal of this paper is to introduce a statistical model that
takes such dependencies into account and show how this model can be used for
sparse signal recovery. We follow the suggestion of two recent works and assume
that the sparsity pattern is modeled by a Boltzmann machine, a commonly used
graphical model. For general dependency models, exact MAP and MMSE estimation
of the sparse representation becomes computationally complex. To simplify the
computations, we propose greedy approximations of the MAP and MMSE estimators.
We then consider a special case in which exact MAP is feasible, by assuming
that the dictionary is unitary and the dependency model corresponds to a
certain sparse graph. Exploiting this structure, we develop an efficient
message passing algorithm that recovers the underlying signal. When the model
parameters defining the underlying graph are unknown, we suggest an algorithm
that learns these parameters directly from the data, leading to an iterative
scheme for adaptive sparse signal recovery. The effectiveness of our approach
is demonstrated on real-life signals - patches of natural images - where we
compare the denoising performance to that of previous recovery methods that do
not exploit the statistical dependencies.
|
1010.5742
|
Stochastic Verification Theorem of Forward-Backward Controlled Systems
for Viscosity Solutions
|
math.OC cs.SY
|
In this paper, we investigate the controlled system described by
forward-backward stochastic differential equations with the control contained
in drift, diffusion and generator of BSDE. A new verification theorem is
derived within the framework of viscosity solutions without involving any
derivatives of the value functions. It is worth to pointing out that this
theorem has wider applicability than the restrictive classical verification
theorems. As a relevant problem, the optimal stochastic feedback controls for
forward-backward system are discussed as well.
|
1010.5764
|
(2,1)-separating systems beyond the probabilistic bound
|
math.CO cs.IT math.AG math.IT
|
Building on previous results of Xing, we give new lower bounds on the rate of
intersecting codes over large alphabets. The proof is constructive, and uses
algebraic geometry, although nothing beyond the basic theory of linear systems
on curves. Then, using these new bounds within a concatenation argument, we
construct binary (2,1)-separating systems of asymptotic rate exceeding the one
given by the probabilistic method, which was the best lower bound available up
to now. This answers (negatively) the question of whether this probabilistic
bound was exact, which has remained open for more than 30 years. (By the way,
we also give a formulation of the separation property in terms of metric
convexity, which may be an inspirational source for new research problems.)
|
1010.5771
|
Reward and cooperation in the spatial public goods game
|
physics.soc-ph cs.SI
|
The promise of punishment and reward in promoting public cooperation is
debatable. While punishment is traditionally considered more successful than
reward, the fact that the cost of punishment frequently fails to offset gains
from enhanced cooperation has lead some to reconsider reward as the main
catalyst behind collaborative efforts. Here we elaborate on the "stick versus
carrot" dilemma by studying the evolution of cooperation in the spatial public
goods game, where besides the traditional cooperators and defectors, rewarding
cooperators supplement the array of possible strategies. The latter are willing
to reward cooperative actions at a personal cost, thus effectively downgrading
pure cooperators to second-order free-riders due to their unwillingness to bear
these additional costs. Consequently, we find that defection remains viable,
especially if the rewarding is costly. Rewards, however, can promote
cooperation, especially if the synergetic effects of cooperation are low.
Surprisingly, moderate rewards may promote cooperation better than high
rewards, which is due to the spontaneous emergence of cyclic dominance between
the three strategies.
|
1010.5793
|
Percolation in self-similar networks
|
cond-mat.dis-nn cs.SI physics.soc-ph
|
We provide a simple proof that graphs in a general class of self-similar
networks have zero percolation threshold. The considered self-similar networks
include random scale-free graphs with given expected node degrees and zero
clustering, scale-free graphs with finite clustering and metric structure,
growing scale-free networks, and many real networks. The proof and the
derivation of the giant component size do not require the assumption that
networks are treelike. Our results rely only on the observation that
self-similar networks possess a hierarchy of nested subgraphs whose average
degree grows with their depth in the hierarchy. We conjecture that this
property is pivotal for percolation in networks.
|
1010.5806
|
Inner and Outer Bounds for the Gaussian Cognitive Interference Channel
and New Capacity Results
|
cs.IT math.IT
|
The capacity of the Gaussian cognitive interference channel, a variation of
the classical two-user interference channel where one of the transmitters
(referred to as cognitive) has knowledge of both messages, is known in several
parameter regimes but remains unknown in general. In this paper we provide a
comparative overview of this channel model as we proceed through our
contributions: we present a new outer bound based on the idea of a broadcast
channel with degraded message sets, and another series of outer bounds obtained
by transforming the cognitive channel into channels with known capacity. We
specialize the largest known inner bound derived for the discrete memoryless
channel to the Gaussian noise channel and present several simplified schemes
evaluated for Gaussian inputs in closed form which we use to prove a number of
results. These include a new set of capacity results for the a) "primary
decodes cognitive" regime, a subset of the "strong interference" regime that is
not included in the "very strong interference" regime for which capacity was
known, and for the b) "S-channel" in which the primary transmitter does not
interfere with the cognitive receiver. Next, for a general Gaussian cognitive
interference channel, we determine the capacity to within one bit/s/Hz and to
within a factor two regardless of channel parameters, thus establishing rate
performance guarantees at high and low SNR, respectively. We also show how
different simplified transmission schemes achieve a constant gap between inner
and outer bound for specific channels. Finally, we numerically evaluate and
compare the various simplified achievable rate regions and outer bounds in
parameter regimes where capacity is unknown, leading to further insight on the
capacity region of the Gaussian cognitive interference channel.
|
1010.5829
|
Robustness of a Network of Networks
|
physics.data-an cs.SI physics.soc-ph
|
Almost all network research has been focused on the properties of a single
network that does not interact and depends on other networks. In reality, many
real-world networks interact with other networks. Here we develop an analytical
framework for studying interacting networks and present an exact percolation
law for a network of $n$ interdependent networks. In particular, we find that
for $n$ Erd\H{o}s-R\'{e}nyi networks each of average degree $k$, the giant
component, $P_{\infty}$, is given by $P_{\infty}=p[1-\exp(-kP_{\infty})]^n$
where $1-p$ is the initial fraction of removed nodes. Our general result
coincides for $n=1$ with the known Erd\H{o}s-R\'{e}nyi second-order phase
transition for a single network. For any $n \geq 2$ cascading failures occur
and the transition becomes a first-order percolation transition. The new law
for $P_{\infty}$ shows that percolation theory that is extensively studied in
physics and mathematics is a limiting case ($n=1$) of a more general general
and different percolation law for interdependent networks.
|
1010.5891
|
A new muscle fatigue and recovery model and its ergonomics application
in human simulation
|
cs.RO
|
Although automatic techniques have been employed in manufacturing industries
to increase productivity and efficiency, there are still lots of manual
handling jobs, especially for assembly and maintenance jobs. In these jobs,
musculoskeletal disorders (MSDs) are one of the major health problems due to
overload and cumulative physical fatigue. With combination of conventional
posture analysis techniques, digital human modelling and simulation (DHM)
techniques have been developed and commercialized to evaluate the potential
physical exposures. However, those ergonomics analysis tools are mainly based
on posture analysis techniques, and until now there is still no fatigue index
available in the commercial software to evaluate the physical fatigue easily
and quickly. In this paper, a new muscle fatigue and recovery model is proposed
and extended to evaluate joint fatigue level in manual handling jobs. A special
application case is described and analyzed by digital human simulation
technique.
|
1010.5938
|
Stable Takens' Embeddings for Linear Dynamical Systems
|
cs.SY cs.IT math.DS math.IT math.OC
|
Takens' Embedding Theorem remarkably established that concatenating M
previous outputs of a dynamical system into a vector (called a delay coordinate
map) can be a one-to-one mapping of a low-dimensional attractor from the system
state space. However, Takens' theorem is fragile in the sense that even small
imperfections can induce arbitrarily large errors in this attractor
representation. We extend Takens' result to establish deterministic, explicit
and non-asymptotic sufficient conditions for a delay coordinate map to form a
stable embedding in the restricted case of linear dynamical systems and
observation functions. Our work is inspired by the field of Compressive Sensing
(CS), where results guarantee that low-dimensional signal families can be
robustly reconstructed if they are stably embedded by a measurement operator.
However, in contrast to typical CS results, i) our sufficient conditions are
independent of the size of the ambient state space, and ii) some system and
measurement pairs have fundamental limits on the conditioning of the embedding
(i.e., how close it is to an isometry), meaning that further measurements
beyond some point add no further significant value. We use several simple
simulations to explore the conditions of the main results, including the
tightness of the bounds and the convergence speed of the stable embedding. We
also present an example task of estimating the attractor dimension from
time-series data to highlight the value of stable embeddings over traditional
Takens' embeddings.
|
1010.5943
|
Random Graph Generator for Bipartite Networks Modeling
|
cs.AI cs.SI physics.soc-ph
|
The purpose of this article is to introduce a new iterative algorithm with
properties resembling real life bipartite graphs. The algorithm enables us to
generate wide range of random bigraphs, which features are determined by a set
of parameters.We adapt the advances of last decade in unipartite complex
networks modeling to the bigraph setting. This data structure can be observed
in several situations. However, only a few datasets are freely available to
test the algorithms (e.g. community detection, influential nodes
identification, information retrieval) which operate on such data. Therefore,
artificial datasets are needed to enhance development and testing of the
algorithms. We are particularly interested in applying the generator to the
analysis of recommender systems. Therefore, we focus on two characteristics
that, besides simple statistics, are in our opinion responsible for the
performance of neighborhood based collaborative filtering algorithms. The
features are node degree distribution and local clustering coeficient.
|
1010.5954
|
Random Graphs for Performance Evaluation of Recommender Systems
|
cs.AI cs.SI physics.soc-ph
|
The purpose of this article is to introduce a new analytical framework
dedicated to measuring performance of recommender systems. The standard
approach is to assess the quality of a system by means of accuracy related
statistics. However, the specificity of the environments in which recommender
systems are deployed requires to pay much attention to speed and memory
requirements of the algorithms. Unfortunately, it is implausible to assess
accurately the complexity of various algorithms with formal tools. This can be
attributed to the fact that such analyses are usually based on an assumption of
dense representation of underlying data structures. Whereas, in real life the
algorithms operate on sparse data and are implemented with collections
dedicated for them. Therefore, we propose to measure the complexity of
recommender systems with artificial datasets that posses real-life properties.
We utilize recently developed bipartite graph generator to evaluate how
state-of-the-art recommender systems' behavior is determined and diversified by
topological properties of the generated datasets.
|
1010.5990
|
The Nature of Explosive Percolation Phase Transition
|
cond-mat.dis-nn cond-mat.stat-mech cs.SI physics.soc-ph
|
In this Letter, we show that the explosive percolation is a novel continuous
phase transition. The order-parameter-distribution histogram at the percolation
threshold is studied in Erd\H{o}s-R\'{e}nyi networks, scale-free networks, and
square lattice. In finite system, two well-defined Gaussian-like peaks coexist,
and the valley between the two peaks is suppressed with the system size
increasing. This finite-size effect always appears in typical first-order phase
transition. However, both of the two peaks shift to zero point in a power law
manner, which indicates the explosive percolation is continuous in the
thermodynamic limit. The nature of explosive percolation in all the three
structures belongs to this novel continuous phase transition. Various scaling
exponents concerning the order-parameter-distribution are obtained.
|
1010.6020
|
The Effect of Spatial Coupling on Compressive Sensing
|
cs.IT math.IT
|
Recently, it was observed that spatially-coupled LDPC code ensembles approach
the Shannon capacity for a class of binary-input memoryless symmetric (BMS)
channels. The fundamental reason for this was attributed to a "threshold
saturation" phenomena derived by Kudekar, Richardson and Urbanke. In
particular, it was shown that the belief propagation (BP) threshold of the
spatially coupled codes is equal to the maximum a posteriori (MAP) decoding
threshold of the underlying constituent codes. In this sense, the BP threshold
is saturated to its maximum value. Moreover, it has been empirically observed
that the same phenomena also occurs when transmitting over more general classes
of BMS channels. In this paper, we show that the effect of spatial coupling is
not restricted to the realm of channel coding. The effect of coupling also
manifests itself in compressed sensing. Specifically, we show that
spatially-coupled measurement matrices have an improved sparsity to sampling
threshold for reconstruction algorithms based on verification decoding. For
BP-based reconstruction algorithms, this phenomenon is also tested empirically
via simulation. At the block lengths accessible via simulation, the effect is
quite small and it seems that spatial coupling is not providing the gains one
might expect. Based on the threshold analysis, however, we believe this
warrants further study.
|
1010.6032
|
Recurrence-based time series analysis by means of complex network
methods
|
nlin.CD cs.SI physics.data-an physics.soc-ph
|
Complex networks are an important paradigm of modern complex systems sciences
which allows quantitatively assessing the structural properties of systems
composed of different interacting entities. During the last years, intensive
efforts have been spent on applying network-based concepts also for the
analysis of dynamically relevant higher-order statistical properties of time
series. Notably, many corresponding approaches are closely related with the
concept of recurrence in phase space. In this paper, we review recent
methodological advances in time series analysis based on complex networks, with
a special emphasis on methods founded on recurrence plots. The potentials and
limitations of the individual methods are discussed and illustrated for
paradigmatic examples of dynamical systems as well as for real-world time
series. Complex network measures are shown to provide information about
structural features of dynamical systems that are complementary to those
characterized by other methods of time series analysis and, hence,
substantially enrich the knowledge gathered from other existing (linear as well
as nonlinear) approaches.
|
1010.6057
|
Ergodic Secret Alignment
|
cs.IT cs.CR math.IT
|
In this paper, we introduce two new achievable schemes for the fading
multiple access wiretap channel (MAC-WT). In the model that we consider, we
assume that perfect knowledge of the state of all channels is available at all
the nodes in a causal fashion. Our schemes use this knowledge together with the
time varying nature of the channel model to align the interference from
different users at the eavesdropper perfectly in a one-dimensional space while
creating a higher dimensionality space for the interfering signals at the
legitimate receiver hence allowing for better chance of recovery. While we
achieve this alignment through signal scaling at the transmitters in our first
scheme (scaling based alignment (SBA)), we let nature provide this alignment
through the ergodicity of the channel coefficients in the second scheme
(ergodic secret alignment (ESA)). For each scheme, we obtain the resulting
achievable secrecy rate region. We show that the secrecy rates achieved by both
schemes scale with SNR as 1/2log(SNR). Hence, we show the sub-optimality of the
i.i.d. Gaussian signaling based schemes with and without cooperative jamming by
showing that the secrecy rates achieved using i.i.d. Gaussian signaling with
cooperative jamming do not scale with SNR. In addition, we introduce an
improved version of our ESA scheme where we incorporate cooperative jamming to
achieve higher secrecy rates. Moreover, we derive the necessary optimality
conditions for the power control policy that maximizes the secrecy sum rate
achievable by our ESA scheme when used solely and with cooperative jamming.
|
1010.6091
|
Network motifs in music sequences
|
physics.soc-ph cs.CL physics.data-an
|
This paper has been withdrawn by the author because it needs a deep
methodological revision.
|
1010.6096
|
Multi-Sensor Fuzzy Data Fusion Using Sensors with Different
Characteristics
|
eess.SY cs.SY
|
This paper proposes a new approach to multi-sensor data fusion. It suggests
that aggregation of data from multiple sensors can be done more efficiently
when we consider information about sensors' different characteristics. Similar
to most research on effective sensors' characteristics, especially in control
systems, our focus is on sensors' accuracy and frequency response. A rule-based
fuzzy system is presented for fusion of raw data obtained from the sensors that
have complement characteristics in accuracy and bandwidth. Furthermore, a fuzzy
predictor system is suggested aiming for extreme accuracy which is a common
need in highly sensitive applications. Advantages of our proposed sensor fusion
system are shown by simulation of a control system utilizing the fusion system
for output estimation.
|
1010.6148
|
On a small-gain approach to distributed event-triggered control
|
math.OC cs.SY
|
In this paper the problem of stabilizing large-scale systems by distributed
controllers, where the controllers exchange information via a shared limited
communication medium is addressed. Event-triggered sampling schemes are
proposed, where each system decides when to transmit new information across the
network based on the crossing of some error thresholds. Stability of the
interconnected large-scale system is inferred by applying a generalized
small-gain theorem. Two variations of the event-triggered controllers which
prevent the occurrence of the Zeno phenomenon are also discussed.
|
1010.6165
|
Sampling of operators
|
math.FA cs.IT math.CA math.IT
|
Sampling and reconstruction of functions is a central tool in science. A key
result is given by the sampling theorem for bandlimited functions attributed to
Whittaker, Shannon, Nyquist, and Kotelnikov. We develop an analogous sampling
theory for operators which we call bandlimited if their Kohn-Nirenberg symbols
are bandlimited. We prove sampling theorems for such operators and show that
they are extensions of the classical sampling theorem.
|
1010.6178
|
Fractionally Predictive Spiking Neurons
|
q-bio.NC cs.NE
|
Recent experimental work has suggested that the neural firing rate can be
interpreted as a fractional derivative, at least when signal variation induces
neural adaptation. Here, we show that the actual neural spike-train itself can
be considered as the fractional derivative, provided that the neural signal is
approximated by a sum of power-law kernels. A simple standard thresholding
spiking neuron suffices to carry out such an approximation, given a suitable
refractory response. Empirically, we find that the online approximation of
signals with a sum of power-law kernels is beneficial for encoding signals with
slowly varying components, like long-memory self-similar signals. For such
signals, the online power-law kernel approximation typically required less than
half the number of spikes for similar SNR as compared to sums of similar but
exponentially decaying kernels. As power-law kernels can be accurately
approximated using sums or cascades of weighted exponentials, we demonstrate
that the corresponding decoding of spike-trains by a receiving neuron allows
for natural and transparent temporal signal filtering by tuning the weights of
the decoding kernel.
|
1010.6214
|
The assembly modes of rigid 11-bar linkages
|
cs.RO cs.SC
|
Designing an m-bar linkage with a maximal number of assembly modes is
important in robot kinematics, and has further applications in structural
biology and computational geometry. A related question concerns the number of
assembly modes of rigid mechanisms as a function of their nodes n, which is
uniquely defined given m. Rigid 11-bar linkages, where n=7, are the simplest
planar linkages for which these questions were still open. It will be proven
that the maximal number of assembly modes of such linkages is exactly 56. The
rigidity of a linkage is captured by a polynomial system derived from distance,
or Cayley-Menger, matrices. The upper bound on the number of assembly modes is
obtained as the mixed volume of a 5x5 system. An 11-bar linkage admitting 56
configurations is constructed using stochastic optimisation methods. This
yields a general lower bound of $\Omega(2.3^n)$ on the number of assembly
modes, slightly improving the current record of $\Omega(2.289^n)$, while the
best known upper bound is roughly $4^n$. Our methods are straightforward and
have been implemented in Maple. They are described in general terms
illustrating the fact that they can be readily extended to other planar or
spatial linkages. The main results have been reported in conference publication
[EM11]. This version (2017) typesets correctly the last Figure 5 so as to
include all 28 configurations modulo reflection.
|
1010.6234
|
Analysing the behaviour of robot teams through relational sequential
pattern mining
|
cs.AI cs.LG cs.MA
|
This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the effective collaboration among the members of a team and comparing
the different multi-agent behaviours. Using external observations of a
Multi-Agent System to analyse, model, recognize agent behaviour could be very
useful to direct team actions. In particular, this report focuses on the
challenge of autonomous unsupervised sequential learning of the team's
behaviour from observations. Our approach allows to learn a symbolic sequence
(a relational representation) to translate raw multi-agent, multi-variate
observations of a dynamic, complex environment, into a set of sequential
behaviours that are characteristic of the team in question, represented by a
set of sequences expressed in first-order logic atoms. We propose to use a
relational learning algorithm to mine meaningful frequent patterns among the
relational sequences to characterise team behaviours. We compared the
performance of two teams in the RoboCup four-legged league environment, that
have a very different approach to the game. One uses a Case Based Reasoning
approach, the other uses a pure reactive behaviour.
|
1010.6242
|
GraphDuplex: visualisation simultan\'ee de N r\'eseaux coupl\'es 2 par 2
|
cs.IR
|
While social network analysis often focuses on graph structure of social
actors, an increasing number of communication networks now provide textual
content within social activity (email, instant messaging, blogging,
collaboration networks). We present an open source visualization software,
GraphDuplex, which brings together social structure and textual content, adding
a semantic dimension to social analysis. GraphDuplex eventually connects any
number of social or semantic graphs together, and through dynamic queries
enables user interaction and exploration across multiple graphs of different
nature.
|
1010.6247
|
Symmetry in Shannon's Noiseless Coding Theorem
|
cs.IT math.IT
|
Statements of Shannon's Noiseless Coding Theorem by various authors,
including the original, are reviewed and clarified. Traditional statements of
the theorem are often unclear as to when it applies. A new notation is
introduced and the domain of application is clarified. An examination of the
bounds of the Theorem leads to a new symmetric restatement. It is shown that
the extended upper bound is an acheivable upper bound, giving symmetry to the
theorem.The relation of information entropy to the physical entropy of Gibbs
and Boltmann is illustrated. Consequently, the study of Shannon Entropy is
strongly related to physics and there is a physical theory of information. This
paper is the beginning of of an attempt to clarify these relationships.
|
1010.6255
|
On the Capacity of the 2-user Gaussian MAC Interfering with a P2P Link
|
cs.IT math.IT
|
A multiple access channel and a point-to-point channel sharing the same
medium for communications are considered. We obtain an outer bound for the
capacity region of this setup, and we show that this outer bound is achievable
in some cases. These cases are mainly when interference is strong or very
strong. A sum capacity upper bound is also obtained, which is nearly tight if
the interference power at the receivers is low. In this case, using Gaussian
codes and treating interference as noise achieves a sum rate close to the upper
bound.
|
1010.6280
|
Optimum Transmission Policies for Battery Limited Energy Harvesting
Nodes
|
cs.IT math.IT
|
Wireless networks with energy harvesting battery powered nodes are quickly
emerging as a viable option for future wireless networks with extended
lifetime. Equally important to their counterpart in the design of energy
harvesting radios are the design principles that this new networking paradigm
calls for. In particular, unlike wireless networks considered up to date, the
energy replenishment process and the storage constraints of the rechargeable
batteries need to be taken into account in designing efficient transmission
strategies. In this work, we consider such transmission policies for
rechargeable nodes, and identify the optimum solution for two related problems.
Specifically, the transmission policy that maximizes the short term throughput,
i.e., the amount of data transmitted in a finite time horizon is found. In
addition, we show the relation of this optimization problem to another, namely,
the minimization of the transmission completion time for a given amount of
data, and solve that as well. The transmission policies are identified under
the constraints on energy causality, i.e., energy replenishment process, as
well as the energy storage, i.e., battery capacity. The power-rate relationship
for this problem is assumed to be an increasing concave function, as dictated
by information theory. For battery replenishment, a model with discrete packets
of energy arrivals is considered. We derive the necessary conditions that the
throughput-optimal allocation satisfies, and then provide the algorithm that
finds the optimal transmission policy with respect to the short-term throughput
and the minimum transmission completion time. Numerical results are presented
to confirm the analytical findings.
|
1010.6290
|
Symmetric Capacity of the Gaussian Interference Channel with an
Out-of-Band Relay to within 1.15 Bits
|
cs.IT math.IT
|
This work studies the Gaussian interference channel (IC) with a relay, which
transmits and receives in a band that is orthogonal to the IC. The channel
associated with the relay is thus an out-of-band relay channel (OBRC). The
focus is on a symmetric channel model, in order to assess the fundamental
impact of the OBRC on the signal interaction of the IC, in the simplest
possible setting. First, the linear deterministic model is investigated and the
sum capacity of this channel is established for all possible channel
parameters. In particular, it is observed that the impact of OBRC, as its links
get stronger, is similar to that of output feedback for the IC. The insights
obtained from the deterministic model are then used to design achievable
schemes for the Gaussian model. The interference links are classified as
extremely strong, very strong, strong, moderate, weak, and very weak. For
strong and moderate interference, separate encoding is near optimal. For very
strong and extremely strong interference, the interference links provide side
information to the destinations, which can help the transmission through the
OBRC. For weak or very weak interference, an extension of the Han-Kobayashi
scheme for the IC is utilized, where the messages are split into common and
private. To achieve higher rates, it is beneficial to further split the common
message into two parts, and the OBRC plays an important role in decoding the
common message. It is shown that our strategy achieves the symmetric capacity
to within 1.14625 bits per channel use with duplexing factor 0.5, and 1.27125
bits per channel use for arbitrary duplexing factors, for all channel
parameters. An important observation from the constant gap result is that
strong interference can be beneficial with the presence of an OBR.
|
1011.0027
|
Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility
Maximization under Imperfect Channel-State Information
|
cs.IT cs.NI math.IT
|
We consider the problem of simultaneous user-scheduling, power-allocation,
and rate-selection in an OFDMA downlink, with the goal of maximizing expected
sum-utility under a sum-power constraint. In doing so, we consider a family of
generic goodput-based utilities that facilitate, e.g., throughput-based
pricing, quality-of-service enforcement, and/or the treatment of practical
modulation-and-coding schemes (MCS). Since perfect knowledge of channel state
information (CSI) may be difficult to maintain at the base-station, especially
when the number of users and/or subchannels is large, we consider scheduling
and resource allocation under imperfect CSI, where the channel state is
described by a generic probability distribution. First, we consider the
"continuous" case where multiple users and/or code rates can time-share a
single OFDMA subchannel and time slot. This yields a non-convex optimization
problem that we convert into a convex optimization problem and solve exactly
using a dual optimization approach. Second, we consider the "discrete" case
where only a single user and code rate is allowed per OFDMA subchannel per time
slot. For the mixed-integer optimization problem that arises, we discuss the
connections it has with the continuous case and show that it can solved exactly
in some situations. For the other situations, we present a bound on the
optimality gap. For both cases, we provide algorithmic implementations of the
obtained solution. Finally, we study, numerically, the performance of the
proposed algorithms under various degrees of CSI uncertainty, utilities, and
OFDMA system configurations. In addition, we demonstrate advantages relative to
existing state-of-the-art algorithms.
|
1011.0041
|
Predictive State Temporal Difference Learning
|
cs.LG cs.AI
|
We propose a new approach to value function approximation which combines
linear temporal difference reinforcement learning with subspace identification.
In practical applications, reinforcement learning (RL) is complicated by the
fact that state is either high-dimensional or partially observable. Therefore,
RL methods are designed to work with features of state rather than state
itself, and the success or failure of learning is often determined by the
suitability of the selected features. By comparison, subspace identification
(SSID) methods are designed to select a feature set which preserves as much
information as possible about state. In this paper we connect the two
approaches, looking at the problem of reinforcement learning with a large set
of features, each of which may only be marginally useful for value function
approximation. We introduce a new algorithm for this situation, called
Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive
state representations, PSTD finds a linear compression operator that projects a
large set of features down to a small set that preserves the maximum amount of
predictive information. As in RL, PSTD then uses a Bellman recursion to
estimate a value function. We discuss the connection between PSTD and prior
approaches in RL and SSID. We prove that PSTD is statistically consistent,
perform several experiments that illustrate its properties, and demonstrate its
potential on a difficult optimal stopping problem.
|
1011.0051
|
Proceedings Fourth Workshop on Membrane Computing and Biologically
Inspired Process Calculi 2010
|
cs.LO cs.CE cs.DC
|
The 4th Workshop on Membrane Computing and Biologically Inspired Process
Calculi (MeCBIC 2010) is organized in Jena as a satellite event of the Eleventh
International Conference on Membrane Computing (CMC11). Biological membranes
play a fundamental role in the complex reactions which take place in cells of
living organisms. The importance of this role has been considered in two
different types of formalisms introduced recently. Membrane systems were
introduced as a class of distributed parallel computing devices inspired by the
observation that any biological system is a complex hierarchical structure,
with a flow of biochemical substances and information that underlies their
functioning. The modeling and analysis of biological systems has also attracted
considerable interest of the process algebra research community. Thus the
notions of membranes and compartments have been explicitly represented in a
family of calculi, such as ambients and brane calculi. A cross fertilization of
these two research areas has recently started. A deeper investigation of the
relationships between these related formalisms is interesting, as it is
important to understand the crucial similarities and the differences. The main
aim of the workshop is to bring together researchers working on membrane
computing, in biologically inspired process calculi, and in other related
fields, in order to present recent results and to discuss new ideas concerning
such formalisms, their properties and relationships.
|
1011.0093
|
Fast Color Quantization Using Weighted Sort-Means Clustering
|
cs.CV cs.GR
|
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.
|
1011.0097
|
Sparse Inverse Covariance Selection via Alternating Linearization
Methods
|
cs.LG math.OC stat.ML
|
Gaussian graphical models are of great interest in statistical learning.
Because the conditional independencies between different nodes correspond to
zero entries in the inverse covariance matrix of the Gaussian distribution, one
can learn the structure of the graph by estimating a sparse inverse covariance
matrix from sample data, by solving a convex maximum likelihood problem with an
$\ell_1$-regularization term. In this paper, we propose a first-order method
based on an alternating linearization technique that exploits the problem's
special structure; in particular, the subproblems solved in each iteration have
closed-form solutions. Moreover, our algorithm obtains an $\epsilon$-optimal
solution in $O(1/\epsilon)$ iterations. Numerical experiments on both synthetic
and real data from gene association networks show that a practical version of
this algorithm outperforms other competitive algorithms.
|
1011.0098
|
Qualitative Reasoning about Relative Direction on Adjustable Levels of
Granularity
|
cs.AI
|
An important issue in Qualitative Spatial Reasoning is the representation of
relative direction. In this paper we present simple geometric rules that enable
reasoning about relative direction between oriented points. This framework, the
Oriented Point Algebra OPRA_m, has a scalable granularity m. We develop a
simple algorithm for computing the OPRA_m composition tables and prove its
correctness. Using a composition table, algebraic closure for a set of OPRA
statements is sufficient to solve spatial navigation tasks. And it turns out
that scalable granularity is useful in these navigation tasks.
|
1011.0187
|
A Distributed AI Aided 3D Domino Game
|
cs.AI
|
In the article a turn-based game played on four computers connected via
network is investigated. There are three computers with natural intelligence
and one with artificial intelligence. Game table is seen by each player's own
view point in all players' monitors. Domino pieces are three dimensional. For
distributed systems TCP/IP protocol is used. In order to get 3D image,
Microsoft XNA technology is applied. Domino 101 game is nondeterministic game
that is result of the game depends on the initial random distribution of the
pieces. Number of the distributions is equal to the multiplication of following
combinations: . Moreover, in this game that is played by four people, players
are divided into 2 pairs. Accordingly, we cannot predict how the player uses
the dominoes that is according to the dominoes of his/her partner or according
to his/her own dominoes. The fact that the natural intelligence can be a player
in any level affects the outcome. These reasons make it difficult to develop an
AI. In the article four levels of AI are developed. The AI in the first level
is equivalent to the intelligence of a child who knows the rules of the game
and recognizes the numbers. The AI in this level plays if it has any domino,
suitable to play or says pass. In most of the games which can be played on the
internet, the AI does the same. But the AI in the last level is a master
player, and it can develop itself according to its competitors' levels.
|
1011.0190
|
Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on
Rough Set Theory
|
cs.AI
|
In this paper it is considered rule reduct generation problem, based on Rough
Set Theory. Rule Reduct Generation (RG) and Modified Rule Generation (MRG)
algorithms are well-known. Alternative to these algorithms Pruning Algorithm of
Generation A Minimal Set of Rule Reducts, or briefly Pruning Rule Generation
(PRG) algorithm is developed. PRG algorithm uses tree structured data type. PRG
algorithm is compared with RG and MRG algorithms
|
1011.0208
|
Network Diversity and Economic Development: a Comment
|
cs.SI physics.soc-ph
|
Network diversity yields context-dependent benefits that are not yet
fully-understood. I elaborate on a recently introduced distinction between tie
strength diversity and information source diversity, and explain when, how, and
why they matter. The issue whether there are benefits to specialization is the
key.
|
1011.0233
|
Reasoning about Cardinal Directions between Extended Objects: The
Hardness Result
|
cs.AI
|
The cardinal direction calculus (CDC) proposed by Goyal and Egenhofer is a
very expressive qualitative calculus for directional information of extended
objects. Early work has shown that consistency checking of complete networks of
basic CDC constraints is tractable while reasoning with the CDC in general is
NP-hard. This paper shows, however, if allowing some constraints unspecified,
then consistency checking of possibly incomplete networks of basic CDC
constraints is already intractable. This draws a sharp boundary between the
tractable and intractable subclasses of the CDC. The result is achieved by a
reduction from the well-known 3-SAT problem.
|
1011.0234
|
Cascade of failures in coupled network systems with multiple
support-dependent relations
|
physics.data-an cs.SI nlin.CD physics.soc-ph
|
We study, both analytically and numerically, the cascade of failures in two
coupled network systems A and B, where multiple support-dependent relations are
randomly built between nodes of networks A and B. In our model we assume that
each node in one network can function only if it has at least a single support
node in the other network. If both networks A and B are Erd\H{o}s-R\'enyi
networks, A and B, with (i) sizes $N^A$ and $N^B$, (ii) average degrees $a$ and
$b$, and (iii) $c^{AB}_0N^B$ support links from network A to B and
$c^{BA}_0N^B$ support links from network B to A, we find that under random
attack with removal of fractions $(1-R^A)N^A$ and $(1-R^B)N^B$ nodes
respectively, the percolating giant components of both networks at the end of
the cascading failures, $\mu^A_\infty$ and $\mu^B_\infty$, are given by the
percolation laws $\mu^A_\infty = R^A [1-\exp{({-c^{BA}_0\mu^B_\infty})}]
[1-\exp{({-a\mu^A_\infty})}]$ and $\mu^B_\infty = R^B
[1-\exp{({-c^{AB}_0\mu^A_\infty})}] [1-\exp{({-b\mu^B_\infty})}]$. In the limit
of $c^{BA}_0 \to \infty$ and $c^{AB}_0 \to \infty$, both networks become
independent, and the giant components are equivalent to a random attack on a
single Erd\H{o}s-R\'enyi network. We also test our theory on two coupled
scale-free networks, and find good agreement with the simulations.
|
1011.0250
|
Delineation of Raw Plethysmograph using Wavelets for Mobile based Pulse
Oximeters
|
cs.CE
|
The non-invasive pulse-oximeter is a crucial parameter in continuous
monitoring systems. It plays a vital role from admission of the patient to
surgeries with general anaesthesia. The paper proposes the application of
wavelet transform to delineate the raw plethysmograph signals obtained from
basic portable and mobile-powered electronic hardware. The paper primarily
focuses on finding peaks and baseline from noisy infrared and red waveforms
which are responsible for calculating heart-rate and oxygen saturation
percentages.
|
1011.0271
|
Spontaneous Formation of Dynamical Groups in an Adaptive Networked
System
|
cond-mat.dis-nn cs.SI nlin.AO physics.soc-ph
|
In this work, we investigate a model of an adaptive networked dynamical
system, where the coupling strengths among phase oscillators coevolve with the
phase states. It is shown that in this model the oscillators can spontaneously
differentiate into two dynamical groups after a long time evolution. Within
each group, the oscillators have similar phases, while oscillators in different
groups have approximately opposite phases. The network gradually converts from
the initial random structure with a uniform distribution of connection
strengths into a modular structure which is characterized by strong intra
connections and weak inter connections. Furthermore, the connection strengths
follow a power law distribution, which is a natural consequence of the
coevolution of the network and the dynamics. Interestingly, it is found that if
the inter connections are weaker than a certain threshold, the two dynamical
groups will almost decouple and evolve independently. These results are helpful
in further understanding the empirical observations in many social and
biological networks.
|
1011.0279
|
Mobile Based Secure Digital Wallet for Peer to Peer Payment System
|
cs.CE
|
E-commerce in today's conditions has the highest dependence on network
infrastructure of banking. However, when the possibility of communicating with
the Banking network is not provided, business activities will suffer. This
paper proposes a new approach of digital wallet based on mobile devices without
the need to exchange physical money or communicate with banking network. A
digital wallet is a software component that allows a user to make an electronic
payment in cash (such as a credit card or a digital coin), and hides the
low-level details of executing the payment protocol that is used to make the
payment. The main features of proposed architecture are secure awareness, fault
tolerance, and infrastructure-less protocol.
|
1011.0298
|
Intuitionistic Fuzzy Ideal Extensions of {\Gamma}-Semigroups
|
cs.IT math.IT
|
In this paper the concept of the extensions of intuitionistic fuzzy ideals in
a semigroup has been extended to a {\Gamma}-Semigroups. Among other results
characterization of prime ideals in a {\Gamma}-Semigroups in terms of
intuitionistic fuzzy ideal extension has been obtained.
|
1011.0306
|
Semantic Query Optimisation with Ontology Simulation
|
cs.IR
|
Semantic Web is, without a doubt, gaining momentum in both industry and
academia. The word "Semantic" refers to "meaning" - a semantic web is a web of
meaning. In this fast changing and result oriented practical world, gone are
the days where an individual had to struggle for finding information on the
Internet where knowledge management was the major issue. The semantic web has a
vision of linking, integrating and analysing data from various data sources and
forming a new information stream, hence a web of databases connected with each
other and machines interacting with other machines to yield results which are
user oriented and accurate. With the emergence of Semantic Web framework the
na\"ive approach of searching information on the syntactic web is clich\'e.
This paper proposes an optimised semantic searching of keywords exemplified by
simulation an ontology of Indian universities with a proposed algorithm which
ramifies the effective semantic retrieval of information which is easy to
access and time saving.
|
1011.0328
|
Mining Frequent Itemsets Using Genetic Algorithm
|
cs.DB
|
In general frequent itemsets are generated from large data sets by applying
association rule mining algorithms like Apriori, Partition, Pincer-Search,
Incremental, Border algorithm etc., which take too much computer time to
compute all the frequent itemsets. By using Genetic Algorithm (GA) we can
improve the scenario. The major advantage of using GA in the discovery of
frequent itemsets is that they perform global search and its time complexity is
less compared to other algorithms as the genetic algorithm is based on the
greedy approach. The main aim of this paper is to find all the frequent
itemsets from given data sets using genetic algorithm.
|
1011.0330
|
Imitation learning of motor primitives and language bootstrapping in
robots
|
cs.AI
|
Imitation learning in robots, also called programing by demonstration, has
made important advances in recent years, allowing humans to teach context
dependant motor skills/tasks to robots. We propose to extend the usual contexts
investigated to also include acoustic linguistic expressions that might denote
a given motor skill, and thus we target joint learning of the motor skills and
their potential acoustic linguistic name. In addition to this, a modification
of a class of existing algorithms within the imitation learning framework is
made so that they can handle the unlabeled demonstration of several tasks/motor
primitives without having to inform the imitator of what task is being
demonstrated or what the number of tasks are, which is a necessity for language
learning, i.e; if one wants to teach naturally an open number of new motor
skills together with their acoustic names. Finally, a mechanism for detecting
whether or not linguistic input is relevant to the task is also proposed, and
our architecture also allows the robot to find the right framing for a given
identified motor primitive. With these additions it becomes possible to build
an imitator that bridges the gap between imitation learning and language
learning by being able to learn linguistic expressions using methods from the
imitation learning community. In this sense the imitator can learn a word by
guessing whether a certain speech pattern present in the context means that a
specific task is to be executed. The imitator is however not assumed to know
that speech is relevant and has to figure this out on its own by looking at the
demonstrations: indeed, the architecture allows the robot to transparently also
learn tasks which should not be triggered by an acoustic word, but for example
by the color or position of an object or a gesture made by someone in the
environment. To demonstrate this ability to find the ...
|
1011.0338
|
Effects of Sequence Partitioning on Compression Rate
|
cs.IT math.IT
|
In the paper, a theoretical work is done for investigating effects of
splitting data sequence into packs of data set. We proved that a partitioning
of data sequence is possible to find such that the entropy rate at each
subsequence is lower than entropy rate of the source. Effects of sequence
partitioning on overall compression rate are argued on the bases of
partitioning statistics, and then, an optimization problem for an optimal
partition is defined to improve overall compression rate of a sequence.
|
1011.0350
|
Developing courses with HoloRena, a framework for scenario- and game
based e-learning environments
|
cs.LG cs.HC cs.SE
|
However utilizing rich, interactive solutions can make learning more
effective and attractive, scenario- and game-based educational resources on the
web are not widely used. Creating these applications is a complex, expensive
and challenging process. Development frameworks and authoring tools hardly
support reusable components, teamwork and learning management
system-independent courseware architecture. In this article we initiate the
concept of a low-level, thick-client solution addressing these problems. With
some example applications we try to demonstrate, how a framework, based on this
concept can be useful for developing scenario- and game-based e-learning
environments.
|
1011.0362
|
Optimization of artificial flockings by means of anisotropy measurements
|
physics.bio-ph cs.AI nlin.AO
|
An effective procedure to determine the optimal parameters appearing in
artificial flockings is proposed in terms of optimization problems. We
numerically examine genetic algorithms (GAs) to determine the optimal set of
parameters such as the weights for three essential interactions in BOIDS by
Reynolds (1987) under `zero-collision' and `no-breaking-up' constraints. As a
fitness function (the energy function) to be maximized by the GA, we choose the
so-called the $\gamma$-value of anisotropy which can be observed empirically in
typical flocks of starling. We confirm that the GA successfully finds the
solution having a large $\gamma$-value leading-up to a strong anisotropy. The
numerical experience shows that the procedure might enable us to make more
realistic and efficient artificial flocking of starling even in our personal
computers. We also evaluate two distinct types of interactions in agents,
namely, metric and topological definitions of interactions. We confirmed that
the topological definition can explain the empirical evidence much better than
the metric definition does.
|
1011.0397
|
Efficient Approximation of Optimal Control for Markov Games
|
cs.GT cs.SY math.OC
|
We study the time-bounded reachability problem for continuous-time Markov
decision processes (CTMDPs) and games (CTMGs). Existing techniques for this
problem use discretisation techniques to break time into discrete intervals,
and optimal control is approximated for each interval separately. Current
techniques provide an accuracy of O(\epsilon^2) on each interval, which leads
to an infeasibly large number of intervals. We propose a sequence of
approximations that achieve accuracies of O(\epsilon^3), O(\epsilon^4), and
O(\epsilon^5), that allow us to drastically reduce the number of intervals that
are considered. For CTMDPs, the performance of the resulting algorithms is
comparable to the heuristic approach given by Buckholz and Schulz, while also
being theoretically justified. All of our results generalise to CTMGs, where
our results yield the first practically implementable algorithms for this
problem. We also provide positional strategies for both players that achieve
similar error bounds.
|
1011.0404
|
A New Email Retrieval Ranking Approach
|
cs.IR
|
Email Retrieval task has recently taken much attention to help the user
retrieve the email(s) related to the submitted query. Up to our knowledge,
existing email retrieval ranking approaches sort the retrieved emails based on
some heuristic rules, which are either search clues or some predefined user
criteria rooted in email fields. Unfortunately, the user usually does not know
the effective rule that acquires best ranking related to his query. This paper
presents a new email retrieval ranking approach to tackle this problem. It
ranks the retrieved emails based on a scoring function that depends on crucial
email fields, namely subject, content, and sender. The paper also proposes an
architecture to allow every user in a network/group of users to be able, if
permissible, to know the most important network senders who are interested in
his submitted query words. The experimental evaluation on Enron corpus prove
that our approach outperforms known email retrieval ranking approaches
|
1011.0415
|
Learning Networks of Stochastic Differential Equations
|
math.ST cond-mat.stat-mech cs.IT cs.LG math.IT stat.TH
|
We consider linear models for stochastic dynamics. To any such model can be
associated a network (namely a directed graph) describing which degrees of
freedom interact under the dynamics. We tackle the problem of learning such a
network from observation of the system trajectory over a time interval $T$.
We analyze the $\ell_1$-regularized least squares algorithm and, in the
setting in which the underlying network is sparse, we prove performance
guarantees that are \emph{uniform in the sampling rate} as long as this is
sufficiently high. This result substantiates the notion of a well defined `time
complexity' for the network inference problem.
|
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