id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1211.6675 | Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization | cs.CV cs.CE stat.ML | Graph embedding techniques are useful to characterize spectral signature
relations for hyperspectral images. However, such images consists of disjoint
classes due to spatial details that are often ignored by existing graph
computing tools. Robust parameter estimation is a challenge for kernel
functions that compute such graphs. Finding a corresponding high quality
coordinate system to map signature relations remains an open research question.
We answer positively on these challenges by first proposing a kernel function
of spatial and spectral information in computing neighborhood graphs. Secondly,
the study exploits the force field interpretation from mechanics and devise a
unifying nonlinear graph embedding framework. The generalized framework leads
to novel unsupervised multidimensional artificial field embedding techniques
that rely on the simple additive assumption of pair-dependent attraction and
repulsion functions. The formulations capture long range and short range
distance related effects often associated with living organisms and help to
establish algorithmic properties that mimic mutual behavior for the purpose of
dimensionality reduction. The main benefits from the proposed models includes
the ability to preserve the local topology of data and produce quality
visualizations i.e. maintaining disjoint meaningful neighborhoods. As part of
evaluation, visualization, gradient field trajectories, and semisupervised
classification experiments are conducted for image scenes acquired by multiple
sensors at various spatial resolutions over different types of objects. The
results demonstrate the superiority of the proposed embedding framework over
various widely used methods.
|
1211.6687 | Robustness Analysis of Hottopixx, a Linear Programming Model for
Factoring Nonnegative Matrices | stat.ML cs.LG cs.NA math.OC | Although nonnegative matrix factorization (NMF) is NP-hard in general, it has
been shown very recently that it is tractable under the assumption that the
input nonnegative data matrix is close to being separable (separability
requires that all columns of the input matrix belongs to the cone spanned by a
small subset of these columns). Since then, several algorithms have been
designed to handle this subclass of NMF problems. In particular, Bittorf,
Recht, R\'e and Tropp (`Factoring nonnegative matrices with linear programs',
NIPS 2012) proposed a linear programming model, referred to as Hottopixx. In
this paper, we provide a new and more general robustness analysis of their
method. In particular, we design a provably more robust variant using a
post-processing strategy which allows us to deal with duplicates and near
duplicates in the dataset.
|
1211.6697 | Refinement of the Sphere-Packing Bound: Asymmetric Channels | cs.IT math.IT | We provide a refinement of the sphere-packing bound for constant composition
codes over asymmetric discrete memoryless channels that improves the pre-factor
in front of the exponential term. The order of our pre-factor is
$\Omega(N^{-1/2(1+\epsilon + \rho_{R}^{\ast})})$ for any $\epsilon >0$, where
$\rho_{R}^{\ast}$ is the maximum absolute-value subdifferential of the
sphere-packing exponent at rate $R$ and $N$ is the blocklength.
|
1211.6719 | Cooperative Sparsity Pattern Recovery in Distributed Networks Via
Distributed-OMP | cs.IT cs.NA math.IT | In this paper, we consider the problem of collaboratively estimating the
sparsity pattern of a sparse signal with multiple measurement data in
distributed networks. We assume that each node makes Compressive Sensing (CS)
based measurements via random projections regarding the same sparse signal. We
propose a distributed greedy algorithm based on Orthogonal Matching Pursuit
(OMP), in which the sparse support is estimated iteratively while fusing
indices estimated at distributed nodes. In the proposed distributed framework,
each node has to perform less number of iterations of OMP compared to the
sparsity index of the sparse signal. Thus, with each node having a very small
number of compressive measurements, a significant performance gain in support
recovery is achieved via the proposed collaborative scheme compared to the case
where each node estimates the sparsity pattern independently and then fusion is
performed to get a global estimate. We further extend the algorithm to estimate
the sparsity pattern in a binary hypothesis testing framework, where the
algorithm first detects the presence of a sparse signal collaborating among
nodes with a fewer number of iterations of OMP and then increases the number of
iterations to estimate the sparsity pattern only if the signal is detected.
|
1211.6727 | Graph Laplacians on Singular Manifolds: Toward understanding complex
spaces: graph Laplacians on manifolds with singularities and boundaries | cs.AI cs.CG cs.LG | Recently, much of the existing work in manifold learning has been done under
the assumption that the data is sampled from a manifold without boundaries and
singularities or that the functions of interest are evaluated away from such
points. At the same time, it can be argued that singularities and boundaries
are an important aspect of the geometry of realistic data.
In this paper we consider the behavior of graph Laplacians at points at or
near boundaries and two main types of other singularities: intersections, where
different manifolds come together and sharp "edges", where a manifold sharply
changes direction. We show that the behavior of graph Laplacian near these
singularities is quite different from that in the interior of the manifolds. In
fact, a phenomenon somewhat reminiscent of the Gibbs effect in the analysis of
Fourier series, can be observed in the behavior of graph Laplacian near such
points. Unlike in the interior of the domain, where graph Laplacian converges
to the Laplace-Beltrami operator, near singularities graph Laplacian tends to a
first-order differential operator, which exhibits different scaling behavior as
a function of the kernel width. One important implication is that while points
near the singularities occupy only a small part of the total volume, the
difference in scaling results in a disproportionately large contribution to the
total behavior. Another significant finding is that while the scaling behavior
of the operator is the same near different types of singularities, they are
very distinct at a more refined level of analysis.
We believe that a comprehensive understanding of these structures in addition
to the standard case of a smooth manifold can take us a long way toward better
methods for analysis of complex non-linear data and can lead to significant
progress in algorithm design.
|
1211.6778 | Efficient parallel algorithms for tandem queueing system simulation | math.NA cs.DC cs.SY | Parallel algorithms designed for simulation and performance evaluation of
single-server tandem queueing systems with both infinite and finite buffers are
presented. The algorithms exploit a simple computational procedure based on
recursive equations as a representation of system dynamics. A brief analysis of
the performance of the algorithms are given to show that they involve low time
and memory requirements.
|
1211.6799 | Context Visualization for Social Bookmark Management | cs.HC cs.IR | We present the design of a new social bookmark manager, named GalViz, as part
of the interface of the GiveA-Link system. Unlike the interfaces of traditional
social tagging tools, which usually display information in a list view, GalViz
visualizes tags, resources, social links, and social context in an interactive
network, combined with the tag cloud. Evaluations through a scenario case study
and log analysis provide evidence of the effectiveness of our design.
|
1211.6807 | Scalable Spectral Algorithms for Community Detection in Directed
Networks | cs.SI physics.soc-ph stat.ML | Community detection has been one of the central problems in network studies
and directed network is particularly challenging due to asymmetry among its
links. In this paper, we found that incorporating the direction of links
reveals new perspectives on communities regarding to two different roles,
source and terminal, that a node plays in each community. Intriguingly, such
communities appear to be connected with unique spectral property of the graph
Laplacian of the adjacency matrix and we exploit this connection by using
regularized SVD methods. We propose harvesting algorithms, coupled with
regularized SVDs, that are linearly scalable for efficient identification of
communities in huge directed networks. The proposed algorithm shows great
performance and scalability on benchmark networks in simulations and
successfully recovers communities in real network applications.
|
1211.6821 | Additive-State-Decomposition Dynamic Inversion Stabilized Control for a
Class of Uncertain MIMO Systems | cs.SY | This paper presents a new control, namely additive-state-decomposition
dynamic inversion stabilized control, that is used to stabilize a class of
multi-input multi-output (MIMO) systems subject to nonparametric time-varying
uncertainties with respect to both state and input. By additive state
decomposition and a new definition of output, the considered uncertain system
is transformed into a minimum-phase uncertainty-free system with relative
degree one, in which all uncertainties are lumped into a new disturbance at the
output. Subsequently, dynamic inversion control is applied to reject the lumped
disturbance. Performance analysis of the resulting closed-loop dynamics shows
that the stability can be ensured. Finally, to demonstrate its effectiveness,
the proposed control is applied to two existing problems by numerical
simulation. Furthermore, in order to show its practicability, the proposed
control is also performed on a real quadrotor to stabilize its attitude when
its inertia moment matrix is subject to a large uncertainty.
|
1211.6827 | Additive-State-Decomposition-Based Tracking Control for TORA Benchmark | cs.SY | In this paper, a new control scheme, called additive state decomposition
based tracking control, is proposed to solve the tracking (rejection) problem
for rotational position of the TORA (a nonlinear nonminimum phase system). By
the additive state decomposition, the tracking (rejection) task for the
considered nonlinear system is decomposed into two independent subtasks: a
tracking (rejection) subtask for a linear time invariant (LTI) system, leaving
a stabilization subtask for a derived nonlinear system. By the decomposition,
the proposed tracking control scheme avoids solving regulation equations and
can tackle the tracking (rejection) problem in the presence of any external
signal (except for the frequencies at +1 or -1) generated by a marginally
stable autonomous LTI system. To demonstrate the effectiveness, numerical
simulation is given.
|
1211.6834 | On unbiased performance evaluation for protein inference | stat.AP cs.LG q-bio.QM | This letter is a response to the comments of Serang (2012) on Huang and He
(2012) in Bioinformatics. Serang (2012) claimed that the parameters for the
Fido algorithm should be specified using the grid search method in Serang et
al. (2010) so as to generate a deserved accuracy in performance comparison. It
seems that it is an argument on parameter tuning. However, it is indeed the
issue of how to conduct an unbiased performance evaluation for comparing
different protein inference algorithms. In this letter, we would explain why we
don't use the grid search for parameter selection in Huang and He (2012) and
show that this procedure may result in an over-estimated performance that is
unfair to competing algorithms. In fact, this issue has also been pointed out
by Li and Radivojac (2012).
|
1211.6839 | Modeling the Multi-layer Nature of the European Air Transport Network:
Resilience and Passengers Re-scheduling under random failures | physics.soc-ph cs.SI | We study the dynamics of the European Air Transport Network by using a
multiplex network formalism. We will consider the set of flights of each
airline as an interdependent network and we analyze the resilience of the
system against random flight failures in the passenger's rescheduling problem.
A comparison between the single-plex approach and the corresponding multiplex
one is presented illustrating that the multiplexity strongly affects the
robustness of the European Air Network.
|
1211.6847 | Letter counting: a stem cell for Cryptology, Quantitative Linguistics,
and Statistics | math.HO cs.CL cs.CR | Counting letters in written texts is a very ancient practice. It has
accompanied the development of Cryptology, Quantitative Linguistics, and
Statistics. In Cryptology, counting frequencies of the different characters in
an encrypted message is the basis of the so called frequency analysis method.
In Quantitative Linguistics, the proportion of vowels to consonants in
different languages was studied long before authorship attribution. In
Statistics, the alternation vowel-consonants was the only example that Markov
ever gave of his theory of chained events. A short history of letter counting
is presented. The three domains, Cryptology, Quantitative Linguistics, and
Statistics, are then examined, focusing on the interactions with the other two
fields through letter counting. As a conclusion, the eclectism of past
centuries scholars, their background in humanities, and their familiarity with
cryptograms, are identified as contributing factors to the mutual enrichment
process which is described here.
|
1211.6851 | Classification Recouvrante Bas\'ee sur les M\'ethodes \`a Noyau | cs.LG stat.CO stat.ME stat.ML | Overlapping clustering problem is an important learning issue in which
clusters are not mutually exclusive and each object may belongs simultaneously
to several clusters. This paper presents a kernel based method that produces
overlapping clusters on a high feature space using mercer kernel techniques to
improve separability of input patterns. The proposed method, called
OKM-K(Overlapping $k$-means based kernel method), extends OKM (Overlapping
$k$-means) method to produce overlapping schemes. Experiments are performed on
overlapping dataset and empirical results obtained with OKM-K outperform
results obtained with OKM.
|
1211.6859 | Overlapping clustering based on kernel similarity metric | stat.ML cs.LG stat.ME | Producing overlapping schemes is a major issue in clustering. Recent proposed
overlapping methods relies on the search of an optimal covering and are based
on different metrics, such as Euclidean distance and I-Divergence, used to
measure closeness between observations. In this paper, we propose the use of
another measure for overlapping clustering based on a kernel similarity metric
.We also estimate the number of overlapped clusters using the Gram matrix.
Experiments on both Iris and EachMovie datasets show the correctness of the
estimation of number of clusters and show that measure based on kernel
similarity metric improves the precision, recall and f-measure in overlapping
clustering.
|
1211.6868 | Simultaneous Information and Power Transfer for Broadband Wireless
Systems | cs.IT math.IT | Far-field microwave power transfer (MPT) will free wireless sensors and other
mobile devices from the constraints imposed by finite battery capacities.
Integrating MPT with wireless communications to support simultaneous
information and power transfer (SIPT) allows the same spectrum to be used for
dual purposes without compromising the quality of service. A novel approach is
presented in this paper for realizing SIPT in a broadband system where
orthogonal frequency division multiplexing and transmit beamforming are
deployed to create a set of parallel sub-channels for SIPT, which simplifies
resource allocation. Supported by a proposed reconfigurable mobile
architecture, different system configurations are considered by combining
single-user/multiuser systems, downlink/uplink information transfer, and
variable/fixed coding rates. Optimizing the power control for these
configurations results in a new class of multiuser power-control problems
featuring circuit-power constraints, specifying that the transferred power must
be sufficiently large to support the operation of the receiver circuitry.
Solving these problems gives a set of power-control algorithms that exploit
channel diversity in frequency for simultaneously enhancing the throughput and
the MPT efficiency. For the system configurations with variable coding rates,
the algorithms are variants of water-filling that account for the circuit-power
constraints. The optimal algorithms for those configurations with fixed coding
rates are shown to sequentially allocate mobiles their required power for
decoding in the ascending order until the entire budgeted power is spent. The
required power for a mobile is derived as simple functions of the minimum
signal-to-noise ratio for correct decoding, the circuit power and sub-channel
gains.
|
1211.6887 | Automating rule generation for grammar checkers | cs.CL cs.LG | In this paper, I describe several approaches to automatic or semi-automatic
development of symbolic rules for grammar checkers from the information
contained in corpora. The rules obtained this way are an important addition to
manually-created rules that seem to dominate in rule-based checkers. However,
the manual process of creation of rules is costly, time-consuming and
error-prone. It seems therefore advisable to use machine-learning algorithms to
create the rules automatically or semi-automatically. The results obtained seem
to corroborate my initial hypothesis that symbolic machine learning algorithms
can be useful for acquiring new rules for grammar checking. It turns out,
however, that for practical uses, error corpora cannot be the sole source of
information used in grammar checking. I suggest therefore that only by using
different approaches, grammar-checkers, or more generally, computer-aided
proofreading tools, will be able to cover most frequent and severe mistakes and
avoid false alarms that seem to distract users.
|
1211.6898 | On the Use of Non-Stationary Policies for Stationary Infinite-Horizon
Markov Decision Processes | cs.LG cs.AI | We consider infinite-horizon stationary $\gamma$-discounted Markov Decision
Processes, for which it is known that there exists a stationary optimal policy.
Using Value and Policy Iteration with some error $\epsilon$ at each iteration,
it is well-known that one can compute stationary policies that are
$\frac{2\gamma}{(1-\gamma)^2}\epsilon$-optimal. After arguing that this
guarantee is tight, we develop variations of Value and Policy Iteration for
computing non-stationary policies that can be up to
$\frac{2\gamma}{1-\gamma}\epsilon$-optimal, which constitutes a significant
improvement in the usual situation when $\gamma$ is close to 1. Surprisingly,
this shows that the problem of "computing near-optimal non-stationary policies"
is much simpler than that of "computing near-optimal stationary policies".
|
1211.6918 | Aspects of Polar-Coded Modulation | cs.IT math.IT | We consider the joint design of polar coding and higher-order modulation
schemes for ever increased spectral efficiency. The close connection between
the polar code construction and the multi-level coding approach is described in
detail. Relations between different modulation schemes such as bit-interleaved
coded modulation (BICM) and multi-level coding (MLC) in case of polar-coded
modulation as well as the influence of the applied labeling rule and the
selection of frozen channels are demonstrated.
|
1211.6950 | Dynamic Network Cartography | cs.NI cs.IT cs.MA math.IT stat.ML | Communication networks have evolved from specialized, research and tactical
transmission systems to large-scale and highly complex interconnections of
intelligent devices, increasingly becoming more commercial, consumer-oriented,
and heterogeneous. Propelled by emergent social networking services and
high-definition streaming platforms, network traffic has grown explosively
thanks to the advances in processing speed and storage capacity of
state-of-the-art communication technologies. As "netizens" demand a seamless
networking experience that entails not only higher speeds, but also resilience
and robustness to failures and malicious cyber-attacks, ample opportunities for
signal processing (SP) research arise. The vision is for ubiquitous smart
network devices to enable data-driven statistical learning algorithms for
distributed, robust, and online network operation and management, adaptable to
the dynamically-evolving network landscape with minimal need for human
intervention. The present paper aims at delineating the analytical background
and the relevance of SP tools to dynamic network monitoring, introducing the SP
readership to the concept of dynamic network cartography -- a framework to
construct maps of the dynamic network state in an efficient and scalable manner
tailored to large-scale heterogeneous networks.
|
1211.6971 | A New Automatic Method to Adjust Parameters for Object Recognition | cs.CV cs.AI | To recognize an object in an image, the user must apply a combination of
operators, where each operator has a set of parameters. These parameters must
be well adjusted in order to reach good results. Usually, this adjustment is
made manually by the user. In this paper we propose a new method to automate
the process of parameter adjustment for an object recognition task. Our method
is based on reinforcement learning, we use two types of agents: User Agent that
gives the necessary information and Parameter Agent that adjusts the parameters
of each operator. Due to the nature of reinforcement learning the results do
not depend only on the system characteristics but also on the user favorite
choices.
|
1211.6984 | New Approach for CCA2-Secure Post-Quantum Cryptosystem Using Knapsack
Problem | cs.CR cs.IT math.IT | Chosen-ciphertext security, which guarantees confidentiality of encrypted
messages even in the presence of a decryption oracle, has become the defacto
notion of security for public-key encryption under active attack. In this
manuscript, for the first time, we propose a new approach for constructing
post-quantum cryptosystems secure against adaptive chosen ciphertext attack
(CCA2-secure) in the standard model using the knapsack problem. The
computational version of the knapsack problem is NP-hard. Thus, this problem is
expected to be difficult to solve using quantum computers. Our construction is
a precoding-based encryption algorithm and uses the knapsack problem to perform
a permutation and pad some random fogged data to the message bits. Compared to
other approaches were introduced today, our approach is very simple and more
efficient.
|
1211.6988 | Simultaneous Distributed Sensor Self-Localization and Target Tracking
Using Belief Propagation and Likelihood Consensus | cs.NI cs.IT math.IT | We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.
|
1211.7012 | Learning-Assisted Automated Reasoning with Flyspeck | cs.AI cs.DL cs.LG cs.LO | The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided.
|
1211.7045 | Orientation Determination from Cryo-EM images Using Least Unsquared
Deviation | cs.LG math.NA math.OC q-bio.BM | A major challenge in single particle reconstruction from cryo-electron
microscopy is to establish a reliable ab-initio three-dimensional model using
two-dimensional projection images with unknown orientations. Common-lines based
methods estimate the orientations without additional geometric information.
However, such methods fail when the detection rate of common-lines is too low
due to the high level of noise in the images. An approximation to the least
squares global self consistency error was obtained using convex relaxation by
semidefinite programming. In this paper we introduce a more robust global self
consistency error and show that the corresponding optimization problem can be
solved via semidefinite relaxation. In order to prevent artificial clustering
of the estimated viewing directions, we further introduce a spectral norm term
that is added as a constraint or as a regularization term to the relaxed
minimization problem. The resulted problems are solved by using either the
alternating direction method of multipliers or an iteratively reweighted least
squares procedure. Numerical experiments with both simulated and real images
demonstrate that the proposed methods significantly reduce the orientation
estimation error when the detection rate of common-lines is low.
|
1211.7052 | Quantifying the effect of temporal resolution on time-varying networks | cond-mat.stat-mech cs.SI physics.soc-ph | Time-varying networks describe a wide array of systems whose constituents and
interactions evolve over time. They are defined by an ordered stream of
interactions between nodes, yet they are often represented in terms of a
sequence of static networks, each aggregating all edges and nodes present in a
time interval of size \Delta t. In this work we quantify the impact of an
arbitrary \Delta t on the description of a dynamical process taking place upon
a time-varying network. We focus on the elementary random walk, and put forth a
simple mathematical framework that well describes the behavior observed on real
datasets. The analytical description of the bias introduced by time integrating
techniques represents a step forward in the correct characterization of
dynamical processes on time-varying graphs.
|
1211.7075 | Secure and Reliable Transmission with Cooperative Relays in Two-Hop
Wireless Networks | cs.NI cs.CR cs.IT math.IT | This work considers the secure and reliable information transmission in
two-hop relay wireless networks without the information of both eavesdropper
channels and locations. While the previous work on this problem mainly studied
infinite networks and their asymptotic behavior and scaling law results, this
papers focuses on a more practical network with finite number of system nodes
and explores the corresponding exact results on the number of eavesdroppers the
network can tolerant to ensure a desired secrecy and reliability. For achieving
secure and reliable information transmission in a finite network, two
transmission protocols are considered in this paper, one adopts an optimal but
complex relay selection process with less load balance capacity while the other
adopts a random but simple relay selection process with good load balance
capacity. Theoretical analysis is further provided to determine the exact and
maximum number of independent and also uniformly distributed eavesdroppers one
network can tolerate to satisfy a specified requirement in terms of the maximum
secrecy outage probability and maximum transmission outage probability allowed.
|
1211.7080 | Virtual Simulation Objects Concept as a Framework for System-Level
Simulation | cs.SY cs.HC cs.SE | This paper presents Virtual Simulation Objects (VSO) concept which forms
theoretical basis for building tools and framework that is developed for
system-level simulations using existing software modules available within
cyber-infrastructure. Presented concept is implemented by the software tool for
building composite solutions using VSO-based GUI and running them using CLAVIRE
simulation environment.
|
1211.7089 | The Convergence Guarantees of a Non-convex Approach for Sparse Recovery | cs.IT math.IT | In the area of sparse recovery, numerous researches hint that non-convex
penalties might induce better sparsity than convex ones, but up until now those
corresponding non-convex algorithms lack convergence guarantees from the
initial solution to the global optimum. This paper aims to provide performance
guarantees of a non-convex approach for sparse recovery. Specifically, the
concept of weak convexity is incorporated into a class of sparsity-inducing
penalties to characterize the non-convexity. Borrowing the idea of the
projected subgradient method, an algorithm is proposed to solve the non-convex
optimization problem. In addition, a uniform approximate projection is adopted
in the projection step to make this algorithm computationally tractable for
large scale problems. The convergence analysis is provided in the noisy
scenario. It is shown that if the non-convexity of the penalty is below a
threshold (which is in inverse proportion to the distance between the initial
solution and the sparse signal), the recovered solution has recovery error
linear in both the step size and the noise term. Numerical simulations are
implemented to test the performance of the proposed approach and verify the
theoretical analysis.
|
1211.7102 | SVD Based Image Processing Applications: State of The Art, Contributions
and Research Challenges | cs.CV cs.MM | Singular Value Decomposition (SVD) has recently emerged as a new paradigm for
processing different types of images. SVD is an attractive algebraic transform
for image processing applications. The paper proposes an experimental survey
for the SVD as an efficient transform in image processing applications. Despite
the well-known fact that SVD offers attractive properties in imaging, the
exploring of using its properties in various image applications is currently at
its infancy. Since the SVD has many attractive properties have not been
utilized, this paper contributes in using these generous properties in newly
image applications and gives a highly recommendation for more research
challenges. In this paper, the SVD properties for images are experimentally
presented to be utilized in developing new SVD-based image processing
applications. The paper offers survey on the developed SVD based image
applications. The paper also proposes some new contributions that were
originated from SVD properties analysis in different image processing. The aim
of this paper is to provide a better understanding of the SVD in image
processing and identify important various applications and open research
directions in this increasingly important area; SVD based image processing in
the future research.
|
1211.7133 | Socializing the h-index | cs.DL cs.IR cs.SI physics.soc-ph | A variety of bibliometric measures have been proposed to quantify the impact
of researchers and their work. The h-index is a notable and widely-used example
which aims to improve over simple metrics such as raw counts of papers or
citations. However, a limitation of this measure is that it considers authors
in isolation and does not account for contributions through a collaborative
team. To address this, we propose a natural variant that we dub the Social
h-index. The idea is to redistribute the h-index score to reflect an
individual's impact on the research community. In addition to describing this
new measure, we provide examples, discuss its properties, and contrast with
other measures.
|
1211.7141 | Pseudometrically Constrained Centroidal Voronoi Tessellations:
Generating uniform antipodally symmetric points on the unit sphere with a
novel acceleration strategy and its applications to Diffusion and 3D radial
MRI | physics.med-ph cs.CE cs.CG math.MG math.OC | Purpose: The purpose of this work is to investigate the hypothesis that
uniform sampling measurements that are endowed with antipodal symmetry play an
important role when the raw data and image data are related through the Fourier
relationship as in q-space diffusion MRI and 3D radial MRI. Currently, it is
extremely challenging to generate large uniform antipodally symmetric point
sets suitable for 3D radial MRI. A novel approach is proposed to solve this
important and long-standing problem.
Methods: The proposed method is based upon constrained centroidal Voronoi
tessellations of the upper hemisphere with a novel pseudometric. Geometrically
intuitive approach to tessellating the upper hemisphere is also proposed.
Results: The average time complexity of the proposed centroidal tessellations
was shown to be effectively on the order of the product of the number of
iterations and the number of generators. For small sample size, the proposed
method was comparable to the state-of-the-art iterative method in terms of the
uniformity. For large sample size, in which the state-of-the-art method is
infeasible, the reconstructed images from the proposed method has less streak
and ringing artifact as compared to those of the commonly used methods.
Conclusion: This work solved a long-standing problem on generating uniform
sampling points for 3D radial MRI.
|
1211.7164 | Group Formation through Indirect Reciprocity | physics.soc-ph cs.SI nlin.AO q-bio.PE | The emergence of structure in cooperative relation is studied in a game
theoretical model. It is proved that specific types of reciprocity norm lead
individuals to split into two groups. The condition for the evolutionary
stability of the norms is also revealed. This result suggests a connection
between group formation and a specific type of reciprocity norm in our society.
|
1211.7180 | Multislice Modularity Optimization in Community Detection and Image
Segmentation | cs.SI cs.CV physics.data-an physics.soc-ph | Because networks can be used to represent many complex systems, they have
attracted considerable attention in physics, computer science, sociology, and
many other disciplines. One of the most important areas of network science is
the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In
this paper, we algorithmically detect communities in social networks and image
data by optimizing multislice modularity. A key advantage of modularity
optimization is that it does not require prior knowledge of the number or sizes
of communities, and it is capable of finding network partitions that are
composed of communities of different sizes. By optimizing multislice modularity
and subsequently calculating diagnostics on the resulting network partitions,
it is thereby possible to obtain information about network structure across
multiple system scales. We illustrate this method on data from both social
networks and images, and we find that optimization of multislice modularity
performs well on these two tasks without the need for extensive
problem-specific adaptation. However, improving the computational speed of this
method remains a challenging open problem.
|
1211.7184 | Erratum: Simplified Drift Analysis for Proving Lower Bounds in
Evolutionary Computation | cs.NE | This erratum points out an error in the simplified drift theorem (SDT)
[Algorithmica 59(3), 369-386, 2011]. It is also shown that a minor modification
of one of its conditions is sufficient to establish a valid result. In many
respects, the new theorem is more general than before. We no longer assume a
Markov process nor a finite search space. Furthermore, the proof of the theorem
is more compact than the previous ones. Finally, previous applications of the
SDT are revisited. It turns out that all of these either meet the modified
condition directly or by means of few additional arguments.
|
1211.7200 | Using Differential Evolution for the Graph Coloring | math.CO cs.NE | Differential evolution was developed for reliable and versatile function
optimization. It has also become interesting for other domains because of its
ease to use. In this paper, we posed the question of whether differential
evolution can also be used by solving of the combinatorial optimization
problems, and in particular, for the graph coloring problem. Therefore, a
hybrid self-adaptive differential evolution algorithm for graph coloring was
proposed that is comparable with the best heuristics for graph coloring today,
i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of
Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the
evolutionary algorithm with method SAW of Eiben et al., which achieved
excellent results for this kind of graphs, was also incorporated into this
study. The extensive experiments show that the differential evolution could
become a competitive tool for the solving of graph coloring problem in the
future.
|
1211.7203 | Robust Filtering for Adaptive Homodyne Estimation of Continuously
Varying Optical Phase | quant-ph cs.SY math.OC | Recently, it has been demonstrated experimentally that adaptive estimation of
a continuously varying optical phase provides superior accuracy in the phase
estimate compared to static estimation. Here, we show that the mean-square
error in the adaptive phase estimate may be further reduced for the stochastic
noise process considered by using an optimal Kalman filter in the feedback
loop. Further, the estimation process can be made robust to fluctuations in the
underlying parameters of the noise process modulating the system phase to be
estimated. This has been done using a guaranteed cost robust filter.
|
1211.7210 | Evolutionarily Stable Sets in Quantum Penny Flip Games | quant-ph cs.AI cs.GT | In game theory, an Evolutionarily Stable Set (ES set) is a set of Nash
Equilibrium (NE) strategies that give the same payoffs. Similar to an
Evolutionarily Stable Strategy (ES strategy), an ES set is also a strict NE.
This work investigates the evolutionary stability of classical and quantum
strategies in the quantum penny flip games. In particular, we developed an
evolutionary game theory model to conduct a series of simulations where a
population of mixed classical strategies from the ES set of the game were
invaded by quantum strategies. We found that when only one of the two players'
mixed classical strategies were invaded, the results were different. In one
case, due to the interference phenomenon of superposition, quantum strategies
provided more payoff, hence successfully replaced the mixed classical
strategies in the ES set. In the other case, the mixed classical strategies
were able to sustain the invasion of quantum strategies and remained in the ES
set. Moreover, when both players' mixed classical strategies were invaded by
quantum strategies, a new quantum ES set emerged. The strategies in the quantum
ES set give both players payoff 0, which is the same as the payoff of the
strategies in the mixed classical ES set of this game.
|
1211.7219 | A recursive divide-and-conquer approach for sparse principal component
analysis | cs.CV cs.LG stat.ML | In this paper, a new method is proposed for sparse PCA based on the recursive
divide-and-conquer methodology. The main idea is to separate the original
sparse PCA problem into a series of much simpler sub-problems, each having a
closed-form solution. By recursively solving these sub-problems in an
analytical way, an efficient algorithm is constructed to solve the sparse PCA
problem. The algorithm only involves simple computations and is thus easy to
implement. The proposed method can also be very easily extended to other sparse
PCA problems with certain constraints, such as the nonnegative sparse PCA
problem. Furthermore, we have shown that the proposed algorithm converges to a
stationary point of the problem, and its computational complexity is
approximately linear in both data size and dimensionality. The effectiveness of
the proposed method is substantiated by extensive experiments implemented on a
series of synthetic and real data in both reconstruction-error-minimization and
data-variance-maximization viewpoints.
|
1211.7232 | Real Time Enhanced Random Sampling of Online Social Networks | cs.SI cs.IR physics.soc-ph | Social graphs can be easily extracted from Online Social Networks. However
these networks are getting larger from day to day. Sampling methods used to
evaluate graph information cannot accurately extract graph properties.
Furthermore Social Networks are limiting the access to their data, making the
crawling process even harder. A novel approach on Random Sampling is proposed,
considering both limitation and resources. We evaluate this proposal with 4
different settings on 5 different Test Graphs, crawled directly from Twitter.
Through comparing the results we observe the pros and cons of its method as
well as their resource allocation. Concluding we present their best area of
application.
|
1211.7239 | Information Leakage Neutralization for the Multi-Antenna
Non-Regenerative Relay-Assisted Multi-Carrier Interference Channel | cs.IT math.IT | In heterogeneous dense networks where spectrum is shared, users privacy
remains one of the major challenges. On a multi-antenna relay-assisted
multi-carrier interference channel, each user shares the frequency and spatial
resources with all other users. When the receivers are not only interested in
their own signals but also in eavesdropping other users' signals, the cross
talk on the frequency and spatial channels becomes information leakage. In this
paper, we propose a novel secrecy rate enhancing relay strategy that utilizes
both frequency and spatial resources, termed as information leakage
neutralization. To this end, the relay matrix is chosen such that the effective
channel from the transmitter to the colluding eavesdropper is equal to the
negative of the effective channel over the relay to the colluding eavesdropper
and thus the information leakage to zero. Interestingly, the optimal relay
matrix in general is not block-diagonal which encourages users' encoding over
the frequency channels. We proposed two information leakage neutralization
strategies, namely efficient information leakage neutralization (EFFIN) and
optimized information leakage neutralization (OPTIN). EFFIN provides a simple
and efficient design of relay processing matrix and precoding matrices at the
transmitters in the scenario of limited power and computational resources.
OPTIN, despite its higher complexity, provides a better sum secrecy rate
performance by optimizing the relay processing matrix and the precoding
matrices jointly. The proposed methods are shown to improve the sum secrecy
rates over several state-of-the-art baseline methods.
|
1211.7276 | Efficient algorithms for robust recovery of images from compressed data | cs.IT cs.LG math.IT stat.ML | Compressed sensing (CS) is an important theory for sub-Nyquist sampling and
recovery of compressible data. Recently, it has been extended by Pham and
Venkatesh to cope with the case where corruption to the CS data is modeled as
impulsive noise. The new formulation, termed as robust CS, combines robust
statistics and CS into a single framework to suppress outliers in the CS
recovery. To solve the newly formulated robust CS problem, Pham and Venkatesh
suggested a scheme that iteratively solves a number of CS problems, the
solutions from which converge to the true robust compressed sensing solution.
However, this scheme is rather inefficient as it has to use existing CS solvers
as a proxy. To overcome limitation with the original robust CS algorithm, we
propose to solve the robust CS problem directly in this paper and drive more
computationally efficient algorithms by following latest advances in
large-scale convex optimization for non-smooth regularization. Furthermore, we
also extend the robust CS formulation to various settings, including additional
affine constraints, $\ell_1$-norm loss function, mixed-norm regularization, and
multi-tasking, so as to further improve robust CS. We also derive simple but
effective algorithms to solve these extensions. We demonstrate that the new
algorithms provide much better computational advantage over the original robust
CS formulation, and effectively solve more sophisticated extensions where the
original methods simply cannot. We demonstrate the usefulness of the extensions
on several CS imaging tasks.
|
1211.7283 | Coherence-based Partial Exact Recovery Condition for OMP/OLS | cs.IT math.IT physics.data-an stat.CO | We address the exact recovery of the support of a k-sparse vector with
Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a
noiseless setting. We consider the scenario where OMP/OLS have selected good
atoms during the first l iterations (l<k) and derive a new sufficient and
worst-case necessary condition for their success in k steps. Our result is
based on the coherence \mu of the dictionary and relaxes Tropp's well-known
condition \mu<1/(2k-1) to the case where OMP/OLS have a partial knowledge of
the support.
|
1211.7302 | Exploiting Metric Structure for Efficient Private Query Release | cs.DS cs.CR cs.DB | We consider the problem of privately answering queries defined on databases
which are collections of points belonging to some metric space. We give simple,
computationally efficient algorithms for answering distance queries defined
over an arbitrary metric. Distance queries are specified by points in the
metric space, and ask for the average distance from the query point to the
points contained in the database, according to the specified metric. Our
algorithms run efficiently in the database size and the dimension of the space,
and operate in both the online query release setting, and the offline setting
in which they must in polynomial time generate a fixed data structure which can
answer all queries of interest. This represents one of the first subclasses of
linear queries for which efficient algorithms are known for the private query
release problem, circumventing known hardness results for generic linear
queries.
|
1211.7326 | Repeated Root Constacyclic Codes of Length $mp^s$ over
$\mathbb{F}_{p^r}+u \mathbb{F}_{p^r}+...+ u^{e-1}\mathbb{F}_{p^r}$ | cs.IT math.IT | We give the structure of $\lambda$-constacyclic codes of length $p^sm$ over
$R=\mathbb{F}_{p^r}+u \mathbb{F}_{p^r}+...+ u^{e-1}\mathbb{F}_{p^r}$ with
$\lambda \in \F_{p^r}^*$. We also give the structure of $\lambda$-constacyclic
codes of length $p^sm$ with $\lambda=\alpha_1+u\alpha_2+...+u^{e-1}
\alpha_{e-1}$, where $\alpha_1,\alpha_2 \neq 0$ and study the self-duality of
these codes.
|
1211.7343 | Persistence and periodicity in a dynamic proximity network | physics.data-an cs.SI physics.soc-ph | The topology of social networks can be understood as being inherently
dynamic, with edges having a distinct position in time. Most characterizations
of dynamic networks discretize time by converting temporal information into a
sequence of network "snapshots" for further analysis. Here we study a highly
resolved data set of a dynamic proximity network of 66 individuals. We show
that the topology of this network evolves over a very broad distribution of
time scales, that its behavior is characterized by strong periodicities driven
by external calendar cycles, and that the conversion of inherently
continuous-time data into a sequence of snapshots can produce highly biased
estimates of network structure. We suggest that dynamic social networks exhibit
a natural time scale \Delta_{nat}, and that the best conversion of such dynamic
data to a discrete sequence of networks is done at this natural rate.
|
1211.7359 | Genetic braid optimization: A heuristic approach to compute
quasiparticle braids | quant-ph cond-mat.mes-hall cs.NE | In topologically-protected quantum computation, quantum gates can be carried
out by adiabatically braiding two-dimensional quasiparticles, reminiscent of
entangled world lines. Bonesteel et al. [Phys. Rev. Lett. 95, 140503 (2005)],
as well as Leijnse and Flensberg [Phys. Rev. B 86, 104511 (2012)] recently
provided schemes for computing quantum gates from quasiparticle braids.
Mathematically, the problem of executing a gate becomes that of finding a
product of the generators (matrices) in that set that approximates the gate
best, up to an error. To date, efficient methods to compute these gates only
strive to optimize for accuracy. We explore the possibility of using a generic
approach applicable to a variety of braiding problems based on evolutionary
(genetic) algorithms. The method efficiently finds optimal braids while
allowing the user to optimize for the relative utilities of accuracy and/or
length. Furthermore, when optimizing for error only, the method can quickly
produce efficient braids.
|
1211.7369 | Approximate Rank-Detecting Factorization of Low-Rank Tensors | stat.ML cs.LG math.NA | We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3
tensor and calculates its factorization into rank-one components. We provide
generative conditions for the algorithm to work and demonstrate on both
synthetic and real world data that AROFAC2 is a potentially outperforming
alternative to the gold standard PARAFAC over which it has the advantages that
it can intrinsically detect the true rank, avoids spurious components, and is
stable with respect to outliers and non-Gaussian noise.
|
1212.0017 | A large-scale and fault-tolerant approach of subgraph mining using
density-based partitioning | cs.DB cs.DC cs.SI | Recently, graph mining approaches have become very popular, especially in
domains such as bioinformatics, chemoinformatics and social networks. In this
scope, one of the most challenging tasks is frequent subgraph discovery. This
task has been motivated by the tremendously increasing size of existing graph
databases. Since then, an important problem of designing efficient and scaling
approaches for frequent subgraph discovery in large clusters, has taken place.
However, failures are a norm rather than being an exception in large clusters.
In this context, the MapReduce framework was designed so that node failures are
automatically handled by the framework. In this paper, we propose a large-scale
and fault-tolerant approach of subgraph mining by means of a density-based
partitioning technique, using MapReduce. Our partitioning aims to balance
computation load on a collection of machines. We experimentally show that our
approach decreases significantly the execution time and scales the subgraph
discovery process to large graph databases.
|
1212.0018 | Collective Phenomena and Non-Finite State Computation in a Human Social
System | cs.SI cs.FL nlin.AO physics.soc-ph q-bio.PE stat.AP | We investigate the computational structure of a paradigmatic example of
distributed social interaction: that of the open-source Wikipedia community. We
examine the statistical properties of its cooperative behavior, and perform
model selection to determine whether this aspect of the system can be described
by a finite-state process, or whether reference to an effectively unbounded
resource allows for a more parsimonious description. We find strong evidence,
in a majority of the most-edited pages, in favor of a collective-state model,
where the probability of a "revert" action declines as the square root of the
number of non-revert actions seen since the last revert. We provide evidence
that the emergence of this social counter is driven by collective interaction
effects, rather than properties of individual users.
|
1212.0023 | Emergence of Self-Organized Amoeboid Movement in a Multi-Agent
Approximation of Physarum polycephalum | cs.MA q-bio.CB | The giant single-celled slime mould Physarum polycephalum exhibits complex
morphological adaptation and amoeboid movement as it forages for food and may
be seen as a minimal example of complex robotic behaviour. Swarm computation
has previously been used to explore how spatiotemporal complexity can emerge
from, and be distributed within, simple component parts and their interactions.
Using a particle based swarm approach we explore the question of how to
generate collective amoeboid movement from simple non-oscillatory component
parts in a model of P. polycephalum. The model collective behaves as a cohesive
and deformable virtual material, approximating the local coupling within the
plasmodium matrix. The collective generates de-novo and complex oscillatory
patterns from simple local interactions. The origin of this motor behaviour is
distributed within the collective rendering is morphologically adaptive,
amenable to external influence, and robust to simulated environmental insult.
We show how to gain external influence over the collective movement by
simulated chemo-attraction (pulling towards nutrient stimuli) and simulated
light irradiation hazards (pushing from stimuli). The amorphous and distributed
properties of the collective are demonstrated by cleaving it into two
independent entities and fusing two separate entities to form a single device,
thus enabling it to traverse narrow, separate or tortuous paths. We conclude by
summarising the contribution of the model to swarm based robotics and
soft-bodied modular robotics and discuss the future potential of such material
approaches to the field.
|
1212.0030 | Viewpoint Invariant Object Detector | cs.CV | Object Detection is the task of identifying the existence of an object class
instance and locating it within an image. Difficulties in handling high
intra-class variations constitute major obstacles to achieving high performance
on standard benchmark datasets (scale, viewpoint, lighting conditions and
orientation variations provide good examples). Suggested model aims at
providing more robustness to detecting objects suffering severe distortion due
to < 60{\deg} viewpoint changes. In addition, several model computational
bottlenecks have been resolved leading to a significant increase in the model
performance (speed and space) without compromising the resulting accuracy.
Finally, we produced two illustrative applications showing the potential of the
object detection technology being deployed in real life applications; namely
content-based image search and content-based video search.
|
1212.0041 | Compression Stress Effect on Dislocations Movement and Crack propagation
in Cubic Crystal | physics.comp-ph cond-mat.mtrl-sci cs.CE | Fracture material is seriously problem in daily life, and it has connection
with mechanical properties itself. The mechanical properties is belief depend
on dislocation movement and crack propagation in the crystal. Information about
this is very important to characterize the material. In FCC crystal structure
the competition between crack propagation and dislocation wake is very
interesting, in a ductile material like copper (Cu) dislocation can be seen in
room temperature, but in a brittle material like Si only cracks can be seen
observed. Different techniques were applied to material to study the mechanical
properties, in this study we did compression test in one direction. Combination
of simulation and experimental on cubic material are reported in this paper. We
found that the deflection of crack direction in Si caused by vacancy of
lattice,while compression stress on Cu cause the atoms displacement in one
direction. Some evidence of dislocation wake in Si crystal under compression
stress at high temperature will reported.
|
1212.0042 | Secure voice based authentication for mobile devices: Vaulted Voice
Verification | cs.CR cs.CV | As the use of biometrics becomes more wide-spread, the privacy concerns that
stem from the use of biometrics are becoming more apparent. As the usage of
mobile devices grows, so does the desire to implement biometric identification
into such devices. A large majority of mobile devices being used are mobile
phones. While work is being done to implement different types of biometrics
into mobile phones, such as photo based biometrics, voice is a more natural
choice. The idea of voice as a biometric identifier has been around a long
time. One of the major concerns with using voice as an identifier is the
instability of voice. We have developed a protocol that addresses those
instabilities and preserves privacy. This paper describes a novel protocol that
allows a user to authenticate using voice on a mobile/remote device without
compromising their privacy. We first discuss the \vv protocol, which has
recently been introduced in research literature, and then describe its
limitations. We then introduce a novel adaptation and extension of the vaulted
verification protocol to voice, dubbed $V^3$. Following that we show a
performance evaluation and then conclude with a discussion of security and
future work.
|
1212.0047 | On the capacity limit of wireless channels under colored scattering | cs.IT math.IT | It has been generally believed that the multiple-input multiple-output (MIMO)
channel capacity grows linearly with the size of antenna arrays. In terms of
degrees of freedom, linear transmit and receive arrays of length $L$ in a
scattering environment of total angular spread $|\Omega|$ asymptotically have
$|\Omega| L$ degrees of freedom. In this paper, it is claimed that the linear
increase in degrees of freedom may not be attained when scattered
electromagnetic fields in the underlying scattering environment are
statistically correlated. After introducing a model of correlated scattering,
which is referred to as the colored scattering model, we derive the number of
degrees of freedom. Unlike the uncorrelated case, the number of degrees of
freedom in the colored scattering channel is asymptotically limited by
$|\Omega| \cdot \min \{L, 1/\Gamma}$, where $\Gamma$ is a parameter determining
the extent of correlation. In other words, for very large arrays in the colored
scattering environment, degrees of freedom can get saturated to an intrinsic
limit rather than increasing linearly with the array size.
|
1212.0059 | Artificial Neural Network Fuzzy Inference System (ANFIS) For Brain Tumor
Detection | cs.CV cs.AI | Detection and segmentation of Brain tumor is very important because it
provides anatomical information of normal and abnormal tissues which helps in
treatment planning and patient follow-up. There are number of techniques for
image segmentation. Proposed research work uses ANFIS (Artificial Neural
Network Fuzzy Inference System) for image classification and then compares the
results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes
benefits of both ANN and the fuzzy logic systems. A comprehensive feature set
and fuzzy rules are selected to classify an abnormal image to the corresponding
tumor type. Experimental results illustrate promising results in terms of
classification accuracy. A comparative analysis is performed with the FCM and
K-NN to show the superior nature of ANFIS systems.
|
1212.0074 | Challenges in Kurdish Text Processing | cs.IR cs.CL | Despite having a large number of speakers, the Kurdish language is among the
less-resourced languages. In this work we highlight the challenges and problems
in providing the required tools and techniques for processing texts written in
Kurdish. From a high-level perspective, the main challenges are: the inherent
diversity of the language, standardization and segmentation issues, and the
lack of language resources.
|
1212.0079 | Computing Strong and Weak Permissions in Defeasible Logic | cs.LO cs.AI | In this paper we propose an extension of Defeasible Logic to represent and
compute three concepts of defeasible permission. In particular, we discuss
different types of explicit permissive norms that work as exceptions to
opposite obligations. Moreover, we show how strong permissions can be
represented both with, and without introducing a new consequence relation for
inferring conclusions from explicit permissive norms. Finally, we illustrate
how a preference operator applicable to contrary-to-duty obligations can be
combined with a new operator representing ordered sequences of strong
permissions which derogate from prohibitions. The logical system is studied
from a computational standpoint and is shown to have liner computational
complexity.
|
1212.0083 | From the decoding of cortical activities to the control of a JACO
robotic arm: a whole processing chain | cs.NE cs.HC cs.RO q-bio.NC | This paper presents a complete processing chain for decoding intracranial
data recorded in the cortex of a monkey and replicates the associated movements
on a JACO robotic arm by Kinova. We developed specific modules inside the
OpenViBE platform in order to build a Brain-Machine Interface able to read the
data, compute the position of the robotic finger and send this position to the
robotic arm. More pre- cisely, two client/server protocols have been tested to
transfer the finger positions: VRPN and a light protocol based on TCP/IP
sockets. According to the requested finger position, the server calls the
associ- ated functions of an API by Kinova to move the fin- gers properly.
Finally, we monitor the gap between the requested and actual fingers positions.
This chain can be generalized to any movement of the arm or wrist.
|
1212.0087 | A scalable mining of frequent quadratic concepts in d-folksonomies | cs.SI | Folksonomy mining is grasping the interest of web 2.0 community since it
represents the core data of social resource sharing systems. However, a
scrutiny of the related works interested in mining folksonomies unveils that
the time stamp dimension has not been considered. For example, the wealthy
number of works dedicated to mining tri-concepts from folksonomies did not take
into account time dimension. In this paper, we will consider a folksonomy
commonly composed of triples <users, tags, resources> and we shall consider the
time as a new dimension. We motivate our approach by highlighting the battery
of potential applications. Then, we present the foundations for mining
quadri-concepts, provide a formal definition of the problem and introduce a new
efficient algorithm, called QUADRICONS for its solution to allow for mining
folksonomies in time, i.e., d-folksonomies. We also introduce a new closure
operator that splits the induced search space into equivalence classes whose
smallest elements are the quadri-minimal generators. Carried out experiments on
large-scale real-world datasets highlight good performances of our algorithm.
|
1212.0094 | A Construction for Periodic ZCZ Sequences | cs.IT math.IT | We introduce a construction for periodic zero correlation zone (ZCZ)
sequences over roots of unity. The sequences share similarities to the perfect
periodic sequence constructions of Liu, Frank, and Milewski. The sequences have
two non-zero off-peak autocorrelation values which asymptotically approach $\pm
2 \pi$, so the sequences are asymptotically perfect.
|
1212.0096 | Predictive Control of a Permanent Magnet Synchronous Machine based on
Real-Time Dynamic Optimization | cs.SY | A predictive control scheme for a permanent-magnet synchronous machine (PMSM)
is presented. It is based on a suboptimal method for computationally efficient
trajectory generation based on continuous parameterization and linear
programming. The torque controller optimizes a quadratic cost consisting of
control error and machine losses in real-time respecting voltage and current
limitations. The multivariable controller decouples the two current components
and exploits cross-coupling effects in the long-range constrained predictive
control strategy. The optimization results in fast and smooth torque dynamics
while inherently using field-weakening to improve the power efficiency and the
current dynamics in high speed operation. The performance of the scheme is
demonstrated by experimental results.
|
1212.0101 | Performance Bounds on a Wiretap Network with Arbitrary Wiretap Sets | cs.IT math.IT | Consider a communication network represented by a directed graph
$\mathcal{G}=(\mathcal{V},\mathcal{E})$, where $\mathcal{V}$ is the set of
nodes and $\mathcal{E}$ is the set of point-to-point channels in the network.
On the network a secure message $M$ is transmitted, and there may exist
wiretappers who want to obtain information about the message. In secure network
coding, we aim to find a network code which can protect the message against the
wiretapper whose power is constrained. Cai and Yeung \cite{cai2002secure}
studied the model in which the wiretapper can access any one but not more than
one set of channels, called a wiretap set, out of a collection $\mathcal{A}$ of
all possible wiretap sets. In order to protect the message, the message needs
to be mixed with a random key $K$. They proved tight fundamental performance
bounds when $\mathcal{A}$ consists of all subsets of $\mathcal{E}$ of a fixed
size $r$. However, beyond this special case, obtaining such bounds is much more
difficult. In this paper, we investigate the problem when $\mathcal{A}$
consists of arbitrary subsets of $\mathcal{E}$ and obtain the following
results: 1) an upper bound on $H(M)$; 2) a lower bound on $H(K)$ in terms of
$H(M)$. The upper bound on $H(M)$ is explicit, while the lower bound on $H(K)$
can be computed in polynomial time when $|\mathcal{A}|$ is fixed. The tightness
of the lower bound for the point-to-point communication system is also proved.
|
1212.0121 | Opinion dynamics with disagreement and modulated information | physics.soc-ph cs.SI | Opinion dynamics concerns social processes through which populations or
groups of individuals agree or disagree on specific issues. As such, modelling
opinion dynamics represents an important research area that has been
progressively acquiring relevance in many different domains. Existing
approaches have mostly represented opinions through discrete binary or
continuous variables by exploring a whole panoply of cases: e.g. independence,
noise, external effects, multiple issues. In most of these cases the crucial
ingredient is an attractive dynamics through which similar or similar enough
agents get closer. Only rarely the possibility of explicit disagreement has
been taken into account (i.e., the possibility for a repulsive interaction
among individuals' opinions), and mostly for discrete or 1-dimensional
opinions, through the introduction of additional model parameters. Here we
introduce a new model of opinion formation, which focuses on the interplay
between the possibility of explicit disagreement, modulated in a
self-consistent way by the existing opinions' overlaps between the interacting
individuals, and the effect of external information on the system. Opinions are
modelled as a vector of continuous variables related to multiple possible
choices for an issue. Information can be modulated to account for promoting
multiple possible choices. Numerical results show that extreme information
results in segregation and has a limited effect on the population, while milder
messages have better success and a cohesion effect. Additionally, the initial
condition plays an important role, with the population forming one or multiple
clusters based on the initial average similarity between individuals, with a
transition point depending on the number of opinion choices.
|
1212.0134 | Fingertip Detection: A Fast Method with Natural Hand | cs.CV | Many vision based applications have used fingertips to track or manipulate
gestures in their applications. Gesture identification is a natural way to pass
the signals to the machine, as the human express its feelings most of the time
with hand expressions. Here a novel time efficient algorithm has been described
for fingertip detection. This method is invariant to hand direction and in
preprocessing it cuts only hand part from the full image, hence further
computation would be much faster than processing full image. Binary silhouette
of the input image is generated using HSV color space based skin filter and
hand cropping done based on intensity histogram of the hand image
|
1212.0139 | Cumulative Step-size Adaptation on Linear Functions | cs.LG stat.ML | The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation,
where the step size is adapted measuring the length of a so-called cumulative
path. The cumulative path is a combination of the previous steps realized by
the algorithm, where the importance of each step decreases with time. This
article studies the CSA-ES on composites of strictly increasing functions with
affine linear functions through the investigation of its underlying Markov
chains. Rigorous results on the change and the variation of the step size are
derived with and without cumulation. The step-size diverges geometrically fast
in most cases. Furthermore, the influence of the cumulation parameter is
studied.
|
1212.0141 | On the Role of Social Identity and Cohesion in Characterizing Online
Social Communities | cs.SI physics.soc-ph | Two prevailing theories for explaining social group or community structure
are cohesion and identity. The social cohesion approach posits that social
groups arise out of an aggregation of individuals that have mutual
interpersonal attraction as they share common characteristics. These
characteristics can range from common interests to kinship ties and from social
values to ethnic backgrounds. In contrast, the social identity approach posits
that an individual is likely to join a group based on an intrinsic
self-evaluation at a cognitive or perceptual level. In other words group
members typically share an awareness of a common category membership.
In this work we seek to understand the role of these two contrasting theories
in explaining the behavior and stability of social communities in Twitter. A
specific focal point of our work is to understand the role of these theories in
disparate contexts ranging from disaster response to socio-political activism.
We extract social identity and social cohesion features-of-interest for large
scale datasets of five real-world events and examine the effectiveness of such
features in capturing behavioral characteristics and the stability of groups.
We also propose a novel measure of social group sustainability based on the
divergence in group discussion. Our main findings are: 1) Sharing of social
identities (especially physical location) among group members has a positive
impact on group sustainability, 2) Structural cohesion (represented by high
group density and low average shortest path length) is a strong indicator of
group sustainability, and 3) Event characteristics play a role in shaping group
sustainability, as social groups in transient events behave differently from
groups in events that last longer.
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1212.0142 | Pedestrian Detection with Unsupervised Multi-Stage Feature Learning | cs.CV cs.LG | Pedestrian detection is a problem of considerable practical interest. Adding
to the list of successful applications of deep learning methods to vision, we
report state-of-the-art and competitive results on all major pedestrian
datasets with a convolutional network model. The model uses a few new twists,
such as multi-stage features, connections that skip layers to integrate global
shape information with local distinctive motif information, and an unsupervised
method based on convolutional sparse coding to pre-train the filters at each
stage.
|
1212.0146 | Efficient Community Detection in Large Networks using Content and Links | cs.SI physics.soc-ph | In this paper we discuss a very simple approach of combining content and link
information in graph structures for the purpose of community discovery, a
fundamental task in network analysis. Our approach hinges on the basic
intuition that many networks contain noise in the link structure and that
content information can help strengthen the community signal. This enables ones
to eliminate the impact of noise (false positives and false negatives), which
is particularly prevalent in online social networks and Web-scale information
networks.
Specifically we introduce a measure of signal strength between two nodes in
the network by fusing their link strength with content similarity. Link
strength is estimated based on whether the link is likely (with high
probability) to reside within a community. Content similarity is estimated
through cosine similarity or Jaccard coefficient. We discuss a simple mechanism
for fusing content and link similarity. We then present a biased edge sampling
procedure which retains edges that are locally relevant for each graph node.
The resulting backbone graph can be clustered using standard community
discovery algorithms such as Metis and Markov clustering.
Through extensive experiments on multiple real-world datasets (Flickr,
Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate
the effectiveness and efficiency of our methods over state-of-the-art learning
and mining approaches several of which also attempt to combine link and content
analysis for the purposes of community discovery. Specifically we always find a
qualitative benefit when combining content with link analysis. Additionally our
biased graph sampling approach realizes a quantitative benefit in that it is
typically several orders of magnitude faster than competing approaches.
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1212.0160 | Fuzzy Based Stator Flux Optimizer Design For Direct Torque Control | cs.SY | Direct Torque Control (DTC) is well known as an effective control technique
for high performance drives in a wide variety of industrial applications and
conventional DTC technique uses two constant reference value: torque and stator
flux. In this paper, a new fuzzy based stator flux optimizer has been proposed
for DTC controlled induction motor drivers and simulation studies have been
carried out with Matlab/Simulink to compare the proposed system behaviours at
vary load conditions. The most important feature of the proposed fuzzy logic
based stator flux optimizer that it self-regulates the stator flux reference
value using the motor load situation without need of any motor parameters.
Simulation results show that the performance of the proposed DTC technique has
been improved and especially at low-load conditions torque ripples are greatly
reduced with respect to the conventional DTC.
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1212.0167 | Follow Whom? Chinese Users Have Different Choice | cs.SI physics.soc-ph | Sina Weibo, which was launched in 2009, is the most popular Chinese
micro-blogging service. It has been reported that Sina Weibo has more than 400
million registered users by the end of the third quarter in 2012. Sina Weibo
and Twitter have a lot in common, however, in terms of the following
preference, Sina Weibo users, most of whom are Chinese, behave differently
compared with those of Twitter.
This work is based on a data set of Sina Weibo which contains 80.8 million
users' profiles and 7.2 billion relations and a large data set of Twitter.
Firstly some basic features of Sina Weibo and Twitter are analyzed such as
degree and activeness distribution, correlation between degree and activeness,
and the degree of separation. Then the following preference is investigated by
studying the assortative mixing, friend similarities, following distribution,
edge balance ratio, and ranking correlation, where edge balance ratio is newly
proposed to measure balance property of graphs. It is found that Sina Weibo has
a lower reciprocity rate, more positive balanced relations and is more
disassortative. Coinciding with Asian traditional culture, the following
preference of Sina Weibo users is more concentrated and hierarchical: they are
more likely to follow people at higher or the same social levels and less
likely to follow people lower than themselves. In contrast, the same kind of
following preference is weaker in Twitter. Twitter users are open as they
follow people from levels, which accords with its global characteristic and the
prevalence of western civilization. The message forwarding behavior is studied
by displaying the propagation levels, delays, and critical users. The following
preference derives from not only the usage habits but also underlying reasons
such as personalities and social moralities that is worthy of future research.
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1212.0170 | An Evolution Strategy Approach toward Rule-set Generation for Network
Intrusion Detection Systems (IDS) | cs.CR cs.NE | With the increasing number of intrusions in system and network
infrastructures, Intrusion Detection Systems (IDS) have become an active area
of research to develop reliable and effective solutions to detect and counter
them. The use of Evolutionary Algorithms in IDS has proved its maturity over
the times. Although most of the research works have been based on the use of
genetic algorithms in IDS, this paper presents an approach toward the
generation of rules for the identification of anomalous connections using
evolution strategies . The emphasis is given on how the problem can be modeled
into ES primitives and how the fitness of the population can be evaluated in
order to find the local optima, therefore resulting in optimal rules that can
be used for detecting intrusions in intrusion detection systems.
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1212.0171 | Message-Passing Algorithms for Quadratic Minimization | cs.IT cs.LG math.IT stat.ML | Gaussian belief propagation (GaBP) is an iterative algorithm for computing
the mean of a multivariate Gaussian distribution, or equivalently, the minimum
of a multivariate positive definite quadratic function. Sufficient conditions,
such as walk-summability, that guarantee the convergence and correctness of
GaBP are known, but GaBP may fail to converge to the correct solution given an
arbitrary positive definite quadratic function. As was observed in previous
work, the GaBP algorithm fails to converge if the computation trees produced by
the algorithm are not positive definite. In this work, we will show that the
failure modes of the GaBP algorithm can be understood via graph covers, and we
prove that a parameterized generalization of the min-sum algorithm can be used
to ensure that the computation trees remain positive definite whenever the
input matrix is positive definite. We demonstrate that the resulting algorithm
is closely related to other iterative schemes for quadratic minimization such
as the Gauss-Seidel and Jacobi algorithms. Finally, we observe, empirically,
that there always exists a choice of parameters such that the above
generalization of the GaBP algorithm converges.
|
1212.0190 | A Comparative Study of Discretization Approaches for Granular
Association Rule Mining | cs.DB cs.IR | Granular association rule mining is a new relational data mining approach to
reveal patterns hidden in multiple tables. The current research of granular
association rule mining considers only nominal data. In this paper, we study
the impact of discretization approaches on mining semantically richer and
stronger rules from numeric data. Specifically, the Equal Width approach and
the Equal Frequency approach are adopted and compared. The setting of interval
numbers is a key issue in discretization approaches, so we compare different
settings through experiments on a well-known real life data set. Experimental
results show that: 1) discretization is an effective preprocessing technique in
mining stronger rules; 2) the Equal Frequency approach helps generating more
rules than the Equal Width approach; 3) with certain settings of interval
numbers, we can obtain much more rules than others.
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1212.0207 | Modelling Multi-Trait Scale-free Networks by Optimization | cs.SI math-ph math.MP nlin.AO physics.soc-ph | Recently, one paper in Nature(Papadopoulos, 2012) raised an old debate on the
origin of the scale-free property of complex networks, which focuses on whether
the scale-free property origins from the optimization or not. Because the
real-world complex networks often have multiple traits, any explanation on the
scale-free property of complex networks should be capable of explaining the
other traits as well. This paper proposed a framework which can model
multi-trait scale-free networks based on optimization, and used three examples
to demonstrate its effectiveness. The results suggested that the optimization
is a more generalized explanation because it can not only explain the origin of
the scale-free property, but also the origin of the other traits in a uniform
way. This paper provides a universal method to get ideal networks for the
researches such as epidemic spreading and synchronization on complex networks.
|
1212.0215 | Artificial Neural Network for Performance Modeling and Optimization of
CMOS Analog Circuits | cs.NE | This paper presents an implementation of multilayer feed forward neural
networks (NN) to optimize CMOS analog circuits. For modeling and design
recently neural network computational modules have got acceptance as an
unorthodox and useful tool. To achieve high performance of active or passive
circuit component neural network can be trained accordingly. A well trained
neural network can produce more accurate outcome depending on its learning
capability. Neural network model can replace empirical modeling solutions
limited by range and accuracy.[2] Neural network models are easy to obtain for
new circuits or devices which can replace analytical methods. Numerical
modeling methods can also be replaced by neural network model due to their
computationally expansive behavior.[2][10][20]. The pro- posed implementation
is aimed at reducing resource requirement, without much compromise on the
speed. The NN ensures proper functioning by assigning the appropriate inputs,
weights, biases, and excitation function of the layer that is currently being
computed. The concept used is shown to be very effective in reducing resource
requirements and enhancing speed.
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1212.0217 | Cultural evolution and personalization | physics.soc-ph cs.SI | In social sciences, there is currently no consensus on the mechanism for
cultural evolution. The evolution of first names of newborn babies offers a
remarkable example for the researches in the field. Here we perform statistical
analyses on over 100 years of data in the United States. We focus in particular
on how the frequency-rank distribution and inequality of baby names change over
time. We propose a stochastic model where name choice is determined by
personalized preference and social influence. Remarkably, variations on the
strength of personalized preference can account satisfactorily for the observed
empirical features. Therefore, we claim that personalization drives cultural
evolution, at least in the example of baby names.
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1212.0220 | Metaheuristic Optimization: Algorithm Analysis and Open Problems | math.OC cs.NE | Metaheuristic algorithms are becoming an important part of modern
optimization. A wide range of metaheuristic algorithms have emerged over the
last two decades, and many metaheuristics such as particle swarm optimization
are becoming increasingly popular. Despite their popularity, mathematical
analysis of these algorithms lacks behind. Convergence analysis still remains
unsolved for the majority of metaheuristic algorithms, while efficiency
analysis is equally challenging. In this paper, we intend to provide an
overview of convergence and efficiency studies of metaheuristics, and try to
provide a framework for analyzing metaheuristics in terms of convergence and
efficiency. This can form a basis for analyzing other algorithms. We also
outline some open questions as further research topics.
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1212.0229 | Simplification and integration in computing and cognition: the SP theory
and the multiple alignment concept | cs.AI cs.CL | The main purpose of this article is to describe potential benefits and
applications of the SP theory, a unique attempt to simplify and integrate ideas
across artificial intelligence, mainstream computing and human cognition, with
information compression as a unifying theme. The theory, including a concept of
multiple alignment, combines conceptual simplicity with descriptive and
explanatory power in several areas including representation of knowledge,
natural language processing, pattern recognition, several kinds of reasoning,
the storage and retrieval of information, planning and problem solving,
unsupervised learning, information compression, and human perception and
cognition. In the SP machine -- an expression of the SP theory which is
currently realised in the form of computer models -- there is potential for an
overall simplification of computing systems, including software. As a theory
with a broad base of support, the SP theory promises useful insights in many
areas and the integration of structures and functions, both within a given area
and amongst different areas. There are potential benefits in natural language
processing (with potential for the understanding and translation of natural
languages), the need for a versatile intelligence in autonomous robots,
computer vision, intelligent databases, maintaining multiple versions of
documents or web pages, software engineering, criminal investigations, the
management of big data and gaining benefits from it, the semantic web, medical
diagnosis, the detection of computer viruses, the economical transmission of
data, and data fusion. Further development of these ideas would be facilitated
by the creation of a high-parallel, web-based, open-source version of the SP
machine, with a good user interface. This would provide a means for researchers
to explore what can be done with the system and to refine it.
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1212.0240 | Onboard Dynamic Rail Track Safety Monitoring System | cs.MA cs.SY | This proposal aims at solving one of the long prevailing problems in the
Indian Railways. This simple method of continuous monitoring and assessment of
the condition of the rail tracks can prevent major disasters and save precious
human lives. Our method is capable of alerting the train in case of any
dislocations in the track or change in strength of the soil. Also it can avert
the collisions of the train with other or with the vehicles trying to move
across the unmanned level crossings.
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1212.0243 | Estimation for Monotone Sampling: Competitiveness and Customization | math.ST cs.DB stat.TH | Random samples are lossy summaries which allow queries posed over the data to
be approximated by applying an appropriate estimator to the sample. The
effectiveness of sampling, however, hinges on estimator selection. The choice
of estimators is subjected to global requirements, such as unbiasedness and
range restrictions on the estimate value, and ideally, we seek estimators that
are both efficient to derive and apply and {\em admissible} (not dominated, in
terms of variance, by other estimators). Nevertheless, for a given data domain,
sampling scheme, and query, there are many admissible estimators. We study the
choice of admissible nonnegative and unbiased estimators for monotone sampling
schemes. Monotone sampling schemes are implicit in many applications of massive
data set analysis. Our main contribution is general derivations of admissible
estimators with desirable properties. We present a construction of {\em
order-optimal} estimators, which minimize variance according to {\em any}
specified priorities over the data domain. Order-optimality allows us to
customize the derivation to common patterns that we can learn or observe in the
data. When we prioritize lower values (e.g., more similar data sets when
estimating difference), we obtain the L$^*$ estimator, which is the unique
monotone admissible estimator. We show that the L$^*$ estimator is
4-competitive and dominates the classic Horvitz-Thompson estimator. These
properties make the L$^*$ estimator a natural default choice. We also present
the U$^*$ estimator, which prioritizes large values (e.g., less similar data
sets). Our estimator constructions are both easy to apply and possess desirable
properties, allowing us to make the most from our summarized data.
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1212.0248 | The structure of Renyi entropic inequalities | quant-ph cs.IT math-ph math.IT math.MP | We investigate the universal inequalities relating the alpha-Renyi entropies
of the marginals of a multi-partite quantum state. This is in analogy to the
same question for the Shannon and von Neumann entropy (alpha=1) which are known
to satisfy several non-trivial inequalities such as strong subadditivity.
Somewhat surprisingly, we find for 0<alpha<1, that the only inequality is
non-negativity: In other words, any collection of non-negative numbers assigned
to the nonempty subsets of n parties can be arbitrarily well approximated by
the alpha-entropies of the 2^n-1 marginals of a quantum state.
For alpha>1 we show analogously that there are no non-trivial homogeneous (in
particular no linear) inequalities. On the other hand, it is known that there
are further, non-linear and indeed non-homogeneous, inequalities delimiting the
alpha-entropies of a general quantum state.
Finally, we also treat the case of Renyi entropies restricted to classical
states (i.e. probability distributions), which in addition to non-negativity
are also subject to monotonicity. For alpha different from 0 and 1 we show that
this is the only other homogeneous relation.
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1212.0254 | Resolution and Datalog Rewriting Under Value Invention and Equality
Constraints | cs.DB | This paper present several refinements of the Datalog +/- framework based on
resolution and Datalog-rewriting. We first present a resolution algorithm which
is complete for arbitrary sets of tgds and egds. We then show that a technique
of saturation can be used to achieve completeness with respect to First-Order
(FO) query rewriting. We then investigate the class of guarded tgds (with a
loose definition of guardedness), and show that every set of tgds in this class
can be rewritten into an equivalent set of standard Datalog rules. On the
negative side, this implies that Datalog +/- has (only) the same expressive
power as standard Datalog in terms of query answering. On the positive side
however, this mean that known results and existing optimization techniques
(such as Magic-Set) may be applied in the context of Datalog +/- despite its
richer syntax.
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1212.0272 | Risk Limiting Dispatch with Fast Ramping Storage | math.OC cs.SY | Risk Limiting Dispatch (RLD) was proposed recently as a mechanism that
utilizes information and market recourse to reduce reserve capacity
requirements, emissions and achieve other system operator objectives. It
induces a set of simple dispatch rules that can be easily embedded into the
existing dispatch systems to provide computationally efficient and reliable
decisions. Storage is emerging as an alternative to mitigate the uncertainty in
the grid. This paper extends the RLD framework to incorporate fast-ramping
storage. It developed a closed form threshold rule for the optimal stochastic
dispatch incorporating a sequence of markets and real-time information. An
efficient algorithm to evaluate the thresholds is developed based on analysis
of the optimal storage operation. Simple approximations that rely on
continuous-time approximations of the solution for the discrete time control
problem are also studied. The benefits of storage with respect to prediction
quality and storage capacity are examined, and the overall effect on dispatch
is quantified. Numerical experiments illustrate the proposed procedures.
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1212.0277 | A Construction for Perfect Periodic Autocorrelation Sequences | cs.IT math.IT | We introduce a construction for perfect periodic autocorrelation sequences
over roots of unity. The sequences share similarities to the perfect periodic
sequence constructions of Liu, Frank, and Milewski.
|
1212.0280 | Error Bounds on Derivatives during Simulations | math.NA cs.CE | The methods commonly used for numerical differentiation, such as the
"center-difference formula" and "four-points formula" are unusable in
simulations or real-time data analysis because they require knowledge of the
future. In Bard'11, an algorithm was shown that generates formulas that require
knowledge only of the past and present values of $f(t)$ to estimate $f'(t)$.
Furthermore, the algorithm can handle irregularly spaced data and higher-order
derivatives. That work did not include a rigorous proof of correctness nor the
error bounds. In this paper, the correctness and error bounds of that algorithm
are proven, explicit forms are given for the coefficients, and several
interesting corollaries are proven.
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1212.0287 | Exploring Relay Cooperation for Secure and Reliable Transmission in
Two-Hop Wireless Networks | cs.CR cs.IT cs.NI math.IT | This work considers the problem of secure and reliable information
transmission via relay cooperation in two-hop relay wireless networks without
the information of both eavesdropper channels and locations. While previous
work on this problem mainly studied infinite networks and their asymptotic
behavior and scaling law results, this papers focuses on a more practical
network with finite number of system nodes and explores the corresponding exact
result on the number of eavesdroppers one network can tolerant to ensure
desired secrecy and reliability. We first study the scenario where path-loss is
equal between all pairs of nodes and consider two transmission protocols there,
one adopts an optimal but complex relay selection process with less load
balance capacity while the other adopts a random but simple relay selection
process with good load balance capacity. Theoretical analysis is then provided
to determine the maximum number of eavesdroppers one network can tolerate to
ensure a desired performance in terms of the secrecy outage probability and
transmission outage probability. We further extend our study to the more
general scenario where path-loss between each pair of nodes also depends the
distance between them, for which a new transmission protocol with both
preferable relay selection and good load balance as well as the corresponding
theoretical analysis are presented.
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1212.0291 | An Image Based Technique for Enhancement of Underwater Images | cs.CV | The underwater images usually suffers from non-uniform lighting, low
contrast, blur and diminished colors. In this paper, we proposed an image based
preprocessing technique to enhance the quality of the underwater images. The
proposed technique comprises a combination of four filters such as homomorphic
filtering, wavelet denoising, bilateral filter and contrast equalization. These
filters are applied sequentially on degraded underwater images. The literature
survey reveals that image based preprocessing algorithms uses standard filter
techniques with various combinations. For smoothing the image, the image based
preprocessing algorithms uses the anisotropic filter. The main drawback of the
anisotropic filter is that iterative in nature and computation time is high
compared to bilateral filter. In the proposed technique, in addition to other
three filters, we employ a bilateral filter for smoothing the image. The
experimentation is carried out in two stages. In the first stage, we have
conducted various experiments on captured images and estimated optimal
parameters for bilateral filter. Similarly, optimal filter bank and optimal
wavelet shrinkage function are estimated for wavelet denoising. In the second
stage, we conducted the experiments using estimated optimal parameters, optimal
filter bank and optimal wavelet shrinkage function for evaluating the proposed
technique. We evaluated the technique using quantitative based criteria such as
a gradient magnitude histogram and Peak Signal to Noise Ratio (PSNR). Further,
the results are qualitatively evaluated based on edge detection results. The
proposed technique enhances the quality of the underwater images and can be
employed prior to apply computer vision techniques.
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1212.0317 | An Improved UP-Growth High Utility Itemset Mining | cs.DB | Efficient discovery of frequent itemsets in large datasets is a crucial task
of data mining. In recent years, several approaches have been proposed for
generating high utility patterns, they arise the problems of producing a large
number of candidate itemsets for high utility itemsets and probably degrades
mining performance in terms of speed and space. Recently proposed compact tree
structure, viz., UP Tree, maintains the information of transactions and
itemsets, facilitate the mining performance and avoid scanning original
database repeatedly. In this paper, UP Tree (Utility Pattern Tree) is adopted,
which scans database only twice to obtain candidate items and manage them in an
efficient data structured way. Applying UP Tree to the UP Growth takes more
execution time for Phase II. Hence this paper presents modified algorithm
aiming to reduce the execution time by effectively identifying high utility
itemsets.
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1212.0318 | Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its
Applications | cs.CV | Image fusion is the process of integrating multiple images of the same scene
into a single fused image to reduce uncertainty and minimizing redundancy while
extracting all the useful information from the source images. Image fusion
process is required for different applications like medical imaging, remote
sensing, medical imaging, machine vision, biometrics and military applications
where quality and critical information is required. In this paper, image fusion
using fuzzy and neuro fuzzy logic approaches utilized to fuse images from
different sensors, in order to enhance visualization. The proposed work further
explores comparison between fuzzy based image fusion and neuro fuzzy fusion
technique along with quality evaluation indices for image fusion like image
quality index, mutual information measure, fusion factor, fusion symmetry,
fusion index, root mean square error, peak signal to noise ratio, entropy,
correlation coefficient and spatial frequency. Experimental results obtained
from fusion process prove that the use of the neuro fuzzy based image fusion
approach shows better performance in first two test cases while in the third
test case fuzzy based image fusion technique gives better results.
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1212.0336 | Analytical model of misinformation of a social network node | cs.SI physics.soc-ph | This paper presents the research of the influence of cognitive, behavioral,
representational factors on the susceptibility of the participants in social
networks to misinformation, as well as on the activity of the nodes in this
regard. The importance of this research consists of method of blocking the
propaganda. This is very important because when people involuntarily acquire
information some of them experience an undesired change in their social
attitude. Such phenomena typically lead towards the information warfare. A
model was developed during this research for calculating the level of
misinformation of the social network participant (network node) based on the
model of iterative learning process.
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1212.0347 | Association schemes related to Delsarte-Goethals codes | math.CO cs.IT math.IT | In this paper, we construct an infinite series of 9-class association schemes
from a refinement of the partition of Delsarte-Goethals codes by their Lee
weights. The explicit expressions of the dual schemes are determined through
direct manipulations of complicated exponential sums. As a byproduct, the other
three infinite families of association schemes are also obtained as fusion
schemes and quotient schemes.
|
1212.0382 | Comments on Proakis Analysis of the Characteristic Function of Complex
Gaussian Quadratic Forms | cs.IT math.IT | An analysis of the characteristic function of Gaussian quadratic forms is
presented in [1] to study the performance of multichannel communication
systems. This technical report reviews this analysis, obtaining alternative
expressions to original ones in compact matrix format.
|
1212.0383 | GLCM-based chi-square histogram distance for automatic detection of
defects on patterned textures | cs.CV | Chi-square histogram distance is one of the distance measures that can be
used to find dissimilarity between two histograms. Motivated by the fact that
texture discrimination by human vision system is based on second-order
statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM)
that is based on second-order statistics and propose a new machine vision
algorithm for automatic defect detection on patterned textures. Input defective
images are split into several periodic blocks and GLCMs are computed after
quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact
and to reduce computation time. Dissimilarity matrix derived from chi-square
distances of the GLCMs is subjected to hierarchical clustering to automatically
identify defective and defect-free blocks. Effectiveness of the proposed method
is demonstrated through experiments on defective real-fabric images of 2 major
wallpaper groups (pmm and p4m groups).
|
1212.0388 | Hypergraph and protein function prediction with gene expression data | stat.ML cs.LG q-bio.QM | Most network-based protein (or gene) function prediction methods are based on
the assumption that the labels of two adjacent proteins in the network are
likely to be the same. However, assuming the pairwise relationship between
proteins or genes is not complete, the information a group of genes that show
very similar patterns of expression and tend to have similar functions (i.e.
the functional modules) is missed. The natural way overcoming the information
loss of the above assumption is to represent the gene expression data as the
hypergraph. Thus, in this paper, the three un-normalized, random walk, and
symmetric normalized hypergraph Laplacian based semi-supervised learning
methods applied to hypergraph constructed from the gene expression data in
order to predict the functions of yeast proteins are introduced. Experiment
results show that the average accuracy performance measures of these three
hypergraph Laplacian based semi-supervised learning methods are the same.
However, their average accuracy performance measures of these three methods are
much greater than the average accuracy performance measures of un-normalized
graph Laplacian based semi-supervised learning method (i.e. the baseline method
of this paper) applied to gene co-expression network created from the gene
expression data.
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1212.0402 | UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild | cs.CV | We introduce UCF101 which is currently the largest dataset of human actions.
It consists of 101 action classes, over 13k clips and 27 hours of video data.
The database consists of realistic user uploaded videos containing camera
motion and cluttered background. Additionally, we provide baseline action
recognition results on this new dataset using standard bag of words approach
with overall performance of 44.5%. To the best of our knowledge, UCF101 is
currently the most challenging dataset of actions due to its large number of
classes, large number of clips and also unconstrained nature of such clips.
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1212.0415 | On the minimum distance and the minimum weight of Goppa codes from a
quotient of the Hermitian curve | math.AG cs.IT math.IT | In this paper we study evaluation codes arising from plane quotients of the
Hermitian curve, defined by affine equations of the form $y^q+y=x^m$, $q$ being
a prime power and $m$ a positive integer which divides $q+1$. The dual minimum
distance and minimum weight of such codes are studied from a geometric point of
view. In many cases we completely describe the minimum-weight codewords of
their dual codes through a geometric characterization of the supports, and
provide their number. Finally, we apply our results to describe Goppa codes of
classical interest on such curves.
|
1212.0433 | Compressive Schlieren Deflectometry | cs.CV | Schlieren deflectometry aims at characterizing the deflections undergone by
refracted incident light rays at any surface point of a transparent object. For
smooth surfaces, each surface location is actually associated with a sparse
deflection map (or spectrum). This paper presents a novel method to
compressively acquire and reconstruct such spectra. This is achieved by
altering the way deflection information is captured in a common Schlieren
Deflectometer, i.e., the deflection spectra are indirectly observed by the
principle of spread spectrum compressed sensing. These observations are
realized optically using a 2-D Spatial Light Modulator (SLM) adjusted to the
corresponding sensing basis and whose modulations encode the light deviation
subsequently recorded by a CCD camera. The efficiency of this approach is
demonstrated experimentally on the observation of few test objects. Further,
using a simple parametrization of the deflection spectra we show that relevant
key parameters can be directly computed using the measurements, avoiding full
reconstruction.
|
1212.0435 | Network Growth with Arbitrary Initial Conditions: Analytical Results for
Uniform and Preferential Attachment | cond-mat.stat-mech cs.SI physics.soc-ph | This paper provides time-dependent expressions for the expected degree
distribution of a given network that is subject to growth, as a function of
time. We consider both uniform attachment, where incoming nodes form links to
existing nodes selected uniformly at random, and preferential attachment, when
probabilities are assigned proportional to the degrees of the existing nodes.
We consider the cases of single and multiple links being formed by each
newly-introduced node. The initial conditions are arbitrary, that is, the
solution depends on the degree distribution of the initial graph which is the
substrate of the growth. Previous work in the literature focuses on the
asymptotic state, that is, when the number of nodes added to the initial graph
tends to infinity, rendering the effect of the initial graph negligible. Our
contribution provides a solution for the expected degree distribution as a
function of time, for arbitrary initial condition. Previous results match our
results in the asymptotic limit.
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1212.0463 | Nonparametric risk bounds for time-series forecasting | math.ST cs.LG stat.ML stat.TH | We derive generalization error bounds for traditional time-series forecasting
models. Our results hold for many standard forecasting tools including
autoregressive models, moving average models, and, more generally, linear
state-space models. These non-asymptotic bounds need only weak assumptions on
the data-generating process, yet allow forecasters to select among competing
models and to guarantee, with high probability, that their chosen model will
perform well. We motivate our techniques with and apply them to standard
economic and financial forecasting tools---a GARCH model for predicting equity
volatility and a dynamic stochastic general equilibrium model (DSGE), the
standard tool in macroeconomic forecasting. We demonstrate in particular how
our techniques can aid forecasters and policy makers in choosing models which
behave well under uncertainty and mis-specification.
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