id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1211.5590 | Theano: new features and speed improvements | cs.SC cs.LG | Theano is a linear algebra compiler that optimizes a user's
symbolically-specified mathematical computations to produce efficient low-level
implementations. In this paper, we present new features and efficiency
improvements to Theano, and benchmarks demonstrating Theano's performance
relative to Torch7, a recently introduced machine learning library, and to
RNNLM, a C++ library targeted at recurrent neural networks.
|
1211.5608 | Blind Deconvolution using Convex Programming | cs.IT math.IT | We consider the problem of recovering two unknown vectors, $\boldsymbol{w}$
and $\boldsymbol{x}$, of length $L$ from their circular convolution. We make
the structural assumption that the two vectors are members of known subspaces,
one with dimension $N$ and the other with dimension $K$. Although the observed
convolution is nonlinear in both $\boldsymbol{w}$ and $\boldsymbol{x}$, it is
linear in the rank-1 matrix formed by their outer product
$\boldsymbol{w}\boldsymbol{x}^*$. This observation allows us to recast the
deconvolution problem as low-rank matrix recovery problem from linear
measurements, whose natural convex relaxation is a nuclear norm minimization
program.
We prove the effectiveness of this relaxation by showing that for "generic"
signals, the program can deconvolve $\boldsymbol{w}$ and $\boldsymbol{x}$
exactly when the maximum of $N$ and $K$ is almost on the order of $L$. That is,
we show that if $\boldsymbol{x}$ is drawn from a random subspace of dimension
$N$, and $\boldsymbol{w}$ is a vector in a subspace of dimension $K$ whose
basis vectors are "spread out" in the frequency domain, then nuclear norm
minimization recovers $\boldsymbol{w}\boldsymbol{x}^*$ without error.
We discuss this result in the context of blind channel estimation in
communications. If we have a message of length $N$ which we code using a random
$L\times N$ coding matrix, and the encoded message travels through an unknown
linear time-invariant channel of maximum length $K$, then the receiver can
recover both the channel response and the message when $L\gtrsim N+K$, to
within constant and log factors.
|
1211.5611 | Distributed Random Projection Algorithm for Convex Optimization | math.OC cs.SY | Random projection algorithm is an iterative gradient method with random
projections. Such an algorithm is of interest for constrained optimization when
the constraint set is not known in advance or the projection operation on the
whole constraint set is computationally prohibitive. This paper presents a
distributed random projection (DRP) algorithm for fully distributed constrained
convex optimization problems that can be used by multiple agents connected over
a time-varying network, where each agent has its own objective function and its
own constrained set. With reasonable assumptions, we prove that the iterates of
all agents converge to the same point in the optimal set almost surely. In
addition, we consider a variant of the method that uses a mini-batch of
consecutive random projections and establish its convergence in almost sure
sense. Experiments on distributed support vector machines demonstrate fast
convergence of the algorithm. It actually shows that the number of iteration
required until convergence is much smaller than scanning over all training
samples just once.
|
1211.5614 | A Hash based Approach for Secure Keyless Steganography in Lossless RGB
Images | cs.CR cs.CV cs.MM | This paper proposes an improved steganography approach for hiding text
messages in lossless RGB images. The objective of this work is to increase the
security level and to improve the storage capacity with compression techniques.
The security level is increased by randomly distributing the text message over
the entire image instead of clustering within specific image portions. Storage
capacity is increased by utilizing all the color channels for storing
information and providing the source text message compression. The degradation
of the images can be minimized by changing only one least significant bit per
color channel for hiding the message, incurring a very little change in the
original image. Using steganography alone with simple LSB has a potential
problem that the secret message is easily detectable from the histogram
analysis method. To improve the security as well as the image embedding
capacity indirectly, a compression based scheme is introduced. Various tests
have been done to check the storage capacity and message distribution. These
testes show the superiority of the proposed approach with respect to other
existing approaches.
|
1211.5617 | Optimal rotation control for a qubit subject to continuous measurement | math.OC cs.SY quant-ph | In this article we analyze the optimal control strategy for rotating a
monitored qubit from an initial pure state to an orthogonal state in minimum
time. This strategy is described for two different cost functions of interest
which do not have the usual regularity properties. Hence, as classically smooth
cost functions may not exist, we interpret these functions as viscosity
solutions to the optimal control problem. Specifically we prove their existence
and uniqueness in this weak-solution setting. In addition, we also give bounds
on the time optimal control to prepare any pure state from a mixed state.
|
1211.5625 | A survey of computational methods for protein complex prediction from
protein interaction networks | cs.CE q-bio.MN | Complexes of physically interacting proteins are one of the fundamental
functional units responsible for driving key biological mechanisms within the
cell. Their identification is therefore necessary not only to understand
complex formation but also the higher level organization of the cell. With the
advent of high-throughput techniques in molecular biology, significant amount
of physical interaction data has been cataloged from organisms such as yeast,
which has in turn fueled computational approaches to systematically mine
complexes from the network of physical interactions among proteins (PPI
network). In this survey, we review, classify and evaluate some of the key
computational methods developed till date for the identification of protein
complexes from PPI networks. We present two insightful taxonomies that reflect
how these methods have evolved over the years towards improving automated
complex prediction. We also discuss some open challenges facing accurate
reconstruction of complexes, the crucial ones being presence of high proportion
of errors and noise in current high-throughput datasets and some key aspects
overlooked by current complex detection methods. We hope this review will not
only help to condense the history of computational complex detection for easy
reference, but also provide valuable insights to drive further research in this
area.
|
1211.5629 | Prototype for Extended XDB Using Wiki | cs.DB cs.SE | This paper describes a prototype of extended XDB. XDB is an open-source and
extensible database architecture developed by National Aeronautics and Space
Administration (NASA) to provide integration of heterogeneous and distributed
information resources for scientific and engineering applications. XDB enables
an unlimited number of desktops and distributed information sources to be
linked seamlessly and efficiently into an information grid using Data Access
and Retrieval Composition (DARC) protocol which provides a contextual search
and retrieval capability useful for lightweight web applications. This paper
shows the usage of XDB on common data management in the enterprise without
burdening users and application developers with unnecessary complexity and
formal schemas. Supported by NASA Ames Research Center through NASA Exploration
System Mission Directorate (ESMD) Higher Education grant, a project team at
Fairfield University extended this concept and developed an extended XDB
protocol and a prototype providing text-searches for Wiki. The technical
specification of the protocol was posted to Source Forge (sourceforge.net) and
a prototype providing text-searches for Wiki was developed. The prototype was
created for 16 tags of the MediaWiki dialect. As part of future works, the
prototype will be further extended to the complete Wiki markups and other
dialects of Wiki.
|
1211.5643 | Shadows and headless shadows: a worlds-based, autobiographical approach
to reasoning | cs.AI | Many cognitive systems deploy multiple, closed, individually consistent
models which can represent interpretations of the present state of the world,
moments in the past, possible futures or alternate versions of reality. While
they appear under different names, these structures can be grouped under the
general term of worlds. The Xapagy architecture is a story-oriented cognitive
system which relies exclusively on the autobiographical memory implemented as a
raw collection of events organized into world-type structures called {\em
scenes}. The system performs reasoning by shadowing current events with events
from the autobiography. The shadows are then extrapolated into headless shadows
corresponding to predictions, hidden events or inferred relations.
|
1211.5644 | Modeling problems of identity in Little Red Riding Hood | cs.AI | This paper argues that the problem of identity is a critical challenge in
agents which are able to reason about stories. The Xapagy architecture has been
built from scratch to perform narrative reasoning and relies on a somewhat
unusual approach to represent instances and identity. We illustrate the
approach by a representation of the story of Little Red Riding Hood in the
architecture, with a focus on the problem of identity raised by the narrative.
|
1211.5687 | Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep
Extensions | cs.LG stat.ML | We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture
modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or
surpasses the state-of-the-art on texture synthesis and inpainting by
parametric models. We also develop a novel RBM model with a spike-and-slab
visible layer and binary variables in the hidden layer. This model is designed
to be stacked on top of the TssRBM. We show the resulting deep belief network
(DBN) is a powerful generative model that improves on single-layer models and
is capable of modeling not only single high-resolution and challenging textures
but also multiple textures.
|
1211.5694 | The Williams Bjerknes Model on Regular Trees | math.PR cs.SI math-ph math.MP | We consider the Williams Bjerknes model, also known as the biased voter model
on the $d$-regular tree $\bbT^d$, where $d \geq 3$. Starting from an initial
configuration of "healthy" and "infected" vertices, infected vertices infect
their neighbors at Poisson rate $\lambda \geq 1$, while healthy vertices heal
their neighbors at Poisson rate 1. All vertices act independently. It is well
known that starting from a configuration with a positive but finite number of
infected vertices, infected vertices will continue to exist at all time with
positive probability iff $\lambda > 1$. We show that there exists a threshold
$\lambda_c \in (1, \infty)$ such that if $\lambda > \lambda_c$ then in the
above setting with positive probability all vertices will become eventually
infected forever, while if $\lambda < \lambda_c$, all vertices will become
eventually healthy with probability 1. In particular, this yields a complete
convergence theorem for the model and its dual, a certain branching coalescing
random walk on $\bbT^d$ -- above $\lambda_c$. We also treat the case of initial
configurations chosen according to a distribution which is invariant or ergodic
with respect to the group of automorphisms of $\bbT^d$.
|
1211.5708 | On Watts' Cascade Model with Random Link Weights | physics.soc-ph cond-mat.dis-nn cs.SI | We study an extension of Duncan Watts' 2002 model of information cascades in
social networks where edge weights are taken to be random, an innovation
motivated by recent applications of cascade analysis to systemic risk in
financial networks. The main result is a probabilistic analysis that
characterizes the cascade in an infinite network as the fixed point of a
vector-valued mapping, explicit in terms of convolution integrals that can be
efficiently evaluated numerically using the fast Fourier transform algorithm. A
second result gives an approximate probabilistic analysis of cascades on "real
world networks", finite networks based on a fixed deterministic graph.
Extensive cross testing with Monte Carlo estimates shows that this approximate
analysis performs surprisingly well, and provides a flexible microscope that
can be used to investigate properties of information cascades in real world
networks over a wide range of model parameters.
|
1211.5712 | Detection of elliptical shapes via cross-entropy clustering | cs.CV | The problem of finding elliptical shapes in an image will be considered. We
discuss the solution which uses cross-entropy clustering. The proposed method
allows the search for ellipses with predefined sizes and position in the space.
Moreover, it works well for search of ellipsoids in higher dimensions.
|
1211.5718 | Deterministic Compression with Uncertain Priors | cs.IT cs.CC math.IT | We consider the task of compression of information when the source of the
information and the destination do not agree on the prior, i.e., the
distribution from which the information is being generated. This setting was
considered previously by Kalai et al. (ICS 2011) who suggested that this was a
natural model for human communication, and efficient schemes for compression
here could give insights into the behavior of natural languages. Kalai et al.
gave a compression scheme with nearly optimal performance, assuming the source
and destination share some uniform randomness. In this work we explore the need
for this randomness, and give some non-trivial upper bounds on the
deterministic communication complexity for this problem. In the process we
introduce a new family of structured graphs of constant fractional chromatic
number whose (integral) chromatic number turns out to be a key component in the
analysis of the communication complexity. We provide some non-trivial upper
bounds on the chromatic number of these graphs to get our upper bound, while
using lower bounds on variants of these graphs to prove lower bounds for some
natural approaches to solve the communication complexity question. Tight
analysis of communication complexity of our problems and the chromatic number
of the underlying graphs remains open.
|
1211.5723 | The Survey of Data Mining Applications And Feature Scope | cs.DB cs.IR | In this paper we have focused a variety of techniques, approaches and
different areas of the research which are helpful and marked as the important
field of data mining Technologies. As we are aware that many Multinational
companies and large organizations are operated in different places of the
different countries.Each place of operation may generate large volumes of data.
Corporate decision makers require access from all such sources and take
strategic decisions.The data warehouse is used in the significant business
value by improving the effectiveness of managerial decision-making. In an
uncertain and highly competitive business environment, the value of strategic
information systems such as these are easily recognized however in todays
business environment,efficiency or speed is not the only key for
competitiveness.This type of huge amount of data are available in the form of
tera-topeta-bytes which has drastically changed in the areas of science and
engineering.To analyze,manage and make a decision of such type of huge amount
of data we need techniques called the data mining which will transforming in
many fields.This paper imparts more number of applications of the data mining
and also focuses scope of the data mining which will helpful in the further
research.
|
1211.5724 | Data Mining: A prediction Technique for the workers in the PR Department
of Orissa (Block and Panchayat) | cs.DB | This paper presents the method of mining the data and which contains the
information about the large information about the PR (Panchayat Raj
Department)of Orissa.We have focused some of the techniques,approaches and
different methodologies of the demand forecasting. Every organizations are
operated in different places of the country. Each place of operation may
generate a huge amount of data. In an organization, worker prediction is the
difficult task of the manager. It is the complex process not only because its
nature of feature prediction but also various approaches methodologies always
makes user confused. This paper aims to deal with the problem selection
process. In this paper we have used some of the approaches from literature are
been introduced and analyzed to find its suitable organization and situation.
Based on this we have designed with automatic selection function to help users
make a prejudgment. This information about each approach will be showed to
users with examples to help understanding. This system also provides
calculation function to help users work out a predication result. Generally the
new developed system has a more comprehensive functions compared with existing
ones. It aims to improve the accuracy of demand forecasting by implementing the
forecasting algorithm. While it is still a decision support system with no
ability of make the final judgment.This type of huge amount of data are are
available in the form of different ways which has drastically changed in the
areas of science and engineering.To analyze, manage and make a decision of such
type of huge amount of data we need techniques called the data mining which
will transforming in many fields. We have implemented the algorithms in JAVA
technology. This paper provides the prediction algorithm Linear Regression,
result which will helpful in the further research.
|
1211.5735 | Generalized Degrees of Freedom for Network-Coded Cognitive Interference
Channel | cs.IT math.IT | We study a two-user cognitive interference channel (CIC) where one of the
transmitters (primary) has knowledge of a linear combination (over an
appropriate finite field) of the two information messages. We refer to this
channel model as Network-Coded CIC, since the linear combination may be the
result of some linear network coding scheme implemented in the backbone wired
network.In this paper, we characterize the generalized degrees of freedom
(GDoF) for the Gaussian Network-Coded CIC. For achievability, we use the novel
Precoded Compute-and-Forward (PCoF) and Dirty Paper Coding (DPC), based on
nested lattice codes. As a consequence of the GDoF characterization, we show
that knowing "mixed data" (linear combinations of the information messages)
provides a {\em multiplicative} gain for the Gaussian CIC, if the power ratio
of signal-to-noise (SNR) to interference-to-noise (INR) is larger than certain
threshold. For example, when $\SNR=\INR$, the Network-Coded cognition yields a
100% gain over the classical Gaussian CIC.
|
1211.5739 | Optimal Selection of Measurement Configurations for Stiffness Model
Calibration of Anthropomorphic Manipulators | cs.RO | The paper focuses on the calibration of elastostatic parameters of spatial
anthropomorphic robots. It proposes a new strategy for optimal selection of the
measurement configurations that essentially increases the efficiency of robot
calibration. This strategy is based on the concept of the robot test-pose and
ensures the best compliance error compensation for the test configuration. The
advantages of the proposed approach and its suitability for practical
applications are illustrated by numerical examples, which deal with calibration
of elastostatic parameters of a 3 degrees of freedom anthropomorphic
manipulator with rigid links and compliant actuated joints
|
1211.5740 | Industry-oriented Performance Measures for Design of Robot Calibration
Experiment | cs.RO | The paper focuses on the accuracy improvement of geometric and elasto-static
calibration of industrial robots. It proposes industry-oriented performance
measures for the calibration experiment design. They are based on the concept
of manipulator test-pose and referred to the end-effector location accuracy
after application of the error compensation algorithm, which implements the
identified parameters. This approach allows the users to define optimal
measurement configurations for robot calibration for given work piece location
and machining forces/torques. These performance measures are suitable for
comparing the calibration plans for both simple and complex trajectories to be
performed. The advantages of the developed techniques are illustrated by an
example that deals with machining using robotic manipulator.
|
1211.5757 | Low-Complexity LP Decoding of Nonbinary Linear Codes | cs.IT math.IT | Linear Programming (LP) decoding of Low-Density Parity-Check (LDPC) codes has
attracted much attention in the research community in the past few years. LP
decoding has been derived for binary and nonbinary linear codes. However, the
most important problem with LP decoding for both binary and nonbinary linear
codes is that the complexity of standard LP solvers such as the simplex
algorithm remains prohibitively large for codes of moderate to large block
length. To address this problem, two low-complexity LP (LCLP) decoding
algorithms for binary linear codes have been proposed by Vontobel and Koetter,
henceforth called the basic LCLP decoding algorithm and the subgradient LCLP
decoding algorithm.
In this paper, we generalize these LCLP decoding algorithms to nonbinary
linear codes. The computational complexity per iteration of the proposed
nonbinary LCLP decoding algorithms scales linearly with the block length of the
code. A modified BCJR algorithm for efficient check-node calculations in the
nonbinary basic LCLP decoding algorithm is also proposed, which has complexity
linear in the check node degree.
Several simulation results are presented for nonbinary LDPC codes defined
over Z_4, GF(4), and GF(8) using quaternary phase-shift keying and
8-phase-shift keying, respectively, over the AWGN channel. It is shown that for
some group-structured LDPC codes, the error-correcting performance of the
nonbinary LCLP decoding algorithms is similar to or better than that of the
min-sum decoding algorithm.
|
1211.5758 | Inversion of Linear and Nonlinear Observable Systems with Series-defined
Output Trajectories | cs.SY math.DS | The problem of inverting a system in presence of a series-defined output is
analyzed. Inverse models are derived that consist of a set of algebraic
equations. The inversion is performed explicitly for an output trajectory
functional, which is a linear combination of some basis functions with
arbitrarily free coefficients. The observer canonical form is exploited, and
the input-output representation is solved using a series method. It is shown
that the only required system characteristic is observability, which implies
that there is no need for output redefinition. An exact inverse model is found
for linear systems. For general nonlinear systems, a good approximation of the
inverse model valid on a finite time interval is found.
|
1211.5759 | Trajectory Tracking Control with Flat Inputs and a Dynamic Compensator | cs.SY math.DS | This paper proposes a tracking controller based on the concept of flat inputs
and a dynamic compensator. Flat inputs represent a dual approach to flat
outputs. In contrast to conventional flatness-based control design, the
regulated output may be a non-flat output, or the system may be non-flat. The
method is applicable to observable systems with stable internal dynamics. The
performance of the new design is demonstrated on the variable-length pendulum,
a non-flat nonlinear system with a singularity in the relative degree.
|
1211.5761 | Computationally Efficient Trajectory Optimization for Linear Control
Systems with Input and State Constraints | cs.SY math.OC | This paper presents a trajectory generation method that optimizes a quadratic
cost functional with respect to linear system dynamics and to linear input and
state constraints. The method is based on continuous-time flatness-based
trajectory generation, and the outputs are parameterized using a polynomial
basis. A method to parameterize the constraints is introduced using a result on
polynomial nonpositivity. The resulting parameterized problem remains
linear-quadratic and can be solved using quadratic programming. The problem can
be further simplified to a linear programming problem by linearization around
the unconstrained optimum. The method promises to be computationally efficient
for constrained systems with a high optimization horizon. As application, a
predictive torque controller for a permanent magnet synchronous motor which is
based on real-time optimization is presented.
|
1211.5766 | Visualization and clustering by 3D cellular automata: Application to
unstructured data | cs.AI cs.IR | Given the limited performance of 2D cellular automata in terms of space when
the number of documents increases and in terms of visualization clusters, our
motivation was to experiment these cellular automata by increasing the size to
view the impact of size on quality of results. The representation of textual
data was carried out by a vector model whose components are derived from the
overall balancing of the used corpus, Term Frequency Inverse Document Frequency
(TF-IDF). The WorldNet thesaurus has been used to address the problem of the
lemmatization of the words because the representation used in this study is
that of the bags of words. Another independent method of the language was used
to represent textual records is that of the n-grams. Several measures of
similarity have been tested. To validate the classification we have used two
measures of assessment based on the recall and precision (f-measure and
entropy). The results are promising and confirm the idea to increase the
dimension to the problem of the spatiality of the classes. The results obtained
in terms of purity class (i.e. the minimum value of entropy) shows that the
number of documents over longer believes the results are better for 3D cellular
automata, which was not obvious to the 2D dimension. In terms of spatial
navigation, cellular automata provide very good 3D performance visualization
than 2D cellular automata.
|
1211.5787 | Fast Rendezvous on a Cycle by Agents with Different Speeds | cs.DC cs.RO | The difference between the speed of the actions of different processes is
typically considered as an obstacle that makes the achievement of cooperative
goals more difficult. In this work, we aim to highlight potential benefits of
such asynchrony phenomena to tasks involving symmetry breaking. Specifically,
in this paper, identical (except for their speeds) mobile agents are placed at
arbitrary locations on a cycle of length $n$ and use their speed difference in
order to rendezvous fast. We normalize the speed of the slower agent to be 1,
and fix the speed of the faster agent to be some $c>1$. (An agent does not know
whether it is the slower agent or the faster one.) The straightforward
distributed-race DR algorithm is the one in which both agents simply start
walking until rendezvous is achieved. It is easy to show that, in the worst
case, the rendezvous time of DR is $n/(c-1)$. Note that in the interesting
case, where $c$ is very close to 1 this bound becomes huge. Our first result is
a lower bound showing that, up to a multiplicative factor of 2, this bound is
unavoidable, even in a model that allows agents to leave arbitrary marks, even
assuming sense of direction, and even assuming $n$ and $c$ are known to agents.
That is, we show that under such assumptions, the rendezvous time of any
algorithm is at least $\frac{n}{2(c-1)}$ if $c\leq 3$ and slightly larger if
$c>3$. We then construct an algorithm that precisely matches the lower bound
for the case $c\leq 2$, and almost matches it when $c>2$. Moreover, our
algorithm performs under weaker assumptions than those stated above, as it does
not assume sense of direction, and it allows agents to leave only a single mark
(a pebble) and only at the place where they start the execution. Finally, we
investigate the setting in which no marks can be used at all, and show tight
bounds for $c\leq 2$, and almost tight bounds for $c>2$.
|
1211.5793 | Compliance error compensation technique for parallel robots composed of
non-perfect serial chains | cs.RO | The paper presents the compliance errors compensation technique for
over-constrained parallel manipulators under external and internal loadings.
This technique is based on the non-linear stiffness modeling which is able to
take into account the influence of non-perfect geometry of serial chains caused
by manufacturing errors. Within the developed technique, the deviation
compensation reduces to an adjustment of a target trajectory that is modified
in the off-line mode. The advantages and practical significance of the proposed
technique are illustrated by an example that deals with groove milling by the
Orthoglide manipulator that considers different locations of the workpiece. It
is also demonstrated that the impact of the compliance errors and the errors
caused by inaccuracy in serial chains cannot be taken into account using the
superposition principle.
|
1211.5795 | Stiffness modeling of non-perfect parallel manipulators | cs.RO | The paper focuses on the stiffness modeling of parallel manipulators composed
of non-perfect serial chains, whose geometrical parameters differ from the
nominal ones. In these manipulators, there usually exist essential internal
forces/torques that considerably affect the stiffness properties and also
change the end-effector location. These internal load-ings are caused by
elastic deformations of the manipulator ele-ments during assembling, while the
geometrical errors in the chains are compensated for by applying appropriate
forces. For this type of manipulators, a non-linear stiffness modeling
tech-nique is proposed that allows us to take into account inaccuracy in the
chains and to aggregate their stiffness models for the case of both small and
large deflections. Advantages of the developed technique and its ability to
compute and compensate for the compliance errors caused by different factors
are illustrated by an example that deals with parallel manipulators of the
Or-thoglide family
|
1211.5803 | Fast community detection by SCORE | stat.ME cs.SI physics.soc-ph | Consider a network where the nodes split into $K$ different communities. The
community labels for the nodes are unknown and it is of major interest to
estimate them (i.e., community detection). Degree Corrected Block Model (DCBM)
is a popular network model. How to detect communities with the DCBM is an
interesting problem, where the main challenge lies in the degree heterogeneity.
We propose a new approach to community detection which we call the Spectral
Clustering On Ratios-of-Eigenvectors (SCORE). Compared to classical spectral
methods, the main innovation is to use the entry-wise ratios between the first
leading eigenvector and each of the other leading eigenvectors for clustering.
Let $A$ be the adjacency matrix of the network. We first obtain the $K$ leading
eigenvectors of $A$, say, $\hat{\eta}_1,\ldots,\hat{\eta}_K$, and let $\hat{R}$
be the $n\times (K-1)$ matrix such that
$\hat{R}(i,k)=\hat{\eta}_{k+1}(i)/\hat{\eta}_1(i)$, $1\leq i\leq n$, $1\leq
k\leq K-1$. We then use $\hat{R}$ for clustering by applying the $k$-means
method. The central surprise is, the effect of degree heterogeneity is largely
ancillary, and can be effectively removed by taking entry-wise ratios between
$\hat{\eta}_{k+1}$ and $\hat{\eta}_1$, $1\leq k\leq K-1$. The method is
successfully applied to the web blogs data and the karate club data, with error
rates of $58/1222$ and $1/34$, respectively. These results are more
satisfactory than those by the classical spectral methods. Additionally,
compared to modularity methods, SCORE is easier to implement, computationally
faster, and also has smaller error rates. We develop a theoretic framework
where we show that under mild conditions, the SCORE stably yields consistent
community detection. In the core of the analysis is the recent development on
Random Matrix Theory (RMT), where the matrix-form Bernstein inequality is
especially helpful.
|
1211.5811 | A max-algebra approach to modeling and simulation of tandem queueing
systems | math.NA cs.SY | Max-algebra models of tandem single-server queueing systems with both finite
and infinite buffers are developed. The dynamics of each system is described by
a linear vector state equation similar to those in the conventional linear
systems theory, and it is determined by a transition matrix inherent in the
system. The departure epochs of a customer from the queues are considered as
state variables, whereas its service times are assumed to be system parameters.
We show how transition matrices may be calculated from the service times, and
present the matrices associated with particular models. We also give a
representation of system performance measures including the system time and the
waiting time of customers, associated with the models. As an application, both
serial and parallel simulation procedures are presented, and their performance
is outlined.
|
1211.5817 | Extending SPARQL to Support Entity Grouping and Path Queries | cs.DB | The ability to efficiently find relevant subgraphs and paths in a large graph
to a given query is important in many applications including scientific data
analysis, social networks, and business intelligence. Currently, there is
little support and no efficient approaches for expressing and executing such
queries. This paper proposes a data model and a query language to address this
problem. The contributions include supporting the construction and selection
of: (i) folder nodes, representing a set of related entities, and (ii) path
nodes, representing a set of paths in which a path is the transitive
relationship of two or more entities in the graph. Folders and paths can be
stored and used for future queries. We introduce FPSPARQL which is an extension
of the SPARQL supporting folder and path nodes. We have implemented a query
engine that supports FPSPARQL and the evaluation results shows its viability
and efficiency for querying large graph datasets.
|
1211.5829 | An Automatic Algorithm for Object Recognition and Detection Based on
ASIFT Keypoints | cs.AI cs.CV | Object recognition is an important task in image processing and computer
vision. This paper presents a perfect method for object recognition with full
boundary detection by combining affine scale invariant feature transform
(ASIFT) and a region merging algorithm. ASIFT is a fully affine invariant
algorithm that means features are invariant to six affine parameters namely
translation (2 parameters), zoom, rotation and two camera axis orientations.
The features are very reliable and give us strong keypoints that can be used
for matching between different images of an object. We trained an object in
several images with different aspects for finding best keypoints of it. Then, a
robust region merging algorithm is used to recognize and detect the object with
full boundary in the other images based on ASIFT keypoints and a similarity
measure for merging regions in the image. Experimental results show that the
presented method is very efficient and powerful to recognize the object and
detect it with high accuracy.
|
1211.5837 | Geosocial Graph-Based Community Detection | cs.SI physics.soc-ph | We apply spectral clustering and multislice modularity optimization to a Los
Angeles Police Department field interview card data set. To detect communities
(i.e., cohesive groups of vertices), we use both geographic and social
information about stops involving street gang members in the LAPD district of
Hollenbeck. We then compare the algorithmically detected communities with known
gang identifications and argue that discrepancies are due to sparsity of social
connections in the data as well as complex underlying sociological factors that
blur distinctions between communities.
|
1211.5856 | Distributed Optimal Power Flow for Smart Microgrids | math.OC cs.SY | Optimal power flow (OPF) is considered for microgrids, with the objective of
minimizing either the power distribution losses, or, the cost of power drawn
from the substation and supplied by distributed generation (DG) units, while
effecting voltage regulation. The microgrid is unbalanced, due to unequal loads
in each phase and non-equilateral conductor spacings on the distribution lines.
Similar to OPF formulations for balanced systems, the considered OPF problem is
nonconvex. Nevertheless, a semidefinite programming (SDP) relaxation technique
is advocated to obtain a convex problem solvable in polynomial-time complexity.
Enticingly, numerical tests demonstrate the ability of the proposed method to
attain the globally optimal solution of the original nonconvex OPF. To ensure
scalability with respect to the number of nodes, robustness to isolated
communication outages, and data privacy and integrity, the proposed SDP is
solved in a distributed fashion by resorting to the alternating direction
method of multipliers. The resulting algorithm entails iterative
message-passing among groups of consumers and guarantees faster convergence
compared to competing alternatives
|
1211.5870 | Super-Resolution by Compressive Sensing Algorithms | cs.IT math.IT physics.optics | In this work, super-resolution by 4 compressive sensing methods (OMP, BP,
BLOOMP, BP-BLOT) with highly coherent partial Fourier measurements is
comparatively studied. An alternative metric more suitable for gauging the
quality of spike recovery is introduced and based on the concept of filtration
with a parameter representing the level of tolerance for support offset. In
terms of the filtered error norm only BLOOMP and BP-BLOT can perform
grid-independent recovery of well separated spikes of Rayleigh index 1 for
arbitrarily large super-resolution factor. Moreover both BLOOMP and BP-BLOT can
localize spike support within a few percent of the Rayleigh length. This is a
weak form of super-resolution. Only BP-BLOT can achieve this feat for closely
spaced spikes separated by a fraction of the Rayleigh length, a strong form of
super-resolution.
|
1211.5877 | A Methodology to Extract Social Network from the Web Snippet | cs.SI cs.IR | The Web has been chosen as a basic infrastructure to gain the social
structure information, through the social network extraction, from all over the
world. However, most of the web documents are unstructured and lack of
semantics. Moreover, that network is subject to all kinds of changes and
dynamics, and a network can be very complex due to the large number of nodes
and links Web contains. In this paper, we discuss a methodology that meant to
assists in extracting and modeling the social network from Web snippet. As the
manual social network extraction of web documents is impractical and
unscalable, and fully automated extraction are still at the very early stage to
be implemented, we proposed a (semi)-automatic extraction based on the
superficial methods.
|
1211.5882 | Multi-User Detection in Multibeam Mobile Satellite Systems: A Fair
Performance Evaluation | cs.IT math.IT | Multi-User Detection (MUD) techniques are currently being examined as
promising technologies for the next generation of broadband, interactive,
multibeam, satellite communication (SatCom) systems. Results in the existing
literature have shown that when full frequency and polarization reuse is
employed and user signals are jointly processed at the gateway, more than
threefold gains in terms of spectral efficiency over conventional systems can
be obtained. However, the information theoretic results for the capacity of the
multibeam satellite channel, are given under ideal assumptions, disregarding
the implementation constraints of such an approach. Considering a real system
implementation, the adoption of full resource reuse is bound to increase the
payload complexity and power consumption. Since the novel techniques require
extra payload resources, fairness issues in the comparison among the two
approaches arise. The present contribution evaluates in a fair manner, the
performance of the return link (RL) of a SatCom system serving mobile users
that are jointly decoded at the receiver. More specifically, the achievable
spectral efficiency of the assumed system is compared to a conventional system
under the constraint of equal physical layer resource utilization. Furthermore,
realistic link budgets for the RL of mobile SatComs are presented, thus
allowing the comparison of the systems in terms of achievable throughput. Since
the proposed systems operate under the same payload requirements as the
conventional systems, the comparison can be regarded as fair. Finally, existing
analytical formulas are also employed to provide closed form descriptions of
the performance of clustered multibeam MUD, thus introducing insights on how
the performance scales with respect to the system parameters.
|
1211.5884 | Low complexity sum rate maximization for single and multiple stream MIMO
AF relay networks | cs.IT math.IT | A multiple-antenna amplify-and-forward two-hop interference network with
multiple links and multiple relays is considered. We optimize transmit
precoders, receive decoders and relay AF matrices to maximize the achievable
sum rate. Under per user and total relay sum power constraints, we propose an
efficient algorithm to maximize the total signal to total interference plus
noise ratio (TSTINR). Computational complexity analysis shows that our proposed
algorithm for TSTINR has lower complexity than the existing weighted minimum
mean square error (WMMSE) algorithm. We analyze and confirm by simulations that
the TSTINR, WMMSE and the total leakage interference plus noise (TLIN)
minimization models with per user and total relay sum power constraints can
only transmit a single data stream for each user. Thus we propose a novel
multiple stream TSTINR model with requirement of orthogonal columns for
precoders, in order to support multiple data streams and thus utilize higher
Degrees of Freedom. Multiple data streams and larger multiplexing gains are
guaranteed. Simulation results show that for single stream models, our TSTINR
algorithm outperforms the TLIN algorithm generally and outperforms WMMSE in
medium to high Signal-to-Noise-Ratio scenarios; the system sum rate
significantly benefits from multiple data streams in medium to high SNR
scenarios.
|
1211.5888 | User Scheduling for Coordinated Dual Satellite Systems with Linear
Precoding | cs.IT math.IT | The constantly increasing demand for interactive broadband satellite
communications is driving current research to explore novel system
architectures that reuse frequency in a more aggressive manner. To this end,
the topic of dual satellite systems, in which satellites share spatial (i.e.
same coverage area) and spectral (i.e. full frequency reuse) degrees of freedom
is introduced. In each multibeam satellite, multiuser interferences are
mitigated by employing zero forcing precoding with realistic per antenna power
constraints. However, the two sets of users that the transmitters are
separately serving, interfere. The present contribution, proposes the partial
cooperation, namely coordination between the two coexisting transmitters in
order to reduce interferences and enhance the performance of the whole system,
while maintaining moderate system complexity. In this direction, a heuristic,
iterative, low complexity algorithm that allocates users in the two interfering
sets is proposed. This novel algorithm, improves the performance of each
satellite and of the overall system, simultaneously. The first is achieved by
maximizing the orthogonality between users allocated in the same set, hence
optimizing the zero forcing performance, whilst the second by minimizing the
level of interferences between the two sets. Simulation results show that the
proposed method, compared to conventional techniques, significantly increases
spectral efficiency.
|
1211.5890 | Adaptive Control of Enterprise | cs.CE | Modern progress in artificial intelligence permits to realize algorithms of
adaptation for critical events (in addition to ERP). A production emergence, an
appearance of new competitive goods, a major change in financial state of
partners, a radical change in exchange rate, a change in custom and tax
legislation, a political and energy crisis, an ecocatastrophe can lead up to a
decrease of profit or bankruptcy of enterprise. Therefore it is necessary to
assess a probability of threat and to take preventive actions. If a critical
event took place, one must estimate restoration expenses and possible
consequences as well as to prepare appropriate propositions. This is provided
using modern methods of diagnostics, prediction, and decision making as well as
an inference engine and semantic analysis. Mathematical methods in use are
called in algorithms of adaptation automatically. Because the enterprise is a
complex system, to overcome complexity of control it is necessary to apply
semantic representations. Such representations are formed from descriptions of
events, facts, persons, organizations, goods, operations, scripts on a natural
language. Semantic representations permit as well to formulate actual problems
and to find ways to resolve these problems.
|
1211.5901 | Bayesian learning of noisy Markov decision processes | stat.ML cs.LG stat.CO | We consider the inverse reinforcement learning problem, that is, the problem
of learning from, and then predicting or mimicking a controller based on
state/action data. We propose a statistical model for such data, derived from
the structure of a Markov decision process. Adopting a Bayesian approach to
inference, we show how latent variables of the model can be estimated, and how
predictions about actions can be made, in a unified framework. A new Markov
chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior
distribution. This step includes a parameter expansion step, which is shown to
be essential for good convergence properties of the MCMC sampler. As an
illustration, the method is applied to learning a human controller.
|
1211.5903 | MMSE Performance Analysis of Generalized Multibeam Satellite Channels | cs.IT math.IT | Aggressive frequency reuse in the return link (RL) of multibeam satellite
communications (SatComs) is crucial towards the implementation of next
generation, interactive satellite services. In this direction, multiuser
detection has shown great potential in mitigating the increased intrasystem
interferences, induced by a tight spectrum reuse. Herein we present an analytic
framework to describe the linear Minimum Mean Square Error (MMSE) performance
of multiuser channels that exhibit full receive correlation: an inherent
attribute of the RL of multibeam SatComs. Analytic, tight approximations on the
MMSE performance are proposed for cases where closed form solutions are not
available in the existing literature. The proposed framework is generic, thus
providing a generalized solution straightforwardly extendable to various fading
models over channels that exhibit full receive correlation. Simulation results
are provided to show the tightness of the proposed approximation with respect
to the available transmit power.
|
1211.5914 | A survey of uncertainty principles and some signal processing
applications | cs.IT math.IT | The goal of this paper is to review the main trends in the domain of
uncertainty principles and localization, emphasize their mutual connections and
investigate practical consequences. The discussion is strongly oriented
towards, and motivated by signal processing problems, from which significant
advances have been made recently. Relations with sparse approximation and
coding problems are emphasized.
|
1211.5931 | Power Allocation Strategies for Fixed-Gain Half-Duplex
Amplify-and-Forward Relaying in Nakagami-m Fading | cs.IT math.IT | In this paper, we study power allocation strategies for a fixed-gain
amplify-and-forward relay network employing multiple relays. We consider two
optimization problems for the relay network: 1) optimal power allocation to
maximize the end-to-end signal-to-noise ratio (SNR) and 2) minimizing the total
consumed power while maintaining the end-to-end SNR over a threshold value. We
investigate these two problems for two relaying protocols of all-participate
relaying and selective relaying and multiple cases of available channel state
information (CSI) at the relays. We show that the SNR maximization problem is
concave and the power minimization problem is convex for all protocols and CSI
cases considered. We obtain closed-form expressions for the two problems in the
case for full CSI and CSI of all the relay-destination links at the relays and
solve the problems through convex programming when full CSI or CSI of the
relay-destination links are not available at the relays. Numerical results show
the benefit of having full CSI at the relays for both optimization problems.
However, they also show that CSI overhead can be reduced by having only partial
CSI at the relays with only a small degradation in performance.
|
1211.5937 | Comparing the reliability of networks by spectral analysis | cond-mat.stat-mech cs.SI physics.soc-ph | We provide a method for the ranking of the reliability of two networks with
the same connectance. Our method is based on the Cheeger constant linking the
topological property of a network with its spectrum. We first analyze a set of
twisted rings with the same connectance and degree distribution, and obtain the
ranking of their reliability using their eigenvalue gaps. The results are
generalized to general networks using the method of rewiring. The success of
our ranking method is verified numerically for the IEEE57, the
Erd\H{o}s-R\'enyi, and the Small-World networks.
|
1211.5938 | Social Network Games | cs.GT cs.SI | One of the natural objectives of the field of the social networks is to
predict agents' behaviour. To better understand the spread of various products
through a social network arXiv:1105.2434 introduced a threshold model, in which
the nodes influenced by their neighbours can adopt one out of several
alternatives. To analyze the consequences of such product adoption we associate
here with each such social network a natural strategic game between the agents.
In these games the payoff of each player weakly increases when more players
choose his strategy, which is exactly opposite to the congestion games. The
possibility of not choosing any product results in two special types of (pure)
Nash equilibria.
We show that such games may have no Nash equilibrium and that determining an
existence of a Nash equilibrium, also of a special type, is NP-complete. This
implies the same result for a more general class of games, namely polymatrix
games. The situation changes when the underlying graph of the social network is
a DAG, a simple cycle, or, more generally, has no source nodes. For these three
classes we determine the complexity of an existence of (a special type of) Nash
equilibria.
We also clarify for these categories of games the status and the complexity
of the finite best response property (FBRP) and the finite improvement property
(FIP). Further, we introduce a new property of the uniform FIP which is
satisfied when the underlying graph is a simple cycle, but determining it is
co-NP-hard in the general case and also when the underlying graph has no source
nodes. The latter complexity results also hold for the property of being a
weakly acyclic game. A preliminary version of this paper appeared as [19].
|
1211.5986 | Signal recognition and adapted filtering by non-commutative tomography | physics.data-an cs.IR math.NA | Tomograms, a generalization of the Radon transform to arbitrary pairs of
non-commuting operators, are positive bilinear transforms with a rigorous
probabilistic interpretation which provide a full characterization of the
signal and are robust in the presence of noise. Tomograms based on the
time-frequency operator pair, were used in the past for component separation
and denoising. Here we show how, by the construction of an operator pair
adapted to the signal, meaningful information with good time resolution is
extracted even in very noisy situations.
|
1211.6013 | Online Stochastic Optimization with Multiple Objectives | cs.LG math.OC | In this paper we propose a general framework to characterize and solve the
stochastic optimization problems with multiple objectives underlying many real
world learning applications. We first propose a projection based algorithm
which attains an $O(T^{-1/3})$ convergence rate. Then, by leveraging on the
theory of Lagrangian in constrained optimization, we devise a novel primal-dual
stochastic approximation algorithm which attains the optimal convergence rate
of $O(T^{-1/2})$ for general Lipschitz continuous objectives.
|
1211.6014 | Exploring the Mobility of Mobile Phone Users | physics.soc-ph cs.SI | Mobile phone datasets allow for the analysis of human behavior on an
unprecedented scale. The social network, temporal dynamics and mobile behavior
of mobile phone users have often been analyzed independently from each other
using mobile phone datasets. In this article, we explore the connections
between various features of human behavior extracted from a large mobile phone
dataset. Our observations are based on the analysis of communication data of
100000 anonymized and randomly chosen individuals in a dataset of
communications in Portugal. We show that clustering and principal component
analysis allow for a significant dimension reduction with limited loss of
information. The most important features are related to geographical location.
In particular, we observe that most people spend most of their time at only a
few locations. With the help of clustering methods, we then robustly identify
home and office locations and compare the results with official census data.
Finally, we analyze the geographic spread of users' frequent locations and show
that commuting distances can be reasonably well explained by a gravity model.
|
1211.6024 | Reconfigurable Antennas, Preemptive Switching and Virtual Channel
Management | cs.IT math.IT | This article considers the performance of wireless communication systems that
utilize reconfigurable or pattern-dynamic antennas. The focus is on
finite-state channels with memory and performance is assessed in terms of
real-time behavior. In a wireless setting, when a slow fading channel enters a
deep fade, the corresponding communication system faces the threat of
successive decoding failures at the destination. Under such circumstances,
rapidly getting out of deep fades becomes a priority. Recent advances in fast
reconfigurable antennas provide new means to alter the statistical profile of
fading channels and thereby reduce the probability of prolonged fades. Fast
reconfigurable antennas are therefore poised to improve overall performance,
especially for delay-sensitive traffic in slow-fading environments. This
potential for enhanced performance motivates this study of the temporal
behavior of point-to-point communication systems with reconfigurable antennas.
Specifically, agile wireless communication schemes over erasure channels are
analyzed; situations where using reconfigurable antennas yield substantial
performance gains in terms of throughput and average delay are identified.
Scenarios where only partial state information is available at the receiver are
also examined, naturally leading to partially observable decision processes.
|
1211.6039 | Rendezvous of two robots with visible bits | cs.MA cs.CG cs.RO | We study the rendezvous problem for two robots moving in the plane (or on a
line). Robots are autonomous, anonymous, oblivious, and carry colored lights
that are visible to both. We consider deterministic distributed algorithms in
which robots do not use distance information, but try to reduce (or increase)
their distance by a constant factor, depending on their lights' colors.
We give a complete characterization of the number of colors that are
necessary to solve the rendezvous problem in every possible model, ranging from
fully synchronous to semi-synchronous to asynchronous, rigid and non-rigid,
with preset or arbitrary initial configuration.
In particular, we show that three colors are sufficient in the non-rigid
asynchronous model with arbitrary initial configuration. In contrast, two
colors are insufficient in the rigid asynchronous model with arbitrary initial
configuration and in the non-rigid asynchronous model with preset initial
configuration.
Additionally, if the robots are able to distinguish between zero and non-zero
distances, we show how they can solve rendezvous and detect termination using
only three colors, even in the non-rigid asynchronous model with arbitrary
initial configuration.
|
1211.6048 | Local sampling and approximation of operators with bandlimited
Kohn-Nirenberg symbols | math.FA cs.IT math.CA math.IT | Recent sampling theorems allow for the recovery of operators with bandlimited
Kohn-Nirenberg symbols from their response to a single discretely supported
identifier signal. The available results are inherently non-local. For example,
we show that in order to recover a bandlimited operator precisely, the
identifier cannot decay in time nor in frequency. Moreover, a concept of local
and discrete representation is missing from the theory. In this paper, we
develop tools that address these shortcomings.
We show that to obtain a local approximation of an operator, it is sufficient
to test the operator on a truncated and mollified delta train, that is, on a
compactly supported Schwarz class function. To compute the operator
numerically, discrete measurements can be obtained from the response function
which are localized in the sense that a local selection of the values yields a
local approximation of the operator.
Central to our analysis is to conceptualize the meaning of localization for
operators with bandlimited Kohn-Nirenberg symbol.
|
1211.6080 | Convexity of reachable sets of nonlinear ordinary differential equations | math.OC cs.SY | We present a necessary and sufficient condition for the reachable set, i.e.,
the set of states reachable from a ball of initial states at some time, of an
ordinary differential equation to be convex. In particular, convexity is
guaranteed if the ball of initial states is sufficiently small, and we provide
an upper bound on the radius of that ball, which can be directly obtained from
the right hand side of the differential equation. In finite dimensions, our
results cover the case of ellipsoids of initial states. A potential application
of our results is inner and outer polyhedral approximation of reachable sets,
which becomes extremely simple and almost universally applicable if these sets
are known to be convex. We demonstrate by means of an example that the balls of
initial states for which the latter property follows from our results are large
enough to be used in actual computations.
|
1211.6085 | Random Projections for Linear Support Vector Machines | cs.LG stat.ML | Let X be a data matrix of rank \rho, whose rows represent n points in
d-dimensional space. The linear support vector machine constructs a hyperplane
separator that maximizes the 1-norm soft margin. We develop a new oblivious
dimension reduction technique which is precomputed and can be applied to any
input matrix X. We prove that, with high probability, the margin and minimum
enclosing ball in the feature space are preserved to within \epsilon-relative
error, ensuring comparable generalization as in the original space in the case
of classification. For regression, we show that the margin is preserved to
\epsilon-relative error with high probability. We present extensive experiments
with real and synthetic data to support our theory.
|
1211.6086 | Finding influential users of an online health community: a new metric
based on sentiment influence | cs.SI cs.CY physics.soc-ph | What characterizes influential users in online health communities (OHCs)? We
hypothesize that (1) the emotional support received by OHC members can be
assessed from their sentiment ex-pressed in online interactions, and (2) such
assessments can help to identify influential OHC members. Through text mining
and sentiment analysis of users' online interactions, we propose a novel metric
that directly measures a user's ability to affect the sentiment of others.
Using dataset from an OHC, we demonstrate that this metric is highly effective
in identifying influential users. In addition, combining the metric with other
traditional measures further improves the identification of influential users.
This study can facilitate online community management and advance our
understanding of social influence in OHCs.
|
1211.6097 | Shadows and Headless Shadows: an Autobiographical Approach to Narrative
Reasoning | cs.AI | The Xapagy architecture is a story-oriented cognitive system which relies
exclusively on the autobiographical memory implemented as a raw collection of
events. Reasoning is performed by shadowing current events with events from the
autobiography. The shadows are then extrapolated into headless shadows (HLSs).
In a story following mood, HLSs can be used to track the level of surprise of
the agent, to infer hidden actions or relations between the participants, and
to summarize ongoing events. In recall mood, the HLSs can be used to create new
stories ranging from exact recall to free-form confabulation.
|
1211.6101 | Design of Calibration Experiments for Identification of Manipulator
Elastostatic Parameters | cs.RO | The paper is devoted to the elastostatic calibration of industrial robots,
which is used for precise machining of large-dimensional parts made of
composite materials. In this technological process, the interaction between the
robot and the workpiece causes essential elastic deflections of the manipulator
components that should be compensated by the robot controller using relevant
elastostatic model of this mechanism. To estimate parameters of this model, an
advanced calibration technique is applied that is based on the non-linear
experiment design theory, which is adopted for this particular application. In
contrast to previous works, it is proposed a concept of the user-defined
test-pose, which is used to evaluate the calibration experiments quality. In
the frame of this concept, the related optimization problem is defined and
numerical routines are developed, which allow generating optimal set of
manipulator configurations and corresponding forces/torques for a given number
of the calibration experiments. Some specific kinematic constraints are also
taken into account, which insure feasibility of calibration experiments for the
obtained configurations and allow avoiding collision between the robotic
manipulator and the measurement equipment. The efficiency of the developed
technique is illustrated by an application example that deals with elastostatic
calibration of the serial manipulator used for robot-based machining.
|
1211.6158 | The Interplay Between Stability and Regret in Online Learning | cs.LG stat.ML | This paper considers the stability of online learning algorithms and its
implications for learnability (bounded regret). We introduce a novel quantity
called {\em forward regret} that intuitively measures how good an online
learning algorithm is if it is allowed a one-step look-ahead into the future.
We show that given stability, bounded forward regret is equivalent to bounded
regret. We also show that the existence of an algorithm with bounded regret
implies the existence of a stable algorithm with bounded regret and bounded
forward regret. The equivalence results apply to general, possibly non-convex
problems. To the best of our knowledge, our analysis provides the first general
connection between stability and regret in the online setting that is not
restricted to a particular class of algorithms. Our stability-regret connection
provides a simple recipe for analyzing regret incurred by any online learning
algorithm. Using our framework, we analyze several existing online learning
algorithms as well as the "approximate" versions of algorithms like RDA that
solve an optimization problem at each iteration. Our proofs are simpler than
existing analysis for the respective algorithms, show a clear trade-off between
stability and forward regret, and provide tighter regret bounds in some cases.
Furthermore, using our recipe, we analyze "approximate" versions of several
algorithms such as follow-the-regularized-leader (FTRL) that requires solving
an optimization problem at each step.
|
1211.6159 | A semantic association page rank algorithm for web search engines | cs.IR | The majority of Semantic Web search engines retrieve information by focusing
on the use of concepts and relations restricted to the query provided by the
user. By trying to guess the implicit meaning between these concepts and
relations, probabilities are calculated to give the pages a score for ranking.
In this study, I propose a relation-based page rank algorithm to be used as a
Semantic Web search engine. Relevance is measured as the probability of finding
the connections made by the user at the time of the query, as well as the
information contained in the base knowledge of the Semantic Web environment. By
the use of "virtual links" between the concepts in a page, which are obtained
from the knowledge base, we can connect concepts and components of a page and
increase the probability score for a better ranking. By creating these
connections, this study also looks to eliminate the possibility of getting
results equal to zero, and to provide a tie-breaker solution when two or more
pages obtain the same score.
|
1211.6166 | Tracking and Quantifying Censorship on a Chinese Microblogging Site | cs.IR cs.CR | We present measurements and analysis of censorship on Weibo, a popular
microblogging site in China. Since we were limited in the rate at which we
could download posts, we identified users likely to participate in sensitive
topics and recursively followed their social contacts. We also leveraged new
natural language processing techniques to pick out trending topics despite the
use of neologisms, named entities, and informal language usage in Chinese
social media. We found that Weibo dynamically adapts to the changing interests
of its users through multiple layers of filtering. The filtering includes both
retroactively searching posts by keyword or repost links to delete them, and
rejecting posts as they are posted. The trend of sensitive topics is
short-lived, suggesting that the censorship is effective in stopping the
"viral" spread of sensitive issues. We also give evidence that sensitive topics
in Weibo only scarcely propagate beyond a core of sensitive posters.
|
1211.6176 | Shark: SQL and Rich Analytics at Scale | cs.DB | Shark is a new data analysis system that marries query processing with
complex analytics on large clusters. It leverages a novel distributed memory
abstraction to provide a unified engine that can run SQL queries and
sophisticated analytics functions (e.g., iterative machine learning) at scale,
and efficiently recovers from failures mid-query. This allows Shark to run SQL
queries up to 100x faster than Apache Hive, and machine learning programs up to
100x faster than Hadoop. Unlike previous systems, Shark shows that it is
possible to achieve these speedups while retaining a MapReduce-like execution
engine, and the fine-grained fault tolerance properties that such engines
provide. It extends such an engine in several ways, including column-oriented
in-memory storage and dynamic mid-query replanning, to effectively execute SQL.
The result is a system that matches the speedups reported for MPP analytic
databases over MapReduce, while offering fault tolerance properties and complex
analytics capabilities that they lack.
|
1211.6181 | Exponential Bounds for Convergence of Entropy Rate Approximations in
Hidden Markov Models Satisfying a Path-Mergeability Condition | math.PR cs.IT math.IT | A hidden Markov model (HMM) is said to have path-mergeable states if for any
two states i,j there exists a word w and state k such that it is possible to
transition from both i and j to k while emitting w. We show that for a finite
HMM with path-mergeable states the block estimates of the entropy rate converge
exponentially fast. We also show that the path-mergeability property is
asymptotically typical in the space of HMM topolgies and easily testable.
|
1211.6189 | Distributed Priority Synthesis | cs.SY cs.LO | Given a set of interacting components with non-deterministic variable update
and given safety requirements, the goal of priority synthesis is to restrict,
by means of priorities, the set of possible interactions in such a way as to
guarantee the given safety conditions for all possible runs. In distributed
priority synthesis we are interested in obtaining local sets of priorities,
which are deployed in terms of local component controllers sharing intended
next moves between components in local neighborhoods only. These possible
communication paths between local controllers are specified by means of a
communication architecture. We formally define the problem of distributed
priority synthesis in terms of a multi-player safety game between players for
(angelically) selecting the next transition of the components and an
environment for (demonically) updating uncontrollable variables. We analyze the
complexity of the problem, and propose several optimizations including a
solution-space exploration based on a diagnosis method using a nested extension
of the usual attractor computation in games together with a reduction to
corresponding SAT problems. When diagnosis fails, the method proposes potential
candidates to guide the exploration. These optimized algorithms for solving
distributed priority synthesis problems have been integrated into the VissBIP
framework. An experimental validation of this implementation is performed using
a range of case studies including scheduling in multicore processors and
modular robotics.
|
1211.6205 | Neuro-Fuzzy Computing System with the Capacity of Implementation on
Memristor-Crossbar and Optimization-Free Hardware Training | cs.NE cs.AI | In this paper, first we present a new explanation for the relation between
logical circuits and artificial neural networks, logical circuits and fuzzy
logic, and artificial neural networks and fuzzy inference systems. Then, based
on these results, we propose a new neuro-fuzzy computing system which can
effectively be implemented on the memristor-crossbar structure. One important
feature of the proposed system is that its hardware can directly be trained
using the Hebbian learning rule and without the need to any optimization. The
system also has a very good capability to deal with huge number of input-out
training data without facing problems like overtraining.
|
1211.6218 | Adaptive Interference Alignment with CSI Uncertainty | cs.IT math.IT | Interference alignment (IA) is known to significantly increase sum-throughput
at high SNR in the presence of multiple interfering nodes, however, the
reliability of IA is little known, which is the subject of this paper. We study
the error performance of IA and compare it with conventional orthogonal
transmission schemes. Since most IA algorithms require extensive channel state
information (CSI), we also investigate the impact of CSI imperfection
(uncertainty) on the error performance. Our results show that under identical
rates, IA attains a better error performance than the orthogonal scheme for
practical signal to noise ratio (SNR) values but is more sensitive to CSI
uncertainty. We design bit loading algorithms that significantly improve error
performance of the existing IA schemes. Furthermore, we propose an adaptive
transmission scheme that not only considerably reduces error probability, but
also produces robustness to CSI uncertainty.
|
1211.6239 | Optimal Power and Range Adaptation for Green Broadcasting | cs.IT math.IT | Improving energy efficiency is key to network providers maintaining profit
levels and an acceptable carbon footprint in the face of rapidly increasing
data traffic in cellular networks in the coming years. The energy-saving
concept studied in this paper is the adaptation of a base station's (BS's)
transmit power levels and coverage area according to channel conditions and
traffic load. The traffic load in cellular networks exhibits significant
fluctuations in both space and time, which can be exploited, through cell range
adaptation, for energy saving. In this paper, we design short- and long-term BS
power control (STPC and LTPC respectively) policies for the OFDMA-based
downlink of a single-cell system, where bandwidth is dynamically and equally
shared among a random number of mobile users (MUs). STPC is a function of all
MUs' channel gains that maintains the required user-level quality of service
(QoS), while LTPC (including BS on-off control) is a function of traffic
density that minimizes the long-term energy consumption at the BS under a
minimum throughput constraint. We first develop a power scaling law that
relates the (short-term) average transmit power at BS with the given cell range
and MU density. Based on this result, we derive the optimal (long-term)
transmit adaptation policy by considering a joint range adaptation and LTPC
problem. By identifying the fact that energy saving at BS essentially comes
from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS
on-off power control, we propose low-complexity suboptimal schemes with various
combinations of the two ESMs to investigate their impacts on system energy
consumption. It is shown that when the network throughput is low, BS on-off
power control is the most effective ESM, while when the network throughput is
higher, range adaptation becomes more effective.
|
1211.6244 | A Computational Model and Convergence Theorem for Rumor Dissemination in
Social Networks | cs.SI cs.GT physics.soc-ph | The spread of rumors, which are known as unverified statements of uncertain
origin, may cause tremendous number of social problems. If it would be possible
to identify factors affecting spreading a rumor (such as agents' desires, trust
network, etc.), then this could be used to slowdown or stop its spreading. A
computational model that includes rumor features and the way a rumor is spread
among society's members, based on their desires, is therefore needed. Our
research is centering on the relation between the homogeneity of the society
and rumor convergence in it and result shows that the homogeneity of the
society is a necessary condition for convergence of the spreading rumor.
|
1211.6248 | A simple non-parametric Topic Mixture for Authors and Documents | cs.LG stat.ML | This article reviews the Author-Topic Model and presents a new non-parametric
extension based on the Hierarchical Dirichlet Process. The extension is
especially suitable when no prior information about the number of components
necessary is available. A blocked Gibbs sampler is described and focus put on
staying as close as possible to the original model with only the minimum of
theoretical and implementation overhead necessary.
|
1211.6255 | Keyhole and Reflection Effects in Network Connectivity Analysis | cs.IT cs.NI math.IT | Recent research has demonstrated the importance of boundary effects on the
overall connection probability of wireless networks, but has largely focused on
convex domains. We consider two generic scenarios of practical importance to
wireless communications, in which one or more nodes are located outside the
convex space where the remaining nodes reside. Consequently, conventional
approaches with the underlying assumption that only line-of-sight (LOS) or
direct connections between nodes are possible, fail to provide the correct
analysis for the connectivity. We present an analytical framework that
explicitly considers the effects of reflections from the system boundaries on
the full connection probability. This study provides a different strategy to
ray tracing tools for predicting the wireless propagation environment. A simple
two-dimensional geometry is first considered, followed by a more practical
three-dimensional system. We investigate the effects of different system
parameters on the connectivity of the network though analysis corroborated by
numerical simulations, and highlight the potential of our approach for more
general non-convex geometries.t system parameters on the connectivity of the
network through simulation and analysis.
|
1211.6273 | A RDF-based Data Integration Framework | cs.DB | Data integration is one of the main problems in distributed data sources. An
approach is to provide an integrated mediated schema for various data sources.
This research work aims at developing a framework for defining an integrated
schema and querying on it. The basic idea is to employ recent standard
languages and tools to provide a unified data integration framework. RDF is
used for integrated schema descriptions as well as providing a unified view of
data. RDQL is used for query reformulation. Furthermore, description logic
inference services provide necessary means for satisfiability checking of
concepts in integrated schema. The framework has tools to display integrated
schema, query on it, and provides enough flexibilities to be used in different
application domains.
|
1211.6279 | Optimal Rate Irregular LDPC Codes in Binary Erasure Channel | cs.IT math.IT | In this paper, we design the optimal rate capacity approaching irregular
Low-Density Parity-Check code ensemble over Binary Erasure Channel, by using
practical Semi-Definite Programming approach. Our method does not use any
relaxation or any approximate solution unlike previous works. Our simulation
results include two parts; first, we present some codes and their degree
distribution functions that their rates are close to the capacity. Second, the
maximum achievable rate behavior of codes in our method is illustrated through
some figures.
|
1211.6302 | Duality between subgradient and conditional gradient methods | cs.LG math.OC stat.ML | Given a convex optimization problem and its dual, there are many possible
first-order algorithms. In this paper, we show the equivalence between mirror
descent algorithms and algorithms generalizing the conditional gradient method.
This is done through convex duality, and implies notably that for certain
problems, such as for supervised machine learning problems with non-smooth
losses or problems regularized by non-smooth regularizers, the primal
subgradient method and the dual conditional gradient method are formally
equivalent. The dual interpretation leads to a form of line search for mirror
descent, as well as guarantees of convergence for primal-dual certificates.
|
1211.6321 | Citation content analysis (cca): A framework for syntactic and semantic
analysis of citation content | cs.DL cs.IR cs.IT math.IT physics.soc-ph | This paper proposes a new framework for Citation Content Analysis (CCA), for
syntactic and semantic analysis of citation content that can be used to better
analyze the rich sociocultural context of research behavior. The framework
could be considered the next generation of citation analysis. This paper
briefly reviews the history and features of content analysis in traditional
social sciences, and its previous application in Library and Information
Science. Based on critical discussion of the theoretical necessity of a new
method as well as the limits of citation analysis, the nature and purposes of
CCA are discussed, and potential procedures to conduct CCA, including
principles to identify the reference scope, a two-dimensional (citing and
cited) and two-modular (syntactic and semantic modules) codebook, are provided
and described. Future works and implications are also suggested.
|
1211.6324 | Graph diameter, eigenvalues, and minimum-time consensus | math.OC cs.MA | We consider the problem of achieving average consensus in the minimum number
of linear iterations on a fixed, undirected graph. We are motivated by the task
of deriving lower bounds for consensus protocols and by the so-called
"definitive consensus conjecture" which states that for an undirected connected
graph G with diameter D there exist D matrices whose nonzero-pattern complies
with the edges in G and whose product equals the all-ones matrix. Our first
result is a counterexample to the definitive consensus conjecture, which is the
first improvement of the diameter lower bound for linear consensus protocols.
We then provide some algebraic conditions under which this conjecture holds,
which we use to establish that all distance-regular graphs satisfy the
definitive consensus conjecture.
|
1211.6340 | An Approach of Improving Students Academic Performance by using k means
clustering algorithm and Decision tree | cs.LG | Improving students academic performance is not an easy task for the academic
community of higher learning. The academic performance of engineering and
science students during their first year at university is a turning point in
their educational path and usually encroaches on their General Point
Average,GPA in a decisive manner. The students evaluation factors like class
quizzes mid and final exam assignment lab work are studied. It is recommended
that all these correlated information should be conveyed to the class teacher
before the conduction of final exam. This study will help the teachers to
reduce the drop out ratio to a significant level and improve the performance of
students. In this paper, we present a hybrid procedure based on Decision Tree
of Data mining method and Data Clustering that enables academicians to predict
students GPA and based on that instructor can take necessary step to improve
student academic performance.
|
1211.6401 | On the Performance Bound of Sparse Estimation with Sensing Matrix
Perturbation | cs.IT math.IT | This paper focusses on the sparse estimation in the situation where both the
the sensing matrix and the measurement vector are corrupted by additive
Gaussian noises. The performance bound of sparse estimation is analyzed and
discussed in depth. Two types of lower bounds, the constrained Cram\'{e}r-Rao
bound (CCRB) and the Hammersley-Chapman-Robbins bound (HCRB), are discussed. It
is shown that the situation with sensing matrix perturbation is more complex
than the one with only measurement noise. For the CCRB, its closed-form
expression is deduced. It demonstrates a gap between the maximal and nonmaximal
support cases. It is also revealed that a gap lies between the CCRB and the MSE
of the oracle pseudoinverse estimator, but it approaches zero asymptotically
when the problem dimensions tend to infinity. For a tighter bound, the HCRB,
despite of the difficulty in obtaining a simple expression for general sensing
matrix, a closed-form expression in the unit sensing matrix case is derived for
a qualitative study of the performance bound. It is shown that the gap between
the maximal and nonmaximal cases is eliminated for the HCRB. Numerical
simulations are performed to verify the theoretical results in this paper.
|
1211.6409 | Obesity Heuristic, New Way On Artificial Immune Systems | cs.AI cs.CR | There is a need for new metaphors from immunology to flourish the application
areas of Artificial Immune Systems. A metaheuristic called Obesity Heuristic
derived from advances in obesity treatment is proposed. The main forces of the
algorithm are the generation omega-6 and omega-3 fatty acids. The algorithm
works with Just-In-Time philosophy; by starting only when desired. A case study
of data cleaning is provided. With experiments conducted on standard tables,
results show that Obesity Heuristic outperforms other algorithms, with 100%
recall. This is a great improvement over other algorithms
|
1211.6410 | New Hoopoe Heuristic Optimization | cs.NE cs.AI | Most optimization problems in real life applications are often highly
nonlinear. Local optimization algorithms do not give the desired performance.
So, only global optimization algorithms should be used to obtain optimal
solutions. This paper introduces a new nature-inspired metaheuristic
optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will
study HH and validate it against some test functions. Investigations show that
it is very promising and could be seen as an optimization of the powerful
algorithm of cuckoo search. Finally, we discuss the features of Hoopoe
Heuristic and propose topics for further studies.
|
1211.6411 | New Heuristics for Interfacing Human Motor System using Brain Waves | cs.HC cs.AI | There are many new forms of interfacing human users to machines. We persevere
here electric mechanical form of interaction between human and machine. The
emergence of brain-computer interface allows mind-to-movement systems. The
story of the Pied Piper inspired us to devise some new heuristics for
interfacing human motor system using brain waves by combining head helmet and
LumbarMotionMonitor For the simulation we use java GridGain Brain responses of
classified subjects during training indicates that Probe can be the best
stimulus to rely on in distinguishing between knowledgeable and not
knowledgeable
|
1211.6462 | Statistical mechanics of reputation systems in autonomous networks | cond-mat.dis-nn cs.SI physics.soc-ph | Reputation systems seek to infer which members of a community can be trusted
based on ratings they issue about each other. We construct a Bayesian inference
model and simulate approximate estimates using belief propagation (BP). The
model is then mapped onto computing equilibrium properties of a spin glass in a
random field and analyzed by employing the replica symmetric cavity approach.
Having the fraction of trustful nodes and environment noise level as control
parameters, we evaluate the theoretical performance in terms of estimation
error and the robustness of the BP approximation in different scenarios.
Regions of degraded performance are then explained by the convergence
properties of the BP algorithm and by the emergence of a glassy phase.
|
1211.6471 | Optimization of measurement configurations for geometrical calibration
of industrial robot | cs.RO | The paper is devoted to the geometrical calibration of industrial robots
employed in precise manufacturing. To identify geometric parameters, an
advanced calibration technique is proposed that is based on the non-linear
experiment design theory, which is adopted for this particular application. In
contrast to previous works, the calibration experiment quality is evaluated
using a concept of the user-defined test-pose. In the frame of this concept,
the related optimization problem is formulated and numerical routines are
developed, which allow user to generate optimal set of manipulator
configurations for a given number of calibration experiments. The efficiency of
the developed technique is illustrated by several examples.
|
1211.6491 | Sum-Rate Optimal Multi-Code CDMA Systems: An Equivalence Result | cs.IT math.IT | In this paper, the sum rate of a multi-code CDMA system with asymmetric-power
users is maximized, given a processing gain and a power profile of users.
Unlike the sum-rate maximization for a single-code CDMA system, the
optimization requires the joint optimal distribution of each user's power to
its multiple data streams as well as the optimal design of signature sequences.
The crucial step is to establish an equivalence of the multi-code CDMA system
to restricted FDMA and TDMA systems. The CDMA system has upper limits on the
numbers of multi-codes of users, while the FDMA and the TDMA systems have upper
limits on the bandwidths and the duty cycles of users, respectively, in
addition to total bandwidth constraint. The equivalence facilitates the
complete characterization of the maximum sum rate of the multi-code CDMA system
and also provides new insights into the single- and the multi-code CDMA systems
in terms of the parameters of the equivalent FDMA and TDMA systems.
|
1211.6496 | TwitterPaul: Extracting and Aggregating Twitter Predictions | cs.SI cs.AI physics.soc-ph | This paper introduces TwitterPaul, a system designed to make use of Social
Media data to help to predict game outcomes for the 2010 FIFA World Cup
tournament. To this end, we extracted over 538K mentions to football games from
a large sample of tweets that occurred during the World Cup, and we classified
into different types with a precision of up to 88%. The different mentions were
aggregated in order to make predictions about the outcomes of the actual games.
We attempt to learn which Twitter users are accurate predictors and explore
several techniques in order to exploit this information to make more accurate
predictions. We compare our results to strong baselines and against the betting
line (prediction market) and found that the quality of extractions is more
important than the quantity, suggesting that high precision methods working on
a medium-sized dataset are preferable over low precision methods that use a
larger amount of data. Finally, by aggregating some classes of predictions, the
system performance is close to the one of the betting line. Furthermore, we
believe that this domain independent framework can help to predict other
sports, elections, product release dates and other future events that people
talk about in social media.
|
1211.6512 | Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks | cs.SI physics.soc-ph | Recent research has focused on the monitoring of global-scale online data for
improved detection of epidemics, mood patterns, movements in the stock market,
political revolutions, box-office revenues, consumer behaviour and many other
important phenomena. However, privacy considerations and the sheer scale of
data available online are quickly making global monitoring infeasible, and
existing methods do not take full advantage of local network structure to
identify key nodes for monitoring. Here, we develop a model of the contagious
spread of information in a global-scale, publicly-articulated social network
and show that a simple method can yield not just early detection, but advance
warning of contagious outbreaks. In this method, we randomly choose a small
fraction of nodes in the network and then we randomly choose a "friend" of each
node to include in a group for local monitoring. Using six months of data from
most of the full Twittersphere, we show that this friend group is more central
in the network and it helps us to detect viral outbreaks of the use of novel
hashtags about 7 days earlier than we could with an equal-sized randomly chosen
group. Moreover, the method actually works better than expected due to network
structure alone because highly central actors are both more active and exhibit
increased diversity in the information they transmit to others. These results
suggest that local monitoring is not just more efficient, it is more effective,
and it is possible that other contagious processes in global-scale networks may
be similarly monitored.
|
1211.6522 | Generalized Distributed Compressive Sensing | cs.IT math.IT | Distributed Compressive Sensing (DCS) improves the signal recovery
performance of multi signal ensembles by exploiting both intra- and
inter-signal correlation and sparsity structure. However, the existing DCS was
proposed for a very limited ensemble of signals that has single common
information \cite{Baron:2009vd}. In this paper, we propose a generalized DCS
(GDCS) which can improve sparse signal detection performance given arbitrary
types of common information which are classified into not just full common
information but also a variety of partial common information. The theoretical
bound on the required number of measurements using the GDCS is obtained.
Unfortunately, the GDCS may require much a priori-knowledge on various inter
common information of ensemble of signals to enhance the performance over the
existing DCS. To deal with this problem, we propose a novel algorithm that can
search for the correlation structure among the signals, with which the proposed
GDCS improves detection performance even without a priori-knowledge on
correlation structure for the case of arbitrarily correlated multi signal
ensembles.
|
1211.6537 | Degree-based network models | math.ST cs.SI math.CO stat.ME stat.TH | We derive the sampling properties of random networks based on weights whose
pairwise products parameterize independent Bernoulli trials. This enables an
understanding of many degree-based network models, in which the structure of
realized networks is governed by properties of their degree sequences. We
provide exact results and large-sample approximations for power-law networks
and other more general forms. This enables us to quantify sampling variability
both within and across network populations, and to characterize the limiting
extremes of variation achievable through such models. Our results highlight
that variation explained through expected degree structure need not be
attributed to more complicated generative mechanisms.
|
1211.6566 | A Unified Framework for the Ergodic Capacity of Spectrum Sharing
Cognitive Radio Systems | cs.IT math.IT | We consider a spectrum sharing communication scenario in which a primary and
a secondary users are communicating, simultaneously, with their respective
destinations using the same frequency carrier. Both optimal power profile and
ergodic capacity are derived for fading channels, under an average transmit
power and an instantaneous interference outage constraints. Unlike previous
studies, we assume that the secondary user has a noisy version of the cross
link and the secondary link Channel State Information (CSI). After deriving the
capacity in this case, we provide an ergodic capacity generalization, through a
unified expression, that encompasses several previously studied spectrum
sharing settings. In addition, we provide an asymptotic capacity analysis at
high and low signal-to-noise ratio (SNR). Numerical results, applied for
independent Rayleigh fading channels, show that at low SNR regime, only the
secondary channel estimation matters with no effect of the cross link on the
capacity; whereas at high SNR regime, the capacity is rather driven by the
cross link CSI. Furthermore, a practical on-off power allocation scheme is
proposed and is shown, through numerical results, to achieve the full capacity
at high and low SNR
|
1211.6572 | Average sampling of band-limited stochastic processes | cs.IT math.IT | We consider the problem of reconstructing a wide sense stationary
band-limited process from its local averages taken either at the Nyquist rate
or above. As a result, we obtain a sufficient condition under which average
sampling expansions hold in mean square and for almost all sample functions.
Truncation and aliasing errors of the expansion are also discussed.
|
1211.6581 | Multi-Target Regression via Input Space Expansion: Treating Targets as
Inputs | cs.LG | In many practical applications of supervised learning the task involves the
prediction of multiple target variables from a common set of input variables.
When the prediction targets are binary the task is called multi-label
classification, while when the targets are continuous the task is called
multi-target regression. In both tasks, target variables often exhibit
statistical dependencies and exploiting them in order to improve predictive
accuracy is a core challenge. A family of multi-label classification methods
address this challenge by building a separate model for each target on an
expanded input space where other targets are treated as additional input
variables. Despite the success of these methods in the multi-label
classification domain, their applicability and effectiveness in multi-target
regression has not been studied until now. In this paper, we introduce two new
methods for multi-target regression, called Stacked Single-Target and Ensemble
of Regressor Chains, by adapting two popular multi-label classification methods
of this family. Furthermore, we highlight an inherent problem of these methods
- a discrepancy of the values of the additional input variables between
training and prediction - and develop extensions that use out-of-sample
estimates of the target variables during training in order to tackle this
problem. The results of an extensive experimental evaluation carried out on a
large and diverse collection of datasets show that, when the discrepancy is
appropriately mitigated, the proposed methods attain consistent improvements
over the independent regressions baseline. Moreover, two versions of Ensemble
of Regression Chains perform significantly better than four state-of-the-art
methods including regularization-based multi-task learning methods and a
multi-objective random forest approach.
|
1211.6598 | Estimation of Bandlimited Signals in Additive Gaussian Noise: a
"Precision Indifference" Principle | cs.IT math.IT | The sampling, quantization, and estimation of a bounded dynamic-range
bandlimited signal affected by additive independent Gaussian noise is studied
in this work. For bandlimited signals, the distortion due to additive
independent Gaussian noise can be reduced by oversampling (statistical
diversity). The pointwise expected mean-squared error is used as a distortion
metric for signal estimate in this work. Two extreme scenarios of quantizer
precision are considered: (i) infinite precision (real scalars); and (ii)
one-bit quantization (sign information). If $N$ is the oversampling ratio with
respect to the Nyquist rate, then the optimal law for distortion is $O(1/N)$.
We show that a distortion of $O(1/N)$ can be achieved irrespective of the
quantizer precision by considering the above-mentioned two extreme scenarios of
quantization. Thus, a quantization precision indifference principle is
discovered, where the reconstruction distortion law, up to a proportionality
constant, is unaffected by quantizer's accuracy.
|
1211.6610 | Intrusion Detection on Smartphones | cs.CR cs.AI | Smartphone technology is more and more becoming the predominant communication
tool for people across the world. People use their smartphones to keep their
contact data, to browse the internet, to exchange messages, to keep notes,
carry their personal files and documents, etc. Users while browsing are also
capable of shopping online, thus provoking a need to type their credit card
numbers and security codes. As the smartphones are becoming widespread so do
the security threats and vulnerabilities facing this technology. Recent news
and articles indicate huge increase in malware and viruses for operating
systems employed on smartphones (primarily Android and iOS). Major limitations
of smartphone technology are its processing power and its scarce energy source
since smartphones rely on battery usage. Since smartphones are devices which
change their network location as the user moves between different places,
intrusion detection systems for smartphone technology are most often classified
as IDSs designed for mobile ad-hoc networks. The aim of this research is to
give a brief overview of IDS technology, give an overview of major machine
learning and pattern recognition algorithms used in IDS technologies, give an
overview of security models of iOS and Android and propose a new host-based IDS
model for smartphones and create proof-of-concept application for Android
platform for the newly proposed model. Keywords: IDS, SVM, Android, iOS;
|
1211.6616 | TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in
Cellular Radio Access Networks | cs.NI cs.AI cs.IT cs.LG math.IT | Recent works have validated the possibility of improving energy efficiency in
radio access networks (RANs), achieved by dynamically turning on/off some base
stations (BSs). In this paper, we extend the research over BS switching
operations, which should match up with traffic load variations. Instead of
depending on the dynamic traffic loads which are still quite challenging to
precisely forecast, we firstly formulate the traffic variations as a Markov
decision process. Afterwards, in order to foresightedly minimize the energy
consumption of RANs, we design a reinforcement learning framework based BS
switching operation scheme. Furthermore, to avoid the underlying curse of
dimensionality in reinforcement learning, a transfer actor-critic algorithm
(TACT), which utilizes the transferred learning expertise in historical periods
or neighboring regions, is proposed and provably converges. In the end, we
evaluate our proposed scheme by extensive simulations under various practical
configurations and show that the proposed TACT algorithm contributes to a
performance jumpstart and demonstrates the feasibility of significant energy
efficiency improvement at the expense of tolerable delay performance.
|
1211.6624 | A contraction theory-based analysis of the stability of the Extended
Kalman Filter | cs.SY math.OC | The contraction properties of the Extended Kalman Filter, viewed as a
deterministic observer for nonlinear systems, are analyzed. This yields new
conditions under which exponential convergence of the state error can be
guaranteed. As contraction analysis studies the evolution of an infinitesimal
discrepancy between neighboring trajectories, and thus stems from a
differential framework, the sufficient convergence conditions are different
from the ones that previously appeared in the literature, which were derived in
a Lyapunov framework. This article sheds another light on the theoretical
properties of this popular observer.
|
1211.6631 | Asymptotic Properties of Likelihood Based Linear Modulation
Classification Systems | cs.IT math.IT stat.AP | The problem of linear modulation classification using likelihood based
methods is considered. Asymptotic properties of most commonly used classifiers
in the literature are derived. These classifiers are based on hybrid likelihood
ratio test (HLRT) and average likelihood ratio test (ALRT), respectively. Both
a single-sensor setting and a multi-sensor setting that uses a distributed
decision fusion approach are analyzed. For a modulation classification system
using a single sensor, it is shown that HLRT achieves asymptotically vanishing
probability of error (Pe) whereas the same result cannot be proven for ALRT. In
a multi-sensor setting using soft decision fusion, conditions are derived under
which Pe vanishes asymptotically. Furthermore, the asymptotic analysis of the
fusion rule that assumes independent sensor decisions is carried out.
|
1211.6636 | Edge Balance Ratio: Power Law from Vertices to Edges in Directed Complex
Network | cs.SI physics.soc-ph | Power law distribution is common in real-world networks including online
social networks. Many studies on complex networks focus on the characteristics
of vertices, which are always proved to follow the power law. However, few
researches have been done on edges in directed networks. In this paper, edge
balance ratio is firstly proposed to measure the balance property of edges in
directed networks. Based on edge balance ratio, balance profile and positivity
are put forward to describe the balance level of the whole network. Then the
distribution of edge balance ratio is theoretically analyzed. In a directed
network whose vertex in-degree follows the power law with scaling exponent
$\gamma$, it is proved that the edge balance ratio follows a piecewise power
law, with the scaling exponent of each section linearly dependents on $\gamma$.
The theoretical analysis is verified by numerical simulations. Moreover, the
theoretical analysis is confirmed by statistics of real-world online social
networks, including Twitter network with 35 million users and Sina Weibo
network with 110 million users.
|
1211.6643 | A Graph-Theoretical Approach for the Analysis and Model Reduction of
Complex-Balanced Chemical Reaction Networks | math.DS cs.SY math.OC physics.chem-ph | In this paper we derive a compact mathematical formulation describing the
dynamics of chemical reaction networks that are complex-balanced and are
governed by mass action kinetics. The formulation is based on the graph of
(substrate and product) complexes and the stoichiometric information of these
complexes, and crucially uses a balanced weighted Laplacian matrix. It is shown
that this formulation leads to elegant methods for characterizing the space of
all equilibria for complex-balanced networks and for deriving stability
properties of such networks. We propose a method for model reduction of
complex-balanced networks, which is similar to the Kron reduction method for
electrical networks and involves the computation of Schur complements of the
balanced weighted Laplacian matrix.
|
1211.6653 | Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process
Approach | cs.LG stat.ML | Multi-task learning models using Gaussian processes (GP) have been developed
and successfully applied in various applications. The main difficulty with this
approach is the computational cost of inference using the union of examples
from all tasks. Therefore sparse solutions, that avoid using the entire data
directly and instead use a set of informative "representatives" are desirable.
The paper investigates this problem for the grouped mixed-effect GP model where
each individual response is given by a fixed-effect, taken from one of a set of
unknown groups, plus a random individual effect function that captures
variations among individuals. Such models have been widely used in previous
work but no sparse solutions have been developed. The paper presents the first
sparse solution for such problems, showing how the sparse approximation can be
obtained by maximizing a variational lower bound on the marginal likelihood,
generalizing ideas from single-task Gaussian processes to handle the
mixed-effect model as well as grouping. Experiments using artificial and real
data validate the approach showing that it can recover the performance of
inference with the full sample, that it outperforms baseline methods, and that
it outperforms state of the art sparse solutions for other multi-task GP
formulations.
|
1211.6658 | Nature-Inspired Mateheuristic Algorithms: Success and New Challenges | math.OC cs.NE | Despite the increasing popularity of metaheuristics, many crucially important
questions remain unanswered. There are two important issues: theoretical
framework and the gap between theory and applications. At the moment, the
practice of metaheuristics is like heuristic itself, to some extent, by trial
and error. Mathematical analysis lags far behind, apart from a few, limited,
studies on convergence analysis and stability, there is no theoretical
framework for analyzing metaheuristic algorithms. I believe mathematical and
statistical methods using Markov chains and dynamical systems can be very
useful in the future work. There is no doubt that any theoretical progress will
provide potentially huge insightful into meteheuristic algorithms.
|
1211.6660 | An Equivalence between Network Coding and Index Coding | cs.IT cs.DM cs.NI math.IT | We show that the network coding and index coding problems are equivalent.
This equivalence holds in the general setting which includes linear and
non-linear codes. Specifically, we present an efficient reduction that maps a
network coding instance to an index coding one while preserving feasibility.
Previous connections were restricted to the linear case.
|
1211.6664 | Compression of structured high-throughput sequencing data | q-bio.QM cs.DB q-bio.GN | Large biological datasets are being produced at a rapid pace and create
substantial storage challenges, particularly in the domain of high-throughput
sequencing (HTS). Most approaches currently used to store HTS data are either
unable to quickly adapt to the requirements of new sequencing or analysis
methods (because they do not support schema evolution), or fail to provide
state of the art compression of the datasets. We have devised new approaches to
store HTS data that support seamless data schema evolution and compress
datasets substantially better than existing approaches. Building on these new
approaches, we discuss and demonstrate how a multi-tier data organization can
dramatically reduce the storage, computational and network burden of
collecting, analyzing, and archiving large sequencing datasets. For instance,
we show that spliced RNA-Seq alignments can be stored in less than 4% the size
of a BAM file with perfect data fidelity. Compared to the previous compression
state of the art, these methods reduce dataset size more than 20% when storing
gene expression and epigenetic datasets. The approaches have been integrated in
a comprehensive suite of software tools (http://goby.campagnelab.org) that
support common analyses for a range of high-throughput sequencing assays.
|
1211.6674 | Some results on the Weiss-Weinstein bound for conditional and
unconditional signal models in array processing | cs.IT math.IT stat.AP | In this paper, the Weiss-Weinstein bound is analyzed in the context of
sources localization with a planar array of sensors. Both conditional and
unconditional source signal models are studied. First, some results are given
in the multiple sources context without specifying the structure of the
steering matrix and of the noise covariance matrix. Moreover, the case of an
uniform or Gaussian prior are analyzed. Second, these results are applied to
the particular case of a single source for two kinds of array geometries: a
non-uniform linear array (elevation only) and an arbitrary planar (azimuth and
elevation) array.
|
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