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1209.0880
|
On Solving the Oriented Two-Dimensional Bin Packing Problem under Free
Guillotine Cutting: Exploiting the Power of Probabilistic Solution
Construction
|
cs.AI
|
Two-dimensional bin packing problems are highly relevant combinatorial
optimization problems. They find a large number of applications, for example,
in the context of transportation or warehousing, and for the cutting of
different materials such as glass, wood or metal. In this work we deal with the
oriented two-dimensional bin packing problem under free guillotine cutting. In
this specific problem a set of oriented rectangular items is given which must
be packed into a minimum number of bins of equal size. The first algorithm
proposed in this work is a randomized multi-start version of a constructive
one-pass heuristic from the literature. Additionally we propose the use of this
randomized one-pass heuristic within an evolutionary algorithm. The results of
the two proposed algorithms are compared to the best approaches from the
literature. In particular the evolutionary algorithm compares very favorably to
current state-of-the-art approaches. The optimal solution for 4 previously
unsolved instances could be found.
|
1209.0911
|
Conquering the rating bound problem in neighborhood-based collaborative
filtering: a function recovery approach
|
cs.IR cs.AI cs.HC
|
As an important tool for information filtering in the era of socialized web,
recommender systems have witnessed rapid development in the last decade. As
benefited from the better interpretability, neighborhood-based collaborative
filtering techniques, such as item-based collaborative filtering adopted by
Amazon, have gained a great success in many practical recommender systems.
However, the neighborhood-based collaborative filtering method suffers from the
rating bound problem, i.e., the rating on a target item that this method
estimates is bounded by the observed ratings of its all neighboring items.
Therefore, it cannot accurately estimate the unobserved rating on a target
item, if its ground truth rating is actually higher (lower) than the highest
(lowest) rating over all items in its neighborhood. In this paper, we address
this problem by formalizing rating estimation as a task of recovering a scalar
rating function. With a linearity assumption, we infer all the ratings by
optimizing the low-order norm, e.g., the $l_1/2$-norm, of the second derivative
of the target scalar function, while remaining its observed ratings unchanged.
Experimental results on three real datasets, namely Douban, Goodreads and
MovieLens, demonstrate that the proposed approach can well overcome the rating
bound problem. Particularly, it can significantly improve the accuracy of
rating estimation by 37% than the conventional neighborhood-based methods.
|
1209.0913
|
Structuring Relevant Feature Sets with Multiple Model Learning
|
cs.LG
|
Feature selection is one of the most prominent learning tasks, especially in
high-dimensional datasets in which the goal is to understand the mechanisms
that underly the learning dataset. However most of them typically deliver just
a flat set of relevant features and provide no further information on what kind
of structures, e.g. feature groupings, might underly the set of relevant
features. In this paper we propose a new learning paradigm in which our goal is
to uncover the structures that underly the set of relevant features for a given
learning problem. We uncover two types of features sets, non-replaceable
features that contain important information about the target variable and
cannot be replaced by other features, and functionally similar features sets
that can be used interchangeably in learned models, given the presence of the
non-replaceable features, with no change in the predictive performance. To do
so we propose a new learning algorithm that learns a number of disjoint models
using a model disjointness regularization constraint together with a constraint
on the predictive agreement of the disjoint models. We explore the behavior of
our approach on a number of high-dimensional datasets, and show that, as
expected by their construction, these satisfy a number of properties. Namely,
model disjointness, a high predictive agreement, and a similar predictive
performance to models learned on the full set of relevant features. The ability
to structure the set of relevant features in such a manner can become a
valuable tool in different applications of scientific knowledge discovery.
|
1209.0935
|
Characterizing Successful Formulas: the Multi-agent Case
|
cs.MA cs.LO
|
Characterization of successful formulas in Public Announcement Logic (PAL) is
a well known open problem in Dynamic Epistemic Logic. Recently, Holliday and
ICard have given a complete characterization for the single agent case.
However, the problem for the multi-agent case is open. This paper gives a
partial solution to the problem, characterizing the subclass of the language
consisting of unary operators, and discusses methods to give a complete
solution.
|
1209.0997
|
Direct computation of diagnoses for ontology debugging
|
cs.AI
|
Modern ontology debugging methods allow efficient identification and
localization of faulty axioms defined by a user while developing an ontology.
The ontology development process in this case is characterized by rather
frequent and regular calls to a reasoner resulting in an early user awareness
of modeling errors. In such a scenario an ontology usually includes only a
small number of conflict sets, i.e. sets of axioms preserving the faults. This
property allows efficient use of standard model-based diagnosis techniques
based on the application of hitting set algorithms to a number of given
conflict sets. However, in many use cases such as ontology alignment the
ontologies might include many more conflict sets than in usual ontology
development settings, thus making precomputation of conflict sets and
consequently ontology diagnosis infeasible. In this paper we suggest a
debugging approach based on a direct computation of diagnoses that omits
calculation of conflict sets. Embedded in an ontology debugger, the proposed
algorithm is able to identify diagnoses for an ontology which includes a large
number of faults and for which application of standard diagnosis methods fails.
The evaluation results show that the approach is practicable and is able to
identify a fault in adequate time.
|
1209.0999
|
Visual Exploration of Simulated and Measured Blood Flow
|
cs.GR cs.CV
|
Morphology of cardiovascular tissue is influenced by the unsteady behavior of
the blood flow and vice versa. Therefore, the pathogenesis of several
cardiovascular diseases is directly affected by the blood-flow dynamics.
Understanding flow behavior is of vital importance to understand the
cardiovascular system and potentially harbors a considerable value for both
diagnosis and risk assessment. The analysis of hemodynamic characteristics
involves qualitative and quantitative inspection of the blood-flow field.
Visualization plays an important role in the qualitative exploration, as well
as the definition of relevant quantitative measures and its validation. There
are two main approaches to obtain information about the blood flow: simulation
by computational fluid dynamics, and in-vivo measurements. Although research on
blood flow simulation has been performed for decades, many open problems remain
concerning accuracy and patient-specific solutions. Possibilities for real
measurement of blood flow have recently increased considerably by new
developments in magnetic resonance imaging which enable the acquisition of 3D
quantitative measurements of blood-flow velocity fields. This chapter presents
the visualization challenges for both simulation and real measurements of
unsteady blood-flow fields.
|
1209.1011
|
Kleisli Database Instances
|
cs.DB math.CT
|
We use monads to relax the atomicity requirement for data in a database.
Depending on the choice of monad, the database fields may contain generalized
values such as lists or sets of values, or they may contain exceptions such as
various types of nulls. The return operation for monads ensures that any
ordinary database instance will count as one of these generalized instances,
and the bind operation ensures that generalized values behave well under joins
of foreign key sequences. Different monads allow for vastly different types of
information to be stored in the database. For example, we show that classical
concepts like Markov chains, graphs, and finite state automata are each
perfectly captured by a different monad on the same schema.
|
1209.1032
|
On Scalable Video Streaming over Cognitive Radio Cellular and Ad Hoc
Networks
|
cs.IT cs.NI math.IT
|
Video content delivery over wireless networks is expected to grow drastically
in the coming years. In this paper, we investigate the challenging problem of
video over cognitive radio (CR) networks. Although having high potential, this
problem brings about a new level of technical challenges. After reviewing
related work, we first address the problem of video over infrastructure-based
CR networks, and then extend the problem to video over non-infrastructure-based
ad hoc CR networks. We present formulations of cross-layer optimization
problems as well as effective algorithms to solving the problems. The proposed
algorithms are analyzed with respect to their optimality and validate with
simulations.
|
1209.1033
|
The Annealing Sparse Bayesian Learning Algorithm
|
cs.IT cs.LG math.IT
|
In this paper we propose a two-level hierarchical Bayesian model and an
annealing schedule to re-enable the noise variance learning capability of the
fast marginalized Sparse Bayesian Learning Algorithms. The performance such as
NMSE and F-measure can be greatly improved due to the annealing technique. This
algorithm tends to produce the most sparse solution under moderate SNR
scenarios and can outperform most concurrent SBL algorithms while pertains
small computational load.
|
1209.1048
|
Performance Analysis Of Neuro Genetic Algorithm Applied On Detecting
Proportion Of Components In Manhole Gas Mixture
|
cs.NE cs.CV
|
The article presents performance analysis of a real valued neuro genetic
algorithm applied for the detection of proportion of the gases found in manhole
gas mixture. The neural network (NN) trained using genetic algorithm (GA) leads
to concept of neuro genetic algorithm, which is used for implementing an
intelligent sensory system for the detection of component gases present in
manhole gas mixture Usually a manhole gas mixture contains several toxic gases
like Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and
Carbon Monoxide. A semiconductor based gas sensor array used for sensing
manhole gas components is an integral part of the proposed intelligent system.
It consists of many sensor elements, where each sensor element is responsible
for sensing particular gas component. Multiple sensors of different gases used
for detecting gas mixture of multiple gases, results in cross-sensitivity. The
cross-sensitivity is a major issue and the problem is viewed as pattern
recognition problem. The objective of this article is to present performance
analysis of the real valued neuro genetic algorithm which is applied for
multiple gas detection.
|
1209.1064
|
A Max-Product EM Algorithm for Reconstructing Markov-tree Sparse Signals
from Compressive Samples
|
stat.ML cs.IT math.IT
|
We propose a Bayesian expectation-maximization (EM) algorithm for
reconstructing Markov-tree sparse signals via belief propagation. The
measurements follow an underdetermined linear model where the
regression-coefficient vector is the sum of an unknown approximately sparse
signal and a zero-mean white Gaussian noise with an unknown variance. The
signal is composed of large- and small-magnitude components identified by
binary state variables whose probabilistic dependence structure is described by
a Markov tree. Gaussian priors are assigned to the signal coefficients given
their state variables and the Jeffreys' noninformative prior is assigned to the
noise variance. Our signal reconstruction scheme is based on an EM iteration
that aims at maximizing the posterior distribution of the signal and its state
variables given the noise variance. We construct the missing data for the EM
iteration so that the complete-data posterior distribution corresponds to a
hidden Markov tree (HMT) probabilistic graphical model that contains no loops
and implement its maximization (M) step via a max-product algorithm. This EM
algorithm estimates the vector of state variables as well as solves iteratively
a linear system of equations to obtain the corresponding signal estimate. We
select the noise variance so that the corresponding estimated signal and state
variables obtained upon convergence of the EM iteration have the largest
marginal posterior distribution. We compare the proposed and existing
state-of-the-art reconstruction methods via signal and image reconstruction
experiments.
|
1209.1073
|
Reply to 'Comments on Integer SEC-DED codes for low power
communications'
|
cs.IT math.IT
|
This paper is a reply to the comments on 'Integer SEC-DED codes for low power
communications'.
|
1209.1077
|
Learning Probability Measures with respect to Optimal Transport Metrics
|
cs.LG stat.ML
|
We study the problem of estimating, in the sense of optimal transport
metrics, a measure which is assumed supported on a manifold embedded in a
Hilbert space. By establishing a precise connection between optimal transport
metrics, optimal quantization, and learning theory, we derive new probabilistic
bounds for the performance of a classic algorithm in unsupervised learning
(k-means), when used to produce a probability measure derived from the data. In
the course of the analysis, we arrive at new lower bounds, as well as
probabilistic upper bounds on the convergence rate of the empirical law of
large numbers, which, unlike existing bounds, are applicable to a wide class of
measures.
|
1209.1086
|
Robustness and Generalization for Metric Learning
|
cs.LG cs.AI stat.ML
|
Metric learning has attracted a lot of interest over the last decade, but the
generalization ability of such methods has not been thoroughly studied. In this
paper, we introduce an adaptation of the notion of algorithmic robustness
(previously introduced by Xu and Mannor) that can be used to derive
generalization bounds for metric learning. We further show that a weak notion
of robustness is in fact a necessary and sufficient condition for a metric
learning algorithm to generalize. To illustrate the applicability of the
proposed framework, we derive generalization results for a large family of
existing metric learning algorithms, including some sparse formulations that
are not covered by previous results.
|
1209.1114
|
Speed Tracking of a Linear Induction Motor - Enumerative Nonlinear Model
Predictive Control
|
cs.SY math.OC
|
Direct torque control is considered as one of the most efficient techniques
for speed and/or position tracking control of induction motor drives. However,
this control scheme has several drawbacks: the switching frequency may exceed
the maximum allowable switching frequency of the inverters, and the ripples in
current and torque, especially at low speed tracking, may be too large. In this
paper we propose a new approach that overcomes these problems. The suggested
controller is a model predictive controller which directly controls the
inverter switches. It is easy to implement in real time and it outperforms all
previous approaches. Simulation results show that the new approach has as good
tracking properties as any other scheme, and that it reduces the average
inverter switching frequency about 95% as compared to classical direct torque
control.
|
1209.1121
|
Learning Manifolds with K-Means and K-Flats
|
cs.LG stat.ML
|
We study the problem of estimating a manifold from random samples. In
particular, we consider piecewise constant and piecewise linear estimators
induced by k-means and k-flats, and analyze their performance. We extend
previous results for k-means in two separate directions. First, we provide new
results for k-means reconstruction on manifolds and, secondly, we prove
reconstruction bounds for higher-order approximation (k-flats), for which no
known results were previously available. While the results for k-means are
novel, some of the technical tools are well-established in the literature. In
the case of k-flats, both the results and the mathematical tools are new.
|
1209.1122
|
On Learning with Finite Memory
|
cs.GT cs.SI
|
We consider an infinite collection of agents who make decisions,
sequentially, about an unknown underlying binary state of the world. Each
agent, prior to making a decision, receives an independent private signal whose
distribution depends on the state of the world. Moreover, each agent also
observes the decisions of its last K immediate predecessors. We study
conditions under which the agent decisions converge to the correct value of the
underlying state. We focus on the case where the private signals have bounded
information content and investigate whether learning is possible, that is,
whether there exist decision rules for the different agents that result in the
convergence of their sequence of individual decisions to the correct state of
the world. We first consider learning in the almost sure sense and show that it
is impossible, for any value of K. We then explore the possibility of
convergence in probability of the decisions to the correct state. Here, a
distinction arises: if K equals 1, learning in probability is impossible under
any decision rule, while for K greater or equal to 2, we design a decision rule
that achieves it. We finally consider a new model, involving forward looking
strategic agents, each of which maximizes the discounted sum (over all agents)
of the probabilities of a correct decision. (The case, studied in previous
literature, of myopic agents who maximize the probability of their own decision
being correct is an extreme special case.) We show that for any value of K, for
any equilibrium of the associated Bayesian game, and under the assumption that
each private signal has bounded information content, learning in probability
fails to obtain.
|
1209.1123
|
Stabilizability and Norm-Optimal Control Design subject to Sparsity
Constraints
|
cs.SY math.DS math.OC
|
Consider that a linear time-invariant (LTI) plant is given and that we wish
to design a stabilizing controller for it. Admissible controllers are LTI and
must comply with a pre-selected sparsity pattern. The sparsity pattern is
assumed to be quadratically invariant (QI) with respect to the plant, which,
from prior results, guarantees that there is a convex parametrization of all
admissible stabilizing controllers provided that an initial admissible stable
stabilizing controller is provided. This paper addresses the previously
unsolved problem of determining necessary and sufficient conditions for the
existence of an admissible stabilizing controller. The main idea is to cast the
existence of such a controller as the feasibility of an exact model-matching
problem with stability restrictions, which can be tackled using existing
methods. Furthermore, we show that, when it exists, the solution of the
model-matching problem can be used to compute an admissible stabilizing
controller. This method also leads to a convex parametrization that may be
viewed as an extension of Youla's classical approach so as to incorporate
sparsity constraints. Applications of this parametrization on the design of
norm-optimal controllers via convex methods are also explored. An illustrative
example is provided, and a special case is discussed for which the exact model
matching problem has a unique and easily computable solution.
|
1209.1125
|
Video Data Visualization System: Semantic Classification And
Personalization
|
cs.IR cs.CV cs.MM
|
We present in this paper an intelligent video data visualization tool, based
on semantic classification, for retrieving and exploring a large scale corpus
of videos. Our work is based on semantic classification resulting from semantic
analysis of video. The obtained classes will be projected in the visualization
space. The graph is represented by nodes and edges, the nodes are the keyframes
of video documents and the edges are the relation between documents and the
classes of documents. Finally, we construct the user's profile, based on the
interaction with the system, to render the system more adequate to its
references.
|
1209.1128
|
Capacity achieving multiwrite WOM codes
|
cs.IT cs.CC math.IT
|
In this paper we give an explicit construction of a capacity achieving family
of binary t-write WOM codes for any number of writes t, that have a polynomial
time encoding and decoding algorithms. The block length of our construction is
N=(t/\epsilon)^{O(t/(\delta\epsilon))} when \epsilon is the gap to capacity and
encoding and decoding run in time N^{1+\delta}. This is the first deterministic
construction achieving these parameters. Our techniques also apply to larger
alphabets.
|
1209.1139
|
Control of Noisy Differential-Drive Vehicles from Time-Bounded Temporal
Logic Specifications
|
cs.RO
|
We address the problem of controlling a noisy differential drive mobile robot
such that the probability of satisfying a specification given as a Bounded
Linear Temporal Logic (BLTL) formula over a set of properties at the regions in
the environment is maximized. We assume that the vehicle can determine its
precise initial position in a known map of the environment. However, inspired
by practical limitations, we assume that the vehicle is equipped with noisy
actuators and, during its motion in the environment, it can only measure the
angular velocity of its wheels using limited accuracy incremental encoders.
Assuming the duration of the motion is finite, we map the measurements to a
Markov Decision Process (MDP). We use recent results in Statistical Model
Checking (SMC) to obtain an MDP control policy that maximizes the probability
of satisfaction. We translate this policy to a vehicle feedback control
strategy and show that the probability that the vehicle satisfies the
specification in the environment is bounded from below by the probability of
satisfying the specification on the MDP. We illustrate our method with
simulations and experimental results.
|
1209.1150
|
On dually flat Randers metrics
|
math.DG cs.IT math.IT
|
In this paper, I will show how to use beta-deformations to deal with dual
flatness of Randers metrics. beta-deformations is a new method in
Riemann-Finsler geometry, it is introduced by the author(see arxiv:1209.0845).
Later on I will provide more applications of the new kind of deformations in
Finsler geometry.
|
1209.1154
|
Generalized Formulation of Weighted Optimal Guidance Laws with Impact
Angle Constraint
|
cs.SY
|
The purpose of this paper is to investigate the generalized formulation of
weighted optimal guidance laws with impact angle constraint. From the
generalized formulation, we explicitly find the feasible set of weighting
functions that lead to analytical forms of weighted optimal guidance laws. This
result has potential significance because it can provide additional degrees of
freedom in designing a guidance law that accomplishes the specified guidance
objective.
|
1209.1180
|
Distributed Optimal Beamformers for Cognitive Radios Robust to Channel
Uncertainties
|
cs.IT math.IT
|
Through spatial multiplexing and diversity, multi-input multi-output (MIMO)
cognitive radio (CR) networks can markedly increase transmission rates and
reliability, while controlling the interference inflicted to peer nodes and
primary users (PUs) via beamforming. The present paper optimizes the design of
transmit- and receive-beamformers for ad hoc CR networks when CR-to-CR channels
are known, but CR-to-PU channels cannot be estimated accurately. Capitalizing
on a norm-bounded channel uncertainty model, the optimal beamforming design is
formulated to minimize the overall mean-square error (MSE) from all data
streams, while enforcing protection of the PU system when the CR-to-PU channels
are uncertain. Even though the resultant optimization problem is non-convex,
algorithms with provable convergence to stationary points are developed by
resorting to block coordinate ascent iterations, along with suitable convex
approximation techniques. Enticingly, the novel schemes also lend themselves
naturally to distributed implementations. Numerical tests are reported to
corroborate the analytical findings.
|
1209.1181
|
FCM Based Blood Vessel Segmentation Method for Retinal Images
|
cs.CV
|
Segmentation of blood vessels in retinal images provides early diagnosis of
diseases like glaucoma, diabetic retinopathy and macular degeneration. Among
these diseases occurrence of Glaucoma is most frequent and has serious ocular
consequences that can even lead to blindness, if it is not detected early. The
clinical criteria for the diagnosis of glaucoma include intraocular pressure
measurement, optic nerve head evaluation, retinal nerve fiber layer and visual
field defects. This form of blood vessel segmentation helps in early detection
for ophthalmic diseases, and potentially reduces the risk of blindness. The
low-contrast images at the retina owing to narrow blood vessels of the retina
are difficult to extract. These low contrast images are, however useful in
revealing certain systemic diseases. Motivated by the goals of improving
detection of such vessels, this present work proposes an algorithm for
segmentation of blood vessels and compares the results between expert
ophthalmologist hand-drawn ground-truths and segmented image(i.e. the output of
the present work).Sensitivity, specificity, positive predictive value (PPV),
positive likelihood ratio (PLR) and accuracy are used to evaluate overall
performance.It is found that this work segments blood vessels successfully with
sensitivity, specificity, PPV, PLR and accuracy of 99.62%, 54.66%, 95.08%,
219.72 and 95.03%, respectively.
|
1209.1198
|
Multivariate Interpolation Formula over Finite Fields and Its
Applications in Coding Theory
|
cs.IT math.IT
|
A multivariate interpolation formula (MVIF) over finite fields is presented
by using the proposed Kronecker delta function. The MVIF can be applied to
yield polynomial relations over the base field among homogeneous symmetric
rational functions. Besides the property that all the coefficients are coming
from the base field, there is also a significant one on the degrees of the
obtained polynomial; namely, the degree of each term satisfies certain
condition. Next, for any cyclic codes the unknown syndrome representation can
also be provided by the proposed MVIF and also has the same properties. By
applying the unknown syndrome representation and the Berlekamp-Massey
algorithm, one-step decoding algorithms can be developed to determine the error
locator polynomials for arbitrary cyclic codes.
|
1209.1224
|
Wavelet Based Normal and Abnormal Heart Sound Identification using
Spectrogram Analysis
|
cs.CV
|
The present work proposes a computer-aided normal and abnormal heart sound
identification based on Discrete Wavelet Transform (DWT), it being useful for
tele-diagnosis of heart diseases. Due to the presence of Cumulative Frequency
components in the spectrogram, DWT is applied on the spectro-gram up to n level
to extract the features from the individual approximation components. One
dimensional feature vector is obtained by evaluating the Row Mean of the
approximation components of these spectrograms. For this present approach, the
set of spectrograms has been considered as the database, rather than raw sound
samples. Minimum Euclidean distance is computed between feature vector of the
test sample and the feature vectors of the stored samples to identify the heart
sound. By applying this algorithm, almost 82% of accuracy was achieved.
|
1209.1236
|
Coordination of autonomic functionalities in communications networks
|
cs.NI cs.SY
|
Future communication networks are expected to feature autonomic (or
self-organizing) mechanisms to ease deployment (self-configuration), tune
parameters automatically (self-optimization) and repair the network
(self-healing). Self-organizing mechanisms have been designed as stand-alone
entities, even though multiple mechanisms will run in parallel in operational
networks. An efficient coordination mechanism will be the major enabler for
large scale deployment of self-organizing networks. We model self-organizing
mechanisms as control loops, and study the conditions for stability when
running control loops in parallel. Based on control theory and Lyapunov
stability, we propose a coordination mechanism to stabilize the system, which
can be implemented in a distributed fashion. The mechanism remains valid in the
presence of measurement noise via stochastic approximation. Instability and
coordination in the context of wireless networks are illustrated with two
examples and the influence of network geometry is investigated. We are
essentially concerned with linear systems, and the applicability of our results
for non-linear systems is discussed.
|
1209.1291
|
The degrees of freedom of MIMO networks with full-duplex receiver
cooperation but no CSIT
|
cs.IT math.IT
|
The question of whether the degrees of freedom (DoF) of multi-user networks
can be enhanced even under isotropic fading and no channel state information
(or output feedback) at the transmitters (CSIT) is investigated. Toward this
end, the two-user MIMO (multiple-input, multiple-output) broadcast and
interference channels are studied with no side-information whatsoever at the
transmitters and with receivers equipped with full-duplex radios. The
full-duplex feature allows for receiver cooperation because each receiver, in
addition to receiving the signals sent by the transmitters, can also
simultaneously transmit a signal in the same band to the other receiver. Unlike
the case of MIMO networks with CSIT and full-duplex receivers, for which DoF
are known, it is shown that for MIMO networks with no CSIT, full-duplex
receiver cooperation is beneficial to such an extent that even the DoF region
is enhanced. Indeed, for important classes of two-user MIMO broadcast and
interference channels, defined by certain relationships on numbers of antennas
at different terminals, the exact DoF regions are established. The key to
achieving DoF-optimal performance for such networks are new retro-cooperative
interference alignment schemes. Their optimality is established via the DoF
analysis of certain genie-aided or enhanced version of those networks.
|
1209.1295
|
Period Distribution of Inversive Pseudorandom Number Generators Over
Finite Fields
|
cs.IT math.IT
|
In this paper, we focus on analyzing the period distribution of the inversive
pseudorandom number generators (IPRNGs) over finite field $({\rm
Z}_{N},+,\times)$, where $N>3$ is a prime. The sequences generated by the
IPRNGs are transformed to 2-dimensional linear feedback shift register (LFSR)
sequences. By employing the generating function method and the finite field
theory, the period distribution is obtained analytically. The analysis process
also indicates how to choose the parameters and the initial values such that
the IPRNGs fit specific periods. The analysis results show that there are many
small periods if $N$ is not chosen properly. The experimental examples show the
effectiveness of the theoretical analysis.
|
1209.1300
|
Input Scheme for Hindi Using Phonetic Mapping
|
cs.CL
|
Written Communication on Computers requires knowledge of writing text for the
desired language using Computer. Mostly people do not use any other language
besides English. This creates a barrier. To resolve this issue we have
developed a scheme to input text in Hindi using phonetic mapping scheme. Using
this scheme we generate intermediate code strings and match them with
pronunciations of input text. Our system show significant success over other
input systems available.
|
1209.1301
|
Evaluation of Computational Grammar Formalisms for Indian Languages
|
cs.CL
|
Natural Language Parsing has been the most prominent research area since the
genesis of Natural Language Processing. Probabilistic Parsers are being
developed to make the process of parser development much easier, accurate and
fast. In Indian context, identification of which Computational Grammar
Formalism is to be used is still a question which needs to be answered. In this
paper we focus on this problem and try to analyze different formalisms for
Indian languages.
|
1209.1317
|
Lossy joint source-channel coding in the finite blocklength regime
|
cs.IT math.IT
|
This paper finds new tight finite-blocklength bounds for the best achievable
lossy joint source-channel code rate, and demonstrates that joint
source-channel code design brings considerable performance advantage over a
separate one in the non-asymptotic regime. A joint source-channel code maps a
block of $k$ source symbols onto a length$-n$ channel codeword, and the
fidelity of reproduction at the receiver end is measured by the probability
$\epsilon$ that the distortion exceeds a given threshold $d$. For memoryless
sources and channels, it is demonstrated that the parameters of the best joint
source-channel code must satisfy $nC - kR(d) \approx \sqrt{nV + k \mathcal
V(d)} Q(\epsilon)$, where $C$ and $V$ are the channel capacity and channel
dispersion, respectively; $R(d)$ and $\mathcal V(d)$ are the source
rate-distortion and rate-dispersion functions; and $Q$ is the standard Gaussian
complementary cdf. Symbol-by-symbol (uncoded) transmission is known to achieve
the Shannon limit when the source and channel satisfy a certain probabilistic
matching condition. In this paper we show that even when this condition is not
satisfied, symbol-by-symbol transmission is, in some cases, the best known
strategy in the non-asymptotic regime.
|
1209.1318
|
Finding and Recommending Scholarly Articles
|
cs.IR astro-ph.IM cs.DL physics.soc-ph
|
The rate at which scholarly literature is being produced has been increasing
at approximately 3.5 percent per year for decades. This means that during a
typical 40 year career the amount of new literature produced each year
increases by a factor of four. The methods scholars use to discover relevant
literature must change. Just like everybody else involved in information
discovery, scholars are confronted with information overload. Two decades ago,
this discovery process essentially consisted of paging through abstract books,
talking to colleagues and librarians, and browsing journals. A time-consuming
process, which could even be longer if material had to be shipped from
elsewhere. Now much of this discovery process is mediated by online scholarly
information systems. All these systems are relatively new, and all are still
changing. They all share a common goal: to provide their users with access to
the literature relevant to their specific needs. To achieve this each system
responds to actions by the user by displaying articles which the system judges
relevant to the user's current needs. Recently search systems which use
particularly sophisticated methodologies to recommend a few specific papers to
the user have been called "recommender systems". These methods are in line with
the current use of the term "recommender system" in computer science. We do not
adopt this definition, rather we view systems like these as components in a
larger whole, which is presented by the scholarly information systems
themselves. In what follows we view the recommender system as an aspect of the
entire information system; one which combines the massive memory capacities of
the machine with the cognitive abilities of the human user to achieve a
human-machine synergy.
|
1209.1322
|
Differentially Private Grids for Geospatial Data
|
cs.CR cs.DB
|
In this paper, we tackle the problem of constructing a differentially private
synopsis for two-dimensional datasets such as geospatial datasets. The current
state-of-the-art methods work by performing recursive binary partitioning of
the data domains, and constructing a hierarchy of partitions. We show that the
key challenge in partition-based synopsis methods lies in choosing the right
partition granularity to balance the noise error and the non-uniformity error.
We study the uniform-grid approach, which applies an equi-width grid of a
certain size over the data domain and then issues independent count queries on
the grid cells. This method has received no attention in the literature,
probably due to the fact that no good method for choosing a grid size was
known. Based on an analysis of the two kinds of errors, we propose a method for
choosing the grid size. Experimental results validate our method, and show that
this approach performs as well as, and often times better than, the
state-of-the-art methods. We further introduce a novel adaptive-grid method.
The adaptive grid method lays a coarse-grained grid over the dataset, and then
further partitions each cell according to its noisy count. Both levels of
partitions are then used in answering queries over the dataset. This method
exploits the need to have finer granularity partitioning over dense regions
and, at the same time, coarse partitioning over sparse regions. Through
extensive experiments on real-world datasets, we show that this approach
consistently and significantly outperforms the uniform-grid method and other
state-of-the-art methods.
|
1209.1323
|
An Empirical Study of How Users Adopt Famous Entities
|
cs.SI physics.soc-ph
|
Users of social networking services construct their personal social networks
by creating asymmetric and symmetric social links. Users usually follow friends
and selected famous entities that include celebrities and news agencies. In
this paper, we investigate how users follow famous entities. We statically and
dynamically analyze data within a huge social networking service with a
manually classified set of famous entities. The results show that the in-degree
of famous entities does not fit to power-law distribution. Conversely, the
maximum number of famous followees in one category for each user shows
power-law property. To our best knowledge, there is no research work on this
topic with human-chosen famous entity dataset in real life. These findings
might be helpful in microblogging marketing and user classification.
|
1209.1351
|
Emergence of influential spreaders in modified rumor models
|
physics.soc-ph cs.SI
|
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.
|
1209.1360
|
Multiclass Learning with Simplex Coding
|
stat.ML cs.LG
|
In this paper we discuss a novel framework for multiclass learning, defined
by a suitable coding/decoding strategy, namely the simplex coding, that allows
to generalize to multiple classes a relaxation approach commonly used in binary
classification. In this framework, a relaxation error analysis can be developed
avoiding constraints on the considered hypotheses class. Moreover, we show that
in this setting it is possible to derive the first provably consistent
regularized method with training/tuning complexity which is independent to the
number of classes. Tools from convex analysis are introduced that can be used
beyond the scope of this paper.
|
1209.1380
|
The Sample Complexity of Search over Multiple Populations
|
cs.IT math.IT stat.ML
|
This paper studies the sample complexity of searching over multiple
populations. We consider a large number of populations, each corresponding to
either distribution P0 or P1. The goal of the search problem studied here is to
find one population corresponding to distribution P1 with as few samples as
possible. The main contribution is to quantify the number of samples needed to
correctly find one such population. We consider two general approaches:
non-adaptive sampling methods, which sample each population a predetermined
number of times until a population following P1 is found, and adaptive sampling
methods, which employ sequential sampling schemes for each population. We first
derive a lower bound on the number of samples required by any sampling scheme.
We then consider an adaptive procedure consisting of a series of sequential
probability ratio tests, and show it comes within a constant factor of the
lower bound. We give explicit expressions for this constant when samples of the
populations follow Gaussian and Bernoulli distributions. An alternative
adaptive scheme is discussed which does not require full knowledge of P1, and
comes within a constant factor of the optimal scheme. For comparison, a lower
bound on the sampling requirements of any non-adaptive scheme is presented.
|
1209.1402
|
Joint Spatial Division and Multiplexing
|
cs.IT math.IT
|
We propose Joint Spatial Division and Multiplexing (JSDM), an approach to
multiuser MIMO downlink that exploits the structure of the correlation of the
channel vectors in order to allow for a large number of antennas at the base
station while requiring reduced-dimensional Channel State Information at the
Transmitter (CSIT). This allows for significant savings both in the downlink
training and in the CSIT feedback from the user terminals to the base station,
thus making the use of a large number of base station antennas potentially
suitable also for Frequency Division Duplexing (FDD) systems, for which
uplink/downlink channel reciprocity cannot be exploited. JSDM forms the
multiuser MIMO downlink precoder by concatenating a pre-beamforming matrix,
which depends only on the channel second-order statistics, with a classical
multiuser precoder, based on the instantaneous knowledge of the resulting
reduced dimensional effective channels. We prove a simple condition under which
JSDM incurs no loss of optimality with respect to the full CSIT case. For
linear uniformly spaced arrays, we show that such condition is closely
approached when the number of antennas is large. For this case, we use Szego
asymptotic theory of large Toeplitz matrices to design a DFT-based
pre-beamforming scheme requiring only coarse information about the users angles
of arrival and angular spread. Finally, we extend these ideas to the case of a
two-dimensional base station antenna array, with 3-dimensional beamforming,
including multiple beams in the elevation angle direction. We provide
guidelines for the pre-beamforming optimization and calculate the system
spectral efficiency under proportional fairness and maxmin fairness criteria,
showing extremely attractive performance. Our numerical results are obtained
via an asymptotic random matrix theory tool known as deterministic equivalent
approximation.
|
1209.1411
|
Connections between Human Dynamics and Network Science
|
physics.soc-ph cond-mat.stat-mech cs.SI physics.data-an
|
The increasing availability of large-scale data on human behavior has
catalyzed simultaneous advances in network theory, capturing the scaling
properties of the interactions between a large number of individuals, and human
dynamics, quantifying the temporal characteristics of human activity patterns.
These two areas remain disjoint, each pursuing as separate lines of inquiry.
Here we report a series of generic relationships between the quantities
characterizing these two areas by demonstrating that the degree and link weight
distributions in social networks can be expressed in terms of the dynamical
exponents characterizing human activity patterns. We test the validity of these
theoretical predictions on datasets capturing various facets of human
interactions, from mobile calls to tweets.
|
1209.1421
|
Blackboard Rules for Coordinating Context-aware Applications in Mobile
Ad Hoc Networks
|
cs.MA
|
Thanks to improvements in wireless communication technologies and increasing
computing power in hand-held devices, mobile ad hoc networks are becoming an
ever-more present reality. Coordination languages are expected to become
important means in supporting this type of interaction. To this extent we argue
the interest of the Bach coordination language as a middleware that can handle
and react to context changes as well as cope with unpredictable physical
interruptions that occur in opportunistic network connections. More concretely,
our proposal is based on blackboard rules that model declaratively the actions
to be taken once the blackboard content reaches a predefined state, but also
that manage the engagement and disengagement of hosts and transient sharing of
blackboards. The idea of reactiveness has already been introduced in previous
work, but as will be appreciated by the reader, this article presents a new
perspective, more focused on a declarative setting.
|
1209.1423
|
A model for cross-cultural reciprocal interactions through mass media
|
physics.soc-ph cond-mat.stat-mech cs.SI nlin.CD
|
We investigate the problem of cross-cultural interactions through mass media
in a model where two populations of social agents, each with its own internal
dynamics, get information about each other through reciprocal global
interactions. As the agent dynamics, we employ Axelrod's model for social
influence. The global interaction fields correspond to the statistical mode of
the states of the agents and represent mass media messages on the cultural
trend originating in each population. Several phases are found in the
collective behavior of either population depending on parameter values: two
homogeneous phases, one having the state of the global field acting on that
population, and the other consisting of a state different from that reached by
the applied global field; and a disordered phase. In addition, the system
displays nontrivial effects: (i) the emergence of a largest minority group of
appreciable size sharing a state different from that of the applied global
field; (ii) the appearance of localized ordered states for some values of
parameters when the entire system is observed, consisting of one population in
a homogeneous state and the other in a disordered state. This last situation
can be considered as a social analogue to a chimera state arising in globally
coupled populations of oscillators.
|
1209.1424
|
Multiuser Diversity for the Cognitive Uplink with Generalized Fading and
Reduced Primary's Cooperation
|
cs.IT math.IT
|
In cognitive multiple access networks, feedback is an important mechanism to
convey secondary transmitter primary base station (STPB) channel gains from the
primary base station (PBS) to the secondary base station (SBS). This paper
investigates the optimal sum-rate capacity scaling laws for cognitive multiple
access networks in feedback limited communication scenarios. First, an
efficient feedback protocol called $K$-smallest channel gains ($K$-SCGs)
feedback protocol is proposed in which the PBS feeds back the $\K$ smallest out
of $N$ STPB channel gains to the SBS. Second, the sum-rate performance of the
$K$-SCG feedback protocol is studied for three network types when transmission
powers of secondary users (SUs) are optimally allocated. The network types
considered are total-power-and-interference-limited (TPIL),
interference-limited (IL) and individual-power-and-interference-limited (IPIL)
networks. For each network type studied, we provide a sufficient condition on
$\K$ such that the $K$-SCG feedback protocol is {\em asymptotically} optimal in
the sense that the secondary network sum-rate scaling behavior under the
$K$-SCG feedback protocol is the same with that under the full-feedback
protocol. We allow distributions of
secondary-transmitter-secondary-base-station (STSB), and STPB channel power
gains to belong to a fairly general class of distributions called class
$\mathcal{C}$-distributions that includes commonly used fading models.
|
1209.1425
|
The End of an Architectural Era for Analytical Databases
|
cs.DB cs.DC
|
Traditional enterprise warehouse solutions center around an analytical
database system that is monolithic and inflexible: data needs to be extracted,
transformed, and loaded into the rigid relational form before analysis. It
takes years of sophisticated planning to provision and deploy a warehouse;
adding new hardware resources to an existing warehouse is an equally lengthy
and daunting task.
Additionally, modern data analysis employs statistical methods that go well
beyond the typical roll-up and drill-down capabilities provided by warehouse
systems. Although it is possible to implement such methods using a combination
of SQL and UDFs, query engines in relational databases are ill-suited for
these.
The Hadoop ecosystem introduces a suite of tools for data analytics that
overcome some of the problems of traditional solutions. These systems, however,
forgo years of warehouse research. Memory is significantly underutilized in
Hadoop clusters, and execution engine is naive compared with its relational
counterparts.
It is time to rethink the design of data warehouse systems and take the best
from both worlds. The new generation of warehouse systems should be modular,
high performance, fault-tolerant, easy to provision, and designed to support
both SQL query processing and machine learning applications.
This paper references the Shark system developed at Berkeley as an initial
attempt.
|
1209.1426
|
Power Control and Multiuser Diversity for the Distributed Cognitive
Uplink
|
cs.IT math.IT
|
This paper studies optimum power control and sum-rate scaling laws for the
distributed cognitive uplink. It is first shown that the optimum distributed
power control policy is in the form of a threshold based water-filling power
control. Each secondary user executes the derived power control policy in a
distributed fashion by using local knowledge of its direct and interference
channel gains such that the resulting aggregate (average) interference does not
disrupt primary's communication. Then, the tight sum-rate scaling laws are
derived as a function of the number of secondary users $N$ under the optimum
distributed power control policy. The fading models considered to derive
sum-rate scaling laws are general enough to include Rayleigh, Rician and
Nakagami fading models as special cases. When transmissions of secondary users
are limited by both transmission and interference power constraints, it is
shown that the secondary network sum-rate scales according to
$\frac{1}{\e{}n_h}\log\logp{N}$, where $n_h$ is a parameter obtained from the
distribution of direct channel power gains. For the case of transmissions
limited only by interference constraints, on the other hand, the secondary
network sum-rate scales according to $\frac{1}{\e{}\gamma_g}\logp{N}$, where
$\gamma_g$ is a parameter obtained from the distribution of interference
channel power gains. These results indicate that the distributed cognitive
uplink is able to achieve throughput scaling behavior similar to that of the
centralized cognitive uplink up to a pre-log multiplier $\frac{1}{\e{}}$,
whilst primary's quality-of-service requirements are met. The factor
$\frac{1}{\e{}}$ can be interpreted as the cost of distributed implementation
of the cognitive uplink.
|
1209.1428
|
Challenges and Directions for Engineering Multi-agent Systems
|
cs.MA cs.SE
|
In this talk I review where we stand regarding the engineering of multi-agent
systems. There is both good news and bad news. The good news is that over the
past decade we've made considerable progress on techniques for engineering
multi-agent systems: we have good, usable methodologies, and mature tools.
Furthermore, we've seen a wide range of demonstrated applications, and have
even begun to quantify the advantages of agent technology. However, industry
involvement in AAMAS appears to be declining (as measured by industry
sponsorship of the conference), and industry affiliated attendants at AAMAS
2012 were few (1-2%). Furthermore, looking at the applications of agents being
reported at recent AAMAS, usage of Agent Oriented Software Engineering (AOSE)
and of Agent Oriented Programming Languages (AOPLs) is quite limited. This
observation is corroborated by the results of a 2008 survey by Frank and
Virginia Dignum. Based on these observations, I make five recommendations: (1)
Re-engage with industry; (2) Stop designing AOPLs and AOSE methodologies ...
and instead ... (3) Move to the "macro" level: develop techniques for designing
and implementing interaction, integrate micro (single cognitive agent) and
macro (MAS) design and implementation; (4) Develop techniques for the Assurance
of MAS; and (5) Re-engage with the US.
|
1209.1434
|
Communicating Processes with Data for Supervisory Coordination
|
cs.SY
|
We employ supervisory controllers to safely coordinate high-level
discrete(-event) behavior of distributed components of complex systems.
Supervisory controllers observe discrete-event system behavior, make a decision
on allowed activities, and communicate the control signals to the involved
parties. Models of the supervisory controllers can be automatically synthesized
based on formal models of the system components and a formalization of the safe
coordination (control) requirements. Based on the obtained models, code
generation can be used to implement the supervisory controllers in software, on
a PLC, or an embedded (micro)processor. In this article, we develop a process
theory with data that supports a model-based systems engineering framework for
supervisory coordination. We employ communication to distinguish between the
different flows of information, i.e., observation and supervision, whereas we
employ data to specify the coordination requirements more compactly, and to
increase the expressivity of the framework. To illustrate the framework, we
remodel an industrial case study involving coordination of maintenance
procedures of a printing process of a high-tech Oce printer.
|
1209.1450
|
On spatial selectivity and prediction across conditions with fMRI
|
stat.ML cs.LG
|
Researchers in functional neuroimaging mostly use activation coordinates to
formulate their hypotheses. Instead, we propose to use the full statistical
images to define regions of interest (ROIs). This paper presents two machine
learning approaches, transfer learning and selection transfer, that are
compared upon their ability to identify the common patterns between brain
activation maps related to two functional tasks. We provide some preliminary
quantification of these similarities, and show that selection transfer makes it
possible to set a spatial scale yielding ROIs that are more specific to the
context of interest than with transfer learning. In particular, selection
transfer outlines well known regions such as the Visual Word Form Area when
discriminating between different visual tasks.
|
1209.1476
|
The effect of network structure on phase transitions in queuing networks
|
physics.soc-ph cs.SI physics.data-an
|
Recently, De Martino et al have presented a general framework for the study
of transportation phenomena on complex networks. One of their most significant
achievements was a deeper understanding of the phase transition from the
uncongested to the congested phase at a critical traffic load. In this paper,
we also study phase transition in transportation networks using a discrete time
random walk model. Our aim is to establish a direct connection between the
structure of the graph and the value of the critical traffic load. Applying
spectral graph theory, we show that the original results of De Martino et al
showing that the critical loading depends only on the degree sequence of the
graph -- suggesting that different graphs with the same degree sequence have
the same critical loading if all other circumstances are fixed -- is valid only
if the graph is dense enough. For sparse graphs, higher order corrections,
related to the local structure of the network, appear.
|
1209.1479
|
Communication dynamics in finite capacity social networks
|
physics.soc-ph cs.SI
|
In communication networks structure and dynamics are tightly coupled. The
structure controls the flow of information and is itself shaped by the
dynamical process of information exchanged between nodes. In order to reconcile
structure and dynamics, a generic model, based on the local interaction between
nodes, is considered for the communication in large social networks. In
agreement with data from a large human organization, we show that the flow is
non-Markovian and controlled by the temporal limitations of individuals. We
confirm the versatility of our model by predicting simultaneously the
degree-dependent node activity, the balance between information input and
output of nodes and the degree distribution. Finally, we quantify the
limitations to network analysis when it is based on data sampled over a finite
period of time.
|
1209.1481
|
Image Mining from Gel Diagrams in Biomedical Publications
|
cs.IR q-bio.QM
|
Authors of biomedical publications often use gel images to report
experimental results such as protein-protein interactions or protein
expressions under different conditions. Gel images offer a way to concisely
communicate such findings, not all of which need to be explicitly discussed in
the article text. This fact together with the abundance of gel images and their
shared common patterns makes them prime candidates for image mining endeavors.
We introduce an approach for the detection of gel images, and present an
automatic workflow to analyze them. We are able to detect gel segments and
panels at high accuracy, and present first results for the identification of
gene names in these images. While we cannot provide a complete solution at this
point, we present evidence that this kind of image mining is feasible.
|
1209.1483
|
Underspecified Scientific Claims in Nanopublications
|
cs.DL cs.IR
|
The application range of nanopublications --- small entities of scientific
results in RDF representation --- could be greatly extended if complete formal
representations are not mandatory. To that aim, we present an approach to
represent and interlink scientific claims in an underspecified way, based on
independent English sentences.
|
1209.1557
|
Learning Model-Based Sparsity via Projected Gradient Descent
|
stat.ML cs.LG math.OC
|
Several convex formulation methods have been proposed previously for
statistical estimation with structured sparsity as the prior. These methods
often require a carefully tuned regularization parameter, often a cumbersome or
heuristic exercise. Furthermore, the estimate that these methods produce might
not belong to the desired sparsity model, albeit accurately approximating the
true parameter. Therefore, greedy-type algorithms could often be more desirable
in estimating structured-sparse parameters. So far, these greedy methods have
mostly focused on linear statistical models. In this paper we study the
projected gradient descent with non-convex structured-sparse parameter model as
the constraint set. Should the cost function have a Stable Model-Restricted
Hessian the algorithm produces an approximation for the desired minimizer. As
an example we elaborate on application of the main results to estimation in
Generalized Linear Model.
|
1209.1558
|
A Comparative Study between Moravec and Harris Corner Detection of Noisy
Images Using Adaptive Wavelet Thresholding Technique
|
cs.CV
|
In this paper a comparative study between Moravec and Harris Corner Detection
has been done for obtaining features required to track and recognize objects
within a noisy image. Corner detection of noisy images is a challenging task in
image processing. Natural images often get corrupted by noise during
acquisition and transmission. As Corner detection of these noisy images does
not provide desired results, hence de-noising is required. Adaptive wavelet
thresholding approach is applied for the same.
|
1209.1563
|
Wavelet Based QRS Complex Detection of ECG Signal
|
cs.CV
|
The Electrocardiogram (ECG) is a sensitive diagnostic tool that is used to
detect various cardiovascular diseases by measuring and recording the
electrical activity of the heart in exquisite detail. A wide range of heart
condition is determined by thorough examination of the features of the ECG
report. Automatic extraction of time plane features is important for
identification of vital cardiac diseases. This paper presents a
multi-resolution wavelet transform based system for detection 'P', 'Q', 'R',
'S', 'T' peaks complex from original ECG signal. 'R-R' time lapse is an
important minutia of the ECG signal that corresponds to the heartbeat of the
concerned person. Abrupt increase in height of the 'R' wave or changes in the
measurement of the 'R-R' denote various anomalies of human heart. Similarly
'P-P', 'Q-Q', 'S-S', 'T-T' also corresponds to different anomalies of heart and
their peak amplitude also envisages other cardiac diseases. In this proposed
method the 'PQRST' peaks are marked and stored over the entire signal and the
time interval between two consecutive 'R' peaks and other peaks interval are
measured to detect anomalies in behavior of heart, if any. The peaks are
achieved by the composition of Daubeheissub bands wavelet of original ECG
signal. The accuracy of the 'PQRST' complex detection and interval measurement
is achieved up to 100% with high exactitude by processing and thresholding the
original ECG signal.
|
1209.1652
|
Power-laws and the Conservation of Information in discrete token
systems: Part 2 The role of defect
|
cs.IT math-ph math.IT math.MP q-bio.GN
|
In a matching paper (arXiv:1207.5027), I proved that Conservation of Size and
Information in a discrete token based system is overwhelmingly likely to lead
to a power-law component size distribution with respect to the size of its
unique alphabet. This was substantiated to a very high level of significance
using some 55 million lines of source code of mixed provenance. The principle
was also applied to show that average gene length should be constant in an
animal kingdom where the same constraints appear to hold, the implication being
that Conservation of Information plays a similar role in discrete token-based
systems as the Conservation of Energy does in physical systems.
In this part 2, the role of defect will be explored and a functional
behaviour for defect derived to be consistent with the power-law behaviour
substantiated above.
This will be supported by further experimental data and the implications
explored.
|
1209.1679
|
Bayesian Quantized Network Coding via Belief Propagation
|
cs.IT math.IT
|
In this paper, we propose an alternative for routing based packet forwarding,
which uses network coding to increase transmission efficiency, in terms of both
compression and error resilience. This non-adaptive encoding is called
quantized network coding, which involves random linear mapping in the real
field, followed by quantization to cope with the finite capacity of the links.
At the gateway node, which collects received quantized network coder packets,
minimum mean squared error decoding is performed, by using belief propagation
in the factor graph representation. Our simulation results show a significant
improvement, in terms of the number of required packets to recover the
messages, which can be interpreted as an embedded distributed source coding for
correlated messages.
|
1209.1688
|
Rank Centrality: Ranking from Pair-wise Comparisons
|
cs.LG stat.ML
|
The question of aggregating pair-wise comparisons to obtain a global ranking
over a collection of objects has been of interest for a very long time: be it
ranking of online gamers (e.g. MSR's TrueSkill system) and chess players,
aggregating social opinions, or deciding which product to sell based on
transactions. In most settings, in addition to obtaining a ranking, finding
`scores' for each object (e.g. player's rating) is of interest for
understanding the intensity of the preferences.
In this paper, we propose Rank Centrality, an iterative rank aggregation
algorithm for discovering scores for objects (or items) from pair-wise
comparisons. The algorithm has a natural random walk interpretation over the
graph of objects with an edge present between a pair of objects if they are
compared; the score, which we call Rank Centrality, of an object turns out to
be its stationary probability under this random walk. To study the efficacy of
the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model
(equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which
each object has an associated score which determines the probabilistic outcomes
of pair-wise comparisons between objects. In terms of the pair-wise marginal
probabilities, which is the main subject of this paper, the MNL model and the
BTL model are identical. We bound the finite sample error rates between the
scores assumed by the BTL model and those estimated by our algorithm. In
particular, the number of samples required to learn the score well with high
probability depends on the structure of the comparison graph. When the
Laplacian of the comparison graph has a strictly positive spectral gap, e.g.
each item is compared to a subset of randomly chosen items, this leads to
dependence on the number of samples that is nearly order-optimal.
|
1209.1695
|
Decentralized Stochastic Control with Partial History Sharing: A Common
Information Approach
|
cs.SY math.OC
|
A general model of decentralized stochastic control called partial history
sharing information structure is presented. In this model, at each step the
controllers share part of their observation and control history with each
other. This general model subsumes several existing models of information
sharing as special cases. Based on the information commonly known to all the
controllers, the decentralized problem is reformulated as an equivalent
centralized problem from the perspective of a coordinator. The coordinator
knows the common information and select prescriptions that map each
controller's local information to its control actions. The optimal control
problem at the coordinator is shown to be a partially observable Markov
decision process (POMDP) which is solved using techniques from Markov decision
theory. This approach provides (a) structural results for optimal strategies,
and (b) a dynamic program for obtaining optimal strategies for all controllers
in the original decentralized problem. Thus, this approach unifies the various
ad-hoc approaches taken in the literature. In addition, the structural results
on optimal control strategies obtained by the proposed approach cannot be
obtained by the existing generic approach (the person-by-person approach) for
obtaining structural results in decentralized problems; and the dynamic program
obtained by the proposed approach is simpler than that obtained by the existing
generic approach (the designer's approach) for obtaining dynamic programs in
decentralized problems.
|
1209.1711
|
Programming Languages for Scientific Computing
|
cs.PL cs.CE cs.MS
|
Scientific computation is a discipline that combines numerical analysis,
physical understanding, algorithm development, and structured programming.
Several yottacycles per year on the world's largest computers are spent
simulating problems as diverse as weather prediction, the properties of
material composites, the behavior of biomolecules in solution, and the quantum
nature of chemical compounds. This article is intended to review specfic
languages features and their use in computational science. We will review the
strengths and weaknesses of different programming styles, with examples taken
from widely used scientific codes.
|
1209.1716
|
Classification of binary systematic codes of small defect
|
cs.IT math.IT
|
In this paper non-trivial non-linear binary systematic AMDS codes are
classified in terms of their weight distributions, employing only elementary
techniques. In particular, we show that their length and minimum distance
completely determine the weight distribution.
|
1209.1719
|
Semi-metric networks for recommender systems
|
cs.IR cond-mat.stat-mech cs.SI
|
Weighted graphs obtained from co-occurrence in user-item relations lead to
non-metric topologies. We use this semi-metric behavior to issue
recommendations, and discuss its relationship to transitive closure on fuzzy
graphs. Finally, we test the performance of this method against other item- and
user-based recommender systems on the Movielens benchmark. We show that
including highly semi-metric edges in our recommendation algorithms leads to
better recommendations.
|
1209.1727
|
Bandits with heavy tail
|
stat.ML cs.LG
|
The stochastic multi-armed bandit problem is well understood when the reward
distributions are sub-Gaussian. In this paper we examine the bandit problem
under the weaker assumption that the distributions have moments of order
1+\epsilon, for some $\epsilon \in (0,1]$. Surprisingly, moments of order 2
(i.e., finite variance) are sufficient to obtain regret bounds of the same
order as under sub-Gaussian reward distributions. In order to achieve such
regret, we define sampling strategies based on refined estimators of the mean
such as the truncated empirical mean, Catoni's M-estimator, and the
median-of-means estimator. We also derive matching lower bounds that also show
that the best achievable regret deteriorates when \epsilon <1.
|
1209.1734
|
Load Distribution Composite Design Pattern for Genetic Algorithm-Based
Autonomic Computing Systems
|
cs.SE cs.DC cs.NE
|
Current autonomic computing systems are ad hoc solutions that are designed
and implemented from the scratch. When designing software, in most cases two or
more patterns are to be composed to solve a bigger problem. A composite design
patterns shows a synergy that makes the composition more than just the sum of
its parts which leads to ready-made software architectures. As far as we know,
there are no studies on composition of design patterns for autonomic computing
domain. In this paper we propose pattern-oriented software architecture for
self-optimization in autonomic computing system using design patterns
composition and multi objective evolutionary algorithms that software designers
and/or programmers can exploit to drive their work. Main objective of the
system is to reduce the load in the server by distributing the population to
clients. We used Case Based Reasoning, Database Access, and Master Slave design
patterns. We evaluate the effectiveness of our architecture with and without
design patterns compositions. The use of composite design patterns in the
architecture and quantitative measurements are presented. A simple UML class
diagram is used to describe the architecture.
|
1209.1739
|
Design of Spectrum Sensing Policy for Multi-user Multi-band Cognitive
Radio Network
|
cs.LG cs.NI
|
Finding an optimal sensing policy for a particular access policy and sensing
scheme is a laborious combinatorial problem that requires the system model
parameters to be known. In practise the parameters or the model itself may not
be completely known making reinforcement learning methods appealing. In this
paper a non-parametric reinforcement learning-based method is developed for
sensing and accessing multi-band radio spectrum in multi-user cognitive radio
networks. A suboptimal sensing policy search algorithm is proposed for a
particular multi-user multi-band access policy and the randomized
Chair-Varshney rule. The randomized Chair-Varshney rule is used to reduce the
probability of false alarms under a constraint on the probability of detection
that protects the primary user. The simulation results show that the proposed
method achieves a sum profit (e.g. data rate) close to the optimal sensing
policy while achieving the desired probability of detection.
|
1209.1751
|
Information content versus word length in random typing
|
physics.data-an cond-mat.stat-mech cs.CL
|
Recently, it has been claimed that a linear relationship between a measure of
information content and word length is expected from word length optimization
and it has been shown that this linearity is supported by a strong correlation
between information content and word length in many languages (Piantadosi et
al. 2011, PNAS 108, 3825-3826). Here, we study in detail some connections
between this measure and standard information theory. The relationship between
the measure and word length is studied for the popular random typing process
where a text is constructed by pressing keys at random from a keyboard
containing letters and a space behaving as a word delimiter. Although this
random process does not optimize word lengths according to information content,
it exhibits a linear relationship between information content and word length.
The exact slope and intercept are presented for three major variants of the
random typing process. A strong correlation between information content and
word length can simply arise from the units making a word (e.g., letters) and
not necessarily from the interplay between a word and its context as proposed
by Piantadosi et al. In itself, the linear relation does not entail the results
of any optimization process.
|
1209.1759
|
Difference of Normals as a Multi-Scale Operator in Unorganized Point
Clouds
|
cs.CV
|
A novel multi-scale operator for unorganized 3D point clouds is introduced.
The Difference of Normals (DoN) provides a computationally efficient,
multi-scale approach to processing large unorganized 3D point clouds. The
application of DoN in the multi-scale filtering of two different real-world
outdoor urban LIDAR scene datasets is quantitatively and qualitatively
demonstrated. In both datasets the DoN operator is shown to segment large 3D
point clouds into scale-salient clusters, such as cars, people, and lamp posts
towards applications in semi-automatic annotation, and as a pre-processing step
in automatic object recognition. The application of the operator to
segmentation is evaluated on a large public dataset of outdoor LIDAR scenes
with ground truth annotations.
|
1209.1788
|
On the Use of Lee's Protocol for Speckle-Reducing Techniques
|
cs.CV
|
This paper presents two new MAP (Maximum a Posteriori) filters for speckle
noise reduction and a Monte Carlo procedure for the assessment of their
performance. In order to quantitatively evaluate the results obtained using
these new filters, with respect to classical ones, a Monte Carlo extension of
Lee's protocol is proposed. This extension of the protocol shows that its
original version leads to inconsistencies that hamper its use as a general
procedure for filter assessment. Some solutions for these inconsistencies are
proposed, and a consistent comparison of speckle-reducing filters is provided.
|
1209.1794
|
A New Similairty Measure For Spatial Personalization
|
cs.DB
|
Extracting the relevant information by exploiting the spatial data warehouse
becomes increasingly hard. In fact, because of the enormous amount of data
stored in the spatial data warehouse, the user, usually, don't know what part
of the cube contain the relevant information and what the forthcoming query
should be. As a solution, we propose to study the similarity between the
behaviors of the users, in term of the spatial MDX queries launched on the
system, as a basis to recommend the next relevant MDX query to the current
user. This paper introduces a new similarity measure for comparing spatial MDX
queries. The proposed similarity measure could directly support the development
of spatial personalization approaches. The proposed similarity measure takes
into account the basic components of the similarity assessment models: the
topology, the direction and the distance.
|
1209.1797
|
Securing Your Transactions: Detecting Anomalous Patterns In XML
Documents
|
cs.CR cs.LG
|
XML transactions are used in many information systems to store data and
interact with other systems. Abnormal transactions, the result of either an
on-going cyber attack or the actions of a benign user, can potentially harm the
interacting systems and therefore they are regarded as a threat. In this paper
we address the problem of anomaly detection and localization in XML
transactions using machine learning techniques. We present a new XML anomaly
detection framework, XML-AD. Within this framework, an automatic method for
extracting features from XML transactions was developed as well as a practical
method for transforming XML features into vectors of fixed dimensionality. With
these two methods in place, the XML-AD framework makes it possible to utilize
general learning algorithms for anomaly detection. Central to the functioning
of the framework is a novel multi-univariate anomaly detection algorithm,
ADIFA. The framework was evaluated on four XML transactions datasets, captured
from real information systems, in which it achieved over 89% true positive
detection rate with less than a 0.2% false positive rate.
|
1209.1800
|
An Empirical Study of MAUC in Multi-class Problems with Uncertain Cost
Matrices
|
cs.LG
|
Cost-sensitive learning relies on the availability of a known and fixed cost
matrix. However, in some scenarios, the cost matrix is uncertain during
training, and re-train a classifier after the cost matrix is specified would
not be an option. For binary classification, this issue can be successfully
addressed by methods maximizing the Area Under the ROC Curve (AUC) metric.
Since the AUC can measure performance of base classifiers independent of cost
during training, and a larger AUC is more likely to lead to a smaller total
cost in testing using the threshold moving method. As an extension of AUC to
multi-class problems, MAUC has attracted lots of attentions and been widely
used. Although MAUC also measures performance of base classifiers independent
of cost, it is unclear whether a larger MAUC of classifiers is more likely to
lead to a smaller total cost. In fact, it is also unclear what kinds of
post-processing methods should be used in multi-class problems to convert base
classifiers into discrete classifiers such that the total cost is as small as
possible. In the paper, we empirically explore the relationship between MAUC
and the total cost of classifiers by applying two categories of post-processing
methods. Our results suggest that a larger MAUC is also beneficial.
Interestingly, simple calibration methods that convert the output matrix into
posterior probabilities perform better than existing sophisticated post
re-optimization methods.
|
1209.1826
|
A spatio-spectral hybridization for edge preservation and noisy image
restoration via local parametric mixtures and Lagrangian relaxation
|
stat.ME cs.CV stat.AP
|
This paper investigates a fully unsupervised statistical method for edge
preserving image restoration and compression using a spatial decomposition
scheme. Smoothed maximum likelihood is used for local estimation of edge pixels
from mixture parametric models of local templates. For the complementary smooth
part the traditional L2-variational problem is solved in the Fourier domain
with Thin Plate Spline (TPS) regularization. It is well known that naive
Fourier compression of the whole image fails to restore a piece-wise smooth
noisy image satisfactorily due to Gibbs phenomenon. Images are interpreted as
relative frequency histograms of samples from bi-variate densities where the
sample sizes might be unknown. The set of discontinuities is assumed to be
completely unsupervised Lebesgue-null, compact subset of the plane in the
continuous formulation of the problem. Proposed spatial decomposition uses a
widely used topological concept, partition of unity. The decision on edge pixel
neighborhoods are made based on the multiple testing procedure of Holms.
Statistical summary of the final output is decomposed into two layers of
information extraction, one for the subset of edge pixels and the other for the
smooth region. Robustness is also demonstrated by applying the technique on
noisy degradation of clean images.
|
1209.1873
|
Stochastic Dual Coordinate Ascent Methods for Regularized Loss
Minimization
|
stat.ML cs.LG math.OC
|
Stochastic Gradient Descent (SGD) has become popular for solving large scale
supervised machine learning optimization problems such as SVM, due to their
strong theoretical guarantees. While the closely related Dual Coordinate Ascent
(DCA) method has been implemented in various software packages, it has so far
lacked good convergence analysis. This paper presents a new analysis of
Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods
enjoy strong theoretical guarantees that are comparable or better than SGD.
This analysis justifies the effectiveness of SDCA for practical applications.
|
1209.1885
|
Parametric Constructive Kripke-Semantics for Standard Multi-Agent Belief
and Knowledge (Knowledge As Unbiased Belief)
|
cs.LO cs.AI cs.DC cs.MA
|
We propose parametric constructive Kripke-semantics for multi-agent
KD45-belief and S5-knowledge in terms of elementary set-theoretic constructions
of two basic functional building blocks, namely bias (or viewpoint) and
visibility, functioning also as the parameters of the doxastic and epistemic
accessibility relation. The doxastic accessibility relates two possible worlds
whenever the application of the composition of bias with visibility to the
first world is equal to the application of visibility to the second world. The
epistemic accessibility is the transitive closure of the union of our doxastic
accessibility and its converse. Therefrom, accessibility relations for common
and distributed belief and knowledge can be constructed in a standard way. As a
result, we obtain a general definition of knowledge in terms of belief that
enables us to view S5-knowledge as accurate (unbiased and thus true)
KD45-belief, negation-complete belief and knowledge as exact KD45-belief and
S5-knowledge, respectively, and perfect S5-knowledge as precise (exact and
accurate) KD45-belief, and all this generically for arbitrary functions of bias
and visibility. Our results can be seen as a semantic complement to previous
foundational results by Halpern et al. about the (un)definability and
(non-)reducibility of knowledge in terms of and to belief, respectively.
|
1209.1899
|
A matrix approach for computing extensions of argumentation frameworks
|
cs.AI
|
The matrices and their sub-blocks are introduced into the study of
determining various extensions in the sense of Dung's theory of argumentation
frameworks. It is showed that each argumentation framework has its matrix
representations, and the core semantics defined by Dung can be characterized by
specific sub-blocks of the matrix. Furthermore, the elementary permutations of
a matrix are employed by which an efficient matrix approach for finding out all
extensions under a given semantics is obtained. Different from several
established approaches, such as the graph labelling algorithm, Constraint
Satisfaction Problem algorithm, the matrix approach not only put the mathematic
idea into the investigation for finding out various extensions, but also
completely achieve the goal to compute all the extensions needed.
|
1209.1911
|
Progressive Differences Convolutional Low-Density Parity-Check Codes
|
cs.IT math.IT
|
We present a new family of low-density parity-check (LDPC) convolutional
codes that can be designed using ordered sets of progressive differences. We
study their properties and define a subset of codes in this class that have
some desirable features, such as fixed minimum distance and Tanner graphs
without short cycles. The design approach we propose ensures that these
properties are guaranteed independently of the code rate. This makes these
codes of interest in many practical applications, particularly when high rate
codes are needed for saving bandwidth. We provide some examples of coded
transmission schemes exploiting this new class of codes.
|
1209.1960
|
A Comparative Study of Efficient Initialization Methods for the K-Means
Clustering Algorithm
|
cs.LG cs.CV
|
K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. In this
paper, we first present an overview of these methods with an emphasis on their
computational efficiency. We then compare eight commonly used linear time
complexity initialization methods on a large and diverse collection of data
sets using various performance criteria. Finally, we analyze the experimental
results using non-parametric statistical tests and provide recommendations for
practitioners. We demonstrate that popular initialization methods often perform
poorly and that there are in fact strong alternatives to these methods.
|
1209.1983
|
Toward a New Protocol to Evaluate Recommender Systems
|
cs.IR cs.PF
|
In this paper, we propose an approach to analyze the performance and the
added value of automatic recommender systems in an industrial context. We show
that recommender systems are multifaceted and can be organized around 4
structuring functions: help users to decide, help users to compare, help users
to discover, help users to explore. A global off line protocol is then proposed
to evaluate recommender systems. This protocol is based on the definition of
appropriate evaluation measures for each aforementioned function. The
evaluation protocol is discussed from the perspective of the usefulness and
trust of the recommendation. A new measure called Average Measure of Impact is
introduced. This measure evaluates the impact of the personalized
recommendation. We experiment with two classical methods, K-Nearest Neighbors
(KNN) and Matrix Factorization (MF), using the well known dataset: Netflix. A
segmentation of both users and items is proposed to finely analyze where the
algorithms perform well or badly. We show that the performance is strongly
dependent on the segments and that there is no clear correlation between the
RMSE and the quality of the recommendation.
|
1209.2058
|
Safe and Stabilizing Distributed Multi-Path Cellular Flows
|
cs.RO cs.DC cs.MA cs.SY
|
We study the problem of distributed traffic control in the partitioned plane,
where the movement of all entities (robots, vehicles, etc.) within each
partition (cell) is coupled. Establishing liveness in such systems is
challenging, but such analysis will be necessary to apply such distributed
traffic control algorithms in applications like coordinating robot swarms and
the intelligent highway system. We present a formal model of a distributed
traffic control protocol that guarantees minimum separation between entities,
even as some cells fail. Once new failures cease occurring, in the case of a
single target, the protocol is guaranteed to self-stabilize and the entities
with feasible paths to the target cell make progress towards it. For multiple
targets, failures may cause deadlocks in the system, so we identify a class of
non-deadlocking failures where all entities are able to make progress to their
respective targets. The algorithm relies on two general principles: temporary
blocking for maintenance of safety and local geographical routing for
guaranteeing progress. Our assertional proofs may serve as a template for the
analysis of other distributed traffic control protocols. We present simulation
results that provide estimates of throughput as a function of entity velocity,
safety separation, single-target path complexity, failure-recovery rates, and
multi-target path complexity.
|
1209.2066
|
Data Processing Bounds for Scalar Lossy Source Codes with Side
Information at the Decoder
|
cs.IT math.IT
|
In this paper, we introduce new lower bounds on the distortion of scalar
fixed-rate codes for lossy compression with side information available at the
receiver. These bounds are derived by presenting the relevant random variables
as a Markov chain and applying generalized data processing inequalities a la
Ziv and Zakai. We show that by replacing the logarithmic function with other
functions, in the data processing theorem we formulate, we obtain new lower
bounds on the distortion of scalar coding with side information at the decoder.
The usefulness of these results is demonstrated for uniform sources and the
convex function $Q(t)=t^{1-\alpha}$, $\alpha>1$. The bounds in this case are
shown to be better than one can obtain from the Wyner-Ziv rate-distortion
function.
|
1209.2070
|
Content-based Multi-media Retrieval Technology
|
cs.MM cs.IR
|
This paper gives a summary of the content-based Image Retrieval and
Content-based Audio Retrieval, which are two parts of the Content-based
Retrieval. Content-based Retrieval is the retrieval based on the features of
the content. Generally, it is a way to extract features of the media data and
find other data with the similar features from the database automatically.
Content-based Retrieval can not only work on discrete media like texts, but
also can be used on continuous media, such as video and audio.
|
1209.2079
|
Error Rate Analysis of GF(q) Network Coded Detect-and-Forward Wireless
Relay Networks Using Equivalent Relay Channel Models
|
cs.IT math.IT
|
This paper investigates simple means of analyzing the error rate performance
of a general q-ary Galois Field network coded detect-and-forward cooperative
relay network with known relay error statistics at the destination. Equivalent
relay channels are used in obtaining an approximate error rate of the relay
network, from which the diversity order is found. Error rate analyses using
equivalent relay channel models are shown to be closely matched with simulation
results. Using the equivalent relay channels, low complexity receivers are
developed whose performances are close to that of the optimal maximum
likelihood receiver.
|
1209.2082
|
Blind Image Deblurring by Spectral Properties of Convolution Operators
|
cs.CV
|
In this paper, we study the problem of recovering a sharp version of a given
blurry image when the blur kernel is unknown. Previous methods often introduce
an image-independent regularizer (such as Gaussian or sparse priors) on the
desired blur kernel. We shall show that the blurry image itself encodes rich
information about the blur kernel. Such information can be found through
analyzing and comparing how the spectrum of an image as a convolution operator
changes before and after blurring. Our analysis leads to an effective convex
regularizer on the blur kernel which depends only on the given blurry image. We
show that the minimizer of this regularizer guarantees to give good
approximation to the blur kernel if the original image is sharp enough. By
combining this powerful regularizer with conventional image deblurring
techniques, we show how we could significantly improve the deblurring results
through simulations and experiments on real images. In addition, our analysis
and experiments help explaining a widely accepted doctrine; that is, the edges
are good features for deblurring.
|
1209.2086
|
On Cooperative Relay Networks with Video Applications
|
cs.IT cs.NI math.IT
|
In this paper, we investigate the problem of cooperative relay in CR networks
for further enhanced network performance. In particular, we focus on the two
representative cooperative relay strategies, and develop optimal spectrum
sensing and $p$-Persistent CSMA for spectrum access. Then, we study the problem
of cooperative relay in CR networks for video streaming. We incorporate
interference alignment to allow transmitters collaboratively send encoded
signals to all CR users. In the cases of a single licensed channel and multiple
licensed channels with channel bonding, we develop an optimal distributed
algorithm with proven convergence and convergence speed. In the case of
multiple channels without channel bonding, we develop a greedy algorithm with
bounded performance.
|
1209.2088
|
Spreading Processes and Large Components in Ordered, Directed Random
Graphs
|
math.CO cs.DM cs.SI
|
Order the vertices of a directed random graph \math{v_1,...,v_n}; edge
\math{(v_i,v_j)} for \math{i<j} exists independently with probability \math{p}.
This random graph model is related to certain spreading processes on networks.
We consider the component reachable from \math{v_1} and prove existence of a
sharp threshold \math{p^*=\log n/n} at which this reachable component
transitions from \math{o(n)} to \math{\Omega(n)}.
|
1209.2097
|
Semantic web applications with regard to math and environment
|
cs.DL cs.IR
|
The following is an outline of possible strategies in using semantic web
techniques and math with regard to environmental issues. The article uses
concrete examples and applications and provides partially a rather basic
treatment of semantic web techniques and math in order to adress a broader
audience.
|
1209.2137
|
Decoding billions of integers per second through vectorization
|
cs.IR cs.DB
|
In many important applications -- such as search engines and relational
database systems -- data is stored in the form of arrays of integers. Encoding
and, most importantly, decoding of these arrays consumes considerable CPU time.
Therefore, substantial effort has been made to reduce costs associated with
compression and decompression. In particular, researchers have exploited the
superscalar nature of modern processors and SIMD instructions. Nevertheless, we
introduce a novel vectorized scheme called SIMD-BP128 that improves over
previously proposed vectorized approaches. It is nearly twice as fast as the
previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the
same time, SIMD-BP128 saves up to 2 bits per integer. For even better
compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has
a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while
being two times faster during decoding.
|
1209.2138
|
Optimality Properties, Distributed Strategies, and Measurement-Based
Evaluation of Coordinated Multicell OFDMA Transmission
|
cs.IT math.IT
|
The throughput of multicell systems is inherently limited by interference and
the available communication resources. Coordinated resource allocation is the
key to efficient performance, but the demand on backhaul signaling and
computational resources grows rapidly with number of cells, terminals, and
subcarriers. To handle this, we propose a novel multicell framework with
dynamic cooperation clusters where each terminal is jointly served by a small
set of base stations. Each base station coordinates interference to neighboring
terminals only, thus limiting backhaul signalling and making the framework
scalable. This framework can describe anything from interference channels to
ideal joint multicell transmission.
The resource allocation (i.e., precoding and scheduling) is formulated as an
optimization problem (P1) with performance described by arbitrary monotonic
functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary
linear power constraints. Although (P1) is non-convex and difficult to solve
optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2)
Conditions for full power usage; and 3) A precoding parametrization based on a
few parameters between zero and one. These optimality properties are used to
propose low-complexity strategies: both a centralized scheme and a distributed
version that only requires local channel knowledge and processing. We evaluate
the performance on measured multicell channels and observe that the proposed
strategies achieve close-to-optimal performance among centralized and
distributed solutions, respectively. In addition, we show that multicell
interference coordination can give substantial improvements in sum performance,
but that joint transmission is very sensitive to synchronization errors and
that some terminals can experience performance degradations.
|
1209.2139
|
Fused Multiple Graphical Lasso
|
cs.LG stat.ML
|
In this paper, we consider the problem of estimating multiple graphical
models simultaneously using the fused lasso penalty, which encourages adjacent
graphs to share similar structures. A motivating example is the analysis of
brain networks of Alzheimer's disease using neuroimaging data. Specifically, we
may wish to estimate a brain network for the normal controls (NC), a brain
network for the patients with mild cognitive impairment (MCI), and a brain
network for Alzheimer's patients (AD). We expect the two brain networks for NC
and MCI to share common structures but not to be identical to each other;
similarly for the two brain networks for MCI and AD. The proposed formulation
can be solved using a second-order method. Our key technical contribution is to
establish the necessary and sufficient condition for the graphs to be
decomposable. Based on this key property, a simple screening rule is presented,
which decomposes the large graphs into small subgraphs and allows an efficient
estimation of multiple independent (small) subgraphs, dramatically reducing the
computational cost. We perform experiments on both synthetic and real data; our
results demonstrate the effectiveness and efficiency of the proposed approach.
|
1209.2163
|
Modeling controversies in the press: the case of the abnormal bees'
death
|
physics.soc-ph cs.CL
|
The controversy about the cause(s) of abnormal death of bee colonies in
France is investigated through an extensive analysis of the french speaking
press. A statistical analysis of textual data is first performed on the lexicon
used by journalists to describe the facts and to present associated
informations during the period 1998-2010. Three states are identified to
explain the phenomenon. The first state asserts a unique cause, the second one
focuses on multifactor causes and the third one states the absence of current
proof. Assigning each article to one of the three states, we are able to follow
the associated opinion dynamics among the journalists over 13 years. Then, we
apply the Galam sequential probabilistic model of opinion dynamic to those
data. Assuming journalists are either open mind or inflexible about their
respective opinions, the results are reproduced precisely provided we account
for a series of annual changes in the proportions of respective inflexibles.
The results shed a new counter intuitive light on the various pressure supposed
to apply on the journalists by either chemical industries or beekeepers and
experts or politicians. The obtained dynamics of respective inflexibles shows
the possible effect of lobbying, the inertia of the debate and the net
advantage gained by the first whistleblowers.
|
1209.2177
|
On-off Threshold Models of Social Contagion
|
physics.soc-ph cs.SI
|
We study binary state contagion dynamics on a social network where nodes act
in response to the average state of their neighborhood. We model the competing
tendencies of imitation and non-conformity by incorporating an off-threshold
into standard threshold models of behavior. In this way, we attempt to capture
important aspects of fashions and general societal trends. Allowing varying
amounts of stochasticity in both the network and node responses, we find
different outcomes in the random and deterministic versions of the model. In
the limit of a large, dense network, however, we show that these dynamics
coincide. The dynamical behavior of the system ranges from steady state to
chaotic depending on network connectivity and update synchronicity. We
construct a mean field theory for general random networks. In the undirected
case, the mean field theory predicts that the dynamics on the network are a
smoothed version of the average node response dynamics. We compare our theory
to extensive simulations on Poisson random graphs with node responses that
average to the chaotic tent map.
|
1209.2178
|
Continuous Queries for Multi-Relational Graphs
|
cs.DB cs.SI
|
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.
|
1209.2179
|
Downlink Noncoherent Cooperation without Transmitter Phase Alignment
|
cs.IT math.IT
|
Multicell joint processing can mitigate inter-cell interference and thereby
increase the spectral efficiency of cellular systems. Most previous work has
assumed phase-aligned (coherent) transmissions from different base transceiver
stations (BTSs), which is difficult to achieve in practice. In this work, a
noncoherent cooperative transmission scheme for the downlink is studied, which
does not require phase alignment. The focus is on jointly serving two users in
adjacent cells sharing the same resource block. The two BTSs partially share
their messages through a backhaul link, and each BTS transmits a superposition
of two codewords, one for each receiver. Each receiver decodes its own message,
and treats the signals for the other receiver as background noise. With
narrowband transmissions the achievable rate region and maximum achievable
weighted sum rate are characterized by optimizing the power allocation (and the
beamforming vectors in the case of multiple transmit antennas) at each BTS
between its two codewords. For a wideband (multicarrier) system, a dual
formulation of the optimal power allocation problem across sub-carriers is
presented, which can be efficiently solved by numerical methods. Results show
that the proposed cooperation scheme can improve the sum rate substantially in
the low to moderate signal-to-noise ratio (SNR) range.
|
1209.2191
|
MapReduce is Good Enough? If All You Have is a Hammer, Throw Away
Everything That's Not a Nail!
|
cs.DC cs.DB
|
Hadoop is currently the large-scale data analysis "hammer" of choice, but
there exist classes of algorithms that aren't "nails", in the sense that they
are not particularly amenable to the MapReduce programming model. To address
this, researchers have proposed MapReduce extensions or alternative programming
models in which these algorithms can be elegantly expressed. This essay
espouses a very different position: that MapReduce is "good enough", and that
instead of trying to invent screwdrivers, we should simply get rid of
everything that's not a nail. To be more specific, much discussion in the
literature surrounds the fact that iterative algorithms are a poor fit for
MapReduce: the simple solution is to find alternative non-iterative algorithms
that solve the same problem. This essay captures my personal experiences as an
academic researcher as well as a software engineer in a "real-world" production
analytics environment. From this combined perspective I reflect on the current
state and future of "big data" research.
|
1209.2192
|
Power Allocation for Conventional and Buffer-Aided Link Adaptive
Relaying Systems with Energy Harvesting Nodes
|
cs.IT math.IT
|
Energy harvesting (EH) nodes can play an important role in cooperative
communication systems which do not have a continuous power supply. In this
paper, we consider the optimization of conventional and buffer-aided link
adaptive EH relaying systems, where an EH source communicates with the
destination via an EH decode-and-forward relay. In conventional relaying,
source and relay transmit signals in consecutive time slots whereas in
buffer-aided link adaptive relaying, the state of the source-relay and
relay-destination channels determines whether the source or the relay is
selected for transmission. Our objective is to maximize the system throughput
over a finite number of transmission time slots for both relaying protocols. In
case of conventional relaying, we propose an offline and several online joint
source and relay transmit power allocation schemes. For offline power
allocation, we formulate an optimization problem which can be solved optimally.
For the online case, we propose a dynamic programming (DP) approach to compute
the optimal online transmit power. To alleviate the complexity inherent to DP,
we also propose several suboptimal online power allocation schemes. For
buffer-aided link adaptive relaying, we show that the joint offline
optimization of the source and relay transmit powers along with the link
selection results in a mixed integer non-linear program which we solve
optimally using the spatial branch-and-bound method. We also propose an
efficient online power allocation scheme and a naive online power allocation
scheme for buffer-aided link adaptive relaying. Our results show that link
adaptive relaying provides performance improvement over conventional relaying
at the expense of a higher computational complexity.
|
1209.2194
|
Cooperative learning in multi-agent systems from intermittent
measurements
|
math.OC cs.LG cs.MA cs.SY
|
Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector $\mu$ from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of $\mu$. Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes.
|
1209.2204
|
How is non-knowledge represented in economic theory?
|
q-fin.GN cs.AI stat.AP
|
In this article, we address the question of how non-knowledge about future
events that influence economic agents' decisions in choice settings has been
formally represented in economic theory up to date. To position our discussion
within the ongoing debate on uncertainty, we provide a brief review of
historical developments in economic theory and decision theory on the
description of economic agents' choice behaviour under conditions of
uncertainty, understood as either (i) ambiguity, or (ii) unawareness.
Accordingly, we identify and discuss two approaches to the formalisation of
non-knowledge: one based on decision-making in the context of a state space
representing the exogenous world, as in Savage's axiomatisation and some
successor concepts (ambiguity as situations with unknown probabilities), and
one based on decision-making over a set of menus of potential future
opportunities, providing the possibility of derivation of agents' subjective
state spaces (unawareness as situation with imperfect subjective knowledge of
all future events possible). We also discuss impeding challenges of the
formalisation of non-knowledge.
|
1209.2262
|
A single-photon sampling architecture for solid-state imaging
|
cs.IT math.IT physics.ins-det
|
Advances in solid-state technology have enabled the development of silicon
photomultiplier sensor arrays capable of sensing individual photons. Combined
with high-frequency time-to-digital converters (TDCs), this technology opens up
the prospect of sensors capable of recording with high accuracy both the time
and location of each detected photon. Such a capability could lead to
significant improvements in imaging accuracy, especially for applications
operating with low photon fluxes such as LiDAR and positron emission
tomography.
The demands placed on on-chip readout circuitry imposes stringent trade-offs
between fill factor and spatio-temporal resolution, causing many contemporary
designs to severely underutilize the technology's full potential. Concentrating
on the low photon flux setting, this paper leverages results from group testing
and proposes an architecture for a highly efficient readout of pixels using
only a small number of TDCs, thereby also reducing both cost and power
consumption. The design relies on a multiplexing technique based on binary
interconnection matrices. We provide optimized instances of these matrices for
various sensor parameters and give explicit upper and lower bounds on the
number of TDCs required to uniquely decode a given maximum number of
simultaneous photon arrivals.
To illustrate the strength of the proposed architecture, we note a typical
digitization result of a 120x120 photodiode sensor on a 30um x 30um pitch with
a 40ps time resolution and an estimated fill factor of approximately 70%, using
only 161 TDCs. The design guarantees registration and unique recovery of up to
4 simultaneous photon arrivals using a fast decoding algorithm. In a series of
realistic simulations of scintillation events in clinical positron emission
tomography the design was able to recover the spatio-temporal location of 98.6%
of all photons that caused pixel firings.
|
1209.2274
|
PCA-Based Relevance Feedback in Document Image Retrieval
|
cs.IR
|
Research has been devoted in the past few years to relevance feedback as an
effective solution to improve performance of information retrieval systems.
Relevance feedback refers to an interactive process that helps to improve the
retrieval performance. In this paper we propose the use of relevance feedback
to improve document image retrieval System (DIRS) performance. This paper
compares a variety of strategies for positive and negative feedback. In
addition, feature subspace is extracted and updated during the feedback process
using a Principal Component Analysis (PCA) technique and based on user's
feedback. That is, in addition to reducing the dimensionality of feature
spaces, a proper subspace for each type of features is obtained in the feedback
process to further improve the retrieval accuracy. Experiments show that using
relevance Feedback in DIR achieves better performance than common DIR.
|
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