id
stringlengths 9
16
| title
stringlengths 4
278
| categories
stringlengths 5
104
| abstract
stringlengths 6
4.09k
|
|---|---|---|---|
1202.4486
|
Deterministic Leader Election Among Disoriented Anonymous Sensors
|
cs.DC cs.MA
|
We address the Leader Election (LE) problem in networks of anonymous sensors
sharing no kind of common coordinate system. Leader Election is a fundamental
symmetry breaking problem in distributed computing. Its goal is to assign value
1 (leader) to one of the entities and value 0 (non-leader) to all others. In
this paper, assuming n > 1 disoriented anonymous sensors, we provide a complete
charac- terization on the sensors positions to deterministically elect a
leader, provided that all the sensors' positions are known by every sensor.
More precisely, our contribution is twofold: First, assuming n anonymous
sensors agreeing on a common handedness (chirality) of their own coordinate
system, we provide a complete characterization on the sensors positions to
deterministically elect a leader. Second, we also provide such a complete
chararacterization for sensors devoided of a common handedness. Both
characterizations rely on a particular object from combinatorics on words,
namely the Lyndon Words.
|
1202.4495
|
Stochastic-Based Pattern Recognition Analysis
|
cs.CV
|
In this work we review the basic principles of stochastic logic and propose
its application to probabilistic-based pattern-recognition analysis. The
proposed technique is intrinsically a parallel comparison of input data to
various pre-stored categories using Bayesian techniques. We design smart
pulse-based stochastic-logic blocks to provide an efficient pattern recognition
analysis. The proposed rchitecture is applied to a specific navigation problem.
The resulting system is orders of magnitude faster than processor-based
solutions.
|
1202.4507
|
A Cryptographic Moving-Knife Cake-Cutting Protocol
|
cs.GT cs.CR cs.MA
|
This paper proposes a cake-cutting protocol using cryptography when the cake
is a heterogeneous good that is represented by an interval on a real line.
Although the Dubins-Spanier moving-knife protocol with one knife achieves
simple fairness, all players must execute the protocol synchronously. Thus, the
protocol cannot be executed on asynchronous networks such as the Internet. We
show that the moving-knife protocol can be executed asynchronously by a
discrete protocol using a secure auction protocol. The number of cuts is n-1
where n is the number of players, which is the minimum.
|
1202.4509
|
Rich Counter-Examples for Temporal-Epistemic Logic Model Checking
|
cs.LO cs.MA
|
Model checking verifies that a model of a system satisfies a given property,
and otherwise produces a counter-example explaining the violation. The verified
properties are formally expressed in temporal logics. Some temporal logics,
such as CTL, are branching: they allow to express facts about the whole
computation tree of the model, rather than on each single linear computation.
This branching aspect is even more critical when dealing with multi-modal
logics, i.e. logics expressing facts about systems with several transition
relations. A prominent example is CTLK, a logic that reasons about temporal and
epistemic properties of multi-agent systems. In general, model checkers produce
linear counter-examples for failed properties, composed of a single computation
path of the model. But some branching properties are only poorly and partially
explained by a linear counter-example.
This paper proposes richer counter-example structures called tree-like
annotated counter-examples (TLACEs), for properties in Action-Restricted CTL
(ARCTL), an extension of CTL quantifying paths restricted in terms of actions
labeling transitions of the model. These counter-examples have a branching
structure that supports more complete description of property violations.
Elements of these counter-examples are annotated with parts of the property to
give a better understanding of their structure. Visualization and browsing of
these richer counter-examples become a critical issue, as the number of
branches and states can grow exponentially for deeply-nested properties.
This paper formally defines the structure of TLACEs, characterizes adequate
counter-examples w.r.t. models and failed properties, and gives a generation
algorithm for ARCTL properties. It also illustrates the approach with examples
in CTLK, using a reduction of CTLK to ARCTL. The proposed approach has been
implemented, first by extending the NuSMV model checker to generate and export
branching counter-examples, secondly by providing an interactive graphical
interface to visualize and browse them.
|
1202.4532
|
Conceptual Level Design of Semi-structured Database System:
Graph-semantic Based Approach
|
cs.SE cs.DB
|
This paper has proposed a Graph - semantic based conceptual model for
semi-structured database system, called GOOSSDM, to conceptualize the different
facets of such system in object oriented paradigm. The model defines a set of
graph based formal constructs, variety of relationship types with participation
constraints and rich set of graphical notations to specify the conceptual level
design of semi-structured database system. The proposed design approach
facilitates modeling of irregular, heterogeneous, hierarchical and
non-hierarchical semi-structured data at the conceptual level. Moreover, the
proposed GOOSSDM is capable to model XML document at conceptual level with the
facility of document-centric design, ordering and disjunction characteristic. A
rule based transformation mechanism of GOOSSDM schema into the equivalent XML
Schema Definition (XSD) also has been proposed in this paper. The concepts of
the proposed conceptual model have been implemented using Generic Modeling
Environment (GME).
|
1202.4533
|
Unified model of voltage/current mode control to predict saddle-node
bifurcation
|
cs.SY math.DS nlin.CD
|
A unified model of voltage mode control (VMC) and current mode control (CMC)
is proposed to predict the saddle-node bifurcation (SNB). Exact SNB boundary
conditions are derived, and can be further simplified in various forms for
design purpose. Many approaches, including steady-state, sampled-data, average,
harmonic balance, and loop gain analyses are applied to predict SNB. Each
approach has its own merits and complement the other approaches.
|
1202.4534
|
Bifurcation Boundary Conditions for Switching DC-DC Converters Under
Constant On-Time Control
|
cs.SY math.DS nlin.CD
|
Sampled-data analysis and harmonic balance analysis are applied to analyze
switching DC-DC converters under constant on-time control. Design-oriented
boundary conditions for the period-doubling bifurcation and the saddle-node
bifurcation are derived. The required ramp slope to avoid the bifurcations and
the assigned pole locations associated with the ramp are also derived. The
derived boundary conditions are more general and accurate than those recently
obtained. Those recently obtained boundary conditions become special cases
under the general modeling approach presented in this paper. Different analyses
give different perspectives on the system dynamics and complement each other.
Under the sampled-data analysis, the boundary conditions are expressed in terms
of signal slopes and the ramp slope. Under the harmonic balance analysis, the
boundary conditions are expressed in terms of signal harmonics. The derived
boundary conditions are useful for a designer to design a converter to avoid
the occurrence of the period-doubling bifurcation and the saddle-node
bifurcation.
|
1202.4535
|
Proceedings First Workshop on CTP Components for Educational Software
|
cs.SY cs.LO cs.MS cs.SC
|
The THedu'11 workshop received thirteen submissions, twelve of which were
accepted and presented during the workshop. For the post-conference proceedings
nine submission where received and accepted. The submissions are within the
scope of the following points, which have been announced in the call of papers:
CTP-based software tools for education; CTP technology combined with novel
interfaces, drag and drop, etc.; technologies to access ITP knowledge relevant
for a certain step of problem solving; usability considerations on representing
ITP knowledge; combination of deduction and computation; formal problem
specifications; effectiveness of ATP in checking user input; formats for
deductive content in proof documents, geometric constructions, etc; formal
domain models for e-learning in mathematics and applications.
|
1202.4537
|
Sampled-Data and Harmonic Balance Analyses of Average Current-Mode
Controlled Buck Converter
|
cs.SY math.DS nlin.CD
|
Dynamics and stability of average current-mode control of buck converters are
analyzed by sampled-data and harmonic balance analyses. An exact sampled-data
model is derived. A new continuous-time model "lifted" from the sampled-data
model is also derived, and has frequency response matched with experimental
data reported previously. Orbital stability is studied and it is found
unrelated to the ripple size of the current-loop compensator output. An
unstable window of the current-loop compensator pole is found by simulations,
and it can be accurately predicted by sampled-data and harmonic balance
analyses. A new S plot accurately predicting the subharmonic oscillation is
proposed. The S plot assists pole assignment and shows the required ramp slope
to avoid instability.
|
1202.4553
|
MIMO capacity for deterministic channel models: sublinear growth
|
cs.IT math-ph math.IT math.MP
|
This is the second paper of the authors in a series concerned with the
development of a deterministic model for the transfer matrix of a MIMO system.
Starting from the Maxwell equations, we have described in \cite{BCFM} the
generic structure of such a deterministic transfer matrix. In the current paper
we apply the results of \cite{BCFM} in order to study the (Shannon-Foschini)
capacity behavior of a MIMO system as a function of the deterministic spread
function of the environment, and the number of transmitting and receiving
antennas. The antennas are assumed to fill in a given, fixed volume. Under some
generic assumptions, we prove that the capacity grows much more slowly than
linearly with the number of antennas. These results reinforce previous
heuristic results obtained from statistical models of the transfer matrix,
which also predict a sublinear behavior.
|
1202.4554
|
On the dynamics of social conflicts: looking for the Black Swan
|
math-ph cs.SI math.MP physics.soc-ph
|
This paper deals with the modeling of social competition, possibly resulting
in the onset of extreme conflicts. More precisely, we discuss models describing
the interplay between individual competition for wealth distribution that, when
coupled with political stances coming from support or opposition to a
government, may give rise to strongly self-enhanced effects. The latter may be
thought of as the early stages of massive, unpredictable events known as Black
Swans, although no analysis of any fully-developed Black Swan is provided here.
Our approach makes use of the framework of the kinetic theory for active
particles, where nonlinear interactions among subjects are modeled according to
game-theoretical tools.
|
1202.4590
|
Noncontinous additive entropies of partitions
|
cs.IT math.IT math.PR
|
In a previous paper: A. Paszkiewicz, T. Sobieszek, Additive Entropies of
Partitions, we have given a description of additive partition entropies that is
real functions $I$ on the set of finite partitions that are additive on
stochastically independent partitions in a given probability space. We now
present an analogical result, this time without assuming continuity.
As a by-product of our efforts we solve a 2-cocycle functional equation for
certain subsets of convex cones.
|
1202.4591
|
Additive Entropies of Partitions
|
cs.IT math.IT math.PR
|
We provide, under minimal continuity assumptions, a description of
\textsl{additive partition entropies}. They are real functions $I$ on the set
of finite partitions that are additive on stochastically independent partitions
in a given probability space.
|
1202.4596
|
Compressive Principal Component Pursuit
|
cs.IT math.IT
|
We consider the problem of recovering a target matrix that is a superposition
of low-rank and sparse components, from a small set of linear measurements.
This problem arises in compressed sensing of structured high-dimensional
signals such as videos and hyperspectral images, as well as in the analysis of
transformation invariant low-rank recovery. We analyze the performance of the
natural convex heuristic for solving this problem, under the assumption that
measurements are chosen uniformly at random. We prove that this heuristic
exactly recovers low-rank and sparse terms, provided the number of observations
exceeds the number of intrinsic degrees of freedom of the component signals by
a polylogarithmic factor. Our analysis introduces several ideas that may be of
independent interest for the more general problem of compressed sensing and
decomposing superpositions of multiple structured signals.
|
1202.4661
|
Delay Asymptotics with Retransmissions and Incremental Redundancy Codes
over Erasure Channels
|
cs.IT cs.PF math.IT
|
Recent studies have shown that retransmissions can cause heavy-tailed
transmission delays even when packet sizes are light-tailed. Moreover, the
impact of heavy-tailed delays persists even when packets size are upper
bounded. The key question we study in this paper is how the use of coding
techniques to transmit information, together with different system
configurations, would affect the distribution of delay. To investigate this
problem, we model the underlying channel as a Markov modulated binary erasure
channel, where transmitted bits are either received successfully or erased.
Erasure codes are used to encode information prior to transmission, which
ensures that a fixed fraction of the bits in the codeword can lead to
successful decoding. We use incremental redundancy codes, where the codeword is
divided into codeword trunks and these trunks are transmitted one at a time to
provide incremental redundancies to the receiver until the information is
recovered. We characterize the distribution of delay under two different
scenarios: (I) Decoder uses memory to cache all previously successfully
received bits. (II) Decoder does not use memory, where received bits are
discarded if the corresponding information cannot be decoded. In both cases, we
consider codeword length with infinite and finite support. From a theoretical
perspective, our results provide a benchmark to quantify the tradeoff between
system complexity and the distribution of delay.
|
1202.4664
|
Super-FEC Codes for 40/100 Gbps Networking
|
cs.IT math.IT
|
This paper presents a simple approach to evaluate the performance bound at
very low bit-error-rate (BER) range for binary pseudo-product codes and
true-product codes. Moreover it introduces a super-product BCH code that can
achieve near-Shannon limit performance with very low decoding complexity. This
work has been accepted by IEEE Communications Letters for future publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible.
|
1202.4707
|
A para-model agent for dynamical systems
|
math.OC cs.SY
|
Consider a dynamical system $u \mapsto x, \dot{x} = f_{nl}(x,u)$ where
$f_{nl}$ is a nonlinear (convex or nonconvex) function, or a combination of
nonlinear functions that can eventually switch. We present, in this preliminary
work, a generalization of the standard model-free control, that can either
control the dynamical system, given an output reference trajectory, or optimize
the dynamical system as a derivative-free optimization based "extremum-seeking"
procedure. Multiple applications are presented and the robustness of the
proposed method is studied in simulation.
|
1202.4720
|
Non-Stationary Random Process for Large-Scale Failure and Recovery of
Power Distributions
|
cs.SY
|
A key objective of the smart grid is to improve reliability of utility
services to end users. This requires strengthening resilience of distribution
networks that lie at the edge of the grid. However, distribution networks are
exposed to external disturbances such as hurricanes and snow storms where
electricity service to customers is disrupted repeatedly. External disturbances
cause large-scale power failures that are neither well-understood, nor
formulated rigorously, nor studied systematically. This work studies resilience
of power distribution networks to large-scale disturbances in three aspects.
First, a non-stationary random process is derived to characterize an entire
life cycle of large-scale failure and recovery. Second, resilience is defined
based on the non-stationary random process. Close form analytical expressions
are derived under specific large-scale failure scenarios. Third, the
non-stationary model and the resilience metric are applied to a real life
example of large-scale disruptions due to Hurricane Ike. Real data on
large-scale failures from an operational network is used to learn time-varying
model parameters and resilience metrics.
|
1202.4736
|
Diversity of MIMO Linear Precoding
|
cs.IT math.IT
|
Linear precoding is a relatively simple method of MIMO signaling that can
also be optimal in certain special cases. This paper is dedicated to high-SNR
analysis of MIMO linear precoding. The Diversity-Multiplexing Tradeoff (DMT) of
a number of linear precoders is analyzed. Furthermore, since the diversity at
finite rate (also known as the fixed-rate regime, corresponding to multiplexing
gain of zero) does not always follow from the DMT, linear precoders are also
analyzed for their diversity at fixed rates. In several cases, the diversity at
multiplexing gain of zero is found not to be unique, but rather to depend on
spectral efficiency. The analysis includes the zero-forcing (ZF), regularized
ZF, matched filtering and Wiener filtering precoders. We calculate the DMT of
ZF precoding under two common design approaches, namely maximizing the
throughput and minimizing the transmit power. It is shown that regularized ZF
(RZF) or Matched filter (MF) suffer from error floors for all positive
multiplexing gains. However, in the fixed rate regime, RZF and MF precoding
achieve full diversity up to a certain spectral efficiency and zero diversity
at rates above it. When the regularization parameter in the RZF is optimized in
the MMSE sense, the structure is known as the Wiener precoder which in the
fixed-rate regime is shown to have diversity that depends not only on the
number of antennas, but also on the spectral efficiency. The diversity in the
presence of both precoding and equalization is also analyzed.
|
1202.4743
|
Real-time detection and tracking of multiple objects with partial
decoding in H.264/AVC bitstream domain
|
cs.MM cs.CV
|
In this paper, we show that we can apply probabilistic spatiotemporal
macroblock filtering (PSMF) and partial decoding processes to effectively
detect and track multiple objects in real time in H.264|AVC bitstreams with
stationary background. Our contribution is that our method cannot only show
fast processing time but also handle multiple moving objects that are
articulated, changing in size or internally have monotonous color, even though
they contain a chaotic set of non-homogeneous motion vectors inside. In
addition, our partial decoding process for H.264|AVC bitstreams enables to
improve the accuracy of object trajectories and overcome long occlusion by
using extracted color information.
|
1202.4805
|
Fast Generation of Large Scale Social Networks with Clustering
|
cs.SI physics.soc-ph
|
A key challenge within the social network literature is the problem of
network generation - that is, how can we create synthetic networks that match
characteristics traditionally found in most real world networks? Important
characteristics that are present in social networks include a power law degree
distribution, small diameter and large amounts of clustering; however, most
current network generators, such as the Chung Lu and Kronecker models, largely
ignore the clustering present in a graph and choose to focus on preserving
other network statistics, such as the power law distribution. Models such as
the exponential random graph model have a transitivity parameter, but are
computationally difficult to learn, making scaling to large real world networks
intractable. In this work, we propose an extension to the Chung Lu ran- dom
graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion
of a random transitive edge. That is, with some probability it will choose to
connect to a node exactly two hops away, having been introduced to a 'friend of
a friend'. In all other cases it will follow the standard Chung Lu model,
selecting a 'random surfer' from anywhere in the graph according to the given
invariant distribution. We prove TCL's expected degree distribution is equal to
the degree distribution of the original graph, while being able to capture the
clustering present in the network. The single parameter required by our model
can be learned in seconds on graphs with millions of edges, while networks can
be generated in time that is linear in the number of edges. We demonstrate the
performance TCL on four real- world social networks, including an email dataset
with hundreds of thousands of nodes and millions of edges, showing TCL
generates graphs that match the degree distribution, clustering coefficients
and hop plots of the original networks.
|
1202.4815
|
Data Mining Applications: A comparative Study for Predicting Student's
performance
|
cs.IR cs.DB
|
Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area
focusing upon methodologies for extracting useful knowledge from data and there
are several useful KDD tools to extracting the knowledge. This knowledge can be
used to increase the quality of education. But educational institution does not
use any knowledge discovery process approach on these data. Data mining can be
used for decision making in educational system. A decision tree classifier is
one of the most widely used supervised learning methods used for data
exploration based on divide & conquer technique. This paper discusses use of
decision trees in educational data mining. Decision tree algorithms are applied
on students' past performance data to generate the model and this model can be
used to predict the students' performance. It helps earlier in identifying the
dropouts and students who need special attention and allow the teacher to
provide appropriate advising/counseling.
|
1202.4818
|
Association Rule Mining Based On Trade List
|
cs.DB
|
In this paper a new mining algorithm is defined based on frequent item set.
Apriori Algorithm scans the database every time when it finds the frequent item
set so it is very time consuming and at each step it generates candidate item
set. So for large databases it takes lots of space to store candidate item set
.In undirected item set graph, it is improvement on apriori but it takes time
and space for tree generation. The defined algorithm scans the database at the
start only once and then from that scanned data base it generates the Trade
List. It contains the information of whole database. By considering minimum
support it finds the frequent item set and by considering the minimum
confidence it generates the association rule. If database and minimum support
is changed, the new algorithm finds the new frequent items by scanning Trade
List. That is why it's executing efficiency is improved distinctly compared to
traditional algorithm.
|
1202.4828
|
Towards an Intelligent Tutor for Mathematical Proofs
|
cs.AI cs.LO cs.MS cs.SC
|
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.
|
1202.4835
|
Isabelle/PIDE as Platform for Educational Tools
|
cs.LO cs.AI cs.MS
|
The Isabelle/PIDE platform addresses the question whether proof assistants of
the LCF family are suitable as technological basis for educational tools. The
traditionally strong logical foundations of systems like HOL, Coq, or Isabelle
have so far been counter-balanced by somewhat inaccessible interaction via the
TTY (or minor variations like the well-known Proof General / Emacs interface).
Thus the fundamental question of math education tools with fully-formal
background theories has often been answered negatively due to accidental
weaknesses of existing proof engines.
The idea of "PIDE" (which means "Prover IDE") is to integrate existing
provers like Isabelle into a larger environment, that facilitates access by
end-users and other tools. We use Scala to expose the proof engine in ML to the
JVM world, where many user-interfaces, editor frameworks, and educational tools
already exist. This shall ultimately lead to combined mathematical assistants,
where the logical engine is in the background, without obstructing the view on
applications of formal methods, formalized mathematics, and math education in
particular.
|
1202.4837
|
The GF Mathematics Library
|
cs.MS cs.CL
|
This paper is devoted to present the Mathematics Grammar Library, a system
for multilingual mathematical text processing. We explain the context in which
it originated, its current design and functionality and the current development
goals. We also present two prototype services and comment on possible future
applications in the area of artificial mathematics assistants.
|
1202.4856
|
Improved Linear Precoding over Block Diagonalization in Multi-cell
Cooperative Networks
|
cs.IT math.IT
|
In downlink multiuser multiple-input multiple-output (MIMO) systems, block
diagonalization (BD) is a practical linear precoding scheme which achieves the
same degrees of freedom (DoF) as the optimal linear/nonlinear precoding
schemes. However, its sum-rate performance is rather poor in the practical SNR
regime due to the transmit power boost problem. In this paper, we propose an
improved linear precoding scheme over BD with a so-called
"effective-SNR-enhancement" technique. The transmit covariance matrices are
obtained by firstly solving a power minimization problem subject to the minimum
rate constraint achieved by BD, and then properly scaling the solution to
satisfy the power constraints. It is proved that such approach equivalently
enhances the system SNR, and hence compensates the transmit power boost problem
associated with BD. The power minimization problem is in general non-convex. We
therefore propose an efficient algorithm that solves the problem heuristically.
Simulation results show significant sum rate gains over the optimal BD and the
existing minimum mean square error (MMSE) based precoding schemes.
|
1202.4871
|
Multilevel Image Encryption
|
cs.CR cs.CV
|
With the fast evolution of digital data exchange and increased usage of multi
media images, it is essential to protect the confidential image data from
unauthorized access. In natural images the values and position of the
neighbouring pixels are strongly correlated. The method proposed in this paper,
breaks this correlation increasing entropy of the position and entropy of pixel
values using block shuffling and encryption by chaotic sequence respectively.
The plain-image is initially row wise shuffled and first level of encryption is
performed using addition modulo operation. The image is divided into blocks and
then block based shuffling is performed using Arnold Cat transformation,
further the blocks are uniformly scrambled across the image. Finally the
shuffled image undergoes second level of encryption by bitwise XOR operation,
and then the image as a whole is shuffled column wise to produce the ciphered
image for transmission. The experimental results show that the proposed
algorithm can successfully encrypt or decrypt the image with the secret keys,
and the analysis of the algorithm also demonstrates that the encrypted image
has good information entropy and low correlation coefficients.
|
1202.4905
|
A Bi-Directional Refinement Algorithm for the Calculus of (Co)Inductive
Constructions
|
cs.LO cs.AI
|
The paper describes the refinement algorithm for the Calculus of
(Co)Inductive Constructions (CIC) implemented in the interactive theorem prover
Matita. The refinement algorithm is in charge of giving a meaning to the terms,
types and proof terms directly written by the user or generated by using
tactics, decision procedures or general automation. The terms are written in an
"external syntax" meant to be user friendly that allows omission of
information, untyped binders and a certain liberal use of user defined
sub-typing. The refiner modifies the terms to obtain related well typed terms
in the internal syntax understood by the kernel of the ITP. In particular, it
acts as a type inference algorithm when all the binders are untyped. The
proposed algorithm is bi-directional: given a term in external syntax and a
type expected for the term, it propagates as much typing information as
possible towards the leaves of the term. Traditional mono-directional
algorithms, instead, proceed in a bottom-up way by inferring the type of a
sub-term and comparing (unifying) it with the type expected by its context only
at the end. We propose some novel bi-directional rules for CIC that are
particularly effective. Among the benefits of bi-directionality we have better
error message reporting and better inference of dependent types. Moreover,
thanks to bi-directionality, the coercion system for sub-typing is more
effective and type inference generates simpler unification problems that are
more likely to be solved by the inherently incomplete higher order unification
algorithms implemented. Finally we introduce in the external syntax the notion
of vector of placeholders that enables to omit at once an arbitrary number of
arguments. Vectors of placeholders allow a trivial implementation of implicit
arguments and greatly simplify the implementation of primitive and simple
tactics.
|
1202.4910
|
Distributed Private Heavy Hitters
|
cs.DS cs.CR cs.DB
|
In this paper, we give efficient algorithms and lower bounds for solving the
heavy hitters problem while preserving differential privacy in the fully
distributed local model. In this model, there are n parties, each of which
possesses a single element from a universe of size N. The heavy hitters problem
is to find the identity of the most common element shared amongst the n
parties. In the local model, there is no trusted database administrator, and so
the algorithm must interact with each of the $n$ parties separately, using a
differentially private protocol. We give tight information-theoretic upper and
lower bounds on the accuracy to which this problem can be solved in the local
model (giving a separation between the local model and the more common
centralized model of privacy), as well as computationally efficient algorithms
even in the case where the data universe N may be exponentially large.
|
1202.4943
|
A new hybrid jpeg image compression scheme using symbol reduction
technique
|
cs.MM cs.CV
|
Lossy JPEG compression is a widely used compression technique. Normally the
JPEG standard technique uses three process mapping reduces interpixel
redundancy, quantization, which is lossy process and entropy encoding, which is
considered lossless process. In this paper, a new technique has been proposed
by combining the JPEG algorithm and Symbol Reduction Huffman technique for
achieving more compression ratio. The symbols reduction technique reduces the
number of symbols by combining together to form a new symbol. As a result of
this technique the number of Huffman code to be generated also reduced. It is
simple fast and easy to implement. The result shows that the performance of
standard JPEG method can be improved by proposed method. This hybrid approach
achieves about 20% more compression ratio than the Standard JPEG.
|
1202.4959
|
Lossy Source Coding via Spatially Coupled LDGM Ensembles
|
cs.IT cond-mat.stat-mech math.IT
|
We study a new encoding scheme for lossy source compression based on
spatially coupled low-density generator-matrix codes. We develop a
belief-propagation guided-decimation algorithm, and show that this algorithm
allows to approach the optimal distortion of spatially coupled ensembles.
Moreover, using the survey propagation formalism, we also observe that the
optimal distortions of the spatially coupled and individual code ensembles are
the same. Since regular low-density generator-matrix codes are known to achieve
the Shannon rate-distortion bound under optimal encoding as the degrees grow,
our results suggest that spatial coupling can be used to reach the
rate-distortion bound, under a {\it low complexity} belief-propagation
guided-decimation algorithm.
This problem is analogous to the MAX-XORSAT problem in computer science.
|
1202.4961
|
Strongly universal string hashing is fast
|
cs.DB cs.DS
|
We present fast strongly universal string hashing families: they can process
data at a rate of 0.2 CPU cycle per byte. Maybe surprisingly, we find that
these families---though they require a large buffer of random numbers---are
often faster than popular hash functions with weaker theoretical guarantees.
Moreover, conventional wisdom is that hash functions with fewer multiplications
are faster. Yet we find that they may fail to be faster due to operation
pipelining. We present experimental results on several processors including
low-powered processors. Our tests include hash functions designed for
processors with the Carry-Less Multiplication (CLMUL) instruction set. We also
prove, using accessible proofs, the strong universality of our families.
|
1202.4974
|
How Clustering Affects Epidemics in Random Networks
|
math.PR cs.SI physics.soc-ph
|
Motivated by the analysis of social networks, we study a model of random
networks that has both a given degree distribution and a tunable clustering
coefficient. We consider two types of growth processes on these graphs:
diffusion and symmetric threshold model. The diffusion process is inspired from
epidemic models. It is characterized by an infection probability, each neighbor
transmitting the epidemic independently. In the symmetric threshold process,
the interactions are still local but the propagation rule is governed by a
threshold (that might vary among the different nodes). An interesting example
of symmetric threshold process is the contagion process, which is inspired by a
simple coordination game played on the network. Both types of processes have
been used to model spread of new ideas, technologies, viruses or worms and
results have been obtained for random graphs with no clustering. In this paper,
we are able to analyze the impact of clustering on the growth processes. While
clustering inhibits the diffusion process, its impact for the contagion process
is more subtle and depends on the connectivity of the graph: in a low
connectivity regime, clustering also inhibits the contagion, while in a high
connectivity regime, clustering favors the appearance of global cascades but
reduces their size.
For both diffusion and symmetric threshold models, we characterize conditions
under which global cascades are possible and compute their size explicitly, as
a function of the degree distribution and the clustering coefficient. Our
results are applied to regular or power-law graphs with exponential cutoff and
shed new light on the impact of clustering.
|
1202.5014
|
Two-way Interference Channels
|
cs.IT math.IT
|
We consider two-way interference channels (ICs) where forward and backward
channels are ICs but not necessarily the same. We first consider a scenario
where there are only two forward messages and feedback is offered through the
backward IC for aiding forward-message transmission. For a linear deterministic
model of this channel, we develop inner and outer bounds that match for a wide
range of channel parameters. We find that the backward IC can be more
efficiently used for feedback rather than if it were used for sending its own
independent backward messages. As a consequence, we show that feedback can
provide a net increase in capacity even if feedback cost is taken into
consideration. Moreover we extend this to a more general scenario with two
additional independent backward messages, from which we find that interaction
can provide an arbitrarily large gain in capacity.
|
1202.5041
|
Information flow in a network model and the law of diminishing marginal
returns
|
physics.data-an cs.SI physics.soc-ph q-bio.NC
|
We analyze a simple dynamical network model which describes the limited
capacity of nodes to process the input information. For a suitable choice of
the parameters, the information flow pattern is characterized by exponential
distribution of the incoming information and a fat-tailed distribution of the
outgoing information, as a signature of the law of diminishing marginal
returns. The analysis of a real EEG data-set shows that similar phenomena may
be relevant for brain signals.
|
1202.5110
|
ISAR Image Formation Using Sequential Minimization of L0 and L2 Norms
|
cs.IT math.IT
|
A sparsity-driven algorithm of inverse synthetic aperture radar (ISAR)
imaging is proposed. Based on the parametric sparse representation of the
received ISAR signal, the problem of ISAR image formation is converted into the
joint estimation of the target rotation rate and the sparse power distribution
in the spatial domain. This goal is achieved by sequential minimization of L0
and L2 norms, which ensure the sparsest ISAR image and the minimum recovery
error, respectively.
|
1202.5187
|
Sphere Decoding for Spatial Modulation Systems with Arbitrary Nt
|
cs.IT math.IT
|
Recently, three Sphere Decoding (SD) algorithms were proposed for Spatial
Modulation (SM) scheme which focus on reducing the transmit-, receive-, and
both transmit and receive-search spaces at the receiver and were termed as
Receiver-centric SD (Rx-SD), Transmitter-centric SD (Tx-SD), and Combined SD
(C-SD) detectors, respectively. The Tx-SD detector was proposed for systems
with Nt \leq Nr, where Nt and Nr are the number of transmit and receive
antennas of the system. In this paper, we show that the existing Tx-SD detector
is not limited to systems with Nt \leq Nr but can be used with systems Nr < Nt
\leq 2Nr - 1 as well. We refer to this detector as the Extended Tx-SD (E-Tx-SD)
detector. Further, we propose an E- Tx-SD based detection scheme for SM systems
with arbitrary Nt by exploiting the Inter-Channel Interference (ICI) free
property of the SM systems. We show with our simulation results that the
proposed detectors are ML-optimal and offer significantly reduced complexity.
|
1202.5198
|
Network Theory, Cracking and Frictional Sliding
|
physics.geo-ph cs.CE nlin.AO
|
We have developed different network approaches to complex patterns of
frictional interfaces (contact areas developments). Here, we analyze the
dynamics of static friction. We found, under the correlation measure, the
fraction of triangles correlates with the detachment fronts. Also, for all
types of the loops (such as triangles), there is a universal power law between
nodes' degree and motifs where motifs frequency follow a power law. This shows
high energy localization is characterized by fast variation of the loops
fraction. Also, this proves that the congestion of loops occurs around hubs.
Furthermore, the motif distributions and modularity space of networks -in terms
of within-module degree and participation coefficient- show universal trends,
indicating an in common aspect of energy flow in shear ruptures. Moreover, we
confirmed that slow ruptures generally hold small localization, while regular
ruptures carry a high level of energy localization. We proposed that
assortativity, as an index to correlation of node's degree, can uncover
acoustic features of the interfaces. We showed that increasing assortativity
induces a nearly silent period of fault's activities. Also, we proposed that
slow ruptures resulted from within-module developments rather than
extra-modules of the networks. Our approach presents a completely new
perspective of the evolution of shear ruptures.
|
1202.5202
|
Secure Compressed Reading in Smart Grids
|
cs.IT cs.PF math.IT
|
Smart Grids measure energy usage in real-time and tailor supply and delivery
accordingly, in order to improve power transmission and distribution. For the
grids to operate effectively, it is critical to collect readings from
massively-installed smart meters to control centers in an efficient and secure
manner. In this paper, we propose a secure compressed reading scheme to address
this critical issue. We observe that our collected real-world meter data
express strong temporal correlations, indicating they are sparse in certain
domains. We adopt Compressed Sensing technique to exploit this sparsity and
design an efficient meter data transmission scheme. Our scheme achieves
substantial efficiency offered by compressed sensing, without the need to know
beforehand in which domain the meter data are sparse. This is in contrast to
traditional compressed-sensing based scheme where such sparse-domain
information is required a priori. We then design specific dependable scheme to
work with our compressed sensing based data transmission scheme to make our
meter reading reliable and secure. We provide performance guarantee for the
correctness, efficiency, and security of our proposed scheme. Through analysis
and simulations, we demonstrate the effectiveness of our schemes and compare
their performance to prior arts.
|
1202.5216
|
Identifying Discriminating Network Motifs in YouTube Spam
|
cs.SI
|
Like other social media websites, YouTube is not immune from the attention of
spammers. In particular, evidence can be found of attempts to attract users to
malicious third-party websites. As this type of spam is often associated with
orchestrated campaigns, it has a discernible network signature, based on
networks derived from comments posted by users to videos. In this paper, we
examine examples of different YouTube spam campaigns of this nature, and use a
feature selection process to identify network motifs that are characteristic of
the corresponding campaign strategies. We demonstrate how these discriminating
motifs can be used as part of a network motif profiling process that tracks the
activity of spam user accounts over time, enabling the process to scale to
larger networks.
|
1202.5230
|
Triadic Measures on Graphs: The Power of Wedge Sampling
|
cs.SI cs.DM
|
Graphs are used to model interactions in a variety of contexts, and there is
a growing need to quickly assess the structure of a graph. Some of the most
useful graph metrics, especially those measuring social cohesion, are based on
triangles. Despite the importance of these triadic measures, associated
algorithms can be extremely expensive. We propose a new method based on wedge
sampling. This versatile technique allows for the fast and accurate
approximation of all current variants of clustering coefficients and enables
rapid uniform sampling of the triangles of a graph. Our methods come with
provable and practical time-approximation tradeoffs for all computations. We
provide extensive results that show our methods are orders of magnitude faster
than the state-of-the-art, while providing nearly the accuracy of full
enumeration. Our results will enable more wide-scale adoption of triadic
measures for analysis of extremely large graphs, as demonstrated on several
real-world examples.
|
1202.5249
|
On the Power of Manifold Samples in Exploring Configuration Spaces and
the Dimensionality of Narrow Passages
|
cs.RO cs.CG
|
We extend our study of Motion Planning via Manifold Samples (MMS), a general
algorithmic framework that combines geometric methods for the exact and
complete analysis of low-dimensional configuration spaces with sampling-based
approaches that are appropriate for higher dimensions. The framework explores
the configuration space by taking samples that are entire low-dimensional
manifolds of the configuration space capturing its connectivity much better
than isolated point samples. The contributions of this paper are as follows:
(i) We present a recursive application of MMS in a six-dimensional
configuration space, enabling the coordination of two polygonal robots
translating and rotating amidst polygonal obstacles. In the adduced experiments
for the more demanding test cases MMS clearly outperforms PRM, with over
20-fold speedup in a coordination-tight setting. (ii) A probabilistic
completeness proof for the most prevalent case, namely MMS with samples that
are affine subspaces. (iii) A closer examination of the test cases reveals that
MMS has, in comparison to standard sampling-based algorithms, a significant
advantage in scenarios containing high-dimensional narrow passages. This
provokes a novel characterization of narrow passages which attempts to capture
their dimensionality, an attribute that had been (to a large extent) unattended
in previous definitions.
|
1202.5259
|
Sequential Coding of Markov Sources over Burst Erasure Channels
|
cs.IT math.IT
|
We study sequential coding of Markov sources under an error propagation
constraint. An encoder sequentially compresses a sequence of vector-sources
that are spatially i.i.d. but temporally correlated according to a first-order
Markov process. The channel erases up to B packets in a single burst, but
reveals all other packets to the destination. The destination is required to
reproduce all the source-vectors instantaneously and in a lossless manner,
except those sequences that occur in an error propagation window of length B +
W following the start of the erasure burst. We define the rate-recovery
function R(B, W) - the minimum achievable compression rate per source sample in
this framework - and develop upper and lower bounds on this function. Our upper
bound is obtained using a random binning technique, whereas our lower bound is
obtained by drawing connections to multi-terminal source coding. Our upper and
lower bounds coincide, yielding R(B, W), in some special cases. More generally,
both the upper and lower bounds equal the rate for predictive coding plus a
term that decreases as 1/(W+1), thus establishing a scaling behaviour of the
rate-recovery function. For a special class of semi-deterministic Markov
sources we propose a new optimal coding scheme: prospicient coding. An
extension of this coding technique to Gaussian sources is also developed. For
the class of symmetric Markov sources and memoryless encoders, we establish the
optimality of random binning. When the destination is required to reproduce
each source sequence with a fixed delay and when W = 0 we also establish the
optimality of binning.
|
1202.5284
|
Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on
Unimodal Functions
|
cs.NE cs.AI
|
In this article we present an Elitism Levels Traverse Mechanism that we
designed to find bounds on population-based Evolutionary algorithms solving
unimodal functions. We prove its efficiency theoretically and test it on OneMax
function deriving bounds c{\mu}n log n - O({\mu} n). This analysis can be
generalized to any similar algorithm using variants of tournament selection and
genetic operators that flip or swap only 1 bit in each string.
|
1202.5298
|
Min Max Generalization for Two-stage Deterministic Batch Mode
Reinforcement Learning: Relaxation Schemes
|
cs.SY cs.LG
|
We study the minmax optimization problem introduced in [22] for computing
policies for batch mode reinforcement learning in a deterministic setting.
First, we show that this problem is NP-hard. In the two-stage case, we provide
two relaxation schemes. The first relaxation scheme works by dropping some
constraints in order to obtain a problem that is solvable in polynomial time.
The second relaxation scheme, based on a Lagrangian relaxation where all
constraints are dualized, leads to a conic quadratic programming problem. We
also theoretically prove and empirically illustrate that both relaxation
schemes provide better results than those given in [22].
|
1202.5299
|
Culturomics meets random fractal theory: Insights into long-range
correlations of social and natural phenomena over the past two centuries
|
physics.soc-ph cond-mat.stat-mech cs.DL cs.SI stat.AP
|
Culturomics was recently introduced as the application of high-throughput
data collection and analysis to the study of human culture. Here we make use of
this data by investigating fluctuations in yearly usage frequencies of specific
words that describe social and natural phenomena, as derived from books that
were published over the course of the past two centuries. We show that the
determination of the Hurst parameter by means of fractal analysis provides
fundamental insights into the nature of long-range correlations contained in
the culturomic trajectories, and by doing so, offers new interpretations as to
what might be the main driving forces behind the examined phenomena. Quite
remarkably, we find that social and natural phenomena are governed by
fundamentally different processes. While natural phenomena have properties that
are typical for processes with persistent long-range correlations, social
phenomena are better described as nonstationary, on-off intermittent, or Levy
walk processes.
|
1202.5302
|
Application of Steganography for Anonymity through the Internet
|
cs.CR cs.IT math.IT
|
In this paper, a novel steganographic scheme based on chaotic iterations is
proposed. This research work takes place into the information hiding security
framework. The applications for anonymity and privacy through the Internet are
regarded too. To guarantee such an anonymity, it should be possible to set up a
secret communication channel into a web page, being both secure and robust. To
achieve this goal, we propose an information hiding scheme being stego-secure,
which is the highest level of security in a well defined and studied category
of attacks called "watermark-only attack". This category of attacks is the best
context to study steganography-based anonymity through the Internet. The
steganalysis of our steganographic process is also studied in order to show it
security in a real test framework.
|
1202.5332
|
A Characterization of Scale Invariant Responses in Enzymatic Networks
|
cs.SY cs.CE q-bio.MN
|
An ubiquitous property of biological sensory systems is adaptation: a step
increase in stimulus triggers an initial change in a biochemical or
physiological response, followed by a more gradual relaxation toward a basal,
pre-stimulus level. Adaptation helps maintain essential variables within
acceptable bounds and allows organisms to readjust themselves to an optimum and
non-saturating sensitivity range when faced with a prolonged change in their
environment. Recently, it was shown theoretically and experimentally that many
adapting systems, both at the organism and single-cell level, enjoy a
remarkable additional feature: scale invariance, meaning that the initial,
transient behavior remains (approximately) the same even when the background
signal level is scaled. In this work, we set out to investigate under what
conditions a broadly used model of biochemical enzymatic networks will exhibit
scale-invariant behavior. An exhaustive computational study led us to discover
a new property of surprising simplicity and generality, uniform linearizations
with fast output (ULFO), whose validity we show is both necessary and
sufficient for scale invariance of enzymatic networks. Based on this study, we
go on to develop a mathematical explanation of how ULFO results in scale
invariance. Our work provides a surprisingly consistent, simple, and general
framework for understanding this phenomenon, and results in concrete
experimental predictions.
|
1202.5349
|
Buffer-Aided Relaying with Adaptive Link Selection
|
cs.IT math.IT
|
In this paper, we consider a simple network consisting of a source, a
half-duplex decode-and-forward relay, and a destination. We propose a new
relaying protocol employing adaptive link selection, i.e., in any given time
slot, based on the channel state information of the source-relay and the
relay-destination link a decision is made whether the source or the relay
transmits. In order to avoid data loss at the relay, adaptive link selection
requires the relay to be equipped with a buffer such that data can be queued
until the relay-destination link is selected for transmission. We study both
delay constrained and delay unconstrained transmission. For the delay
unconstrained case, we characterize the optimal link selection policy, derive
the corresponding throughput, and develop an optimal power allocation scheme.
For the delay constrained case, we propose to starve the buffer of the relay by
choosing the decision threshold of the link selection policy smaller than the
optimal one and derive a corresponding upper bound on the average delay.
Furthermore, we propose a modified link selection protocol which avoids buffer
overflow by limiting the queue size. Our analytical and numerical results show
that buffer-aided relaying with adaptive link selection achieves significant
throughput gains compared to conventional relaying protocols with and without
buffers where the relay employs a fixed schedule for reception and
transmission.
|
1202.5358
|
DPCube: Differentially Private Histogram Release through
Multidimensional Partitioning
|
cs.DB
|
Differential privacy is a strong notion for protecting individual privacy in
privacy preserving data analysis or publishing. In this paper, we study the
problem of differentially private histogram release for random workloads. We
study two multidimensional partitioning strategies including: 1) a baseline
cell-based partitioning strategy for releasing an equi-width cell histogram,
and 2) an innovative 2-phase kd-tree based partitioning strategy for releasing
a v-optimal histogram. We formally analyze the utility of the released
histograms and quantify the errors for answering linear queries such as
counting queries. We formally characterize the property of the input data that
will guarantee the optimality of the algorithm. Finally, we implement and
experimentally evaluate several applications using the released histograms,
including counting queries, classification, and blocking for record linkage and
show the benefit of our approach.
|
1202.5398
|
Mod-CSA: Modularity optimization by conformational space annealing
|
physics.comp-ph cs.SI physics.data-an physics.soc-ph
|
We propose a new modularity optimization method, Mod-CSA, based on stochastic
global optimization algorithm, conformational space annealing (CSA). Our method
outperforms simulated annealing in terms of both efficiency and accuracy,
finding higher modularity partitions with less computational resources
required. The high modularity values found by our method are higher than, or
equal to, the largest values previously reported. In addition, the method can
be combined with other heuristic methods, and implemented in parallel fashion,
allowing it to be applicable to large graphs with more than 10000 nodes.
|
1202.5413
|
On the Joint Error-and-Erasure Decoding for Irreducible Polynomial
Remainder Codes
|
cs.IT math.IT math.RA
|
A general class of polynomial remainder codes is considered. Such codes are
very flexible in rate and length and include Reed-Solomon codes as a special
case.
As an extension of previous work, two joint error-and-erasure decoding
approaches are proposed. In particular, both the decoding approaches by means
of a fixed transform are treated in a way compatible with the error-only
decoding. In the end, a collection of gcd-based decoding algorithm is obtained,
some of which appear to be new even when specialized to Reed-Solomon codes.
|
1202.5414
|
Left-Invariant Diffusion on the Motion Group in terms of the Irreducible
Representations of SO(3)
|
math.AP cs.CV cs.NA math.RT
|
In this work we study the formulation of convection/diffusion equations on
the 3D motion group SE(3) in terms of the irreducible representations of SO(3).
Therefore, the left-invariant vector-fields on SE(3) are expressed as linear
operators, that are differential forms in the translation coordinate and
algebraic in the rotation. In the context of 3D image processing this approach
avoids the explicit discretization of SO(3) or $S_2$, respectively. This is
particular important for SO(3), where a direct discretization is infeasible due
to the enormous memory consumption. We show two applications of the framework:
one in the context of diffusion-weighted magnetic resonance imaging and one in
the context of object detection.
|
1202.5447
|
Global $H_\infty$ Consensus of Multi-Agent Systems with Lipschitz
Nonlinear Dynamics
|
cs.SY math.OC
|
This paper addresses the global consensus problems of a class of nonlinear
multi-agent systems with Lipschitz nonlinearity and directed communication
graphs, by using a distributed consensus protocol based on the relative states
of neighboring agents. A two-step algorithm is presented to construct a
protocol, under which a Lipschitz multi-agent system without disturbances can
reach global consensus for a strongly connected directed communication graph.
Another algorithm is then given to design a protocol which can achieve global
consensus with a guaranteed $H_\infty$ performance for a Lipschitz multiagent
system subject to external disturbances. The case with a leader-follower
communication graph is also discussed. Finally, the effectiveness of the
theoretical results is demonstrated through a network of single-link
manipulators.
|
1202.5469
|
Enhancing Navigation on Wikipedia with Social Tags
|
cs.IR cs.DL cs.HC cs.SI
|
Social tagging has become an interesting approach to improve search and
navigation over the actual Web, since it aggregates the tags added by different
users to the same resource in a collaborative way. This way, it results in a
list of weighted tags describing its resource. Combined to a classical
taxonomic classification system such as that by Wikipedia, social tags can
enhance document navigation and search. On the one hand, social tags suggest
alternative navigation ways, including pivot-browsing, popularity-driven
navigation, and filtering. On the other hand, it provides new metadata,
sometimes uncovered by documents' content, that can substantially improve
document search. In this work, the inclusion of an interface to add
user-defined tags describing Wikipedia articles is proposed, as a way to
improve article navigation and retrieval. As a result, a prototype on applying
tags over Wikipedia is proposed in order to evaluate its effectiveness.
|
1202.5470
|
Convergence analysis of the FOCUSS algorithm
|
cs.IT math.IT
|
FOCal Underdetermined System Solver (FOCUSS) is a powerful tool for sparse
representation and underdetermined inverse problems, which is extremely easy to
implement. In this paper, we give a comprehensive convergence analysis on the
FOCUSS algorithm towards establishing a systematic convergence theory by
providing three primary contributions as follows. First, we give a rigorous
derivation for this algorithm exploiting the auxiliary function. Then, we prove
its convergence. Third, we systematically study its convergence rate with
respect to the sparsity parameter p and demonstrate its convergence rate by
numerical experiments.
|
1202.5471
|
L1-norm minimization for quaternion signals
|
cs.NA cs.DS cs.IT math.IT
|
The l1-norm minimization problem plays an important role in the compressed
sensing (CS) theory. We present in this letter an algorithm for solving the
problem of l1-norm minimization for quaternion signals by converting it to
second-order cone programming. An application example of the proposed algorithm
is also given for practical guidelines of perfect recovery of quaternion
signals. The proposed algorithm may find its potential application when CS
theory meets the quaternion signal processing.
|
1202.5474
|
Pareto Boundary of the Rate Region for Single-Stream MIMO Interference
Channels: Linear Transceiver Design
|
cs.IT math.IT
|
We consider a multiple-input multiple-output (MIMO) interference channel
(IC), where a single data stream per user is transmitted and each receiver
treats interference as noise. The paper focuses on the open problem of
computing the outermost boundary (so-called Pareto boundary-PB) of the
achievable rate region under linear transceiver design. The Pareto boundary
consists of the strict PB and non-strict PB. For the two user case, we compute
the non-strict PB and the two ending points of the strict PB exactly. For the
strict PB, we formulate the problem to maximize one rate while the other rate
is fixed such that a strict PB point is reached. To solve this non-convex
optimization problem which results from the hard-coupled two transmit
beamformers, we propose an alternating optimization algorithm. Furthermore, we
extend the algorithm to the multi-user scenario and show convergence. Numerical
simulations illustrate that the proposed algorithm computes a sequence of
well-distributed operating points that serve as a reasonable and complete inner
bound of the strict PB compared with existing methods.
|
1202.5477
|
Analyzing Tag Distributions in Folksonomies for Resource Classification
|
cs.DL cs.IR
|
Recent research has shown the usefulness of social tags as a data source to
feed resource classification. Little is known about the effect of settings on
folksonomies created on social tagging systems. In this work, we consider the
settings of social tagging systems to further understand tag distributions in
folksonomies. We analyze in depth the tag distributions on three large-scale
social tagging datasets, and analyze the effect on a resource classification
task. To this end, we study the appropriateness of applying weighting schemes
based on the well-known TF-IDF for resource classification. We show the great
importance of settings as to altering tag distributions. Among those settings,
tag suggestions produce very different folksonomies, which condition the
success of the employed weighting schemes. Our findings and analyses are
relevant for researchers studying tag-based resource classification, user
behavior in social networks, the structure of folksonomies and tag
distributions, as well as for developers of social tagging systems in search of
an appropriate setting.
|
1202.5509
|
Organizing the Aggregate: Languages for Spatial Computing
|
cs.PL cs.DC cs.MA
|
As the number of computing devices embedded into engineered systems continues
to rise, there is a widening gap between the needs of the user to control
aggregates of devices and the complex technology of individual devices. Spatial
computing attempts to bridge this gap for systems with local communication by
exploiting the connection between physical locality and device connectivity. A
large number of spatial computing domain specific languages (DSLs) have emerged
across diverse domains, from biology and reconfigurable computing, to sensor
networks and agent-based systems. In this chapter, we develop a framework for
analyzing and comparing spatial computing DSLs, survey the current state of the
art, and provide a roadmap for future spatial computing DSL investigation.
|
1202.5514
|
Classification approach based on association rules mining for unbalanced
data
|
stat.ML cs.LG
|
This paper deals with the binary classification task when the target class
has the lower probability of occurrence. In such situation, it is not possible
to build a powerful classifier by using standard methods such as logistic
regression, classification tree, discriminant analysis, etc. To overcome this
short-coming of these methods which yield classifiers with low sensibility, we
tackled the classification problem here through an approach based on the
association rules learning. This approach has the advantage of allowing the
identification of the patterns that are well correlated with the target class.
Association rules learning is a well known method in the area of data-mining.
It is used when dealing with large database for unsupervised discovery of local
patterns that expresses hidden relationships between input variables. In
considering association rules from a supervised learning point of view, a
relevant set of weak classifiers is obtained from which one derives a
classifier that performs well.
|
1202.5517
|
Research Traceability using Provenance Services for Biomedical Analysis
|
cs.DB cs.SE
|
We outline the approach being developed in the neuGRID project to use
provenance management techniques for the purposes of capturing and preserving
the provenance data that emerges in the specification and execution of
workflows in biomedical analyses. In the neuGRID project a provenance service
has been designed and implemented that is intended to capture, store, retrieve
and reconstruct the workflow information needed to facilitate users in
conducting user analyses. We describe the architecture of the neuGRID
provenance service and discuss how the CRISTAL system from CERN is being
adapted to address the requirements of the project and then consider how a
generalised approach for provenance management could emerge for more generic
application to the (Health)Grid community.
|
1202.5528
|
Hierarchical Resource Allocation in Femtocell Networks using Graph
Algorithms
|
cs.IT cs.NI math.IT
|
This paper presents a hierarchical approach to resource allocation in
open-access femtocell networks. The major challenge in femtocell networks is
interference management which in our system, based on the Long Term Evolution
(LTE) standard, translates to which user should be allocated which physical
resource block (or fraction thereof) from which femtocell access point (FAP).
The globally optimal solution requires integer programming and is
mathematically intractable. We propose a hierarchical three-stage solution:
first, the load of each FAP is estimated considering the number of users
connected to the FAP, their average channel gain and required data rates.
Second, based on each FAP's load, the physical resource blocks (PRBs) are
allocated to FAPs in a manner that minimizes the interference by coloring the
modified interference graph. Finally, the resource allocation is performed at
each FAP considering users' instantaneous channel gain. The two major
advantages of this suboptimal approach are the significantly reduced
computation complexity and the fact that the proposed algorithm only uses
information that is already likely to be available at the nodes executing the
relevant optimization step. The performance of the proposed solution is
evaluated in networks based on the LTE standard.
|
1202.5529
|
On Secure Communication with Constrained Randomization
|
cs.IT math.IT
|
In this paper, we investigate how constraints on the randomization in the
encoding process affect the secrecy rates achievable over wiretap channels. In
particular, we characterize the secrecy capacity with a rate-limited local
source of randomness and a less capable eavesdropper's channel, which shows
that limited rate incurs a secrecy rate penalty but does not preclude secrecy.
We also discuss a more practical aspect of rate-limited randomization in the
context of cooperative jamming. Finally, we show that secure communication is
possible with a non-uniform source for randomness; this suggests the
possibility of designing robust coding schemes.
|
1202.5544
|
An Incremental Sampling-based Algorithm for Stochastic Optimal Control
|
cs.RO cs.SY math.DS math.OC math.PR
|
In this paper, we consider a class of continuous-time, continuous-space
stochastic optimal control problems. Building upon recent advances in Markov
chain approximation methods and sampling-based algorithms for deterministic
path planning, we propose a novel algorithm called the incremental Markov
Decision Process (iMDP) to compute incrementally control policies that
approximate arbitrarily well an optimal policy in terms of the expected cost.
The main idea behind the algorithm is to generate a sequence of finite
discretizations of the original problem through random sampling of the state
space. At each iteration, the discretized problem is a Markov Decision Process
that serves as an incrementally refined model of the original problem. We show
that with probability one, (i) the sequence of the optimal value functions for
each of the discretized problems converges uniformly to the optimal value
function of the original stochastic optimal control problem, and (ii) the
original optimal value function can be computed efficiently in an incremental
manner using asynchronous value iterations. Thus, the proposed algorithm
provides an anytime approach to the computation of optimal control policies of
the continuous problem. The effectiveness of the proposed approach is
demonstrated on motion planning and control problems in cluttered environments
in the presence of process noise.
|
1202.5597
|
Hybrid Batch Bayesian Optimization
|
cs.AI cs.LG
|
Bayesian Optimization aims at optimizing an unknown non-convex/concave
function that is costly to evaluate. We are interested in application scenarios
where concurrent function evaluations are possible. Under such a setting, BO
could choose to either sequentially evaluate the function, one input at a time
and wait for the output of the function before making the next selection, or
evaluate the function at a batch of multiple inputs at once. These two
different settings are commonly referred to as the sequential and batch
settings of Bayesian Optimization. In general, the sequential setting leads to
better optimization performance as each function evaluation is selected with
more information, whereas the batch setting has an advantage in terms of the
total experimental time (the number of iterations). In this work, our goal is
to combine the strength of both settings. Specifically, we systematically
analyze Bayesian optimization using Gaussian process as the posterior estimator
and provide a hybrid algorithm that, based on the current state, dynamically
switches between a sequential policy and a batch policy with variable batch
sizes. We provide theoretical justification for our algorithm and present
experimental results on eight benchmark BO problems. The results show that our
method achieves substantial speedup (up to %78) compared to a pure sequential
policy, without suffering any significant performance loss.
|
1202.5598
|
Clustering using Max-norm Constrained Optimization
|
cs.LG stat.ML
|
We suggest using the max-norm as a convex surrogate constraint for
clustering. We show how this yields a better exact cluster recovery guarantee
than previously suggested nuclear-norm relaxation, and study the effectiveness
of our method, and other related convex relaxations, compared to other
clustering approaches.
|
1202.5599
|
On the Ingleton-Violations in Finite Groups
|
cs.IT math.IT
|
Given $n$ discrete random variables, its entropy vector is the $2^n-1$
dimensional vector obtained from the joint entropies of all non-empty subsets
of the random variables. It is well known that there is a one-to-one
correspondence between such an entropy vector and a certain
group-characterizable vector obtained from a finite group and $n$ of its
subgroups [3]. This correspondence may be useful for characterizing the space
of entropic vectors and for designing network codes. If one restricts attention
to abelian groups then not all entropy vectors can be obtained. This is an
explanation for the fact shown by Dougherty et al [4] that linear network codes
cannot achieve capacity in general network coding problems. All abelian
group-characterizable vectors, and by fiat all entropy vectors generated by
linear network codes, satisfy a linear inequality called the Ingleton
inequality. It is therefore of interest to identify groups that violate the
Ingleton inequality. In this paper, we study the problem of finding nonabelian
finite groups that yield characterizable vectors which violate the Ingleton
inequality. Using a refined computer search, we find the symmetric group $S_5$
to be the smallest group that violates the Ingleton inequality. Careful study
of the structure of this group, and its subgroups, reveals that it belongs to
the Ingleton-violating family $PGL(2,q)$ with a prime power $q \geq 5$, i.e.,
the projective group of $2\times 2$ nonsingular matrices with entries in
$\mathbb{F}_q$. We further interpret this family using the theory of group
actions. We also extend the construction to more general groups such as
$PGL(n,q)$ and $GL(n,q)$. The families of groups identified here are therefore
good candidates for constructing network codes more powerful than linear
network codes, and we discuss some considerations for constructing such group
network codes.
|
1202.5600
|
Interaction Histories and Short Term Memory: Enactive Development of
Turn-taking Behaviors in a Childlike Humanoid Robot
|
cs.AI nlin.AO
|
In this article, an enactive architecture is described that allows a humanoid
robot to learn to compose simple actions into turn-taking behaviors while
playing interaction games with a human partner. The robot's action choices are
reinforced by social feedback from the human in the form of visual attention
and measures of behavioral synchronization. We demonstrate that the system can
acquire and switch between behaviors learned through interaction based on
social feedback from the human partner. The role of reinforcement based on a
short term memory of the interaction is experimentally investigated. Results
indicate that feedback based only on the immediate state is insufficient to
learn certain turn-taking behaviors. Therefore some history of the interaction
must be considered in the acquisition of turn-taking, which can be efficiently
handled through the use of short term memory.
|
1202.5618
|
An equation-free approach to coarse-graining the dynamics of networks
|
cs.SI nlin.AO physics.comp-ph physics.soc-ph
|
We propose and illustrate an approach to coarse-graining the dynamics of
evolving networks (networks whose connectivity changes dynamically). The
approach is based on the equation-free framework: short bursts of detailed
network evolution simulations are coupled with lifting and restriction
operators that translate between actual network realizations and their
(appropriately chosen) coarse observables. This framework is used here to
accelerate temporal simulations (through coarse projective integration), and to
implement coarsegrained fixed point algorithms (through matrix-free
Newton-Krylov GMRES). The approach is illustrated through a simple network
evolution example, for which analytical approximations to the coarse-grained
dynamics can be independently obtained, so as to validate the computational
results. The scope and applicability of the approach, as well as the issue of
selection of good coarse observables are discussed.
|
1202.5657
|
Design of a Fractional Order Phase Shaper for Iso-damped Control of a
PHWR under Step-back Condition
|
math.OC cs.SY
|
Phase shaping using fractional order (FO) phase shapers has been proposed by
many contemporary researchers as a means of producing systems with iso-damped
closed loop response due to a stepped variation in input. Such systems, with
the closed loop damping remaining invariant to gain changes can be used to
produce dead-beat step response with only rise time varying with gain. This
technique is used to achieve an active step-back in a Pressurized Heavy Water
Reactor (PHWR) where it is desired to change the reactor power to a
pre-determined value within a short interval keeping the power undershoot as
low as possible. This paper puts forward an approach as an alternative for the
present day practice of a passive step-back mechanism where the control rods
are allowed to drop during a step-back action by gravity, with release of
electromagnetic clutches. The reactor under a step-back condition is identified
as a system using practical test data and a suitable Proportional plus Integral
plus Derivative (PID) controller is designed for it. Then the combined plant is
augmented with a phase shaper to achieve a dead-beat response in terms of power
drop. The fact that the identified static gain of the system depends on the
initial power level at which a step-back is initiated, makes this application
particularly suited for using a FO phase shaper. In this paper, a model of a
nuclear reactor is developed for a control rod drop scenario involving rapid
power reduction in a 500MWe Canadian Deuterium Uranium (CANDU) reactor using
AutoRegressive Exogenous (ARX) algorithm. The system identification and reduced
order modeling are developed from practical test data. For closed loop active
control of the identified reactor model, the fractional order phase shaper
along with a PID controller is shown to perform better than the present Reactor
Regulating System (RRS) due to its iso-damped nature.
|
1202.5665
|
q-Gaussian based Smoothed Functional Algorithm for Stochastic
Optimization
|
cs.SY cs.IT math.IT
|
The q-Gaussian distribution results from maximizing certain generalizations
of Shannon entropy under some constraints. The importance of q-Gaussian
distributions stems from the fact that they exhibit power-law behavior, and
also generalize Gaussian distributions. In this paper, we propose a Smoothed
Functional (SF) scheme for gradient estimation using q-Gaussian distribution,
and also propose an algorithm for optimization based on the above scheme.
Convergence results of the algorithm are presented. Performance of the proposed
algorithm is shown by simulation results on a queuing model.
|
1202.5667
|
Fractional Order Phase Shaper Design with Routh's Criterion for
Iso-damped Control System
|
cs.SY
|
Phase curve of an open loop system is flat in nature if the derivative of
phase with respect to frequency is zero. With a flat phase curve, the
corresponding closed-loop system exhibits an iso-damped property i.e. maintains
constant overshoot with the change of gain and with other parametric
variations. In recent past application, fractional order (FO) phase shapers
have been proposed by contemporary researchers to achieve enhanced parametric
robustness. In this paper, a simple Routh tabulation based methodology is
proposed to design an appropriate FO phase shaper to achieve phase flattening
in a control loop, comprising a system, controlled by a classical PID
controller. The method is demonstrated using MATLAB simulation of a second
order DC motor plant and also a first order with time delay system.
|
1202.5674
|
Handling Packet Dropouts and Random Delays for Unstable Delayed
Processes in NCS by Optimal Tuning of PI{\lambda}D{\mu} Controllers with
Evolutionary Algorithms
|
cs.SY
|
The issues of stochastically varying network delays and packet dropouts in
Networked Control System (NCS) applications have been simultaneously addressed
by time domain optimal tuning of fractional order (FO) PID controllers.
Different variants of evolutionary algorithms are used for the tuning process
and their performances are compared. Also the effectiveness of the fractional
order PI{\lambda}D{\mu} controllers over their integer order counterparts is
looked into. Two standard test bench plants with time delay and unstable poles
which are encountered in process control applications are tuned with the
proposed method to establish the validity of the tuning methodology. The
proposed tuning methodology is independent of the specific choice of plant and
is also applicable for less complicated systems. Thus it is useful in a wide
variety of scenarios. The paper also shows the superiority of FOPID controllers
over their conventional PID counterparts for NCS applications.
|
1202.5677
|
On the Selection of Tuning Methodology of FOPID Controllers for the
Control of Higher Order Processes
|
cs.SY
|
In this paper, a comparative study is done on the time and frequency domain
tuning strategies for fractional order (FO) PID controllers to handle higher
order processes. A new fractional order template for reduced parameter modeling
of stable minimum/non-minimum phase higher order processes is introduced and
its advantage in frequency domain tuning of FOPID controllers is also
presented. The time domain optimal tuning of FOPID controllers have also been
carried out to handle these higher order processes by performing optimization
with various integral performance indices. The paper highlights on the
practical control system implementation issues like flexibility of online
autotuning, reduced control signal and actuator size, capability of measurement
noise filtration, load disturbance suppression, robustness against parameter
uncertainties etc. in light of the above tuning methodologies.
|
1202.5680
|
A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time
Domain Tuning Based on Integral Performance Indices
|
cs.SY
|
A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID)
controller has been proposed in this paper which works on the closed loop error
and its fractional derivative as the input and has a fractional integrator in
its output. The fractional order differ-integrations in the proposed fuzzy
logic controller (FLC) are kept as design variables along with the input-output
scaling factors (SF) and are optimized with Genetic Algorithm (GA) while
minimizing several integral error indices along with the control signal as the
objective function. Simulations studies are carried out to control a delayed
nonlinear process and an open loop unstable process with time delay. The closed
loop performances and controller efforts in each case are compared with
conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to
different integral performance indices. Simulation results show that the
proposed fractional order fuzzy PID controller outperforms the others in most
cases.
|
1202.5683
|
Improved Model Reduction and Tuning of Fractional Order
PI{\lambda}D{\mu} Controllers for Analytical Rule Extraction with Genetic
Programming
|
cs.SY cs.NE
|
Genetic Algorithm (GA) has been used in this paper for a new approach of
sub-optimal model reduction in the Nyquist plane and optimal time domain tuning
of PID and fractional order (FO) PI{\lambda}D{\mu} controllers. Simulation
studies show that the Nyquist based new model reduction technique outperforms
the conventional H2 norm based reduced parameter modeling technique. With the
tuned controller parameters and reduced order model parameter data-set, optimum
tuning rules have been developed with a test-bench of higher order processes
via Genetic Programming (GP). The GP performs a symbolic regression on the
reduced process parameters to evolve a tuning rule which provides the best
analytical expression to map the data. The tuning rules are developed for a
minimum time domain integral performance index described by weighted sum of
error index and controller effort. From the reported Pareto optimal front of GP
based optimal rule extraction technique a trade-off can be made between the
complexity of the tuning formulae and the control performance. The efficacy of
the single-gene and multi-gene GP based tuning rules has been compared with
original GA based control performance for the PID and PI{\lambda}D{\mu}
controllers, handling four different class of representative higher order
processes. These rules are very useful for process control engineers as they
inherit the power of the GA based tuning methodology, but can be easily
calculated without the requirement for running the computationally intensive GA
every time. Three dimensional plots of the required variation in PID/FOPID
controller parameters with reduced process parameters have been shown as a
guideline for the operator. Parametric robustness of the reported GP based
tuning rules has also been shown with credible simulation examples.
|
1202.5684
|
Fractional Order Modeling of a PHWR Under Step-Back Condition and
Control of Its Global Power with a Robust PI{\lambda}D{\mu} Controller
|
cs.SY
|
Bulk reduction of reactor power within a small finite time interval under
abnormal conditions is referred to as step-back. In this paper, a 500MWe
Canadian Deuterium Uranium (CANDU) type Pressurized Heavy Water Reactor (PHWR)
is modeled using few variants of Least Square Estimator (LSE) from practical
test data under a control rod drop scenario in order to design a control system
to achieve a dead-beat response during a stepped reduction of its global power.
A new fractional order (FO) model reduction technique is attempted which
increases the parametric robustness of the control loop due to lesser modeling
error and ensures iso-damped closed loop response with a PI{\lambda}D{\mu} or
FOPID controller. Such a controller can, therefore, be used to achieve active
step-back under varying load conditions for which the system dynamics change
significantly. For closed loop active control of the reduced FO reactor models,
the PI{\lambda}D{\mu} controller is shown to perform better than the classical
integer order PID controllers and present operating Reactor Regulating System
(RRS) due to its robustness against shift in system parameters.
|
1202.5685
|
Information inequalities and Generalized Graph Entropies
|
cs.IT math.IT
|
In this article, we discuss the problem of establishing relations between
information measures assessed for network structures. Two types of entropy
based measures namely, the Shannon entropy and its generalization, the
R\'{e}nyi entropy have been considered for this study. Our main results involve
establishing formal relationship, in the form of implicit inequalities, between
these two kinds of measures when defined for graphs. Further, we also state and
prove inequalities connecting the classical partition-based graph entropies and
the functional-based entropy measures. In addition, several explicit
inequalities are derived for special classes of graphs.
|
1202.5686
|
Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist
Plane for Optimal Tuning Rule Extraction of PID and PI{\lambda}D{\mu}
Controllers via Genetic Programming
|
cs.SY
|
Genetic Algorithm (GA) has been used in this paper for a new Nyquist based
sub-optimal model reduction and optimal time domain tuning of PID and
fractional order (FO) PI{\lambda}D{\mu} controllers. Comparative studies show
that the new model reduction technique outperforms the conventional H2-norm
based reduced order modeling techniques. Optimum tuning rule has been developed
next with a test-bench of higher order processes via Genetic Programming (GP)
with minimum value of weighted integral error index and control signal. From
the Pareto optimal front which is a trade-off between the complexity of the
formulae and control performance, an efficient set of tuning rules has been
generated for time domain optimal PID and PI{\lambda}D{\mu} controllers.
|
1202.5689
|
Estimation, Analysis and Smoothing of Self-Similar Network Induced
Delays in Feedback Control of Nuclear Reactors
|
cs.SY
|
This paper analyzes a nuclear reactor power signal that suffers from network
induced random delays in the shared data network while being fed-back to the
Reactor Regulating System (RRS). A detailed study is carried out to investigate
the self similarity of random delay dynamics due to the network traffic in
shared medium. The fractionality or selfsimilarity in the network induced delay
that corrupts the measured power signal coming from Self Powered Neutron
Detectors (SPND) is estimated and analyzed. As any fractional order randomness
is intrinsically different from conventional Gaussian kind of randomness, these
delay dynamics need to be handled efficiently, before reaching the controller
within the RRS. An attempt has been made to minimize the effect of the
randomness in the reactor power transient data with few classes of smoothing
filters. The performance measure of the smoothers with fractional order noise
consideration is also investigated into.
|
1202.5690
|
Embedded Network Test-Bed for Validating Real-Time Control Algorithms to
Ensure Optimal Time Domain Performance
|
cs.SY
|
The paper presents a Stateflow based network test-bed to validate real-time
optimal control algorithms. Genetic Algorithm (GA) based time domain
performance index minimization is attempted for tuning of PI controller to
handle a balanced lag and delay type First Order Plus Time Delay (FOPTD)
process over network. The tuning performance is validated on a real-time
communication network with artificially simulated stochastic delay, packet loss
and out-of order packets characterizing the network.
|
1202.5692
|
Adaptive Gain and Order Scheduling of Optimal Fractional Order
PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network
|
cs.SY
|
Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu}
controllers are studied in this paper considering four different classes of
higher order processes. The mapping between the optimum PID/FOPID controller
parameters and the reduced order process models are done using Radial Basis
Function (RBF) type Artificial Neural Network (ANN). Simulation studies have
been done to show the effectiveness of the RBFNN for online scheduling of such
controllers with random change in set-point and process parameters.
|
1202.5693
|
Optimizing Continued Fraction Expansion Based IIR Realization of
Fractional Order Differ-Integrators with Genetic Algorithm
|
cs.SY
|
Rational approximation of fractional order (FO) differ-integrators via
Continued Fraction Expansion (CFE) is a well known technique. In this paper,
the nominal structures of various generating functions are optimized using
Genetic Algorithm (GA) to minimize the deviation in magnitude and phase
response between the original FO element and the rationalized discrete time
filter in Infinite Impulse Response (IIR) structure. The optimized filter based
realizations show better approximation of the FO elements in comparison with
the existing methods and is demonstrated by the frequency response of the IIR
filters.
|
1202.5695
|
Training Restricted Boltzmann Machines on Word Observations
|
cs.LG stat.ML
|
The restricted Boltzmann machine (RBM) is a flexible tool for modeling
complex data, however there have been significant computational difficulties in
using RBMs to model high-dimensional multinomial observations. In natural
language processing applications, words are naturally modeled by K-ary discrete
distributions, where K is determined by the vocabulary size and can easily be
in the hundreds of thousands. The conventional approach to training RBMs on
word observations is limited because it requires sampling the states of K-way
softmax visible units during block Gibbs updates, an operation that takes time
linear in K. In this work, we address this issue by employing a more general
class of Markov chain Monte Carlo operators on the visible units, yielding
updates with computational complexity independent of K. We demonstrate the
success of our approach by training RBMs on hundreds of millions of word
n-grams using larger vocabularies than previously feasible and using the
learned features to improve performance on chunking and sentiment
classification tasks, achieving state-of-the-art results on the latter.
|
1202.5713
|
The warm-start bias of Yelp ratings
|
cs.SI
|
Yelp ratings are often viewed as a reputation metric for local businesses. In
this paper we study how Yelp ratings evolve over time. Our main finding is that
on average the first ratings that businesses receive overestimate their
eventual reputation. In particular, the first review that a business receives
in our dataset averages 4.1 stars, while the 20th review averages just 3.69
stars. This significant warm-start bias which may be attributed to the limited
exposure of a business in its first steps may mask analysis performed on
ratings and reputational ramifications. Therefore, we study techniques to
identify and correct for this bias. Further, we perform a case study to explore
the effect of a Groupon deal on the merchant's subsequent ratings and show both
that previous research has overestimated Groupon's effect to merchants'
reputation and that average ratings anticorrelate with the number of reviews
received. Our analysis points to the importance of identifying and removing
biases from Yelp reviews.
|
1202.5722
|
S3A: Secure System Simplex Architecture for Enhanced Security of
Cyber-Physical Systems
|
cs.CR cs.SY
|
Until recently, cyber-physical systems, especially those with safety-critical
properties that manage critical infrastructure (e.g. power generation plants,
water treatment facilities, etc.) were considered to be invulnerable against
software security breaches. The recently discovered 'W32.Stuxnet' worm has
drastically changed this perception by demonstrating that such systems are
susceptible to external attacks. Here we present an architecture that enhances
the security of safety-critical cyber-physical systems despite the presence of
such malware. Our architecture uses the property that control systems have
deterministic execution behavior, to detect an intrusion within 0.6 {\mu}s
while still guaranteeing the safety of the plant. We also show that even if an
attack is successful, the overall state of the physical system will still
remain safe. Even if the operating system's administrative privileges have been
compromised, our architecture will still be able to protect the physical system
from coming to harm.
|
1202.5820
|
Tag-Aware Recommender Systems: A State-of-the-art Survey
|
cs.IR cs.SI
|
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.
|
1202.5830
|
On Secrecy Rate of the Generalized Artificial-Noise Assisted Secure
Beamforming for Wiretap Channels
|
cs.IT math.IT
|
In this paper we consider the secure transmission in fast Rayleigh fading
channels with full knowledge of the main channel and only the statistics of the
eavesdropper's channel state information at the transmitter. For the
multiple-input, single-output, single-antenna eavesdropper systems, we
generalize Goel and Negi's celebrated artificial-noise (AN) assisted
beamforming, which just selects the directions to transmit AN heuristically.
Our scheme may inject AN to the direction of the message, which outperforms
Goel and Negi's scheme where AN is only injected in the directions orthogonal
to the main channel. The ergodic secrecy rate of the proposed AN scheme can be
represented by a highly simplified power allocation problem. To attain it, we
prove that the optimal transmission scheme for the message bearing signal is a
beamformer, which is aligned to the direction of the legitimate channel. After
characterizing the optimal eigenvectors of the covariance matrices of signal
and AN, we also provide the necessary condition for transmitting AN in the main
channel to be optimal. Since the resulting secrecy rate is a non-convex power
allocation problem, we develop an algorithm to efficiently solve it. Simulation
results show that our generalized AN scheme outperforms Goel and Negi's,
especially when the quality of legitimate channel is much worse than that of
eavesdropper's. In particular, the regime with non-zero secrecy rate is
enlarged, which can significantly improve the connectivity of the secure
network when the proposed AN assisted beamforming is applied.
|
1202.5844
|
Divide-and-Conquer Method for L1 Norm Matrix Factorization in the
Presence of Outliers and Missing Data
|
cs.NA cs.CV
|
The low-rank matrix factorization as a L1 norm minimization problem has
recently attracted much attention due to its intrinsic robustness to the
presence of outliers and missing data. In this paper, we propose a new method,
called the divide-and-conquer method, for solving this problem. The main idea
is to break the original problem into a series of smallest possible
sub-problems, each involving only unique scalar parameter. Each of these
subproblems is proved to be convex and has closed-form solution. By recursively
optimizing these small problems in an analytical way, efficient algorithm,
entirely avoiding the time-consuming numerical optimization as an inner loop,
for solving the original problem can naturally be constructed. The
computational complexity of the proposed algorithm is approximately linear in
both data size and dimensionality, making it possible to handle large-scale L1
norm matrix factorization problems. The algorithm is also theoretically proved
to be convergent. Based on a series of experiment results, it is substantiated
that our method always achieves better results than the current
state-of-the-art methods on $L1$ matrix factorization calculation in both
computational time and accuracy, especially on large-scale applications such as
face recognition and structure from motion.
|
1202.5857
|
Algebraic Fast-Decodable Relay Codes for Distributed Communications
|
cs.IT math.IT math.RA
|
In this paper, fast-decodable lattice code constructions are designed for the
nonorthogonal amplify-and-forward (NAF) multiple-input multiple-output (MIMO)
channel. The constructions are based on different types of algebraic
structures, e.g. quaternion division algebras. When satisfying certain
properties, these algebras provide us with codes whose structure naturally
reduces the decoding complexity. The complexity can be further reduced by
shortening the block length, i.e., by considering rectangular codes called less
than minimum delay (LMD) codes.
|
1202.5895
|
Asymptotic behaviour of gossip processes and small world networks
|
math.PR cs.SI physics.soc-ph
|
Both small world models of random networks with occasional long range
connections and gossip processes with occasional long range transmission of
information have similar characteristic behaviour. The long range elements
appreciably reduce the effective distances, measured in space or in time,
between pairs of typical points. In this paper, we show that their common
behaviour can be interpreted as a product of the locally branching nature of
the models. In particular, it is shown that both typical distances between
points and the proportion of space that can be reached within a given distance
or time can be approximated by formulae involving the limit random variable of
the branching process.
|
1202.5909
|
Closed benchmarks for network community structure characterization
|
physics.soc-ph cond-mat.stat-mech cs.SI
|
Characterizing the community structure of complex networks is a key challenge
in many scientific fields. Very diverse algorithms and methods have been
proposed to this end, many working reasonably well in specific situations.
However, no consensus has emerged on which of these methods is the best to use
in practice. In part, this is due to the fact that testing their performance
requires the generation of a comprehensive, standard set of synthetic
benchmarks, a goal not yet fully achieved. Here, we present a type of benchmark
that we call "closed", in which an initial network of known community structure
is progressively converted into a second network whose communities are also
known. This approach differs from all previously published ones, in which
networks evolve toward randomness. The use of this type of benchmark allows us
to monitor the transformation of the community structure of a network.
Moreover, we can predict the optimal behavior of the variation of information,
a measure of the quality of the partitions obtained, at any moment of the
process. This enables us in many cases to determine the best partition among
those suggested by different algorithms. Also, since any network can be used as
a starting point, extensive studies and comparisons can be performed using a
heterogeneous set of structures, including random ones. These properties make
our benchmarks a general standard for comparing community detection algorithms.
|
1202.5913
|
Fly out-smarts man
|
q-bio.PE cs.CL physics.bio-ph
|
Precopulatory courtship is a high-cost, non-well understood animal world
mystery. Drosophila's (=D.'s) precopulatory courtship not only shows marked
structural similarities with mammalian courtship, but also with human spoken
language. This suggests the study of purpose, modalities and in particular of
the power of this language and to compare it to human language. Following a
mathematical symbolic dynamics approach, we translate courtship videos of D.'s
body language into a formal language. This approach made it possible to show
that D. may use its body language to express individual information -
information that may be important for evolutionary optimization, on top of the
sexual group membership. Here, we use Chomsky's hierarchical language
classification to characterize the power of D.'s body language, and then
compare it with the power of languages spoken by humans. We find that from a
formal language point of view, D.'s body language is at least as powerful as
the languages spoken by humans. From this we conclude that human intellect
cannot be the direct consequence of the formal grammar complexity of human
language.
|
1202.5938
|
Intelligent Car System
|
cs.RO
|
In modern life the road safety has becomes the core issue. One single move of
a driver can cause horrifying accident. The main goal of intelligent car system
is to make communication with other cars on the road. The system is able to
control to speed, direction and the distance between the cars the intelligent
car system is able to recognize traffic light and is able to take decision
according to it. This paper presents a framework of the intelligent car system.
I validate several aspect of our system using simulation.
|
1202.5953
|
On an Ethical Use of Neural Networks: A Case Study on a North Indian
Raga
|
cs.NE cs.SD
|
The paper gives an artificial neural network (ANN) approach to time series
modeling, the data being instance versus notes (characterized by pitch)
depicting the structure of a North Indian raga, namely, Bageshree. Respecting
the sentiments of the artists' community, the paper argues why it is more
ethical to model a structure than try and "manufacture" an artist by training
the neural network to copy performances of artists. Indian Classical Music
centers on the ragas, where emotion and devotion are both important and neither
can be substituted by such "calculated artistry" which the ANN generated copies
are ultimately up to.
|
1202.5967
|
Joint Source-Channel Cooperative Transmission over Relay-Broadcast
Networks
|
cs.IT math.IT
|
Reliable transmission of a discrete memoryless source over a multiple-relay
relay-broadcast network is considered. Motivated by sensor network
applications, it is assumed that the relays and the destinations all have
access to side information correlated with the underlying source signal. Joint
source-channel cooperative transmission is studied in which the relays help the
transmission of the source signal to the destinations by using both their
overheard signals, as in the classical channel cooperation scenario, as well as
the available correlated side information. Decode-and-forward (DF) based
cooperative transmission is considered in a network of multiple relay terminals
and two different achievability schemes are proposed: i) a regular encoding and
sliding-window decoding scheme without explicit source binning at the encoder,
and ii) a semi-regular encoding and backward decoding scheme with binning based
on the side information statistics. It is shown that both of these schemes lead
to the same source-channel code rate, which is shown to be the "source-channel
capacity" in the case of i) a physically degraded relay network in which the
side information signals are also degraded in the same order as the channel;
and ii) a relay-broadcast network in which all the terminals want to
reconstruct the source reliably, while at most one of them can act as a relay.
|
1202.6001
|
Efficiently Sampling Multiplicative Attribute Graphs Using a
Ball-Dropping Process
|
stat.ML cs.LG
|
We introduce a novel and efficient sampling algorithm for the Multiplicative
Attribute Graph Model (MAGM - Kim and Leskovec (2010)}). Our algorithm is
\emph{strictly} more efficient than the algorithm proposed by Yun and
Vishwanathan (2012), in the sense that our method extends the \emph{best} time
complexity guarantee of their algorithm to a larger fraction of parameter
space. Both in theory and in empirical evaluation on sparse graphs, our new
algorithm outperforms the previous one. To design our algorithm, we first
define a stochastic \emph{ball-dropping process} (BDP). Although a special case
of this process was introduced as an efficient approximate sampling algorithm
for the Kronecker Product Graph Model (KPGM - Leskovec et al. (2010)}), neither
\emph{why} such an approximation works nor \emph{what} is the actual
distribution this process is sampling from has been addressed so far to the
best of our knowledge. Our rigorous treatment of the BDP enables us to clarify
the rational behind a BDP approximation of KPGM, and design an efficient
sampling algorithm for the MAGM.
|
1202.6009
|
Marginality: a numerical mapping for enhanced treatment of nominal and
hierarchical attributes
|
cs.AI
|
The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data
anonymization or privacy-preserving data mining, is to publish data sets
containing the answers of individual respondents in such a way that the
respondents corresponding to the released records cannot be re-identified and
the released data are analytically useful. SDC methods are either based on
masking the original data, generating synthetic versions of them or creating
hybrid versions by combining original and synthetic data. The choice of SDC
methods for categorical data, especially nominal data, is much smaller than the
choice of methods for numerical data. We mitigate this problem by introducing a
numerical mapping for hierarchical nominal data which allows computing means,
variances and covariances on them.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.