id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1309.4396 | Routing Directions: Keeping it Fast and Simple | cs.DS cs.DB | The problem of providing meaningful routing directions over road networks is
of great importance. In many real-life cases, the fastest route may not be the
ideal choice for providing directions in written, spoken text, or for an
unfamiliar neighborhood, or in cases of emergency. Rather, it is often more
preferable to offer "simple" directions that are easy to memorize, explain,
understand or follow. However, there exist cases where the simplest route is
considerably longer than the fastest. This paper tries to address this issue,
by finding near-simplest routes which are as short as possible and near-fastest
routes which are as simple as possible. Particularly, we focus on efficiency,
and propose novel algorithms, which are theoretically and experimentally shown
to be significantly faster than existing approaches.
|
1309.4408 | Lambda Dependency-Based Compositional Semantics | cs.AI | This short note presents a new formal language, lambda dependency-based
compositional semantics (lambda DCS) for representing logical forms in semantic
parsing. By eliminating variables and making existential quantification
implicit, lambda DCS logical forms are generally more compact than those in
lambda calculus.
|
1309.4411 | Emergence of overlap in ensembles of spatial multiplexes and statistical
mechanics of spatial interacting networks ensembles | physics.soc-ph cond-mat.dis-nn cs.SI | Spatial networks range from the brain networks, to transportation networks
and infrastructures. Recently interacting and multiplex networks are attracting
great attention because their dynamics and robustness cannot be understood
without treating at the same time several networks. Here we present maximal
entropy ensembles of spatial multiplex and spatial interacting networks that
can be used in order to model spatial multilayer network structures and to
build null models of real datasets. We show that spatial multiplex naturally
develop a significant overlap of the links, a noticeable property of many
multiplexes that can affect significantly the dynamics taking place on them.
Additionally, we characterize ensembles of spatial interacting networks and we
analyse the structure of interacting airport and railway networks in India,
showing the effect of space in determining the link probability.
|
1309.4426 | GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic
and Noisy Images | cs.CV | Analysis of microscopy images can provide insight into many biological
processes. One particularly challenging problem is cell nuclear segmentation in
highly anisotropic and noisy 3D image data. Manually localizing and segmenting
each and every cell nuclei is very time consuming, which remains a bottleneck
in large scale biological experiments. In this work we present a tool for
automated segmentation of cell nuclei from 3D fluorescent microscopic data. Our
tool is based on state-of-the-art image processing and machine learning
techniques and supports a friendly graphical user interface (GUI). We show that
our tool is as accurate as manual annotation but greatly reduces the time for
the registration.
|
1309.4429 | Comsol Simulations of Cracking in Point Loaded Masonry with Randomly
Distributed Material Properties | cs.CE | This paper describes COMSOL simulations of the stress and crack development
in the area where a masonry wall supports a floor. In these simulations one of
the main material properties of calcium silicate, its E-value, was assigned
randomly to the finite elements of the modeled specimen. Calcium silicate is a
frequently used building material with a relatively brittle fracture
characteristic. Its initial E-value varies, as well as tensile strength and
post peak behavior. Therefore, in the simulation, initial E-values were
randomly assigned to the elements of the model and a step function used for
describing the descending branch. The method also allows for variation in
strength to be taken into account in future research. The performed non-linear
simulation results are compared with experimental findings. They show the
stress distribution and cracking behavior in point loaded masonry when varying
material properties are used.
|
1309.4496 | Evaluating socio-economic state of a country analyzing airtime credit
and mobile phone datasets | cs.CY cs.SI physics.soc-ph | Reliable statistical information is important to make political decisions on
a sound basis and to help measure the impact of policies. Unfortunately,
statistics offices in developing countries have scarce resources and
statistical censuses are therefore conducted sporadically. Based on mobile
phone communications and history of airtime credit purchases, we estimate the
relative income of individuals, the diversity and inequality of income, and an
indicator for socioeconomic segregation for fine-grained regions of an African
country. Our study shows how to use mobile phone datasets as a starting point
to understand the socio-economic state of a country, which can be especially
useful in countries with few resources to conduct large surveys.
|
1309.4501 | A fully automatic problem solver with human-style output | cs.AI | This paper describes a program that solves elementary mathematical problems,
mostly in metric space theory, and presents solutions that are hard to
distinguish from solutions that might be written by human mathematicians. The
program is part of a more general project, which we also discuss.
|
1309.4531 | Power Optimization for Network Localization | cs.IT math.IT | Reliable and accurate localization of mobile objects is essential for many
applications in wireless networks. In range-based localization, the position of
the object can be inferred using the distance measurements from wireless
signals exchanged with active objects or reflected by passive ones. Power
allocation for ranging signals is important since it affects not only network
lifetime and throughput but also localization accuracy. In this paper, we
establish a unifying optimization framework for power allocation in both active
and passive localization networks. In particular, we first determine the
functional properties of the localization accuracy metric, which enable us to
transform the power allocation problems into second-order cone programs
(SOCPs). We then propose the robust counterparts of the problems in the
presence of parameter uncertainty and develop asymptotically optimal and
efficient near-optimal SOCP-based algorithms. Our simulation results validate
the efficiency and robustness of the proposed algorithms.
|
1309.4545 | Further results on "Velocity-position integration formula, part
I-Application to in-flight alignment" | cs.RO | This note improves our above-mentioned recent work by effectively depressing
the adverse effect of the lever arm on attitude estimation.
|
1309.4550 | Cable-Driven Robots with Wireless Control Capability for Pedagogical
Illustration in Science | cs.RO | Science teaching in secondary schools is often abstract for students. Even if
some experiments can be conducted in classrooms, mainly for chemistry or some
physics fields, mathematics is not an experimental science. Teachers have to
convince students that theorems have practical implications. We present
teachers an original and easy-to-use pedagogical tool: a cable-driven robot
with a Web-based remote control interface. The robot implements several
scientific concepts such as 3D-geometry and kinematics. The remote control
enables the teacher to move freely in the classroom.
|
1309.4573 | A novel approach for nose tip detection using smoothing by weighted
median filtering applied to 3D face images in variant poses | cs.CV | This paper is based on an application of smoothing of 3D face images followed
by feature detection i.e. detecting the nose tip. The present method uses a
weighted mesh median filtering technique for smoothing. In this present
smoothing technique we have built the neighborhood surrounding a particular
point in 3D face and replaced that with the weighted value of the surrounding
points in 3D face image. After applying the smoothing technique to the 3D face
images our experimental results show that we have obtained considerable
improvement as compared to the algorithm without smoothing. We have used here
the maximum intensity algorithm for detecting the nose-tip and this method
correctly detects the nose-tip in case of any pose i.e. along X, Y, and Z axes.
The present technique gave us worked successfully on 535 out of 542 3D face
images as compared to the method without smoothing which worked only on 521 3D
face images out of 542 face images. Thus we have obtained a 98.70% performance
rate over 96.12% performance rate of the algorithm without smoothing. All the
experiments have been performed on the FRAV3D database.
|
1309.4576 | Dynamics of interacting information waves in networks | physics.soc-ph cs.SI | To better understand the inner workings of information spreading, network
researchers often use simple models to capture the spreading dynamics. But most
models only highlight the effect of local interactions on the global spreading
of a single information wave, and ignore the effects of interactions between
multiple waves. Here we take into account the effect of multiple interacting
waves by using an agent-based model in which the interaction between
information waves is based on their novelty. We analyzed the global effects of
such interactions and found that information that actually reaches nodes
reaches them faster. This effect is caused by selection between information
waves: slow waves die out and only fast waves survive. As a result, and in
contrast to models with non-interacting information dynamics, the access to
information decays with the distance from the source. Moreover, when we
analyzed the model on various synthetic and real spatial road networks, we
found that the decay rate also depends on the path redundancy and the effective
dimension of the system. In general, the decay of the information wave
frequency as a function of distance from the source follows a power law
distribution with an exponent between -0.2 for a two-dimensional system with
high path redundancy and -0.5 for a tree-like system with no path redundancy.
We found that the real spatial networks provide an infrastructure for
information spreading that lies in between these two extremes. Finally, to
better understand the mechanics behind the scaling results, we provide analytic
calculations of the scaling for a one-dimensional system.
|
1309.4577 | Detection of pose orientation across single and multiple axes in case of
3D face images | cs.CV | In this paper, we propose a new approach that takes as input a 3D face image
across X, Y and Z axes as well as both Y and X axes and gives output as its
pose i.e. it tells whether the face is oriented with respect the X, Y or Z axes
or is it oriented across multiple axes with angles of rotation up to 42 degree.
All the experiments have been performed on the FRAV3D, GAVADB and Bosphorus
database which has two figures of each individual across multiple axes. After
applying the proposed algorithm to the 3D facial surface from FRAV3D on 848 3D
faces, 566 3D faces were correctly recognized for pose thus giving 67% of
correct identification rate. We had experimented on 420 images from the GAVADB
database, and only 336 images were detected for correct pose identification
rate i.e. 80% and from Bosphorus database on 560 images only 448 images were
detected for correct pose identification i.e. 80%.abstract goes here.
|
1309.4582 | A novel approach to nose-tip and eye corners detection using H-K
Curvature Analysis in case of 3D images | cs.CV | In this paper we present a novel method that combines a HK curvature-based
approach for three-dimensional (3D) face detection in different poses (X-axis,
Y-axis and Z-axis). Salient face features, such as the eyes and nose, are
detected through an analysis of the curvature of the entire facial surface. All
the experiments have been performed on the FRAV3D Database. After applying the
proposed algorithm to the 3D facial surface we have obtained considerably good
results i.e. on 752 3D face images our method detected the eye corners for 543
face images, thus giving a 72.20% of eye corners detection and 743 face images
for nose-tip detection thus giving a 98.80% of good nose tip localization
|
1309.4628 | Text segmentation with character-level text embeddings | cs.CL | Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.
|
1309.4638 | Dispersion Analysis of Infinite Constellations in Ergodic Fading
Channels | cs.IT math.IT | This thesis considers infinite constellations in fading channels, without
power constraint and with perfect channel state information available at the
receiver. Infinite constellations are the framework, proposed by Poltyrev, for
analyzing coded modulation codes. The Poltyrev's capacity, is the highest
achievable normalized log density (NLD) of codewords per unit volume, at
possibly large block length, that guarantees a vanishing error probability. For
a given finite block length and a fixed error probability, there is a gap
between the highest achievable NLD and Poltyrev's capacity. The dispersion
analysis quantifies asymptotically this gap.
The thesis begins by the dispersion analysis of infinite constellations in
scalar fading channels. Later on, we extend the analysis to the case of
multiple input multiple output fading channels. As in other channels, we show
that the gap between the highest achievable NLD and the Poltyrev's capacity,
vanishes asymptotically as the square root of the channel dispersion over the
block length, multiplied by the inverse Q-function of the allowed error
probability.
Moreover, exact terms for Poltyrev's capacity and channel dispersion, are
derived in the thesis. The relations to the amplitude and to the power
constrained fading channels are also discussed, especially in terms of
capacity, channel dispersion and error exponents. These relations hint that in
typical cases the unconstrained model can be interpreted as the limit of the
constrained model, when the signal to noise ratio tends to infinity.
|
1309.4651 | Overhead-Optimized Gamma Network Codes | cs.IT math.IT | We design a network coding scheme with minimum reception overhead and linear
encoding/decoding complexity.
|
1309.4662 | DNA origami and the complexity of Eulerian circuits with turning costs | math.CO cs.CE cs.DS q-bio.BM | Building a structure using self-assembly of DNA molecules by origami folding
requires finding a route for the scaffolding strand through the desired
structure. When the target structure is a 1-complex (or the geometric
realization of a graph), an optimal route corresponds to an Eulerian circuit
through the graph with minimum turning cost. By showing that it leads to a
solution to the 3-SAT problem, we prove that the general problem of finding an
optimal route for a scaffolding strand for such structures is NP-hard. We then
show that the problem may readily be transformed into a Traveling Salesman
Problem (TSP), so that machinery that has been developed for the TSP may be
applied to find optimal routes for the scaffolding strand in a DNA origami
self-assembly process. We give results for a few special cases, showing for
example that the problem remains intractable for graphs with maximum degree 8,
but is polynomial time for 4-regular plane graphs if the circuit is restricted
to following faces. We conclude with some implications of these results for
related problems, such as biomolecular computing and mill routing problems.
|
1309.4714 | Temporal-Difference Learning to Assist Human Decision Making during the
Control of an Artificial Limb | cs.AI cs.LG cs.RO | In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems.
|
1309.4720 | Robustness of Network Measures to Link Errors | physics.soc-ph cond-mat.stat-mech cs.SI q-bio.MN | In various applications involving complex networks, network measures are
employed to assess the relative importance of network nodes. However, the
robustness of such measures in the presence of link inaccuracies has not been
well characterized. Here we present two simple stochastic models of false and
missing links and study the effect of link errors on three commonly used node
centrality measures: degree centrality, betweenness centrality, and dynamical
importance. We perform numerical simulations to assess robustness of these
three centrality measures. We also develop an analytical theory, which we
compare with our simulations, obtaining very good agreement.
|
1309.4744 | Modeling the Role of Context Dependency in the Recognition and
Manifestation of Entrepreneurial Opportunity | q-bio.NC cs.AI | The paper uses the SCOP theory of concepts to model the role of environmental
context on three levels of entrepreneurial opportunity: idea generation, idea
development, and entrepreneurial decision. The role of contextual-fit in the
generation and development of ideas is modeled as the collapse of their
superposition state into one of the potential states that composes this
superposition. The projection of this collapsed state on the socio-economic
basis results in interference of the developed idea with the perceptions of the
supporting community, undergoing an eventual collapse for an entrepreneurial
decision that reflects the shared vision of its stakeholders. The developed
idea may continue to evolve due to continuous or discontinuous changes in the
environment. The model offers unique insights into the effects of external
influences on entrepreneurial decisions.
|
1309.4796 | Bayesian Degree-Corrected Stochastic Blockmodels for Community Detection | stat.ME cs.SI physics.soc-ph | Community detection in networks has drawn much attention in diverse fields,
especially social sciences. Given its significance, there has been a large body
of literature with approaches from many fields. Here we present a statistical
framework that is representative, extensible, and that yields an estimator with
good properties. Our proposed approach considers a stochastic blockmodel based
on a logistic regression formulation with node correction terms. We follow a
Bayesian approach that explicitly captures the community behavior via prior
specification. We further adopt a data augmentation strategy with latent
Polya-Gamma variables to obtain posterior samples. We conduct inference based
on a principled, canonically mapped centroid estimator that formally addresses
label non-identifiability and captures representative community assignments. We
demonstrate the proposed model and estimation on real-world as well as
simulated benchmark networks and show that the proposed model and estimator are
more flexible, representative, and yield smaller error rates when compared to
the MAP estimator from classical degree-corrected stochastic blockmodels.
|
1309.4844 | Network Anomaly Detection: A Survey and Comparative Analysis of
Stochastic and Deterministic Methods | stat.ML cs.LG cs.NI | We present five methods to the problem of network anomaly detection. These
methods cover most of the common techniques in the anomaly detection field,
including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and
clustering analysis. We evaluate all methods in a simulated network that
consists of nominal data, three flow-level anomalies and one packet-level
attack. Through analyzing the results, we point out the advantages and
disadvantages of each method and conclude that combining the results of the
individual methods can yield improved anomaly detection results.
|
1309.4846 | A Robust Information Source Estimator with Sparse Observations | cs.SI | In this paper, we consider the problem of locating the information source
with sparse observations. We assume that a piece of information spreads in a
network following a heterogeneous susceptible-infected-recovered (SIR) model
and that a small subset of infected nodes are reported, from which we need to
find the source of the information. We adopt the sample path based estimator
developed in [1], and prove that on infinite trees, the sample path based
estimator is a Jordan infection center with respect to the set of observed
infected nodes. In other words, the sample path based estimator minimizes the
maximum distance to observed infected nodes. We further prove that the distance
between the estimator and the actual source is upper bounded by a constant
independent of the number of infected nodes with a high probability on infinite
trees. Our simulations on tree networks and real world networks show that the
sample path based estimator is closer to the actual source than several other
algorithms.
|
1309.4860 | Modeling complex spatial dynamics of two-population interaction in
urbanization process | physics.soc-ph cs.SI | This paper is mainly devoted to lay an empirical foundation for further
research on complex spatial dynamics of two-population interaction. Based on
the US population census data, a rural and urban population interaction model
is developed. Subsequently a logistic equation on percentage urban is derived
from the urbanization model so that spatial interaction can be connected
mathematically with logistic growth. The numerical experiment by using the
discretized urban-rural population interaction model of urbanization shows a
period-doubling bifurcation and chaotic behavior, which is identical in
patterns to those from the simple mathematical models of logistic growth in
ecology. This suggests that the complicated dynamics of logistic growth may
come from some kind of the nonlinear interaction. The results from this study
help to understand urbanization, urban-rural population interaction, chaotic
dynamics, and spatial complexity of geographical systems.
|
1309.4863 | Hierarchical Bass model | physics.soc-ph cs.SI | We propose a new model about diffusion of a product which includes a memory
of how many adopters or advertisements a non-adopter met, where (non-)adopters
mean people (not) possessing the product. This effect is lacking in the Bass
model. As an application, we utilize the model to fit the iPod sales data, and
so the better agreement is obtained than the Bass model.
|
1309.4873 | Sub-Stream Fairness and Numerical Correctness in MIMO Interference
Channels | cs.IT math.IT | Stream fairness, fairness between all streams in the system, is a more
restrictive condition than sub-stream fairness, fairness between all streams of
each user. Thus sub-stream fairness alleviates utility loss as well as
complexity and overhead compared to stream fairness. Moreover, depending on
algorithmic parameters, conventional algorithms including distributed
interference alignment (DIA) may not provide sub-stream fairness, and generate
sub-streams with poor signal-to-interference plus noise ratios (SINRs), thus
with poor bit error rates (BERs). To this end, we propose a distributed power
control algorithm to render sub-stream fairness in the system, and establish
initiatory connections between sub-stream SINRs, BERs, and rates. Algorithms
have particular responses to parameters. In the paper, important algorithmic
parameters are analyzed to exhibit numerical correctness in benchmarking. The
distinction between separate filtering schemes that design each stream of a
user separately and group filtering schemes that jointly design the streams of
a user is also underscored in the paper. Finally, the power control law used in
the proposed algorithm is proven to linearly converge to a unique fixed-point,
and the algorithm is shown to achieve feasible SINR targets.
|
1309.4907 | On Adaptive Measurement Inclusion Rate In Real-Time Moving-Horizon
Observers | cs.SY math.OC | This paper investigates a self adaptation mechanism regarding the rate with
which new measurements have to be incorporated in Moving-Horizon state
estimation algorithms. This investigation can be viewed as the dual of the one
proposed by the author in the context of real-time model predictive control. An
illustrative example is provided in order to assess the relevance of the
proposed updating rule.
|
1309.4923 | Quantum Walks in artificial electric and gravitational Fields | quant-ph cs.IT gr-qc math-ph math.IT math.MP | The continuous limit of quantum walks (QWs) on the line is revisited through
a recently developed method. In all cases but one, the limit coincides with the
dynamics of a Dirac fermion coupled to an artificial electric and/or
relativistic gravitational field. All results are carefully discussed and
illustrated by numerical simulations.
|
1309.4927 | A finite axiomatization of conditional independence and inclusion
dependencies | math.LO cs.AI cs.DB cs.LO | We present a complete finite axiomatization of the unrestricted implication
problem for inclusion and conditional independence atoms in the context of
dependence logic. For databases, our result implies a finite axiomatization of
the unrestricted implication problem for inclusion, functional, and embedded
multivalued dependencies in the unirelational case.
|
1309.4930 | The Zero-Undetected-Error Capacity Approaches the Sperner Capacity | cs.IT math.IT | Ahlswede, Cai, and Zhang proved that, in the noise-free limit, the
zero-undetected-error capacity is lower bounded by the Sperner capacity of the
channel graph, and they conjectured equality. Here we derive an upper bound
that proves the conjecture.
|
1309.4938 | Improving Query Expansion Using WordNet | cs.IR | This study proposes a new way of using WordNet for Query Expansion (QE). We
choose candidate expansion terms, as usual, from a set of pseudo relevant
documents; however, the usefulness of these terms is measured based on their
definitions provided in a hand-crafted lexical resource like WordNet.
Experiments with a number of standard TREC collections show that this method
outperforms existing WordNet based methods. It also compares favorably with
established QE methods such as KLD and RM3. Leveraging earlier work in which a
combination of QE methods was found to outperform each individual method (as
well as other well-known QE methods), we next propose a combination-based QE
method that takes into account three different aspects of a candidate expansion
term's usefulness: (i) its distribution in the pseudo relevant documents and in
the target corpus, (ii) its statistical association with query terms, and (iii)
its semantic relation with the query, as determined by the overlap between the
WordNet definitions of the term and query terms. This combination of diverse
sources of information appears to work well on a number of test collections,
viz., TREC123, TREC5, TREC678, TREC robust new and TREC910 collections, and
yields significant improvements over competing methods on most of these
collections.
|
1309.4942 | HetNets and Massive MIMO: Modeling, Potential Gains, and Performance
Analysis | cs.IT math.IT | We consider a heterogeneous cellular network (HetNet) where a macrocell tier
with a large antenna array base station (BS) is overlaid with a dense tier of
small cells (SCs). We investigate the potential benefits of incorporating a
massive MIMO BS in a TDD-based HetNet and we provide analytical expressions for
the coverage probability and the area spectral efficiency using stochastic
geometry. The duplexing mode in which SCs should operate during uplink
macrocell transmissions is optimized. Furthermore, we consider a reverse TDD
scheme, in which the massive MIMO BS can estimate the SC interference
covariance matrix. Our results suggest that significant throughput improvement
can be achieved by exploiting interference nulling and implicit coordination
across the tiers due to flexible and asymmetric TDD operation.
|
1309.4959 | Four-Pose Synthesis of Angle-Symmetric 6R Linkages | cs.RO | We use the recently introduced factorization theory of motion polynomials
over the dual quaternions for the synthesis of closed kinematic loops with six
revolute joints that visit four prescribed poses. Our approach admits either no
or a one-parametric family of solutions. We suggest strategies for picking good
solutions from this family.
|
1309.4962 | HOL(y)Hammer: Online ATP Service for HOL Light | cs.AI cs.DL cs.LG cs.LO cs.MS | HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable)
mathematics encoded in the HOL Light system. The service allows its users to
upload and automatically process an arbitrary formal development (project)
based on HOL Light, and to attack arbitrary conjectures that use the concepts
defined in some of the uploaded projects. For that, the service uses several
automated reasoning systems combined with several premise selection methods
trained on all the project proofs. The projects that are readily available on
the server for such query answering include the recent versions of the
Flyspeck, Multivariate Analysis and Complex Analysis libraries. The service
runs on a 48-CPU server, currently employing in parallel for each task 7 AI/ATP
combinations and 4 decision procedures that contribute to its overall
performance. The system is also available for local installation by interested
users, who can customize it for their own proof development. An Emacs interface
allowing parallel asynchronous queries to the service is also provided. The
overall structure of the service is outlined, problems that arise and their
solutions are discussed, and an initial account of using the system is given.
|
1309.4978 | An Analytical Model of Packet Collisions in IEEE 802.15.4 Wireless
Networks | cs.NI cs.IT math.IT | Numerous studies showed that concurrent transmissions can boost wireless
network performance despite collisions. While these works provide empirical
evidence that concurrent transmissions may be received reliably, existing
signal capture models only partially explain the root causes of this
phenomenon. We present a comprehensive mathematical model that reveals the
reasons and provides insights on the key parameters affecting the performance
of MSK-modulated transmissions. A major contribution is a closed-form
derivation of the receiver bit decision variable for arbitrary numbers of
colliding signals and constellations of power ratios, timing offsets, and
carrier phase offsets. We systematically explore the root causes for successful
packet delivery under concurrent transmissions across the whole parameter space
of the model. We confirm the capture threshold behavior observed in previous
studies but also reveal new insights relevant for the design of optimal
protocols: We identify capture zones depending not only on the signal power
ratio but also on time and phase offsets.
|
1309.4999 | Bayesian rules and stochastic models for high accuracy prediction of
solar radiation | cs.LG stat.AP | It is essential to find solar predictive methods to massively insert
renewable energies on the electrical distribution grid. The goal of this study
is to find the best methodology allowing predicting with high accuracy the
hourly global radiation. The knowledge of this quantity is essential for the
grid manager or the private PV producer in order to anticipate fluctuations
related to clouds occurrences and to stabilize the injected PV power. In this
paper, we test both methodologies: single and hybrid predictors. In the first
class, we include the multi-layer perceptron (MLP), auto-regressive and moving
average (ARMA), and persistence models. In the second class, we mix these
predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian
averages of outputs related to single models. If MLP and ARMA are equivalent
(nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain
upper than 14 percentage points compared to the persistence estimation
(nRMSE=37% versus 51%).
|
1309.5004 | Blind Deconvolution via Maximum Kurtosis Adaptive Filtering | cs.CV | In this paper, we present an algorithm for identifying a parametrically
described destructive unknown system based on a non-gaussianity measure. It is
known that under certain conditions the output of a linear system is more
gaussian than the input. Hence, an inverse filter is searched, such that its
output is minimally gaussian. We use the kurtosis as a measure of the
non-gaussianity of the signal. A maximum of the kurtosis as a function of the
deconvolving filter coefficients is searched. The search is done iteratively
using the gradient ascent algorithm, and the coefficients at the maximum point
correspond to the inverse filter coefficients. This filter may be applied to
the distorted signal to obtain the original undistorted signal. While a similar
approach has been used before, it was always directed at a particular kind of a
signal, commonly of impulsive characteristics. In this paper a successful
attempt has been made to apply the algorithm to a wider range of signals, such
as to process distorted audio signals and destructed images. This innovative
implementation required the revelation of a way to preprocess the distorted
signal at hand. The experimental results show very good performance in terms of
recovering audio signals and blurred images, both for an FIR and IIR distorting
filters.
|
1309.5014 | Characterizing and modeling an electoral campaign in the context of
Twitter: 2011 Spanish Presidential Election as a case study | physics.soc-ph cs.CY cs.SI | Transmitting messages in the most efficient way as possible has always been
one of politicians main concerns during electoral processes. Due to the rapidly
growing number of users, online social networks have become ideal platforms for
politicians to interact with their potential voters. Exploiting the available
potential of these tools to maximize their influence over voters is one of
politicians actual challenges. To step in this direction, we have analyzed the
user activity in the online social network Twitter, during the 2011 Spanish
Presidential electoral process, and found that such activity is correlated with
the election results. We introduce a new measure to study political support in
Twitter, which we call the Relative Support. We have also characterized user
behavior by analyzing the structural and dynamical patterns of the complex
networks emergent from the mention and retweet networks. Our results suggest
that the collective attention is driven by a very small fraction of users.
Furthermore we have analyzed the interactions taking place among politicians,
observing a lack of debate. Finally we develop a network growth model to
reproduce the interactions taking place among politicians.
|
1309.5018 | Semantic Advertising | cs.AI cs.CY cs.IR | We present the concept of Semantic Advertising which we see as the future of
online advertising. Semantic Advertising is online advertising powered by
semantic technology which essentially enables us to represent and reason with
concepts and the meaning of things. This paper aims to 1) Define semantic
advertising, 2) Place it in the context of broader and more widely used
concepts such as the Semantic Web and Semantic Search, 3) Provide a survey of
work in related areas such as context matching, and 4) Provide a perspective on
successful emerging technologies and areas of future work. We base our work on
our experience as a company developing semantic technologies aimed at realizing
the full potential of online advertising.
|
1309.5047 | A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics | cs.LG q-bio.GN stat.ML | The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.
|
1309.5067 | Distributed coordination of self-organizing mechanisms in communication
networks | cs.NI cs.SY | The fast development of the Self-Organizing Network (SON) technology in
mobile networks renders the problem of coordinating SON functionalities
operating simultaneously critical. SON functionalities can be viewed as control
loops that may need to be coordinated to guarantee conflict free operation, to
enforce stability of the network and to achieve performance gain. This paper
proposes a distributed solution for coordinating SON functionalities. It uses
Rosen's concave games framework in conjunction with convex optimization. The
SON functionalities are modeled as linear Ordinary Differential Equation
(ODE)s. The stability of the system is first evaluated using a basic control
theory approach. The coordination solution consists in finding a linear map
(called coordination matrix) that stabilizes the system of SON functionalities.
It is proven that the solution remains valid in a noisy environment using
Stochastic Approximation. A practical example involving three different SON
functionalities deployed in Base Stations (BSs) of a Long Term Evolution (LTE)
network demonstrates the usefulness of the proposed method.
|
1309.5069 | Securing the IEEE 802.16 OFDM WiMAX PHYSICAL AND MAC Layer Using STBC
Coding and Encryption | cs.CR cs.IT cs.NI math.IT | This work proposes model design in securing the IEEE 802.16 WiMAX Physical
and MAC layer, using Orthogonal Frequency Division Multiplexing (OFDM) and STBC
model. Typically, it addresses the physical and MAC layer security concerns,
using a Space Time Block Coding (STBC), link encryption, and Message
Authentication Code (MAC) technique. The model conforms to Multiple Input
Single Output (MISO) fading channels which model two or more transmitters and a
receiver in multiuser environment. The two fading link parameters are assumed
to be same. Channel estimate for each link, in combination to the received
signal is based on Reed Solomon Convolution Coding (RS-CC) algorithm, which
occurs as a result of the Space-Time Diversity Combiner block. In addition the
model explore using communication blocks to measure and display bit error rate
after encryption algorithm and Message Authentication Code (MAC) have been
adapted in Forward Error Correction (FEC) mode. Channel SNR and estimation in
rate ID is applied. The final results shows authentication, and the
Reed-Solomon decoding of the final information or data received.
|
1309.5105 | Subspace identification of large-scale interconnected systems | cs.SY | We propose a decentralized subspace algorithm for identification of
large-scale, interconnected systems that are described by sparse (multi) banded
state-space matrices. First, we prove that the state of a local subsystem can
be approximated by a linear combination of inputs and outputs of the local
subsystems that are in its neighborhood. Furthermore, we prove that for
interconnected systems with well-conditioned, finite-time observability
Gramians (or observability matrices), the size of this neighborhood is
relatively small. On the basis of these results, we develop a subspace
identification algorithm that identifies a state-space model of a local
subsystem from the local input-output data. Consequently, the developed
algorithm is computationally feasible for interconnected systems with a large
number of local subsystems. Numerical results confirm the effectiveness of the
new identification algorithm.
|
1309.5109 | Network Structure and Biased Variance Estimation in Respondent Driven
Sampling | stat.AP cs.SI stat.ME | This paper explores bias in the estimation of sampling variance in Respondent
Driven Sampling (RDS). Prior methodological work on RDS has focused on its
problematic assumptions and the biases and inefficiencies of its estimators of
the population mean. Nonetheless, researchers have given only slight attention
to the topic of estimating sampling variance in RDS, despite the importance of
variance estimation for the construction of confidence intervals and hypothesis
tests. In this paper, we show that the estimators of RDS sampling variance rely
on a critical assumption that the network is First Order Markov (FOM) with
respect to the dependent variable of interest. We demonstrate, through
intuitive examples, mathematical generalizations, and computational experiments
that current RDS variance estimators will always underestimate the population
sampling variance of RDS in empirical networks that do not conform to the FOM
assumption. Analysis of 215 observed university and school networks from
Facebook and Add Health indicates that the FOM assumption is violated in every
empirical network we analyze, and that these violations lead to substantially
biased RDS estimators of sampling variance. We propose and test two alternative
variance estimators that show some promise for reducing biases, but which also
illustrate the limits of estimating sampling variance with only partial
information on the underlying population social network.
|
1309.5110 | An ant colony optimization algorithm for job shop scheduling problem | cs.AI cs.NE | The nature has inspired several metaheuristics, outstanding among these is
Ant Colony Optimization (ACO), which have proved to be very effective and
efficient in problems of high complexity (NP-hard) in combinatorial
optimization. This paper describes the implementation of an ACO model algorithm
known as Elitist Ant System (EAS), applied to a combinatorial optimization
problem called Job Shop Scheduling Problem (JSSP). We propose a method that
seeks to reduce delays designating the operation immediately available, but
considering the operations that lack little to be available and have a greater
amount of pheromone. The performance of the algorithm was evaluated for
problems of JSSP reference, comparing the quality of the solutions obtained
regarding the best known solution of the most effective methods. The solutions
were of good quality and obtained with a remarkable efficiency by having to
make a very low number of objective function evaluations.
|
1309.5124 | Multi-layer graph analysis for dynamic social networks | cs.SI physics.soc-ph stat.CO | Modern social networks frequently encompass multiple distinct types of
connectivity information; for instance, explicitly acknowledged friend
relationships might complement behavioral measures that link users according to
their actions or interests. One way to represent these networks is as
multi-layer graphs, where each layer contains a unique set of edges over the
same underlying vertices (users). Edges in different layers typically have
related but distinct semantics; depending on the application multiple layers
might be used to reduce noise through averaging, to perform multifaceted
analyses, or a combination of the two. However, it is not obvious how to extend
standard graph analysis techniques to the multi-layer setting in a flexible
way. In this paper we develop latent variable models and methods for mining
multi-layer networks for connectivity patterns based on noisy data.
|
1309.5126 | The third-order term in the normal approximation for singular channels | cs.IT math.IT | For a singular and symmetric discrete memoryless channel with positive
dispersion, the third-order term in the normal approximation is shown to be
upper bounded by a constant. This finding completes the characterization of the
third-order term for symmetric discrete memoryless channels. The proof method
is extended to asymmetric and singular channels with constant composition
codes, and its connection to existing results, as well as its limitation in the
error exponents regime, are discussed.
|
1309.5145 | The Immune System: the ultimate fractionated cyber-physical system | cs.CE q-bio.OT | In this little vision paper we analyze the human immune system from a
computer science point of view with the aim of understanding the architecture
and features that allow robust, effective behavior to emerge from local sensing
and actions. We then recall the notion of fractionated cyber-physical systems,
and compare and contrast this to the immune system. We conclude with some
challenges.
|
1309.5174 | Saying What You're Looking For: Linguistics Meets Video Search | cs.CV cs.CL cs.IR | We present an approach to searching large video corpora for video clips which
depict a natural-language query in the form of a sentence. This approach uses
compositional semantics to encode subtle meaning that is lost in other systems,
such as the difference between two sentences which have identical words but
entirely different meaning: "The person rode the horse} vs. \emph{The horse
rode the person". Given a video-sentence pair and a natural-language parser,
along with a grammar that describes the space of sentential queries, we produce
a score which indicates how well the video depicts the sentence. We produce
such a score for each video clip in a corpus and return a ranked list of clips.
Furthermore, this approach addresses two fundamental problems simultaneously:
detecting and tracking objects, and recognizing whether those tracks depict the
query. Because both tracking and object detection are unreliable, this uses
knowledge about the intended sentential query to focus the tracker on the
relevant participants and ensures that the resulting tracks are described by
the sentential query. While earlier work was limited to single-word queries
which correspond to either verbs or nouns, we show how one can search for
complex queries which contain multiple phrases, such as prepositional phrases,
and modifiers, such as adverbs. We demonstrate this approach by searching for
141 queries involving people and horses interacting with each other in 10
full-length Hollywood movies.
|
1309.5201 | Diffusive Molecular Communication with Disruptive Flows | cs.IT math.IT | In this paper, we study the performance of detectors in a diffusive molecular
communication environment where steady uniform flow is present. We derive the
expected number of information molecules to be observed in a passive spherical
receiver, and determine the impact of flow on the assumption that the
concentration of molecules throughout the receiver is uniform. Simulation
results show the impact of advection on detector performance as a function of
the flow's magnitude and direction. We highlight that there are disruptive
flows, i.e., flows that are not in the direction of information transmission,
that lead to an improvement in detector performance as long as the disruptive
flow does not dominate diffusion and sufficient samples are taken.
|
1309.5223 | JRC EuroVoc Indexer JEX - A freely available multi-label categorisation
tool | cs.CL | EuroVoc (2012) is a highly multilingual thesaurus consisting of over 6,700
hierarchically organised subject domains used by European Institutions and many
authorities in Member States of the European Union (EU) for the classification
and retrieval of official documents. JEX is JRC-developed multi-label
classification software that learns from manually labelled data to
automatically assign EuroVoc descriptors to new documents in a profile-based
category-ranking task. The JEX release consists of trained classifiers for 22
official EU languages, of parallel training data in the same languages, of an
interface that allows viewing and amending the assignment results, and of a
module that allows users to re-train the tool on their own document
collections. JEX allows advanced users to change the document representation so
as to possibly improve the categorisation result through linguistic
pre-processing. JEX can be used as a tool for interactive EuroVoc descriptor
assignment to increase speed and consistency of the human categorisation
process, or it can be used fully automatically. The output of JEX is a
language-independent EuroVoc feature vector lending itself also as input to
various other Language Technology tasks, including cross-lingual clustering and
classification, cross-lingual plagiarism detection, sentence selection and
ranking, and more.
|
1309.5226 | DGT-TM: A freely Available Translation Memory in 22 Languages | cs.CL | The European Commission's (EC) Directorate General for Translation, together
with the EC's Joint Research Centre, is making available a large translation
memory (TM; i.e. sentences and their professionally produced translations)
covering twenty-two official European Union (EU) languages and their 231
language pairs. Such a resource is typically used by translation professionals
in combination with TM software to improve speed and consistency of their
translations. However, this resource has also many uses for translation studies
and for language technology applications, including Statistical Machine
Translation (SMT), terminology extraction, Named Entity Recognition (NER),
multilingual classification and clustering, and many more. In this reference
paper for DGT-TM, we introduce this new resource, provide statistics regarding
its size, and explain how it was produced and how to use it.
|
1309.5247 | Rotating Non-Uniform and High-Dimensional Constellations Using Geodesic
Flow on Lie Groups | cs.IT math.IT | We use a numerical algorithm on the Lie group of rotation matrices to obtain
rotated constellations for Rayleigh fading channels. Our approach minimizes the
union bound for the pairwise error probability to produce rotations optimized
for a given signal-to-noise ratio. This approach circumvents explicit
parametrization of rotation matrices, which has previously prevented robust
numerical methods from being applied to constellation rotation. Our algorithm
is applicable to arbitrary finite constellations in arbitrary dimensions, and
one can thus apply our method to non-uniform constellations, which are of
interest for practical concerns due to their ability to increase BICM capacity.
We show how our rotations can improve the codeword error performance of
non-uniform constellations, and we also apply our method to reproduce and
improve rotations given by ideal lattices in cyclotomic fields.
|
1309.5262 | Near-Field Passive RFID Communication: Channel Model and Code Design | cs.IT math.IT | This paper discusses a new channel model and code design for the
reader-to-tag channel in near-field passive radio frequency identification
(RFID) systems using inductive coupling as a power transfer mechanism. If the
receiver resynchronizes its internal clock each time a bit is detected, the
bit-shift channel used previously in the literature to model the reader-to-tag
channel needs to be modified. In particular, we propose a discretized Gaussian
shift channel as a new channel model in this scenario. We introduce the concept
of quantifiable error avoidance, which is much simpler than error correction.
The capacity is computed numerically, and we also design some new simple codes
for error avoidance on this channel model based on insights gained from the
capacity calculations. Finally, some simulation results are presented to
compare the proposed codes to the Manchester code and two previously proposed
codes for the bit-shift channel model.
|
1309.5290 | An introduction to the Europe Media Monitor family of applications | cs.CL | Most large organizations have dedicated departments that monitor the media to
keep up-to-date with relevant developments and to keep an eye on how they are
represented in the news. Part of this media monitoring work can be automated.
In the European Union with its 23 official languages, it is particularly
important to cover media reports in many languages in order to capture the
complementary news content published in the different countries. It is also
important to be able to access the news content across languages and to merge
the extracted information. We present here the four publicly accessible systems
of the Europe Media Monitor (EMM) family of applications, which cover between
19 and 50 languages (see http://press.jrc.it/overview.html). We give an
overview of their functionality and discuss some of the implications of the
fact that they cover quite so many languages. We discuss design issues
necessary to be able to achieve this high multilinguality, as well as the
benefits of this multilinguality.
|
1309.5304 | Adaptive model predictive control with exploring property for
constrained linear systems that uses basis function model parametrization | cs.SY math.OC | This manuscript contains technical details of recent results developed by the
authors on adaptive model predictive control for constrained linear systems
that exhibits exploring property and uses basis function model parametrization.
|
1309.5310 | Conditioning of Random Block Subdictionaries with Applications to
Block-Sparse Recovery and Regression | math.ST cs.IT math.IT stat.TH | The linear model, in which a set of observations is assumed to be given by a
linear combination of columns of a matrix, has long been the mainstay of the
statistics and signal processing literature. One particular challenge for
inference under linear models is understanding the conditions on the dictionary
under which reliable inference is possible. This challenge has attracted
renewed attention in recent years since many modern inference problems deal
with the "underdetermined" setting, in which the number of observations is much
smaller than the number of columns in the dictionary. This paper makes several
contributions for this setting when the set of observations is given by a
linear combination of a small number of groups of columns of the dictionary,
termed the "block-sparse" case. First, it specifies conditions on the
dictionary under which most block subdictionaries are well conditioned. This
result is fundamentally different from prior work on block-sparse inference
because (i) it provides conditions that can be explicitly computed in
polynomial time, (ii) the given conditions translate into near-optimal scaling
of the number of columns of the block subdictionaries as a function of the
number of observations for a large class of dictionaries, and (iii) it suggests
that the spectral norm and the quadratic-mean block coherence of the dictionary
(rather than the worst-case coherences) fundamentally limit the scaling of
dimensions of the well-conditioned block subdictionaries. Second, this paper
investigates the problems of block-sparse recovery and block-sparse regression
in underdetermined settings. Near-optimal block-sparse recovery and regression
are possible for certain dictionaries as long as the dictionary satisfies
easily computable conditions and the coefficients describing the linear
combination of groups of columns can be modeled through a mild statistical
prior.
|
1309.5316 | A modeling approach to design a software sensor and analyze agronomical
features - Application to sap flow and grape quality relationship | cs.AI | This work proposes a framework using temporal data and domain knowledge in
order to analyze complex agronomical features. The expertise is first
formalized in an ontology, under the form of concepts and relationships between
them, and then used in conjunction with raw data and mathematical models to
design a software sensor. Next the software sensor outputs are put in relation
to product quality, assessed by quantitative measurements. This requires the
use of advanced data analysis methods, such as functional regression. The
methodology is applied to a case study involving an experimental design in
French vineyards. The temporal data consist of sap flow measurements, and the
goal is to explain fruit quality (sugar concentration and weight), using vine's
water courses through the various vine phenological stages. The results are
discussed, as well as the method genericity and robustness.
|
1309.5319 | Recognizing Speech in a Novel Accent: The Motor Theory of Speech
Perception Reframed | cs.CL cs.LG q-bio.NC | The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory.
|
1309.5333 | Power Grid Simulation using Matrix Exponential Method with Rational
Krylov Subspaces | cs.CE cs.NA math.DS | One well adopted power grid simulation methodology is to factorize matrix
once and perform only backward forward substitution with a deliberately chosen
step size along the simulation. Since the required simulation time is usually
long for the power grid design, the costly factorization is amortized. However,
such fixed step size cannot exploit larger step size for the low frequency
response in the power grid to speedup the simulation. In this work, we utilize
the matrix exponential method with the rational Krylov subspace approximation
to enable adaptive step size in the power grid simulation. The kernel operation
in our method only demands one factorization and backward forward
substitutions. Moreover, the rational Krylov subspace approximation can relax
the stiffness constraint of the previous works. The cheap computation of
adaptivity in our method could exploit the long low frequency response in a
power grid and significantly accelerate the simulation. The experimental
results show that our method achieves up to 18X speedup over the trapezoidal
method with fixed step size.
|
1309.5357 | Development of Comprehensive Devnagari Numeral and Character Database
for Offline Handwritten Character Recognition | cs.CV | In handwritten character recognition, benchmark database plays an important
role in evaluating the performance of various algorithms and the results
obtained by various researchers. In Devnagari script, there is lack of such
official benchmark. This paper focuses on the generation of offline benchmark
database for Devnagari handwritten numerals and characters. The present work
generated 5137 and 20305 isolated samples for numeral and character database,
respectively, from 750 writers of all ages, sex, education, and profession. The
offline sample images are stored in TIFF image format as it occupies less
memory. Also, the data is presented in binary level so that memory requirement
is further reduced. It will facilitate research on handwriting recognition of
Devnagari script through free access to the researchers.
|
1309.5390 | Information Acquisition with Sensing Robots: Algorithms and Error Bounds | cs.SY cs.RO math.DS math.OC | Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.
|
1309.5391 | Even the Abstract have Colour: Consensus in Word-Colour Associations | cs.CL | Colour is a key component in the successful dissemination of information.
Since many real-world concepts are associated with colour, for example danger
with red, linguistic information is often complemented with the use of
appropriate colours in information visualization and product marketing. Yet,
there is no comprehensive resource that captures concept-colour associations.
We present a method to create a large word-colour association lexicon by
crowdsourcing. A word-choice question was used to obtain sense-level
annotations and to ensure data quality. We focus especially on abstract
concepts and emotions to show that even they tend to have strong colour
associations. Thus, using the right colours can not only improve semantic
coherence, but also inspire the desired emotional response.
|
1309.5396 | Bayesian Quickest Change Point Detection with Sampling Right Constraints | cs.IT math.IT | In this paper, Bayesian quickest change detection problems with sampling
right constraints are considered. Specifically, there is a sequence of random
variables whose probability density function will change at an unknown time.
The goal is to detect this change in a way such that a linear combination of
the average detection delay and the false alarm probability is minimized. Two
types of sampling right constrains are discussed. The first one is a limited
sampling right constraint, in which the observer can take at most $N$
observations from this random sequence. Under this setup, we show that the cost
function can be written as a set of iterative functions, which can be solved by
Markov optimal stopping theory. The optimal stopping rule is shown to be a
threshold rule. An asymptotic upper bound of the average detection delay is
developed as the false alarm probability goes to zero. This upper bound
indicates that the performance of the limited sampling right problem is close
to that of the classic Bayesian quickest detection for several scenarios of
practical interest. The second constraint discussed in this paper is a
stochastic sampling right constraint, in which sampling rights are consumed by
taking observations and are replenished randomly. The observer cannot take
observations if there are no sampling rights left. We characterize the optimal
solution, which has a very complex structure. For practical applications, we
propose a low complexity algorithm, in which the sampling rule is to take
observations as long as the observer has sampling rights left and the detection
scheme is a threshold rule. We show that this low complexity scheme is first
order asymptotically optimal as the false alarm probability goes to zero.
|
1309.5401 | Nonmyopic View Planning for Active Object Detection | cs.RO cs.CV cs.SY | One of the central problems in computer vision is the detection of
semantically important objects and the estimation of their pose. Most of the
work in object detection has been based on single image processing and its
performance is limited by occlusions and ambiguity in appearance and geometry.
This paper proposes an active approach to object detection by controlling the
point of view of a mobile depth camera. When an initial static detection phase
identifies an object of interest, several hypotheses are made about its class
and orientation. The sensor then plans a sequence of views, which balances the
amount of energy used to move with the chance of identifying the correct
hypothesis. We formulate an active hypothesis testing problem, which includes
sensor mobility, and solve it using a point-based approximate POMDP algorithm.
The validity of our approach is verified through simulation and real-world
experiments with the PR2 robot. The results suggest that our approach
outperforms the widely-used greedy view point selection and provides a
significant improvement over static object detection.
|
1309.5406 | A new and improved quantitative recovery analysis for iterative hard
thresholding algorithms in compressed sensing | math.NA cs.IT math.IT | We present a new recovery analysis for a standard compressed sensing
algorithm, Iterative Hard Thresholding (IHT) (Blumensath and Davies, 2008),
which considers the fixed points of the algorithm. In the context of arbitrary
measurement matrices, we derive a sufficient condition for convergence of IHT
to a fixed point and a necessary condition for the existence of fixed points.
These conditions allow us to perform a sparse signal recovery analysis in the
deterministic noiseless case by implying that the original sparse signal is the
unique fixed point and limit point of IHT, and in the case of Gaussian
measurement matrices and noise by generating a bound on the approximation error
of the IHT limit as a multiple of the noise level. By generalizing the notion
of fixed points, we extend our analysis to the variable stepsize Normalised IHT
(N-IHT) (Blumensath and Davies, 2010). For both stepsize schemes, we obtain
lower bounds on asymptotic phase transitions in a proportional-dimensional
framework, quantifying the sparsity/undersampling trade-off for which recovery
is guaranteed. Exploiting the reasonable average-case assumption that the
underlying signal and measurement matrix are independent, comparison with
previous results within this framework shows a substantial quantitative
improvement.
|
1309.5414 | An Algebraic Approach to the Control of Decentralized Systems | cs.SY math.OC math.RA | Optimal decentralized controller design is notoriously difficult, but recent
research has identified large subclasses of such problems that may be
convexified and thus are amenable to solution via efficient numerical methods.
One recently discovered sufficient condition for convexity is quadratic
invariance (QI). Despite the simple algebraic characterization of QI, which
relates the plant and controller maps, proving convexity of the set of
achievable closed-loop maps requires tools from functional analysis. In this
work, we present a new formulation of quadratic invariance that is purely
algebraic. While our results are similar in flavor to those from traditional QI
theory, they do not follow from that body of work. Furthermore, they are
applicable to new types of systems that are difficult to treat using functional
analysis. Examples discussed include rational transfer matrices, systems with
delays, and multidimensional systems.
|
1309.5422 | Compositional Transient Stability Analysis of Multi-Machine Power
Networks | cs.SY math.OC | During the normal operation of a power system all the voltages and currents
are sinusoids with a frequency of 60 Hz in America and parts of Asia, or of
50Hz in the rest of the world. Forcing all the currents and voltages to be
sinusoids with the right frequency is one of the most important problems in
power systems. This problem is known as the transient stability problem in the
power systems literature.
The classical models used to study transient stability are based on several
implicit assumptions that are violated when transients occur. One such
assumption is the use of phasors to study transients. While phasors require
sinusoidal waveforms to be well defined, there is no guarantee that waveforms
will remain sinusoidal during transients. In this paper, we use energy-based
models derived from first principles that are not subject to hard-to-justify
classical assumptions. In addition to eliminate assumptions that are known not
to hold during transient stages, we derive intuitive conditions ensuring the
transient stability of power systems with lossy transmission lines.
Furthermore, the conditions for transient stability are compositional in the
sense that one infers transient stability of a large power system by checking
simple conditions for individual generators.
|
1309.5427 | Latent Fisher Discriminant Analysis | cs.LG cs.CV stat.ML | Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.
|
1309.5440 | Capacity of a POST Channel with and without Feedback | cs.IT math.IT | We consider finite state channels where the state of the channel is its
previous output. We refer to these as POST (Previous Output is the STate)
channels. We first focus on POST($\alpha$) channels. These channels have binary
inputs and outputs, where the state determines if the channel behaves as a $Z$
or an $S$ channel, both with parameter $\alpha$. %with parameter $\alpha.$ We
show that the non feedback capacity of the POST($\alpha$) channel equals its
feedback capacity, despite the memory of the channel. The proof of this
surprising result is based on showing that the induced output distribution,
when maximizing the directed information in the presence of feedback, can also
be achieved by an input distribution that does not utilize of the feedback. We
show that this is a sufficient condition for the feedback capacity to equal the
non feedback capacity for any finite state channel. We show that the result
carries over from the POST($\alpha$) channel to a binary POST channel where the
previous output determines whether the current channel will be binary with
parameters $(a,b)$ or $(b,a)$. Finally, we show that, in general, feedback may
increase the capacity of a POST channel.
|
1309.5450 | Assortative mixing in functional brain networks during epileptic
seizures | physics.data-an cs.SI physics.comp-ph physics.soc-ph | We investigate assortativity of functional brain networks before, during, and
after one-hundred epileptic seizures with different anatomical onset locations.
We construct binary functional networks from multi-channel
electroencephalographic data recorded from 60 epilepsy patients, and from
time-resolved estimates of the assortativity coefficient we conclude that
positive degree-degree correlations are inherent to seizure dynamics. While
seizures evolve, an increasing assortativity indicates a segregation of the
underlying functional network into groups of brain regions that are only
sparsely interconnected, if at all. Interestingly, assortativity decreases
already prior to seizure end. Together with previous observations of
characteristic temporal evolutions of global statistical properties and
synchronizability of epileptic brain networks, our findings may help to gain
deeper insights into the complicated dynamics underlying generation,
propagation, and termination of seizures.
|
1309.5488 | Emergent Behaviors over Signed Random Networks in Dynamical Environments | cs.SI physics.soc-ph | We study asymptotic dynamical patterns that emerge among a set of nodes that
interact in a dynamically evolving signed random network. Node interactions
take place at random on a sequence of deterministic signed graphs. Each node
receives positive or negative recommendations from its neighbors depending on
the sign of the interaction arcs, and updates its state accordingly. Positive
recommendations follow the standard consensus update while two types of
negative recommendations, each modeling a different type of antagonistic or
malicious interaction, are considered. Nodes may weigh positive and negative
recommendations differently, and random processes are introduced to model the
time-varying attention that nodes pay to the positive and negative
recommendations. Various conditions for almost sure convergence, divergence,
and clustering of the node states are established. Some fundamental
similarities and differences are established for the two notions of negative
recommendations.
|
1309.5502 | The multi-vehicle covering tour problem: building routes for urban
patrolling | cs.AI cs.DS | In this paper we study a particular aspect of the urban community policing:
routine patrol route planning. We seek routes that guarantee visibility, as
this has a sizable impact on the community perceived safety, allowing quick
emergency responses and providing surveillance of selected sites (e.g.,
hospitals, schools). The planning is restricted to the availability of vehicles
and strives to achieve balanced routes. We study an adaptation of the model for
the multi-vehicle covering tour problem, in which a set of locations must be
visited, whereas another subset must be close enough to the planned routes. It
constitutes an NP-complete integer programming problem. Suboptimal solutions
are obtained with several heuristics, some adapted from the literature and
others developed by us. We solve some adapted instances from TSPLIB and an
instance with real data, the former being compared with results from
literature, and latter being compared with empirical data.
|
1309.5504 | Chaos Forgets and Remembers: Measuring Information Creation,
Destruction, and Storage | nlin.CD cond-mat.stat-mech cs.IT math.DS math.IT | The hallmark of deterministic chaos is that it creates information---the rate
being given by the Kolmogorov-Sinai metric entropy. Since its introduction half
a century ago, the metric entropy has been used as a unitary quantity to
measure a system's intrinsic unpredictability. Here, we show that it naturally
decomposes into two structurally meaningful components: A portion of the
created information---the ephemeral information---is forgotten and a
portion---the bound information---is remembered. The bound information is a new
kind of intrinsic computation that differs fundamentally from information
creation: it measures the rate of active information storage. We show that it
can be directly and accurately calculated via symbolic dynamics, revealing a
hitherto unknown richness in how dynamical systems compute.
|
1309.5511 | On the asymptotic hyperstability of switched systems | cs.SY math.DS | Asymptotic hyperstability is achievable under certain switching laws if at
least one of the feed-forward parameterization: 1) possesses a strictly
positive real transfer function, 2) a minimum residence time interval is
respected for each activation time interval of such a parameterization, and 3)
a maximum allowable residence time interval is guaranteed for all active
parameterization which is not positive real.
|
1309.5540 | Detection and Isolation of Failures in Linear Multi-Agent Networks | cs.SY cs.DM math.OC | In this paper the focus is on the relationship between the occurrence of
failures in a (directed or undirected) network of linear single integrator
agents and the presence of jump discontinuities in the derivatives of the
network output. Based on this relationship, an algorithm for sensor placement
is proposed, which enables the designer to detect and isolate any link failures
across the network, based on the jump discontinuities observed by the sensor
nodes. These results are explained through elaborative examples and computer
experiments.
|
1309.5552 | The co-evolution of brand effect and competitiveness in evolving
networks | physics.soc-ph cs.SI | The principle that 'the brand effect is attractive' underlies preferential
attachment. Here we show that the brand effect is just one dimension of
attractiveness. Another dimension is competitiveness. We firstly develop a
general framework that allows us to investigate the competitive aspect of real
networks, instead of simply preferring popular nodes. Our model accurately
describes the evolution of social and technological networks. The phenomenon
which more competitive nodes become richer links can help us to understand the
evolution of many competitive systems in nature and society. In general, the
paper provides an explicit analytical expression of degree distributions of the
network. In particular, the model yields a nontrivial time evolution of nodes'
properties and scale-free behavior with exponents depending on the microscopic
parameters characterizing the competition rules. Secondly, through theoretical
analysis and numerical simulations, it reveals that our model has not only the
universality for the homogeneous weighted network, but also the character for
the heterogeneous weighted network. Thirdly, the paper also develops a model
based on a profit-driven mechanism. It can better describe the observed
phenomenon in enterprise cooperation networks. We show that standard
preferential attachment, the growing random graph, the initial attractiveness
model, the fitness model and weighted networks, can all be seen as degenerate
cases of our model.
|
1309.5574 | Image-guided therapy system for interstitial gynecologic brachytherapy
in a multimodality operating suite | cs.CE physics.med-ph | In this contribution, an image-guided therapy system supporting gynecologic
radiation therapy is introduced. The overall workflow of the presented system
starts with the arrival of the patient and ends with follow-up examinations by
imaging and a superimposed visualization of the modeled device from a PACS
system. Thereby, the system covers all treatments stages (pre-, intra- and
postoperative) and has been designed and constructed by a computer scientist
with feedback from an interdisciplinary team of physicians and engineers. This
integrated medical system enables dispatch of diagnostic images directly after
acquisition to a processing workstation that has an on-board 3D Computer Aided
Design model of a medical device. Thus, allowing precise identification of
catheter location in the 3D imaging model which later provides rapid feedback
to the clinician regarding device location. Moreover, the system enables the
ability to perform patient-specific pre-implant evaluation by assessing the
placement of interstitial needles prior to an intervention via virtual template
matching with a diagnostic scan.
|
1309.5587 | High-rate quantum low-density parity-check codes assisted by reliable
qubits | quant-ph cs.IT math.IT | Quantum error correction is an important building block for reliable quantum
information processing. A challenging hurdle in the theory of quantum error
correction is that it is significantly more difficult to design
error-correcting codes with desirable properties for quantum information
processing than for traditional digital communications and computation. A
typical obstacle to constructing a variety of strong quantum error-correcting
codes is the complicated restrictions imposed on the structure of a code.
Recently, promising solutions to this problem have been proposed in quantum
information science, where in principle any binary linear code can be turned
into a quantum error-correcting code by assuming a small number of reliable
quantum bits. This paper studies how best to take advantage of these latest
ideas to construct desirable quantum error-correcting codes of very high
information rate. Our methods exploit structured high-rate low-density
parity-check codes available in the classical domain and provide quantum
analogues that inherit their characteristic low decoding complexity and high
error correction performance even at moderate code lengths. Our approach to
designing high-rate quantum error-correcting codes also allows for making
direct use of other major syndrome decoding methods for linear codes, making it
possible to deal with a situation where promising quantum analogues of
low-density parity-check codes are difficult to find.
|
1309.5594 | Generic Image Classification Approaches Excel on Face Recognition | cs.CV | The main finding of this work is that the standard image classification
pipeline, which consists of dictionary learning, feature encoding, spatial
pyramid pooling and linear classification, outperforms all state-of-the-art
face recognition methods on the tested benchmark datasets (we have tested on
AR, Extended Yale B, the challenging FERET, and LFW-a datasets). This
surprising and prominent result suggests that those advances in generic image
classification can be directly applied to improve face recognition systems. In
other words, face recognition may not need to be viewed as a separate object
classification problem.
While recently a large body of residual based face recognition methods focus
on developing complex dictionary learning algorithms, in this work we show that
a dictionary of randomly extracted patches (even from non-face images) can
achieve very promising results using the image classification pipeline. That
means, the choice of dictionary learning methods may not be important. Instead,
we find that learning multiple dictionaries using different low-level image
features often improve the final classification accuracy. Our proposed face
recognition approach offers the best reported results on the widely-used face
recognition benchmark datasets. In particular, on the challenging FERET and
LFW-a datasets, we improve the best reported accuracies in the literature by
about 20% and 30% respectively.
|
1309.5598 | Stabilizer formalism for generalized concatenated quantum codes | quant-ph cs.IT math.IT | The concept of generalized concatenated quantum codes (GCQC) provides a
systematic way for constructing good quantum codes from short component codes.
We introduce a stabilizer formalism for GCQCs, which is achieved by defining
quantum coset codes. This formalism offers a new perspective for GCQCs and
enables us to derive a lower bound on the code distance of stabilizer GCQCs
from component codes parameters,for both non-degenerate and degenerate
component codes. Our formalism also shows how to exploit the error-correcting
capacity of component codes to design good GCQCs efficiently.
|
1309.5605 | Stochastic Bound Majorization | cs.LG | Recently a majorization method for optimizing partition functions of
log-linear models was proposed alongside a novel quadratic variational
upper-bound. In the batch setting, it outperformed state-of-the-art first- and
second-order optimization methods on various learning tasks. We propose a
stochastic version of this bound majorization method as well as a low-rank
modification for high-dimensional data-sets. The resulting stochastic
second-order method outperforms stochastic gradient descent (across variations
and various tunings) both in terms of the number of iterations and computation
time till convergence while finding a better quality parameter setting. The
proposed method bridges first- and second-order stochastic optimization methods
by maintaining a computational complexity that is linear in the data dimension
and while exploiting second order information about the pseudo-global curvature
of the objective function (as opposed to the local curvature in the Hessian).
|
1309.5643 | Multiple Instance Learning with Bag Dissimilarities | stat.ML cs.LG | Multiple instance learning (MIL) is concerned with learning from sets (bags)
of objects (instances), where the individual instance labels are ambiguous. In
this setting, supervised learning cannot be applied directly. Often,
specialized MIL methods learn by making additional assumptions about the
relationship of the bag labels and instance labels. Such assumptions may fit a
particular dataset, but do not generalize to the whole range of MIL problems.
Other MIL methods shift the focus of assumptions from the labels to the overall
(dis)similarity of bags, and therefore learn from bags directly. We propose to
represent each bag by a vector of its dissimilarities to other bags in the
training set, and treat these dissimilarities as a feature representation. We
show several alternatives to define a dissimilarity between bags and discuss
which definitions are more suitable for particular MIL problems. The
experimental results show that the proposed approach is computationally
inexpensive, yet very competitive with state-of-the-art algorithms on a wide
range of MIL datasets.
|
1309.5652 | LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual | cs.CL | The Linguistic Data Consortium (LDC) has developed hundreds of data corpora
for natural language processing (NLP) research. Among these are a number of
annotated treebank corpora for Arabic. Typically, these corpora consist of a
single collection of annotated documents. NLP research, however, usually
requires multiple data sets for the purposes of training models, developing
techniques, and final evaluation. Therefore it becomes necessary to divide the
corpora used into the required data sets (divisions). This document details a
set of rules that have been defined to enable consistent divisions for old and
new Arabic treebanks (ATB) and related corpora.
|
1309.5655 | A new look at reweighted message passing | cs.AI cs.CV cs.LG | We propose a new family of message passing techniques for MAP estimation in
graphical models which we call {\em Sequential Reweighted Message Passing}
(SRMP). Special cases include well-known techniques such as {\em Min-Sum
Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing}
(TRW-S). Importantly, our derivation is simpler than the original derivation of
TRW-S, and does not involve a decomposition into trees. This allows easy
generalizations. We present such a generalization for the case of higher-order
graphical models, and test it on several real-world problems with promising
results.
|
1309.5657 | A Hybrid Algorithm for Matching Arabic Names | cs.CL | In this paper, a new hybrid algorithm which combines both of token-based and
character-based approaches is presented. The basic Levenshtein approach has
been extended to token-based distance metric. The distance metric is enhanced
to set the proper granularity level behavior of the algorithm. It smoothly maps
a threshold of misspellings differences at the character level, and the
importance of token level errors in terms of token's position and frequency.
Using a large Arabic dataset, the experimental results show that the proposed
algorithm overcomes successfully many types of errors such as: typographical
errors, omission or insertion of middle name components, omission of
non-significant popular name components, and different writing styles character
variations. When compared the results with other classical algorithms, using
the same dataset, the proposed algorithm was found to increase the minimum
success level of best tested algorithms, while achieving higher upper limits .
|
1309.5660 | Spike Synchronization Dynamics of Small-World Networks | cs.NE nlin.AO q-bio.NC | In this research report, we examine the effects of small-world network
organization on spike synchronization dynamics in networks of Izhikevich
spiking units. We interpolate network organizations from regular ring lattices,
through the small-world region, to random networks, and measure global spike
synchronization dynamics. We examine how average path length and clustering
effect the dynamics of global and neighborhood clique spike organization and
propagation. We show that the emergence of global synchronization undergoes a
phase transition in the small-world region, between the clustering and path
length phase transitions that are known to exist. We add additional realistic
constraints on the dynamics by introducing propagation delays of spiking
signals proportional to wiring length. The addition of delays interferes with
the ability of random networks to sustain global synchronization, in relation
to the breakdown of clustering in the networks. The addition of delays further
enhances the finding that small-world organization is beneficial for balancing
neighborhood synchronized waves of organization with global synchronization
dynamics.
|
1309.5674 | A note on the five valued conjectures of Johansen and Helleseth and zeta
functions | cs.IT math.IT | For the complete five-valued cross-correlation distribution between two
$m$-sequences ${s_t}$ and ${s_{dt}}$ of period $2^m-1$ that differ by the
decimation $d={{2^{2k}+1}\over {2^k+1}}$ where $m$ is odd and
$\mbox{gcd}(k,m)=1$, Johansen and Hellseth expressed it in terms of some
exponential sums. And two conjectures are presented that are of interest in
their own right. In this correspondence we study these conjectures for the
particular case where $k=3$, and the cases $k=1,2$ can also be analyzed in a
similar process. When $k>3$, the degrees of the relevant polynomials will
become higher.
Here the multiplicity of the biggest absolute value of the cross-correlation
is no more than one-sixth of the multiplicity corresponding the smallest
absolute value.
|
1309.5676 | Implementation of a language driven Backpropagation algorithm | cs.NE | Inspired by the importance of both communication and feedback on errors in
human learning, our main goal was to implement a similar mechanism in
supervised learning of artificial neural networks. The starting point in our
study was the observation that words should accompany the input vectors
included in the training set, thus extending the ANN input space. This had as
consequence the necessity to take into consideration a modified sigmoid
activation function for neurons in the first hidden layer (in agreement with a
specific MLP apartment structure), and also a modified version of the
Backpropagation algorithm, which allows using of unspecified (null) desired
output components. Following the belief that basic concepts should be tested on
simple examples, the previous mentioned mechanism was applied on both the XOR
problem and a didactic color case study. In this context, we noticed the
interesting fact that the ANN was capable to categorize all desired input
vectors in the absence of their corresponding words, even though the training
set included only word accompanied inputs, in both positive and negative
examples. Further analysis along applying this approach to more complex
scenarios is currently in progress, as we consider the proposed language-driven
algorithm might contribute to a better understanding of learning in humans,
opening as well the possibility to create a specific category of artificial
neural networks, with abstraction capabilities.
|
1309.5677 | Checkerboard Problem to Topology Optimization of Continuum Structures | cs.CE | The area of topology optimization of continuum structures of which is allowed
to change in order to improve the performance is now dominated by methods that
employ the material distribution concept. The typical methods of the topology
optimization based on the structural optimization of two phase composites are
the so-called variable density ones, like the SIMP (Solid Isotropic Material
with Penalization) and the BESO (Bi-directional Evolutional Structure
Optimization). The topology optimization problem refers to the saddle-point
variation one as well as the so-called Stokes flow problem of the compressive
fluid. The checkerboard patterns often appear in the results computed by the
SIMP and the BESO in which the Q1-P0 element is used for FEM (Finite Element
Method), since these patterns are more favourable than uniform density regions.
Computational experiments of SIMP and BESO have shown that filtering of
sensitivity information of the optimization problem is a highly efficient way
that the checkerboard patterns disappeared and to ensure mesh-independency. SIn
this paper, we discuss the theoretical basis for the filtering method of the
SIMP and the BESO and as a result, the filtering method can be understood by
the theorem of partition of unity and the convolution operator of low-pass
filter.
|
1309.5686 | On the tradeoff of average delay and average power for fading
point-to-point links with monotone policies | cs.NI cs.IT cs.PF math.IT | We consider a fading point-to-point link with packets arriving randomly at
rate $\lambda$ per slot to the transmitter queue. We assume that the
transmitter can control the number of packets served in a slot by varying the
transmit power for the slot. We restrict to transmitter scheduling policies
that are monotone and stationary, i.e., the number of packets served is a
non-decreasing function of the queue length at the beginning of the slot for
every slot fade state. For such policies, we obtain asymptotic lower bounds for
the minimum average delay of the packets, when average transmitter power is a
small positive quantity $V$ more than the minimum average power required for
transmitter queue stability. We show that the minimum average delay grows
either to a finite value or as $\Omega\brap{\log(1/V)}$ or $\Omega\brap{1/V}$
when $V \downarrow 0$, for certain sets of values of $\lambda$. These sets are
determined by the distribution of fading gain, the maximum number of packets
which can be transmitted in a slot, and the transmit power function of the
fading gain and the number of packets transmitted that is assumed. We identify
a case where the above behaviour of the tradeoff differs from that obtained
from a previously considered approximate model, in which the random queue
length process is assumed to evolve on the non-negative real line, and the
transmit power function is strictly convex. We also consider a fading
point-to-point link, where the transmitter, in addition to controlling the
number of packets served, can also control the number of packets admitted in
every slot. Our approach, which uses bounds on the stationary probability
distribution of the queue length, also leads to an intuitive explanation of the
asymptotic behaviour of average delay in the regime where $V \downarrow 0$.
|
1309.5702 | 3-D Visual Coverage Based on Gradient Descent Techniques on Matrix
Manifold and Its Application to Moving Objects Monitoring | cs.SY | This paper investigates coverage control for visual sensor networks based on
gradient descent techniques on matrix manifolds. We consider the scenario that
networked vision sensors with controllable orientations are distributed over
3-D space to monitor 2-D environment. Then, the decision variable must be
constrained on the Lie group SO(3). The contribution of this paper is two
folds. The first one is technical, namely we formulate the coverage problem as
an optimization problem on SO(3) without introducing local parameterization
like Eular angles and directly apply the gradient descent algorithm on the
manifold. The second technological contribution is to present not only the
coverage control scheme but also the density estimation process including image
processing and curve fitting while exemplifying its effectiveness through
simulation of moving objects monitoring.
|
1309.5749 | Adaptive Variable Step Algorithm for Missing Samples Recovery in Sparse
Signals | cs.IT math.IT | Recovery of arbitrarily positioned samples that are missing in sparse signals
recently attracted significant research interest. Sparse signals with heavily
corrupted arbitrary positioned samples could be analyzed in the same way as
compressive sensed signals by omitting the corrupted samples and considering
them as unavailable during the recovery process. The reconstruction of missing
samples is done by using one of the well known reconstruction algorithms. In
this paper we will propose a very simple and efficient adaptive variable step
algorithm, applied directly to the concentration measures, without
reformulating the reconstruction problem within the standard linear programming
form. Direct application of the gradient approach to the nondifferentiable
forms of measures lead us to introduce a variable step size algorithm. A
criterion for changing adaptive algorithm parameters is presented. The results
are illustrated on the examples with sparse signals, including approximately
sparse signals and noisy sparse signals.
|
1309.5762 | A new hierarchical clustering algorithm to identify non-overlapping
like-minded communities | cs.SI physics.soc-ph | A network has a non-overlapping community structure if the nodes of the
network can be partitioned into disjoint sets such that each node in a set is
densely connected to other nodes inside the set and sparsely connected to the
nodes out- side it. There are many metrics to validate the efficacy of such a
structure, such as clustering coefficient, betweenness, centrality, modularity
and like-mindedness. Many methods have been proposed to optimize some of these
metrics, but none of these works well on the recently introduced metric
like-mindedness. To solve this problem, we propose a be- havioral property
based algorithm to identify communities that optimize the like-mindedness
metric and compare its performance on this metric with other behavioral data
based methodologies as well as community detection methods that rely only on
structural data. We execute these algorithms on real-life datasets of
Filmtipset and Twitter and show that our algorithm performs better than the
existing algorithms with respect to the like-mindedness metric.
|
1309.5802 | Lower Bound on the BER of a Decode-and-Forward Relay Network Under Chaos
Shift Keying Communication System | cs.IT cs.PF math.IT | This paper carries out the first-ever investigation of the analysis of a
cooperative Decode-and-Forward (DF) relay network with Chaos Shift Keying (CSK)
modulation. The performance analysis of DF-CSK in this paper takes into account
the dynamical nature of chaotic signal, which is not similar to a conventional
binary modulation performance computation methodology. The expression of a
lower bound bit error rate (BER) is derived in order to investigate the
performance of the cooperative system under independently and identically
distributed (i.i.d.) Gaussian fading wireless environments. The effect of the
non-periodic nature of chaotic sequence leading to a non constant bit energy of
the considered modulation is also investigated. A computation approach of the
BER expression based on the probability density function of the bit energy of
the chaotic sequence, channel distribution, and number of relays is presented.
Simulation results prove the accuracy of our BER computation methodology.
|
1309.5803 | Scalable Anomaly Detection in Large Homogenous Populations | cs.LG cs.DC cs.SY math.OC | Anomaly detection in large populations is a challenging but highly relevant
problem. The problem is essentially a multi-hypothesis problem, with a
hypothesis for every division of the systems into normal and anomal systems.
The number of hypothesis grows rapidly with the number of systems and
approximate solutions become a necessity for any problems of practical
interests. In the current paper we take an optimization approach to this
multi-hypothesis problem. We first observe that the problem is equivalent to a
non-convex combinatorial optimization problem. We then relax the problem to a
convex problem that can be solved distributively on the systems and that stays
computationally tractable as the number of systems increase. An interesting
property of the proposed method is that it can under certain conditions be
shown to give exactly the same result as the combinatorial multi-hypothesis
problem and the relaxation is hence tight.
|
1309.5821 | Undefined By Data: A Survey of Big Data Definitions | cs.DB | The term big data has become ubiquitous. Owing to a shared origin between
academia, industry and the media there is no single unified definition, and
various stakeholders provide diverse and often contradictory definitions. The
lack of a consistent definition introduces ambiguity and hampers discourse
relating to big data. This short paper attempts to collate the various
definitions which have gained some degree of traction and to furnish a clear
and concise definition of an otherwise ambiguous term.
|
1309.5822 | Querying the Guarded Fragment | cs.LO cs.DB | Evaluating a Boolean conjunctive query Q against a guarded first-order theory
F is equivalent to checking whether "F and not Q" is unsatisfiable. This
problem is relevant to the areas of database theory and description logic.
Since Q may not be guarded, well known results about the decidability,
complexity, and finite-model property of the guarded fragment do not obviously
carry over to conjunctive query answering over guarded theories, and had been
left open in general. By investigating finite guarded bisimilar covers of
hypergraphs and relational structures, and by substantially generalising
Rosati's finite chase, we prove for guarded theories F and (unions of)
conjunctive queries Q that (i) Q is true in each model of F iff Q is true in
each finite model of F and (ii) determining whether F implies Q is
2EXPTIME-complete. We further show the following results: (iii) the existence
of polynomial-size conformal covers of arbitrary hypergraphs; (iv) a new proof
of the finite model property of the clique-guarded fragment; (v) the small
model property of the guarded fragment with optimal bounds; (vi) a
polynomial-time solution to the canonisation problem modulo guarded
bisimulation, which yields (vii) a capturing result for guarded bisimulation
invariant PTIME.
|
1309.5823 | A Kernel Classification Framework for Metric Learning | cs.LG | Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel
classification framework. First, doublets and triplets are constructed from the
training samples, and a family of degree-2 polynomial kernel functions are
proposed for pairs of doublets or triplets. Then, a kernel classification
framework is established, which can not only generalize many popular metric
learning methods such as LMNN and ITML, but also suggest new metric learning
methods, which can be efficiently implemented, interestingly, by using the
standard support vector machine (SVM) solvers. Two novel metric learning
methods, namely doublet-SVM and triplet-SVM, are then developed under the
proposed framework. Experimental results show that doublet-SVM and triplet-SVM
achieve competitive classification accuracies with state-of-the-art metric
learning methods such as ITML and LMNN but with significantly less training
time.
|
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