id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1309.1829 | On the $k$-error linear complexity for $2^n$-periodic binary sequences
via Cube Theory | cs.CR cs.IT math.IT | The linear complexity and k-error linear complexity of a sequence have been
used as important measures of keystream strength, hence designing a sequence
with high linear complexity and $k$-error linear complexity is a popular
research topic in cryptography. In this paper, the concept of stable $k$-error
linear complexity is proposed to study sequences with stable and large
$k$-error linear complexity. In order to study k-error linear complexity of
binary sequences with period $2^n$, a new tool called cube theory is developed.
By using the cube theory, one can easily construct sequences with the maximum
stable $k$-error linear complexity. For such purpose, we first prove that a
binary sequence with period $2^n$ can be decomposed into some disjoint cubes
and further give a general decomposition approach. Second, it is proved that
the maximum $k$-error linear complexity is $2^n-(2^l-1)$ over all
$2^n$-periodic binary sequences, where $2^{l-1}\le k<2^{l}$. Thirdly, a
characterization is presented about the $t$th ($t>1$) decrease in the $k$-error
linear complexity for a $2^n$-periodic binary sequence $s$ and this is a
continuation of Kurosawa et al. recent work for the first decrease of k-error
linear complexity. Finally, A counting formula for $m$-cubes with the same
linear complexity is derived, which is equivalent to the counting formula for
$k$-error vectors. The counting formula of $2^n$-periodic binary sequences
which can be decomposed into more than one cube is also investigated, which
extends an important result by Etzion et al..
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1309.1830 | Radar shadow detection in SAR images using DEM and projections | cs.CV | Synthetic aperture radar (SAR) images are widely used in target recognition
tasks nowadays. In this letter, we propose an automatic approach for radar
shadow detection and extraction from SAR images utilizing geometric projections
along with the digital elevation model (DEM) which corresponds to the given
geo-referenced SAR image. First, the DEM is rotated into the radar geometry so
that each row would match that of a radar line of sight. Next, we extract the
shadow regions by processing row by row until the image is covered fully. We
test the proposed shadow detection approach on different DEMs and a simulated
1D signals and 2D hills and volleys modeled by various variance based Gaussian
functions. Experimental results indicate the proposed algorithm produces good
results in detecting shadows in SAR images with high resolution.
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1309.1853 | A General Two-Step Approach to Learning-Based Hashing | cs.LG cs.CV | Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.
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1309.1862 | Observability transitions in correlated networks | physics.soc-ph cond-mat.stat-mech cs.SI | Yang, Wang, and Motter [Phys. Rev. Lett. 109, 258701 (2012)] analyzed a model
for network observability transitions in which a sensor placed on a node makes
the node and the adjacent nodes observable. The size of the connected
components comprising the observable nodes is a major concern of the model. We
analyze this model in random heterogeneous networks with degree correlation.
With numerical simulations and analytical arguments based on generating
functions, we find that negative degree correlation makes networks more
observable. This result holds true both when the sensors are placed on nodes
one by one in a random order and when hubs preferentially receive the sensors.
Finally, we numerically optimize networks with a fixed degree sequence with
respect to the size of the largest observable component. Optimized networks
have negative degree correlation induced by the resulting hub-repulsive
structure; the largest hubs are rarely connected to each other, in contrast to
the rich-club phenomenon of networks.
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1309.1864 | Timing estimation in distributed sensor and control systems with central
processing | cs.SY stat.AP | We consider the problem of estimating timing of measurements and actuation in
distributed sensor and control systems with central processing. The focus is on
direct timing estimation for scenarios where clock synchronization is not
feasible or desirable. Models of the timing and central and peripheral time
stamps are motivated and derived from underlying clock and communication delay
definitions and models. Heuristics for constructing a system time is presented
and it is outlined how the joint timing and the plant state estimation can be
handled. For a simple set of underlying clock and communication delay models,
inclusion of peripheral unit time stamps is shown to reduce jitter, and it is
argued that in general it will give significant jitter reduction. Finally, a
numerical example is given of a contemporary system design.
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1309.1884 | Tractable vs. Intractable Cases of Matching Dependencies for Query
Answering under Entity Resolution | cs.DB cs.CC cs.LO | Matching Dependencies (MDs) are a relatively recent proposal for declarative
entity resolution. They are rules that specify, on the basis of similarities
satisfied by values in a database, what values should be considered duplicates,
and have to be matched. On the basis of a chase-like procedure for MD
enforcement, we can obtain clean (duplicate-free) instances; actually possibly
several of them. The resolved answers to queries are those that are invariant
under the resulting class of resolved instances. Previous work identified
certain classes of queries and sets of MDs for which resolved query answering
is tractable. Special emphasis was placed on cyclic sets of MDs. In this work
we further investigate the complexity of this problem, identifying intractable
cases, and exploring the frontier between tractability and intractability. We
concentrate mostly on acyclic sets of MDs. For a special case we obtain a
dichotomy result relative to NP-hardness.
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1309.1890 | Evolution of the Chilean Web: A Larger Study | cs.SI physics.soc-ph | In this paper we extend our previous and only study on the dynamics of the
Chilean Web. This new study doubles the time period and to the best of our
knowledge is the only study of its type known about any country in the Web. The
new results corroborate the trends found before, in particular the exponential
growth of the Web, and reinforce the conclusion that the Web is more chaotic
than we would like. Hence, modeling most Web characteristics is not trivial.
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1309.1913 | Dynamic Team Theory of Stochastic Differential Decision Systems with
Decentralized Noisy Information Structures via Girsanov's Measure
Transformation | math.OC cs.SY math.ST stat.TH | In this paper, we present two methods which generalize static team theory to
dynamic team theory, in the context of continuous-time stochastic nonlinear
differential decentralized decision systems, with relaxed strategies, which are
measurable to different noisy information structures. For both methods we apply
Girsanov's measure transformation to obtain an equivalent dynamic team problem
under a reference probability measure, so that the observations and information
structures available for decisions, are not affected by any of the team
decisions. The first method is based on function space integration with respect
to products of Wiener measures, and generalizes Witsenhausen's [1] definition
of equivalence between discrete-time static and dynamic team problems. The
second method is based on stochastic Pontryagin's maximum principle. The team
optimality conditions are given by a "Hamiltonian System" consisting of forward
and backward stochastic differential equations, and a conditional variational
Hamiltonian with respect to the information structure of each team member,
expressed under the initial and a reference probability space via Girsanov's
measure transformation. Under global convexity conditions, we show that that
PbP optimality implies team optimality. In addition, we also show existence of
team and PbP optimal relaxed decentralized strategies (conditional
distributions), in the weak$^*$ sense, without imposing convexity on the action
spaces of the team members. Moreover, using the embedding of regular strategies
into relaxed strategies, we also obtain team and PbP optimality conditions for
regular team strategies, which are measurable functions of decentralized
information structures, and we use the Krein-Millman theorem to show
realizability of relaxed strategies by regular strategies.
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1309.1928 | Rollover Preventive Force Synthesis at Active Suspensions in a Vehicle
Performing a Severe Maneuver with Wheels Lifted off | cs.SY | Among the intelligent safety technologies for road vehicles, active
suspensions controlled by embedded computing elements for preventing rollover
have received a lot of attention. The existing models for synthesizing and
allocating forces in such suspensions are conservatively based on the
constraint that no wheels lift off the ground. However, in practice,
smart/active suspensions are more necessary in the situation where the wheels
have just lifted off the ground. The difficulty in computing control in the
last situation is that the problem requires satisfying disjunctive constraints
on the dynamics. To the authors',knowledge, no efficient solution method is
available for the simulation of dynamics with disjunctive constraints and thus
hardware realizable and accurate force allocation in an active suspension tends
to be a difficulty. In this work we give an algorithm for and simulate
numerical solutions of the force allocation problem as an optimal control
problem constrained by dynamics with disjunctive constraints. In particular we
study the allocation and synthesis of time-dependent active suspension forces
in terms of sensor output data in order to stabilize the roll motion of the
road vehicle. An equivalent constraint in the form of a convex combination
(hull) is proposed to satisfy the disjunctive constraints. The validated
numerical simulations show that it is possible to allocate and synthesize
control forces at the active suspensions from sensor output data such that the
forces stabilize the roll moment of the vehicle with its wheels just lifted off
the ground during arbitrary fish-hook maneuvers.
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1309.1939 | The placement of the head that minimizes online memory: a complex
systems approach | cs.CL nlin.AO physics.data-an physics.soc-ph | It is well known that the length of a syntactic dependency determines its
online memory cost. Thus, the problem of the placement of a head and its
dependents (complements or modifiers) that minimizes online memory is
equivalent to the problem of the minimum linear arrangement of a star tree.
However, how that length is translated into cognitive cost is not known. This
study shows that the online memory cost is minimized when the head is placed at
the center, regardless of the function that transforms length into cost,
provided only that this function is strictly monotonically increasing. Online
memory defines a quasi-convex adaptive landscape with a single central minimum
if the number of elements is odd and two central minima if that number is even.
We discuss various aspects of the dynamics of word order of subject (S), verb
(V) and object (O) from a complex systems perspective and suggest that word
orders tend to evolve by swapping adjacent constituents from an initial or
early SOV configuration that is attracted towards a central word order by
online memory minimization. We also suggest that the stability of SVO is due to
at least two factors, the quasi-convex shape of the adaptive landscape in the
online memory dimension and online memory adaptations that avoid regression to
SOV. Although OVS is also optimal for placing the verb at the center, its low
frequency is explained by its long distance to the seminal SOV in the
permutation space.
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1309.1952 | A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries | stat.ML cs.LG math.OC | We consider the problem of learning overcomplete dictionaries in the context
of sparse coding, where each sample selects a sparse subset of dictionary
elements. Our main result is a strategy to approximately recover the unknown
dictionary using an efficient algorithm. Our algorithm is a clustering-style
procedure, where each cluster is used to estimate a dictionary element. The
resulting solution can often be further cleaned up to obtain a high accuracy
estimate, and we provide one simple scenario where $\ell_1$-regularized
regression can be used for such a second stage.
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1309.1973 | Regret-Based Multi-Agent Coordination with Uncertain Task Rewards | cs.AI | Many multi-agent coordination problems can be represented as DCOPs. Motivated
by task allocation in disaster response, we extend standard DCOP models to
consider uncertain task rewards where the outcome of completing a task depends
on its current state, which is randomly drawn from unknown distributions. The
goal of solving this problem is to find a solution for all agents that
minimizes the overall worst-case loss. This is a challenging problem for
centralized algorithms because the search space grows exponentially with the
number of agents and is nontrivial for standard DCOP algorithms we have. To
address this, we propose a novel decentralized algorithm that incorporates
Max-Sum with iterative constraint generation to solve the problem by passing
messages among agents. By so doing, our approach scales well and can solve
instances of the task allocation problem with hundreds of agents and tasks.
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1309.1976 | Source Broadcasting to the Masses: Separation has a Bounded Loss | cs.IT math.IT | This work discusses the source broadcasting problem, i.e. transmitting a
source to many receivers via a broadcast channel. The optimal rate-distortion
region for this problem is unknown. The separation approach divides the problem
into two complementary problems: source successive refinement and broadcast
channel transmission. We provide bounds on the loss incorporated by applying
time-sharing and separation in source broadcasting. If the broadcast channel is
degraded, it turns out that separation-based time-sharing achieves at least a
factor of the joint source-channel optimal rate, and this factor has a positive
limit even if the number of receivers increases to infinity. For the AWGN
broadcast channel a better bound is introduced, implying that all achievable
joint source-channel schemes have a rate within one bit of the separation-based
achievable rate region for two receivers, or within $\log_2 T$ bits for $T$
receivers.
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1309.2002 | Plug-and-play distributed state estimation for linear systems | cs.SY math.OC | This paper proposes a state estimator for large-scale linear systems
described by the interaction of state-coupled subsystems affected by bounded
disturbances. We equip each subsystem with a Local State Estimator (LSE) for
the reconstruction of the subsystem states using pieces of information from
parent subsystems only. Moreover we provide conditions guaranteeing that the
estimation errors are confined into prescribed polyhedral sets and converge to
zero in absence of disturbances. Quite remarkably, the design of an LSE is
recast into an optimization problem that requires data from the corresponding
subsystem and its parents only. This allows one to synthesize LSEs in a
Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of
the whole estimator requires at most the design of an LSE for the subsystem and
its parents. Theoretical results are backed up by numerical experiments on a
mechanical system.
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1309.2018 | A Direct Power Controlled and Series Compensated EHV Transmission Line | cs.SY | This paper presents the design and analysis of a compensation method with
application to a 345 kV 480 MVA three-phase transmission line. The compensator
system includes a series injected voltage source converter that minimizes the
resonance effects of capacitor line reactance. This creates an ability to
compensate for the effects of subsynchronous resonance and thereby increase
line loadability and control real and reactive power flows. The granularity of
power flow control and simultaneous stabilization is achieved by the method of
direct decoupled power control (DPC). The design process is detailed with
respect to optimal response characteristics considering variations of line
parameters, realistic transformer impedances, and maximum ramp response rates.
Line effects are demonstrated in a PLECS model in MATLAB, and compensation
control system functionality is verified. A case study is provided of a 345 kV
transmission line from an EMTP simulation in PSCAD that accounts for
distributed parameter effects that are encountered in physical EHV transmission
lines. This demonstrates the improvement in stability to power system
transients as well as damping of power system oscillations.
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1309.2024 | A Robust Continuous Time Fixed Lag Smoother for Nonlinear Uncertain
Systems | cs.SY | This paper presents a robust fixed lag smoother for a class of nonlinear
uncertain systems. A unified scheme, which combines a nonlinear robust
estimator with a stable fixed lag smoother, is presented to improve the error
covariance of the estimation. The robust fixed lag smoother is based on the use
of Integral Quadratic Constraints and minimax LQG control. The state estimator
uses a copy of the system nonlinearity in the estimator and combines an
approximate model of the delayed states to produce a smoothed signal. In order
to see the effectiveness of the method, it is applied to a quantum optical
phase estimation problem. Results show significant improvement in the error
covariance of the estimator using fixed lag smoother in the presence of
nonlinear uncertainty.
|
1309.2031 | Cooperative Wireless Sensor Network Positioning via Implicit Convex
Feasibility | cs.IT math.IT math.OC | We propose a distributed positioning algorithm to estimate the unknown
positions of a number of target nodes, given distance measurements between
target nodes and between target nodes and a number of reference nodes at known
positions. Based on a geometric interpretation, we formulate the positioning
problem as an implicit convex feasibility problem in which some of the sets
depend on the unknown target positions, and apply a parallel projection onto
convex sets approach to estimate the unknown target node positions. The
proposed technique is suitable for parallel implementation in which every
target node in parallel can update its position and share the estimate of its
location with other targets. We mathematically prove convergence of the
proposed algorithm. Simulation results reveal enhanced performance for the
proposed approach compared to available techniques based on projections,
especially for sparse networks.
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1309.2052 | On the Strategic Allocation of Social Gratification | cs.SI physics.soc-ph | Members of social networks are given opportunities to bestow positive
recognition upon one another by means of constructs such as "likes" and
"retweets." Although recipients no doubt experience utility from these actions,
one might question why these constructs with no intrinsic value for the sender
are exchanged at all. Here we formulate a metric for the prestige of a member
of a social network based on his or her place within the network and the rate
at which "likes" are exchanged within his or her social circle. Simulation
reveals that the 1% most strategically-optimized networks exchange likes at an
average rate 23.5% higher than that of their random counterparts. This suggests
that purely strategic agents, even with no concern for altruism or the general
welfare, experience utility from giving social gratification. Further, we show
that prestige-maximization creates a selective pressure for structural features
associated with social networks including clustering and the small-world
property.
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1309.2057 | Single image super resolution in spatial and wavelet domain | cs.CV | Recently single image super resolution is very important research area to
generate high resolution image from given low resolution image. Algorithms of
single image resolution are mainly based on wavelet domain and spatial domain.
Filters support to model the regularity of natural images is exploited in
wavelet domain while edges of images get sharp during up sampling in spatial
domain. Here single image super resolution algorithm is presented which based
on both spatial and wavelet domain and take the advantage of both. Algorithm is
iterative and use back projection to minimize reconstruction error. Wavelet
based denoising method is also introduced to remove noise.
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1309.2074 | Learning Transformations for Clustering and Classification | cs.CV cs.LG stat.ML | A low-rank transformation learning framework for subspace clustering and
classification is here proposed. Many high-dimensional data, such as face
images and motion sequences, approximately lie in a union of low-dimensional
subspaces. The corresponding subspace clustering problem has been extensively
studied in the literature to partition such high-dimensional data into clusters
corresponding to their underlying low-dimensional subspaces. However,
low-dimensional intrinsic structures are often violated for real-world
observations, as they can be corrupted by errors or deviate from ideal models.
We propose to address this by learning a linear transformation on subspaces
using matrix rank, via its convex surrogate nuclear norm, as the optimization
criteria. The learned linear transformation restores a low-rank structure for
data from the same subspace, and, at the same time, forces a a maximally
separated structure for data from different subspaces. In this way, we reduce
variations within subspaces, and increase separation between subspaces for a
more robust subspace clustering. This proposed learned robust subspace
clustering framework significantly enhances the performance of existing
subspace clustering methods. Basic theoretical results here presented help to
further support the underlying framework. To exploit the low-rank structures of
the transformed subspaces, we further introduce a fast subspace clustering
technique, which efficiently combines robust PCA with sparse modeling. When
class labels are present at the training stage, we show this low-rank
transformation framework also significantly enhances classification
performance. Extensive experiments using public datasets are presented, showing
that the proposed approach significantly outperforms state-of-the-art methods
for subspace clustering and classification.
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1309.2077 | An optimal fuzzy-PI force/motion controller to increase industrial robot
autonomy | cs.RO | This paper presents a method for robot self-recognition and self-adaptation
through the analysis of the contact between the robot end effector and its
surrounding environment. Often, in off-line robot programming, the idealized
robotic environment (the virtual one) does not reflect accurately the real one.
In this situation, we are in the presence of a partially unknown environment
(PUE). Thus, robotic systems must have some degree of autonomy to overcome this
situation, especially when contact exists. The proposed force/motion control
system has an external control loop based on forces and torques exerted on the
robot end effector and an internal control loop based on robot motion. The
external control loop is tested with an optimal proportional integrative (PI)
and a fuzzy-PI controller. The system performance is validated with real-world
experiments involving contact in PUEs.
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1309.2078 | Direct off-line robot programming via a common CAD package | cs.RO | This paper focuses on intuitive and direct off-line robot programming from a
CAD drawing running on a common 3-D CAD package. It explores the most suitable
way to represent robot motion in a CAD drawing, how to automatically extract
such motion data from the drawing, make the mapping of data from the virtual
(CAD model) to the real environment and the process of automatic generation of
robot paths/programs. In summary, this study aims to present a novel CAD-based
robot programming system accessible to anyone with basic knowledge of CAD and
robotics. Experiments on different manipulation tasks show the effectiveness
and versatility of the proposed approach.
|
1309.2079 | CAD-based robot programming: The role of Fuzzy-PI force control in
unstructured environments | cs.RO | More and more, new ways of interaction between humans and robots are desired,
something that allow us to program a robot in an intuitive way, quickly and
with a high-level of abstraction from the robot language. In this paper is
presented a CAD-based system that allows users with basic skills in CAD and
without skills in robot programming to generate robot programs from a CAD model
of a robotic cell. When the CAD model reproduces exactly the real scenario, the
system presents a satisfactory performance. On the contrary, when the CAD model
does not reproduce exactly the real scenario or the calibration process is
poorly done, we are dealing with uncertain (unstructured environment). In order
to minimize or eliminate the previously mentioned problems, it was introduced
sensory feedback (force and torque sensing) in the robotic framework. By
controlling the end-effector pose and specifying its relationship to the
interaction/contact forces, robot programmers can ensure that the robot
maneuvers in an unstructured environment, damping possible impacts and also
increasing the tolerance to positioning errors from the calibration process.
Fuzzy-PI reasoning was used as a force control technique. The effectiveness of
the proposed approach was evaluated in a series of experiments.
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1309.2080 | Structure Learning of Probabilistic Logic Programs by Searching the
Clause Space | cs.LG cs.AI | Learning probabilistic logic programming languages is receiving an increasing
attention and systems are available for learning the parameters (PRISM,
LeProbLog, LFI-ProbLog and EMBLEM) or both the structure and the parameters
(SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the
algorithm SLIPCOVER for "Structure LearnIng of Probabilistic logic programs by
searChing OVER the clause space". It performs a beam search in the space of
probabilistic clauses and a greedy search in the space of theories, using the
log likelihood of the data as the guiding heuristics. To estimate the log
likelihood SLIPCOVER performs Expectation Maximization with EMBLEM. The
algorithm has been tested on five real world datasets and compared with
SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic
Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER
achieves higher areas under the precision-recall and ROC curves in most cases.
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1309.2081 | Discretization and fitting of nominal data for autonomous robots | cs.RO | This paper presents methodologies to discretize nominal robot paths extracted
from 3-D CAD drawings. Behind robot path discretization is the ability to have
a robot adjusting the traversed paths so that the contact between robot tool
and work-piece is properly maintained. In addition, a hybrid force/motion
control system based on Fuzzy-PI control is proposed to adjust robot paths with
external sensory feedback. All these capabilities allow to facilitate the robot
programming process and to increase the robots autonomy.
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1309.2084 | Real-Time and Continuous Hand Gesture Spotting: an Approach Based on
Artificial Neural Networks | cs.RO cs.CV | New and more natural human-robot interfaces are of crucial interest to the
evolution of robotics. This paper addresses continuous and real-time hand
gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture
patterns are recognized by using artificial neural networks (ANNs) specifically
adapted to the process of controlling an industrial robot. Since in continuous
gesture recognition the communicative gestures appear intermittently with the
noncommunicative, we are proposing a new architecture with two ANNs in series
to recognize both kinds of gesture. A data glove is used as interface
technology. Experimental results demonstrated that the proposed solution
presents high recognition rates (over 99% for a library of ten gestures and
over 96% for a library of thirty gestures), low training and learning time and
a good capacity to generalize from particular situations.
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1309.2086 | High-level robot programming based on CAD: dealing with unpredictable
environments | cs.RO | Purpose - The purpose of this paper is to present a CAD-based human-robot
interface that allows non-expert users to teach a robot in a manner similar to
that used by human beings to teach each other.
Design/methodology/approach - Intuitive robot programming is achieved by
using CAD drawings to generate robot programs off-line. Sensory feedback allows
minimization of the effects of uncertainty, providing information to adjust the
robot paths during robot operation.
Findings - It was found that it is possible to generate a robot program from
a common CAD drawing and run it without any major concerns about calibration or
CAD model accuracy.
Research limitations/implications - A limitation of the proposed system has
to do with the fact that it was designed to be used for particular
technological applications.
Practical implications - Since most manufacturing companies have CAD packages
in their facilities today, CAD-based robot programming may be a good option to
program robots without the need for skilled robot programmers.
Originality/value - The paper proposes a new CAD-based robot programming
system. Robot programs are directly generated from a CAD drawing running on a
commonly available 3D CAD package (Autodesk Inventor) and not from a
commercial, computer aided robotics (CAR) software, making it a simple CAD
integrated solution. This is a low-cost and low-setup time system where no
advanced robot programming skills are required to operate it. In summary, robot
programs are generated with a high-level of abstraction from the robot
language.
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1309.2089 | A low-cost laser scanning solution for flexible robotic cells: spray
coating | cs.RO | In this paper, an adaptive and low-cost robotic coating platform for small
production series is presented. This new platform presents a flexible
architecture that enables fast/automatic system adaptive behaviour without
human intervention. The concept is based on contactless technology, using
artificial vision and laser scanning to identify and characterize different
workpieces travelling on a conveyor. Using laser triangulation, the workpieces
are virtually reconstructed through a simplified cloud of three-dimensional
(3D) points. From those reconstructed models, several algorithms are
implemented to extract information about workpieces profile (pattern
recognition), size, boundary and pose. Such information is then used to on-line
adjust the base robot programmes. These robot programmes are off-line generated
from a 3D computer-aided design model of each different workpiece profile.
Finally, the robotic manipulator executes the coating process after its base
programmes have been adjusted. This is a low-cost and fully autonomous system
that allows adapting the robots behaviour to different manufacturing
situations. It means that the robot is ready to work over any piece at any
time, and thus, small production series can be reduced to as much as a
one-object series. No skilled workers and large setup times are needed to
operate it. Experimental results showed that this solution proved to be
efficient and can be applied not only for spray coating purposes but also for
many other industrial processes (automatic manipulation, pick-and-place,
inspection, etc.).
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1309.2090 | Accelerometer-based control of an industrial robotic arm | cs.RO | Most of industrial robots are still programmed using the typical teaching
process, through the use of the robot teach pendant. In this paper is proposed
an accelerometer-based system to control an industrial robot using two low-cost
and small 3-axis wireless accelerometers. These accelerometers are attached to
the human arms, capturing its behavior (gestures and postures). An Artificial
Neural Network (ANN) trained with a back-propagation algorithm was used to
recognize arm gestures and postures, which then will be used as input in the
control of the robot. The aim is that the robot starts the movement almost at
the same time as the user starts to perform a gesture or posture (low response
time). The results show that the system allows the control of an industrial
robot in an intuitive way. However, the achieved recognition rate of gestures
and postures (92%) should be improved in future, keeping the compromise with
the system response time (160 milliseconds). Finally, the results of some tests
performed with an industrial robot are presented and discussed.
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1309.2093 | High-level programming and control for industrial robotics: using a
hand-held accelerometer-based input device for gesture and posture
recognition | cs.RO | Purpose - Most industrial robots are still programmed using the typical
teaching process, through the use of the robot teach pendant. This is a tedious
and time-consuming task that requires some technical expertise, and hence new
approaches to robot programming are required. The purpose of this paper is to
present a robotic system that allows users to instruct and program a robot with
a high-level of abstraction from the robot language.
Design/methodology/approach - The paper presents in detail a robotic system
that allows users, especially non-expert programmers, to instruct and program a
robot just showing it what it should do, in an intuitive way. This is done
using the two most natural human interfaces (gestures and speech), a force
control system and several code generation techniques. Special attention will
be given to the recognition of gestures, where the data extracted from a motion
sensor (three-axis accelerometer) embedded in the Wii remote controller was
used to capture human hand behaviours. Gestures (dynamic hand positions) as
well as manual postures (static hand positions) are recognized using a
statistical approach and artificial neural networks.
Practical implications - The key contribution of this paper is that it offers
a practical method to program robots by means of gestures and speech, improving
work efficiency and saving time.
Originality/value - This paper presents an alternative to the typical robot
teaching process, extending the concept of human-robot interaction and
co-worker scenario. Since most companies do not have engineering resources to
make changes or add new functionalities to their robotic manufacturing systems,
this system constitutes a major advantage for small- to medium-sized
enterprises.
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1309.2094 | The Linearized Bregman Method via Split Feasibility Problems: Analysis
and Generalizations | math.OC cs.CV cs.NA math.NA | The linearized Bregman method is a method to calculate sparse solutions to
systems of linear equations. We formulate this problem as a split feasibility
problem, propose an algorithmic framework based on Bregman projections and
prove a general convergence result for this framework. Convergence of the
linearized Bregman method will be obtained as a special case. Our approach also
allows for several generalizations such as other objective functions,
incremental iterations, incorporation of non-gaussian noise models or box
constraints.
|
1309.2143 | Secure Layered Transmission in Multicast Systems with Wireless
Information and Power Transfer | cs.IT math.IT | This paper considers downlink multicast transmit beamforming for secure
layered transmission systems with wireless simultaneous information and power
transfer. We study the power allocation algorithm design for minimizing the
total transmit power in the presence of passive eavesdroppers and energy
harvesting receivers. The algorithm design is formulated as a non-convex
optimization problem. Our problem formulation promotes the dual use of energy
signals in providing secure communication and facilitating efficient energy
transfer. Besides, we take into account a minimum required power for energy
harvesting at the idle receivers and heterogeneous quality of service (QoS)
requirements for the multicast video receivers. In light of the intractability
of the problem, we reformulate the considered problem by replacing a non-convex
probabilistic constraint with a convex deterministic constraint. Then, a
semidefinite programming relaxation (SDR) approach is adopted to obtain an
upper solution for the reformulated problem. Subsequently, sufficient
conditions for the global optimal solution of the reformulated problem are
revealed. Furthermore, we propose two suboptimal power allocation schemes based
on the upper bound solution. Simulation results demonstrate the excellent
performance and significant transmit power savings achieved by the proposed
schemes compared to isotropic energy signal generation.
|
1309.2168 | Large-scale optimization with the primal-dual column generation method | math.OC cs.LG cs.NA | The primal-dual column generation method (PDCGM) is a general-purpose column
generation technique that relies on the primal-dual interior point method to
solve the restricted master problems. The use of this interior point method
variant allows to obtain suboptimal and well-centered dual solutions which
naturally stabilizes the column generation. As recently presented in the
literature, reductions in the number of calls to the oracle and in the CPU
times are typically observed when compared to the standard column generation,
which relies on extreme optimal dual solutions. However, these results are
based on relatively small problems obtained from linear relaxations of
combinatorial applications. In this paper, we investigate the behaviour of the
PDCGM in a broader context, namely when solving large-scale convex optimization
problems. We have selected applications that arise in important real-life
contexts such as data analysis (multiple kernel learning problem),
decision-making under uncertainty (two-stage stochastic programming problems)
and telecommunication and transportation networks (multicommodity network flow
problem). In the numerical experiments, we use publicly available benchmark
instances to compare the performance of the PDCGM against recent results for
different methods presented in the literature, which were the best available
results to date. The analysis of these results suggests that the PDCGM offers
an attractive alternative over specialized methods since it remains competitive
in terms of number of iterations and CPU times even for large-scale
optimization problems.
|
1309.2172 | Average resistance of toroidal graphs | math.OC cs.SY | The average effective resistance of a graph is a relevant performance index
in many applications, including distributed estimation and control of network
systems. In this paper, we study how the average resistance depends on the
graph topology and specifically on the dimension of the graph. We concentrate
on $d$-dimensional toroidal grids and we exploit the connection between
resistance and Laplacian eigenvalues. Our analysis provides tight estimates of
the average resistance, which are key to study its asymptotic behavior when the
number of nodes grows to infinity. In dimension two, the average resistance
diverges: in this case, we are able to capture its rate of growth when the
sides of the grid grow at different rates. In higher dimensions, the average
resistance is bounded uniformly in the number of nodes: in this case, we
conjecture that its value is of order $1/d$ for large $d$. We prove this fact
for hypercubes and when the side lengths go to infinity.
|
1309.2175 | Cascading failures in spatially-embedded random networks | physics.soc-ph cond-mat.stat-mech cs.SI | Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.
|
1309.2183 | Application of Artificial Neural Networks in Estimating Participation in
Elections | cs.NE cs.CY | It is approved that artificial neural networks can be considerable effective
in anticipating and analyzing flows in which traditional methods and statics
are not able to solve. in this article, by using two-layer feedforward network
with tan-sigmoid transmission function in input and output layers, we can
anticipate participation rate of public in kohgiloye and boyerahmad province in
future presidential election of islamic republic of iran with 91% accuracy. the
assessment standards of participation such as confusion matrix and roc diagrams
have been approved our claims.
|
1309.2199 | Distinguishing Topical and Social Groups Based on Common Identity and
Bond Theory | cs.SI cs.CY physics.soc-ph | Social groups play a crucial role in social media platforms because they form
the basis for user participation and engagement. Groups are created explicitly
by members of the community, but also form organically as members interact. Due
to their importance, they have been studied widely (e.g., community detection,
evolution, activity, etc.). One of the key questions for understanding how such
groups evolve is whether there are different types of groups and how they
differ. In Sociology, theories have been proposed to help explain how such
groups form. In particular, the common identity and common bond theory states
that people join groups based on identity (i.e., interest in the topics
discussed) or bond attachment (i.e., social relationships). The theory has been
applied qualitatively to small groups to classify them as either topical or
social. We use the identity and bond theory to define a set of features to
classify groups into those two categories. Using a dataset from Flickr, we
extract user-defined groups and automatically-detected groups, obtained from a
community detection algorithm. We discuss the process of manual labeling of
groups into social or topical and present results of predicting the group label
based on the defined features. We directly validate the predictions of the
theory showing that the metrics are able to forecast the group type with high
accuracy. In addition, we present a comparison between declared and detected
groups along topicality and sociality dimensions.
|
1309.2236 | The Cost of an Epidemic over a Complex Network: A Random Matrix Approach | cs.SI physics.soc-ph | In this paper we quantify the total economic impact of an epidemic over a
complex network using tools from random matrix theory. Incorporating the direct
and indirect costs of infection, we calculate the disease cost in the large
graph limit for an SIS (Susceptible - Infected - Susceptible) infection
process. We also give an upper bound on this cost for arbitrary finite graphs
and illustrate both calculated costs using extensive simulations on random and
real-world networks. We extend these calculations by considering the total
social cost of an epidemic, accounting for both the immunization and disease
costs for various immunization strategies and determining the optimal
immunization. Our work focuses on the transient behavior of the epidemic, in
contrast to previous research, which typically focuses on determining the
steady-state system equilibrium.
|
1309.2238 | Probability and the Classical/Quantum Divide | quant-ph cs.IT math.IT | This paper considers the problem of distinguishing between classical and
quantum domains in macroscopic phenomena using tests based on probability and
it presents a condition on the ratios of the outcomes being the same (Ps) to
being different (Pn). Given three events, Ps/Pn for the classical case, where
there are no 3-way coincidences, is one-half whereas for the quantum state it
is one-third. For non-maximally entangled objects we find that so long as r <
5.83, we can separate them from classical objects using a probability test. For
maximally entangled particles (r = 1), we propose that the value of 5/12 be
used for Ps/Pn to separate classical and quantum states when no other
information is available and measurements are noisy.
|
1309.2240 | Contour Manifolds and Optimal Transport | math.DG cs.CV | Describing shapes by suitable measures in object segmentation, as proposed in
[24], allows to combine the advantages of the representations as parametrized
contours and indicator functions. The pseudo-Riemannian structure of optimal
transport can be used to model shapes in ways similar as with contours, while
the Kantorovich functional enables the application of convex optimization
methods for global optimality of the segmentation functional.
In this paper we provide a mathematical study of the shape measure
representation and its relation to the contour description. In particular we
show that the pseudo-Riemannian structure of optimal transport, when restricted
to the set of shape measures, yields a manifold which is diffeomorphic to the
manifold of closed contours. A discussion of the metric induced by optimal
transport and the corresponding geodesic equation is given.
|
1309.2250 | A Search Algorithm to Find Multiple Sets of One Dimensional Unipolar
(Optical) Orthogonal Codes with Same Code-length and Low Weight | cs.IT math.IT | This paper describes a search algorithm to find multiple sets of one
dimensional unipolar (optical) orthogonal codes characterized by parameters,
binary code sequence of length (n bits) and weight w (number of bit 1s in the
sequence) as well as auto-correlation and cross-correlation constraint
respectively for the codes within a set. For a given code length n and code
weight w all possible difference sets, with auto-correlation constraints lying
from 1 to w-1 can be designed with distinct code serial number. For given
cross-correlation constraint from 1 to w-1 Multiple sets can be searched out of
the codes with auto-correlation constraints less than or equal to given
auto-correlation constraint using proposed algorithm. The searched multiple
sets can be sorted as having number of codes not less than the upper bound of
the sets given by Johnson bound. These one dimensional unipolar orthogonal
codes have their application in incoherent optical code division multiple
access systems.
|
1309.2254 | Design of Two Dimensional Unipolar (Optical) Orthogonal Codes Through
One Dimensional Unipolar (Optical) Orthogonal Codes | cs.IT math.IT | In this paper, an algorithm for construction of multiple sets of two
dimensional (2D) or matrix unipolar (optical) orthogonal codes has been
proposed. Representations of these 2D codes in difference of positions
representation (DoPR) have also been discussed along-with conventional weighted
positions representation (WPR) of the code. This paper also proposes less
complex methods for calculation of auto-correlation as well as
cross-correlation constraints within set of matrix codes. The multiple sets of
matrix codes provide flexibility for selection of optical orthogonal codes set
in wavelength-hopping time-spreading (WHTS) optical code division multiple
access (CDMA) system.
|
1309.2304 | Information Theory and Moduli of Riemann Surfaces | math.AG cs.IT math.IT | One interpretation of Torelli's Theorem, which asserts that a compact Riemann
Surface $X$ of genus $g > 1$ is determined by the $g(g+1)/2$ entries of the
period matrix, is that the period matrix is a message about $X$. Since this
message depends on only $3g-3$ moduli, it is sparse, or at least approximately
so, in the sense of information theory. Thus, methods from information theory
may be useful in reconstructing the period matrix, and hence the Riemann
surface, from a small subset of the periods. The results here show that, with
high probability, any set of $3g-3$ periods form moduli for the surface.
|
1309.2343 | A Finite-Blocklength Perspective on Gaussian Multi-Access Channels | cs.IT math.IT | Motivated by the growing application of wireless multi-access networks with
stringent delay constraints, we investigate the Gaussian multiple access
channel (MAC) in the finite blocklength regime. Building upon information
spectrum concepts, we develop several non-asymptotic inner bounds on channel
coding rates over the Gaussian MAC with a given finite blocklength, positive
average error probability, and maximal power constraints. Employing Central
Limit Theorem (CLT) approximations, we also obtain achievable second-order
coding rates for the Gaussian MAC based on an explicit expression for its
dispersion matrix. We observe that, unlike the pentagon shape of the asymptotic
capacity region, the second-order region has a curved shape with no sharp
corners.
A main emphasis of the paper is to provide a new perspective on the procedure
of handling input cost constraints for tight achievability proofs. Contrary to
the complicated achievability techniques in the literature, we show that with a
proper choice of input distribution, tight bounds can be achieved via the
standard random coding argument and a modified typicality decoding. In
particular, we prove that codebooks generated randomly according to independent
uniform distributions on the respective "power shells" perform far better than
both independent and identically distributed (i.i.d.) Gaussian inputs and TDMA
with power control. Interestingly, analogous to an error exponent result of
Gallager, the resulting achievable region lies roughly halfway between that of
the i.i.d. Gaussian inputs and that of a hypothetical "sum-power shell" input.
However, dealing with such a non-i.i.d. input requires additional analysis such
as a new change of measure technique and application of a Berry-Esseen CLT for
functions of random variables.
|
1309.2350 | Exponentially Fast Parameter Estimation in Networks Using Distributed
Dual Averaging | cs.LG cs.SI math.OC stat.ML | In this paper we present an optimization-based view of distributed parameter
estimation and observational social learning in networks. Agents receive a
sequence of random, independent and identically distributed (i.i.d.) signals,
each of which individually may not be informative about the underlying true
state, but the signals together are globally informative enough to make the
true state identifiable. Using an optimization-based characterization of
Bayesian learning as proximal stochastic gradient descent (with
Kullback-Leibler divergence from a prior as a proximal function), we show how
to efficiently use a distributed, online variant of Nesterov's dual averaging
method to solve the estimation with purely local information. When the true
state is globally identifiable, and the network is connected, we prove that
agents eventually learn the true parameter using a randomized gossip scheme. We
demonstrate that with high probability the convergence is exponentially fast
with a rate dependent on the KL divergence of observations under the true state
from observations under the second likeliest state. Furthermore, our work also
highlights the possibility of learning under continuous adaptation of network
which is a consequence of employing constant, unit stepsize for the algorithm.
|
1309.2351 | Elementos de ingenier\'ia de explotaci\'on de la informaci\'on aplicados
a la investigaci\'on tributaria fiscal | cs.AI | By introducing elements of information mining to tax analysis, by means of
data mining software and advanced computational concepts of artificial
intelligence, the problem of tax evader's crime against public property has
been addressed. Through an empirical approach from a hypothetical case of use,
induction algorithms, neural networks and bayesian networks are applied to
determine the feasibility of its heuristic application by the tax public
administrator. Different strategies are explored to facilitate the work of
local and regional federal tax inspectors, considering their limited
computational capabilities, but equally effective for those social scientist
committed to handcrafting tax research.
-----
Apresentando a introdu\c{c}\~ao de elementos de explora\c{c}\~ao de
informa\c{c}\~oes para an\'alise fiscal, por meio de software de
minera\c{c}\~ao de dados e conceitos avan\c{c}ados computacionais de
intelig\^encia artificial, foi abordado o problema do crime de sonegador fiscal
contra o patrim\^onio p\'ublico. Atrav\'es de uma abordagem emp\'irica a partir
de um caso hipot\'etico de uso, os algoritmos de indu\c{c}\~ao, redes neurais e
redes bayesianas s\~ao aplicados para determinar a viabilidade de sua
aplica\c{c}\~ao heur\'istica pelo administrador p\'ublico tribut\'ario.
Diferentes estrat\'egias s\~ao exploradas para facilitar o trabalho dos
inspectores tribut\'arios federais locais e regionais, tendo em conta as suas
capacidades computacionais limitados, mas igualmente eficaz para aqueles
cientista social comprometido com a investiga\c{c}\~ao fiscal.
|
1309.2355 | An Optimal Load-Frequency Control Method for Inverter-Based Renewable
Energy Transmission | cs.SY | The frequency droop response of conventional turbine driven synchronous
generators with respect to load increases is normally used in order to have
stable operating characteristics for multiple generators operating in parallel
over large geographical regions. This presents a challenge for renewable energy
sources that interface to the transmission grid through static inverters that
do not exhibit an intrinsic frequency droop characteristic. This paper provides
a technique for designing optimal load frequency controllers as transmission
line inverters fed from renewable energy sources that allows for fast dynamic
response due to variable solar and wind conditions while maintain stability to
interconnected synchronous generators. A control technique based on LQG
optimization theory is presented. Detailed analysis of a three-area system in a
region of mixed wind and solar photovoltaic sources is modeled in a manner that
confirms the effectiveness of the disclosed load-frequency control method.
|
1309.2371 | Performance analysis of modified algorithm for finding multilevel
association rules | cs.DB | Multilevel association rules explore the concept hierarchy at multiple levels
which provides more specific information. Apriori algorithm explores the single
level association rules. Many implementations are available of Apriori
algorithm. Fast Apriori implementation is modified to develop new algorithm for
finding multilevel association rules. In this study the performance of this new
algorithm is analyzed in terms of running time in seconds.
|
1309.2375 | Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized
Loss Minimization | stat.ML cs.LG cs.NA stat.CO | We introduce a proximal version of the stochastic dual coordinate ascent
method and show how to accelerate the method using an inner-outer iteration
procedure. We analyze the runtime of the framework and obtain rates that
improve state-of-the-art results for various key machine learning optimization
problems including SVM, logistic regression, ridge regression, Lasso, and
multiclass SVM. Experiments validate our theoretical findings.
|
1309.2388 | Minimizing Finite Sums with the Stochastic Average Gradient | math.OC cs.LG stat.CO stat.ML | We propose the stochastic average gradient (SAG) method for optimizing the
sum of a finite number of smooth convex functions. Like stochastic gradient
(SG) methods, the SAG method's iteration cost is independent of the number of
terms in the sum. However, by incorporating a memory of previous gradient
values the SAG method achieves a faster convergence rate than black-box SG
methods. The convergence rate is improved from O(1/k^{1/2}) to O(1/k) in
general, and when the sum is strongly-convex the convergence rate is improved
from the sub-linear O(1/k) to a linear convergence rate of the form O(p^k) for
p \textless{} 1. Further, in many cases the convergence rate of the new method
is also faster than black-box deterministic gradient methods, in terms of the
number of gradient evaluations. Numerical experiments indicate that the new
algorithm often dramatically outperforms existing SG and deterministic gradient
methods, and that the performance may be further improved through the use of
non-uniform sampling strategies.
|
1309.2402 | Anger is More Influential Than Joy: Sentiment Correlation in Weibo | cs.SI physics.soc-ph | Recent years have witnessed the tremendous growth of the online social media.
In China, Weibo, a Twitter-like service, has attracted more than 500 million
users in less than four years. Connected by online social ties, different users
influence each other emotionally. We find the correlation of anger among users
is significantly higher than that of joy, which indicates that angry emotion
could spread more quickly and broadly in the network. While the correlation of
sadness is surprisingly low and highly fluctuated. Moreover, there is a
stronger sentiment correlation between a pair of users if they share more
interactions. And users with larger number of friends posses more significant
sentiment influence to their neighborhoods. Our findings could provide insights
for modeling sentiment influence and propagation in online social networks.
|
1309.2471 | Implementation of nlization framework for verbs, pronouns and
determiners with eugene | cs.CL | UNL system is designed and implemented by a nonprofit organization, UNDL
Foundation at Geneva in 1999. UNL applications are application softwares that
allow end users to accomplish natural language tasks, such as translating,
summarizing, retrieving or extracting information, etc. Two major web based
application softwares are Interactive ANalyzer (IAN), which is a natural
language analysis system. It represents natural language sentences as semantic
networks in the UNL format. Other application software is dEep-to-sUrface
GENErator (EUGENE), which is an open-source interactive NLizer. It generates
natural language sentences out of semantic networks represented in the UNL
format. In this paper, NLization framework with EUGENE is focused, while using
UNL system for accomplishing the task of machine translation. In whole
NLization process, EUGENE takes a UNL input and delivers an output in natural
language without any human intervention. It is language-independent and has to
be parametrized to the natural language input through a dictionary and a
grammar, provided as separate interpretable files. In this paper, it is
explained that how UNL input is syntactically and semantically analyzed with
the UNL-NL T-Grammar for NLization of UNL sentences involving verbs, pronouns
and determiners for Punjabi natural language.
|
1309.2473 | Interference Alignment with Diversity for the $2 \times 2$ $X$-Network
with three antennas | cs.IT math.IT | Interference alignment is known to achieve the maximum sum DoF of
$\frac{4M}{3}$ in the $2 \times 2$ $X$-Network (i.e., two-transmitter (Tx)
two-receiver (Rx) $X$-Network) with $M$ antennas at each node, as demonstrated
by Jafar and Shamai. Recently, an Alamouti code based transmission scheme,
which we call the Li-Jafarkhani-Jafar (LJJ) scheme, was proposed for the $2
\times 2$ $X$-Network with two antennas at each node. This scheme achieves a
sum degrees of freedom (DoF) of $\frac{8}{3}$ and also a diversity gain of two
when fixed finite constellations are employed at each Tx. In the LJJ scheme,
each Tx required the knowledge of only its own channel unlike the Jafar-Shamai
scheme which required global CSIT to achieve the maximum possible sum DoF of
$\frac{8}{3}$. Bit error rate (BER) is an important performance metric when the
coding length is finite. This work first proposes a new STBC for a three
transmit antenna single user MIMO system. Building on this STBC, we extend the
LJJ scheme to the $2 \times 2$ $X$-Network with three antennas at each node.
Local channel knowledge is assumed at each Tx. It is shown that the proposed
scheme achieves the maximum possible sum DoF of 4. A diversity gain of 3 is
also guaranteed when fixed finite constellation inputs are used.
|
1309.2502 | Distributed Maximum Likelihood Sensor Network Localization | cs.IT math.IT | We propose a class of convex relaxations to solve the sensor network
localization problem, based on a maximum likelihood (ML) formulation. This
class, as well as the tightness of the relaxations, depends on the noise
probability density function (PDF) of the collected measurements. We derive a
computational efficient edge-based version of this ML convex relaxation class
and we design a distributed algorithm that enables the sensor nodes to solve
these edge-based convex programs locally by communicating only with their close
neighbors. This algorithm relies on the alternating direction method of
multipliers (ADMM), it converges to the centralized solution, it can run
asynchronously, and it is computation error-resilient. Finally, we compare our
proposed distributed scheme with other available methods, both analytically and
numerically, and we argue the added value of ADMM, especially for large-scale
networks.
|
1309.2505 | Compressed Sensing for Block-Sparse Smooth Signals | stat.ML cs.IT math.IT math.ST stat.TH | We present reconstruction algorithms for smooth signals with block sparsity
from their compressed measurements. We tackle the issue of varying group size
via group-sparse least absolute shrinkage selection operator (LASSO) as well as
via latent group LASSO regularizations. We achieve smoothness in the signal via
fusion. We develop low-complexity solvers for our proposed formulations through
the alternating direction method of multipliers.
|
1309.2506 | A multi-stream hmm approach to offline handwritten arabic word
recognition | cs.CV | In This paper we presented new approach for cursive Arabic text recognition
system. The objective is to propose methodology analytical offline recognition
of handwritten Arabic for rapid implementation. The first part in the writing
recognition system is the preprocessing phase is the preprocessing phase to
prepare the data was introduces and extracts a set of simple statistical
features by two methods : from a window which is sliding long that text line
the right to left and the approach VH2D (consists in projecting every character
on the abscissa, on the ordinate and the diagonals 45{\deg} and 135{\deg}) . It
then injects the resulting feature vectors to Hidden Markov Model (HMM) and
combined the two HMM by multi-stream approach.
|
1309.2517 | Forecasting Stock Time-Series using Data Approximation and Pattern
Sequence Similarity | cs.DB | Time series analysis is the process of building a model using statistical
techniques to represent characteristics of time series data. Processing and
forecasting huge time series data is a challenging task. This paper presents
Approximation and Prediction of Stock Time-series data (APST), which is a two
step approach to predict the direction of change of stock price indices. First,
performs data approximation by using the technique called Multilevel Segment
Mean (MSM). In second phase, prediction is performed for the approximated data
using Euclidian distance and Nearest-Neighbour technique. The computational
cost of data approximation is O(n ni) and computational cost of prediction task
is O(m |NN|). Thus, the accuracy and the time required for prediction in the
proposed method is comparatively efficient than the existing Label Based
Forecasting (LBF) method [1].
|
1309.2558 | On differential passivity of physical systems | cs.SY math.DS | Differential passivity is a property that allows to check with a pointwise
criterion that a system is incrementally passive, a property that is relevant
to study interconnected systems in the context of regulation, synchronization,
and estimation. The paper investigates how restrictive is the property,
focusing on a class of open gradient systems encountered in the coenergy
modeling framework of physical systems, in particular the Brayton-Moser
formalism for nonlinear electrical circuits.
|
1309.2574 | Randomized Consensus with Attractive and Repulsive Links | cs.SY cs.MA math.OC | We study convergence properties of a randomized consensus algorithm over a
graph with both attractive and repulsive links. At each time instant, a node is
randomly selected to interact with a random neighbor. Depending on if the link
between the two nodes belongs to a given subgraph of attractive or repulsive
links, the node update follows a standard attractive weighted average or a
repulsive weighted average, respectively. The repulsive update has the opposite
sign of the standard consensus update. In this way, it counteracts the
consensus formation and can be seen as a model of link faults or malicious
attacks in a communication network, or the impact of trust and antagonism in a
social network. Various probabilistic convergence and divergence conditions are
established. A threshold condition for the strength of the repulsive action is
given for convergence in expectation: when the repulsive weight crosses this
threshold value, the algorithm transits from convergence to divergence. An
explicit value of the threshold is derived for classes of attractive and
repulsive graphs. The results show that a single repulsive link can sometimes
drastically change the behavior of the consensus algorithm. They also
explicitly show how the robustness of the consensus algorithm depends on the
size and other properties of the graphs.
|
1309.2593 | Maximizing submodular functions using probabilistic graphical models | cs.LG math.OC | We consider the problem of maximizing submodular functions; while this
problem is known to be NP-hard, several numerically efficient local search
techniques with approximation guarantees are available. In this paper, we
propose a novel convex relaxation which is based on the relationship between
submodular functions, entropies and probabilistic graphical models. In a
graphical model, the entropy of the joint distribution decomposes as a sum of
marginal entropies of subsets of variables; moreover, for any distribution, the
entropy of the closest distribution factorizing in the graphical model provides
an bound on the entropy. For directed graphical models, this last property
turns out to be a direct consequence of the submodularity of the entropy
function, and allows the generalization of graphical-model-based upper bounds
to any submodular functions. These upper bounds may then be jointly maximized
with respect to a set, while minimized with respect to the graph, leading to a
convex variational inference scheme for maximizing submodular functions, based
on outer approximations of the marginal polytope and maximum likelihood bounded
treewidth structures. By considering graphs of increasing treewidths, we may
then explore the trade-off between computational complexity and tightness of
the relaxation. We also present extensions to constrained problems and
maximizing the difference of submodular functions, which include all possible
set functions.
|
1309.2597 | Mine Blood Donors Information through Improved K-Means Clustering | cs.DB | The number of accidents and health diseases which are increasing at an
alarming rate are resulting in a huge increase in the demand for blood. There
is a necessity for the organized analysis of the blood donor database or blood
banks repositories. Clustering analysis is one of the data mining applications
and K-means clustering algorithm is the fundamental algorithm for modern
clustering techniques. K-means clustering algorithm is traditional approach and
iterative algorithm. At every iteration, it attempts to find the distance from
the centroid of each cluster to each and every data point. This paper gives the
improvement to the original k-means algorithm by improving the initial
centroids with distribution of data. Results and discussions show that improved
K-means algorithm produces accurate clusters in less computation time to find
the donors information.
|
1309.2643 | Noisy Interactive Quantum Communication | quant-ph cs.CC cs.IT math.IT | We study the problem of simulating protocols in a quantum communication
setting over noisy channels. This problem falls at the intersection of quantum
information theory and quantum communication complexity, and it will be of
importance for eventual real-world applications of interactive quantum
protocols, which can be proved to have exponentially lower communication costs
than their classical counterparts for some problems. These are the first
results concerning the quantum version of this problem, originally studied by
Schulman in a classical setting (FOCS '92, STOC '93). We simulate a length $N$
quantum communication protocol by a length $O(N)$ protocol with arbitrarily
small error. Under adversarial noise, our strategy can withstand, for
arbitrarily small $\epsilon > 0$, error rates as high as $1/2 -\epsilon$ when
parties pre-share perfect entanglement, but the classical channel is noisy. We
show that this is optimal. We provide extension of these results in several
other models of communication, including when also the entanglement is noisy,
and when there is no pre-shared entanglement but communication is quantum and
noisy. We also study the case of random noise, for which we provide simulation
protocols with positive communication rates and no pre-shared entanglement over
some quantum channels with quantum capacity $C_Q=0$, proving that $C_Q$ is in
general not the right characterization of a channel's capacity for interactive
quantum communication. Our results are stated for a general quantum
communication protocol in which Alice and Bob collaborate, and these results
hold in particular in the quantum communication complexity settings of the Yao
and Cleve--Buhrman models.
|
1309.2648 | Resurrecting My Revolution: Using Social Link Neighborhood in Bringing
Context to the Disappearing Web | cs.IR cs.DL | In previous work we reported that resources linked in tweets disappeared at
the rate of 11% in the first year followed by 7.3% each year afterwards. We
also found that in the first year 6.7%, and 14.6% in each subsequent year, of
the resources were archived in public web archives. In this paper we revisit
the same dataset of tweets and find that our prior model still holds and the
calculated error for estimating percentages missing was about 4%, but we found
the rate of archiving produced a higher error of about 11.5%. We also
discovered that resources have disappeared from the archives themselves (7.89%)
as well as reappeared on the live web after being declared missing (6.54%). We
have also tested the availability of the tweets themselves and found that
10.34% have disappeared from the live web. To mitigate the loss of resources on
the live web, we propose the use of a "tweet signature". Using the Topsy API,
we extract the top five most frequent terms from the union of all tweets about
a resource, and use these five terms as a query to Google. We found that using
tweet signatures results in discovering replacement resources with 70+% textual
similarity to the missing resource 41% of the time.
|
1309.2655 | First-Order Provenance Games | cs.DB cs.LO | We propose a new model of provenance, based on a game-theoretic approach to
query evaluation. First, we study games G in their own right, and ask how to
explain that a position x in G is won, lost, or drawn. The resulting notion of
game provenance is closely related to winning strategies, and excludes from
provenance all "bad moves", i.e., those which unnecessarily allow the opponent
to improve the outcome of a play. In this way, the value of a position is
determined by its game provenance. We then define provenance games by viewing
the evaluation of a first-order query as a game between two players who argue
whether a tuple is in the query answer. For RA+ queries, we show that game
provenance is equivalent to the most general semiring of provenance polynomials
N[X]. Variants of our game yield other known semirings. However, unlike
semiring provenance, game provenance also provides a "built-in" way to handle
negation and thus to answer why-not questions: In (provenance) games, the
reason why x is not won, is the same as why x is lost or drawn (the latter is
possible for games with draws). Since first-order provenance games are
draw-free, they yield a new provenance model that combines how- and why-not
provenance.
|
1309.2660 | Data-Driven Grasp Synthesis - A Survey | cs.RO | We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.
|
1309.2675 | A Brief Study of Open Source Graph Databases | cs.DB cs.DS cs.SE | With the proliferation of large irregular sparse relational datasets, new
storage and analysis platforms have arisen to fill gaps in performance and
capability left by conventional approaches built on traditional database
technologies and query languages. Many of these platforms apply graph
structures and analysis techniques to enable users to ingest, update, query and
compute on the topological structure of these relationships represented as
set(s) of edges between set(s) of vertices. To store and process Facebook-scale
datasets, they must be able to support data sources with billions of edges,
update rates of millions of updates per second, and complex analysis kernels.
These platforms must provide intuitive interfaces that enable graph experts and
novice programmers to write implementations of common graph algorithms. In this
paper, we explore a variety of graph analysis and storage platforms. We compare
their capabil- ities, interfaces, and performance by implementing and computing
a set of real-world graph algorithms on synthetic graphs with up to 256 million
edges. In the spirit of full disclosure, several authors are affiliated with
the development of STINGER.
|
1309.2676 | Greedy Signal Space Methods for incoherence and beyond | math.NA cs.IT math.IT | Compressive sampling (CoSa) has provided many methods for signal recovery of
signals compressible with respect to an orthonormal basis. However, modern
applications have sparked the emergence of approaches for signals not sparse in
an orthonormal basis but in some arbitrary, perhaps highly overcomplete,
dictionary. Recently, several "signal-space" greedy methods have been proposed
to address signal recovery in this setting. However, such methods inherently
rely on the existence of fast and accurate projections which allow one to
identify the most relevant atoms in a dictionary for any given signal, up to a
very strict accuracy. When the dictionary is highly overcomplete, no such
projections are currently known; the requirements on such projections do not
even hold for incoherent or well-behaved dictionaries. In this work, we provide
an alternate analysis for signal space greedy methods which enforce assumptions
on these projections which hold in several settings including those when the
dictionary is incoherent or structurally coherent. These results align more
closely with traditional results in the standard CoSa literature and improve
upon previous work in the signal space setting.
|
1309.2677 | Language change in a multiple group society | physics.soc-ph cs.SI | The processes leading to change in languages are manifold. In order to reduce
ambiguity in the transmission of information, agreement on a set of conventions
for recurring problems is favored. In addition to that, speakers tend to use
particular linguistic variants associated with the social groups they identify
with. The influence of other groups propagating across the speech community as
new variant forms sustains the competition between linguistic variants. With
the utterance selection model, an evolutionary description of language change,
Baxter et al. [Phys. Rev. E 73, 046118 (2006)] have provided a mathematical
formulation of the interactions inside a group of speakers, exploring the
mechanisms that lead to or inhibit the fixation of linguistic variants. In this
paper, we take the utterance selection model one step further by describing a
speech community consisting of multiple interacting groups. Tuning the
interaction strength between groups allows us to gain deeper understanding
about the way in which linguistic variants propagate and how their distribution
depends on the group partitioning. Both for the group size and the number of
groups we find scaling behaviors with two asymptotic regimes. If groups are
strongly connected, the dynamics is that of the standard utterance selection
model, whereas if their coupling is weak, the magnitude of the latter along
with the system size governs the way consensus is reached. Furthermore, we find
that a high influence of the interlocutor on a speaker's utterances can act as
a counterweight to group segregation.
|
1309.2679 | Caracterizando la Web Chilena | cs.SI | This article presents a characterization of the web space from Chile in 2007.
The characterization shows distributions of sites and domains, analysis of
document content and server configuration. In addition, the network structure
of the chilean Web is analyzed, determining components based on hyperlink
structure at the document and site levels.
Original Abstract: En este art\'iculo se muestra una caracterizaci\'on del
espacio web de Chile para el a\~no 2007. Se muestran distribuciones de sitios y
dominios, caracterizaci\'on del contenido en base a tipos de documento, asi
como configuraci\'on de los servidores. Se estudia la estructura de la red
creada mediante hiperv\'inculos en los documentos y c\'omo las diferentes
componentes de esta estructura var\'ian cuando los hiperv\'inculos son
agregados a nivel de sitios.
|
1309.2687 | CrowdPlanner: A Crowd-Based Route Recommendation System | cs.DB | CrowdPlanner -- a novel crowd-based route recommendation system has been
developed, which requests human workers to evaluate candidates routes
recommended by different sources and methods, and determine the best route
based on the feedbacks of these workers. Our system addresses two critical
issues in its core components: a) task generation component generates a series
of informative and concise questions with optimized ordering for a given
candidate route set so that workers feel comfortable and easy to answer; and b)
worker selection component utilizes a set of selection criteria and an
efficient algorithm to find the most eligible workers to answer the questions
with high accuracy.
|
1309.2690 | Energt Efficient MAC Protocols for Wireless Sensor Network: A Survey | cs.IT cs.NI math.IT | Wireless Sensor Network (WSN) is an attractive choice for a variety of
applications as no wired infrastructure is needed. Other wireless networks are
not as energy constrained as WSNs, because they may be plugged into the mains
supply or equipped with batteries that are rechargeable and replaceable. Among
others, one of the main sources of energy depletion in WSN is communications
controlled by the Medium Access Control (MAC) protocols. An extensive survey of
energy efficient MAC protocols is presented in this article. We categorise WSN
MAC protocols in the following categories: controlled access (CA), random
access (RA), slotted protocols (SP) and hybrid protocols (HP). We further
discuss how energy efficient MAC protocols have developed from fixed sleep/wake
cycles through adaptive to dynamic cycles, thus becoming more responsive to
traffic load variations. Finally we present open research questions on MAC
layer design for WSNs in terms of energy efficiency
|
1309.2693 | A Conflict-Based Path-Generation Heuristic for Evacuation Planning | cs.AI math.OC | Evacuation planning and scheduling is a critical aspect of disaster
management and national security applications. This paper proposes a
conflict-based path-generation approach for evacuation planning. Its key idea
is to generate evacuation routes lazily for evacuated areas and to optimize the
evacuation over these routes in a master problem. Each new path is generated to
remedy conflicts in the evacuation and adds new columns and a new row in the
master problem. The algorithm is applied to massive flood scenarios in the
Hawkesbury-Nepean river (West Sydney, Australia) which require evacuating in
the order of 70,000 persons. The proposed approach reduces the number of
variables from 4,500,000 in a Mixed Integer Programming (MIP) formulation to
30,000 in the case study. With this approach, realistic evacuations scenarios
can be solved near-optimally in real time, supporting both evacuation planning
in strategic, tactical, and operational environments.
|
1309.2712 | On Block Security of Regenerating Codes at the MBR Point for Distributed
Storage Systems | cs.IT math.CO math.IT | A passive adversary can eavesdrop stored content or downloaded content of
some storage nodes, in order to learn illegally about the file stored across a
distributed storage system (DSS). Previous work in the literature focuses on
code constructions that trade storage capacity for perfect security. In other
words, by decreasing the amount of original data that it can store, the system
can guarantee that the adversary, which eavesdrops up to a certain number of
storage nodes, obtains no information (in Shannon's sense) about the original
data. In this work we introduce the concept of block security for DSS and
investigate minimum bandwidth regenerating (MBR) codes that are block secure
against adversaries of varied eavesdropping strengths. Such MBR codes guarantee
that no information about any group of original data units up to a certain size
is revealed, without sacrificing the storage capacity of the system. The size
of such secure groups varies according to the number of nodes that the
adversary can eavesdrop. We show that code constructions based on Cauchy
matrices provide block security. The opposite conclusion is drawn for codes
based on Vandermonde matrices.
|
1309.2721 | Asymptotically Optimal Beamforming for Video Streaming in Multi-Antenna
Interference Networks | cs.IT math.IT | In this paper, we consider queue-aware beamforming control for video
streaming applications in multi-antenna interference network. Using heavy
traffic approximation technique, we first derive the diffusion limit for the
discrete time queuing system. Based on the diffusion limit, we formulate an
infinite horizon ergodic control problem to minimize the average power costs of
the base stations subject to the constraints on the playback interruption costs
and buffer overflow costs of the mobile users. To deal with the queue coupling
challenge, we utilize the weak interference coupling property in the network to
derive a closed-form approximate value function of the optimality equation as
well as the associated error bound using perturbation analysis. Based on the
closed-form approximate value function, we propose a low complexity queue-aware
beamforming control algorithm, which is asymptotically optimal for sufficiently
small cross-channel path gain. Finally, the proposed scheme is compared with
various baselines through simulations and it is shown that significant
performance gain can be achieved.
|
1309.2747 | Approximate Counting CSP Solutions Using Partition Function | cs.AI | We propose a new approximate method for counting the number of the solutions
for constraint satisfaction problem (CSP). The method derives from the
partition function based on introducing the free energy and capturing the
relationship of probabilities of variables and constraints, which requires the
marginal probabilities. It firstly obtains the marginal probabilities using the
belief propagation, and then computes the number of solutions according to the
partition function. This allows us to directly plug the marginal probabilities
into the partition function and efficiently count the number of solutions for
CSP. The experimental results show that our method can solve both random
problems and structural problems efficiently.
|
1309.2752 | Robust Periocular Recognition By Fusing Sparse Representations of Color
and Geometry Information | cs.CV | In this paper, we propose a re-weighted elastic net (REN) model for biometric
recognition. The new model is applied to data separated into geometric and
color spatial components. The geometric information is extracted using a fast
cartoon - texture decomposition model based on a dual formulation of the total
variation norm allowing us to carry information about the overall geometry of
images. Color components are defined using linear and nonlinear color spaces,
namely the red-green-blue (RGB), chromaticity-brightness (CB) and
hue-saturation-value (HSV). Next, according to a Bayesian fusion-scheme, sparse
representations for classification purposes are obtained. The scheme is
numerically solved using a gradient projection (GP) algorithm. In the empirical
validation of the proposed model, we have chosen the periocular region, which
is an emerging trait known for its robustness against low quality data. Our
results were obtained in the publicly available UBIRIS.v2 data set and show
consistent improvements in recognition effectiveness when compared to related
state-of-the-art techniques.
|
1309.2765 | Enhancements of Multi-class Support Vector Machine Construction from
Binary Learners using Generalization Performance | cs.LG stat.ML | We propose several novel methods for enhancing the multi-class SVMs by
applying the generalization performance of binary classifiers as the core idea.
This concept will be applied on the existing algorithms, i.e., the Decision
Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and
Max Wins. Although in the previous approaches there have been many attempts to
use some information such as the margin size and the number of support vectors
as performance estimators for binary SVMs, they may not accurately reflect the
actual performance of the binary SVMs. We show that the generalization ability
evaluated via a cross-validation mechanism is more suitable to directly extract
the actual performance of binary SVMs. Our methods are built around this
performance measure, and each of them is crafted to overcome the weakness of
the previous algorithm. The proposed methods include the Reordering Adaptive
Directed Acyclic Graph (RADAG), Strong Elimination of the classifiers (SE),
Weak Elimination of the classifiers (WE), and Voting based Candidate Filtering
(VCF). Experimental results demonstrate that our methods give significantly
higher accuracy than all of the traditional ones. Especially, WE provides
significantly superior results compared to Max Wins which is recognized as the
state of the art algorithm in terms of both accuracy and classification speed
with two times faster in average.
|
1309.2796 | Decision Trees for Function Evaluation - Simultaneous Optimization of
Worst and Expected Cost | cs.DS cs.AI cs.LG | In several applications of automatic diagnosis and active learning a central
problem is the evaluation of a discrete function by adaptively querying the
values of its variables until the values read uniquely determine the value of
the function. In general, the process of reading the value of a variable might
involve some cost, computational or even a fee to be paid for the experiment
required for obtaining the value. This cost should be taken into account when
deciding the next variable to read. The goal is to design a strategy for
evaluating the function incurring little cost (in the worst case or in
expectation according to a prior distribution on the possible variables'
assignments). Our algorithm builds a strategy (decision tree) which attains a
logarithmic approxima- tion simultaneously for the expected and worst cost
spent. This is best possible under the assumption that $P \neq NP.$
|
1309.2797 | Revealing the intricate effect of collaboration on innovation | physics.soc-ph cs.DL cs.SI physics.data-an | We study the Japan and U.S. patent records of several decades to demonstrate
the effect of collaboration on innovation. We find that statistically inventor
teams slightly outperform solo inventors while company teams perform equally
well as solo companies. By tracking the performance record of individual teams
we find that inventor teams' performance generally degrades with more repeat
collaborations. Though company teams' performance displays strongly bursty
behavior, long-term collaboration does not significantly help innovation at
all. To systematically study the effect of repeat collaboration, we define the
repeat collaboration number of a team as the average number of collaborations
over all the teammate pairs. We find that mild repeat collaboration improves
the performance of Japanese inventor teams and U.S. company teams. Yet,
excessive repeat collaboration does not significantly help innovation at both
the inventor and company levels in both countries. To control for unobserved
heterogeneity, we perform a detailed regression analysis and the results are
consistent with our simple observations. The presented results reveal the
intricate effect of collaboration on innovation, which may also be observed in
other creative projects.
|
1309.2805 | Containing epidemic outbreaks by message-passing techniques | physics.soc-ph cond-mat.dis-nn cs.SI q-bio.PE | The problem of targeted network immunization can be defined as the one of
finding a subset of nodes in a network to immunize or vaccinate in order to
minimize a tradeoff between the cost of vaccination and the final (stationary)
expected infection under a given epidemic model. Although computing the
expected infection is a hard computational problem, simple and efficient
mean-field approximations have been put forward in the literature in recent
years. The optimization problem can be recast into a constrained one in which
the constraints enforce local mean-field equations describing the average
stationary state of the epidemic process. For a wide class of epidemic models,
including the susceptible-infected-removed and the
susceptible-infected-susceptible models, we define a message-passing approach
to network immunization that allows us to study the statistical properties of
epidemic outbreaks in the presence of immunized nodes as well as to find
(nearly) optimal immunization sets for a given choice of parameters and costs.
The algorithm scales linearly with the size of the graph and it can be made
efficient even on large networks. We compare its performance with topologically
based heuristics, greedy methods, and simulated annealing.
|
1309.2819 | Stochastic processes with random contexts: a characterization, and
adaptive estimators for the transition probabilities | math.PR cs.IT math.IT math.ST stat.TH | This paper introduces the concept of random context representations for the
transition probabilities of a finite-alphabet stochastic process. Processes
with these representations generalize context tree processes (a.k.a. variable
length Markov chains), and are proven to coincide with processes whose
transition probabilities are almost surely continuous functions of the
(infinite) past. This is similar to a classical result by Kalikow about
continuous transition probabilities. Existence and uniqueness of a minimal
random context representation are proven, and an estimator of the transition
probabilities based on this representation is shown to have very good "pastwise
adaptativity" properties. In particular, it achieves minimax performance, up to
logarithmic factors, for binary renewal processes with bounded $2+\gamma$
moments.
|
1309.2827 | Geometrical aspects of quantum walks on random two-dimensional
structures | quant-ph cond-mat.stat-mech cs.IT math.IT | We study the transport properties of continuous-time quantum walks (CTQW)
over finite two-dimensional structures with a given number of randomly placed
bonds and with different aspect ratios (AR). Here, we focus on the transport
from, say, the left side to the right side of the structure where absorbing
sites are placed. We do so by analyzing the long-time average of the survival
probability of CTQW. We compare the results to the classical continuous-time
random walk case (CTRW). For small AR (landscape configurations) we observe
only small differences between the quantum and the classical transport
properties, i.e., roughly the same number of bonds is needed to facilitate the
transport. However, with increasing AR (portrait configurations) a much larger
number of bonds is needed in the CTQW case than in the CTRW case. While for
CTRW the number of bonds needed decreases when going from small AR to large AR,
for CTRW this number is large for small AR, has a minimum for the square
configuration, and increases again for increasing AR. We corroborate our
findings for large AR by showing that the corresponding quantum eigenstates are
strongly localized in situations in which the transport is facilitated in the
CTRW case.
|
1309.2848 | High-dimensional cluster analysis with the Masked EM Algorithm | q-bio.QM cs.LG q-bio.NC stat.AP | Cluster analysis faces two problems in high dimensions: first, the `curse of
dimensionality' that can lead to overfitting and poor generalization
performance; and second, the sheer time taken for conventional algorithms to
process large amounts of high-dimensional data. In many applications, only a
small subset of features provide information about the cluster membership of
any one data point, however this informative feature subset may not be the same
for all data points. Here we introduce a `Masked EM' algorithm for fitting
mixture of Gaussians models in such cases. We show that the algorithm performs
close to optimally on simulated Gaussian data, and in an application of `spike
sorting' of high channel-count neuronal recordings.
|
1309.2853 | General Purpose Textual Sentiment Analysis and Emotion Detection Tools | cs.CL | Textual sentiment analysis and emotion detection consists in retrieving the
sentiment or emotion carried by a text or document. This task can be useful in
many domains: opinion mining, prediction, feedbacks, etc. However, building a
general purpose tool for doing sentiment analysis and emotion detection raises
a number of issues, theoretical issues like the dependence to the domain or to
the language but also pratical issues like the emotion representation for
interoperability. In this paper we present our sentiment/emotion analysis
tools, the way we propose to circumvent the di culties and the applications
they are used for.
|
1309.2870 | Analytical Framework of LDGM-based Iterative Quantization with
Decimation | cs.IT math.IT | While iterative quantizers based on low-density generator-matrix (LDGM) codes
have been shown to be able to achieve near-ideal distortion performance with
comparatively moderate block length and computational complexity requirements,
their analysis remains difficult due to the presence of decimation steps. In
this paper, considering the use of LDGM-based quantizers in a class of
symmetric source coding problems, with the alphabet being either binary or
non-binary, it is proved rigorously that, as long as the degree distribution
satisfies certain conditions that can be evaluated with density evolution (DE),
the belief propagation (BP) marginals used in the decimation step have
vanishing mean-square error compared to the exact marginals when the block
length and iteration count goes to infinity, which potentially allows
near-ideal distortion performances to be achieved. This provides a sound
theoretical basis for the degree distribution optimization methods previously
proposed in the literature and already found to be effective in practice.
|
1309.2900 | Mining for Spatially-Near Communities in Geo-Located Social Networks | cs.SI physics.soc-ph | Current approaches to community detection in social networks often ignore the
spatial location of the nodes. In this paper, we look to extract spatially-near
communities in a social network. We introduce a new metric to measure the
quality of a community partition in a geolocated social networks called
"spatially-near modularity" a value that increases based on aspects of the
network structure but decreases based on the distance between nodes in the
communities. We then look to find an optimal partition with respect to this
measure - which should be an "ideal" community with respect to both social ties
and geographic location. Though an NP-hard problem, we introduce two heuristic
algorithms that attempt to maximize this measure and outperform non-geographic
community finding by an order of magnitude. Applications to counter-terrorism
are also discussed.
|
1309.2915 | Randomized Quantization and Source Coding with Constrained Output
Distribution | cs.IT math.IT | This paper studies fixed-rate randomized vector quantization under the
constraint that the quantizer's output has a given fixed probability
distribution. A general representation of randomized quantizers that includes
the common models in the literature is introduced via appropriate mixtures of
joint probability measures on the product of the source and reproduction
alphabets. Using this representation and results from optimal transport theory,
the existence of an optimal (minimum distortion) randomized quantizer having a
given output distribution is shown under various conditions. For sources with
densities and the mean square distortion measure, it is shown that this optimum
can be attained by randomizing quantizers having convex codecells. For
stationary and memoryless source and output distributions a rate-distortion
theorem is proved, providing a single-letter expression for the optimum
distortion in the limit of large block-lengths.
|
1309.2920 | Evolutionary Information Diffusion over Social Networks | cs.GT cs.SI physics.soc-ph | Social networks have become ubiquitous in our daily life, as such it has
attracted great research interests recently. A key challenge is that it is of
extremely large-scale with tremendous information flow, creating the phenomenon
of "Big Data". Under such a circumstance, understanding information diffusion
over social networks has become an important research issue. Most of the
existing works on information diffusion analysis are based on either network
structure modeling or empirical approach with dataset mining. However, the
information diffusion is also heavily influenced by network users' decisions,
actions and their socio-economic connections, which is generally ignored in
existing works. In this paper, we propose an evolutionary game theoretic
framework to model the dynamic information diffusion process in social
networks. Specifically, we analyze the framework in uniform degree and
non-uniform degree networks and derive the closed-form expressions of the
evolutionary stable network states. Moreover, the information diffusion over
two special networks, Erd\H{o}s-R\'enyi random network and the
Barab\'asi-Albert scale-free network, are also highlighted. To verify our
theoretical analysis, we conduct experiments by using both synthetic networks
and real-world Facebook network, as well as real-world information spreading
dataset of Twitter and Memetracker. Experiments shows that the proposed game
theoretic framework is effective and practical in modeling the social network
users' information forwarding behaviors.
|
1309.2963 | A Scalable Heuristic for Viral Marketing Under the Tipping Model | cs.SI physics.soc-ph | In a "tipping" model, each node in a social network, representing an
individual, adopts a property or behavior if a certain number of his incoming
neighbors currently exhibit the same. In viral marketing, a key problem is to
select an initial "seed" set from the network such that the entire network
adopts any behavior given to the seed. Here we introduce a method for quickly
finding seed sets that scales to very large networks. Our approach finds a set
of nodes that guarantees spreading to the entire network under the tipping
model. After experimentally evaluating 31 real-world networks, we found that
our approach often finds seed sets that are several orders of magnitude smaller
than the population size and outperform nodal centrality measures in most
cases. In addition, our approach scales well - on a Friendster social network
consisting of 5.6 million nodes and 28 million edges we found a seed set in
under 3.6 hours. Our experiments also indicate that our algorithm provides
small seed sets even if high-degree nodes are removed. Lastly, we find that
highly clustered local neighborhoods, together with dense network-wide
community structures, suppress a trend's ability to spread under the tipping
model.
|
1309.3006 | The Classification Accuracy of Multiple-Metric Learning Algorithm on
Multi-Sensor Fusion | cs.CV | This paper focuses on two main issues; first one is the impact of Similarity
Search to learning the training sample in metric space, and searching based on
supervised learning classi-fication. In particular, four metrics space
searching are based on spatial information that are introduced as the
following; Cheby-shev Distance (CD); Bray Curtis Distance (BCD); Manhattan
Distance (MD) and Euclidean Distance(ED) classifiers. The second issue
investigates the performance of combination of mul-ti-sensor images on the
supervised learning classification accura-cy. QuickBird multispectral data (MS)
and panchromatic data (PAN) have been used in this study to demonstrate the
enhance-ment and accuracy assessment of fused image over the original images.
The supervised classification results of fusion image generated better than the
MS did. QuickBird and the best results with ED classifier than the other did.
|
1309.3014 | Hypercontractivity of spherical averages in Hamming space | math.PR cs.IT math.CO math.FA math.IT | Consider the linear space of functions on the binary hypercube and the linear
operator $S_\delta$ acting by averaging a function over a Hamming sphere of
radius $\delta n$ around every point. It is shown that this operator has a
dimension-independent bound on the norm $L_p \to L_2$ with $p =
1+(1-2\delta)^2$. This result evidently parallels a classical estimate of
Bonami and Gross for $L_p \to L_q$ norms for the operator of convolution with a
Bernoulli noise. The estimate for $S_\delta$ is harder to obtain since the
latter is neither a part of a semigroup, nor a tensor power. The result is
shown by a detailed study of the eigenvalues of $S_\delta$ and $L_p\to L_2$
norms of the Fourier multiplier operators $\Pi_a$ with symbol equal to a
characteristic function of the Hamming sphere of radius $a$ (in the notation
common in boolean analysis $\Pi_a f=f^{=a}$, where $f^{=a}$ is a degree-$a$
component of function $f$). A sample application of the result is given: Any
set $A\subset \FF_2^n$ with the property that $A+A$ contains a large portion of
some Hamming sphere (counted with multiplicity) must have cardinality a
constant multiple of $2^n$.
|
1309.3029 | On the Chi square and higher-order Chi distances for approximating
f-divergences | cs.IT math.IT | We report closed-form formula for calculating the Chi square and higher-order
Chi distances between statistical distributions belonging to the same
exponential family with affine natural space, and instantiate those formula for
the Poisson and isotropic Gaussian families. We then describe an analytic
formula for the $f$-divergences based on Taylor expansions and relying on an
extended class of Chi-type distances.
|
1309.3039 | How Relevant Are Chess Composition Conventions? | cs.AI | Composition conventions are guidelines used by human composers in composing
chess problems. They are particularly significant in composition tournaments.
Examples include, not having any check in the first move of the solution and
not dressing up the board with unnecessary pieces. Conventions are often
associated or even directly conflated with the overall aesthetics or beauty of
a composition. Using an existing experimentally-validated computational
aesthetics model for three-move mate problems, we analyzed sets of
computer-generated compositions adhering to at least 2, 3 and 4 comparable
conventions to test if simply conforming to more conventions had a positive
effect on their aesthetics, as is generally believed by human composers. We
found slight but statistically significant evidence that it does, but only to a
point. We also analyzed human judge scores of 145 three-move mate problems
composed by humans to see if they had any positive correlation with the
computational aesthetic scores of those problems. We found that they did not.
These seemingly conflicting findings suggest two main things. First, the right
amount of adherence to composition conventions in a composition has a positive
effect on its perceived aesthetics. Second, human judges either do not look at
the same conventions related to aesthetics in the model used or emphasize
others that have less to do with beauty as perceived by the majority of
players, even though they may mistakenly consider their judgements beautiful in
the traditional, non-esoteric sense. Human judges may also be relying
significantly on personal tastes as we found no correlation between their
individual scores either.
|
1309.3060 | On SAT representations of XOR constraints | cs.CC cs.AI | We study the representation of systems S of linear equations over the
two-element field (aka xor- or parity-constraints) via conjunctive normal forms
F (boolean clause-sets). First we consider the problem of finding an
"arc-consistent" representation ("AC"), meaning that unit-clause propagation
will fix all forced assignments for all possible instantiations of the
xor-variables. Our main negative result is that there is no polysize
AC-representation in general. On the positive side we show that finding such an
AC-representation is fixed-parameter tractable (fpt) in the number of
equations. Then we turn to a stronger criterion of representation, namely
propagation completeness ("PC") --- while AC only covers the variables of S,
now all the variables in F (the variables in S plus auxiliary variables) are
considered for PC. We show that the standard translation actually yields a PC
representation for one equation, but fails so for two equations (in fact
arbitrarily badly). We show that with a more intelligent translation we can
also easily compute a translation to PC for two equations. We conjecture that
computing a representation in PC is fpt in the number of equations.
|
1309.3103 | Temporal Autoencoding Improves Generative Models of Time Series | stat.ML cs.LG | Restricted Boltzmann Machines (RBMs) are generative models which can learn
useful representations from samples of a dataset in an unsupervised fashion.
They have been widely employed as an unsupervised pre-training method in
machine learning. RBMs have been modified to model time series in two main
ways: The Temporal RBM stacks a number of RBMs laterally and introduces
temporal dependencies between the hidden layer units; The Conditional RBM, on
the other hand, considers past samples of the dataset as a conditional bias and
learns a representation which takes these into account. Here we propose a new
training method for both the TRBM and the CRBM, which enforces the dynamic
structure of temporal datasets. We do so by treating the temporal models as
denoising autoencoders, considering past frames of the dataset as corrupted
versions of the present frame and minimizing the reconstruction error of the
present data by the model. We call this approach Temporal Autoencoding. This
leads to a significant improvement in the performance of both models in a
filling-in-frames task across a number of datasets. The error reduction for
motion capture data is 56\% for the CRBM and 80\% for the TRBM. Taking the
posterior mean prediction instead of single samples further improves the
model's estimates, decreasing the error by as much as 91\% for the CRBM on
motion capture data. We also trained the model to perform forecasting on a
large number of datasets and have found TA pretraining to consistently improve
the performance of the forecasts. Furthermore, by looking at the prediction
error across time, we can see that this improvement reflects a better
representation of the dynamics of the data as opposed to a bias towards
reconstructing the observed data on a short time scale.
|
1309.3117 | Convex relaxations of structured matrix factorizations | cs.LG math.OC | We consider the factorization of a rectangular matrix $X $ into a positive
linear combination of rank-one factors of the form $u v^\top$, where $u$ and
$v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode
specific structures regarding the factors, such as positivity or sparsity. In
this paper, we show that computing the optimal decomposition is equivalent to
computing a certain gauge function of $X$ and we provide a detailed analysis of
these gauge functions and their polars. Since these gauge functions are
typically hard to compute, we present semi-definite relaxations and several
algorithms that may recover approximate decompositions with approximation
guarantees. We illustrate our results with simulations on finding
decompositions with elements in $\{0,1\}$. As side contributions, we present a
detailed analysis of variational quadratic representations of norms as well as
a new iterative basis pursuit algorithm that can deal with inexact first-order
oracles.
|
1309.3126 | Distributed Business Processes - A Framework for Modeling and Execution | cs.MA cs.SE | Commercially available business process management systems (BPMS) still
suffer to support organizations to enact their business processes in an
effective and efficient way. Current BPMS, in general, are based on BPMN 2.0
and/or BPEL. It is well known, that these approaches have some restrictions
according modeling and immediate transfer of the model into executable code.
Recently, a method for modeling and execution of business processes, named
subject-oriented business process management (S-BPM), gained attention. This
methodology facilitates modeling of any business process using only five
symbols and allows direct execution based on such models. Further on, this
methodology has a strong theoretical and formal basis realizing distributed
systems; any process is defined as a network of independent and distributed
agents - i.e. instances of subjects - which coordinate work through the
exchange of messages. In this work, we present a framework and a prototype
based on off-the-shelf technologies as a possible realization of the S-BPM
methodology. We can prove and demonstrate the principal architecture concept;
these results should also stimulate a discussion about actual BPMS and its
underlying concepts.
|
1309.3132 | Combination of Multiple Bipartite Ranking for Web Content Quality
Evaluation | cs.IR | Web content quality estimation is crucial to various web content processing
applications. Our previous work applied Bagging + C4.5 to achive the best
results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of
many point-wise rankinig models. In this paper, we combine multiple pair-wise
bipartite ranking learner to solve the multi-partite ranking problems for the
web quality estimation. In encoding stage, we present the ternary encoding and
the binary coding extending each rank value to $L - 1$ (L is the number of the
different ranking value). For the decoding, we discuss the combination of
multiple ranking results from multiple bipartite ranking models with the
predefined weighting and the adaptive weighting. The experiments on ECML/PKDD
2010 Discovery Challenge datasets show that \textit{binary coding} +
\textit{predefined weighting} yields the highest performance in all four
combinations and furthermore it is better than the best results reported in
ECML/PKDD 2010 Discovery Challenge competition.
|
1309.3139 | Exploiting Interference for Efficient Distributed Computation in
Cluster-based Wireless Sensor Networks | cs.DC cs.IT math.IT | This invited paper presents some novel ideas on how to enhance the
performance of consensus algorithms in distributed wireless sensor networks,
when communication costs are considered. Of particular interest are consensus
algorithms that exploit the broadcast property of the wireless channel to boost
the performance in terms of convergence speeds. To this end, we propose a novel
clustering based consensus algorithm that exploits interference for
computation, while reducing the energy consumption in the network. The
resulting optimization problem is a semidefinite program, which can be solved
offline prior to system startup.
|
1309.3147 | Improved Stability Design of Interconnected Distributed Generation
Resources | cs.SY | This work provides a design method for achieving a specified level of
stability for inverter-based interconnected distributed generation. The
stability of parallel connected distributed energy resources determined from a
linearized state-space model of the inverter dynamics that includes the
admittance matrix of the interconnecting distribution lines. Each inverter uses
a localized droop control scheme with the associated voltage and frequency
measurements obtained through the application of an enhanced phase locked loop.
Previous work on this topic has focused on single inverters connected to an
infinite bus without modeling of delays from a phase locked loop
implementation. This proposed method overcomes both of these limitations of
previous research. A detailed large-signal simulation of a three-bus
interconnected power system is analyzed under two different network admittance
values. Results confirm the effectiveness of the proposed stability design
method.
|
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